From 2fc012687f428c7dacbc14e5d57e92d5d5f49013 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 4 Dec 2023 11:50:01 +0800 Subject: [PATCH 001/136] wholeBody_ct_segmentation failed to be download (#7280) Fixes model-zoo#537 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: KumoLiu --- monai/bundle/scripts.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/bundle/scripts.py b/monai/bundle/scripts.py index 20a491e493..2565a3cf64 100644 --- a/monai/bundle/scripts.py +++ b/monai/bundle/scripts.py @@ -221,7 +221,7 @@ def _download_from_ngc( def _get_latest_bundle_version_monaihosting(name): url = "https://api.ngc.nvidia.com/v2/models/nvidia/monaihosting" - full_url = f"{url}/{name}" + full_url = f"{url}/{name.lower()}" requests_get, has_requests = optional_import("requests", name="get") if has_requests: resp = requests_get(full_url) From 88f8dd275dbe8ca856b45ca34d6d01f4ea4c0128 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 5 Dec 2023 13:20:01 +0800 Subject: [PATCH 002/136] update the Python version requirements for transformers (#7275) Part of #7250. ### Description Fix the Python version for transformers smaller than 3.10. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: KumoLiu --- docs/requirements.txt | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- tests/test_transchex.py | 3 ++- 4 files changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index a9bbc384f8..e5bedf8552 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -21,7 +21,7 @@ sphinxcontrib-serializinghtml sphinx-autodoc-typehints==1.11.1 pandas einops -transformers<4.22 # https://github.com/Project-MONAI/MONAI/issues/5157 +transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 mlflow>=1.28.0 clearml>=1.10.0rc0 tensorboardX diff --git a/requirements-dev.txt b/requirements-dev.txt index 6332d5b0a5..cacbefe234 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -33,7 +33,7 @@ tifffile; platform_system == "Linux" or platform_system == "Darwin" pandas requests einops -transformers<4.22 # https://github.com/Project-MONAI/MONAI/issues/5157 +transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 mlflow>=1.28.0 clearml>=1.10.0rc0 matplotlib!=3.5.0 diff --git a/setup.cfg b/setup.cfg index 123da68dfa..0370d0062d 100644 --- a/setup.cfg +++ b/setup.cfg @@ -65,7 +65,7 @@ all = imagecodecs pandas einops - transformers<4.22 + transformers<4.22; python_version <= '3.10' mlflow>=1.28.0 clearml>=1.10.0rc0 matplotlib @@ -123,7 +123,7 @@ pandas = einops = einops transformers = - transformers<4.22 + transformers<4.22; python_version <= '3.10' mlflow = mlflow matplotlib = diff --git a/tests/test_transchex.py b/tests/test_transchex.py index 9ad847cdaa..8fb1f56715 100644 --- a/tests/test_transchex.py +++ b/tests/test_transchex.py @@ -18,7 +18,7 @@ from monai.networks import eval_mode from monai.networks.nets.transchex import Transchex -from tests.utils import skip_if_quick +from tests.utils import SkipIfAtLeastPyTorchVersion, skip_if_quick TEST_CASE_TRANSCHEX = [] for drop_out in [0.4]: @@ -46,6 +46,7 @@ @skip_if_quick +@SkipIfAtLeastPyTorchVersion((1, 10)) class TestTranschex(unittest.TestCase): @parameterized.expand(TEST_CASE_TRANSCHEX) def test_shape(self, input_param, expected_shape): From d5585c34fc44f46af9ffb28522ce44c7ed869654 Mon Sep 17 00:00:00 2001 From: Kaibo Tang Date: Tue, 5 Dec 2023 03:46:24 -0500 Subject: [PATCH 003/136] 7263 add diffusion loss (#7272) Fixes #7263. ### Description Add diffusion loss. I also made a [demo notebook](https://github.com/kvttt/deep-atlas/blob/main/diffusion_loss_scale_test.ipynb) to provide some explanations and analyses of diffusion loss. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: kaibo Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/source/losses.rst | 5 ++ monai/losses/__init__.py | 2 +- monai/losses/deform.py | 82 +++++++++++++++++++++++++ tests/test_diffusion_loss.py | 116 +++++++++++++++++++++++++++++++++++ 4 files changed, 204 insertions(+), 1 deletion(-) create mode 100644 tests/test_diffusion_loss.py diff --git a/docs/source/losses.rst b/docs/source/losses.rst index 568c7dfc77..e929e9d605 100644 --- a/docs/source/losses.rst +++ b/docs/source/losses.rst @@ -96,6 +96,11 @@ Registration Losses .. autoclass:: BendingEnergyLoss :members: +`DiffusionLoss` +~~~~~~~~~~~~~~~ +.. autoclass:: DiffusionLoss + :members: + `LocalNormalizedCrossCorrelationLoss` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: LocalNormalizedCrossCorrelationLoss diff --git a/monai/losses/__init__.py b/monai/losses/__init__.py index d734a9d44d..92898c81ca 100644 --- a/monai/losses/__init__.py +++ b/monai/losses/__init__.py @@ -14,7 +14,7 @@ from .adversarial_loss import PatchAdversarialLoss from .cldice import SoftclDiceLoss, SoftDiceclDiceLoss from .contrastive import ContrastiveLoss -from .deform import BendingEnergyLoss +from .deform import BendingEnergyLoss, DiffusionLoss from .dice import ( Dice, DiceCELoss, diff --git a/monai/losses/deform.py b/monai/losses/deform.py index dd03a8eb3d..129abeedd2 100644 --- a/monai/losses/deform.py +++ b/monai/losses/deform.py @@ -116,3 +116,85 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor: raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].') return energy + + +class DiffusionLoss(_Loss): + """ + Calculate the diffusion based on first-order differentiation of pred using central finite difference. + For the original paper, please refer to + VoxelMorph: A Learning Framework for Deformable Medical Image Registration, + Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca + IEEE TMI: Transactions on Medical Imaging. 2019. eprint arXiv:1809.05231. + + Adapted from: + VoxelMorph (https://github.com/voxelmorph/voxelmorph) + """ + + def __init__(self, normalize: bool = False, reduction: LossReduction | str = LossReduction.MEAN) -> None: + """ + Args: + normalize: + Whether to divide out spatial sizes in order to make the computation roughly + invariant to image scale (i.e. vector field sampling resolution). Defaults to False. + reduction: {``"none"``, ``"mean"``, ``"sum"``} + Specifies the reduction to apply to the output. Defaults to ``"mean"``. + + - ``"none"``: no reduction will be applied. + - ``"mean"``: the sum of the output will be divided by the number of elements in the output. + - ``"sum"``: the output will be summed. + """ + super().__init__(reduction=LossReduction(reduction).value) + self.normalize = normalize + + def forward(self, pred: torch.Tensor) -> torch.Tensor: + """ + Args: + pred: + Predicted dense displacement field (DDF) with shape BCH[WD], + where C is the number of spatial dimensions. + Note that diffusion loss can only be calculated + when the sizes of the DDF along all spatial dimensions are greater than 2. + + Raises: + ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"]. + ValueError: When ``pred`` is not 3-d, 4-d or 5-d. + ValueError: When any spatial dimension of ``pred`` has size less than or equal to 2. + ValueError: When the number of channels of ``pred`` does not match the number of spatial dimensions. + + """ + if pred.ndim not in [3, 4, 5]: + raise ValueError(f"Expecting 3-d, 4-d or 5-d pred, instead got pred of shape {pred.shape}") + for i in range(pred.ndim - 2): + if pred.shape[-i - 1] <= 2: + raise ValueError(f"All spatial dimensions must be > 2, got spatial dimensions {pred.shape[2:]}") + if pred.shape[1] != pred.ndim - 2: + raise ValueError( + f"Number of vector components, i.e. number of channels of the input DDF, {pred.shape[1]}, " + f"does not match number of spatial dimensions, {pred.ndim - 2}" + ) + + # first order gradient + first_order_gradient = [spatial_gradient(pred, dim) for dim in range(2, pred.ndim)] + + # spatial dimensions in a shape suited for broadcasting below + if self.normalize: + spatial_dims = torch.tensor(pred.shape, device=pred.device)[2:].reshape((1, -1) + (pred.ndim - 2) * (1,)) + + diffusion = torch.tensor(0) + for dim_1, g in enumerate(first_order_gradient): + dim_1 += 2 + if self.normalize: + # We divide the partial derivative for each vector component at each voxel by the spatial size + # corresponding to that component relative to the spatial size of the vector component with respect + # to which the partial derivative is taken. + g *= pred.shape[dim_1] / spatial_dims + diffusion = diffusion + g**2 + + if self.reduction == LossReduction.MEAN.value: + diffusion = torch.mean(diffusion) # the batch and channel average + elif self.reduction == LossReduction.SUM.value: + diffusion = torch.sum(diffusion) # sum over the batch and channel dims + elif self.reduction != LossReduction.NONE.value: + raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].') + + return diffusion diff --git a/tests/test_diffusion_loss.py b/tests/test_diffusion_loss.py new file mode 100644 index 0000000000..05dfab95fb --- /dev/null +++ b/tests/test_diffusion_loss.py @@ -0,0 +1,116 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.losses.deform import DiffusionLoss + +device = "cuda" if torch.cuda.is_available() else "cpu" + +TEST_CASES = [ + # all first partials are zero, so the diffusion loss is also zero + [{}, {"pred": torch.ones((1, 3, 5, 5, 5), device=device)}, 0.0], + # all first partials are one, so the diffusion loss is also one + [{}, {"pred": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5)}, 1.0], + # before expansion, the first partials are 2, 4, 6, so the diffusion loss is (2^2 + 4^2 + 6^2) / 3 = 18.67 + [ + {"normalize": False}, + {"pred": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5) ** 2}, + 56.0 / 3.0, + ], + # same as the previous case + [ + {"normalize": False}, + {"pred": torch.arange(0, 5, device=device)[None, None, None, :].expand(1, 2, 5, 5) ** 2}, + 56.0 / 3.0, + ], + # same as the previous case + [{"normalize": False}, {"pred": torch.arange(0, 5, device=device)[None, None, :].expand(1, 1, 5) ** 2}, 56.0 / 3.0], + # we have shown in the demo notebook that + # diffusion loss is scale-invariant when the all axes have the same resolution + [ + {"normalize": True}, + {"pred": torch.arange(0, 5, device=device)[None, None, None, None, :].expand(1, 3, 5, 5, 5) ** 2}, + 56.0 / 3.0, + ], + [ + {"normalize": True}, + {"pred": torch.arange(0, 5, device=device)[None, None, None, :].expand(1, 2, 5, 5) ** 2}, + 56.0 / 3.0, + ], + [{"normalize": True}, {"pred": torch.arange(0, 5, device=device)[None, None, :].expand(1, 1, 5) ** 2}, 56.0 / 3.0], + # for the following case, consider the following 2D matrix: + # tensor([[[[0, 1, 2], + # [1, 2, 3], + # [2, 3, 4], + # [3, 4, 5], + # [4, 5, 6]], + # [[0, 1, 2], + # [1, 2, 3], + # [2, 3, 4], + # [3, 4, 5], + # [4, 5, 6]]]]) + # the first partials wrt x are all ones, and so are the first partials wrt y + # the diffusion loss, when normalization is not applied, is 1^2 + 1^2 = 2 + [{"normalize": False}, {"pred": torch.stack([torch.arange(i, i + 3) for i in range(5)]).expand(1, 2, 5, 3)}, 2.0], + # consider the same matrix, this time with normalization applied, using the same notation as in the demo notebook, + # the coefficients to be divided out are (1, 5/3) for partials wrt x and (3/5, 1) for partials wrt y + # the diffusion loss is then (1/1)^2 + (1/(5/3))^2 + (1/(3/5))^2 + (1/1)^2 = (1 + 9/25 + 25/9 + 1) / 2 = 2.5689 + [ + {"normalize": True}, + {"pred": torch.stack([torch.arange(i, i + 3) for i in range(5)]).expand(1, 2, 5, 3)}, + (1.0 + 9.0 / 25.0 + 25.0 / 9.0 + 1.0) / 2.0, + ], +] + + +class TestDiffusionLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) + def test_shape(self, input_param, input_data, expected_val): + result = DiffusionLoss(**input_param).forward(**input_data) + np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5) + + def test_ill_shape(self): + loss = DiffusionLoss() + # not in 3-d, 4-d, 5-d + with self.assertRaisesRegex(ValueError, "Expecting 3-d, 4-d or 5-d"): + loss.forward(torch.ones((1, 3), device=device)) + with self.assertRaisesRegex(ValueError, "Expecting 3-d, 4-d or 5-d"): + loss.forward(torch.ones((1, 4, 5, 5, 5, 5), device=device)) + with self.assertRaisesRegex(ValueError, "All spatial dimensions"): + loss.forward(torch.ones((1, 3, 2, 5, 5), device=device)) + with self.assertRaisesRegex(ValueError, "All spatial dimensions"): + loss.forward(torch.ones((1, 3, 5, 2, 5))) + with self.assertRaisesRegex(ValueError, "All spatial dimensions"): + loss.forward(torch.ones((1, 3, 5, 5, 2))) + + # number of vector components unequal to number of spatial dims + with self.assertRaisesRegex(ValueError, "Number of vector components"): + loss.forward(torch.ones((1, 2, 5, 5, 5))) + with self.assertRaisesRegex(ValueError, "Number of vector components"): + loss.forward(torch.ones((1, 2, 5, 5, 5))) + + def test_ill_opts(self): + pred = torch.rand(1, 3, 5, 5, 5).to(device=device) + with self.assertRaisesRegex(ValueError, ""): + DiffusionLoss(reduction="unknown")(pred) + with self.assertRaisesRegex(ValueError, ""): + DiffusionLoss(reduction=None)(pred) + + +if __name__ == "__main__": + unittest.main() From 82124587c20eb5f86af2a8667fbc075978bc110b Mon Sep 17 00:00:00 2001 From: Yufan He <59374597+heyufan1995@users.noreply.github.com> Date: Thu, 7 Dec 2023 21:36:21 -0500 Subject: [PATCH 004/136] Fix swinunetrv2 2D bug (#7302) Fixes # . ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: heyufan1995 --- monai/networks/nets/swin_unetr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/networks/nets/swin_unetr.py b/monai/networks/nets/swin_unetr.py index 10c4ce3d8e..6f96dfd291 100644 --- a/monai/networks/nets/swin_unetr.py +++ b/monai/networks/nets/swin_unetr.py @@ -1024,7 +1024,7 @@ def __init__( self.layers4.append(layer) if self.use_v2: layerc = UnetrBasicBlock( - spatial_dims=3, + spatial_dims=spatial_dims, in_channels=embed_dim * 2**i_layer, out_channels=embed_dim * 2**i_layer, kernel_size=3, From b1c8b42d03bf3c1daabd6c39a35fcbdadb57e8b0 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 12 Dec 2023 11:07:04 +0800 Subject: [PATCH 005/136] Fix `RuntimeError` in `DataAnalyzer` (#7310) Fixes #7309 ### Description `DataAnalyzer` only catch error when data is on GPU, add catching error when data is on CPU. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/auto3dseg/data_analyzer.py | 26 ++++++++++++++++---------- monai/auto3dseg/analyzer.py | 2 +- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/monai/apps/auto3dseg/data_analyzer.py b/monai/apps/auto3dseg/data_analyzer.py index 9280fb5be5..15e56abfea 100644 --- a/monai/apps/auto3dseg/data_analyzer.py +++ b/monai/apps/auto3dseg/data_analyzer.py @@ -28,7 +28,7 @@ from monai.data import DataLoader, Dataset, partition_dataset from monai.data.utils import no_collation from monai.transforms import Compose, EnsureTyped, LoadImaged, Orientationd -from monai.utils import StrEnum, min_version, optional_import +from monai.utils import ImageMetaKey, StrEnum, min_version, optional_import from monai.utils.enums import DataStatsKeys, ImageStatsKeys @@ -343,19 +343,25 @@ def _get_all_case_stats( d = summarizer(batch_data) except BaseException as err: if "image_meta_dict" in batch_data.keys(): - filename = batch_data["image_meta_dict"]["filename_or_obj"] + filename = batch_data["image_meta_dict"][ImageMetaKey.FILENAME_OR_OBJ] else: - filename = batch_data[self.image_key].meta["filename_or_obj"] + filename = batch_data[self.image_key].meta[ImageMetaKey.FILENAME_OR_OBJ] logger.info(f"Unable to process data {filename} on {device}. {err}") if self.device.type == "cuda": logger.info("DataAnalyzer `device` set to GPU execution hit an exception. Falling back to `cpu`.") - batch_data[self.image_key] = batch_data[self.image_key].to("cpu") - if self.label_key is not None: - label = batch_data[self.label_key] - if not _label_argmax: - label = torch.argmax(label, dim=0) if label.shape[0] > 1 else label[0] - batch_data[self.label_key] = label.to("cpu") - d = summarizer(batch_data) + try: + batch_data[self.image_key] = batch_data[self.image_key].to("cpu") + if self.label_key is not None: + label = batch_data[self.label_key] + if not _label_argmax: + label = torch.argmax(label, dim=0) if label.shape[0] > 1 else label[0] + batch_data[self.label_key] = label.to("cpu") + d = summarizer(batch_data) + except BaseException as err: + logger.info(f"Unable to process data {filename} on {device}. {err}") + continue + else: + continue stats_by_cases = { DataStatsKeys.BY_CASE_IMAGE_PATH: d[DataStatsKeys.BY_CASE_IMAGE_PATH], diff --git a/monai/auto3dseg/analyzer.py b/monai/auto3dseg/analyzer.py index 654999d439..d5cfb21dab 100644 --- a/monai/auto3dseg/analyzer.py +++ b/monai/auto3dseg/analyzer.py @@ -460,7 +460,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe torch.set_grad_enabled(False) ndas: list[MetaTensor] = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])] # type: ignore - ndas_label: MetaTensor = d[self.label_key] # (H,W,D) + ndas_label: MetaTensor = d[self.label_key].astype(torch.int8) # (H,W,D) if ndas_label.shape != ndas[0].shape: raise ValueError(f"Label shape {ndas_label.shape} is different from image shape {ndas[0].shape}") From 6126a6e51a8c85a2105a104389eec82eeb20ce9b Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 14 Dec 2023 10:14:19 +0800 Subject: [PATCH 006/136] Support specified filenames in `Saveimage` (#7318) Fixes #7317 ### Description Add support specified filename for users to save like nibabel.save. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/transforms/io/array.py | 17 ++++++++++++++--- tests/test_save_image.py | 16 ++++++++++++++++ 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py index cd7e4ef090..7222a26fc3 100644 --- a/monai/transforms/io/array.py +++ b/monai/transforms/io/array.py @@ -414,6 +414,9 @@ def __init__( self.fname_formatter = output_name_formatter self.output_ext = output_ext.lower() or output_format.lower() + self.output_ext = ( + f".{self.output_ext}" if self.output_ext and not self.output_ext.startswith(".") else self.output_ext + ) if isinstance(writer, str): writer_, has_built_in = optional_import("monai.data", name=f"{writer}") # search built-in if not has_built_in: @@ -458,15 +461,23 @@ def set_options(self, init_kwargs=None, data_kwargs=None, meta_kwargs=None, writ self.write_kwargs.update(write_kwargs) return self - def __call__(self, img: torch.Tensor | np.ndarray, meta_data: dict | None = None): + def __call__( + self, img: torch.Tensor | np.ndarray, meta_data: dict | None = None, filename: str | PathLike | None = None + ): """ Args: img: target data content that save into file. The image should be channel-first, shape: `[C,H,W,[D]]`. meta_data: key-value pairs of metadata corresponding to the data. + filename: str or file-like object which to save img. + If specified, will ignore `self.output_name_formatter` and `self.folder_layout`. """ meta_data = img.meta if isinstance(img, MetaTensor) else meta_data - kw = self.fname_formatter(meta_data, self) - filename = self.folder_layout.filename(**kw) + if filename is not None: + filename = f"{filename}{self.output_ext}" + else: + kw = self.fname_formatter(meta_data, self) + filename = self.folder_layout.filename(**kw) + if meta_data: meta_spatial_shape = ensure_tuple(meta_data.get("spatial_shape", ())) if len(meta_spatial_shape) >= len(img.shape): diff --git a/tests/test_save_image.py b/tests/test_save_image.py index ba94ab5087..d88db201ce 100644 --- a/tests/test_save_image.py +++ b/tests/test_save_image.py @@ -37,6 +37,8 @@ False, ] +TEST_CASE_5 = [torch.randint(0, 255, (3, 2, 4, 5), dtype=torch.uint8), ".dcm", False] + @unittest.skipUnless(has_itk, "itk not installed") class TestSaveImage(unittest.TestCase): @@ -58,6 +60,20 @@ def test_saved_content(self, test_data, meta_data, output_ext, resample): filepath = "testfile0" if meta_data is not None else "0" self.assertTrue(os.path.exists(os.path.join(tempdir, filepath + "_trans" + output_ext))) + @parameterized.expand([TEST_CASE_5]) + def test_saved_content_with_filename(self, test_data, output_ext, resample): + with tempfile.TemporaryDirectory() as tempdir: + trans = SaveImage( + output_dir=tempdir, + output_ext=output_ext, + resample=resample, + separate_folder=False, # test saving into the same folder + ) + filename = str(os.path.join(tempdir, "test")) + trans(test_data, filename=filename) + + self.assertTrue(os.path.exists(filename + output_ext)) + if __name__ == "__main__": unittest.main() From 4c546b3af0818767ce1cb6136346caf06461854e Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 15 Dec 2023 11:32:18 +0800 Subject: [PATCH 007/136] Fix typo (#7321) Fix typo. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/metrics/hausdorff_distance.py | 2 +- monai/metrics/surface_dice.py | 2 +- monai/metrics/surface_distance.py | 2 +- monai/metrics/utils.py | 6 +++--- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/monai/metrics/hausdorff_distance.py b/monai/metrics/hausdorff_distance.py index d9bbf17db3..d727eb0567 100644 --- a/monai/metrics/hausdorff_distance.py +++ b/monai/metrics/hausdorff_distance.py @@ -190,7 +190,7 @@ def compute_hausdorff_distance( y[b, c], distance_metric=distance_metric, spacing=spacing_list[b], - symetric=not directed, + symmetric=not directed, class_index=c, ) percentile_distances = [_compute_percentile_hausdorff_distance(d, percentile) for d in distances] diff --git a/monai/metrics/surface_dice.py b/monai/metrics/surface_dice.py index 635eb1bc24..b20b47a1a5 100644 --- a/monai/metrics/surface_dice.py +++ b/monai/metrics/surface_dice.py @@ -253,7 +253,7 @@ def compute_surface_dice( distance_metric=distance_metric, spacing=spacing_list[b], use_subvoxels=use_subvoxels, - symetric=True, + symmetric=True, class_index=c, ) boundary_correct: int | torch.Tensor | float diff --git a/monai/metrics/surface_distance.py b/monai/metrics/surface_distance.py index 7ce632c588..3cb336d6a0 100644 --- a/monai/metrics/surface_distance.py +++ b/monai/metrics/surface_distance.py @@ -177,7 +177,7 @@ def compute_average_surface_distance( y[b, c], distance_metric=distance_metric, spacing=spacing_list[b], - symetric=symmetric, + symmetric=symmetric, class_index=c, ) surface_distance = torch.cat(distances) diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py index 62e6520b96..d4b8f6e9b6 100644 --- a/monai/metrics/utils.py +++ b/monai/metrics/utils.py @@ -295,7 +295,7 @@ def get_edge_surface_distance( distance_metric: str = "euclidean", spacing: int | float | np.ndarray | Sequence[int | float] | None = None, use_subvoxels: bool = False, - symetric: bool = False, + symmetric: bool = False, class_index: int = -1, ) -> tuple[ tuple[torch.Tensor, torch.Tensor], @@ -314,7 +314,7 @@ def get_edge_surface_distance( See :py:func:`monai.metrics.utils.get_surface_distance`. use_subvoxels: whether to use subvoxel resolution (using the spacing). This will return the areas of the edges. - symetric: whether to compute the surface distance from `y_pred` to `y` and from `y` to `y_pred`. + symmetric: whether to compute the surface distance from `y_pred` to `y` and from `y` to `y_pred`. class_index: The class-index used for context when warning about empty ground truth or prediction. Returns: @@ -338,7 +338,7 @@ def get_edge_surface_distance( " this may result in nan/inf distance." ) distances: tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor] - if symetric: + if symmetric: distances = ( get_surface_distance(edges_pred, edges_gt, distance_metric, spacing), get_surface_distance(edges_gt, edges_pred, distance_metric, spacing), From 01a0ee1cc2c6f97b690cd0c04222d60d01198df7 Mon Sep 17 00:00:00 2001 From: binliunls <107988372+binliunls@users.noreply.github.com> Date: Fri, 15 Dec 2023 22:00:24 +0800 Subject: [PATCH 008/136] fix optimizer pararmeter issue (#7322) Fixes # . ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: binliu --- monai/handlers/mlflow_handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/handlers/mlflow_handler.py b/monai/handlers/mlflow_handler.py index a2bd345dc6..df209c1c8b 100644 --- a/monai/handlers/mlflow_handler.py +++ b/monai/handlers/mlflow_handler.py @@ -401,7 +401,7 @@ def _default_iteration_log(self, engine: Engine) -> None: cur_optimizer = engine.optimizer for param_name in self.optimizer_param_names: params = { - f"{param_name} group_{i}": float(param_group[param_name]) + f"{param_name}_group_{i}": float(param_group[param_name]) for i, param_group in enumerate(cur_optimizer.param_groups) } self._log_metrics(params, step=engine.state.iteration) From 90d2acb54d5325e6727f2cc2329cb40adc8577af Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 18 Dec 2023 12:00:43 +0800 Subject: [PATCH 009/136] Fix `lazy` ignored in `SpatialPadd` (#7316) Fixes #7314 #7315. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Ben Murray --- monai/transforms/croppad/dictionary.py | 9 +++------ tests/padders.py | 3 +++ 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/monai/transforms/croppad/dictionary.py b/monai/transforms/croppad/dictionary.py index 56d214c51d..be9441dc4a 100644 --- a/monai/transforms/croppad/dictionary.py +++ b/monai/transforms/croppad/dictionary.py @@ -221,9 +221,8 @@ def __init__( note that `np.pad` treats channel dimension as the first dimension. """ - LazyTransform.__init__(self, lazy) padder = SpatialPad(spatial_size, method, lazy=lazy, **kwargs) - Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys) + Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys, lazy=lazy) class BorderPadd(Padd): @@ -274,9 +273,8 @@ def __init__( note that `np.pad` treats channel dimension as the first dimension. """ - LazyTransform.__init__(self, lazy) padder = BorderPad(spatial_border=spatial_border, lazy=lazy, **kwargs) - Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys) + Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys, lazy=lazy) class DivisiblePadd(Padd): @@ -324,9 +322,8 @@ def __init__( See also :py:class:`monai.transforms.SpatialPad` """ - LazyTransform.__init__(self, lazy) padder = DivisiblePad(k=k, method=method, lazy=lazy, **kwargs) - Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys) + Padd.__init__(self, keys, padder=padder, mode=mode, allow_missing_keys=allow_missing_keys, lazy=lazy) class Cropd(MapTransform, InvertibleTransform, LazyTransform): diff --git a/tests/padders.py b/tests/padders.py index 02d7b40af6..ae1153bdfd 100644 --- a/tests/padders.py +++ b/tests/padders.py @@ -136,6 +136,9 @@ def pad_test_pending_ops(self, input_param, input_shape): # TODO: mode="bilinear" may report error overrides = {"mode": "nearest", "padding_mode": mode[1], "align_corners": False} result = apply_pending(pending_result, overrides=overrides)[0] + # lazy in constructor + pad_fn_lazy = self.Padder(mode=mode[0], lazy=True, **input_param) + self.assertTrue(pad_fn_lazy.lazy) # compare assert_allclose(result, expected, rtol=1e-5) if isinstance(result, MetaTensor) and not isinstance(pad_fn, MapTransform): From f5327ad59a996508d6ca52a505ab798a98289987 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 28 Dec 2023 22:22:52 +0800 Subject: [PATCH 010/136] Update openslide-python version (#7344) --- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index cacbefe234..2639c0a3e7 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -27,7 +27,7 @@ ninja torchvision psutil cucim>=23.2.0; platform_system == "Linux" -openslide-python==1.1.2 +openslide-python imagecodecs; platform_system == "Linux" or platform_system == "Darwin" tifffile; platform_system == "Linux" or platform_system == "Darwin" pandas diff --git a/setup.cfg b/setup.cfg index 0370d0062d..b38974a1a4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -60,7 +60,7 @@ all = lmdb psutil cucim>=23.2.0 - openslide-python==1.1.2 + openslide-python tifffile imagecodecs pandas @@ -113,7 +113,7 @@ psutil = cucim = cucim>=23.2.0 openslide = - openslide-python==1.1.2 + openslide-python tifffile = tifffile imagecodecs = From b3d7a48afb15f6590e02302d3b048a4f62d1cdee Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 29 Dec 2023 12:33:46 +0800 Subject: [PATCH 011/136] Upgrade the version of `transformers` (#7343) Fixes #7338 ### Description transformers' version is pinned to v4.22 since https://github.com/Project-MONAI/MONAI/issues/5157. Updated the version refer to https://github.com/huggingface/transformers/issues/21678. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/nets/transchex.py | 49 +++++++++----------------------- requirements-dev.txt | 2 +- tests/test_transchex.py | 3 +- 3 files changed, 15 insertions(+), 39 deletions(-) diff --git a/monai/networks/nets/transchex.py b/monai/networks/nets/transchex.py index ff27903cef..6bfff3c956 100644 --- a/monai/networks/nets/transchex.py +++ b/monai/networks/nets/transchex.py @@ -12,20 +12,17 @@ from __future__ import annotations import math -import os -import shutil -import tarfile -import tempfile from collections.abc import Sequence import torch from torch import nn +from monai.config.type_definitions import PathLike from monai.utils import optional_import transformers = optional_import("transformers") load_tf_weights_in_bert = optional_import("transformers", name="load_tf_weights_in_bert")[0] -cached_path = optional_import("transformers.file_utils", name="cached_path")[0] +cached_file = optional_import("transformers.utils", name="cached_file")[0] BertEmbeddings = optional_import("transformers.models.bert.modeling_bert", name="BertEmbeddings")[0] BertLayer = optional_import("transformers.models.bert.modeling_bert", name="BertLayer")[0] @@ -63,44 +60,16 @@ def from_pretrained( state_dict=None, cache_dir=None, from_tf=False, + path_or_repo_id="bert-base-uncased", + filename="pytorch_model.bin", *inputs, **kwargs, ): - archive_file = "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz" - resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir) - tempdir = None - if os.path.isdir(resolved_archive_file) or from_tf: - serialization_dir = resolved_archive_file - else: - tempdir = tempfile.mkdtemp() - with tarfile.open(resolved_archive_file, "r:gz") as archive: - - def is_within_directory(directory, target): - abs_directory = os.path.abspath(directory) - abs_target = os.path.abspath(target) - - prefix = os.path.commonprefix([abs_directory, abs_target]) - - return prefix == abs_directory - - def safe_extract(tar, path=".", members=None, *, numeric_owner=False): - for member in tar.getmembers(): - member_path = os.path.join(path, member.name) - if not is_within_directory(path, member_path): - raise Exception("Attempted Path Traversal in Tar File") - - tar.extractall(path, members, numeric_owner=numeric_owner) - - safe_extract(archive, tempdir) - serialization_dir = tempdir + weights_path = cached_file(path_or_repo_id, filename, cache_dir=cache_dir) model = cls(num_language_layers, num_vision_layers, num_mixed_layers, bert_config, *inputs, **kwargs) if state_dict is None and not from_tf: - weights_path = os.path.join(serialization_dir, "pytorch_model.bin") state_dict = torch.load(weights_path, map_location="cpu" if not torch.cuda.is_available() else None) - if tempdir: - shutil.rmtree(tempdir) if from_tf: - weights_path = os.path.join(serialization_dir, "model.ckpt") return load_tf_weights_in_bert(model, weights_path) old_keys = [] new_keys = [] @@ -304,6 +273,8 @@ def __init__( chunk_size_feed_forward: int = 0, is_decoder: bool = False, add_cross_attention: bool = False, + path_or_repo_id: str | PathLike = "bert-base-uncased", + filename: str = "pytorch_model.bin", ) -> None: """ Args: @@ -315,6 +286,10 @@ def __init__( num_vision_layers: number of vision transformer layers. num_mixed_layers: number of mixed transformer layers. drop_out: fraction of the input units to drop. + path_or_repo_id: This can be either: + - a string, the *model id* of a model repo on huggingface.co. + - a path to a *directory* potentially containing the file. + filename: The name of the file to locate in `path_or_repo`. The other parameters are part of the `bert_config` to `MultiModal.from_pretrained`. @@ -369,6 +344,8 @@ def __init__( num_vision_layers=num_vision_layers, num_mixed_layers=num_mixed_layers, bert_config=bert_config, + path_or_repo_id=path_or_repo_id, + filename=filename, ) self.patch_size = patch_size diff --git a/requirements-dev.txt b/requirements-dev.txt index 2639c0a3e7..4685cd1572 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -33,7 +33,7 @@ tifffile; platform_system == "Linux" or platform_system == "Darwin" pandas requests einops -transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 +transformers>=4.36.0 mlflow>=1.28.0 clearml>=1.10.0rc0 matplotlib!=3.5.0 diff --git a/tests/test_transchex.py b/tests/test_transchex.py index 8fb1f56715..9ad847cdaa 100644 --- a/tests/test_transchex.py +++ b/tests/test_transchex.py @@ -18,7 +18,7 @@ from monai.networks import eval_mode from monai.networks.nets.transchex import Transchex -from tests.utils import SkipIfAtLeastPyTorchVersion, skip_if_quick +from tests.utils import skip_if_quick TEST_CASE_TRANSCHEX = [] for drop_out in [0.4]: @@ -46,7 +46,6 @@ @skip_if_quick -@SkipIfAtLeastPyTorchVersion((1, 10)) class TestTranschex(unittest.TestCase): @parameterized.expand(TEST_CASE_TRANSCHEX) def test_shape(self, input_param, expected_shape): From 02973f0e2bac4f799bb9f522de5bb7427f257ed4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jan 2024 03:34:50 +0000 Subject: [PATCH 012/136] Bump github/codeql-action from 2 to 3 (#7354) Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3.
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CodeQL Bundle v2.15.5

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/codeql-analysis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index 3d32ae407a..18f1519b5a 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -42,7 +42,7 @@ jobs: # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v2 + uses: github/codeql-action/init@v3 with: languages: ${{ matrix.language }} # If you wish to specify custom queries, you can do so here or in a config file. @@ -72,4 +72,4 @@ jobs: BUILD_MONAI=1 ./runtests.sh --build - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v2 + uses: github/codeql-action/analyze@v3 From 38ac573dcd76181ad5c8a9a3cbc294e7d2fdb80d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jan 2024 06:42:41 +0000 Subject: [PATCH 013/136] Bump actions/upload-artifact from 3 to 4 (#7350) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 3 to 4.
Release notes

Sourced from actions/upload-artifact's releases.

v4.0.0

What's Changed

The release of upload-artifact@v4 and download-artifact@v4 are major changes to the backend architecture of Artifacts. They have numerous performance and behavioral improvements.

For more information, see the @​actions/artifact documentation.

New Contributors

Full Changelog: https://github.com/actions/upload-artifact/compare/v3...v4.0.0

v3.1.3

What's Changed

Full Changelog: https://github.com/actions/upload-artifact/compare/v3...v3.1.3

v3.1.2

  • Update all @actions/* NPM packages to their latest versions- #374
  • Update all dev dependencies to their most recent versions - #375

v3.1.1

  • Update actions/core package to latest version to remove set-output deprecation warning #351

v3.1.0

What's Changed

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/upload-artifact&package-manager=github_actions&previous-version=3&new-version=4)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/docker.yml | 2 +- .github/workflows/release.yml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index f51e4fdf76..f80a4c2c96 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -36,7 +36,7 @@ jobs: python setup.py build cat build/lib/monai/_version.py - name: Upload version - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: _version.py path: build/lib/monai/_version.py diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7197215486..e9817e1c4c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -66,7 +66,7 @@ jobs: - if: matrix.python-version == '3.9' && startsWith(github.ref, 'refs/tags/') name: Upload artifacts - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: dist path: dist/ @@ -108,7 +108,7 @@ jobs: python setup.py build cat build/lib/monai/_version.py - name: Upload version - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: _version.py path: build/lib/monai/_version.py From 454f89cddeac992481e5237150dc656f83b89c6e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jan 2024 17:29:09 +0800 Subject: [PATCH 014/136] Bump actions/setup-python from 4 to 5 (#7351) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5.
Release notes

Sourced from actions/setup-python's releases.

v5.0.0

What's Changed

In scope of this release, we update node version runtime from node16 to node20 (actions/setup-python#772). Besides, we update dependencies to the latest versions.

Full Changelog: https://github.com/actions/setup-python/compare/v4.8.0...v5.0.0

v4.8.0

What's Changed

In scope of this release we added support for GraalPy (actions/setup-python#694). You can use this snippet to set up GraalPy:

steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v4
  with:
    python-version: 'graalpy-22.3'
- run: python my_script.py

Besides, the release contains such changes as:

New Contributors

Full Changelog: https://github.com/actions/setup-python/compare/v4...v4.8.0

v4.7.1

What's Changed

Full Changelog: https://github.com/actions/setup-python/compare/v4...v4.7.1

v4.7.0

In scope of this release, the support for reading python version from pyproject.toml was added (actions/setup-python#669).

      - name: Setup Python
        uses: actions/setup-python@v4
</tr></table>

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/setup-python&package-manager=github_actions&previous-version=4&new-version=5)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/cron-ngc-bundle.yml | 2 +- .github/workflows/docker.yml | 2 +- .github/workflows/pythonapp-min.yml | 6 +++--- .github/workflows/pythonapp.yml | 8 ++++---- .github/workflows/release.yml | 4 ++-- .github/workflows/setupapp.yml | 4 ++-- .github/workflows/weekly-preview.yml | 2 +- 7 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.github/workflows/cron-ngc-bundle.yml b/.github/workflows/cron-ngc-bundle.yml index 0bba630d03..84666204a9 100644 --- a/.github/workflows/cron-ngc-bundle.yml +++ b/.github/workflows/cron-ngc-bundle.yml @@ -19,7 +19,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: cache weekly timestamp diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index f80a4c2c96..c375e82e74 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -26,7 +26,7 @@ jobs: ref: dev fetch-depth: 0 - name: Set up Python 3.9 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.9' - shell: bash diff --git a/.github/workflows/pythonapp-min.yml b/.github/workflows/pythonapp-min.yml index 558c270e33..7b7930bdf5 100644 --- a/.github/workflows/pythonapp-min.yml +++ b/.github/workflows/pythonapp-min.yml @@ -30,7 +30,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: Prepare pip wheel @@ -76,7 +76,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Prepare pip wheel @@ -121,7 +121,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: Prepare pip wheel diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index ad8b555dd4..29a79759e0 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -28,7 +28,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: cache weekly timestamp @@ -69,7 +69,7 @@ jobs: disk-root: "D:" - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: Prepare pip wheel @@ -128,7 +128,7 @@ jobs: with: fetch-depth: 0 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: cache weekly timestamp @@ -209,7 +209,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: cache weekly timestamp diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index e9817e1c4c..9334908bfc 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -19,7 +19,7 @@ jobs: with: fetch-depth: 0 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install setuptools @@ -97,7 +97,7 @@ jobs: with: fetch-depth: 0 - name: Set up Python 3.9 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.9' - shell: bash diff --git a/.github/workflows/setupapp.yml b/.github/workflows/setupapp.yml index 0ff7162bee..82394a86dd 100644 --- a/.github/workflows/setupapp.yml +++ b/.github/workflows/setupapp.yml @@ -83,7 +83,7 @@ jobs: with: fetch-depth: 0 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: cache weekly timestamp @@ -120,7 +120,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Set up Python 3.8 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.8' - name: cache weekly timestamp diff --git a/.github/workflows/weekly-preview.yml b/.github/workflows/weekly-preview.yml index c631982745..e94e1dac5a 100644 --- a/.github/workflows/weekly-preview.yml +++ b/.github/workflows/weekly-preview.yml @@ -14,7 +14,7 @@ jobs: ref: dev fetch-depth: 0 - name: Set up Python 3.9 - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.9' - name: Install setuptools From db6e81d9dbe0613c900cec664ad6e96fa277c9a4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jan 2024 23:42:25 +0800 Subject: [PATCH 015/136] Bump actions/download-artifact from 3 to 4 (#7352) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 3 to 4.
Release notes

Sourced from actions/download-artifact's releases.

v4.0.0

What's Changed

The release of upload-artifact@v4 and download-artifact@v4 are major changes to the backend architecture of Artifacts. They have numerous performance and behavioral improvements.

For more information, see the @​actions/artifact documentation.

New Contributors

Full Changelog: https://github.com/actions/download-artifact/compare/v3...v4.0.0

v3.0.2

  • Bump @actions/artifact to v1.1.1 - actions/download-artifact#195
  • Fixed a bug in Node16 where if an HTTP download finished too quickly (<1ms, e.g. when it's mocked) we attempt to delete a temp file that has not been created yet actions/toolkit#1278

v3.0.1

Commits
  • f44cd7b Merge pull request #259 from actions/robherley/glob-downloads
  • 3181fe8 add some migration docs
  • aaaac7b licensed cache
  • 7c9182f update readme
  • b94e701 licensed cache
  • 0b55470 add test case for globbed downloads to same directory
  • 0b51c2e update prettier/eslint versions
  • c4c6db7 support globbing artifact list & merging download directory
  • 1bd0606 Merge pull request #252 from stchr/patch-1
  • eff4d42 fix default for run-id
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/download-artifact&package-manager=github_actions&previous-version=3&new-version=4)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/docker.yml | 2 +- .github/workflows/release.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index c375e82e74..229ae675f5 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -56,7 +56,7 @@ jobs: with: ref: dev - name: Download version - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: _version.py - name: docker_build diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 9334908bfc..a03d2cea6c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -125,7 +125,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Download version - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: _version.py - name: Set tag From 8fa6931b14ba9617a595fff1d396ac44cc82e207 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jan 2024 10:41:55 +0800 Subject: [PATCH 016/136] Bump peter-evans/slash-command-dispatch from 3.0.1 to 3.0.2 (#7353) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [peter-evans/slash-command-dispatch](https://github.com/peter-evans/slash-command-dispatch) from 3.0.1 to 3.0.2.
Release notes

Sourced from peter-evans/slash-command-dispatch's releases.

Slash Command Dispatch v3.0.2

What's Changed

New Contributors

Full Changelog: https://github.com/peter-evans/slash-command-dispatch/compare/v3.0.1...v3.0.2

Commits
  • f996d7b Fix the CollaboratorPermission GraphQL query (#301)
  • 05b97d6 build(deps-dev): bump @​types/node from 16.18.65 to 16.18.67 (#300)
  • 8e70073 build(deps-dev): bump eslint from 8.54.0 to 8.55.0 (#299)
  • bd00135 build(deps-dev): bump @​types/node from 16.18.62 to 16.18.65 (#298)
  • ee873b6 build(deps-dev): bump eslint from 8.53.0 to 8.54.0 (#296)
  • 44abc47 build(deps-dev): bump @​types/node from 16.18.61 to 16.18.62 (#295)
  • 19ad7b8 build(deps-dev): bump @​types/node from 16.18.60 to 16.18.61 (#294)
  • 29a9815 build(deps-dev): bump prettier from 3.0.3 to 3.1.0 (#293)
  • ade0309 build(deps-dev): bump eslint from 8.52.0 to 8.53.0 (#292)
  • fc8222e build(deps-dev): bump @​types/node from 16.18.59 to 16.18.60 (#291)
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=peter-evans/slash-command-dispatch&package-manager=github_actions&previous-version=3.0.1&new-version=3.0.2)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/chatops.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/chatops.yml b/.github/workflows/chatops.yml index b4e201a0d9..59c7d070b4 100644 --- a/.github/workflows/chatops.yml +++ b/.github/workflows/chatops.yml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest steps: - name: dispatch - uses: peter-evans/slash-command-dispatch@v3.0.1 + uses: peter-evans/slash-command-dispatch@v3.0.2 with: token: ${{ secrets.PR_MAINTAIN }} reaction-token: ${{ secrets.GITHUB_TOKEN }} From 445d7505f0ab51558c6820ec6fb945438d7a6d76 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:18:45 +0800 Subject: [PATCH 017/136] Give more useful exception when batch is considered during matrix multiplication (#7326) Fixes #7323 ### Description Give more useful exception when batch is considered during matrix multiplication. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/transforms/inverse.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/monai/transforms/inverse.py b/monai/transforms/inverse.py index 41fabb35aa..f94f11eca9 100644 --- a/monai/transforms/inverse.py +++ b/monai/transforms/inverse.py @@ -185,7 +185,17 @@ def track_transform_meta( # not lazy evaluation, directly update the metatensor affine (don't push to the stack) orig_affine = data_t.peek_pending_affine() orig_affine = convert_to_dst_type(orig_affine, affine, dtype=torch.float64)[0] - affine = orig_affine @ to_affine_nd(len(orig_affine) - 1, affine, dtype=torch.float64) + try: + affine = orig_affine @ to_affine_nd(len(orig_affine) - 1, affine, dtype=torch.float64) + except RuntimeError as e: + if orig_affine.ndim > 2: + if data_t.is_batch: + msg = "Transform applied to batched tensor, should be applied to instances only" + else: + msg = "Mismatch affine matrix, ensured that the batch dimension is not included in the calculation." + raise RuntimeError(msg) from e + else: + raise out_obj.meta[MetaKeys.AFFINE] = convert_to_tensor(affine, device=torch.device("cpu"), dtype=torch.float64) if not (get_track_meta() and transform_info and transform_info.get(TraceKeys.TRACING)): From d7137cf410617f4ec54c621d9511e982855a2892 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 9 Jan 2024 11:13:14 +0800 Subject: [PATCH 018/136] Fix incorrectly size compute in auto3dseg analyzer (#7374) Fixes #7222 ### Description remove int convert here. https://github.com/Project-MONAI/MONAI/blob/8fa6931b14ba9617a595fff1d396ac44cc82e207/monai/auto3dseg/analyzer.py#L259 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/auto3dseg/analyzer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/auto3dseg/analyzer.py b/monai/auto3dseg/analyzer.py index d5cfb21dab..56419da4cb 100644 --- a/monai/auto3dseg/analyzer.py +++ b/monai/auto3dseg/analyzer.py @@ -256,7 +256,7 @@ def __call__(self, data): ) report[ImageStatsKeys.SIZEMM] = [ - int(a * b) for a, b in zip(report[ImageStatsKeys.SHAPE][0], report[ImageStatsKeys.SPACING]) + a * b for a, b in zip(report[ImageStatsKeys.SHAPE][0], report[ImageStatsKeys.SPACING]) ] report[ImageStatsKeys.INTENSITY] = [ From e1ffa7ebd38963bdced326d86979f5a9e2851517 Mon Sep 17 00:00:00 2001 From: Kaibo Tang Date: Tue, 9 Jan 2024 22:41:30 -0500 Subject: [PATCH 019/136] 7380 mention demo in bending energy and diffusion docstrings (#7381) Fixes #7380. ### Description Mention [demo](https://github.com/Project-MONAI/tutorials/blob/main/modules/bending_energy_diffusion_loss_notes.ipynb) in bending energy and diffusion docstrings. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: kaibo --- monai/losses/deform.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/monai/losses/deform.py b/monai/losses/deform.py index 129abeedd2..37e4468d4b 100644 --- a/monai/losses/deform.py +++ b/monai/losses/deform.py @@ -46,7 +46,10 @@ def spatial_gradient(x: torch.Tensor, dim: int) -> torch.Tensor: class BendingEnergyLoss(_Loss): """ - Calculate the bending energy based on second-order differentiation of pred using central finite difference. + Calculate the bending energy based on second-order differentiation of ``pred`` using central finite difference. + + For more information, + see https://github.com/Project-MONAI/tutorials/blob/main/modules/bending_energy_diffusion_loss_notes.ipynb. Adapted from: DeepReg (https://github.com/DeepRegNet/DeepReg) @@ -75,6 +78,9 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor: Raises: ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"]. + ValueError: When ``pred`` is not 3-d, 4-d or 5-d. + ValueError: When any spatial dimension of ``pred`` has size less than or equal to 4. + ValueError: When the number of channels of ``pred`` does not match the number of spatial dimensions. """ if pred.ndim not in [3, 4, 5]: @@ -84,7 +90,8 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor: raise ValueError(f"All spatial dimensions must be > 4, got spatial dimensions {pred.shape[2:]}") if pred.shape[1] != pred.ndim - 2: raise ValueError( - f"Number of vector components, {pred.shape[1]}, does not match number of spatial dimensions, {pred.ndim-2}" + f"Number of vector components, i.e. number of channels of the input DDF, {pred.shape[1]}, " + f"does not match number of spatial dimensions, {pred.ndim - 2}" ) # first order gradient @@ -120,12 +127,15 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor: class DiffusionLoss(_Loss): """ - Calculate the diffusion based on first-order differentiation of pred using central finite difference. + Calculate the diffusion based on first-order differentiation of ``pred`` using central finite difference. For the original paper, please refer to VoxelMorph: A Learning Framework for Deformable Medical Image Registration, Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca IEEE TMI: Transactions on Medical Imaging. 2019. eprint arXiv:1809.05231. + For more information, + see https://github.com/Project-MONAI/tutorials/blob/main/modules/bending_energy_diffusion_loss_notes.ipynb. + Adapted from: VoxelMorph (https://github.com/voxelmorph/voxelmorph) """ From 25f190148c7c3e1326e55e296ef7c374903b3c52 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 12 Jan 2024 20:25:42 +0800 Subject: [PATCH 020/136] Pin gdown version to v4.6.3 (#7384) Workaround for #7382 #7383 ### Description Based on the comment [here](https://github.com/wkentaro/gdown/issues/291#issuecomment-1887060708), pin the gdown version as a workaround. Will review this one once gdown has some update internal. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/utils.py | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/monai/apps/utils.py b/monai/apps/utils.py index d2dd63b958..442dbabba0 100644 --- a/monai/apps/utils.py +++ b/monai/apps/utils.py @@ -30,7 +30,7 @@ from monai.config.type_definitions import PathLike from monai.utils import look_up_option, min_version, optional_import -gdown, has_gdown = optional_import("gdown", "4.4") +gdown, has_gdown = optional_import("gdown", "4.6.3") if TYPE_CHECKING: from tqdm import tqdm diff --git a/requirements-dev.txt b/requirements-dev.txt index 4685cd1572..f8bc9d5a3e 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,7 +1,7 @@ # Full requirements for developments -r requirements-min.txt pytorch-ignite==0.4.11 -gdown>=4.4.0 +gdown>=4.4.0, <=4.6.3 scipy>=1.7.1 itk>=5.2 nibabel diff --git a/setup.cfg b/setup.cfg index b38974a1a4..1141dc0ef8 100644 --- a/setup.cfg +++ b/setup.cfg @@ -52,7 +52,7 @@ all = scipy>=1.7.1 pillow tensorboard - gdown>=4.4.0 + gdown==4.6.3 pytorch-ignite==0.4.11 torchvision itk>=5.2 @@ -97,7 +97,7 @@ pillow = tensorboard = tensorboard gdown = - gdown>=4.4.0 + gdown==4.6.3 ignite = pytorch-ignite==0.4.11 torchvision = From 78295c7bfbd86c79765bff228040004dcbbcaae3 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 18 Jan 2024 18:01:02 +0800 Subject: [PATCH 021/136] Fix Premerge (#7397) Fixes #7396 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- setup.cfg | 2 ++ tests/test_flexible_unet.py | 2 +- tests/test_invertd.py | 12 ++++++------ 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/setup.cfg b/setup.cfg index 1141dc0ef8..0069214de3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -173,6 +173,7 @@ max_line_length = 120 # B028 https://github.com/Project-MONAI/MONAI/issues/5855 # B907 https://github.com/Project-MONAI/MONAI/issues/5868 # B908 https://github.com/Project-MONAI/MONAI/issues/6503 +# B036 https://github.com/Project-MONAI/MONAI/issues/7396 ignore = E203 E501 @@ -186,6 +187,7 @@ ignore = B028 B907 B908 + B036 per_file_ignores = __init__.py: F401, __main__.py: F401 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py diff --git a/tests/test_flexible_unet.py b/tests/test_flexible_unet.py index 1218ce6e85..1d831f0976 100644 --- a/tests/test_flexible_unet.py +++ b/tests/test_flexible_unet.py @@ -39,7 +39,7 @@ class DummyEncoder(BaseEncoder): def get_encoder_parameters(cls): basic_dict = {"spatial_dims": 2, "in_channels": 3, "pretrained": False} param_dict_list = [basic_dict] - for key in basic_dict: + for key in basic_dict.keys(): cur_dict = basic_dict.copy() del cur_dict[key] param_dict_list.append(cur_dict) diff --git a/tests/test_invertd.py b/tests/test_invertd.py index cd2e91257a..2e6ee35981 100644 --- a/tests/test_invertd.py +++ b/tests/test_invertd.py @@ -112,15 +112,15 @@ def test_invert(self): self.assertTupleEqual(i.shape[1:], (101, 100, 107)) # check the case that different items use different interpolation mode to invert transforms - d = item["image_inverted1"] + j = item["image_inverted1"] # if the interpolation mode is nearest, accumulated diff should be smaller than 1 - self.assertLess(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 1.0) - self.assertTupleEqual(d.shape, (1, 101, 100, 107)) + self.assertLess(torch.sum(j.to(torch.float) - j.to(torch.uint8).to(torch.float)).item(), 1.0) + self.assertTupleEqual(j.shape, (1, 101, 100, 107)) - d = item["label_inverted1"] + k = item["label_inverted1"] # if the interpolation mode is not nearest, accumulated diff should be greater than 10000 - self.assertGreater(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 10000.0) - self.assertTupleEqual(d.shape, (1, 101, 100, 107)) + self.assertGreater(torch.sum(k.to(torch.float) - k.to(torch.uint8).to(torch.float)).item(), 10000.0) + self.assertTupleEqual(k.shape, (1, 101, 100, 107)) # check labels match reverted = item["label_inverted"].detach().cpu().numpy().astype(np.int32) From 80be1c37f07f58d1343f21dfb8a3fb07bef7ef22 Mon Sep 17 00:00:00 2001 From: "axel.vlaminck" Date: Thu, 18 Jan 2024 18:07:22 +0100 Subject: [PATCH 022/136] Track applied operations in image filter (#7395) Fixes #7394 ### Description When ImageFilter is in the transformation sequence it didn't pass the applied_operations. Now it is passed when present. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: axel.vlaminck --- monai/transforms/utility/array.py | 11 ++++++++--- tests/test_image_filter.py | 16 ++++++++++++++++ 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py index 2322f2123f..5dfbcb0e91 100644 --- a/monai/transforms/utility/array.py +++ b/monai/transforms/utility/array.py @@ -1562,17 +1562,22 @@ def __init__(self, filter: str | NdarrayOrTensor | nn.Module, filter_size: int | self.filter_size = filter_size self.additional_args_for_filter = kwargs - def __call__(self, img: NdarrayOrTensor, meta_dict: dict | None = None) -> NdarrayOrTensor: + def __call__( + self, img: NdarrayOrTensor, meta_dict: dict | None = None, applied_operations: list | None = None + ) -> NdarrayOrTensor: """ Args: img: torch tensor data to apply filter to with shape: [channels, height, width[, depth]] meta_dict: An optional dictionary with metadata + applied_operations: An optional list of operations that have been applied to the data Returns: A MetaTensor with the same shape as `img` and identical metadata """ if isinstance(img, MetaTensor): meta_dict = img.meta + applied_operations = img.applied_operations + img_, prev_type, device = convert_data_type(img, torch.Tensor) ndim = img_.ndim - 1 # assumes channel first format @@ -1582,8 +1587,8 @@ def __call__(self, img: NdarrayOrTensor, meta_dict: dict | None = None) -> Ndarr self.filter = ApplyFilter(self.filter) img_ = self._apply_filter(img_) - if meta_dict: - img_ = MetaTensor(img_, meta=meta_dict) + if meta_dict is not None or applied_operations is not None: + img_ = MetaTensor(img_, meta=meta_dict, applied_operations=applied_operations) else: img_, *_ = convert_data_type(img_, prev_type, device) return img_ diff --git a/tests/test_image_filter.py b/tests/test_image_filter.py index 841a5d5cd5..985ea95e79 100644 --- a/tests/test_image_filter.py +++ b/tests/test_image_filter.py @@ -17,6 +17,7 @@ import torch from parameterized import parameterized +from monai.data.meta_tensor import MetaTensor from monai.networks.layers.simplelayers import GaussianFilter from monai.transforms import ImageFilter, ImageFilterd, RandImageFilter, RandImageFilterd @@ -115,6 +116,21 @@ def test_call_3d(self, filter_name): out_tensor = filter(SAMPLE_IMAGE_3D) self.assertEqual(out_tensor.shape[1:], SAMPLE_IMAGE_3D.shape[1:]) + def test_pass_applied_operations(self): + "Test that applied operations are passed through" + applied_operations = ["op1", "op2"] + image = MetaTensor(SAMPLE_IMAGE_2D, applied_operations=applied_operations) + filter = ImageFilter(SUPPORTED_FILTERS[0], 3, **ADDITIONAL_ARGUMENTS) + out_tensor = filter(image) + self.assertEqual(out_tensor.applied_operations, applied_operations) + + def test_pass_empty_metadata_dict(self): + "Test that applied operations are passed through" + image = MetaTensor(SAMPLE_IMAGE_2D, meta={}) + filter = ImageFilter(SUPPORTED_FILTERS[0], 3, **ADDITIONAL_ARGUMENTS) + out_tensor = filter(image) + self.assertTrue(isinstance(out_tensor, MetaTensor)) + class TestImageFilterDict(unittest.TestCase): @parameterized.expand(SUPPORTED_FILTERS) From facf17693410d41170edd8e94364b4f341369aea Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 19 Jan 2024 16:00:13 +0800 Subject: [PATCH 023/136] Add `compile` support in `SupervisedTrainer` and `SupervisedEvaluator` (#7375) Fixes # . ### Description Add `compile` support in `SupervisedTrainer` and `SupervisedEvaluator`. Convert to `torch.Tensor` internally. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- monai/engines/evaluator.py | 51 +++++++++++++++++++++++++++++++++++-- monai/engines/trainer.py | 52 ++++++++++++++++++++++++++++++++++++-- 2 files changed, 99 insertions(+), 4 deletions(-) diff --git a/monai/engines/evaluator.py b/monai/engines/evaluator.py index 119853d5c5..2c8dfe6b85 100644 --- a/monai/engines/evaluator.py +++ b/monai/engines/evaluator.py @@ -11,12 +11,14 @@ from __future__ import annotations +import warnings from typing import TYPE_CHECKING, Any, Callable, Iterable, Sequence import torch from torch.utils.data import DataLoader from monai.config import IgniteInfo, KeysCollection +from monai.data import MetaTensor from monai.engines.utils import IterationEvents, default_metric_cmp_fn, default_prepare_batch from monai.engines.workflow import Workflow from monai.inferers import Inferer, SimpleInferer @@ -25,7 +27,7 @@ from monai.utils import ForwardMode, ensure_tuple, min_version, optional_import from monai.utils.enums import CommonKeys as Keys from monai.utils.enums import EngineStatsKeys as ESKeys -from monai.utils.module import look_up_option +from monai.utils.module import look_up_option, pytorch_after if TYPE_CHECKING: from ignite.engine import Engine, EventEnum @@ -213,6 +215,10 @@ class SupervisedEvaluator(Evaluator): `device`, `non_blocking`. amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details: https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast. + compile: whether to use `torch.compile`, default is False. If True, MetaTensor inputs will be converted to + `torch.Tensor` before forward pass, then converted back afterward with copied meta information. + compile_kwargs: dict of the args for `torch.compile()` API, for more details: + https://pytorch.org/docs/stable/generated/torch.compile.html#torch-compile. """ @@ -238,6 +244,8 @@ def __init__( decollate: bool = True, to_kwargs: dict | None = None, amp_kwargs: dict | None = None, + compile: bool = False, + compile_kwargs: dict | None = None, ) -> None: super().__init__( device=device, @@ -259,8 +267,16 @@ def __init__( to_kwargs=to_kwargs, amp_kwargs=amp_kwargs, ) - + if compile: + if pytorch_after(2, 1): + compile_kwargs = {} if compile_kwargs is None else compile_kwargs + network = torch.compile(network, **compile_kwargs) # type: ignore[assignment] + else: + warnings.warn( + "Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done" + ) self.network = network + self.compile = compile self.inferer = SimpleInferer() if inferer is None else inferer def _iteration(self, engine: SupervisedEvaluator, batchdata: dict[str, torch.Tensor]) -> dict: @@ -288,6 +304,24 @@ def _iteration(self, engine: SupervisedEvaluator, batchdata: dict[str, torch.Ten kwargs: dict = {} else: inputs, targets, args, kwargs = batch + # FIXME: workaround for https://github.com/pytorch/pytorch/issues/117026 + if self.compile: + inputs_meta, targets_meta, inputs_applied_operations, targets_applied_operations = None, None, None, None + if isinstance(inputs, MetaTensor): + warnings.warn( + "Will convert to PyTorch Tensor if using compile, and casting back to MetaTensor after the forward pass." + ) + inputs, inputs_meta, inputs_applied_operations = ( + inputs.as_tensor(), + inputs.meta, + inputs.applied_operations, + ) + if isinstance(targets, MetaTensor): + targets, targets_meta, targets_applied_operations = ( + targets.as_tensor(), + targets.meta, + targets.applied_operations, + ) # put iteration outputs into engine.state engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} @@ -298,6 +332,19 @@ def _iteration(self, engine: SupervisedEvaluator, batchdata: dict[str, torch.Ten engine.state.output[Keys.PRED] = engine.inferer(inputs, engine.network, *args, **kwargs) else: engine.state.output[Keys.PRED] = engine.inferer(inputs, engine.network, *args, **kwargs) + # copy back meta info + if self.compile: + if inputs_meta is not None: + engine.state.output[Keys.IMAGE] = MetaTensor( + inputs, meta=inputs_meta, applied_operations=inputs_applied_operations + ) + engine.state.output[Keys.PRED] = MetaTensor( + engine.state.output[Keys.PRED], meta=inputs_meta, applied_operations=inputs_applied_operations + ) + if targets_meta is not None: + engine.state.output[Keys.LABEL] = MetaTensor( + targets, meta=targets_meta, applied_operations=targets_applied_operations + ) engine.fire_event(IterationEvents.FORWARD_COMPLETED) engine.fire_event(IterationEvents.MODEL_COMPLETED) diff --git a/monai/engines/trainer.py b/monai/engines/trainer.py index 61b7028e11..f1513ea73b 100644 --- a/monai/engines/trainer.py +++ b/monai/engines/trainer.py @@ -11,6 +11,7 @@ from __future__ import annotations +import warnings from typing import TYPE_CHECKING, Any, Callable, Iterable, Sequence import torch @@ -18,6 +19,7 @@ from torch.utils.data import DataLoader from monai.config import IgniteInfo +from monai.data import MetaTensor from monai.engines.utils import IterationEvents, default_make_latent, default_metric_cmp_fn, default_prepare_batch from monai.engines.workflow import Workflow from monai.inferers import Inferer, SimpleInferer @@ -25,6 +27,7 @@ from monai.utils import GanKeys, min_version, optional_import from monai.utils.enums import CommonKeys as Keys from monai.utils.enums import EngineStatsKeys as ESKeys +from monai.utils.module import pytorch_after if TYPE_CHECKING: from ignite.engine import Engine, EventEnum @@ -125,7 +128,10 @@ class SupervisedTrainer(Trainer): `device`, `non_blocking`. amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details: https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast. - + compile: whether to use `torch.compile`, default is False. If True, MetaTensor inputs will be converted to + `torch.Tensor` before forward pass, then converted back afterward with copied meta information. + compile_kwargs: dict of the args for `torch.compile()` API, for more details: + https://pytorch.org/docs/stable/generated/torch.compile.html#torch-compile. """ def __init__( @@ -153,6 +159,8 @@ def __init__( optim_set_to_none: bool = False, to_kwargs: dict | None = None, amp_kwargs: dict | None = None, + compile: bool = False, + compile_kwargs: dict | None = None, ) -> None: super().__init__( device=device, @@ -174,8 +182,16 @@ def __init__( to_kwargs=to_kwargs, amp_kwargs=amp_kwargs, ) - + if compile: + if pytorch_after(2, 1): + compile_kwargs = {} if compile_kwargs is None else compile_kwargs + network = torch.compile(network, **compile_kwargs) # type: ignore[assignment] + else: + warnings.warn( + "Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done" + ) self.network = network + self.compile = compile self.optimizer = optimizer self.loss_function = loss_function self.inferer = SimpleInferer() if inferer is None else inferer @@ -207,6 +223,25 @@ def _iteration(self, engine: SupervisedTrainer, batchdata: dict[str, torch.Tenso kwargs: dict = {} else: inputs, targets, args, kwargs = batch + # FIXME: workaround for https://github.com/pytorch/pytorch/issues/117026 + if self.compile: + inputs_meta, targets_meta, inputs_applied_operations, targets_applied_operations = None, None, None, None + if isinstance(inputs, MetaTensor): + warnings.warn( + "Will convert to PyTorch Tensor if using compile, and casting back to MetaTensor after the forward pass." + ) + inputs, inputs_meta, inputs_applied_operations = ( + inputs.as_tensor(), + inputs.meta, + inputs.applied_operations, + ) + if isinstance(targets, MetaTensor): + targets, targets_meta, targets_applied_operations = ( + targets.as_tensor(), + targets.meta, + targets.applied_operations, + ) + # put iteration outputs into engine.state engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets} @@ -231,6 +266,19 @@ def _compute_pred_loss(): engine.state.output[Keys.LOSS].backward() engine.fire_event(IterationEvents.BACKWARD_COMPLETED) engine.optimizer.step() + # copy back meta info + if self.compile: + if inputs_meta is not None: + engine.state.output[Keys.IMAGE] = MetaTensor( + inputs, meta=inputs_meta, applied_operations=inputs_applied_operations + ) + engine.state.output[Keys.PRED] = MetaTensor( + engine.state.output[Keys.PRED], meta=inputs_meta, applied_operations=inputs_applied_operations + ) + if targets_meta is not None: + engine.state.output[Keys.LABEL] = MetaTensor( + targets, meta=targets_meta, applied_operations=targets_applied_operations + ) engine.fire_event(IterationEvents.MODEL_COMPLETED) return engine.state.output From abe2e3151e77fdbcad68268111aaabf1e73c3dbe Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 22 Jan 2024 23:58:29 +0800 Subject: [PATCH 024/136] Fix CUDA_VISIBLE_DEVICES setting ignored (#7408) Fixes #7407 ### Description Move `optional import cucim` inside the function to avoid using all GPUs. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- monai/metrics/utils.py | 6 ++---- tests/test_set_visible_devices.py | 7 +++++++ 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py index d4b8f6e9b6..e7057256fb 100644 --- a/monai/metrics/utils.py +++ b/monai/metrics/utils.py @@ -38,10 +38,6 @@ binary_erosion, _ = optional_import("scipy.ndimage.morphology", name="binary_erosion") distance_transform_edt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_edt") distance_transform_cdt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_cdt") -cucim_binary_erosion, has_cucim_binary_erosion = optional_import("cucim.skimage.morphology", name="binary_erosion") -cucim_distance_transform_edt, has_cucim_distance_transform_edt = optional_import( - "cucim.core.operations.morphology", name="distance_transform_edt" -) __all__ = [ "ignore_background", @@ -179,6 +175,8 @@ def get_mask_edges( always_return_as_numpy: whether to a numpy array regardless of the input type. If False, return the same type as inputs. """ + # move in the funciton to avoid using all the GPUs + cucim_binary_erosion, has_cucim_binary_erosion = optional_import("cucim.skimage.morphology", name="binary_erosion") if seg_pred.shape != seg_gt.shape: raise ValueError(f"seg_pred and seg_gt should have same shapes, got {seg_pred.shape} and {seg_gt.shape}.") converter: Any diff --git a/tests/test_set_visible_devices.py b/tests/test_set_visible_devices.py index 53703e107a..993e8a4ac2 100644 --- a/tests/test_set_visible_devices.py +++ b/tests/test_set_visible_devices.py @@ -35,6 +35,13 @@ def test_visible_devices(self): ) self.assertEqual(num_gpus_before, num_gpus_after) + # test import monai won't affect setting CUDA_VISIBLE_DEVICES + num_gpus_after_monai = self.run_process_and_get_exit_code( + 'python -c "import os; import torch; import monai; ' + + "os.environ['CUDA_VISIBLE_DEVICES'] = '0'; exit(torch.cuda.device_count())\"" + ) + self.assertEqual(num_gpus_after_monai, 1) + if __name__ == "__main__": unittest.main() From 6c9e49ee045c345ecd8d3518cb0d503ed02c61b7 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 25 Jan 2024 09:47:36 +0800 Subject: [PATCH 025/136] Fix Incorrect updated affine in `NrrdReader` and update docstring in `ITKReader` (#7415) Fixes #7414 Fixes #7371 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/data/image_reader.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py index 0823d11834..2361bb63a7 100644 --- a/monai/data/image_reader.py +++ b/monai/data/image_reader.py @@ -168,8 +168,8 @@ class ITKReader(ImageReader): series_name: the name of the DICOM series if there are multiple ones. used when loading DICOM series. reverse_indexing: whether to use a reversed spatial indexing convention for the returned data array. - If ``False``, the spatial indexing follows the numpy convention; - otherwise, the spatial indexing convention is reversed to be compatible with ITK. Default is ``False``. + If ``False``, the spatial indexing convention is reversed to be compatible with ITK; + otherwise, the spatial indexing follows the numpy convention. Default is ``False``. This option does not affect the metadata. series_meta: whether to load the metadata of the DICOM series (using the metadata from the first slice). This flag is checked only when loading DICOM series. Default is ``False``. @@ -1323,7 +1323,7 @@ def get_data(self, img: NrrdImage | list[NrrdImage]) -> tuple[np.ndarray, dict]: header = dict(i.header) if self.index_order == "C": header = self._convert_f_to_c_order(header) - header[MetaKeys.ORIGINAL_AFFINE] = self._get_affine(i) + header[MetaKeys.ORIGINAL_AFFINE] = self._get_affine(header) if self.affine_lps_to_ras: header = self._switch_lps_ras(header) @@ -1344,7 +1344,7 @@ def get_data(self, img: NrrdImage | list[NrrdImage]) -> tuple[np.ndarray, dict]: return _stack_images(img_array, compatible_meta), compatible_meta - def _get_affine(self, img: NrrdImage) -> np.ndarray: + def _get_affine(self, header: dict) -> np.ndarray: """ Get the affine matrix of the image, it can be used to correct spacing, orientation or execute spatial transforms. @@ -1353,8 +1353,8 @@ def _get_affine(self, img: NrrdImage) -> np.ndarray: img: A `NrrdImage` loaded from image file """ - direction = img.header["space directions"] - origin = img.header["space origin"] + direction = header["space directions"] + origin = header["space origin"] x, y = direction.shape affine_diam = min(x, y) + 1 From df4cf922e048aeba2207641654e2b61995e1c055 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 30 Jan 2024 09:42:03 +0800 Subject: [PATCH 026/136] Ignore E704 after update black (#7422) Fixes #7421 ### Description https://pypi.org/project/black/24.1.1/ ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/detection/utils/anchor_utils.py | 8 ++++++-- monai/data/decathlon_datalist.py | 6 ++---- monai/losses/image_dissimilarity.py | 4 +--- monai/transforms/utility/dictionary.py | 6 +++--- monai/utils/dist.py | 9 +++------ monai/utils/misc.py | 6 ++---- setup.cfg | 2 ++ tests/test_hilbert_transform.py | 20 +++++++++++--------- tests/test_spacing.py | 8 +++++--- 9 files changed, 35 insertions(+), 34 deletions(-) diff --git a/monai/apps/detection/utils/anchor_utils.py b/monai/apps/detection/utils/anchor_utils.py index baaa7ce874..283169b653 100644 --- a/monai/apps/detection/utils/anchor_utils.py +++ b/monai/apps/detection/utils/anchor_utils.py @@ -369,8 +369,12 @@ class AnchorGeneratorWithAnchorShape(AnchorGenerator): def __init__( self, feature_map_scales: Sequence[int] | Sequence[float] = (1, 2, 4, 8), - base_anchor_shapes: Sequence[Sequence[int]] - | Sequence[Sequence[float]] = ((32, 32, 32), (48, 20, 20), (20, 48, 20), (20, 20, 48)), + base_anchor_shapes: Sequence[Sequence[int]] | Sequence[Sequence[float]] = ( + (32, 32, 32), + (48, 20, 20), + (20, 48, 20), + (20, 20, 48), + ), indexing: str = "ij", ) -> None: nn.Module.__init__(self) diff --git a/monai/data/decathlon_datalist.py b/monai/data/decathlon_datalist.py index 6f163f972e..14765dcfaa 100644 --- a/monai/data/decathlon_datalist.py +++ b/monai/data/decathlon_datalist.py @@ -24,13 +24,11 @@ @overload -def _compute_path(base_dir: PathLike, element: PathLike, check_path: bool = False) -> str: - ... +def _compute_path(base_dir: PathLike, element: PathLike, check_path: bool = False) -> str: ... @overload -def _compute_path(base_dir: PathLike, element: list[PathLike], check_path: bool = False) -> list[str]: - ... +def _compute_path(base_dir: PathLike, element: list[PathLike], check_path: bool = False) -> list[str]: ... def _compute_path(base_dir, element, check_path=False): diff --git a/monai/losses/image_dissimilarity.py b/monai/losses/image_dissimilarity.py index 39219e059a..dd132770ec 100644 --- a/monai/losses/image_dissimilarity.py +++ b/monai/losses/image_dissimilarity.py @@ -277,9 +277,7 @@ def parzen_windowing_b_spline(self, img: torch.Tensor, order: int) -> tuple[torc if order == 0: weight = weight + (sample_bin_matrix < 0.5) + (sample_bin_matrix == 0.5) * 0.5 elif order == 3: - weight = ( - weight + (4 - 6 * sample_bin_matrix**2 + 3 * sample_bin_matrix**3) * (sample_bin_matrix < 1) / 6 - ) + weight = weight + (4 - 6 * sample_bin_matrix**2 + 3 * sample_bin_matrix**3) * (sample_bin_matrix < 1) / 6 weight = weight + (2 - sample_bin_matrix) ** 3 * (sample_bin_matrix >= 1) * (sample_bin_matrix < 2) / 6 else: raise ValueError(f"Do not support b-spline {order}-order parzen windowing") diff --git a/monai/transforms/utility/dictionary.py b/monai/transforms/utility/dictionary.py index ec10bd8537..1cd9ff6323 100644 --- a/monai/transforms/utility/dictionary.py +++ b/monai/transforms/utility/dictionary.py @@ -1765,9 +1765,9 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N LabelToMaskD = LabelToMaskDict = LabelToMaskd FgBgToIndicesD = FgBgToIndicesDict = FgBgToIndicesd ClassesToIndicesD = ClassesToIndicesDict = ClassesToIndicesd -ConvertToMultiChannelBasedOnBratsClassesD = ( - ConvertToMultiChannelBasedOnBratsClassesDict -) = ConvertToMultiChannelBasedOnBratsClassesd +ConvertToMultiChannelBasedOnBratsClassesD = ConvertToMultiChannelBasedOnBratsClassesDict = ( + ConvertToMultiChannelBasedOnBratsClassesd +) AddExtremePointsChannelD = AddExtremePointsChannelDict = AddExtremePointsChanneld TorchVisionD = TorchVisionDict = TorchVisiond RandTorchVisionD = RandTorchVisionDict = RandTorchVisiond diff --git a/monai/utils/dist.py b/monai/utils/dist.py index 20f09628ac..2418b43591 100644 --- a/monai/utils/dist.py +++ b/monai/utils/dist.py @@ -50,18 +50,15 @@ def get_dist_device(): @overload -def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[True]) -> torch.Tensor: - ... +def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[True]) -> torch.Tensor: ... @overload -def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[False]) -> list[torch.Tensor]: - ... +def evenly_divisible_all_gather(data: torch.Tensor, concat: Literal[False]) -> list[torch.Tensor]: ... @overload -def evenly_divisible_all_gather(data: torch.Tensor, concat: bool) -> torch.Tensor | list[torch.Tensor]: - ... +def evenly_divisible_all_gather(data: torch.Tensor, concat: bool) -> torch.Tensor | list[torch.Tensor]: ... def evenly_divisible_all_gather(data: torch.Tensor, concat: bool = True) -> torch.Tensor | list[torch.Tensor]: diff --git a/monai/utils/misc.py b/monai/utils/misc.py index d6ff370f69..2a5c5da136 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -103,13 +103,11 @@ def star_zip_with(op, *vals): @overload -def first(iterable: Iterable[T], default: T) -> T: - ... +def first(iterable: Iterable[T], default: T) -> T: ... @overload -def first(iterable: Iterable[T]) -> T | None: - ... +def first(iterable: Iterable[T]) -> T | None: ... def first(iterable: Iterable[T], default: T | None = None) -> T | None: diff --git a/setup.cfg b/setup.cfg index 0069214de3..4180ced917 100644 --- a/setup.cfg +++ b/setup.cfg @@ -174,6 +174,7 @@ max_line_length = 120 # B907 https://github.com/Project-MONAI/MONAI/issues/5868 # B908 https://github.com/Project-MONAI/MONAI/issues/6503 # B036 https://github.com/Project-MONAI/MONAI/issues/7396 +# E704 https://github.com/Project-MONAI/MONAI/issues/7421 ignore = E203 E501 @@ -188,6 +189,7 @@ ignore = B907 B908 B036 + E704 per_file_ignores = __init__.py: F401, __main__.py: F401 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py diff --git a/tests/test_hilbert_transform.py b/tests/test_hilbert_transform.py index 4c49aecd8b..68fa0b1192 100644 --- a/tests/test_hilbert_transform.py +++ b/tests/test_hilbert_transform.py @@ -180,15 +180,17 @@ def test_value(self, arguments, image, expected_data, atol): @SkipIfNoModule("torch.fft") class TestHilbertTransformGPU(unittest.TestCase): @parameterized.expand( - [] - if not torch.cuda.is_available() - else [ - TEST_CASE_1D_SINE_GPU, - TEST_CASE_2D_SINE_GPU, - TEST_CASE_3D_SINE_GPU, - TEST_CASE_1D_2CH_SINE_GPU, - TEST_CASE_2D_2CH_SINE_GPU, - ], + ( + [] + if not torch.cuda.is_available() + else [ + TEST_CASE_1D_SINE_GPU, + TEST_CASE_2D_SINE_GPU, + TEST_CASE_3D_SINE_GPU, + TEST_CASE_1D_2CH_SINE_GPU, + TEST_CASE_2D_2CH_SINE_GPU, + ] + ), skip_on_empty=True, ) def test_value(self, arguments, image, expected_data, atol): diff --git a/tests/test_spacing.py b/tests/test_spacing.py index 1ff1518297..8b664641d7 100644 --- a/tests/test_spacing.py +++ b/tests/test_spacing.py @@ -74,9 +74,11 @@ torch.ones((1, 2, 1, 2)), # data torch.tensor([[2, 1, 0, 4], [-1, -3, 0, 5], [0, 0, 2.0, 5], [0, 0, 0, 1]]), {}, - torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]]) - if USE_COMPILED - else torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]]), + ( + torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]]) + if USE_COMPILED + else torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]]) + ), *device, ] ) From cd36c1cf677e7137ea15f418e22e17343ac808e9 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 1 Feb 2024 19:47:32 +0800 Subject: [PATCH 027/136] update `rm -rf /opt/hostedtoolcache` avoid change the python version (#7424) Fixes #7416 ### Description update `rm -rf /opt/hostedtoolcache` avoid change the python version ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .github/workflows/docker.yml | 2 +- .github/workflows/release.yml | 6 +++--- .github/workflows/setupapp.yml | 4 ++-- monai/transforms/smooth_field/array.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 229ae675f5..065125cc33 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -62,7 +62,7 @@ jobs: - name: docker_build shell: bash run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; docker --version # get tag info for versioning cat _version.py diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index a03d2cea6c..c134724665 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -27,7 +27,7 @@ jobs: python -m pip install --user --upgrade setuptools wheel - name: Build and test source archive and wheel file run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; git fetch --depth=1 origin +refs/tags/*:refs/tags/* root_dir=$PWD echo "$root_dir" @@ -102,7 +102,7 @@ jobs: python-version: '3.9' - shell: bash run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; git describe python -m pip install --user --upgrade setuptools wheel python setup.py build @@ -143,7 +143,7 @@ jobs: RELEASE_VERSION: ${{ steps.versioning.outputs.tag }} shell: bash run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; # get tag info for versioning mv _version.py monai/ # version checks diff --git a/.github/workflows/setupapp.yml b/.github/workflows/setupapp.yml index 82394a86dd..a6407deb33 100644 --- a/.github/workflows/setupapp.yml +++ b/.github/workflows/setupapp.yml @@ -100,7 +100,7 @@ jobs: key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ steps.pip-cache.outputs.datew }} - name: Install the dependencies run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; python -m pip install --upgrade pip wheel python -m pip install -r requirements-dev.txt - name: Run quick tests CPU ubuntu @@ -146,7 +146,7 @@ jobs: - name: Install the default branch with build (dev branch only) if: github.ref == 'refs/heads/dev' run: | - rm -rf /opt/hostedtoolcache + find /opt/hostedtoolcache/* -maxdepth 0 ! -name 'Python' -exec rm -rf {} \; BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI python -c 'import monai; monai.config.print_config()' - name: Get the test cases (dev branch only) diff --git a/monai/transforms/smooth_field/array.py b/monai/transforms/smooth_field/array.py index c9df5f1dbb..9d19263f8b 100644 --- a/monai/transforms/smooth_field/array.py +++ b/monai/transforms/smooth_field/array.py @@ -96,7 +96,7 @@ def __init__( self.set_spatial_size(spatial_size) def randomize(self, data: Any | None = None) -> None: - self.field[self.rand_slices] = torch.from_numpy(self.R.uniform(self.low, self.high, self.crand_size)) + self.field[self.rand_slices] = torch.from_numpy(self.R.uniform(self.low, self.high, self.crand_size)) # type: ignore[index] def set_spatial_size(self, spatial_size: Sequence[int] | None) -> None: """ From 6def75e3db78bf606ad3e21685c9aa15bdb64425 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 1 Feb 2024 13:53:01 +0000 Subject: [PATCH 028/136] Bump peter-evans/slash-command-dispatch from 3.0.2 to 4.0.0 (#7428) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [peter-evans/slash-command-dispatch](https://github.com/peter-evans/slash-command-dispatch) from 3.0.2 to 4.0.0.
Release notes

Sourced from peter-evans/slash-command-dispatch's releases.

Slash Command Dispatch v4.0.0

⚙️ Updated runtime to Node.js 20

  • The action now requires a minimum version of v2.308.0 for the Actions runner. Update self-hosted runners to v2.308.0 or later to ensure compatibility.

What's Changed

Full Changelog: https://github.com/peter-evans/slash-command-dispatch/compare/v3.0.2...v4.0.0

Commits
  • 13bc097 feat: update runtime to node 20 (#316)
  • 128d8b9 build(deps-dev): bump @​types/node from 16.18.74 to 16.18.76 (#318)
  • df481df build(deps): bump peter-evans/create-or-update-comment from 3 to 4 (#317)
  • d4579a0 build(deps-dev): bump @​types/node from 16.18.70 to 16.18.74 (#315)
  • 8f053ea build(deps-dev): bump prettier from 3.2.2 to 3.2.4 (#314)
  • b3eb783 build(deps-dev): bump prettier from 3.1.1 to 3.2.2 (#312)
  • c0334d0 build(deps-dev): bump eslint-plugin-prettier from 5.1.2 to 5.1.3 (#311)
  • e627c61 build(deps-dev): bump @​types/node from 16.18.69 to 16.18.70 (#310)
  • 5c23a33 build(deps-dev): bump @​types/node from 16.18.68 to 16.18.69 (#309)
  • 8dd62d5 build(deps-dev): bump eslint-plugin-prettier from 5.0.1 to 5.1.2 (#308)
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=peter-evans/slash-command-dispatch&package-manager=github_actions&previous-version=3.0.2&new-version=4.0.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/chatops.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/chatops.yml b/.github/workflows/chatops.yml index 59c7d070b4..6f3b1c293d 100644 --- a/.github/workflows/chatops.yml +++ b/.github/workflows/chatops.yml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest steps: - name: dispatch - uses: peter-evans/slash-command-dispatch@v3.0.2 + uses: peter-evans/slash-command-dispatch@v4.0.0 with: token: ${{ secrets.PR_MAINTAIN }} reaction-token: ${{ secrets.GITHUB_TOKEN }} From 6ea3e3957b0103e8bff9f6d5d33d185495dce15b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 2 Feb 2024 16:14:04 +0800 Subject: [PATCH 029/136] Bump peter-evans/create-or-update-comment from 3 to 4 (#7429) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [peter-evans/create-or-update-comment](https://github.com/peter-evans/create-or-update-comment) from 3 to 4.
Release notes

Sourced from peter-evans/create-or-update-comment's releases.

Create or Update Comment v4.0.0

⚙️ Updated runtime to Node.js 20

  • The action now requires a minimum version of v2.308.0 for the Actions runner. Update self-hosted runners to v2.308.0 or later to ensure compatibility.

What's Changed

Full Changelog: https://github.com/peter-evans/create-or-update-comment/compare/v3.1.0...v4.0.0

Create or Update Comment v3.1.0

What's Changed

Full Changelog: https://github.com/peter-evans/create-or-update-comment/compare/v3.0.2...v3.1.0

Create or Update Comment v3.0.2

What's Changed

... (truncated)

Commits
  • 71345be feat: update runtime to node 20 (#306)
  • d41bfe3 build(deps-dev): bump prettier from 3.2.3 to 3.2.4 (#305)
  • 73b4b9e build(deps-dev): bump @​types/node from 18.19.7 to 18.19.8 (#304)
  • b865fac build(deps-dev): bump @​types/node from 18.19.6 to 18.19.7 (#303)
  • 52b668a build(deps-dev): bump eslint-plugin-jest from 27.6.1 to 27.6.3 (#302)
  • 974f56a build(deps-dev): bump prettier from 3.1.1 to 3.2.3 (#301)
  • 2cbfe8b build(deps-dev): bump @​types/node from 18.19.4 to 18.19.6 (#300)
  • 761872a build(deps-dev): bump eslint-plugin-prettier from 5.1.2 to 5.1.3 (#299)
  • 72c3238 build(deps-dev): bump @​types/node from 18.19.3 to 18.19.4 (#298)
  • 07daf7b build(deps-dev): bump eslint-plugin-jest from 27.6.0 to 27.6.1 (#297)
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=peter-evans/create-or-update-comment&package-manager=github_actions&previous-version=3&new-version=4)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/integration.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/integration.yml b/.github/workflows/integration.yml index 952b2d8deb..c239f9d5fe 100644 --- a/.github/workflows/integration.yml +++ b/.github/workflows/integration.yml @@ -71,7 +71,7 @@ jobs: run: ./runtests.sh --build --net - name: Add reaction - uses: peter-evans/create-or-update-comment@v3 + uses: peter-evans/create-or-update-comment@v4 with: token: ${{ secrets.PR_MAINTAIN }} repository: ${{ github.event.client_payload.github.payload.repository.full_name }} @@ -151,7 +151,7 @@ jobs: python -m tests.test_integration_gpu_customization - name: Add reaction - uses: peter-evans/create-or-update-comment@v3 + uses: peter-evans/create-or-update-comment@v4 with: token: ${{ secrets.PR_MAINTAIN }} repository: ${{ github.event.client_payload.github.payload.repository.full_name }} From 53014437eba6ee46b6073954af59238c6aaa89f3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 2 Feb 2024 09:11:02 +0000 Subject: [PATCH 030/136] Bump actions/cache from 3 to 4 (#7430) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/cache](https://github.com/actions/cache) from 3 to 4.
Release notes

Sourced from actions/cache's releases.

v4.0.0

What's Changed

New Contributors

Full Changelog: https://github.com/actions/cache/compare/v3...v4.0.0

v3.3.3

What's Changed

New Contributors

Full Changelog: https://github.com/actions/cache/compare/v3...v3.3.3

v3.3.2

What's Changed

New Contributors

Full Changelog: https://github.com/actions/cache/compare/v3...v3.3.2

v3.3.1

What's Changed

Full Changelog: https://github.com/actions/cache/compare/v3...v3.3.1

v3.3.0

What's Changed

... (truncated)

Changelog

Sourced from actions/cache's changelog.

Releases

3.0.0

  • Updated minimum runner version support from node 12 -> node 16

3.0.1

  • Added support for caching from GHES 3.5.
  • Fixed download issue for files > 2GB during restore.

3.0.2

  • Added support for dynamic cache size cap on GHES.

3.0.3

  • Fixed avoiding empty cache save when no files are available for caching. (issue)

3.0.4

  • Fixed tar creation error while trying to create tar with path as ~/ home folder on ubuntu-latest. (issue)

3.0.5

  • Removed error handling by consuming actions/cache 3.0 toolkit, Now cache server error handling will be done by toolkit. (PR)

3.0.6

  • Fixed #809 - zstd -d: no such file or directory error
  • Fixed #833 - cache doesn't work with github workspace directory

3.0.7

  • Fixed #810 - download stuck issue. A new timeout is introduced in the download process to abort the download if it gets stuck and doesn't finish within an hour.

3.0.8

  • Fix zstd not working for windows on gnu tar in issues #888 and #891.
  • Allowing users to provide a custom timeout as input for aborting download of a cache segment using an environment variable SEGMENT_DOWNLOAD_TIMEOUT_MINS. Default is 60 minutes.

3.0.9

  • Enhanced the warning message for cache unavailablity in case of GHES.

3.0.10

  • Fix a bug with sorting inputs.
  • Update definition for restore-keys in README.md

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/cache&package-manager=github_actions&previous-version=3&new-version=4)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .github/workflows/cron-ngc-bundle.yml | 2 +- .github/workflows/integration.yml | 4 ++-- .github/workflows/pythonapp-min.yml | 6 +++--- .github/workflows/pythonapp.yml | 8 ++++---- .github/workflows/setupapp.yml | 6 +++--- 5 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/workflows/cron-ngc-bundle.yml b/.github/workflows/cron-ngc-bundle.yml index 84666204a9..bd45bc8d1e 100644 --- a/.github/workflows/cron-ngc-bundle.yml +++ b/.github/workflows/cron-ngc-bundle.yml @@ -26,7 +26,7 @@ jobs: id: pip-cache run: echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ~/.cache/pip diff --git a/.github/workflows/integration.yml b/.github/workflows/integration.yml index c239f9d5fe..c82530a551 100644 --- a/.github/workflows/integration.yml +++ b/.github/workflows/integration.yml @@ -22,7 +22,7 @@ jobs: id: pip-cache run: echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | @@ -95,7 +95,7 @@ jobs: id: pip-cache run: echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | diff --git a/.github/workflows/pythonapp-min.yml b/.github/workflows/pythonapp-min.yml index 7b7930bdf5..bbe7579774 100644 --- a/.github/workflows/pythonapp-min.yml +++ b/.github/workflows/pythonapp-min.yml @@ -44,7 +44,7 @@ jobs: echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT shell: bash - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ${{ steps.pip-cache.outputs.dir }} @@ -90,7 +90,7 @@ jobs: echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT shell: bash - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ${{ steps.pip-cache.outputs.dir }} @@ -135,7 +135,7 @@ jobs: echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT shell: bash - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ${{ steps.pip-cache.outputs.dir }} diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index 29a79759e0..b011e65cf1 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -36,7 +36,7 @@ jobs: run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ~/.cache/pip @@ -83,7 +83,7 @@ jobs: echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT shell: bash - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: ${{ steps.pip-cache.outputs.dir }} @@ -136,7 +136,7 @@ jobs: run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | @@ -217,7 +217,7 @@ jobs: run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | diff --git a/.github/workflows/setupapp.yml b/.github/workflows/setupapp.yml index a6407deb33..8f95ffdfc0 100644 --- a/.github/workflows/setupapp.yml +++ b/.github/workflows/setupapp.yml @@ -35,7 +35,7 @@ jobs: echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip if: ${{ startsWith(github.ref, 'refs/heads/dev') }} - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | @@ -91,7 +91,7 @@ jobs: run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | @@ -128,7 +128,7 @@ jobs: run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - name: cache for pip - uses: actions/cache@v3 + uses: actions/cache@v4 id: cache with: path: | From aa4f578e8886311dd5539d462a6cf78f12323076 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 2 Feb 2024 21:29:44 +0800 Subject: [PATCH 031/136] Bump codecov/codecov-action from 3 to 4 (#7431) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 3 to 4.
Release notes

Sourced from codecov/codecov-action's releases.

v4.0.0

v4 of the Codecov Action uses the CLI as the underlying upload. The CLI has helped to power new features including local upload, the global upload token, and new upcoming features.

Breaking Changes

  • The Codecov Action runs as a node20 action due to node16 deprecation. See this post from GitHub on how to migrate.
  • Tokenless uploading is unsupported. However, PRs made from forks to the upstream public repos will support tokenless (e.g. contributors to OS projects do not need the upstream repo's Codecov token). This doc shows instructions on how to add the Codecov token.
  • OS platforms have been added, though some may not be automatically detected. To see a list of platforms, see our CLI download page
  • Various arguments to the Action have been changed. Please be aware that the arguments match with the CLI's needs

v3 versions and below will not have access to CLI features (e.g. global upload token, ATS).

What's Changed

... (truncated)

Changelog

Sourced from codecov/codecov-action's changelog.

4.0.0-beta.2

Fixes

  • #1085 not adding -n if empty to do-upload command

4.0.0-beta.1

v4 represents a move from the universal uploader to the Codecov CLI. Although this will unlock new features for our users, the CLI is not yet at feature parity with the universal uploader.

Breaking Changes

  • No current support for aarch64 and alpine architectures.
  • Tokenless uploading is unsuported
  • Various arguments to the Action have been removed

3.1.4

Fixes

  • #967 Fix typo in README.md
  • #971 fix: add back in working dir
  • #969 fix: CLI option names for uploader

Dependencies

  • #970 build(deps-dev): bump @​types/node from 18.15.12 to 18.16.3
  • #979 build(deps-dev): bump @​types/node from 20.1.0 to 20.1.2
  • #981 build(deps-dev): bump @​types/node from 20.1.2 to 20.1.4

3.1.3

Fixes

  • #960 fix: allow for aarch64 build

Dependencies

  • #957 build(deps-dev): bump jest-junit from 15.0.0 to 16.0.0
  • #958 build(deps): bump openpgp from 5.7.0 to 5.8.0
  • #959 build(deps-dev): bump @​types/node from 18.15.10 to 18.15.12

3.1.2

Fixes

  • #718 Update README.md
  • #851 Remove unsupported path_to_write_report argument
  • #898 codeql-analysis.yml
  • #901 Update README to contain correct information - inputs and negate feature
  • #955 fix: add in all the extra arguments for uploader

Dependencies

  • #819 build(deps): bump openpgp from 5.4.0 to 5.5.0
  • #835 build(deps): bump node-fetch from 3.2.4 to 3.2.10
  • #840 build(deps): bump ossf/scorecard-action from 1.1.1 to 2.0.4
  • #841 build(deps): bump @​actions/core from 1.9.1 to 1.10.0
  • #843 build(deps): bump @​actions/github from 5.0.3 to 5.1.1
  • #869 build(deps): bump node-fetch from 3.2.10 to 3.3.0
  • #872 build(deps-dev): bump jest-junit from 13.2.0 to 15.0.0
  • #879 build(deps): bump decode-uri-component from 0.2.0 to 0.2.2

... (truncated)

Commits
  • f30e495 fix: update action.yml (#1240)
  • a7b945c fix: allow for other archs (#1239)
  • 98ab2c5 Update package.json (#1238)
  • 43235cc Update README.md (#1237)
  • 0cf8684 chore(ci): bump to node20 (#1236)
  • 8e1e730 build(deps-dev): bump @​typescript-eslint/eslint-plugin from 6.19.1 to 6.20.0 ...
  • 61293af build(deps-dev): bump @​typescript-eslint/parser from 6.19.1 to 6.20.0 (#1235)
  • 7a070cb build(deps): bump github/codeql-action from 3.23.1 to 3.23.2 (#1231)
  • 9097165 build(deps): bump actions/upload-artifact from 4.2.0 to 4.3.0 (#1232)
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/cron.yml | 6 +++--- .github/workflows/pythonapp-gpu.yml | 2 +- .github/workflows/setupapp.yml | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/cron.yml b/.github/workflows/cron.yml index e981280ff9..792fda5279 100644 --- a/.github/workflows/cron.yml +++ b/.github/workflows/cron.yml @@ -67,7 +67,7 @@ jobs: if pgrep python; then pkill python; fi shell: bash - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: false files: ./coverage.xml @@ -111,7 +111,7 @@ jobs: if pgrep python; then pkill python; fi shell: bash - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: false files: ./coverage.xml @@ -212,7 +212,7 @@ jobs: if pgrep python; then pkill python; fi shell: bash - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: false files: ./coverage.xml diff --git a/.github/workflows/pythonapp-gpu.yml b/.github/workflows/pythonapp-gpu.yml index 0baef949f0..a6d7981814 100644 --- a/.github/workflows/pythonapp-gpu.yml +++ b/.github/workflows/pythonapp-gpu.yml @@ -137,6 +137,6 @@ jobs: shell: bash - name: Upload coverage if: ${{ github.head_ref != 'dev' && github.event.pull_request.merged != true }} - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: files: ./coverage.xml diff --git a/.github/workflows/setupapp.yml b/.github/workflows/setupapp.yml index 8f95ffdfc0..c6ad243b81 100644 --- a/.github/workflows/setupapp.yml +++ b/.github/workflows/setupapp.yml @@ -68,7 +68,7 @@ jobs: if pgrep python; then pkill python; fi shell: bash - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: false files: ./coverage.xml @@ -111,7 +111,7 @@ jobs: BUILD_MONAI=1 ./runtests.sh --build --quick --min coverage xml --ignore-errors - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: false files: ./coverage.xml From 33afaefff925dce45b8331e8290f234a4d95fd51 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 2 Feb 2024 22:56:30 +0800 Subject: [PATCH 032/136] Update tensorboard version to fix deadlock (#7435) Fixes #7434 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index f8bc9d5a3e..706980576c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -6,7 +6,7 @@ scipy>=1.7.1 itk>=5.2 nibabel pillow!=8.3.0 # https://github.com/python-pillow/Pillow/issues/5571 -tensorboard>=2.6 # https://github.com/Project-MONAI/MONAI/issues/5776 +tensorboard>=2.12.0 # https://github.com/Project-MONAI/MONAI/issues/7434 scikit-image>=0.19.0 tqdm>=4.47.0 lmdb From 718e4befda6262aed0f1e2d41985b0a7edd8b011 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:43:23 +0000 Subject: [PATCH 033/136] auto updates (#7439) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/apps/datasets.py | 1 + monai/apps/deepedit/transforms.py | 3 +++ monai/apps/detection/metrics/coco.py | 1 + monai/apps/detection/utils/ATSS_matcher.py | 1 + monai/apps/pathology/transforms/post/array.py | 6 +++--- monai/data/dataset.py | 1 + monai/fl/client/client_algo.py | 1 + monai/handlers/ignite_metric.py | 1 + monai/metrics/f_beta_score.py | 1 + monai/networks/blocks/dynunet_block.py | 1 + monai/networks/blocks/localnet_block.py | 2 ++ monai/networks/blocks/pos_embed_utils.py | 1 + monai/networks/layers/gmm.py | 1 + monai/networks/layers/simplelayers.py | 2 ++ monai/networks/layers/spatial_transforms.py | 5 +++++ monai/networks/nets/ahnet.py | 6 ++++++ monai/networks/nets/attentionunet.py | 4 ++++ monai/networks/nets/basic_unet.py | 1 + monai/networks/nets/basic_unetplusplus.py | 1 + monai/networks/nets/densenet.py | 3 +++ monai/networks/nets/dints.py | 4 ++++ monai/networks/nets/efficientnet.py | 4 ++++ monai/networks/nets/highresnet.py | 1 + monai/networks/nets/hovernet.py | 6 ++++++ monai/networks/nets/milmodel.py | 1 + monai/networks/nets/regunet.py | 2 ++ monai/networks/nets/vnet.py | 5 +++++ monai/optimizers/lr_finder.py | 2 ++ monai/optimizers/utils.py | 2 ++ monai/transforms/adaptors.py | 4 ++++ monai/transforms/inverse_batch_transform.py | 1 + monai/transforms/post/array.py | 1 + monai/transforms/spatial/array.py | 2 +- monai/transforms/utility/dictionary.py | 1 + monai/utils/misc.py | 1 + monai/utils/module.py | 2 ++ monai/utils/profiling.py | 1 + monai/visualize/class_activation_maps.py | 2 ++ monai/visualize/gradient_based.py | 1 + tests/croppers.py | 1 + tests/hvd_evenly_divisible_all_gather.py | 1 + tests/ngc_bundle_download.py | 2 ++ tests/padders.py | 1 + tests/profile_subclass/min_classes.py | 1 + tests/test_acn_block.py | 1 + tests/test_activations.py | 1 + tests/test_activationsd.py | 1 + tests/test_adaptors.py | 11 +++++++++++ tests/test_add_coordinate_channels.py | 1 + tests/test_add_coordinate_channelsd.py | 1 + tests/test_add_extreme_points_channel.py | 1 + tests/test_add_extreme_points_channeld.py | 1 + tests/test_adjust_contrast.py | 1 + tests/test_adjust_contrastd.py | 1 + tests/test_adn.py | 2 ++ tests/test_adversarial_loss.py | 1 + tests/test_affine.py | 2 ++ tests/test_affine_grid.py | 1 + tests/test_affine_transform.py | 3 +++ tests/test_affined.py | 1 + tests/test_ahnet.py | 6 ++++++ tests/test_anchor_box.py | 1 + tests/test_apply.py | 1 + tests/test_apply_filter.py | 1 + tests/test_arraydataset.py | 2 ++ tests/test_as_channel_last.py | 1 + tests/test_as_channel_lastd.py | 1 + tests/test_as_discrete.py | 1 + tests/test_as_discreted.py | 1 + tests/test_atss_box_matcher.py | 1 + tests/test_attentionunet.py | 1 + tests/test_auto3dseg.py | 1 + tests/test_auto3dseg_bundlegen.py | 1 + tests/test_auto3dseg_ensemble.py | 1 + tests/test_auto3dseg_hpo.py | 2 ++ tests/test_autoencoder.py | 1 + tests/test_avg_merger.py | 1 + tests/test_basic_unet.py | 1 + tests/test_basic_unetplusplus.py | 1 + tests/test_bending_energy.py | 1 + tests/test_bilateral_approx_cpu.py | 1 + tests/test_bilateral_approx_cuda.py | 1 + tests/test_bilateral_precise.py | 2 ++ tests/test_blend_images.py | 1 + tests/test_bounding_rect.py | 1 + tests/test_bounding_rectd.py | 1 + tests/test_box_coder.py | 1 + tests/test_box_transform.py | 1 + tests/test_box_utils.py | 1 + tests/test_bundle_ckpt_export.py | 1 + tests/test_bundle_download.py | 3 +++ tests/test_bundle_get_data.py | 1 + tests/test_bundle_init_bundle.py | 1 + tests/test_bundle_onnx_export.py | 1 + tests/test_bundle_push_to_hf_hub.py | 1 + tests/test_bundle_trt_export.py | 1 + tests/test_bundle_utils.py | 2 ++ tests/test_bundle_verify_metadata.py | 1 + tests/test_bundle_verify_net.py | 1 + tests/test_bundle_workflow.py | 1 + tests/test_cachedataset.py | 1 + tests/test_cachedataset_parallel.py | 1 + tests/test_cachedataset_persistent_workers.py | 1 + tests/test_cachentransdataset.py | 1 + tests/test_call_dist.py | 1 + tests/test_cast_to_type.py | 1 + tests/test_cast_to_typed.py | 1 + tests/test_channel_pad.py | 1 + tests/test_check_hash.py | 1 + tests/test_check_missing_files.py | 1 + tests/test_classes_to_indices.py | 1 + tests/test_classes_to_indicesd.py | 1 + tests/test_cldice_loss.py | 1 + tests/test_complex_utils.py | 1 + tests/test_component_locator.py | 1 + tests/test_component_store.py | 1 + tests/test_compose.py | 18 ++++++++++++++++++ tests/test_compose_get_number_conversions.py | 7 +++++++ tests/test_compute_confusion_matrix.py | 1 + tests/test_compute_f_beta.py | 1 + tests/test_compute_fid_metric.py | 1 + tests/test_compute_froc.py | 3 +++ tests/test_compute_generalized_dice.py | 1 + tests/test_compute_ho_ver_maps.py | 1 + tests/test_compute_ho_ver_maps_d.py | 1 + tests/test_compute_meandice.py | 1 + tests/test_compute_meaniou.py | 1 + tests/test_compute_mmd_metric.py | 1 + tests/test_compute_multiscalessim_metric.py | 1 + tests/test_compute_panoptic_quality.py | 1 + tests/test_compute_regression_metrics.py | 1 + tests/test_compute_roc_auc.py | 1 + tests/test_compute_variance.py | 1 + tests/test_concat_itemsd.py | 1 + tests/test_config_item.py | 1 + tests/test_config_parser.py | 2 ++ tests/test_contrastive_loss.py | 1 + tests/test_convert_data_type.py | 1 + tests/test_convert_to_multi_channel.py | 1 + tests/test_convert_to_multi_channeld.py | 1 + tests/test_convert_to_onnx.py | 1 + tests/test_convert_to_torchscript.py | 1 + tests/test_convert_to_trt.py | 1 + tests/test_convolutions.py | 3 +++ tests/test_copy_itemsd.py | 1 + tests/test_copy_model_state.py | 3 +++ tests/test_correct_crop_centers.py | 1 + tests/test_create_cross_validation_datalist.py | 1 + tests/test_create_grid_and_affine.py | 2 ++ tests/test_crf_cpu.py | 1 + tests/test_crf_cuda.py | 1 + tests/test_crop_foreground.py | 1 + tests/test_crop_foregroundd.py | 1 + tests/test_cross_validation.py | 1 + tests/test_csv_dataset.py | 1 + tests/test_csv_iterable_dataset.py | 1 + tests/test_csv_saver.py | 1 + tests/test_cucim_dict_transform.py | 1 + tests/test_cucim_transform.py | 1 + tests/test_cumulative.py | 1 + tests/test_cumulative_average.py | 1 + tests/test_cumulative_average_dist.py | 1 + tests/test_cv2_dist.py | 1 + tests/test_daf3d.py | 1 + tests/test_data_stats.py | 1 + tests/test_data_statsd.py | 1 + tests/test_dataloader.py | 2 ++ tests/test_dataset.py | 1 + tests/test_dataset_func.py | 1 + tests/test_dataset_summary.py | 1 + tests/test_decathlondataset.py | 1 + tests/test_decollate.py | 2 ++ tests/test_deepedit_interaction.py | 1 + tests/test_deepedit_transforms.py | 11 +++++++++++ tests/test_deepgrow_dataset.py | 1 + tests/test_deepgrow_interaction.py | 1 + tests/test_deepgrow_transforms.py | 11 +++++++++++ tests/test_delete_itemsd.py | 1 + tests/test_denseblock.py | 4 ++++ tests/test_densenet.py | 2 ++ tests/test_deprecated.py | 8 ++++++++ tests/test_detect_envelope.py | 2 ++ tests/test_detection_coco_metrics.py | 1 + tests/test_detector_boxselector.py | 1 + tests/test_detector_utils.py | 1 + tests/test_dev_collate.py | 1 + tests/test_dice_ce_loss.py | 1 + tests/test_dice_focal_loss.py | 1 + tests/test_dice_loss.py | 1 + tests/test_diffusion_loss.py | 1 + tests/test_dints_cell.py | 1 + tests/test_dints_mixop.py | 1 + tests/test_dints_network.py | 2 ++ tests/test_discriminator.py | 1 + tests/test_distance_transform_edt.py | 1 + tests/test_download_and_extract.py | 1 + tests/test_download_url_yandex.py | 1 + tests/test_downsample_block.py | 1 + tests/test_drop_path.py | 1 + tests/test_ds_loss.py | 4 ++++ tests/test_dvf2ddf.py | 1 + tests/test_dynunet.py | 3 +++ tests/test_dynunet_block.py | 2 ++ tests/test_efficientnet.py | 2 ++ tests/test_ensemble_evaluator.py | 3 +++ tests/test_ensure_channel_first.py | 1 + tests/test_ensure_channel_firstd.py | 1 + tests/test_ensure_tuple.py | 1 + tests/test_ensure_type.py | 1 + tests/test_ensure_typed.py | 1 + tests/test_enum_bound_interp.py | 1 + tests/test_eval_mode.py | 1 + tests/test_evenly_divisible_all_gather_dist.py | 1 + tests/test_factorized_increase.py | 1 + tests/test_factorized_reduce.py | 1 + tests/test_fastmri_reader.py | 1 + tests/test_fft_utils.py | 1 + tests/test_fg_bg_to_indices.py | 1 + tests/test_fg_bg_to_indicesd.py | 1 + tests/test_file_basename.py | 1 + tests/test_fill_holes.py | 1 + tests/test_fill_holesd.py | 1 + tests/test_fl_exchange_object.py | 1 + tests/test_fl_monai_algo.py | 1 + tests/test_fl_monai_algo_dist.py | 1 + tests/test_fl_monai_algo_stats.py | 1 + tests/test_flatten_sub_keysd.py | 1 + tests/test_flexible_unet.py | 3 +++ tests/test_flip.py | 1 + tests/test_flipd.py | 1 + tests/test_focal_loss.py | 1 + tests/test_folder_layout.py | 1 + tests/test_foreground_mask.py | 1 + tests/test_foreground_maskd.py | 1 + tests/test_fourier.py | 1 + tests/test_fpn_block.py | 2 ++ tests/test_freeze_layers.py | 1 + tests/test_from_engine_hovernet.py | 1 + tests/test_fullyconnectednet.py | 1 + tests/test_gaussian.py | 1 + tests/test_gaussian_filter.py | 2 ++ tests/test_gaussian_sharpen.py | 1 + tests/test_gaussian_sharpend.py | 1 + tests/test_gaussian_smooth.py | 1 + tests/test_gaussian_smoothd.py | 1 + tests/test_gdsdataset.py | 2 ++ tests/test_generalized_dice_focal_loss.py | 1 + tests/test_generalized_dice_loss.py | 1 + .../test_generalized_wasserstein_dice_loss.py | 2 ++ tests/test_generate_distance_map.py | 1 + tests/test_generate_distance_mapd.py | 1 + tests/test_generate_instance_border.py | 1 + tests/test_generate_instance_borderd.py | 1 + tests/test_generate_instance_centroid.py | 1 + tests/test_generate_instance_centroidd.py | 1 + tests/test_generate_instance_contour.py | 1 + tests/test_generate_instance_contourd.py | 1 + tests/test_generate_instance_type.py | 1 + tests/test_generate_instance_typed.py | 1 + ...test_generate_label_classes_crop_centers.py | 1 + tests/test_generate_param_groups.py | 1 + ...test_generate_pos_neg_label_crop_centers.py | 1 + tests/test_generate_spatial_bounding_box.py | 1 + tests/test_generate_succinct_contour.py | 1 + tests/test_generate_succinct_contourd.py | 1 + tests/test_generate_watershed_markers.py | 1 + tests/test_generate_watershed_markersd.py | 1 + tests/test_generate_watershed_mask.py | 1 + tests/test_generate_watershed_maskd.py | 1 + tests/test_generator.py | 1 + tests/test_get_equivalent_dtype.py | 1 + tests/test_get_extreme_points.py | 1 + tests/test_get_layers.py | 2 ++ tests/test_get_package_version.py | 1 + tests/test_get_unique_labels.py | 1 + tests/test_gibbs_noise.py | 1 + tests/test_gibbs_noised.py | 1 + tests/test_giou_loss.py | 1 + tests/test_global_mutual_information_loss.py | 2 ++ tests/test_globalnet.py | 2 ++ tests/test_gmm.py | 1 + tests/test_grid_dataset.py | 1 + tests/test_grid_distortion.py | 1 + tests/test_grid_distortiond.py | 1 + tests/test_grid_patch.py | 1 + tests/test_grid_patchd.py | 1 + tests/test_grid_pull.py | 1 + tests/test_grid_split.py | 1 + tests/test_grid_splitd.py | 1 + tests/test_handler_checkpoint_loader.py | 1 + tests/test_handler_checkpoint_saver.py | 1 + tests/test_handler_classification_saver.py | 1 + .../test_handler_classification_saver_dist.py | 1 + tests/test_handler_clearml_image.py | 1 + tests/test_handler_clearml_stats.py | 1 + tests/test_handler_confusion_matrix_dist.py | 1 + tests/test_handler_decollate_batch.py | 1 + tests/test_handler_early_stop.py | 3 +++ tests/test_handler_garbage_collector.py | 1 + tests/test_handler_ignite_metric.py | 1 + tests/test_handler_logfile.py | 1 + tests/test_handler_lr_scheduler.py | 1 + tests/test_handler_metric_logger.py | 1 + tests/test_handler_metrics_reloaded.py | 2 ++ tests/test_handler_metrics_saver.py | 1 + tests/test_handler_metrics_saver_dist.py | 1 + tests/test_handler_mlflow.py | 2 ++ tests/test_handler_nvtx.py | 1 + tests/test_handler_panoptic_quality.py | 1 + tests/test_handler_parameter_scheduler.py | 3 +++ tests/test_handler_post_processing.py | 1 + tests/test_handler_prob_map_producer.py | 3 +++ tests/test_handler_regression_metrics.py | 1 + tests/test_handler_regression_metrics_dist.py | 4 ++++ tests/test_handler_rocauc.py | 1 + tests/test_handler_rocauc_dist.py | 1 + tests/test_handler_smartcache.py | 1 + tests/test_handler_stats.py | 2 ++ tests/test_handler_tb_image.py | 1 + tests/test_handler_tb_stats.py | 2 ++ tests/test_handler_validation.py | 2 ++ tests/test_hardnegsampler.py | 1 + tests/test_hashing.py | 2 ++ tests/test_hausdorff_distance.py | 1 + tests/test_hausdorff_loss.py | 2 ++ tests/test_header_correct.py | 1 + tests/test_highresnet.py | 1 + tests/test_hilbert_transform.py | 3 +++ tests/test_histogram_normalize.py | 1 + tests/test_histogram_normalized.py | 1 + tests/test_hovernet.py | 1 + ...st_hovernet_instance_map_post_processing.py | 1 + ...t_hovernet_instance_map_post_processingd.py | 1 + tests/test_hovernet_loss.py | 2 ++ ...st_hovernet_nuclear_type_post_processing.py | 1 + ...t_hovernet_nuclear_type_post_processingd.py | 1 + tests/test_identity.py | 1 + tests/test_identityd.py | 1 + tests/test_image_dataset.py | 2 ++ tests/test_image_filter.py | 5 +++++ tests/test_image_rw.py | 4 ++++ tests/test_img2tensorboard.py | 1 + tests/test_init_reader.py | 1 + tests/test_integration_autorunner.py | 1 + tests/test_integration_bundle_run.py | 3 +++ tests/test_integration_classification_2d.py | 2 ++ tests/test_integration_determinism.py | 3 +++ tests/test_integration_fast_train.py | 1 + tests/test_integration_gpu_customization.py | 1 + tests/test_integration_lazy_samples.py | 1 + tests/test_integration_nnunetv2_runner.py | 1 + tests/test_integration_segmentation_3d.py | 1 + tests/test_integration_sliding_window.py | 1 + tests/test_integration_stn.py | 1 + tests/test_integration_unet_2d.py | 3 +++ tests/test_integration_workers.py | 1 + tests/test_integration_workflows.py | 3 +++ tests/test_integration_workflows_gan.py | 1 + tests/test_intensity_stats.py | 1 + tests/test_intensity_statsd.py | 1 + tests/test_inverse_array.py | 1 + tests/test_invert.py | 1 + tests/test_invertd.py | 1 + tests/test_is_supported_format.py | 1 + tests/test_iterable_dataset.py | 2 ++ tests/test_itk_torch_bridge.py | 2 ++ tests/test_itk_writer.py | 1 + tests/test_k_space_spike_noise.py | 1 + tests/test_k_space_spike_noised.py | 1 + tests/test_keep_largest_connected_component.py | 1 + .../test_keep_largest_connected_componentd.py | 1 + tests/test_kspace_mask.py | 1 + tests/test_label_filter.py | 1 + tests/test_label_filterd.py | 1 + tests/test_label_quality_score.py | 1 + tests/test_label_to_contour.py | 1 + tests/test_label_to_contourd.py | 1 + tests/test_label_to_mask.py | 1 + tests/test_label_to_maskd.py | 1 + tests/test_lambda.py | 1 + tests/test_lambdad.py | 1 + tests/test_lesion_froc.py | 1 + tests/test_list_data_collate.py | 1 + tests/test_list_to_dict.py | 1 + tests/test_lltm.py | 1 + tests/test_lmdbdataset.py | 2 ++ tests/test_lmdbdataset_dist.py | 2 ++ tests/test_load_decathlon_datalist.py | 1 + tests/test_load_image.py | 2 ++ tests/test_load_imaged.py | 3 +++ tests/test_load_spacing_orientation.py | 1 + tests/test_loader_semaphore.py | 1 + ..._local_normalized_cross_correlation_loss.py | 1 + tests/test_localnet.py | 1 + tests/test_localnet_block.py | 3 +++ tests/test_look_up_option.py | 1 + tests/test_loss_metric.py | 1 + tests/test_lr_finder.py | 1 + tests/test_lr_scheduler.py | 2 ++ tests/test_make_nifti.py | 1 + tests/test_map_binary_to_indices.py | 1 + tests/test_map_classes_to_indices.py | 1 + tests/test_map_label_value.py | 1 + tests/test_map_label_valued.py | 1 + tests/test_map_transform.py | 2 ++ tests/test_mask_intensity.py | 1 + tests/test_mask_intensityd.py | 1 + tests/test_masked_dice_loss.py | 1 + tests/test_masked_loss.py | 1 + tests/test_masked_patch_wsi_dataset.py | 3 +++ tests/test_matshow3d.py | 1 + tests/test_mean_ensemble.py | 1 + tests/test_mean_ensembled.py | 1 + tests/test_median_filter.py | 1 + tests/test_median_smooth.py | 1 + tests/test_median_smoothd.py | 1 + tests/test_mednistdataset.py | 1 + tests/test_meta_affine.py | 1 + tests/test_meta_tensor.py | 1 + tests/test_metatensor_integration.py | 1 + tests/test_metrics_reloaded.py | 1 + tests/test_milmodel.py | 1 + tests/test_mlp.py | 1 + tests/test_mmar_download.py | 1 + tests/test_module_list.py | 1 + tests/test_monai_env_vars.py | 1 + tests/test_monai_utils_misc.py | 4 ++++ tests/test_mri_utils.py | 1 + tests/test_multi_scale.py | 1 + tests/test_net_adapter.py | 1 + tests/test_network_consistency.py | 1 + tests/test_nifti_endianness.py | 1 + tests/test_nifti_header_revise.py | 1 + tests/test_nifti_rw.py | 1 + tests/test_normalize_intensity.py | 1 + tests/test_normalize_intensityd.py | 1 + tests/test_npzdictitemdataset.py | 1 + tests/test_nrrd_reader.py | 1 + tests/test_nuclick_transforms.py | 9 +++++++++ tests/test_numpy_reader.py | 1 + tests/test_nvtx_decorator.py | 1 + tests/test_nvtx_transform.py | 1 + tests/test_occlusion_sensitivity.py | 2 ++ tests/test_one_of.py | 12 ++++++++++++ tests/test_optional_import.py | 1 + tests/test_ori_ras_lps.py | 1 + tests/test_orientation.py | 1 + tests/test_orientationd.py | 1 + tests/test_p3d_block.py | 1 + tests/test_pad_collation.py | 2 ++ tests/test_pad_mode.py | 1 + tests/test_partition_dataset.py | 1 + tests/test_partition_dataset_classes.py | 1 + tests/test_patch_dataset.py | 1 + tests/test_patch_inferer.py | 1 + tests/test_patch_wsi_dataset.py | 3 +++ tests/test_patchembedding.py | 2 ++ tests/test_pathology_he_stain.py | 2 ++ tests/test_pathology_he_stain_dict.py | 2 ++ tests/test_pathology_prob_nms.py | 1 + tests/test_perceptual_loss.py | 1 + tests/test_persistentdataset.py | 2 ++ tests/test_persistentdataset_dist.py | 3 +++ tests/test_phl_cpu.py | 1 + tests/test_phl_cuda.py | 1 + tests/test_pil_reader.py | 1 + tests/test_plot_2d_or_3d_image.py | 1 + tests/test_png_rw.py | 1 + tests/test_polyval.py | 1 + tests/test_prepare_batch_default.py | 2 ++ tests/test_prepare_batch_default_dist.py | 2 ++ tests/test_prepare_batch_extra_input.py | 2 ++ tests/test_prepare_batch_hovernet.py | 2 ++ tests/test_preset_filters.py | 6 ++++++ tests/test_print_info.py | 1 + tests/test_print_transform_backends.py | 1 + tests/test_probnms.py | 1 + tests/test_probnmsd.py | 1 + tests/test_profiling.py | 1 + tests/test_pytorch_version_after.py | 1 + tests/test_query_memory.py | 1 + tests/test_quicknat.py | 1 + tests/test_rand_adjust_contrast.py | 1 + tests/test_rand_adjust_contrastd.py | 1 + tests/test_rand_affine.py | 1 + tests/test_rand_affine_grid.py | 1 + tests/test_rand_affined.py | 1 + tests/test_rand_axis_flip.py | 1 + tests/test_rand_axis_flipd.py | 1 + tests/test_rand_bias_field.py | 1 + tests/test_rand_bias_fieldd.py | 1 + tests/test_rand_coarse_dropout.py | 1 + tests/test_rand_coarse_dropoutd.py | 1 + tests/test_rand_coarse_shuffle.py | 1 + tests/test_rand_coarse_shuffled.py | 1 + tests/test_rand_crop_by_label_classes.py | 1 + tests/test_rand_crop_by_label_classesd.py | 1 + tests/test_rand_crop_by_pos_neg_label.py | 1 + tests/test_rand_crop_by_pos_neg_labeld.py | 1 + tests/test_rand_cucim_dict_transform.py | 1 + tests/test_rand_cucim_transform.py | 1 + tests/test_rand_deform_grid.py | 1 + tests/test_rand_elastic_2d.py | 1 + tests/test_rand_elastic_3d.py | 1 + tests/test_rand_elasticd_2d.py | 1 + tests/test_rand_elasticd_3d.py | 1 + tests/test_rand_flip.py | 1 + tests/test_rand_flipd.py | 1 + tests/test_rand_gaussian_noise.py | 1 + tests/test_rand_gaussian_noised.py | 1 + tests/test_rand_gaussian_sharpen.py | 1 + tests/test_rand_gaussian_sharpend.py | 1 + tests/test_rand_gaussian_smooth.py | 1 + tests/test_rand_gaussian_smoothd.py | 1 + tests/test_rand_gibbs_noise.py | 1 + tests/test_rand_gibbs_noised.py | 1 + tests/test_rand_grid_distortion.py | 1 + tests/test_rand_grid_distortiond.py | 1 + tests/test_rand_grid_patch.py | 1 + tests/test_rand_grid_patchd.py | 1 + tests/test_rand_histogram_shift.py | 1 + tests/test_rand_histogram_shiftd.py | 1 + tests/test_rand_k_space_spike_noise.py | 1 + tests/test_rand_k_space_spike_noised.py | 1 + tests/test_rand_lambda.py | 1 + tests/test_rand_lambdad.py | 1 + tests/test_rand_rician_noise.py | 1 + tests/test_rand_rician_noised.py | 1 + tests/test_rand_rotate.py | 3 +++ tests/test_rand_rotate90.py | 1 + tests/test_rand_rotate90d.py | 1 + tests/test_rand_rotated.py | 2 ++ tests/test_rand_scale_intensity.py | 1 + tests/test_rand_scale_intensity_fixed_mean.py | 1 + tests/test_rand_scale_intensity_fixed_meand.py | 1 + tests/test_rand_scale_intensityd.py | 1 + tests/test_rand_shift_intensity.py | 1 + tests/test_rand_shift_intensityd.py | 1 + tests/test_rand_simulate_low_resolution.py | 1 + tests/test_rand_simulate_low_resolutiond.py | 1 + tests/test_rand_spatial_crop_samplesd.py | 1 + tests/test_rand_std_shift_intensity.py | 1 + tests/test_rand_std_shift_intensityd.py | 1 + tests/test_rand_weighted_cropd.py | 1 + tests/test_rand_zoom.py | 1 + tests/test_rand_zoomd.py | 1 + tests/test_randidentity.py | 2 ++ tests/test_random_order.py | 4 ++++ tests/test_randomizable.py | 2 ++ tests/test_randomizable_transform_type.py | 2 ++ tests/test_randtorchvisiond.py | 1 + tests/test_rankfilter_dist.py | 2 ++ tests/test_recon_net_utils.py | 1 + ...test_reference_based_normalize_intensity.py | 1 + tests/test_reference_based_spatial_cropd.py | 1 + tests/test_reference_resolver.py | 1 + tests/test_reg_loss_integration.py | 2 ++ tests/test_regunet.py | 1 + tests/test_regunet_block.py | 3 +++ tests/test_remove_repeated_channel.py | 1 + tests/test_remove_repeated_channeld.py | 1 + tests/test_remove_small_objects.py | 1 + tests/test_repeat_channel.py | 1 + tests/test_repeat_channeld.py | 1 + tests/test_replace_module.py | 1 + tests/test_require_pkg.py | 4 ++++ tests/test_resample.py | 1 + tests/test_resample_backends.py | 1 + tests/test_resample_datalist.py | 1 + tests/test_resample_to_match.py | 1 + tests/test_resample_to_matchd.py | 1 + tests/test_resampler.py | 1 + tests/test_resize.py | 1 + tests/test_resize_with_pad_or_crop.py | 1 + tests/test_resize_with_pad_or_cropd.py | 1 + tests/test_resized.py | 1 + tests/test_resnet.py | 1 + tests/test_retinanet.py | 1 + tests/test_retinanet_detector.py | 2 ++ tests/test_retinanet_predict_utils.py | 3 +++ tests/test_rotate.py | 2 ++ tests/test_rotate90.py | 3 +++ tests/test_rotate90d.py | 1 + tests/test_rotated.py | 3 +++ tests/test_safe_dtype_range.py | 1 + tests/test_saliency_inferer.py | 1 + tests/test_sample_slices.py | 1 + tests/test_sampler_dist.py | 1 + tests/test_save_classificationd.py | 1 + tests/test_save_image.py | 1 + tests/test_save_imaged.py | 3 +++ tests/test_save_state.py | 1 + tests/test_savitzky_golay_filter.py | 4 ++++ tests/test_savitzky_golay_smooth.py | 1 + tests/test_savitzky_golay_smoothd.py | 1 + tests/test_scale_intensity.py | 1 + tests/test_scale_intensity_fixed_mean.py | 1 + tests/test_scale_intensity_range.py | 1 + .../test_scale_intensity_range_percentiles.py | 1 + .../test_scale_intensity_range_percentilesd.py | 1 + tests/test_scale_intensity_ranged.py | 1 + tests/test_scale_intensityd.py | 1 + tests/test_se_block.py | 1 + tests/test_se_blocks.py | 2 ++ tests/test_seg_loss_integration.py | 2 ++ tests/test_segresnet.py | 2 ++ tests/test_segresnet_block.py | 1 + tests/test_segresnet_ds.py | 1 + tests/test_select_cross_validation_folds.py | 1 + tests/test_select_itemsd.py | 1 + tests/test_selfattention.py | 1 + tests/test_senet.py | 2 ++ tests/test_separable_filter.py | 1 + tests/test_set_determinism.py | 2 ++ tests/test_set_visible_devices.py | 1 + tests/test_shift_intensity.py | 1 + tests/test_shift_intensityd.py | 1 + tests/test_shuffle_buffer.py | 1 + tests/test_signal_continuouswavelet.py | 1 + tests/test_signal_fillempty.py | 2 ++ tests/test_signal_fillemptyd.py | 2 ++ tests/test_signal_rand_add_gaussiannoise.py | 2 ++ tests/test_signal_rand_add_sine.py | 2 ++ tests/test_signal_rand_add_sine_partial.py | 2 ++ tests/test_signal_rand_add_squarepulse.py | 2 ++ ...test_signal_rand_add_squarepulse_partial.py | 2 ++ tests/test_signal_rand_drop.py | 2 ++ tests/test_signal_rand_scale.py | 2 ++ tests/test_signal_rand_shift.py | 2 ++ tests/test_signal_remove_frequency.py | 2 ++ tests/test_simple_aspp.py | 1 + tests/test_simulatedelay.py | 1 + tests/test_simulatedelayd.py | 1 + tests/test_skip_connection.py | 1 + tests/test_slice_inferer.py | 1 + tests/test_sliding_patch_wsi_dataset.py | 3 +++ .../test_sliding_window_hovernet_inference.py | 1 + tests/test_sliding_window_inference.py | 2 ++ tests/test_sliding_window_splitter.py | 1 + tests/test_smartcachedataset.py | 1 + tests/test_smooth_field.py | 1 + tests/test_some_of.py | 6 ++++++ tests/test_spacing.py | 1 + tests/test_spacingd.py | 1 + tests/test_spatial_combine_transforms.py | 1 + tests/test_spatial_resample.py | 1 + tests/test_spatial_resampled.py | 1 + tests/test_spectral_loss.py | 1 + tests/test_splitdim.py | 1 + tests/test_squeeze_unsqueeze.py | 1 + tests/test_squeezedim.py | 1 + tests/test_squeezedimd.py | 1 + tests/test_ssim_loss.py | 1 + tests/test_ssim_metric.py | 1 + tests/test_state_cacher.py | 1 + tests/test_std_shift_intensity.py | 1 + tests/test_std_shift_intensityd.py | 1 + tests/test_str2bool.py | 1 + tests/test_str2list.py | 1 + tests/test_subpixel_upsample.py | 1 + tests/test_surface_dice.py | 1 + tests/test_surface_distance.py | 1 + tests/test_swin_unetr.py | 1 + tests/test_synthetic.py | 1 + tests/test_tciadataset.py | 1 + tests/test_testtimeaugmentation.py | 1 + tests/test_text_encoding.py | 1 + tests/test_thread_buffer.py | 1 + tests/test_threadcontainer.py | 1 + tests/test_threshold_intensity.py | 1 + tests/test_threshold_intensityd.py | 1 + tests/test_timedcall_dist.py | 1 + tests/test_to_contiguous.py | 1 + tests/test_to_cupy.py | 1 + tests/test_to_cupyd.py | 1 + tests/test_to_device.py | 1 + tests/test_to_deviced.py | 1 + tests/test_to_from_meta_tensord.py | 1 + tests/test_to_numpy.py | 1 + tests/test_to_numpyd.py | 1 + tests/test_to_onehot.py | 1 + tests/test_to_pil.py | 1 + tests/test_to_pild.py | 1 + tests/test_to_tensor.py | 1 + tests/test_to_tensord.py | 1 + tests/test_torchscript_utils.py | 2 ++ tests/test_torchvision.py | 1 + tests/test_torchvision_fc_model.py | 2 ++ tests/test_torchvisiond.py | 1 + tests/test_traceable_transform.py | 2 ++ tests/test_train_mode.py | 1 + tests/test_trainable_bilateral.py | 2 ++ tests/test_trainable_joint_bilateral.py | 2 ++ tests/test_transchex.py | 1 + tests/test_transform.py | 2 ++ tests/test_transformerblock.py | 1 + tests/test_transpose.py | 1 + tests/test_transposed.py | 1 + tests/test_tversky_loss.py | 1 + ...test_ultrasound_confidence_map_transform.py | 1 + tests/test_unet.py | 1 + tests/test_unetr.py | 1 + tests/test_unetr_block.py | 3 +++ tests/test_unified_focal_loss.py | 1 + tests/test_upsample_block.py | 1 + tests/test_utils_pytorch_numpy_unification.py | 1 + tests/test_varautoencoder.py | 1 + tests/test_varnet.py | 1 + tests/test_version.py | 1 + tests/test_video_datasets.py | 1 + tests/test_vis_cam.py | 1 + tests/test_vis_gradbased.py | 2 ++ tests/test_vis_gradcam.py | 2 ++ tests/test_vit.py | 1 + tests/test_vitautoenc.py | 1 + tests/test_vnet.py | 1 + tests/test_vote_ensemble.py | 1 + tests/test_vote_ensembled.py | 1 + tests/test_voxelmorph.py | 1 + tests/test_warp.py | 1 + tests/test_watershed.py | 1 + tests/test_watershedd.py | 1 + tests/test_weight_init.py | 1 + tests/test_weighted_random_sampler_dist.py | 1 + tests/test_with_allow_missing_keys.py | 1 + tests/test_write_metrics_reports.py | 1 + tests/test_wsi_sliding_window_splitter.py | 2 ++ tests/test_wsireader.py | 4 ++++ tests/test_zarr_avg_merger.py | 1 + tests/test_zipdataset.py | 2 ++ tests/test_zoom.py | 1 + tests/test_zoom_affine.py | 1 + tests/test_zoomd.py | 1 + tests/utils.py | 2 ++ 734 files changed, 1046 insertions(+), 4 deletions(-) diff --git a/monai/apps/datasets.py b/monai/apps/datasets.py index bb10eb6b11..67ea3059cc 100644 --- a/monai/apps/datasets.py +++ b/monai/apps/datasets.py @@ -737,6 +737,7 @@ def get_dataset(self, folds: Sequence[int] | int, **dataset_params: Any) -> obje dataset_params_.update(dataset_params) class _NsplitsDataset(self.dataset_cls): # type: ignore + def _split_datalist(self, datalist: list[dict]) -> list[dict]: data = partition_dataset(data=datalist, num_partitions=nfolds, shuffle=True, seed=seed) return select_cross_validation_folds(partitions=data, folds=folds) diff --git a/monai/apps/deepedit/transforms.py b/monai/apps/deepedit/transforms.py index 4c92b42c08..6d0825f54a 100644 --- a/monai/apps/deepedit/transforms.py +++ b/monai/apps/deepedit/transforms.py @@ -34,6 +34,7 @@ class DiscardAddGuidanced(MapTransform): + def __init__( self, keys: KeysCollection, @@ -84,6 +85,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda class NormalizeLabelsInDatasetd(MapTransform): + def __init__( self, keys: KeysCollection, label_names: dict[str, int] | None = None, allow_missing_keys: bool = False ): @@ -121,6 +123,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda class SingleLabelSelectiond(MapTransform): + def __init__( self, keys: KeysCollection, label_names: Sequence[str] | None = None, allow_missing_keys: bool = False ): diff --git a/monai/apps/detection/metrics/coco.py b/monai/apps/detection/metrics/coco.py index 033b763be5..a856f14fb8 100644 --- a/monai/apps/detection/metrics/coco.py +++ b/monai/apps/detection/metrics/coco.py @@ -72,6 +72,7 @@ class COCOMetric: + def __init__( self, classes: Sequence[str], diff --git a/monai/apps/detection/utils/ATSS_matcher.py b/monai/apps/detection/utils/ATSS_matcher.py index cc9e238862..5b8f950ab3 100644 --- a/monai/apps/detection/utils/ATSS_matcher.py +++ b/monai/apps/detection/utils/ATSS_matcher.py @@ -164,6 +164,7 @@ def compute_matches( class ATSSMatcher(Matcher): + def __init__( self, num_candidates: int = 4, diff --git a/monai/apps/pathology/transforms/post/array.py b/monai/apps/pathology/transforms/post/array.py index ef9c535725..99e94f89c0 100644 --- a/monai/apps/pathology/transforms/post/array.py +++ b/monai/apps/pathology/transforms/post/array.py @@ -162,7 +162,7 @@ def __call__(self, prob_map: NdarrayOrTensor) -> NdarrayOrTensor: pred = label(pred)[0] if self.remove_small_objects is not None: pred = self.remove_small_objects(pred) - pred[pred > 0] = 1 # type: ignore + pred[pred > 0] = 1 return convert_to_dst_type(pred, prob_map, dtype=self.dtype)[0] @@ -338,7 +338,7 @@ def __call__(self, mask: NdarrayOrTensor, instance_border: NdarrayOrTensor) -> N instance_border = instance_border >= self.threshold # uncertain area marker = mask - convert_to_dst_type(instance_border, mask)[0] # certain foreground - marker[marker < 0] = 0 # type: ignore + marker[marker < 0] = 0 marker = self.postprocess_fn(marker) marker = convert_to_numpy(marker) @@ -634,7 +634,7 @@ def __call__( # type: ignore seg_map_crop = convert_to_dst_type(seg_map_crop == instance_id, type_map_crop, dtype=bool)[0] - inst_type = type_map_crop[seg_map_crop] # type: ignore + inst_type = type_map_crop[seg_map_crop] type_list, type_pixels = unique(inst_type, return_counts=True) type_list = list(zip(type_list, type_pixels)) type_list = sorted(type_list, key=lambda x: x[1], reverse=True) diff --git a/monai/data/dataset.py b/monai/data/dataset.py index eba850225d..531893d768 100644 --- a/monai/data/dataset.py +++ b/monai/data/dataset.py @@ -1275,6 +1275,7 @@ def __len__(self) -> int: return min(len(dataset) for dataset in self.data) def _transform(self, index: int): + def to_list(x): return list(x) if isinstance(x, (tuple, list)) else [x] diff --git a/monai/fl/client/client_algo.py b/monai/fl/client/client_algo.py index 25a88a9e66..3dc9f5785d 100644 --- a/monai/fl/client/client_algo.py +++ b/monai/fl/client/client_algo.py @@ -57,6 +57,7 @@ def abort(self, extra: dict | None = None) -> None: class ClientAlgoStats(BaseClient): + def get_data_stats(self, extra: dict | None = None) -> ExchangeObject: """ Get summary statistics about the local data. diff --git a/monai/handlers/ignite_metric.py b/monai/handlers/ignite_metric.py index 0382b8cb64..021154d705 100644 --- a/monai/handlers/ignite_metric.py +++ b/monai/handlers/ignite_metric.py @@ -157,6 +157,7 @@ def attach(self, engine: Engine, name: str) -> None: # type: ignore[override] @deprecated(since="1.2", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.") class IgniteMetric(IgniteMetricHandler): + def __init__( self, metric_fn: CumulativeIterationMetric | None = None, diff --git a/monai/metrics/f_beta_score.py b/monai/metrics/f_beta_score.py index 61e4525662..bb9371c8bf 100644 --- a/monai/metrics/f_beta_score.py +++ b/monai/metrics/f_beta_score.py @@ -22,6 +22,7 @@ class FBetaScore(CumulativeIterationMetric): + def __init__( self, beta: float = 1.0, diff --git a/monai/networks/blocks/dynunet_block.py b/monai/networks/blocks/dynunet_block.py index 12afab3464..801b49de8b 100644 --- a/monai/networks/blocks/dynunet_block.py +++ b/monai/networks/blocks/dynunet_block.py @@ -245,6 +245,7 @@ def forward(self, inp, skip): class UnetOutBlock(nn.Module): + def __init__( self, spatial_dims: int, in_channels: int, out_channels: int, dropout: tuple | str | float | None = None ): diff --git a/monai/networks/blocks/localnet_block.py b/monai/networks/blocks/localnet_block.py index 11808eabf7..6e0efc8588 100644 --- a/monai/networks/blocks/localnet_block.py +++ b/monai/networks/blocks/localnet_block.py @@ -72,6 +72,7 @@ def get_deconv_block(spatial_dims: int, in_channels: int, out_channels: int) -> class ResidualBlock(nn.Module): + def __init__( self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Sequence[int] | int ) -> None: @@ -95,6 +96,7 @@ def forward(self, x) -> torch.Tensor: class LocalNetResidualBlock(nn.Module): + def __init__(self, spatial_dims: int, in_channels: int, out_channels: int) -> None: super().__init__() if in_channels != out_channels: diff --git a/monai/networks/blocks/pos_embed_utils.py b/monai/networks/blocks/pos_embed_utils.py index 138149cac6..e03553307e 100644 --- a/monai/networks/blocks/pos_embed_utils.py +++ b/monai/networks/blocks/pos_embed_utils.py @@ -23,6 +23,7 @@ # From PyTorch internals def _ntuple(n): + def parse(x): if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): return tuple(x) diff --git a/monai/networks/layers/gmm.py b/monai/networks/layers/gmm.py index 94d619bb7a..6ebe66832f 100644 --- a/monai/networks/layers/gmm.py +++ b/monai/networks/layers/gmm.py @@ -78,6 +78,7 @@ def apply(self, features): class _ApplyFunc(torch.autograd.Function): + @staticmethod def forward(ctx, params, features, compiled_extension): return compiled_extension.apply(params, features) diff --git a/monai/networks/layers/simplelayers.py b/monai/networks/layers/simplelayers.py index a1122ceaa2..4ac621967f 100644 --- a/monai/networks/layers/simplelayers.py +++ b/monai/networks/layers/simplelayers.py @@ -552,6 +552,7 @@ def forward(self, in_tensor: torch.Tensor, number_of_passes=1) -> torch.Tensor: class GaussianFilter(nn.Module): + def __init__( self, spatial_dims: int, @@ -607,6 +608,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class LLTMFunction(Function): + @staticmethod def forward(ctx, input, weights, bias, old_h, old_cell): outputs = _C.lltm_forward(input, weights, bias, old_h, old_cell) diff --git a/monai/networks/layers/spatial_transforms.py b/monai/networks/layers/spatial_transforms.py index 53f35e63f2..2d39dfdbc1 100644 --- a/monai/networks/layers/spatial_transforms.py +++ b/monai/networks/layers/spatial_transforms.py @@ -33,6 +33,7 @@ class _GridPull(torch.autograd.Function): + @staticmethod def forward(ctx, input, grid, interpolation, bound, extrapolate): opt = (bound, interpolation, extrapolate) @@ -132,6 +133,7 @@ def grid_pull( class _GridPush(torch.autograd.Function): + @staticmethod def forward(ctx, input, grid, shape, interpolation, bound, extrapolate): opt = (bound, interpolation, extrapolate) @@ -236,6 +238,7 @@ def grid_push( class _GridCount(torch.autograd.Function): + @staticmethod def forward(ctx, grid, shape, interpolation, bound, extrapolate): opt = (bound, interpolation, extrapolate) @@ -335,6 +338,7 @@ def grid_count(grid: torch.Tensor, shape=None, interpolation="linear", bound="ze class _GridGrad(torch.autograd.Function): + @staticmethod def forward(ctx, input, grid, interpolation, bound, extrapolate): opt = (bound, interpolation, extrapolate) @@ -433,6 +437,7 @@ def grid_grad(input: torch.Tensor, grid: torch.Tensor, interpolation="linear", b class AffineTransform(nn.Module): + def __init__( self, spatial_size: Sequence[int] | int | None = None, diff --git a/monai/networks/nets/ahnet.py b/monai/networks/nets/ahnet.py index b0ad1eabbd..5e280d7f24 100644 --- a/monai/networks/nets/ahnet.py +++ b/monai/networks/nets/ahnet.py @@ -87,6 +87,7 @@ def forward(self, x): class Projection(nn.Sequential): + def __init__(self, spatial_dims: int, num_input_features: int, num_output_features: int): super().__init__() @@ -100,6 +101,7 @@ def __init__(self, spatial_dims: int, num_input_features: int, num_output_featur class DenseBlock(nn.Sequential): + def __init__( self, spatial_dims: int, @@ -118,6 +120,7 @@ def __init__( class UpTransition(nn.Sequential): + def __init__( self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = "transpose" ): @@ -143,6 +146,7 @@ def __init__( class Final(nn.Sequential): + def __init__( self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = "transpose" ): @@ -178,6 +182,7 @@ def __init__( class Pseudo3DLayer(nn.Module): + def __init__(self, spatial_dims: int, num_input_features: int, growth_rate: int, bn_size: int, dropout_prob: float): super().__init__() # 1x1x1 @@ -244,6 +249,7 @@ def forward(self, x): class PSP(nn.Module): + def __init__(self, spatial_dims: int, psp_block_num: int, in_ch: int, upsample_mode: str = "transpose"): super().__init__() self.up_modules = nn.ModuleList() diff --git a/monai/networks/nets/attentionunet.py b/monai/networks/nets/attentionunet.py index 362d63d636..5689cf1071 100644 --- a/monai/networks/nets/attentionunet.py +++ b/monai/networks/nets/attentionunet.py @@ -23,6 +23,7 @@ class ConvBlock(nn.Module): + def __init__( self, spatial_dims: int, @@ -67,6 +68,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class UpConv(nn.Module): + def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size=3, strides=2, dropout=0.0): super().__init__() self.up = Convolution( @@ -88,6 +90,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class AttentionBlock(nn.Module): + def __init__(self, spatial_dims: int, f_int: int, f_g: int, f_l: int, dropout=0.0): super().__init__() self.W_g = nn.Sequential( @@ -145,6 +148,7 @@ def forward(self, g: torch.Tensor, x: torch.Tensor) -> torch.Tensor: class AttentionLayer(nn.Module): + def __init__( self, spatial_dims: int, diff --git a/monai/networks/nets/basic_unet.py b/monai/networks/nets/basic_unet.py index 7fc57edc42..b9970d4113 100644 --- a/monai/networks/nets/basic_unet.py +++ b/monai/networks/nets/basic_unet.py @@ -176,6 +176,7 @@ def forward(self, x: torch.Tensor, x_e: Optional[torch.Tensor]): class BasicUNet(nn.Module): + def __init__( self, spatial_dims: int = 3, diff --git a/monai/networks/nets/basic_unetplusplus.py b/monai/networks/nets/basic_unetplusplus.py index 28d4b4668a..f7ae768513 100644 --- a/monai/networks/nets/basic_unetplusplus.py +++ b/monai/networks/nets/basic_unetplusplus.py @@ -24,6 +24,7 @@ class BasicUNetPlusPlus(nn.Module): + def __init__( self, spatial_dims: int = 3, diff --git a/monai/networks/nets/densenet.py b/monai/networks/nets/densenet.py index 2100272d91..5ccb429c91 100644 --- a/monai/networks/nets/densenet.py +++ b/monai/networks/nets/densenet.py @@ -42,6 +42,7 @@ class _DenseLayer(nn.Module): + def __init__( self, spatial_dims: int, @@ -88,6 +89,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class _DenseBlock(nn.Sequential): + def __init__( self, spatial_dims: int, @@ -119,6 +121,7 @@ def __init__( class _Transition(nn.Sequential): + def __init__( self, spatial_dims: int, diff --git a/monai/networks/nets/dints.py b/monai/networks/nets/dints.py index 6e3420d136..129e0925d3 100644 --- a/monai/networks/nets/dints.py +++ b/monai/networks/nets/dints.py @@ -73,6 +73,7 @@ def _dfs(node, paths): class _IdentityWithRAMCost(nn.Identity): + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.ram_cost = 0 @@ -105,6 +106,7 @@ def __init__( class _P3DActiConvNormBlockWithRAMCost(P3DActiConvNormBlock): + def __init__( self, in_channel: int, @@ -122,6 +124,7 @@ def __init__( class _FactorizedIncreaseBlockWithRAMCost(FactorizedIncreaseBlock): + def __init__( self, in_channel: int, @@ -138,6 +141,7 @@ def __init__( class _FactorizedReduceBlockWithRAMCost(FactorizedReduceBlock): + def __init__( self, in_channel: int, diff --git a/monai/networks/nets/efficientnet.py b/monai/networks/nets/efficientnet.py index d89ab53ea2..4e6c327b23 100644 --- a/monai/networks/nets/efficientnet.py +++ b/monai/networks/nets/efficientnet.py @@ -73,6 +73,7 @@ class MBConvBlock(nn.Module): + def __init__( self, spatial_dims: int, @@ -227,6 +228,7 @@ def set_swish(self, memory_efficient: bool = True) -> None: class EfficientNet(nn.Module): + def __init__( self, blocks_args_str: list[str], @@ -472,6 +474,7 @@ def _initialize_weights(self) -> None: class EfficientNetBN(EfficientNet): + def __init__( self, model_name: str, @@ -558,6 +561,7 @@ def __init__( class EfficientNetBNFeatures(EfficientNet): + def __init__( self, model_name: str, diff --git a/monai/networks/nets/highresnet.py b/monai/networks/nets/highresnet.py index e71f8d193d..4959f0713f 100644 --- a/monai/networks/nets/highresnet.py +++ b/monai/networks/nets/highresnet.py @@ -36,6 +36,7 @@ class HighResBlock(nn.Module): + def __init__( self, spatial_dims: int, diff --git a/monai/networks/nets/hovernet.py b/monai/networks/nets/hovernet.py index 3ec1cea37e..5f340c9be6 100644 --- a/monai/networks/nets/hovernet.py +++ b/monai/networks/nets/hovernet.py @@ -49,6 +49,7 @@ class _DenseLayerDecoder(nn.Module): + def __init__( self, num_features: int, @@ -103,6 +104,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class _DecoderBlock(nn.Sequential): + def __init__( self, layers: int, @@ -159,6 +161,7 @@ def __init__( class _DenseLayer(nn.Sequential): + def __init__( self, num_features: int, @@ -219,6 +222,7 @@ def __init__( class _Transition(nn.Sequential): + def __init__( self, in_channels: int, act: str | tuple = ("relu", {"inplace": True}), norm: str | tuple = "batch" ) -> None: @@ -235,6 +239,7 @@ def __init__( class _ResidualBlock(nn.Module): + def __init__( self, layers: int, @@ -312,6 +317,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class _DecoderBranch(nn.ModuleList): + def __init__( self, decode_config: Sequence[int] = (8, 4), diff --git a/monai/networks/nets/milmodel.py b/monai/networks/nets/milmodel.py index 0a25b7feec..ad6b77bf3d 100644 --- a/monai/networks/nets/milmodel.py +++ b/monai/networks/nets/milmodel.py @@ -83,6 +83,7 @@ def __init__( if mil_mode == "att_trans_pyramid": # register hooks to capture outputs of intermediate layers def forward_hook(layer_name): + def hook(module, input, output): self.extra_outputs[layer_name] = output diff --git a/monai/networks/nets/regunet.py b/monai/networks/nets/regunet.py index a7c5158240..4d6150ea1b 100644 --- a/monai/networks/nets/regunet.py +++ b/monai/networks/nets/regunet.py @@ -234,6 +234,7 @@ def forward(self, x): class AffineHead(nn.Module): + def __init__( self, spatial_dims: int, @@ -375,6 +376,7 @@ def build_output_block(self): class AdditiveUpSampleBlock(nn.Module): + def __init__( self, spatial_dims: int, diff --git a/monai/networks/nets/vnet.py b/monai/networks/nets/vnet.py index d89eb8ae03..2815224e08 100644 --- a/monai/networks/nets/vnet.py +++ b/monai/networks/nets/vnet.py @@ -30,6 +30,7 @@ def get_acti_layer(act: tuple[str, dict] | str, nchan: int = 0): class LUConv(nn.Module): + def __init__(self, spatial_dims: int, nchan: int, act: tuple[str, dict] | str, bias: bool = False): super().__init__() @@ -58,6 +59,7 @@ def _make_nconv(spatial_dims: int, nchan: int, depth: int, act: tuple[str, dict] class InputTransition(nn.Module): + def __init__( self, spatial_dims: int, in_channels: int, out_channels: int, act: tuple[str, dict] | str, bias: bool = False ): @@ -91,6 +93,7 @@ def forward(self, x): class DownTransition(nn.Module): + def __init__( self, spatial_dims: int, @@ -127,6 +130,7 @@ def forward(self, x): class UpTransition(nn.Module): + def __init__( self, spatial_dims: int, @@ -165,6 +169,7 @@ def forward(self, x, skipx): class OutputTransition(nn.Module): + def __init__( self, spatial_dims: int, in_channels: int, out_channels: int, act: tuple[str, dict] | str, bias: bool = False ): diff --git a/monai/optimizers/lr_finder.py b/monai/optimizers/lr_finder.py index 75e108ae71..045135628d 100644 --- a/monai/optimizers/lr_finder.py +++ b/monai/optimizers/lr_finder.py @@ -43,6 +43,7 @@ class DataLoaderIter: + def __init__(self, data_loader: DataLoader, image_extractor: Callable, label_extractor: Callable) -> None: if not isinstance(data_loader, DataLoader): raise ValueError( @@ -71,6 +72,7 @@ def __next__(self): class TrainDataLoaderIter(DataLoaderIter): + def __init__( self, data_loader: DataLoader, image_extractor: Callable, label_extractor: Callable, auto_reset: bool = True ) -> None: diff --git a/monai/optimizers/utils.py b/monai/optimizers/utils.py index 7e566abb46..75a125f076 100644 --- a/monai/optimizers/utils.py +++ b/monai/optimizers/utils.py @@ -70,12 +70,14 @@ def generate_param_groups( lr_values = ensure_tuple_rep(lr_values, len(layer_matches)) def _get_select(f): + def _select(): return f(network).parameters() return _select def _get_filter(f): + def _filter(): # should eventually generate a list of network parameters return (x[1] for x in filter(f, network.named_parameters())) diff --git a/monai/transforms/adaptors.py b/monai/transforms/adaptors.py index 5729740690..f5f1a4fc18 100644 --- a/monai/transforms/adaptors.py +++ b/monai/transforms/adaptors.py @@ -132,6 +132,7 @@ def __call__(self, img, seg): @_monai_export("monai.transforms") def adaptor(function, outputs, inputs=None): + def must_be_types_or_none(variable_name, variable, types): if variable is not None: if not isinstance(variable, types): @@ -216,6 +217,7 @@ def _inner(ditems): @_monai_export("monai.transforms") def apply_alias(fn, name_map): + def _inner(data): # map names pre_call = dict(data) @@ -236,6 +238,7 @@ def _inner(data): @_monai_export("monai.transforms") def to_kwargs(fn): + def _inner(data): return fn(**data) @@ -243,6 +246,7 @@ def _inner(data): class FunctionSignature: + def __init__(self, function: Callable) -> None: import inspect diff --git a/monai/transforms/inverse_batch_transform.py b/monai/transforms/inverse_batch_transform.py index 73149f1be5..1a7d16fb8c 100644 --- a/monai/transforms/inverse_batch_transform.py +++ b/monai/transforms/inverse_batch_transform.py @@ -30,6 +30,7 @@ class _BatchInverseDataset(Dataset): + def __init__(self, data: Sequence[Any], transform: InvertibleTransform, pad_collation_used: bool) -> None: self.data = data self.invertible_transform = transform diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py index 3d5d30be92..da9b23ce57 100644 --- a/monai/transforms/post/array.py +++ b/monai/transforms/post/array.py @@ -631,6 +631,7 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: class Ensemble: + @staticmethod def get_stacked_torch(img: Sequence[NdarrayOrTensor] | NdarrayOrTensor) -> torch.Tensor: """Get either a sequence or single instance of np.ndarray/torch.Tensor. Return single torch.Tensor.""" diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py index 8ad86b72dd..094afdd3c4 100644 --- a/monai/transforms/spatial/array.py +++ b/monai/transforms/spatial/array.py @@ -3441,7 +3441,7 @@ def filter_count(self, image_np: NdarrayOrTensor, locations: np.ndarray) -> tupl idx = self.R.permutation(image_np.shape[0]) idx = idx[: self.num_patches] idx_np = convert_data_type(idx, np.ndarray)[0] - image_np = image_np[idx] # type: ignore + image_np = image_np[idx] locations = locations[idx_np] return image_np, locations elif self.sort_fn not in (None, GridPatchSort.MIN, GridPatchSort.MAX): diff --git a/monai/transforms/utility/dictionary.py b/monai/transforms/utility/dictionary.py index 1cd9ff6323..7e3a7b0454 100644 --- a/monai/transforms/utility/dictionary.py +++ b/monai/transforms/utility/dictionary.py @@ -670,6 +670,7 @@ def __init__(self, keys: KeysCollection, sep: str = ".", use_re: Sequence[bool] self.use_re = ensure_tuple_rep(use_re, len(self.keys)) def __call__(self, data): + def _delete_item(keys, d, use_re: bool = False): key = keys[0] if len(keys) > 1: diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 2a5c5da136..caa7c067df 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -742,6 +742,7 @@ def check_key_duplicates(ordered_pairs: Sequence[tuple[Any, Any]]) -> dict[Any, class CheckKeyDuplicatesYamlLoader(SafeLoader): + def construct_mapping(self, node, deep=False): mapping = set() for key_node, _ in node.value: diff --git a/monai/utils/module.py b/monai/utils/module.py index f46ba7c1b3..db62e1e72b 100644 --- a/monai/utils/module.py +++ b/monai/utils/module.py @@ -418,6 +418,7 @@ def optional_import( msg += f" ({exception_str})" class _LazyRaise: + def __init__(self, *_args, **_kwargs): _default_msg = ( f"{msg}." @@ -453,6 +454,7 @@ def __iter__(self): return _LazyRaise(), False class _LazyCls(_LazyRaise): + def __init__(self, *_args, **kwargs): super().__init__() if not as_type.startswith("decorator"): diff --git a/monai/utils/profiling.py b/monai/utils/profiling.py index da5c0ac05c..5c880bbe1f 100644 --- a/monai/utils/profiling.py +++ b/monai/utils/profiling.py @@ -336,6 +336,7 @@ def profile_iter(self, name, iterable): """Wrapper around anything iterable to profile how long it takes to generate items.""" class _Iterable: + def __iter__(_self): # noqa: B902, N805 pylint: disable=E0213 do_iter = True orig_iter = iter(iterable) diff --git a/monai/visualize/class_activation_maps.py b/monai/visualize/class_activation_maps.py index 81d0bb32c4..6d1e8dfd03 100644 --- a/monai/visualize/class_activation_maps.py +++ b/monai/visualize/class_activation_maps.py @@ -96,12 +96,14 @@ def __init__( warnings.warn(f"Not all target_layers exist in the network module: targets: {self.target_layers}.") def backward_hook(self, name): + def _hook(_module, _grad_input, grad_output): self.gradients[name] = grad_output[0] return _hook def forward_hook(self, name): + def _hook(_module, _input, output): self.activations[name] = output diff --git a/monai/visualize/gradient_based.py b/monai/visualize/gradient_based.py index e2e938d86b..c54c9cd4ca 100644 --- a/monai/visualize/gradient_based.py +++ b/monai/visualize/gradient_based.py @@ -26,6 +26,7 @@ class _AutoGradReLU(torch.autograd.Function): + @staticmethod def forward(ctx, x): pos_mask = (x > 0).type_as(x) diff --git a/tests/croppers.py b/tests/croppers.py index 8c9b43bf0a..cfececfa9f 100644 --- a/tests/croppers.py +++ b/tests/croppers.py @@ -24,6 +24,7 @@ class CropTest(unittest.TestCase): + @staticmethod def get_arr(shape): return np.random.randint(100, size=shape).astype(float) diff --git a/tests/hvd_evenly_divisible_all_gather.py b/tests/hvd_evenly_divisible_all_gather.py index c7baac2bc9..78c6ca06bc 100644 --- a/tests/hvd_evenly_divisible_all_gather.py +++ b/tests/hvd_evenly_divisible_all_gather.py @@ -21,6 +21,7 @@ class HvdEvenlyDivisibleAllGather: + def test_data(self): # initialize Horovod hvd.init() diff --git a/tests/ngc_bundle_download.py b/tests/ngc_bundle_download.py index ba35f2b80c..01dc044870 100644 --- a/tests/ngc_bundle_download.py +++ b/tests/ngc_bundle_download.py @@ -70,6 +70,7 @@ @skip_if_windows class TestNgcBundleDownload(unittest.TestCase): + @parameterized.expand([TEST_CASE_NGC_1, TEST_CASE_NGC_2]) @skip_if_quick def test_ngc_download_bundle(self, bundle_name, version, remove_prefix, download_name, file_path, hash_val): @@ -101,6 +102,7 @@ def test_ngc_download_bundle(self, bundle_name, version, remove_prefix, download @unittest.skip("deprecating mmar tests") class TestAllDownloadingMMAR(unittest.TestCase): + def setUp(self): print_debug_info() self.test_dir = "./" diff --git a/tests/padders.py b/tests/padders.py index ae1153bdfd..a7dce263bb 100644 --- a/tests/padders.py +++ b/tests/padders.py @@ -51,6 +51,7 @@ class PadTest(unittest.TestCase): + @staticmethod def get_arr(shape): return np.random.randint(100, size=shape).astype(float) diff --git a/tests/profile_subclass/min_classes.py b/tests/profile_subclass/min_classes.py index 7104ffcd59..3e7c52476f 100644 --- a/tests/profile_subclass/min_classes.py +++ b/tests/profile_subclass/min_classes.py @@ -25,5 +25,6 @@ class SubTensor(torch.Tensor): class SubWithTorchFunc(torch.Tensor): + def __torch_function__(self, func, types, args=(), kwargs=None): return super().__torch_function__(func, types, args, {} if kwargs is None else kwargs) diff --git a/tests/test_acn_block.py b/tests/test_acn_block.py index 2f3783cbb8..1cbf3ea168 100644 --- a/tests/test_acn_block.py +++ b/tests/test_acn_block.py @@ -29,6 +29,7 @@ class TestACNBlock(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_acn_block(self, input_param, input_shape, expected_shape): net = ActiConvNormBlock(**input_param) diff --git a/tests/test_activations.py b/tests/test_activations.py index 0e83c73304..ad18e2bbec 100644 --- a/tests/test_activations.py +++ b/tests/test_activations.py @@ -94,6 +94,7 @@ class TestActivations(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value_shape(self, input_param, img, out, expected_shape): result = Activations(**input_param)(img) diff --git a/tests/test_activationsd.py b/tests/test_activationsd.py index 22a275997c..74968c0bb4 100644 --- a/tests/test_activationsd.py +++ b/tests/test_activationsd.py @@ -50,6 +50,7 @@ class TestActivationsd(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value_shape(self, input_param, test_input, output, expected_shape): result = Activationsd(**input_param)(test_input) diff --git a/tests/test_adaptors.py b/tests/test_adaptors.py index 257c4346ad..2495fdc72e 100644 --- a/tests/test_adaptors.py +++ b/tests/test_adaptors.py @@ -18,13 +18,16 @@ class TestAdaptors(unittest.TestCase): + def test_function_signature(self): + def foo(image, label=None, *a, **kw): pass _ = FunctionSignature(foo) def test_single_in_single_out(self): + def foo(image): return image * 2 @@ -55,6 +58,7 @@ def foo(image): self.assertEqual(dres["img"], 4) def test_multi_in_single_out(self): + def foo(image, label): return image * label @@ -86,6 +90,7 @@ def foo(image, label): self.assertEqual(dres["lbl"], 3) def test_default_arg_single_out(self): + def foo(a, b=2): return a * b @@ -98,6 +103,7 @@ def foo(a, b=2): dres = adaptor(foo, "c")(d) def test_multi_out(self): + def foo(a, b): return a * b, a / b @@ -107,6 +113,7 @@ def foo(a, b): self.assertEqual(dres["d"], 3 / 4) def test_dict_out(self): + def foo(a): return {"a": a * 2} @@ -120,7 +127,9 @@ def foo(a): class TestApplyAlias(unittest.TestCase): + def test_apply_alias(self): + def foo(d): d["x"] *= 2 return d @@ -131,7 +140,9 @@ def foo(d): class TestToKwargs(unittest.TestCase): + def test_to_kwargs(self): + def foo(**kwargs): results = {k: v * 2 for k, v in kwargs.items()} return results diff --git a/tests/test_add_coordinate_channels.py b/tests/test_add_coordinate_channels.py index cd33f98fd5..199fe071e3 100644 --- a/tests/test_add_coordinate_channels.py +++ b/tests/test_add_coordinate_channels.py @@ -29,6 +29,7 @@ class TestAddCoordinateChannels(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input, expected_shape): result = AddCoordinateChannels(**input_param)(input) diff --git a/tests/test_add_coordinate_channelsd.py b/tests/test_add_coordinate_channelsd.py index f5784928fd..c00240c2d5 100644 --- a/tests/test_add_coordinate_channelsd.py +++ b/tests/test_add_coordinate_channelsd.py @@ -42,6 +42,7 @@ class TestAddCoordinateChannels(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input, expected_shape): result = AddCoordinateChannelsd(**input_param)(input)["img"] diff --git a/tests/test_add_extreme_points_channel.py b/tests/test_add_extreme_points_channel.py index 140caa34ba..c453322d6b 100644 --- a/tests/test_add_extreme_points_channel.py +++ b/tests/test_add_extreme_points_channel.py @@ -69,6 +69,7 @@ class TestAddExtremePointsChannel(unittest.TestCase): + @parameterized.expand(TESTS) def test_correct_results(self, input_data, expected): add_extreme_points_channel = AddExtremePointsChannel() diff --git a/tests/test_add_extreme_points_channeld.py b/tests/test_add_extreme_points_channeld.py index 5640e696fc..026f71200a 100644 --- a/tests/test_add_extreme_points_channeld.py +++ b/tests/test_add_extreme_points_channeld.py @@ -64,6 +64,7 @@ class TestAddExtremePointsChanneld(unittest.TestCase): + @parameterized.expand(TESTS) def test_correct_results(self, input_data, expected): add_extreme_points_channel = AddExtremePointsChanneld( diff --git a/tests/test_adjust_contrast.py b/tests/test_adjust_contrast.py index 9fa0247115..2236056558 100644 --- a/tests/test_adjust_contrast.py +++ b/tests/test_adjust_contrast.py @@ -30,6 +30,7 @@ class TestAdjustContrast(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, gamma, invert_image, retain_stats): adjuster = AdjustContrast(gamma=gamma, invert_image=invert_image, retain_stats=retain_stats) diff --git a/tests/test_adjust_contrastd.py b/tests/test_adjust_contrastd.py index 4a671ef7be..38eb001226 100644 --- a/tests/test_adjust_contrastd.py +++ b/tests/test_adjust_contrastd.py @@ -30,6 +30,7 @@ class TestAdjustContrastd(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, gamma, invert_image, retain_stats): adjuster = AdjustContrastd("img", gamma=gamma, invert_image=invert_image, retain_stats=retain_stats) diff --git a/tests/test_adn.py b/tests/test_adn.py index 27e23a08d3..327bf7b20c 100644 --- a/tests/test_adn.py +++ b/tests/test_adn.py @@ -59,6 +59,7 @@ class TestADN2D(TorchImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_adn_2d(self, args): adn = ADN(**args) @@ -73,6 +74,7 @@ def test_no_input(self): class TestADN3D(TorchImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_adn_3d(self, args): adn = ADN(**args) diff --git a/tests/test_adversarial_loss.py b/tests/test_adversarial_loss.py index 77880725ec..f7b9ae7eb0 100644 --- a/tests/test_adversarial_loss.py +++ b/tests/test_adversarial_loss.py @@ -39,6 +39,7 @@ class TestPatchAdversarialLoss(unittest.TestCase): + def get_input(self, shape, is_positive): """ Get tensor for the tests. The tensor is around (-1) or (+1), depending on diff --git a/tests/test_affine.py b/tests/test_affine.py index 9c2f4197a6..a08a22ae6f 100644 --- a/tests/test_affine.py +++ b/tests/test_affine.py @@ -167,6 +167,7 @@ class TestAffine(unittest.TestCase): + @parameterized.expand(TESTS) def test_affine(self, input_param, input_data, expected_val): input_copy = deepcopy(input_data["img"]) @@ -199,6 +200,7 @@ def test_affine(self, input_param, input_data, expected_val): @unittest.skipUnless(optional_import("scipy")[1], "Requires scipy library.") class TestAffineConsistency(unittest.TestCase): + @parameterized.expand([[7], [8], [9]]) def test_affine_resize(self, s): """s""" diff --git a/tests/test_affine_grid.py b/tests/test_affine_grid.py index f3febbe0f3..2d89725bb7 100644 --- a/tests/test_affine_grid.py +++ b/tests/test_affine_grid.py @@ -135,6 +135,7 @@ class TestAffineGrid(unittest.TestCase): + @parameterized.expand(TESTS) def test_affine_grid(self, input_param, input_data, expected_val): g = AffineGrid(**input_param) diff --git a/tests/test_affine_transform.py b/tests/test_affine_transform.py index 39dc609167..6ea036bce8 100644 --- a/tests/test_affine_transform.py +++ b/tests/test_affine_transform.py @@ -83,6 +83,7 @@ class TestNormTransform(unittest.TestCase): + @parameterized.expand(TEST_NORM_CASES) def test_norm_xform(self, input_shape, align_corners, expected, zero_centered=False): norm = normalize_transform( @@ -107,6 +108,7 @@ def test_norm_xform(self, input_shape, align_corners, expected, zero_centered=Fa class TestToNormAffine(unittest.TestCase): + @parameterized.expand(TEST_TO_NORM_AFFINE_CASES) def test_to_norm_affine(self, affine, src_size, dst_size, align_corners, expected, zero_centered=False): affine = torch.as_tensor(affine, device=torch.device("cpu:0"), dtype=torch.float32) @@ -130,6 +132,7 @@ def test_to_norm_affine_ill(self, affine, src_size, dst_size, align_corners): class TestAffineTransform(unittest.TestCase): + def test_affine_shift(self): affine = torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]]) image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]]) diff --git a/tests/test_affined.py b/tests/test_affined.py index a35b35758a..94903ff8c7 100644 --- a/tests/test_affined.py +++ b/tests/test_affined.py @@ -168,6 +168,7 @@ class TestAffined(unittest.TestCase): + @parameterized.expand(TESTS) def test_affine(self, input_param, input_data, expected_val): input_copy = deepcopy(input_data) diff --git a/tests/test_ahnet.py b/tests/test_ahnet.py index 5707cf0452..99a177f395 100644 --- a/tests/test_ahnet.py +++ b/tests/test_ahnet.py @@ -126,6 +126,7 @@ class TestFCN(unittest.TestCase): + @parameterized.expand([TEST_CASE_FCN_1, TEST_CASE_FCN_2, TEST_CASE_FCN_3]) @skip_if_quick def test_fcn_shape(self, input_param, input_shape, expected_shape): @@ -136,6 +137,7 @@ def test_fcn_shape(self, input_param, input_shape, expected_shape): class TestFCNWithPretrain(unittest.TestCase): + @parameterized.expand([TEST_CASE_FCN_WITH_PRETRAIN_1, TEST_CASE_FCN_WITH_PRETRAIN_2]) @skip_if_quick def test_fcn_shape(self, input_param, input_shape, expected_shape): @@ -146,6 +148,7 @@ def test_fcn_shape(self, input_param, input_shape, expected_shape): class TestMCFCN(unittest.TestCase): + @parameterized.expand([TEST_CASE_MCFCN_1, TEST_CASE_MCFCN_2, TEST_CASE_MCFCN_3]) def test_mcfcn_shape(self, input_param, input_shape, expected_shape): net = MCFCN(**input_param).to(device) @@ -155,6 +158,7 @@ def test_mcfcn_shape(self, input_param, input_shape, expected_shape): class TestMCFCNWithPretrain(unittest.TestCase): + @parameterized.expand([TEST_CASE_MCFCN_WITH_PRETRAIN_1, TEST_CASE_MCFCN_WITH_PRETRAIN_2]) def test_mcfcn_shape(self, input_param, input_shape, expected_shape): net = test_pretrained_networks(MCFCN, input_param, device) @@ -164,6 +168,7 @@ def test_mcfcn_shape(self, input_param, input_shape, expected_shape): class TestAHNET(unittest.TestCase): + @parameterized.expand([TEST_CASE_AHNET_2D_1, TEST_CASE_AHNET_2D_2, TEST_CASE_AHNET_2D_3]) def test_ahnet_shape_2d(self, input_param, input_shape, expected_shape): net = AHNet(**input_param).to(device) @@ -192,6 +197,7 @@ def test_script(self): class TestAHNETWithPretrain(unittest.TestCase): + @parameterized.expand( [TEST_CASE_AHNET_3D_WITH_PRETRAIN_1, TEST_CASE_AHNET_3D_WITH_PRETRAIN_2, TEST_CASE_AHNET_3D_WITH_PRETRAIN_3] ) diff --git a/tests/test_anchor_box.py b/tests/test_anchor_box.py index c29296e8ae..301ce78361 100644 --- a/tests/test_anchor_box.py +++ b/tests/test_anchor_box.py @@ -42,6 +42,7 @@ @SkipIfBeforePyTorchVersion((1, 11)) @unittest.skipUnless(has_torchvision, "Requires torchvision") class TestAnchorGenerator(unittest.TestCase): + @parameterized.expand(TEST_CASES_2D) def test_anchor_2d(self, input_param, image_shape, feature_maps_shapes): torch_anchor_utils, _ = optional_import("torchvision.models.detection.anchor_utils") diff --git a/tests/test_apply.py b/tests/test_apply.py index 4784d46413..ca37e945ba 100644 --- a/tests/test_apply.py +++ b/tests/test_apply.py @@ -39,6 +39,7 @@ def single_2d_transform_cases(): class TestApply(unittest.TestCase): + def _test_apply_impl(self, tensor, pending_transforms, expected_shape): result = apply_pending(tensor, pending_transforms) self.assertListEqual(result[1], pending_transforms) diff --git a/tests/test_apply_filter.py b/tests/test_apply_filter.py index 0de77bfb4d..e8db6da4b9 100644 --- a/tests/test_apply_filter.py +++ b/tests/test_apply_filter.py @@ -20,6 +20,7 @@ class ApplyFilterTestCase(unittest.TestCase): + def test_1d(self): a = torch.tensor([[list(range(10))]], dtype=torch.float) out = apply_filter(a, torch.tensor([-1, 0, 1]), stride=1) diff --git a/tests/test_arraydataset.py b/tests/test_arraydataset.py index a3b78fc6e0..efc014a267 100644 --- a/tests/test_arraydataset.py +++ b/tests/test_arraydataset.py @@ -40,6 +40,7 @@ class TestCompose(Compose): + def __call__(self, input_, lazy): img = self.transforms[0](input_) metadata = img.meta @@ -77,6 +78,7 @@ def __call__(self, input_, lazy): class TestArrayDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_shape(self, img_transform, label_transform, indices, expected_shape): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=(128, 128, 128)).astype(float), np.eye(4)) diff --git a/tests/test_as_channel_last.py b/tests/test_as_channel_last.py index 8f88fb2928..51e1a5c0fd 100644 --- a/tests/test_as_channel_last.py +++ b/tests/test_as_channel_last.py @@ -27,6 +27,7 @@ class TestAsChannelLast(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, in_type, input_param, expected_shape): test_data = in_type(np.random.randint(0, 2, size=[1, 2, 3, 4])) diff --git a/tests/test_as_channel_lastd.py b/tests/test_as_channel_lastd.py index 16086b769c..aa51ab6056 100644 --- a/tests/test_as_channel_lastd.py +++ b/tests/test_as_channel_lastd.py @@ -27,6 +27,7 @@ class TestAsChannelLastd(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, in_type, input_param, expected_shape): test_data = { diff --git a/tests/test_as_discrete.py b/tests/test_as_discrete.py index 2802c7d9ff..bf59752920 100644 --- a/tests/test_as_discrete.py +++ b/tests/test_as_discrete.py @@ -65,6 +65,7 @@ class TestAsDiscrete(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value_shape(self, input_param, img, out, expected_shape): result = AsDiscrete(**input_param)(img) diff --git a/tests/test_as_discreted.py b/tests/test_as_discreted.py index ec394fc3af..ed1b3c5b3e 100644 --- a/tests/test_as_discreted.py +++ b/tests/test_as_discreted.py @@ -68,6 +68,7 @@ class TestAsDiscreted(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value_shape(self, input_param, test_input, output, expected_shape): result = AsDiscreted(**input_param)(test_input) diff --git a/tests/test_atss_box_matcher.py b/tests/test_atss_box_matcher.py index a614497bc9..6133d4839d 100644 --- a/tests/test_atss_box_matcher.py +++ b/tests/test_atss_box_matcher.py @@ -33,6 +33,7 @@ class TestATSS(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_atss(self, input_param, boxes, anchors, num_anchors_per_level, num_anchors_per_loc, expected_matches): matcher = ATSSMatcher(**input_param, debug=True) diff --git a/tests/test_attentionunet.py b/tests/test_attentionunet.py index d5c67cee38..83f6cabc5e 100644 --- a/tests/test_attentionunet.py +++ b/tests/test_attentionunet.py @@ -20,6 +20,7 @@ class TestAttentionUnet(unittest.TestCase): + def test_attention_block(self): for dims in [2, 3]: block = att.AttentionBlock(dims, f_int=2, f_g=6, f_l=6) diff --git a/tests/test_auto3dseg.py b/tests/test_auto3dseg.py index 5964ddd6e9..e2097679e2 100644 --- a/tests/test_auto3dseg.py +++ b/tests/test_auto3dseg.py @@ -165,6 +165,7 @@ def __call__(self, data): class TestDataAnalyzer(unittest.TestCase): + def setUp(self): self.test_dir = tempfile.TemporaryDirectory() work_dir = self.test_dir.name diff --git a/tests/test_auto3dseg_bundlegen.py b/tests/test_auto3dseg_bundlegen.py index 1d2d6611bb..e7bf6820bc 100644 --- a/tests/test_auto3dseg_bundlegen.py +++ b/tests/test_auto3dseg_bundlegen.py @@ -107,6 +107,7 @@ def run_auto3dseg_before_bundlegen(test_path, work_dir): @SkipIfBeforePyTorchVersion((1, 11, 1)) @skip_if_quick class TestBundleGen(unittest.TestCase): + def setUp(self) -> None: set_determinism(0) self.test_dir = tempfile.TemporaryDirectory() diff --git a/tests/test_auto3dseg_ensemble.py b/tests/test_auto3dseg_ensemble.py index 367f66581c..7ac553cc0c 100644 --- a/tests/test_auto3dseg_ensemble.py +++ b/tests/test_auto3dseg_ensemble.py @@ -112,6 +112,7 @@ def create_sim_data(dataroot, sim_datalist, sim_dim, **kwargs): @SkipIfBeforePyTorchVersion((1, 11, 1)) @unittest.skipIf(not has_tb, "no tensorboard summary writer") class TestEnsembleBuilder(unittest.TestCase): + def setUp(self) -> None: set_determinism(0) self.test_dir = tempfile.TemporaryDirectory() diff --git a/tests/test_auto3dseg_hpo.py b/tests/test_auto3dseg_hpo.py index 0441116dc9..34d00336ec 100644 --- a/tests/test_auto3dseg_hpo.py +++ b/tests/test_auto3dseg_hpo.py @@ -79,6 +79,7 @@ def skip_if_no_optuna(obj): @SkipIfBeforePyTorchVersion((1, 11, 1)) @unittest.skipIf(not has_tb, "no tensorboard summary writer") class TestHPO(unittest.TestCase): + def setUp(self) -> None: self.test_dir = tempfile.TemporaryDirectory() test_path = self.test_dir.name @@ -154,6 +155,7 @@ def test_run_optuna(self) -> None: algo = algo_dict[AlgoKeys.ALGO] class OptunaGenLearningRate(OptunaGen): + def get_hyperparameters(self): return {"learning_rate": self.trial.suggest_float("learning_rate", 0.00001, 0.1)} diff --git a/tests/test_autoencoder.py b/tests/test_autoencoder.py index 485049c2d1..6408f6a6d0 100644 --- a/tests/test_autoencoder.py +++ b/tests/test_autoencoder.py @@ -74,6 +74,7 @@ class TestAutoEncoder(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_shape, expected_shape): net = AutoEncoder(**input_param).to(device) diff --git a/tests/test_avg_merger.py b/tests/test_avg_merger.py index adef2a759a..7995d63271 100644 --- a/tests/test_avg_merger.py +++ b/tests/test_avg_merger.py @@ -137,6 +137,7 @@ class AvgMergerTests(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_0_DEFAULT_DTYPE, diff --git a/tests/test_basic_unet.py b/tests/test_basic_unet.py index 23e19dd536..770750851f 100644 --- a/tests/test_basic_unet.py +++ b/tests/test_basic_unet.py @@ -83,6 +83,7 @@ class TestBasicUNET(unittest.TestCase): + @parameterized.expand(CASES_1D + CASES_2D + CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_basic_unetplusplus.py b/tests/test_basic_unetplusplus.py index 19ed5977fd..6438b5e0d4 100644 --- a/tests/test_basic_unetplusplus.py +++ b/tests/test_basic_unetplusplus.py @@ -83,6 +83,7 @@ class TestBasicUNETPlusPlus(unittest.TestCase): + @parameterized.expand(CASES_1D + CASES_2D + CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_bending_energy.py b/tests/test_bending_energy.py index f29d4f256b..2e8ab32dbd 100644 --- a/tests/test_bending_energy.py +++ b/tests/test_bending_energy.py @@ -50,6 +50,7 @@ class TestBendingEnergy(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = BendingEnergyLoss(**input_param).forward(**input_data) diff --git a/tests/test_bilateral_approx_cpu.py b/tests/test_bilateral_approx_cpu.py index da30d5d7de..e8a55e1f76 100644 --- a/tests/test_bilateral_approx_cpu.py +++ b/tests/test_bilateral_approx_cpu.py @@ -365,6 +365,7 @@ @skip_if_no_cpp_extension class BilateralFilterTestCaseCpuApprox(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cpu_approx(self, test_case_description, sigmas, input, expected): # Params to determine the implementation to test diff --git a/tests/test_bilateral_approx_cuda.py b/tests/test_bilateral_approx_cuda.py index b9be7d9ccf..4ad15d9646 100644 --- a/tests/test_bilateral_approx_cuda.py +++ b/tests/test_bilateral_approx_cuda.py @@ -366,6 +366,7 @@ @skip_if_no_cuda @skip_if_no_cpp_extension class BilateralFilterTestCaseCudaApprox(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cuda_approx(self, test_case_description, sigmas, input, expected): # Skip this test diff --git a/tests/test_bilateral_precise.py b/tests/test_bilateral_precise.py index 1a68dc8b4e..e13ede5bfd 100644 --- a/tests/test_bilateral_precise.py +++ b/tests/test_bilateral_precise.py @@ -366,6 +366,7 @@ @skip_if_no_cpp_extension @skip_if_quick class BilateralFilterTestCaseCpuPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cpu_precise(self, test_case_description, sigmas, input, expected): # Params to determine the implementation to test @@ -399,6 +400,7 @@ def test_cpu_precise_backwards(self, test_case_description, sigmas, input, expec @skip_if_no_cuda @skip_if_no_cpp_extension class BilateralFilterTestCaseCudaPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cuda_precise(self, test_case_description, sigmas, input, expected): # Skip this test diff --git a/tests/test_blend_images.py b/tests/test_blend_images.py index 9814a5a3f8..700ae1fe58 100644 --- a/tests/test_blend_images.py +++ b/tests/test_blend_images.py @@ -44,6 +44,7 @@ def get_alpha(img): @skipUnless(has_matplotlib, "Matplotlib required") class TestBlendImages(unittest.TestCase): + @parameterized.expand(TESTS) def test_blend(self, image, label, alpha): blended = blend_images(image, label, alpha) diff --git a/tests/test_bounding_rect.py b/tests/test_bounding_rect.py index b9c232e2d2..b879fa6093 100644 --- a/tests/test_bounding_rect.py +++ b/tests/test_bounding_rect.py @@ -28,6 +28,7 @@ class TestBoundingRect(unittest.TestCase): + def setUp(self): monai.utils.set_determinism(1) diff --git a/tests/test_bounding_rectd.py b/tests/test_bounding_rectd.py index 248a0a8e47..96435036b1 100644 --- a/tests/test_bounding_rectd.py +++ b/tests/test_bounding_rectd.py @@ -28,6 +28,7 @@ class TestBoundingRectD(unittest.TestCase): + def setUp(self): monai.utils.set_determinism(1) diff --git a/tests/test_box_coder.py b/tests/test_box_coder.py index 5835341139..75ff650d6c 100644 --- a/tests/test_box_coder.py +++ b/tests/test_box_coder.py @@ -21,6 +21,7 @@ class TestBoxTransform(unittest.TestCase): + def test_value(self): box_coder = BoxCoder(weights=[1, 1, 1, 1, 1, 1]) test_dtype = [torch.float32, torch.float16] diff --git a/tests/test_box_transform.py b/tests/test_box_transform.py index e114f8869f..e99f95fa32 100644 --- a/tests/test_box_transform.py +++ b/tests/test_box_transform.py @@ -79,6 +79,7 @@ class TestBoxTransform(unittest.TestCase): + @parameterized.expand(TESTS_2D_mask) def test_value_2d_mask(self, mask, expected_box_label): box_label = convert_mask_to_box(mask) diff --git a/tests/test_box_utils.py b/tests/test_box_utils.py index c4fefb5a98..3c05efe0d0 100644 --- a/tests/test_box_utils.py +++ b/tests/test_box_utils.py @@ -140,6 +140,7 @@ class TestCreateBoxList(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_data, mode2, expected_box, expected_area): expected_box = convert_data_type(expected_box, dtype=np.float32)[0] diff --git a/tests/test_bundle_ckpt_export.py b/tests/test_bundle_ckpt_export.py index d9b3bedab2..8f376a06d5 100644 --- a/tests/test_bundle_ckpt_export.py +++ b/tests/test_bundle_ckpt_export.py @@ -32,6 +32,7 @@ @skip_if_windows class TestCKPTExport(unittest.TestCase): + def setUp(self): self.device = os.environ.get("CUDA_VISIBLE_DEVICES") if not self.device: diff --git a/tests/test_bundle_download.py b/tests/test_bundle_download.py index fa96c6f28d..89fbe5e8b2 100644 --- a/tests/test_bundle_download.py +++ b/tests/test_bundle_download.py @@ -93,6 +93,7 @@ class TestDownload(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) @skip_if_quick def test_github_download_bundle(self, bundle_name, version): @@ -192,6 +193,7 @@ def test_monaihosting_source_download_bundle(self, bundle_files, bundle_name, ve @skip_if_no_cuda class TestLoad(unittest.TestCase): + @parameterized.expand([TEST_CASE_7]) @skip_if_quick def test_load_weights(self, bundle_files, bundle_name, repo, device, model_file): @@ -336,6 +338,7 @@ def test_load_ts_module(self, bundle_files, bundle_name, version, repo, device, class TestDownloadLargefiles(unittest.TestCase): + @parameterized.expand([TEST_CASE_10]) @skip_if_quick def test_url_download_large_files(self, bundle_files, bundle_name, url, hash_val): diff --git a/tests/test_bundle_get_data.py b/tests/test_bundle_get_data.py index 88bfed758a..605b3945bb 100644 --- a/tests/test_bundle_get_data.py +++ b/tests/test_bundle_get_data.py @@ -45,6 +45,7 @@ @skip_if_windows @SkipIfNoModule("requests") class TestGetBundleData(unittest.TestCase): + @parameterized.expand([TEST_CASE_3, TEST_CASE_4]) @skip_if_quick def test_get_all_bundles_list(self, params): diff --git a/tests/test_bundle_init_bundle.py b/tests/test_bundle_init_bundle.py index 08f921da01..eb831093d5 100644 --- a/tests/test_bundle_init_bundle.py +++ b/tests/test_bundle_init_bundle.py @@ -23,6 +23,7 @@ @skip_if_windows class TestBundleInit(unittest.TestCase): + def test_bundle(self): with tempfile.TemporaryDirectory() as tempdir: net = UNet(2, 1, 1, [4, 8], [2]) diff --git a/tests/test_bundle_onnx_export.py b/tests/test_bundle_onnx_export.py index ffd5fa636d..ee22d7caef 100644 --- a/tests/test_bundle_onnx_export.py +++ b/tests/test_bundle_onnx_export.py @@ -29,6 +29,7 @@ @SkipIfNoModule("onnx") @SkipIfBeforePyTorchVersion((1, 10)) class TestONNXExport(unittest.TestCase): + def setUp(self): self.device = os.environ.get("CUDA_VISIBLE_DEVICES") if not self.device: diff --git a/tests/test_bundle_push_to_hf_hub.py b/tests/test_bundle_push_to_hf_hub.py index 375c5d81e8..39368c6f40 100644 --- a/tests/test_bundle_push_to_hf_hub.py +++ b/tests/test_bundle_push_to_hf_hub.py @@ -28,6 +28,7 @@ class TestPushToHuggingFaceHub(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) @skip_if_quick @skipUnless(has_huggingface_hub, "Requires `huggingface_hub` package.") diff --git a/tests/test_bundle_trt_export.py b/tests/test_bundle_trt_export.py index 72743f5fcb..47034852ef 100644 --- a/tests/test_bundle_trt_export.py +++ b/tests/test_bundle_trt_export.py @@ -48,6 +48,7 @@ @skip_if_no_cuda @skip_if_quick class TestTRTExport(unittest.TestCase): + def setUp(self): self.device = os.environ.get("CUDA_VISIBLE_DEVICES") if not self.device: diff --git a/tests/test_bundle_utils.py b/tests/test_bundle_utils.py index 181c08475c..47c534f3b6 100644 --- a/tests/test_bundle_utils.py +++ b/tests/test_bundle_utils.py @@ -51,6 +51,7 @@ @skip_if_windows class TestLoadBundleConfig(unittest.TestCase): + def setUp(self): self.bundle_dir = tempfile.TemporaryDirectory() self.dir_name = os.path.join(self.bundle_dir.name, "TestBundle") @@ -134,6 +135,7 @@ def test_load_config_ts(self): class TestPPrintEdges(unittest.TestCase): + def test_str(self): self.assertEqual(pprint_edges("", 0), "''") self.assertEqual(pprint_edges({"a": 1, "b": 2}, 0), "{'a': 1, 'b': 2}") diff --git a/tests/test_bundle_verify_metadata.py b/tests/test_bundle_verify_metadata.py index 0701e905b9..f6c2192621 100644 --- a/tests/test_bundle_verify_metadata.py +++ b/tests/test_bundle_verify_metadata.py @@ -28,6 +28,7 @@ @skip_if_windows class TestVerifyMetaData(unittest.TestCase): + def setUp(self): self.config = testing_data_config("configs", "test_meta_file") download_url_or_skip_test( diff --git a/tests/test_bundle_verify_net.py b/tests/test_bundle_verify_net.py index 6f516fdd48..f55fdd597b 100644 --- a/tests/test_bundle_verify_net.py +++ b/tests/test_bundle_verify_net.py @@ -28,6 +28,7 @@ @skip_if_windows class TestVerifyNetwork(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_verify(self, meta_file, config_file): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_bundle_workflow.py b/tests/test_bundle_workflow.py index 4291eedf3f..f7da37acef 100644 --- a/tests/test_bundle_workflow.py +++ b/tests/test_bundle_workflow.py @@ -37,6 +37,7 @@ class TestBundleWorkflow(unittest.TestCase): + def setUp(self): self.data_dir = tempfile.mkdtemp() self.expected_shape = (128, 128, 128) diff --git a/tests/test_cachedataset.py b/tests/test_cachedataset.py index dcae5fdce1..dbb1b8f8f1 100644 --- a/tests/test_cachedataset.py +++ b/tests/test_cachedataset.py @@ -39,6 +39,7 @@ class TestCacheDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, transform, expected_shape): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4)) diff --git a/tests/test_cachedataset_parallel.py b/tests/test_cachedataset_parallel.py index c3fc2cc362..6a01a82512 100644 --- a/tests/test_cachedataset_parallel.py +++ b/tests/test_cachedataset_parallel.py @@ -30,6 +30,7 @@ class TestCacheDatasetParallel(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_shape(self, num_workers, dataset_size, transform): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[8, 8, 8]).astype(float), np.eye(4)) diff --git a/tests/test_cachedataset_persistent_workers.py b/tests/test_cachedataset_persistent_workers.py index e60862238d..78092906c6 100644 --- a/tests/test_cachedataset_persistent_workers.py +++ b/tests/test_cachedataset_persistent_workers.py @@ -18,6 +18,7 @@ class TestTransformsWCacheDatasetAndPersistentWorkers(unittest.TestCase): + def test_duplicate_transforms(self): data = [{"img": create_test_image_2d(128, 128, num_seg_classes=1, channel_dim=0)[0]} for _ in range(2)] diff --git a/tests/test_cachentransdataset.py b/tests/test_cachentransdataset.py index d50fe4f8dd..90e86c2eb0 100644 --- a/tests/test_cachentransdataset.py +++ b/tests/test_cachentransdataset.py @@ -34,6 +34,7 @@ class TestCacheNTransDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_n_trans(self, transform, expected_shape): data_array = np.random.randint(0, 2, size=[128, 128, 128]).astype(float) diff --git a/tests/test_call_dist.py b/tests/test_call_dist.py index 0621824b65..503cb5e792 100644 --- a/tests/test_call_dist.py +++ b/tests/test_call_dist.py @@ -17,6 +17,7 @@ class DistributedCallTest(DistTestCase): + def test_constructor(self): with self.assertRaises(ValueError): DistCall(nnodes=1, nproc_per_node=0) diff --git a/tests/test_cast_to_type.py b/tests/test_cast_to_type.py index 6dd994120c..035260804e 100644 --- a/tests/test_cast_to_type.py +++ b/tests/test_cast_to_type.py @@ -37,6 +37,7 @@ class TestCastToType(unittest.TestCase): + @parameterized.expand(TESTS) def test_type(self, out_dtype, input_data, expected_type): result = CastToType(dtype=out_dtype)(input_data) diff --git a/tests/test_cast_to_typed.py b/tests/test_cast_to_typed.py index 687deeda1d..81e17117a9 100644 --- a/tests/test_cast_to_typed.py +++ b/tests/test_cast_to_typed.py @@ -53,6 +53,7 @@ class TestCastToTyped(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_type(self, input_param, input_data, expected_type): result = CastToTyped(**input_param)(input_data) diff --git a/tests/test_channel_pad.py b/tests/test_channel_pad.py index 2d8c57fd68..77dd172378 100644 --- a/tests/test_channel_pad.py +++ b/tests/test_channel_pad.py @@ -34,6 +34,7 @@ class TestChannelPad(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): net = ChannelPad(**input_param) diff --git a/tests/test_check_hash.py b/tests/test_check_hash.py index bb3d0ff12e..263c18703c 100644 --- a/tests/test_check_hash.py +++ b/tests/test_check_hash.py @@ -32,6 +32,7 @@ class TestCheckMD5(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_result(self, md5_value, t, expected_result): test_image = np.ones((5, 5, 3)) diff --git a/tests/test_check_missing_files.py b/tests/test_check_missing_files.py index efbe5a95fb..2b5c17a1ec 100644 --- a/tests/test_check_missing_files.py +++ b/tests/test_check_missing_files.py @@ -23,6 +23,7 @@ class TestCheckMissingFiles(unittest.TestCase): + def test_content(self): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4)) with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_classes_to_indices.py b/tests/test_classes_to_indices.py index e7dd7abfe5..a7377dac16 100644 --- a/tests/test_classes_to_indices.py +++ b/tests/test_classes_to_indices.py @@ -82,6 +82,7 @@ class TestClassesToIndices(unittest.TestCase): + @parameterized.expand(TESTS_CASES) def test_value(self, input_args, label, image, expected_indices): indices = ClassesToIndices(**input_args)(label, image) diff --git a/tests/test_classes_to_indicesd.py b/tests/test_classes_to_indicesd.py index 7a34cc06b4..dead1ae753 100644 --- a/tests/test_classes_to_indicesd.py +++ b/tests/test_classes_to_indicesd.py @@ -97,6 +97,7 @@ class TestClassesToIndicesd(unittest.TestCase): + @parameterized.expand(TESTS_CASES) def test_value(self, input_args, input_data, expected_indices): result = ClassesToIndicesd(**input_args)(input_data) diff --git a/tests/test_cldice_loss.py b/tests/test_cldice_loss.py index 071bd20d6c..14d3575e3b 100644 --- a/tests/test_cldice_loss.py +++ b/tests/test_cldice_loss.py @@ -23,6 +23,7 @@ class TestclDiceLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, y_pred_data, expected_val): loss = SoftclDiceLoss() diff --git a/tests/test_complex_utils.py b/tests/test_complex_utils.py index 77eaa924a2..fdcee4babe 100644 --- a/tests/test_complex_utils.py +++ b/tests/test_complex_utils.py @@ -51,6 +51,7 @@ class TestMRIUtils(unittest.TestCase): + @parameterized.expand(TESTS) def test_to_tensor_complex(self, test_data, expected_shape): result = convert_to_tensor_complex(test_data) diff --git a/tests/test_component_locator.py b/tests/test_component_locator.py index 3b54a13706..9378fc159d 100644 --- a/tests/test_component_locator.py +++ b/tests/test_component_locator.py @@ -21,6 +21,7 @@ class TestComponentLocator(unittest.TestCase): + def test_locate(self): locator = ComponentLocator(excludes=None if has_ignite else ["monai.handlers"]) # test init mapping table and get the module path of component diff --git a/tests/test_component_store.py b/tests/test_component_store.py index 614f387754..424eceb3d1 100644 --- a/tests/test_component_store.py +++ b/tests/test_component_store.py @@ -17,6 +17,7 @@ class TestComponentStore(unittest.TestCase): + def setUp(self): self.cs = ComponentStore("TestStore", "I am a test store, please ignore") diff --git a/tests/test_compose.py b/tests/test_compose.py index a1952b102f..309767833b 100644 --- a/tests/test_compose.py +++ b/tests/test_compose.py @@ -39,6 +39,7 @@ def data_from_keys(keys, h, w): class _RandXform(Randomizable): + def randomize(self): self.val = self.R.random_sample() @@ -48,12 +49,14 @@ def __call__(self, __unused): class TestCompose(unittest.TestCase): + def test_empty_compose(self): c = mt.Compose() i = 1 self.assertEqual(c(i), 1) def test_non_dict_compose(self): + def a(i): return i + "a" @@ -64,6 +67,7 @@ def b(i): self.assertEqual(c(""), "abab") def test_dict_compose(self): + def a(d): d = dict(d) d["a"] += 1 @@ -82,6 +86,7 @@ def b(d): self.assertDictEqual(execute_compose(data, transforms), expected) def test_list_dict_compose(self): + def a(d): # transform to handle dict data d = dict(d) d["a"] += 1 @@ -109,6 +114,7 @@ def c(d): # transform to handle dict data self.assertDictEqual(item, expected) def test_non_dict_compose_with_unpack(self): + def a(i, i2): return i + "a", i2 + "a2" @@ -122,6 +128,7 @@ def b(i, i2): self.assertEqual(execute_compose(data, transforms, map_items=False, unpack_items=True), expected) def test_list_non_dict_compose_with_unpack(self): + def a(i, i2): return i + "a", i2 + "a2" @@ -135,6 +142,7 @@ def b(i, i2): self.assertEqual(execute_compose(data, transforms, unpack_items=True), expected) def test_list_dict_compose_no_map(self): + def a(d): # transform to handle dict data d = dict(d) d["a"] += 1 @@ -163,6 +171,7 @@ def c(d): # transform to handle dict data self.assertDictEqual(item, expected) def test_random_compose(self): + class _Acc(Randomizable): self.rand = 0.0 @@ -182,7 +191,9 @@ def __call__(self, data): self.assertAlmostEqual(c(1), 1.90734751) def test_randomize_warn(self): + class _RandomClass(Randomizable): + def randomize(self, foo1, foo2): pass @@ -267,6 +278,7 @@ def test_backwards_compatible_imports(self): class TestComposeExecute(unittest.TestCase): + @parameterized.expand(TEST_COMPOSE_EXECUTE_TEST_CASES) def test_compose_execute_equivalence(self, keys, pipeline): data = data_from_keys(keys, 12, 16) @@ -657,8 +669,10 @@ def test_compose_lazy_on_call_with_logging(self, compose_type, pipeline, lazy_on class TestOps: + @staticmethod def concat(value): + def _inner(data): return data + value @@ -666,6 +680,7 @@ def _inner(data): @staticmethod def concatd(value): + def _inner(data): return {k: v + value for k, v in data.items()} @@ -673,6 +688,7 @@ def _inner(data): @staticmethod def concata(value): + def _inner(data1, data2): return data1 + value, data2 + value @@ -688,6 +704,7 @@ def _inner(data1, data2): class TestComposeExecuteWithFlags(unittest.TestCase): + @parameterized.expand(TEST_COMPOSE_EXECUTE_FLAG_TEST_CASES) def test_compose_execute_equivalence_with_flags(self, flags, data, pipeline): expected = mt.Compose(pipeline, **flags)(data) @@ -711,6 +728,7 @@ def test_compose_execute_equivalence_with_flags(self, flags, data, pipeline): class TestComposeCallableInput(unittest.TestCase): + def test_value_error_when_not_sequence(self): data = torch.tensor(np.random.randn(1, 5, 5)) diff --git a/tests/test_compose_get_number_conversions.py b/tests/test_compose_get_number_conversions.py index 664558d9cd..2623bab69c 100644 --- a/tests/test_compose_get_number_conversions.py +++ b/tests/test_compose_get_number_conversions.py @@ -38,6 +38,7 @@ def _apply(x, fn): class Load(Transform): + def __init__(self, as_tensor): self.fn = lambda _: PT_ARR if as_tensor else NP_ARR @@ -46,26 +47,31 @@ def __call__(self, x): class N(Transform): + def __call__(self, x): return _apply(x, convert_to_numpy) class T(Transform): + def __call__(self, x): return _apply(x, convert_to_tensor) class NT(Transform): + def __call__(self, x): return _apply(x, lambda x: x) class TCPU(Transform): + def __call__(self, x): return _apply(x, lambda x: convert_to_tensor(x).cpu()) class TGPU(Transform): + def __call__(self, x): return _apply(x, lambda x: convert_to_tensor(x).cuda()) @@ -103,6 +109,7 @@ def __call__(self, x): class TestComposeNumConversions(unittest.TestCase): + @parameterized.expand(TESTS) def test_get_number_of_conversions(self, transforms, is_dict, input, expected): input = input if not is_dict else {KEY: input, "Other": NP_ARR} diff --git a/tests/test_compute_confusion_matrix.py b/tests/test_compute_confusion_matrix.py index e0a92aec67..248f16a7fe 100644 --- a/tests/test_compute_confusion_matrix.py +++ b/tests/test_compute_confusion_matrix.py @@ -220,6 +220,7 @@ class TestConfusionMatrix(unittest.TestCase): + @parameterized.expand([TEST_CASE_CONFUSION_MATRIX]) def test_value(self, input_data, expected_value): # include or ignore background diff --git a/tests/test_compute_f_beta.py b/tests/test_compute_f_beta.py index c8ed5aa887..85997577cf 100644 --- a/tests/test_compute_f_beta.py +++ b/tests/test_compute_f_beta.py @@ -23,6 +23,7 @@ class TestFBetaScore(unittest.TestCase): + def test_expecting_success_and_device(self): metric = FBetaScore() y_pred = torch.tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]], device=_device) diff --git a/tests/test_compute_fid_metric.py b/tests/test_compute_fid_metric.py index 1c7c3273fe..bd867f5296 100644 --- a/tests/test_compute_fid_metric.py +++ b/tests/test_compute_fid_metric.py @@ -24,6 +24,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy") class TestFIDMetric(unittest.TestCase): + def test_results(self): x = torch.Tensor([[1, 2], [1, 2], [1, 2]]) y = torch.Tensor([[2, 2], [1, 2], [1, 2]]) diff --git a/tests/test_compute_froc.py b/tests/test_compute_froc.py index 0a48dc099a..4dc0507366 100644 --- a/tests/test_compute_froc.py +++ b/tests/test_compute_froc.py @@ -111,6 +111,7 @@ class TestComputeFpTp(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_value(self, input_data, expected_fp, expected_tp, expected_num): fp_probs, tp_probs, num_tumors = compute_fp_tp_probs(**input_data) @@ -120,6 +121,7 @@ def test_value(self, input_data, expected_fp, expected_tp, expected_num): class TestComputeFpTpNd(unittest.TestCase): + @parameterized.expand([TEST_CASE_ND_1, TEST_CASE_ND_2]) def test_value(self, input_data, expected_fp, expected_tp, expected_num): fp_probs, tp_probs, num_tumors = compute_fp_tp_probs_nd(**input_data) @@ -129,6 +131,7 @@ def test_value(self, input_data, expected_fp, expected_tp, expected_num): class TestComputeFrocScore(unittest.TestCase): + @parameterized.expand([TEST_CASE_4, TEST_CASE_5]) def test_value(self, input_data, thresholds, expected_score): fps_per_image, total_sensitivity = compute_froc_curve_data(**input_data) diff --git a/tests/test_compute_generalized_dice.py b/tests/test_compute_generalized_dice.py index ab3d012c97..e04444e988 100644 --- a/tests/test_compute_generalized_dice.py +++ b/tests/test_compute_generalized_dice.py @@ -119,6 +119,7 @@ class TestComputeGeneralizedDiceScore(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_device(self, input_data, _expected_value): result = compute_generalized_dice(**input_data) diff --git a/tests/test_compute_ho_ver_maps.py b/tests/test_compute_ho_ver_maps.py index 50598cb57b..bbd5230f04 100644 --- a/tests/test_compute_ho_ver_maps.py +++ b/tests/test_compute_ho_ver_maps.py @@ -62,6 +62,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class ComputeHoVerMapsTests(unittest.TestCase): + @parameterized.expand(TESTS) def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask): input_image = in_type(mask) diff --git a/tests/test_compute_ho_ver_maps_d.py b/tests/test_compute_ho_ver_maps_d.py index 27bb57988c..7b5ac0d9d7 100644 --- a/tests/test_compute_ho_ver_maps_d.py +++ b/tests/test_compute_ho_ver_maps_d.py @@ -63,6 +63,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class ComputeHoVerMapsDictTests(unittest.TestCase): + @parameterized.expand(TESTS) def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask): hv_key = list(hv_mask.keys())[0] diff --git a/tests/test_compute_meandice.py b/tests/test_compute_meandice.py index 46e1d67b1b..aae15483b5 100644 --- a/tests/test_compute_meandice.py +++ b/tests/test_compute_meandice.py @@ -252,6 +252,7 @@ class TestComputeMeanDice(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_9, TEST_CASE_11, TEST_CASE_12]) def test_value(self, input_data, expected_value): result = compute_dice(**input_data) diff --git a/tests/test_compute_meaniou.py b/tests/test_compute_meaniou.py index d39edaa6f3..0b7a2bbce2 100644 --- a/tests/test_compute_meaniou.py +++ b/tests/test_compute_meaniou.py @@ -187,6 +187,7 @@ class TestComputeMeanIoU(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_9, TEST_CASE_11, TEST_CASE_12]) def test_value(self, input_data, expected_value): result = compute_iou(**input_data) diff --git a/tests/test_compute_mmd_metric.py b/tests/test_compute_mmd_metric.py index d1b69b3dfe..96b5cbc089 100644 --- a/tests/test_compute_mmd_metric.py +++ b/tests/test_compute_mmd_metric.py @@ -36,6 +36,7 @@ class TestMMDMetric(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_results(self, input_param, input_data, expected_val): metric = MMDMetric(**input_param) diff --git a/tests/test_compute_multiscalessim_metric.py b/tests/test_compute_multiscalessim_metric.py index 4ebc5b7935..3df8026c2b 100644 --- a/tests/test_compute_multiscalessim_metric.py +++ b/tests/test_compute_multiscalessim_metric.py @@ -20,6 +20,7 @@ class TestMultiScaleSSIMMetric(unittest.TestCase): + def test2d_gaussian(self): set_determinism(0) preds = torch.abs(torch.randn(1, 1, 64, 64)) diff --git a/tests/test_compute_panoptic_quality.py b/tests/test_compute_panoptic_quality.py index a5858e91d1..a916ea32b2 100644 --- a/tests/test_compute_panoptic_quality.py +++ b/tests/test_compute_panoptic_quality.py @@ -92,6 +92,7 @@ @SkipIfNoModule("scipy.optimize") class TestPanopticQualityMetric(unittest.TestCase): + @parameterized.expand([TEST_FUNC_CASE_1, TEST_FUNC_CASE_2, TEST_FUNC_CASE_3, TEST_FUNC_CASE_4]) def test_value(self, input_params, expected_value): result = compute_panoptic_quality(**input_params) diff --git a/tests/test_compute_regression_metrics.py b/tests/test_compute_regression_metrics.py index b0fde3afe9..a8b7f03e47 100644 --- a/tests/test_compute_regression_metrics.py +++ b/tests/test_compute_regression_metrics.py @@ -45,6 +45,7 @@ def psnrmetric_np(max_val, y_pred, y): class TestRegressionMetrics(unittest.TestCase): + def test_shape_reduction(self): set_determinism(seed=123) device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_compute_roc_auc.py b/tests/test_compute_roc_auc.py index 2f080c76cb..f2cb816db4 100644 --- a/tests/test_compute_roc_auc.py +++ b/tests/test_compute_roc_auc.py @@ -100,6 +100,7 @@ class TestComputeROCAUC(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_1, diff --git a/tests/test_compute_variance.py b/tests/test_compute_variance.py index 8eaac10a6c..486a1e9f6f 100644 --- a/tests/test_compute_variance.py +++ b/tests/test_compute_variance.py @@ -109,6 +109,7 @@ class TestComputeVariance(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_value(self, input_data, expected_value): result = compute_variance(**input_data) diff --git a/tests/test_concat_itemsd.py b/tests/test_concat_itemsd.py index 322a95d7df..64c5d6e255 100644 --- a/tests/test_concat_itemsd.py +++ b/tests/test_concat_itemsd.py @@ -22,6 +22,7 @@ class TestConcatItemsd(unittest.TestCase): + def test_tensor_values(self): device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu:0") input_data = { diff --git a/tests/test_config_item.py b/tests/test_config_item.py index cb1e7ad552..72f54adf0a 100644 --- a/tests/test_config_item.py +++ b/tests/test_config_item.py @@ -52,6 +52,7 @@ class TestConfigItem(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_item(self, test_input, expected): item = ConfigItem(config=test_input) diff --git a/tests/test_config_parser.py b/tests/test_config_parser.py index 63254e7336..41d7aa7a4e 100644 --- a/tests/test_config_parser.py +++ b/tests/test_config_parser.py @@ -72,6 +72,7 @@ def case_pdb_inst(sarg=None): class TestClass: + @staticmethod def compute(a, b, func=lambda x, y: x + y): return func(a, b) @@ -126,6 +127,7 @@ def __call__(self, a, b): class TestConfigParser(unittest.TestCase): + def test_config_content(self): test_config = {"preprocessing": [{"_target_": "LoadImage"}], "dataset": {"_target_": "Dataset"}} parser = ConfigParser(config=test_config) diff --git a/tests/test_contrastive_loss.py b/tests/test_contrastive_loss.py index 4cafa0a905..21a9e76417 100644 --- a/tests/test_contrastive_loss.py +++ b/tests/test_contrastive_loss.py @@ -55,6 +55,7 @@ class TestContrastiveLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, input_param, input_data, expected_val): contrastiveloss = ContrastiveLoss(**input_param) diff --git a/tests/test_convert_data_type.py b/tests/test_convert_data_type.py index c3e4490ffe..b95539f4b7 100644 --- a/tests/test_convert_data_type.py +++ b/tests/test_convert_data_type.py @@ -77,6 +77,7 @@ class TestTensor(torch.Tensor): class TestConvertDataType(unittest.TestCase): + @parameterized.expand(TESTS) def test_convert_data_type(self, in_image, im_out, out_dtype, safe): converted_im, orig_type, orig_device = convert_data_type(in_image, type(im_out), dtype=out_dtype, safe=safe) diff --git a/tests/test_convert_to_multi_channel.py b/tests/test_convert_to_multi_channel.py index 78c3c90688..98bbea1ebf 100644 --- a/tests/test_convert_to_multi_channel.py +++ b/tests/test_convert_to_multi_channel.py @@ -48,6 +48,7 @@ class TestConvertToMultiChannel(unittest.TestCase): + @parameterized.expand(TESTS) def test_type_shape(self, data, expected_result): result = ConvertToMultiChannelBasedOnBratsClasses()(data) diff --git a/tests/test_convert_to_multi_channeld.py b/tests/test_convert_to_multi_channeld.py index 351adddb13..e482770497 100644 --- a/tests/test_convert_to_multi_channeld.py +++ b/tests/test_convert_to_multi_channeld.py @@ -26,6 +26,7 @@ class TestConvertToMultiChanneld(unittest.TestCase): + @parameterized.expand([TEST_CASE]) def test_type_shape(self, keys, data, expected_result): result = ConvertToMultiChannelBasedOnBratsClassesd(**keys)(data) diff --git a/tests/test_convert_to_onnx.py b/tests/test_convert_to_onnx.py index 7560c98703..398d260c52 100644 --- a/tests/test_convert_to_onnx.py +++ b/tests/test_convert_to_onnx.py @@ -36,6 +36,7 @@ @SkipIfBeforePyTorchVersion((1, 9)) @skip_if_quick class TestConvertToOnnx(unittest.TestCase): + @parameterized.expand(TESTS) def test_unet(self, device, use_trace, use_ort): if use_ort: diff --git a/tests/test_convert_to_torchscript.py b/tests/test_convert_to_torchscript.py index 0b8e9a8141..c78b8e78c0 100644 --- a/tests/test_convert_to_torchscript.py +++ b/tests/test_convert_to_torchscript.py @@ -22,6 +22,7 @@ class TestConvertToTorchScript(unittest.TestCase): + def test_value(self): model = UNet( spatial_dims=2, in_channels=1, out_channels=3, channels=(16, 32, 64), strides=(2, 2), num_res_units=0 diff --git a/tests/test_convert_to_trt.py b/tests/test_convert_to_trt.py index 108ed66f31..5579539764 100644 --- a/tests/test_convert_to_trt.py +++ b/tests/test_convert_to_trt.py @@ -39,6 +39,7 @@ @skip_if_no_cuda @skip_if_quick class TestConvertToTRT(unittest.TestCase): + def setUp(self): self.gpu_device = torch.cuda.current_device() diff --git a/tests/test_convolutions.py b/tests/test_convolutions.py index 1311401f1d..77bc12770f 100644 --- a/tests/test_convolutions.py +++ b/tests/test_convolutions.py @@ -18,6 +18,7 @@ class TestConvolution2D(TorchImageTestCase2D): + def test_conv1(self): conv = Convolution(2, self.input_channels, self.output_channels) out = conv(self.imt) @@ -69,6 +70,7 @@ def test_transpose2(self): class TestConvolution3D(TorchImageTestCase3D): + def test_conv1(self): conv = Convolution(3, self.input_channels, self.output_channels, dropout=0.1, adn_ordering="DAN") out = conv(self.imt) @@ -126,6 +128,7 @@ def test_transpose2(self): class TestResidualUnit2D(TorchImageTestCase2D): + def test_conv_only1(self): conv = ResidualUnit(2, 1, self.output_channels) out = conv(self.imt) diff --git a/tests/test_copy_itemsd.py b/tests/test_copy_itemsd.py index ff4799a094..a78e08897b 100644 --- a/tests/test_copy_itemsd.py +++ b/tests/test_copy_itemsd.py @@ -32,6 +32,7 @@ class TestCopyItemsd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_numpy_values(self, keys, times, names): input_data = {"img": np.array([[0, 1], [1, 2]]), "seg": np.array([[3, 4], [4, 5]])} diff --git a/tests/test_copy_model_state.py b/tests/test_copy_model_state.py index 2e7513b234..26b01d930a 100644 --- a/tests/test_copy_model_state.py +++ b/tests/test_copy_model_state.py @@ -22,6 +22,7 @@ class _TestModelOne(torch.nn.Module): + def __init__(self, n_n, n_m, n_class): super().__init__() self.layer = torch.nn.Linear(n_n, n_m) @@ -34,6 +35,7 @@ def forward(self, x): class _TestModelTwo(torch.nn.Module): + def __init__(self, n_n, n_m, n_d, n_class): super().__init__() self.layer = torch.nn.Linear(n_n, n_m) @@ -55,6 +57,7 @@ def forward(self, x): class TestModuleState(unittest.TestCase): + def tearDown(self): set_determinism(None) diff --git a/tests/test_correct_crop_centers.py b/tests/test_correct_crop_centers.py index d2a95bf684..82b0b93b53 100644 --- a/tests/test_correct_crop_centers.py +++ b/tests/test_correct_crop_centers.py @@ -23,6 +23,7 @@ class TestCorrectCropCenters(unittest.TestCase): + @parameterized.expand(TESTS) def test_torch(self, spatial_size, centers, label_spatial_shape): result1 = correct_crop_centers(centers, spatial_size, label_spatial_shape) diff --git a/tests/test_create_cross_validation_datalist.py b/tests/test_create_cross_validation_datalist.py index d05a94f59e..0e80be1cd0 100644 --- a/tests/test_create_cross_validation_datalist.py +++ b/tests/test_create_cross_validation_datalist.py @@ -20,6 +20,7 @@ class TestCreateCrossValidationDatalist(unittest.TestCase): + def test_content(self): with tempfile.TemporaryDirectory() as tempdir: datalist = [] diff --git a/tests/test_create_grid_and_affine.py b/tests/test_create_grid_and_affine.py index 2b5890a777..4910a10470 100644 --- a/tests/test_create_grid_and_affine.py +++ b/tests/test_create_grid_and_affine.py @@ -28,6 +28,7 @@ class TestCreateGrid(unittest.TestCase): + def test_create_grid(self): with self.assertRaisesRegex(TypeError, ""): create_grid(None) @@ -168,6 +169,7 @@ def test_assert(func, params, expected): class TestCreateAffine(unittest.TestCase): + def test_create_rotate(self): with self.assertRaisesRegex(TypeError, ""): create_rotate(2, None) diff --git a/tests/test_crf_cpu.py b/tests/test_crf_cpu.py index e29a4d69eb..a7ae0ff2df 100644 --- a/tests/test_crf_cpu.py +++ b/tests/test_crf_cpu.py @@ -495,6 +495,7 @@ @skip_if_no_cpp_extension class CRFTestCaseCpu(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test(self, test_case_description, params, input, features, expected): # Create input tensors diff --git a/tests/test_crf_cuda.py b/tests/test_crf_cuda.py index 8529e2e6de..d5329aab15 100644 --- a/tests/test_crf_cuda.py +++ b/tests/test_crf_cuda.py @@ -496,6 +496,7 @@ @skip_if_no_cpp_extension @skip_if_no_cuda class CRFTestCaseCuda(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test(self, test_case_description, params, input, features, expected): # Create input tensors diff --git a/tests/test_crop_foreground.py b/tests/test_crop_foreground.py index 4435b128ba..f63cb3e8b0 100644 --- a/tests/test_crop_foreground.py +++ b/tests/test_crop_foreground.py @@ -99,6 +99,7 @@ class TestCropForeground(unittest.TestCase): + @parameterized.expand(TEST_COORDS + TESTS) def test_value(self, arguments, image, expected_data, _): cropper = CropForeground(**arguments) diff --git a/tests/test_crop_foregroundd.py b/tests/test_crop_foregroundd.py index 776776f6c5..92954aa81e 100644 --- a/tests/test_crop_foregroundd.py +++ b/tests/test_crop_foregroundd.py @@ -158,6 +158,7 @@ class TestCropForegroundd(unittest.TestCase): + @parameterized.expand(TEST_POSITION + TESTS) def test_value(self, arguments, input_data, expected_data, _): cropper = CropForegroundd(**arguments) diff --git a/tests/test_cross_validation.py b/tests/test_cross_validation.py index de1122eeae..6d0f2319fb 100644 --- a/tests/test_cross_validation.py +++ b/tests/test_cross_validation.py @@ -21,6 +21,7 @@ class TestCrossValidation(unittest.TestCase): + @skip_if_quick def test_values(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_csv_dataset.py b/tests/test_csv_dataset.py index 82a0f7afbd..71be4fdd22 100644 --- a/tests/test_csv_dataset.py +++ b/tests/test_csv_dataset.py @@ -23,6 +23,7 @@ class TestCSVDataset(unittest.TestCase): + def test_values(self): with tempfile.TemporaryDirectory() as tempdir: test_data1 = [ diff --git a/tests/test_csv_iterable_dataset.py b/tests/test_csv_iterable_dataset.py index 65a0a420a5..e06da0c41b 100644 --- a/tests/test_csv_iterable_dataset.py +++ b/tests/test_csv_iterable_dataset.py @@ -26,6 +26,7 @@ @skip_if_windows class TestCSVIterableDataset(unittest.TestCase): + def test_values(self): with tempfile.TemporaryDirectory() as tempdir: test_data1 = [ diff --git a/tests/test_csv_saver.py b/tests/test_csv_saver.py index 833d1134cf..234b3f1057 100644 --- a/tests/test_csv_saver.py +++ b/tests/test_csv_saver.py @@ -23,6 +23,7 @@ class TestCSVSaver(unittest.TestCase): + def test_saved_content(self): with tempfile.TemporaryDirectory() as tempdir: saver = CSVSaver(output_dir=tempdir, filename="predictions.csv", delimiter="\t") diff --git a/tests/test_cucim_dict_transform.py b/tests/test_cucim_dict_transform.py index 6ebfd8bac7..d2dcc6aa5f 100644 --- a/tests/test_cucim_dict_transform.py +++ b/tests/test_cucim_dict_transform.py @@ -66,6 +66,7 @@ @unittest.skipUnless(HAS_CUPY, "CuPy is required.") @unittest.skipUnless(has_cut, "cuCIM transforms are required.") class TestCuCIMDict(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_COLOR_JITTER_1, diff --git a/tests/test_cucim_transform.py b/tests/test_cucim_transform.py index 5884358a74..5f16c11589 100644 --- a/tests/test_cucim_transform.py +++ b/tests/test_cucim_transform.py @@ -66,6 +66,7 @@ @unittest.skipUnless(HAS_CUPY, "CuPy is required.") @unittest.skipUnless(has_cut, "cuCIM transforms are required.") class TestCuCIM(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_COLOR_JITTER_1, diff --git a/tests/test_cumulative.py b/tests/test_cumulative.py index 3377fa815c..d3b6ba094c 100644 --- a/tests/test_cumulative.py +++ b/tests/test_cumulative.py @@ -20,6 +20,7 @@ class TestCumulative(unittest.TestCase): + def test_single(self): c = Cumulative() c.extend([2, 3]) diff --git a/tests/test_cumulative_average.py b/tests/test_cumulative_average.py index d815d9be77..624da2c7bb 100644 --- a/tests/test_cumulative_average.py +++ b/tests/test_cumulative_average.py @@ -32,6 +32,7 @@ class TestAverageMeter(unittest.TestCase): + @parameterized.expand(TEST_CASE_1) def test_value_all(self, data): # test orig diff --git a/tests/test_cumulative_average_dist.py b/tests/test_cumulative_average_dist.py index 17f4164838..30c01c21ee 100644 --- a/tests/test_cumulative_average_dist.py +++ b/tests/test_cumulative_average_dist.py @@ -23,6 +23,7 @@ @SkipIfBeforePyTorchVersion((1, 8)) class DistributedCumulativeAverage(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_value(self): rank = dist.get_rank() diff --git a/tests/test_cv2_dist.py b/tests/test_cv2_dist.py index edd2e1ec42..562c205763 100644 --- a/tests/test_cv2_dist.py +++ b/tests/test_cv2_dist.py @@ -42,6 +42,7 @@ def main_worker(rank, ngpus_per_node, port): @skip_if_no_cuda class TestCV2Dist(unittest.TestCase): + def test_cv2_cuda_ops(self): print_config() ngpus_per_node = torch.cuda.device_count() diff --git a/tests/test_daf3d.py b/tests/test_daf3d.py index 34e25cc6be..d20cb3cfd1 100644 --- a/tests/test_daf3d.py +++ b/tests/test_daf3d.py @@ -42,6 +42,7 @@ @unittest.skipUnless(has_tv, "torchvision not installed") class TestDAF3D(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_data_stats.py b/tests/test_data_stats.py index 6ef51bef92..05453b0694 100644 --- a/tests/test_data_stats.py +++ b/tests/test_data_stats.py @@ -137,6 +137,7 @@ class TestDataStats(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7]) def test_value(self, input_param, input_data, expected_print): transform = DataStats(**input_param) diff --git a/tests/test_data_statsd.py b/tests/test_data_statsd.py index 374bc815ac..ef88300c10 100644 --- a/tests/test_data_statsd.py +++ b/tests/test_data_statsd.py @@ -157,6 +157,7 @@ class TestDataStatsd(unittest.TestCase): + @parameterized.expand( [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8] ) diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 2ee69687a6..73e27799f7 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -29,6 +29,7 @@ class TestDataLoader(unittest.TestCase): + def test_values(self): datalist = [ {"image": "spleen_19.nii.gz", "label": "spleen_label_19.nii.gz"}, @@ -59,6 +60,7 @@ def test_exception(self, datalist): class _RandomDataset(torch.utils.data.Dataset, Randomizable): + def __getitem__(self, index): return self.R.randint(0, 1000, (1,)) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index c7c2b77697..1398009c63 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -30,6 +30,7 @@ class TestDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_shape(self, expected_shape): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4)) diff --git a/tests/test_dataset_func.py b/tests/test_dataset_func.py index afccd129fe..166d888d9e 100644 --- a/tests/test_dataset_func.py +++ b/tests/test_dataset_func.py @@ -20,6 +20,7 @@ class TestDatasetFunc(unittest.TestCase): + def test_seg_values(self): with tempfile.TemporaryDirectory() as tempdir: # prepare test datalist file diff --git a/tests/test_dataset_summary.py b/tests/test_dataset_summary.py index 87538425d5..21cc53de90 100644 --- a/tests/test_dataset_summary.py +++ b/tests/test_dataset_summary.py @@ -36,6 +36,7 @@ def test_collate(batch): class TestDatasetSummary(unittest.TestCase): + def test_spacing_intensity(self): set_determinism(seed=0) with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_decathlondataset.py b/tests/test_decathlondataset.py index 345cc487c5..d220cd9097 100644 --- a/tests/test_decathlondataset.py +++ b/tests/test_decathlondataset.py @@ -23,6 +23,7 @@ class TestDecathlonDataset(unittest.TestCase): + @skip_if_quick def test_values(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_decollate.py b/tests/test_decollate.py index 26b9e7a4f4..92f7c89e28 100644 --- a/tests/test_decollate.py +++ b/tests/test_decollate.py @@ -81,6 +81,7 @@ class TestDeCollate(unittest.TestCase): + def setUp(self) -> None: set_determinism(seed=0) @@ -159,6 +160,7 @@ def test_decollation_list(self, *transforms): class TestBasicDeCollate(unittest.TestCase): + @parameterized.expand(TEST_BASIC) def test_decollation_examples(self, input_val, expected_out): out = decollate_batch(input_val) diff --git a/tests/test_deepedit_interaction.py b/tests/test_deepedit_interaction.py index 5dcc6205f7..8baf4dc827 100644 --- a/tests/test_deepedit_interaction.py +++ b/tests/test_deepedit_interaction.py @@ -40,6 +40,7 @@ def add_one(engine): class TestInteractions(unittest.TestCase): + def run_interaction(self, train): label_names = {"spleen": 1, "background": 0} np.random.seed(0) diff --git a/tests/test_deepedit_transforms.py b/tests/test_deepedit_transforms.py index 7f4d4eee1e..18d6567fd7 100644 --- a/tests/test_deepedit_transforms.py +++ b/tests/test_deepedit_transforms.py @@ -209,6 +209,7 @@ class TestAddGuidanceFromPointsCustomd(unittest.TestCase): + @parameterized.expand([ADD_GUIDANCE_FROM_POINTS_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = AddGuidanceFromPointsDeepEditd(**arguments) @@ -217,6 +218,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddGuidanceSignalCustomd(unittest.TestCase): + @parameterized.expand([ADD_GUIDANCE_CUSTOM_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = AddGuidanceSignalDeepEditd(**arguments) @@ -225,6 +227,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddInitialSeedPointMissingLabelsd(unittest.TestCase): + @parameterized.expand([ADD_INITIAL_POINT_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): seed = 0 @@ -235,6 +238,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddRandomGuidanceCustomd(unittest.TestCase): + @parameterized.expand([ADD_RANDOM_GUIDANCE_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = AddRandomGuidanceDeepEditd(**arguments) @@ -244,6 +248,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestDiscardAddGuidanced(unittest.TestCase): + @parameterized.expand([DISCARD_ADD_GUIDANCE_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = DiscardAddGuidanced(**arguments) @@ -252,6 +257,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestFindAllValidSlicesMissingLabelsd(unittest.TestCase): + @parameterized.expand([FIND_SLICE_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = FindAllValidSlicesMissingLabelsd(**arguments) @@ -260,6 +266,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestFindDiscrepancyRegionsCustomd(unittest.TestCase): + @parameterized.expand([FIND_DISCREPANCY_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = FindDiscrepancyRegionsDeepEditd(**arguments) @@ -268,6 +275,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestNormalizeLabelsDatasetd(unittest.TestCase): + @parameterized.expand([NormalizeLabelsDatasetd_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = NormalizeLabelsInDatasetd(**arguments) @@ -276,6 +284,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestResizeGuidanceMultipleLabelCustomd(unittest.TestCase): + @parameterized.expand([RESIZE_GUIDANCE_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = ResizeGuidanceMultipleLabelDeepEditd(**arguments) @@ -284,6 +293,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestSingleLabelSelectiond(unittest.TestCase): + @parameterized.expand([SingleLabelSelectiond_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = SingleLabelSelectiond(**arguments) @@ -292,6 +302,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestSplitPredsLabeld(unittest.TestCase): + @parameterized.expand([SplitPredsLabeld_TEST_CASE]) def test_correct_results(self, arguments, input_data, expected_result): add_fn = SplitPredsLabeld(**arguments) diff --git a/tests/test_deepgrow_dataset.py b/tests/test_deepgrow_dataset.py index d8a412ade9..b8d630960c 100644 --- a/tests/test_deepgrow_dataset.py +++ b/tests/test_deepgrow_dataset.py @@ -51,6 +51,7 @@ class TestCreateDataset(unittest.TestCase): + def setUp(self): set_determinism(1) self.tempdir = tempfile.mkdtemp() diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py index 7cdbeed9f9..35759699f8 100644 --- a/tests/test_deepgrow_interaction.py +++ b/tests/test_deepgrow_interaction.py @@ -38,6 +38,7 @@ def add_one(engine): class TestInteractions(unittest.TestCase): + def run_interaction(self, train, compose): data = [{"image": np.ones((1, 2, 2, 2)).astype(np.float32), "label": np.ones((1, 2, 2, 2))} for _ in range(5)] network = torch.nn.Linear(2, 2) diff --git a/tests/test_deepgrow_transforms.py b/tests/test_deepgrow_transforms.py index 1328e13439..a491a8004b 100644 --- a/tests/test_deepgrow_transforms.py +++ b/tests/test_deepgrow_transforms.py @@ -337,6 +337,7 @@ class TestFindAllValidSlicesd(unittest.TestCase): + @parameterized.expand([FIND_SLICE_TEST_CASE_1, FIND_SLICE_TEST_CASE_2]) def test_correct_results(self, arguments, input_data, expected_result): result = FindAllValidSlicesd(**arguments)(input_data) @@ -344,6 +345,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestSpatialCropForegroundd(unittest.TestCase): + @parameterized.expand([CROP_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = SpatialCropForegroundd(**arguments)(input_data) @@ -368,6 +370,7 @@ def test_foreground_position(self, arguments, input_data, _): class TestAddInitialSeedPointd(unittest.TestCase): + @parameterized.expand([ADD_INITIAL_POINT_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): seed = 0 @@ -378,6 +381,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddGuidanceSignald(unittest.TestCase): + @parameterized.expand([ADD_GUIDANCE_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = AddGuidanceSignald(**arguments)(input_data) @@ -385,6 +389,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestFindDiscrepancyRegionsd(unittest.TestCase): + @parameterized.expand([FIND_DISCREPANCY_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = FindDiscrepancyRegionsd(**arguments)(input_data) @@ -392,6 +397,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddRandomGuidanced(unittest.TestCase): + @parameterized.expand([ADD_RANDOM_GUIDANCE_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): seed = 0 @@ -402,6 +408,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestAddGuidanceFromPointsd(unittest.TestCase): + @parameterized.expand( [ ADD_GUIDANCE_FROM_POINTS_TEST_CASE_1, @@ -419,6 +426,7 @@ def test_correct_results(self, arguments, input_data, expected_pos, expected_neg class TestSpatialCropGuidanced(unittest.TestCase): + @parameterized.expand( [SPATIAL_CROP_GUIDANCE_TEST_CASE_1, SPATIAL_CROP_GUIDANCE_TEST_CASE_2, SPATIAL_CROP_GUIDANCE_TEST_CASE_3] ) @@ -428,6 +436,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestResizeGuidanced(unittest.TestCase): + @parameterized.expand([RESIZE_GUIDANCE_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = ResizeGuidanced(**arguments)(input_data) @@ -435,6 +444,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestRestoreLabeld(unittest.TestCase): + @parameterized.expand([RESTORE_LABEL_TEST_CASE_1, RESTORE_LABEL_TEST_CASE_2]) def test_correct_results(self, arguments, input_data, expected_result): result = RestoreLabeld(**arguments)(input_data) @@ -442,6 +452,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestFetch2DSliced(unittest.TestCase): + @parameterized.expand([FETCH_2D_SLICE_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = Fetch2DSliced(**arguments)(input_data) diff --git a/tests/test_delete_itemsd.py b/tests/test_delete_itemsd.py index 1ec77f29fd..c57184cd9f 100644 --- a/tests/test_delete_itemsd.py +++ b/tests/test_delete_itemsd.py @@ -28,6 +28,7 @@ class TestDeleteItemsd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_memory(self, input_param, expected_key_size): input_data = {"image": {}} if "sep" in input_param else {} diff --git a/tests/test_denseblock.py b/tests/test_denseblock.py index c14ca2ae7a..b741582422 100644 --- a/tests/test_denseblock.py +++ b/tests/test_denseblock.py @@ -20,6 +20,7 @@ class TestDenseBlock2D(TorchImageTestCase2D): + def test_block_empty(self): block = DenseBlock([]) out = block(self.imt) @@ -36,6 +37,7 @@ def test_block_conv(self): class TestDenseBlock3D(TorchImageTestCase3D): + def test_block_conv(self): conv1 = nn.Conv3d(self.input_channels, self.output_channels, 3, padding=1) conv2 = nn.Conv3d(self.input_channels + self.output_channels, self.input_channels, 3, padding=1) @@ -52,6 +54,7 @@ def test_block_conv(self): class TestConvDenseBlock2D(TorchImageTestCase2D): + def test_block_empty(self): conv = ConvDenseBlock(spatial_dims=2, in_channels=self.input_channels, channels=[]) out = conv(self.imt) @@ -79,6 +82,7 @@ def test_block2(self): class TestConvDenseBlock3D(TorchImageTestCase3D): + def test_block_empty(self): conv = ConvDenseBlock(spatial_dims=3, in_channels=self.input_channels, channels=[]) out = conv(self.imt) diff --git a/tests/test_densenet.py b/tests/test_densenet.py index 1b44baf0c2..ee4be9003b 100644 --- a/tests/test_densenet.py +++ b/tests/test_densenet.py @@ -79,6 +79,7 @@ class TestPretrainedDENSENET(unittest.TestCase): + @parameterized.expand([TEST_PRETRAINED_2D_CASE_1, TEST_PRETRAINED_2D_CASE_2]) @skip_if_quick def test_121_2d_shape_pretrain(self, model, input_param, input_shape, expected_shape): @@ -103,6 +104,7 @@ def test_pretrain_consistency(self, model, input_param, input_shape): class TestDENSENET(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_densenet_shape(self, model, input_param, input_shape, expected_shape): net = model(**input_param).to(device) diff --git a/tests/test_deprecated.py b/tests/test_deprecated.py index 5d511f3821..3171a67e2c 100644 --- a/tests/test_deprecated.py +++ b/tests/test_deprecated.py @@ -18,6 +18,7 @@ class TestDeprecatedRC(unittest.TestCase): + def setUp(self): self.test_version_rc = "0.6.0rc1" self.test_version = "0.6.0" @@ -61,6 +62,7 @@ def foo3(): class TestDeprecated(unittest.TestCase): + def setUp(self): self.test_version = "0.5.3+96.g1fa03c2.dirty" self.prev_version = "0.4.3+96.g1fa03c2.dirty" @@ -142,6 +144,7 @@ def test_meth_warning1(self): """Test deprecated decorator with just `since` set.""" class Foo5: + @deprecated(since=self.prev_version, version_val=self.test_version) def meth1(self): pass @@ -152,6 +155,7 @@ def test_meth_except1(self): """Test deprecated decorator with just `since` set.""" class Foo6: + @deprecated(version_val=self.test_version) def meth1(self): pass @@ -389,6 +393,7 @@ def test_deprecated_arg_default_errors(self): # since > replaced def since_grater_than_replaced(): + @deprecated_arg_default( "b", old_default="a", @@ -404,6 +409,7 @@ def foo(a, b=None): # argname doesnt exist def argname_doesnt_exist(): + @deprecated_arg_default( "other", old_default="a", new_default="b", since=self.test_version, version_val=self.test_version ) @@ -414,6 +420,7 @@ def foo(a, b=None): # argname has no default def argname_has_no_default(): + @deprecated_arg_default( "a", old_default="a", @@ -429,6 +436,7 @@ def foo(a): # new default is used but version < replaced def argname_was_replaced_before_specified_version(): + @deprecated_arg_default( "a", old_default="a", diff --git a/tests/test_detect_envelope.py b/tests/test_detect_envelope.py index 105d3a4ace..e2efefeb77 100644 --- a/tests/test_detect_envelope.py +++ b/tests/test_detect_envelope.py @@ -116,6 +116,7 @@ @SkipIfNoModule("torch.fft") class TestDetectEnvelope(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_1D_SINE, @@ -151,6 +152,7 @@ def test_value_error(self, arguments, image, method): @SkipIfModule("torch.fft") class TestHilbertTransformNoFFTMod(unittest.TestCase): + def test_no_fft_module_error(self): self.assertRaises(OptionalImportError, DetectEnvelope(), np.random.rand(1, 10)) diff --git a/tests/test_detection_coco_metrics.py b/tests/test_detection_coco_metrics.py index 780031ee0c..a85eb37db7 100644 --- a/tests/test_detection_coco_metrics.py +++ b/tests/test_detection_coco_metrics.py @@ -23,6 +23,7 @@ class TestCOCOMetrics(unittest.TestCase): + def test_coco_run(self): coco_metric = COCOMetric(classes=["c0", "c1", "c2"], iou_list=[0.1], max_detection=[10]) diff --git a/tests/test_detector_boxselector.py b/tests/test_detector_boxselector.py index 8cc9b15911..326ecd5773 100644 --- a/tests/test_detector_boxselector.py +++ b/tests/test_detector_boxselector.py @@ -56,6 +56,7 @@ class TestBoxSelector(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_box_selector(self, input_param, boxes, logits, image_shape, expected_results): box_selector = BoxSelector(**input_param) diff --git a/tests/test_detector_utils.py b/tests/test_detector_utils.py index 41716934b5..352e1c2faf 100644 --- a/tests/test_detector_utils.py +++ b/tests/test_detector_utils.py @@ -79,6 +79,7 @@ class TestDetectorUtils(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_detector_utils(self, input_param, input_shape, expected_shape): size_divisible = 32 * ensure_tuple(input_param["conv1_t_stride"])[0] diff --git a/tests/test_dev_collate.py b/tests/test_dev_collate.py index 97028f2597..44c4d2c598 100644 --- a/tests/test_dev_collate.py +++ b/tests/test_dev_collate.py @@ -36,6 +36,7 @@ class DevCollateTest(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_dev_collate(self, inputs, msg): with self.assertLogs(level=logging.CRITICAL) as log: diff --git a/tests/test_dice_ce_loss.py b/tests/test_dice_ce_loss.py index 58b9f4c191..225618ed2c 100644 --- a/tests/test_dice_ce_loss.py +++ b/tests/test_dice_ce_loss.py @@ -86,6 +86,7 @@ class TestDiceCELoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, input_param, input_data, expected_val): diceceloss = DiceCELoss(**input_param) diff --git a/tests/test_dice_focal_loss.py b/tests/test_dice_focal_loss.py index 845ef40cd5..13899da003 100644 --- a/tests/test_dice_focal_loss.py +++ b/tests/test_dice_focal_loss.py @@ -22,6 +22,7 @@ class TestDiceFocalLoss(unittest.TestCase): + def test_result_onehot_target_include_bg(self): size = [3, 3, 5, 5] label = torch.randint(low=0, high=2, size=size) diff --git a/tests/test_dice_loss.py b/tests/test_dice_loss.py index 370d2dd5af..14aa6ec241 100644 --- a/tests/test_dice_loss.py +++ b/tests/test_dice_loss.py @@ -168,6 +168,7 @@ class TestDiceLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = DiceLoss(**input_param).forward(**input_data) diff --git a/tests/test_diffusion_loss.py b/tests/test_diffusion_loss.py index 05dfab95fb..93df77cc51 100644 --- a/tests/test_diffusion_loss.py +++ b/tests/test_diffusion_loss.py @@ -79,6 +79,7 @@ class TestDiffusionLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = DiffusionLoss(**input_param).forward(**input_data) diff --git a/tests/test_dints_cell.py b/tests/test_dints_cell.py index 21cef39d68..13990da373 100644 --- a/tests/test_dints_cell.py +++ b/tests/test_dints_cell.py @@ -98,6 +98,7 @@ class TestCell(unittest.TestCase): + @parameterized.expand(TEST_CASES_2D + TEST_CASES_3D) def test_cell_3d(self, input_param, ops, weight, input_shape, expected_shape): net = Cell(**input_param) diff --git a/tests/test_dints_mixop.py b/tests/test_dints_mixop.py index 09d2e7a423..683a8d1005 100644 --- a/tests/test_dints_mixop.py +++ b/tests/test_dints_mixop.py @@ -61,6 +61,7 @@ class TestMixOP(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_mixop_3d(self, input_param, ops, weight, input_shape, expected_shape): net = MixedOp(ops=Cell.OPS3D, **input_param) diff --git a/tests/test_dints_network.py b/tests/test_dints_network.py index 532c31886b..5ee4db7a4e 100644 --- a/tests/test_dints_network.py +++ b/tests/test_dints_network.py @@ -115,6 +115,7 @@ @skip_if_quick class TestDints(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D) def test_dints_inference(self, dints_grid_params, dints_params, input_shape, expected_shape): grid = TopologySearch(**dints_grid_params) @@ -155,6 +156,7 @@ def test_dints_search(self, dints_grid_params, dints_params, input_shape, expect @SkipIfBeforePyTorchVersion((1, 9)) class TestDintsTS(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D + TEST_CASES_2D) def test_script(self, dints_grid_params, dints_params, input_shape, _): grid = TopologyInstance(**dints_grid_params) diff --git a/tests/test_discriminator.py b/tests/test_discriminator.py index 62635e286e..f615605e56 100644 --- a/tests/test_discriminator.py +++ b/tests/test_discriminator.py @@ -42,6 +42,7 @@ class TestDiscriminator(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_data, expected_shape): net = Discriminator(**input_param) diff --git a/tests/test_distance_transform_edt.py b/tests/test_distance_transform_edt.py index 83b9348348..cf5c253c0c 100644 --- a/tests/test_distance_transform_edt.py +++ b/tests/test_distance_transform_edt.py @@ -146,6 +146,7 @@ class TestDistanceTransformEDT(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_scipy_transform(self, input, expected_output): transform = DistanceTransformEDT() diff --git a/tests/test_download_and_extract.py b/tests/test_download_and_extract.py index 696bcfc78f..555f7dc250 100644 --- a/tests/test_download_and_extract.py +++ b/tests/test_download_and_extract.py @@ -24,6 +24,7 @@ class TestDownloadAndExtract(unittest.TestCase): + @skip_if_quick def test_actions(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_download_url_yandex.py b/tests/test_download_url_yandex.py index a08105a93f..54d39b06ff 100644 --- a/tests/test_download_url_yandex.py +++ b/tests/test_download_url_yandex.py @@ -29,6 +29,7 @@ class TestDownloadUrlYandex(unittest.TestCase): + @unittest.skip("data source unstable") def test_verify(self): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_downsample_block.py b/tests/test_downsample_block.py index cd40be4306..34afa248ad 100644 --- a/tests/test_downsample_block.py +++ b/tests/test_downsample_block.py @@ -37,6 +37,7 @@ class TestMaxAvgPool(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape, expected_shape): net = MaxAvgPool(**input_param) diff --git a/tests/test_drop_path.py b/tests/test_drop_path.py index ab2150e548..1b9974791a 100644 --- a/tests/test_drop_path.py +++ b/tests/test_drop_path.py @@ -28,6 +28,7 @@ class TestDropPath(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape): im = torch.rand(input_shape) diff --git a/tests/test_ds_loss.py b/tests/test_ds_loss.py index de7aec1ced..daa4ed1e5e 100644 --- a/tests/test_ds_loss.py +++ b/tests/test_ds_loss.py @@ -135,6 +135,7 @@ class TestDSLossDiceCE(unittest.TestCase): + @parameterized.expand(TEST_CASES_DICECE) def test_result(self, input_param, input_param2, input_data, expected_val): diceceloss = DeepSupervisionLoss(DiceCELoss(**input_param), **input_param2) @@ -160,6 +161,7 @@ def test_script(self): @SkipIfBeforePyTorchVersion((1, 11)) class TestDSLossDiceCE2(unittest.TestCase): + @parameterized.expand(TEST_CASES_DICECE2) def test_result(self, input_param, input_param2, input_data, expected_val): diceceloss = DeepSupervisionLoss(DiceCELoss(**input_param), **input_param2) @@ -169,6 +171,7 @@ def test_result(self, input_param, input_param2, input_data, expected_val): @SkipIfBeforePyTorchVersion((1, 11)) class TestDSLossDice(unittest.TestCase): + @parameterized.expand(TEST_CASES_DICE) def test_result(self, input_param, input_data, expected_val): loss = DeepSupervisionLoss(DiceLoss(**input_param)) @@ -178,6 +181,7 @@ def test_result(self, input_param, input_data, expected_val): @SkipIfBeforePyTorchVersion((1, 11)) class TestDSLossDiceFocal(unittest.TestCase): + @parameterized.expand(TEST_CASES_DICEFOCAL) def test_result(self, input_param, input_data, expected_val): loss = DeepSupervisionLoss(DiceFocalLoss(**input_param)) diff --git a/tests/test_dvf2ddf.py b/tests/test_dvf2ddf.py index f18b5b7297..b385b897e5 100644 --- a/tests/test_dvf2ddf.py +++ b/tests/test_dvf2ddf.py @@ -42,6 +42,7 @@ class TestDVF2DDF(unittest.TestCase): + def setUp(self): set_determinism(0) diff --git a/tests/test_dynunet.py b/tests/test_dynunet.py index 247da14b7d..b0137ae245 100644 --- a/tests/test_dynunet.py +++ b/tests/test_dynunet.py @@ -109,6 +109,7 @@ class TestDynUNet(unittest.TestCase): + @parameterized.expand(TEST_CASE_DYNUNET_3D) def test_shape(self, input_param, input_shape, expected_shape): net = DynUNet(**input_param).to(device) @@ -128,6 +129,7 @@ def test_script(self): @skip_if_no_cuda @skip_if_windows class TestDynUNetWithInstanceNorm3dNVFuser(unittest.TestCase): + def setUp(self): try: layer = InstanceNorm3dNVFuser(num_features=1, affine=False).to("cuda:0") @@ -161,6 +163,7 @@ def test_consistency(self, input_param, input_shape, _): class TestDynUNetDeepSupervision(unittest.TestCase): + @parameterized.expand(TEST_CASE_DEEP_SUPERVISION) def test_shape(self, input_param, input_shape, expected_shape): net = DynUNet(**input_param).to(device) diff --git a/tests/test_dynunet_block.py b/tests/test_dynunet_block.py index b34ccb31ba..4d9e06670b 100644 --- a/tests/test_dynunet_block.py +++ b/tests/test_dynunet_block.py @@ -73,6 +73,7 @@ class TestResBasicBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_RES_BASIC_BLOCK) def test_shape(self, input_param, input_shape, expected_shape): for net in [UnetResBlock(**input_param), UnetBasicBlock(**input_param)]: @@ -96,6 +97,7 @@ def test_script(self): class TestUpBlock(unittest.TestCase): + @parameterized.expand(TEST_UP_BLOCK) def test_shape(self, input_param, input_shape, expected_shape, skip_shape): net = UnetUpBlock(**input_param) diff --git a/tests/test_efficientnet.py b/tests/test_efficientnet.py index 5bdad5a568..c16526eaa3 100644 --- a/tests/test_efficientnet.py +++ b/tests/test_efficientnet.py @@ -248,6 +248,7 @@ def make_shape_cases( class TestEFFICIENTNET(unittest.TestCase): + @parameterized.expand(CASES_1D + CASES_2D + CASES_3D + CASES_VARIATIONS) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" @@ -376,6 +377,7 @@ def test_script(self): class TestExtractFeatures(unittest.TestCase): + @parameterized.expand(CASE_EXTRACT_FEATURES) def test_shape(self, input_param, input_shape, expected_shapes): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_ensemble_evaluator.py b/tests/test_ensemble_evaluator.py index 40a4a72dd5..ad81d35d52 100644 --- a/tests/test_ensemble_evaluator.py +++ b/tests/test_ensemble_evaluator.py @@ -26,11 +26,13 @@ class TestEnsembleEvaluator(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_content(self, pred_keys): device = torch.device("cpu:0") class TestDataset(torch.utils.data.Dataset): + def __len__(self): return 8 @@ -40,6 +42,7 @@ def __getitem__(self, index): val_loader = torch.utils.data.DataLoader(TestDataset()) class TestNet(torch.nn.Module): + def __init__(self, func): super().__init__() self.func = func diff --git a/tests/test_ensure_channel_first.py b/tests/test_ensure_channel_first.py index 027b18b7dd..0c9ad5869e 100644 --- a/tests/test_ensure_channel_first.py +++ b/tests/test_ensure_channel_first.py @@ -46,6 +46,7 @@ class TestEnsureChannelFirst(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]) @unittest.skipUnless(has_itk, "itk not installed") def test_load_nifti(self, input_param, filenames, original_channel_dim): diff --git a/tests/test_ensure_channel_firstd.py b/tests/test_ensure_channel_firstd.py index 08e2709641..63a437894b 100644 --- a/tests/test_ensure_channel_firstd.py +++ b/tests/test_ensure_channel_firstd.py @@ -32,6 +32,7 @@ class TestEnsureChannelFirstd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_load_nifti(self, input_param, filenames, original_channel_dim): if original_channel_dim is None: diff --git a/tests/test_ensure_tuple.py b/tests/test_ensure_tuple.py index dc6649ec4c..ec8c92785a 100644 --- a/tests/test_ensure_tuple.py +++ b/tests/test_ensure_tuple.py @@ -37,6 +37,7 @@ class TestEnsureTuple(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input, expected_value, wrap_array=False): result = ensure_tuple(input, wrap_array) diff --git a/tests/test_ensure_type.py b/tests/test_ensure_type.py index 7d6b7ca586..00b01898b3 100644 --- a/tests/test_ensure_type.py +++ b/tests/test_ensure_type.py @@ -22,6 +22,7 @@ class TestEnsureType(unittest.TestCase): + def test_array_input(self): test_datas = [np.array([[1, 2], [3, 4]]), torch.as_tensor([[1, 2], [3, 4]])] if torch.cuda.is_available(): diff --git a/tests/test_ensure_typed.py b/tests/test_ensure_typed.py index 4fa942e742..09aa1f04b5 100644 --- a/tests/test_ensure_typed.py +++ b/tests/test_ensure_typed.py @@ -22,6 +22,7 @@ class TestEnsureTyped(unittest.TestCase): + def test_array_input(self): test_datas = [np.array([[1, 2], [3, 4]]), torch.as_tensor([[1, 2], [3, 4]])] if torch.cuda.is_available(): diff --git a/tests/test_enum_bound_interp.py b/tests/test_enum_bound_interp.py index 5a63fc05af..cd3119f91c 100644 --- a/tests/test_enum_bound_interp.py +++ b/tests/test_enum_bound_interp.py @@ -22,6 +22,7 @@ @skip_if_no_cpp_extension class TestEnumBoundInterp(unittest.TestCase): + def test_bound(self): self.assertEqual(str(b.replicate), "BoundType.replicate") self.assertEqual(str(b.nearest), "BoundType.replicate") diff --git a/tests/test_eval_mode.py b/tests/test_eval_mode.py index 8458753e1f..b40bb78327 100644 --- a/tests/test_eval_mode.py +++ b/tests/test_eval_mode.py @@ -19,6 +19,7 @@ class TestEvalMode(unittest.TestCase): + def test_eval_mode(self): t = torch.rand(1, 1, 4, 4) p = torch.nn.Conv2d(1, 1, 3) diff --git a/tests/test_evenly_divisible_all_gather_dist.py b/tests/test_evenly_divisible_all_gather_dist.py index f338944daa..d6d26c7e23 100644 --- a/tests/test_evenly_divisible_all_gather_dist.py +++ b/tests/test_evenly_divisible_all_gather_dist.py @@ -21,6 +21,7 @@ class DistributedEvenlyDivisibleAllGather(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_data(self): self._run() diff --git a/tests/test_factorized_increase.py b/tests/test_factorized_increase.py index f7642ff357..b082c70090 100644 --- a/tests/test_factorized_increase.py +++ b/tests/test_factorized_increase.py @@ -25,6 +25,7 @@ class TestFactInc(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_factorized_increase_3d(self, input_param, input_shape, expected_shape): net = FactorizedIncreaseBlock(**input_param) diff --git a/tests/test_factorized_reduce.py b/tests/test_factorized_reduce.py index 224a0cb351..5e879c3cb5 100644 --- a/tests/test_factorized_reduce.py +++ b/tests/test_factorized_reduce.py @@ -25,6 +25,7 @@ class TestFactRed(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_factorized_reduce_3d(self, input_param, input_shape, expected_shape): net = FactorizedReduceBlock(**input_param) diff --git a/tests/test_fastmri_reader.py b/tests/test_fastmri_reader.py index b15bd4b6a2..af2eed7db5 100644 --- a/tests/test_fastmri_reader.py +++ b/tests/test_fastmri_reader.py @@ -65,6 +65,7 @@ class TestMRIUtils(unittest.TestCase): + @parameterized.expand([TEST_CASE1, TEST_CASE2]) def test_get_data(self, test_data, test_res, test_meta): reader = FastMRIReader() diff --git a/tests/test_fft_utils.py b/tests/test_fft_utils.py index 971df2b411..7c7035770a 100644 --- a/tests/test_fft_utils.py +++ b/tests/test_fft_utils.py @@ -42,6 +42,7 @@ class TestFFT(unittest.TestCase): + @parameterized.expand(TESTS) def test(self, test_data, res_data): result = fftn_centered(test_data, spatial_dims=2, is_complex=False) diff --git a/tests/test_fg_bg_to_indices.py b/tests/test_fg_bg_to_indices.py index 7d88bb7ee9..a28c491333 100644 --- a/tests/test_fg_bg_to_indices.py +++ b/tests/test_fg_bg_to_indices.py @@ -72,6 +72,7 @@ class TestFgBgToIndices(unittest.TestCase): + @parameterized.expand(TESTS_CASES) def test_type_shape(self, input_data, label, image, expected_fg, expected_bg): fg_indices, bg_indices = FgBgToIndices(**input_data)(label, image) diff --git a/tests/test_fg_bg_to_indicesd.py b/tests/test_fg_bg_to_indicesd.py index d0d1ae5fb6..c6dd2059f4 100644 --- a/tests/test_fg_bg_to_indicesd.py +++ b/tests/test_fg_bg_to_indicesd.py @@ -67,6 +67,7 @@ class TestFgBgToIndicesd(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_type_shape(self, input_data, data, expected_fg, expected_bg): result = FgBgToIndicesd(**input_data)(data) diff --git a/tests/test_file_basename.py b/tests/test_file_basename.py index 93e2027575..27e2d98c7d 100644 --- a/tests/test_file_basename.py +++ b/tests/test_file_basename.py @@ -20,6 +20,7 @@ class TestFilename(unittest.TestCase): + def test_value(self): with tempfile.TemporaryDirectory() as tempdir: output_tmp = os.path.join(tempdir, "output") diff --git a/tests/test_fill_holes.py b/tests/test_fill_holes.py index 65c59d49eb..241f7f8254 100644 --- a/tests/test_fill_holes.py +++ b/tests/test_fill_holes.py @@ -195,6 +195,7 @@ class TestFillHoles(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_results(self, _, args, input_image, expected): converter = FillHoles(**args) diff --git a/tests/test_fill_holesd.py b/tests/test_fill_holesd.py index 3f98dab1bf..28c17b00ac 100644 --- a/tests/test_fill_holesd.py +++ b/tests/test_fill_holesd.py @@ -196,6 +196,7 @@ class TestFillHoles(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_results(self, _, args, input_image, expected): key = CommonKeys.IMAGE diff --git a/tests/test_fl_exchange_object.py b/tests/test_fl_exchange_object.py index 293f9d518b..dab4eae037 100644 --- a/tests/test_fl_exchange_object.py +++ b/tests/test_fl_exchange_object.py @@ -46,6 +46,7 @@ @SkipIfNoModule("torchvision") @SkipIfNoModule("ignite") class TestFLExchangeObject(unittest.TestCase): + @parameterized.expand([TEST_INIT_1, TEST_INIT_2]) def test_init(self, input_params, expected_str): eo = ExchangeObject(**input_params) diff --git a/tests/test_fl_monai_algo.py b/tests/test_fl_monai_algo.py index ca781ff166..54bec24b98 100644 --- a/tests/test_fl_monai_algo.py +++ b/tests/test_fl_monai_algo.py @@ -181,6 +181,7 @@ @SkipIfNoModule("ignite") @SkipIfNoModule("mlflow") class TestFLMonaiAlgo(unittest.TestCase): + @parameterized.expand([TEST_TRAIN_1, TEST_TRAIN_2, TEST_TRAIN_3, TEST_TRAIN_4]) def test_train(self, input_params): # initialize algo diff --git a/tests/test_fl_monai_algo_dist.py b/tests/test_fl_monai_algo_dist.py index 1302ab6618..d8dbfa5339 100644 --- a/tests/test_fl_monai_algo_dist.py +++ b/tests/test_fl_monai_algo_dist.py @@ -32,6 +32,7 @@ @SkipIfNoModule("ignite") @SkipIfBeforePyTorchVersion((1, 11, 1)) class TestFLMonaiAlgo(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2, init_method="no_init") @skip_if_no_cuda def test_train(self): diff --git a/tests/test_fl_monai_algo_stats.py b/tests/test_fl_monai_algo_stats.py index 307b3f539c..6e58f8af88 100644 --- a/tests/test_fl_monai_algo_stats.py +++ b/tests/test_fl_monai_algo_stats.py @@ -64,6 +64,7 @@ @SkipIfNoModule("ignite") class TestFLMonaiAlgo(unittest.TestCase): + @parameterized.expand([TEST_GET_DATA_STATS_1, TEST_GET_DATA_STATS_2, TEST_GET_DATA_STATS_3]) def test_get_data_stats(self, input_params): # initialize algo diff --git a/tests/test_flatten_sub_keysd.py b/tests/test_flatten_sub_keysd.py index 997f203870..1a642e5fc4 100644 --- a/tests/test_flatten_sub_keysd.py +++ b/tests/test_flatten_sub_keysd.py @@ -46,6 +46,7 @@ class TestFlattenSubKeysd(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_dict(self, params, input_data, expected): result = FlattenSubKeysd(**params)(input_data) diff --git a/tests/test_flexible_unet.py b/tests/test_flexible_unet.py index 1d831f0976..404855c9a8 100644 --- a/tests/test_flexible_unet.py +++ b/tests/test_flexible_unet.py @@ -35,6 +35,7 @@ class DummyEncoder(BaseEncoder): + @classmethod def get_encoder_parameters(cls): basic_dict = {"spatial_dims": 2, "in_channels": 3, "pretrained": False} @@ -364,6 +365,7 @@ def make_error_case(): @skip_if_quick class TestFLEXIBLEUNET(unittest.TestCase): + @parameterized.expand(CASES_2D + CASES_3D + CASES_VARIATIONS) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" @@ -404,6 +406,7 @@ def test_error_raise(self, input_param): class TestFlexUNetEncoderRegister(unittest.TestCase): + @parameterized.expand(CASE_REGISTER_ENCODER) def test_regist(self, encoder): tmp_backbone = FlexUNetEncoderRegister() diff --git a/tests/test_flip.py b/tests/test_flip.py index d7df55fde0..789ec86920 100644 --- a/tests/test_flip.py +++ b/tests/test_flip.py @@ -34,6 +34,7 @@ class TestFlip(NumpyImageTestCase2D): + @parameterized.expand(INVALID_CASES) def test_invalid_inputs(self, _, spatial_axis, raises): with self.assertRaises(raises): diff --git a/tests/test_flipd.py b/tests/test_flipd.py index 19f9ed0882..277f387051 100644 --- a/tests/test_flipd.py +++ b/tests/test_flipd.py @@ -35,6 +35,7 @@ class TestFlipd(NumpyImageTestCase2D): + @parameterized.expand(INVALID_CASES) def test_invalid_cases(self, _, spatial_axis, raises): with self.assertRaises(raises): diff --git a/tests/test_focal_loss.py b/tests/test_focal_loss.py index 57df6a3460..de8d625058 100644 --- a/tests/test_focal_loss.py +++ b/tests/test_focal_loss.py @@ -79,6 +79,7 @@ class TestFocalLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, input_param, input_data, expected_val): focal_loss = FocalLoss(**input_param) diff --git a/tests/test_folder_layout.py b/tests/test_folder_layout.py index d6d4bdf679..6f72eee51f 100644 --- a/tests/test_folder_layout.py +++ b/tests/test_folder_layout.py @@ -60,6 +60,7 @@ class TestFolderLayout(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value(self, con_params, f_params, expected): fname = FolderLayout(**con_params).filename(**f_params) diff --git a/tests/test_foreground_mask.py b/tests/test_foreground_mask.py index eb59ae2db6..1aa54f4d3a 100644 --- a/tests/test_foreground_mask.py +++ b/tests/test_foreground_mask.py @@ -81,6 +81,7 @@ @unittest.skipUnless(has_skimage, "Requires sci-kit image") class TestForegroundMask(unittest.TestCase): + @parameterized.expand(TESTS) def test_foreground_mask(self, in_type, arguments, image, mask): input_image = in_type(image) diff --git a/tests/test_foreground_maskd.py b/tests/test_foreground_maskd.py index 24cb233c30..dc7b6cfb24 100644 --- a/tests/test_foreground_maskd.py +++ b/tests/test_foreground_maskd.py @@ -89,6 +89,7 @@ @unittest.skipUnless(has_skimage, "Requires sci-kit image") class TestForegroundMaskd(unittest.TestCase): + @parameterized.expand(TESTS) def test_foreground_mask(self, in_type, arguments, data_dict, mask): data_dict[arguments["keys"]] = in_type(data_dict[arguments["keys"]]) diff --git a/tests/test_fourier.py b/tests/test_fourier.py index 3613db989f..177fc280f7 100644 --- a/tests/test_fourier.py +++ b/tests/test_fourier.py @@ -28,6 +28,7 @@ @SkipIfBeforePyTorchVersion((1, 8)) @SkipIfNoModule("torch.fft") class TestFourier(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_fpn_block.py b/tests/test_fpn_block.py index c6121c5b98..969800e80a 100644 --- a/tests/test_fpn_block.py +++ b/tests/test_fpn_block.py @@ -44,6 +44,7 @@ class TestFPNBlock(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_fpn_block(self, input_param, input_shape, expected_shape): net = FeaturePyramidNetwork(**input_param) @@ -67,6 +68,7 @@ def test_script(self, input_param, input_shape, expected_shape): @unittest.skipUnless(has_torchvision, "Requires torchvision") class TestFPN(unittest.TestCase): + @parameterized.expand(TEST_CASES2) def test_fpn(self, input_param, input_shape, expected_shape): net = _resnet_fpn_extractor(backbone=resnet50(), spatial_dims=input_param["spatial_dims"], returned_layers=[1]) diff --git a/tests/test_freeze_layers.py b/tests/test_freeze_layers.py index 29594ed98a..1bea4ed1b5 100644 --- a/tests/test_freeze_layers.py +++ b/tests/test_freeze_layers.py @@ -27,6 +27,7 @@ class TestModuleState(unittest.TestCase): + def tearDown(self): set_determinism(None) diff --git a/tests/test_from_engine_hovernet.py b/tests/test_from_engine_hovernet.py index 227fa66baa..7d1a784466 100644 --- a/tests/test_from_engine_hovernet.py +++ b/tests/test_from_engine_hovernet.py @@ -28,6 +28,7 @@ class TestFromEngineHovernet(unittest.TestCase): + @parameterized.expand(CASES) def test_results(self, input, expected): output = from_engine_hovernet(keys=["A", "B"], nested_key="C")(input) diff --git a/tests/test_fullyconnectednet.py b/tests/test_fullyconnectednet.py index 94fc4caa6e..863d1399a9 100644 --- a/tests/test_fullyconnectednet.py +++ b/tests/test_fullyconnectednet.py @@ -42,6 +42,7 @@ class TestFullyConnectedNet(unittest.TestCase): + def setUp(self): self.batch_size = 10 self.inSize = 10 diff --git a/tests/test_gaussian.py b/tests/test_gaussian.py index b98507b793..689d8088f9 100644 --- a/tests/test_gaussian.py +++ b/tests/test_gaussian.py @@ -224,6 +224,7 @@ class TestGaussian1d(unittest.TestCase): + def test_gaussian(self): np.testing.assert_allclose( gaussian_1d(0.5, 8), diff --git a/tests/test_gaussian_filter.py b/tests/test_gaussian_filter.py index 1beee579e8..4ab689c565 100644 --- a/tests/test_gaussian_filter.py +++ b/tests/test_gaussian_filter.py @@ -35,6 +35,7 @@ class TestGaussianFilterBackprop(unittest.TestCase): + def code_to_run(self, input_args): input_dims = input_args.get("dims", (2, 3, 8)) device = ( @@ -94,6 +95,7 @@ def test_train_slow(self, input_args): class GaussianFilterTestCase(unittest.TestCase): + def test_1d(self): a = torch.ones(1, 8, 10) g = GaussianFilter(1, 3, 3).to(torch.device("cpu:0")) diff --git a/tests/test_gaussian_sharpen.py b/tests/test_gaussian_sharpen.py index 2509a4fc26..392a7b376b 100644 --- a/tests/test_gaussian_sharpen.py +++ b/tests/test_gaussian_sharpen.py @@ -82,6 +82,7 @@ class TestGaussianSharpen(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = GaussianSharpen(**arguments)(image) diff --git a/tests/test_gaussian_sharpend.py b/tests/test_gaussian_sharpend.py index 75ea915d96..15b219fd2c 100644 --- a/tests/test_gaussian_sharpend.py +++ b/tests/test_gaussian_sharpend.py @@ -82,6 +82,7 @@ class TestGaussianSharpend(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = GaussianSharpend(**arguments)(image) diff --git a/tests/test_gaussian_smooth.py b/tests/test_gaussian_smooth.py index 38b29bbd17..9f99ebe0f8 100644 --- a/tests/test_gaussian_smooth.py +++ b/tests/test_gaussian_smooth.py @@ -86,6 +86,7 @@ class TestGaussianSmooth(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = GaussianSmooth(**arguments)(image) diff --git a/tests/test_gaussian_smoothd.py b/tests/test_gaussian_smoothd.py index 8702c073c8..a6de4a159b 100644 --- a/tests/test_gaussian_smoothd.py +++ b/tests/test_gaussian_smoothd.py @@ -86,6 +86,7 @@ class TestGaussianSmoothd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = GaussianSmoothd(**arguments)(image) diff --git a/tests/test_gdsdataset.py b/tests/test_gdsdataset.py index 29f2d0096b..f0a419dcf5 100644 --- a/tests/test_gdsdataset.py +++ b/tests/test_gdsdataset.py @@ -64,6 +64,7 @@ class _InplaceXform(Transform): + def __call__(self, data): data[0] = data[0] + 1 return data @@ -73,6 +74,7 @@ def __call__(self, data): @unittest.skipUnless(has_nib, "Requires nibabel package.") @unittest.skipUnless(has_kvikio_numpy, "Requires scikit-image library.") class TestDataset(unittest.TestCase): + def test_cache(self): """testing no inplace change to the hashed item""" for p in TEST_NDARRAYS[:2]: diff --git a/tests/test_generalized_dice_focal_loss.py b/tests/test_generalized_dice_focal_loss.py index 33f6653212..8a0a80865e 100644 --- a/tests/test_generalized_dice_focal_loss.py +++ b/tests/test_generalized_dice_focal_loss.py @@ -21,6 +21,7 @@ class TestGeneralizedDiceFocalLoss(unittest.TestCase): + def test_result_onehot_target_include_bg(self): size = [3, 3, 5, 5] label = torch.randint(low=0, high=2, size=size) diff --git a/tests/test_generalized_dice_loss.py b/tests/test_generalized_dice_loss.py index d8ba496d03..7499507129 100644 --- a/tests/test_generalized_dice_loss.py +++ b/tests/test_generalized_dice_loss.py @@ -142,6 +142,7 @@ class TestGeneralizedDiceLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = GeneralizedDiceLoss(**input_param).forward(**input_data) diff --git a/tests/test_generalized_wasserstein_dice_loss.py b/tests/test_generalized_wasserstein_dice_loss.py index 7b85fdc5b6..6b9d57e831 100644 --- a/tests/test_generalized_wasserstein_dice_loss.py +++ b/tests/test_generalized_wasserstein_dice_loss.py @@ -24,6 +24,7 @@ class TestGeneralizedWassersteinDiceLoss(unittest.TestCase): + def test_bin_seg_2d(self): target = torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]]) @@ -160,6 +161,7 @@ def test_convergence(self): # define a model with one layer class OnelayerNet(nn.Module): + def __init__(self): super().__init__() self.layer = nn.Linear(num_voxels, num_voxels * num_classes) diff --git a/tests/test_generate_distance_map.py b/tests/test_generate_distance_map.py index 724a335e1a..42f5664647 100644 --- a/tests/test_generate_distance_map.py +++ b/tests/test_generate_distance_map.py @@ -36,6 +36,7 @@ class TestGenerateDistanceMap(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, probmap, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_distance_mapd.py b/tests/test_generate_distance_mapd.py index 17c5aa782b..2bddadf5b8 100644 --- a/tests/test_generate_distance_mapd.py +++ b/tests/test_generate_distance_mapd.py @@ -55,6 +55,7 @@ class TestGenerateDistanceMapd(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, border_map, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_instance_border.py b/tests/test_generate_instance_border.py index 8634bb7d77..fc1035dfe5 100644 --- a/tests/test_generate_instance_border.py +++ b/tests/test_generate_instance_border.py @@ -34,6 +34,7 @@ class TestGenerateInstanceBorder(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, hover_map, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_instance_borderd.py b/tests/test_generate_instance_borderd.py index fc81e8f87c..cdfbee4193 100644 --- a/tests/test_generate_instance_borderd.py +++ b/tests/test_generate_instance_borderd.py @@ -44,6 +44,7 @@ class TestGenerateInstanceBorderd(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, hover_map, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_instance_centroid.py b/tests/test_generate_instance_centroid.py index f9fdc602a9..6b4d533401 100644 --- a/tests/test_generate_instance_centroid.py +++ b/tests/test_generate_instance_centroid.py @@ -41,6 +41,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestGenerateInstanceCentroid(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, test_data, offset, expected): inst_bbox = get_bbox(test_data[None]) diff --git a/tests/test_generate_instance_centroidd.py b/tests/test_generate_instance_centroidd.py index 92e45cdf84..d381ad8c0e 100644 --- a/tests/test_generate_instance_centroidd.py +++ b/tests/test_generate_instance_centroidd.py @@ -41,6 +41,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestGenerateInstanceCentroidd(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, test_data, offset, expected): inst_bbox = get_bbox(test_data[None]) diff --git a/tests/test_generate_instance_contour.py b/tests/test_generate_instance_contour.py index 9058855e62..7f4290747d 100644 --- a/tests/test_generate_instance_contour.py +++ b/tests/test_generate_instance_contour.py @@ -46,6 +46,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestGenerateInstanceContour(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, test_data, min_num_points, offset, expected): inst_bbox = get_bbox(test_data[None]) diff --git a/tests/test_generate_instance_contourd.py b/tests/test_generate_instance_contourd.py index 22e3669850..5c831ee680 100644 --- a/tests/test_generate_instance_contourd.py +++ b/tests/test_generate_instance_contourd.py @@ -46,6 +46,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestGenerateInstanceContourd(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, test_data, min_num_points, offset, expected): inst_bbox = get_bbox(test_data[None]) diff --git a/tests/test_generate_instance_type.py b/tests/test_generate_instance_type.py index 354f8640ae..24e1d1b6d0 100644 --- a/tests/test_generate_instance_type.py +++ b/tests/test_generate_instance_type.py @@ -41,6 +41,7 @@ class TestGenerateInstanceType(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, type_pred, seg_pred, bbox, expected): result = GenerateInstanceType()(in_type(type_pred[None]), in_type(seg_pred[None]), bbox, 1) diff --git a/tests/test_generate_instance_typed.py b/tests/test_generate_instance_typed.py index 84a5344503..958f68d6bb 100644 --- a/tests/test_generate_instance_typed.py +++ b/tests/test_generate_instance_typed.py @@ -41,6 +41,7 @@ class TestGenerateInstanceTyped(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, in_type, type_pred, seg_pred, bbox, expected): test_data = {"type_pred": in_type(type_pred[None]), "seg": in_type(seg_pred[None]), "bbox": bbox, "id": 1} diff --git a/tests/test_generate_label_classes_crop_centers.py b/tests/test_generate_label_classes_crop_centers.py index c276171bd5..1cbb5f05c3 100644 --- a/tests/test_generate_label_classes_crop_centers.py +++ b/tests/test_generate_label_classes_crop_centers.py @@ -48,6 +48,7 @@ class TestGenerateLabelClassesCropCenters(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_type_shape(self, input_data, expected_type, expected_count, expected_shape): results = [] diff --git a/tests/test_generate_param_groups.py b/tests/test_generate_param_groups.py index 8301e40188..a78dba9f03 100644 --- a/tests/test_generate_param_groups.py +++ b/tests/test_generate_param_groups.py @@ -68,6 +68,7 @@ class TestGenerateParamGroups(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]) def test_lr_values(self, input_param, expected_values, expected_groups): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_generate_pos_neg_label_crop_centers.py b/tests/test_generate_pos_neg_label_crop_centers.py index 13b7b728b4..de127b33df 100644 --- a/tests/test_generate_pos_neg_label_crop_centers.py +++ b/tests/test_generate_pos_neg_label_crop_centers.py @@ -51,6 +51,7 @@ class TestGeneratePosNegLabelCropCenters(unittest.TestCase): + @parameterized.expand(TESTS) def test_type_shape(self, input_data, expected_type, expected_count, expected_shape): results = [] diff --git a/tests/test_generate_spatial_bounding_box.py b/tests/test_generate_spatial_bounding_box.py index a67e7d0175..6d5b415ec2 100644 --- a/tests/test_generate_spatial_bounding_box.py +++ b/tests/test_generate_spatial_bounding_box.py @@ -104,6 +104,7 @@ class TestGenerateSpatialBoundingBox(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_data, expected_box): result = generate_spatial_bounding_box(**input_data) diff --git a/tests/test_generate_succinct_contour.py b/tests/test_generate_succinct_contour.py index 1c60e99546..fc4f5660d9 100644 --- a/tests/test_generate_succinct_contour.py +++ b/tests/test_generate_succinct_contour.py @@ -44,6 +44,7 @@ class TestGenerateSuccinctContour(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, test_data, height, width, expected): result = GenerateSuccinctContour(height=height, width=width)(test_data) diff --git a/tests/test_generate_succinct_contourd.py b/tests/test_generate_succinct_contourd.py index e94a02fed5..7b023d8618 100644 --- a/tests/test_generate_succinct_contourd.py +++ b/tests/test_generate_succinct_contourd.py @@ -45,6 +45,7 @@ class TestGenerateSuccinctContour(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, data, height, width, expected): test_data = {"contour": data} diff --git a/tests/test_generate_watershed_markers.py b/tests/test_generate_watershed_markers.py index a763361913..238fb00ee0 100644 --- a/tests/test_generate_watershed_markers.py +++ b/tests/test_generate_watershed_markers.py @@ -38,6 +38,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestGenerateWatershedMarkers(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, probmap, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_watershed_markersd.py b/tests/test_generate_watershed_markersd.py index 76d4ec1ae6..a3c2b9c231 100644 --- a/tests/test_generate_watershed_markersd.py +++ b/tests/test_generate_watershed_markersd.py @@ -68,6 +68,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestGenerateWatershedMarkersd(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, mask, border_map, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_watershed_mask.py b/tests/test_generate_watershed_mask.py index 1cc35dca5c..5224a912b0 100644 --- a/tests/test_generate_watershed_mask.py +++ b/tests/test_generate_watershed_mask.py @@ -58,6 +58,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestGenerateWatershedMask(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generate_watershed_maskd.py b/tests/test_generate_watershed_maskd.py index aa6d5bf03a..9d0f2c274a 100644 --- a/tests/test_generate_watershed_maskd.py +++ b/tests/test_generate_watershed_maskd.py @@ -58,6 +58,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestGenerateWatershedMaskd(unittest.TestCase): + @parameterized.expand(EXCEPTION_TESTS) def test_value(self, arguments, exception_type): with self.assertRaises(exception_type): diff --git a/tests/test_generator.py b/tests/test_generator.py index c336acf7ef..f531f928da 100644 --- a/tests/test_generator.py +++ b/tests/test_generator.py @@ -42,6 +42,7 @@ class TestGenerator(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_data, expected_shape): net = Generator(**input_param) diff --git a/tests/test_get_equivalent_dtype.py b/tests/test_get_equivalent_dtype.py index 299a3963b7..2b4de1bc2a 100644 --- a/tests/test_get_equivalent_dtype.py +++ b/tests/test_get_equivalent_dtype.py @@ -29,6 +29,7 @@ class TestGetEquivalentDtype(unittest.TestCase): + @parameterized.expand(TESTS) def test_get_equivalent_dtype(self, im, input_dtype): out_dtype = get_equivalent_dtype(input_dtype, type(im)) diff --git a/tests/test_get_extreme_points.py b/tests/test_get_extreme_points.py index 1338ba0e2c..e60715e2fe 100644 --- a/tests/test_get_extreme_points.py +++ b/tests/test_get_extreme_points.py @@ -47,6 +47,7 @@ class TestGetExtremePoints(unittest.TestCase): + @parameterized.expand(TESTS) def test_type_shape(self, input_data, expected): result = get_extreme_points(**input_data) diff --git a/tests/test_get_layers.py b/tests/test_get_layers.py index ad0be1a5c4..5c020892ed 100644 --- a/tests/test_get_layers.py +++ b/tests/test_get_layers.py @@ -37,6 +37,7 @@ class TestGetLayers(unittest.TestCase): + @parameterized.expand(TEST_CASE_NORM) def test_norm_layer(self, input_param, expected): layer = get_norm_layer(**input_param) @@ -54,6 +55,7 @@ def test_dropout_layer(self, input_param, expected): class TestSuggestion(unittest.TestCase): + def test_suggested(self): with self.assertRaisesRegex(ValueError, "did you mean 'GROUP'?"): get_norm_layer(name="grop", spatial_dims=2) diff --git a/tests/test_get_package_version.py b/tests/test_get_package_version.py index 1881d79602..ab9e69cd31 100644 --- a/tests/test_get_package_version.py +++ b/tests/test_get_package_version.py @@ -17,6 +17,7 @@ class TestGetVersion(unittest.TestCase): + def test_default(self): output = get_package_version("42foobarnoexist") self.assertTrue("UNKNOWN" in output) diff --git a/tests/test_get_unique_labels.py b/tests/test_get_unique_labels.py index e550882243..0a88145489 100644 --- a/tests/test_get_unique_labels.py +++ b/tests/test_get_unique_labels.py @@ -35,6 +35,7 @@ class TestGetUniqueLabels(unittest.TestCase): + @parameterized.expand(TESTS) def test_correct_results(self, args, expected): result = get_unique_labels(**args) diff --git a/tests/test_gibbs_noise.py b/tests/test_gibbs_noise.py index aad5d6fea6..bdc66b9495 100644 --- a/tests/test_gibbs_noise.py +++ b/tests/test_gibbs_noise.py @@ -32,6 +32,7 @@ class TestGibbsNoise(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_gibbs_noised.py b/tests/test_gibbs_noised.py index 3aa69b7280..3b2cae7e84 100644 --- a/tests/test_gibbs_noised.py +++ b/tests/test_gibbs_noised.py @@ -33,6 +33,7 @@ class TestGibbsNoised(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_giou_loss.py b/tests/test_giou_loss.py index e794ddab30..34ee22e0ad 100644 --- a/tests/test_giou_loss.py +++ b/tests/test_giou_loss.py @@ -35,6 +35,7 @@ class TestGIoULoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, input_data, expected_val): loss = BoxGIoULoss() diff --git a/tests/test_global_mutual_information_loss.py b/tests/test_global_mutual_information_loss.py index b67ed71725..36a1978c93 100644 --- a/tests/test_global_mutual_information_loss.py +++ b/tests/test_global_mutual_information_loss.py @@ -54,6 +54,7 @@ @skip_if_quick class TestGlobalMutualInformationLoss(unittest.TestCase): + def setUp(self): config = testing_data_config("images", "Prostate_T2W_AX_1") download_url_or_skip_test( @@ -114,6 +115,7 @@ def transformation(translate_params=(0.0, 0.0, 0.0), rotate_params=(0.0, 0.0, 0. class TestGlobalMutualInformationLossIll(unittest.TestCase): + def test_ill_shape(self): loss = GlobalMutualInformationLoss() with self.assertRaisesRegex(ValueError, ""): diff --git a/tests/test_globalnet.py b/tests/test_globalnet.py index 1ab8db5926..626053377c 100644 --- a/tests/test_globalnet.py +++ b/tests/test_globalnet.py @@ -65,6 +65,7 @@ class TestAffineHead(unittest.TestCase): + @parameterized.expand(TEST_CASES_AFFINE_TRANSFORM) def test_shape(self, input_param, theta, expected_val): layer = AffineHead(**input_param) @@ -78,6 +79,7 @@ def test_shape(self, input_param, theta, expected_val): class TestGlobalNet(unittest.TestCase): + @parameterized.expand(TEST_CASES_GLOBAL_NET) def test_shape(self, input_param, input_shape, expected_shape): net = GlobalNet(**input_param).to(device) diff --git a/tests/test_gmm.py b/tests/test_gmm.py index eb638f5479..549e8f1ec4 100644 --- a/tests/test_gmm.py +++ b/tests/test_gmm.py @@ -261,6 +261,7 @@ @skip_if_quick class GMMTestCase(unittest.TestCase): + def setUp(self): self._var = os.environ.get("TORCH_EXTENSIONS_DIR") self.tempdir = tempfile.mkdtemp() diff --git a/tests/test_grid_dataset.py b/tests/test_grid_dataset.py index d937a5e266..4a3b4b6340 100644 --- a/tests/test_grid_dataset.py +++ b/tests/test_grid_dataset.py @@ -58,6 +58,7 @@ def identity_generator(x): class TestGridPatchDataset(unittest.TestCase): + def setUp(self): set_determinism(seed=1234) diff --git a/tests/test_grid_distortion.py b/tests/test_grid_distortion.py index 1a698140af..9ec85250e8 100644 --- a/tests/test_grid_distortion.py +++ b/tests/test_grid_distortion.py @@ -99,6 +99,7 @@ class TestGridDistortion(unittest.TestCase): + @parameterized.expand(TESTS) def test_grid_distortion(self, input_param, input_data, expected_val): g = GridDistortion(**input_param) diff --git a/tests/test_grid_distortiond.py b/tests/test_grid_distortiond.py index a645eb4f87..ce73593dc7 100644 --- a/tests/test_grid_distortiond.py +++ b/tests/test_grid_distortiond.py @@ -75,6 +75,7 @@ class TestGridDistortiond(unittest.TestCase): + @parameterized.expand(TESTS) def test_grid_distortiond(self, input_param, input_data, expected_val_img, expected_val_mask): g = GridDistortiond(**input_param) diff --git a/tests/test_grid_patch.py b/tests/test_grid_patch.py index cd1c5b6531..4b324eda1a 100644 --- a/tests/test_grid_patch.py +++ b/tests/test_grid_patch.py @@ -97,6 +97,7 @@ class TestGridPatch(unittest.TestCase): + @parameterized.expand(TEST_CASES) @SkipIfBeforePyTorchVersion((1, 11, 1)) def test_grid_patch(self, in_type, input_parameters, image, expected): diff --git a/tests/test_grid_patchd.py b/tests/test_grid_patchd.py index 4f317e4677..53313b3a8f 100644 --- a/tests/test_grid_patchd.py +++ b/tests/test_grid_patchd.py @@ -77,6 +77,7 @@ class TestGridPatchd(unittest.TestCase): + @parameterized.expand(TEST_SINGLE) @SkipIfBeforePyTorchVersion((1, 11, 1)) def test_grid_patchd(self, in_type, input_parameters, image_dict, expected): diff --git a/tests/test_grid_pull.py b/tests/test_grid_pull.py index 8877b0c121..f80874d216 100644 --- a/tests/test_grid_pull.py +++ b/tests/test_grid_pull.py @@ -63,6 +63,7 @@ def make_grid(shape, dtype=None, device=None, requires_grad=True): @skip_if_no_cpp_extension class TestGridPull(unittest.TestCase): + @parameterized.expand(TEST_1D_GP, skip_on_empty=True) def test_grid_pull(self, input_param, expected): result = grid_pull(**input_param) diff --git a/tests/test_grid_split.py b/tests/test_grid_split.py index 3ccf6e75a8..852a4847a6 100644 --- a/tests/test_grid_split.py +++ b/tests/test_grid_split.py @@ -66,6 +66,7 @@ class TestGridSplit(unittest.TestCase): + @parameterized.expand(TEST_SINGLE) def test_split_patch_single_call(self, in_type, input_parameters, image, expected): input_image = in_type(image) diff --git a/tests/test_grid_splitd.py b/tests/test_grid_splitd.py index d8519b2121..215076d5a3 100644 --- a/tests/test_grid_splitd.py +++ b/tests/test_grid_splitd.py @@ -70,6 +70,7 @@ class TestGridSplitd(unittest.TestCase): + @parameterized.expand(TEST_SINGLE) def test_split_patch_single_call(self, in_type, input_parameters, img_dict, expected): input_dict = {} diff --git a/tests/test_handler_checkpoint_loader.py b/tests/test_handler_checkpoint_loader.py index 7dfb802bba..7b281665b4 100644 --- a/tests/test_handler_checkpoint_loader.py +++ b/tests/test_handler_checkpoint_loader.py @@ -23,6 +23,7 @@ class TestHandlerCheckpointLoader(unittest.TestCase): + def test_one_save_one_load(self): net1 = torch.nn.PReLU() data1 = net1.state_dict() diff --git a/tests/test_handler_checkpoint_saver.py b/tests/test_handler_checkpoint_saver.py index 70810e018f..42f99e57c9 100644 --- a/tests/test_handler_checkpoint_saver.py +++ b/tests/test_handler_checkpoint_saver.py @@ -111,6 +111,7 @@ class TestHandlerCheckpointSaver(unittest.TestCase): + @parameterized.expand( [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8] ) diff --git a/tests/test_handler_classification_saver.py b/tests/test_handler_classification_saver.py index 905e326a66..5330e48dda 100644 --- a/tests/test_handler_classification_saver.py +++ b/tests/test_handler_classification_saver.py @@ -26,6 +26,7 @@ class TestHandlerClassificationSaver(unittest.TestCase): + def test_saved_content(self): with tempfile.TemporaryDirectory() as tempdir: # set up engine diff --git a/tests/test_handler_classification_saver_dist.py b/tests/test_handler_classification_saver_dist.py index ef06b69683..47dca2d999 100644 --- a/tests/test_handler_classification_saver_dist.py +++ b/tests/test_handler_classification_saver_dist.py @@ -27,6 +27,7 @@ class DistributedHandlerClassificationSaver(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_saved_content(self): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_handler_clearml_image.py b/tests/test_handler_clearml_image.py index 13eebed120..91aa297b7f 100644 --- a/tests/test_handler_clearml_image.py +++ b/tests/test_handler_clearml_image.py @@ -29,6 +29,7 @@ @unittest.skipUnless(has_tb, "Requires SummaryWriter installation") @unittest.skipIf(not has_get_active_config_file, "ClearML 'get_active_config_file' not found") class TestHandlerClearMLImageHandler(unittest.TestCase): + def test_task_init(self): handle, path = tempfile.mkstemp() with open(handle, "w") as new_config: diff --git a/tests/test_handler_clearml_stats.py b/tests/test_handler_clearml_stats.py index a460bc2391..159f6af4eb 100644 --- a/tests/test_handler_clearml_stats.py +++ b/tests/test_handler_clearml_stats.py @@ -29,6 +29,7 @@ @unittest.skipUnless(has_tb, "Requires SummaryWriter installation") @unittest.skipIf(not has_get_active_config_file, "ClearML 'get_active_config_file' not found") class TestHandlerClearMLStatsHandler(unittest.TestCase): + def test_task_init(self): handle, path = tempfile.mkstemp() with open(handle, "w") as new_config: diff --git a/tests/test_handler_confusion_matrix_dist.py b/tests/test_handler_confusion_matrix_dist.py index b74b7e57c4..dd30f04142 100644 --- a/tests/test_handler_confusion_matrix_dist.py +++ b/tests/test_handler_confusion_matrix_dist.py @@ -23,6 +23,7 @@ class DistributedConfusionMatrix(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_compute(self): self._compute() diff --git a/tests/test_handler_decollate_batch.py b/tests/test_handler_decollate_batch.py index 5bc5584515..37ca7f6870 100644 --- a/tests/test_handler_decollate_batch.py +++ b/tests/test_handler_decollate_batch.py @@ -22,6 +22,7 @@ class TestHandlerDecollateBatch(unittest.TestCase): + def test_compute(self): data = [ {"image": torch.tensor([[[[2.0], [3.0]]]]), "filename": ["test1"]}, diff --git a/tests/test_handler_early_stop.py b/tests/test_handler_early_stop.py index 675a804472..5fbb828330 100644 --- a/tests/test_handler_early_stop.py +++ b/tests/test_handler_early_stop.py @@ -19,7 +19,9 @@ class TestHandlerEarlyStop(unittest.TestCase): + def test_early_stop_train_loss(self): + def _train_func(engine, batch): return {"loss": 1.5} @@ -33,6 +35,7 @@ def _train_func(engine, batch): self.assertEqual(trainer.state.epoch, 2) def test_early_stop_val_metric(self): + def _train_func(engine, batch): pass diff --git a/tests/test_handler_garbage_collector.py b/tests/test_handler_garbage_collector.py index f64039b6fb..317eba1b11 100644 --- a/tests/test_handler_garbage_collector.py +++ b/tests/test_handler_garbage_collector.py @@ -34,6 +34,7 @@ class TestHandlerGarbageCollector(unittest.TestCase): + @skipUnless(has_ignite, "Requires ignite") @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]) def test_content(self, data, trigger_event): diff --git a/tests/test_handler_ignite_metric.py b/tests/test_handler_ignite_metric.py index dbdc765b45..28e0b69621 100644 --- a/tests/test_handler_ignite_metric.py +++ b/tests/test_handler_ignite_metric.py @@ -99,6 +99,7 @@ class TestHandlerIgniteMetricHandler(unittest.TestCase): + @SkipIfNoModule("ignite") @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_metric_fn(self, loss_params, metric_params, handler_params, expected_avg): diff --git a/tests/test_handler_logfile.py b/tests/test_handler_logfile.py index f09876ab0a..457aca2ebc 100644 --- a/tests/test_handler_logfile.py +++ b/tests/test_handler_logfile.py @@ -30,6 +30,7 @@ class TestHandlerLogfile(unittest.TestCase): + def setUp(self): if has_ignite: # set up engine diff --git a/tests/test_handler_lr_scheduler.py b/tests/test_handler_lr_scheduler.py index f1d3f45f06..3efb4a789f 100644 --- a/tests/test_handler_lr_scheduler.py +++ b/tests/test_handler_lr_scheduler.py @@ -25,6 +25,7 @@ class TestHandlerLrSchedule(unittest.TestCase): + def test_content(self): data = [0] * 8 test_lr = 0.1 diff --git a/tests/test_handler_metric_logger.py b/tests/test_handler_metric_logger.py index 016af1e8b5..06d50e97ff 100644 --- a/tests/test_handler_metric_logger.py +++ b/tests/test_handler_metric_logger.py @@ -28,6 +28,7 @@ class TestHandlerMetricLogger(unittest.TestCase): + @SkipIfNoModule("ignite") def test_metric_logging(self): dummy_name = "dummy" diff --git a/tests/test_handler_metrics_reloaded.py b/tests/test_handler_metrics_reloaded.py index e080204d6f..b8fb39d2e8 100644 --- a/tests/test_handler_metrics_reloaded.py +++ b/tests/test_handler_metrics_reloaded.py @@ -73,6 +73,7 @@ @unittest.skipIf(not has_metrics, "MetricsReloaded not available.") class TestHandlerMetricsReloadedBinary(unittest.TestCase): + @parameterized.expand([TEST_CASE_BIN_1, TEST_CASE_BIN_2, TEST_CASE_BIN_3]) def test_compute(self, input_params, y_pred, y, expected_value): input_params["output_transform"] = from_engine(["pred", "label"]) @@ -113,6 +114,7 @@ def test_shape_mismatch(self, input_params, _y_pred, _y, _expected_value): @unittest.skipIf(not has_metrics, "MetricsReloaded not available.") class TestMetricsReloadedCategorical(unittest.TestCase): + @parameterized.expand([TEST_CASE_CAT_1, TEST_CASE_CAT_2]) def test_compute(self, input_params, y_pred, y, expected_value): input_params["output_transform"] = from_engine(["pred", "label"]) diff --git a/tests/test_handler_metrics_saver.py b/tests/test_handler_metrics_saver.py index 9888a73e5f..d5ad2f4841 100644 --- a/tests/test_handler_metrics_saver.py +++ b/tests/test_handler_metrics_saver.py @@ -24,6 +24,7 @@ class TestHandlerMetricsSaver(unittest.TestCase): + def test_content(self): with tempfile.TemporaryDirectory() as tempdir: metrics_saver = MetricsSaver( diff --git a/tests/test_handler_metrics_saver_dist.py b/tests/test_handler_metrics_saver_dist.py index 11d7db168b..46c9ad27d7 100644 --- a/tests/test_handler_metrics_saver_dist.py +++ b/tests/test_handler_metrics_saver_dist.py @@ -27,6 +27,7 @@ class DistributedMetricsSaver(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_content(self): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_handler_mlflow.py b/tests/test_handler_mlflow.py index 92cf17eadb..44adc49fc2 100644 --- a/tests/test_handler_mlflow.py +++ b/tests/test_handler_mlflow.py @@ -33,6 +33,7 @@ def get_event_filter(e): + def event_filter(_, event): if event in e: return True @@ -65,6 +66,7 @@ def _train_func(engine, batch): class TestHandlerMLFlow(unittest.TestCase): + def setUp(self): self.tmpdir_list = [] diff --git a/tests/test_handler_nvtx.py b/tests/test_handler_nvtx.py index 75cc5bc1f4..a0d1cdb4d5 100644 --- a/tests/test_handler_nvtx.py +++ b/tests/test_handler_nvtx.py @@ -36,6 +36,7 @@ class TestHandlerDecollateBatch(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1]) @unittest.skipUnless(has_nvtx, "CUDA is required for NVTX!") def test_compute(self, data, expected): diff --git a/tests/test_handler_panoptic_quality.py b/tests/test_handler_panoptic_quality.py index 1595b5ad2c..337f9c7b49 100644 --- a/tests/test_handler_panoptic_quality.py +++ b/tests/test_handler_panoptic_quality.py @@ -60,6 +60,7 @@ @SkipIfNoModule("scipy.optimize") class TestHandlerPanopticQuality(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_compute(self, input_params, expected_avg): metric = PanopticQuality(**input_params) diff --git a/tests/test_handler_parameter_scheduler.py b/tests/test_handler_parameter_scheduler.py index 1e7bbb7588..0bcc794381 100644 --- a/tests/test_handler_parameter_scheduler.py +++ b/tests/test_handler_parameter_scheduler.py @@ -21,6 +21,7 @@ class ToyNet(Module): + def __init__(self, value): super().__init__() self.value = value @@ -36,6 +37,7 @@ def set_value(self, value): class TestHandlerParameterScheduler(unittest.TestCase): + def test_linear_scheduler(self): # Testing step_constant net = ToyNet(value=-1) @@ -116,6 +118,7 @@ def test_multistep_scheduler(self): assert_allclose(net.get_value(), 10 * 0.99 * 0.99) def test_custom_scheduler(self): + def custom_logic(initial_value, gamma, current_step): return initial_value * gamma ** (current_step % 9) diff --git a/tests/test_handler_post_processing.py b/tests/test_handler_post_processing.py index c449665c1e..0dd518325b 100644 --- a/tests/test_handler_post_processing.py +++ b/tests/test_handler_post_processing.py @@ -40,6 +40,7 @@ class TestHandlerPostProcessing(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_compute(self, input_params, decollate, expected): data = [ diff --git a/tests/test_handler_prob_map_producer.py b/tests/test_handler_prob_map_producer.py index 153a00b1ac..347f8cb92c 100644 --- a/tests/test_handler_prob_map_producer.py +++ b/tests/test_handler_prob_map_producer.py @@ -30,6 +30,7 @@ class TestDataset(Dataset): + def __init__(self, name, size): super().__init__( data=[ @@ -63,11 +64,13 @@ def __getitem__(self, index): class TestEvaluator(Evaluator): + def _iteration(self, engine, batchdata): return batchdata class TestHandlerProbMapGenerator(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]) def test_prob_map_generator(self, name, size): # set up dataset diff --git a/tests/test_handler_regression_metrics.py b/tests/test_handler_regression_metrics.py index a06452c54d..a3ec9f071a 100644 --- a/tests/test_handler_regression_metrics.py +++ b/tests/test_handler_regression_metrics.py @@ -46,6 +46,7 @@ def psnrmetric_np(max_val, y_pred, y): class TestHandlerRegressionMetrics(unittest.TestCase): + def test_compute(self): set_determinism(seed=123) device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_handler_regression_metrics_dist.py b/tests/test_handler_regression_metrics_dist.py index a2e96b97d9..f57db429e8 100644 --- a/tests/test_handler_regression_metrics_dist.py +++ b/tests/test_handler_regression_metrics_dist.py @@ -57,6 +57,7 @@ def psnrmetric_np(max_val, y_pred, y): class DistributedMeanSquaredError(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_compute(self): set_determinism(123) @@ -103,6 +104,7 @@ def _val_func(engine, batch): class DistributedMeanAbsoluteError(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_compute(self): set_determinism(123) @@ -149,6 +151,7 @@ def _val_func(engine, batch): class DistributedRootMeanSquaredError(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_compute(self): set_determinism(123) @@ -195,6 +198,7 @@ def _val_func(engine, batch): class DistributedPeakSignalToNoiseRatio(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_compute(self): set_determinism(123) diff --git a/tests/test_handler_rocauc.py b/tests/test_handler_rocauc.py index ce2351a9f5..2c771340f9 100644 --- a/tests/test_handler_rocauc.py +++ b/tests/test_handler_rocauc.py @@ -21,6 +21,7 @@ class TestHandlerROCAUC(unittest.TestCase): + def test_compute(self): auc_metric = ROCAUC() act = Activations(softmax=True) diff --git a/tests/test_handler_rocauc_dist.py b/tests/test_handler_rocauc_dist.py index 5b6ea045c7..6088251b11 100644 --- a/tests/test_handler_rocauc_dist.py +++ b/tests/test_handler_rocauc_dist.py @@ -23,6 +23,7 @@ class DistributedROCAUC(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2, node_rank=0) def test_compute(self): auc_metric = ROCAUC() diff --git a/tests/test_handler_smartcache.py b/tests/test_handler_smartcache.py index c3b4d72cb4..e544d39c72 100644 --- a/tests/test_handler_smartcache.py +++ b/tests/test_handler_smartcache.py @@ -22,6 +22,7 @@ class TestHandlerSmartCache(unittest.TestCase): + def test_content(self): data = [0, 1, 2, 3, 4, 5, 6, 7, 8] expected = [[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]] diff --git a/tests/test_handler_stats.py b/tests/test_handler_stats.py index 1842e08635..f876cff2a3 100644 --- a/tests/test_handler_stats.py +++ b/tests/test_handler_stats.py @@ -26,6 +26,7 @@ def get_event_filter(e): + def event_filter(_, event): if event in e: return True @@ -35,6 +36,7 @@ def event_filter(_, event): class TestHandlerStats(unittest.TestCase): + @parameterized.expand([[True], [get_event_filter([1, 2])]]) def test_metrics_print(self, epoch_log): log_stream = StringIO() diff --git a/tests/test_handler_tb_image.py b/tests/test_handler_tb_image.py index 68b71ff7f9..197b175278 100644 --- a/tests/test_handler_tb_image.py +++ b/tests/test_handler_tb_image.py @@ -33,6 +33,7 @@ @unittest.skipUnless(has_tb, "Requires SummaryWriter installation") @SkipIfBeforePyTorchVersion((1, 13)) # issue 6683 class TestHandlerTBImage(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_tb_image_shape(self, shape): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_handler_tb_stats.py b/tests/test_handler_tb_stats.py index 883827a1ac..b96bea13a1 100644 --- a/tests/test_handler_tb_stats.py +++ b/tests/test_handler_tb_stats.py @@ -26,6 +26,7 @@ def get_event_filter(e): + def event_filter(_, event): if event in e: return True @@ -36,6 +37,7 @@ def event_filter(_, event): @unittest.skipUnless(has_tb, "Requires SummaryWriter installation") class TestHandlerTBStats(unittest.TestCase): + def test_metrics_print(self): with tempfile.TemporaryDirectory() as tempdir: # set up engine diff --git a/tests/test_handler_validation.py b/tests/test_handler_validation.py index e1ccba2294..752b1d3df7 100644 --- a/tests/test_handler_validation.py +++ b/tests/test_handler_validation.py @@ -22,12 +22,14 @@ class TestEvaluator(Evaluator): + def _iteration(self, engine, batchdata): engine.state.output = "called" return engine.state.output class TestHandlerValidation(unittest.TestCase): + def test_content(self): data = [0] * 8 diff --git a/tests/test_hardnegsampler.py b/tests/test_hardnegsampler.py index b33cea1537..5385abd1db 100644 --- a/tests/test_hardnegsampler.py +++ b/tests/test_hardnegsampler.py @@ -37,6 +37,7 @@ class TestSampleSlices(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_shape(self, target_label0, target_label1, concat_fg_probs, expected_result_pos, expected_result_neg): compute_dtypes = [torch.float16, torch.float32] diff --git a/tests/test_hashing.py b/tests/test_hashing.py index 093de47cf9..61b3e7056b 100644 --- a/tests/test_hashing.py +++ b/tests/test_hashing.py @@ -20,6 +20,7 @@ class TestPickleHashing(unittest.TestCase): + def test_pickle(self): set_determinism(0) data1 = np.random.rand(10) @@ -45,6 +46,7 @@ def test_pickle(self): class TestJSONHashing(unittest.TestCase): + def test_json(self): data_dict1 = {"b": "str2", "a": "str1"} data_dict2 = {"a": "str1", "b": "str2"} diff --git a/tests/test_hausdorff_distance.py b/tests/test_hausdorff_distance.py index 71bbad36d2..20276a1832 100644 --- a/tests/test_hausdorff_distance.py +++ b/tests/test_hausdorff_distance.py @@ -168,6 +168,7 @@ def _describe_test_case(test_func, test_number, params): class TestHausdorffDistance(unittest.TestCase): + @parameterized.expand(TEST_CASES_EXPANDED, doc_func=_describe_test_case) def test_value(self, device, metric, directed, input_data, expected_value): percentile = None diff --git a/tests/test_hausdorff_loss.py b/tests/test_hausdorff_loss.py index 5ed20f5f3b..f279d45b14 100644 --- a/tests/test_hausdorff_loss.py +++ b/tests/test_hausdorff_loss.py @@ -198,6 +198,7 @@ def _describe_test_case(test_func, test_number, params): @skipUnless(has_scipy, "Scipy required") class TestHausdorffDTLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES, doc_func=_describe_test_case) def test_shape(self, input_param, input_data, expected_val): result = HausdorffDTLoss(**input_param).forward(**input_data) @@ -234,6 +235,7 @@ def test_input_warnings(self): @skipUnless(has_scipy, "Scipy required") class TesLogtHausdorffDTLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES_LOG, doc_func=_describe_test_case) def test_shape(self, input_param, input_data, expected_val): result = LogHausdorffDTLoss(**input_param).forward(**input_data) diff --git a/tests/test_header_correct.py b/tests/test_header_correct.py index 71fed1e35d..c0ea2a8643 100644 --- a/tests/test_header_correct.py +++ b/tests/test_header_correct.py @@ -20,6 +20,7 @@ class TestCorrection(unittest.TestCase): + def test_correct(self): test_img = nib.Nifti1Image(np.zeros((1, 2, 3)), np.eye(4)) test_img.header.set_zooms((100, 100, 100)) diff --git a/tests/test_highresnet.py b/tests/test_highresnet.py index 04520419b7..bcc5739900 100644 --- a/tests/test_highresnet.py +++ b/tests/test_highresnet.py @@ -48,6 +48,7 @@ class TestHighResNet(DistTestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_shape(self, input_param, input_shape, expected_shape): net = HighResNet(**input_param).to(device) diff --git a/tests/test_hilbert_transform.py b/tests/test_hilbert_transform.py index 68fa0b1192..879a74969d 100644 --- a/tests/test_hilbert_transform.py +++ b/tests/test_hilbert_transform.py @@ -161,6 +161,7 @@ def create_expected_numpy_output(input_datum, **kwargs): @SkipIfNoModule("torch.fft") class TestHilbertTransformCPU(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_1D_SINE_CPU, @@ -179,6 +180,7 @@ def test_value(self, arguments, image, expected_data, atol): @skip_if_no_cuda @SkipIfNoModule("torch.fft") class TestHilbertTransformGPU(unittest.TestCase): + @parameterized.expand( ( [] @@ -201,6 +203,7 @@ def test_value(self, arguments, image, expected_data, atol): @SkipIfModule("torch.fft") class TestHilbertTransformNoFFTMod(unittest.TestCase): + def test_no_fft_module_error(self): self.assertRaises(OptionalImportError, HilbertTransform(), torch.randn(1, 1, 10)) diff --git a/tests/test_histogram_normalize.py b/tests/test_histogram_normalize.py index 3a340db52a..25c0afb64d 100644 --- a/tests/test_histogram_normalize.py +++ b/tests/test_histogram_normalize.py @@ -48,6 +48,7 @@ class TestHistogramNormalize(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = HistogramNormalize(**arguments)(image) diff --git a/tests/test_histogram_normalized.py b/tests/test_histogram_normalized.py index 24f27d225e..a390375441 100644 --- a/tests/test_histogram_normalized.py +++ b/tests/test_histogram_normalized.py @@ -48,6 +48,7 @@ class TestHistogramNormalized(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = HistogramNormalized(**arguments)(image)["img"] diff --git a/tests/test_hovernet.py b/tests/test_hovernet.py index d768895bdc..fb4946b011 100644 --- a/tests/test_hovernet.py +++ b/tests/test_hovernet.py @@ -154,6 +154,7 @@ def check_kernels(net, mode): class TestHoverNet(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_shape, expected_shapes): input_param["decoder_padding"] = False diff --git a/tests/test_hovernet_instance_map_post_processing.py b/tests/test_hovernet_instance_map_post_processing.py index 990e2d9a10..ce272fba1a 100644 --- a/tests/test_hovernet_instance_map_post_processing.py +++ b/tests/test_hovernet_instance_map_post_processing.py @@ -42,6 +42,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestHoVerNetInstanceMapPostProcessing(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_value(self, in_type, test_data, kwargs, expected_info, expected_map): nuclear_prediction = in_type(test_data.astype(float)) diff --git a/tests/test_hovernet_instance_map_post_processingd.py b/tests/test_hovernet_instance_map_post_processingd.py index 69e42d3495..c982156caa 100644 --- a/tests/test_hovernet_instance_map_post_processingd.py +++ b/tests/test_hovernet_instance_map_post_processingd.py @@ -43,6 +43,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestHoVerNetInstanceMapPostProcessingd(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_value(self, in_type, test_data, kwargs, expected_info, expected_map): input = { diff --git a/tests/test_hovernet_loss.py b/tests/test_hovernet_loss.py index 10db4518fa..b7cd1f3104 100644 --- a/tests/test_hovernet_loss.py +++ b/tests/test_hovernet_loss.py @@ -35,6 +35,7 @@ class PrepareTestInputs: + def __init__(self, inputs): self.inputs = {HoVerNetBranch.NP: inputs[1], HoVerNetBranch.HV: inputs[3]} self.targets = {HoVerNetBranch.NP: inputs[0], HoVerNetBranch.HV: inputs[2]} @@ -171,6 +172,7 @@ def test_shape_generator(num_classes=1, num_objects=3, batch_size=1, height=5, w class TestHoverNetLoss(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, expected_loss): loss = HoVerNetLoss() diff --git a/tests/test_hovernet_nuclear_type_post_processing.py b/tests/test_hovernet_nuclear_type_post_processing.py index f2b33c96ae..e97b7abd2c 100644 --- a/tests/test_hovernet_nuclear_type_post_processing.py +++ b/tests/test_hovernet_nuclear_type_post_processing.py @@ -41,6 +41,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestHoVerNetNuclearTypePostProcessing(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_value(self, in_type, test_data, kwargs, expected_info, expected_map): nuclear_prediction = in_type(test_data.astype(float)) diff --git a/tests/test_hovernet_nuclear_type_post_processingd.py b/tests/test_hovernet_nuclear_type_post_processingd.py index 01478b7961..26cf80592c 100644 --- a/tests/test_hovernet_nuclear_type_post_processingd.py +++ b/tests/test_hovernet_nuclear_type_post_processingd.py @@ -42,6 +42,7 @@ @unittest.skipUnless(has_scipy, "Requires scipy library.") @unittest.skipUnless(has_skimage, "Requires scikit-image library.") class TestHoVerNetNuclearTypePostProcessingd(unittest.TestCase): + @parameterized.expand(TEST_CASE) def test_value(self, in_type, test_data, kwargs, expected): input = { diff --git a/tests/test_identity.py b/tests/test_identity.py index 19116cbb8f..4243a7f19a 100644 --- a/tests/test_identity.py +++ b/tests/test_identity.py @@ -18,6 +18,7 @@ class TestIdentity(NumpyImageTestCase2D): + def test_identity(self): for p in TEST_NDARRAYS: img = p(self.imt) diff --git a/tests/test_identityd.py b/tests/test_identityd.py index 98499def01..6b81ad9f16 100644 --- a/tests/test_identityd.py +++ b/tests/test_identityd.py @@ -18,6 +18,7 @@ class TestIdentityd(NumpyImageTestCase2D): + def test_identityd(self): for p in TEST_NDARRAYS: img = p(self.imt) diff --git a/tests/test_image_dataset.py b/tests/test_image_dataset.py index 7f7bdec513..fc8b4b6ccb 100644 --- a/tests/test_image_dataset.py +++ b/tests/test_image_dataset.py @@ -47,6 +47,7 @@ def __call__(self, data): class _TestCompose(Compose): + def __call__(self, data, meta, lazy): data = self.transforms[0](data) # ensure channel first data = self.transforms[1](data, lazy=lazy) # spacing @@ -57,6 +58,7 @@ def __call__(self, data, meta, lazy): class TestImageDataset(unittest.TestCase): + def test_use_case(self): with tempfile.TemporaryDirectory() as tempdir: img_ = nib.Nifti1Image(np.random.randint(0, 2, size=(20, 20, 20)).astype(float), np.eye(4)) diff --git a/tests/test_image_filter.py b/tests/test_image_filter.py index 985ea95e79..adc9dade9c 100644 --- a/tests/test_image_filter.py +++ b/tests/test_image_filter.py @@ -38,6 +38,7 @@ class TestModule(torch.nn.Module): + def __init__(self): super().__init__() @@ -50,6 +51,7 @@ class TestNotAModuleOrTransform: class TestImageFilter(unittest.TestCase): + @parameterized.expand(SUPPORTED_FILTERS) def test_init_from_string(self, filter_name): "Test init from string" @@ -133,6 +135,7 @@ def test_pass_empty_metadata_dict(self): class TestImageFilterDict(unittest.TestCase): + @parameterized.expand(SUPPORTED_FILTERS) def test_init_from_string_dict(self, filter_name): "Test init from string and assert an error is thrown if no size is passed" @@ -162,6 +165,7 @@ def test_call_3d(self, filter_name): class TestRandImageFilter(unittest.TestCase): + @parameterized.expand(SUPPORTED_FILTERS) def test_init_from_string(self, filter_name): "Test init from string and assert an error is thrown if no size is passed" @@ -205,6 +209,7 @@ def test_call_3d_prob_0(self, filter_name): class TestRandImageFilterDict(unittest.TestCase): + @parameterized.expand(SUPPORTED_FILTERS) def test_init_from_string_dict(self, filter_name): "Test init from string and assert an error is thrown if no size is passed" diff --git a/tests/test_image_rw.py b/tests/test_image_rw.py index 79e51c53eb..7e1c1deecc 100644 --- a/tests/test_image_rw.py +++ b/tests/test_image_rw.py @@ -33,6 +33,7 @@ @unittest.skipUnless(has_itk, "itk not installed") class TestLoadSaveNifti(unittest.TestCase): + def setUp(self): self.test_dir = tempfile.mkdtemp() @@ -97,6 +98,7 @@ def test_4d(self, reader, writer): @unittest.skipUnless(has_itk, "itk not installed") class TestLoadSavePNG(unittest.TestCase): + def setUp(self): self.test_dir = tempfile.mkdtemp() @@ -137,6 +139,7 @@ def test_rgb(self, reader, writer): class TestRegRes(unittest.TestCase): + def test_0_default(self): self.assertTrue(len(resolve_writer(".png")) > 0, "has png writer") self.assertTrue(len(resolve_writer(".nrrd")) > 0, "has nrrd writer") @@ -153,6 +156,7 @@ def test_1_new(self): @unittest.skipUnless(has_itk, "itk not installed") class TestLoadSaveNrrd(unittest.TestCase): + def setUp(self): self.test_dir = tempfile.mkdtemp() diff --git a/tests/test_img2tensorboard.py b/tests/test_img2tensorboard.py index 7825f9b4d7..901ca77e7f 100644 --- a/tests/test_img2tensorboard.py +++ b/tests/test_img2tensorboard.py @@ -21,6 +21,7 @@ class TestImg2Tensorboard(unittest.TestCase): + def test_write_gray(self): nparr = np.ones(shape=(1, 32, 32, 32), dtype=np.float32) summary_object_np = make_animated_gif_summary( diff --git a/tests/test_init_reader.py b/tests/test_init_reader.py index 1350146220..cb45cb5146 100644 --- a/tests/test_init_reader.py +++ b/tests/test_init_reader.py @@ -19,6 +19,7 @@ class TestInitLoadImage(unittest.TestCase): + def test_load_image(self): instance1 = LoadImage(image_only=False, dtype=None) instance2 = LoadImage(image_only=True, dtype=None) diff --git a/tests/test_integration_autorunner.py b/tests/test_integration_autorunner.py index 7110db568d..31a0813abc 100644 --- a/tests/test_integration_autorunner.py +++ b/tests/test_integration_autorunner.py @@ -71,6 +71,7 @@ @SkipIfBeforePyTorchVersion((1, 11, 1)) # for mem_get_info @unittest.skipIf(not has_tb, "no tensorboard summary writer") class TestAutoRunner(unittest.TestCase): + def setUp(self) -> None: self.test_dir = tempfile.TemporaryDirectory() test_path = self.test_dir.name diff --git a/tests/test_integration_bundle_run.py b/tests/test_integration_bundle_run.py index bd96f50c55..c2e0fb55b7 100644 --- a/tests/test_integration_bundle_run.py +++ b/tests/test_integration_bundle_run.py @@ -37,6 +37,7 @@ class _Runnable42: + def __init__(self, val=1): self.val = val @@ -46,6 +47,7 @@ def run(self): class _Runnable43: + def __init__(self, func): self.func = func @@ -54,6 +56,7 @@ def run(self): class TestBundleRun(unittest.TestCase): + def setUp(self): self.data_dir = tempfile.mkdtemp() diff --git a/tests/test_integration_classification_2d.py b/tests/test_integration_classification_2d.py index 4fc92c4068..b137fc9b75 100644 --- a/tests/test_integration_classification_2d.py +++ b/tests/test_integration_classification_2d.py @@ -45,6 +45,7 @@ class MedNISTDataset(torch.utils.data.Dataset): + def __init__(self, image_files, labels, transforms): self.image_files = image_files self.labels = labels @@ -182,6 +183,7 @@ def run_inference_test(root_dir, test_x, test_y, device="cuda:0", num_workers=10 @skip_if_quick class IntegrationClassification2D(DistTestCase): + def setUp(self): set_determinism(seed=0) self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_integration_determinism.py b/tests/test_integration_determinism.py index 6821279080..3e88f05620 100644 --- a/tests/test_integration_determinism.py +++ b/tests/test_integration_determinism.py @@ -26,7 +26,9 @@ def run_test(batch_size=64, train_steps=200, device="cuda:0"): + class _TestBatch(Dataset): + def __init__(self, transforms): self.transforms = transforms @@ -76,6 +78,7 @@ def __len__(self): class TestDeterminism(DistTestCase): + def setUp(self): self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu:0") diff --git a/tests/test_integration_fast_train.py b/tests/test_integration_fast_train.py index 497fe22dab..071eb5cf78 100644 --- a/tests/test_integration_fast_train.py +++ b/tests/test_integration_fast_train.py @@ -58,6 +58,7 @@ @skip_if_no_cuda @skip_if_quick class IntegrationFastTrain(DistTestCase): + def setUp(self): set_determinism(seed=0) monai.config.print_config() diff --git a/tests/test_integration_gpu_customization.py b/tests/test_integration_gpu_customization.py index 44165b967c..043405a580 100644 --- a/tests/test_integration_gpu_customization.py +++ b/tests/test_integration_gpu_customization.py @@ -70,6 +70,7 @@ @SkipIfBeforePyTorchVersion((1, 11, 1)) # module 'torch.cuda' has no attribute 'mem_get_info' @unittest.skipIf(not has_tb, "no tensorboard summary writer") class TestEnsembleGpuCustomization(unittest.TestCase): + def setUp(self) -> None: self.test_dir = tempfile.TemporaryDirectory() diff --git a/tests/test_integration_lazy_samples.py b/tests/test_integration_lazy_samples.py index c365616bc8..51d80e7305 100644 --- a/tests/test_integration_lazy_samples.py +++ b/tests/test_integration_lazy_samples.py @@ -160,6 +160,7 @@ def run_training_test(root_dir, device="cuda:0", cachedataset=0, readers=(None, @skip_if_quick @SkipIfBeforePyTorchVersion((1, 11)) class IntegrationLazyResampling(DistTestCase): + def setUp(self): monai.config.print_config() set_determinism(seed=0) diff --git a/tests/test_integration_nnunetv2_runner.py b/tests/test_integration_nnunetv2_runner.py index d35737f86f..822d454f52 100644 --- a/tests/test_integration_nnunetv2_runner.py +++ b/tests/test_integration_nnunetv2_runner.py @@ -49,6 +49,7 @@ @unittest.skipIf(not has_tb, "no tensorboard summary writer") @unittest.skipIf(not has_nnunet, "no nnunetv2") class TestnnUNetV2Runner(unittest.TestCase): + def setUp(self) -> None: self.test_dir = tempfile.TemporaryDirectory() test_path = self.test_dir.name diff --git a/tests/test_integration_segmentation_3d.py b/tests/test_integration_segmentation_3d.py index 2e4cc31645..c72369b151 100644 --- a/tests/test_integration_segmentation_3d.py +++ b/tests/test_integration_segmentation_3d.py @@ -235,6 +235,7 @@ def run_inference_test(root_dir, device="cuda:0"): @skip_if_quick class IntegrationSegmentation3D(DistTestCase): + def setUp(self): set_determinism(seed=0) diff --git a/tests/test_integration_sliding_window.py b/tests/test_integration_sliding_window.py index bcc66a687e..8b53e94941 100644 --- a/tests/test_integration_sliding_window.py +++ b/tests/test_integration_sliding_window.py @@ -72,6 +72,7 @@ def save_func(engine): @skip_if_quick class TestIntegrationSlidingWindow(DistTestCase): + def setUp(self): set_determinism(seed=0) diff --git a/tests/test_integration_stn.py b/tests/test_integration_stn.py index c858060c31..750a20ea5c 100644 --- a/tests/test_integration_stn.py +++ b/tests/test_integration_stn.py @@ -98,6 +98,7 @@ def compare_2d(is_ref=True, device=None, reverse_indexing=False): class TestSpatialTransformerCore(DistTestCase): + def setUp(self): self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu:0") diff --git a/tests/test_integration_unet_2d.py b/tests/test_integration_unet_2d.py index 90c0098d36..918190775c 100644 --- a/tests/test_integration_unet_2d.py +++ b/tests/test_integration_unet_2d.py @@ -25,7 +25,9 @@ def run_test(net_name="basicunet", batch_size=64, train_steps=100, device="cuda:0"): + class _TestBatch(Dataset): + def __getitem__(self, _unused_id): im, seg = create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1) return im[None], seg[None].astype(np.float32) @@ -54,6 +56,7 @@ def __len__(self): @skip_if_quick class TestIntegrationUnet2D(DistTestCase): + @TimedCall(seconds=20, daemon=False) def test_unet_training(self): for n in ["basicunet", "unet"]: diff --git a/tests/test_integration_workers.py b/tests/test_integration_workers.py index 33c26cedf8..123b1ddc6f 100644 --- a/tests/test_integration_workers.py +++ b/tests/test_integration_workers.py @@ -44,6 +44,7 @@ def run_loading_test(num_workers=50, device=None, pw=False): @skip_if_no_cuda @SkipIfBeforePyTorchVersion((1, 9)) class IntegrationLoading(DistTestCase): + def tearDown(self): set_determinism(seed=None) diff --git a/tests/test_integration_workflows.py b/tests/test_integration_workflows.py index 7c6f35f3d3..fafb66f675 100644 --- a/tests/test_integration_workflows.py +++ b/tests/test_integration_workflows.py @@ -118,6 +118,7 @@ def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4): ) class _TestEvalIterEvents: + def attach(self, engine): engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed) @@ -160,6 +161,7 @@ def _forward_completed(self, engine): ) class _TestTrainIterEvents: + def attach(self, engine): engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed) engine.add_event_handler(IterationEvents.LOSS_COMPLETED, self._loss_completed) @@ -284,6 +286,7 @@ def save_func(engine): @skip_if_quick class IntegrationWorkflows(DistTestCase): + def setUp(self): set_determinism(seed=0) diff --git a/tests/test_integration_workflows_gan.py b/tests/test_integration_workflows_gan.py index 6896241d35..1428506020 100644 --- a/tests/test_integration_workflows_gan.py +++ b/tests/test_integration_workflows_gan.py @@ -127,6 +127,7 @@ def generator_loss(gen_images): @skip_if_quick class IntegrationWorkflowsGAN(DistTestCase): + def setUp(self): set_determinism(seed=0) diff --git a/tests/test_intensity_stats.py b/tests/test_intensity_stats.py index 243fcd0dd4..e45c2acbad 100644 --- a/tests/test_intensity_stats.py +++ b/tests/test_intensity_stats.py @@ -53,6 +53,7 @@ class TestIntensityStats(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, img, meta_dict, expected): _, meta_dict = IntensityStats(**input_param)(img, meta_dict) diff --git a/tests/test_intensity_statsd.py b/tests/test_intensity_statsd.py index 3fe82b1df7..d164f249db 100644 --- a/tests/test_intensity_statsd.py +++ b/tests/test_intensity_statsd.py @@ -52,6 +52,7 @@ class TestIntensityStatsd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_value(self, input_param, data, meta_key, expected): meta = IntensityStatsd(**input_param)(data)[meta_key] diff --git a/tests/test_inverse_array.py b/tests/test_inverse_array.py index c0b1a77e55..4da9ee34b9 100644 --- a/tests/test_inverse_array.py +++ b/tests/test_inverse_array.py @@ -33,6 +33,7 @@ @unittest.skipUnless(has_nib, "Requires nibabel") class TestInverseArray(unittest.TestCase): + @staticmethod def get_image(dtype, device) -> MetaTensor: affine = torch.tensor([[0, 0, 1, 0], [-1, 0, 0, 0], [0, 10, 0, 0], [0, 0, 0, 1]]).to(dtype).to(device) diff --git a/tests/test_invert.py b/tests/test_invert.py index 9c57b11331..69d31edfc8 100644 --- a/tests/test_invert.py +++ b/tests/test_invert.py @@ -41,6 +41,7 @@ class TestInvert(unittest.TestCase): + def test_invert(self): set_determinism(seed=0) im_fname = make_nifti_image(create_test_image_3d(101, 100, 107, noise_max=100)[1]) # label image, discrete diff --git a/tests/test_invertd.py b/tests/test_invertd.py index 2e6ee35981..c32a3af643 100644 --- a/tests/test_invertd.py +++ b/tests/test_invertd.py @@ -43,6 +43,7 @@ class TestInvertd(unittest.TestCase): + def test_invert(self): set_determinism(seed=0) im_fname, seg_fname = (make_nifti_image(i) for i in create_test_image_3d(101, 100, 107, noise_max=100)) diff --git a/tests/test_is_supported_format.py b/tests/test_is_supported_format.py index 591772bb3a..fb488eb054 100644 --- a/tests/test_is_supported_format.py +++ b/tests/test_is_supported_format.py @@ -33,6 +33,7 @@ class TestIsSupportedFormat(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7]) def test_value(self, input_param, result): self.assertEqual(is_supported_format(**input_param), result) diff --git a/tests/test_iterable_dataset.py b/tests/test_iterable_dataset.py index 38be9ec30c..cfa711e4c0 100644 --- a/tests/test_iterable_dataset.py +++ b/tests/test_iterable_dataset.py @@ -24,6 +24,7 @@ class _Stream: + def __init__(self, data): self.data = data @@ -32,6 +33,7 @@ def __iter__(self): class TestIterableDataset(unittest.TestCase): + def test_shape(self): expected_shape = (128, 128, 128) test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4)) diff --git a/tests/test_itk_torch_bridge.py b/tests/test_itk_torch_bridge.py index b368230c53..22ae019271 100644 --- a/tests/test_itk_torch_bridge.py +++ b/tests/test_itk_torch_bridge.py @@ -49,6 +49,7 @@ @unittest.skipUnless(has_itk, "Requires `itk` package.") class TestITKTorchAffineMatrixBridge(unittest.TestCase): + def setUp(self): set_determinism(seed=0) self.data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") @@ -493,6 +494,7 @@ def test_use_reference_space(self, ref_filepath, filepath): @unittest.skipUnless(has_nib, "Requires `nibabel` package.") @skip_if_quick class TestITKTorchRW(unittest.TestCase): + def setUp(self): TestITKTorchAffineMatrixBridge.setUp(self) diff --git a/tests/test_itk_writer.py b/tests/test_itk_writer.py index c9707b1b5a..6625339dd0 100644 --- a/tests/test_itk_writer.py +++ b/tests/test_itk_writer.py @@ -27,6 +27,7 @@ @unittest.skipUnless(has_itk, "Requires `itk` package.") class TestITKWriter(unittest.TestCase): + def test_channel_shape(self): with tempfile.TemporaryDirectory() as tempdir: for c in (0, 1, 2, 3): diff --git a/tests/test_k_space_spike_noise.py b/tests/test_k_space_spike_noise.py index 4d820573a6..17acedf319 100644 --- a/tests/test_k_space_spike_noise.py +++ b/tests/test_k_space_spike_noise.py @@ -32,6 +32,7 @@ class TestKSpaceSpikeNoise(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_k_space_spike_noised.py b/tests/test_k_space_spike_noised.py index 76a79d4b12..ce542af0aa 100644 --- a/tests/test_k_space_spike_noised.py +++ b/tests/test_k_space_spike_noised.py @@ -33,6 +33,7 @@ class TestKSpaceSpikeNoised(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_keep_largest_connected_component.py b/tests/test_keep_largest_connected_component.py index 7da3c4b21f..2dfac1142e 100644 --- a/tests/test_keep_largest_connected_component.py +++ b/tests/test_keep_largest_connected_component.py @@ -381,6 +381,7 @@ def to_onehot(x): class TestKeepLargestConnectedComponent(unittest.TestCase): + @parameterized.expand(TESTS) def test_correct_results(self, _, args, input_image, expected): converter = KeepLargestConnectedComponent(**args) diff --git a/tests/test_keep_largest_connected_componentd.py b/tests/test_keep_largest_connected_componentd.py index aac91a2de9..4d3172741d 100644 --- a/tests/test_keep_largest_connected_componentd.py +++ b/tests/test_keep_largest_connected_componentd.py @@ -337,6 +337,7 @@ class TestKeepLargestConnectedComponentd(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_results(self, _, args, input_dict, expected): converter = KeepLargestConnectedComponentd(**args) diff --git a/tests/test_kspace_mask.py b/tests/test_kspace_mask.py index 5d6d9c18ea..cfbd7864c8 100644 --- a/tests/test_kspace_mask.py +++ b/tests/test_kspace_mask.py @@ -26,6 +26,7 @@ class TestMRIUtils(unittest.TestCase): + @parameterized.expand(TESTSM) def test_mask(self, test_data): # random mask diff --git a/tests/test_label_filter.py b/tests/test_label_filter.py index 47a8706491..93cf95a2a0 100644 --- a/tests/test_label_filter.py +++ b/tests/test_label_filter.py @@ -58,6 +58,7 @@ class TestLabelFilter(unittest.TestCase): + @parameterized.expand(VALID_TESTS) def test_correct_results(self, _, args, input_image, expected): converter = LabelFilter(**args) diff --git a/tests/test_label_filterd.py b/tests/test_label_filterd.py index f27df08c2a..fba8100f25 100644 --- a/tests/test_label_filterd.py +++ b/tests/test_label_filterd.py @@ -58,6 +58,7 @@ class TestLabelFilter(unittest.TestCase): + @parameterized.expand(VALID_TESTS) def test_correct_results(self, _, args, input_image, expected): converter = LabelFilterd(keys="image", **args) diff --git a/tests/test_label_quality_score.py b/tests/test_label_quality_score.py index aa243b4236..a46b78b1d4 100644 --- a/tests/test_label_quality_score.py +++ b/tests/test_label_quality_score.py @@ -99,6 +99,7 @@ class TestLabelQualityScore(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_value(self, input_data, expected_value): result = label_quality_score(**input_data) diff --git a/tests/test_label_to_contour.py b/tests/test_label_to_contour.py index 590fd5d4e4..d7fbfc9b8d 100644 --- a/tests/test_label_to_contour.py +++ b/tests/test_label_to_contour.py @@ -142,6 +142,7 @@ def gen_fixed_img(array_type): class TestContour(unittest.TestCase): + def test_contour(self): input_param = {"kernel_type": "Laplace"} diff --git a/tests/test_label_to_contourd.py b/tests/test_label_to_contourd.py index 6fcec72dd8..a91a712da6 100644 --- a/tests/test_label_to_contourd.py +++ b/tests/test_label_to_contourd.py @@ -143,6 +143,7 @@ def gen_fixed_img(array_type): class TestContourd(unittest.TestCase): + def test_contour(self): input_param = {"keys": "img", "kernel_type": "Laplace"} diff --git a/tests/test_label_to_mask.py b/tests/test_label_to_mask.py index 2eba825cf3..47a58cc989 100644 --- a/tests/test_label_to_mask.py +++ b/tests/test_label_to_mask.py @@ -59,6 +59,7 @@ class TestLabelToMask(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = LabelToMask(**arguments)(image) diff --git a/tests/test_label_to_maskd.py b/tests/test_label_to_maskd.py index 35f54ca5b9..44b537128d 100644 --- a/tests/test_label_to_maskd.py +++ b/tests/test_label_to_maskd.py @@ -59,6 +59,7 @@ class TestLabelToMaskd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, input_data, expected_data): result = LabelToMaskd(**arguments)(input_data) diff --git a/tests/test_lambda.py b/tests/test_lambda.py index e2276d671c..e0a5cf84db 100644 --- a/tests/test_lambda.py +++ b/tests/test_lambda.py @@ -23,6 +23,7 @@ class TestLambda(NumpyImageTestCase2D): + def test_lambda_identity(self): for p in TEST_NDARRAYS: img = p(self.imt) diff --git a/tests/test_lambdad.py b/tests/test_lambdad.py index 02e4423b74..fad5ebeee4 100644 --- a/tests/test_lambdad.py +++ b/tests/test_lambdad.py @@ -23,6 +23,7 @@ class TestLambdad(NumpyImageTestCase2D): + def test_lambdad_identity(self): for p in TEST_NDARRAYS: img = p(self.imt) diff --git a/tests/test_lesion_froc.py b/tests/test_lesion_froc.py index 10682c2bb7..0622809102 100644 --- a/tests/test_lesion_froc.py +++ b/tests/test_lesion_froc.py @@ -298,6 +298,7 @@ def prepare_test_data(): class TestEvaluateTumorFROC(unittest.TestCase): + @skipUnless(has_cucim, "Requires cucim") @skipUnless(has_skimage, "Requires skimage") @skipUnless(has_sp, "Requires scipy") diff --git a/tests/test_list_data_collate.py b/tests/test_list_data_collate.py index 9be61e3999..56ee040758 100644 --- a/tests/test_list_data_collate.py +++ b/tests/test_list_data_collate.py @@ -37,6 +37,7 @@ class TestListDataCollate(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_type_shape(self, input_data, expected_type, expected_shape): result = list_data_collate(input_data) diff --git a/tests/test_list_to_dict.py b/tests/test_list_to_dict.py index 4e6bb8cdf7..abb61ea182 100644 --- a/tests/test_list_to_dict.py +++ b/tests/test_list_to_dict.py @@ -32,6 +32,7 @@ class TestListToDict(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_value_shape(self, input, output): result = list_to_dict(input) diff --git a/tests/test_lltm.py b/tests/test_lltm.py index 6ee716e1ef..cc64672e77 100644 --- a/tests/test_lltm.py +++ b/tests/test_lltm.py @@ -29,6 +29,7 @@ class TestLLTM(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) @SkipIfNoModule("monai._C") def test_value(self, input_param, expected_h, expected_c): diff --git a/tests/test_lmdbdataset.py b/tests/test_lmdbdataset.py index 155b4eb0fc..9d128dd728 100644 --- a/tests/test_lmdbdataset.py +++ b/tests/test_lmdbdataset.py @@ -81,6 +81,7 @@ class _InplaceXform(Transform): + def __call__(self, data): if data: data[0] = data[0] + np.pi @@ -91,6 +92,7 @@ def __call__(self, data): @skip_if_windows class TestLMDBDataset(unittest.TestCase): + def test_cache(self): """testing no inplace change to the hashed item""" items = [[list(range(i))] for i in range(5)] diff --git a/tests/test_lmdbdataset_dist.py b/tests/test_lmdbdataset_dist.py index 0b4c7c35fa..1acb89beb3 100644 --- a/tests/test_lmdbdataset_dist.py +++ b/tests/test_lmdbdataset_dist.py @@ -23,6 +23,7 @@ class _InplaceXform(Transform): + def __call__(self, data): if data: data[0] = data[0] + np.pi @@ -33,6 +34,7 @@ def __call__(self, data): @skip_if_windows class TestMPLMDBDataset(DistTestCase): + def setUp(self): self.tempdir = tempfile.mkdtemp() diff --git a/tests/test_load_decathlon_datalist.py b/tests/test_load_decathlon_datalist.py index b0e390cd73..7281034498 100644 --- a/tests/test_load_decathlon_datalist.py +++ b/tests/test_load_decathlon_datalist.py @@ -21,6 +21,7 @@ class TestLoadDecathlonDatalist(unittest.TestCase): + def test_seg_values(self): with tempfile.TemporaryDirectory() as tempdir: test_data = { diff --git a/tests/test_load_image.py b/tests/test_load_image.py index b6a10bceb4..0207079d7d 100644 --- a/tests/test_load_image.py +++ b/tests/test_load_image.py @@ -160,6 +160,7 @@ def get_data(self, _obj): @unittest.skipUnless(has_itk, "itk not installed") class TestLoadImage(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() @@ -379,6 +380,7 @@ def test_channel_dim(self, input_param, filename, expected_shape): @unittest.skipUnless(has_itk, "itk not installed") class TestLoadImageMeta(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py index 534cbb6618..699ed70059 100644 --- a/tests/test_load_imaged.py +++ b/tests/test_load_imaged.py @@ -46,6 +46,7 @@ @unittest.skipUnless(has_itk, "itk not installed") class TestLoadImaged(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, input_param, expected_shape): test_image = nib.Nifti1Image(np.random.rand(128, 128, 128), np.eye(4)) @@ -94,6 +95,7 @@ def test_no_file(self): @unittest.skipUnless(has_itk, "itk not installed") class TestConsistency(unittest.TestCase): + def _cmp(self, filename, ch_shape, reader_1, reader_2, outname, ext): data_dict = {"img": filename} keys = data_dict.keys() @@ -155,6 +157,7 @@ def test_png(self): @unittest.skipUnless(has_itk, "itk not installed") class TestLoadImagedMeta(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_load_spacing_orientation.py b/tests/test_load_spacing_orientation.py index a121bdd3cd..63422761ca 100644 --- a/tests/test_load_spacing_orientation.py +++ b/tests/test_load_spacing_orientation.py @@ -30,6 +30,7 @@ class TestLoadSpacingOrientation(unittest.TestCase): + @staticmethod def load_image(filename): data = {"image": filename} diff --git a/tests/test_loader_semaphore.py b/tests/test_loader_semaphore.py index 859ee1f8d5..83557d830d 100644 --- a/tests/test_loader_semaphore.py +++ b/tests/test_loader_semaphore.py @@ -39,6 +39,7 @@ def _run_test(): class TestImportLock(unittest.TestCase): + def test_start(self): _run_test() diff --git a/tests/test_local_normalized_cross_correlation_loss.py b/tests/test_local_normalized_cross_correlation_loss.py index 21fe8b973f..35a24cd0ca 100644 --- a/tests/test_local_normalized_cross_correlation_loss.py +++ b/tests/test_local_normalized_cross_correlation_loss.py @@ -117,6 +117,7 @@ class TestLocalNormalizedCrossCorrelationLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = LocalNormalizedCrossCorrelationLoss(**input_param).forward(**input_data) diff --git a/tests/test_localnet.py b/tests/test_localnet.py index f557147960..97aa94d2c5 100644 --- a/tests/test_localnet.py +++ b/tests/test_localnet.py @@ -62,6 +62,7 @@ class TestLocalNet(unittest.TestCase): + @parameterized.expand(TEST_CASE_LOCALNET_2D + TEST_CASE_LOCALNET_3D) def test_shape(self, input_param, input_shape, expected_shape): net = LocalNet(**input_param).to(device) diff --git a/tests/test_localnet_block.py b/tests/test_localnet_block.py index 27ea4cd1a6..340a8e94ba 100644 --- a/tests/test_localnet_block.py +++ b/tests/test_localnet_block.py @@ -48,6 +48,7 @@ class TestLocalNetDownSampleBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_DOWN_SAMPLE) def test_shape(self, input_param): net = LocalNetDownSampleBlock(**input_param) @@ -74,6 +75,7 @@ def test_ill_shape(self, input_param): class TestLocalNetUpSampleBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_UP_SAMPLE) def test_shape(self, input_param): net = LocalNetUpSampleBlock(**input_param) @@ -100,6 +102,7 @@ def test_ill_shape(self, input_param): class TestExtractBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_EXTRACT) def test_shape(self, input_param): net = LocalNetFeatureExtractorBlock(**input_param) diff --git a/tests/test_look_up_option.py b/tests/test_look_up_option.py index 5f81fb8d43..d40b7eaa8c 100644 --- a/tests/test_look_up_option.py +++ b/tests/test_look_up_option.py @@ -44,6 +44,7 @@ class _CaseStrEnum(StrEnum): class TestLookUpOption(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_look_up(self, input_str, supported, expected): output = look_up_option(input_str, supported) diff --git a/tests/test_loss_metric.py b/tests/test_loss_metric.py index 682221f5f5..365dc10670 100644 --- a/tests/test_loss_metric.py +++ b/tests/test_loss_metric.py @@ -36,6 +36,7 @@ class TestComputeLossMetric(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_value_class(self, input_data, expected_value): loss_fn = input_data["loss_class"](**input_data["loss_kwargs"]) diff --git a/tests/test_lr_finder.py b/tests/test_lr_finder.py index 46375890eb..d26cb23a90 100644 --- a/tests/test_lr_finder.py +++ b/tests/test_lr_finder.py @@ -48,6 +48,7 @@ @unittest.skipUnless(sys.platform == "linux", "requires linux") @unittest.skipUnless(has_pil, "requires PIL") class TestLRFinder(unittest.TestCase): + def setUp(self): self.root_dir = MONAIEnvVars.data_dir() if not self.root_dir: diff --git a/tests/test_lr_scheduler.py b/tests/test_lr_scheduler.py index 54092ba931..1a61796fe0 100644 --- a/tests/test_lr_scheduler.py +++ b/tests/test_lr_scheduler.py @@ -20,6 +20,7 @@ class SchedulerTestNet(torch.nn.Module): + def __init__(self): super().__init__() self.conv1 = torch.nn.Conv2d(1, 1, 1) @@ -43,6 +44,7 @@ def forward(self, x): class TestLRSCHEDULER(unittest.TestCase): + @parameterized.expand(TEST_CASE_LRSCHEDULER) def test_shape(self, input_param, expected_lr): net = SchedulerTestNet() diff --git a/tests/test_make_nifti.py b/tests/test_make_nifti.py index 4560507c6c..08d3a731ab 100644 --- a/tests/test_make_nifti.py +++ b/tests/test_make_nifti.py @@ -34,6 +34,7 @@ @unittest.skipUnless(has_nib, "Requires nibabel") class TestMakeNifti(unittest.TestCase): + @parameterized.expand(TESTS) def test_make_nifti(self, params): im, _ = create_test_image_2d(100, 88) diff --git a/tests/test_map_binary_to_indices.py b/tests/test_map_binary_to_indices.py index 1080c2a513..9931d997bb 100644 --- a/tests/test_map_binary_to_indices.py +++ b/tests/test_map_binary_to_indices.py @@ -64,6 +64,7 @@ class TestMapBinaryToIndices(unittest.TestCase): + @parameterized.expand(TESTS) def test_type_shape(self, input_data, expected_fg, expected_bg): fg_indices, bg_indices = map_binary_to_indices(**input_data) diff --git a/tests/test_map_classes_to_indices.py b/tests/test_map_classes_to_indices.py index 9c8b4b4793..902744ab65 100644 --- a/tests/test_map_classes_to_indices.py +++ b/tests/test_map_classes_to_indices.py @@ -124,6 +124,7 @@ class TestMapClassesToIndices(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_data, expected_indices): indices = map_classes_to_indices(**input_data) diff --git a/tests/test_map_label_value.py b/tests/test_map_label_value.py index 6b8121b6df..cd311df6bd 100644 --- a/tests/test_map_label_value.py +++ b/tests/test_map_label_value.py @@ -75,6 +75,7 @@ class TestMapLabelValue(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input_data, expected_value): result = MapLabelValue(**input_param)(input_data) diff --git a/tests/test_map_label_valued.py b/tests/test_map_label_valued.py index fa0d094393..0fb46f2515 100644 --- a/tests/test_map_label_valued.py +++ b/tests/test_map_label_valued.py @@ -69,6 +69,7 @@ class TestMapLabelValued(unittest.TestCase): + @parameterized.expand( [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_5_1, TEST_CASE_6, TEST_CASE_7] ) diff --git a/tests/test_map_transform.py b/tests/test_map_transform.py index 7430cf09c7..a7be7b9f5d 100644 --- a/tests/test_map_transform.py +++ b/tests/test_map_transform.py @@ -23,11 +23,13 @@ class MapTest(MapTransform): + def __call__(self, data): pass class TestRandomizable(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_keys(self, keys, expected): transform = MapTest(keys=keys) diff --git a/tests/test_mask_intensity.py b/tests/test_mask_intensity.py index 2b831ba415..b7ff324946 100644 --- a/tests/test_mask_intensity.py +++ b/tests/test_mask_intensity.py @@ -55,6 +55,7 @@ class TestMaskIntensity(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_value(self, arguments, image, expected_data): for p in TEST_NDARRAYS: diff --git a/tests/test_mask_intensityd.py b/tests/test_mask_intensityd.py index 6a39416de4..0efd1f835f 100644 --- a/tests/test_mask_intensityd.py +++ b/tests/test_mask_intensityd.py @@ -57,6 +57,7 @@ class TestMaskIntensityd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_value(self, arguments, image, expected_data): result = MaskIntensityd(**arguments)(image) diff --git a/tests/test_masked_dice_loss.py b/tests/test_masked_dice_loss.py index b868f4d3a1..c971723615 100644 --- a/tests/test_masked_dice_loss.py +++ b/tests/test_masked_dice_loss.py @@ -113,6 +113,7 @@ class TestDiceLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = MaskedDiceLoss(**input_param).forward(**input_data) diff --git a/tests/test_masked_loss.py b/tests/test_masked_loss.py index 708d507523..3c04ffadcb 100644 --- a/tests/test_masked_loss.py +++ b/tests/test_masked_loss.py @@ -40,6 +40,7 @@ class TestMaskedLoss(unittest.TestCase): + def setUp(self): set_determinism(0) diff --git a/tests/test_masked_patch_wsi_dataset.py b/tests/test_masked_patch_wsi_dataset.py index 35509b32f6..8d24075595 100644 --- a/tests/test_masked_patch_wsi_dataset.py +++ b/tests/test_masked_patch_wsi_dataset.py @@ -74,6 +74,7 @@ def setUpModule(): class MaskedPatchWSIDatasetTests: + class Tests(unittest.TestCase): backend = None @@ -100,6 +101,7 @@ def test_gen_patches(self, input_parameters, expected): @skipUnless(has_cucim, "Requires cucim") class TestSlidingPatchWSIDatasetCuCIM(MaskedPatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "cucim" @@ -107,6 +109,7 @@ def setUpClass(cls): @skipUnless(has_osl, "Requires openslide") class TestSlidingPatchWSIDatasetOpenSlide(MaskedPatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "openslide" diff --git a/tests/test_matshow3d.py b/tests/test_matshow3d.py index a6cb3fcee3..e513025e69 100644 --- a/tests/test_matshow3d.py +++ b/tests/test_matshow3d.py @@ -35,6 +35,7 @@ @SkipIfNoModule("matplotlib") class TestMatshow3d(unittest.TestCase): + def test_3d(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") keys = "image" diff --git a/tests/test_mean_ensemble.py b/tests/test_mean_ensemble.py index 09b7f94dc4..6b463f8530 100644 --- a/tests/test_mean_ensemble.py +++ b/tests/test_mean_ensemble.py @@ -58,6 +58,7 @@ class TestMeanEnsemble(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, img, expected_value): result = MeanEnsemble(**input_param)(img) diff --git a/tests/test_mean_ensembled.py b/tests/test_mean_ensembled.py index 01123b0729..795ae47368 100644 --- a/tests/test_mean_ensembled.py +++ b/tests/test_mean_ensembled.py @@ -72,6 +72,7 @@ class TestMeanEnsembled(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, data, expected_value): result = MeanEnsembled(**input_param)(data) diff --git a/tests/test_median_filter.py b/tests/test_median_filter.py index 9f27adff4c..1f5e623260 100644 --- a/tests/test_median_filter.py +++ b/tests/test_median_filter.py @@ -20,6 +20,7 @@ class MedianFilterTestCase(unittest.TestCase): + def test_3d_big(self): a = torch.ones(1, 1, 2, 3, 5) g = MedianFilter([1, 2, 4]).to(torch.device("cpu:0")) diff --git a/tests/test_median_smooth.py b/tests/test_median_smooth.py index 21cd45f28e..5930c0c6b6 100644 --- a/tests/test_median_smooth.py +++ b/tests/test_median_smooth.py @@ -31,6 +31,7 @@ class TestMedianSmooth(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = MedianSmooth(**arguments)(image) diff --git a/tests/test_median_smoothd.py b/tests/test_median_smoothd.py index b8d3452c86..e0bdb331c8 100644 --- a/tests/test_median_smoothd.py +++ b/tests/test_median_smoothd.py @@ -55,6 +55,7 @@ class TestMedianSmoothd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): result = MedianSmoothd(**arguments)(image) diff --git a/tests/test_mednistdataset.py b/tests/test_mednistdataset.py index b5a809ccaa..baf3bf4f2d 100644 --- a/tests/test_mednistdataset.py +++ b/tests/test_mednistdataset.py @@ -25,6 +25,7 @@ class TestMedNISTDataset(unittest.TestCase): + @skip_if_quick def test_values(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_meta_affine.py b/tests/test_meta_affine.py index b95ea3f1ac..95764a0c89 100644 --- a/tests/test_meta_affine.py +++ b/tests/test_meta_affine.py @@ -123,6 +123,7 @@ def _resample_to_affine(itk_obj, ref_obj): @unittest.skipUnless(has_itk, "Requires itk package.") class TestAffineConsistencyITK(unittest.TestCase): + @classmethod def setUpClass(cls): super().setUpClass() diff --git a/tests/test_meta_tensor.py b/tests/test_meta_tensor.py index 0cd0522036..1e0f188b63 100644 --- a/tests/test_meta_tensor.py +++ b/tests/test_meta_tensor.py @@ -50,6 +50,7 @@ def rand_string(min_len=5, max_len=10): class TestMetaTensor(unittest.TestCase): + @staticmethod def get_im(shape=None, dtype=None, device=None): if shape is None: diff --git a/tests/test_metatensor_integration.py b/tests/test_metatensor_integration.py index 6a4c67d160..d647e47e74 100644 --- a/tests/test_metatensor_integration.py +++ b/tests/test_metatensor_integration.py @@ -39,6 +39,7 @@ @unittest.skipUnless(has_nib, "Requires nibabel package.") class TestMetaTensorIntegration(unittest.TestCase): + @classmethod def setUpClass(cls): super().setUpClass() diff --git a/tests/test_metrics_reloaded.py b/tests/test_metrics_reloaded.py index 010326b87d..562693c07c 100644 --- a/tests/test_metrics_reloaded.py +++ b/tests/test_metrics_reloaded.py @@ -76,6 +76,7 @@ @unittest.skipIf(not has_metrics, "MetricsReloaded not available.") class TestMetricsReloaded(unittest.TestCase): + @parameterized.expand(TEST_CASES_BINARY) def test_binary(self, input_param, input_data, expected_val): metric = MetricsReloadedBinary(**input_param) diff --git a/tests/test_milmodel.py b/tests/test_milmodel.py index 9178e0bccb..42116e8220 100644 --- a/tests/test_milmodel.py +++ b/tests/test_milmodel.py @@ -63,6 +63,7 @@ class TestMilModel(unittest.TestCase): + @parameterized.expand(TEST_CASE_MILMODEL) def test_shape(self, input_param, input_shape, expected_shape): with skip_if_downloading_fails(): diff --git a/tests/test_mlp.py b/tests/test_mlp.py index 8ad66ebc6e..54f70d3318 100644 --- a/tests/test_mlp.py +++ b/tests/test_mlp.py @@ -33,6 +33,7 @@ class TestMLPBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_MLP) def test_shape(self, input_param, input_shape, expected_shape): net = MLPBlock(**input_param) diff --git a/tests/test_mmar_download.py b/tests/test_mmar_download.py index 66fca6bb7f..6af3d09fb2 100644 --- a/tests/test_mmar_download.py +++ b/tests/test_mmar_download.py @@ -116,6 +116,7 @@ @unittest.skip("deprecating mmar tests") class TestMMMARDownload(unittest.TestCase): + @parameterized.expand(TEST_CASES) @skip_if_quick def test_download(self, idx): diff --git a/tests/test_module_list.py b/tests/test_module_list.py index 293da95d5a..d21ba53b7c 100644 --- a/tests/test_module_list.py +++ b/tests/test_module_list.py @@ -21,6 +21,7 @@ class TestAllImport(unittest.TestCase): + def test_public_api(self): """ This is to check "monai.__all__" should be consistent with diff --git a/tests/test_monai_env_vars.py b/tests/test_monai_env_vars.py index 6e0d6f0ddf..f5ef28a0ac 100644 --- a/tests/test_monai_env_vars.py +++ b/tests/test_monai_env_vars.py @@ -18,6 +18,7 @@ class TestMONAIEnvVars(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_monai_utils_misc.py b/tests/test_monai_utils_misc.py index 742c9e4047..a2a4ed62f7 100644 --- a/tests/test_monai_utils_misc.py +++ b/tests/test_monai_utils_misc.py @@ -40,11 +40,13 @@ class MiscClass: + def __init__(self, arg1, arg2, kwargs1=None, kwargs2=None): pass class TestToTupleOfDictionaries(unittest.TestCase): + @parameterized.expand(TO_TUPLE_OF_DICTIONARIES_TEST_CASES) def test_to_tuple_of_dictionaries(self, dictionary, keys, expected): self._test_to_tuple_of_dictionaries(dictionary, keys, expected) @@ -61,6 +63,7 @@ def _test_to_tuple_of_dictionaries(self, dictionary, keys, expected): class TestMiscKwargs(unittest.TestCase): + def test_kwargs(self): present, extra_args = self._custom_user_function(MiscClass, 1, kwargs1="value1", kwargs2="value2") self.assertEqual(present, True) @@ -74,6 +77,7 @@ def _custom_user_function(self, cls, *args, **kwargs): class TestCommandRunner(unittest.TestCase): + def setUp(self): self.orig_flag = str(MONAIEnvVars.debug()) diff --git a/tests/test_mri_utils.py b/tests/test_mri_utils.py index 2f67816e2e..aabf06d02e 100644 --- a/tests/test_mri_utils.py +++ b/tests/test_mri_utils.py @@ -27,6 +27,7 @@ class TestMRIUtils(unittest.TestCase): + @parameterized.expand(TESTS) def test_rss(self, test_data, res_data): result = root_sum_of_squares(test_data, spatial_dim=1) diff --git a/tests/test_multi_scale.py b/tests/test_multi_scale.py index 8b8acb2503..6681f266a8 100644 --- a/tests/test_multi_scale.py +++ b/tests/test_multi_scale.py @@ -52,6 +52,7 @@ class TestMultiScale(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = MultiScaleLoss(**input_param).forward(**input_data) diff --git a/tests/test_net_adapter.py b/tests/test_net_adapter.py index 74a2daab9d..242326e242 100644 --- a/tests/test_net_adapter.py +++ b/tests/test_net_adapter.py @@ -42,6 +42,7 @@ class TestNetAdapter(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_shape(self, input_param, input_shape, expected_shape): spatial_dims = input_param["dim"] diff --git a/tests/test_network_consistency.py b/tests/test_network_consistency.py index aca145a03d..4182501808 100644 --- a/tests/test_network_consistency.py +++ b/tests/test_network_consistency.py @@ -38,6 +38,7 @@ class TestNetworkConsistency(unittest.TestCase): + def setUp(self): set_determinism(0) diff --git a/tests/test_nifti_endianness.py b/tests/test_nifti_endianness.py index 2539d95fd5..4475d8aaab 100644 --- a/tests/test_nifti_endianness.py +++ b/tests/test_nifti_endianness.py @@ -46,6 +46,7 @@ class TestNiftiEndianness(unittest.TestCase): + def setUp(self): self.im, _ = create_test_image_2d(100, 100) self.fname = tempfile.NamedTemporaryFile(suffix=".nii.gz").name diff --git a/tests/test_nifti_header_revise.py b/tests/test_nifti_header_revise.py index 3d000160e1..411c783fb5 100644 --- a/tests/test_nifti_header_revise.py +++ b/tests/test_nifti_header_revise.py @@ -20,6 +20,7 @@ class TestRectifyHeaderSformQform(unittest.TestCase): + def test_revise_q(self): img = nib.Nifti1Image(np.zeros((10, 10, 10)), np.eye(4)) img.header.set_zooms((0.1, 0.2, 0.3)) diff --git a/tests/test_nifti_rw.py b/tests/test_nifti_rw.py index f45c2ac5a7..8543fcea30 100644 --- a/tests/test_nifti_rw.py +++ b/tests/test_nifti_rw.py @@ -72,6 +72,7 @@ class TestNiftiLoadRead(unittest.TestCase): + @parameterized.expand(TESTS) def test_orientation(self, array, affine, reader_param, expected): test_image = make_nifti_image(array, affine) diff --git a/tests/test_normalize_intensity.py b/tests/test_normalize_intensity.py index 193b5cc4b2..72ebf579e1 100644 --- a/tests/test_normalize_intensity.py +++ b/tests/test_normalize_intensity.py @@ -83,6 +83,7 @@ class TestNormalizeIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_default(self, im_type): im = im_type(self.imt.copy()) diff --git a/tests/test_normalize_intensityd.py b/tests/test_normalize_intensityd.py index 451269b1c4..229dcd00ff 100644 --- a/tests/test_normalize_intensityd.py +++ b/tests/test_normalize_intensityd.py @@ -51,6 +51,7 @@ class TestNormalizeIntensityd(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_image_normalize_intensityd(self, im_type): key = "img" diff --git a/tests/test_npzdictitemdataset.py b/tests/test_npzdictitemdataset.py index 4ff4577b72..e2196f1907 100644 --- a/tests/test_npzdictitemdataset.py +++ b/tests/test_npzdictitemdataset.py @@ -21,6 +21,7 @@ class TestNPZDictItemDataset(unittest.TestCase): + def test_load_stream(self): dat0 = np.random.rand(10, 1, 4, 4) dat1 = np.random.rand(10, 1, 4, 4) diff --git a/tests/test_nrrd_reader.py b/tests/test_nrrd_reader.py index 01fabe65a8..649b9fa94d 100644 --- a/tests/test_nrrd_reader.py +++ b/tests/test_nrrd_reader.py @@ -48,6 +48,7 @@ @skipUnless(has_nrrd, "nrrd required") class TestNrrdReader(unittest.TestCase): + def test_verify_suffix(self): reader = NrrdReader() self.assertFalse(reader.verify_suffix("test_image.nrd")) diff --git a/tests/test_nuclick_transforms.py b/tests/test_nuclick_transforms.py index fcdd362b01..a6e66c3658 100644 --- a/tests/test_nuclick_transforms.py +++ b/tests/test_nuclick_transforms.py @@ -179,6 +179,7 @@ class TestFilterImaged(unittest.TestCase): + @parameterized.expand([FILTER_IMAGE_TEST_CASE_1]) def test_correct_shape(self, arguments, input_data, expected_shape): result = FilterImaged(**arguments)(input_data) @@ -186,6 +187,7 @@ def test_correct_shape(self, arguments, input_data, expected_shape): class TestFlattenLabeld(unittest.TestCase): + @parameterized.expand([FLATTEN_LABEL_TEST_CASE_1, FLATTEN_LABEL_TEST_CASE_2, FLATTEN_LABEL_TEST_CASE_3]) def test_correct_num_labels(self, arguments, input_data, expected_result): result = FlattenLabeld(**arguments)(input_data) @@ -193,6 +195,7 @@ def test_correct_num_labels(self, arguments, input_data, expected_result): class TestExtractPatchd(unittest.TestCase): + @parameterized.expand([EXTRACT_TEST_CASE_1, EXTRACT_TEST_CASE_2, EXTRACT_TEST_CASE_3]) def test_correct_patch_size(self, arguments, input_data, expected_shape): result = ExtractPatchd(**arguments)(input_data) @@ -205,6 +208,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestSplitLabelsd(unittest.TestCase): + @parameterized.expand([SPLIT_TEST_CASE_1, SPLIT_TEST_CASE_2]) def test_correct_results(self, arguments, input_data, expected_result): result = SplitLabeld(**arguments)(input_data) @@ -212,6 +216,7 @@ def test_correct_results(self, arguments, input_data, expected_result): class TestGuidanceSignal(unittest.TestCase): + @parameterized.expand([GUIDANCE_TEST_CASE_1, GUIDANCE_TEST_CASE_2]) def test_correct_shape(self, arguments, input_data, expected_shape): result = AddPointGuidanceSignald(**arguments)(input_data) @@ -219,6 +224,7 @@ def test_correct_shape(self, arguments, input_data, expected_shape): class TestClickSignal(unittest.TestCase): + @parameterized.expand([CLICK_TEST_CASE_1, CLICK_TEST_CASE_2]) def test_correct_shape(self, arguments, input_data, expected_shape): result = AddClickSignalsd(**arguments)(input_data) @@ -226,6 +232,7 @@ def test_correct_shape(self, arguments, input_data, expected_shape): class TestPostFilterLabel(unittest.TestCase): + @parameterized.expand([LABEL_FILTER_TEST_CASE_1]) def test_correct_shape(self, arguments, input_data, expected_shape): result = PostFilterLabeld(**arguments)(input_data) @@ -233,6 +240,7 @@ def test_correct_shape(self, arguments, input_data, expected_shape): class TestAddLabelAsGuidance(unittest.TestCase): + @parameterized.expand([LABEL_GUIDANCE_TEST_CASE_1]) def test_correct_shape(self, arguments, input_data, expected_shape): result = AddLabelAsGuidanced(**arguments)(input_data) @@ -240,6 +248,7 @@ def test_correct_shape(self, arguments, input_data, expected_shape): class TestSetLabelClass(unittest.TestCase): + @parameterized.expand([LABEL_CLASS_TEST_CASE_1]) def test_correct_results(self, arguments, input_data, expected_result): result = SetLabelClassd(**arguments)(input_data) diff --git a/tests/test_numpy_reader.py b/tests/test_numpy_reader.py index eeff2922ad..6303598bb7 100644 --- a/tests/test_numpy_reader.py +++ b/tests/test_numpy_reader.py @@ -24,6 +24,7 @@ class TestNumpyReader(unittest.TestCase): + def test_npy(self): test_data = np.random.randint(0, 256, size=[3, 4, 4]) with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_nvtx_decorator.py b/tests/test_nvtx_decorator.py index 574fd49592..efd2906972 100644 --- a/tests/test_nvtx_decorator.py +++ b/tests/test_nvtx_decorator.py @@ -72,6 +72,7 @@ @unittest.skipUnless(has_nvtx, "Required torch._C._nvtx for NVTX Range!") class TestNVTXRangeDecorator(unittest.TestCase): + @parameterized.expand([TEST_CASE_ARRAY_0, TEST_CASE_ARRAY_1]) def test_tranform_array(self, input): transforms = Compose([Range("random flip")(Flip()), Range()(ToTensor())]) diff --git a/tests/test_nvtx_transform.py b/tests/test_nvtx_transform.py index 3a5314c35f..af15c53d1b 100644 --- a/tests/test_nvtx_transform.py +++ b/tests/test_nvtx_transform.py @@ -43,6 +43,7 @@ class TestNVTXTransforms(unittest.TestCase): + @parameterized.expand([TEST_CASE_ARRAY_0, TEST_CASE_ARRAY_1, TEST_CASE_DICT_0, TEST_CASE_DICT_1]) @unittest.skipUnless(has_nvtx, "CUDA is required for NVTX!") def test_nvtx_transfroms_alone(self, input): diff --git a/tests/test_occlusion_sensitivity.py b/tests/test_occlusion_sensitivity.py index c7ac5ef533..d821c9bcd9 100644 --- a/tests/test_occlusion_sensitivity.py +++ b/tests/test_occlusion_sensitivity.py @@ -22,6 +22,7 @@ class DenseNetAdjoint(DenseNet121): + def __call__(self, x, adjoint_info): if adjoint_info != 42: raise ValueError @@ -104,6 +105,7 @@ def __call__(self, x, adjoint_info): class TestComputeOcclusionSensitivity(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, init_data, call_data, map_expected_shape, most_prob_expected_shape): occ_sens = OcclusionSensitivity(**init_data) diff --git a/tests/test_one_of.py b/tests/test_one_of.py index 2909597507..ecf1cb3319 100644 --- a/tests/test_one_of.py +++ b/tests/test_one_of.py @@ -39,31 +39,37 @@ class X(Transform): + def __call__(self, x): return x class Y(Transform): + def __call__(self, x): return x class A(Transform): + def __call__(self, x): return x + 1 class B(Transform): + def __call__(self, x): return x + 2 class C(Transform): + def __call__(self, x): return x + 3 class MapBase(MapTransform): + def __init__(self, keys): super().__init__(keys) self.fwd_fn, self.inv_fn = None, None @@ -76,12 +82,14 @@ def __call__(self, data): class NonInv(MapBase): + def __init__(self, keys): super().__init__(keys) self.fwd_fn = lambda x: x * 2 class Inv(MapBase, InvertibleTransform): + def __call__(self, data): d = deepcopy(dict(data)) for key in self.key_iterator(d): @@ -98,6 +106,7 @@ def inverse(self, data): class InvA(Inv): + def __init__(self, keys): super().__init__(keys) self.fwd_fn = lambda x: x + 1 @@ -105,6 +114,7 @@ def __init__(self, keys): class InvB(Inv): + def __init__(self, keys): super().__init__(keys) self.fwd_fn = lambda x: x + 100 @@ -123,6 +133,7 @@ def __init__(self, keys): class TestOneOf(unittest.TestCase): + @parameterized.expand(TESTS) def test_normalize_weights(self, transforms, input_weights, expected_weights): tr = OneOf(transforms, input_weights) @@ -240,6 +251,7 @@ def test_one_of(self): class TestOneOfAPITests(unittest.TestCase): + @staticmethod def data_from_keys(keys): if keys is None: diff --git a/tests/test_optional_import.py b/tests/test_optional_import.py index 03db7b3fc6..e7e1c03fd0 100644 --- a/tests/test_optional_import.py +++ b/tests/test_optional_import.py @@ -17,6 +17,7 @@ class TestOptionalImport(unittest.TestCase): + def test_default(self): my_module, flag = optional_import("not_a_module") self.assertFalse(flag) diff --git a/tests/test_ori_ras_lps.py b/tests/test_ori_ras_lps.py index 824793f927..39c0a57877 100644 --- a/tests/test_ori_ras_lps.py +++ b/tests/test_ori_ras_lps.py @@ -38,6 +38,7 @@ class TestITKWriter(unittest.TestCase): + @parameterized.expand(TEST_CASES_AFFINE) def test_ras_to_lps(self, param, expected): assert_allclose(orientation_ras_lps(param), expected) diff --git a/tests/test_orientation.py b/tests/test_orientation.py index aa1c326bdf..2f3334e622 100644 --- a/tests/test_orientation.py +++ b/tests/test_orientation.py @@ -177,6 +177,7 @@ class TestOrientationCase(unittest.TestCase): + @parameterized.expand(TESTS) def test_ornt_meta( self, diff --git a/tests/test_orientationd.py b/tests/test_orientationd.py index cf4eb23d42..b885266c69 100644 --- a/tests/test_orientationd.py +++ b/tests/test_orientationd.py @@ -65,6 +65,7 @@ class TestOrientationdCase(unittest.TestCase): + @parameterized.expand(TESTS) def test_orntd( self, init_param, img: torch.Tensor, affine: torch.Tensor | None, expected_shape, expected_code, device diff --git a/tests/test_p3d_block.py b/tests/test_p3d_block.py index db9e9c284d..1a4ea6c884 100644 --- a/tests/test_p3d_block.py +++ b/tests/test_p3d_block.py @@ -62,6 +62,7 @@ class TestP3D(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_3d(self, input_param, input_shape, expected_shape): net = P3DActiConvNormBlock(**input_param) diff --git a/tests/test_pad_collation.py b/tests/test_pad_collation.py index cd98f29abf..ee6e001438 100644 --- a/tests/test_pad_collation.py +++ b/tests/test_pad_collation.py @@ -60,6 +60,7 @@ def _testing_collate(x): class _Dataset(torch.utils.data.Dataset): + def __init__(self, images, labels, transforms): self.images = images self.labels = labels @@ -73,6 +74,7 @@ def __getitem__(self, index): class TestPadCollation(unittest.TestCase): + def setUp(self) -> None: set_determinism(seed=0) # image is non square to throw rotation errors diff --git a/tests/test_pad_mode.py b/tests/test_pad_mode.py index 722d5b573f..54ee2c6d75 100644 --- a/tests/test_pad_mode.py +++ b/tests/test_pad_mode.py @@ -23,6 +23,7 @@ @SkipIfBeforePyTorchVersion((1, 10, 1)) class TestPadMode(unittest.TestCase): + def test_pad(self): expected_shapes = {3: (1, 15, 10), 4: (1, 10, 6, 7)} for t in (float, int, np.uint8, np.int16, np.float32, bool): diff --git a/tests/test_partition_dataset.py b/tests/test_partition_dataset.py index 8640d8cc73..c93a6c7682 100644 --- a/tests/test_partition_dataset.py +++ b/tests/test_partition_dataset.py @@ -118,6 +118,7 @@ class TestPartitionDataset(unittest.TestCase): + @parameterized.expand( [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8] ) diff --git a/tests/test_partition_dataset_classes.py b/tests/test_partition_dataset_classes.py index c4fa5ed199..4c13b2f463 100644 --- a/tests/test_partition_dataset_classes.py +++ b/tests/test_partition_dataset_classes.py @@ -76,6 +76,7 @@ class TestPartitionDatasetClasses(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_value(self, input_param, result): self.assertListEqual(partition_dataset_classes(**input_param), result) diff --git a/tests/test_patch_dataset.py b/tests/test_patch_dataset.py index eb705f0c61..9a81d84363 100644 --- a/tests/test_patch_dataset.py +++ b/tests/test_patch_dataset.py @@ -27,6 +27,7 @@ def identity(x): class TestPatchDataset(unittest.TestCase): + def test_shape(self): test_dataset = ["vwxyz", "hello", "world"] n_per_image = len(test_dataset[0]) diff --git a/tests/test_patch_inferer.py b/tests/test_patch_inferer.py index 032d22bb98..c6308224b0 100644 --- a/tests/test_patch_inferer.py +++ b/tests/test_patch_inferer.py @@ -245,6 +245,7 @@ class PatchInfererTests(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_0_TENSOR, diff --git a/tests/test_patch_wsi_dataset.py b/tests/test_patch_wsi_dataset.py index cb9ebcf7e3..70e01eaaf4 100644 --- a/tests/test_patch_wsi_dataset.py +++ b/tests/test_patch_wsi_dataset.py @@ -128,6 +128,7 @@ def setUpModule(): class PatchWSIDatasetTests: + class Tests(unittest.TestCase): backend = None @@ -182,6 +183,7 @@ def test_read_patches_str_multi(self, input_parameters, expected): @skipUnless(has_cim, "Requires cucim") class TestPatchWSIDatasetCuCIM(PatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "cucim" @@ -189,6 +191,7 @@ def setUpClass(cls): @skipUnless(has_osl, "Requires openslide") class TestPatchWSIDatasetOpenSlide(PatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "openslide" diff --git a/tests/test_patchembedding.py b/tests/test_patchembedding.py index 77ade984eb..f8610d9214 100644 --- a/tests/test_patchembedding.py +++ b/tests/test_patchembedding.py @@ -77,6 +77,7 @@ @SkipIfBeforePyTorchVersion((1, 11, 1)) class TestPatchEmbeddingBlock(unittest.TestCase): + def setUp(self): self.threads = torch.get_num_threads() torch.set_num_threads(4) @@ -162,6 +163,7 @@ def test_ill_arg(self): class TestPatchEmbed(unittest.TestCase): + def setUp(self): self.threads = torch.get_num_threads() torch.set_num_threads(4) diff --git a/tests/test_pathology_he_stain.py b/tests/test_pathology_he_stain.py index 7ddad4ad6f..26941c6abb 100644 --- a/tests/test_pathology_he_stain.py +++ b/tests/test_pathology_he_stain.py @@ -73,6 +73,7 @@ class TestExtractHEStains(unittest.TestCase): + @parameterized.expand( [NEGATIVE_VALUE_TEST_CASE, INVALID_VALUE_TEST_CASE, EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_1] ) @@ -145,6 +146,7 @@ def test_result_value(self, image, expected_data): class TestNormalizeHEStains(unittest.TestCase): + @parameterized.expand( [NEGATIVE_VALUE_TEST_CASE, INVALID_VALUE_TEST_CASE, NORMALIZE_STAINS_TEST_CASE_0, NORMALIZE_STAINS_TEST_CASE_1] ) diff --git a/tests/test_pathology_he_stain_dict.py b/tests/test_pathology_he_stain_dict.py index 07db1c3e48..975dc4ffb8 100644 --- a/tests/test_pathology_he_stain_dict.py +++ b/tests/test_pathology_he_stain_dict.py @@ -67,6 +67,7 @@ class TestExtractHEStainsD(unittest.TestCase): + @parameterized.expand([EXTRACT_STAINS_TEST_CASE_0, EXTRACT_STAINS_TEST_CASE_1]) def test_transparent_image(self, image): """ @@ -140,6 +141,7 @@ def test_result_value(self, image, expected_data): class TestNormalizeHEStainsD(unittest.TestCase): + @parameterized.expand([NORMALIZE_STAINS_TEST_CASE_0, NORMALIZE_STAINS_TEST_CASE_1]) def test_transparent_image(self, image): """ diff --git a/tests/test_pathology_prob_nms.py b/tests/test_pathology_prob_nms.py index 0053500437..b3d7da2c1d 100644 --- a/tests/test_pathology_prob_nms.py +++ b/tests/test_pathology_prob_nms.py @@ -43,6 +43,7 @@ class TestPathologyProbNMS(unittest.TestCase): + @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D]) def test_output(self, class_args, call_args, probs_map, expected): nms = PathologyProbNMS(**class_args) diff --git a/tests/test_perceptual_loss.py b/tests/test_perceptual_loss.py index 7e4860e7f9..ba204af697 100644 --- a/tests/test_perceptual_loss.py +++ b/tests/test_perceptual_loss.py @@ -52,6 +52,7 @@ @unittest.skipUnless(has_torchvision, "Requires torchvision") @skip_if_quick class TestPerceptualLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape, target_shape): with skip_if_downloading_fails(): diff --git a/tests/test_persistentdataset.py b/tests/test_persistentdataset.py index 1b8245e318..b7bf2fbb11 100644 --- a/tests/test_persistentdataset.py +++ b/tests/test_persistentdataset.py @@ -45,6 +45,7 @@ class _InplaceXform(Transform): + def __call__(self, data): if data: data[0] = data[0] + np.pi @@ -54,6 +55,7 @@ def __call__(self, data): class TestDataset(unittest.TestCase): + def test_cache(self): """testing no inplace change to the hashed item""" items = [[list(range(i))] for i in range(5)] diff --git a/tests/test_persistentdataset_dist.py b/tests/test_persistentdataset_dist.py index e69c32b1eb..c369af9e92 100644 --- a/tests/test_persistentdataset_dist.py +++ b/tests/test_persistentdataset_dist.py @@ -25,6 +25,7 @@ class _InplaceXform(Transform): + def __call__(self, data): if data: data[0] = data[0] + np.pi @@ -34,6 +35,7 @@ def __call__(self, data): class TestDistDataset(DistTestCase): + def setUp(self): self.tempdir = tempfile.mkdtemp() @@ -58,6 +60,7 @@ def test_mp_dataset(self): class TestDistCreateDataset(DistTestCase): + def setUp(self): self.tempdir = tempfile.mkdtemp() diff --git a/tests/test_phl_cpu.py b/tests/test_phl_cpu.py index 98a5018d8e..6f872a4776 100644 --- a/tests/test_phl_cpu.py +++ b/tests/test_phl_cpu.py @@ -242,6 +242,7 @@ @skip_if_no_cpp_extension class PHLFilterTestCaseCpu(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cpu(self, test_case_description, sigmas, input, features, expected): # Create input tensors diff --git a/tests/test_phl_cuda.py b/tests/test_phl_cuda.py index 0ddfd5eaae..b410ea8722 100644 --- a/tests/test_phl_cuda.py +++ b/tests/test_phl_cuda.py @@ -150,6 +150,7 @@ @skip_if_no_cuda @skip_if_no_cpp_extension class PHLFilterTestCaseCuda(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cuda(self, test_case_description, sigmas, input, features, expected): # Create input tensors diff --git a/tests/test_pil_reader.py b/tests/test_pil_reader.py index dfa5eb725d..078812513d 100644 --- a/tests/test_pil_reader.py +++ b/tests/test_pil_reader.py @@ -37,6 +37,7 @@ class TestPNGReader(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]) def test_shape_value(self, data_shape, filenames, expected_shape, meta_shape, reverse=True): test_image = np.random.randint(0, 256, size=data_shape) diff --git a/tests/test_plot_2d_or_3d_image.py b/tests/test_plot_2d_or_3d_image.py index 180a6c3443..16241853b3 100644 --- a/tests/test_plot_2d_or_3d_image.py +++ b/tests/test_plot_2d_or_3d_image.py @@ -40,6 +40,7 @@ @unittest.skipUnless(has_tb, "Requires SummaryWriter installation") @SkipIfBeforePyTorchVersion((1, 13)) # issue 6683 class TestPlot2dOr3dImage(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_tb_image(self, shape): with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_png_rw.py b/tests/test_png_rw.py index 0b6e8184ea..058cd616cb 100644 --- a/tests/test_png_rw.py +++ b/tests/test_png_rw.py @@ -22,6 +22,7 @@ class TestPngWrite(unittest.TestCase): + def test_write_gray(self): with tempfile.TemporaryDirectory() as out_dir: image_name = os.path.join(out_dir, "test.png") diff --git a/tests/test_polyval.py b/tests/test_polyval.py index 113c862cb3..f0215678af 100644 --- a/tests/test_polyval.py +++ b/tests/test_polyval.py @@ -31,6 +31,7 @@ class TestPolyval(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_floats(self, coef, x, expected): result = polyval(coef, x) diff --git a/tests/test_prepare_batch_default.py b/tests/test_prepare_batch_default.py index e440f5cfe3..d5a5fbf57e 100644 --- a/tests/test_prepare_batch_default.py +++ b/tests/test_prepare_batch_default.py @@ -20,11 +20,13 @@ class TestNet(torch.nn.Module): + def forward(self, x: torch.Tensor): return x class TestPrepareBatchDefault(unittest.TestCase): + def test_dict_content(self): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") dataloader = [ diff --git a/tests/test_prepare_batch_default_dist.py b/tests/test_prepare_batch_default_dist.py index d015cf4b2f..0c53a74834 100644 --- a/tests/test_prepare_batch_default_dist.py +++ b/tests/test_prepare_batch_default_dist.py @@ -43,11 +43,13 @@ class TestNet(torch.nn.Module): + def forward(self, x: torch.Tensor): return x class DistributedPrepareBatchDefault(DistTestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) @DistCall(nnodes=1, nproc_per_node=2, node_rank=0) def test_compute(self, dataloaders): diff --git a/tests/test_prepare_batch_extra_input.py b/tests/test_prepare_batch_extra_input.py index 1769a19e4a..f20c6e7352 100644 --- a/tests/test_prepare_batch_extra_input.py +++ b/tests/test_prepare_batch_extra_input.py @@ -36,11 +36,13 @@ class TestNet(torch.nn.Module): + def forward(self, x: torch.Tensor, t1=None, t2=None, t3=None): return {"x": x, "t1": t1, "t2": t2, "t3": t3} class TestPrepareBatchExtraInput(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2]) def test_content(self, input_args, expected_value): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") diff --git a/tests/test_prepare_batch_hovernet.py b/tests/test_prepare_batch_hovernet.py index 5a7080a225..773fcb53bf 100644 --- a/tests/test_prepare_batch_hovernet.py +++ b/tests/test_prepare_batch_hovernet.py @@ -28,11 +28,13 @@ class TestNet(torch.nn.Module): + def forward(self, x: torch.Tensor): return {HoVerNetBranch.NP: torch.tensor([1, 2]), HoVerNetBranch.NC: torch.tensor([4, 4]), HoVerNetBranch.HV: 16} class TestPrepareBatchHoVerNet(unittest.TestCase): + @parameterized.expand([TEST_CASE_0]) def test_content(self, input_args, expected_value): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") diff --git a/tests/test_preset_filters.py b/tests/test_preset_filters.py index 9bca24cef3..46ed461f7d 100644 --- a/tests/test_preset_filters.py +++ b/tests/test_preset_filters.py @@ -63,6 +63,7 @@ class _TestFilter: + def test_init(self, spatial_dims, size, expected): test_filter = self.filter_class(spatial_dims=spatial_dims, size=size) torch.testing.assert_allclose(expected, test_filter.filter) @@ -75,6 +76,7 @@ def test_forward(self): class TestApplyFilter(unittest.TestCase): + def test_init_and_forward_2d(self): filter_2d = torch.ones(3, 3) image_2d = torch.ones(1, 3, 3) @@ -91,6 +93,7 @@ def test_init_and_forward_3d(self): class MeanFilterTestCase(_TestFilter, unittest.TestCase): + def setUp(self) -> None: self.filter_class = MeanFilter @@ -100,6 +103,7 @@ def test_init(self, spatial_dims, size, expected): class LaplaceFilterTestCase(_TestFilter, unittest.TestCase): + def setUp(self) -> None: self.filter_class = LaplaceFilter @@ -109,6 +113,7 @@ def test_init(self, spatial_dims, size, expected): class EllipticalTestCase(_TestFilter, unittest.TestCase): + def setUp(self) -> None: self.filter_class = EllipticalFilter @@ -118,6 +123,7 @@ def test_init(self, spatial_dims, size, expected): class SharpenTestCase(_TestFilter, unittest.TestCase): + def setUp(self) -> None: self.filter_class = SharpenFilter diff --git a/tests/test_print_info.py b/tests/test_print_info.py index bb748c3f7b..aa152e183c 100644 --- a/tests/test_print_info.py +++ b/tests/test_print_info.py @@ -17,6 +17,7 @@ class TestPrintInfo(unittest.TestCase): + def test_print_info(self): print_debug_info() diff --git a/tests/test_print_transform_backends.py b/tests/test_print_transform_backends.py index 4cd93c3fb2..2072aa4cfa 100644 --- a/tests/test_print_transform_backends.py +++ b/tests/test_print_transform_backends.py @@ -17,6 +17,7 @@ class TestPrintTransformBackends(unittest.TestCase): + def test_get_number_of_conversions(self): tr_t_or_np, *_ = get_transform_backends() self.assertGreater(len(tr_t_or_np), 0) diff --git a/tests/test_probnms.py b/tests/test_probnms.py index 8da5396fac..2b52583ad4 100644 --- a/tests/test_probnms.py +++ b/tests/test_probnms.py @@ -61,6 +61,7 @@ class TestProbNMS(unittest.TestCase): + @parameterized.expand(TESTS) def test_output(self, class_args, probs_map, expected): nms = ProbNMS(**class_args) diff --git a/tests/test_probnmsd.py b/tests/test_probnmsd.py index 1f0288811e..aeb32bdb79 100644 --- a/tests/test_probnmsd.py +++ b/tests/test_probnmsd.py @@ -68,6 +68,7 @@ class TestProbNMS(unittest.TestCase): + @parameterized.expand(TESTS) def test_output(self, class_args, probs_map, expected): nms = ProbNMSD(keys="prob_map", **class_args) diff --git a/tests/test_profiling.py b/tests/test_profiling.py index 2b93fae196..6bee7ba262 100644 --- a/tests/test_profiling.py +++ b/tests/test_profiling.py @@ -29,6 +29,7 @@ class TestWorkflowProfiler(unittest.TestCase): + def setUp(self): super().setUp() diff --git a/tests/test_pytorch_version_after.py b/tests/test_pytorch_version_after.py index 4c8c032c80..147707d2c0 100644 --- a/tests/test_pytorch_version_after.py +++ b/tests/test_pytorch_version_after.py @@ -38,6 +38,7 @@ class TestPytorchVersionCompare(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_compare(self, a, b, p, current, expected=True): """Test pytorch_after with a and b""" diff --git a/tests/test_query_memory.py b/tests/test_query_memory.py index 5e57913acb..77c34ede39 100644 --- a/tests/test_query_memory.py +++ b/tests/test_query_memory.py @@ -17,6 +17,7 @@ class TestQueryMemory(unittest.TestCase): + def test_output_str(self): self.assertTrue(isinstance(query_memory(2), str)) all_device = query_memory(-1) diff --git a/tests/test_quicknat.py b/tests/test_quicknat.py index b4b89b7d62..f6786405d2 100644 --- a/tests/test_quicknat.py +++ b/tests/test_quicknat.py @@ -38,6 +38,7 @@ @unittest.skipUnless(has_se, "squeeze_and_excitation not installed") class TestQuicknat(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape, expected_shape): device = "cuda" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_rand_adjust_contrast.py b/tests/test_rand_adjust_contrast.py index bfeedc2fcf..72d0df141e 100644 --- a/tests/test_rand_adjust_contrast.py +++ b/tests/test_rand_adjust_contrast.py @@ -25,6 +25,7 @@ class TestRandAdjustContrast(NumpyImageTestCase2D): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_correct_results(self, gamma): adjuster = RandAdjustContrast(prob=1.0, gamma=gamma) diff --git a/tests/test_rand_adjust_contrastd.py b/tests/test_rand_adjust_contrastd.py index 4037266da4..bbd5c22009 100644 --- a/tests/test_rand_adjust_contrastd.py +++ b/tests/test_rand_adjust_contrastd.py @@ -25,6 +25,7 @@ class TestRandAdjustContrastd(NumpyImageTestCase2D): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_correct_results(self, gamma): adjuster = RandAdjustContrastd("img", prob=1.0, gamma=gamma) diff --git a/tests/test_rand_affine.py b/tests/test_rand_affine.py index 915b14bf51..f37f7827bb 100644 --- a/tests/test_rand_affine.py +++ b/tests/test_rand_affine.py @@ -140,6 +140,7 @@ class TestRandAffine(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_affine(self, input_param, input_data, expected_val): g = RandAffine(**input_param) diff --git a/tests/test_rand_affine_grid.py b/tests/test_rand_affine_grid.py index 113987a85c..91558ebd03 100644 --- a/tests/test_rand_affine_grid.py +++ b/tests/test_rand_affine_grid.py @@ -198,6 +198,7 @@ class TestRandAffineGrid(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_affine_grid(self, input_param, input_data, expected_val): g = RandAffineGrid(**input_param) diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py index a607029c1a..20c50954e2 100644 --- a/tests/test_rand_affined.py +++ b/tests/test_rand_affined.py @@ -216,6 +216,7 @@ class TestRandAffined(unittest.TestCase): + @parameterized.expand(x + [y] for x, y in itertools.product(TESTS, (False, True))) def test_rand_affined(self, input_param, input_data, expected_val, track_meta): set_track_meta(track_meta) diff --git a/tests/test_rand_axis_flip.py b/tests/test_rand_axis_flip.py index 81e42372db..9c465a0bcb 100644 --- a/tests/test_rand_axis_flip.py +++ b/tests/test_rand_axis_flip.py @@ -23,6 +23,7 @@ class TestRandAxisFlip(NumpyImageTestCase2D): + def test_correct_results(self): for p in TEST_NDARRAYS_ALL: flip = RandAxisFlip(prob=1.0) diff --git a/tests/test_rand_axis_flipd.py b/tests/test_rand_axis_flipd.py index 75357b23e1..d3abef1be4 100644 --- a/tests/test_rand_axis_flipd.py +++ b/tests/test_rand_axis_flipd.py @@ -23,6 +23,7 @@ class TestRandAxisFlip(NumpyImageTestCase3D): + def test_correct_results(self): for p in TEST_NDARRAYS_ALL: flip = RandAxisFlipd(keys="img", prob=1.0) diff --git a/tests/test_rand_bias_field.py b/tests/test_rand_bias_field.py index 16f615146f..333a9ecba5 100644 --- a/tests/test_rand_bias_field.py +++ b/tests/test_rand_bias_field.py @@ -30,6 +30,7 @@ class TestRandBiasField(unittest.TestCase): + @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D]) def test_output_shape(self, class_args, img_shape): for p in TEST_NDARRAYS: diff --git a/tests/test_rand_bias_fieldd.py b/tests/test_rand_bias_fieldd.py index 2b8a60289d..1f174fa397 100644 --- a/tests/test_rand_bias_fieldd.py +++ b/tests/test_rand_bias_fieldd.py @@ -28,6 +28,7 @@ class TestRandBiasFieldd(unittest.TestCase): + @parameterized.expand([TEST_CASES_2D, TEST_CASES_3D]) def test_output_shape(self, class_args, img_shape): key = "img" diff --git a/tests/test_rand_coarse_dropout.py b/tests/test_rand_coarse_dropout.py index 8c3876f10b..ac857f9184 100644 --- a/tests/test_rand_coarse_dropout.py +++ b/tests/test_rand_coarse_dropout.py @@ -63,6 +63,7 @@ class TestRandCoarseDropout(unittest.TestCase): + @parameterized.expand( [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7] ) diff --git a/tests/test_rand_coarse_dropoutd.py b/tests/test_rand_coarse_dropoutd.py index 7b16f992b7..bfc6a2f27f 100644 --- a/tests/test_rand_coarse_dropoutd.py +++ b/tests/test_rand_coarse_dropoutd.py @@ -63,6 +63,7 @@ class TestRandCoarseDropoutd(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]) def test_value(self, input_param, input_data): dropout = RandCoarseDropoutd(**input_param) diff --git a/tests/test_rand_coarse_shuffle.py b/tests/test_rand_coarse_shuffle.py index adfb722b42..39e62c22a8 100644 --- a/tests/test_rand_coarse_shuffle.py +++ b/tests/test_rand_coarse_shuffle.py @@ -52,6 +52,7 @@ class TestRandCoarseShuffle(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shuffle(self, input_param, input_data, expected_val): g = RandCoarseShuffle(**input_param) diff --git a/tests/test_rand_coarse_shuffled.py b/tests/test_rand_coarse_shuffled.py index 3b5a1434f4..f49066efd9 100644 --- a/tests/test_rand_coarse_shuffled.py +++ b/tests/test_rand_coarse_shuffled.py @@ -46,6 +46,7 @@ class TestRandCoarseShuffled(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shuffle(self, input_param, input_data, expected_val): g = RandCoarseShuffled(**input_param) diff --git a/tests/test_rand_crop_by_label_classes.py b/tests/test_rand_crop_by_label_classes.py index 88d2631ca5..743b894d75 100644 --- a/tests/test_rand_crop_by_label_classes.py +++ b/tests/test_rand_crop_by_label_classes.py @@ -127,6 +127,7 @@ class TestRandCropByLabelClasses(unittest.TestCase): + @parameterized.expand(TESTS_INDICES + TESTS_SHAPE) def test_type_shape(self, input_param, input_data, expected_type, expected_shape): result = RandCropByLabelClasses(**input_param)(**input_data) diff --git a/tests/test_rand_crop_by_label_classesd.py b/tests/test_rand_crop_by_label_classesd.py index 748f26f1ff..8908c456ee 100644 --- a/tests/test_rand_crop_by_label_classesd.py +++ b/tests/test_rand_crop_by_label_classesd.py @@ -120,6 +120,7 @@ class TestRandCropByLabelClassesd(unittest.TestCase): + @parameterized.expand(TESTS) def test_type_shape(self, input_param, input_data, expected_type, expected_shape): result = RandCropByLabelClassesd(**input_param)(input_data) diff --git a/tests/test_rand_crop_by_pos_neg_label.py b/tests/test_rand_crop_by_pos_neg_label.py index 98af6b0b5e..66e7a5e849 100644 --- a/tests/test_rand_crop_by_pos_neg_label.py +++ b/tests/test_rand_crop_by_pos_neg_label.py @@ -96,6 +96,7 @@ class TestRandCropByPosNegLabel(unittest.TestCase): + @staticmethod def convert_data_type(im_type, d, keys=("img", "image", "label")): out = deepcopy(d) diff --git a/tests/test_rand_crop_by_pos_neg_labeld.py b/tests/test_rand_crop_by_pos_neg_labeld.py index 1b57548d12..11381e226d 100644 --- a/tests/test_rand_crop_by_pos_neg_labeld.py +++ b/tests/test_rand_crop_by_pos_neg_labeld.py @@ -107,6 +107,7 @@ class TestRandCropByPosNegLabeld(unittest.TestCase): + @staticmethod def convert_data_type(im_type, d, keys=("img", "image", "label")): out = deepcopy(d) diff --git a/tests/test_rand_cucim_dict_transform.py b/tests/test_rand_cucim_dict_transform.py index 33e0667723..3f473897dd 100644 --- a/tests/test_rand_cucim_dict_transform.py +++ b/tests/test_rand_cucim_dict_transform.py @@ -78,6 +78,7 @@ @unittest.skipUnless(HAS_CUPY, "CuPy is required.") @unittest.skipUnless(has_cut, "cuCIM transforms are required.") class TestRandCuCIMDict(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_COLOR_JITTER_1, diff --git a/tests/test_rand_cucim_transform.py b/tests/test_rand_cucim_transform.py index 37d8e29f1d..ce731a05ae 100644 --- a/tests/test_rand_cucim_transform.py +++ b/tests/test_rand_cucim_transform.py @@ -78,6 +78,7 @@ @unittest.skipUnless(HAS_CUPY, "CuPy is required.") @unittest.skipUnless(has_cut, "cuCIM transforms are required.") class TestRandCuCIM(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_COLOR_JITTER_1, diff --git a/tests/test_rand_deform_grid.py b/tests/test_rand_deform_grid.py index 58b64ae596..88fc1333ec 100644 --- a/tests/test_rand_deform_grid.py +++ b/tests/test_rand_deform_grid.py @@ -126,6 +126,7 @@ class TestRandDeformGrid(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_rand_deform_grid(self, input_param, input_data, expected_val): g = RandDeformGrid(**input_param) diff --git a/tests/test_rand_elastic_2d.py b/tests/test_rand_elastic_2d.py index c59052854f..1f3d389a93 100644 --- a/tests/test_rand_elastic_2d.py +++ b/tests/test_rand_elastic_2d.py @@ -110,6 +110,7 @@ class TestRand2DElastic(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_2d_elastic(self, input_param, input_data, expected_val): g = Rand2DElastic(**input_param) diff --git a/tests/test_rand_elastic_3d.py b/tests/test_rand_elastic_3d.py index 0ff3ef6129..5bfa8a6e83 100644 --- a/tests/test_rand_elastic_3d.py +++ b/tests/test_rand_elastic_3d.py @@ -86,6 +86,7 @@ class TestRand3DElastic(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_3d_elastic(self, input_param, input_data, expected_val): g = Rand3DElastic(**input_param) diff --git a/tests/test_rand_elasticd_2d.py b/tests/test_rand_elasticd_2d.py index d0fbd5aa88..10aa116192 100644 --- a/tests/test_rand_elasticd_2d.py +++ b/tests/test_rand_elasticd_2d.py @@ -160,6 +160,7 @@ class TestRand2DElasticd(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_2d_elasticd(self, input_param, input_data, expected_val): g = Rand2DElasticd(**input_param) diff --git a/tests/test_rand_elasticd_3d.py b/tests/test_rand_elasticd_3d.py index e058293584..3838f43f29 100644 --- a/tests/test_rand_elasticd_3d.py +++ b/tests/test_rand_elasticd_3d.py @@ -139,6 +139,7 @@ class TestRand3DElasticd(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_3d_elasticd(self, input_param, input_data, expected_val): g = Rand3DElasticd(**input_param) diff --git a/tests/test_rand_flip.py b/tests/test_rand_flip.py index c3b0bfdede..faeae94cab 100644 --- a/tests/test_rand_flip.py +++ b/tests/test_rand_flip.py @@ -28,6 +28,7 @@ class TestRandFlip(NumpyImageTestCase2D): + @parameterized.expand(INVALID_CASES) def test_invalid_inputs(self, _, spatial_axis, raises): with self.assertRaises(raises): diff --git a/tests/test_rand_flipd.py b/tests/test_rand_flipd.py index be5394c172..a34aa58ed2 100644 --- a/tests/test_rand_flipd.py +++ b/tests/test_rand_flipd.py @@ -26,6 +26,7 @@ class TestRandFlipd(NumpyImageTestCase2D): + @parameterized.expand(VALID_CASES) def test_correct_results(self, _, spatial_axis): for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_rand_gaussian_noise.py b/tests/test_rand_gaussian_noise.py index 7d4d04ff3f..a56e54fe31 100644 --- a/tests/test_rand_gaussian_noise.py +++ b/tests/test_rand_gaussian_noise.py @@ -27,6 +27,7 @@ class TestRandGaussianNoise(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, _, im_type, mean, std): seed = 0 diff --git a/tests/test_rand_gaussian_noised.py b/tests/test_rand_gaussian_noised.py index 24fc19f226..bcbed98b5a 100644 --- a/tests/test_rand_gaussian_noised.py +++ b/tests/test_rand_gaussian_noised.py @@ -29,6 +29,7 @@ class TestRandGaussianNoised(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, _, im_type, keys, mean, std): gaussian_fn = RandGaussianNoised(keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64) diff --git a/tests/test_rand_gaussian_sharpen.py b/tests/test_rand_gaussian_sharpen.py index 8dff69cd4c..ee8604c14b 100644 --- a/tests/test_rand_gaussian_sharpen.py +++ b/tests/test_rand_gaussian_sharpen.py @@ -128,6 +128,7 @@ class TestRandGaussianSharpen(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): converter = RandGaussianSharpen(**arguments) diff --git a/tests/test_rand_gaussian_sharpend.py b/tests/test_rand_gaussian_sharpend.py index 4c32880053..b9bae529db 100644 --- a/tests/test_rand_gaussian_sharpend.py +++ b/tests/test_rand_gaussian_sharpend.py @@ -131,6 +131,7 @@ class TestRandGaussianSharpend(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): converter = RandGaussianSharpend(**arguments) diff --git a/tests/test_rand_gaussian_smooth.py b/tests/test_rand_gaussian_smooth.py index 9fb91a38a1..8bb36ca0fa 100644 --- a/tests/test_rand_gaussian_smooth.py +++ b/tests/test_rand_gaussian_smooth.py @@ -86,6 +86,7 @@ class TestRandGaussianSmooth(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): converter = RandGaussianSmooth(**arguments) diff --git a/tests/test_rand_gaussian_smoothd.py b/tests/test_rand_gaussian_smoothd.py index d312494e46..a93b355184 100644 --- a/tests/test_rand_gaussian_smoothd.py +++ b/tests/test_rand_gaussian_smoothd.py @@ -86,6 +86,7 @@ class TestRandGaussianSmoothd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): converter = RandGaussianSmoothd(**arguments) diff --git a/tests/test_rand_gibbs_noise.py b/tests/test_rand_gibbs_noise.py index a0d18ae7f3..4befeffbe2 100644 --- a/tests/test_rand_gibbs_noise.py +++ b/tests/test_rand_gibbs_noise.py @@ -32,6 +32,7 @@ class TestRandGibbsNoise(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_rand_gibbs_noised.py b/tests/test_rand_gibbs_noised.py index 4120f967e2..6580189af6 100644 --- a/tests/test_rand_gibbs_noised.py +++ b/tests/test_rand_gibbs_noised.py @@ -34,6 +34,7 @@ class TestRandGibbsNoised(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_rand_grid_distortion.py b/tests/test_rand_grid_distortion.py index 8131a2382a..e07c311b25 100644 --- a/tests/test_rand_grid_distortion.py +++ b/tests/test_rand_grid_distortion.py @@ -84,6 +84,7 @@ class TestRandGridDistortion(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_grid_distortion(self, input_param, seed, input_data, expected_val): g = RandGridDistortion(**input_param) diff --git a/tests/test_rand_grid_distortiond.py b/tests/test_rand_grid_distortiond.py index 9f8ed3b9e6..f28e0ae86e 100644 --- a/tests/test_rand_grid_distortiond.py +++ b/tests/test_rand_grid_distortiond.py @@ -77,6 +77,7 @@ class TestRandGridDistortiond(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_grid_distortiond(self, input_param, seed, input_data, expected_val_img, expected_val_mask): g = RandGridDistortiond(**input_param) diff --git a/tests/test_rand_grid_patch.py b/tests/test_rand_grid_patch.py index 494330584a..26863f01b2 100644 --- a/tests/test_rand_grid_patch.py +++ b/tests/test_rand_grid_patch.py @@ -105,6 +105,7 @@ class TestRandGridPatch(unittest.TestCase): + def setUp(self): set_determinism(seed=1234) diff --git a/tests/test_rand_grid_patchd.py b/tests/test_rand_grid_patchd.py index 23ca4a7881..031e834512 100644 --- a/tests/test_rand_grid_patchd.py +++ b/tests/test_rand_grid_patchd.py @@ -85,6 +85,7 @@ class TestRandGridPatchd(unittest.TestCase): + def setUp(self): set_determinism(seed=1234) diff --git a/tests/test_rand_histogram_shift.py b/tests/test_rand_histogram_shift.py index 318dad9dfa..785e24e53b 100644 --- a/tests/test_rand_histogram_shift.py +++ b/tests/test_rand_histogram_shift.py @@ -56,6 +56,7 @@ class TestRandHistogramShift(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_histogram_shift(self, input_param, input_data, expected_val): g = RandHistogramShift(**input_param) diff --git a/tests/test_rand_histogram_shiftd.py b/tests/test_rand_histogram_shiftd.py index 45e81ab012..fced270e90 100644 --- a/tests/test_rand_histogram_shiftd.py +++ b/tests/test_rand_histogram_shiftd.py @@ -61,6 +61,7 @@ class TestRandHistogramShiftD(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_histogram_shiftd(self, input_param, input_data, expected_val): g = RandHistogramShiftd(**input_param) diff --git a/tests/test_rand_k_space_spike_noise.py b/tests/test_rand_k_space_spike_noise.py index 4e7d59329b..7a9dd4288d 100644 --- a/tests/test_rand_k_space_spike_noise.py +++ b/tests/test_rand_k_space_spike_noise.py @@ -29,6 +29,7 @@ class TestRandKSpaceSpikeNoise(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_rand_k_space_spike_noised.py b/tests/test_rand_k_space_spike_noised.py index 3e1c11b2d9..86d4256637 100644 --- a/tests/test_rand_k_space_spike_noised.py +++ b/tests/test_rand_k_space_spike_noised.py @@ -30,6 +30,7 @@ class TestKSpaceSpikeNoised(unittest.TestCase): + def setUp(self): set_determinism(0) super().setUp() diff --git a/tests/test_rand_lambda.py b/tests/test_rand_lambda.py index 1f14499bc0..98a324aec5 100644 --- a/tests/test_rand_lambda.py +++ b/tests/test_rand_lambda.py @@ -37,6 +37,7 @@ def __call__(self, data): class TestRandLambda(unittest.TestCase): + def check(self, tr: RandLambda, img, img_orig_type, out, expected=None): # input shouldn't change self.assertIsInstance(img, img_orig_type) diff --git a/tests/test_rand_lambdad.py b/tests/test_rand_lambdad.py index 6b60a3fe70..5247d79843 100644 --- a/tests/test_rand_lambdad.py +++ b/tests/test_rand_lambdad.py @@ -37,6 +37,7 @@ def __call__(self, data): class TestRandLambdad(unittest.TestCase): + def check(self, tr: RandLambdad, input: dict, out: dict, expected: dict): if isinstance(input["img"], MetaTensor): self.assertEqual(len(input["img"].applied_operations), 0) diff --git a/tests/test_rand_rician_noise.py b/tests/test_rand_rician_noise.py index fe7135835e..8dd1c48e29 100644 --- a/tests/test_rand_rician_noise.py +++ b/tests/test_rand_rician_noise.py @@ -27,6 +27,7 @@ class TestRandRicianNoise(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, _, in_type, mean, std): seed = 0 diff --git a/tests/test_rand_rician_noised.py b/tests/test_rand_rician_noised.py index ae0acab4eb..a190ba866d 100644 --- a/tests/test_rand_rician_noised.py +++ b/tests/test_rand_rician_noised.py @@ -29,6 +29,7 @@ class TestRandRicianNoisedNumpy(NumpyImageTestCase2D): + @parameterized.expand(TESTS) def test_correct_results(self, _, in_type, keys, mean, std): rician_fn = RandRicianNoised(keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64) diff --git a/tests/test_rand_rotate.py b/tests/test_rand_rotate.py index ca3eda3b12..c54229dcfe 100644 --- a/tests/test_rand_rotate.py +++ b/tests/test_rand_rotate.py @@ -73,6 +73,7 @@ class TestRandRotate2D(NumpyImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, align_corners): init_param = { @@ -112,6 +113,7 @@ def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, @unittest.skipIf(USE_COMPILED, "unit tests not for compiled version.") class TestRandRotate3D(NumpyImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, x, y, z, keep_size, mode, padding_mode, align_corners, expected): init_param = { @@ -146,6 +148,7 @@ def test_correct_results(self, im_type, x, y, z, keep_size, mode, padding_mode, class TestRandRotateDtype(NumpyImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, align_corners): rotate_fn = RandRotate( diff --git a/tests/test_rand_rotate90.py b/tests/test_rand_rotate90.py index 88f88bf422..be2e658b78 100644 --- a/tests/test_rand_rotate90.py +++ b/tests/test_rand_rotate90.py @@ -23,6 +23,7 @@ class TestRandRotate90(NumpyImageTestCase2D): + def test_default(self): rotate = RandRotate90() for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_rand_rotate90d.py b/tests/test_rand_rotate90d.py index 23e9025c08..02836b5dd8 100644 --- a/tests/test_rand_rotate90d.py +++ b/tests/test_rand_rotate90d.py @@ -23,6 +23,7 @@ class TestRandRotate90d(NumpyImageTestCase2D): + def test_default(self): key = "test" rotate = RandRotate90d(keys=key) diff --git a/tests/test_rand_rotated.py b/tests/test_rand_rotated.py index a5a377b02f..71d0f67b63 100644 --- a/tests/test_rand_rotated.py +++ b/tests/test_rand_rotated.py @@ -109,6 +109,7 @@ class TestRandRotated2D(NumpyImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, align_corners): init_param = { @@ -153,6 +154,7 @@ def test_correct_results(self, im_type, degrees, keep_size, mode, padding_mode, @unittest.skipIf(USE_COMPILED, "unit tests not for compiled version.") class TestRandRotated3D(NumpyImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_correct_shapes(self, im_type, x, y, z, keep_size, mode, padding_mode, align_corners, expected): init_param = { diff --git a/tests/test_rand_scale_intensity.py b/tests/test_rand_scale_intensity.py index a857c0cefb..7e999c00b3 100644 --- a/tests/test_rand_scale_intensity.py +++ b/tests/test_rand_scale_intensity.py @@ -21,6 +21,7 @@ class TestRandScaleIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_value(self, p): scaler = RandScaleIntensity(factors=0.5, prob=1.0) diff --git a/tests/test_rand_scale_intensity_fixed_mean.py b/tests/test_rand_scale_intensity_fixed_mean.py index f43adab32f..9324c711fa 100644 --- a/tests/test_rand_scale_intensity_fixed_mean.py +++ b/tests/test_rand_scale_intensity_fixed_mean.py @@ -21,6 +21,7 @@ class TestRandScaleIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_value(self, p): scaler = RandScaleIntensityFixedMean(prob=1.0, factors=0.5) diff --git a/tests/test_rand_scale_intensity_fixed_meand.py b/tests/test_rand_scale_intensity_fixed_meand.py index c85c764a55..8c127ac130 100644 --- a/tests/test_rand_scale_intensity_fixed_meand.py +++ b/tests/test_rand_scale_intensity_fixed_meand.py @@ -20,6 +20,7 @@ class TestRandScaleIntensityFixedMeand(NumpyImageTestCase2D): + def test_value(self): key = "img" for p in TEST_NDARRAYS: diff --git a/tests/test_rand_scale_intensityd.py b/tests/test_rand_scale_intensityd.py index 8d928ac157..32c96f0313 100644 --- a/tests/test_rand_scale_intensityd.py +++ b/tests/test_rand_scale_intensityd.py @@ -20,6 +20,7 @@ class TestRandScaleIntensityd(NumpyImageTestCase2D): + def test_value(self): key = "img" for p in TEST_NDARRAYS: diff --git a/tests/test_rand_shift_intensity.py b/tests/test_rand_shift_intensity.py index 01ac55f7b8..907773ccf5 100644 --- a/tests/test_rand_shift_intensity.py +++ b/tests/test_rand_shift_intensity.py @@ -21,6 +21,7 @@ class TestRandShiftIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_value(self, p): shifter = RandShiftIntensity(offsets=1.0, prob=1.0) diff --git a/tests/test_rand_shift_intensityd.py b/tests/test_rand_shift_intensityd.py index 7522676eb0..51675e324c 100644 --- a/tests/test_rand_shift_intensityd.py +++ b/tests/test_rand_shift_intensityd.py @@ -21,6 +21,7 @@ class TestRandShiftIntensityd(NumpyImageTestCase2D): + def test_value(self): key = "img" for p in TEST_NDARRAYS: diff --git a/tests/test_rand_simulate_low_resolution.py b/tests/test_rand_simulate_low_resolution.py index 7d05faad36..6aa586fb0b 100644 --- a/tests/test_rand_simulate_low_resolution.py +++ b/tests/test_rand_simulate_low_resolution.py @@ -71,6 +71,7 @@ class TestRandGaussianSmooth(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): randsimlowres = RandSimulateLowResolution(**arguments) diff --git a/tests/test_rand_simulate_low_resolutiond.py b/tests/test_rand_simulate_low_resolutiond.py index f058ec3b2b..5ec84eba1d 100644 --- a/tests/test_rand_simulate_low_resolutiond.py +++ b/tests/test_rand_simulate_low_resolutiond.py @@ -60,6 +60,7 @@ class TestRandGaussianSmoothd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, arguments, image, expected_data): converter = RandSimulateLowResolutiond(**arguments) diff --git a/tests/test_rand_spatial_crop_samplesd.py b/tests/test_rand_spatial_crop_samplesd.py index b37dacd643..cb53e94b7d 100644 --- a/tests/test_rand_spatial_crop_samplesd.py +++ b/tests/test_rand_spatial_crop_samplesd.py @@ -90,6 +90,7 @@ class TestRandSpatialCropSamplesd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, *TEST_CASE_2]) def test_shape(self, input_param, input_data, expected_shape, expected_last): xform = RandSpatialCropSamplesd(**input_param) diff --git a/tests/test_rand_std_shift_intensity.py b/tests/test_rand_std_shift_intensity.py index 535fb7cb20..0ac5e9482e 100644 --- a/tests/test_rand_std_shift_intensity.py +++ b/tests/test_rand_std_shift_intensity.py @@ -22,6 +22,7 @@ class TestRandStdShiftIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_value(self, p): np.random.seed(0) diff --git a/tests/test_rand_std_shift_intensityd.py b/tests/test_rand_std_shift_intensityd.py index 31209ee754..1fd0c5d2a8 100644 --- a/tests/test_rand_std_shift_intensityd.py +++ b/tests/test_rand_std_shift_intensityd.py @@ -20,6 +20,7 @@ class TestRandStdShiftIntensityd(NumpyImageTestCase2D): + def test_value(self): for p in TEST_NDARRAYS: key = "img" diff --git a/tests/test_rand_weighted_cropd.py b/tests/test_rand_weighted_cropd.py index 9d37779613..1524442f61 100644 --- a/tests/test_rand_weighted_cropd.py +++ b/tests/test_rand_weighted_cropd.py @@ -148,6 +148,7 @@ def get_data(ndim): class TestRandWeightedCrop(unittest.TestCase): + @parameterized.expand(TESTS) def test_rand_weighted_cropd(self, _, init_params, input_data, expected_shape, expected_centers): crop = RandWeightedCropd(**init_params) diff --git a/tests/test_rand_zoom.py b/tests/test_rand_zoom.py index d52b79d8cf..2da04fd652 100644 --- a/tests/test_rand_zoom.py +++ b/tests/test_rand_zoom.py @@ -33,6 +33,7 @@ class TestRandZoom(NumpyImageTestCase2D): + @parameterized.expand(VALID_CASES) def test_correct_results(self, min_zoom, max_zoom, mode, keep_size, align_corners=None): for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_rand_zoomd.py b/tests/test_rand_zoomd.py index bb0495c793..bcbf188310 100644 --- a/tests/test_rand_zoomd.py +++ b/tests/test_rand_zoomd.py @@ -31,6 +31,7 @@ class TestRandZoomd(NumpyImageTestCase2D): + @parameterized.expand(VALID_CASES) def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size): key = "img" diff --git a/tests/test_randidentity.py b/tests/test_randidentity.py index 09dc055b4e..3a8936f2d2 100644 --- a/tests/test_randidentity.py +++ b/tests/test_randidentity.py @@ -19,11 +19,13 @@ class T(mt.Transform): + def __call__(self, x): return x * 2 class TestIdentity(NumpyImageTestCase2D): + def test_identity(self): for p in TEST_NDARRAYS: img = p(self.imt) diff --git a/tests/test_random_order.py b/tests/test_random_order.py index e5507fafca..b38d2398fb 100644 --- a/tests/test_random_order.py +++ b/tests/test_random_order.py @@ -30,6 +30,7 @@ class InvC(Inv): + def __init__(self, keys): super().__init__(keys) self.fwd_fn = lambda x: x + 1 @@ -37,6 +38,7 @@ def __init__(self, keys): class InvD(Inv): + def __init__(self, keys): super().__init__(keys) self.fwd_fn = lambda x: x * 100 @@ -55,6 +57,7 @@ def __init__(self, keys): class TestRandomOrder(unittest.TestCase): + def test_empty_compose(self): c = RandomOrder() i = 1 @@ -113,6 +116,7 @@ def test_inverse(self, transform, invertible, use_metatensor): class TestRandomOrderAPITests(unittest.TestCase): + @staticmethod def data_from_keys(keys): if keys is None: diff --git a/tests/test_randomizable.py b/tests/test_randomizable.py index 96854a6db8..56d5293130 100644 --- a/tests/test_randomizable.py +++ b/tests/test_randomizable.py @@ -19,11 +19,13 @@ class RandTest(Randomizable): + def randomize(self, data=None): pass class TestRandomizable(unittest.TestCase): + def test_default(self): inst = RandTest() r1 = inst.R.rand() diff --git a/tests/test_randomizable_transform_type.py b/tests/test_randomizable_transform_type.py index 3a0995be68..919f9299bf 100644 --- a/tests/test_randomizable_transform_type.py +++ b/tests/test_randomizable_transform_type.py @@ -21,11 +21,13 @@ class InheritsInterface(RandomizableTrait): class InheritsImplementation(RandomizableTransform): + def __call__(self, data): return data class TestRandomizableTransformType(unittest.TestCase): + def test_is_randomizable_transform_type(self): inst = InheritsInterface() self.assertIsInstance(inst, RandomizableTrait) diff --git a/tests/test_randtorchvisiond.py b/tests/test_randtorchvisiond.py index 82f9adf473..7ad06dfd2a 100644 --- a/tests/test_randtorchvisiond.py +++ b/tests/test_randtorchvisiond.py @@ -52,6 +52,7 @@ class TestRandTorchVisiond(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_value(self, input_param, input_data, expected_value): set_determinism(seed=0) diff --git a/tests/test_rankfilter_dist.py b/tests/test_rankfilter_dist.py index 40cd36f31d..fd02e3bdc9 100644 --- a/tests/test_rankfilter_dist.py +++ b/tests/test_rankfilter_dist.py @@ -23,6 +23,7 @@ class DistributedRankFilterTest(DistTestCase): + def setUp(self): self.log_dir = tempfile.TemporaryDirectory() @@ -50,6 +51,7 @@ def tearDown(self) -> None: class SingleRankFilterTest(unittest.TestCase): + def tearDown(self) -> None: self.log_dir.cleanup() diff --git a/tests/test_recon_net_utils.py b/tests/test_recon_net_utils.py index 38adb9617b..1815000777 100644 --- a/tests/test_recon_net_utils.py +++ b/tests/test_recon_net_utils.py @@ -49,6 +49,7 @@ class TestReconNetUtils(unittest.TestCase): + @parameterized.expand(TEST_RESHAPE) def test_reshape_channel_complex(self, test_data): result = reshape_complex_to_channel_dim(test_data) diff --git a/tests/test_reference_based_normalize_intensity.py b/tests/test_reference_based_normalize_intensity.py index 8d2715f983..2d946af118 100644 --- a/tests/test_reference_based_normalize_intensity.py +++ b/tests/test_reference_based_normalize_intensity.py @@ -52,6 +52,7 @@ class TestDetailedNormalizeIntensityd(unittest.TestCase): + @parameterized.expand(TESTS) def test_target_mean_std(self, args, data, normalized_data, normalized_target, mean, std): dtype = data[args["keys"][0]].dtype diff --git a/tests/test_reference_based_spatial_cropd.py b/tests/test_reference_based_spatial_cropd.py index d5777482c0..83cd9c4a5d 100644 --- a/tests/test_reference_based_spatial_cropd.py +++ b/tests/test_reference_based_spatial_cropd.py @@ -46,6 +46,7 @@ class TestTargetBasedSpatialCropd(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, args, data, expected_shape): cropper = ReferenceBasedSpatialCropd(keys=args["keys"], ref_key=args["ref_key"]) diff --git a/tests/test_reference_resolver.py b/tests/test_reference_resolver.py index 07d56a16df..1f02bb01a7 100644 --- a/tests/test_reference_resolver.py +++ b/tests/test_reference_resolver.py @@ -70,6 +70,7 @@ class TestReferenceResolver(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2] + ([TEST_CASE_3] if has_tv else [])) def test_resolve(self, configs, expected_id, output_type): locator = ComponentLocator() diff --git a/tests/test_reg_loss_integration.py b/tests/test_reg_loss_integration.py index 6cd973c32e..e8f82eb0c2 100644 --- a/tests/test_reg_loss_integration.py +++ b/tests/test_reg_loss_integration.py @@ -32,6 +32,7 @@ class TestRegLossIntegration(unittest.TestCase): + def setUp(self): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False @@ -61,6 +62,7 @@ def test_convergence(self, loss_type, loss_args, forward_args, pred_channels=1): # define a one layer model class OnelayerNet(nn.Module): + def __init__(self): super().__init__() self.layer = nn.Sequential( diff --git a/tests/test_regunet.py b/tests/test_regunet.py index 04ff60ef30..3100d7660c 100644 --- a/tests/test_regunet.py +++ b/tests/test_regunet.py @@ -63,6 +63,7 @@ class TestREGUNET(unittest.TestCase): + @parameterized.expand(TEST_CASE_REGUNET_2D + TEST_CASE_REGUNET_3D) def test_shape(self, input_param, input_shape, expected_shape): net = RegUNet(**input_param).to(device) diff --git a/tests/test_regunet_block.py b/tests/test_regunet_block.py index eebe9d8694..fa07671d03 100644 --- a/tests/test_regunet_block.py +++ b/tests/test_regunet_block.py @@ -65,6 +65,7 @@ class TestRegistrationResidualConvBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_RESIDUAL) def test_shape(self, input_param, input_shape, expected_shape): net = RegistrationResidualConvBlock(**input_param) @@ -74,6 +75,7 @@ def test_shape(self, input_param, input_shape, expected_shape): class TestRegistrationDownSampleBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_DOWN_SAMPLE) def test_shape(self, input_param, input_shape, expected_shape): net = RegistrationDownSampleBlock(**input_param) @@ -88,6 +90,7 @@ def test_ill_shape(self): class TestRegistrationExtractionBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_EXTRACTION) def test_shape(self, input_param, input_shapes, image_size, expected_shape): net = RegistrationExtractionBlock(**input_param) diff --git a/tests/test_remove_repeated_channel.py b/tests/test_remove_repeated_channel.py index 90b1b79b03..7da00ee75d 100644 --- a/tests/test_remove_repeated_channel.py +++ b/tests/test_remove_repeated_channel.py @@ -24,6 +24,7 @@ class TestRemoveRepeatedChannel(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_shape): result = RemoveRepeatedChannel(**input_param)(input_data) diff --git a/tests/test_remove_repeated_channeld.py b/tests/test_remove_repeated_channeld.py index 6d36d32f6f..08ec7fb44c 100644 --- a/tests/test_remove_repeated_channeld.py +++ b/tests/test_remove_repeated_channeld.py @@ -34,6 +34,7 @@ class TestRemoveRepeatedChanneld(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input_data, expected_shape): result = RemoveRepeatedChanneld(**input_param)(input_data) diff --git a/tests/test_remove_small_objects.py b/tests/test_remove_small_objects.py index 200f4ed9b2..633a6d9a99 100644 --- a/tests/test_remove_small_objects.py +++ b/tests/test_remove_small_objects.py @@ -55,6 +55,7 @@ @SkipIfNoModule("skimage.morphology") class TestRemoveSmallObjects(unittest.TestCase): + @parameterized.expand(TESTS) def test_remove_small_objects(self, dtype, im_type, lbl, expected, params=None): params = params or {} diff --git a/tests/test_repeat_channel.py b/tests/test_repeat_channel.py index 0ae5743836..82d1d92bd2 100644 --- a/tests/test_repeat_channel.py +++ b/tests/test_repeat_channel.py @@ -24,6 +24,7 @@ class TestRepeatChannel(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input_data, expected_shape): result = RepeatChannel(**input_param)(input_data) diff --git a/tests/test_repeat_channeld.py b/tests/test_repeat_channeld.py index 9f7872135d..2be13a08d1 100644 --- a/tests/test_repeat_channeld.py +++ b/tests/test_repeat_channeld.py @@ -31,6 +31,7 @@ class TestRepeatChanneld(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, input_data, expected_shape): result = RepeatChanneld(**input_param)(input_data) diff --git a/tests/test_replace_module.py b/tests/test_replace_module.py index cac3fd39e5..f3964ac65d 100644 --- a/tests/test_replace_module.py +++ b/tests/test_replace_module.py @@ -32,6 +32,7 @@ class TestReplaceModule(unittest.TestCase): + def setUp(self): self.net = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3) self.num_relus = self.get_num_modules(torch.nn.ReLU) diff --git a/tests/test_require_pkg.py b/tests/test_require_pkg.py index b1a3d82a17..065a7509a4 100644 --- a/tests/test_require_pkg.py +++ b/tests/test_require_pkg.py @@ -17,7 +17,9 @@ class TestRequirePkg(unittest.TestCase): + def test_class(self): + @require_pkg(pkg_name="torch", version="1.4", version_checker=min_version) class TestClass: pass @@ -25,6 +27,7 @@ class TestClass: TestClass() def test_function(self): + @require_pkg(pkg_name="torch", version="1.4", version_checker=min_version) def test_func(x): return x @@ -32,6 +35,7 @@ def test_func(x): test_func(x=None) def test_warning(self): + @require_pkg(pkg_name="test123", raise_error=False) def test_func(x): return x diff --git a/tests/test_resample.py b/tests/test_resample.py index c90dc5f13d..68b08b8b87 100644 --- a/tests/test_resample.py +++ b/tests/test_resample.py @@ -35,6 +35,7 @@ def rotate_90_2d(): class TestResampleFunction(unittest.TestCase): + @parameterized.expand(RESAMPLE_FUNCTION_CASES) def test_resample_function_impl(self, img, matrix, expected): out = resample(convert_to_tensor(img), matrix, {"lazy_shape": img.shape[1:], "lazy_padding_mode": "border"}) diff --git a/tests/test_resample_backends.py b/tests/test_resample_backends.py index 97ee0731e8..7ddd9c7ec2 100644 --- a/tests/test_resample_backends.py +++ b/tests/test_resample_backends.py @@ -44,6 +44,7 @@ @SkipIfBeforePyTorchVersion((1, 9, 1)) class TestResampleBackends(unittest.TestCase): + @parameterized.expand(TEST_IDENTITY) def test_resample_identity(self, input_param, im_type, interp, padding, input_shape): """test resampling of an identity grid with padding 2, im_type, interp, padding, input_shape""" diff --git a/tests/test_resample_datalist.py b/tests/test_resample_datalist.py index ae52492953..ac5cb25bb3 100644 --- a/tests/test_resample_datalist.py +++ b/tests/test_resample_datalist.py @@ -32,6 +32,7 @@ class TestResampleDatalist(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_value_shape(self, input_param, expected): result = resample_datalist(**input_param) diff --git a/tests/test_resample_to_match.py b/tests/test_resample_to_match.py index b12ffd04be..f0d34547a7 100644 --- a/tests/test_resample_to_match.py +++ b/tests/test_resample_to_match.py @@ -46,6 +46,7 @@ def get_rand_fname(len=10, suffix=".nii.gz"): @unittest.skipUnless(has_itk, "itk not installed") class TestResampleToMatch(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_resample_to_matchd.py b/tests/test_resample_to_matchd.py index 748e830bdd..9d104bf392 100644 --- a/tests/test_resample_to_matchd.py +++ b/tests/test_resample_to_matchd.py @@ -36,6 +36,7 @@ def update_fname(d): class TestResampleToMatchd(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_resampler.py b/tests/test_resampler.py index 50ea344090..af0db657aa 100644 --- a/tests/test_resampler.py +++ b/tests/test_resampler.py @@ -152,6 +152,7 @@ class TestResample(unittest.TestCase): + @parameterized.expand(TESTS) def test_resample(self, input_param, input_data, expected_val): g = Resample(**input_param) diff --git a/tests/test_resize.py b/tests/test_resize.py index 97a8f8dab2..33abfe4e1f 100644 --- a/tests/test_resize.py +++ b/tests/test_resize.py @@ -39,6 +39,7 @@ class TestResize(NumpyImageTestCase2D): + def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resize(spatial_size=(128, 128, 3), mode="order") diff --git a/tests/test_resize_with_pad_or_crop.py b/tests/test_resize_with_pad_or_crop.py index 287df039b8..daf257f89f 100644 --- a/tests/test_resize_with_pad_or_crop.py +++ b/tests/test_resize_with_pad_or_crop.py @@ -48,6 +48,7 @@ class TestResizeWithPadOrCrop(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_pad_shape(self, input_param, input_shape, expected_shape, _): for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_resize_with_pad_or_cropd.py b/tests/test_resize_with_pad_or_cropd.py index 471144a609..391e0feb22 100644 --- a/tests/test_resize_with_pad_or_cropd.py +++ b/tests/test_resize_with_pad_or_cropd.py @@ -46,6 +46,7 @@ class TestResizeWithPadOrCropd(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_pad_shape(self, input_param, input_data, expected_val): for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_resized.py b/tests/test_resized.py index bd711b33d8..ab4c9815ea 100644 --- a/tests/test_resized.py +++ b/tests/test_resized.py @@ -59,6 +59,7 @@ class TestResized(NumpyImageTestCase2D): + def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resized(keys="img", spatial_size=(128, 128, 3), mode="order") diff --git a/tests/test_resnet.py b/tests/test_resnet.py index 15ec6353f9..ad1aad8fc6 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -192,6 +192,7 @@ class TestResNet(unittest.TestCase): + def setUp(self): self.tmp_ckpt_filename = os.path.join("tests", "monai_unittest_tmp_ckpt.pth") diff --git a/tests/test_retinanet.py b/tests/test_retinanet.py index 074a5b63fa..f36708d5b3 100644 --- a/tests/test_retinanet.py +++ b/tests/test_retinanet.py @@ -101,6 +101,7 @@ @unittest.skipUnless(has_torchvision, "Requires torchvision") @skip_if_quick class TestRetinaNet(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_retina_shape(self, model, input_param, input_shape): backbone = model(**input_param) diff --git a/tests/test_retinanet_detector.py b/tests/test_retinanet_detector.py index 7292bc0c49..691254fd87 100644 --- a/tests/test_retinanet_detector.py +++ b/tests/test_retinanet_detector.py @@ -93,6 +93,7 @@ class NaiveNetwork(torch.nn.Module): + def __init__(self, spatial_dims, num_classes, **kwargs): super().__init__() self.spatial_dims = spatial_dims @@ -114,6 +115,7 @@ def forward(self, images): @unittest.skipUnless(has_torchvision, "Requires torchvision") @skip_if_quick class TestRetinaNetDetector(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_retina_detector_resnet_backbone_shape(self, input_param, input_shape): returned_layers = [1] diff --git a/tests/test_retinanet_predict_utils.py b/tests/test_retinanet_predict_utils.py index d97806e91c..d909699469 100644 --- a/tests/test_retinanet_predict_utils.py +++ b/tests/test_retinanet_predict_utils.py @@ -85,6 +85,7 @@ class NaiveNetwork(torch.nn.Module): + def __init__(self, spatial_dims, num_classes, **kwargs): super().__init__() self.spatial_dims = spatial_dims @@ -103,6 +104,7 @@ def forward(self, images): class NaiveNetwork2(torch.nn.Module): + def __init__(self, spatial_dims, num_classes, **kwargs): super().__init__() self.spatial_dims = spatial_dims @@ -121,6 +123,7 @@ def forward(self, images): class TestPredictor(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_naive_predictor(self, input_param, input_shape): net = NaiveNetwork(**input_param) diff --git a/tests/test_rotate.py b/tests/test_rotate.py index 95c63e65f7..19fbd1409f 100644 --- a/tests/test_rotate.py +++ b/tests/test_rotate.py @@ -52,6 +52,7 @@ class TestRotate2D(NumpyImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): init_param = { @@ -90,6 +91,7 @@ def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, al class TestRotate3D(NumpyImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): init_param = { diff --git a/tests/test_rotate90.py b/tests/test_rotate90.py index 0948469df9..ebc3fba7e0 100644 --- a/tests/test_rotate90.py +++ b/tests/test_rotate90.py @@ -31,6 +31,7 @@ class TestRotate90(NumpyImageTestCase2D): + def test_rotate90_default(self): rotate = Rotate90() for p in TEST_NDARRAYS_ALL: @@ -102,6 +103,7 @@ def test_prob_k_spatial_axes(self): class TestRotate903d(NumpyImageTestCase3D): + def test_rotate90_default(self): rotate = Rotate90() for p in TEST_NDARRAYS_ALL: @@ -169,6 +171,7 @@ def test_prob_k_spatial_axes(self): @unittest.skipUnless(optional_import("scipy")[1], "Requires scipy library.") class TestRot90Consistency(unittest.TestCase): + @parameterized.expand([[2], [3], [4]]) def test_affine_rot90(self, s): """s""" diff --git a/tests/test_rotate90d.py b/tests/test_rotate90d.py index 08d3a97498..ffe920992a 100644 --- a/tests/test_rotate90d.py +++ b/tests/test_rotate90d.py @@ -22,6 +22,7 @@ class TestRotate90d(NumpyImageTestCase2D): + def test_rotate90_default(self): key = "test" rotate = Rotate90d(keys=key) diff --git a/tests/test_rotated.py b/tests/test_rotated.py index 3755ab1344..28ca755661 100644 --- a/tests/test_rotated.py +++ b/tests/test_rotated.py @@ -43,6 +43,7 @@ @unittest.skipIf(USE_COMPILED, "unittests are not designed for both USE_COMPILED=True/False") class TestRotated2D(NumpyImageTestCase2D): + @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): init_param = { @@ -94,6 +95,7 @@ def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, al @unittest.skipIf(USE_COMPILED, "unittests are not designed for both USE_COMPILED=True/False") class TestRotated3D(NumpyImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): init_param = { @@ -143,6 +145,7 @@ def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, al @unittest.skipIf(USE_COMPILED, "unittests are not designed for both USE_COMPILED=True/False") class TestRotated3DXY(NumpyImageTestCase3D): + @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): rotate_fn = Rotated( diff --git a/tests/test_safe_dtype_range.py b/tests/test_safe_dtype_range.py index 73f9607d7d..61b55635ae 100644 --- a/tests/test_safe_dtype_range.py +++ b/tests/test_safe_dtype_range.py @@ -54,6 +54,7 @@ class TesSafeDtypeRange(unittest.TestCase): + @parameterized.expand(TESTS) def test_safe_dtype_range(self, in_image, im_out, out_dtype): result = safe_dtype_range(in_image, out_dtype) diff --git a/tests/test_saliency_inferer.py b/tests/test_saliency_inferer.py index 4efe30d7a6..70ec048d1c 100644 --- a/tests/test_saliency_inferer.py +++ b/tests/test_saliency_inferer.py @@ -28,6 +28,7 @@ class TestSaliencyInferer(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_shape(self, cam_name): model = DenseNet( diff --git a/tests/test_sample_slices.py b/tests/test_sample_slices.py index 02b7926392..a183689970 100644 --- a/tests/test_sample_slices.py +++ b/tests/test_sample_slices.py @@ -32,6 +32,7 @@ class TestSampleSlices(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_shape(self, input_data, dim, as_indices, vals, expected_result): for p in TEST_NDARRAYS: diff --git a/tests/test_sampler_dist.py b/tests/test_sampler_dist.py index b2f86c54cc..b8bd1c7a9f 100644 --- a/tests/test_sampler_dist.py +++ b/tests/test_sampler_dist.py @@ -24,6 +24,7 @@ class DistributedSamplerTest(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_even(self): data = [1, 2, 3, 4, 5] diff --git a/tests/test_save_classificationd.py b/tests/test_save_classificationd.py index dd0b213bd6..9a7d4fc3f5 100644 --- a/tests/test_save_classificationd.py +++ b/tests/test_save_classificationd.py @@ -26,6 +26,7 @@ class TestSaveClassificationd(unittest.TestCase): + def test_saved_content(self): with tempfile.TemporaryDirectory() as tempdir: data = [ diff --git a/tests/test_save_image.py b/tests/test_save_image.py index d88db201ce..ed7061095d 100644 --- a/tests/test_save_image.py +++ b/tests/test_save_image.py @@ -42,6 +42,7 @@ @unittest.skipUnless(has_itk, "itk not installed") class TestSaveImage(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_saved_content(self, test_data, meta_data, output_ext, resample): if meta_data is not None: diff --git a/tests/test_save_imaged.py b/tests/test_save_imaged.py index ab0b9c0d9f..d2095a7554 100644 --- a/tests/test_save_imaged.py +++ b/tests/test_save_imaged.py @@ -54,6 +54,7 @@ @unittest.skipUnless(has_itk, "itk not installed") class TestSaveImaged(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_saved_content(self, test_data, output_ext, resample): with tempfile.TemporaryDirectory() as tempdir: @@ -73,7 +74,9 @@ def test_saved_content(self, test_data, output_ext, resample): @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_custom_folderlayout(self, test_data, output_ext, resample): + class TestFolderLayout(FolderLayoutBase): + def __init__(self, basepath: Path, extension: str, makedirs: bool): self.basepath = basepath self.ext = extension diff --git a/tests/test_save_state.py b/tests/test_save_state.py index 8ab7080700..0581a3ce1f 100644 --- a/tests/test_save_state.py +++ b/tests/test_save_state.py @@ -43,6 +43,7 @@ class TestSaveState(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_1, diff --git a/tests/test_savitzky_golay_filter.py b/tests/test_savitzky_golay_filter.py index b7f89cdfde..7c60287e2d 100644 --- a/tests/test_savitzky_golay_filter.py +++ b/tests/test_savitzky_golay_filter.py @@ -100,6 +100,7 @@ class TestSavitzkyGolayCPU(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE, TEST_CASE_1D, TEST_CASE_2D_AXIS_2, TEST_CASE_2D_AXIS_3, TEST_CASE_SINE_SMOOTH] ) @@ -109,6 +110,7 @@ def test_value(self, arguments, image, expected_data, atol, rtol=1e-5): class TestSavitzkyGolayCPUREP(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE_REP, TEST_CASE_1D_REP, TEST_CASE_2D_AXIS_2_REP, TEST_CASE_2D_AXIS_3_REP] ) @@ -119,6 +121,7 @@ def test_value(self, arguments, image, expected_data, atol, rtol=1e-5): @skip_if_no_cuda class TestSavitzkyGolayGPU(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE, TEST_CASE_1D, TEST_CASE_2D_AXIS_2, TEST_CASE_2D_AXIS_3, TEST_CASE_SINE_SMOOTH] ) @@ -129,6 +132,7 @@ def test_value(self, arguments, image, expected_data, atol, rtol=1e-5): @skip_if_no_cuda class TestSavitzkyGolayGPUREP(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE_REP, TEST_CASE_1D_REP, TEST_CASE_2D_AXIS_2_REP, TEST_CASE_2D_AXIS_3_REP] ) diff --git a/tests/test_savitzky_golay_smooth.py b/tests/test_savitzky_golay_smooth.py index 6da4f24c62..14e403e238 100644 --- a/tests/test_savitzky_golay_smooth.py +++ b/tests/test_savitzky_golay_smooth.py @@ -60,6 +60,7 @@ class TestSavitzkyGolaySmooth(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE, TEST_CASE_2D_AXIS_2, TEST_CASE_SINE_SMOOTH, TEST_CASE_SINGLE_VALUE_REP] ) diff --git a/tests/test_savitzky_golay_smoothd.py b/tests/test_savitzky_golay_smoothd.py index 7e7176e2bb..3bb4056046 100644 --- a/tests/test_savitzky_golay_smoothd.py +++ b/tests/test_savitzky_golay_smoothd.py @@ -60,6 +60,7 @@ class TestSavitzkyGolaySmoothd(unittest.TestCase): + @parameterized.expand( [TEST_CASE_SINGLE_VALUE, TEST_CASE_2D_AXIS_2, TEST_CASE_SINE_SMOOTH, TEST_CASE_SINGLE_VALUE_REP] ) diff --git a/tests/test_scale_intensity.py b/tests/test_scale_intensity.py index 57a7da1780..17dfe305b2 100644 --- a/tests/test_scale_intensity.py +++ b/tests/test_scale_intensity.py @@ -22,6 +22,7 @@ class TestScaleIntensity(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_range_scale(self, p): scaler = ScaleIntensity(minv=1.0, maxv=2.0) diff --git a/tests/test_scale_intensity_fixed_mean.py b/tests/test_scale_intensity_fixed_mean.py index afbcd46141..35d38ef0b1 100644 --- a/tests/test_scale_intensity_fixed_mean.py +++ b/tests/test_scale_intensity_fixed_mean.py @@ -21,6 +21,7 @@ class TestScaleIntensityFixedMean(NumpyImageTestCase2D): + def test_factor_scale(self): for p in TEST_NDARRAYS: scaler = ScaleIntensityFixedMean(factor=0.1, fixed_mean=False) diff --git a/tests/test_scale_intensity_range.py b/tests/test_scale_intensity_range.py index 898f4dfb45..6013a237db 100644 --- a/tests/test_scale_intensity_range.py +++ b/tests/test_scale_intensity_range.py @@ -20,6 +20,7 @@ class IntensityScaleIntensityRange(NumpyImageTestCase2D): + def test_image_scale_intensity_range(self): scaler = ScaleIntensityRange(a_min=20, a_max=108, b_min=50, b_max=80, dtype=np.uint8) for p in TEST_NDARRAYS: diff --git a/tests/test_scale_intensity_range_percentiles.py b/tests/test_scale_intensity_range_percentiles.py index 583dcec07e..7c3a684a00 100644 --- a/tests/test_scale_intensity_range_percentiles.py +++ b/tests/test_scale_intensity_range_percentiles.py @@ -20,6 +20,7 @@ class TestScaleIntensityRangePercentiles(NumpyImageTestCase2D): + def test_scaling(self): img = self.imt[0] lower = 10 diff --git a/tests/test_scale_intensity_range_percentilesd.py b/tests/test_scale_intensity_range_percentilesd.py index 8e2511d9e4..ab0347fbbf 100644 --- a/tests/test_scale_intensity_range_percentilesd.py +++ b/tests/test_scale_intensity_range_percentilesd.py @@ -20,6 +20,7 @@ class TestScaleIntensityRangePercentilesd(NumpyImageTestCase2D): + def test_scaling(self): img = self.imt lower = 10 diff --git a/tests/test_scale_intensity_ranged.py b/tests/test_scale_intensity_ranged.py index 724acf1c73..cc3f1220e7 100644 --- a/tests/test_scale_intensity_ranged.py +++ b/tests/test_scale_intensity_ranged.py @@ -18,6 +18,7 @@ class IntensityScaleIntensityRanged(NumpyImageTestCase2D): + def test_image_scale_intensity_ranged(self): key = "img" scaler = ScaleIntensityRanged(keys=key, a_min=20, a_max=108, b_min=50, b_max=80) diff --git a/tests/test_scale_intensityd.py b/tests/test_scale_intensityd.py index 6705cfda9d..88beece894 100644 --- a/tests/test_scale_intensityd.py +++ b/tests/test_scale_intensityd.py @@ -20,6 +20,7 @@ class TestScaleIntensityd(NumpyImageTestCase2D): + def test_range_scale(self): key = "img" for p in TEST_NDARRAYS: diff --git a/tests/test_se_block.py b/tests/test_se_block.py index de129f4d55..ca60643635 100644 --- a/tests/test_se_block.py +++ b/tests/test_se_block.py @@ -63,6 +63,7 @@ class TestSEBlockLayer(unittest.TestCase): + @parameterized.expand(TEST_CASES + TEST_CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): net = SEBlock(**input_param).to(device) diff --git a/tests/test_se_blocks.py b/tests/test_se_blocks.py index c97e459f50..c1e72749cc 100644 --- a/tests/test_se_blocks.py +++ b/tests/test_se_blocks.py @@ -41,6 +41,7 @@ class TestChannelSELayer(unittest.TestCase): + @parameterized.expand(TEST_CASES + TEST_CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): net = ChannelSELayer(**input_param) @@ -60,6 +61,7 @@ def test_ill_arg(self): class TestResidualSELayer(unittest.TestCase): + @parameterized.expand(TEST_CASES[:1]) def test_shape(self, input_param, input_shape, expected_shape): net = ResidualSELayer(**input_param) diff --git a/tests/test_seg_loss_integration.py b/tests/test_seg_loss_integration.py index 23bc63fbf6..6713e7bba9 100644 --- a/tests/test_seg_loss_integration.py +++ b/tests/test_seg_loss_integration.py @@ -47,6 +47,7 @@ class TestSegLossIntegration(unittest.TestCase): + def setUp(self): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False @@ -92,6 +93,7 @@ def test_convergence(self, loss_type, loss_args, forward_args): # define a one layer model class OnelayerNet(nn.Module): + def __init__(self): super().__init__() self.layer_1 = nn.Linear(num_voxels, 200) diff --git a/tests/test_segresnet.py b/tests/test_segresnet.py index cb34445efa..728699c434 100644 --- a/tests/test_segresnet.py +++ b/tests/test_segresnet.py @@ -83,6 +83,7 @@ class TestResNet(unittest.TestCase): + @parameterized.expand(TEST_CASE_SEGRESNET + TEST_CASE_SEGRESNET_2) def test_shape(self, input_param, input_shape, expected_shape): net = SegResNet(**input_param).to(device) @@ -102,6 +103,7 @@ def test_script(self): class TestResNetVAE(unittest.TestCase): + @parameterized.expand(TEST_CASE_SEGRESNET_VAE) def test_vae_shape(self, input_param, input_shape, expected_shape): net = SegResNetVAE(**input_param).to(device) diff --git a/tests/test_segresnet_block.py b/tests/test_segresnet_block.py index 343f39d72c..633507a06a 100644 --- a/tests/test_segresnet_block.py +++ b/tests/test_segresnet_block.py @@ -38,6 +38,7 @@ class TestResBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_RESBLOCK) def test_shape(self, input_param, input_shape, expected_shape): net = ResBlock(**input_param) diff --git a/tests/test_segresnet_ds.py b/tests/test_segresnet_ds.py index a5b88f9724..5372fcc8ae 100644 --- a/tests/test_segresnet_ds.py +++ b/tests/test_segresnet_ds.py @@ -72,6 +72,7 @@ class TestResNetDS(unittest.TestCase): + @parameterized.expand(TEST_CASE_SEGRESNET_DS) def test_shape(self, input_param, input_shape, expected_shape): net = SegResNetDS(**input_param).to(device) diff --git a/tests/test_select_cross_validation_folds.py b/tests/test_select_cross_validation_folds.py index 3ab6c0a9c5..c7d19f34ab 100644 --- a/tests/test_select_cross_validation_folds.py +++ b/tests/test_select_cross_validation_folds.py @@ -43,6 +43,7 @@ class TestSelectCrossValidationFolds(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_value(self, input_param, result): partitions = partition_dataset(**input_param) diff --git a/tests/test_select_itemsd.py b/tests/test_select_itemsd.py index 5eb4a1c51b..f025917b9d 100644 --- a/tests/test_select_itemsd.py +++ b/tests/test_select_itemsd.py @@ -23,6 +23,7 @@ class TestSelectItemsd(unittest.TestCase): + @parameterized.expand([TEST_CASE_1]) def test_memory(self, input_param, expected_key_size): input_data = {} diff --git a/tests/test_selfattention.py b/tests/test_selfattention.py index 6062b5352f..b8be4fd1b6 100644 --- a/tests/test_selfattention.py +++ b/tests/test_selfattention.py @@ -37,6 +37,7 @@ class TestResBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_SABLOCK) @skipUnless(has_einops, "Requires einops") def test_shape(self, input_param, input_shape, expected_shape): diff --git a/tests/test_senet.py b/tests/test_senet.py index 92b5f39ace..6809d4562b 100644 --- a/tests/test_senet.py +++ b/tests/test_senet.py @@ -58,6 +58,7 @@ class TestSENET(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7]) def test_senet_shape(self, net, net_args): input_data = torch.randn(2, 2, 64, 64, 64).to(device) @@ -75,6 +76,7 @@ def test_script(self, net, net_args): class TestPretrainedSENET(unittest.TestCase): + def setUp(self): self.original_urls = se_mod.SE_NET_MODELS.copy() replace_url = test_is_quick() diff --git a/tests/test_separable_filter.py b/tests/test_separable_filter.py index 1797a649e0..d712f05ee1 100644 --- a/tests/test_separable_filter.py +++ b/tests/test_separable_filter.py @@ -20,6 +20,7 @@ class SeparableFilterTestCase(unittest.TestCase): + def test_1d(self): a = torch.tensor([[list(range(10))]], dtype=torch.float) out = separable_filtering(a, torch.tensor([-1, 0, 1])) diff --git a/tests/test_set_determinism.py b/tests/test_set_determinism.py index aab7af1079..7d64aed244 100644 --- a/tests/test_set_determinism.py +++ b/tests/test_set_determinism.py @@ -21,6 +21,7 @@ class TestSetDeterminism(unittest.TestCase): + def test_values(self): # check system default flags set_determinism(None) @@ -55,6 +56,7 @@ def test_values(self): class TestSetFlag(unittest.TestCase): + def setUp(self): set_determinism(1, use_deterministic_algorithms=True) diff --git a/tests/test_set_visible_devices.py b/tests/test_set_visible_devices.py index 993e8a4ac2..7860656b3d 100644 --- a/tests/test_set_visible_devices.py +++ b/tests/test_set_visible_devices.py @@ -18,6 +18,7 @@ class TestVisibleDevices(unittest.TestCase): + @staticmethod def run_process_and_get_exit_code(code_to_execute): value = os.system(code_to_execute) diff --git a/tests/test_shift_intensity.py b/tests/test_shift_intensity.py index f1bc36036e..90aa0f9271 100644 --- a/tests/test_shift_intensity.py +++ b/tests/test_shift_intensity.py @@ -20,6 +20,7 @@ class TestShiftIntensity(NumpyImageTestCase2D): + def test_value(self): shifter = ShiftIntensity(offset=1.0) result = shifter(self.imt) diff --git a/tests/test_shift_intensityd.py b/tests/test_shift_intensityd.py index e8d163b34a..22336b4415 100644 --- a/tests/test_shift_intensityd.py +++ b/tests/test_shift_intensityd.py @@ -21,6 +21,7 @@ class TestShiftIntensityd(NumpyImageTestCase2D): + def test_value(self): key = "img" for p in TEST_NDARRAYS: diff --git a/tests/test_shuffle_buffer.py b/tests/test_shuffle_buffer.py index 9fcd3a23f6..e75321616b 100644 --- a/tests/test_shuffle_buffer.py +++ b/tests/test_shuffle_buffer.py @@ -23,6 +23,7 @@ @SkipIfBeforePyTorchVersion((1, 12)) class TestShuffleBuffer(unittest.TestCase): + def test_shape(self): buffer = ShuffleBuffer([1, 2, 3, 4], seed=0) num_workers = 2 if sys.platform == "linux" else 0 diff --git a/tests/test_signal_continuouswavelet.py b/tests/test_signal_continuouswavelet.py index 4886168a00..7e6ee8b105 100644 --- a/tests/test_signal_continuouswavelet.py +++ b/tests/test_signal_continuouswavelet.py @@ -29,6 +29,7 @@ @skipUnless(has_pywt, "pywt required") class TestSignalContinousWavelet(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, type, length, frequency): self.assertIsInstance(SignalContinuousWavelet(type, length, frequency), SignalContinuousWavelet) diff --git a/tests/test_signal_fillempty.py b/tests/test_signal_fillempty.py index ee606d960c..a3ee623cc5 100644 --- a/tests/test_signal_fillempty.py +++ b/tests/test_signal_fillempty.py @@ -26,6 +26,7 @@ @SkipIfBeforePyTorchVersion((1, 9)) class TestSignalFillEmptyNumpy(unittest.TestCase): + def test_correct_parameters_multi_channels(self): self.assertIsInstance(SignalFillEmpty(replacement=0.0), SignalFillEmpty) sig = np.load(TEST_SIGNAL) @@ -37,6 +38,7 @@ def test_correct_parameters_multi_channels(self): @SkipIfBeforePyTorchVersion((1, 9)) class TestSignalFillEmptyTorch(unittest.TestCase): + def test_correct_parameters_multi_channels(self): self.assertIsInstance(SignalFillEmpty(replacement=0.0), SignalFillEmpty) sig = convert_to_tensor(np.load(TEST_SIGNAL)) diff --git a/tests/test_signal_fillemptyd.py b/tests/test_signal_fillemptyd.py index 5b12055e7d..ee8c571ef8 100644 --- a/tests/test_signal_fillemptyd.py +++ b/tests/test_signal_fillemptyd.py @@ -26,6 +26,7 @@ @SkipIfBeforePyTorchVersion((1, 9)) class TestSignalFillEmptyNumpy(unittest.TestCase): + def test_correct_parameters_multi_channels(self): self.assertIsInstance(SignalFillEmptyd(replacement=0.0), SignalFillEmptyd) sig = np.load(TEST_SIGNAL) @@ -41,6 +42,7 @@ def test_correct_parameters_multi_channels(self): @SkipIfBeforePyTorchVersion((1, 9)) class TestSignalFillEmptyTorch(unittest.TestCase): + def test_correct_parameters_multi_channels(self): self.assertIsInstance(SignalFillEmptyd(replacement=0.0), SignalFillEmptyd) sig = convert_to_tensor(np.load(TEST_SIGNAL)) diff --git a/tests/test_signal_rand_add_gaussiannoise.py b/tests/test_signal_rand_add_gaussiannoise.py index 2090df876f..e5c9eba8a2 100644 --- a/tests/test_signal_rand_add_gaussiannoise.py +++ b/tests/test_signal_rand_add_gaussiannoise.py @@ -25,6 +25,7 @@ class TestSignalRandAddGaussianNoiseNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandAddGaussianNoise(boundaries), SignalRandAddGaussianNoise) @@ -35,6 +36,7 @@ def test_correct_parameters_multi_channels(self, boundaries): class TestSignalRandAddGaussianNoiseTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandAddGaussianNoise(boundaries), SignalRandAddGaussianNoise) diff --git a/tests/test_signal_rand_add_sine.py b/tests/test_signal_rand_add_sine.py index ae0684d608..4ba91247dd 100644 --- a/tests/test_signal_rand_add_sine.py +++ b/tests/test_signal_rand_add_sine.py @@ -25,6 +25,7 @@ class TestSignalRandAddSineNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, freqs): self.assertIsInstance(SignalRandAddSine(boundaries, freqs), SignalRandAddSine) @@ -35,6 +36,7 @@ def test_correct_parameters_multi_channels(self, boundaries, freqs): class TestSignalRandAddSineTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, freqs): self.assertIsInstance(SignalRandAddSine(boundaries, freqs), SignalRandAddSine) diff --git a/tests/test_signal_rand_add_sine_partial.py b/tests/test_signal_rand_add_sine_partial.py index 109fb006ea..71b67747a2 100644 --- a/tests/test_signal_rand_add_sine_partial.py +++ b/tests/test_signal_rand_add_sine_partial.py @@ -25,6 +25,7 @@ class TestSignalRandAddSinePartialNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies, fraction): self.assertIsInstance(SignalRandAddSinePartial(boundaries, frequencies, fraction), SignalRandAddSinePartial) @@ -35,6 +36,7 @@ def test_correct_parameters_multi_channels(self, boundaries, frequencies, fracti class TestSignalRandAddSinePartialTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies, fraction): self.assertIsInstance(SignalRandAddSinePartial(boundaries, frequencies, fraction), SignalRandAddSinePartial) diff --git a/tests/test_signal_rand_add_squarepulse.py b/tests/test_signal_rand_add_squarepulse.py index efbdc9af09..e1432029ea 100644 --- a/tests/test_signal_rand_add_squarepulse.py +++ b/tests/test_signal_rand_add_squarepulse.py @@ -31,6 +31,7 @@ @skipUnless(has_scipy, "scipy required") @SkipIfBeforePyTorchVersion((1, 10, 1)) class TestSignalRandAddSquarePulseNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies): self.assertIsInstance(SignalRandAddSquarePulse(boundaries, frequencies), SignalRandAddSquarePulse) @@ -43,6 +44,7 @@ def test_correct_parameters_multi_channels(self, boundaries, frequencies): @skipUnless(has_scipy, "scipy required") @SkipIfBeforePyTorchVersion((1, 10, 1)) class TestSignalRandAddSquarePulseTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies): self.assertIsInstance(SignalRandAddSquarePulse(boundaries, frequencies), SignalRandAddSquarePulse) diff --git a/tests/test_signal_rand_add_squarepulse_partial.py b/tests/test_signal_rand_add_squarepulse_partial.py index eee3f5596d..7e1c2bb9d8 100644 --- a/tests/test_signal_rand_add_squarepulse_partial.py +++ b/tests/test_signal_rand_add_squarepulse_partial.py @@ -31,6 +31,7 @@ @skipUnless(has_scipy, "scipy required") @SkipIfBeforePyTorchVersion((1, 10, 1)) class TestSignalRandAddSquarePulsePartialNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies, fraction): self.assertIsInstance( @@ -45,6 +46,7 @@ def test_correct_parameters_multi_channels(self, boundaries, frequencies, fracti @skipUnless(has_scipy, "scipy required") @SkipIfBeforePyTorchVersion((1, 10, 1)) class TestSignalRandAddSquarePulsePartialTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries, frequencies, fraction): self.assertIsInstance( diff --git a/tests/test_signal_rand_drop.py b/tests/test_signal_rand_drop.py index 5dcd466481..bf2db75a6a 100644 --- a/tests/test_signal_rand_drop.py +++ b/tests/test_signal_rand_drop.py @@ -25,6 +25,7 @@ class TestSignalRandDropNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandDrop(boundaries), SignalRandDrop) @@ -35,6 +36,7 @@ def test_correct_parameters_multi_channels(self, boundaries): class TestSignalRandDropTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandDrop(boundaries), SignalRandDrop) diff --git a/tests/test_signal_rand_scale.py b/tests/test_signal_rand_scale.py index 126d7cca65..c040c59a1f 100644 --- a/tests/test_signal_rand_scale.py +++ b/tests/test_signal_rand_scale.py @@ -25,6 +25,7 @@ class TestSignalRandScaleNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandScale(boundaries), SignalRandScale) @@ -35,6 +36,7 @@ def test_correct_parameters_multi_channels(self, boundaries): class TestSignalRandScaleTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, boundaries): self.assertIsInstance(SignalRandScale(boundaries), SignalRandScale) diff --git a/tests/test_signal_rand_shift.py b/tests/test_signal_rand_shift.py index ed25cc8b1f..96809e7446 100644 --- a/tests/test_signal_rand_shift.py +++ b/tests/test_signal_rand_shift.py @@ -29,6 +29,7 @@ @skipUnless(has_scipy, "scipy required") class TestSignalRandShiftNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, mode, filling, boundaries): self.assertIsInstance(SignalRandShift(mode, filling, boundaries), SignalRandShift) @@ -40,6 +41,7 @@ def test_correct_parameters_multi_channels(self, mode, filling, boundaries): @skipUnless(has_scipy, "scipy required") class TestSignalRandShiftTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, mode, filling, boundaries): self.assertIsInstance(SignalRandShift(mode, filling, boundaries), SignalRandShift) diff --git a/tests/test_signal_remove_frequency.py b/tests/test_signal_remove_frequency.py index b18de36c08..9f795ce68b 100644 --- a/tests/test_signal_remove_frequency.py +++ b/tests/test_signal_remove_frequency.py @@ -31,6 +31,7 @@ @skipUnless(has_scipy and has_torchaudio, "scipy and torchaudio are required") class TestSignalRemoveFrequencyNumpy(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, frequency, quality_factor, sampling_freq): self.assertIsInstance(SignalRemoveFrequency(frequency, quality_factor, sampling_freq), SignalRemoveFrequency) @@ -49,6 +50,7 @@ def test_correct_parameters_multi_channels(self, frequency, quality_factor, samp @skipUnless(has_scipy and has_torchaudio, "scipy and torchaudio are required") class TestSignalRemoveFrequencyTorch(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct_parameters_multi_channels(self, frequency, quality_factor, sampling_freq): self.assertIsInstance(SignalRemoveFrequency(frequency, quality_factor, sampling_freq), SignalRemoveFrequency) diff --git a/tests/test_simple_aspp.py b/tests/test_simple_aspp.py index f18b208e9c..da7540d45e 100644 --- a/tests/test_simple_aspp.py +++ b/tests/test_simple_aspp.py @@ -69,6 +69,7 @@ class TestChannelSELayer(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape, expected_shape): net = SimpleASPP(**input_param) diff --git a/tests/test_simulatedelay.py b/tests/test_simulatedelay.py index 5cf47b245e..0a4f23450a 100644 --- a/tests/test_simulatedelay.py +++ b/tests/test_simulatedelay.py @@ -22,6 +22,7 @@ class TestSimulateDelay(NumpyImageTestCase2D): + @parameterized.expand([(0.45,), (1,)]) def test_value(self, delay_test_time: float): resize = SimulateDelay(delay_time=delay_test_time) diff --git a/tests/test_simulatedelayd.py b/tests/test_simulatedelayd.py index 827fe69510..419e21f24d 100644 --- a/tests/test_simulatedelayd.py +++ b/tests/test_simulatedelayd.py @@ -22,6 +22,7 @@ class TestSimulateDelay(NumpyImageTestCase2D): + @parameterized.expand([(0.45,), (1,)]) def test_value(self, delay_test_time: float): resize = SimulateDelayd(keys="imgd", delay_time=delay_test_time) diff --git a/tests/test_skip_connection.py b/tests/test_skip_connection.py index 0ac8ef0d7a..5ee166cf10 100644 --- a/tests/test_skip_connection.py +++ b/tests/test_skip_connection.py @@ -31,6 +31,7 @@ class TestSkipConnection(unittest.TestCase): + @parameterized.expand(TEST_CASES_3D) def test_shape(self, input_param, input_shape, expected_shape): net = SkipConnection(submodule=torch.nn.Softmax(dim=1), **input_param) diff --git a/tests/test_slice_inferer.py b/tests/test_slice_inferer.py index 4d7dea026f..526542943e 100644 --- a/tests/test_slice_inferer.py +++ b/tests/test_slice_inferer.py @@ -23,6 +23,7 @@ class TestSliceInferer(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, spatial_dim): spatial_dim = int(spatial_dim) diff --git a/tests/test_sliding_patch_wsi_dataset.py b/tests/test_sliding_patch_wsi_dataset.py index 518e94552f..6369613426 100644 --- a/tests/test_sliding_patch_wsi_dataset.py +++ b/tests/test_sliding_patch_wsi_dataset.py @@ -213,6 +213,7 @@ def setUpModule(): class SlidingPatchWSIDatasetTests: + class Tests(unittest.TestCase): backend = None @@ -252,6 +253,7 @@ def test_read_patches_large(self, input_parameters, expected): @skipUnless(has_cucim, "Requires cucim") class TestSlidingPatchWSIDatasetCuCIM(SlidingPatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "cucim" @@ -259,6 +261,7 @@ def setUpClass(cls): @skipUnless(has_osl, "Requires openslide") class TestSlidingPatchWSIDatasetOpenSlide(SlidingPatchWSIDatasetTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "openslide" diff --git a/tests/test_sliding_window_hovernet_inference.py b/tests/test_sliding_window_hovernet_inference.py index 276bd1e372..6fc9240a13 100644 --- a/tests/test_sliding_window_hovernet_inference.py +++ b/tests/test_sliding_window_hovernet_inference.py @@ -36,6 +36,7 @@ class TestSlidingWindowHoVerNetInference(unittest.TestCase): + @parameterized.expand(TEST_CASES_PADDING) def test_sliding_window_with_padding( self, key, image_shape, roi_shape, sw_batch_size, overlap, mode, device, extra_input_padding diff --git a/tests/test_sliding_window_inference.py b/tests/test_sliding_window_inference.py index 8f0c074403..33b38a5bc7 100644 --- a/tests/test_sliding_window_inference.py +++ b/tests/test_sliding_window_inference.py @@ -70,8 +70,10 @@ class TestSlidingWindowInference(unittest.TestCase): + @parameterized.expand(BUFFER_CASES) def test_buffers(self, size_params, buffer_steps, buffer_dim, device_params): + def mult_two(patch, *args, **kwargs): return 2.0 * patch diff --git a/tests/test_sliding_window_splitter.py b/tests/test_sliding_window_splitter.py index 015293cbee..ad136c61a4 100644 --- a/tests/test_sliding_window_splitter.py +++ b/tests/test_sliding_window_splitter.py @@ -236,6 +236,7 @@ def missing_parameter_filter(patch): class SlidingWindowSplitterTests(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_TENSOR_0, diff --git a/tests/test_smartcachedataset.py b/tests/test_smartcachedataset.py index 0e2a79fef3..bb43060469 100644 --- a/tests/test_smartcachedataset.py +++ b/tests/test_smartcachedataset.py @@ -38,6 +38,7 @@ class TestSmartCacheDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5]) def test_shape(self, replace_rate, num_replace_workers, transform): test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[8, 8, 8]).astype(float), np.eye(4)) diff --git a/tests/test_smooth_field.py b/tests/test_smooth_field.py index c525311478..ca010641c4 100644 --- a/tests/test_smooth_field.py +++ b/tests/test_smooth_field.py @@ -88,6 +88,7 @@ class TestSmoothField(unittest.TestCase): + @parameterized.expand(TESTS_CONTRAST) def test_rand_smooth_field_adjust_contrastd(self, input_param, input_data, expected_val): g = RandSmoothFieldAdjustContrastd(**input_param) diff --git a/tests/test_some_of.py b/tests/test_some_of.py index 8880c376b9..3723732d51 100644 --- a/tests/test_some_of.py +++ b/tests/test_some_of.py @@ -31,21 +31,25 @@ class A(Transform): + def __call__(self, x): return 2 * x class B(Transform): + def __call__(self, x): return 3 * x class C(Transform): + def __call__(self, x): return 5 * x class D(Transform): + def __call__(self, x): return 7 * x @@ -71,6 +75,7 @@ def __call__(self, x): class TestSomeOf(unittest.TestCase): + def setUp(self): set_determinism(seed=0) @@ -221,6 +226,7 @@ def test_bad_num_transforms(self): class TestSomeOfAPITests(unittest.TestCase): + @staticmethod def data_from_keys(keys): if keys is None: diff --git a/tests/test_spacing.py b/tests/test_spacing.py index 8b664641d7..c9a6291c78 100644 --- a/tests/test_spacing.py +++ b/tests/test_spacing.py @@ -271,6 +271,7 @@ @skip_if_quick class TestSpacingCase(unittest.TestCase): + @parameterized.expand(TESTS) def test_spacing( self, diff --git a/tests/test_spacingd.py b/tests/test_spacingd.py index 36986b2706..1cecaabced 100644 --- a/tests/test_spacingd.py +++ b/tests/test_spacingd.py @@ -105,6 +105,7 @@ class TestSpacingDCase(unittest.TestCase): + @parameterized.expand(TESTS) def test_spacingd(self, _, data, kw_args, expected_shape, expected_affine, device): data = {k: v.to(device) for k, v in data.items()} diff --git a/tests/test_spatial_combine_transforms.py b/tests/test_spatial_combine_transforms.py index 8594daed16..8479e9084b 100644 --- a/tests/test_spatial_combine_transforms.py +++ b/tests/test_spatial_combine_transforms.py @@ -132,6 +132,7 @@ class CombineLazyTest(unittest.TestCase): + @parameterized.expand(TEST_2D + TEST_3D) def test_combine_transforms(self, input_shape, funcs): for device in ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]: diff --git a/tests/test_spatial_resample.py b/tests/test_spatial_resample.py index b513bd0f05..e64b242128 100644 --- a/tests/test_spatial_resample.py +++ b/tests/test_spatial_resample.py @@ -133,6 +133,7 @@ class TestSpatialResample(unittest.TestCase): + @parameterized.expand(TESTS) def test_flips(self, img, device, data_param, expected_output): for p in TEST_NDARRAYS_ALL: diff --git a/tests/test_spatial_resampled.py b/tests/test_spatial_resampled.py index ebe3eb6e4f..541015cc34 100644 --- a/tests/test_spatial_resampled.py +++ b/tests/test_spatial_resampled.py @@ -87,6 +87,7 @@ class TestSpatialResample(unittest.TestCase): + @parameterized.expand(TESTS) def test_flips_inverse(self, img, device, dst_affine, kwargs, expected_output): img = MetaTensor(img, affine=torch.eye(4)).to(device) diff --git a/tests/test_spectral_loss.py b/tests/test_spectral_loss.py index 21b5c48de4..f62ae9030b 100644 --- a/tests/test_spectral_loss.py +++ b/tests/test_spectral_loss.py @@ -63,6 +63,7 @@ class TestJukeboxLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_results(self, input_param, input_data, expected_val): results = JukeboxLoss(**input_param).forward(**input_data) diff --git a/tests/test_splitdim.py b/tests/test_splitdim.py index 6c678a6bc2..f557f44142 100644 --- a/tests/test_splitdim.py +++ b/tests/test_splitdim.py @@ -26,6 +26,7 @@ class TestSplitDim(unittest.TestCase): + @parameterized.expand(TESTS) def test_correct_shape(self, shape, keepdim, im_type): arr = im_type(np.random.rand(*shape)) diff --git a/tests/test_squeeze_unsqueeze.py b/tests/test_squeeze_unsqueeze.py index 130a214345..3f818f905b 100644 --- a/tests/test_squeeze_unsqueeze.py +++ b/tests/test_squeeze_unsqueeze.py @@ -61,6 +61,7 @@ class TestUnsqueeze(unittest.TestCase): + @parameterized.expand(RIGHT_CASES + ALL_CASES) def test_unsqueeze_right(self, arr, ndim, shape): self.assertEqual(unsqueeze_right(arr, ndim).shape, shape) diff --git a/tests/test_squeezedim.py b/tests/test_squeezedim.py index 6673fd25c1..a295d20ef5 100644 --- a/tests/test_squeezedim.py +++ b/tests/test_squeezedim.py @@ -32,6 +32,7 @@ class TestSqueezeDim(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, test_data, expected_shape): result = SqueezeDim(**input_param)(test_data) diff --git a/tests/test_squeezedimd.py b/tests/test_squeezedimd.py index 9fa9d84030..934479563d 100644 --- a/tests/test_squeezedimd.py +++ b/tests/test_squeezedimd.py @@ -80,6 +80,7 @@ class TestSqueezeDim(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, input_param, test_data, expected_shape): result = SqueezeDimd(**input_param)(test_data) diff --git a/tests/test_ssim_loss.py b/tests/test_ssim_loss.py index db80eb80db..7fa593b956 100644 --- a/tests/test_ssim_loss.py +++ b/tests/test_ssim_loss.py @@ -23,6 +23,7 @@ class TestSSIMLoss(unittest.TestCase): + def test_shape(self): set_determinism(0) preds = torch.abs(torch.randn(2, 3, 16, 16)) diff --git a/tests/test_ssim_metric.py b/tests/test_ssim_metric.py index 467e478937..d79107e999 100644 --- a/tests/test_ssim_metric.py +++ b/tests/test_ssim_metric.py @@ -20,6 +20,7 @@ class TestSSIMMetric(unittest.TestCase): + def test2d_gaussian(self): set_determinism(0) preds = torch.abs(torch.randn(2, 3, 16, 16)) diff --git a/tests/test_state_cacher.py b/tests/test_state_cacher.py index 2037dc3951..22c2836239 100644 --- a/tests/test_state_cacher.py +++ b/tests/test_state_cacher.py @@ -36,6 +36,7 @@ class TestStateCacher(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_state_cacher(self, data_obj, params): key = "data_obj" diff --git a/tests/test_std_shift_intensity.py b/tests/test_std_shift_intensity.py index af18c18aa2..b4dc1db568 100644 --- a/tests/test_std_shift_intensity.py +++ b/tests/test_std_shift_intensity.py @@ -21,6 +21,7 @@ class TestStdShiftIntensity(NumpyImageTestCase2D): + def test_value(self): for p in TEST_NDARRAYS: imt = p(self.imt) diff --git a/tests/test_std_shift_intensityd.py b/tests/test_std_shift_intensityd.py index 6cb7d416c7..73617ef4a3 100644 --- a/tests/test_std_shift_intensityd.py +++ b/tests/test_std_shift_intensityd.py @@ -21,6 +21,7 @@ class TestStdShiftIntensityd(NumpyImageTestCase2D): + def test_value(self): key = "img" factor = np.random.rand() diff --git a/tests/test_str2bool.py b/tests/test_str2bool.py index 36f99b4064..af932b1df8 100644 --- a/tests/test_str2bool.py +++ b/tests/test_str2bool.py @@ -17,6 +17,7 @@ class TestStr2Bool(unittest.TestCase): + def test_str_2_bool(self): for i in ("yes", "true", "t", "y", "1", True): self.assertTrue(str2bool(i)) diff --git a/tests/test_str2list.py b/tests/test_str2list.py index b442925fb3..e1531373cb 100644 --- a/tests/test_str2list.py +++ b/tests/test_str2list.py @@ -17,6 +17,7 @@ class TestStr2List(unittest.TestCase): + def test_str_2_list(self): for i in ("1,2,3", "1, 2, 3", "1,2e-0,3.0", [1, 2, 3]): self.assertEqual(str2list(i), [1, 2, 3]) diff --git a/tests/test_subpixel_upsample.py b/tests/test_subpixel_upsample.py index a6de8dd846..5abbe57e11 100644 --- a/tests/test_subpixel_upsample.py +++ b/tests/test_subpixel_upsample.py @@ -68,6 +68,7 @@ class TestSUBPIXEL(unittest.TestCase): + @parameterized.expand(TEST_CASE_SUBPIXEL) def test_subpixel_shape(self, input_param, input_shape, expected_shape): net = SubpixelUpsample(**input_param) diff --git a/tests/test_surface_dice.py b/tests/test_surface_dice.py index 53b0d38bb2..2ef19a4eea 100644 --- a/tests/test_surface_dice.py +++ b/tests/test_surface_dice.py @@ -24,6 +24,7 @@ class TestAllSurfaceDiceMetrics(unittest.TestCase): + def test_tolerance_euclidean_distance_with_spacing(self): batch_size = 2 n_class = 2 diff --git a/tests/test_surface_distance.py b/tests/test_surface_distance.py index 81ddee107b..85db389f80 100644 --- a/tests/test_surface_distance.py +++ b/tests/test_surface_distance.py @@ -142,6 +142,7 @@ def create_spherical_seg_3d( class TestAllSurfaceMetrics(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_value(self, input_data, expected_value): if len(input_data) == 3: diff --git a/tests/test_swin_unetr.py b/tests/test_swin_unetr.py index e34e5a3c8e..5b33475c7e 100644 --- a/tests/test_swin_unetr.py +++ b/tests/test_swin_unetr.py @@ -76,6 +76,7 @@ class TestSWINUNETR(unittest.TestCase): + @parameterized.expand(TEST_CASE_SWIN_UNETR) @skipUnless(has_einops, "Requires einops") def test_shape(self, input_param, input_shape, expected_shape): diff --git a/tests/test_synthetic.py b/tests/test_synthetic.py index 116897e67d..7db3c3e77a 100644 --- a/tests/test_synthetic.py +++ b/tests/test_synthetic.py @@ -41,6 +41,7 @@ class TestDiceCELoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_create_test_image(self, dim, input_param, expected_img, expected_seg, expected_shape, expected_max_cls): set_determinism(seed=0) diff --git a/tests/test_tciadataset.py b/tests/test_tciadataset.py index 2a3928f9aa..d996922e20 100644 --- a/tests/test_tciadataset.py +++ b/tests/test_tciadataset.py @@ -23,6 +23,7 @@ class TestTciaDataset(unittest.TestCase): + @skip_if_quick def test_values(self): testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data") diff --git a/tests/test_testtimeaugmentation.py b/tests/test_testtimeaugmentation.py index cbb78ec64d..746ad122b2 100644 --- a/tests/test_testtimeaugmentation.py +++ b/tests/test_testtimeaugmentation.py @@ -52,6 +52,7 @@ class TestTestTimeAugmentation(unittest.TestCase): + @staticmethod def get_data(num_examples, input_size, data_type=np.asarray, include_label=True): custom_create_test_image_2d = partial( diff --git a/tests/test_text_encoding.py b/tests/test_text_encoding.py index 831803de8c..902f7a4b1d 100644 --- a/tests/test_text_encoding.py +++ b/tests/test_text_encoding.py @@ -18,6 +18,7 @@ class TestTextEncoder(unittest.TestCase): + def test_test_encoding_shape(self): with skip_if_downloading_fails(): # test 2D encoder diff --git a/tests/test_thread_buffer.py b/tests/test_thread_buffer.py index ab5dba77be..2b7da2c0b0 100644 --- a/tests/test_thread_buffer.py +++ b/tests/test_thread_buffer.py @@ -24,6 +24,7 @@ class TestDataLoader(unittest.TestCase): + def setUp(self): super().setUp() diff --git a/tests/test_threadcontainer.py b/tests/test_threadcontainer.py index ca9fb244fc..9551dec703 100644 --- a/tests/test_threadcontainer.py +++ b/tests/test_threadcontainer.py @@ -36,6 +36,7 @@ class TestThreadContainer(unittest.TestCase): + @SkipIfNoModule("ignite") def test_container(self): net = torch.nn.Conv2d(1, 1, 3, padding=1) diff --git a/tests/test_threshold_intensity.py b/tests/test_threshold_intensity.py index 7fb28d413f..97c80eebcd 100644 --- a/tests/test_threshold_intensity.py +++ b/tests/test_threshold_intensity.py @@ -27,6 +27,7 @@ class TestThresholdIntensity(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, in_type, input_param, expected_value): test_data = in_type(np.arange(10)) diff --git a/tests/test_threshold_intensityd.py b/tests/test_threshold_intensityd.py index d5e7e5f517..867ebfe952 100644 --- a/tests/test_threshold_intensityd.py +++ b/tests/test_threshold_intensityd.py @@ -45,6 +45,7 @@ class TestThresholdIntensityd(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, in_type, input_param, expected_value): test_data = {"image": in_type(np.arange(10)), "label": in_type(np.arange(10)), "extra": in_type(np.arange(10))} diff --git a/tests/test_timedcall_dist.py b/tests/test_timedcall_dist.py index af7cf8720f..a814a99b25 100644 --- a/tests/test_timedcall_dist.py +++ b/tests/test_timedcall_dist.py @@ -50,6 +50,7 @@ def case_1_seconds_bad(arg=None): class TestTimedCall(unittest.TestCase): + def test_good_call(self): output = case_1_seconds() self.assertEqual(output, "good") diff --git a/tests/test_to_contiguous.py b/tests/test_to_contiguous.py index 03733b9775..73a9ca27f6 100644 --- a/tests/test_to_contiguous.py +++ b/tests/test_to_contiguous.py @@ -21,6 +21,7 @@ class TestToContiguous(unittest.TestCase): + def test_contiguous_dict(self): tochange = np.moveaxis(np.zeros((2, 3, 4)), 0, -1) test_dict = {"test_key": [[1]], 0: np.array(0), 1: np.array([0]), "nested": {"nested": [tochange]}} diff --git a/tests/test_to_cupy.py b/tests/test_to_cupy.py index 12a377181d..5a1754e7c5 100644 --- a/tests/test_to_cupy.py +++ b/tests/test_to_cupy.py @@ -26,6 +26,7 @@ @skipUnless(HAS_CUPY, "CuPy is required.") class TestToCupy(unittest.TestCase): + def test_cupy_input(self): test_data = cp.array([[1, 2], [3, 4]], dtype=cp.float32) test_data = cp.rot90(test_data) diff --git a/tests/test_to_cupyd.py b/tests/test_to_cupyd.py index e9a3488489..a07ab671e1 100644 --- a/tests/test_to_cupyd.py +++ b/tests/test_to_cupyd.py @@ -26,6 +26,7 @@ @skipUnless(HAS_CUPY, "CuPy is required.") class TestToCupyd(unittest.TestCase): + def test_cupy_input(self): test_data = cp.array([[1, 2], [3, 4]]) test_data = cp.rot90(test_data) diff --git a/tests/test_to_device.py b/tests/test_to_device.py index cad2b65316..6a13ffca99 100644 --- a/tests/test_to_device.py +++ b/tests/test_to_device.py @@ -30,6 +30,7 @@ @skip_if_no_cuda class TestToDevice(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_value(self, device): converter = ToDevice(device=device, non_blocking=True) diff --git a/tests/test_to_deviced.py b/tests/test_to_deviced.py index 093c3b0c4d..19c2d0761f 100644 --- a/tests/test_to_deviced.py +++ b/tests/test_to_deviced.py @@ -22,6 +22,7 @@ @skip_if_no_cuda class TestToDeviced(unittest.TestCase): + def test_value(self): device = "cuda:0" data = [{"img": torch.tensor(i)} for i in range(4)] diff --git a/tests/test_to_from_meta_tensord.py b/tests/test_to_from_meta_tensord.py index 470826313a..fe777cec77 100644 --- a/tests/test_to_from_meta_tensord.py +++ b/tests/test_to_from_meta_tensord.py @@ -42,6 +42,7 @@ def rand_string(min_len=5, max_len=10): @unittest.skipIf(config.USE_META_DICT, "skipping not metatensor") class TestToFromMetaTensord(unittest.TestCase): + @staticmethod def get_im(shape=None, dtype=None, device=None): if shape is None: diff --git a/tests/test_to_numpy.py b/tests/test_to_numpy.py index 0c604fb9d4..8f7cf34865 100644 --- a/tests/test_to_numpy.py +++ b/tests/test_to_numpy.py @@ -25,6 +25,7 @@ class TestToNumpy(unittest.TestCase): + @skipUnless(HAS_CUPY, "CuPy is required.") def test_cupy_input(self): test_data = cp.array([[1, 2], [3, 4]]) diff --git a/tests/test_to_numpyd.py b/tests/test_to_numpyd.py index d25bdf14a5..ae9b4c84b3 100644 --- a/tests/test_to_numpyd.py +++ b/tests/test_to_numpyd.py @@ -25,6 +25,7 @@ class TestToNumpyd(unittest.TestCase): + @skipUnless(HAS_CUPY, "CuPy is required.") def test_cupy_input(self): test_data = cp.array([[1, 2], [3, 4]]) diff --git a/tests/test_to_onehot.py b/tests/test_to_onehot.py index 52307900af..48dba6fa68 100644 --- a/tests/test_to_onehot.py +++ b/tests/test_to_onehot.py @@ -44,6 +44,7 @@ class TestToOneHot(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_shape(self, input_data, expected_shape, expected_result=None): result = one_hot(**input_data) diff --git a/tests/test_to_pil.py b/tests/test_to_pil.py index e4f74f6e1e..352e10bcc1 100644 --- a/tests/test_to_pil.py +++ b/tests/test_to_pil.py @@ -40,6 +40,7 @@ class TestToPIL(unittest.TestCase): + @parameterized.expand(TESTS) @skipUnless(has_pil, "Requires `pillow` package.") def test_value(self, test_data): diff --git a/tests/test_to_pild.py b/tests/test_to_pild.py index 4eb5999b15..1a0232e134 100644 --- a/tests/test_to_pild.py +++ b/tests/test_to_pild.py @@ -38,6 +38,7 @@ class TestToPIL(unittest.TestCase): + @parameterized.expand(TESTS) @skipUnless(has_pil, "Requires `pillow` package.") def test_values(self, input_param, test_data): diff --git a/tests/test_to_tensor.py b/tests/test_to_tensor.py index cde845c246..50df80128b 100644 --- a/tests/test_to_tensor.py +++ b/tests/test_to_tensor.py @@ -33,6 +33,7 @@ class TestToTensor(unittest.TestCase): + @parameterized.expand(TESTS) def test_array_input(self, test_data, expected_shape): result = ToTensor(dtype=torch.float32, device="cpu", wrap_sequence=True)(test_data) diff --git a/tests/test_to_tensord.py b/tests/test_to_tensord.py index 82456786fd..1eab7b9485 100644 --- a/tests/test_to_tensord.py +++ b/tests/test_to_tensord.py @@ -34,6 +34,7 @@ class TestToTensord(unittest.TestCase): + @parameterized.expand(TESTS) def test_array_input(self, test_data, expected_shape): test_data = {"img": test_data} diff --git a/tests/test_torchscript_utils.py b/tests/test_torchscript_utils.py index ec24f388f1..6f8f231829 100644 --- a/tests/test_torchscript_utils.py +++ b/tests/test_torchscript_utils.py @@ -23,11 +23,13 @@ class TestModule(torch.nn.Module): + def forward(self, x): return x + 10 class TestTorchscript(unittest.TestCase): + def test_save_net_with_metadata(self): """Save a network without metadata to a file.""" m = torch.jit.script(TestModule()) diff --git a/tests/test_torchvision.py b/tests/test_torchvision.py index 9cd536aa6f..2931b0c1a8 100644 --- a/tests/test_torchvision.py +++ b/tests/test_torchvision.py @@ -55,6 +55,7 @@ class TestTorchVision(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, input_data, expected_value): set_determinism(seed=0) diff --git a/tests/test_torchvision_fc_model.py b/tests/test_torchvision_fc_model.py index e913b2b9b1..322cce1161 100644 --- a/tests/test_torchvision_fc_model.py +++ b/tests/test_torchvision_fc_model.py @@ -153,6 +153,7 @@ class TestTorchVisionFCModel(unittest.TestCase): + @parameterized.expand( [TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7] + ([TEST_CASE_8] if has_enum else []) @@ -187,6 +188,7 @@ def test_with_pretrained(self, input_param, input_shape, expected_shape, expecte class TestLookup(unittest.TestCase): + def test_get_module(self): net = UNet(spatial_dims=2, in_channels=1, out_channels=1, channels=(4, 8, 16, 32, 64), strides=(2, 2, 2, 2)) self.assertEqual(look_up_named_module("", net), net) diff --git a/tests/test_torchvisiond.py b/tests/test_torchvisiond.py index b2a6bcafc5..ec09692df9 100644 --- a/tests/test_torchvisiond.py +++ b/tests/test_torchvisiond.py @@ -52,6 +52,7 @@ class TestTorchVisiond(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_value(self, input_param, input_data, expected_value): set_determinism(seed=0) diff --git a/tests/test_traceable_transform.py b/tests/test_traceable_transform.py index 42906c84d2..dd139053e3 100644 --- a/tests/test_traceable_transform.py +++ b/tests/test_traceable_transform.py @@ -17,6 +17,7 @@ class _TraceTest(TraceableTransform): + def __call__(self, data): self.push_transform(data) return data @@ -27,6 +28,7 @@ def pop(self, data): class TestTraceable(unittest.TestCase): + def test_default(self): expected_key = "_transforms" a = _TraceTest() diff --git a/tests/test_train_mode.py b/tests/test_train_mode.py index 6136e2f7db..ae99f91363 100644 --- a/tests/test_train_mode.py +++ b/tests/test_train_mode.py @@ -19,6 +19,7 @@ class TestEvalMode(unittest.TestCase): + def test_eval_mode(self): t = torch.rand(1, 1, 4, 4) p = torch.nn.Conv2d(1, 1, 3) diff --git a/tests/test_trainable_bilateral.py b/tests/test_trainable_bilateral.py index 43b628be80..c69eff4071 100644 --- a/tests/test_trainable_bilateral.py +++ b/tests/test_trainable_bilateral.py @@ -273,6 +273,7 @@ @skip_if_no_cpp_extension class BilateralFilterTestCaseCpuPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cpu_precise(self, test_case_description, sigmas, input, expected): # Params to determine the implementation to test @@ -371,6 +372,7 @@ def test_cpu_precise_backwards(self, test_case_description, sigmas, input, expec @skip_if_no_cuda @skip_if_no_cpp_extension class BilateralFilterTestCaseCudaPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cuda_precise(self, test_case_description, sigmas, input, expected): # Skip this test diff --git a/tests/test_trainable_joint_bilateral.py b/tests/test_trainable_joint_bilateral.py index 8a9c69bda4..4263683ce2 100644 --- a/tests/test_trainable_joint_bilateral.py +++ b/tests/test_trainable_joint_bilateral.py @@ -358,6 +358,7 @@ @skip_if_no_cpp_extension @skip_if_quick class JointBilateralFilterTestCaseCpuPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cpu_precise(self, test_case_description, sigmas, input, guide, expected): # Params to determine the implementation to test @@ -481,6 +482,7 @@ def test_cpu_precise_backwards(self, test_case_description, sigmas, input, guide @skip_if_no_cuda @skip_if_no_cpp_extension class JointBilateralFilterTestCaseCudaPrecise(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_cuda_precise(self, test_case_description, sigmas, input, guide, expected): # Skip this test diff --git a/tests/test_transchex.py b/tests/test_transchex.py index 9ad847cdaa..481c20e285 100644 --- a/tests/test_transchex.py +++ b/tests/test_transchex.py @@ -47,6 +47,7 @@ @skip_if_quick class TestTranschex(unittest.TestCase): + @parameterized.expand(TEST_CASE_TRANSCHEX) def test_shape(self, input_param, expected_shape): net = Transchex(**input_param) diff --git a/tests/test_transform.py b/tests/test_transform.py index ea738eaac3..9b05133391 100644 --- a/tests/test_transform.py +++ b/tests/test_transform.py @@ -20,6 +20,7 @@ class FaultyTransform(mt.Transform): + def __call__(self, _): raise RuntimeError @@ -29,6 +30,7 @@ def faulty_lambda(_): class TestTransform(unittest.TestCase): + @classmethod def setUpClass(cls): super(__class__, cls).setUpClass() diff --git a/tests/test_transformerblock.py b/tests/test_transformerblock.py index 914336668d..5a8dbba83c 100644 --- a/tests/test_transformerblock.py +++ b/tests/test_transformerblock.py @@ -39,6 +39,7 @@ class TestTransformerBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_TRANSFORMERBLOCK) def test_shape(self, input_param, input_shape, expected_shape): net = TransformerBlock(**input_param) diff --git a/tests/test_transpose.py b/tests/test_transpose.py index 0c9ae1c7e3..2f5ccd1235 100644 --- a/tests/test_transpose.py +++ b/tests/test_transpose.py @@ -27,6 +27,7 @@ class TestTranspose(unittest.TestCase): + @parameterized.expand(TESTS) def test_transpose(self, im, indices): tr = Transpose(indices) diff --git a/tests/test_transposed.py b/tests/test_transposed.py index ab80520fc9..e7c6ecbe8a 100644 --- a/tests/test_transposed.py +++ b/tests/test_transposed.py @@ -30,6 +30,7 @@ class TestTranspose(unittest.TestCase): + @parameterized.expand(TESTS) def test_transpose(self, im, indices): data = {"i": deepcopy(im), "j": deepcopy(im)} diff --git a/tests/test_tversky_loss.py b/tests/test_tversky_loss.py index d1175f40c5..efe1f2cdf3 100644 --- a/tests/test_tversky_loss.py +++ b/tests/test_tversky_loss.py @@ -148,6 +148,7 @@ class TestTverskyLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = TverskyLoss(**input_param).forward(**input_data) diff --git a/tests/test_ultrasound_confidence_map_transform.py b/tests/test_ultrasound_confidence_map_transform.py index fbf0c4fe97..f672961700 100644 --- a/tests/test_ultrasound_confidence_map_transform.py +++ b/tests/test_ultrasound_confidence_map_transform.py @@ -518,6 +518,7 @@ class TestUltrasoundConfidenceMapTransform(unittest.TestCase): + def setUp(self): self.input_img_np = np.expand_dims(TEST_INPUT, axis=0) # mock image (numpy array) self.input_mask_np = np.expand_dims(TEST_MASK, axis=0) # mock mask (numpy array) diff --git a/tests/test_unet.py b/tests/test_unet.py index 9cb4af3379..1fb98f84b0 100644 --- a/tests/test_unet.py +++ b/tests/test_unet.py @@ -165,6 +165,7 @@ class TestUNET(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_shape, expected_shape): net = UNet(**input_param).to(device) diff --git a/tests/test_unetr.py b/tests/test_unetr.py index 406d30aa12..46018d2bc0 100644 --- a/tests/test_unetr.py +++ b/tests/test_unetr.py @@ -57,6 +57,7 @@ @skip_if_quick class TestUNETR(unittest.TestCase): + @parameterized.expand(TEST_CASE_UNETR) def test_shape(self, input_param, input_shape, expected_shape): net = UNETR(**input_param) diff --git a/tests/test_unetr_block.py b/tests/test_unetr_block.py index 60004be25e..9701557ed6 100644 --- a/tests/test_unetr_block.py +++ b/tests/test_unetr_block.py @@ -102,6 +102,7 @@ class TestResBasicBlock(unittest.TestCase): + @parameterized.expand(TEST_CASE_UNETR_BASIC_BLOCK) def test_shape(self, input_param, input_shape, expected_shape): for net in [UnetrBasicBlock(**input_param)]: @@ -124,6 +125,7 @@ def test_script(self): class TestUpBlock(unittest.TestCase): + @parameterized.expand(TEST_UP_BLOCK) def test_shape(self, input_param, input_shape, expected_shape, skip_shape): net = UnetrUpBlock(**input_param) @@ -140,6 +142,7 @@ def test_script(self): class TestPrUpBlock(unittest.TestCase): + @parameterized.expand(TEST_PRUP_BLOCK) def test_shape(self, input_param, input_shape, expected_shape): net = UnetrPrUpBlock(**input_param) diff --git a/tests/test_unified_focal_loss.py b/tests/test_unified_focal_loss.py index 0e7217e2b4..3b868a560e 100644 --- a/tests/test_unified_focal_loss.py +++ b/tests/test_unified_focal_loss.py @@ -38,6 +38,7 @@ class TestAsymmetricUnifiedFocalLoss(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_result(self, input_data, expected_val): loss = AsymmetricUnifiedFocalLoss() diff --git a/tests/test_upsample_block.py b/tests/test_upsample_block.py index a82a31b064..e4890c83bc 100644 --- a/tests/test_upsample_block.py +++ b/tests/test_upsample_block.py @@ -121,6 +121,7 @@ class TestUpsample(unittest.TestCase): + @parameterized.expand(TEST_CASES + TEST_CASES_EQ + TEST_CASES_EQ2) def test_shape(self, input_param, input_shape, expected_shape): net = UpSample(**input_param) diff --git a/tests/test_utils_pytorch_numpy_unification.py b/tests/test_utils_pytorch_numpy_unification.py index 619ae8aee3..6e655289e4 100644 --- a/tests/test_utils_pytorch_numpy_unification.py +++ b/tests/test_utils_pytorch_numpy_unification.py @@ -29,6 +29,7 @@ class TestPytorchNumpyUnification(unittest.TestCase): + def setUp(self) -> None: set_determinism(0) diff --git a/tests/test_varautoencoder.py b/tests/test_varautoencoder.py index b050983d2c..e957dcfb61 100644 --- a/tests/test_varautoencoder.py +++ b/tests/test_varautoencoder.py @@ -108,6 +108,7 @@ class TestVarAutoEncoder(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_shape, expected_shape): net = VarAutoEncoder(**input_param).to(device) diff --git a/tests/test_varnet.py b/tests/test_varnet.py index 3ec6b0f087..a46d58d6a2 100644 --- a/tests/test_varnet.py +++ b/tests/test_varnet.py @@ -32,6 +32,7 @@ class TestVarNet(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, coil_sens_model, refinement_model, num_cascades, input_shape, expected_shape): net = VariationalNetworkModel(coil_sens_model, refinement_model, num_cascades).to(device) diff --git a/tests/test_version.py b/tests/test_version.py index 15f8cd36c6..35ce8d9a2f 100644 --- a/tests/test_version.py +++ b/tests/test_version.py @@ -75,6 +75,7 @@ def _pairwise(iterable): class TestVersionCompare(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_compare_leq(self, a, b, expected=True): """Test version_leq with `a` and `b`""" diff --git a/tests/test_video_datasets.py b/tests/test_video_datasets.py index 790feb51ee..6e344e1caa 100644 --- a/tests/test_video_datasets.py +++ b/tests/test_video_datasets.py @@ -31,6 +31,7 @@ class Base: + class TestVideoDataset(unittest.TestCase): video_source: int | str ds: type[VideoDataset] diff --git a/tests/test_vis_cam.py b/tests/test_vis_cam.py index bb3ff7237a..b641599af2 100644 --- a/tests/test_vis_cam.py +++ b/tests/test_vis_cam.py @@ -67,6 +67,7 @@ class TestClassActivationMap(unittest.TestCase): + @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_shape(self, input_data, expected_shape): if input_data["model"] == "densenet2d": diff --git a/tests/test_vis_gradbased.py b/tests/test_vis_gradbased.py index 0fbe328c83..e9db0af240 100644 --- a/tests/test_vis_gradbased.py +++ b/tests/test_vis_gradbased.py @@ -21,6 +21,7 @@ class DenseNetAdjoint(DenseNet121): + def __call__(self, x, adjoint_info): if adjoint_info != 42: raise ValueError @@ -48,6 +49,7 @@ def __call__(self, x, adjoint_info): class TestGradientClassActivationMap(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, vis_type, model, shape): device = "cuda:0" if torch.cuda.is_available() else "cpu" diff --git a/tests/test_vis_gradcam.py b/tests/test_vis_gradcam.py index 4b554de0aa..325b74b3ce 100644 --- a/tests/test_vis_gradcam.py +++ b/tests/test_vis_gradcam.py @@ -24,6 +24,7 @@ class DenseNetAdjoint(DenseNet121): + def __call__(self, x, adjoint_info): if adjoint_info != 42: raise ValueError @@ -149,6 +150,7 @@ def __call__(self, x, adjoint_info): @skip_if_quick class TestGradientClassActivationMap(unittest.TestCase): + @parameterized.expand(TESTS) def test_shape(self, cam_class, input_data, expected_shape): if input_data["model"] == "densenet2d": diff --git a/tests/test_vit.py b/tests/test_vit.py index f911c2d5c9..a84883cba0 100644 --- a/tests/test_vit.py +++ b/tests/test_vit.py @@ -61,6 +61,7 @@ @skip_if_quick class TestViT(unittest.TestCase): + @parameterized.expand(TEST_CASE_Vit) def test_shape(self, input_param, input_shape, expected_shape): net = ViT(**input_param) diff --git a/tests/test_vitautoenc.py b/tests/test_vitautoenc.py index 5e95d3c7fb..cc3d493bb3 100644 --- a/tests/test_vitautoenc.py +++ b/tests/test_vitautoenc.py @@ -66,6 +66,7 @@ @skip_if_quick class TestVitAutoenc(unittest.TestCase): + def setUp(self): self.threads = torch.get_num_threads() torch.set_num_threads(4) diff --git a/tests/test_vnet.py b/tests/test_vnet.py index 633893ce51..0ebf060434 100644 --- a/tests/test_vnet.py +++ b/tests/test_vnet.py @@ -55,6 +55,7 @@ class TestVNet(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_VNET_2D_1, diff --git a/tests/test_vote_ensemble.py b/tests/test_vote_ensemble.py index 32ff120c5d..4abdd0b050 100644 --- a/tests/test_vote_ensemble.py +++ b/tests/test_vote_ensemble.py @@ -71,6 +71,7 @@ class TestVoteEnsemble(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, img, expected_value): result = VoteEnsemble(**input_param)(img) diff --git a/tests/test_vote_ensembled.py b/tests/test_vote_ensembled.py index 17f9d54835..957133d7fc 100644 --- a/tests/test_vote_ensembled.py +++ b/tests/test_vote_ensembled.py @@ -86,6 +86,7 @@ class TestVoteEnsembled(unittest.TestCase): + @parameterized.expand(TESTS) def test_value(self, input_param, img, expected_value): result = VoteEnsembled(**input_param)(img) diff --git a/tests/test_voxelmorph.py b/tests/test_voxelmorph.py index 53ef2fc18f..ef420ef20c 100644 --- a/tests/test_voxelmorph.py +++ b/tests/test_voxelmorph.py @@ -245,6 +245,7 @@ class TestVOXELMORPH(unittest.TestCase): + @parameterized.expand(CASES) def test_shape(self, input_param, input_shape, expected_shape): net = VoxelMorphUNet(**input_param).to(device) diff --git a/tests/test_warp.py b/tests/test_warp.py index e614973f90..bac595224f 100644 --- a/tests/test_warp.py +++ b/tests/test_warp.py @@ -106,6 +106,7 @@ @skip_if_quick class TestWarp(unittest.TestCase): + def setUp(self): config = testing_data_config("images", "Prostate_T2W_AX_1") download_url_or_skip_test( diff --git a/tests/test_watershed.py b/tests/test_watershed.py index a5a232ba3c..3f7a29bfe7 100644 --- a/tests/test_watershed.py +++ b/tests/test_watershed.py @@ -43,6 +43,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestWatershed(unittest.TestCase): + @parameterized.expand(TESTS) def test_output(self, args, image, hover_map, expected_shape): mask = GenerateWatershedMask()(image) diff --git a/tests/test_watershedd.py b/tests/test_watershedd.py index c12f5ad140..fc44996be4 100644 --- a/tests/test_watershedd.py +++ b/tests/test_watershedd.py @@ -48,6 +48,7 @@ @unittest.skipUnless(has_skimage, "Requires scikit-image library.") @unittest.skipUnless(has_scipy, "Requires scipy library.") class TestWatershedd(unittest.TestCase): + @parameterized.expand(TESTS) def test_output(self, args, image, hover_map, expected_shape): data = {"output": image, "hover_map": hover_map} diff --git a/tests/test_weight_init.py b/tests/test_weight_init.py index 376faacc56..a682ec6cc9 100644 --- a/tests/test_weight_init.py +++ b/tests/test_weight_init.py @@ -32,6 +32,7 @@ class TestWeightInit(unittest.TestCase): + @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_shape): im = torch.rand(input_shape) diff --git a/tests/test_weighted_random_sampler_dist.py b/tests/test_weighted_random_sampler_dist.py index d38bab54f0..8e37482da6 100644 --- a/tests/test_weighted_random_sampler_dist.py +++ b/tests/test_weighted_random_sampler_dist.py @@ -24,6 +24,7 @@ @skip_if_windows @skip_if_darwin class DistributedWeightedRandomSamplerTest(DistTestCase): + @DistCall(nnodes=1, nproc_per_node=2) def test_sampling(self): data = [1, 2, 3, 4, 5] diff --git a/tests/test_with_allow_missing_keys.py b/tests/test_with_allow_missing_keys.py index ec55654f07..427f64c705 100644 --- a/tests/test_with_allow_missing_keys.py +++ b/tests/test_with_allow_missing_keys.py @@ -19,6 +19,7 @@ class TestWithAllowMissingKeysMode(unittest.TestCase): + def setUp(self): self.data = {"image": np.arange(16, dtype=float).reshape(1, 4, 4)} diff --git a/tests/test_write_metrics_reports.py b/tests/test_write_metrics_reports.py index 4f61e43fe1..1013f15d85 100644 --- a/tests/test_write_metrics_reports.py +++ b/tests/test_write_metrics_reports.py @@ -23,6 +23,7 @@ class TestWriteMetricsReports(unittest.TestCase): + def test_content(self): with tempfile.TemporaryDirectory() as tempdir: write_metrics_reports( diff --git a/tests/test_wsi_sliding_window_splitter.py b/tests/test_wsi_sliding_window_splitter.py index ac1a136489..c510ece272 100644 --- a/tests/test_wsi_sliding_window_splitter.py +++ b/tests/test_wsi_sliding_window_splitter.py @@ -102,6 +102,7 @@ # Filtering functions test cases def gen_location_filter(locations): + def my_filter(patch, loc): if loc in locations: return False @@ -198,6 +199,7 @@ def setUpModule(): class WSISlidingWindowSplitterTests(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_WSI_0_BASE, diff --git a/tests/test_wsireader.py b/tests/test_wsireader.py index aae2b0dbaf..99a86c5ac8 100644 --- a/tests/test_wsireader.py +++ b/tests/test_wsireader.py @@ -402,6 +402,7 @@ def setUpModule(): class WSIReaderTests: + class Tests(unittest.TestCase): backend = None @@ -640,6 +641,7 @@ def test_errors(self, file_path, reader_kwargs, patch_info, exception): @skipUnless(has_cucim, "Requires cucim") class TestCuCIM(WSIReaderTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "cucim" @@ -647,6 +649,7 @@ def setUpClass(cls): @skipUnless(has_osl, "Requires openslide") class TestOpenSlide(WSIReaderTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "openslide" @@ -654,6 +657,7 @@ def setUpClass(cls): @skipUnless(has_tiff, "Requires tifffile") class TestTiffFile(WSIReaderTests.Tests): + @classmethod def setUpClass(cls): cls.backend = "tifffile" diff --git a/tests/test_zarr_avg_merger.py b/tests/test_zarr_avg_merger.py index c4c7fad5da..de7fad48da 100644 --- a/tests/test_zarr_avg_merger.py +++ b/tests/test_zarr_avg_merger.py @@ -256,6 +256,7 @@ @unittest.skipUnless(has_zarr and has_numcodecs, "Requires zarr (and numcodecs) packages.)") class ZarrAvgMergerTests(unittest.TestCase): + @parameterized.expand( [ TEST_CASE_0_DEFAULT_DTYPE, diff --git a/tests/test_zipdataset.py b/tests/test_zipdataset.py index de8a8e80d6..2939ff3f49 100644 --- a/tests/test_zipdataset.py +++ b/tests/test_zipdataset.py @@ -20,6 +20,7 @@ class Dataset_(torch.utils.data.Dataset): + def __init__(self, length, index_only=True): self.len = length self.index_only = index_only @@ -48,6 +49,7 @@ def __getitem__(self, index): class TestZipDataset(unittest.TestCase): + @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) def test_value(self, datasets, transform, expected_output, expected_length): test_dataset = ZipDataset(datasets=datasets, transform=transform) diff --git a/tests/test_zoom.py b/tests/test_zoom.py index e1ea3c25a3..2db2df4486 100644 --- a/tests/test_zoom.py +++ b/tests/test_zoom.py @@ -43,6 +43,7 @@ class TestZoom(NumpyImageTestCase2D): + @parameterized.expand(VALID_CASES) def test_pending_ops(self, zoom, mode, align_corners=False, keep_size=False): im = MetaTensor(self.imt[0], meta={"a": "b", "affine": DEFAULT_TEST_AFFINE}) diff --git a/tests/test_zoom_affine.py b/tests/test_zoom_affine.py index dc39a4f1c2..ae8e688d96 100644 --- a/tests/test_zoom_affine.py +++ b/tests/test_zoom_affine.py @@ -64,6 +64,7 @@ class TestZoomAffine(unittest.TestCase): + @parameterized.expand(VALID_CASES) def test_correct(self, affine, scale, expected): output = zoom_affine(affine, scale, diagonal=False) diff --git a/tests/test_zoomd.py b/tests/test_zoomd.py index 1dcbf98572..ad91f398ff 100644 --- a/tests/test_zoomd.py +++ b/tests/test_zoomd.py @@ -34,6 +34,7 @@ class TestZoomd(NumpyImageTestCase2D): + @parameterized.expand(VALID_CASES) def test_correct_results(self, zoom, mode, keep_size, align_corners=None): key = "img" diff --git a/tests/utils.py b/tests/utils.py index ee800598bb..ea73a3ed81 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -677,6 +677,7 @@ def setUp(self): class TorchImageTestCase2D(NumpyImageTestCase2D): + def setUp(self): NumpyImageTestCase2D.setUp(self) self.imt = torch.tensor(self.imt) @@ -707,6 +708,7 @@ def setUp(self): class TorchImageTestCase3D(NumpyImageTestCase3D): + def setUp(self): NumpyImageTestCase3D.setUp(self) self.imt = torch.tensor(self.imt) From ec2cc83e64f53c390724e6f2ac8cbab22d0a5efa Mon Sep 17 00:00:00 2001 From: Ibrahim Hadzic Date: Mon, 5 Feb 2024 22:30:53 -0500 Subject: [PATCH 034/136] Instantiation mode `"partial"` to `"callable"`. Return the `_target_` component as-is when in `_mode_="callable"` and no kwargs are specified (#7413) ### Description A `_target_` component with `_mode_="partial"` will still be wrapped in `functools.partial` even when no kwargs are passed: `functool.partial(component)`. In such cases, the component can just be returned as-is. If you agree with this, I will add tests for it. Thank you! ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Ibrahim Hadzic --- docs/source/config_syntax.md | 7 ++++--- monai/bundle/config_item.py | 2 +- monai/utils/enums.py | 2 +- monai/utils/module.py | 11 +++++++---- tests/test_config_item.py | 2 +- tests/test_config_parser.py | 6 ++---- 6 files changed, 16 insertions(+), 14 deletions(-) diff --git a/docs/source/config_syntax.md b/docs/source/config_syntax.md index c1e3d5cbe9..c932879b5a 100644 --- a/docs/source/config_syntax.md +++ b/docs/source/config_syntax.md @@ -168,9 +168,10 @@ _Description:_ `_requires_`, `_disabled_`, `_desc_`, and `_mode_` are optional k - `_mode_` specifies the operating mode when the component is instantiated or the callable is called. it currently supports the following values: - `"default"` (default) -- return the return value of ``_target_(**kwargs)`` - - `"partial"` -- return a partial function of ``functools.partial(_target_, **kwargs)`` (this is often - useful when some portion of the full set of arguments are supplied to the ``_target_``, and the user wants to - call it with additional arguments later). + - `"callable"` -- return a callable, either as ``_target_`` itself or, if ``kwargs`` are provided, as a + partial function of ``functools.partial(_target_, **kwargs)``. Useful for defining a class or function + that will be instantied or called later. User can pre-define some arguments to the ``_target_`` and call + it with additional arguments later. - `"debug"` -- execute with debug prompt and return the return value of ``pdb.runcall(_target_, **kwargs)``, see also [`pdb.runcall`](https://docs.python.org/3/library/pdb.html#pdb.runcall). diff --git a/monai/bundle/config_item.py b/monai/bundle/config_item.py index c6da0a73de..844d5b30bf 100644 --- a/monai/bundle/config_item.py +++ b/monai/bundle/config_item.py @@ -181,7 +181,7 @@ class ConfigComponent(ConfigItem, Instantiable): - ``"_mode_"`` (optional): operating mode for invoking the callable ``component`` defined by ``"_target_"``: - ``"default"``: returns ``component(**kwargs)`` - - ``"partial"``: returns ``functools.partial(component, **kwargs)`` + - ``"callable"``: returns ``component`` or, if ``kwargs`` are provided, ``functools.partial(component, **kwargs)`` - ``"debug"``: returns ``pdb.runcall(component, **kwargs)`` Other fields in the config content are input arguments to the python module. diff --git a/monai/utils/enums.py b/monai/utils/enums.py index a0847dd76c..b786e92151 100644 --- a/monai/utils/enums.py +++ b/monai/utils/enums.py @@ -411,7 +411,7 @@ class CompInitMode(StrEnum): """ DEFAULT = "default" - PARTIAL = "partial" + CALLABLE = "callable" DEBUG = "debug" diff --git a/monai/utils/module.py b/monai/utils/module.py index db62e1e72b..0dcf22fcd7 100644 --- a/monai/utils/module.py +++ b/monai/utils/module.py @@ -231,11 +231,14 @@ def instantiate(__path: str, __mode: str, **kwargs: Any) -> Any: Args: __path: if a string is provided, it's interpreted as the full path of the target class or function component. - If a callable is provided, ``__path(**kwargs)`` or ``functools.partial(__path, **kwargs)`` will be returned. + If a callable is provided, ``__path(**kwargs)`` will be invoked and returned for ``__mode="default"``. + For ``__mode="callable"``, the callable will be returned as ``__path`` or, if ``kwargs`` are provided, + as ``functools.partial(__path, **kwargs)`` for future invoking. + __mode: the operating mode for invoking the (callable) ``component`` represented by ``__path``: - ``"default"``: returns ``component(**kwargs)`` - - ``"partial"``: returns ``functools.partial(component, **kwargs)`` + - ``"callable"``: returns ``component`` or, if ``kwargs`` are provided, ``functools.partial(component, **kwargs)`` - ``"debug"``: returns ``pdb.runcall(component, **kwargs)`` kwargs: keyword arguments to the callable represented by ``__path``. @@ -259,8 +262,8 @@ def instantiate(__path: str, __mode: str, **kwargs: Any) -> Any: return component if m == CompInitMode.DEFAULT: return component(**kwargs) - if m == CompInitMode.PARTIAL: - return partial(component, **kwargs) + if m == CompInitMode.CALLABLE: + return partial(component, **kwargs) if kwargs else component if m == CompInitMode.DEBUG: warnings.warn( f"\n\npdb: instantiating component={component}, mode={m}\n" diff --git a/tests/test_config_item.py b/tests/test_config_item.py index 72f54adf0a..4909ecf6be 100644 --- a/tests/test_config_item.py +++ b/tests/test_config_item.py @@ -37,7 +37,7 @@ TEST_CASE_5 = [{"_target_": "LoadImaged", "_disabled_": "true", "keys": ["image"]}, dict] # test non-monai modules and excludes TEST_CASE_6 = [{"_target_": "torch.optim.Adam", "params": torch.nn.PReLU().parameters(), "lr": 1e-4}, torch.optim.Adam] -TEST_CASE_7 = [{"_target_": "decollate_batch", "detach": True, "pad": True, "_mode_": "partial"}, partial] +TEST_CASE_7 = [{"_target_": "decollate_batch", "detach": True, "pad": True, "_mode_": "callable"}, partial] # test args contains "name" field TEST_CASE_8 = [ {"_target_": "RandTorchVisiond", "keys": "image", "name": "ColorJitter", "brightness": 0.25}, diff --git a/tests/test_config_parser.py b/tests/test_config_parser.py index 41d7aa7a4e..8947158857 100644 --- a/tests/test_config_parser.py +++ b/tests/test_config_parser.py @@ -72,7 +72,6 @@ def case_pdb_inst(sarg=None): class TestClass: - @staticmethod def compute(a, b, func=lambda x, y: x + y): return func(a, b) @@ -127,7 +126,6 @@ def __call__(self, a, b): class TestConfigParser(unittest.TestCase): - def test_config_content(self): test_config = {"preprocessing": [{"_target_": "LoadImage"}], "dataset": {"_target_": "Dataset"}} parser = ConfigParser(config=test_config) @@ -183,7 +181,7 @@ def test_function(self, config): parser = ConfigParser(config=config, globals={"TestClass": TestClass}) for id in config: if id in ("compute", "cls_compute"): - parser[f"{id}#_mode_"] = "partial" + parser[f"{id}#_mode_"] = "callable" func = parser.get_parsed_content(id=id) self.assertTrue(id in parser.ref_resolver.resolved_content) if id == "error_func": @@ -279,7 +277,7 @@ def test_lambda_reference(self): def test_non_str_target(self): configs = { - "fwd": {"_target_": "$@model.forward", "x": "$torch.rand(1, 3, 256, 256)", "_mode_": "partial"}, + "fwd": {"_target_": "$@model.forward", "x": "$torch.rand(1, 3, 256, 256)", "_mode_": "callable"}, "model": {"_target_": "monai.networks.nets.resnet.resnet18", "pretrained": False, "spatial_dims": 2}, } self.assertTrue(callable(ConfigParser(config=configs).fwd)) From c27cc980e078113cec3355f892344a4e6f5a9515 Mon Sep 17 00:00:00 2001 From: "Dr. Behrooz Hashemian" <3968947+drbeh@users.noreply.github.com> Date: Tue, 6 Feb 2024 03:58:15 -0500 Subject: [PATCH 035/136] Add support for mlflow experiment name in auto3dseg (#7442) Fixes #7441 This PR enable Auto3DSeg users to manage their runs and experiment more efficiently in MLFlow under arbitrary experiment names, by providing experiment name as an input parameter. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). change). - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. --------- Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com> --- monai/apps/auto3dseg/auto_runner.py | 12 ++++++++- monai/apps/auto3dseg/bundle_gen.py | 32 ++++++++++++++++++++++-- monai/apps/auto3dseg/ensemble_builder.py | 2 +- 3 files changed, 42 insertions(+), 4 deletions(-) diff --git a/monai/apps/auto3dseg/auto_runner.py b/monai/apps/auto3dseg/auto_runner.py index e4c2d908b7..52a0824227 100644 --- a/monai/apps/auto3dseg/auto_runner.py +++ b/monai/apps/auto3dseg/auto_runner.py @@ -85,6 +85,7 @@ class AutoRunner: can be skipped based on the analysis on the dataset from Auto3DSeg DataAnalyzer. mlflow_tracking_uri: a tracking URI for MLflow server which could be local directory or address of the remote tracking Server; MLflow runs will be recorded locally in algorithms' model folder if the value is None. + mlflow_experiment_name: the name of the experiment in MLflow server. kwargs: image writing parameters for the ensemble inference. The kwargs format follows the SaveImage transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage. @@ -212,6 +213,7 @@ def __init__( templates_path_or_url: str | None = None, allow_skip: bool = True, mlflow_tracking_uri: str | None = None, + mlflow_experiment_name: str | None = None, **kwargs: Any, ): if input is None and os.path.isfile(os.path.join(os.path.abspath(work_dir), "input.yaml")): @@ -253,6 +255,7 @@ def __init__( self.hpo = hpo and has_nni self.hpo_backend = hpo_backend self.mlflow_tracking_uri = mlflow_tracking_uri + self.mlflow_experiment_name = mlflow_experiment_name self.kwargs = deepcopy(kwargs) # parse input config for AutoRunner param overrides @@ -268,7 +271,13 @@ def __init__( if param in self.data_src_cfg and isinstance(self.data_src_cfg[param], bool): setattr(self, param, self.data_src_cfg[param]) # e.g. self.analyze = self.data_src_cfg["analyze"] - for param in ["algos", "hpo_backend", "templates_path_or_url", "mlflow_tracking_uri"]: # override from config + for param in [ + "algos", + "hpo_backend", + "templates_path_or_url", + "mlflow_tracking_uri", + "mlflow_experiment_name", + ]: # override from config if param in self.data_src_cfg: setattr(self, param, self.data_src_cfg[param]) # e.g. self.algos = self.data_src_cfg["algos"] @@ -813,6 +822,7 @@ def run(self): data_stats_filename=self.datastats_filename, data_src_cfg_name=self.data_src_cfg_name, mlflow_tracking_uri=self.mlflow_tracking_uri, + mlflow_experiment_name=self.mlflow_experiment_name, ) if self.gpu_customization: diff --git a/monai/apps/auto3dseg/bundle_gen.py b/monai/apps/auto3dseg/bundle_gen.py index 03b9c8bbf4..8a54d18be7 100644 --- a/monai/apps/auto3dseg/bundle_gen.py +++ b/monai/apps/auto3dseg/bundle_gen.py @@ -85,7 +85,8 @@ def __init__(self, template_path: PathLike): self.template_path = template_path self.data_stats_files = "" self.data_list_file = "" - self.mlflow_tracking_uri = None + self.mlflow_tracking_uri: str | None = None + self.mlflow_experiment_name: str | None = None self.output_path = "" self.name = "" self.best_metric = None @@ -139,7 +140,16 @@ def set_mlflow_tracking_uri(self, mlflow_tracking_uri: str | None) -> None: the remote tracking Server; MLflow runs will be recorded locally in algorithms' model folder if the value is None. """ - self.mlflow_tracking_uri = mlflow_tracking_uri # type: ignore + self.mlflow_tracking_uri = mlflow_tracking_uri + + def set_mlflow_experiment_name(self, mlflow_experiment_name: str | None) -> None: + """ + Set the experiment name for MLflow server + + Args: + mlflow_experiment_name: a string to specify the experiment name for MLflow server. + """ + self.mlflow_experiment_name = mlflow_experiment_name def fill_template_config(self, data_stats_filename: str, algo_path: str, **kwargs: Any) -> dict: """ @@ -447,6 +457,7 @@ class BundleGen(AlgoGen): mlflow_tracking_uri: a tracking URI for MLflow server which could be local directory or address of the remote tracking Server; MLflow runs will be recorded locally in algorithms' model folder if the value is None. + mlfow_experiment_name: a string to specify the experiment name for MLflow server. .. code-block:: bash python -m monai.apps.auto3dseg BundleGen generate --data_stats_filename="../algorithms/datastats.yaml" @@ -460,6 +471,7 @@ def __init__( data_stats_filename: str | None = None, data_src_cfg_name: str | None = None, mlflow_tracking_uri: str | None = None, + mlflow_experiment_name: str | None = None, ): if algos is None or isinstance(algos, (list, tuple, str)): if templates_path_or_url is None: @@ -513,6 +525,7 @@ def __init__( self.data_stats_filename = data_stats_filename self.data_src_cfg_name = data_src_cfg_name self.mlflow_tracking_uri = mlflow_tracking_uri + self.mlflow_experiment_name = mlflow_experiment_name self.history: list[dict] = [] def set_data_stats(self, data_stats_filename: str) -> None: @@ -552,10 +565,23 @@ def set_mlflow_tracking_uri(self, mlflow_tracking_uri): """ self.mlflow_tracking_uri = mlflow_tracking_uri + def set_mlflow_experiment_name(self, mlflow_experiment_name): + """ + Set the experiment name for MLflow server + + Args: + mlflow_experiment_name: a string to specify the experiment name for MLflow server. + """ + self.mlflow_experiment_name = mlflow_experiment_name + def get_mlflow_tracking_uri(self): """Get the tracking URI for MLflow server""" return self.mlflow_tracking_uri + def get_mlflow_experiment_name(self): + """Get the experiment name for MLflow server""" + return self.mlflow_experiment_name + def get_history(self) -> list: """Get the history of the bundleAlgo object with their names/identifiers""" return self.history @@ -608,10 +634,12 @@ def generate( data_stats = self.get_data_stats() data_src_cfg = self.get_data_src() mlflow_tracking_uri = self.get_mlflow_tracking_uri() + mlflow_experiment_name = self.get_mlflow_experiment_name() gen_algo = deepcopy(algo) gen_algo.set_data_stats(data_stats) gen_algo.set_data_source(data_src_cfg) gen_algo.set_mlflow_tracking_uri(mlflow_tracking_uri) + gen_algo.set_mlflow_experiment_name(mlflow_experiment_name) name = f"{gen_algo.name}_{f_id}" if allow_skip: diff --git a/monai/apps/auto3dseg/ensemble_builder.py b/monai/apps/auto3dseg/ensemble_builder.py index e29745e5cf..b2bea806de 100644 --- a/monai/apps/auto3dseg/ensemble_builder.py +++ b/monai/apps/auto3dseg/ensemble_builder.py @@ -464,7 +464,7 @@ def set_ensemble_method(self, ensemble_method_name: str = "AlgoEnsembleBestByFol ensemble_method_name, supported=["AlgoEnsembleBestN", "AlgoEnsembleBestByFold"] ) if self.ensemble_method_name == "AlgoEnsembleBestN": - n_best = kwargs.pop("n_best", False) or 2 + n_best = kwargs.pop("n_best", 2) self.ensemble_method = AlgoEnsembleBestN(n_best=n_best) elif self.ensemble_method_name == "AlgoEnsembleBestByFold": self.ensemble_method = AlgoEnsembleBestByFold(n_fold=self.num_fold) # type: ignore From ff430286c37e78d7592372a5a97377f0cbb0219c Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 7 Feb 2024 21:47:23 +0800 Subject: [PATCH 036/136] Update gdown version (#7448) gdown library has been updated to fix https://github.com/Project-MONAI/MONAI/issues/7383 since https://github.com/wkentaro/gdown/pull/295 Update the gdown version for this PR https://github.com/Project-MONAI/MONAI/pull/7384 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/utils.py | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/monai/apps/utils.py b/monai/apps/utils.py index 442dbabba0..db541923b5 100644 --- a/monai/apps/utils.py +++ b/monai/apps/utils.py @@ -30,7 +30,7 @@ from monai.config.type_definitions import PathLike from monai.utils import look_up_option, min_version, optional_import -gdown, has_gdown = optional_import("gdown", "4.6.3") +gdown, has_gdown = optional_import("gdown", "4.7.3") if TYPE_CHECKING: from tqdm import tqdm diff --git a/requirements-dev.txt b/requirements-dev.txt index 706980576c..b08fef874b 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,7 +1,7 @@ # Full requirements for developments -r requirements-min.txt pytorch-ignite==0.4.11 -gdown>=4.4.0, <=4.6.3 +gdown>=4.7.3 scipy>=1.7.1 itk>=5.2 nibabel diff --git a/setup.cfg b/setup.cfg index 4180ced917..229e2ace56 100644 --- a/setup.cfg +++ b/setup.cfg @@ -52,7 +52,7 @@ all = scipy>=1.7.1 pillow tensorboard - gdown==4.6.3 + gdown>=4.7.3 pytorch-ignite==0.4.11 torchvision itk>=5.2 @@ -97,7 +97,7 @@ pillow = tensorboard = tensorboard gdown = - gdown==4.6.3 + gdown>=4.7.3 ignite = pytorch-ignite==0.4.11 torchvision = From a8080fce21677d509b528b7a06bb28339901eef8 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 20 Feb 2024 22:18:00 +0800 Subject: [PATCH 037/136] Skip "test_gaussian_filter" as a workaround for blossom killed (#7474) workaround for #7445 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_gaussian_filter.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/test_gaussian_filter.py b/tests/test_gaussian_filter.py index 4ab689c565..2167591c66 100644 --- a/tests/test_gaussian_filter.py +++ b/tests/test_gaussian_filter.py @@ -18,7 +18,7 @@ from parameterized import parameterized from monai.networks.layers import GaussianFilter -from tests.utils import skip_if_quick +from tests.utils import SkipIfAtLeastPyTorchVersion, skip_if_quick TEST_CASES = [[{"type": "erf", "gt": 2.0}], [{"type": "scalespace", "gt": 3.0}], [{"type": "sampled", "gt": 5.0}]] TEST_CASES_GPU = [[{"type": "erf", "gt": 0.8, "device": "cuda"}], [{"type": "sampled", "gt": 5.0, "device": "cuda"}]] @@ -34,6 +34,7 @@ ] +@SkipIfAtLeastPyTorchVersion((2, 2, 0)) # https://github.com/Project-MONAI/MONAI/issues/7445 class TestGaussianFilterBackprop(unittest.TestCase): def code_to_run(self, input_args): @@ -94,6 +95,7 @@ def test_train_slow(self, input_args): self.code_to_run(input_args) +@SkipIfAtLeastPyTorchVersion((2, 2, 0)) # https://github.com/Project-MONAI/MONAI/issues/7445 class GaussianFilterTestCase(unittest.TestCase): def test_1d(self): From 50f9aea67fb1ea7967020ad613ce83409261f2de Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Tue, 20 Feb 2024 14:58:30 +0000 Subject: [PATCH 038/136] auto updates (#7463) Signed-off-by: monai-bot Signed-off-by: monai-bot --- tests/test_config_parser.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test_config_parser.py b/tests/test_config_parser.py index 8947158857..cc890a0522 100644 --- a/tests/test_config_parser.py +++ b/tests/test_config_parser.py @@ -72,6 +72,7 @@ def case_pdb_inst(sarg=None): class TestClass: + @staticmethod def compute(a, b, func=lambda x, y: x + y): return func(a, b) @@ -126,6 +127,7 @@ def __call__(self, a, b): class TestConfigParser(unittest.TestCase): + def test_config_content(self): test_config = {"preprocessing": [{"_target_": "LoadImage"}], "dataset": {"_target_": "Dataset"}} parser = ConfigParser(config=test_config) From df37a22f8ef94f922af35f8d0a1d6a85fcf45f95 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 21 Feb 2024 20:48:37 +0800 Subject: [PATCH 039/136] Skip "test_resize" as a workaround for blossom killed (#7484) workaround for #7445 ### Description `Resize` also uses `GaussianFilter` inside. https://github.com/Project-MONAI/MONAI/blob/50f9aea67fb1ea7967020ad613ce83409261f2de/monai/transforms/spatial/functional.py#L332 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_resize.py | 11 +++++++++-- tests/test_resized.py | 10 ++++++++-- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/tests/test_resize.py b/tests/test_resize.py index 33abfe4e1f..65b33ea649 100644 --- a/tests/test_resize.py +++ b/tests/test_resize.py @@ -21,7 +21,14 @@ from monai.data import MetaTensor, set_track_meta from monai.transforms import Resize from tests.lazy_transforms_utils import test_resampler_lazy -from tests.utils import TEST_NDARRAYS_ALL, NumpyImageTestCase2D, assert_allclose, is_tf32_env, pytorch_after +from tests.utils import ( + TEST_NDARRAYS_ALL, + NumpyImageTestCase2D, + SkipIfAtLeastPyTorchVersion, + assert_allclose, + is_tf32_env, + pytorch_after, +) TEST_CASE_0 = [{"spatial_size": 15}, (6, 10, 15)] @@ -39,7 +46,6 @@ class TestResize(NumpyImageTestCase2D): - def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resize(spatial_size=(128, 128, 3), mode="order") @@ -112,6 +118,7 @@ def test_correct_results(self, spatial_size, mode, anti_aliasing): ) @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_2_1, TEST_CASE_3, TEST_CASE_4]) + @SkipIfAtLeastPyTorchVersion((2, 2, 0)) # https://github.com/Project-MONAI/MONAI/issues/7445 def test_longest_shape(self, input_param, expected_shape): input_data = np.random.randint(0, 2, size=[3, 4, 7, 10]) input_param["size_mode"] = "longest" diff --git a/tests/test_resized.py b/tests/test_resized.py index ab4c9815ea..d62f29ab5c 100644 --- a/tests/test_resized.py +++ b/tests/test_resized.py @@ -21,7 +21,13 @@ from monai.data import MetaTensor, set_track_meta from monai.transforms import Invertd, Resize, Resized from tests.lazy_transforms_utils import test_resampler_lazy -from tests.utils import TEST_NDARRAYS_ALL, NumpyImageTestCase2D, assert_allclose, test_local_inversion +from tests.utils import ( + TEST_NDARRAYS_ALL, + NumpyImageTestCase2D, + SkipIfAtLeastPyTorchVersion, + assert_allclose, + test_local_inversion, +) TEST_CASE_0 = [{"keys": "img", "spatial_size": 15}, (6, 10, 15)] @@ -58,8 +64,8 @@ ] +@SkipIfAtLeastPyTorchVersion((2, 2, 0)) # https://github.com/Project-MONAI/MONAI/issues/7445 class TestResized(NumpyImageTestCase2D): - def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resized(keys="img", spatial_size=(128, 128, 3), mode="order") From 1b9398819786c06aa075f379234176671820afee Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 21 Feb 2024 21:30:25 +0800 Subject: [PATCH 040/136] Fix Python 3.12 import AttributeError (#7482) Fixes #7458 ### Description https://github.com/python/cpython/blob/a21c0c7def9a8495f1166d9b434dfc301cb92bff/Lib/importlib/abc.py#L68 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/utils/module.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/module.py b/monai/utils/module.py index 0dcf22fcd7..5e058c105b 100644 --- a/monai/utils/module.py +++ b/monai/utils/module.py @@ -209,7 +209,7 @@ def load_submodules( if (is_pkg or load_all) and name not in sys.modules and match(exclude_pattern, name) is None: try: mod = import_module(name) - importer.find_module(name).load_module(name) # type: ignore + importer.find_spec(name).loader.load_module(name) # type: ignore submodules.append(mod) except OptionalImportError: pass # could not import the optional deps., they are ignored From f4103c5fd9d8b511041ff0e20d9db5d6f03dd039 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 22 Feb 2024 10:29:13 +0800 Subject: [PATCH 041/136] Update test_nnunetv2runner (#7483) Fixes #7013 #7478 ### Description replace `predict_from_raw_data` with `nnUNetPredictor` in test_nnunetv2runner ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/nnunet/nnunetv2_runner.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/monai/apps/nnunet/nnunetv2_runner.py b/monai/apps/nnunet/nnunetv2_runner.py index e62809403e..44b3c24256 100644 --- a/monai/apps/nnunet/nnunetv2_runner.py +++ b/monai/apps/nnunet/nnunetv2_runner.py @@ -37,6 +37,7 @@ class nnUNetV2Runner: # noqa: N801 """ ``nnUNetV2Runner`` provides an interface in MONAI to use `nnU-Net` V2 library to analyze, train, and evaluate neural networks for medical image segmentation tasks. + A version of nnunetv2 higher than 2.2 is needed for this class. ``nnUNetV2Runner`` can be used in two ways: @@ -770,7 +771,7 @@ def find_best_configuration( def predict( self, list_of_lists_or_source_folder: str | list[list[str]], - output_folder: str, + output_folder: str | None | list[str], model_training_output_dir: str, use_folds: tuple[int, ...] | str | None = None, tile_step_size: float = 0.5, @@ -824,7 +825,7 @@ def predict( """ os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}" - from nnunetv2.inference.predict_from_raw_data import predict_from_raw_data + from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor n_processes_preprocessing = ( self.default_num_processes if num_processes_preprocessing < 0 else num_processes_preprocessing @@ -832,20 +833,21 @@ def predict( n_processes_segmentation_export = ( self.default_num_processes if num_processes_segmentation_export < 0 else num_processes_segmentation_export ) - - predict_from_raw_data( - list_of_lists_or_source_folder=list_of_lists_or_source_folder, - output_folder=output_folder, - model_training_output_dir=model_training_output_dir, - use_folds=use_folds, + predictor = nnUNetPredictor( tile_step_size=tile_step_size, use_gaussian=use_gaussian, use_mirroring=use_mirroring, - perform_everything_on_gpu=perform_everything_on_gpu, + perform_everything_on_device=perform_everything_on_gpu, verbose=verbose, + ) + predictor.initialize_from_trained_model_folder( + model_training_output_dir=model_training_output_dir, use_folds=use_folds, checkpoint_name=checkpoint_name + ) + predictor.predict_from_files( + list_of_lists_or_source_folder=list_of_lists_or_source_folder, + output_folder_or_list_of_truncated_output_files=output_folder, save_probabilities=save_probabilities, overwrite=overwrite, - checkpoint_name=checkpoint_name, num_processes_preprocessing=n_processes_preprocessing, num_processes_segmentation_export=n_processes_segmentation_export, folder_with_segs_from_prev_stage=folder_with_segs_from_prev_stage, From 9c77a04f1468318adb19508c64a9fbae6edbcf5d Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 23 Feb 2024 11:07:43 +0800 Subject: [PATCH 042/136] Fix github resource issue when build latest docker (#7450) Fixes #7449 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .github/workflows/docker.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 065125cc33..65716f86f9 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -17,7 +17,8 @@ jobs: versioning_dev: # compute versioning file from python setup.py # upload as artifact - if: github.repository == 'Project-MONAI/MONAI' + # if: github.repository == 'Project-MONAI/MONAI' + if: ${{ false }} # disable docker build job project-monai/monai#7450 runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -47,8 +48,8 @@ jobs: rm -rf {*,.[^.]*} docker_build_dev: - # builds projectmonai/monai:latest - if: github.repository == 'Project-MONAI/MONAI' + # if: github.repository == 'Project-MONAI/MONAI' + if: ${{ false }} # disable docker build job project-monai/monai#7450 needs: versioning_dev runs-on: ubuntu-latest steps: From f6f9e817892090f6c1647f60f51fbc5ed80b74f1 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Sat, 24 Feb 2024 00:49:00 +0800 Subject: [PATCH 043/136] Use int16 instead of int8 in `LabelStats` (#7489) Use uint8 instead of int8 in `LabelStats`. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/auto3dseg/analyzer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/monai/auto3dseg/analyzer.py b/monai/auto3dseg/analyzer.py index 56419da4cb..37f3faea21 100644 --- a/monai/auto3dseg/analyzer.py +++ b/monai/auto3dseg/analyzer.py @@ -460,7 +460,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe torch.set_grad_enabled(False) ndas: list[MetaTensor] = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])] # type: ignore - ndas_label: MetaTensor = d[self.label_key].astype(torch.int8) # (H,W,D) + ndas_label: MetaTensor = d[self.label_key].astype(torch.int16) # (H,W,D) if ndas_label.shape != ndas[0].shape: raise ValueError(f"Label shape {ndas_label.shape} is different from image shape {ndas[0].shape}") @@ -472,7 +472,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe if isinstance(ndas_label, (MetaTensor, torch.Tensor)): unique_label = unique_label.data.cpu().numpy() - unique_label = unique_label.astype(np.int8).tolist() + unique_label = unique_label.astype(np.int16).tolist() label_substats = [] # each element is one label pixel_sum = 0 From 20512d30e40182f8f68a9784405cfe499e76c884 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 26 Feb 2024 07:01:44 +0000 Subject: [PATCH 044/136] auto updates (#7495) Signed-off-by: monai-bot Signed-off-by: monai-bot --- tests/test_resize.py | 1 + tests/test_resized.py | 1 + 2 files changed, 2 insertions(+) diff --git a/tests/test_resize.py b/tests/test_resize.py index 65b33ea649..d4c57e2742 100644 --- a/tests/test_resize.py +++ b/tests/test_resize.py @@ -46,6 +46,7 @@ class TestResize(NumpyImageTestCase2D): + def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resize(spatial_size=(128, 128, 3), mode="order") diff --git a/tests/test_resized.py b/tests/test_resized.py index d62f29ab5c..243a4e6622 100644 --- a/tests/test_resized.py +++ b/tests/test_resized.py @@ -66,6 +66,7 @@ @SkipIfAtLeastPyTorchVersion((2, 2, 0)) # https://github.com/Project-MONAI/MONAI/issues/7445 class TestResized(NumpyImageTestCase2D): + def test_invalid_inputs(self): with self.assertRaises(ValueError): resize = Resized(keys="img", spatial_size=(128, 128, 3), mode="order") From 7cfa2c9e281da51c78d823c7fd88e0e888fff3e1 Mon Sep 17 00:00:00 2001 From: "Timothy J. Baker" <62781117+tim-the-baker@users.noreply.github.com> Date: Mon, 26 Feb 2024 11:32:23 -0500 Subject: [PATCH 045/136] Add sample_std parameter to RandGaussianNoise. (#7492) Fixes issue #7425 ### Description Add a `sample_std` parameter to `RandGaussianNoise` and `RandGaussianNoised`. When True, the Gaussian's standard deviation is sampled uniformly from 0 to std (i.e., what is currently done). When False, the noise's standard deviation is non-random and set to std. The default for sample_std would be True for backwards compatibility. Changes were based on RandRicianNoise which already has a `sample_std` parameter and is similar to RandGaussianNoise in concept and implementation. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Timothy Baker --- monai/transforms/intensity/array.py | 15 ++++++++++++--- monai/transforms/intensity/dictionary.py | 6 ++++-- tests/test_rand_gaussian_noise.py | 12 +++++++----- tests/test_rand_gaussian_noised.py | 14 +++++++++----- 4 files changed, 32 insertions(+), 15 deletions(-) diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py index f9667402c9..a2f63a7482 100644 --- a/monai/transforms/intensity/array.py +++ b/monai/transforms/intensity/array.py @@ -91,24 +91,33 @@ class RandGaussianNoise(RandomizableTransform): mean: Mean or “centre” of the distribution. std: Standard deviation (spread) of distribution. dtype: output data type, if None, same as input image. defaults to float32. + sample_std: If True, sample the spread of the Gaussian distribution uniformly from 0 to std. """ backend = [TransformBackends.TORCH, TransformBackends.NUMPY] - def __init__(self, prob: float = 0.1, mean: float = 0.0, std: float = 0.1, dtype: DtypeLike = np.float32) -> None: + def __init__( + self, + prob: float = 0.1, + mean: float = 0.0, + std: float = 0.1, + dtype: DtypeLike = np.float32, + sample_std: bool = True, + ) -> None: RandomizableTransform.__init__(self, prob) self.mean = mean self.std = std self.dtype = dtype self.noise: np.ndarray | None = None + self.sample_std = sample_std def randomize(self, img: NdarrayOrTensor, mean: float | None = None) -> None: super().randomize(None) if not self._do_transform: return None - rand_std = self.R.uniform(0, self.std) - noise = self.R.normal(self.mean if mean is None else mean, rand_std, size=img.shape) + std = self.R.uniform(0, self.std) if self.sample_std else self.std + noise = self.R.normal(self.mean if mean is None else mean, std, size=img.shape) # noise is float64 array, convert to the output dtype to save memory self.noise, *_ = convert_data_type(noise, dtype=self.dtype) diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py index 058ef87b95..7e93464e64 100644 --- a/monai/transforms/intensity/dictionary.py +++ b/monai/transforms/intensity/dictionary.py @@ -172,7 +172,7 @@ class RandGaussianNoised(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandGaussianNoise`. - Add Gaussian noise to image. This transform assumes all the expected fields have same shape, if want to add + Add Gaussian noise to image. This transform assumes all the expected fields have same shape, if you want to add different noise for every field, please use this transform separately. Args: @@ -183,6 +183,7 @@ class RandGaussianNoised(RandomizableTransform, MapTransform): std: Standard deviation (spread) of distribution. dtype: output data type, if None, same as input image. defaults to float32. allow_missing_keys: don't raise exception if key is missing. + sample_std: If True, sample the spread of the Gaussian distribution uniformly from 0 to std. """ backend = RandGaussianNoise.backend @@ -195,10 +196,11 @@ def __init__( std: float = 0.1, dtype: DtypeLike = np.float32, allow_missing_keys: bool = False, + sample_std: bool = True, ) -> None: MapTransform.__init__(self, keys, allow_missing_keys) RandomizableTransform.__init__(self, prob) - self.rand_gaussian_noise = RandGaussianNoise(mean=mean, std=std, prob=1.0, dtype=dtype) + self.rand_gaussian_noise = RandGaussianNoise(mean=mean, std=std, prob=1.0, dtype=dtype, sample_std=sample_std) def set_random_state( self, seed: int | None = None, state: np.random.RandomState | None = None diff --git a/tests/test_rand_gaussian_noise.py b/tests/test_rand_gaussian_noise.py index a56e54fe31..233b4dd1b6 100644 --- a/tests/test_rand_gaussian_noise.py +++ b/tests/test_rand_gaussian_noise.py @@ -22,22 +22,24 @@ TESTS = [] for p in TEST_NDARRAYS: - TESTS.append(("test_zero_mean", p, 0, 0.1)) - TESTS.append(("test_non_zero_mean", p, 1, 0.5)) + TESTS.append(("test_zero_mean", p, 0, 0.1, True)) + TESTS.append(("test_non_zero_mean", p, 1, 0.5, True)) + TESTS.append(("test_no_sample_std", p, 1, 0.5, False)) class TestRandGaussianNoise(NumpyImageTestCase2D): @parameterized.expand(TESTS) - def test_correct_results(self, _, im_type, mean, std): + def test_correct_results(self, _, im_type, mean, std, sample_std): seed = 0 - gaussian_fn = RandGaussianNoise(prob=1.0, mean=mean, std=std) + gaussian_fn = RandGaussianNoise(prob=1.0, mean=mean, std=std, sample_std=sample_std) gaussian_fn.set_random_state(seed) im = im_type(self.imt) noised = gaussian_fn(im) np.random.seed(seed) np.random.random() - expected = self.imt + np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape) + _std = np.random.uniform(0, std) if sample_std else std + expected = self.imt + np.random.normal(mean, _std, size=self.imt.shape) if isinstance(noised, torch.Tensor): noised = noised.cpu() np.testing.assert_allclose(expected, noised, atol=1e-5) diff --git a/tests/test_rand_gaussian_noised.py b/tests/test_rand_gaussian_noised.py index bcbed98b5a..e3df196be2 100644 --- a/tests/test_rand_gaussian_noised.py +++ b/tests/test_rand_gaussian_noised.py @@ -22,8 +22,9 @@ TESTS = [] for p in TEST_NDARRAYS: - TESTS.append(["test_zero_mean", p, ["img1", "img2"], 0, 0.1]) - TESTS.append(["test_non_zero_mean", p, ["img1", "img2"], 1, 0.5]) + TESTS.append(["test_zero_mean", p, ["img1", "img2"], 0, 0.1, True]) + TESTS.append(["test_non_zero_mean", p, ["img1", "img2"], 1, 0.5, True]) + TESTS.append(["test_no_sample_std", p, ["img1", "img2"], 1, 0.5, False]) seed = 0 @@ -31,15 +32,18 @@ class TestRandGaussianNoised(NumpyImageTestCase2D): @parameterized.expand(TESTS) - def test_correct_results(self, _, im_type, keys, mean, std): - gaussian_fn = RandGaussianNoised(keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64) + def test_correct_results(self, _, im_type, keys, mean, std, sample_std): + gaussian_fn = RandGaussianNoised( + keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64, sample_std=sample_std + ) gaussian_fn.set_random_state(seed) im = im_type(self.imt) noised = gaussian_fn({k: im for k in keys}) np.random.seed(seed) # simulate the randomize() of transform np.random.random() - noise = np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape) + _std = np.random.uniform(0, std) if sample_std else std + noise = np.random.normal(mean, _std, size=self.imt.shape) for k in keys: expected = self.imt + noise if isinstance(noised[k], torch.Tensor): From 98305257b376aaeff82679967d4e639b346b9b14 Mon Sep 17 00:00:00 2001 From: Mathijs de Boer <8137653+MathijsdeBoer@users.noreply.github.com> Date: Wed, 28 Feb 2024 06:10:40 +0100 Subject: [PATCH 046/136] Add __repr__ and __str__ to Metrics baseclass (#7487) ### Description When training a model using MONAI metrics for experiment tracking, I tend to log which metrics I am using. Unfortunately, just sending the metrics objects to Tensorboard will result in a list like [CustomMetric1, CustomMetric2, , etc.] Adding `__repr__` and `__str__` methods to the base class will solve this small annoyance. The current implementation will only return the class name, but if a certain metric would wish to report more data for its `__repr__` string, this can be easily overridden in any subclass. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Signed-off-by: Mathijs de Boer Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Mathijs de Boer --- monai/metrics/metric.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/monai/metrics/metric.py b/monai/metrics/metric.py index a6dc1a49a2..249b2dc951 100644 --- a/monai/metrics/metric.py +++ b/monai/metrics/metric.py @@ -37,6 +37,9 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any: """ raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.") + def __str__(self): + return self.__class__.__name__ + class IterationMetric(Metric): """ From 02c7f53a2ea5835623df6ad12943d58202a42e04 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 1 Mar 2024 16:23:09 +0800 Subject: [PATCH 047/136] Bump al-cheb/configure-pagefile-action from 1.3 to 1.4 (#7510) Bumps [al-cheb/configure-pagefile-action](https://github.com/al-cheb/configure-pagefile-action) from 1.3 to 1.4.
Release notes

Sourced from al-cheb/configure-pagefile-action's releases.

v1.4: Update task node version to 20

configure-pagefile-action

This action is intended to configure Pagefile size and location for Windows images in GitHub Actions.

Available parameters

Argument Description Format Default value
minimum-size Set minimum size of Pagefile 2048MB, 4GB, 8GB and etc 8GB
maximum-size Set maximum size of Pagefile The same like minimum-size minimum-size
disk-root Set disk root where Pagefile will be located C: or D: D:

Usage

name: CI
on: [push]
jobs:
  build:
    runs-on: windows-latest
    steps:
    - name: configure Pagefile
      uses: al-cheb/configure-pagefile-action@v1.4
      with:
        minimum-size: 8

- name: configure Pagefile
  uses: al-cheb/configure-pagefile-action@v1.4
  with:
    minimum-size: 8
    maximum-size: 16
    disk-root: &quot;D:&quot;

License

The scripts and documentation in this project are released under the MIT License

Commits
  • a3b6ebd Merge pull request #20 from mikehardy/mikehardy-patch-1
  • 850626f build(deps): bump javascript dependencies / forward-port as needed
  • e7aac1b fix: use node 20
  • d940d24 build(deps): use v4 of setup-node action, use node 20
  • dfdc038 build(deps): bump actions/checkout from 3 to 4
  • See full diff in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/conda.yml | 2 +- .github/workflows/pythonapp.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/conda.yml b/.github/workflows/conda.yml index a387c77ebd..367a24cbde 100644 --- a/.github/workflows/conda.yml +++ b/.github/workflows/conda.yml @@ -26,7 +26,7 @@ jobs: steps: - if: runner.os == 'windows' name: Config pagefile (Windows only) - uses: al-cheb/configure-pagefile-action@v1.3 + uses: al-cheb/configure-pagefile-action@v1.4 with: minimum-size: 8GB maximum-size: 16GB diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index b011e65cf1..b7f2cfb9db 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -62,7 +62,7 @@ jobs: steps: - if: runner.os == 'windows' name: Config pagefile (Windows only) - uses: al-cheb/configure-pagefile-action@v1.3 + uses: al-cheb/configure-pagefile-action@v1.4 with: minimum-size: 8GB maximum-size: 16GB From e9e273858b6904659b0cd581d357662dc5c782f6 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Sun, 3 Mar 2024 23:09:21 +0800 Subject: [PATCH 048/136] Add arm support (#7500) Fixes # . ### Description Add arm support ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- Dockerfile | 4 ++++ requirements-dev.txt | 4 ++-- setup.cfg | 4 ++-- tests/test_convert_to_onnx.py | 19 +++++++++++++------ tests/test_dynunet.py | 9 ++++++++- tests/test_rand_affine.py | 2 +- tests/test_rand_affined.py | 4 +++- tests/test_spatial_resampled.py | 9 ++++++++- 8 files changed, 41 insertions(+), 14 deletions(-) diff --git a/Dockerfile b/Dockerfile index cb1300ea90..7383837585 100644 --- a/Dockerfile +++ b/Dockerfile @@ -16,6 +16,10 @@ FROM ${PYTORCH_IMAGE} LABEL maintainer="monai.contact@gmail.com" +# TODO: remark for issue [revise the dockerfile](https://github.com/zarr-developers/numcodecs/issues/431) +WORKDIR /opt +RUN git clone --recursive https://github.com/zarr-developers/numcodecs.git && pip wheel numcodecs + WORKDIR /opt/monai # install full deps diff --git a/requirements-dev.txt b/requirements-dev.txt index b08fef874b..af1b8b89d5 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -26,7 +26,7 @@ mypy>=1.5.0 ninja torchvision psutil -cucim>=23.2.0; platform_system == "Linux" +cucim-cu12; platform_system == "Linux" and python_version >= "3.9" and python_version <= "3.10" openslide-python imagecodecs; platform_system == "Linux" or platform_system == "Darwin" tifffile; platform_system == "Linux" or platform_system == "Darwin" @@ -46,7 +46,7 @@ pynrrd pre-commit pydicom h5py -nni; platform_system == "Linux" +nni; platform_system == "Linux" and "arm" not in platform_machine and "aarch" not in platform_machine optuna git+https://github.com/Project-MONAI/MetricsReloaded@monai-support#egg=MetricsReloaded onnx>=1.13.0 diff --git a/setup.cfg b/setup.cfg index 229e2ace56..d7cb703d25 100644 --- a/setup.cfg +++ b/setup.cfg @@ -59,7 +59,7 @@ all = tqdm>=4.47.0 lmdb psutil - cucim>=23.2.0 + cucim-cu12; python_version >= '3.9' and python_version <= '3.10' openslide-python tifffile imagecodecs @@ -111,7 +111,7 @@ lmdb = psutil = psutil cucim = - cucim>=23.2.0 + cucim-cu12 openslide = openslide-python tifffile = diff --git a/tests/test_convert_to_onnx.py b/tests/test_convert_to_onnx.py index 398d260c52..798c510800 100644 --- a/tests/test_convert_to_onnx.py +++ b/tests/test_convert_to_onnx.py @@ -12,6 +12,7 @@ from __future__ import annotations import itertools +import platform import unittest import torch @@ -29,6 +30,12 @@ TESTS = list(itertools.product(TORCH_DEVICE_OPTIONS, [True, False], [True, False])) TESTS_ORT = list(itertools.product(TORCH_DEVICE_OPTIONS, [True])) +ON_AARCH64 = platform.machine() == "aarch64" +if ON_AARCH64: + rtol, atol = 1e-1, 1e-2 +else: + rtol, atol = 1e-3, 1e-4 + onnx, _ = optional_import("onnx") @@ -56,8 +63,8 @@ def test_unet(self, device, use_trace, use_ort): device=device, use_ort=use_ort, use_trace=use_trace, - rtol=1e-3, - atol=1e-4, + rtol=rtol, + atol=atol, ) else: # https://github.com/pytorch/pytorch/blob/release/1.9/torch/onnx/__init__.py#L182 @@ -72,8 +79,8 @@ def test_unet(self, device, use_trace, use_ort): device=device, use_ort=use_ort, use_trace=use_trace, - rtol=1e-3, - atol=1e-4, + rtol=rtol, + atol=atol, ) self.assertTrue(isinstance(onnx_model, onnx.ModelProto)) @@ -107,8 +114,8 @@ def test_seg_res_net(self, device, use_ort): device=device, use_ort=use_ort, use_trace=True, - rtol=1e-3, - atol=1e-4, + rtol=rtol, + atol=atol, ) self.assertTrue(isinstance(onnx_model, onnx.ModelProto)) diff --git a/tests/test_dynunet.py b/tests/test_dynunet.py index b0137ae245..f3c982056c 100644 --- a/tests/test_dynunet.py +++ b/tests/test_dynunet.py @@ -11,6 +11,7 @@ from __future__ import annotations +import platform import unittest from typing import Any, Sequence @@ -24,6 +25,12 @@ InstanceNorm3dNVFuser, _ = optional_import("apex.normalization", name="InstanceNorm3dNVFuser") +ON_AARCH64 = platform.machine() == "aarch64" +if ON_AARCH64: + rtol, atol = 1e-2, 1e-2 +else: + rtol, atol = 1e-4, 1e-4 + device = "cuda" if torch.cuda.is_available() else "cpu" strides: Sequence[Sequence[int] | int] @@ -159,7 +166,7 @@ def test_consistency(self, input_param, input_shape, _): with eval_mode(net_fuser): result_fuser = net_fuser(input_tensor) - assert_allclose(result, result_fuser, rtol=1e-4, atol=1e-4) + assert_allclose(result, result_fuser, rtol=rtol, atol=atol) class TestDynUNetDeepSupervision(unittest.TestCase): diff --git a/tests/test_rand_affine.py b/tests/test_rand_affine.py index f37f7827bb..23e3fd148c 100644 --- a/tests/test_rand_affine.py +++ b/tests/test_rand_affine.py @@ -147,7 +147,7 @@ def test_rand_affine(self, input_param, input_data, expected_val): g.set_random_state(123) result = g(**input_data) g.rand_affine_grid.affine = torch.eye(4, dtype=torch.float64) # reset affine - test_resampler_lazy(g, result, input_param, input_data, seed=123) + test_resampler_lazy(g, result, input_param, input_data, seed=123, rtol=_rtol) if input_param.get("cache_grid", False): self.assertTrue(g._cached_grid is not None) assert_allclose(result, expected_val, rtol=_rtol, atol=1e-4, type_test="tensor") diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py index 20c50954e2..32fde8dc0f 100644 --- a/tests/test_rand_affined.py +++ b/tests/test_rand_affined.py @@ -234,7 +234,9 @@ def test_rand_affined(self, input_param, input_data, expected_val, track_meta): lazy_init_param["keys"], lazy_init_param["mode"] = key, mode resampler = RandAffined(**lazy_init_param).set_random_state(123) expected_output = resampler(**call_param) - test_resampler_lazy(resampler, expected_output, lazy_init_param, call_param, seed=123, output_key=key) + test_resampler_lazy( + resampler, expected_output, lazy_init_param, call_param, seed=123, output_key=key, rtol=_rtol + ) resampler.lazy = False if input_param.get("cache_grid", False): diff --git a/tests/test_spatial_resampled.py b/tests/test_spatial_resampled.py index 541015cc34..d5c86258d7 100644 --- a/tests/test_spatial_resampled.py +++ b/tests/test_spatial_resampled.py @@ -11,6 +11,7 @@ from __future__ import annotations +import platform import unittest import numpy as np @@ -23,6 +24,12 @@ from tests.lazy_transforms_utils import test_resampler_lazy from tests.utils import TEST_DEVICES, assert_allclose +ON_AARCH64 = platform.machine() == "aarch64" +if ON_AARCH64: + rtol, atol = 1e-1, 1e-2 +else: + rtol, atol = 1e-3, 1e-4 + TESTS = [] destinations_3d = [ @@ -104,7 +111,7 @@ def test_flips_inverse(self, img, device, dst_affine, kwargs, expected_output): # check lazy lazy_xform = SpatialResampled(**init_param) - test_resampler_lazy(lazy_xform, output_data, init_param, call_param, output_key="img") + test_resampler_lazy(lazy_xform, output_data, init_param, call_param, output_key="img", rtol=rtol, atol=atol) # check inverse inverted = xform.inverse(output_data)["img"] From 95f69dea3d2ff9fb3d0695d922213aefaf5f0c39 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Sun, 10 Mar 2024 23:21:28 +0800 Subject: [PATCH 049/136] Fix error in "test_bundle_trt_export" (#7524) Fixes #7523 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/monai/networks/utils.py b/monai/networks/utils.py index 42e537648a..4e6699f16b 100644 --- a/monai/networks/utils.py +++ b/monai/networks/utils.py @@ -850,7 +850,10 @@ def _onnx_trt_compile( # wrap the serialized TensorRT engine back to a TorchScript module. trt_model = torch_tensorrt.ts.embed_engine_in_new_module( - f.getvalue(), torch.device(f"cuda:{device}"), input_names, output_names + f.getvalue(), + device=torch.device(f"cuda:{device}"), + input_binding_names=input_names, + output_binding_names=output_names, ) return trt_model From 6b7568d02f1b5d58e28e18285bbf6139c00e28ef Mon Sep 17 00:00:00 2001 From: Fabian Klopfer Date: Fri, 15 Mar 2024 03:19:57 +0100 Subject: [PATCH 050/136] Fix typo in the PerceptualNetworkType Enum (#7548) Fixes #7547 ### Description Previously it was 'medical_resnet50_23datasets' for both identifier and string, which doesn't correspond to the name in the hubconf.py of Warvito's repo. Now it is the correct version (according to Warvitos repo) 'medicalnet_resnet50_23datasets'. The docs state it correctly already. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ x] New tests added to cover the changes. - [ x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Fabian Klopfer --- monai/losses/perceptual.py | 2 +- tests/test_perceptual_loss.py | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/monai/losses/perceptual.py b/monai/losses/perceptual.py index 2207de5e64..fd61603b03 100644 --- a/monai/losses/perceptual.py +++ b/monai/losses/perceptual.py @@ -29,7 +29,7 @@ class PercetualNetworkType(StrEnum): squeeze = "squeeze" radimagenet_resnet50 = "radimagenet_resnet50" medicalnet_resnet10_23datasets = "medicalnet_resnet10_23datasets" - medical_resnet50_23datasets = "medical_resnet50_23datasets" + medicalnet_resnet50_23datasets = "medicalnet_resnet50_23datasets" resnet50 = "resnet50" diff --git a/tests/test_perceptual_loss.py b/tests/test_perceptual_loss.py index ba204af697..02232e6f8d 100644 --- a/tests/test_perceptual_loss.py +++ b/tests/test_perceptual_loss.py @@ -40,6 +40,11 @@ (2, 1, 64, 64, 64), (2, 1, 64, 64, 64), ], + [ + {"spatial_dims": 3, "network_type": "medicalnet_resnet50_23datasets", "is_fake_3d": False}, + (2, 1, 64, 64, 64), + (2, 1, 64, 64, 64), + ], [ {"spatial_dims": 3, "network_type": "resnet50", "is_fake_3d": True, "pretrained": True, "fake_3d_ratio": 0.2}, (2, 1, 64, 64, 64), From ec63e068fec85dff5f043d73f2c029b359332707 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 18 Mar 2024 11:56:45 +0800 Subject: [PATCH 051/136] Update to use `log_sigmoid` in `FocalLoss` (#7534) Fixes #7533 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/losses/focal_loss.py | 5 ++--- tests/test_focal_loss.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/monai/losses/focal_loss.py b/monai/losses/focal_loss.py index 98c1a071b6..28d1c0cdc9 100644 --- a/monai/losses/focal_loss.py +++ b/monai/losses/focal_loss.py @@ -234,9 +234,8 @@ def sigmoid_focal_loss( """ # computing binary cross entropy with logits # equivalent to F.binary_cross_entropy_with_logits(input, target, reduction='none') - # see also https://github.com/pytorch/pytorch/blob/v1.9.0/aten/src/ATen/native/Loss.cpp#L231 - max_val = (-input).clamp(min=0) - loss: torch.Tensor = input - input * target + max_val + ((-max_val).exp() + (-input - max_val).exp()).log() + # see also https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/Loss.cpp#L363 + loss: torch.Tensor = input - input * target - F.logsigmoid(input) # sigmoid(-i) if t==1; sigmoid(i) if t==0 <=> # 1-sigmoid(i) if t==1; sigmoid(i) if t==0 <=> diff --git a/tests/test_focal_loss.py b/tests/test_focal_loss.py index de8d625058..0bb8a078ae 100644 --- a/tests/test_focal_loss.py +++ b/tests/test_focal_loss.py @@ -132,7 +132,7 @@ def test_consistency_with_cross_entropy_2d_no_reduction(self): error = np.abs(a - b) max_error = np.maximum(error, max_error) - assert np.allclose(max_error, 0) + assert np.allclose(max_error, 0, atol=1e-6) def test_consistency_with_cross_entropy_2d_onehot_label(self): """For gamma=0 the focal loss reduces to the cross entropy loss""" From 35c93fd08669ee41a70c297c509c7ff88f06c307 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 22 Mar 2024 16:03:00 +0800 Subject: [PATCH 052/136] Update integration_segmentation_3d result for PyTorch2403 (#7551) Fixes #7550 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Yiheng Wang <68361391+yiheng-wang-nv@users.noreply.github.com> --- runtests.sh | 2 + tests/testing_data/integration_answers.py | 56 +++++++++++++++++++++++ 2 files changed, 58 insertions(+) diff --git a/runtests.sh b/runtests.sh index 0c60bc0f58..0b3e20ce49 100755 --- a/runtests.sh +++ b/runtests.sh @@ -738,12 +738,14 @@ fi # network training/inference/eval integration tests if [ $doNetTests = true ] then + set +e # disable exit on failure so that diagnostics can be given on failure echo "${separator}${blue}integration${noColor}" for i in tests/*integration_*.py do echo "$i" ${cmdPrefix}${cmd} "$i" done + set -e # enable exit on failure fi # run model zoo tests diff --git a/tests/testing_data/integration_answers.py b/tests/testing_data/integration_answers.py index c0dd973418..e02b9ae995 100644 --- a/tests/testing_data/integration_answers.py +++ b/tests/testing_data/integration_answers.py @@ -600,6 +600,62 @@ ], } }, + { # test answers for 24.03 + "integration_segmentation_3d": { + "losses": [ + 0.5442982316017151, + 0.4741817444562912, + 0.4535954713821411, + 0.44163046181201937, + 0.4307525992393494, + 0.428487154841423, + ], + "best_metric": 0.9314384460449219, + "infer_metric": 0.9315622448921204, + "output_sums": [ + 0.14268704426414708, + 0.1528672845845743, + 0.1521782248125706, + 0.14028769128068194, + 0.1889830671664784, + 0.16999075690664475, + 0.14736282992708227, + 0.16877952654821815, + 0.15779597155181269, + 0.17987829927082263, + 0.16320253928314676, + 0.16854299322173155, + 0.14497470986956967, + 0.11437140546369519, + 0.1624117412960871, + 0.20156009294443875, + 0.1764654154256958, + 0.0982348259217418, + 0.1942436068604293, + 0.20359421536407518, + 0.19661953116976483, + 0.2088326101468625, + 0.16273043545239807, + 0.1326107887439663, + 0.1489245275752285, + 0.143107476635514, + 0.23189027677929547, + 0.1613818424566088, + 0.14889532196775188, + 0.10332622984492143, + 0.11940054688302351, + 0.13040496302762658, + 0.11472123087193181, + 0.15307044007394474, + 0.16371989575844717, + 0.1942898223272055, + 0.2230120930471398, + 0.1814679187634795, + 0.19069496508164732, + 0.07537197031940022, + ], + } + }, ] From c64993431198774ef896f525bad0fd6cc932e788 Mon Sep 17 00:00:00 2001 From: Lucas Robinet <67736918+Lucas-rbnt@users.noreply.github.com> Date: Fri, 22 Mar 2024 11:58:39 +0100 Subject: [PATCH 053/136] Add Barlow Twins loss for representation learning (#7530) ### Description Addition of the BarlowTwinsLoss class. This cost function is introduced in the http://proceedings.mlr.press/v139/zbontar21a/zbontar21a.pdf paper with the aim of disentangling the representations learned on two views of the same sample, making it a powerful tool for multimodal and unsupervised learning. This cost function is similar to the InfoNCE Loss function already implemented in MONAI (https://docs.monai.io/en/latest/_modules/monai/losses/contrastive.html#ContrastiveLoss). However, it differs in several respects: there is no l2-normalisation, but rather a z-normalisation. In addition, rather than working between pairs of embeddings, Barlow Twins seeks to decorrelate the components of the representations. ```math \mathcal{L}_{BT} := \sum_i (1 - \mathcal{C}_{ii})^2 + \lambda \sum_i \sum_{i\neq j} \mathcal{C}_{ij}^2 ``` with $\lambda$ a positive hyperparameters and $\mathcal{C}$ the cross-correlation matrix ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Lucas Robinet Signed-off-by: Lucas Robinet <67736918+Lucas-rbnt@users.noreply.github.com> Co-authored-by: Lucas Robinet Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- docs/source/losses.rst | 5 ++ monai/losses/__init__.py | 1 + monai/losses/barlow_twins.py | 84 ++++++++++++++++++++++++ tests/test_barlow_twins_loss.py | 109 ++++++++++++++++++++++++++++++++ 4 files changed, 199 insertions(+) create mode 100644 monai/losses/barlow_twins.py create mode 100644 tests/test_barlow_twins_loss.py diff --git a/docs/source/losses.rst b/docs/source/losses.rst index e929e9d605..61dd959807 100644 --- a/docs/source/losses.rst +++ b/docs/source/losses.rst @@ -73,6 +73,11 @@ Segmentation Losses .. autoclass:: ContrastiveLoss :members: +`BarlowTwinsLoss` +~~~~~~~~~~~~~~~~~ +.. autoclass:: BarlowTwinsLoss + :members: + `HausdorffDTLoss` ~~~~~~~~~~~~~~~~~ .. autoclass:: HausdorffDTLoss diff --git a/monai/losses/__init__.py b/monai/losses/__init__.py index 92898c81ca..4ebedb2084 100644 --- a/monai/losses/__init__.py +++ b/monai/losses/__init__.py @@ -12,6 +12,7 @@ from __future__ import annotations from .adversarial_loss import PatchAdversarialLoss +from .barlow_twins import BarlowTwinsLoss from .cldice import SoftclDiceLoss, SoftDiceclDiceLoss from .contrastive import ContrastiveLoss from .deform import BendingEnergyLoss, DiffusionLoss diff --git a/monai/losses/barlow_twins.py b/monai/losses/barlow_twins.py new file mode 100644 index 0000000000..a61acca66e --- /dev/null +++ b/monai/losses/barlow_twins.py @@ -0,0 +1,84 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import torch +from torch.nn.modules.loss import _Loss + + +class BarlowTwinsLoss(_Loss): + """ + The Barlow Twins cost function takes the representations extracted by a neural network from two + distorted views and seeks to make the cross-correlation matrix of the two representations tend + towards identity. This encourages the neural network to learn similar representations with the least + amount of redundancy. This cost function can be used in particular in multimodal learning to work on + representations from two modalities. The most common use case is for unsupervised learning, where data + augmentations are used to generate 2 distorted views of the same sample to force the encoder to + extract useful features for downstream tasks. + + Zbontar, Jure, et al. "Barlow Twins: Self-Supervised Learning via Redundancy Reduction" International + conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v139/zbontar21a/zbontar21a.pdf) + + Adapted from: + https://github.com/facebookresearch/barlowtwins + + """ + + def __init__(self, lambd: float = 5e-3) -> None: + """ + Args: + lamb: Can be any float to handle the informativeness and invariance trade-off. Ideally set to 5e-3. + + Raises: + ValueError: When an input of dimension length > 2 is passed + ValueError: When input and target are of different shapes + ValueError: When batch size is less than or equal to 1 + + """ + super().__init__() + self.lambd = lambd + + def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """ + Args: + input: the shape should be B[F]. + target: the shape should be B[F]. + """ + if len(target.shape) > 2 or len(input.shape) > 2: + raise ValueError( + f"Either target or input has dimensions greater than 2 where target " + f"shape is ({target.shape}) and input shape is ({input.shape})" + ) + + if target.shape != input.shape: + raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})") + + if target.size(0) <= 1: + raise ValueError( + f"Batch size must be greater than 1 to compute Barlow Twins Loss, but got {target.size(0)}" + ) + + lambd_tensor = torch.as_tensor(self.lambd).to(input.device) + batch_size = input.shape[0] + + # normalize input and target + input_norm = (input - input.mean(0)) / input.std(0).add(1e-6) + target_norm = (target - target.mean(0)) / target.std(0).add(1e-6) + + # cross-correlation matrix + c = torch.mm(input_norm.t(), target_norm) / batch_size # input_norm.t() is FxB, target_norm is BxF so c is FxF + + # loss + c_diff = (c - torch.eye(c.size(0), device=c.device)).pow_(2) # FxF + c_diff[~torch.eye(c.size(0), device=c.device).bool()] *= lambd_tensor + + return c_diff.sum() diff --git a/tests/test_barlow_twins_loss.py b/tests/test_barlow_twins_loss.py new file mode 100644 index 0000000000..81f4032e0c --- /dev/null +++ b/tests/test_barlow_twins_loss.py @@ -0,0 +1,109 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.losses import BarlowTwinsLoss + +TEST_CASES = [ + [ # shape: (2, 4), (2, 4) + {"lambd": 5e-3}, + { + "input": torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]), + "target": torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]), + }, + 4.0, + ], + [ # shape: (2, 4), (2, 4) + {"lambd": 5e-3}, + { + "input": torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]), + "target": torch.tensor([[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]), + }, + 4.0, + ], + [ # shape: (2, 4), (2, 4) + {"lambd": 5e-3}, + { + "input": torch.tensor([[1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 1.0, 0.0]]), + "target": torch.tensor([[1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 0.0, 1.0]]), + }, + 5.2562, + ], + [ # shape: (2, 4), (2, 4) + {"lambd": 5e-4}, + { + "input": torch.tensor([[2.0, 3.0, 1.0, 2.0], [0.0, 1.0, 2.0, 5.0]]), + "target": torch.tensor([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]), + }, + 5.0015, + ], + [ # shape: (4, 4), (4, 4) + {"lambd": 5e-3}, + { + "input": torch.tensor( + [[1.0, 2.0, 1.0, 1.0], [3.0, 1.0, 1.0, 2.0], [1.0, 1.0, 1.0, 1.0], [2.0, 1.0, 1.0, 0.0]] + ), + "target": torch.tensor( + [ + [0.0, 1.0, -1.0, 0.0], + [1 / 3, 0.0, -2 / 3, 1 / 3], + [-2 / 3, -1.0, 7 / 3, 1 / 3], + [1 / 3, 0.0, 1 / 3, -2 / 3], + ] + ), + }, + 1.4736, + ], +] + + +class TestBarlowTwinsLoss(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_result(self, input_param, input_data, expected_val): + barlowtwinsloss = BarlowTwinsLoss(**input_param) + result = barlowtwinsloss(**input_data) + np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4) + + def test_ill_shape(self): + loss = BarlowTwinsLoss(lambd=5e-3) + with self.assertRaises(ValueError): + loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + + def test_ill_batch_size(self): + loss = BarlowTwinsLoss(lambd=5e-3) + with self.assertRaises(ValueError): + loss(torch.ones((1, 2)), torch.ones((1, 2))) + + def test_with_cuda(self): + loss = BarlowTwinsLoss(lambd=5e-3) + i = torch.ones((2, 10)) + j = torch.ones((2, 10)) + if torch.cuda.is_available(): + i = i.cuda() + j = j.cuda() + output = loss(i, j) + np.testing.assert_allclose(output.detach().cpu().numpy(), 10.0, atol=1e-4, rtol=1e-4) + + def check_warning_raised(self): + with self.assertWarns(Warning): + BarlowTwinsLoss(lambd=5e-3, batch_size=1) + + +if __name__ == "__main__": + unittest.main() From c3a7383494b35f82bc3193797a3b07629e94c0c0 Mon Sep 17 00:00:00 2001 From: cxlcl Date: Fri, 22 Mar 2024 09:54:40 -0700 Subject: [PATCH 054/136] Stein's Unbiased Risk Estimator (SURE) loss and Conjugate Gradient (#7308) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Description Based on the discussion topic [here](https://github.com/Project-MONAI/MONAI/discussions/7161#discussion-5773293), we implemented the Conjugate-Gradient algorithm for linear operator inversion, and Stein's Unbiased Risk Estimator (SURE) [1] loss for ground-truth-date free diffusion process guidance that is proposed in [2] and illustrated in the algorithm below: Screenshot 2023-12-10 at 10 19 25 PM The Conjugate-Gradient (CG) algorithm is used to solve for the inversion of the linear operator in Line-4 in the algorithm above, where the linear operator is too large to store explicitly as a matrix (such as FFT/IFFT of an image) and invert directly. Instead, we can solve for the linear inversion iteratively as in CG. The SURE loss is applied for Line-6 above. This is a differentiable loss function that can be used to train/giude an operator (e.g. neural network), where the pseudo ground truth is available but the reference ground truth is not. For example, in the MRI reconstruction, the pseudo ground truth is the zero-filled reconstruction and the reference ground truth is the fully sampled reconstruction. The reference ground truth is not available due to the lack of fully sampled. **Reference** [1] Stein, C.M.: Estimation of the mean of a multivariate normal distribution. Annals of Statistics 1981 [[paper link](https://projecteuclid.org/journals/annals-of-statistics/volume-9/issue-6/Estimation-of-the-Mean-of-a-Multivariate-Normal-Distribution/10.1214/aos/1176345632.full)] [2] B. Ozturkler et al. SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models. MICCAI 2023 [[paper link](https://arxiv.org/pdf/2310.01799.pdf)] ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: chaoliu Signed-off-by: cxlcl Signed-off-by: chaoliu Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- docs/source/losses.rst | 5 + docs/source/networks.rst | 5 + monai/losses/__init__.py | 1 + monai/losses/sure_loss.py | 200 ++++++++++++++++++++ monai/networks/layers/__init__.py | 1 + monai/networks/layers/conjugate_gradient.py | 112 +++++++++++ tests/test_conjugate_gradient.py | 55 ++++++ tests/test_sure_loss.py | 71 +++++++ 8 files changed, 450 insertions(+) create mode 100644 monai/losses/sure_loss.py create mode 100644 monai/networks/layers/conjugate_gradient.py create mode 100644 tests/test_conjugate_gradient.py create mode 100644 tests/test_sure_loss.py diff --git a/docs/source/losses.rst b/docs/source/losses.rst index 61dd959807..ba794af3eb 100644 --- a/docs/source/losses.rst +++ b/docs/source/losses.rst @@ -139,6 +139,11 @@ Reconstruction Losses .. autoclass:: JukeboxLoss :members: +`SURELoss` +~~~~~~~~~~ +.. autoclass:: SURELoss + :members: + Loss Wrappers ------------- diff --git a/docs/source/networks.rst b/docs/source/networks.rst index 8eada7933f..b59c8af5fc 100644 --- a/docs/source/networks.rst +++ b/docs/source/networks.rst @@ -408,6 +408,11 @@ Layers .. autoclass:: LLTM :members: +`ConjugateGradient` +~~~~~~~~~~~~~~~~~~~ +.. autoclass:: ConjugateGradient + :members: + `Utilities` ~~~~~~~~~~~ .. automodule:: monai.networks.layers.convutils diff --git a/monai/losses/__init__.py b/monai/losses/__init__.py index 4ebedb2084..e937b53fa4 100644 --- a/monai/losses/__init__.py +++ b/monai/losses/__init__.py @@ -41,5 +41,6 @@ from .spatial_mask import MaskedLoss from .spectral_loss import JukeboxLoss from .ssim_loss import SSIMLoss +from .sure_loss import SURELoss from .tversky import TverskyLoss from .unified_focal_loss import AsymmetricUnifiedFocalLoss diff --git a/monai/losses/sure_loss.py b/monai/losses/sure_loss.py new file mode 100644 index 0000000000..ebf25613a6 --- /dev/null +++ b/monai/losses/sure_loss.py @@ -0,0 +1,200 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from typing import Callable, Optional + +import torch +import torch.nn as nn +from torch.nn.modules.loss import _Loss + + +def complex_diff_abs_loss(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + """ + First compute the difference in the complex domain, + then get the absolute value and take the mse + + Args: + x, y - B, 2, H, W real valued tensors representing complex numbers + or B,1,H,W complex valued tensors + Returns: + l2_loss - scalar + """ + if not x.is_complex(): + x = torch.view_as_complex(x.permute(0, 2, 3, 1).contiguous()) + if not y.is_complex(): + y = torch.view_as_complex(y.permute(0, 2, 3, 1).contiguous()) + + diff = torch.abs(x - y) + return nn.functional.mse_loss(diff, torch.zeros_like(diff), reduction="mean") + + +def sure_loss_function( + operator: Callable, + x: torch.Tensor, + y_pseudo_gt: torch.Tensor, + y_ref: Optional[torch.Tensor] = None, + eps: Optional[float] = -1.0, + perturb_noise: Optional[torch.Tensor] = None, + complex_input: Optional[bool] = False, +) -> torch.Tensor: + """ + Args: + operator (function): The operator function that takes in an input + tensor x and returns an output tensor y. We will use this to compute + the divergence. More specifically, we will perturb the input x by a + small amount and compute the divergence between the perturbed output + and the reference output + + x (torch.Tensor): The input tensor of shape (B, C, H, W) to the + operator. For complex input, the shape is (B, 2, H, W) aka C=2 real. + For real input, the shape is (B, 1, H, W) real. + + y_pseudo_gt (torch.Tensor): The pseudo ground truth tensor of shape + (B, C, H, W) used to compute the L2 loss. For complex input, the shape is + (B, 2, H, W) aka C=2 real. For real input, the shape is (B, 1, H, W) + real. + + y_ref (torch.Tensor, optional): The reference output tensor of shape + (B, C, H, W) used to compute the divergence. Defaults to None. For + complex input, the shape is (B, 2, H, W) aka C=2 real. For real input, + the shape is (B, 1, H, W) real. + + eps (float, optional): The perturbation scalar. Set to -1 to set it + automatically estimated based on y_pseudo_gtk + + perturb_noise (torch.Tensor, optional): The noise vector of shape (B, C, H, W). + Defaults to None. For complex input, the shape is (B, 2, H, W) aka C=2 real. + For real input, the shape is (B, 1, H, W) real. + + complex_input(bool, optional): Whether the input is complex or not. + Defaults to False. + + Returns: + sure_loss (torch.Tensor): The SURE loss scalar. + """ + # perturb input + if perturb_noise is None: + perturb_noise = torch.randn_like(x) + if eps == -1.0: + eps = float(torch.abs(y_pseudo_gt.max())) / 1000 + # get y_ref if not provided + if y_ref is None: + y_ref = operator(x) + + # get perturbed output + x_perturbed = x + eps * perturb_noise + y_perturbed = operator(x_perturbed) + # divergence + divergence = torch.sum(1.0 / eps * torch.matmul(perturb_noise.permute(0, 1, 3, 2), y_perturbed - y_ref)) # type: ignore + # l2 loss between y_ref, y_pseudo_gt + if complex_input: + l2_loss = complex_diff_abs_loss(y_ref, y_pseudo_gt) + else: + # real input + l2_loss = nn.functional.mse_loss(y_ref, y_pseudo_gt, reduction="mean") + + # sure loss + sure_loss = l2_loss * divergence / (x.shape[0] * x.shape[2] * x.shape[3]) + return sure_loss + + +class SURELoss(_Loss): + """ + Calculate the Stein's Unbiased Risk Estimator (SURE) loss for a given operator. + + This is a differentiable loss function that can be used to train/guide an + operator (e.g. neural network), where the pseudo ground truth is available + but the reference ground truth is not. For example, in the MRI + reconstruction, the pseudo ground truth is the zero-filled reconstruction + and the reference ground truth is the fully sampled reconstruction. Often, + the reference ground truth is not available due to the lack of fully sampled + data. + + The original SURE loss is proposed in [1]. The SURE loss used for guiding + the diffusion model based MRI reconstruction is proposed in [2]. + + Reference + + [1] Stein, C.M.: Estimation of the mean of a multivariate normal distribution. Annals of Statistics + + [2] B. Ozturkler et al. SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models. + (https://arxiv.org/pdf/2310.01799.pdf) + """ + + def __init__(self, perturb_noise: Optional[torch.Tensor] = None, eps: Optional[float] = None) -> None: + """ + Args: + perturb_noise (torch.Tensor, optional): The noise vector of shape + (B, C, H, W). Defaults to None. For complex input, the shape is (B, 2, H, W) aka C=2 real. + For real input, the shape is (B, 1, H, W) real. + + eps (float, optional): The perturbation scalar. Defaults to None. + """ + super().__init__() + self.perturb_noise = perturb_noise + self.eps = eps + + def forward( + self, + operator: Callable, + x: torch.Tensor, + y_pseudo_gt: torch.Tensor, + y_ref: Optional[torch.Tensor] = None, + complex_input: Optional[bool] = False, + ) -> torch.Tensor: + """ + Args: + operator (function): The operator function that takes in an input + tensor x and returns an output tensor y. We will use this to compute + the divergence. More specifically, we will perturb the input x by a + small amount and compute the divergence between the perturbed output + and the reference output + + x (torch.Tensor): The input tensor of shape (B, C, H, W) to the + operator. C=1 or 2: For complex input, the shape is (B, 2, H, W) aka + C=2 real. For real input, the shape is (B, 1, H, W) real. + + y_pseudo_gt (torch.Tensor): The pseudo ground truth tensor of shape + (B, C, H, W) used to compute the L2 loss. C=1 or 2: For complex + input, the shape is (B, 2, H, W) aka C=2 real. For real input, the + shape is (B, 1, H, W) real. + + y_ref (torch.Tensor, optional): The reference output tensor of the + same shape as y_pseudo_gt + + Returns: + sure_loss (torch.Tensor): The SURE loss scalar. + """ + + # check inputs shapes + if x.dim() != 4: + raise ValueError(f"Input tensor x should be 4D, got {x.dim()}.") + if y_pseudo_gt.dim() != 4: + raise ValueError(f"Input tensor y_pseudo_gt should be 4D, but got {y_pseudo_gt.dim()}.") + if y_ref is not None and y_ref.dim() != 4: + raise ValueError(f"Input tensor y_ref should be 4D, but got {y_ref.dim()}.") + if x.shape != y_pseudo_gt.shape: + raise ValueError( + f"Input tensor x and y_pseudo_gt should have the same shape, but got x shape {x.shape}, " + f"y_pseudo_gt shape {y_pseudo_gt.shape}." + ) + if y_ref is not None and y_pseudo_gt.shape != y_ref.shape: + raise ValueError( + f"Input tensor y_pseudo_gt and y_ref should have the same shape, but got y_pseudo_gt shape {y_pseudo_gt.shape}, " + f"y_ref shape {y_ref.shape}." + ) + + # compute loss + loss = sure_loss_function(operator, x, y_pseudo_gt, y_ref, self.eps, self.perturb_noise, complex_input) + + return loss diff --git a/monai/networks/layers/__init__.py b/monai/networks/layers/__init__.py index d61ed57f7f..3a6e4aa554 100644 --- a/monai/networks/layers/__init__.py +++ b/monai/networks/layers/__init__.py @@ -11,6 +11,7 @@ from __future__ import annotations +from .conjugate_gradient import ConjugateGradient from .convutils import calculate_out_shape, gaussian_1d, polyval, same_padding, stride_minus_kernel_padding from .drop_path import DropPath from .factories import Act, Conv, Dropout, LayerFactory, Norm, Pad, Pool, split_args diff --git a/monai/networks/layers/conjugate_gradient.py b/monai/networks/layers/conjugate_gradient.py new file mode 100644 index 0000000000..93a45930d7 --- /dev/null +++ b/monai/networks/layers/conjugate_gradient.py @@ -0,0 +1,112 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from typing import Callable + +import torch +from torch import nn + + +def _zdot(x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: + """ + Complex dot product between tensors x1 and x2: sum(x1.*x2) + """ + if torch.is_complex(x1): + assert torch.is_complex(x2), "x1 and x2 must both be complex" + return torch.sum(x1.conj() * x2) + else: + return torch.sum(x1 * x2) + + +def _zdot_single(x: torch.Tensor) -> torch.Tensor: + """ + Complex dot product between tensor x and itself + """ + res = _zdot(x, x) + if torch.is_complex(res): + return res.real + else: + return res + + +class ConjugateGradient(nn.Module): + """ + Congugate Gradient (CG) solver for linear systems Ax = y. + + For linear_op that is positive definite and self-adjoint, CG is + guaranteed to converge CG is often used to solve linear systems of the form + Ax = y, where A is too large to store explicitly, but can be computed via a + linear operator. + + As a result, here we won't set A explicitly as a matrix, but rather as a + linear operator. For example, A could be a FFT/IFFT operation + """ + + def __init__(self, linear_op: Callable, num_iter: int): + """ + Args: + linear_op: Linear operator + num_iter: Number of iterations to run CG + """ + super().__init__() + + self.linear_op = linear_op + self.num_iter = num_iter + + def update( + self, x: torch.Tensor, p: torch.Tensor, r: torch.Tensor, rsold: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + perform one iteration of the CG method. It takes the current solution x, + the current search direction p, the current residual r, and the old + residual norm rsold as inputs. Then it computes the new solution, search + direction, residual, and residual norm, and returns them. + """ + + dy = self.linear_op(p) + p_dot_dy = _zdot(p, dy) + alpha = rsold / p_dot_dy + x = x + alpha * p + r = r - alpha * dy + rsnew = _zdot_single(r) + beta = rsnew / rsold + rsold = rsnew + p = beta * p + r + return x, p, r, rsold + + def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + """ + run conjugate gradient for num_iter iterations to solve Ax = y + + Args: + x: tensor (real or complex); Initial guess for linear system Ax = y. + The size of x should be applicable to the linear operator. For + example, if the linear operator is FFT, then x is HCHW; if the + linear operator is a matrix multiplication, then x is a vector + + y: tensor (real or complex); Measurement. Same size as x + + Returns: + x: Solution to Ax = y + """ + # Compute residual + r = y - self.linear_op(x) + rsold = _zdot_single(r) + p = r + + # Update + for _i in range(self.num_iter): + x, p, r, rsold = self.update(x, p, r, rsold) + if rsold < 1e-10: + break + return x diff --git a/tests/test_conjugate_gradient.py b/tests/test_conjugate_gradient.py new file mode 100644 index 0000000000..239dbe3ecd --- /dev/null +++ b/tests/test_conjugate_gradient.py @@ -0,0 +1,55 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import torch + +from monai.networks.layers import ConjugateGradient + + +class TestConjugateGradient(unittest.TestCase): + def test_real_valued_inverse(self): + """Test ConjugateGradient with real-valued input: when the input is real + value, the output should be the inverse of the matrix.""" + a_dim = 3 + a_mat = torch.tensor([[1, 2, 3], [2, 1, 2], [3, 2, 1]], dtype=torch.float) + + def a_op(x): + return a_mat @ x + + cg_solver = ConjugateGradient(a_op, num_iter=100) + # define the measurement + y = torch.tensor([1, 2, 3], dtype=torch.float) + # solve for x + x = cg_solver(torch.zeros(a_dim), y) + x_ref = torch.linalg.solve(a_mat, y) + # assert torch.allclose(x, x_ref, atol=1e-6), 'CG solver failed to converge to reference solution' + self.assertTrue(torch.allclose(x, x_ref, atol=1e-6)) + + def test_complex_valued_inverse(self): + a_dim = 3 + a_mat = torch.tensor([[1, 2, 3], [2, 1, 2], [3, 2, 1]], dtype=torch.complex64) + + def a_op(x): + return a_mat @ x + + cg_solver = ConjugateGradient(a_op, num_iter=100) + y = torch.tensor([1, 2, 3], dtype=torch.complex64) + x = cg_solver(torch.zeros(a_dim, dtype=torch.complex64), y) + x_ref = torch.linalg.solve(a_mat, y) + self.assertTrue(torch.allclose(x, x_ref, atol=1e-6)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_sure_loss.py b/tests/test_sure_loss.py new file mode 100644 index 0000000000..945da657bf --- /dev/null +++ b/tests/test_sure_loss.py @@ -0,0 +1,71 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import torch + +from monai.losses import SURELoss + + +class TestSURELoss(unittest.TestCase): + def test_real_value(self): + """Test SURELoss with real-valued input: when the input is real value, the loss should be 0.0.""" + sure_loss_real = SURELoss(perturb_noise=torch.zeros(2, 1, 128, 128), eps=0.1) + + def operator(x): + return x + + y_pseudo_gt = torch.randn(2, 1, 128, 128) + x = torch.randn(2, 1, 128, 128) + loss = sure_loss_real(operator, x, y_pseudo_gt, complex_input=False) + self.assertAlmostEqual(loss.item(), 0.0) + + def test_complex_value(self): + """Test SURELoss with complex-valued input: when the input is complex value, the loss should be 0.0.""" + + def operator(x): + return x + + sure_loss_complex = SURELoss(perturb_noise=torch.zeros(2, 2, 128, 128), eps=0.1) + y_pseudo_gt = torch.randn(2, 2, 128, 128) + x = torch.randn(2, 2, 128, 128) + loss = sure_loss_complex(operator, x, y_pseudo_gt, complex_input=True) + self.assertAlmostEqual(loss.item(), 0.0) + + def test_complex_general_input(self): + """Test SURELoss with complex-valued input: when the input is general complex value, the loss should be 0.0.""" + + def operator(x): + return x + + perturb_noise_real = torch.randn(2, 1, 128, 128) + perturb_noise_complex = torch.zeros(2, 2, 128, 128) + perturb_noise_complex[:, 0, :, :] = perturb_noise_real.squeeze() + y_pseudo_gt_real = torch.randn(2, 1, 128, 128) + y_pseudo_gt_complex = torch.zeros(2, 2, 128, 128) + y_pseudo_gt_complex[:, 0, :, :] = y_pseudo_gt_real.squeeze() + x_real = torch.randn(2, 1, 128, 128) + x_complex = torch.zeros(2, 2, 128, 128) + x_complex[:, 0, :, :] = x_real.squeeze() + + sure_loss_real = SURELoss(perturb_noise=perturb_noise_real, eps=0.1) + sure_loss_complex = SURELoss(perturb_noise=perturb_noise_complex, eps=0.1) + + loss_real = sure_loss_real(operator, x_real, y_pseudo_gt_real, complex_input=False) + loss_complex = sure_loss_complex(operator, x_complex, y_pseudo_gt_complex, complex_input=True) + self.assertAlmostEqual(loss_real.item(), loss_complex.abs().item(), places=6) + + +if __name__ == "__main__": + unittest.main() From c86e790d56f3a5ad07db6e642c6a65e7779b7b21 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 25 Mar 2024 07:26:43 +0000 Subject: [PATCH 055/136] auto updates (#7577) Signed-off-by: monai-bot Signed-off-by: monai-bot --- tests/test_conjugate_gradient.py | 1 + tests/test_sure_loss.py | 1 + 2 files changed, 2 insertions(+) diff --git a/tests/test_conjugate_gradient.py b/tests/test_conjugate_gradient.py index 239dbe3ecd..64efe3b168 100644 --- a/tests/test_conjugate_gradient.py +++ b/tests/test_conjugate_gradient.py @@ -19,6 +19,7 @@ class TestConjugateGradient(unittest.TestCase): + def test_real_valued_inverse(self): """Test ConjugateGradient with real-valued input: when the input is real value, the output should be the inverse of the matrix.""" diff --git a/tests/test_sure_loss.py b/tests/test_sure_loss.py index 945da657bf..903f9bd2ca 100644 --- a/tests/test_sure_loss.py +++ b/tests/test_sure_loss.py @@ -19,6 +19,7 @@ class TestSURELoss(unittest.TestCase): + def test_real_value(self): """Test SURELoss with real-valued input: when the input is real value, the loss should be 0.0.""" sure_loss_real = SURELoss(perturb_noise=torch.zeros(2, 1, 128, 128), eps=0.1) From 97678fa75a851c411c713658059bac3b2ce5cf70 Mon Sep 17 00:00:00 2001 From: Suraj Pai Date: Mon, 25 Mar 2024 22:13:56 -0400 Subject: [PATCH 056/136] Remove nested error propagation on `ConfigComponent` instantiate (#7569) Fixes #7451 ### Description Reduces the length of error messages and error messages being propagated twice. This helps debug better when long `ConfigComponent`s are being instantiated. Refer to issue #7451 for more details ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Suraj Pai Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/bundle/config_item.py | 5 +---- monai/utils/module.py | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/monai/bundle/config_item.py b/monai/bundle/config_item.py index 844d5b30bf..e5122bf3de 100644 --- a/monai/bundle/config_item.py +++ b/monai/bundle/config_item.py @@ -289,10 +289,7 @@ def instantiate(self, **kwargs: Any) -> object: mode = self.get_config().get("_mode_", CompInitMode.DEFAULT) args = self.resolve_args() args.update(kwargs) - try: - return instantiate(modname, mode, **args) - except Exception as e: - raise RuntimeError(f"Failed to instantiate {self}") from e + return instantiate(modname, mode, **args) class ConfigExpression(ConfigItem): diff --git a/monai/utils/module.py b/monai/utils/module.py index 5e058c105b..6f301d8067 100644 --- a/monai/utils/module.py +++ b/monai/utils/module.py @@ -272,7 +272,7 @@ def instantiate(__path: str, __mode: str, **kwargs: Any) -> Any: return pdb.runcall(component, **kwargs) except Exception as e: raise RuntimeError( - f"Failed to instantiate component '{__path}' with kwargs: {kwargs}" + f"Failed to instantiate component '{__path}' with keywords: {','.join(kwargs.keys())}" f"\n set '_mode_={CompInitMode.DEBUG}' to enter the debugging mode." ) from e From e5bebfc7cd2e48499159743365230115d1a7f460 Mon Sep 17 00:00:00 2001 From: Juampa <1523654+juampatronics@users.noreply.github.com> Date: Tue, 26 Mar 2024 03:57:36 +0100 Subject: [PATCH 057/136] 2872 implementation of mixup, cutmix and cutout (#7198) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fixes #2872 ### Description Implementation of mixup, cutmix and cutout as described in the original papers. Current implementation support both, the dictionary-based batches and tuples of tensors. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Juan Pablo de la Cruz Gutiérrez Signed-off-by: monai-bot Signed-off-by: elitap Signed-off-by: Felix Schnabel Signed-off-by: YanxuanLiu Signed-off-by: ytl0623 Signed-off-by: Dženan Zukić Signed-off-by: KumoLiu Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Signed-off-by: Ishan Dutta Signed-off-by: dependabot[bot] Signed-off-by: kaibo Signed-off-by: heyufan1995 Signed-off-by: binliu Signed-off-by: axel.vlaminck Signed-off-by: Ibrahim Hadzic Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com> Signed-off-by: Timothy Baker Signed-off-by: Mathijs de Boer Signed-off-by: Fabian Klopfer Signed-off-by: Lucas Robinet Signed-off-by: Lucas Robinet <67736918+Lucas-rbnt@users.noreply.github.com> Signed-off-by: chaoliu Signed-off-by: cxlcl Signed-off-by: chaoliu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: monai-bot <64792179+monai-bot@users.noreply.github.com> Co-authored-by: elitap Co-authored-by: Felix Schnabel Co-authored-by: YanxuanLiu <104543031+YanxuanLiu@users.noreply.github.com> Co-authored-by: ytl0623 Co-authored-by: Dženan Zukić Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Ishan Dutta Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Kaibo Tang Co-authored-by: Yufan He <59374597+heyufan1995@users.noreply.github.com> Co-authored-by: binliunls <107988372+binliunls@users.noreply.github.com> Co-authored-by: Ben Murray Co-authored-by: axel.vlaminck Co-authored-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Co-authored-by: Ibrahim Hadzic Co-authored-by: Dr. Behrooz Hashemian <3968947+drbeh@users.noreply.github.com> Co-authored-by: Timothy J. Baker <62781117+tim-the-baker@users.noreply.github.com> Co-authored-by: Mathijs de Boer <8137653+MathijsdeBoer@users.noreply.github.com> Co-authored-by: Mathijs de Boer Co-authored-by: Fabian Klopfer Co-authored-by: Yiheng Wang <68361391+yiheng-wang-nv@users.noreply.github.com> Co-authored-by: Lucas Robinet <67736918+Lucas-rbnt@users.noreply.github.com> Co-authored-by: Lucas Robinet Co-authored-by: cxlcl --- docs/source/transforms.rst | 42 +++++ docs/source/transforms_idx.rst | 10 + monai/transforms/__init__.py | 12 ++ monai/transforms/regularization/__init__.py | 10 + monai/transforms/regularization/array.py | 173 ++++++++++++++++++ monai/transforms/regularization/dictionary.py | 97 ++++++++++ tests/test_regularization.py | 90 +++++++++ 7 files changed, 434 insertions(+) create mode 100644 monai/transforms/regularization/__init__.py create mode 100644 monai/transforms/regularization/array.py create mode 100644 monai/transforms/regularization/dictionary.py create mode 100644 tests/test_regularization.py diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index 8990e7991d..bd3feb3497 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -661,6 +661,27 @@ Post-processing :members: :special-members: __call__ +Regularization +^^^^^^^^^^^^^^ + +`CutMix` +"""""""" +.. autoclass:: CutMix + :members: + :special-members: __call__ + +`CutOut` +"""""""" +.. autoclass:: CutOut + :members: + :special-members: __call__ + +`MixUp` +""""""" +.. autoclass:: MixUp + :members: + :special-members: __call__ + Signal ^^^^^^^ @@ -1707,6 +1728,27 @@ Post-processing (Dict) :members: :special-members: __call__ +Regularization (Dict) +^^^^^^^^^^^^^^^^^^^^^ + +`CutMixd` +""""""""" +.. autoclass:: CutMixd + :members: + :special-members: __call__ + +`CutOutd` +""""""""" +.. autoclass:: CutOutd + :members: + :special-members: __call__ + +`MixUpd` +"""""""" +.. autoclass:: MixUpd + :members: + :special-members: __call__ + Signal (Dict) ^^^^^^^^^^^^^ diff --git a/docs/source/transforms_idx.rst b/docs/source/transforms_idx.rst index f4d02a483f..650d45db71 100644 --- a/docs/source/transforms_idx.rst +++ b/docs/source/transforms_idx.rst @@ -74,6 +74,16 @@ Post-processing post.array post.dictionary +Regularization +^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: _gen + :nosignatures: + + regularization.array + regularization.dictionary + Signal ^^^^^^ diff --git a/monai/transforms/__init__.py b/monai/transforms/__init__.py index 2aa8fbf8a1..349533fb3e 100644 --- a/monai/transforms/__init__.py +++ b/monai/transforms/__init__.py @@ -336,6 +336,18 @@ VoteEnsembled, VoteEnsembleDict, ) +from .regularization.array import CutMix, CutOut, MixUp +from .regularization.dictionary import ( + CutMixd, + CutMixD, + CutMixDict, + CutOutd, + CutOutD, + CutOutDict, + MixUpd, + MixUpD, + MixUpDict, +) from .signal.array import ( SignalContinuousWavelet, SignalFillEmpty, diff --git a/monai/transforms/regularization/__init__.py b/monai/transforms/regularization/__init__.py new file mode 100644 index 0000000000..1e97f89407 --- /dev/null +++ b/monai/transforms/regularization/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py new file mode 100644 index 0000000000..6c9022d647 --- /dev/null +++ b/monai/transforms/regularization/array.py @@ -0,0 +1,173 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from abc import abstractmethod +from math import ceil, sqrt + +import torch + +from ..transform import RandomizableTransform + +__all__ = ["MixUp", "CutMix", "CutOut", "Mixer"] + + +class Mixer(RandomizableTransform): + def __init__(self, batch_size: int, alpha: float = 1.0) -> None: + """ + Mixer is a base class providing the basic logic for the mixup-class of + augmentations. In all cases, we need to sample the mixing weights for each + sample (lambda in the notation used in the papers). Also, pairs of samples + being mixed are picked by randomly shuffling the batch samples. + + Args: + batch_size (int): number of samples per batch. That is, samples are expected tp + be of size batchsize x channels [x depth] x height x width. + alpha (float, optional): mixing weights are sampled from the Beta(alpha, alpha) + distribution. Defaults to 1.0, the uniform distribution. + """ + super().__init__() + if alpha <= 0: + raise ValueError(f"Expected positive number, but got {alpha = }") + self.alpha = alpha + self.batch_size = batch_size + + @abstractmethod + def apply(self, data: torch.Tensor): + raise NotImplementedError() + + def randomize(self, data=None) -> None: + """ + Sometimes you need may to apply the same transform to different tensors. + The idea is to get a sample and then apply it with apply() as often + as needed. You need to call this method everytime you apply the transform to a new + batch. + """ + self._params = ( + torch.from_numpy(self.R.beta(self.alpha, self.alpha, self.batch_size)).type(torch.float32), + self.R.permutation(self.batch_size), + ) + + +class MixUp(Mixer): + """MixUp as described in: + Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz. + mixup: Beyond Empirical Risk Minimization, ICLR 2018 + + Class derived from :py:class:`monai.transforms.Mixer`. See corresponding + documentation for details on the constructor parameters. + """ + + def apply(self, data: torch.Tensor): + weight, perm = self._params + nsamples, *dims = data.shape + if len(weight) != nsamples: + raise ValueError(f"Expected batch of size: {len(weight)}, but got {nsamples}") + + if len(dims) not in [3, 4]: + raise ValueError("Unexpected number of dimensions") + + mixweight = weight[(Ellipsis,) + (None,) * len(dims)] + return mixweight * data + (1 - mixweight) * data[perm, ...] + + def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None): + self.randomize() + if labels is None: + return self.apply(data) + return self.apply(data), self.apply(labels) + + +class CutMix(Mixer): + """CutMix augmentation as described in: + Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo. + CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features, + ICCV 2019 + + Class derived from :py:class:`monai.transforms.Mixer`. See corresponding + documentation for details on the constructor parameters. Here, alpha not only determines + the mixing weight but also the size of the random rectangles used during for mixing. + Please refer to the paper for details. + + The most common use case is something close to: + + .. code-block:: python + + cm = CutMix(batch_size=8, alpha=0.5) + for batch in loader: + images, labels = batch + augimg, auglabels = cm(images, labels) + output = model(augimg) + loss = loss_function(output, auglabels) + ... + + """ + + def apply(self, data: torch.Tensor): + weights, perm = self._params + nsamples, _, *dims = data.shape + if len(weights) != nsamples: + raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") + + mask = torch.ones_like(data) + for s, weight in enumerate(weights): + coords = [torch.randint(0, d, size=(1,)) for d in dims] + lengths = [d * sqrt(1 - weight) for d in dims] + idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)] + mask[s][idx] = 0 + + return mask * data + (1 - mask) * data[perm, ...] + + def apply_on_labels(self, labels: torch.Tensor): + weights, perm = self._params + nsamples, *dims = labels.shape + if len(weights) != nsamples: + raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") + + mixweight = weights[(Ellipsis,) + (None,) * len(dims)] + return mixweight * labels + (1 - mixweight) * labels[perm, ...] + + def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None): + self.randomize() + augmented = self.apply(data) + return (augmented, self.apply_on_labels(labels)) if labels is not None else augmented + + +class CutOut(Mixer): + """Cutout as described in the paper: + Terrance DeVries, Graham W. Taylor. + Improved Regularization of Convolutional Neural Networks with Cutout, + arXiv:1708.04552 + + Class derived from :py:class:`monai.transforms.Mixer`. See corresponding + documentation for details on the constructor parameters. Here, alpha not only determines + the mixing weight but also the size of the random rectangles being cut put. + Please refer to the paper for details. + """ + + def apply(self, data: torch.Tensor): + weights, _ = self._params + nsamples, _, *dims = data.shape + if len(weights) != nsamples: + raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") + + mask = torch.ones_like(data) + for s, weight in enumerate(weights): + coords = [torch.randint(0, d, size=(1,)) for d in dims] + lengths = [d * sqrt(1 - weight) for d in dims] + idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)] + mask[s][idx] = 0 + + return mask * data + + def __call__(self, data: torch.Tensor): + self.randomize() + return self.apply(data) diff --git a/monai/transforms/regularization/dictionary.py b/monai/transforms/regularization/dictionary.py new file mode 100644 index 0000000000..373913da99 --- /dev/null +++ b/monai/transforms/regularization/dictionary.py @@ -0,0 +1,97 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from monai.config import KeysCollection +from monai.utils.misc import ensure_tuple + +from ..transform import MapTransform +from .array import CutMix, CutOut, MixUp + +__all__ = ["MixUpd", "MixUpD", "MixUpDict", "CutMixd", "CutMixD", "CutMixDict", "CutOutd", "CutOutD", "CutOutDict"] + + +class MixUpd(MapTransform): + """ + Dictionary-based version :py:class:`monai.transforms.MixUp`. + + Notice that the mixup transformation will be the same for all entries + for consistency, i.e. images and labels must be applied the same augmenation. + """ + + def __init__( + self, keys: KeysCollection, batch_size: int, alpha: float = 1.0, allow_missing_keys: bool = False + ) -> None: + super().__init__(keys, allow_missing_keys) + self.mixup = MixUp(batch_size, alpha) + + def __call__(self, data): + self.mixup.randomize() + result = dict(data) + for k in self.keys: + result[k] = self.mixup.apply(data[k]) + return result + + +class CutMixd(MapTransform): + """ + Dictionary-based version :py:class:`monai.transforms.CutMix`. + + Notice that the mixture weights will be the same for all entries + for consistency, i.e. images and labels must be aggregated with the same weights, + but the random crops are not. + """ + + def __init__( + self, + keys: KeysCollection, + batch_size: int, + label_keys: KeysCollection | None = None, + alpha: float = 1.0, + allow_missing_keys: bool = False, + ) -> None: + super().__init__(keys, allow_missing_keys) + self.mixer = CutMix(batch_size, alpha) + self.label_keys = ensure_tuple(label_keys) if label_keys is not None else [] + + def __call__(self, data): + self.mixer.randomize() + result = dict(data) + for k in self.keys: + result[k] = self.mixer.apply(data[k]) + for k in self.label_keys: + result[k] = self.mixer.apply_on_labels(data[k]) + return result + + +class CutOutd(MapTransform): + """ + Dictionary-based version :py:class:`monai.transforms.CutOut`. + + Notice that the cutout is different for every entry in the dictionary. + """ + + def __init__(self, keys: KeysCollection, batch_size: int, allow_missing_keys: bool = False) -> None: + super().__init__(keys, allow_missing_keys) + self.cutout = CutOut(batch_size) + + def __call__(self, data): + result = dict(data) + self.cutout.randomize() + for k in self.keys: + result[k] = self.cutout(data[k]) + return result + + +MixUpD = MixUpDict = MixUpd +CutMixD = CutMixDict = CutMixd +CutOutD = CutOutDict = CutOutd diff --git a/tests/test_regularization.py b/tests/test_regularization.py new file mode 100644 index 0000000000..d381ea72ca --- /dev/null +++ b/tests/test_regularization.py @@ -0,0 +1,90 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import torch + +from monai.transforms import CutMix, CutMixd, CutOut, MixUp, MixUpd + + +class TestMixup(unittest.TestCase): + def test_mixup(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + sample = torch.rand(*shape, dtype=torch.float32) + mixup = MixUp(6, 1.0) + output = mixup(sample) + self.assertEqual(output.shape, sample.shape) + self.assertTrue(any(not torch.allclose(sample, mixup(sample)) for _ in range(10))) + + with self.assertRaises(ValueError): + MixUp(6, -0.5) + + mixup = MixUp(6, 0.5) + for dims in [2, 3]: + with self.assertRaises(ValueError): + shape = (5, 3) + (32,) * dims + sample = torch.rand(*shape, dtype=torch.float32) + mixup(sample) + + def test_mixupd(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + t = torch.rand(*shape, dtype=torch.float32) + sample = {"a": t, "b": t} + mixup = MixUpd(["a", "b"], 6) + output = mixup(sample) + self.assertTrue(torch.allclose(output["a"], output["b"])) + + with self.assertRaises(ValueError): + MixUpd(["k1", "k2"], 6, -0.5) + + +class TestCutMix(unittest.TestCase): + def test_cutmix(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + sample = torch.rand(*shape, dtype=torch.float32) + cutmix = CutMix(6, 1.0) + output = cutmix(sample) + self.assertEqual(output.shape, sample.shape) + self.assertTrue(any(not torch.allclose(sample, cutmix(sample)) for _ in range(10))) + + def test_cutmixd(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + t = torch.rand(*shape, dtype=torch.float32) + label = torch.randint(0, 1, shape) + sample = {"a": t, "b": t, "lbl1": label, "lbl2": label} + cutmix = CutMixd(["a", "b"], 6, label_keys=("lbl1", "lbl2")) + output = cutmix(sample) + # croppings are different on each application + self.assertTrue(not torch.allclose(output["a"], output["b"])) + # but mixing of labels is not affected by it + self.assertTrue(torch.allclose(output["lbl1"], output["lbl2"])) + + +class TestCutOut(unittest.TestCase): + def test_cutout(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + sample = torch.rand(*shape, dtype=torch.float32) + cutout = CutOut(6, 1.0) + output = cutout(sample) + self.assertEqual(output.shape, sample.shape) + self.assertTrue(any(not torch.allclose(sample, cutout(sample)) for _ in range(10))) + + +if __name__ == "__main__": + unittest.main() From 2716b6aec1d3e26e030fae3a57869ece06992148 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 27 Mar 2024 12:16:19 +0800 Subject: [PATCH 058/136] Remove device count cache when import monai (#7581) workaround for #7575 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/monai/__init__.py b/monai/__init__.py index 638220f6df..eb05ac993d 100644 --- a/monai/__init__.py +++ b/monai/__init__.py @@ -83,6 +83,11 @@ from .utils.tf32 import detect_default_tf32 detect_default_tf32() + import torch + + # workaround related to https://github.com/Project-MONAI/MONAI/issues/7575 + if hasattr(torch.cuda.device_count, "cache_clear"): + torch.cuda.device_count.cache_clear() except BaseException: from .utils.misc import MONAIEnvVars From c9fed96f8f09302de98bf1b5ff73a81fe85158a7 Mon Sep 17 00:00:00 2001 From: Lucas Robinet Date: Wed, 27 Mar 2024 06:10:45 +0100 Subject: [PATCH 059/136] Fixing gradient in sincos positional encoding in monai/networks/blocks/patchembedding.py (#7564) ### Description When you choose to put the argument `pos_embed_type='sincos'` in the `PatchEmbeddingBlock` class, it still return a learnable positional encoding To reproduce: ```python from monai.networks.blocks import PatchEmbeddingBlock patcher = PatchEmbeddingBlock( in_channels=1, img_size=(32, 32, 32), patch_size=(8, 8, 8), hidden_size=96, num_heads=8, pos_embed_type="sincos", dropout_rate=0.5, ) print(patcher.position_embeddings.requires_grad) >>> True ``` In the literature, we sometimes use either positional encoding in sincos which are fixed and non-trainable as in the original Attention Is All You Need [paper](https://arxiv.org/abs/1706.03762) or a learnable positional embedding as in the ViT [paper](https://arxiv.org/abs/2010.11929). If you choose to use a sincos, then it seems that is must be fixed which is not the case here. I'm not completely sure of the desired result in MONAI since there's already a learnable possibility, so if we choose sincos we'd like gradient-free parameters. However the documentation of `build_sincos_position_embedding`in the `pos_embed_utils.py`files stipulate: "The sin-cos position embedding as a learnable parameter" which seems a bit confusing. Especially as the encoding construction function seems to aim to set the require gradient to False (see below) ```python pos_embed = nn.Parameter(pos_emb) pos_embed.requires_grad = False return pos_embed ``` But these changes are not maintained by torch's `copy_` function, which does not copy gradient parameters (see the cpp code https://github.com/pytorch/pytorch/blob/148a8de6397be6e4b4ca1508b03b82d117bfb03c/torch/csrc/lazy/ts_backend/tensor_aten_ops.cpp#L51). This `copy_`is used in the `PatchEmbeddingBlock` class to instantiate the positional embedding. I propose a small fix to overcome this problem as well as test cases to ensure that positional embedding behaves correctly. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Lucas Robinet Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/blocks/patchembedding.py | 1 + monai/networks/blocks/pos_embed_utils.py | 2 +- tests/test_patchembedding.py | 26 ++++++++++++++++++++++++ 3 files changed, 28 insertions(+), 1 deletion(-) diff --git a/monai/networks/blocks/patchembedding.py b/monai/networks/blocks/patchembedding.py index 7d56045814..44774ce5da 100644 --- a/monai/networks/blocks/patchembedding.py +++ b/monai/networks/blocks/patchembedding.py @@ -123,6 +123,7 @@ def __init__( with torch.no_grad(): pos_embeddings = build_sincos_position_embedding(grid_size, hidden_size, spatial_dims) self.position_embeddings.data.copy_(pos_embeddings.float()) + self.position_embeddings.requires_grad = False else: raise ValueError(f"pos_embed_type {self.pos_embed_type} not supported.") diff --git a/monai/networks/blocks/pos_embed_utils.py b/monai/networks/blocks/pos_embed_utils.py index e03553307e..21586e56da 100644 --- a/monai/networks/blocks/pos_embed_utils.py +++ b/monai/networks/blocks/pos_embed_utils.py @@ -46,7 +46,7 @@ def build_sincos_position_embedding( temperature (float): The temperature for the sin-cos position embedding. Returns: - pos_embed (nn.Parameter): The sin-cos position embedding as a learnable parameter. + pos_embed (nn.Parameter): The sin-cos position embedding as a fixed parameter. """ if spatial_dims == 2: diff --git a/tests/test_patchembedding.py b/tests/test_patchembedding.py index f8610d9214..d059145033 100644 --- a/tests/test_patchembedding.py +++ b/tests/test_patchembedding.py @@ -93,6 +93,32 @@ def test_shape(self, input_param, input_shape, expected_shape): result = net(torch.randn(input_shape)) self.assertEqual(result.shape, expected_shape) + def test_sincos_pos_embed(self): + net = PatchEmbeddingBlock( + in_channels=1, + img_size=(32, 32, 32), + patch_size=(8, 8, 8), + hidden_size=96, + num_heads=8, + pos_embed_type="sincos", + dropout_rate=0.5, + ) + + self.assertEqual(net.position_embeddings.requires_grad, False) + + def test_learnable_pos_embed(self): + net = PatchEmbeddingBlock( + in_channels=1, + img_size=(32, 32, 32), + patch_size=(8, 8, 8), + hidden_size=96, + num_heads=8, + pos_embed_type="learnable", + dropout_rate=0.5, + ) + + self.assertEqual(net.position_embeddings.requires_grad, True) + def test_ill_arg(self): with self.assertRaises(ValueError): PatchEmbeddingBlock( From ba3c72cc784fcad9c3745b3a46af5bb8d1f4024b Mon Sep 17 00:00:00 2001 From: johnzielke Date: Wed, 27 Mar 2024 02:50:20 -0400 Subject: [PATCH 060/136] Fix inconsistent alpha parameter/docs for RandGibbsNoise/RandGibbsNoised (#7584) Fixes inconsistent alpha parameter/docs for RandGibbsNoise/RandGibbsNoised ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: John Zielke Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/transforms/intensity/array.py | 8 ++++++-- monai/transforms/intensity/dictionary.py | 5 +++-- tests/test_rand_gibbs_noise.py | 9 +++++++++ tests/test_rand_gibbs_noised.py | 8 ++++++++ 4 files changed, 26 insertions(+), 4 deletions(-) diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py index a2f63a7482..0085050ee3 100644 --- a/monai/transforms/intensity/array.py +++ b/monai/transforms/intensity/array.py @@ -1840,15 +1840,19 @@ class RandGibbsNoise(RandomizableTransform): Args: prob (float): probability of applying the transform. - alpha (Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes + alpha (float, Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes values in the interval [0,1] with alpha = 0 acting as the identity mapping. If a length-2 list is given as [a,b] then the value of alpha will be sampled uniformly from the interval [a,b]. 0 <= a <= b <= 1. + If a float is given, then the value of alpha will be sampled uniformly from the interval [0, alpha]. """ backend = GibbsNoise.backend - def __init__(self, prob: float = 0.1, alpha: Sequence[float] = (0.0, 1.0)) -> None: + def __init__(self, prob: float = 0.1, alpha: float | Sequence[float] = (0.0, 1.0)) -> None: + if isinstance(alpha, float): + alpha = (0, alpha) + alpha = ensure_tuple(alpha) if len(alpha) != 2: raise ValueError("alpha length must be 2.") if alpha[1] > 1 or alpha[0] < 0: diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py index 7e93464e64..5b911904b0 100644 --- a/monai/transforms/intensity/dictionary.py +++ b/monai/transforms/intensity/dictionary.py @@ -1423,10 +1423,11 @@ class RandGibbsNoised(RandomizableTransform, MapTransform): keys: 'image', 'label', or ['image', 'label'] depending on which data you need to transform. prob (float): probability of applying the transform. - alpha (float, List[float]): Parametrizes the intensity of the Gibbs noise filter applied. Takes + alpha (float, Sequence[float]): Parametrizes the intensity of the Gibbs noise filter applied. Takes values in the interval [0,1] with alpha = 0 acting as the identity mapping. If a length-2 list is given as [a,b] then the value of alpha will be sampled uniformly from the interval [a,b]. + If a float is given, then the value of alpha will be sampled uniformly from the interval [0, alpha]. allow_missing_keys: do not raise exception if key is missing. """ @@ -1436,7 +1437,7 @@ def __init__( self, keys: KeysCollection, prob: float = 0.1, - alpha: Sequence[float] = (0.0, 1.0), + alpha: float | Sequence[float] = (0.0, 1.0), allow_missing_keys: bool = False, ) -> None: MapTransform.__init__(self, keys, allow_missing_keys) diff --git a/tests/test_rand_gibbs_noise.py b/tests/test_rand_gibbs_noise.py index 4befeffbe2..5ef249a1f4 100644 --- a/tests/test_rand_gibbs_noise.py +++ b/tests/test_rand_gibbs_noise.py @@ -90,6 +90,15 @@ def test_alpha(self, im_shape, input_type): self.assertGreaterEqual(t.sampled_alpha, 0.5) self.assertLessEqual(t.sampled_alpha, 0.51) + @parameterized.expand(TEST_CASES) + def test_alpha_single_value(self, im_shape, input_type): + im = self.get_data(im_shape, input_type) + alpha = 0.01 + t = RandGibbsNoise(1.0, alpha) + _ = t(deepcopy(im)) + self.assertGreaterEqual(t.sampled_alpha, 0) + self.assertLessEqual(t.sampled_alpha, 0.01) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_rand_gibbs_noised.py b/tests/test_rand_gibbs_noised.py index 6580189af6..382290dd39 100644 --- a/tests/test_rand_gibbs_noised.py +++ b/tests/test_rand_gibbs_noised.py @@ -105,6 +105,14 @@ def test_alpha(self, im_shape, input_type): _ = t(deepcopy(data)) self.assertTrue(0.5 <= t.rand_gibbs_noise.sampled_alpha <= 0.51) + @parameterized.expand(TEST_CASES) + def test_alpha_single_value(self, im_shape, input_type): + data = self.get_data(im_shape, input_type) + alpha = 0.01 + t = RandGibbsNoised(KEYS, 1.0, alpha) + _ = t(deepcopy(data)) + self.assertTrue(0 <= t.rand_gibbs_noise.sampled_alpha <= 0.01) + if __name__ == "__main__": unittest.main() From 7c0b10e8fd6529188779663b8a2ccbcfafbc8f70 Mon Sep 17 00:00:00 2001 From: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Date: Wed, 27 Mar 2024 17:32:24 +0800 Subject: [PATCH 061/136] Fix bundle_root for NNIGen (#7586) Fixes #7585 . ### Description Because the NNI test takes too much time, the previous behavior did not get caught with the dry-run mode of HPO Gen ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). --------- Signed-off-by: Mingxin Zheng Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/auto3dseg/hpo_gen.py | 4 +++- tests/test_auto3dseg_hpo.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/monai/apps/auto3dseg/hpo_gen.py b/monai/apps/auto3dseg/hpo_gen.py index 688bf2b916..b755b99feb 100644 --- a/monai/apps/auto3dseg/hpo_gen.py +++ b/monai/apps/auto3dseg/hpo_gen.py @@ -193,7 +193,9 @@ def generate(self, output_folder: str = ".") -> None: self.obj_filename = os.path.join(write_path, "algo_object.pkl") if isinstance(self.algo, BundleAlgo): - self.algo.export_to_disk(output_folder, task_prefix + task_id, fill_with_datastats=False) + self.algo.export_to_disk( + output_folder, task_prefix + task_id, bundle_root=write_path, fill_with_datastats=False + ) else: ConfigParser.export_config_file(self.params, write_path) logger.info(write_path) diff --git a/tests/test_auto3dseg_hpo.py b/tests/test_auto3dseg_hpo.py index 34d00336ec..53d09defa0 100644 --- a/tests/test_auto3dseg_hpo.py +++ b/tests/test_auto3dseg_hpo.py @@ -181,7 +181,7 @@ def test_get_history(self) -> None: NNIGen().run_algo(obj_filename, self.work_dir) history = import_bundle_algo_history(self.work_dir, only_trained=True) - assert len(history) == 3 + assert len(history) == 1 def tearDown(self) -> None: self.test_dir.cleanup() From 2d463a7d19166cff6a83a313f339228bc812912d Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Wed, 27 Mar 2024 22:11:13 +0000 Subject: [PATCH 062/136] Auto3DSeg algo_template hash update (#7589) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index caa7c067df..c30eb0904d 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "249bf4b") + return os.environ.get("MONAI_ALGO_HASH", "c51bc6a") @staticmethod def trace_transform() -> str | None: From 15d2abf93eed3cacc16ccab58da092d011c59afa Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 1 Apr 2024 11:41:00 +0800 Subject: [PATCH 063/136] Utilizing subprocess for nnUNet training. (#7576) Workaround for #7575 ### Description - Due to the impact of #7575, the operation to set the device within nnUNetV2Runner will become ineffective. This PR is intended to resolve this issue. - Add a version check for #7575, will revisit after the update from pytorch team. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/nnunet/nnunetv2_runner.py | 94 +++++++++++++--------------- tests/test_set_visible_devices.py | 3 +- 2 files changed, 45 insertions(+), 52 deletions(-) diff --git a/monai/apps/nnunet/nnunetv2_runner.py b/monai/apps/nnunet/nnunetv2_runner.py index 44b3c24256..8a10849904 100644 --- a/monai/apps/nnunet/nnunetv2_runner.py +++ b/monai/apps/nnunet/nnunetv2_runner.py @@ -22,6 +22,7 @@ from monai.apps.nnunet.utils import analyze_data, create_new_data_copy, create_new_dataset_json from monai.bundle import ConfigParser from monai.utils import ensure_tuple, optional_import +from monai.utils.misc import run_cmd load_pickle, _ = optional_import("batchgenerators.utilities.file_and_folder_operations", name="load_pickle") join, _ = optional_import("batchgenerators.utilities.file_and_folder_operations", name="join") @@ -495,65 +496,64 @@ def train_single_model(self, config: Any, fold: int, gpu_id: tuple | list | int fold: fold of the 5-fold cross-validation. Should be an int between 0 and 4. gpu_id: an integer to select the device to use, or a tuple/list of GPU device indices used for multi-GPU training (e.g., (0,1)). Default: 0. - from nnunetv2.run.run_training import run_training kwargs: this optional parameter allows you to specify additional arguments in - ``nnunetv2.run.run_training.run_training``. Currently supported args are - - plans_identifier: custom plans identifier. Default: "nnUNetPlans". - - pretrained_weights: path to nnU-Net checkpoint file to be used as pretrained model. Will only be - used when actually training. Beta. Use with caution. Default: False. - - use_compressed_data: True to use compressed data for training. Reading compressed data is much - more CPU and (potentially) RAM intensive and should only be used if you know what you are - doing. Default: False. - - continue_training: continue training from latest checkpoint. Default: False. - - only_run_validation: True to run the validation only. Requires training to have finished. - Default: False. - - disable_checkpointing: True to disable checkpointing. Ideal for testing things out and you - don't want to flood your hard drive with checkpoints. Default: False. + ``nnunetv2.run.run_training.run_training_entry``. + + Currently supported args are: + + - p: custom plans identifier. Default: "nnUNetPlans". + - pretrained_weights: path to nnU-Net checkpoint file to be used as pretrained model. Will only be + used when actually training. Beta. Use with caution. Default: False. + - use_compressed: True to use compressed data for training. Reading compressed data is much + more CPU and (potentially) RAM intensive and should only be used if you know what you are + doing. Default: False. + - c: continue training from latest checkpoint. Default: False. + - val: True to run the validation only. Requires training to have finished. + Default: False. + - disable_checkpointing: True to disable checkpointing. Ideal for testing things out and you + don't want to flood your hard drive with checkpoints. Default: False. """ if "num_gpus" in kwargs: kwargs.pop("num_gpus") logger.warning("please use gpu_id to set the GPUs to use") - if "trainer_class_name" in kwargs: - kwargs.pop("trainer_class_name") + if "tr" in kwargs: + kwargs.pop("tr") logger.warning("please specify the `trainer_class_name` in the __init__ of `nnUNetV2Runner`.") - if "export_validation_probabilities" in kwargs: - kwargs.pop("export_validation_probabilities") + if "npz" in kwargs: + kwargs.pop("npz") logger.warning("please specify the `export_validation_probabilities` in the __init__ of `nnUNetV2Runner`.") + cmd = self.train_single_model_command(config, fold, gpu_id, kwargs) + run_cmd(cmd, shell=True) + + def train_single_model_command(self, config, fold, gpu_id, kwargs): if isinstance(gpu_id, (tuple, list)): if len(gpu_id) > 1: gpu_ids_str = "" for _i in range(len(gpu_id)): gpu_ids_str += f"{gpu_id[_i]}," - os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids_str[:-1] + device_setting = f"CUDA_VISIBLE_DEVICES={gpu_ids_str[:-1]}" else: - os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id[0]}" - else: - os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}" - - from nnunetv2.run.run_training import run_training - - if isinstance(gpu_id, int) or len(gpu_id) == 1: - run_training( - dataset_name_or_id=self.dataset_name_or_id, - configuration=config, - fold=fold, - trainer_class_name=self.trainer_class_name, - export_validation_probabilities=self.export_validation_probabilities, - **kwargs, - ) + device_setting = f"CUDA_VISIBLE_DEVICES={gpu_id[0]}" else: - run_training( - dataset_name_or_id=self.dataset_name_or_id, - configuration=config, - fold=fold, - num_gpus=len(gpu_id), - trainer_class_name=self.trainer_class_name, - export_validation_probabilities=self.export_validation_probabilities, - **kwargs, - ) + device_setting = f"CUDA_VISIBLE_DEVICES={gpu_id}" + num_gpus = 1 if isinstance(gpu_id, int) or len(gpu_id) == 1 else len(gpu_id) + + cmd = ( + f"{device_setting} nnUNetv2_train " + + f"{self.dataset_name_or_id} {config} {fold} " + + f"-tr {self.trainer_class_name} -num_gpus {num_gpus}" + ) + if self.export_validation_probabilities: + cmd += " --npz" + for _key, _value in kwargs.items(): + if _key == "p" or _key == "pretrained_weights": + cmd += f" -{_key} {_value}" + else: + cmd += f" --{_key} {_value}" + return cmd def train( self, @@ -637,15 +637,7 @@ def train_parallel_cmd( if _config in ensure_tuple(configs): for _i in range(self.num_folds): the_device = gpu_id_for_all[_index % n_devices] # type: ignore - cmd = ( - "python -m monai.apps.nnunet nnUNetV2Runner train_single_model " - + f"--input_config '{self.input_config_or_dict}' --work_dir '{self.work_dir}' " - + f"--config '{_config}' --fold {_i} --gpu_id {the_device} " - + f"--trainer_class_name {self.trainer_class_name} " - + f"--export_validation_probabilities {self.export_validation_probabilities}" - ) - for _key, _value in kwargs.items(): - cmd += f" --{_key} {_value}" + cmd = self.train_single_model_command(_config, _i, the_device, kwargs) all_cmds[-1][the_device].append(cmd) _index += 1 return all_cmds diff --git a/tests/test_set_visible_devices.py b/tests/test_set_visible_devices.py index 7860656b3d..b4f44957a2 100644 --- a/tests/test_set_visible_devices.py +++ b/tests/test_set_visible_devices.py @@ -14,7 +14,7 @@ import os import unittest -from tests.utils import skip_if_no_cuda +from tests.utils import SkipIfAtLeastPyTorchVersion, skip_if_no_cuda class TestVisibleDevices(unittest.TestCase): @@ -25,6 +25,7 @@ def run_process_and_get_exit_code(code_to_execute): return int(bin(value).replace("0b", "").rjust(16, "0")[:8], 2) @skip_if_no_cuda + @SkipIfAtLeastPyTorchVersion((2, 2, 1)) def test_visible_devices(self): num_gpus_before = self.run_process_and_get_exit_code( 'python -c "import os; import torch; ' From ec4d946571937ad162b415b1ea5977efc5adcfcd Mon Sep 17 00:00:00 2001 From: Vladimir Chernyi <57420464+scalyvladimir@users.noreply.github.com> Date: Mon, 1 Apr 2024 07:28:56 +0300 Subject: [PATCH 064/136] typo fix (#7595) Fixes # 1. ### Description Fixed typo. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [x] In-line docstrings updated. Signed-off-by: Vladimir Chernyi <57420464+scalyvladimir@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/data/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/data/dataset.py b/monai/data/dataset.py index 531893d768..79e066303e 100644 --- a/monai/data/dataset.py +++ b/monai/data/dataset.py @@ -427,7 +427,7 @@ def _transform(self, index: int): class CacheNTransDataset(PersistentDataset): """ - Extension of `PersistentDataset`, tt can also cache the result of first N transforms, no matter it's random or not. + Extension of `PersistentDataset`, it can also cache the result of first N transforms, no matter it's random or not. """ From a7c2589133ab01afd75a762885dce69a73abfe1a Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 1 Apr 2024 08:13:31 +0100 Subject: [PATCH 065/136] auto updates (#7599) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/transforms/regularization/array.py | 1 + tests/test_regularization.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py index 6c9022d647..0b495c8623 100644 --- a/monai/transforms/regularization/array.py +++ b/monai/transforms/regularization/array.py @@ -22,6 +22,7 @@ class Mixer(RandomizableTransform): + def __init__(self, batch_size: int, alpha: float = 1.0) -> None: """ Mixer is a base class providing the basic logic for the mixup-class of diff --git a/tests/test_regularization.py b/tests/test_regularization.py index d381ea72ca..22faf1027d 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -19,6 +19,7 @@ class TestMixup(unittest.TestCase): + def test_mixup(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims @@ -52,6 +53,7 @@ def test_mixupd(self): class TestCutMix(unittest.TestCase): + def test_cutmix(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims @@ -76,6 +78,7 @@ def test_cutmixd(self): class TestCutOut(unittest.TestCase): + def test_cutout(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims From c885100e951e5b7b1fedf5e9bc965201592e8541 Mon Sep 17 00:00:00 2001 From: Yiheng Wang <68361391+yiheng-wang-nv@users.noreply.github.com> Date: Mon, 1 Apr 2024 15:56:38 +0800 Subject: [PATCH 066/136] 7540 change bundle workflow args (#7549) Fixes #7540 . ### Description This PR: 1. add logging file and meta file into BundleWorkflow 2. add the sequence form of meta files check for ConfigWorkflow ### Types of changes - [x] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Yiheng Wang Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/bundle/workflows.py | 70 ++++++++++++++++++++++++++--------- tests/nonconfig_workflow.py | 4 +- tests/test_bundle_workflow.py | 8 ++++ 3 files changed, 63 insertions(+), 19 deletions(-) diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py index da3aa30141..804b3b06f0 100644 --- a/monai/bundle/workflows.py +++ b/monai/bundle/workflows.py @@ -46,6 +46,9 @@ class BundleWorkflow(ABC): or "infer", "inference", "eval", "evaluation" for a inference workflow, other unsupported string will raise a ValueError. default to `None` for common workflow. + meta_file: filepath of the metadata file, if this is a list of file paths, their contents will be merged in order. + logging_file: config file for `logging` module in the program. for more details: + https://docs.python.org/3/library/logging.config.html#logging.config.fileConfig. """ @@ -59,11 +62,40 @@ class BundleWorkflow(ABC): new_name="workflow_type", msg_suffix="please use `workflow_type` instead.", ) - def __init__(self, workflow_type: str | None = None, workflow: str | None = None): + def __init__( + self, + workflow_type: str | None = None, + workflow: str | None = None, + meta_file: str | Sequence[str] | None = None, + logging_file: str | None = None, + ): + if logging_file is not None: + if not os.path.isfile(logging_file): + raise FileNotFoundError(f"Cannot find the logging config file: {logging_file}.") + logger.info(f"Setting logging properties based on config: {logging_file}.") + fileConfig(logging_file, disable_existing_loggers=False) + + if meta_file is not None: + if isinstance(meta_file, str) and not os.path.isfile(meta_file): + logger.error( + f"Cannot find the metadata config file: {meta_file}. " + "Please see: https://docs.monai.io/en/stable/mb_specification.html" + ) + meta_file = None + if isinstance(meta_file, list): + for f in meta_file: + if not os.path.isfile(f): + logger.error( + f"Cannot find the metadata config file: {f}. " + "Please see: https://docs.monai.io/en/stable/mb_specification.html" + ) + meta_file = None + workflow_type = workflow if workflow is not None else workflow_type if workflow_type is None: self.properties = copy(MetaProperties) self.workflow_type = None + self.meta_file = meta_file return if workflow_type.lower() in self.supported_train_type: self.properties = {**TrainProperties, **MetaProperties} @@ -74,6 +106,8 @@ def __init__(self, workflow_type: str | None = None, workflow: str | None = None else: raise ValueError(f"Unsupported workflow type: '{workflow_type}'.") + self.meta_file = meta_file + @abstractmethod def initialize(self, *args: Any, **kwargs: Any) -> Any: """ @@ -142,6 +176,13 @@ def get_workflow_type(self): """ return self.workflow_type + def get_meta_file(self): + """ + Get the meta file. + + """ + return self.meta_file + def add_property(self, name: str, required: str, desc: str | None = None) -> None: """ Besides the default predefined properties, some 3rd party applications may need the bundle @@ -233,25 +274,26 @@ def __init__( **override: Any, ) -> None: workflow_type = workflow if workflow is not None else workflow_type - super().__init__(workflow_type=workflow_type) if config_file is not None: _config_files = ensure_tuple(config_file) - self.config_root_path = Path(_config_files[0]).parent + config_root_path = Path(_config_files[0]).parent for _config_file in _config_files: _config_file = Path(_config_file) - if _config_file.parent != self.config_root_path: + if _config_file.parent != config_root_path: logger.warn( - f"Not all config files are in {self.config_root_path}. If logging_file and meta_file are" - f"not specified, {self.config_root_path} will be used as the default config root directory." + f"Not all config files are in {config_root_path}. If logging_file and meta_file are" + f"not specified, {config_root_path} will be used as the default config root directory." ) if not _config_file.is_file(): raise FileNotFoundError(f"Cannot find the config file: {_config_file}.") else: - self.config_root_path = Path("configs") - + config_root_path = Path("configs") + meta_file = str(config_root_path / "metadata.json") if meta_file is None else meta_file + super().__init__(workflow_type=workflow_type, meta_file=meta_file) + self.config_root_path = config_root_path logging_file = str(self.config_root_path / "logging.conf") if logging_file is None else logging_file if logging_file is not None: - if not os.path.exists(logging_file): + if not os.path.isfile(logging_file): if logging_file == str(self.config_root_path / "logging.conf"): logger.warn(f"Default logging file in {logging_file} does not exist, skipping logging.") else: @@ -262,14 +304,8 @@ def __init__( self.parser = ConfigParser() self.parser.read_config(f=config_file) - meta_file = str(self.config_root_path / "metadata.json") if meta_file is None else meta_file - if isinstance(meta_file, str) and not os.path.exists(meta_file): - logger.error( - f"Cannot find the metadata config file: {meta_file}. " - "Please see: https://docs.monai.io/en/stable/mb_specification.html" - ) - else: - self.parser.read_meta(f=meta_file) + if self.meta_file is not None: + self.parser.read_meta(f=self.meta_file) # the rest key-values in the _args are to override config content self.parser.update(pairs=override) diff --git a/tests/nonconfig_workflow.py b/tests/nonconfig_workflow.py index 7b5328bf72..b2c44c12c6 100644 --- a/tests/nonconfig_workflow.py +++ b/tests/nonconfig_workflow.py @@ -36,8 +36,8 @@ class NonConfigWorkflow(BundleWorkflow): """ - def __init__(self, filename, output_dir): - super().__init__(workflow_type="inference") + def __init__(self, filename, output_dir, meta_file=None, logging_file=None): + super().__init__(workflow_type="inference", meta_file=meta_file, logging_file=logging_file) self.filename = filename self.output_dir = output_dir self._bundle_root = "will override" diff --git a/tests/test_bundle_workflow.py b/tests/test_bundle_workflow.py index f7da37acef..0b0d51cbfb 100644 --- a/tests/test_bundle_workflow.py +++ b/tests/test_bundle_workflow.py @@ -35,6 +35,8 @@ TEST_CASE_3 = [os.path.join(os.path.dirname(__file__), "testing_data", "config_fl_train.json")] +TEST_CASE_NON_CONFIG_WRONG_LOG = [None, "logging.conf", "Cannot find the logging config file: logging.conf."] + class TestBundleWorkflow(unittest.TestCase): @@ -144,8 +146,14 @@ def test_train_config(self, config_file): def test_non_config(self): # test user defined python style workflow inferer = NonConfigWorkflow(self.filename, self.data_dir) + self.assertEqual(inferer.meta_file, None) self._test_inferer(inferer) + @parameterized.expand([TEST_CASE_NON_CONFIG_WRONG_LOG]) + def test_non_config_wrong_log_cases(self, meta_file, logging_file, expected_error): + with self.assertRaisesRegex(FileNotFoundError, expected_error): + NonConfigWorkflow(self.filename, self.data_dir, meta_file, logging_file) + if __name__ == "__main__": unittest.main() From 264b9e4700667e895c272f8da6a5a43d0c50d865 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 1 Apr 2024 18:09:35 +0800 Subject: [PATCH 067/136] Add "properties_path" in BundleWorkflow (#7542) Fixes #7541 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- monai/bundle/workflows.py | 23 +++++-- tests/test_bundle_workflow.py | 10 +++ tests/test_regularization.py | 16 +++++ tests/testing_data/fl_infer_properties.json | 67 +++++++++++++++++++++ 4 files changed, 112 insertions(+), 4 deletions(-) create mode 100644 tests/testing_data/fl_infer_properties.json diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py index 804b3b06f0..d876f6d7ae 100644 --- a/monai/bundle/workflows.py +++ b/monai/bundle/workflows.py @@ -11,6 +11,7 @@ from __future__ import annotations +import json import os import sys import time @@ -24,6 +25,7 @@ from monai.bundle.config_parser import ConfigParser from monai.bundle.properties import InferProperties, MetaProperties, TrainProperties from monai.bundle.utils import DEFAULT_EXP_MGMT_SETTINGS, EXPR_KEY, ID_REF_KEY, ID_SEP_KEY +from monai.config import PathLike from monai.utils import BundleProperty, BundlePropertyConfig, deprecated_arg, deprecated_arg_default, ensure_tuple __all__ = ["BundleWorkflow", "ConfigWorkflow"] @@ -46,6 +48,7 @@ class BundleWorkflow(ABC): or "infer", "inference", "eval", "evaluation" for a inference workflow, other unsupported string will raise a ValueError. default to `None` for common workflow. + properties_path: the path to the JSON file of properties. meta_file: filepath of the metadata file, if this is a list of file paths, their contents will be merged in order. logging_file: config file for `logging` module in the program. for more details: https://docs.python.org/3/library/logging.config.html#logging.config.fileConfig. @@ -66,6 +69,7 @@ def __init__( self, workflow_type: str | None = None, workflow: str | None = None, + properties_path: PathLike | None = None, meta_file: str | Sequence[str] | None = None, logging_file: str | None = None, ): @@ -92,15 +96,24 @@ def __init__( meta_file = None workflow_type = workflow if workflow is not None else workflow_type - if workflow_type is None: + if workflow_type is None and properties_path is None: self.properties = copy(MetaProperties) self.workflow_type = None self.meta_file = meta_file return - if workflow_type.lower() in self.supported_train_type: + if properties_path is not None: + properties_path = Path(properties_path) + if not properties_path.is_file(): + raise ValueError(f"Property file {properties_path} does not exist.") + with open(properties_path) as json_file: + self.properties = json.load(json_file) + self.workflow_type = None + self.meta_file = meta_file + return + if workflow_type.lower() in self.supported_train_type: # type: ignore[union-attr] self.properties = {**TrainProperties, **MetaProperties} self.workflow_type = "train" - elif workflow_type.lower() in self.supported_infer_type: + elif workflow_type.lower() in self.supported_infer_type: # type: ignore[union-attr] self.properties = {**InferProperties, **MetaProperties} self.workflow_type = "infer" else: @@ -247,6 +260,7 @@ class ConfigWorkflow(BundleWorkflow): or "infer", "inference", "eval", "evaluation" for a inference workflow, other unsupported string will raise a ValueError. default to `None` for common workflow. + properties_path: the path to the JSON file of properties. override: id-value pairs to override or add the corresponding config content. e.g. ``--net#input_chns 42``, ``--net %/data/other.json#net_arg`` @@ -271,6 +285,7 @@ def __init__( tracking: str | dict | None = None, workflow_type: str | None = None, workflow: str | None = None, + properties_path: PathLike | None = None, **override: Any, ) -> None: workflow_type = workflow if workflow is not None else workflow_type @@ -289,7 +304,7 @@ def __init__( else: config_root_path = Path("configs") meta_file = str(config_root_path / "metadata.json") if meta_file is None else meta_file - super().__init__(workflow_type=workflow_type, meta_file=meta_file) + super().__init__(workflow_type=workflow_type, meta_file=meta_file, properties_path=properties_path) self.config_root_path = config_root_path logging_file = str(self.config_root_path / "logging.conf") if logging_file is None else logging_file if logging_file is not None: diff --git a/tests/test_bundle_workflow.py b/tests/test_bundle_workflow.py index 0b0d51cbfb..9a276b577f 100644 --- a/tests/test_bundle_workflow.py +++ b/tests/test_bundle_workflow.py @@ -105,6 +105,16 @@ def test_inference_config(self, config_file): ) self._test_inferer(inferer) + # test property path + inferer = ConfigWorkflow( + config_file=config_file, + properties_path=os.path.join(os.path.dirname(__file__), "testing_data", "fl_infer_properties.json"), + logging_file=os.path.join(os.path.dirname(__file__), "testing_data", "logging.conf"), + **override, + ) + self._test_inferer(inferer) + self.assertEqual(inferer.workflow_type, None) + @parameterized.expand([TEST_CASE_3]) def test_train_config(self, config_file): # test standard MONAI model-zoo config workflow diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 22faf1027d..c6f727cb54 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -16,9 +16,15 @@ import torch from monai.transforms import CutMix, CutMixd, CutOut, MixUp, MixUpd +from monai.utils import set_determinism class TestMixup(unittest.TestCase): + def setUp(self) -> None: + set_determinism(seed=0) + + def tearDown(self) -> None: + set_determinism(None) def test_mixup(self): for dims in [2, 3]: @@ -53,6 +59,11 @@ def test_mixupd(self): class TestCutMix(unittest.TestCase): + def setUp(self) -> None: + set_determinism(seed=0) + + def tearDown(self) -> None: + set_determinism(None) def test_cutmix(self): for dims in [2, 3]: @@ -78,6 +89,11 @@ def test_cutmixd(self): class TestCutOut(unittest.TestCase): + def setUp(self) -> None: + set_determinism(seed=0) + + def tearDown(self) -> None: + set_determinism(None) def test_cutout(self): for dims in [2, 3]: diff --git a/tests/testing_data/fl_infer_properties.json b/tests/testing_data/fl_infer_properties.json new file mode 100644 index 0000000000..72e97cd2c6 --- /dev/null +++ b/tests/testing_data/fl_infer_properties.json @@ -0,0 +1,67 @@ +{ + "bundle_root": { + "description": "root path of the bundle.", + "required": true, + "id": "bundle_root" + }, + "device": { + "description": "target device to execute the bundle workflow.", + "required": true, + "id": "device" + }, + "dataset_dir": { + "description": "directory path of the dataset.", + "required": true, + "id": "dataset_dir" + }, + "dataset": { + "description": "PyTorch dataset object for the inference / evaluation logic.", + "required": true, + "id": "dataset" + }, + "evaluator": { + "description": "inference / evaluation workflow engine.", + "required": true, + "id": "evaluator" + }, + "network_def": { + "description": "network module for the inference.", + "required": true, + "id": "network_def" + }, + "inferer": { + "description": "MONAI Inferer object to execute the model computation in inference.", + "required": true, + "id": "inferer" + }, + "dataset_data": { + "description": "data source for the inference / evaluation dataset.", + "required": false, + "id": "dataset::data", + "refer_id": null + }, + "handlers": { + "description": "event-handlers for the inference / evaluation logic.", + "required": false, + "id": "handlers", + "refer_id": "evaluator::val_handlers" + }, + "preprocessing": { + "description": "preprocessing for the input data.", + "required": false, + "id": "preprocessing", + "refer_id": "dataset::transform" + }, + "postprocessing": { + "description": "postprocessing for the model output data.", + "required": false, + "id": "postprocessing", + "refer_id": "evaluator::postprocessing" + }, + "key_metric": { + "description": "the key metric during evaluation.", + "required": false, + "id": "key_metric", + "refer_id": "evaluator::key_val_metric" + } +} From bbaaf4c7d75976b0289152541afa9fa03fa44f7a Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 1 Apr 2024 15:39:10 +0100 Subject: [PATCH 068/136] Auto3DSeg algo_template hash update (#7603) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index c30eb0904d..dd0ccada3d 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "c51bc6a") + return os.environ.get("MONAI_ALGO_HASH", "b910ab8") @staticmethod def trace_transform() -> str | None: From 5ec7305abfa406fec7f013953ec4da7cfcae15ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?L=C5=91rincz-Moln=C3=A1r=20Szabolcs-Botond?= Date: Tue, 2 Apr 2024 04:13:28 +0200 Subject: [PATCH 069/136] ENH: generate_label_classes_crop_centers: warn only if ratio of missing class is not set to 0 (#7602) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fixes #7594 ### Description Only warn users that the ratio of a missing class is set to 0, when it wasn't already set to 0 by the user, in `generate_label_classes_crop_centers`, function being used by `RandCropByLabelClasses` transform. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. ⚠️ See notes - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. ⚠️ See notes - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. ### Notes regarding tests Some tests were failing, see details:
====================================================================== ERROR: test_cuda_0_2_batches_1_dimensions_1_channels_2_classes_2_mixtures (tests.test_gmm.GMMTestCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/usr/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_gmm.py", line 288, in test_cuda gmm = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True) File "/home/szabolcslorincz/MONAI/monai/networks/layers/gmm.py", line 44, in __init__ self.compiled_extension = load_module( File "/home/szabolcslorincz/MONAI/monai/_extensions/loader.py", line 89, in load_module module = load( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1306, in load return _jit_compile( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1710, in _jit_compile _write_ninja_file_and_build_library( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1823, in _write_ninja_file_and_build_library _run_ninja_build( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'gmm_1_2_1_Linux_3_10_12_22_12_1' ====================================================================== ERROR: test_cuda_1_1_batches_1_dimensions_5_channels_2_classes_1_mixtures (tests.test_gmm.GMMTestCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/usr/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_gmm.py", line 288, in test_cuda gmm = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True) File "/home/szabolcslorincz/MONAI/monai/networks/layers/gmm.py", line 44, in __init__ self.compiled_extension = load_module( File "/home/szabolcslorincz/MONAI/monai/_extensions/loader.py", line 89, in load_module module = load( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1306, in load return _jit_compile( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1710, in _jit_compile _write_ninja_file_and_build_library( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1823, in _write_ninja_file_and_build_library _run_ninja_build( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'gmm_5_2_1_Linux_3_10_12_22_12_1' ====================================================================== ERROR: test_cuda_2_1_batches_2_dimensions_2_channels_4_classes_4_mixtures (tests.test_gmm.GMMTestCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/usr/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_gmm.py", line 288, in test_cuda gmm = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True) File "/home/szabolcslorincz/MONAI/monai/networks/layers/gmm.py", line 44, in __init__ self.compiled_extension = load_module( File "/home/szabolcslorincz/MONAI/monai/_extensions/loader.py", line 89, in load_module module = load( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1306, in load return _jit_compile( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1710, in _jit_compile _write_ninja_file_and_build_library( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1823, in _write_ninja_file_and_build_library _run_ninja_build( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'gmm_2_4_1_Linux_3_10_12_22_12_1' ====================================================================== ERROR: test_cuda_3_1_batches_3_dimensions_1_channels_2_classes_1_mixtures (tests.test_gmm.GMMTestCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/usr/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_gmm.py", line 288, in test_cuda gmm = GaussianMixtureModel(features_tensor.size(1), mixture_count, class_count, verbose_build=True) File "/home/szabolcslorincz/MONAI/monai/networks/layers/gmm.py", line 44, in __init__ self.compiled_extension = load_module( File "/home/szabolcslorincz/MONAI/monai/_extensions/loader.py", line 89, in load_module module = load( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1306, in load return _jit_compile( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1710, in _jit_compile _write_ninja_file_and_build_library( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1823, in _write_ninja_file_and_build_library _run_ninja_build( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'gmm_1_2_1_Linux_3_10_12_22_12_1_v1' ====================================================================== ERROR: test_load (tests.test_gmm.GMMTestCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/usr/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/tests/test_gmm.py", line 311, in test_load load_module("gmm", {"CHANNEL_COUNT": 2, "MIXTURE_COUNT": 2, "MIXTURE_SIZE": 3}, verbose_build=True) File "/home/szabolcslorincz/MONAI/monai/_extensions/loader.py", line 89, in load_module module = load( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1306, in load return _jit_compile( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1710, in _jit_compile _write_ninja_file_and_build_library( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1823, in _write_ninja_file_and_build_library _run_ninja_build( File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'gmm_2_2_3_Linux_3_10_12_22_12_1' ====================================================================== ERROR: test_spacing_35 (tests.test_spacing.TestSpacingCase) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_spacing.py", line 289, in test_spacing res: MetaTensor = tr(**call_param) # type: ignore File "/home/szabolcslorincz/MONAI/monai/transforms/spatial/array.py", line 525, in __call__ data_array = self.sp_resample( File "/home/szabolcslorincz/MONAI/monai/transforms/spatial/array.py", line 223, in __call__ return spatial_resample( File "/home/szabolcslorincz/MONAI/monai/transforms/spatial/functional.py", line 178, in spatial_resample img = affine_xform(img.unsqueeze(0), theta=xform.to(img), spatial_size=spatial_size).squeeze(0) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl return forward_call(*args, **kwargs) File "/home/szabolcslorincz/MONAI/monai/networks/layers/spatial_transforms.py", line 584, in forward grid = nn.functional.affine_grid(theta=theta[:, :sr], size=list(dst_size), align_corners=self.align_corners) File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/nn/functional.py", line 4418, in affine_grid return torch.affine_grid_generator(theta, size, align_corners) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 840.00 MiB. GPU 0 has a total capacity of 8.00 GiB of which 0 bytes is free. Including non-PyTorch memory, this process has 17179869184.00 GiB memory in use. Of the allocated memory 6.47 GiB is allocated by PyTorch, and 156.63 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) ====================================================================== FAIL: test_seg_res_net_1_cuda (tests.test_convert_to_onnx.TestConvertToOnnx) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_convert_to_onnx.py", line 108, in test_seg_res_net onnx_model = convert_to_onnx( File "/home/szabolcslorincz/MONAI/monai/networks/utils.py", line 709, in convert_to_onnx assert_fn(r1.cpu(), convert_to_tensor(r2, dtype=r1.dtype), rtol=rtol, atol=atol) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1520, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 233543 / 1451520 (16.1%) Greatest absolute difference: 0.0014852285385131836 at index (0, 19, 12, 13, 22) (up to 0.0001 allowed) Greatest relative difference: 589.0405883789062 at index (0, 32, 21, 16, 15) (up to 0.001 allowed) ====================================================================== FAIL: test_unet_4_cuda (tests.test_convert_to_onnx.TestConvertToOnnx) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_convert_to_onnx.py", line 57, in test_unet onnx_model = convert_to_onnx( File "/home/szabolcslorincz/MONAI/monai/networks/utils.py", line 709, in convert_to_onnx assert_fn(r1.cpu(), convert_to_tensor(r2, dtype=r1.dtype), rtol=rtol, atol=atol) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1520, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 6705 / 49152 (13.6%) Greatest absolute difference: 0.0015408992767333984 at index (11, 0, 21, 27) (up to 0.0001 allowed) Greatest relative difference: 51.80112838745117 at index (14, 0, 25, 19) (up to 0.001 allowed) ====================================================================== FAIL: test_unet_5_cuda (tests.test_convert_to_onnx.TestConvertToOnnx) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_convert_to_onnx.py", line 57, in test_unet onnx_model = convert_to_onnx( File "/home/szabolcslorincz/MONAI/monai/networks/utils.py", line 709, in convert_to_onnx assert_fn(r1.cpu(), convert_to_tensor(r2, dtype=r1.dtype), rtol=rtol, atol=atol) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1520, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 6218 / 49152 (12.7%) Greatest absolute difference: 0.0015670061111450195 at index (2, 0, 23, 14) (up to 0.0001 allowed) Greatest relative difference: 7.987473964691162 at index (8, 0, 27, 8) (up to 0.001 allowed) ====================================================================== FAIL: test_unet_6_cuda (tests.test_convert_to_onnx.TestConvertToOnnx) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_convert_to_onnx.py", line 57, in test_unet onnx_model = convert_to_onnx( File "/home/szabolcslorincz/MONAI/monai/networks/utils.py", line 709, in convert_to_onnx assert_fn(r1.cpu(), convert_to_tensor(r2, dtype=r1.dtype), rtol=rtol, atol=atol) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1520, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 6743 / 49152 (13.7%) Greatest absolute difference: 0.0015552043914794922 at index (1, 1, 9, 11) (up to 0.0001 allowed) Greatest relative difference: 2.0317020416259766 at index (11, 0, 19, 21) (up to 0.001 allowed) ====================================================================== FAIL: test_unet_7_cuda (tests.test_convert_to_onnx.TestConvertToOnnx) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_convert_to_onnx.py", line 57, in test_unet onnx_model = convert_to_onnx( File "/home/szabolcslorincz/MONAI/monai/networks/utils.py", line 709, in convert_to_onnx assert_fn(r1.cpu(), convert_to_tensor(r2, dtype=r1.dtype), rtol=rtol, atol=atol) # type: ignore File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1520, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close! Mismatched elements: 6823 / 49152 (13.9%) Greatest absolute difference: 0.0018431544303894043 at index (10, 0, 9, 19) (up to 0.0001 allowed) Greatest relative difference: 4.297887325286865 at index (13, 0, 12, 13) (up to 0.001 allowed) ====================================================================== FAIL: test_shape_2 (tests.test_multi_scale.TestMultiScale) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/parameterized/parameterized.py", line 620, in standalone_func return func(*(a + p.args), **p.kwargs, **kw) File "/home/szabolcslorincz/MONAI/tests/test_multi_scale.py", line 59, in test_shape np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5) File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 1504, in assert_allclose assert_array_compare(compare, actual, desired, err_msg=str(err_msg), File "/usr/lib/python3.10/contextlib.py", line 79, in inner return func(*args, **kwds) File "/home/szabolcslorincz/MONAI/venv/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 797, in assert_array_compare raise AssertionError(msg) AssertionError: Not equal to tolerance rtol=1e-05, atol=0 Mismatched elements: 1 / 1 (100%) Max absolute difference: 0. Max relative difference: 0. x: array(0.715212, dtype=float32) y: array(0.715228) ---------------------------------------------------------------------- Ran 14392 tests in 2252.916s FAILED (failures=6, errors=6, skipped=450) --------- Signed-off-by: Szabolcs Botond Lorincz Molnar Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/transforms/utils.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index e282ecff24..455b0cfb7d 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -625,9 +625,12 @@ def generate_label_classes_crop_centers( for i, array in enumerate(indices): if len(array) == 0: - ratios_[i] = 0 - if warn: - warnings.warn(f"no available indices of class {i} to crop, set the crop ratio of this class to zero.") + if ratios_[i] != 0: + ratios_[i] = 0 + if warn: + warnings.warn( + f"no available indices of class {i} to crop, setting the crop ratio of this class to zero." + ) centers = [] classes = rand_state.choice(len(ratios_), size=num_samples, p=np.asarray(ratios_) / np.sum(ratios_)) From 763347d0c945d5ea87916b86240c4368d818a286 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 3 Apr 2024 21:12:58 +0800 Subject: [PATCH 070/136] Update base image to 2403 (#7600) ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Nic Ma --- .github/workflows/cron.yml | 16 ++++++++-------- .github/workflows/pythonapp-gpu.yml | 8 ++++---- Dockerfile | 2 +- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/workflows/cron.yml b/.github/workflows/cron.yml index 792fda5279..0f9e6cd480 100644 --- a/.github/workflows/cron.yml +++ b/.github/workflows/cron.yml @@ -19,18 +19,18 @@ jobs: - "PTLATEST+CUDA121" include: # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes - - environment: PT191+CUDA113 - pytorch: "torch==1.9.1 torchvision==0.10.1 --extra-index-url https://download.pytorch.org/whl/cu113" - base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3 - environment: PT110+CUDA113 pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu113" base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3 - environment: PT113+CUDA113 pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113" base: "nvcr.io/nvidia/pytorch:21.06-py3" # CUDA 11.3 - - environment: PTLATEST+CUDA121 - pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118" + - environment: PT113+CUDA122 + pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121" base: "nvcr.io/nvidia/pytorch:23.08-py3" # CUDA 12.2 + - environment: PTLATEST+CUDA124 + pytorch: "-U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu121" + base: "nvcr.io/nvidia/pytorch:24.03-py3" # CUDA 12.4 container: image: ${{ matrix.base }} options: "--gpus all" @@ -76,7 +76,7 @@ jobs: if: github.repository == 'Project-MONAI/MONAI' strategy: matrix: - container: ["pytorch:22.10", "pytorch:23.08"] + container: ["pytorch:23.08", "pytorch:24.03"] container: image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image options: "--gpus all" @@ -121,7 +121,7 @@ jobs: if: github.repository == 'Project-MONAI/MONAI' strategy: matrix: - container: ["pytorch:23.08"] + container: ["pytorch:24.03"] container: image: nvcr.io/nvidia/${{ matrix.container }}-py3 # testing with the latest pytorch base image options: "--gpus all" @@ -221,7 +221,7 @@ jobs: if: github.repository == 'Project-MONAI/MONAI' needs: cron-gpu # so that monai itself is verified first container: - image: nvcr.io/nvidia/pytorch:23.08-py3 # testing with the latest pytorch base image + image: nvcr.io/nvidia/pytorch:24.03-py3 # testing with the latest pytorch base image options: "--gpus all --ipc=host" runs-on: [self-hosted, linux, x64, integration] steps: diff --git a/.github/workflows/pythonapp-gpu.yml b/.github/workflows/pythonapp-gpu.yml index a6d7981814..f83d52f8e3 100644 --- a/.github/workflows/pythonapp-gpu.yml +++ b/.github/workflows/pythonapp-gpu.yml @@ -29,10 +29,6 @@ jobs: - "PT210+CUDA121DOCKER" include: # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes - - environment: PT19+CUDA114DOCKER - # 21.10: 1.10.0a0+0aef44c - pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error - base: "nvcr.io/nvidia/pytorch:21.10-py3" - environment: PT110+CUDA111 pytorch: "torch==1.10.2 torchvision==0.11.3 --extra-index-url https://download.pytorch.org/whl/cu111" base: "nvcr.io/nvidia/cuda:11.1.1-devel-ubuntu18.04" @@ -47,6 +43,10 @@ jobs: # 23.08: 2.1.0a0+29c30b1 pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error base: "nvcr.io/nvidia/pytorch:23.08-py3" + - environment: PT210+CUDA121DOCKER + # 24.03: 2.3.0a0+40ec155e58.nv24.3 + pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error + base: "nvcr.io/nvidia/pytorch:24.03-py3" container: image: ${{ matrix.base }} options: --gpus all --env NVIDIA_DISABLE_REQUIRE=true # workaround for unsatisfied condition: cuda>=11.6 diff --git a/Dockerfile b/Dockerfile index 7383837585..d5777104c8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,7 +11,7 @@ # To build with a different base image # please run `docker build` using the `--build-arg PYTORCH_IMAGE=...` flag. -ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:23.08-py3 +ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.03-py3 FROM ${PYTORCH_IMAGE} LABEL maintainer="monai.contact@gmail.com" From 195d7ddc3669426e39567d90d083684cd948e388 Mon Sep 17 00:00:00 2001 From: Lucas Robinet Date: Thu, 4 Apr 2024 09:16:17 +0200 Subject: [PATCH 071/136] simplification of the sincos positional encoding in patchembedding.py (#7605) Fixes #7564 . ### Description As discussed, a small simplification for the creation of sincos positional encoding where we don't need to use the `torch.no_grad()` context or copy the tensor with `copy_` from torch which doesn't preserve the `requires_grad` attribute here. The changes are simple and are linked to the corresponding comment #7564, the output is already in float32 so it doesn't seem particularly necessary to apply the conversion previously done. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Lucas Robinet Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/blocks/patchembedding.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/monai/networks/blocks/patchembedding.py b/monai/networks/blocks/patchembedding.py index 44774ce5da..91bd73ebbb 100644 --- a/monai/networks/blocks/patchembedding.py +++ b/monai/networks/blocks/patchembedding.py @@ -120,10 +120,7 @@ def __init__( for in_size, pa_size in zip(img_size, patch_size): grid_size.append(in_size // pa_size) - with torch.no_grad(): - pos_embeddings = build_sincos_position_embedding(grid_size, hidden_size, spatial_dims) - self.position_embeddings.data.copy_(pos_embeddings.float()) - self.position_embeddings.requires_grad = False + self.position_embeddings = build_sincos_position_embedding(grid_size, hidden_size, spatial_dims) else: raise ValueError(f"pos_embed_type {self.pos_embed_type} not supported.") From 625967c483e6ad98c2e41d7d046040c91329cd8c Mon Sep 17 00:00:00 2001 From: Lucas Robinet Date: Fri, 5 Apr 2024 11:21:24 +0200 Subject: [PATCH 072/136] harmonization and clarification of dice losses variants docs and associated tests (#7587) ### Description This PR aims to clarify and harmonise the code for the DiceLoss variants in the `monai/losses/dice.py` file. With the `to_onehot_y` `softmax` and `sigmoid` arguments, I didn't necessarily understand the ValueError that occurred when I passed a target of size NH[WD]. I had a bit of trouble reading the documentation and understanding it. I thought that they had to be the same shape as they are displayed, unlike the number of dimensions in the input, so I added that. Besides, in the documentation is written: ```python """ raises: ValueError: When number of channels for target is neither 1 nor the same as input. """ ``` Trying to reproduce this, we give an input with a number of channels $N$ and target a number of channels of $M$, with $M \neq N$ and $M > 1$. ```python loss = DiceCELoss() input = torch.rand(1, 4, 3, 3) target = torch.randn(1, 2, 3, 3) loss(input, target) >: AssertionError: ground truth has different shape (torch.Size([1, 2, 3, 3])) from input (torch.Size([1, 4, 3, 3])) ``` This error in the Dice is an `AssertionError` and not a `ValueError` as expected and the explanation can be confusing and doesn't give a clear idea of the error here. The classes concerned and harmonised are `DiceFocalLoss`, `DiceCELoss` and `GeneralizedDiceFocalLoss` with the addition of tests that behave correctly and handle this harmonisation. Also, feel free to modify or make suggestions regarding the changes made in the docstring to make them more understandable (in my opinion, but other readers and users will probably have a different view). ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Lucas Robinet Signed-off-by: Lucas Robinet Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- monai/losses/dice.py | 42 +++++++++++++++++++---- tests/test_dice_ce_loss.py | 18 +++++++--- tests/test_dice_focal_loss.py | 14 ++++++-- tests/test_generalized_dice_focal_loss.py | 14 ++++++-- 4 files changed, 73 insertions(+), 15 deletions(-) diff --git a/monai/losses/dice.py b/monai/losses/dice.py index b3c0f57c6e..f1c357d31f 100644 --- a/monai/losses/dice.py +++ b/monai/losses/dice.py @@ -778,12 +778,22 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: Raises: ValueError: When number of dimensions for input and target are different. - ValueError: When number of channels for target is neither 1 nor the same as input. + ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input. + + Returns: + torch.Tensor: value of the loss. """ - if len(input.shape) != len(target.shape): + if input.dim() != target.dim(): raise ValueError( "the number of dimensions for input and target should be the same, " + f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). " + "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]." + ) + + if target.shape[1] != 1 and target.shape[1] != input.shape[1]: + raise ValueError( + "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, " f"got shape {input.shape} and {target.shape}." ) @@ -899,14 +909,24 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: Raises: ValueError: When number of dimensions for input and target are different. - ValueError: When number of channels for target is neither 1 nor the same as input. + ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input. + Returns: + torch.Tensor: value of the loss. """ - if len(input.shape) != len(target.shape): + if input.dim() != target.dim(): raise ValueError( "the number of dimensions for input and target should be the same, " + f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). " + "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]." + ) + + if target.shape[1] != 1 and target.shape[1] != input.shape[1]: + raise ValueError( + "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, " f"got shape {input.shape} and {target.shape}." ) + if self.to_onehot_y: n_pred_ch = input.shape[1] if n_pred_ch == 1: @@ -1015,15 +1035,23 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: target (torch.Tensor): the shape should be BNH[WD] or B1H[WD]. Raises: - ValueError: When the input and target tensors have different numbers of dimensions, or the target - channel isn't either one-hot encoded or categorical with the same shape of the input. + ValueError: When number of dimensions for input and target are different. + ValueError: When number of channels for target is neither 1 (without one-hot encoding) nor the same as input. Returns: torch.Tensor: value of the loss. """ if input.dim() != target.dim(): raise ValueError( - f"Input - {input.shape} - and target - {target.shape} - must have the same number of dimensions." + "the number of dimensions for input and target should be the same, " + f"got shape {input.shape} (nb dims: {len(input.shape)}) and {target.shape} (nb dims: {len(target.shape)}). " + "if target is not one-hot encoded, please provide a tensor with shape B1H[WD]." + ) + + if target.shape[1] != 1 and target.shape[1] != input.shape[1]: + raise ValueError( + "number of channels for target is neither 1 (without one-hot encoding) nor the same as input, " + f"got shape {input.shape} and {target.shape}." ) gdl_loss = self.generalized_dice(input, target) diff --git a/tests/test_dice_ce_loss.py b/tests/test_dice_ce_loss.py index 225618ed2c..97c7ae5050 100644 --- a/tests/test_dice_ce_loss.py +++ b/tests/test_dice_ce_loss.py @@ -93,10 +93,20 @@ def test_result(self, input_param, input_data, expected_val): result = diceceloss(**input_data) np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, atol=1e-4, rtol=1e-4) - # def test_ill_shape(self): - # loss = DiceCELoss() - # with self.assertRaisesRegex(ValueError, ""): - # loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + def test_ill_shape(self): + loss = DiceCELoss() + with self.assertRaises(AssertionError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5))) + + def test_ill_shape2(self): + loss = DiceCELoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + + def test_ill_shape3(self): + loss = DiceCELoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4))) # def test_ill_reduction(self): # with self.assertRaisesRegex(ValueError, ""): diff --git a/tests/test_dice_focal_loss.py b/tests/test_dice_focal_loss.py index 13899da003..814a174762 100644 --- a/tests/test_dice_focal_loss.py +++ b/tests/test_dice_focal_loss.py @@ -69,8 +69,18 @@ def test_result_no_onehot_no_bg(self, size, onehot): def test_ill_shape(self): loss = DiceFocalLoss() - with self.assertRaisesRegex(ValueError, ""): - loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + with self.assertRaises(AssertionError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5))) + + def test_ill_shape2(self): + loss = DiceFocalLoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + + def test_ill_shape3(self): + loss = DiceFocalLoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4))) def test_ill_lambda(self): with self.assertRaisesRegex(ValueError, ""): diff --git a/tests/test_generalized_dice_focal_loss.py b/tests/test_generalized_dice_focal_loss.py index 8a0a80865e..65252611ca 100644 --- a/tests/test_generalized_dice_focal_loss.py +++ b/tests/test_generalized_dice_focal_loss.py @@ -59,8 +59,18 @@ def test_result_no_onehot_no_bg(self): def test_ill_shape(self): loss = GeneralizedDiceFocalLoss() - with self.assertRaisesRegex(ValueError, ""): - loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + with self.assertRaises(AssertionError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 2, 5))) + + def test_ill_shape2(self): + loss = GeneralizedDiceFocalLoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3))) + + def test_ill_shape3(self): + loss = GeneralizedDiceFocalLoss() + with self.assertRaises(ValueError): + loss.forward(torch.ones((1, 3, 4, 4)), torch.ones((1, 2, 4, 4))) def test_ill_lambda(self): with self.assertRaisesRegex(ValueError, ""): From c0b9cc0b00459563196b9ce1d430f5d86406c5e6 Mon Sep 17 00:00:00 2001 From: Lucas Robinet Date: Fri, 5 Apr 2024 14:56:25 +0200 Subject: [PATCH 073/136] Implementation of intensity clipping transform: bot hard and soft approaches (#7535) Fixes Issue #7512. ### Description Addition of a transformation allowing values above or below a certain percentile to be clipped. Clipping can be hard or soft. With soft clipping, the function remains derivable and the order of the values is respected, with smoother corners. The soft clipping function is based on this medium article https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291 It's important to note that I've chosen to switch from Nones values to percentiles to take account of the fact that soft clipping can be one-sided or two-sided. In fact, providing percentiles of 100 or 0 doesn't change anything in the case of hard clipping, but it does in the case of soft clipping because the function is smoothed. Hence the interest in introducing the possibility of putting None to avoid smoothing the function on one side or the other. To implement this we had to define a `softplus` function in `monai.transforms.utils_pytorch_numpy_unification.py`. One of the problems is that `np.logaddexp` do not exactly yields same outputs as `torch.logaddexp`. I've left it as is and lowered the tolerance of the tests slightly, but it's possible to force the conversion to numpy and then switch back to torch to ensure better unification between the frameworks. I've also added the `soft_clip` function in `monai.transforms.utils.py` with the associated unit tests to ensure that the transformation works properly. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Lucas Robinet Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- docs/source/transforms.rst | 12 ++ monai/transforms/__init__.py | 4 + monai/transforms/intensity/array.py | 148 ++++++++++++- monai/transforms/intensity/dictionary.py | 35 +++ monai/transforms/utils.py | 37 ++++ .../utils_pytorch_numpy_unification.py | 15 ++ tests/test_clip_intensity_percentiles.py | 185 ++++++++++++++++ tests/test_clip_intensity_percentilesd.py | 203 ++++++++++++++++++ tests/test_soft_clip.py | 125 +++++++++++ 9 files changed, 763 insertions(+), 1 deletion(-) create mode 100644 tests/test_clip_intensity_percentiles.py create mode 100644 tests/test_clip_intensity_percentilesd.py create mode 100644 tests/test_soft_clip.py diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index bd3feb3497..8bd5bfd99f 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -309,6 +309,12 @@ Intensity :members: :special-members: __call__ +`ClipIntensityPercentiles` +"""""""""""""""""""""""""" +.. autoclass:: ClipIntensityPercentiles + :members: + :special-members: __call__ + `RandScaleIntensity` """""""""""""""""""" .. image:: https://raw.githubusercontent.com/Project-MONAI/DocImages/main/transforms/RandScaleIntensity.png @@ -1405,6 +1411,12 @@ Intensity (Dict) :members: :special-members: __call__ +`ClipIntensityPercentilesd` +""""""""""""""""""""""""""" +.. autoclass:: ClipIntensityPercentilesd + :members: + :special-members: __call__ + `RandScaleIntensityd` """"""""""""""""""""" .. image:: https://raw.githubusercontent.com/Project-MONAI/DocImages/main/transforms/RandScaleIntensityd.png diff --git a/monai/transforms/__init__.py b/monai/transforms/__init__.py index 349533fb3e..ab9adb6a99 100644 --- a/monai/transforms/__init__.py +++ b/monai/transforms/__init__.py @@ -92,6 +92,7 @@ from .croppad.functional import crop_func, crop_or_pad_nd, pad_func, pad_nd from .intensity.array import ( AdjustContrast, + ClipIntensityPercentiles, ComputeHoVerMaps, DetectEnvelope, ForegroundMask, @@ -135,6 +136,9 @@ AdjustContrastd, AdjustContrastD, AdjustContrastDict, + ClipIntensityPercentilesd, + ClipIntensityPercentilesD, + ClipIntensityPercentilesDict, ComputeHoVerMapsd, ComputeHoVerMapsD, ComputeHoVerMapsDict, diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py index 0085050ee3..f656475a36 100644 --- a/monai/transforms/intensity/array.py +++ b/monai/transforms/intensity/array.py @@ -30,7 +30,7 @@ from monai.data.utils import get_random_patch, get_valid_patch_size from monai.networks.layers import GaussianFilter, HilbertTransform, MedianFilter, SavitzkyGolayFilter from monai.transforms.transform import RandomizableTransform, Transform -from monai.transforms.utils import Fourier, equalize_hist, is_positive, rescale_array +from monai.transforms.utils import Fourier, equalize_hist, is_positive, rescale_array, soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile, where from monai.utils.enums import TransformBackends from monai.utils.misc import ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple @@ -54,6 +54,7 @@ "NormalizeIntensity", "ThresholdIntensity", "ScaleIntensityRange", + "ClipIntensityPercentiles", "AdjustContrast", "RandAdjustContrast", "ScaleIntensityRangePercentiles", @@ -1007,6 +1008,151 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: return ret +class ClipIntensityPercentiles(Transform): + """ + Apply clip based on the intensity distribution of input image. + If `sharpness_factor` is provided, the intensity values will be soft clipped according to + f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)) + From https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291 + + Soft clipping preserves the order of the values and maintains the gradient everywhere. + For example: + + .. code-block:: python + :emphasize-lines: 11, 22 + + image = torch.Tensor( + [[[1, 2, 3, 4, 5], + [1, 2, 3, 4, 5], + [1, 2, 3, 4, 5], + [1, 2, 3, 4, 5], + [1, 2, 3, 4, 5], + [1, 2, 3, 4, 5]]]) + + # Hard clipping from lower and upper image intensity percentiles + hard_clipper = ClipIntensityPercentiles(30, 70) + print(hard_clipper(image)) + metatensor([[[2., 2., 3., 4., 4.], + [2., 2., 3., 4., 4.], + [2., 2., 3., 4., 4.], + [2., 2., 3., 4., 4.], + [2., 2., 3., 4., 4.], + [2., 2., 3., 4., 4.]]]) + + + # Soft clipping from lower and upper image intensity percentiles + soft_clipper = ClipIntensityPercentiles(30, 70, 10.) + print(soft_clipper(image)) + metatensor([[[2.0000, 2.0693, 3.0000, 3.9307, 4.0000], + [2.0000, 2.0693, 3.0000, 3.9307, 4.0000], + [2.0000, 2.0693, 3.0000, 3.9307, 4.0000], + [2.0000, 2.0693, 3.0000, 3.9307, 4.0000], + [2.0000, 2.0693, 3.0000, 3.9307, 4.0000], + [2.0000, 2.0693, 3.0000, 3.9307, 4.0000]]]) + + See Also: + + - :py:class:`monai.transforms.ScaleIntensityRangePercentiles` + """ + + backend = [TransformBackends.TORCH, TransformBackends.NUMPY] + + def __init__( + self, + lower: float | None, + upper: float | None, + sharpness_factor: float | None = None, + channel_wise: bool = False, + return_clipping_values: bool = False, + dtype: DtypeLike = np.float32, + ) -> None: + """ + Args: + lower: lower intensity percentile. In the case of hard clipping, None will have the same effect as 0 by + not clipping the lowest input values. However, in the case of soft clipping, None and zero will have + two different effects: None will not apply clipping to low values, whereas zero will still transform + the lower values according to the soft clipping transformation. Please check for more details: + https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291. + upper: upper intensity percentile. The same as for lower, but this time with the highest values. If we + are looking to perform soft clipping, if None then there will be no effect on this side whereas if set + to 100, the values will be passed via the corresponding clipping equation. + sharpness_factor: if not None, the intensity values will be soft clipped according to + f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)). + defaults to None. + channel_wise: if True, compute intensity percentile and normalize every channel separately. + default to False. + return_clipping_values: whether to return the calculated percentiles in tensor meta information. + If soft clipping and requested percentile is None, return None as the corresponding clipping + values in meta information. Clipping values are stored in a list with each element corresponding + to a channel if channel_wise is set to True. defaults to False. + dtype: output data type, if None, same as input image. defaults to float32. + """ + if lower is None and upper is None: + raise ValueError("lower or upper percentiles must be provided") + if lower is not None and (lower < 0.0 or lower > 100.0): + raise ValueError("Percentiles must be in the range [0, 100]") + if upper is not None and (upper < 0.0 or upper > 100.0): + raise ValueError("Percentiles must be in the range [0, 100]") + if upper is not None and lower is not None and upper < lower: + raise ValueError("upper must be greater than or equal to lower") + if sharpness_factor is not None and sharpness_factor <= 0: + raise ValueError("sharpness_factor must be greater than 0") + + self.lower = lower + self.upper = upper + self.sharpness_factor = sharpness_factor + self.channel_wise = channel_wise + if return_clipping_values: + self.clipping_values: list[tuple[float | None, float | None]] = [] + self.return_clipping_values = return_clipping_values + self.dtype = dtype + + def _clip(self, img: NdarrayOrTensor) -> NdarrayOrTensor: + if self.sharpness_factor is not None: + lower_percentile = percentile(img, self.lower) if self.lower is not None else None + upper_percentile = percentile(img, self.upper) if self.upper is not None else None + img = soft_clip(img, self.sharpness_factor, lower_percentile, upper_percentile, self.dtype) + else: + lower_percentile = percentile(img, self.lower) if self.lower is not None else percentile(img, 0) + upper_percentile = percentile(img, self.upper) if self.upper is not None else percentile(img, 100) + img = clip(img, lower_percentile, upper_percentile) + + if self.return_clipping_values: + self.clipping_values.append( + ( + ( + lower_percentile + if lower_percentile is None + else lower_percentile.item() if hasattr(lower_percentile, "item") else lower_percentile + ), + ( + upper_percentile + if upper_percentile is None + else upper_percentile.item() if hasattr(upper_percentile, "item") else upper_percentile + ), + ) + ) + img = convert_to_tensor(img, track_meta=False) + return img + + def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor: + """ + Apply the transform to `img`. + """ + img = convert_to_tensor(img, track_meta=get_track_meta()) + img_t = convert_to_tensor(img, track_meta=False) + if self.channel_wise: + img_t = torch.stack([self._clip(img=d) for d in img_t]) # type: ignore + else: + img_t = self._clip(img=img_t) + + img = convert_to_dst_type(img_t, dst=img)[0] + if self.return_clipping_values: + img.meta["clipping_values"] = self.clipping_values # type: ignore + + return img + + class AdjustContrast(Transform): """ Changes image intensity with gamma transform. Each pixel/voxel intensity is updated as:: diff --git a/monai/transforms/intensity/dictionary.py b/monai/transforms/intensity/dictionary.py index 5b911904b0..5dbac485fe 100644 --- a/monai/transforms/intensity/dictionary.py +++ b/monai/transforms/intensity/dictionary.py @@ -26,6 +26,7 @@ from monai.data.meta_obj import get_track_meta from monai.transforms.intensity.array import ( AdjustContrast, + ClipIntensityPercentiles, ComputeHoVerMaps, ForegroundMask, GaussianSharpen, @@ -77,6 +78,7 @@ "NormalizeIntensityd", "ThresholdIntensityd", "ScaleIntensityRanged", + "ClipIntensityPercentilesd", "AdjustContrastd", "RandAdjustContrastd", "ScaleIntensityRangePercentilesd", @@ -122,6 +124,8 @@ "ThresholdIntensityDict", "ScaleIntensityRangeD", "ScaleIntensityRangeDict", + "ClipIntensityPercentilesD", + "ClipIntensityPercentilesDict", "AdjustContrastD", "AdjustContrastDict", "RandAdjustContrastD", @@ -886,6 +890,36 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N return d +class ClipIntensityPercentilesd(MapTransform): + """ + Dictionary-based wrapper of :py:class:`monai.transforms.ClipIntensityPercentiles`. + Clip the intensity values of input image to a specific range based on the intensity distribution of the input. + If `sharpness_factor` is provided, the intensity values will be soft clipped according to + f(x) = x + (1/sharpness_factor) * softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)) + """ + + def __init__( + self, + keys: KeysCollection, + lower: float | None, + upper: float | None, + sharpness_factor: float | None = None, + channel_wise: bool = False, + dtype: DtypeLike = np.float32, + allow_missing_keys: bool = False, + ) -> None: + super().__init__(keys, allow_missing_keys) + self.scaler = ClipIntensityPercentiles( + lower=lower, upper=upper, sharpness_factor=sharpness_factor, channel_wise=channel_wise, dtype=dtype + ) + + def __call__(self, data: dict) -> dict: + d = dict(data) + for key in self.key_iterator(d): + d[key] = self.scaler(d[key]) + return d + + class AdjustContrastd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.AdjustContrast`. @@ -1929,6 +1963,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N NormalizeIntensityD = NormalizeIntensityDict = NormalizeIntensityd ThresholdIntensityD = ThresholdIntensityDict = ThresholdIntensityd ScaleIntensityRangeD = ScaleIntensityRangeDict = ScaleIntensityRanged +ClipIntensityPercentilesD = ClipIntensityPercentilesDict = ClipIntensityPercentilesd AdjustContrastD = AdjustContrastDict = AdjustContrastd RandAdjustContrastD = RandAdjustContrastDict = RandAdjustContrastd ScaleIntensityRangePercentilesD = ScaleIntensityRangePercentilesDict = ScaleIntensityRangePercentilesd diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 455b0cfb7d..14f35e1219 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -38,6 +38,7 @@ nonzero, ravel, searchsorted, + softplus, unique, unravel_index, where, @@ -131,9 +132,45 @@ "resolves_modes", "has_status_keys", "distance_transform_edt", + "soft_clip", ] +def soft_clip( + arr: NdarrayOrTensor, + sharpness_factor: float = 1.0, + minv: NdarrayOrTensor | float | int | None = None, + maxv: NdarrayOrTensor | float | int | None = None, + dtype: DtypeLike | torch.dtype = np.float32, +) -> NdarrayOrTensor: + """ + Apply soft clip to the input array or tensor. + The intensity values will be soft clipped according to + f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)) + From https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291 + + To perform one-sided clipping, set either minv or maxv to None. + Args: + arr: input array to clip. + sharpness_factor: the sharpness of the soft clip function, default to 1. + minv: minimum value of target clipped array. + maxv: maximum value of target clipped array. + dtype: if not None, convert input array to dtype before computation. + + """ + + if dtype is not None: + arr, *_ = convert_data_type(arr, dtype=dtype) + + v = arr + if minv is not None: + v = v + softplus(-sharpness_factor * (arr - minv)) / sharpness_factor + if maxv is not None: + v = v - softplus(sharpness_factor * (arr - maxv)) / sharpness_factor + + return v + + def rand_choice(prob: float = 0.5) -> bool: """ Returns True if a randomly chosen number is less than or equal to `prob`, by default this is a 50/50 chance. diff --git a/monai/transforms/utils_pytorch_numpy_unification.py b/monai/transforms/utils_pytorch_numpy_unification.py index 0774d50314..020d99af16 100644 --- a/monai/transforms/utils_pytorch_numpy_unification.py +++ b/monai/transforms/utils_pytorch_numpy_unification.py @@ -52,9 +52,24 @@ "median", "mean", "std", + "softplus", ] +def softplus(x: NdarrayOrTensor) -> NdarrayOrTensor: + """stable softplus through `np.logaddexp` with equivalent implementation for torch. + + Args: + x: array/tensor. + + Returns: + Softplus of the input. + """ + if isinstance(x, np.ndarray): + return np.logaddexp(np.zeros_like(x), x) + return torch.logaddexp(torch.zeros_like(x), x) + + def allclose(a: NdarrayTensor, b: NdarrayOrTensor, rtol=1e-5, atol=1e-8, equal_nan=False) -> bool: """`np.allclose` with equivalent implementation for torch.""" b, *_ = convert_to_dst_type(b, a, wrap_sequence=True) diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py new file mode 100644 index 0000000000..f5fe07a323 --- /dev/null +++ b/tests/test_clip_intensity_percentiles.py @@ -0,0 +1,185 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms import ClipIntensityPercentiles +from monai.transforms.utils import soft_clip +from monai.transforms.utils_pytorch_numpy_unification import clip +from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose + + +class TestClipIntensityPercentiles2D(NumpyImageTestCase2D): + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_two_sided(self, p): + hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_high(self, p): + hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (0, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_low(self, p): + hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (5, 100)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_two_sided(self, p): + soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_high(self, p): + soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + upper = np.percentile(self.imt, 95) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_low(self, p): + soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + lower = np.percentile(self.imt, 5) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_channel_wise(self, p): + clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) + im = p(self.imt) + result = clipper(im) + for i, c in enumerate(self.imt): + lower, upper = np.percentile(c, (5, 95)) + expected = clip(c, lower, upper) + assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + def test_ill_sharpness_factor(self): + with self.assertRaises(ValueError): + ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=0.0) + + def test_ill_lower_percentile(self): + with self.assertRaises(ValueError): + ClipIntensityPercentiles(upper=None, lower=-1) + + def test_ill_upper_percentile(self): + with self.assertRaises(ValueError): + ClipIntensityPercentiles(upper=101, lower=None) + + def test_ill_percentiles(self): + with self.assertRaises(ValueError): + ClipIntensityPercentiles(upper=95, lower=96) + + def test_ill_both_none(self): + with self.assertRaises(ValueError): + ClipIntensityPercentiles(upper=None, lower=None) + + +class TestClipIntensityPercentiles3D(NumpyImageTestCase3D): + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_two_sided(self, p): + hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_high(self, p): + hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (0, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_low(self, p): + hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) + im = p(self.imt) + result = hard_clipper(im) + lower, upper = np.percentile(self.imt, (5, 100)) + expected = clip(self.imt, lower, upper) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_two_sided(self, p): + soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_high(self, p): + soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + upper = np.percentile(self.imt, 95) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_low(self, p): + soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper(im) + lower = np.percentile(self.imt, 5) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_channel_wise(self, p): + clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) + im = p(self.imt) + result = clipper(im) + for i, c in enumerate(self.imt): + lower, upper = np.percentile(c, (5, 95)) + expected = clip(c, lower, upper) + assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py new file mode 100644 index 0000000000..193fa8b487 --- /dev/null +++ b/tests/test_clip_intensity_percentilesd.py @@ -0,0 +1,203 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import numpy as np +from parameterized import parameterized + +from monai.transforms import ClipIntensityPercentilesd +from monai.transforms.utils import soft_clip +from monai.transforms.utils_pytorch_numpy_unification import clip +from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose + + +class TestClipIntensityPercentilesd2D(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_two_sided(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_high(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (0, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_low(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 100)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_two_sided(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_high(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + upper = np.percentile(self.imt, 95) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_low(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + lower = np.percentile(self.imt, 5) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_channel_wise(self, p): + key = "img" + clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) + im = p(self.imt) + result = clipper({key: im}) + for i, c in enumerate(self.imt): + lower, upper = np.percentile(c, (5, 95)) + expected = clip(c, lower, upper) + assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + def test_ill_sharpness_factor(self): + key = "img" + with self.assertRaises(ValueError): + ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=0.0) + + def test_ill_lower_percentile(self): + key = "img" + with self.assertRaises(ValueError): + ClipIntensityPercentilesd(keys=[key], upper=None, lower=-1) + + def test_ill_upper_percentile(self): + key = "img" + with self.assertRaises(ValueError): + ClipIntensityPercentilesd(keys=[key], upper=101, lower=None) + + def test_ill_percentiles(self): + key = "img" + with self.assertRaises(ValueError): + ClipIntensityPercentilesd(keys=[key], upper=95, lower=96) + + def test_ill_both_none(self): + key = "img" + with self.assertRaises(ValueError): + ClipIntensityPercentilesd(keys=[key], upper=None, lower=None) + + +class TestClipIntensityPercentilesd3D(NumpyImageTestCase3D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_two_sided(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_high(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (0, 95)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_hard_clipping_one_sided_low(self, p): + key = "img" + hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) + im = p(self.imt) + result = hard_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 100)) + expected = clip(self.imt, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_two_sided(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + lower, upper = np.percentile(self.imt, (5, 95)) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_high(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + upper = np.percentile(self.imt, 95) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_soft_clipping_one_sided_low(self, p): + key = "img" + soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) + im = p(self.imt) + result = soft_clipper({key: im}) + lower = np.percentile(self.imt, 5) + expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + + @parameterized.expand([[p] for p in TEST_NDARRAYS]) + def test_channel_wise(self, p): + key = "img" + clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) + im = p(self.imt) + result = clipper({key: im}) + for i, c in enumerate(self.imt): + lower, upper = np.percentile(c, (5, 95)) + expected = clip(c, lower, upper) + assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_soft_clip.py b/tests/test_soft_clip.py new file mode 100644 index 0000000000..de5122e982 --- /dev/null +++ b/tests/test_soft_clip.py @@ -0,0 +1,125 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest + +import numpy as np +import torch +from parameterized import parameterized + +from monai.transforms.utils import soft_clip + +TEST_CASES = [ + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 10}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([2.0000, 2.0000, 2.0693, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 7.9307, 8.0000]), + }, + ], + [ + {"minv": 2, "maxv": None, "sharpness_factor": 10}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([2.0000, 2.0000, 2.0693, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000]), + }, + ], + [ + {"minv": None, "maxv": 7, "sharpness_factor": 10}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([0.0000, 1.0000, 2.0000, 3.0000, 4.0000, 5.0000, 6.0000, 6.9307, 7.0000, 7.0000]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 1.0}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([2.1266, 2.3124, 2.6907, 3.3065, 4.1088, 5.0000, 5.8912, 6.6935, 7.3093, 7.6877]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 3.0}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([2.0008, 2.0162, 2.2310, 3.0162, 4.0008, 5.0000, 5.9992, 6.9838, 7.7690, 7.9838]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 5.0}, + { + "input": torch.arange(10).float(), + "clipped": torch.tensor([2.0000, 2.0013, 2.1386, 3.0013, 4.0000, 5.0000, 6.0000, 6.9987, 7.8614, 7.9987]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 10}, + { + "input": np.arange(10).astype(np.float32), + "clipped": np.array([2.0000, 2.0000, 2.0693, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 7.9307, 8.0000]), + }, + ], + [ + {"minv": 2, "maxv": None, "sharpness_factor": 10}, + { + "input": np.arange(10).astype(float), + "clipped": np.array([2.0000, 2.0000, 2.0693, 3.0000, 4.0000, 5.0000, 6.0000, 7.0000, 8.0000, 9.0000]), + }, + ], + [ + {"minv": None, "maxv": 7, "sharpness_factor": 10}, + { + "input": np.arange(10).astype(float), + "clipped": np.array([0.0000, 1.0000, 2.0000, 3.0000, 4.0000, 5.0000, 6.0000, 6.9307, 7.0000, 7.0000]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 1.0}, + { + "input": np.arange(10).astype(float), + "clipped": np.array([2.1266, 2.3124, 2.6907, 3.3065, 4.1088, 5.0000, 5.8912, 6.6935, 7.3093, 7.6877]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 3.0}, + { + "input": np.arange(10).astype(float), + "clipped": np.array([2.0008, 2.0162, 2.2310, 3.0162, 4.0008, 5.0000, 5.9992, 6.9838, 7.7690, 7.9838]), + }, + ], + [ + {"minv": 2, "maxv": 8, "sharpness_factor": 5.0}, + { + "input": np.arange(10).astype(float), + "clipped": np.array([2.0000, 2.0013, 2.1386, 3.0013, 4.0000, 5.0000, 6.0000, 6.9987, 7.8614, 7.9987]), + }, + ], +] + + +class TestSoftClip(unittest.TestCase): + + @parameterized.expand(TEST_CASES) + def test_result(self, input_param, input_data): + outputs = soft_clip(input_data["input"], **input_param) + expected_val = input_data["clipped"] + if isinstance(outputs, torch.Tensor): + np.testing.assert_allclose( + outputs.detach().cpu().numpy(), expected_val.detach().cpu().numpy(), atol=1e-4, rtol=1e-4 + ) + else: + np.testing.assert_allclose(outputs, expected_val, atol=1e-4, rtol=1e-4) + + +if __name__ == "__main__": + unittest.main() From 87152d106557bdc37de0ee923be4e9a25c4c003c Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 8 Apr 2024 12:51:23 +0800 Subject: [PATCH 074/136] Fix typo in `SSIMMetric` (#7612) Fixes #7610 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/metrics/regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/metrics/regression.py b/monai/metrics/regression.py index 9d29654ee3..4c8b8aa71b 100644 --- a/monai/metrics/regression.py +++ b/monai/metrics/regression.py @@ -303,7 +303,7 @@ def _compute_metric(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor if self.spatial_dims == 3 and dims != 5: raise ValueError( - f"y_pred should have 4 dimensions (batch, channel, height, width, depth) when using {self.spatial_dims}" + f"y_pred should have 5 dimensions (batch, channel, height, width, depth) when using {self.spatial_dims}" f" spatial dimensions, got {dims}." ) From e9a5bfe4693ce490c75616a97eb0812e1828539b Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Wed, 10 Apr 2024 04:03:54 +0100 Subject: [PATCH 075/136] auto updates (#7614) Signed-off-by: monai-bot Signed-off-by: monai-bot --- tests/test_clip_intensity_percentilesd.py | 2 ++ tests/test_regularization.py | 3 +++ 2 files changed, 5 insertions(+) diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 193fa8b487..7e00ef09de 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -23,6 +23,7 @@ class TestClipIntensityPercentilesd2D(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_two_sided(self, p): key = "img" @@ -124,6 +125,7 @@ def test_ill_both_none(self): class TestClipIntensityPercentilesd3D(NumpyImageTestCase3D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_two_sided(self, p): key = "img" diff --git a/tests/test_regularization.py b/tests/test_regularization.py index c6f727cb54..4df60b9808 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -20,6 +20,7 @@ class TestMixup(unittest.TestCase): + def setUp(self) -> None: set_determinism(seed=0) @@ -59,6 +60,7 @@ def test_mixupd(self): class TestCutMix(unittest.TestCase): + def setUp(self) -> None: set_determinism(seed=0) @@ -89,6 +91,7 @@ def test_cutmixd(self): class TestCutOut(unittest.TestCase): + def setUp(self) -> None: set_determinism(seed=0) From 54a6991c7e319c30751d017da670391eb8551179 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 11 Apr 2024 18:26:50 +0800 Subject: [PATCH 076/136] Fix test error in `test_soft_clipping_one_sided_high` (#7624) Fixes #7616 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- tests/test_clip_intensity_percentiles.py | 60 +++++++++++------------ tests/test_clip_intensity_percentilesd.py | 60 +++++++++++------------ 2 files changed, 60 insertions(+), 60 deletions(-) diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index f5fe07a323..82471e25ce 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -12,12 +12,12 @@ import unittest -import numpy as np +import torch from parameterized import parameterized from monai.transforms import ClipIntensityPercentiles from monai.transforms.utils import soft_clip -from monai.transforms.utils_pytorch_numpy_unification import clip +from monai.transforms.utils_pytorch_numpy_unification import clip, percentile from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -28,8 +28,8 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 95)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -37,8 +37,8 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (0, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (0, 95)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -46,8 +46,8 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (5, 100)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 100)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -55,8 +55,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + lower, upper = percentile(im, (5, 95)) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -65,8 +65,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = np.percentile(self.imt, 95) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + upper = percentile(im, 95) + expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) @@ -75,8 +75,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = np.percentile(self.imt, 5) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + lower = percentile(im, 5) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -85,8 +85,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(self.imt): - lower, upper = np.percentile(c, (5, 95)) + for i, c in enumerate(im): + lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) @@ -118,8 +118,8 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 95)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -127,8 +127,8 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (0, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (0, 95)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -136,8 +136,8 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = np.percentile(self.imt, (5, 100)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 100)) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -145,8 +145,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + lower, upper = percentile(im, (5, 95)) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -155,8 +155,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = np.percentile(self.imt, 95) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + upper = percentile(im, 95) + expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) @@ -165,8 +165,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = np.percentile(self.imt, 5) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + lower = percentile(im, 5) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -175,8 +175,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(self.imt): - lower, upper = np.percentile(c, (5, 95)) + for i, c in enumerate(im): + lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 7e00ef09de..2b49383182 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -13,12 +13,12 @@ import unittest -import numpy as np +import torch from parameterized import parameterized from monai.transforms import ClipIntensityPercentilesd from monai.transforms.utils import soft_clip -from monai.transforms.utils_pytorch_numpy_unification import clip +from monai.transforms.utils_pytorch_numpy_unification import clip, percentile from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -30,8 +30,8 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 95)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -40,8 +40,8 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (0, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (0, 95)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -50,8 +50,8 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 100)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 100)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -60,8 +60,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + lower, upper = percentile(im, (5, 95)) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -71,8 +71,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = np.percentile(self.imt, 95) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + upper = percentile(im, 95) + expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) @@ -82,8 +82,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = np.percentile(self.imt, 5) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + lower = percentile(im, 5) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -93,8 +93,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(self.imt): - lower, upper = np.percentile(c, (5, 95)) + for i, c in enumerate(im): + lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) @@ -132,8 +132,8 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 95)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -142,8 +142,8 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (0, 95)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (0, 95)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -152,8 +152,8 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 100)) - expected = clip(self.imt, lower, upper) + lower, upper = percentile(im, (5, 100)) + expected = clip(im, lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -162,8 +162,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = np.percentile(self.imt, (5, 95)) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=upper) + lower, upper = percentile(im, (5, 95)) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -173,8 +173,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = np.percentile(self.imt, 95) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=None, maxv=upper) + upper = percentile(im, 95) + expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) @@ -184,8 +184,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = np.percentile(self.imt, 5) - expected = soft_clip(self.imt, sharpness_factor=1.0, minv=lower, maxv=None) + lower = percentile(im, 5) + expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) @@ -195,8 +195,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(self.imt): - lower, upper = np.percentile(c, (5, 95)) + for i, c in enumerate(im): + lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) From 3856c456bd8140c70decaeb89c4f58d1dfabb74b Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 11 Apr 2024 21:19:18 +0800 Subject: [PATCH 077/136] Fix deprecated warning in ruff (#7625) Fixes #7623 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- pyproject.toml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 14b41bbeb8..08b75b1e97 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -27,7 +27,7 @@ repos: - id: end-of-file-fixer - id: mixed-line-ending - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: v0.0.261 + rev: v0.3.5 hooks: - id: ruff args: diff --git a/pyproject.toml b/pyproject.toml index cd8a510b04..50d0b09672 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,8 +38,8 @@ exclude = "monai/bundle/__main__.py" [tool.ruff] line-length = 133 -ignore-init-module-imports = true -ignore = ["F401", "E741"] +lint.ignore-init-module-imports = true +lint.ignore = ["F401", "E741"] [tool.pytype] # Space-separated list of files or directories to exclude. From da3ecdd52e0ba68c765eb5010ec202811c3cb1ec Mon Sep 17 00:00:00 2001 From: binliunls <107988372+binliunls@users.noreply.github.com> Date: Fri, 12 Apr 2024 15:11:52 +0800 Subject: [PATCH 078/136] 7601 fix mlflow artifacts (#7604) Fixes #7601 . ### Description Support not save artifacts. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: binliu Co-authored-by: Nic Ma Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/bundle/scripts.py | 17 +++++++++++++---- monai/bundle/utils.py | 6 +++--- monai/bundle/workflows.py | 26 ++++++++++++++++---------- tests/test_fl_monai_algo.py | 6 +++--- 4 files changed, 35 insertions(+), 20 deletions(-) diff --git a/monai/bundle/scripts.py b/monai/bundle/scripts.py index 2565a3cf64..598d938cbd 100644 --- a/monai/bundle/scripts.py +++ b/monai/bundle/scripts.py @@ -778,10 +778,19 @@ def run( https://docs.python.org/3/library/logging.config.html#logging.config.fileConfig. Default to None. tracking: if not None, enable the experiment tracking at runtime with optionally configurable and extensible. - if "mlflow", will add `MLFlowHandler` to the parsed bundle with default tracking settings, - if other string, treat it as file path to load the tracking settings. - if `dict`, treat it as tracking settings. - will patch the target config content with `tracking handlers` and the top-level items of `configs`. + If "mlflow", will add `MLFlowHandler` to the parsed bundle with default tracking settings where a set of + common parameters shown below will be added and can be passed through the `override` parameter of this method. + + - ``"output_dir"``: the path to save mlflow tracking outputs locally, default to "/eval". + - ``"tracking_uri"``: uri to save mlflow tracking outputs, default to "/output_dir/mlruns". + - ``"experiment_name"``: experiment name for this run, default to "monai_experiment". + - ``"run_name"``: the name of current run. + - ``"save_execute_config"``: whether to save the executed config files. It can be `False`, `/path/to/artifacts` + or `True`. If set to `True`, will save to the default path "/eval". Default to `True`. + + If other string, treat it as file path to load the tracking settings. + If `dict`, treat it as tracking settings. + Will patch the target config content with `tracking handlers` and the top-level items of `configs`. for detailed usage examples, please check the tutorial: https://github.com/Project-MONAI/tutorials/blob/main/experiment_management/bundle_integrate_mlflow.ipynb. args_file: a JSON or YAML file to provide default values for `run_id`, `meta_file`, diff --git a/monai/bundle/utils.py b/monai/bundle/utils.py index b187159c89..a0f39d236f 100644 --- a/monai/bundle/utils.py +++ b/monai/bundle/utils.py @@ -113,7 +113,7 @@ "experiment_name": "monai_experiment", "run_name": None, # may fill it at runtime - "execute_config": None, + "save_execute_config": True, "is_not_rank0": ( "$torch.distributed.is_available() \ and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0" @@ -125,7 +125,7 @@ "tracking_uri": "@tracking_uri", "experiment_name": "@experiment_name", "run_name": "@run_name", - "artifacts": "@execute_config", + "artifacts": "@save_execute_config", "iteration_log": True, "epoch_log": True, "tag_name": "train_loss", @@ -148,7 +148,7 @@ "tracking_uri": "@tracking_uri", "experiment_name": "@experiment_name", "run_name": "@run_name", - "artifacts": "@execute_config", + "artifacts": "@save_execute_config", "iteration_log": False, "close_on_complete": True, }, diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py index d876f6d7ae..471088994b 100644 --- a/monai/bundle/workflows.py +++ b/monai/bundle/workflows.py @@ -506,13 +506,19 @@ def patch_bundle_tracking(parser: ConfigParser, settings: dict) -> None: parser[k] = v # save the executed config into file default_name = f"config_{time.strftime('%Y%m%d_%H%M%S')}.json" - filepath = parser.get("execute_config", None) - if filepath is None: - if "output_dir" not in parser: - # if no "output_dir" in the bundle config, default to "/eval" - parser["output_dir"] = f"{EXPR_KEY}{ID_REF_KEY}bundle_root + '/eval'" - # experiment management tools can refer to this config item to track the config info - parser["execute_config"] = parser["output_dir"] + f" + '/{default_name}'" - filepath = os.path.join(parser.get_parsed_content("output_dir"), default_name) - Path(filepath).parent.mkdir(parents=True, exist_ok=True) - parser.export_config_file(parser.get(), filepath) + # Users can set the `save_execute_config` to `False`, `/path/to/artifacts` or `True`. + # If set to False, nothing will be recorded. If set to True, the default path will be logged. + # If set to a file path, the given path will be logged. + filepath = parser.get("save_execute_config", True) + if filepath: + if isinstance(filepath, bool): + if "output_dir" not in parser: + # if no "output_dir" in the bundle config, default to "/eval" + parser["output_dir"] = f"{EXPR_KEY}{ID_REF_KEY}bundle_root + '/eval'" + # experiment management tools can refer to this config item to track the config info + parser["save_execute_config"] = parser["output_dir"] + f" + '/{default_name}'" + filepath = os.path.join(parser.get_parsed_content("output_dir"), default_name) + Path(filepath).parent.mkdir(parents=True, exist_ok=True) + parser.export_config_file(parser.get(), filepath) + else: + parser["save_execute_config"] = None diff --git a/tests/test_fl_monai_algo.py b/tests/test_fl_monai_algo.py index 54bec24b98..c8cb3451fc 100644 --- a/tests/test_fl_monai_algo.py +++ b/tests/test_fl_monai_algo.py @@ -75,7 +75,7 @@ tracking={ "handlers_id": DEFAULT_HANDLERS_ID, "configs": { - "execute_config": f"{_data_dir}/config_executed.json", + "save_execute_config": f"{_data_dir}/config_executed.json", "trainer": { "_target_": "MLFlowHandler", "tracking_uri": path_to_uri(_data_dir) + "/mlflow_override", @@ -201,7 +201,7 @@ def test_train(self, input_params): algo.finalize() # test experiment management - if "execute_config" in algo.train_workflow.parser: + if "save_execute_config" in algo.train_workflow.parser: self.assertTrue(os.path.exists(f"{_data_dir}/mlflow_override")) shutil.rmtree(f"{_data_dir}/mlflow_override") self.assertTrue(os.path.exists(f"{_data_dir}/config_executed.json")) @@ -224,7 +224,7 @@ def test_evaluate(self, input_params): algo.evaluate(data=data, extra={}) # test experiment management - if "execute_config" in algo.eval_workflow.parser: + if "save_execute_config" in algo.eval_workflow.parser: self.assertGreater(len(list(glob.glob(f"{_data_dir}/mlflow_*"))), 0) for f in list(glob.glob(f"{_data_dir}/mlflow_*")): shutil.rmtree(f) From 12684884a93173bed23190ff7a3d0e674a3e6a9f Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 12 Apr 2024 16:19:16 +0800 Subject: [PATCH 079/136] Uninstall opencv included in base image (#7626) Fixes [tutorial #1689](https://github.com/Project-MONAI/tutorials/issues/1689) ### Description Uninstall opencv included in base image. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- Dockerfile | 3 +++ monai/data/video_dataset.py | 2 +- requirements-dev.txt | 1 + 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index d5777104c8..45673fd775 100644 --- a/Dockerfile +++ b/Dockerfile @@ -22,6 +22,9 @@ RUN git clone --recursive https://github.com/zarr-developers/numcodecs.git && pi WORKDIR /opt/monai +# remove opencv-python before opencv-python-headless installation +RUN pip uninstall -y opencv && rm /usr/local/lib/python3.10/dist-packages/cv2 -r + # install full deps COPY requirements.txt requirements-min.txt requirements-dev.txt /tmp/ RUN cp /tmp/requirements.txt /tmp/req.bak \ diff --git a/monai/data/video_dataset.py b/monai/data/video_dataset.py index be3bcf5bd5..ed5c37d777 100644 --- a/monai/data/video_dataset.py +++ b/monai/data/video_dataset.py @@ -177,7 +177,7 @@ def get_available_codecs() -> dict[str, str]: with tempfile.TemporaryDirectory() as tmp_dir: for codec, ext in all_codecs.items(): fname = os.path.join(tmp_dir, f"test{ext}") - fourcc = cv2.VideoWriter_fourcc(*codec) + fourcc = cv2.VideoWriter_fourcc(*codec) # type: ignore[attr-defined] noviderr = writer.open(fname, fourcc, 1, (10, 10)) if noviderr: codecs[codec] = ext diff --git a/requirements-dev.txt b/requirements-dev.txt index af1b8b89d5..2855b56ad5 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -57,3 +57,4 @@ zarr lpips==0.1.4 nvidia-ml-py huggingface_hub +opencv-python-headless From 9e2904afdae311b91fa7b5a1f2171e1cc3336e19 Mon Sep 17 00:00:00 2001 From: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Date: Fri, 12 Apr 2024 22:41:37 +0800 Subject: [PATCH 080/136] Add checks for num_fold and fail early if wrong (#7634) Fixes #7628 . ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Mingxin Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/auto3dseg/auto_runner.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/monai/apps/auto3dseg/auto_runner.py b/monai/apps/auto3dseg/auto_runner.py index 52a0824227..05c961f999 100644 --- a/monai/apps/auto3dseg/auto_runner.py +++ b/monai/apps/auto3dseg/auto_runner.py @@ -298,9 +298,13 @@ def __init__( pass # inspect and update folds - num_fold = self.inspect_datalist_folds(datalist_filename=datalist_filename) + self.max_fold = self.inspect_datalist_folds(datalist_filename=datalist_filename) if "num_fold" in self.data_src_cfg: num_fold = int(self.data_src_cfg["num_fold"]) # override from config + logger.info(f"Setting num_fold {num_fold} based on the input config.") + else: + num_fold = self.max_fold + logger.info(f"Setting num_fold {num_fold} based on the input datalist {datalist_filename}.") self.data_src_cfg["datalist"] = datalist_filename # update path to a version in work_dir and save user input ConfigParser.export_config_file( @@ -398,7 +402,10 @@ def inspect_datalist_folds(self, datalist_filename: str) -> int: if len(fold_list) > 0: num_fold = max(fold_list) + 1 - logger.info(f"Setting num_fold {num_fold} based on the input datalist {datalist_filename}.") + logger.info(f"Found num_fold {num_fold} based on the input datalist {datalist_filename}.") + # check if every fold is present + if len(set(fold_list)) != num_fold: + raise ValueError(f"Fold numbers are not continuous from 0 to {num_fold - 1}") elif "validation" in datalist and len(datalist["validation"]) > 0: logger.info("No fold numbers provided, attempting to use a single fold based on the validation key") # update the datalist file @@ -492,6 +499,11 @@ def set_num_fold(self, num_fold: int = 5) -> AutoRunner: if num_fold <= 0: raise ValueError(f"num_fold is expected to be an integer greater than zero. Now it gets {num_fold}") + if num_fold > self.max_fold + 1: + # Auto3DSeg allows no validation set, so the maximum fold number is max_fold + 1 + raise ValueError( + f"num_fold is greater than the maximum fold number {self.max_fold} in {self.datalist_filename}." + ) self.num_fold = num_fold return self From 049744842adf24803324a269c7573661cbe06882 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 15 Apr 2024 07:45:31 +0100 Subject: [PATCH 081/136] Auto3DSeg algo_template hash update (#7642) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index dd0ccada3d..4611ff958e 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "b910ab8") + return os.environ.get("MONAI_ALGO_HASH", "bd09a2a") @staticmethod def trace_transform() -> str | None: From 605ffe1c25a4416b3050f324a30210cebe744590 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 15 Apr 2024 10:26:42 +0100 Subject: [PATCH 082/136] Auto3DSeg algo_template hash update (#7643) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 4611ff958e..4388c1387e 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "bd09a2a") + return os.environ.get("MONAI_ALGO_HASH", "f65128f") @staticmethod def trace_transform() -> str | None: From bff4b1559a147f570fc881717eadc7b3e8a98b96 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 15 Apr 2024 21:06:59 +0800 Subject: [PATCH 083/136] Remove source code of numcodecs in the Dockerfile (#7644) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Remove source code of numcodecs. - Add check for only build from source when arm architecture. - Removed the hardcoding of “python3.10”. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- Dockerfile | 9 +++++++-- monai/data/video_dataset.py | 4 ++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/Dockerfile b/Dockerfile index 45673fd775..fc97227351 100644 --- a/Dockerfile +++ b/Dockerfile @@ -17,8 +17,13 @@ FROM ${PYTORCH_IMAGE} LABEL maintainer="monai.contact@gmail.com" # TODO: remark for issue [revise the dockerfile](https://github.com/zarr-developers/numcodecs/issues/431) -WORKDIR /opt -RUN git clone --recursive https://github.com/zarr-developers/numcodecs.git && pip wheel numcodecs +RUN if [[ $(uname -m) =~ "aarch64" ]]; then \ + cd /opt && \ + git clone --branch v0.12.1 --recursive https://github.com/zarr-developers/numcodecs && \ + pip wheel numcodecs && \ + rm -r /opt/*.whl && \ + rm -rf /opt/numcodecs; \ + fi WORKDIR /opt/monai diff --git a/monai/data/video_dataset.py b/monai/data/video_dataset.py index ed5c37d777..9ff23ebeff 100644 --- a/monai/data/video_dataset.py +++ b/monai/data/video_dataset.py @@ -173,15 +173,15 @@ def get_available_codecs() -> dict[str, str]: all_codecs = {"mp4v": ".mp4", "X264": ".avi", "H264": ".mp4", "MP42": ".mp4", "MJPG": ".mjpeg", "DIVX": ".avi"} codecs = {} with SuppressStderr(): - writer = cv2.VideoWriter() with tempfile.TemporaryDirectory() as tmp_dir: for codec, ext in all_codecs.items(): + writer = cv2.VideoWriter() fname = os.path.join(tmp_dir, f"test{ext}") fourcc = cv2.VideoWriter_fourcc(*codec) # type: ignore[attr-defined] noviderr = writer.open(fname, fourcc, 1, (10, 10)) if noviderr: codecs[codec] = ext - writer.release() + writer.release() return codecs def get_num_frames(self) -> int: From 16d4e2fcdbc6a7be5bf9df02fc12f34eee5f26b9 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 16 Apr 2024 22:05:35 +0800 Subject: [PATCH 084/136] Remove memory_pool_limit in trt config (#7647) Fixes https://github.com/Project-MONAI/model-zoo/issues/568. ### Description Remove memory_pool_limit in trt config ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/monai/networks/utils.py b/monai/networks/utils.py index 4e6699f16b..7e3d2e2170 100644 --- a/monai/networks/utils.py +++ b/monai/networks/utils.py @@ -840,7 +840,6 @@ def _onnx_trt_compile( # set up the conversion configuration config = builder.create_builder_config() - config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 31) config.add_optimization_profile(profile) if precision == "fp16": config.set_flag(trt.BuilderFlag.FP16) From d6e6b249b1acfd043d2f599be3bef526127131ff Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 19 Apr 2024 11:16:03 +0800 Subject: [PATCH 085/136] Add version requirement for mlflow (#7659) Fixes #7658. ### Description set mlflow>=1.28.0,<=2.11.3 https://github.com/mlflow/mlflow/pull/11740 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/requirements.txt | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- tests/test_clip_intensity_percentiles.py | 3 ++- tests/test_clip_intensity_percentilesd.py | 3 ++- 5 files changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index e5bedf8552..5acc437391 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -22,7 +22,7 @@ sphinx-autodoc-typehints==1.11.1 pandas einops transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 -mlflow>=1.28.0 +mlflow>=1.28.0, <=2.11.3 clearml>=1.10.0rc0 tensorboardX imagecodecs; platform_system == "Linux" or platform_system == "Darwin" diff --git a/requirements-dev.txt b/requirements-dev.txt index 2855b56ad5..bfa8961ecf 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -34,7 +34,7 @@ pandas requests einops transformers>=4.36.0 -mlflow>=1.28.0 +mlflow>=1.28.0, <=2.11.3 clearml>=1.10.0rc0 matplotlib!=3.5.0 tensorboardX diff --git a/setup.cfg b/setup.cfg index d7cb703d25..c8ae1630f7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -66,7 +66,7 @@ all = pandas einops transformers<4.22; python_version <= '3.10' - mlflow>=1.28.0 + mlflow>=1.28.0, <=2.11.3 clearml>=1.10.0rc0 matplotlib tensorboardX @@ -125,7 +125,7 @@ einops = transformers = transformers<4.22; python_version <= '3.10' mlflow = - mlflow + mlflow>=1.28.0, <=2.11.3 matplotlib = matplotlib clearml = diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index 82471e25ce..f584a0bb41 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -18,6 +18,7 @@ from monai.transforms import ClipIntensityPercentiles from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -29,7 +30,7 @@ def test_hard_clipping_two_sided(self, p): im = p(self.imt) result = hard_clipper(im) lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = clip(convert_to_tensor(im), lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 2b49383182..97c08f9f4e 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -19,6 +19,7 @@ from monai.transforms import ClipIntensityPercentilesd from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -31,7 +32,7 @@ def test_hard_clipping_two_sided(self, p): im = p(self.imt) result = hard_clipper({key: im}) lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = clip(convert_to_tensor(im), lower, upper) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) From ffd4454b576abd4eaae30b364f41c213e30dca4c Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Fri, 19 Apr 2024 05:28:48 +0100 Subject: [PATCH 086/136] Auto3DSeg algo_template hash update (#7674) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 4388c1387e..de4bfdc2bb 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "f65128f") + return os.environ.get("MONAI_ALGO_HASH", "def5f26") @staticmethod def trace_transform() -> str | None: From 224c47a5741f0e0b454b6ffda5a65bf598af6a7c Mon Sep 17 00:00:00 2001 From: Han Wang Date: Fri, 19 Apr 2024 07:11:26 +0100 Subject: [PATCH 087/136] Fixed four test issues within test code. (#7662) ### Description This PR fixed the three issues within the test code that could cause problems during CICD. Three issues: tests/test_pad_collation.py line 120: supposed to be assertEqual, but not assertTure? or it will always be true. tests/test_to_numpy.py line 74: Same as above. tests/test_auto3dseg.py line 370: typo? two same asserts next to each other. tests/test_compose.py line 719, 725, 727: Should be assertEqual instead of assertTrue ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Han Wang Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_auto3dseg.py | 1 - tests/test_compose.py | 6 +++--- tests/test_pad_collation.py | 2 +- tests/test_to_numpy.py | 2 +- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/tests/test_auto3dseg.py b/tests/test_auto3dseg.py index e2097679e2..6be33bf6ca 100644 --- a/tests/test_auto3dseg.py +++ b/tests/test_auto3dseg.py @@ -367,7 +367,6 @@ def test_filename_case_analyzer(self): for batch_data in self.dataset: d = transform(batch_data[0]) assert DataStatsKeys.BY_CASE_IMAGE_PATH in d - assert DataStatsKeys.BY_CASE_IMAGE_PATH in d def test_filename_case_analyzer_image_only(self): analyzer_image = FilenameStats("image", DataStatsKeys.BY_CASE_IMAGE_PATH) diff --git a/tests/test_compose.py b/tests/test_compose.py index 309767833b..3c53ac4a22 100644 --- a/tests/test_compose.py +++ b/tests/test_compose.py @@ -716,15 +716,15 @@ def test_compose_execute_equivalence_with_flags(self, flags, data, pipeline): for k in actual.keys(): self.assertEqual(expected[k], actual[k]) else: - self.assertTrue(expected, actual) + self.assertEqual(expected, actual) p = deepcopy(pipeline) actual = execute_compose(execute_compose(data, p, start=0, end=cutoff, **flags), p, start=cutoff, **flags) if isinstance(actual, dict): for k in actual.keys(): - self.assertTrue(expected[k], actual[k]) + self.assertEqual(expected[k], actual[k]) else: - self.assertTrue(expected, actual) + self.assertEqual(expected, actual) class TestComposeCallableInput(unittest.TestCase): diff --git a/tests/test_pad_collation.py b/tests/test_pad_collation.py index ee6e001438..17f49611df 100644 --- a/tests/test_pad_collation.py +++ b/tests/test_pad_collation.py @@ -117,7 +117,7 @@ def test_pad_collation(self, t_type, collate_method, transform): batch_inverse = BatchInverseTransform(dataset.transform, loader) for data in loader: output = batch_inverse(data) - self.assertTrue(output[0]["image"].shape, (1, 10, 9)) + self.assertEqual(output[0]["image"].shape, (1, 10, 9)) if __name__ == "__main__": diff --git a/tests/test_to_numpy.py b/tests/test_to_numpy.py index 8f7cf34865..f92b7c0075 100644 --- a/tests/test_to_numpy.py +++ b/tests/test_to_numpy.py @@ -71,7 +71,7 @@ def test_list_tuple(self): assert_allclose(result, np.asarray(test_data), type_test=False) test_data = ((1, 2), (3, 4)) result = ToNumpy(wrap_sequence=False)(test_data) - self.assertTrue(type(result), tuple) + self.assertIsInstance(result, tuple) assert_allclose(result, ((np.asarray(1), np.asarray(2)), (np.asarray(3), np.asarray(4)))) def test_single_value(self): From 7a6b69fc22b25fbdc747e0e6d28f85c90947cfd4 Mon Sep 17 00:00:00 2001 From: Han Wang Date: Fri, 19 Apr 2024 10:15:38 +0100 Subject: [PATCH 088/136] Adapt to use assert raises (#7670) ### Description Some test cases use try/except command, instead of catch exceptions with assertRaise. This will cause extra execution time according to the experimental tests. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Han Wang Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_decathlondataset.py | 5 +---- tests/test_mednistdataset.py | 5 +---- tests/test_monai_utils_misc.py | 9 ++++----- tests/test_tciadataset.py | 4 +--- 4 files changed, 7 insertions(+), 16 deletions(-) diff --git a/tests/test_decathlondataset.py b/tests/test_decathlondataset.py index d220cd9097..70a2a6c06c 100644 --- a/tests/test_decathlondataset.py +++ b/tests/test_decathlondataset.py @@ -80,7 +80,7 @@ def _test_dataset(dataset): self.assertDictEqual(properties["labels"], {"0": "background", "1": "Anterior", "2": "Posterior"}) shutil.rmtree(os.path.join(testing_dir, "Task04_Hippocampus")) - try: + with self.assertRaisesRegex(RuntimeError, "^Cannot find dataset directory"): DecathlonDataset( root_dir=testing_dir, task="Task04_Hippocampus", @@ -88,9 +88,6 @@ def _test_dataset(dataset): section="validation", download=False, ) - except RuntimeError as e: - print(str(e)) - self.assertTrue(str(e).startswith("Cannot find dataset directory")) if __name__ == "__main__": diff --git a/tests/test_mednistdataset.py b/tests/test_mednistdataset.py index baf3bf4f2d..1db632c144 100644 --- a/tests/test_mednistdataset.py +++ b/tests/test_mednistdataset.py @@ -65,11 +65,8 @@ def _test_dataset(dataset): self.assertEqual(data[0]["class_name"], "AbdomenCT") self.assertEqual(data[0]["label"], 0) shutil.rmtree(os.path.join(testing_dir, "MedNIST")) - try: + with self.assertRaisesRegex(RuntimeError, "^Cannot find dataset directory"): MedNISTDataset(root_dir=testing_dir, transform=transform, section="test", download=False) - except RuntimeError as e: - print(str(e)) - self.assertTrue(str(e).startswith("Cannot find dataset directory")) if __name__ == "__main__": diff --git a/tests/test_monai_utils_misc.py b/tests/test_monai_utils_misc.py index a2a4ed62f7..f4eb5d3956 100644 --- a/tests/test_monai_utils_misc.py +++ b/tests/test_monai_utils_misc.py @@ -92,12 +92,11 @@ def test_run_cmd(self): cmd2 = "-c" cmd3 = 'import sys; print("\\tThis is on stderr\\n", file=sys.stderr); sys.exit(1)' os.environ["MONAI_DEBUG"] = str(True) - try: + with self.assertRaises(RuntimeError) as cm: run_cmd([cmd1, cmd2, cmd3], check=True) - except RuntimeError as err: - self.assertIn("This is on stderr", str(err)) - self.assertNotIn("\\n", str(err)) - self.assertNotIn("\\t", str(err)) + self.assertIn("This is on stderr", str(cm.exception)) + self.assertNotIn("\\n", str(cm.exception)) + self.assertNotIn("\\t", str(cm.exception)) if __name__ == "__main__": diff --git a/tests/test_tciadataset.py b/tests/test_tciadataset.py index d996922e20..5a16bb4816 100644 --- a/tests/test_tciadataset.py +++ b/tests/test_tciadataset.py @@ -108,7 +108,7 @@ def _test_dataset(dataset): )[0] shutil.rmtree(os.path.join(testing_dir, collection)) - try: + with self.assertRaisesRegex(RuntimeError, "^Cannot find dataset directory"): TciaDataset( root_dir=testing_dir, collection=collection, @@ -117,8 +117,6 @@ def _test_dataset(dataset): download=False, val_frac=val_frac, ) - except RuntimeError as e: - self.assertTrue(str(e).startswith("Cannot find dataset directory")) if __name__ == "__main__": From 03a5fa695ad02fcb916d5495e84f8f01883806e2 Mon Sep 17 00:00:00 2001 From: Fabian Klopfer Date: Fri, 19 Apr 2024 16:21:14 +0200 Subject: [PATCH 089/136] MedicalNetPerceptualSimilarity: Add multi-channel (#7568) Fixes #7567 . ### Description MedicalNetPerceptualSimilarity: Add multi-channel support for 3Dvolumes. The current version of the code in the dev branch already largely supports that besides the following: medicalnet_* require inputs to have a single channel. This PR passes the multi-channel volume channel-wise to the networks and concatenates the resulting feature vectors. The existing code takes care of averaging over channels and spatially. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Fabian Klopfer Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/losses/perceptual.py | 56 ++++++++++++++++++++++++++++++----- tests/test_perceptual_loss.py | 34 +++++++++++++++++++-- 2 files changed, 80 insertions(+), 10 deletions(-) diff --git a/monai/losses/perceptual.py b/monai/losses/perceptual.py index fd61603b03..02493f412b 100644 --- a/monai/losses/perceptual.py +++ b/monai/losses/perceptual.py @@ -45,6 +45,7 @@ class PerceptualLoss(nn.Module): The fake 3D implementation is based on a 2.5D approach where we calculate the 2D perceptual loss on slices from all three axes and average. The full 3D approach uses a 3D network to calculate the perceptual loss. + MedicalNet networks are only compatible with 3D inputs and support channel-wise loss. Args: spatial_dims: number of spatial dimensions. @@ -62,6 +63,8 @@ class PerceptualLoss(nn.Module): pretrained_state_dict_key: if `pretrained_path` is not `None`, this argument is used to extract the expected state dict. This argument only works when ``"network_type"`` is "resnet50". Defaults to `None`. + channel_wise: if True, the loss is returned per channel. Otherwise the loss is averaged over the channels. + Defaults to ``False``. """ def __init__( @@ -74,6 +77,7 @@ def __init__( pretrained: bool = True, pretrained_path: str | None = None, pretrained_state_dict_key: str | None = None, + channel_wise: bool = False, ): super().__init__() @@ -86,6 +90,9 @@ def __init__( "Argument is_fake_3d must be set to False." ) + if channel_wise and "medicalnet_" not in network_type: + raise ValueError("Channel-wise loss is only compatible with MedicalNet networks.") + if network_type.lower() not in list(PercetualNetworkType): raise ValueError( "Unrecognised criterion entered for Adversarial Loss. Must be one in: %s" @@ -102,7 +109,9 @@ def __init__( self.spatial_dims = spatial_dims self.perceptual_function: nn.Module if spatial_dims == 3 and is_fake_3d is False: - self.perceptual_function = MedicalNetPerceptualSimilarity(net=network_type, verbose=False) + self.perceptual_function = MedicalNetPerceptualSimilarity( + net=network_type, verbose=False, channel_wise=channel_wise + ) elif "radimagenet_" in network_type: self.perceptual_function = RadImageNetPerceptualSimilarity(net=network_type, verbose=False) elif network_type == "resnet50": @@ -172,7 +181,12 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: # 2D and real 3D cases loss = self.perceptual_function(input, target) - return torch.mean(loss) + if self.channel_wise: + loss = torch.mean(loss.squeeze(), dim=0) + else: + loss = torch.mean(loss) + + return loss class MedicalNetPerceptualSimilarity(nn.Module): @@ -185,14 +199,20 @@ class MedicalNetPerceptualSimilarity(nn.Module): net: {``"medicalnet_resnet10_23datasets"``, ``"medicalnet_resnet50_23datasets"``} Specifies the network architecture to use. Defaults to ``"medicalnet_resnet10_23datasets"``. verbose: if false, mute messages from torch Hub load function. + channel_wise: if True, the loss is returned per channel. Otherwise the loss is averaged over the channels. + Defaults to ``False``. """ - def __init__(self, net: str = "medicalnet_resnet10_23datasets", verbose: bool = False) -> None: + def __init__( + self, net: str = "medicalnet_resnet10_23datasets", verbose: bool = False, channel_wise: bool = False + ) -> None: super().__init__() torch.hub._validate_not_a_forked_repo = lambda a, b, c: True self.model = torch.hub.load("warvito/MedicalNet-models", model=net, verbose=verbose) self.eval() + self.channel_wise = channel_wise + for param in self.parameters(): param.requires_grad = False @@ -206,20 +226,42 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: Args: input: 3D input tensor with shape BCDHW. target: 3D target tensor with shape BCDHW. + """ input = medicalnet_intensity_normalisation(input) target = medicalnet_intensity_normalisation(target) # Get model outputs - outs_input = self.model.forward(input) - outs_target = self.model.forward(target) + feats_per_ch = 0 + for ch_idx in range(input.shape[1]): + input_channel = input[:, ch_idx, ...].unsqueeze(1) + target_channel = target[:, ch_idx, ...].unsqueeze(1) + + if ch_idx == 0: + outs_input = self.model.forward(input_channel) + outs_target = self.model.forward(target_channel) + feats_per_ch = outs_input.shape[1] + else: + outs_input = torch.cat([outs_input, self.model.forward(input_channel)], dim=1) + outs_target = torch.cat([outs_target, self.model.forward(target_channel)], dim=1) # Normalise through the channels feats_input = normalize_tensor(outs_input) feats_target = normalize_tensor(outs_target) - results: torch.Tensor = (feats_input - feats_target) ** 2 - results = spatial_average_3d(results.sum(dim=1, keepdim=True), keepdim=True) + feats_diff: torch.Tensor = (feats_input - feats_target) ** 2 + if self.channel_wise: + results = torch.zeros( + feats_diff.shape[0], input.shape[1], feats_diff.shape[2], feats_diff.shape[3], feats_diff.shape[4] + ) + for i in range(input.shape[1]): + l_idx = i * feats_per_ch + r_idx = (i + 1) * feats_per_ch + results[:, i, ...] = feats_diff[:, l_idx : i + r_idx, ...].sum(dim=1) + else: + results = feats_diff.sum(dim=1, keepdim=True) + + results = spatial_average_3d(results, keepdim=True) return results diff --git a/tests/test_perceptual_loss.py b/tests/test_perceptual_loss.py index 02232e6f8d..548613e2ae 100644 --- a/tests/test_perceptual_loss.py +++ b/tests/test_perceptual_loss.py @@ -18,7 +18,7 @@ from monai.losses import PerceptualLoss from monai.utils import optional_import -from tests.utils import SkipIfBeforePyTorchVersion, skip_if_downloading_fails, skip_if_quick +from tests.utils import SkipIfBeforePyTorchVersion, assert_allclose, skip_if_downloading_fails, skip_if_quick _, has_torchvision = optional_import("torchvision") TEST_CASES = [ @@ -40,11 +40,31 @@ (2, 1, 64, 64, 64), (2, 1, 64, 64, 64), ], + [ + {"spatial_dims": 3, "network_type": "medicalnet_resnet10_23datasets", "is_fake_3d": False}, + (2, 6, 64, 64, 64), + (2, 6, 64, 64, 64), + ], + [ + { + "spatial_dims": 3, + "network_type": "medicalnet_resnet10_23datasets", + "is_fake_3d": False, + "channel_wise": True, + }, + (2, 6, 64, 64, 64), + (2, 6, 64, 64, 64), + ], [ {"spatial_dims": 3, "network_type": "medicalnet_resnet50_23datasets", "is_fake_3d": False}, (2, 1, 64, 64, 64), (2, 1, 64, 64, 64), ], + [ + {"spatial_dims": 3, "network_type": "medicalnet_resnet50_23datasets", "is_fake_3d": False}, + (2, 6, 64, 64, 64), + (2, 6, 64, 64, 64), + ], [ {"spatial_dims": 3, "network_type": "resnet50", "is_fake_3d": True, "pretrained": True, "fake_3d_ratio": 0.2}, (2, 1, 64, 64, 64), @@ -63,7 +83,11 @@ def test_shape(self, input_param, input_shape, target_shape): with skip_if_downloading_fails(): loss = PerceptualLoss(**input_param) result = loss(torch.randn(input_shape), torch.randn(target_shape)) - self.assertEqual(result.shape, torch.Size([])) + + if "channel_wise" in input_param.keys() and input_param["channel_wise"]: + self.assertEqual(result.shape, torch.Size([input_shape[1]])) + else: + self.assertEqual(result.shape, torch.Size([])) @parameterized.expand(TEST_CASES) def test_identical_input(self, input_param, input_shape, target_shape): @@ -71,7 +95,11 @@ def test_identical_input(self, input_param, input_shape, target_shape): loss = PerceptualLoss(**input_param) tensor = torch.randn(input_shape) result = loss(tensor, tensor) - self.assertEqual(result, torch.Tensor([0.0])) + + if "channel_wise" in input_param.keys() and input_param["channel_wise"]: + assert_allclose(result, torch.Tensor([0.0] * input_shape[1])) + else: + self.assertEqual(result, torch.Tensor([0.0])) def test_different_shape(self): with skip_if_downloading_fails(): From c6bf8e9c52ccecbdfe289151772940df4356f358 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 22 Apr 2024 16:07:18 +0800 Subject: [PATCH 090/136] Workaround for B909 in flake8-bugbear (#7691) Workaround for #7690 ### Description Updated with flake8-bugbear, causing the B909 error. A workaround fix was made for the flake8-bugbear version because the 24.4.21 update contained some false positives. Fore more information, see https://github.com/PyCQA/flake8-bugbear/issues/467 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- requirements-dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 08b75b1e97..b71a2bac43 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -58,7 +58,7 @@ repos: name: Unused noqa additional_dependencies: - flake8>=3.8.1 - - flake8-bugbear + - flake8-bugbear<=24.2.6 - flake8-comprehensions - pep8-naming exclude: | diff --git a/requirements-dev.txt b/requirements-dev.txt index bfa8961ecf..b207b56b19 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -11,7 +11,7 @@ scikit-image>=0.19.0 tqdm>=4.47.0 lmdb flake8>=3.8.1 -flake8-bugbear +flake8-bugbear<=24.2.6 # https://github.com/Project-MONAI/MONAI/issues/7690 flake8-comprehensions mccabe pep8-naming From 178ebc8cc9352f3d7f67286a3856de67d355576f Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 22 Apr 2024 22:49:41 +0800 Subject: [PATCH 091/136] Fix AttributeError in 'PerceptualLoss' (#7693) Fixes #7692 ### Description Fix AttributeError in 'PerceptualLoss' ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- monai/losses/perceptual.py | 1 + tests/test_clip_intensity_percentiles.py | 16 ++++++++-------- tests/test_clip_intensity_percentilesd.py | 19 +++++++++---------- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/monai/losses/perceptual.py b/monai/losses/perceptual.py index 02493f412b..a8ae90993a 100644 --- a/monai/losses/perceptual.py +++ b/monai/losses/perceptual.py @@ -125,6 +125,7 @@ def __init__( self.perceptual_function = LPIPS(pretrained=pretrained, net=network_type, verbose=False) self.is_fake_3d = is_fake_3d self.fake_3d_ratio = fake_3d_ratio + self.channel_wise = channel_wise def _calculate_axis_loss(self, input: torch.Tensor, target: torch.Tensor, spatial_axis: int) -> torch.Tensor: """ diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index f584a0bb41..01820e7115 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -31,7 +31,7 @@ def test_hard_clipping_two_sided(self, p): result = hard_clipper(im) lower, upper = percentile(im, (5, 95)) expected = clip(convert_to_tensor(im), lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_high(self, p): @@ -40,7 +40,7 @@ def test_hard_clipping_one_sided_high(self, p): result = hard_clipper(im) lower, upper = percentile(im, (0, 95)) expected = clip(im, lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_low(self, p): @@ -49,7 +49,7 @@ def test_hard_clipping_one_sided_low(self, p): result = hard_clipper(im) lower, upper = percentile(im, (5, 100)) expected = clip(im, lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_two_sided(self, p): @@ -89,7 +89,7 @@ def test_channel_wise(self, p): for i, c in enumerate(im): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) - assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) def test_ill_sharpness_factor(self): with self.assertRaises(ValueError): @@ -121,7 +121,7 @@ def test_hard_clipping_two_sided(self, p): result = hard_clipper(im) lower, upper = percentile(im, (5, 95)) expected = clip(im, lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_high(self, p): @@ -130,7 +130,7 @@ def test_hard_clipping_one_sided_high(self, p): result = hard_clipper(im) lower, upper = percentile(im, (0, 95)) expected = clip(im, lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_low(self, p): @@ -139,7 +139,7 @@ def test_hard_clipping_one_sided_low(self, p): result = hard_clipper(im) lower, upper = percentile(im, (5, 100)) expected = clip(im, lower, upper) - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_two_sided(self, p): @@ -179,7 +179,7 @@ def test_channel_wise(self, p): for i, c in enumerate(im): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) - assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) if __name__ == "__main__": diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 97c08f9f4e..fa727b6adb 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -19,7 +19,6 @@ from monai.transforms import ClipIntensityPercentilesd from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile -from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -32,8 +31,8 @@ def test_hard_clipping_two_sided(self, p): im = p(self.imt) result = hard_clipper({key: im}) lower, upper = percentile(im, (5, 95)) - expected = clip(convert_to_tensor(im), lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + expected = clip(im, lower, upper) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_high(self, p): @@ -43,7 +42,7 @@ def test_hard_clipping_one_sided_high(self, p): result = hard_clipper({key: im}) lower, upper = percentile(im, (0, 95)) expected = clip(im, lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_low(self, p): @@ -53,7 +52,7 @@ def test_hard_clipping_one_sided_low(self, p): result = hard_clipper({key: im}) lower, upper = percentile(im, (5, 100)) expected = clip(im, lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_two_sided(self, p): @@ -97,7 +96,7 @@ def test_channel_wise(self, p): for i, c in enumerate(im): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) - assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-4, atol=0) def test_ill_sharpness_factor(self): key = "img" @@ -135,7 +134,7 @@ def test_hard_clipping_two_sided(self, p): result = hard_clipper({key: im}) lower, upper = percentile(im, (5, 95)) expected = clip(im, lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_high(self, p): @@ -145,7 +144,7 @@ def test_hard_clipping_one_sided_high(self, p): result = hard_clipper({key: im}) lower, upper = percentile(im, (0, 95)) expected = clip(im, lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_one_sided_low(self, p): @@ -155,7 +154,7 @@ def test_hard_clipping_one_sided_low(self, p): result = hard_clipper({key: im}) lower, upper = percentile(im, (5, 100)) expected = clip(im, lower, upper) - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_two_sided(self, p): @@ -199,7 +198,7 @@ def test_channel_wise(self, p): for i, c in enumerate(im): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) - assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-7, atol=0) + assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-4, atol=0) if __name__ == "__main__": From ac9b1862f540bebdc3ff45a1655a0667602a0bce Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 23 Apr 2024 09:28:13 +0800 Subject: [PATCH 092/136] Always convert input to C-order in distance_transform_edt (#7675) Fixes #7660 `indices_` may not always be a shallow copy for F-order input as it constantly converts to C-order https://github.com/Project-MONAI/MONAI/blob/ffd4454b576abd4eaae30b364f41c213e30dca4c/monai/transforms/utils.py#L2209 https://github.com/Project-MONAI/MONAI/blob/ffd4454b576abd4eaae30b364f41c213e30dca4c/monai/utils/type_conversion.py#L262 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/transforms/utils.py | 2 +- tests/test_clip_intensity_percentiles.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 14f35e1219..560dbac346 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -2190,7 +2190,7 @@ def distance_transform_edt( if return_distances: dtype = torch.float64 if float64_distances else torch.float32 if distances is None: - distances = torch.zeros_like(img, dtype=dtype) # type: ignore + distances = torch.zeros_like(img, memory_format=torch.contiguous_format, dtype=dtype) # type: ignore else: if not isinstance(distances, torch.Tensor) and distances.device != img.device: raise TypeError("distances must be a torch.Tensor on the same device as img") diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index 01820e7115..af157446f6 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -18,7 +18,6 @@ from monai.transforms import ClipIntensityPercentiles from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile -from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose @@ -30,7 +29,7 @@ def test_hard_clipping_two_sided(self, p): im = p(self.imt) result = hard_clipper(im) lower, upper = percentile(im, (5, 95)) - expected = clip(convert_to_tensor(im), lower, upper) + expected = clip(im, lower, upper) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) From a59676ff1be98190fbd8ab1d804aa822cc40ca14 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Tue, 23 Apr 2024 04:47:12 +0100 Subject: [PATCH 093/136] Auto3DSeg algo_template hash update (#7695) Signed-off-by: monai-bot Signed-off-by: monai-bot Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index de4bfdc2bb..2b4b30fe48 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "def5f26") + return os.environ.get("MONAI_ALGO_HASH", "4403f94") @staticmethod def trace_transform() -> str | None: From ec6aa33a4a528d96733c380b50136c81f662bf38 Mon Sep 17 00:00:00 2001 From: Han Wang Date: Tue, 23 Apr 2024 06:15:20 +0100 Subject: [PATCH 094/136] Merge similar test components with parameterized (#7663) ### Description I noticed some test cases contain same duplicated asserts. Having multiple asserts in one test cases can cause potential issues like when the first assert fails, the test case stops and won't check the second assert. By using @parameterized.expand, this issue can be resolved and the caching also saves execution time. Added sign-offs from #7648 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Han Wang Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_affine_transform.py | 27 +-- tests/test_compute_f_beta.py | 36 ++-- tests/test_global_mutual_information_loss.py | 40 ++-- tests/test_hausdorff_loss.py | 22 +-- tests/test_median_filter.py | 21 +- tests/test_multi_scale.py | 29 +-- tests/test_optional_import.py | 27 +-- tests/test_perceptual_loss.py | 8 +- tests/test_prepare_batch_default.py | 100 +++------- tests/test_rand_affine.py | 7 +- tests/test_rand_affined.py | 9 +- tests/test_tversky_loss.py | 11 +- ...est_ultrasound_confidence_map_transform.py | 183 ++++++------------ tests/test_vit.py | 99 +++------- tests/test_vitautoenc.py | 97 +++------- 15 files changed, 243 insertions(+), 473 deletions(-) diff --git a/tests/test_affine_transform.py b/tests/test_affine_transform.py index 6ea036bce8..11464070e0 100644 --- a/tests/test_affine_transform.py +++ b/tests/test_affine_transform.py @@ -133,28 +133,17 @@ def test_to_norm_affine_ill(self, affine, src_size, dst_size, align_corners): class TestAffineTransform(unittest.TestCase): - def test_affine_shift(self): - affine = torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]]) - image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]]) - out = AffineTransform(align_corners=False)(image, affine) - out = out.detach().cpu().numpy() - expected = [[[[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]]] - np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol) - - def test_affine_shift_1(self): - affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, -1.0]]) - image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]]) - out = AffineTransform(align_corners=False)(image, affine) - out = out.detach().cpu().numpy() - expected = [[[[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]]] - np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol) - - def test_affine_shift_2(self): - affine = torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0]]) + @parameterized.expand( + [ + (torch.as_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, -1.0]]), [[[[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]]]), + (torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, -1.0]]), [[[[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]]]), + (torch.as_tensor([[1.0, 0.0, -1.0], [0.0, 1.0, 0.0]]), [[[[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]]]), + ] + ) + def test_affine_transforms(self, affine, expected): image = torch.as_tensor([[[[4.0, 1.0, 3.0, 2.0], [7.0, 6.0, 8.0, 5.0], [3.0, 5.0, 3.0, 6.0]]]]) out = AffineTransform(align_corners=False)(image, affine) out = out.detach().cpu().numpy() - expected = [[[[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]]] np.testing.assert_allclose(out, expected, atol=1e-5, rtol=_rtol) def test_zoom(self): diff --git a/tests/test_compute_f_beta.py b/tests/test_compute_f_beta.py index 85997577cf..43ebb6a6d5 100644 --- a/tests/test_compute_f_beta.py +++ b/tests/test_compute_f_beta.py @@ -15,6 +15,7 @@ import numpy as np import torch +from parameterized import parameterized from monai.metrics import FBetaScore from tests.utils import assert_allclose @@ -33,26 +34,21 @@ def test_expecting_success_and_device(self): assert_allclose(result, torch.Tensor([0.714286]), atol=1e-6, rtol=1e-6) np.testing.assert_equal(result.device, y_pred.device) - def test_expecting_success2(self): - metric = FBetaScore(beta=0.5) - metric( - y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]) - ) - assert_allclose(metric.aggregate()[0], torch.Tensor([0.609756]), atol=1e-6, rtol=1e-6) - - def test_expecting_success3(self): - metric = FBetaScore(beta=2) - metric( - y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]) - ) - assert_allclose(metric.aggregate()[0], torch.Tensor([0.862069]), atol=1e-6, rtol=1e-6) - - def test_denominator_is_zero(self): - metric = FBetaScore(beta=2) - metric( - y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=torch.Tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]) - ) - assert_allclose(metric.aggregate()[0], torch.Tensor([0.0]), atol=1e-6, rtol=1e-6) + @parameterized.expand( + [ + (0.5, torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]), torch.Tensor([0.609756])), # success_beta_0_5 + (2, torch.Tensor([[1, 0, 1], [0, 1, 0], [1, 0, 1]]), torch.Tensor([0.862069])), # success_beta_2 + ( + 2, # success_beta_2, denominator_zero + torch.Tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]), + torch.Tensor([0.0]), + ), + ] + ) + def test_success_and_zero(self, beta, y, expected_score): + metric = FBetaScore(beta=beta) + metric(y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), y=y) + assert_allclose(metric.aggregate()[0], expected_score, atol=1e-6, rtol=1e-6) def test_number_of_dimensions_less_than_2_should_raise_error(self): metric = FBetaScore() diff --git a/tests/test_global_mutual_information_loss.py b/tests/test_global_mutual_information_loss.py index 36a1978c93..22f5e88431 100644 --- a/tests/test_global_mutual_information_loss.py +++ b/tests/test_global_mutual_information_loss.py @@ -15,6 +15,7 @@ import numpy as np import torch +from parameterized import parameterized from monai import transforms from monai.losses.image_dissimilarity import GlobalMutualInformationLoss @@ -116,24 +117,33 @@ def transformation(translate_params=(0.0, 0.0, 0.0), rotate_params=(0.0, 0.0, 0. class TestGlobalMutualInformationLossIll(unittest.TestCase): - def test_ill_shape(self): + @parameterized.expand( + [ + (torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float)), # mismatched_simple_dims + ( + torch.ones((1, 3, 3), dtype=torch.float), + torch.ones((1, 3), dtype=torch.float), + ), # mismatched_advanced_dims + ] + ) + def test_ill_shape(self, input1, input2): loss = GlobalMutualInformationLoss() - with self.assertRaisesRegex(ValueError, ""): - loss.forward(torch.ones((1, 2), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device)) - with self.assertRaisesRegex(ValueError, ""): - loss.forward(torch.ones((1, 3, 3), dtype=torch.float), torch.ones((1, 3), dtype=torch.float, device=device)) - - def test_ill_opts(self): + with self.assertRaises(ValueError): + loss.forward(input1, input2) + + @parameterized.expand( + [ + (0, "mean", ValueError, ""), # num_bins_zero + (-1, "mean", ValueError, ""), # num_bins_negative + (64, "unknown", ValueError, ""), # reduction_unknown + (64, None, ValueError, ""), # reduction_none + ] + ) + def test_ill_opts(self, num_bins, reduction, expected_exception, expected_message): pred = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device) target = torch.ones((1, 3, 3, 3, 3), dtype=torch.float, device=device) - with self.assertRaisesRegex(ValueError, ""): - GlobalMutualInformationLoss(num_bins=0)(pred, target) - with self.assertRaisesRegex(ValueError, ""): - GlobalMutualInformationLoss(num_bins=-1)(pred, target) - with self.assertRaisesRegex(ValueError, ""): - GlobalMutualInformationLoss(reduction="unknown")(pred, target) - with self.assertRaisesRegex(ValueError, ""): - GlobalMutualInformationLoss(reduction=None)(pred, target) + with self.assertRaisesRegex(expected_exception, expected_message): + GlobalMutualInformationLoss(num_bins=num_bins, reduction=reduction)(pred, target) if __name__ == "__main__": diff --git a/tests/test_hausdorff_loss.py b/tests/test_hausdorff_loss.py index f279d45b14..f2211008c2 100644 --- a/tests/test_hausdorff_loss.py +++ b/tests/test_hausdorff_loss.py @@ -219,17 +219,12 @@ def test_ill_opts(self): with self.assertRaisesRegex(ValueError, ""): HausdorffDTLoss(reduction=None)(chn_input, chn_target) - def test_input_warnings(self): + @parameterized.expand([(False, False, False), (False, True, False), (False, False, True)]) + def test_input_warnings(self, include_background, softmax, to_onehot_y): chn_input = torch.ones((1, 1, 1, 3)) chn_target = torch.ones((1, 1, 1, 3)) with self.assertWarns(Warning): - loss = HausdorffDTLoss(include_background=False) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = HausdorffDTLoss(softmax=True) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = HausdorffDTLoss(to_onehot_y=True) + loss = HausdorffDTLoss(include_background=include_background, softmax=softmax, to_onehot_y=to_onehot_y) loss.forward(chn_input, chn_target) @@ -256,17 +251,12 @@ def test_ill_opts(self): with self.assertRaisesRegex(ValueError, ""): LogHausdorffDTLoss(reduction=None)(chn_input, chn_target) - def test_input_warnings(self): + @parameterized.expand([(False, False, False), (False, True, False), (False, False, True)]) + def test_input_warnings(self, include_background, softmax, to_onehot_y): chn_input = torch.ones((1, 1, 1, 3)) chn_target = torch.ones((1, 1, 1, 3)) with self.assertWarns(Warning): - loss = LogHausdorffDTLoss(include_background=False) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = LogHausdorffDTLoss(softmax=True) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = LogHausdorffDTLoss(to_onehot_y=True) + loss = LogHausdorffDTLoss(include_background=include_background, softmax=softmax, to_onehot_y=to_onehot_y) loss.forward(chn_input, chn_target) diff --git a/tests/test_median_filter.py b/tests/test_median_filter.py index 1f5e623260..516388afce 100644 --- a/tests/test_median_filter.py +++ b/tests/test_median_filter.py @@ -15,27 +15,20 @@ import numpy as np import torch +from parameterized import parameterized from monai.networks.layers import MedianFilter class MedianFilterTestCase(unittest.TestCase): + @parameterized.expand([(torch.ones(1, 1, 2, 3, 5), [1, 2, 4]), (torch.ones(1, 1, 4, 3, 4), 1)]) # 3d_big # 3d + def test_3d(self, input_tensor, radius): + filter = MedianFilter(radius).to(torch.device("cpu:0")) - def test_3d_big(self): - a = torch.ones(1, 1, 2, 3, 5) - g = MedianFilter([1, 2, 4]).to(torch.device("cpu:0")) + expected = input_tensor.numpy() + output = filter(input_tensor).cpu().numpy() - expected = a.numpy() - out = g(a).cpu().numpy() - np.testing.assert_allclose(out, expected, rtol=1e-5) - - def test_3d(self): - a = torch.ones(1, 1, 4, 3, 4) - g = MedianFilter(1).to(torch.device("cpu:0")) - - expected = a.numpy() - out = g(a).cpu().numpy() - np.testing.assert_allclose(out, expected, rtol=1e-5) + np.testing.assert_allclose(output, expected, rtol=1e-5) def test_3d_radii(self): a = torch.ones(1, 1, 4, 3, 2) diff --git a/tests/test_multi_scale.py b/tests/test_multi_scale.py index 6681f266a8..0b49087216 100644 --- a/tests/test_multi_scale.py +++ b/tests/test_multi_scale.py @@ -58,17 +58,24 @@ def test_shape(self, input_param, input_data, expected_val): result = MultiScaleLoss(**input_param).forward(**input_data) np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5) - def test_ill_opts(self): - with self.assertRaisesRegex(ValueError, ""): - MultiScaleLoss(loss=dice_loss, kernel="none") - with self.assertRaisesRegex(ValueError, ""): - MultiScaleLoss(loss=dice_loss, scales=[-1])( - torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device) - ) - with self.assertRaisesRegex(ValueError, ""): - MultiScaleLoss(loss=dice_loss, scales=[-1], reduction="none")( - torch.ones((1, 1, 3), device=device), torch.ones((1, 1, 3), device=device) - ) + @parameterized.expand( + [ + ({"loss": dice_loss, "kernel": "none"}, None, None), # kernel_none + ({"loss": dice_loss, "scales": [-1]}, torch.ones((1, 1, 3)), torch.ones((1, 1, 3))), # scales_negative + ( + {"loss": dice_loss, "scales": [-1], "reduction": "none"}, + torch.ones((1, 1, 3)), + torch.ones((1, 1, 3)), + ), # scales_negative_reduction_none + ] + ) + def test_ill_opts(self, kwargs, input, target): + if input is None and target is None: + with self.assertRaisesRegex(ValueError, ""): + MultiScaleLoss(**kwargs) + else: + with self.assertRaisesRegex(ValueError, ""): + MultiScaleLoss(**kwargs)(input, target) def test_script(self): input_param, input_data, expected_val = TEST_CASES[0] diff --git a/tests/test_optional_import.py b/tests/test_optional_import.py index e7e1c03fd0..2f640f88d0 100644 --- a/tests/test_optional_import.py +++ b/tests/test_optional_import.py @@ -13,22 +13,20 @@ import unittest +from parameterized import parameterized + from monai.utils import OptionalImportError, exact_version, optional_import class TestOptionalImport(unittest.TestCase): - def test_default(self): - my_module, flag = optional_import("not_a_module") + @parameterized.expand(["not_a_module", "torch.randint"]) + def test_default(self, import_module): + my_module, flag = optional_import(import_module) self.assertFalse(flag) with self.assertRaises(OptionalImportError): my_module.test - my_module, flag = optional_import("torch.randint") - with self.assertRaises(OptionalImportError): - self.assertFalse(flag) - print(my_module.test) - def test_import_valid(self): my_module, flag = optional_import("torch") self.assertTrue(flag) @@ -47,18 +45,9 @@ def test_import_wrong_number(self): self.assertTrue(flag) print(my_module.randint(1, 2, (1, 2))) - def test_import_good_number(self): - my_module, flag = optional_import("torch", "0") - my_module.nn - self.assertTrue(flag) - print(my_module.randint(1, 2, (1, 2))) - - my_module, flag = optional_import("torch", "0.0.0.1") - my_module.nn - self.assertTrue(flag) - print(my_module.randint(1, 2, (1, 2))) - - my_module, flag = optional_import("torch", "1.1.0") + @parameterized.expand(["0", "0.0.0.1", "1.1.0"]) + def test_import_good_number(self, version_number): + my_module, flag = optional_import("torch", version_number) my_module.nn self.assertTrue(flag) print(my_module.randint(1, 2, (1, 2))) diff --git a/tests/test_perceptual_loss.py b/tests/test_perceptual_loss.py index 548613e2ae..b8aa2e5982 100644 --- a/tests/test_perceptual_loss.py +++ b/tests/test_perceptual_loss.py @@ -113,12 +113,10 @@ def test_1d(self): with self.assertRaises(NotImplementedError): PerceptualLoss(spatial_dims=1) - def test_medicalnet_on_2d_data(self): + @parameterized.expand(["medicalnet_resnet10_23datasets", "medicalnet_resnet50_23datasets"]) + def test_medicalnet_on_2d_data(self, network_type): with self.assertRaises(ValueError): - PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet10_23datasets") - - with self.assertRaises(ValueError): - PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet50_23datasets") + PerceptualLoss(spatial_dims=2, network_type=network_type) if __name__ == "__main__": diff --git a/tests/test_prepare_batch_default.py b/tests/test_prepare_batch_default.py index d5a5fbf57e..9aa498866f 100644 --- a/tests/test_prepare_batch_default.py +++ b/tests/test_prepare_batch_default.py @@ -14,6 +14,7 @@ import unittest import torch +from parameterized import parameterized from monai.engines import PrepareBatchDefault, SupervisedEvaluator from tests.utils import assert_allclose @@ -27,85 +28,48 @@ def forward(self, x: torch.Tensor): class TestPrepareBatchDefault(unittest.TestCase): - def test_dict_content(self): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - dataloader = [ - { - "image": torch.tensor([1, 2]), - "label": torch.tensor([3, 4]), - "extra1": torch.tensor([5, 6]), - "extra2": 16, - "extra3": "test", - } + @parameterized.expand( + [ + ( + [ + { + "image": torch.tensor([1, 2]), + "label": torch.tensor([3, 4]), + "extra1": torch.tensor([5, 6]), + "extra2": 16, + "extra3": "test", + } + ], + TestNet(), + True, + ), # dict_content + ([torch.tensor([1, 2])], torch.nn.Identity(), True), # tensor_content + ([(torch.tensor([1, 2]), torch.tensor([3, 4]))], torch.nn.Identity(), True), # pair_content + ([], TestNet(), False), # empty_data ] - # set up engine - evaluator = SupervisedEvaluator( - device=device, - val_data_loader=dataloader, - epoch_length=1, - network=TestNet(), - non_blocking=False, - prepare_batch=PrepareBatchDefault(), - decollate=False, - mode="eval", - ) - evaluator.run() - output = evaluator.state.output - assert_allclose(output["image"], torch.tensor([1, 2], device=device)) - assert_allclose(output["label"], torch.tensor([3, 4], device=device)) - - def test_tensor_content(self): + ) + def test_prepare_batch(self, dataloader, network, should_run): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - dataloader = [torch.tensor([1, 2])] - - # set up engine evaluator = SupervisedEvaluator( device=device, val_data_loader=dataloader, - epoch_length=1, - network=torch.nn.Identity(), + epoch_length=len(dataloader) if should_run else 0, + network=network, non_blocking=False, prepare_batch=PrepareBatchDefault(), decollate=False, - mode="eval", + mode="eval" if should_run else "train", ) evaluator.run() - output = evaluator.state.output - assert_allclose(output["image"], torch.tensor([1, 2], device=device)) - self.assertTrue(output["label"] is None) - def test_pair_content(self): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - dataloader = [(torch.tensor([1, 2]), torch.tensor([3, 4]))] - - # set up engine - evaluator = SupervisedEvaluator( - device=device, - val_data_loader=dataloader, - epoch_length=1, - network=torch.nn.Identity(), - non_blocking=False, - prepare_batch=PrepareBatchDefault(), - decollate=False, - mode="eval", - ) - evaluator.run() - output = evaluator.state.output - assert_allclose(output["image"], torch.tensor([1, 2], device=device)) - assert_allclose(output["label"], torch.tensor([3, 4], device=device)) - - def test_empty_data(self): - dataloader = [] - evaluator = SupervisedEvaluator( - val_data_loader=dataloader, - device=torch.device("cpu"), - epoch_length=0, - network=TestNet(), - non_blocking=False, - prepare_batch=PrepareBatchDefault(), - decollate=False, - ) - evaluator.run() + if should_run: + output = evaluator.state.output + if isinstance(dataloader[0], dict) or isinstance(dataloader[0], tuple): + assert_allclose(output["image"], torch.tensor([1, 2], device=device)) + assert_allclose(output["label"], torch.tensor([3, 4], device=device)) + else: + assert_allclose(output["image"], torch.tensor([1, 2], device=device)) + self.assertTrue(output["label"] is None) if __name__ == "__main__": diff --git a/tests/test_rand_affine.py b/tests/test_rand_affine.py index 23e3fd148c..2c827b7426 100644 --- a/tests/test_rand_affine.py +++ b/tests/test_rand_affine.py @@ -152,11 +152,10 @@ def test_rand_affine(self, input_param, input_data, expected_val): self.assertTrue(g._cached_grid is not None) assert_allclose(result, expected_val, rtol=_rtol, atol=1e-4, type_test="tensor") - def test_ill_cache(self): + @parameterized.expand([(None,), ((1, 1, -1),)]) + def test_ill_cache(self, spatial_size): with self.assertWarns(UserWarning): - RandAffine(cache_grid=True) - with self.assertWarns(UserWarning): - RandAffine(cache_grid=True, spatial_size=(1, 1, -1)) + RandAffine(cache_grid=True, spatial_size=spatial_size) @parameterized.expand(TEST_CASES_SKIPPED_CONSISTENCY) def test_skipped_transform_consistency(self, im, in_dtype): diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py index 32fde8dc0f..950058a9e9 100644 --- a/tests/test_rand_affined.py +++ b/tests/test_rand_affined.py @@ -272,13 +272,10 @@ def test_rand_affined(self, input_param, input_data, expected_val, track_meta): self.assertEqual(len(v.applied_operations), 0) self.assertTupleEqual(v.shape, input_data[k].shape) - def test_ill_cache(self): + @parameterized.expand([(None,), ((2, -1),)]) # spatial size is None # spatial size is dynamic + def test_ill_cache(self, spatial_size): with self.assertWarns(UserWarning): - # spatial size is None - RandAffined(device=device, spatial_size=None, prob=1.0, cache_grid=True, keys=("img", "seg")) - with self.assertWarns(UserWarning): - # spatial size is dynamic - RandAffined(device=device, spatial_size=(2, -1), prob=1.0, cache_grid=True, keys=("img", "seg")) + RandAffined(device=device, spatial_size=spatial_size, prob=1.0, cache_grid=True, keys=("img", "seg")) if __name__ == "__main__": diff --git a/tests/test_tversky_loss.py b/tests/test_tversky_loss.py index efe1f2cdf3..0365503ea2 100644 --- a/tests/test_tversky_loss.py +++ b/tests/test_tversky_loss.py @@ -165,17 +165,12 @@ def test_ill_shape(self): with self.assertRaisesRegex(ValueError, ""): TverskyLoss(reduction=None)(chn_input, chn_target) - def test_input_warnings(self): + @parameterized.expand([(False, False, False), (False, True, False), (False, False, True)]) + def test_input_warnings(self, include_background, softmax, to_onehot_y): chn_input = torch.ones((1, 1, 3)) chn_target = torch.ones((1, 1, 3)) with self.assertWarns(Warning): - loss = TverskyLoss(include_background=False) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = TverskyLoss(softmax=True) - loss.forward(chn_input, chn_target) - with self.assertWarns(Warning): - loss = TverskyLoss(to_onehot_y=True) + loss = TverskyLoss(include_background=include_background, softmax=softmax, to_onehot_y=to_onehot_y) loss.forward(chn_input, chn_target) def test_script(self): diff --git a/tests/test_ultrasound_confidence_map_transform.py b/tests/test_ultrasound_confidence_map_transform.py index f672961700..63ce7d58e4 100644 --- a/tests/test_ultrasound_confidence_map_transform.py +++ b/tests/test_ultrasound_confidence_map_transform.py @@ -15,6 +15,7 @@ import numpy as np import torch +from parameterized import parameterized from monai.transforms import UltrasoundConfidenceMapTransform from tests.utils import assert_allclose @@ -535,162 +536,92 @@ def test_parameters(self): with self.assertRaises(ValueError): UltrasoundConfidenceMapTransform(sink_mode="unknown") - def test_rgb(self): + @parameterized.expand( + [("all", SINK_ALL_OUTPUT), ("mid", SINK_MID_OUTPUT), ("min", SINK_MIN_OUTPUT), ("mask", SINK_MASK_OUTPUT, True)] + ) + def test_ultrasound_confidence_map_transform(self, sink_mode, expected_output, use_mask=False): # RGB image input_img_rgb = np.expand_dims(np.repeat(self.input_img_np, 3, axis=0), axis=0) input_img_rgb_torch = torch.from_numpy(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="all") - result_torch = transform(input_img_rgb_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_ALL_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_ALL_OUTPUT, rtol=1e-4, atol=1e-4) + transform = UltrasoundConfidenceMapTransform(sink_mode=sink_mode) - transform = UltrasoundConfidenceMapTransform(sink_mode="mid") - result_torch = transform(input_img_rgb_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MID_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MID_OUTPUT, rtol=1e-4, atol=1e-4) + if use_mask: + result_torch = transform(input_img_rgb_torch, self.input_mask_torch) + result_np = transform(input_img_rgb, self.input_mask_np) + else: + result_torch = transform(input_img_rgb_torch) + result_np = transform(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="min") - result_torch = transform(input_img_rgb_torch) self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MIN_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) + assert_allclose(result_torch, torch.tensor(expected_output), rtol=1e-4, atol=1e-4) self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MIN_OUTPUT, rtol=1e-4, atol=1e-4) + assert_allclose(result_np, expected_output, rtol=1e-4, atol=1e-4) - transform = UltrasoundConfidenceMapTransform(sink_mode="mask") - result_torch = transform(input_img_rgb_torch, self.input_mask_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MASK_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb, self.input_mask_np) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MASK_OUTPUT, rtol=1e-4, atol=1e-4) - - def test_multi_channel_2d(self): - # 2D multi-channel image + @parameterized.expand( + [ + ("all", SINK_ALL_OUTPUT), + ("mid", SINK_MID_OUTPUT), + ("min", SINK_MIN_OUTPUT), + ("mask", SINK_MASK_OUTPUT, True), # Adding a flag for mask cases + ] + ) + def test_multi_channel_2d(self, sink_mode, expected_output, use_mask=False): input_img_rgb = np.expand_dims(np.repeat(self.input_img_np, 17, axis=0), axis=0) input_img_rgb_torch = torch.from_numpy(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="all") - result_torch = transform(input_img_rgb_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_ALL_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_ALL_OUTPUT, rtol=1e-4, atol=1e-4) - - transform = UltrasoundConfidenceMapTransform(sink_mode="mid") - result_torch = transform(input_img_rgb_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MID_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MID_OUTPUT, rtol=1e-4, atol=1e-4) + transform = UltrasoundConfidenceMapTransform(sink_mode=sink_mode) - transform = UltrasoundConfidenceMapTransform(sink_mode="min") - result_torch = transform(input_img_rgb_torch) - self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MIN_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb) - self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MIN_OUTPUT, rtol=1e-4, atol=1e-4) + if use_mask: + result_torch = transform(input_img_rgb_torch, self.input_mask_torch) + result_np = transform(input_img_rgb, self.input_mask_np) + else: + result_torch = transform(input_img_rgb_torch) + result_np = transform(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="mask") - result_torch = transform(input_img_rgb_torch, self.input_mask_torch) self.assertIsInstance(result_torch, torch.Tensor) - assert_allclose(result_torch, torch.tensor(SINK_MASK_OUTPUT), rtol=1e-4, atol=1e-4) - result_np = transform(input_img_rgb, self.input_mask_np) + assert_allclose(result_torch, torch.tensor(expected_output), rtol=1e-4, atol=1e-4) self.assertIsInstance(result_np, np.ndarray) - assert_allclose(result_np, SINK_MASK_OUTPUT, rtol=1e-4, atol=1e-4) + assert_allclose(result_np, expected_output, rtol=1e-4, atol=1e-4) - def test_non_one_first_dim(self): - # Image without first dimension as 1 + @parameterized.expand([("all",), ("mid",), ("min",), ("mask",)]) + def test_non_one_first_dim(self, sink_mode): + transform = UltrasoundConfidenceMapTransform(sink_mode=sink_mode) input_img_rgb = np.repeat(self.input_img_np, 3, axis=0) input_img_rgb_torch = torch.from_numpy(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="all") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb) - - transform = UltrasoundConfidenceMapTransform(sink_mode="mid") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb) - - transform = UltrasoundConfidenceMapTransform(sink_mode="min") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb) - - transform = UltrasoundConfidenceMapTransform(sink_mode="mask") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch, self.input_mask_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb, self.input_mask_np) - - def test_no_first_dim(self): - # Image without first dimension + if sink_mode == "mask": + with self.assertRaises(ValueError): + transform(input_img_rgb_torch, self.input_mask_torch) + with self.assertRaises(ValueError): + transform(input_img_rgb, self.input_mask_np) + else: + with self.assertRaises(ValueError): + transform(input_img_rgb_torch) + with self.assertRaises(ValueError): + transform(input_img_rgb) + + @parameterized.expand([("all",), ("mid",), ("min",), ("mask",)]) + def test_no_first_dim(self, sink_mode): input_img_rgb = self.input_img_np[0] input_img_rgb_torch = torch.from_numpy(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="all") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb) + transform = UltrasoundConfidenceMapTransform(sink_mode=sink_mode) - transform = UltrasoundConfidenceMapTransform(sink_mode="mid") with self.assertRaises(ValueError): transform(input_img_rgb_torch) with self.assertRaises(ValueError): transform(input_img_rgb) - transform = UltrasoundConfidenceMapTransform(sink_mode="min") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb) - - transform = UltrasoundConfidenceMapTransform(sink_mode="mask") - with self.assertRaises(ValueError): - transform(input_img_rgb_torch, self.input_mask_torch) - with self.assertRaises(ValueError): - transform(input_img_rgb, self.input_mask_np) - - def test_sink_all(self): - transform = UltrasoundConfidenceMapTransform(sink_mode="all") - - # This should not raise an exception for torch tensor - result_torch = transform(self.input_img_torch) - self.assertIsInstance(result_torch, torch.Tensor) - - # This should not raise an exception for numpy array - result_np = transform(self.input_img_np) - self.assertIsInstance(result_np, np.ndarray) - - def test_sink_mid(self): - transform = UltrasoundConfidenceMapTransform(sink_mode="mid") - - # This should not raise an exception for torch tensor - result_torch = transform(self.input_img_torch) - self.assertIsInstance(result_torch, torch.Tensor) - - # This should not raise an exception for numpy array - result_np = transform(self.input_img_np) - self.assertIsInstance(result_np, np.ndarray) + if sink_mode == "mask": + with self.assertRaises(ValueError): + transform(input_img_rgb_torch, self.input_mask_torch) + with self.assertRaises(ValueError): + transform(input_img_rgb, self.input_mask_np) - def test_sink_min(self): - transform = UltrasoundConfidenceMapTransform(sink_mode="min") + @parameterized.expand([("all",), ("mid",), ("min",)]) + def test_sink_mode(self, mode): + transform = UltrasoundConfidenceMapTransform(sink_mode=mode) # This should not raise an exception for torch tensor result_torch = transform(self.input_img_torch) diff --git a/tests/test_vit.py b/tests/test_vit.py index a84883cba0..d27c10f95e 100644 --- a/tests/test_vit.py +++ b/tests/test_vit.py @@ -69,75 +69,40 @@ def test_shape(self, input_param, input_shape, expected_shape): result, _ = net(torch.randn(input_shape)) self.assertEqual(result.shape, expected_shape) - def test_ill_arg(self): + @parameterized.expand( + [ + (1, (128, 128, 128), (16, 16, 16), 128, 3072, 12, 12, "conv", False, 5.0), + (1, (32, 32, 32), (64, 64, 64), 512, 3072, 12, 8, "perceptron", False, 0.3), + (1, (96, 96, 96), (8, 8, 8), 512, 3072, 12, 14, "conv", False, 0.3), + (1, (97, 97, 97), (4, 4, 4), 768, 3072, 12, 8, "perceptron", True, 0.3), + (4, (96, 96, 96), (16, 16, 16), 768, 3072, 12, 12, "perc", False, 0.3), + ] + ) + def test_ill_arg( + self, + in_channels, + img_size, + patch_size, + hidden_size, + mlp_dim, + num_layers, + num_heads, + pos_embed, + classification, + dropout_rate, + ): with self.assertRaises(ValueError): ViT( - in_channels=1, - img_size=(128, 128, 128), - patch_size=(16, 16, 16), - hidden_size=128, - mlp_dim=3072, - num_layers=12, - num_heads=12, - pos_embed="conv", - classification=False, - dropout_rate=5.0, - ) - - with self.assertRaises(ValueError): - ViT( - in_channels=1, - img_size=(32, 32, 32), - patch_size=(64, 64, 64), - hidden_size=512, - mlp_dim=3072, - num_layers=12, - num_heads=8, - pos_embed="perceptron", - classification=False, - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViT( - in_channels=1, - img_size=(96, 96, 96), - patch_size=(8, 8, 8), - hidden_size=512, - mlp_dim=3072, - num_layers=12, - num_heads=14, - pos_embed="conv", - classification=False, - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViT( - in_channels=1, - img_size=(97, 97, 97), - patch_size=(4, 4, 4), - hidden_size=768, - mlp_dim=3072, - num_layers=12, - num_heads=8, - pos_embed="perceptron", - classification=True, - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViT( - in_channels=4, - img_size=(96, 96, 96), - patch_size=(16, 16, 16), - hidden_size=768, - mlp_dim=3072, - num_layers=12, - num_heads=12, - pos_embed="perc", - classification=False, - dropout_rate=0.3, + in_channels=in_channels, + img_size=img_size, + patch_size=patch_size, + hidden_size=hidden_size, + mlp_dim=mlp_dim, + num_layers=num_layers, + num_heads=num_heads, + pos_embed=pos_embed, + classification=classification, + dropout_rate=dropout_rate, ) @parameterized.expand(TEST_CASE_Vit) diff --git a/tests/test_vitautoenc.py b/tests/test_vitautoenc.py index cc3d493bb3..c68c583a0e 100644 --- a/tests/test_vitautoenc.py +++ b/tests/test_vitautoenc.py @@ -82,83 +82,30 @@ def test_shape(self, input_param, input_shape, expected_shape): result, _ = net(torch.randn(input_shape)) self.assertEqual(result.shape, expected_shape) - def test_ill_arg(self): + @parameterized.expand( + [ + (1, (32, 32, 32), (64, 64, 64), 512, 3072, 12, 8, "perceptron", 0.3), # img_size_too_large_for_patch_size + (1, (96, 96, 96), (8, 8, 8), 512, 3072, 12, 14, "conv", 0.3), # num_heads_out_of_bound + (1, (97, 97, 97), (4, 4, 4), 768, 3072, 12, 8, "perceptron", 0.3), # img_size_not_divisible_by_patch_size + (4, (96, 96, 96), (16, 16, 16), 768, 3072, 12, 12, "perc", 0.3), # invalid_pos_embed + (4, (96, 96, 96), (9, 9, 9), 768, 3072, 12, 12, "perc", 0.3), # patch_size_not_divisible + # Add more test cases as needed + ] + ) + def test_ill_arg( + self, in_channels, img_size, patch_size, hidden_size, mlp_dim, num_layers, num_heads, pos_embed, dropout_rate + ): with self.assertRaises(ValueError): ViTAutoEnc( - in_channels=1, - img_size=(128, 128, 128), - patch_size=(16, 16, 16), - hidden_size=128, - mlp_dim=3072, - num_layers=12, - num_heads=12, - pos_embed="conv", - dropout_rate=5.0, - ) - - with self.assertRaises(ValueError): - ViTAutoEnc( - in_channels=1, - img_size=(32, 32, 32), - patch_size=(64, 64, 64), - hidden_size=512, - mlp_dim=3072, - num_layers=12, - num_heads=8, - pos_embed="perceptron", - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViTAutoEnc( - in_channels=1, - img_size=(96, 96, 96), - patch_size=(8, 8, 8), - hidden_size=512, - mlp_dim=3072, - num_layers=12, - num_heads=14, - pos_embed="conv", - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViTAutoEnc( - in_channels=1, - img_size=(97, 97, 97), - patch_size=(4, 4, 4), - hidden_size=768, - mlp_dim=3072, - num_layers=12, - num_heads=8, - pos_embed="perceptron", - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViTAutoEnc( - in_channels=4, - img_size=(96, 96, 96), - patch_size=(16, 16, 16), - hidden_size=768, - mlp_dim=3072, - num_layers=12, - num_heads=12, - pos_embed="perc", - dropout_rate=0.3, - ) - - with self.assertRaises(ValueError): - ViTAutoEnc( - in_channels=4, - img_size=(96, 96, 96), - patch_size=(9, 9, 9), - hidden_size=768, - mlp_dim=3072, - num_layers=12, - num_heads=12, - pos_embed="perc", - dropout_rate=0.3, + in_channels=in_channels, + img_size=img_size, + patch_size=patch_size, + hidden_size=hidden_size, + mlp_dim=mlp_dim, + num_layers=num_layers, + num_heads=num_heads, + pos_embed=pos_embed, + dropout_rate=dropout_rate, ) From dc58e5c2b7997e8cefdef4698e342937fccea956 Mon Sep 17 00:00:00 2001 From: Konstantin Sukharev <50718389+k-sukharev@users.noreply.github.com> Date: Tue, 23 Apr 2024 11:30:21 +0500 Subject: [PATCH 095/136] Add ResNet backbones for FlexibleUNet (#7571) Fixes #7570. ### Description Add ResNet backbones (with option to use pretrained Med3D weights) for FlexibleUNet. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Konstantin Sukharev Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/source/networks.rst | 5 + monai/networks/nets/__init__.py | 2 + monai/networks/nets/flexible_unet.py | 13 ++- monai/networks/nets/resnet.py | 145 ++++++++++++++++++++++++++- tests/test_flexible_unet.py | 135 +++++-------------------- tests/test_resnet.py | 39 ++++++- 6 files changed, 222 insertions(+), 117 deletions(-) diff --git a/docs/source/networks.rst b/docs/source/networks.rst index b59c8af5fc..249375dfc1 100644 --- a/docs/source/networks.rst +++ b/docs/source/networks.rst @@ -491,6 +491,11 @@ Nets .. autoclass:: ResNet :members: +`ResNetFeatures` +~~~~~~~~~~~~~~~~ +.. autoclass:: ResNetFeatures + :members: + `SENet` ~~~~~~~ .. autoclass:: SENet diff --git a/monai/networks/nets/__init__.py b/monai/networks/nets/__init__.py index 9247aaee85..de5d1adc7e 100644 --- a/monai/networks/nets/__init__.py +++ b/monai/networks/nets/__init__.py @@ -59,6 +59,8 @@ ResNet, ResNetBlock, ResNetBottleneck, + ResNetEncoder, + ResNetFeatures, get_medicalnet_pretrained_resnet_args, get_pretrained_resnet_medicalnet, resnet10, diff --git a/monai/networks/nets/flexible_unet.py b/monai/networks/nets/flexible_unet.py index ac2124b5f9..c27b0fc17b 100644 --- a/monai/networks/nets/flexible_unet.py +++ b/monai/networks/nets/flexible_unet.py @@ -24,6 +24,7 @@ from monai.networks.layers.utils import get_act_layer from monai.networks.nets import EfficientNetEncoder from monai.networks.nets.basic_unet import UpCat +from monai.networks.nets.resnet import ResNetEncoder from monai.utils import InterpolateMode, optional_import __all__ = ["FlexibleUNet", "FlexUNet", "FLEXUNET_BACKBONE", "FlexUNetEncoderRegister"] @@ -78,6 +79,7 @@ def register_class(self, name: type[Any] | str): FLEXUNET_BACKBONE = FlexUNetEncoderRegister() FLEXUNET_BACKBONE.register_class(EfficientNetEncoder) +FLEXUNET_BACKBONE.register_class(ResNetEncoder) class UNetDecoder(nn.Module): @@ -238,7 +240,7 @@ def __init__( ) -> None: """ A flexible implement of UNet, in which the backbone/encoder can be replaced with - any efficient network. Currently the input must have a 2 or 3 spatial dimension + any efficient or residual network. Currently the input must have a 2 or 3 spatial dimension and the spatial size of each dimension must be a multiple of 32 if is_pad parameter is False. Please notice each output of backbone must be 2x downsample in spatial dimension @@ -248,10 +250,11 @@ def __init__( Args: in_channels: number of input channels. out_channels: number of output channels. - backbone: name of backbones to initialize, only support efficientnet right now, - can be from [efficientnet-b0,..., efficientnet-b8, efficientnet-l2]. - pretrained: whether to initialize pretrained ImageNet weights, only available - for spatial_dims=2 and batch norm is used, default to False. + backbone: name of backbones to initialize, only support efficientnet and resnet right now, + can be from [efficientnet-b0, ..., efficientnet-b8, efficientnet-l2, resnet10, ..., resnet200]. + pretrained: whether to initialize pretrained weights. ImageNet weights are available for efficient networks + if spatial_dims=2 and batch norm is used. MedicalNet weights are available for residual networks + if spatial_dims=3 and in_channels=1. Default to False. decoder_channels: number of output channels for all feature maps in decoder. `len(decoder_channels)` should equal to `len(encoder_channels) - 1`,default to (256, 128, 64, 32, 16). diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index 34a4b7057e..99975271da 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -21,6 +21,7 @@ import torch import torch.nn as nn +from monai.networks.blocks.encoder import BaseEncoder from monai.networks.layers.factories import Conv, Norm, Pool from monai.networks.layers.utils import get_pool_layer from monai.utils import ensure_tuple_rep @@ -45,6 +46,19 @@ "resnet200", ] + +resnet_params = { + # model_name: (block, layers, shortcut_type, bias_downsample, datasets23) + "resnet10": ("basic", [1, 1, 1, 1], "B", False, True), + "resnet18": ("basic", [2, 2, 2, 2], "A", True, True), + "resnet34": ("basic", [3, 4, 6, 3], "A", True, True), + "resnet50": ("bottleneck", [3, 4, 6, 3], "B", False, True), + "resnet101": ("bottleneck", [3, 4, 23, 3], "B", False, False), + "resnet152": ("bottleneck", [3, 8, 36, 3], "B", False, False), + "resnet200": ("bottleneck", [3, 24, 36, 3], "B", False, False), +} + + logger = logging.getLogger(__name__) @@ -335,6 +349,120 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x +class ResNetFeatures(ResNet): + + def __init__(self, model_name: str, pretrained: bool = True, spatial_dims: int = 3, in_channels: int = 1) -> None: + """Initialize resnet18 to resnet200 models as a backbone, the backbone can be used as an encoder for + segmentation and objection models. + + Compared with the class `ResNet`, the only different place is the forward function. + + Args: + model_name: name of model to initialize, can be from [resnet10, ..., resnet200]. + pretrained: whether to initialize pretrained MedicalNet weights, + only available for spatial_dims=3 and in_channels=1. + spatial_dims: number of spatial dimensions of the input image. + in_channels: number of input channels for first convolutional layer. + """ + if model_name not in resnet_params: + model_name_string = ", ".join(resnet_params.keys()) + raise ValueError(f"invalid model_name {model_name} found, must be one of {model_name_string} ") + + block, layers, shortcut_type, bias_downsample, datasets23 = resnet_params[model_name] + + super().__init__( + block=block, + layers=layers, + block_inplanes=get_inplanes(), + spatial_dims=spatial_dims, + n_input_channels=in_channels, + conv1_t_stride=2, + shortcut_type=shortcut_type, + feed_forward=False, + bias_downsample=bias_downsample, + ) + if pretrained: + if spatial_dims == 3 and in_channels == 1: + _load_state_dict(self, model_name, datasets23=datasets23) + else: + raise ValueError("Pretrained resnet models are only available for in_channels=1 and spatial_dims=3.") + + def forward(self, inputs: torch.Tensor): + """ + Args: + inputs: input should have spatially N dimensions + ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, N is defined by `dimensions`. + + Returns: + a list of torch Tensors. + """ + x = self.conv1(inputs) + x = self.bn1(x) + x = self.relu(x) + + features = [] + features.append(x) + + if not self.no_max_pool: + x = self.maxpool(x) + + x = self.layer1(x) + features.append(x) + + x = self.layer2(x) + features.append(x) + + x = self.layer3(x) + features.append(x) + + x = self.layer4(x) + features.append(x) + + return features + + +class ResNetEncoder(ResNetFeatures, BaseEncoder): + """Wrap the original resnet to an encoder for flexible-unet.""" + + backbone_names = ["resnet10", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet200"] + + @classmethod + def get_encoder_parameters(cls) -> list[dict]: + """Get the initialization parameter for resnet backbones.""" + parameter_list = [] + for backbone_name in cls.backbone_names: + parameter_list.append( + {"model_name": backbone_name, "pretrained": True, "spatial_dims": 3, "in_channels": 1} + ) + return parameter_list + + @classmethod + def num_channels_per_output(cls) -> list[tuple[int, ...]]: + """Get number of resnet backbone output feature maps channel.""" + return [ + (64, 64, 128, 256, 512), + (64, 64, 128, 256, 512), + (64, 64, 128, 256, 512), + (64, 256, 512, 1024, 2048), + (64, 256, 512, 1024, 2048), + (64, 256, 512, 1024, 2048), + (64, 256, 512, 1024, 2048), + ] + + @classmethod + def num_outputs(cls) -> list[int]: + """Get number of resnet backbone output feature maps. + + Since every backbone contains the same 5 output feature maps, the number list should be `[5] * 7`. + """ + return [5] * 7 + + @classmethod + def get_encoder_names(cls) -> list[str]: + """Get names of resnet backbones.""" + return cls.backbone_names + + def _resnet( arch: str, block: type[ResNetBlock | ResNetBottleneck], @@ -477,7 +605,7 @@ def resnet200(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> def get_pretrained_resnet_medicalnet(resnet_depth: int, device: str = "cpu", datasets23: bool = True): """ - Donwlad resnet pretrained weights from https://huggingface.co/TencentMedicalNet + Download resnet pretrained weights from https://huggingface.co/TencentMedicalNet Args: resnet_depth: depth of the pretrained model. Supported values are 10, 18, 34, 50, 101, 152 and 200 @@ -533,7 +661,7 @@ def get_pretrained_resnet_medicalnet(resnet_depth: int, device: str = "cpu", dat def get_medicalnet_pretrained_resnet_args(resnet_depth: int): """ Return correct shortcut_type and bias_downsample - for pretrained MedicalNet weights according to resnet depth + for pretrained MedicalNet weights according to resnet depth. """ # After testing # False: 10, 50, 101, 152, 200 @@ -541,3 +669,16 @@ def get_medicalnet_pretrained_resnet_args(resnet_depth: int): bias_downsample = -1 if resnet_depth in [18, 34] else 0 # 18, 10, 34 shortcut_type = "A" if resnet_depth in [18, 34] else "B" return bias_downsample, shortcut_type + + +def _load_state_dict(model: nn.Module, model_name: str, datasets23: bool = True) -> None: + search_res = re.search(r"resnet(\d+)", model_name) + if search_res: + resnet_depth = int(search_res.group(1)) + datasets23 = model_name.endswith("_23datasets") + else: + raise ValueError("model_name argument should contain resnet depth. Example: resnet18 or resnet18_23datasets.") + + model_state_dict = get_pretrained_resnet_medicalnet(resnet_depth, device="cpu", datasets23=datasets23) + model_state_dict = {key.replace("module.", ""): value for key, value in model_state_dict.items()} + model.load_state_dict(model_state_dict) diff --git a/tests/test_flexible_unet.py b/tests/test_flexible_unet.py index 404855c9a8..42baa28b71 100644 --- a/tests/test_flexible_unet.py +++ b/tests/test_flexible_unet.py @@ -23,12 +23,11 @@ EfficientNetBNFeatures, FlexibleUNet, FlexUNetEncoderRegister, - ResNet, - ResNetBlock, - ResNetBottleneck, + ResNetEncoder, + ResNetFeatures, ) from monai.utils import optional_import -from tests.utils import skip_if_downloading_fails, skip_if_quick +from tests.utils import SkipIfNoModule, skip_if_downloading_fails, skip_if_quick torchvision, has_torchvision = optional_import("torchvision") PIL, has_pil = optional_import("PIL") @@ -59,101 +58,6 @@ def get_encoder_names(cls): return ["encoder_wrong_channels", "encoder_no_param1", "encoder_no_param2", "encoder_no_param3"] -class ResNetEncoder(ResNet, BaseEncoder): - backbone_names = ["resnet10", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet200"] - output_feature_channels = [(64, 128, 256, 512)] * 3 + [(256, 512, 1024, 2048)] * 4 - parameter_layers = [ - [1, 1, 1, 1], - [2, 2, 2, 2], - [3, 4, 6, 3], - [3, 4, 6, 3], - [3, 4, 23, 3], - [3, 8, 36, 3], - [3, 24, 36, 3], - ] - - def __init__(self, in_channels, pretrained, **kargs): - super().__init__(**kargs, n_input_channels=in_channels) - if pretrained: - # Author of paper zipped the state_dict on googledrive, - # so would need to download, unzip and read (2.8gb file for a ~150mb state dict). - # Would like to load dict from url but need somewhere to save the state dicts. - raise NotImplementedError( - "Currently not implemented. You need to manually download weights provided by the paper's author" - " and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet" - ) - - @staticmethod - def get_inplanes(): - return [64, 128, 256, 512] - - @classmethod - def get_encoder_parameters(cls) -> list[dict]: - """ - Get parameter list to initialize encoder networks. - Each parameter dict must have `spatial_dims`, `in_channels` - and `pretrained` parameters. - """ - parameter_list = [] - res_type: type[ResNetBlock] | type[ResNetBottleneck] - for backbone in range(len(cls.backbone_names)): - if backbone < 3: - res_type = ResNetBlock - else: - res_type = ResNetBottleneck - parameter_list.append( - { - "block": res_type, - "layers": cls.parameter_layers[backbone], - "block_inplanes": ResNetEncoder.get_inplanes(), - "spatial_dims": 2, - "in_channels": 3, - "pretrained": False, - } - ) - return parameter_list - - @classmethod - def num_channels_per_output(cls): - """ - Get number of output features' channel. - """ - return cls.output_feature_channels - - @classmethod - def num_outputs(cls): - """ - Get number of output feature. - """ - return [4] * 7 - - @classmethod - def get_encoder_names(cls): - """ - Get the name string of backbones which will be used to initialize flexible unet. - """ - return cls.backbone_names - - def forward(self, x: torch.Tensor): - feature_list = [] - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - if not self.no_max_pool: - x = self.maxpool(x) - x = self.layer1(x) - feature_list.append(x) - x = self.layer2(x) - feature_list.append(x) - x = self.layer3(x) - feature_list.append(x) - x = self.layer4(x) - feature_list.append(x) - - return feature_list - - -FLEXUNET_BACKBONE.register_class(ResNetEncoder) FLEXUNET_BACKBONE.register_class(DummyEncoder) @@ -204,9 +108,7 @@ def make_shape_cases( def make_error_case(): - error_dummy_backbones = DummyEncoder.get_encoder_names() - error_resnet_backbones = ResNetEncoder.get_encoder_names() - error_backbones = error_dummy_backbones + error_resnet_backbones + error_backbones = DummyEncoder.get_encoder_names() error_param_list = [] for backbone in error_backbones: error_param_list.append( @@ -232,7 +134,7 @@ def make_error_case(): norm="instance", ) CASES_3D = make_shape_cases( - models=[SEL_MODELS[0]], + models=[SEL_MODELS[0], SEL_MODELS[2]], spatial_dims=[3], batches=[1], pretrained=[False], @@ -345,6 +247,7 @@ def make_error_case(): "spatial_dims": 2, "norm": ("batch", {"eps": 1e-3, "momentum": 0.01}), }, + EfficientNetBNFeatures, { "in_channels": 3, "num_classes": 10, @@ -354,7 +257,20 @@ def make_error_case(): "norm": ("batch", {"eps": 1e-3, "momentum": 0.01}), }, ["_conv_stem.weight"], - ) + ), + ( + { + "in_channels": 1, + "out_channels": 10, + "backbone": SEL_MODELS[2], + "pretrained": True, + "spatial_dims": 3, + "norm": ("batch", {"eps": 1e-3, "momentum": 0.01}), + }, + ResNetFeatures, + {"model_name": SEL_MODELS[2], "pretrained": True, "spatial_dims": 3, "in_channels": 1}, + ["conv1.weight"], + ), ] CASE_ERRORS = make_error_case() @@ -363,6 +279,7 @@ def make_error_case(): CASE_REGISTER_ENCODER = ["EfficientNetEncoder", "monai.networks.nets.EfficientNetEncoder"] +@SkipIfNoModule("hf_hub_download") @skip_if_quick class TestFLEXIBLEUNET(unittest.TestCase): @@ -381,19 +298,19 @@ def test_shape(self, input_param, input_shape, expected_shape): self.assertEqual(result.shape, expected_shape) @parameterized.expand(CASES_PRETRAIN) - def test_pretrain(self, input_param, efficient_input_param, weight_list): + def test_pretrain(self, flexunet_input_param, feature_extractor_class, feature_extractor_input_param, weight_list): device = "cuda" if torch.cuda.is_available() else "cpu" with skip_if_downloading_fails(): - net = FlexibleUNet(**input_param).to(device) + net = FlexibleUNet(**flexunet_input_param).to(device) with skip_if_downloading_fails(): - eff_net = EfficientNetBNFeatures(**efficient_input_param).to(device) + feature_extractor_net = feature_extractor_class(**feature_extractor_input_param).to(device) for weight_name in weight_list: - if weight_name in net.encoder.state_dict() and weight_name in eff_net.state_dict(): + if weight_name in net.encoder.state_dict() and weight_name in feature_extractor_net.state_dict(): net_weight = net.encoder.state_dict()[weight_name] - download_weight = eff_net.state_dict()[weight_name] + download_weight = feature_extractor_net.state_dict()[weight_name] weight_diff = torch.abs(net_weight - download_weight) diff_sum = torch.sum(weight_diff) # check if a weight in weight_list equals to the downloaded weight. diff --git a/tests/test_resnet.py b/tests/test_resnet.py index ad1aad8fc6..449edba4bf 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -24,6 +24,7 @@ from monai.networks import eval_mode from monai.networks.nets import ( ResNet, + ResNetFeatures, get_medicalnet_pretrained_resnet_args, get_pretrained_resnet_medicalnet, resnet10, @@ -36,7 +37,14 @@ ) from monai.networks.nets.resnet import ResNetBlock from monai.utils import optional_import -from tests.utils import equal_state_dict, skip_if_downloading_fails, skip_if_no_cuda, skip_if_quick, test_script_save +from tests.utils import ( + SkipIfNoModule, + equal_state_dict, + skip_if_downloading_fails, + skip_if_no_cuda, + skip_if_quick, + test_script_save, +) if TYPE_CHECKING: import torchvision @@ -191,6 +199,15 @@ ] +CASE_EXTRACT_FEATURES = [ + ( + {"model_name": "resnet10", "pretrained": True, "spatial_dims": 3, "in_channels": 1}, + [1, 1, 64, 64, 64], + ([1, 64, 32, 32, 32], [1, 64, 16, 16, 16], [1, 128, 8, 8, 8], [1, 256, 4, 4, 4], [1, 512, 2, 2, 2]), + ) +] + + class TestResNet(unittest.TestCase): def setUp(self): @@ -270,5 +287,25 @@ def test_script(self, model, input_param, input_shape, expected_shape): test_script_save(net, test_data) +@SkipIfNoModule("hf_hub_download") +class TestExtractFeatures(unittest.TestCase): + + @parameterized.expand(CASE_EXTRACT_FEATURES) + def test_shape(self, input_param, input_shape, expected_shapes): + device = "cuda" if torch.cuda.is_available() else "cpu" + + with skip_if_downloading_fails(): + net = ResNetFeatures(**input_param).to(device) + + # run inference with random tensor + with eval_mode(net): + features = net(torch.randn(input_shape).to(device)) + + # check output shape + self.assertEqual(len(features), len(expected_shapes)) + for feature, expected_shape in zip(features, expected_shapes): + self.assertEqual(feature.shape, torch.Size(expected_shape)) + + if __name__ == "__main__": unittest.main() From 1c07a176409bdb99bd06505dd59296c7ac274a92 Mon Sep 17 00:00:00 2001 From: Han Wang Date: Tue, 23 Apr 2024 15:38:28 +0200 Subject: [PATCH 096/136] Refactored test assertions that have suboptimal tests with numbers (#7671) ### Description As discussed in PR #7609, I tried to break the suboptimal test refactors to smaller pieces. Suboptimal Assert: Instead of using statements such as assertIsNone, assertIsInstance, always simply use assertTrue or assertFalse. This will decrease the code overall readability and increase the execution time as extra logic needed. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Han Wang Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_bundle_get_data.py | 2 +- tests/test_compute_regression_metrics.py | 10 ++++++---- tests/test_handler_stats.py | 16 ++++++++-------- tests/test_invertd.py | 2 +- tests/test_load_spacing_orientation.py | 4 ++-- tests/test_meta_affine.py | 4 ++-- tests/test_persistentdataset.py | 2 +- tests/test_rand_weighted_cropd.py | 2 +- tests/test_recon_net_utils.py | 2 +- tests/test_reg_loss_integration.py | 2 +- tests/test_sobel_gradient.py | 4 ++-- tests/test_sobel_gradientd.py | 4 ++-- tests/test_threadcontainer.py | 2 +- tests/test_warp.py | 2 +- 14 files changed, 30 insertions(+), 28 deletions(-) diff --git a/tests/test_bundle_get_data.py b/tests/test_bundle_get_data.py index 605b3945bb..3731752b65 100644 --- a/tests/test_bundle_get_data.py +++ b/tests/test_bundle_get_data.py @@ -53,7 +53,7 @@ def test_get_all_bundles_list(self, params): output = get_all_bundles_list(**params) self.assertTrue(isinstance(output, list)) self.assertTrue(isinstance(output[0], tuple)) - self.assertTrue(len(output[0]) == 2) + self.assertEqual(len(output[0]), 2) @parameterized.expand([TEST_CASE_1, TEST_CASE_5]) @skip_if_quick diff --git a/tests/test_compute_regression_metrics.py b/tests/test_compute_regression_metrics.py index a8b7f03e47..c407ab6ba6 100644 --- a/tests/test_compute_regression_metrics.py +++ b/tests/test_compute_regression_metrics.py @@ -70,22 +70,24 @@ def test_shape_reduction(self): mt = mt_fn(reduction="mean") mt(in_tensor, in_tensor) out_tensor = mt.aggregate() - self.assertTrue(len(out_tensor.shape) == 1) + self.assertEqual(len(out_tensor.shape), 1) mt = mt_fn(reduction="sum") mt(in_tensor, in_tensor) out_tensor = mt.aggregate() - self.assertTrue(len(out_tensor.shape) == 0) + self.assertEqual(len(out_tensor.shape), 0) mt = mt_fn(reduction="sum") # test reduction arg overriding mt(in_tensor, in_tensor) out_tensor = mt.aggregate(reduction="mean_channel") - self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch) + self.assertEqual(len(out_tensor.shape), 1) + self.assertEqual(out_tensor.shape[0], batch) mt = mt_fn(reduction="sum_channel") mt(in_tensor, in_tensor) out_tensor = mt.aggregate() - self.assertTrue(len(out_tensor.shape) == 1 and out_tensor.shape[0] == batch) + self.assertEqual(len(out_tensor.shape), 1) + self.assertEqual(out_tensor.shape[0], batch) def test_compare_numpy(self): set_determinism(seed=123) diff --git a/tests/test_handler_stats.py b/tests/test_handler_stats.py index f876cff2a3..52da5c179b 100644 --- a/tests/test_handler_stats.py +++ b/tests/test_handler_stats.py @@ -76,9 +76,9 @@ def _update_metric(engine): if has_key_word.match(line): content_count += 1 if epoch_log is True: - self.assertTrue(content_count == max_epochs) + self.assertEqual(content_count, max_epochs) else: - self.assertTrue(content_count == 2) # 2 = len([1, 2]) from event_filter + self.assertEqual(content_count, 2) # 2 = len([1, 2]) from event_filter @parameterized.expand([[True], [get_event_filter([1, 3])]]) def test_loss_print(self, iteration_log): @@ -116,9 +116,9 @@ def _train_func(engine, batch): if has_key_word.match(line): content_count += 1 if iteration_log is True: - self.assertTrue(content_count == num_iters * max_epochs) + self.assertEqual(content_count, num_iters * max_epochs) else: - self.assertTrue(content_count == 2) # 2 = len([1, 3]) from event_filter + self.assertEqual(content_count, 2) # 2 = len([1, 3]) from event_filter def test_loss_dict(self): log_stream = StringIO() @@ -150,7 +150,7 @@ def _train_func(engine, batch): for line in output_str.split("\n"): if has_key_word.match(line): content_count += 1 - self.assertTrue(content_count > 0) + self.assertGreater(content_count, 0) def test_loss_file(self): key_to_handler = "test_logging" @@ -184,7 +184,7 @@ def _train_func(engine, batch): for line in output_str.split("\n"): if has_key_word.match(line): content_count += 1 - self.assertTrue(content_count > 0) + self.assertGreater(content_count, 0) def test_exception(self): # set up engine @@ -239,7 +239,7 @@ def _update_metric(engine): for line in output_str.split("\n"): if has_key_word.match(line): content_count += 1 - self.assertTrue(content_count > 0) + self.assertGreater(content_count, 0) def test_default_logger(self): log_stream = StringIO() @@ -274,7 +274,7 @@ def _train_func(engine, batch): for line in output_str.split("\n"): if has_key_word.match(line): content_count += 1 - self.assertTrue(content_count > 0) + self.assertGreater(content_count, 0) if __name__ == "__main__": diff --git a/tests/test_invertd.py b/tests/test_invertd.py index c32a3af643..f6e8fc40e7 100644 --- a/tests/test_invertd.py +++ b/tests/test_invertd.py @@ -134,7 +134,7 @@ def test_invert(self): # 25300: 2 workers (cpu, non-macos) # 1812: 0 workers (gpu or macos) # 1821: windows torch 1.10.0 - self.assertTrue((reverted.size - n_good) < 40000, f"diff. {reverted.size - n_good}") + self.assertLess((reverted.size - n_good), 40000, f"diff. {reverted.size - n_good}") set_determinism(seed=None) diff --git a/tests/test_load_spacing_orientation.py b/tests/test_load_spacing_orientation.py index 63422761ca..cbc730e1bb 100644 --- a/tests/test_load_spacing_orientation.py +++ b/tests/test_load_spacing_orientation.py @@ -48,7 +48,7 @@ def test_load_spacingd(self, filename): ref = resample_to_output(anat, (1, 0.2, 1), order=1) t2 = time.time() print(f"time scipy: {t2 - t1}") - self.assertTrue(t2 >= t1) + self.assertGreaterEqual(t2, t1) np.testing.assert_allclose(res_dict["image"].affine, ref.affine) np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape) np.testing.assert_allclose(ref.get_fdata(), res_dict["image"][0], atol=0.05) @@ -68,7 +68,7 @@ def test_load_spacingd_rotate(self, filename): ref = resample_to_output(anat, (1, 2, 3), order=1) t2 = time.time() print(f"time scipy: {t2 - t1}") - self.assertTrue(t2 >= t1) + self.assertGreaterEqual(t2, t1) np.testing.assert_allclose(res_dict["image"].affine, ref.affine) if "anatomical" not in filename: np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape) diff --git a/tests/test_meta_affine.py b/tests/test_meta_affine.py index 95764a0c89..890734391f 100644 --- a/tests/test_meta_affine.py +++ b/tests/test_meta_affine.py @@ -160,7 +160,7 @@ def test_linear_consistent(self, xform_cls, input_dict, atol): diff = np.abs(itk.GetArrayFromImage(ref_2) - itk.GetArrayFromImage(expected)) avg_diff = np.mean(diff) - self.assertTrue(avg_diff < atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}") + self.assertLess(avg_diff, atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}") @parameterized.expand(TEST_CASES_DICT) def test_linear_consistent_dict(self, xform_cls, input_dict, atol): @@ -175,7 +175,7 @@ def test_linear_consistent_dict(self, xform_cls, input_dict, atol): diff = {k: np.abs(itk.GetArrayFromImage(ref_2[k]) - itk.GetArrayFromImage(expected[k])) for k in keys} avg_diff = {k: np.mean(diff[k]) for k in keys} for k in keys: - self.assertTrue(avg_diff[k] < atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}") + self.assertLess(avg_diff[k], atol, f"{xform_cls} avg_diff: {avg_diff}, tol: {atol}") if __name__ == "__main__": diff --git a/tests/test_persistentdataset.py b/tests/test_persistentdataset.py index b7bf2fbb11..7c4969e283 100644 --- a/tests/test_persistentdataset.py +++ b/tests/test_persistentdataset.py @@ -165,7 +165,7 @@ def test_different_transforms(self): im1 = PersistentDataset([im], Identity(), cache_dir=path, hash_transform=json_hashing)[0] im2 = PersistentDataset([im], Flip(1), cache_dir=path, hash_transform=json_hashing)[0] l2 = ((im1 - im2) ** 2).sum() ** 0.5 - self.assertTrue(l2 > 1) + self.assertGreater(l2, 1) if __name__ == "__main__": diff --git a/tests/test_rand_weighted_cropd.py b/tests/test_rand_weighted_cropd.py index 1524442f61..a1414df0ac 100644 --- a/tests/test_rand_weighted_cropd.py +++ b/tests/test_rand_weighted_cropd.py @@ -154,7 +154,7 @@ def test_rand_weighted_cropd(self, _, init_params, input_data, expected_shape, e crop = RandWeightedCropd(**init_params) crop.set_random_state(10) result = crop(input_data) - self.assertTrue(len(result) == init_params["num_samples"]) + self.assertEqual(len(result), init_params["num_samples"]) _len = len(tuple(input_data.keys())) self.assertTupleEqual(tuple(result[0].keys())[:_len], tuple(input_data.keys())) diff --git a/tests/test_recon_net_utils.py b/tests/test_recon_net_utils.py index 1815000777..48d3b59a17 100644 --- a/tests/test_recon_net_utils.py +++ b/tests/test_recon_net_utils.py @@ -64,7 +64,7 @@ def test_reshape_channel_complex(self, test_data): def test_complex_normalize(self, test_data): result, mean, std = complex_normalize(test_data) result = result * std + mean - self.assertTrue((((result - test_data) ** 2).mean() ** 0.5).item() < 1e-5) + self.assertLess((((result - test_data) ** 2).mean() ** 0.5).item(), 1e-5) @parameterized.expand(TEST_PAD) def test_pad(self, test_data): diff --git a/tests/test_reg_loss_integration.py b/tests/test_reg_loss_integration.py index e8f82eb0c2..1fb81689e6 100644 --- a/tests/test_reg_loss_integration.py +++ b/tests/test_reg_loss_integration.py @@ -99,7 +99,7 @@ def forward(self, x): # backward pass loss_val.backward() optimizer.step() - self.assertTrue(init_loss > loss_val, "loss did not decrease") + self.assertGreater(init_loss, loss_val, "loss did not decrease") if __name__ == "__main__": diff --git a/tests/test_sobel_gradient.py b/tests/test_sobel_gradient.py index 3d995a60c9..a0d7cf5a8b 100644 --- a/tests/test_sobel_gradient.py +++ b/tests/test_sobel_gradient.py @@ -164,8 +164,8 @@ def test_sobel_gradients(self, image, arguments, expected_grad): ) def test_sobel_kernels(self, arguments, expected_kernels): sobel = SobelGradients(**arguments) - self.assertTrue(sobel.kernel_diff.dtype == expected_kernels[0].dtype) - self.assertTrue(sobel.kernel_smooth.dtype == expected_kernels[0].dtype) + self.assertEqual(sobel.kernel_diff.dtype, expected_kernels[0].dtype) + self.assertEqual(sobel.kernel_smooth.dtype, expected_kernels[0].dtype) assert_allclose(sobel.kernel_diff, expected_kernels[0]) assert_allclose(sobel.kernel_smooth, expected_kernels[1]) diff --git a/tests/test_sobel_gradientd.py b/tests/test_sobel_gradientd.py index 7499a0410b..03524823a5 100644 --- a/tests/test_sobel_gradientd.py +++ b/tests/test_sobel_gradientd.py @@ -187,8 +187,8 @@ def test_sobel_gradients(self, image_dict, arguments, expected_grad): ) def test_sobel_kernels(self, arguments, expected_kernels): sobel = SobelGradientsd(**arguments) - self.assertTrue(sobel.kernel_diff.dtype == expected_kernels[0].dtype) - self.assertTrue(sobel.kernel_smooth.dtype == expected_kernels[0].dtype) + self.assertEqual(sobel.kernel_diff.dtype, expected_kernels[0].dtype) + self.assertEqual(sobel.kernel_smooth.dtype, expected_kernels[0].dtype) assert_allclose(sobel.kernel_diff, expected_kernels[0]) assert_allclose(sobel.kernel_smooth, expected_kernels[1]) diff --git a/tests/test_threadcontainer.py b/tests/test_threadcontainer.py index 9551dec703..568461748b 100644 --- a/tests/test_threadcontainer.py +++ b/tests/test_threadcontainer.py @@ -62,7 +62,7 @@ def test_container(self): self.assertTrue(con.is_alive) self.assertIsNotNone(con.status()) - self.assertTrue(len(con.status_dict) > 0) + self.assertGreater(len(con.status_dict), 0) con.join() diff --git a/tests/test_warp.py b/tests/test_warp.py index bac595224f..55f40764c3 100644 --- a/tests/test_warp.py +++ b/tests/test_warp.py @@ -124,7 +124,7 @@ def test_itk_benchmark(self): relative_diff = np.mean( np.divide(monai_result - itk_result, itk_result, out=np.zeros_like(itk_result), where=(itk_result != 0)) ) - self.assertTrue(relative_diff < 0.01) + self.assertLess(relative_diff, 0.01) @parameterized.expand(TEST_CASES, skip_on_empty=True) def test_resample(self, input_param, input_data, expected_val): From 07a78d20b30ab4abe8fd776063ab6903439df264 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Wed, 24 Apr 2024 03:31:28 +0100 Subject: [PATCH 097/136] Auto3DSeg algo_template hash update (#7700) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 2b4b30fe48..84e979abf4 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "4403f94") + return os.environ.get("MONAI_ALGO_HASH", "07acb39") @staticmethod def trace_transform() -> str | None: From c3e4457f98e80785a946892bdd6741b9fead9113 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 24 Apr 2024 17:15:58 +0800 Subject: [PATCH 098/136] Update pycln version (#7704) Fixes #7703 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .github/workflows/pythonapp-min.yml | 2 ++ .github/workflows/pythonapp.yml | 2 ++ .pre-commit-config.yaml | 2 +- 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pythonapp-min.yml b/.github/workflows/pythonapp-min.yml index bbe7579774..dffae10558 100644 --- a/.github/workflows/pythonapp-min.yml +++ b/.github/workflows/pythonapp-min.yml @@ -9,6 +9,8 @@ on: - main - releasing/* pull_request: + head_ref-ignore: + - dev concurrency: # automatically cancel the previously triggered workflows when there's a newer version diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index b7f2cfb9db..d4043585cc 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -9,6 +9,8 @@ on: - main - releasing/* pull_request: + head_ref-ignore: + - dev concurrency: # automatically cancel the previously triggered workflows when there's a newer version diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b71a2bac43..b9debaf08f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -69,7 +69,7 @@ repos: )$ - repo: https://github.com/hadialqattan/pycln - rev: v2.1.3 + rev: v2.4.0 hooks: - id: pycln args: [--config=pyproject.toml] From bfe09b8c3c9538cee577d3608330879952a54fde Mon Sep 17 00:00:00 2001 From: Han Wang Date: Wed, 24 Apr 2024 17:09:21 +0200 Subject: [PATCH 099/136] Refactored others type of subotimal asserts (#7672) ### Description As discussed in PR #7609, I tried to break the suboptimal test refactors to smaller pieces. In this PR all asserts are checking textual content or instance of a parameter. Suboptimal Assert: Instead of using statements such as assertIsNone, assertIsInstance, always simply use assertTrue or assertFalse. This will decrease the code overall readability and increase the execution time as extra logic needed. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Han Wang Signed-off-by: Ben Murray Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Ben Murray --- tests/test_bundle_ckpt_export.py | 6 +++--- tests/test_bundle_get_data.py | 17 ++++++++------- tests/test_bundle_trt_export.py | 12 +++++------ tests/test_bundle_workflow.py | 6 +++--- tests/test_component_store.py | 8 +++---- tests/test_compute_ho_ver_maps.py | 4 ++-- tests/test_compute_ho_ver_maps_d.py | 4 ++-- tests/test_concat_itemsd.py | 8 +++---- tests/test_config_parser.py | 2 +- tests/test_cucim_dict_transform.py | 16 +++++++------- tests/test_cucim_transform.py | 16 +++++++------- tests/test_detect_envelope.py | 2 +- tests/test_ensure_typed.py | 32 ++++++++++++++-------------- tests/test_flipd.py | 2 +- tests/test_freeze_layers.py | 8 +++---- tests/test_generalized_dice_loss.py | 4 ++-- tests/test_get_package_version.py | 6 +++--- tests/test_grid_patch.py | 6 +++--- tests/test_integration_bundle_run.py | 6 ++---- tests/test_inverse_collation.py | 2 +- tests/test_load_imaged.py | 2 +- tests/test_look_up_option.py | 2 +- tests/test_matshow3d.py | 2 +- tests/test_mednistdataset.py | 2 +- tests/test_meta_tensor.py | 4 ++-- tests/test_mmar_download.py | 2 +- tests/test_rand_affined.py | 2 +- tests/test_rand_bias_field.py | 2 +- tests/test_resnet.py | 2 +- tests/test_to_cupy.py | 16 +++++++------- tests/test_to_numpy.py | 12 +++++------ tests/test_torchvision_fc_model.py | 4 ++-- tests/test_traceable_transform.py | 4 ++-- 33 files changed, 111 insertions(+), 112 deletions(-) diff --git a/tests/test_bundle_ckpt_export.py b/tests/test_bundle_ckpt_export.py index 8f376a06d5..cfcadcfc4c 100644 --- a/tests/test_bundle_ckpt_export.py +++ b/tests/test_bundle_ckpt_export.py @@ -72,9 +72,9 @@ def test_export(self, key_in_ckpt, use_trace): _, metadata, extra_files = load_net_with_metadata( ts_file, more_extra_files=["inference.json", "def_args.json"] ) - self.assertTrue("schema" in metadata) - self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"])) - self.assertTrue("network_def" in json.loads(extra_files["inference.json"])) + self.assertIn("schema", metadata) + self.assertIn("meta_file", json.loads(extra_files["def_args.json"])) + self.assertIn("network_def", json.loads(extra_files["inference.json"])) @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_default_value(self, key_in_ckpt, use_trace): diff --git a/tests/test_bundle_get_data.py b/tests/test_bundle_get_data.py index 3731752b65..f84713fbe3 100644 --- a/tests/test_bundle_get_data.py +++ b/tests/test_bundle_get_data.py @@ -51,25 +51,26 @@ class TestGetBundleData(unittest.TestCase): def test_get_all_bundles_list(self, params): with skip_if_downloading_fails(): output = get_all_bundles_list(**params) - self.assertTrue(isinstance(output, list)) - self.assertTrue(isinstance(output[0], tuple)) - self.assertEqual(len(output[0]), 2) + self.assertIsInstance(output, list) + self.assertIsInstance(output[0], tuple) + self.assertTrue(len(output[0]) == 2) @parameterized.expand([TEST_CASE_1, TEST_CASE_5]) @skip_if_quick def test_get_bundle_versions(self, params): with skip_if_downloading_fails(): output = get_bundle_versions(**params) - self.assertTrue(isinstance(output, dict)) - self.assertTrue("latest_version" in output and "all_versions" in output) - self.assertTrue("0.1.0" in output["all_versions"]) + self.assertIsInstance(output, dict) + self.assertIn("latest_version", output) + self.assertIn("all_versions", output) + self.assertIn("0.1.0", output["all_versions"]) @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) @skip_if_quick def test_get_bundle_info(self, params): with skip_if_downloading_fails(): output = get_bundle_info(**params) - self.assertTrue(isinstance(output, dict)) + self.assertIsInstance(output, dict) for key in ["id", "name", "size", "download_count", "browser_download_url"]: self.assertTrue(key in output) @@ -78,7 +79,7 @@ def test_get_bundle_info(self, params): def test_get_bundle_info_monaihosting(self, params): with skip_if_downloading_fails(): output = get_bundle_info(**params) - self.assertTrue(isinstance(output, dict)) + self.assertIsInstance(output, dict) for key in ["name", "browser_download_url"]: self.assertTrue(key in output) diff --git a/tests/test_bundle_trt_export.py b/tests/test_bundle_trt_export.py index 47034852ef..833a0ca1dc 100644 --- a/tests/test_bundle_trt_export.py +++ b/tests/test_bundle_trt_export.py @@ -91,9 +91,9 @@ def test_trt_export(self, convert_precision, input_shape, dynamic_batch): _, metadata, extra_files = load_net_with_metadata( ts_file, more_extra_files=["inference.json", "def_args.json"] ) - self.assertTrue("schema" in metadata) - self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"])) - self.assertTrue("network_def" in json.loads(extra_files["inference.json"])) + self.assertIn("schema", metadata) + self.assertIn("meta_file", json.loads(extra_files["def_args.json"])) + self.assertIn("network_def", json.loads(extra_files["inference.json"])) @parameterized.expand([TEST_CASE_3, TEST_CASE_4]) @unittest.skipUnless( @@ -129,9 +129,9 @@ def test_onnx_trt_export(self, convert_precision, input_shape, dynamic_batch): _, metadata, extra_files = load_net_with_metadata( ts_file, more_extra_files=["inference.json", "def_args.json"] ) - self.assertTrue("schema" in metadata) - self.assertTrue("meta_file" in json.loads(extra_files["def_args.json"])) - self.assertTrue("network_def" in json.loads(extra_files["inference.json"])) + self.assertIn("schema", metadata) + self.assertIn("meta_file", json.loads(extra_files["def_args.json"])) + self.assertIn("network_def", json.loads(extra_files["inference.json"])) if __name__ == "__main__": diff --git a/tests/test_bundle_workflow.py b/tests/test_bundle_workflow.py index 9a276b577f..1727fcdf53 100644 --- a/tests/test_bundle_workflow.py +++ b/tests/test_bundle_workflow.py @@ -138,11 +138,11 @@ def test_train_config(self, config_file): self.assertListEqual(trainer.check_properties(), []) # test read / write the properties dataset = trainer.train_dataset - self.assertTrue(isinstance(dataset, Dataset)) + self.assertIsInstance(dataset, Dataset) inferer = trainer.train_inferer - self.assertTrue(isinstance(inferer, SimpleInferer)) + self.assertIsInstance(inferer, SimpleInferer) # test optional properties get - self.assertTrue(trainer.train_key_metric is None) + self.assertIsNone(trainer.train_key_metric) trainer.train_dataset = deepcopy(dataset) trainer.train_inferer = deepcopy(inferer) # test optional properties set diff --git a/tests/test_component_store.py b/tests/test_component_store.py index 424eceb3d1..7e7c6dd19d 100644 --- a/tests/test_component_store.py +++ b/tests/test_component_store.py @@ -48,17 +48,17 @@ def test_add2(self): self.cs.add("test_obj2", "Test object", test_obj2) self.assertEqual(len(self.cs), 2) - self.assertTrue("test_obj1" in self.cs) - self.assertTrue("test_obj2" in self.cs) + self.assertIn("test_obj1", self.cs) + self.assertIn("test_obj2", self.cs) def test_add_def(self): - self.assertFalse("test_func" in self.cs) + self.assertNotIn("test_func", self.cs) @self.cs.add_def("test_func", "Test function") def test_func(): return 123 - self.assertTrue("test_func" in self.cs) + self.assertIn("test_func", self.cs) self.assertEqual(len(self.cs), 1) self.assertEqual(list(self.cs), [("test_func", test_func)]) diff --git a/tests/test_compute_ho_ver_maps.py b/tests/test_compute_ho_ver_maps.py index bbd5230f04..6e46cf2b1e 100644 --- a/tests/test_compute_ho_ver_maps.py +++ b/tests/test_compute_ho_ver_maps.py @@ -67,8 +67,8 @@ class ComputeHoVerMapsTests(unittest.TestCase): def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask): input_image = in_type(mask) result = ComputeHoVerMaps(**arguments)(input_image) - self.assertTrue(isinstance(result, torch.Tensor)) - self.assertTrue(str(result.dtype).split(".")[1] == arguments.get("dtype", "float32")) + self.assertIsInstance(result, torch.Tensor) + self.assertEqual(str(result.dtype).split(".")[1], arguments.get("dtype", "float32")) assert_allclose(result, hv_mask, type_test="tensor") diff --git a/tests/test_compute_ho_ver_maps_d.py b/tests/test_compute_ho_ver_maps_d.py index 7b5ac0d9d7..0734e2e731 100644 --- a/tests/test_compute_ho_ver_maps_d.py +++ b/tests/test_compute_ho_ver_maps_d.py @@ -71,8 +71,8 @@ def test_horizontal_certical_maps(self, in_type, arguments, mask, hv_mask): for k in mask.keys(): input_image[k] = in_type(mask[k]) result = ComputeHoVerMapsd(keys="mask", **arguments)(input_image)[hv_key] - self.assertTrue(isinstance(result, torch.Tensor)) - self.assertTrue(str(result.dtype).split(".")[1] == arguments.get("dtype", "float32")) + self.assertIsInstance(result, torch.Tensor) + self.assertEqual(str(result.dtype).split(".")[1], arguments.get("dtype", "float32")) assert_allclose(result, hv_mask[hv_key], type_test="tensor") diff --git a/tests/test_concat_itemsd.py b/tests/test_concat_itemsd.py index 64c5d6e255..564ddf5c1f 100644 --- a/tests/test_concat_itemsd.py +++ b/tests/test_concat_itemsd.py @@ -30,7 +30,7 @@ def test_tensor_values(self): "img2": torch.tensor([[0, 1], [1, 2]], device=device), } result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data) - self.assertTrue("cat_img" in result) + self.assertIn("cat_img", result) result["cat_img"] += 1 assert_allclose(result["img1"], torch.tensor([[0, 1], [1, 2]], device=device)) assert_allclose(result["cat_img"], torch.tensor([[1, 2], [2, 3], [1, 2], [2, 3]], device=device)) @@ -42,8 +42,8 @@ def test_metatensor_values(self): "img2": MetaTensor([[0, 1], [1, 2]], device=device), } result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data) - self.assertTrue("cat_img" in result) - self.assertTrue(isinstance(result["cat_img"], MetaTensor)) + self.assertIn("cat_img", result) + self.assertIsInstance(result["cat_img"], MetaTensor) self.assertEqual(result["img1"].meta, result["cat_img"].meta) result["cat_img"] += 1 assert_allclose(result["img1"], torch.tensor([[0, 1], [1, 2]], device=device)) @@ -52,7 +52,7 @@ def test_metatensor_values(self): def test_numpy_values(self): input_data = {"img1": np.array([[0, 1], [1, 2]]), "img2": np.array([[0, 1], [1, 2]])} result = ConcatItemsd(keys=["img1", "img2"], name="cat_img")(input_data) - self.assertTrue("cat_img" in result) + self.assertIn("cat_img", result) result["cat_img"] += 1 np.testing.assert_allclose(result["img1"], np.array([[0, 1], [1, 2]])) np.testing.assert_allclose(result["cat_img"], np.array([[1, 2], [2, 3], [1, 2], [2, 3]])) diff --git a/tests/test_config_parser.py b/tests/test_config_parser.py index cc890a0522..cf1edc8f08 100644 --- a/tests/test_config_parser.py +++ b/tests/test_config_parser.py @@ -185,7 +185,7 @@ def test_function(self, config): if id in ("compute", "cls_compute"): parser[f"{id}#_mode_"] = "callable" func = parser.get_parsed_content(id=id) - self.assertTrue(id in parser.ref_resolver.resolved_content) + self.assertIn(id, parser.ref_resolver.resolved_content) if id == "error_func": with self.assertRaises(TypeError): func(1, 2) diff --git a/tests/test_cucim_dict_transform.py b/tests/test_cucim_dict_transform.py index d2dcc6aa5f..3c5703a34c 100644 --- a/tests/test_cucim_dict_transform.py +++ b/tests/test_cucim_dict_transform.py @@ -80,8 +80,8 @@ class TestCuCIMDict(unittest.TestCase): def test_tramsforms_numpy_single(self, params, input, expected): input = {"image": input} output = CuCIMd(keys="image", **params)(input)["image"] - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, np.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, np.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -98,8 +98,8 @@ def test_tramsforms_numpy_batch(self, params, input, expected): input = {"image": input[cp.newaxis, ...]} expected = expected[cp.newaxis, ...] output = CuCIMd(keys="image", **params)(input)["image"] - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, np.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, np.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -116,8 +116,8 @@ def test_tramsforms_cupy_single(self, params, input, expected): input = {"image": cp.asarray(input)} expected = cp.asarray(expected) output = CuCIMd(keys="image", **params)(input)["image"] - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, cp.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, cp.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -134,8 +134,8 @@ def test_tramsforms_cupy_batch(self, params, input, expected): input = {"image": cp.asarray(input)[cp.newaxis, ...]} expected = cp.asarray(expected)[cp.newaxis, ...] output = CuCIMd(keys="image", **params)(input)["image"] - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, cp.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, cp.ndarray) cp.testing.assert_allclose(output, expected) diff --git a/tests/test_cucim_transform.py b/tests/test_cucim_transform.py index 5f16c11589..162e16b52a 100644 --- a/tests/test_cucim_transform.py +++ b/tests/test_cucim_transform.py @@ -79,8 +79,8 @@ class TestCuCIM(unittest.TestCase): ) def test_tramsforms_numpy_single(self, params, input, expected): output = CuCIM(**params)(input) - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, np.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, np.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -97,8 +97,8 @@ def test_tramsforms_numpy_batch(self, params, input, expected): input = input[cp.newaxis, ...] expected = expected[cp.newaxis, ...] output = CuCIM(**params)(input) - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, np.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, np.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -115,8 +115,8 @@ def test_tramsforms_cupy_single(self, params, input, expected): input = cp.asarray(input) expected = cp.asarray(expected) output = CuCIM(**params)(input) - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, cp.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, cp.ndarray) cp.testing.assert_allclose(output, expected) @parameterized.expand( @@ -133,8 +133,8 @@ def test_tramsforms_cupy_batch(self, params, input, expected): input = cp.asarray(input)[cp.newaxis, ...] expected = cp.asarray(expected)[cp.newaxis, ...] output = CuCIM(**params)(input) - self.assertTrue(output.dtype == expected.dtype) - self.assertTrue(isinstance(output, cp.ndarray)) + self.assertEqual(output.dtype, expected.dtype) + self.assertIsInstance(output, cp.ndarray) cp.testing.assert_allclose(output, expected) diff --git a/tests/test_detect_envelope.py b/tests/test_detect_envelope.py index e2efefeb77..f9c2b5ac53 100644 --- a/tests/test_detect_envelope.py +++ b/tests/test_detect_envelope.py @@ -147,7 +147,7 @@ def test_value_error(self, arguments, image, method): elif method == "__call__": self.assertRaises(ValueError, DetectEnvelope(**arguments), image) else: - raise ValueError("Expected raising method invalid. Should be __init__ or __call__.") + self.fail("Expected raising method invalid. Should be __init__ or __call__.") @SkipIfModule("torch.fft") diff --git a/tests/test_ensure_typed.py b/tests/test_ensure_typed.py index 09aa1f04b5..fe543347de 100644 --- a/tests/test_ensure_typed.py +++ b/tests/test_ensure_typed.py @@ -33,8 +33,8 @@ def test_array_input(self): keys="data", data_type=dtype, dtype=np.float32 if dtype == "NUMPY" else None, device="cpu" )({"data": test_data})["data"] if dtype == "NUMPY": - self.assertTrue(result.dtype == np.float32) - self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray)) + self.assertEqual(result.dtype, np.float32) + self.assertIsInstance(result, torch.Tensor if dtype == "tensor" else np.ndarray) assert_allclose(result, test_data, type_test=False) self.assertTupleEqual(result.shape, (2, 2)) @@ -45,7 +45,7 @@ def test_single_input(self): for test_data in test_datas: for dtype in ("tensor", "numpy"): result = EnsureTyped(keys="data", data_type=dtype)({"data": test_data})["data"] - self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray)) + self.assertIsInstance(result, torch.Tensor if dtype == "tensor" else np.ndarray) if isinstance(test_data, bool): self.assertFalse(result) else: @@ -56,11 +56,11 @@ def test_string(self): for dtype in ("tensor", "numpy"): # string input result = EnsureTyped(keys="data", data_type=dtype)({"data": "test_string"})["data"] - self.assertTrue(isinstance(result, str)) + self.assertIsInstance(result, str) self.assertEqual(result, "test_string") # numpy array of string result = EnsureTyped(keys="data", data_type=dtype)({"data": np.array(["test_string"])})["data"] - self.assertTrue(isinstance(result, np.ndarray)) + self.assertIsInstance(result, np.ndarray) self.assertEqual(result[0], "test_string") def test_list_tuple(self): @@ -68,15 +68,15 @@ def test_list_tuple(self): result = EnsureTyped(keys="data", data_type=dtype, wrap_sequence=False, track_meta=True)( {"data": [[1, 2], [3, 4]]} )["data"] - self.assertTrue(isinstance(result, list)) - self.assertTrue(isinstance(result[0][1], MetaTensor if dtype == "tensor" else np.ndarray)) + self.assertIsInstance(result, list) + self.assertIsInstance(result[0][1], MetaTensor if dtype == "tensor" else np.ndarray) assert_allclose(result[1][0], torch.as_tensor(3), type_test=False) # tuple of numpy arrays result = EnsureTyped(keys="data", data_type=dtype, wrap_sequence=False)( {"data": (np.array([1, 2]), np.array([3, 4]))} )["data"] - self.assertTrue(isinstance(result, tuple)) - self.assertTrue(isinstance(result[0], torch.Tensor if dtype == "tensor" else np.ndarray)) + self.assertIsInstance(result, tuple) + self.assertIsInstance(result[0], torch.Tensor if dtype == "tensor" else np.ndarray) assert_allclose(result[1], torch.as_tensor([3, 4]), type_test=False) def test_dict(self): @@ -92,19 +92,19 @@ def test_dict(self): ) for key in ("data", "label"): result = trans[key] - self.assertTrue(isinstance(result, dict)) - self.assertTrue(isinstance(result["img"], torch.Tensor if dtype == "tensor" else np.ndarray)) - self.assertTrue(isinstance(result["meta"]["size"], torch.Tensor if dtype == "tensor" else np.ndarray)) + self.assertIsInstance(result, dict) + self.assertIsInstance(result["img"], torch.Tensor if dtype == "tensor" else np.ndarray) + self.assertIsInstance(result["meta"]["size"], torch.Tensor if dtype == "tensor" else np.ndarray) self.assertEqual(result["meta"]["path"], "temp/test") self.assertEqual(result["extra"], None) assert_allclose(result["img"], torch.as_tensor([1.0, 2.0]), type_test=False) assert_allclose(result["meta"]["size"], torch.as_tensor([1, 2, 3]), type_test=False) if dtype == "numpy": - self.assertTrue(trans["data"]["img"].dtype == np.float32) - self.assertTrue(trans["label"]["img"].dtype == np.int8) + self.assertEqual(trans["data"]["img"].dtype, np.float32) + self.assertEqual(trans["label"]["img"].dtype, np.int8) else: - self.assertTrue(trans["data"]["img"].dtype == torch.float32) - self.assertTrue(trans["label"]["img"].dtype == torch.int8) + self.assertEqual(trans["data"]["img"].dtype, torch.float32) + self.assertEqual(trans["label"]["img"].dtype, torch.int8) if __name__ == "__main__": diff --git a/tests/test_flipd.py b/tests/test_flipd.py index 277f387051..1df6d34056 100644 --- a/tests/test_flipd.py +++ b/tests/test_flipd.py @@ -78,7 +78,7 @@ def test_torch(self, spatial_axis, img: torch.Tensor, track_meta: bool, device): def test_meta_dict(self): xform = Flipd("image", [0, 1]) res = xform({"image": torch.zeros(1, 3, 4)}) - self.assertTrue(res["image"].applied_operations == res["image_transforms"]) + self.assertEqual(res["image"].applied_operations, res["image_transforms"]) if __name__ == "__main__": diff --git a/tests/test_freeze_layers.py b/tests/test_freeze_layers.py index 1bea4ed1b5..7be8e576bf 100644 --- a/tests/test_freeze_layers.py +++ b/tests/test_freeze_layers.py @@ -40,9 +40,9 @@ def test_freeze_vars(self, device): for name, param in model.named_parameters(): if "class_layer" in name: - self.assertEqual(param.requires_grad, False) + self.assertFalse(param.requires_grad) else: - self.assertEqual(param.requires_grad, True) + self.assertTrue(param.requires_grad) @parameterized.expand(TEST_CASES) def test_exclude_vars(self, device): @@ -53,9 +53,9 @@ def test_exclude_vars(self, device): for name, param in model.named_parameters(): if "class_layer" in name: - self.assertEqual(param.requires_grad, True) + self.assertTrue(param.requires_grad) else: - self.assertEqual(param.requires_grad, False) + self.assertFalse(param.requires_grad) if __name__ == "__main__": diff --git a/tests/test_generalized_dice_loss.py b/tests/test_generalized_dice_loss.py index 7499507129..5738f4a089 100644 --- a/tests/test_generalized_dice_loss.py +++ b/tests/test_generalized_dice_loss.py @@ -184,7 +184,7 @@ def test_differentiability(self): generalized_dice_loss = GeneralizedDiceLoss() loss = generalized_dice_loss(prediction, target) - self.assertNotEqual(loss.grad_fn, None) + self.assertIsNotNone(loss.grad_fn) def test_batch(self): prediction = torch.zeros(2, 3, 3, 3) @@ -194,7 +194,7 @@ def test_batch(self): generalized_dice_loss = GeneralizedDiceLoss(batch=True) loss = generalized_dice_loss(prediction, target) - self.assertNotEqual(loss.grad_fn, None) + self.assertIsNotNone(loss.grad_fn) def test_script(self): loss = GeneralizedDiceLoss() diff --git a/tests/test_get_package_version.py b/tests/test_get_package_version.py index ab9e69cd31..e9e1d8eca6 100644 --- a/tests/test_get_package_version.py +++ b/tests/test_get_package_version.py @@ -20,14 +20,14 @@ class TestGetVersion(unittest.TestCase): def test_default(self): output = get_package_version("42foobarnoexist") - self.assertTrue("UNKNOWN" in output) + self.assertIn("UNKNOWN", output) output = get_package_version("numpy") - self.assertFalse("UNKNOWN" in output) + self.assertNotIn("UNKNOWN", output) def test_msg(self): output = get_package_version("42foobarnoexist", "test") - self.assertTrue("test" in output) + self.assertIn("test", output) if __name__ == "__main__": diff --git a/tests/test_grid_patch.py b/tests/test_grid_patch.py index 4b324eda1a..56af123548 100644 --- a/tests/test_grid_patch.py +++ b/tests/test_grid_patch.py @@ -124,11 +124,11 @@ def test_grid_patch_meta(self, input_parameters, image, expected, expected_meta) self.assertTrue(output.meta["path"] == expected_meta[0]["path"]) for output_patch, expected_patch, expected_patch_meta in zip(output, expected, expected_meta): assert_allclose(output_patch, expected_patch, type_test=False) - self.assertTrue(isinstance(output_patch, MetaTensor)) - self.assertTrue(output_patch.meta["location"] == expected_patch_meta["location"]) + self.assertIsInstance(output_patch, MetaTensor) + self.assertEqual(output_patch.meta["location"], expected_patch_meta["location"]) self.assertTrue(output_patch.meta["spatial_shape"], list(output_patch.shape[1:])) if "path" in expected_meta[0]: - self.assertTrue(output_patch.meta["path"] == expected_patch_meta["path"]) + self.assertEqual(output_patch.meta["path"], expected_patch_meta["path"]) if __name__ == "__main__": diff --git a/tests/test_integration_bundle_run.py b/tests/test_integration_bundle_run.py index c2e0fb55b7..60aaef05bf 100644 --- a/tests/test_integration_bundle_run.py +++ b/tests/test_integration_bundle_run.py @@ -135,9 +135,8 @@ def test_scripts_fold(self): command_run = cmd + ["run", "training", "--config_file", config_file, "--meta_file", meta_file] completed_process = subprocess.run(command_run, check=True, capture_output=True, text=True) output = repr(completed_process.stdout).replace("\\n", "\n").replace("\\t", "\t") # Get the captured output - print(output) - self.assertTrue(expected_condition in output) + self.assertIn(expected_condition, output) command_run_workflow = cmd + [ "run_workflow", "--run_id", @@ -149,8 +148,7 @@ def test_scripts_fold(self): ] completed_process = subprocess.run(command_run_workflow, check=True, capture_output=True, text=True) output = repr(completed_process.stdout).replace("\\n", "\n").replace("\\t", "\t") # Get the captured output - print(output) - self.assertTrue(expected_condition in output) + self.assertIn(expected_condition, output) # test missing meta file self.assertIn("ERROR", command_line_tests(cmd + ["run", "training", "--config_file", config_file])) diff --git a/tests/test_inverse_collation.py b/tests/test_inverse_collation.py index f33b5c67eb..bf3972e6bd 100644 --- a/tests/test_inverse_collation.py +++ b/tests/test_inverse_collation.py @@ -133,7 +133,7 @@ def test_collation(self, _, transform, collate_fn, ndim): d = decollate_batch(item) self.assertTrue(len(d) <= self.batch_size) for b in d: - self.assertTrue(isinstance(b["image"], MetaTensor)) + self.assertIsInstance(b["image"], MetaTensor) np.testing.assert_array_equal( b["image"].applied_operations[-1]["orig_size"], b["label"].applied_operations[-1]["orig_size"] ) diff --git a/tests/test_load_imaged.py b/tests/test_load_imaged.py index 699ed70059..914240c705 100644 --- a/tests/test_load_imaged.py +++ b/tests/test_load_imaged.py @@ -190,7 +190,7 @@ def test_correct(self, input_p, expected_shape, track_meta): self.assertTrue(hasattr(r, "affine")) self.assertIsInstance(r.affine, torch.Tensor) self.assertEqual(r.meta["space"], "RAS") - self.assertTrue("qform_code" not in r.meta) + self.assertNotIn("qform_code", r.meta) else: self.assertIsInstance(r, torch.Tensor) self.assertNotIsInstance(r, MetaTensor) diff --git a/tests/test_look_up_option.py b/tests/test_look_up_option.py index d40b7eaa8c..75560b4ac4 100644 --- a/tests/test_look_up_option.py +++ b/tests/test_look_up_option.py @@ -56,7 +56,7 @@ def test_default(self): def test_str_enum(self): output = look_up_option("C", {"A", "B"}, default=None) - self.assertEqual(output, None) + self.assertIsNone(output) self.assertEqual(list(_CaseStrEnum), ["A", "B"]) self.assertEqual(_CaseStrEnum.MODE_A, "A") self.assertEqual(str(_CaseStrEnum.MODE_A), "A") diff --git a/tests/test_matshow3d.py b/tests/test_matshow3d.py index e513025e69..e54bb523e4 100644 --- a/tests/test_matshow3d.py +++ b/tests/test_matshow3d.py @@ -78,7 +78,7 @@ def test_samples(self): fig, mat = matshow3d( [im[keys] for im in ims], title=f"testing {keys}", figsize=(2, 2), frames_per_row=5, every_n=2, show=False ) - self.assertTrue(mat.dtype == np.float32) + self.assertEqual(mat.dtype, np.float32) with tempfile.TemporaryDirectory() as tempdir: tempimg = f"{tempdir}/matshow3d_patch_test.png" diff --git a/tests/test_mednistdataset.py b/tests/test_mednistdataset.py index 1db632c144..c1b21e9373 100644 --- a/tests/test_mednistdataset.py +++ b/tests/test_mednistdataset.py @@ -41,7 +41,7 @@ def _test_dataset(dataset): self.assertEqual(len(dataset), int(MEDNIST_FULL_DATASET_LENGTH * dataset.test_frac)) self.assertTrue("image" in dataset[0]) self.assertTrue("label" in dataset[0]) - self.assertTrue(isinstance(dataset[0]["image"], MetaTensor)) + self.assertIsInstance(dataset[0]["image"], MetaTensor) self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64)) with skip_if_downloading_fails(): diff --git a/tests/test_meta_tensor.py b/tests/test_meta_tensor.py index 1e0f188b63..f31a07eba4 100644 --- a/tests/test_meta_tensor.py +++ b/tests/test_meta_tensor.py @@ -222,9 +222,9 @@ def test_stack(self, device, dtype): def test_get_set_meta_fns(self): set_track_meta(False) - self.assertEqual(get_track_meta(), False) + self.assertFalse(get_track_meta()) set_track_meta(True) - self.assertEqual(get_track_meta(), True) + self.assertTrue(get_track_meta()) @parameterized.expand(TEST_DEVICES) def test_torchscript(self, device): diff --git a/tests/test_mmar_download.py b/tests/test_mmar_download.py index 6af3d09fb2..2ac73a8149 100644 --- a/tests/test_mmar_download.py +++ b/tests/test_mmar_download.py @@ -142,7 +142,7 @@ def test_load_ckpt(self, input_args, expected_name, expected_val): def test_unique(self): # model ids are unique keys = sorted(m["id"] for m in MODEL_DESC) - self.assertTrue(keys == sorted(set(keys))) + self.assertEqual(keys, sorted(set(keys))) def test_search(self): self.assertEqual(_get_val({"a": 1, "b": 2}, key="b"), 2) diff --git a/tests/test_rand_affined.py b/tests/test_rand_affined.py index 950058a9e9..eb8ebd06c5 100644 --- a/tests/test_rand_affined.py +++ b/tests/test_rand_affined.py @@ -240,7 +240,7 @@ def test_rand_affined(self, input_param, input_data, expected_val, track_meta): resampler.lazy = False if input_param.get("cache_grid", False): - self.assertTrue(g.rand_affine._cached_grid is not None) + self.assertIsNotNone(g.rand_affine._cached_grid) for key in res: if isinstance(key, str) and key.endswith("_transforms"): continue diff --git a/tests/test_rand_bias_field.py b/tests/test_rand_bias_field.py index 333a9ecba5..328f46b7ee 100644 --- a/tests/test_rand_bias_field.py +++ b/tests/test_rand_bias_field.py @@ -39,7 +39,7 @@ def test_output_shape(self, class_args, img_shape): img = p(np.random.rand(*img_shape)) output = bias_field(img) np.testing.assert_equal(output.shape, img_shape) - self.assertTrue(output.dtype in (np.float32, torch.float32)) + self.assertIn(output.dtype, (np.float32, torch.float32)) img_zero = np.zeros([*img_shape]) output_zero = bias_field(img_zero) diff --git a/tests/test_resnet.py b/tests/test_resnet.py index 449edba4bf..22890daeea 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -228,7 +228,7 @@ def test_resnet_shape(self, model, input_param, input_shape, expected_shape): if input_param.get("feed_forward", True): self.assertEqual(result.shape, expected_shape) else: - self.assertTrue(result.shape in expected_shape) + self.assertIn(result.shape, expected_shape) @parameterized.expand(PRETRAINED_TEST_CASES) @skip_if_quick diff --git a/tests/test_to_cupy.py b/tests/test_to_cupy.py index 5a1754e7c5..38400f0d3f 100644 --- a/tests/test_to_cupy.py +++ b/tests/test_to_cupy.py @@ -62,8 +62,8 @@ def test_numpy_input_dtype(self): test_data = np.rot90(test_data) self.assertFalse(test_data.flags["C_CONTIGUOUS"]) result = ToCupy(np.uint8)(test_data) - self.assertTrue(result.dtype == cp.uint8) - self.assertTrue(isinstance(result, cp.ndarray)) + self.assertEqual(result.dtype, cp.uint8) + self.assertIsInstance(result, cp.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) cp.testing.assert_allclose(result, test_data) @@ -72,8 +72,8 @@ def test_tensor_input(self): test_data = test_data.rot90() self.assertFalse(test_data.is_contiguous()) result = ToCupy()(test_data) - self.assertTrue(result.dtype == cp.float32) - self.assertTrue(isinstance(result, cp.ndarray)) + self.assertEqual(result.dtype, cp.float32) + self.assertIsInstance(result, cp.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) cp.testing.assert_allclose(result, test_data) @@ -83,8 +83,8 @@ def test_tensor_cuda_input(self): test_data = test_data.rot90() self.assertFalse(test_data.is_contiguous()) result = ToCupy()(test_data) - self.assertTrue(result.dtype == cp.float32) - self.assertTrue(isinstance(result, cp.ndarray)) + self.assertEqual(result.dtype, cp.float32) + self.assertIsInstance(result, cp.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) cp.testing.assert_allclose(result, test_data) @@ -95,8 +95,8 @@ def test_tensor_cuda_input_dtype(self): self.assertFalse(test_data.is_contiguous()) result = ToCupy(dtype="float32")(test_data) - self.assertTrue(result.dtype == cp.float32) - self.assertTrue(isinstance(result, cp.ndarray)) + self.assertEqual(result.dtype, cp.float32) + self.assertIsInstance(result, cp.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) cp.testing.assert_allclose(result, test_data) diff --git a/tests/test_to_numpy.py b/tests/test_to_numpy.py index f92b7c0075..f4e5f80a29 100644 --- a/tests/test_to_numpy.py +++ b/tests/test_to_numpy.py @@ -32,7 +32,7 @@ def test_cupy_input(self): test_data = cp.rot90(test_data) self.assertFalse(test_data.flags["C_CONTIGUOUS"]) result = ToNumpy()(test_data) - self.assertTrue(isinstance(result, np.ndarray)) + self.assertIsInstance(result, np.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) assert_allclose(result, test_data.get(), type_test=False) @@ -41,8 +41,8 @@ def test_numpy_input(self): test_data = np.rot90(test_data) self.assertFalse(test_data.flags["C_CONTIGUOUS"]) result = ToNumpy(dtype="float32")(test_data) - self.assertTrue(isinstance(result, np.ndarray)) - self.assertTrue(result.dtype == np.float32) + self.assertIsInstance(result, np.ndarray) + self.assertEqual(result.dtype, np.float32) self.assertTrue(result.flags["C_CONTIGUOUS"]) assert_allclose(result, test_data, type_test=False) @@ -51,7 +51,7 @@ def test_tensor_input(self): test_data = test_data.rot90() self.assertFalse(test_data.is_contiguous()) result = ToNumpy(dtype=torch.uint8)(test_data) - self.assertTrue(isinstance(result, np.ndarray)) + self.assertIsInstance(result, np.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) assert_allclose(result, test_data, type_test=False) @@ -61,7 +61,7 @@ def test_tensor_cuda_input(self): test_data = test_data.rot90() self.assertFalse(test_data.is_contiguous()) result = ToNumpy()(test_data) - self.assertTrue(isinstance(result, np.ndarray)) + self.assertIsInstance(result, np.ndarray) self.assertTrue(result.flags["C_CONTIGUOUS"]) assert_allclose(result, test_data, type_test=False) @@ -77,7 +77,7 @@ def test_list_tuple(self): def test_single_value(self): for test_data in [5, np.array(5), torch.tensor(5)]: result = ToNumpy(dtype=np.uint8)(test_data) - self.assertTrue(isinstance(result, np.ndarray)) + self.assertIsInstance(result, np.ndarray) assert_allclose(result, np.asarray(test_data), type_test=False) self.assertEqual(result.ndim, 0) diff --git a/tests/test_torchvision_fc_model.py b/tests/test_torchvision_fc_model.py index 322cce1161..9cc19db62c 100644 --- a/tests/test_torchvision_fc_model.py +++ b/tests/test_torchvision_fc_model.py @@ -195,8 +195,8 @@ def test_get_module(self): mod = look_up_named_module("model.1.submodule.1.submodule.1.submodule.0.conv", net) self.assertTrue(str(mod).startswith("Conv2d")) self.assertIsInstance(set_named_module(net, "model", torch.nn.Identity()).model, torch.nn.Identity) - self.assertEqual(look_up_named_module("model.1.submodule.1.submodule.1.submodule.conv", net), None) - self.assertEqual(look_up_named_module("test attribute", net), None) + self.assertIsNone(look_up_named_module("model.1.submodule.1.submodule.1.submodule.conv", net)) + self.assertIsNone(look_up_named_module("test attribute", net)) if __name__ == "__main__": diff --git a/tests/test_traceable_transform.py b/tests/test_traceable_transform.py index dd139053e3..6a499b2dd9 100644 --- a/tests/test_traceable_transform.py +++ b/tests/test_traceable_transform.py @@ -33,12 +33,12 @@ def test_default(self): expected_key = "_transforms" a = _TraceTest() for x in a.transform_info_keys(): - self.assertTrue(x in a.get_transform_info()) + self.assertIn(x, a.get_transform_info()) self.assertEqual(a.trace_key(), expected_key) data = {"image": "test"} data = a(data) # adds to the stack - self.assertTrue(isinstance(data[expected_key], list)) + self.assertIsInstance(data[expected_key], list) self.assertEqual(data[expected_key][0]["class"], "_TraceTest") data = a(data) # adds to the stack From 8c709de12010929307060628a450741e1263e8e5 Mon Sep 17 00:00:00 2001 From: Matthew Vine <32849887+MattTheCuber@users.noreply.github.com> Date: Wed, 24 Apr 2024 23:10:30 -0400 Subject: [PATCH 100/136] Fix download failing on FIPS machines (#7698) ### Description This PR fixes downloads failing on FIPS enabled machines due to insecure MD5 hashing. The two solutions are to disable MD5 hashing (SHA1 is allowed and faster), or use the `usedforsecurity=False` flag. This PR uses the second method. However, the `usedforsecurity` flag only works for Python 3.9 and later (which was accounted for). Let me know if you have a better implementation to solve this issue. The error thrown on FIPS enabled machine is: ```ValueError: [digital envelope routines: EVP_DigestInit_ex] disabled for FIPS``` ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Matthew Vine <32849887+MattTheCuber@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/utils.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/monai/apps/utils.py b/monai/apps/utils.py index db541923b5..0c998146a3 100644 --- a/monai/apps/utils.py +++ b/monai/apps/utils.py @@ -135,7 +135,12 @@ def check_hash(filepath: PathLike, val: str | None = None, hash_type: str = "md5 logger.info(f"Expected {hash_type} is None, skip {hash_type} check for file {filepath}.") return True actual_hash_func = look_up_option(hash_type.lower(), SUPPORTED_HASH_TYPES) - actual_hash = actual_hash_func() + + if sys.version_info >= (3, 9): + actual_hash = actual_hash_func(usedforsecurity=False) # allows checks on FIPS enabled machines + else: + actual_hash = actual_hash_func() + try: with open(filepath, "rb") as f: for chunk in iter(lambda: f.read(1024 * 1024), b""): From 6a130cc7f659efe66cf651369e35d4a93b2ec28a Mon Sep 17 00:00:00 2001 From: binliunls <107988372+binliunls@users.noreply.github.com> Date: Fri, 26 Apr 2024 13:18:06 +0800 Subject: [PATCH 101/136] 7713 Update TRT parameter (#7714) Fixes #7713 . ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: binliu --- monai/networks/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/monai/networks/utils.py b/monai/networks/utils.py index 7e3d2e2170..152911f443 100644 --- a/monai/networks/utils.py +++ b/monai/networks/utils.py @@ -986,6 +986,7 @@ def scale_batch_size(input_shape: Sequence[int], scale_num: int): inputs=input_placeholder, enabled_precisions=convert_precision, device=target_device, + ir="torchscript", **kwargs, ) From 4c193ead5aa16c23bee55f62cb225349df340c72 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 29 Apr 2024 14:18:00 +0800 Subject: [PATCH 102/136] Fix itk install error when python=3.8 (#7719) Fixes #7716 set python 3.9 for pythonapp.yml ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .github/workflows/pythonapp.yml | 4 ++-- tests/test_clip_intensity_percentilesd.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index d4043585cc..b8b73907d4 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -70,10 +70,10 @@ jobs: maximum-size: 16GB disk-root: "D:" - uses: actions/checkout@v4 - - name: Set up Python 3.8 + - name: Set up Python 3.9 uses: actions/setup-python@v5 with: - python-version: '3.8' + python-version: '3.9' - name: Prepare pip wheel run: | which python diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index fa727b6adb..ed4fc588cb 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -96,7 +96,7 @@ def test_channel_wise(self, p): for i, c in enumerate(im): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) - assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-4, atol=0) + assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-3, atol=0) def test_ill_sharpness_factor(self): key = "img" From 56508992c32e328667740e18f99b82a6a19f2c2a Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 29 Apr 2024 09:07:04 +0100 Subject: [PATCH 103/136] auto updates (#7723) Signed-off-by: monai-bot Signed-off-by: monai-bot Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/nets/resnet.py | 2 -- tests/test_median_filter.py | 1 + tests/test_resnet.py | 1 - 3 files changed, 1 insertion(+), 3 deletions(-) diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index 99975271da..de16cd4cca 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -46,7 +46,6 @@ "resnet200", ] - resnet_params = { # model_name: (block, layers, shortcut_type, bias_downsample, datasets23) "resnet10": ("basic", [1, 1, 1, 1], "B", False, True), @@ -58,7 +57,6 @@ "resnet200": ("bottleneck", [3, 24, 36, 3], "B", False, False), } - logger = logging.getLogger(__name__) diff --git a/tests/test_median_filter.py b/tests/test_median_filter.py index 516388afce..02fa812380 100644 --- a/tests/test_median_filter.py +++ b/tests/test_median_filter.py @@ -21,6 +21,7 @@ class MedianFilterTestCase(unittest.TestCase): + @parameterized.expand([(torch.ones(1, 1, 2, 3, 5), [1, 2, 4]), (torch.ones(1, 1, 4, 3, 4), 1)]) # 3d_big # 3d def test_3d(self, input_tensor, radius): filter = MedianFilter(radius).to(torch.device("cpu:0")) diff --git a/tests/test_resnet.py b/tests/test_resnet.py index 22890daeea..ecab133154 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -198,7 +198,6 @@ [model, *TEST_CASE_1] for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200] ] - CASE_EXTRACT_FEATURES = [ ( {"model_name": "resnet10", "pretrained": True, "spatial_dims": 3, "in_channels": 1}, From e1a69b03c86ce065db2816b696ea4a6b57d46435 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Tue, 30 Apr 2024 06:27:19 +0100 Subject: [PATCH 104/136] Auto3DSeg algo_template hash update (#7728) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/utils/misc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/utils/misc.py b/monai/utils/misc.py index 84e979abf4..ab9fe259aa 100644 --- a/monai/utils/misc.py +++ b/monai/utils/misc.py @@ -527,7 +527,7 @@ def doc_images() -> str | None: @staticmethod def algo_hash() -> str | None: - return os.environ.get("MONAI_ALGO_HASH", "07acb39") + return os.environ.get("MONAI_ALGO_HASH", "e4cf5a1") @staticmethod def trace_transform() -> str | None: From fe733b0ff1951ee752ab87ebfe5c4b7c82d30579 Mon Sep 17 00:00:00 2001 From: Pkaps25 <43655728+Pkaps25@users.noreply.github.com> Date: Mon, 6 May 2024 23:55:31 -0400 Subject: [PATCH 105/136] Propagate kernel size through attention Attention-UNet (#7734) Fixes #7726. ### Description Passes the `kernel_size` parameter to `ConvBlocks` within Attention UNet, creating a net with the expected number of parameters. Using the example in #7726 on this branch: ``` from monai.networks.nets import AttentionUnet model = AttentionUnet( spatial_dims = 2, in_channels = 1, out_channels = 1, channels = (2, 4, 8, 16), strides = (2,2,2), kernel_size = 5, up_kernel_size = 5 ) ``` outputs the expected values: ``` Total params: 18,846 Trainable params: 18,846 Non-trainable params: 0 Total mult-adds (M): 0.37 ``` ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Peter Kaplinsky Co-authored-by: Peter Kaplinsky --- monai/networks/nets/attentionunet.py | 12 ++++++++++-- tests/test_attentionunet.py | 20 ++++++++++++++++++++ 2 files changed, 30 insertions(+), 2 deletions(-) diff --git a/monai/networks/nets/attentionunet.py b/monai/networks/nets/attentionunet.py index 5689cf1071..fdf31d9701 100644 --- a/monai/networks/nets/attentionunet.py +++ b/monai/networks/nets/attentionunet.py @@ -29,7 +29,7 @@ def __init__( spatial_dims: int, in_channels: int, out_channels: int, - kernel_size: int = 3, + kernel_size: Sequence[int] | int = 3, strides: int = 1, dropout=0.0, ): @@ -219,7 +219,13 @@ def __init__( self.kernel_size = kernel_size self.dropout = dropout - head = ConvBlock(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=channels[0], dropout=dropout) + head = ConvBlock( + spatial_dims=spatial_dims, + in_channels=in_channels, + out_channels=channels[0], + dropout=dropout, + kernel_size=self.kernel_size, + ) reduce_channels = Convolution( spatial_dims=spatial_dims, in_channels=channels[0], @@ -245,6 +251,7 @@ def _create_block(channels: Sequence[int], strides: Sequence[int]) -> nn.Module: out_channels=channels[1], strides=strides[0], dropout=self.dropout, + kernel_size=self.kernel_size, ), subblock, ), @@ -271,6 +278,7 @@ def _get_bottom_layer(self, in_channels: int, out_channels: int, strides: int) - out_channels=out_channels, strides=strides, dropout=self.dropout, + kernel_size=self.kernel_size, ), up_kernel_size=self.up_kernel_size, strides=strides, diff --git a/tests/test_attentionunet.py b/tests/test_attentionunet.py index 83f6cabc5e..6a577f763f 100644 --- a/tests/test_attentionunet.py +++ b/tests/test_attentionunet.py @@ -14,11 +14,17 @@ import unittest import torch +import torch.nn as nn import monai.networks.nets.attentionunet as att from tests.utils import skip_if_no_cuda, skip_if_quick +def get_net_parameters(net: nn.Module) -> int: + """Returns the total number of parameters in a Module.""" + return sum(param.numel() for param in net.parameters()) + + class TestAttentionUnet(unittest.TestCase): def test_attention_block(self): @@ -50,6 +56,20 @@ def test_attentionunet(self): self.assertEqual(output.shape[0], input.shape[0]) self.assertEqual(output.shape[1], 2) + def test_attentionunet_kernel_size(self): + args_dict = { + "spatial_dims": 2, + "in_channels": 1, + "out_channels": 2, + "channels": (3, 4, 5), + "up_kernel_size": 5, + "strides": (1, 2), + } + model_a = att.AttentionUnet(**args_dict, kernel_size=5) + model_b = att.AttentionUnet(**args_dict, kernel_size=7) + self.assertEqual(get_net_parameters(model_a), 3534) + self.assertEqual(get_net_parameters(model_b), 5574) + @skip_if_no_cuda def test_attentionunet_gpu(self): for dims in [2, 3]: From ecaf5a1c9df5a462e3e1989db381865a3bed56a2 Mon Sep 17 00:00:00 2001 From: Simon Jensen <61684806+simojens@users.noreply.github.com> Date: Tue, 7 May 2024 16:48:58 +0200 Subject: [PATCH 106/136] Fixed misguiding weight mode documentation (#7746) ### Description DeepSupervisionLoss has a small typo in the documentation for 'exp' weight mode. The correct weights for this mode should be starting at 1, not 0 and then decrease by power of two. Might be worth fixing as it made me check the source code. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Simon Jensen <61684806+simojens@users.noreply.github.com> --- monai/losses/ds_loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/losses/ds_loss.py b/monai/losses/ds_loss.py index 57fcff6b87..aacc16874d 100644 --- a/monai/losses/ds_loss.py +++ b/monai/losses/ds_loss.py @@ -33,7 +33,7 @@ def __init__(self, loss: _Loss, weight_mode: str = "exp", weights: list[float] | weight_mode: {``"same"``, ``"exp"``, ``"two"``} Specifies the weights calculation for each image level. Defaults to ``"exp"``. - ``"same"``: all weights are equal to 1. - - ``"exp"``: exponentially decreasing weights by a power of 2: 0, 0.5, 0.25, 0.125, etc . + - ``"exp"``: exponentially decreasing weights by a power of 2: 1, 0.5, 0.25, 0.125, etc . - ``"two"``: equal smaller weights for lower levels: 1, 0.5, 0.5, 0.5, 0.5, etc weights: a list of weights to apply to each deeply supervised sub-loss, if provided, this will be used regardless of the weight_mode From 32b7754e98486b5edf01b8de3c08798a5e176566 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 8 May 2024 09:17:56 +0800 Subject: [PATCH 107/136] Enhance logging logic in `ConfigWorkflow` (#7745) When using nvflare with monai bundle, the logging logic may be overrided by the logging logic in the bundle. Add an option to disable logging logic in the bundle. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/bundle/workflows.py | 9 ++++++--- monai/fl/client/monai_algo.py | 14 +++++++++----- monai/fl/utils/constants.py | 1 + 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py index 471088994b..11c9bf0562 100644 --- a/monai/bundle/workflows.py +++ b/monai/bundle/workflows.py @@ -239,6 +239,7 @@ class ConfigWorkflow(BundleWorkflow): logging_file: config file for `logging` module in the program. for more details: https://docs.python.org/3/library/logging.config.html#logging.config.fileConfig. If None, default to "configs/logging.conf", which is commonly used for bundles in MONAI model zoo. + If False, the logging logic for the bundle will not be modified. init_id: ID name of the expected config expression to initialize before running, default to "initialize". allow a config to have no `initialize` logic and the ID. run_id: ID name of the expected config expression to run, default to "run". @@ -278,7 +279,7 @@ def __init__( self, config_file: str | Sequence[str], meta_file: str | Sequence[str] | None = None, - logging_file: str | None = None, + logging_file: str | bool | None = None, init_id: str = "initialize", run_id: str = "run", final_id: str = "finalize", @@ -307,7 +308,9 @@ def __init__( super().__init__(workflow_type=workflow_type, meta_file=meta_file, properties_path=properties_path) self.config_root_path = config_root_path logging_file = str(self.config_root_path / "logging.conf") if logging_file is None else logging_file - if logging_file is not None: + if logging_file is False: + logger.warn(f"Logging file is set to {logging_file}, skipping logging.") + else: if not os.path.isfile(logging_file): if logging_file == str(self.config_root_path / "logging.conf"): logger.warn(f"Default logging file in {logging_file} does not exist, skipping logging.") @@ -315,7 +318,7 @@ def __init__( raise FileNotFoundError(f"Cannot find the logging config file: {logging_file}.") else: logger.info(f"Setting logging properties based on config: {logging_file}.") - fileConfig(logging_file, disable_existing_loggers=False) + fileConfig(str(logging_file), disable_existing_loggers=False) self.parser = ConfigParser() self.parser.read_config(f=config_file) diff --git a/monai/fl/client/monai_algo.py b/monai/fl/client/monai_algo.py index 9acf131bd9..a3ac58c221 100644 --- a/monai/fl/client/monai_algo.py +++ b/monai/fl/client/monai_algo.py @@ -134,12 +134,14 @@ def initialize(self, extra=None): Args: extra: Dict with additional information that should be provided by FL system, - i.e., `ExtraItems.CLIENT_NAME` and `ExtraItems.APP_ROOT`. + i.e., `ExtraItems.CLIENT_NAME`, `ExtraItems.APP_ROOT` and `ExtraItems.LOGGING_FILE`. + You can diable the logging logic in the monai bundle by setting {ExtraItems.LOGGING_FILE} to False. """ if extra is None: extra = {} self.client_name = extra.get(ExtraItems.CLIENT_NAME, "noname") + logging_file = extra.get(ExtraItems.LOGGING_FILE, None) self.logger.info(f"Initializing {self.client_name} ...") # FL platform needs to provide filepath to configuration files @@ -149,7 +151,7 @@ def initialize(self, extra=None): if self.workflow is None: config_train_files = self._add_config_files(self.config_train_filename) self.workflow = ConfigWorkflow( - config_file=config_train_files, meta_file=None, logging_file=None, workflow_type="train" + config_file=config_train_files, meta_file=None, logging_file=logging_file, workflow_type="train" ) self.workflow.initialize() self.workflow.bundle_root = self.bundle_root @@ -412,13 +414,15 @@ def initialize(self, extra=None): Args: extra: Dict with additional information that should be provided by FL system, - i.e., `ExtraItems.CLIENT_NAME` and `ExtraItems.APP_ROOT`. + i.e., `ExtraItems.CLIENT_NAME`, `ExtraItems.APP_ROOT` and `ExtraItems.LOGGING_FILE`. + You can diable the logging logic in the monai bundle by setting {ExtraItems.LOGGING_FILE} to False. """ self._set_cuda_device() if extra is None: extra = {} self.client_name = extra.get(ExtraItems.CLIENT_NAME, "noname") + logging_file = extra.get(ExtraItems.LOGGING_FILE, None) timestamp = time.strftime("%Y%m%d_%H%M%S") self.logger.info(f"Initializing {self.client_name} ...") # FL platform needs to provide filepath to configuration files @@ -434,7 +438,7 @@ def initialize(self, extra=None): self.train_workflow = ConfigWorkflow( config_file=config_train_files, meta_file=None, - logging_file=None, + logging_file=logging_file, workflow_type="train", **self.train_kwargs, ) @@ -459,7 +463,7 @@ def initialize(self, extra=None): self.eval_workflow = ConfigWorkflow( config_file=config_eval_files, meta_file=None, - logging_file=None, + logging_file=logging_file, workflow_type=self.eval_workflow_name, **self.eval_kwargs, ) diff --git a/monai/fl/utils/constants.py b/monai/fl/utils/constants.py index eda1a6b4f9..18beceeaee 100644 --- a/monai/fl/utils/constants.py +++ b/monai/fl/utils/constants.py @@ -30,6 +30,7 @@ class ExtraItems(StrEnum): CLIENT_NAME = "fl_client_name" APP_ROOT = "fl_app_root" STATS_SENDER = "fl_stats_sender" + LOGGING_FILE = "logging_file" class FlPhase(StrEnum): From f278e51b8c13fa46d3f5e763452d9e70e24df955 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Wed, 8 May 2024 12:32:50 +0800 Subject: [PATCH 108/136] Add version requirement for filelock and nni (#7744) Add version requirement for filelock and nni ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- Dockerfile | 12 ++++-------- requirements-dev.txt | 5 ++--- 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/Dockerfile b/Dockerfile index fc97227351..8e255597d1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -18,18 +18,14 @@ LABEL maintainer="monai.contact@gmail.com" # TODO: remark for issue [revise the dockerfile](https://github.com/zarr-developers/numcodecs/issues/431) RUN if [[ $(uname -m) =~ "aarch64" ]]; then \ - cd /opt && \ - git clone --branch v0.12.1 --recursive https://github.com/zarr-developers/numcodecs && \ - pip wheel numcodecs && \ - rm -r /opt/*.whl && \ - rm -rf /opt/numcodecs; \ + export CFLAGS="-O3" && \ + export DISABLE_NUMCODECS_SSE2=true && \ + export DISABLE_NUMCODECS_AVX2=true && \ + pip install numcodecs; \ fi WORKDIR /opt/monai -# remove opencv-python before opencv-python-headless installation -RUN pip uninstall -y opencv && rm /usr/local/lib/python3.10/dist-packages/cv2 -r - # install full deps COPY requirements.txt requirements-min.txt requirements-dev.txt /tmp/ RUN cp /tmp/requirements.txt /tmp/req.bak \ diff --git a/requirements-dev.txt b/requirements-dev.txt index b207b56b19..e8792c86f3 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -46,15 +46,14 @@ pynrrd pre-commit pydicom h5py -nni; platform_system == "Linux" and "arm" not in platform_machine and "aarch" not in platform_machine +nni==2.10.1; platform_system == "Linux" and "arm" not in platform_machine and "aarch" not in platform_machine optuna git+https://github.com/Project-MONAI/MetricsReloaded@monai-support#egg=MetricsReloaded onnx>=1.13.0 onnxruntime; python_version <= '3.10' typeguard<3 # https://github.com/microsoft/nni/issues/5457 -filelock!=3.12.0 # https://github.com/microsoft/nni/issues/5523 +filelock<3.12.0 # https://github.com/microsoft/nni/issues/5523 zarr lpips==0.1.4 nvidia-ml-py huggingface_hub -opencv-python-headless From d83fa5660e87d99f9ddcfcfb4695cbdf53a3884c Mon Sep 17 00:00:00 2001 From: NabJa <32510324+NabJa@users.noreply.github.com> Date: Wed, 8 May 2024 17:51:47 +0200 Subject: [PATCH 109/136] Add dimensionality of heads argument to SABlock (#7664) Fixes #7661. ### Description The changes made add a parameter (_dim_head_) to set the output paramters of all the heads in the Self-attention Block (SABlock). Currently the output dimension is set to be _hidden_size_ and when increasing the number of heads this is equally distributed among all heads. ### Example The original implementation automatically determines **_equally_distributed_head_dim_**: (qkv * num_heds * equally_distributed_head_dim = 3*hidden_size in this example -> 3 * 8 * 16 = 384) ``` block = SABlock(hidden_size=128, num_heads=8) x = torch.zeros(1, 256, 128) x = block.qkv(x) print(x.shape) x = block.input_rearrange(x) print(x.shape) > torch.Size([1, 256, 384]) > torch.Size([3, 1, 8, 256, 16]) # <- This corresponds to (qkv batch num_heads sequence_length equally_distributed_head_dim) ``` The propesed implementation fixes this by setting the new argument **_dim_head_:** ``` block_new = SABlock(hidden_size=128, num_heads=8, dim_head=32) x = torch.zeros(1, 256, 128) x = block_new.qkv(x) print(x.shape) x = block_new.input_rearrange(x) print(x.shape) > torch.Size([1, 256, 384]) > torch.Size([3, 1, 8, 256, 32]) # <- This corresponds to (qkv batch num_heads sequence_length dim_head) ``` ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: NabJa Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/blocks/selfattention.py | 12 ++++++--- tests/test_selfattention.py | 34 ++++++++++++++++++++++++++ 2 files changed, 42 insertions(+), 4 deletions(-) diff --git a/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py index 7c81c1704f..7b410b1a7c 100644 --- a/monai/networks/blocks/selfattention.py +++ b/monai/networks/blocks/selfattention.py @@ -32,6 +32,7 @@ def __init__( dropout_rate: float = 0.0, qkv_bias: bool = False, save_attn: bool = False, + dim_head: int | None = None, ) -> None: """ Args: @@ -40,6 +41,7 @@ def __init__( dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0. qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False. save_attn (bool, optional): to make accessible the attention matrix. Defaults to False. + dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads. """ @@ -52,14 +54,16 @@ def __init__( raise ValueError("hidden size should be divisible by num_heads.") self.num_heads = num_heads - self.out_proj = nn.Linear(hidden_size, hidden_size) - self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias) + self.dim_head = hidden_size // num_heads if dim_head is None else dim_head + self.inner_dim = self.dim_head * num_heads + + self.out_proj = nn.Linear(self.inner_dim, hidden_size) + self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias) self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads) self.out_rearrange = Rearrange("b h l d -> b l (h d)") self.drop_output = nn.Dropout(dropout_rate) self.drop_weights = nn.Dropout(dropout_rate) - self.head_dim = hidden_size // num_heads - self.scale = self.head_dim**-0.5 + self.scale = self.dim_head**-0.5 self.save_attn = save_attn self.att_mat = torch.Tensor() diff --git a/tests/test_selfattention.py b/tests/test_selfattention.py index b8be4fd1b6..0ebed84159 100644 --- a/tests/test_selfattention.py +++ b/tests/test_selfattention.py @@ -74,6 +74,40 @@ def test_access_attn_matrix(self): matrix_acess_blk(torch.randn(input_shape)) assert matrix_acess_blk.att_mat.shape == (input_shape[0], input_shape[0], input_shape[1], input_shape[1]) + def test_number_of_parameters(self): + + def count_sablock_params(*args, **kwargs): + """Count the number of parameters in a SABlock.""" + sablock = SABlock(*args, **kwargs) + return sum([x.numel() for x in sablock.parameters() if x.requires_grad]) + + hidden_size = 128 + num_heads = 8 + default_dim_head = hidden_size // num_heads + + # Default dim_head is hidden_size // num_heads + nparams_default = count_sablock_params(hidden_size=hidden_size, num_heads=num_heads) + nparams_like_default = count_sablock_params( + hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head + ) + self.assertEqual(nparams_default, nparams_like_default) + + # Increasing dim_head should increase the number of parameters + nparams_custom_large = count_sablock_params( + hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head * 2 + ) + self.assertGreater(nparams_custom_large, nparams_default) + + # Decreasing dim_head should decrease the number of parameters + nparams_custom_small = count_sablock_params( + hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head // 2 + ) + self.assertGreater(nparams_default, nparams_custom_small) + + # Increasing the number of heads with the default behaviour should not change the number of params. + nparams_default_more_heads = count_sablock_params(hidden_size=hidden_size, num_heads=num_heads * 2) + self.assertEqual(nparams_default, nparams_default_more_heads) + if __name__ == "__main__": unittest.main() From 258f56de80b38378ca4dfbf86436924bbb93ff7e Mon Sep 17 00:00:00 2001 From: Pkaps25 <43655728+Pkaps25@users.noreply.github.com> Date: Thu, 9 May 2024 09:46:02 -0400 Subject: [PATCH 110/136] Add activation parameter to ResNet (#7749) Fixes #7653 . ### Description Includes an `act` parameter to `ResNet` and its submodules to allow for passing the `inplace` param. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Peter Kaplinsky Co-authored-by: Peter Kaplinsky --- monai/networks/nets/resnet.py | 28 +++++++++++++++++----------- tests/test_resnet.py | 19 ++++++++++++++++++- 2 files changed, 35 insertions(+), 12 deletions(-) diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index de16cd4cca..93a77d7b2a 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -23,7 +23,7 @@ from monai.networks.blocks.encoder import BaseEncoder from monai.networks.layers.factories import Conv, Norm, Pool -from monai.networks.layers.utils import get_pool_layer +from monai.networks.layers.utils import get_act_layer, get_pool_layer from monai.utils import ensure_tuple_rep from monai.utils.module import look_up_option, optional_import @@ -78,6 +78,7 @@ def __init__( spatial_dims: int = 3, stride: int = 1, downsample: nn.Module | partial | None = None, + act: str | tuple = ("relu", {"inplace": True}), ) -> None: """ Args: @@ -86,6 +87,7 @@ def __init__( spatial_dims: number of spatial dimensions of the input image. stride: stride to use for first conv layer. downsample: which downsample layer to use. + act: activation type and arguments. Defaults to relu. """ super().__init__() @@ -94,7 +96,7 @@ def __init__( self.conv1 = conv_type(in_planes, planes, kernel_size=3, padding=1, stride=stride, bias=False) self.bn1 = norm_type(planes) - self.relu = nn.ReLU(inplace=True) + self.act = get_act_layer(name=act) self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, bias=False) self.bn2 = norm_type(planes) self.downsample = downsample @@ -105,7 +107,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: out: torch.Tensor = self.conv1(x) out = self.bn1(out) - out = self.relu(out) + out = self.act(out) out = self.conv2(out) out = self.bn2(out) @@ -114,7 +116,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: residual = self.downsample(x) out += residual - out = self.relu(out) + out = self.act(out) return out @@ -129,6 +131,7 @@ def __init__( spatial_dims: int = 3, stride: int = 1, downsample: nn.Module | partial | None = None, + act: str | tuple = ("relu", {"inplace": True}), ) -> None: """ Args: @@ -137,6 +140,7 @@ def __init__( spatial_dims: number of spatial dimensions of the input image. stride: stride to use for second conv layer. downsample: which downsample layer to use. + act: activation type and arguments. Defaults to relu. """ super().__init__() @@ -150,7 +154,7 @@ def __init__( self.bn2 = norm_type(planes) self.conv3 = conv_type(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = norm_type(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) + self.act = get_act_layer(name=act) self.downsample = downsample self.stride = stride @@ -159,11 +163,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: out: torch.Tensor = self.conv1(x) out = self.bn1(out) - out = self.relu(out) + out = self.act(out) out = self.conv2(out) out = self.bn2(out) - out = self.relu(out) + out = self.act(out) out = self.conv3(out) out = self.bn3(out) @@ -172,7 +176,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: residual = self.downsample(x) out += residual - out = self.relu(out) + out = self.act(out) return out @@ -202,6 +206,7 @@ class ResNet(nn.Module): num_classes: number of output (classifications). feed_forward: whether to add the FC layer for the output, default to `True`. bias_downsample: whether to use bias term in the downsampling block when `shortcut_type` is 'B', default to `True`. + act: activation type and arguments. Defaults to relu. """ @@ -220,6 +225,7 @@ def __init__( num_classes: int = 400, feed_forward: bool = True, bias_downsample: bool = True, # for backwards compatibility (also see PR #5477) + act: str | tuple = ("relu", {"inplace": True}), ) -> None: super().__init__() @@ -257,7 +263,7 @@ def __init__( bias=False, ) self.bn1 = norm_type(self.in_planes) - self.relu = nn.ReLU(inplace=True) + self.act = get_act_layer(name=act) self.maxpool = pool_type(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, block_inplanes[0], layers[0], spatial_dims, shortcut_type) self.layer2 = self._make_layer(block, block_inplanes[1], layers[1], spatial_dims, shortcut_type, stride=2) @@ -329,7 +335,7 @@ def _make_layer( def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.conv1(x) x = self.bn1(x) - x = self.relu(x) + x = self.act(x) if not self.no_max_pool: x = self.maxpool(x) @@ -396,7 +402,7 @@ def forward(self, inputs: torch.Tensor): """ x = self.conv1(inputs) x = self.bn1(x) - x = self.relu(x) + x = self.act(x) features = [] features.append(x) diff --git a/tests/test_resnet.py b/tests/test_resnet.py index ecab133154..3a58d1c955 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -107,6 +107,7 @@ "num_classes": 3, "conv1_t_size": [3], "conv1_t_stride": 1, + "act": ("relu", {"inplace": False}), }, (1, 2, 32), (1, 3), @@ -185,13 +186,29 @@ (1, 3), ] +TEST_CASE_8 = [ + { + "block": "bottleneck", + "layers": [3, 4, 6, 3], + "block_inplanes": [64, 128, 256, 512], + "spatial_dims": 1, + "n_input_channels": 2, + "num_classes": 3, + "conv1_t_size": [3], + "conv1_t_stride": 1, + "act": ("relu", {"inplace": False}), + }, + (1, 2, 32), + (1, 3), +] + TEST_CASES = [] PRETRAINED_TEST_CASES = [] for case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]: for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]: TEST_CASES.append([model, *case]) PRETRAINED_TEST_CASES.append([model, *case]) -for case in [TEST_CASE_5, TEST_CASE_5_A, TEST_CASE_6, TEST_CASE_7]: +for case in [TEST_CASE_5, TEST_CASE_5_A, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]: TEST_CASES.append([ResNet, *case]) TEST_SCRIPT_CASES = [ From 4af23069ccbbd26d7816bafc3762365ce4c5da77 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 10 May 2024 13:33:37 +0800 Subject: [PATCH 111/136] Revert version requirement for mlflow (#7742) Revert https://github.com/Project-MONAI/MONAI/pull/7659 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- .github/workflows/pythonapp.yml | 1 + docs/requirements.txt | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml index b8b73907d4..d1e77bb567 100644 --- a/.github/workflows/pythonapp.yml +++ b/.github/workflows/pythonapp.yml @@ -100,6 +100,7 @@ jobs: python -m pip install --pre -U itk - name: Install the dependencies run: | + python -m pip install --user --upgrade pip wheel python -m pip install torch==1.13.1 torchvision==0.14.1 cat "requirements-dev.txt" python -m pip install -r requirements-dev.txt diff --git a/docs/requirements.txt b/docs/requirements.txt index 5acc437391..468545dc19 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -22,7 +22,7 @@ sphinx-autodoc-typehints==1.11.1 pandas einops transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 -mlflow>=1.28.0, <=2.11.3 +mlflow>=2.12.2 clearml>=1.10.0rc0 tensorboardX imagecodecs; platform_system == "Linux" or platform_system == "Darwin" diff --git a/requirements-dev.txt b/requirements-dev.txt index e8792c86f3..83bdae2a5c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -34,7 +34,7 @@ pandas requests einops transformers>=4.36.0 -mlflow>=1.28.0, <=2.11.3 +mlflow>=2.12.2 clearml>=1.10.0rc0 matplotlib!=3.5.0 tensorboardX diff --git a/setup.cfg b/setup.cfg index c8ae1630f7..711d5c5558 100644 --- a/setup.cfg +++ b/setup.cfg @@ -66,7 +66,7 @@ all = pandas einops transformers<4.22; python_version <= '3.10' - mlflow>=1.28.0, <=2.11.3 + mlflow>=2.12.2 clearml>=1.10.0rc0 matplotlib tensorboardX @@ -125,7 +125,7 @@ einops = transformers = transformers<4.22; python_version <= '3.10' mlflow = - mlflow>=1.28.0, <=2.11.3 + mlflow>=2.12.2 matplotlib = matplotlib clearml = From ab4bd43ae4faa983d48b67e17bc0a2ddb597d038 Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 13 May 2024 09:35:15 +0100 Subject: [PATCH 112/136] auto updates (#7760) Signed-off-by: monai-bot Signed-off-by: monai-bot --- monai/data/video_dataset.py | 2 +- monai/networks/nets/daf3d.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/monai/data/video_dataset.py b/monai/data/video_dataset.py index 9ff23ebeff..031e85db26 100644 --- a/monai/data/video_dataset.py +++ b/monai/data/video_dataset.py @@ -177,7 +177,7 @@ def get_available_codecs() -> dict[str, str]: for codec, ext in all_codecs.items(): writer = cv2.VideoWriter() fname = os.path.join(tmp_dir, f"test{ext}") - fourcc = cv2.VideoWriter_fourcc(*codec) # type: ignore[attr-defined] + fourcc = cv2.VideoWriter_fourcc(*codec) noviderr = writer.open(fname, fourcc, 1, (10, 10)) if noviderr: codecs[codec] = ext diff --git a/monai/networks/nets/daf3d.py b/monai/networks/nets/daf3d.py index c9a18c746a..31c2bf4c31 100644 --- a/monai/networks/nets/daf3d.py +++ b/monai/networks/nets/daf3d.py @@ -196,7 +196,7 @@ def __init__(self, in_planes, planes, spatial_dims=3, stride=1, downsample=None) self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, stride=stride, groups=32, bias=False) # adapt activation function - self.relu = nn.PReLU() # type: ignore + self.relu = nn.PReLU() class Daf3dResNetDilatedBottleneck(Daf3dResNetBottleneck): @@ -287,7 +287,7 @@ def __init__( n_input_channels, self.in_planes, kernel_size=7, stride=(1, 2, 2), padding=(3, 3, 3), bias=False ) self.bn1 = norm_type(32, 64) - self.relu = nn.PReLU() # type: ignore + self.relu = nn.PReLU() # adapt layers to our needs self.layer1 = self._make_layer(Daf3dResNetBottleneck, block_inplanes[0], layers[0], spatial_dims, shortcut_type) From daf2e714bb8a1cfa7c17d9aa7fb066dd37f5429e Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 13 May 2024 23:26:14 +0800 Subject: [PATCH 113/136] Fix HTTPError when Too Many Requests for huggingface hub (#7765) Fixes #7764 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/utils.py b/tests/utils.py index ea73a3ed81..d1939e590b 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -156,6 +156,7 @@ def skip_if_downloading_fails(): "limit", # HTTP Error 503: Egress is over the account limit "authenticate", "timed out", # urlopen error [Errno 110] Connection timed out + "HTTPError", # HTTPError: 429 Client Error: Too Many Requests for huggingface hub ) ): raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e # incomplete download From 1bcf97f808718eb39b9a42ec770afbf581a5d83c Mon Sep 17 00:00:00 2001 From: johnzielke Date: Wed, 15 May 2024 22:29:51 -0400 Subject: [PATCH 114/136] Add direct links to github source code to docs (#7738) ### Description This adds direct links to the respective source code of classes, methods, etc. to the docs. In my opinion these nicer and more useful than viewing the source code in copy in the docs. ![image](https://github.com/Project-MONAI/MONAI/assets/20091488/4a6a2650-9fd2-4cd9-a2a1-084cc3a5fb36) I currently added it in addition to the links to the source files copied into the docs. If desired this could also be done instead of. I also used a github logo instead of another `[source]` link. I am not sure whether there is any easier way to change that into the logo than the way I have done, but this one seems to work. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: John Zielke --- docs/source/conf.py | 55 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 54 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index fdb10fbe03..782b585c9f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -13,6 +13,8 @@ import os import subprocess import sys +import importlib +import inspect sys.path.insert(0, os.path.abspath("..")) sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) @@ -95,7 +97,7 @@ def generate_apidocs(*args): "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx.ext.autodoc", - "sphinx.ext.viewcode", + "sphinx.ext.linkcode", "sphinx.ext.autosectionlabel", "sphinx.ext.autosummary", "sphinx_autodoc_typehints", @@ -162,3 +164,54 @@ def setup(app): # Hook to allow for automatic generation of API docs # before doc deployment begins. app.connect("builder-inited", generate_apidocs) + + +# -- Linkcode configuration -------------------------------------------------- +DEFAULT_REF = "dev" +if os.environ.get("GITHUB_REF_TYPE", "branch") == "tag": + # When building a tag, link to the tag itself + git_ref = os.environ.get("GITHUB_REF", DEFAULT_REF) +else: + git_ref = os.environ.get("GITHUB_SHA", DEFAULT_REF) + +DEFAULT_REPOSITORY = "Project-MONAI/MONAI" +repository = os.environ.get("GITHUB_REPOSITORY", DEFAULT_REPOSITORY) + +base_code_url = f"https://github.com/{repository}/blob/{git_ref}" +MODULE_ROOT_FOLDER = "monai" + + +# Adjusted from https://github.com/python-websockets/websockets/blob/main/docs/conf.py +def linkcode_resolve(domain, info): + if domain != "py": + raise ValueError( + f"expected domain to be 'py', got {domain}." + "Please adjust linkcode_resolve to either handle this domain or ignore it." + ) + + mod = importlib.import_module(info["module"]) + if "." in info["fullname"]: + objname, attrname = info["fullname"].split(".") + obj = getattr(mod, objname) + try: + # object is a method of a class + obj = getattr(obj, attrname) + except AttributeError: + # object is an attribute of a class + return None + else: + obj = getattr(mod, info["fullname"]) + + try: + file = inspect.getsourcefile(obj) + source, lineno = inspect.getsourcelines(obj) + except TypeError: + # e.g. object is a typing.Union + return None + file = os.path.relpath(file, os.path.abspath("..")) + if not file.startswith(MODULE_ROOT_FOLDER): + # e.g. object is a typing.NewType + return None + start, end = lineno, lineno + len(source) - 1 + url = f"{base_code_url}/{file}#L{start}-L{end}" + return url From 5e9ac1bf4e3bfa73dcdb1c604554c5f7f41075b3 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 17 May 2024 16:08:14 +0800 Subject: [PATCH 115/136] Fix matplotlib 3.9.0 has no attribute 'get_cmap` (#7780) Fixes #7776 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/source/conf.py | 13 +++---------- monai/visualize/utils.py | 4 +--- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- tests/test_matshow3d.py | 1 + 5 files changed, 8 insertions(+), 16 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 782b585c9f..543372ebc7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -139,7 +139,7 @@ def generate_apidocs(*args): "github_user": "Project-MONAI", "github_repo": "MONAI", "github_version": "dev", - "doc_path": "docs/", + "doc_path": "docs/source", "conf_py_path": "/docs/", "VERSION": version, } @@ -167,17 +167,10 @@ def setup(app): # -- Linkcode configuration -------------------------------------------------- -DEFAULT_REF = "dev" -if os.environ.get("GITHUB_REF_TYPE", "branch") == "tag": - # When building a tag, link to the tag itself - git_ref = os.environ.get("GITHUB_REF", DEFAULT_REF) -else: - git_ref = os.environ.get("GITHUB_SHA", DEFAULT_REF) - DEFAULT_REPOSITORY = "Project-MONAI/MONAI" repository = os.environ.get("GITHUB_REPOSITORY", DEFAULT_REPOSITORY) -base_code_url = f"https://github.com/{repository}/blob/{git_ref}" +base_code_url = f"https://github.com/{repository}/blob/{version}" MODULE_ROOT_FOLDER = "monai" @@ -208,7 +201,7 @@ def linkcode_resolve(domain, info): except TypeError: # e.g. object is a typing.Union return None - file = os.path.relpath(file, os.path.abspath("..")) + file = os.path.relpath(file, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) if not file.startswith(MODULE_ROOT_FOLDER): # e.g. object is a typing.NewType return None diff --git a/monai/visualize/utils.py b/monai/visualize/utils.py index f6718fe7a5..88c9a0d66a 100644 --- a/monai/visualize/utils.py +++ b/monai/visualize/utils.py @@ -24,11 +24,9 @@ from monai.utils.type_conversion import convert_data_type, convert_to_dst_type if TYPE_CHECKING: - from matplotlib import cm from matplotlib import pyplot as plt else: plt, _ = optional_import("matplotlib", name="pyplot") - cm, _ = optional_import("matplotlib", name="cm") __all__ = ["matshow3d", "blend_images"] @@ -210,7 +208,7 @@ def blend_images( image = repeat(image, 3, axis=0) def get_label_rgb(cmap: str, label: NdarrayOrTensor) -> NdarrayOrTensor: - _cmap = cm.get_cmap(cmap) + _cmap = plt.colormaps.get_cmap(cmap) label_np, *_ = convert_data_type(label, np.ndarray) label_rgb_np = _cmap(label_np[0]) label_rgb_np = np.moveaxis(label_rgb_np, -1, 0)[:3] diff --git a/requirements-dev.txt b/requirements-dev.txt index 83bdae2a5c..09aeb22cac 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -36,7 +36,7 @@ einops transformers>=4.36.0 mlflow>=2.12.2 clearml>=1.10.0rc0 -matplotlib!=3.5.0 +matplotlib>=3.6.3 tensorboardX types-PyYAML pyyaml diff --git a/setup.cfg b/setup.cfg index 711d5c5558..086722914e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -68,7 +68,7 @@ all = transformers<4.22; python_version <= '3.10' mlflow>=2.12.2 clearml>=1.10.0rc0 - matplotlib + matplotlib>=3.6.3 tensorboardX pyyaml fire @@ -127,7 +127,7 @@ transformers = mlflow = mlflow>=2.12.2 matplotlib = - matplotlib + matplotlib>=3.6.3 clearml = clearml tensorboardX = diff --git a/tests/test_matshow3d.py b/tests/test_matshow3d.py index e54bb523e4..2eba310f4e 100644 --- a/tests/test_matshow3d.py +++ b/tests/test_matshow3d.py @@ -114,6 +114,7 @@ def test_3d_rgb(self): every_n=2, frame_dim=-1, channel_dim=0, + fill_value=0, show=False, ) From 7429e2a0633defa415f644e23e83a85c07dc3ba1 Mon Sep 17 00:00:00 2001 From: johnzielke Date: Fri, 17 May 2024 07:44:00 -0400 Subject: [PATCH 116/136] Fix doc source links for read the docs (#7779) Fixes the current docs build. Currently no [source] links are shown anymore. It seems like read the docs uses a different working directory, therefore breaking the source links. It also will not have the correct git tag referenced. This should fix both of these issues. If there is a way to test if this fix works without merging to dev, let me know. ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: John Zielke Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/source/conf.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 543372ebc7..a91f38081f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -140,7 +140,7 @@ def generate_apidocs(*args): "github_repo": "MONAI", "github_version": "dev", "doc_path": "docs/source", - "conf_py_path": "/docs/", + "conf_py_path": "/docs/source", "VERSION": version, } html_scaled_image_link = False @@ -167,11 +167,24 @@ def setup(app): # -- Linkcode configuration -------------------------------------------------- +DEFAULT_REF = "dev" +read_the_docs_ref = os.environ.get("READTHEDOCS_GIT_IDENTIFIER", None) +if read_the_docs_ref: + # When building on ReadTheDocs, link to the specific commit + # https://docs.readthedocs.io/en/stable/reference/environment-variables.html#envvar-READTHEDOCS_GIT_IDENTIFIER + git_ref = read_the_docs_ref +elif os.environ.get("GITHUB_REF_TYPE", "branch") == "tag": + # When building a tag, link to the tag itself + git_ref = os.environ.get("GITHUB_REF", DEFAULT_REF) +else: + git_ref = os.environ.get("GITHUB_SHA", DEFAULT_REF) + DEFAULT_REPOSITORY = "Project-MONAI/MONAI" repository = os.environ.get("GITHUB_REPOSITORY", DEFAULT_REPOSITORY) -base_code_url = f"https://github.com/{repository}/blob/{version}" +base_code_url = f"https://github.com/{repository}/blob/{git_ref}" MODULE_ROOT_FOLDER = "monai" +repo_root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) # Adjusted from https://github.com/python-websockets/websockets/blob/main/docs/conf.py @@ -201,7 +214,7 @@ def linkcode_resolve(domain, info): except TypeError: # e.g. object is a typing.Union return None - file = os.path.relpath(file, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) + file = os.path.relpath(file, repo_root_path) if not file.startswith(MODULE_ROOT_FOLDER): # e.g. object is a typing.NewType return None From d0d129246f3759f88bc0c23488e819cbac263889 Mon Sep 17 00:00:00 2001 From: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Date: Fri, 17 May 2024 20:48:31 +0800 Subject: [PATCH 117/136] Restrict Auto3DSeg fold input based on datalist (#7778) Fixes #7777. ### Description Lower the maximum `num_fold` allowed for user inputs in Auto3DSeg AutoRunner ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [x] In-line docstrings updated. Signed-off-by: Mingxin Zheng Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/apps/auto3dseg/auto_runner.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/monai/apps/auto3dseg/auto_runner.py b/monai/apps/auto3dseg/auto_runner.py index 05c961f999..5b6b501555 100644 --- a/monai/apps/auto3dseg/auto_runner.py +++ b/monai/apps/auto3dseg/auto_runner.py @@ -499,8 +499,8 @@ def set_num_fold(self, num_fold: int = 5) -> AutoRunner: if num_fold <= 0: raise ValueError(f"num_fold is expected to be an integer greater than zero. Now it gets {num_fold}") - if num_fold > self.max_fold + 1: - # Auto3DSeg allows no validation set, so the maximum fold number is max_fold + 1 + if num_fold > self.max_fold: + # Auto3DSeg must contain validation set, so the maximum fold number is max_fold. raise ValueError( f"num_fold is greater than the maximum fold number {self.max_fold} in {self.datalist_filename}." ) From 25e78a2b3d2333f7effad7ae3ca2d45e5081e82b Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Fri, 17 May 2024 22:13:12 +0800 Subject: [PATCH 118/136] 7753 update changelog for v1.3.1 (#7773) Part of #7753 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- CHANGELOG.md | 95 +++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 94 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 61be8f07c1..38336505ed 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,98 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/). ## [Unreleased] +## [1.3.1] - 2024-05-17 +### Added +* Support for `by_measure` argument in `RemoveSmallObjects` (#7137) +* Support for `pretrained` flag in `ResNet` (#7095) +* Support for uploading and downloading bundles to and from the Hugging Face Hub (#6454) +* Added weight parameter in DiceLoss to apply weight to voxels of each class (#7158) +* Support for returning dice for each class in `DiceMetric` (#7163) +* Introduced `ComponentStore` for storage purposes (#7159) +* Added utilities used in MONAI Generative (#7134) +* Enabled Python 3.11 support for `convert_to_torchscript` and `convert_to_onnx` (#7182) +* Support for MLflow in `AutoRunner` (#7176) +* `fname_regex` option in PydicomReader (#7181) +* Allowed setting AutoRunner parameters from config (#7175) +* `VoxelMorphUNet` and `VoxelMorph` (#7178) +* Enabled `cache` option in `GridPatchDataset` (#7180) +* Introduced `class_labels` option in `write_metrics_reports` for improved readability (#7249) +* `DiffusionLoss` for image registration task (#7272) +* Supported specifying `filename` in `Saveimage` (#7318) +* Compile support in `SupervisedTrainer` and `SupervisedEvaluator` (#7375) +* `mlflow_experiment_name` support in `Auto3DSeg` (#7442) +* Arm support (#7500) +* `BarlowTwinsLoss` for representation learning (#7530) +* `SURELoss` and `ConjugateGradient` for diffusion models (#7308) +* Support for `CutMix`, `CutOut`, and `MixUp` augmentation techniques (#7198) +* `meta_file` and `logging_file` options to `BundleWorkflow` (#7549) +* `properties_path` option to `BundleWorkflow` for customized properties (#7542) +* Support for both soft and hard clipping in `ClipIntensityPercentiles` (#7535) +* Support for not saving artifacts in `MLFlowHandler` (#7604) +* Support for multi-channel images in `PerceptualLoss` (#7568) +* Added ResNet backbone for `FlexibleUNet` (#7571) +* Introduced `dim_head` option in `SABlock` to set dimensions for each head (#7664) +* Direct links to github source code to docs (#7738, #7779) +#### misc. +* Refactored `list_data_collate` and `collate_meta_tensor` to utilize the latest PyTorch API (#7165) +* Added __str__ method in `Metric` base class (#7487) +* Made enhancements for testing files (#7662, #7670, #7663, #7671, #7672) +* Improved documentation for bundles (#7116) +### Fixed +#### transforms +* Addressed issue where lazy mode was ignored in `SpatialPadd` (#7316) +* Tracked applied operations in `ImageFilter` (#7395) +* Warnings are now given only if missing class is not set to 0 in `generate_label_classes_crop_centers` (#7602) +* Input is now always converted to C-order in `distance_transform_edt` to ensure consistent behavior (#7675) +#### data +* Modified .npz file behavior to use keys in `NumpyReader` (#7148) +* Handled corrupted cached files in `PersistentDataset` (#7244) +* Corrected affine update in `NrrdReader` (#7415) +#### metrics and losses +* Addressed precision issue in `get_confusion_matrix` (#7187) +* Harmonized and clarified documentation and tests for dice losses variants (#7587) +#### networks +* Removed hard-coded `spatial_dims` in `SwinTransformer` (#7302) +* Fixed learnable `position_embeddings` in `PatchEmbeddingBlock` (#7564, #7605) +* Removed `memory_pool_limit` in TRT config (#7647) +* Propagated `kernel_size` to `ConvBlocks` within `AttentionUnet` (#7734) +* Addressed hard-coded activation layer in `ResNet` (#7749) +#### bundle +* Resolved bundle download issue (#7280) +* Updated `bundle_root` directory for `NNIGen` (#7586) +* Checked for `num_fold` and failed early if incorrect (#7634) +* Enhanced logging logic in `ConfigWorkflow` (#7745) +#### misc. +* Enabled chaining in `Auto3DSeg` CLI (#7168) +* Addressed useless error message in `nnUNetV2Runner` (#7217) +* Resolved typing and deprecation issues in Mypy (#7231) +* Quoted `$PY_EXE` variable to handle Python path that contains spaces in Bash (#7268) +* Improved documentation, code examples, and warning messages in various modules (#7234, #7213, #7271, #7326, #7569, #7584) +* Fixed typos in various modules (#7321, #7322, #7458, #7595, #7612) +* Enhanced docstrings in various modules (#7245, #7381, #7746) +* Handled error when data is on CPU in `DataAnalyzer` (#7310) +* Updated version requirements for third-party packages (#7343, #7344, #7384, #7448, #7659, #7704, #7744, #7742, #7780) +* Addressed incorrect slice compute in `ImageStats` (#7374) +* Avoided editing a loop's mutable iterable to address B308 (#7397) +* Fixed issue with `CUDA_VISIBLE_DEVICES` setting being ignored (#7408, #7581) +* Avoided changing Python version in CICD (#7424) +* Renamed partial to callable in instantiate mode (#7413) +* Imported AttributeError for Python 3.12 compatibility (#7482) +* Updated `nnUNetV2Runner` to support nnunetv2 2.2 (#7483) +* Used uint8 instead of int8 in `LabelStats` (#7489) +* Utilized subprocess for nnUNet training (#7576) +* Addressed deprecated warning in ruff (#7625) +* Fixed downloading failure on FIPS machine (#7698) +* Updated `torch_tensorrt` compile parameters to avoid warning (#7714) +* Restrict `Auto3DSeg` fold input based on datalist (#7778) +### Changed +* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:24.03-py3` from `nvcr.io/nvidia/pytorch:23.08-py3` +### Removed +* Removed unrecommended star-arg unpacking after a keyword argument, addressed B026 (#7262) +* Skipped old PyTorch version test for `SwinUNETR` (#7266) +* Dropped docker build workflow and migrated to Nvidia Blossom system (#7450) +* Dropped Python 3.8 test on quick-py3 workflow (#7719) + ## [1.3.0] - 2023-10-12 ### Added * Intensity transforms `ScaleIntensityFixedMean` and `RandScaleIntensityFixedMean` (#6542) @@ -943,7 +1035,8 @@ the postprocessing steps should be used before calling the metrics methods [highlights]: https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md -[Unreleased]: https://github.com/Project-MONAI/MONAI/compare/1.3.0...HEAD +[Unreleased]: https://github.com/Project-MONAI/MONAI/compare/1.3.1...HEAD +[1.3.1]: https://github.com/Project-MONAI/MONAI/compare/1.3.0...1.3.1 [1.3.0]: https://github.com/Project-MONAI/MONAI/compare/1.2.0...1.3.0 [1.2.0]: https://github.com/Project-MONAI/MONAI/compare/1.1.0...1.2.0 [1.1.0]: https://github.com/Project-MONAI/MONAI/compare/1.0.1...1.1.0 From b16f54ab82e665f8643043ffba5c917fa417121b Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Sat, 18 May 2024 23:49:49 +0800 Subject: [PATCH 119/136] Pin transformer's version (#7782) workaround for #7781 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- docs/requirements.txt | 2 +- requirements-dev.txt | 2 +- setup.cfg | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 468545dc19..007281ac35 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -21,7 +21,7 @@ sphinxcontrib-serializinghtml sphinx-autodoc-typehints==1.11.1 pandas einops -transformers<4.22; python_version <= '3.10' # https://github.com/Project-MONAI/MONAI/issues/5157 +transformers>=4.36.0, <4.41.0; python_version <= '3.10' mlflow>=2.12.2 clearml>=1.10.0rc0 tensorboardX diff --git a/requirements-dev.txt b/requirements-dev.txt index 09aeb22cac..a8ba25966b 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -33,7 +33,7 @@ tifffile; platform_system == "Linux" or platform_system == "Darwin" pandas requests einops -transformers>=4.36.0 +transformers>=4.36.0, <4.41.0; python_version <= '3.10' mlflow>=2.12.2 clearml>=1.10.0rc0 matplotlib>=3.6.3 diff --git a/setup.cfg b/setup.cfg index 086722914e..7b82784a8a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -65,7 +65,7 @@ all = imagecodecs pandas einops - transformers<4.22; python_version <= '3.10' + transformers>=4.36.0, <4.41.0; python_version <= '3.10' mlflow>=2.12.2 clearml>=1.10.0rc0 matplotlib>=3.6.3 @@ -123,7 +123,7 @@ pandas = einops = einops transformers = - transformers<4.22; python_version <= '3.10' + transformers>=4.36.0, <4.41.0; python_version <= '3.10' mlflow = mlflow>=2.12.2 matplotlib = From 96bfda00c6bd290297f5e3514ea227c6be4d08b4 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 21 May 2024 02:03:26 +0800 Subject: [PATCH 120/136] Skip failed tests (#7783) ### Description - skip release_tag_docker for resource issue - increase tolerance for tests/test_clip_intensity_percentiles.py - skip tests/test_regularization.py for non-deterministic ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .github/workflows/release.yml | 3 ++- README.md | 1 - tests/test_clip_intensity_percentiles.py | 15 +++++++-------- tests/test_clip_intensity_percentilesd.py | 14 +++++++------- tests/test_regularization.py | 3 +++ 5 files changed, 19 insertions(+), 17 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index c134724665..60b610565e 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -119,7 +119,8 @@ jobs: rm -rf {*,.[^.]*} release_tag_docker: - if: github.repository == 'Project-MONAI/MONAI' + # if: github.repository == 'Project-MONAI/MONAI' + if: ${{ false }} needs: versioning runs-on: ubuntu-latest steps: diff --git a/README.md b/README.md index 7565fea1b7..5345cdb926 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,6 @@ [![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) -[![docker](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/) [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI) diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index af157446f6..a821558fb7 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -22,7 +22,6 @@ class TestClipIntensityPercentiles2D(NumpyImageTestCase2D): - @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) @@ -58,7 +57,7 @@ def test_soft_clipping_two_sided(self, p): lower, upper = percentile(im, (5, 95)) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_high(self, p): @@ -68,7 +67,7 @@ def test_soft_clipping_one_sided_high(self, p): upper = percentile(im, 95) expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_low(self, p): @@ -78,7 +77,7 @@ def test_soft_clipping_one_sided_low(self, p): lower = percentile(im, 5) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_channel_wise(self, p): @@ -147,8 +146,8 @@ def test_soft_clipping_two_sided(self, p): result = soft_clipper(im) lower, upper = percentile(im, (5, 95)) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_high(self, p): @@ -158,7 +157,7 @@ def test_soft_clipping_one_sided_high(self, p): upper = percentile(im, 95) expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=5e-5, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_low(self, p): @@ -168,7 +167,7 @@ def test_soft_clipping_one_sided_low(self, p): lower = percentile(im, 5) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result, p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_channel_wise(self, p): diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index ed4fc588cb..98840419a0 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -63,7 +63,7 @@ def test_soft_clipping_two_sided(self, p): lower, upper = percentile(im, (5, 95)) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_high(self, p): @@ -74,7 +74,7 @@ def test_soft_clipping_one_sided_high(self, p): upper = percentile(im, 95) expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_low(self, p): @@ -85,7 +85,7 @@ def test_soft_clipping_one_sided_low(self, p): lower = percentile(im, 5) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_channel_wise(self, p): @@ -164,8 +164,8 @@ def test_soft_clipping_two_sided(self, p): result = soft_clipper({key: im}) lower, upper = percentile(im, (5, 95)) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_high(self, p): @@ -176,7 +176,7 @@ def test_soft_clipping_one_sided_high(self, p): upper = percentile(im, 95) expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=5e-5, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_soft_clipping_one_sided_low(self, p): @@ -187,7 +187,7 @@ def test_soft_clipping_one_sided_low(self, p): lower = percentile(im, 5) expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy - assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-6, atol=0) + assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_channel_wise(self, p): diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 4df60b9808..32df2f7b41 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -19,6 +19,7 @@ from monai.utils import set_determinism +@unittest.skip("Mixup is non-deterministic. Skip it temporarily") class TestMixup(unittest.TestCase): def setUp(self) -> None: @@ -59,6 +60,7 @@ def test_mixupd(self): MixUpd(["k1", "k2"], 6, -0.5) +@unittest.skip("CutMix is non-deterministic. Skip it temporarily") class TestCutMix(unittest.TestCase): def setUp(self) -> None: @@ -90,6 +92,7 @@ def test_cutmixd(self): self.assertTrue(torch.allclose(output["lbl1"], output["lbl2"])) +@unittest.skip("CutOut is non-deterministic. Skip it temporarily") class TestCutOut(unittest.TestCase): def setUp(self) -> None: From 244148d76e3ce2fe83c36226027d9f02acd43c02 Mon Sep 17 00:00:00 2001 From: ytl0623 Date: Tue, 21 May 2024 15:16:38 +0800 Subject: [PATCH 121/136] Add function in monai.transforms.utils.py (#7712) Fixes #6704 ### Description Combined `map_classes_to_indices` and `generate_label_classes_crop_centers` to a single function `map_and_generate_sampling_centers` in monai.transforms.utils.py. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: ytl0623 Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yu <146002968+Yu0610@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/transforms/__init__.py | 1 + monai/transforms/utils.py | 65 ++++++++++++++ .../test_map_and_generate_sampling_centers.py | 87 +++++++++++++++++++ 3 files changed, 153 insertions(+) create mode 100644 tests/test_map_and_generate_sampling_centers.py diff --git a/monai/transforms/__init__.py b/monai/transforms/__init__.py index ab9adb6a99..ef1da2d855 100644 --- a/monai/transforms/__init__.py +++ b/monai/transforms/__init__.py @@ -671,6 +671,7 @@ in_bounds, is_empty, is_positive, + map_and_generate_sampling_centers, map_binary_to_indices, map_classes_to_indices, map_spatial_axes, diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py index 560dbac346..d8461d927b 100644 --- a/monai/transforms/utils.py +++ b/monai/transforms/utils.py @@ -108,6 +108,7 @@ "in_bounds", "is_empty", "is_positive", + "map_and_generate_sampling_centers", "map_binary_to_indices", "map_classes_to_indices", "map_spatial_axes", @@ -368,6 +369,70 @@ def check_non_lazy_pending_ops( warnings.warn(msg) +def map_and_generate_sampling_centers( + label: NdarrayOrTensor, + spatial_size: Sequence[int] | int, + num_samples: int, + label_spatial_shape: Sequence[int] | None = None, + num_classes: int | None = None, + image: NdarrayOrTensor | None = None, + image_threshold: float = 0.0, + max_samples_per_class: int | None = None, + ratios: list[float | int] | None = None, + rand_state: np.random.RandomState | None = None, + allow_smaller: bool = False, + warn: bool = True, +) -> tuple[tuple]: + """ + Combine "map_classes_to_indices" and "generate_label_classes_crop_centers" functions, return crop center coordinates. + This calls `map_classes_to_indices` to get indices from `label`, gets the shape from `label_spatial_shape` + is given otherwise from the labels, calls `generate_label_classes_crop_centers`, and returns its results. + + Args: + label: use the label data to get the indices of every class. + spatial_size: spatial size of the ROIs to be sampled. + num_samples: total sample centers to be generated. + label_spatial_shape: spatial shape of the original label data to unravel selected centers. + indices: sequence of pre-computed foreground indices of every class in 1 dimension. + num_classes: number of classes for argmax label, not necessary for One-Hot label. + image: if image is not None, only return the indices of every class that are within the valid + region of the image (``image > image_threshold``). + image_threshold: if enabled `image`, use ``image > image_threshold`` to + determine the valid image content area and select class indices only in this area. + max_samples_per_class: maximum length of indices in each class to reduce memory consumption. + Default is None, no subsampling. + ratios: ratios of every class in the label to generate crop centers, including background class. + if None, every class will have the same ratio to generate crop centers. + rand_state: numpy randomState object to align with other modules. + allow_smaller: if `False`, an exception will be raised if the image is smaller than + the requested ROI in any dimension. If `True`, any smaller dimensions will be set to + match the cropped size (i.e., no cropping in that dimension). + warn: if `True` prints a warning if a class is not present in the label. + Returns: + Tuple of crop centres + """ + if label is None: + raise ValueError("label must not be None.") + indices = map_classes_to_indices(label, num_classes, image, image_threshold, max_samples_per_class) + + if label_spatial_shape is not None: + _shape = label_spatial_shape + elif isinstance(label, monai.data.MetaTensor): + _shape = label.peek_pending_shape() + else: + _shape = label.shape[1:] + + if _shape is None: + raise ValueError( + "label_spatial_shape or label with a known shape must be provided to infer the output spatial shape." + ) + centers = generate_label_classes_crop_centers( + spatial_size, num_samples, _shape, indices, ratios, rand_state, allow_smaller, warn + ) + + return ensure_tuple(centers) + + def map_binary_to_indices( label: NdarrayOrTensor, image: NdarrayOrTensor | None = None, image_threshold: float = 0.0 ) -> tuple[NdarrayOrTensor, NdarrayOrTensor]: diff --git a/tests/test_map_and_generate_sampling_centers.py b/tests/test_map_and_generate_sampling_centers.py new file mode 100644 index 0000000000..ff74f974b9 --- /dev/null +++ b/tests/test_map_and_generate_sampling_centers.py @@ -0,0 +1,87 @@ +# Copyright (c) MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import unittest +from copy import deepcopy + +import numpy as np +from parameterized import parameterized + +from monai.transforms import map_and_generate_sampling_centers +from monai.utils.misc import set_determinism +from tests.utils import TEST_NDARRAYS, assert_allclose + +TEST_CASE_1 = [ + # test Argmax data + { + "label": (np.array([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])), + "spatial_size": [2, 2, 2], + "num_samples": 2, + "label_spatial_shape": [3, 3, 3], + "num_classes": 3, + "image": None, + "ratios": [0, 1, 2], + "image_threshold": 0.0, + }, + tuple, + 2, + 3, +] + +TEST_CASE_2 = [ + { + "label": ( + np.array( + [ + [[1, 0, 0], [0, 1, 0], [0, 0, 1]], + [[0, 1, 0], [0, 0, 1], [1, 0, 0]], + [[0, 0, 1], [1, 0, 0], [0, 1, 0]], + ] + ) + ), + "spatial_size": [2, 2, 2], + "num_samples": 1, + "ratios": None, + "label_spatial_shape": [3, 3, 3], + "image": None, + "image_threshold": 0.0, + }, + tuple, + 1, + 3, +] + + +class TestMapAndGenerateSamplingCenters(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) + def test_map_and_generate_sampling_centers(self, input_data, expected_type, expected_count, expected_shape): + results = [] + for p in TEST_NDARRAYS + (None,): + input_data = deepcopy(input_data) + if p is not None: + input_data["label"] = p(input_data["label"]) + set_determinism(0) + result = map_and_generate_sampling_centers(**input_data) + self.assertIsInstance(result, expected_type) + self.assertEqual(len(result), expected_count) + self.assertEqual(len(result[0]), expected_shape) + # check for consistency between numpy, torch and torch.cuda + results.append(result) + if len(results) > 1: + for x, y in zip(result[0], result[-1]): + assert_allclose(x, y, type_test=False) + + +if __name__ == "__main__": + unittest.main() From 66a2fae406f8d83a4421edd86a04fa787dbbd091 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 21 May 2024 19:05:34 +0800 Subject: [PATCH 122/136] 7753 update releasing 1.3.1 (#7788) Fixes #7753 ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- CITATION.cff | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index cac47faae4..4754c5b2e3 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -6,8 +6,8 @@ title: "MONAI: Medical Open Network for AI" abstract: "AI Toolkit for Healthcare Imaging" authors: - name: "MONAI Consortium" -date-released: 2023-10-12 -version: "1.3.0" +date-released: 2024-05-21 +version: "1.3.1" identifiers: - description: "This DOI represents all versions of MONAI, and will always resolve to the latest one." type: doi From 373c003d94009d021b66fcf08a1b650228d7079b Mon Sep 17 00:00:00 2001 From: Pkaps25 <43655728+Pkaps25@users.noreply.github.com> Date: Thu, 23 May 2024 03:00:22 -0400 Subject: [PATCH 123/136] Add norm param to ResNet (#7752) Fixes #7294 . ### Description Adds a `norm` param to ResNet ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Peter Kaplinsky Co-authored-by: Peter Kaplinsky Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/nets/daf3d.py | 24 ++++++++++++------- monai/networks/nets/resnet.py | 44 ++++++++++++++++++++++------------- tests/test_resnet.py | 19 ++++++++++++++- 3 files changed, 62 insertions(+), 25 deletions(-) diff --git a/monai/networks/nets/daf3d.py b/monai/networks/nets/daf3d.py index 31c2bf4c31..02e5bb022a 100644 --- a/monai/networks/nets/daf3d.py +++ b/monai/networks/nets/daf3d.py @@ -13,6 +13,7 @@ from collections import OrderedDict from collections.abc import Callable, Sequence +from functools import partial import torch import torch.nn as nn @@ -25,6 +26,7 @@ from monai.networks.blocks.convolutions import Convolution from monai.networks.blocks.feature_pyramid_network import ExtraFPNBlock, FeaturePyramidNetwork from monai.networks.layers.factories import Conv, Norm +from monai.networks.layers.utils import get_norm_layer from monai.networks.nets.resnet import ResNet, ResNetBottleneck __all__ = [ @@ -170,27 +172,31 @@ class Daf3dResNetBottleneck(ResNetBottleneck): spatial_dims: number of spatial dimensions of the input image. stride: stride to use for second conv layer. downsample: which downsample layer to use. + norm: which normalization layer to use. Defaults to group. """ expansion = 2 - def __init__(self, in_planes, planes, spatial_dims=3, stride=1, downsample=None): - norm_type: Callable = Norm[Norm.GROUP, spatial_dims] + def __init__( + self, in_planes, planes, spatial_dims=3, stride=1, downsample=None, norm=("group", {"num_groups": 32}) + ): conv_type: Callable = Conv[Conv.CONV, spatial_dims] + norm_layer = partial(get_norm_layer, name=norm, spatial_dims=spatial_dims) + # in case downsample uses batch norm, change to group norm if isinstance(downsample, nn.Sequential): downsample = nn.Sequential( conv_type(in_planes, planes * self.expansion, kernel_size=1, stride=stride, bias=False), - norm_type(num_groups=32, num_channels=planes * self.expansion), + norm_layer(channels=planes * self.expansion), ) super().__init__(in_planes, planes, spatial_dims, stride, downsample) # change norm from batch to group norm - self.bn1 = norm_type(num_groups=32, num_channels=planes) - self.bn2 = norm_type(num_groups=32, num_channels=planes) - self.bn3 = norm_type(num_groups=32, num_channels=planes * self.expansion) + self.bn1 = norm_layer(channels=planes) + self.bn2 = norm_layer(channels=planes) + self.bn3 = norm_layer(channels=planes * self.expansion) # adapt second convolution to work with groups self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, stride=stride, groups=32, bias=False) @@ -212,8 +218,10 @@ class Daf3dResNetDilatedBottleneck(Daf3dResNetBottleneck): downsample: which downsample layer to use. """ - def __init__(self, in_planes, planes, spatial_dims=3, stride=1, downsample=None): - super().__init__(in_planes, planes, spatial_dims, stride, downsample) + def __init__( + self, in_planes, planes, spatial_dims=3, stride=1, downsample=None, norm=("group", {"num_groups": 32}) + ): + super().__init__(in_planes, planes, spatial_dims, stride, downsample, norm) # add dilation in second convolution conv_type: Callable = Conv[Conv.CONV, spatial_dims] diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index 93a77d7b2a..2cd7c8102a 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -22,8 +22,8 @@ import torch.nn as nn from monai.networks.blocks.encoder import BaseEncoder -from monai.networks.layers.factories import Conv, Norm, Pool -from monai.networks.layers.utils import get_act_layer, get_pool_layer +from monai.networks.layers.factories import Conv, Pool +from monai.networks.layers.utils import get_act_layer, get_norm_layer, get_pool_layer from monai.utils import ensure_tuple_rep from monai.utils.module import look_up_option, optional_import @@ -79,6 +79,7 @@ def __init__( stride: int = 1, downsample: nn.Module | partial | None = None, act: str | tuple = ("relu", {"inplace": True}), + norm: str | tuple = "batch", ) -> None: """ Args: @@ -88,17 +89,18 @@ def __init__( stride: stride to use for first conv layer. downsample: which downsample layer to use. act: activation type and arguments. Defaults to relu. + norm: feature normalization type and arguments. Defaults to batch norm. """ super().__init__() conv_type: Callable = Conv[Conv.CONV, spatial_dims] - norm_type: Callable = Norm[Norm.BATCH, spatial_dims] + norm_layer = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=planes) self.conv1 = conv_type(in_planes, planes, kernel_size=3, padding=1, stride=stride, bias=False) - self.bn1 = norm_type(planes) + self.bn1 = norm_layer self.act = get_act_layer(name=act) self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, bias=False) - self.bn2 = norm_type(planes) + self.bn2 = norm_layer self.downsample = downsample self.stride = stride @@ -132,6 +134,7 @@ def __init__( stride: int = 1, downsample: nn.Module | partial | None = None, act: str | tuple = ("relu", {"inplace": True}), + norm: str | tuple = "batch", ) -> None: """ Args: @@ -141,19 +144,20 @@ def __init__( stride: stride to use for second conv layer. downsample: which downsample layer to use. act: activation type and arguments. Defaults to relu. + norm: feature normalization type and arguments. Defaults to batch norm. """ super().__init__() conv_type: Callable = Conv[Conv.CONV, spatial_dims] - norm_type: Callable = Norm[Norm.BATCH, spatial_dims] + norm_layer = partial(get_norm_layer, name=norm, spatial_dims=spatial_dims) self.conv1 = conv_type(in_planes, planes, kernel_size=1, bias=False) - self.bn1 = norm_type(planes) + self.bn1 = norm_layer(channels=planes) self.conv2 = conv_type(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = norm_type(planes) + self.bn2 = norm_layer(channels=planes) self.conv3 = conv_type(planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = norm_type(planes * self.expansion) + self.bn3 = norm_layer(channels=planes * self.expansion) self.act = get_act_layer(name=act) self.downsample = downsample self.stride = stride @@ -207,6 +211,7 @@ class ResNet(nn.Module): feed_forward: whether to add the FC layer for the output, default to `True`. bias_downsample: whether to use bias term in the downsampling block when `shortcut_type` is 'B', default to `True`. act: activation type and arguments. Defaults to relu. + norm: feature normalization type and arguments. Defaults to batch norm. """ @@ -226,6 +231,7 @@ def __init__( feed_forward: bool = True, bias_downsample: bool = True, # for backwards compatibility (also see PR #5477) act: str | tuple = ("relu", {"inplace": True}), + norm: str | tuple = "batch", ) -> None: super().__init__() @@ -238,7 +244,6 @@ def __init__( raise ValueError("Unknown block '%s', use basic or bottleneck" % block) conv_type: type[nn.Conv1d | nn.Conv2d | nn.Conv3d] = Conv[Conv.CONV, spatial_dims] - norm_type: type[nn.BatchNorm1d | nn.BatchNorm2d | nn.BatchNorm3d] = Norm[Norm.BATCH, spatial_dims] pool_type: type[nn.MaxPool1d | nn.MaxPool2d | nn.MaxPool3d] = Pool[Pool.MAX, spatial_dims] avgp_type: type[nn.AdaptiveAvgPool1d | nn.AdaptiveAvgPool2d | nn.AdaptiveAvgPool3d] = Pool[ Pool.ADAPTIVEAVG, spatial_dims @@ -262,7 +267,9 @@ def __init__( padding=tuple(k // 2 for k in conv1_kernel_size), bias=False, ) - self.bn1 = norm_type(self.in_planes) + + norm_layer = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=self.in_planes) + self.bn1 = norm_layer self.act = get_act_layer(name=act) self.maxpool = pool_type(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, block_inplanes[0], layers[0], spatial_dims, shortcut_type) @@ -275,7 +282,7 @@ def __init__( for m in self.modules(): if isinstance(m, conv_type): nn.init.kaiming_normal_(torch.as_tensor(m.weight), mode="fan_out", nonlinearity="relu") - elif isinstance(m, norm_type): + elif isinstance(m, type(norm_layer)): nn.init.constant_(torch.as_tensor(m.weight), 1) nn.init.constant_(torch.as_tensor(m.bias), 0) elif isinstance(m, nn.Linear): @@ -295,9 +302,9 @@ def _make_layer( spatial_dims: int, shortcut_type: str, stride: int = 1, + norm: str | tuple = "batch", ) -> nn.Sequential: conv_type: Callable = Conv[Conv.CONV, spatial_dims] - norm_type: Callable = Norm[Norm.BATCH, spatial_dims] downsample: nn.Module | partial | None = None if stride != 1 or self.in_planes != planes * block.expansion: @@ -317,18 +324,23 @@ def _make_layer( stride=stride, bias=self.bias_downsample, ), - norm_type(planes * block.expansion), + get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=planes * block.expansion), ) layers = [ block( - in_planes=self.in_planes, planes=planes, spatial_dims=spatial_dims, stride=stride, downsample=downsample + in_planes=self.in_planes, + planes=planes, + spatial_dims=spatial_dims, + stride=stride, + downsample=downsample, + norm=norm, ) ] self.in_planes = planes * block.expansion for _i in range(1, blocks): - layers.append(block(self.in_planes, planes, spatial_dims=spatial_dims)) + layers.append(block(self.in_planes, planes, spatial_dims=spatial_dims, norm=norm)) return nn.Sequential(*layers) diff --git a/tests/test_resnet.py b/tests/test_resnet.py index 3a58d1c955..e873f1238a 100644 --- a/tests/test_resnet.py +++ b/tests/test_resnet.py @@ -202,13 +202,30 @@ (1, 3), ] +TEST_CASE_9 = [ # Layer norm + { + "block": ResNetBlock, + "layers": [3, 4, 6, 3], + "block_inplanes": [64, 128, 256, 512], + "spatial_dims": 1, + "n_input_channels": 2, + "num_classes": 3, + "conv1_t_size": [3], + "conv1_t_stride": 1, + "act": ("relu", {"inplace": False}), + "norm": ("layer", {"normalized_shape": (64, 32)}), + }, + (1, 2, 32), + (1, 3), +] + TEST_CASES = [] PRETRAINED_TEST_CASES = [] for case in [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_2_A, TEST_CASE_3_A]: for model in [resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200]: TEST_CASES.append([model, *case]) PRETRAINED_TEST_CASES.append([model, *case]) -for case in [TEST_CASE_5, TEST_CASE_5_A, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]: +for case in [TEST_CASE_5, TEST_CASE_5_A, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8, TEST_CASE_9]: TEST_CASES.append([ResNet, *case]) TEST_SCRIPT_CASES = [ From e5afa4341b18149ca8d44f680f669b33ab423722 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 23 May 2024 22:35:37 +0800 Subject: [PATCH 124/136] Fix tests/test_warp.py (#7794) Fixes #7793 ### Description The Update() method in ITK is used to trigger the execution of the pipeline. Add it to checks if the output data is up-to-date. If it is not (for example, if the input data or parameters have changed), it will recompute the output. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_warp.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_warp.py b/tests/test_warp.py index 55f40764c3..0e5f2466db 100644 --- a/tests/test_warp.py +++ b/tests/test_warp.py @@ -217,6 +217,7 @@ def itk_warp(img, ddf): # warp warp_filter.SetDisplacementField(displacement_field) warp_filter.SetInput(itk_img) + warp_filter.Update() warped_img = warp_filter.GetOutput() warped_img = np.asarray(warped_img) From ad6a4339cd5c8d9272c3381a22fce7441e3e0c01 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Mon, 27 May 2024 14:54:14 +0800 Subject: [PATCH 125/136] Fix Resnet (#7805) Fixes #7802. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- monai/networks/nets/resnet.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/monai/networks/nets/resnet.py b/monai/networks/nets/resnet.py index 2cd7c8102a..6e61db07ca 100644 --- a/monai/networks/nets/resnet.py +++ b/monai/networks/nets/resnet.py @@ -94,13 +94,12 @@ def __init__( super().__init__() conv_type: Callable = Conv[Conv.CONV, spatial_dims] - norm_layer = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=planes) self.conv1 = conv_type(in_planes, planes, kernel_size=3, padding=1, stride=stride, bias=False) - self.bn1 = norm_layer + self.bn1 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=planes) self.act = get_act_layer(name=act) self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, bias=False) - self.bn2 = norm_layer + self.bn2 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=planes) self.downsample = downsample self.stride = stride From 94ab632dca0fb39be4a119683183bb7027106f1a Mon Sep 17 00:00:00 2001 From: monai-bot <64792179+monai-bot@users.noreply.github.com> Date: Mon, 27 May 2024 11:00:36 +0100 Subject: [PATCH 126/136] auto updates (#7807) Signed-off-by: monai-bot Signed-off-by: monai-bot Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> --- tests/test_clip_intensity_percentiles.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index a821558fb7..cab2a89a47 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -22,6 +22,7 @@ class TestClipIntensityPercentiles2D(NumpyImageTestCase2D): + @parameterized.expand([[p] for p in TEST_NDARRAYS]) def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) From 762b5253efa364b1b740876eb2f9bf838ac7bed0 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 28 May 2024 22:36:50 +0800 Subject: [PATCH 127/136] Fix precision issue in TestClipIntensityPercentiles3D (#7808) Fixes #7797 ### Description Ensure the same dtype when test to avoid precision issue. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- tests/test_clip_intensity_percentiles.py | 75 +++++++++++++---------- tests/test_clip_intensity_percentilesd.py | 55 +++++++---------- 2 files changed, 67 insertions(+), 63 deletions(-) diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py index cab2a89a47..77f811db87 100644 --- a/tests/test_clip_intensity_percentiles.py +++ b/tests/test_clip_intensity_percentiles.py @@ -18,9 +18,32 @@ from monai.transforms import ClipIntensityPercentiles from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose +def test_hard_clip_func(im, lower, upper): + im_t = convert_to_tensor(im) + if lower is None: + upper = percentile(im_t, upper) + elif upper is None: + lower = percentile(im_t, lower) + else: + lower, upper = percentile(im_t, (lower, upper)) + return clip(im_t, lower, upper) + + +def test_soft_clip_func(im, lower, upper): + im_t = convert_to_tensor(im) + if lower is None: + upper = percentile(im_t, upper) + elif upper is None: + lower = percentile(im_t, lower) + else: + lower, upper = percentile(im_t, (lower, upper)) + return soft_clip(im_t, minv=lower, maxv=upper, sharpness_factor=1.0, dtype=torch.float32) + + class TestClipIntensityPercentiles2D(NumpyImageTestCase2D): @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -28,8 +51,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -37,8 +59,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -46,8 +67,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -55,9 +75,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -65,9 +84,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -75,9 +93,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -85,7 +102,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -118,8 +136,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -127,8 +144,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentiles(upper=95, lower=None) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -136,8 +152,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentiles(upper=None, lower=5) im = p(self.imt) result = hard_clipper(im) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -145,8 +160,7 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, 95) # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -155,9 +169,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentiles(upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -165,9 +178,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentiles(upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper(im) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result, p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -175,7 +187,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentiles(upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper(im) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0) diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index 98840419a0..3e06b18418 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -13,14 +13,15 @@ import unittest -import torch from parameterized import parameterized from monai.transforms import ClipIntensityPercentilesd -from monai.transforms.utils import soft_clip from monai.transforms.utils_pytorch_numpy_unification import clip, percentile +from monai.utils.type_conversion import convert_to_tensor from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose +from .test_clip_intensity_percentiles import test_hard_clip_func, test_soft_clip_func + class TestClipIntensityPercentilesd2D(NumpyImageTestCase2D): @@ -30,8 +31,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -40,8 +40,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -50,8 +49,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -60,9 +58,8 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -71,9 +68,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -82,9 +78,8 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) - # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, 5, None) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -93,7 +88,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(im): + im_t = convert_to_tensor(self.imt) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-3, atol=0) @@ -132,8 +128,7 @@ def test_hard_clipping_two_sided(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -142,8 +137,7 @@ def test_hard_clipping_one_sided_high(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (0, 95)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 0, 95) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -152,8 +146,7 @@ def test_hard_clipping_one_sided_low(self, p): hard_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5) im = p(self.imt) result = hard_clipper({key: im}) - lower, upper = percentile(im, (5, 100)) - expected = clip(im, lower, upper) + expected = test_hard_clip_func(im, 5, 100) assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -162,8 +155,7 @@ def test_soft_clipping_two_sided(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower, upper = percentile(im, (5, 95)) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=upper, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, 95) # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -173,9 +165,8 @@ def test_soft_clipping_one_sided_high(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=None, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - upper = percentile(im, 95) - expected = soft_clip(im, sharpness_factor=1.0, minv=None, maxv=upper, dtype=torch.float32) - # the rtol is set to 5e-5 because the logaddexp function used in softplus is not stable accross torch and numpy + expected = test_soft_clip_func(im, None, 95) + # the rtol is set to 1e-4 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @parameterized.expand([[p] for p in TEST_NDARRAYS]) @@ -184,8 +175,7 @@ def test_soft_clipping_one_sided_low(self, p): soft_clipper = ClipIntensityPercentilesd(keys=[key], upper=None, lower=5, sharpness_factor=1.0) im = p(self.imt) result = soft_clipper({key: im}) - lower = percentile(im, 5) - expected = soft_clip(im, sharpness_factor=1.0, minv=lower, maxv=None, dtype=torch.float32) + expected = test_soft_clip_func(im, 5, None) # the rtol is set to 1e-6 because the logaddexp function used in softplus is not stable accross torch and numpy assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-4, atol=0) @@ -195,7 +185,8 @@ def test_channel_wise(self, p): clipper = ClipIntensityPercentilesd(keys=[key], upper=95, lower=5, channel_wise=True) im = p(self.imt) result = clipper({key: im}) - for i, c in enumerate(im): + im_t = convert_to_tensor(im) + for i, c in enumerate(im_t): lower, upper = percentile(c, (5, 95)) expected = clip(c, lower, upper) assert_allclose(result[key][i], p(expected), type_test="tensor", rtol=1e-4, atol=0) From a0935d95a1746687576bca34666407ebbe38ad71 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 28 May 2024 23:34:47 +0800 Subject: [PATCH 128/136] Fix negative strides issue in `NrrdReader` (#7809) Fixes #7798 ### Description Add copy to avoid negative strides when save spatial shape. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/data/image_reader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/data/image_reader.py b/monai/data/image_reader.py index 2361bb63a7..f5e199e2a3 100644 --- a/monai/data/image_reader.py +++ b/monai/data/image_reader.py @@ -1331,7 +1331,7 @@ def get_data(self, img: NrrdImage | list[NrrdImage]) -> tuple[np.ndarray, dict]: header[MetaKeys.SPACE] = SpaceKeys.LPS # assuming LPS if not specified header[MetaKeys.AFFINE] = header[MetaKeys.ORIGINAL_AFFINE].copy() - header[MetaKeys.SPATIAL_SHAPE] = header["sizes"] + header[MetaKeys.SPATIAL_SHAPE] = header["sizes"].copy() [header.pop(k) for k in ("sizes", "space origin", "space directions")] # rm duplicated data in header if self.channel_dim is None: # default to "no_channel" or -1 From 0d7f77225669d1165a0d923d4ff54fd4bfa28607 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Thu, 30 May 2024 21:58:51 +0800 Subject: [PATCH 129/136] Ensure deterministic in MixUp, CutMix, CutOut (#7813) Fixes #7697 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- monai/transforms/regularization/array.py | 52 ++++++++---- monai/transforms/regularization/dictionary.py | 80 ++++++++++++------ tests/test_regularization.py | 83 ++++++++++++------- 3 files changed, 146 insertions(+), 69 deletions(-) diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py index 0b495c8623..a7436bda84 100644 --- a/monai/transforms/regularization/array.py +++ b/monai/transforms/regularization/array.py @@ -16,6 +16,9 @@ import torch +from monai.data.meta_obj import get_track_meta +from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor + from ..transform import RandomizableTransform __all__ = ["MixUp", "CutMix", "CutOut", "Mixer"] @@ -53,9 +56,11 @@ def randomize(self, data=None) -> None: as needed. You need to call this method everytime you apply the transform to a new batch. """ + super().randomize(None) self._params = ( torch.from_numpy(self.R.beta(self.alpha, self.alpha, self.batch_size)).type(torch.float32), self.R.permutation(self.batch_size), + [torch.from_numpy(self.R.randint(0, d, size=(1,))) for d in data.shape[2:]] if data is not None else [], ) @@ -69,7 +74,7 @@ class MixUp(Mixer): """ def apply(self, data: torch.Tensor): - weight, perm = self._params + weight, perm, _ = self._params nsamples, *dims = data.shape if len(weight) != nsamples: raise ValueError(f"Expected batch of size: {len(weight)}, but got {nsamples}") @@ -80,11 +85,18 @@ def apply(self, data: torch.Tensor): mixweight = weight[(Ellipsis,) + (None,) * len(dims)] return mixweight * data + (1 - mixweight) * data[perm, ...] - def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None): - self.randomize() + def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): + data_t = convert_to_tensor(data, track_meta=get_track_meta()) + if labels is not None: + labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) + if randomize: + self.randomize() if labels is None: - return self.apply(data) - return self.apply(data), self.apply(labels) + return convert_to_dst_type(self.apply(data_t), dst=data)[0] + return ( + convert_to_dst_type(self.apply(data_t), dst=data)[0], + convert_to_dst_type(self.apply(labels_t), dst=labels)[0], + ) class CutMix(Mixer): @@ -113,14 +125,13 @@ class CutMix(Mixer): """ def apply(self, data: torch.Tensor): - weights, perm = self._params + weights, perm, coords = self._params nsamples, _, *dims = data.shape if len(weights) != nsamples: raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") mask = torch.ones_like(data) for s, weight in enumerate(weights): - coords = [torch.randint(0, d, size=(1,)) for d in dims] lengths = [d * sqrt(1 - weight) for d in dims] idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)] mask[s][idx] = 0 @@ -128,7 +139,7 @@ def apply(self, data: torch.Tensor): return mask * data + (1 - mask) * data[perm, ...] def apply_on_labels(self, labels: torch.Tensor): - weights, perm = self._params + weights, perm, _ = self._params nsamples, *dims = labels.shape if len(weights) != nsamples: raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") @@ -136,10 +147,16 @@ def apply_on_labels(self, labels: torch.Tensor): mixweight = weights[(Ellipsis,) + (None,) * len(dims)] return mixweight * labels + (1 - mixweight) * labels[perm, ...] - def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None): - self.randomize() - augmented = self.apply(data) - return (augmented, self.apply_on_labels(labels)) if labels is not None else augmented + def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): + data_t = convert_to_tensor(data, track_meta=get_track_meta()) + if labels is not None: + labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) + if randomize: + self.randomize(data) + augmented = convert_to_dst_type(self.apply(data_t), dst=data)[0] + if labels is not None: + augmented_label = convert_to_dst_type(self.apply(labels_t), dst=labels)[0] + return (augmented, augmented_label) if labels is not None else augmented class CutOut(Mixer): @@ -155,20 +172,21 @@ class CutOut(Mixer): """ def apply(self, data: torch.Tensor): - weights, _ = self._params + weights, _, coords = self._params nsamples, _, *dims = data.shape if len(weights) != nsamples: raise ValueError(f"Expected batch of size: {len(weights)}, but got {nsamples}") mask = torch.ones_like(data) for s, weight in enumerate(weights): - coords = [torch.randint(0, d, size=(1,)) for d in dims] lengths = [d * sqrt(1 - weight) for d in dims] idx = [slice(None)] + [slice(c, min(ceil(c + ln), d)) for c, ln, d in zip(coords, lengths, dims)] mask[s][idx] = 0 return mask * data - def __call__(self, data: torch.Tensor): - self.randomize() - return self.apply(data) + def __call__(self, data: torch.Tensor, randomize=True): + data_t = convert_to_tensor(data, track_meta=get_track_meta()) + if randomize: + self.randomize(data) + return convert_to_dst_type(self.apply(data_t), dst=data)[0] diff --git a/monai/transforms/regularization/dictionary.py b/monai/transforms/regularization/dictionary.py index 373913da99..d8815e47b9 100644 --- a/monai/transforms/regularization/dictionary.py +++ b/monai/transforms/regularization/dictionary.py @@ -11,16 +11,23 @@ from __future__ import annotations +from collections.abc import Hashable + +import numpy as np + from monai.config import KeysCollection +from monai.config.type_definitions import NdarrayOrTensor +from monai.data.meta_obj import get_track_meta +from monai.utils import convert_to_tensor from monai.utils.misc import ensure_tuple -from ..transform import MapTransform +from ..transform import MapTransform, RandomizableTransform from .array import CutMix, CutOut, MixUp __all__ = ["MixUpd", "MixUpD", "MixUpDict", "CutMixd", "CutMixD", "CutMixDict", "CutOutd", "CutOutD", "CutOutDict"] -class MixUpd(MapTransform): +class MixUpd(MapTransform, RandomizableTransform): """ Dictionary-based version :py:class:`monai.transforms.MixUp`. @@ -31,18 +38,24 @@ class MixUpd(MapTransform): def __init__( self, keys: KeysCollection, batch_size: int, alpha: float = 1.0, allow_missing_keys: bool = False ) -> None: - super().__init__(keys, allow_missing_keys) + MapTransform.__init__(self, keys, allow_missing_keys) self.mixup = MixUp(batch_size, alpha) + def set_random_state(self, seed: int | None = None, state: np.random.RandomState | None = None) -> MixUpd: + super().set_random_state(seed, state) + self.mixup.set_random_state(seed, state) + return self + def __call__(self, data): - self.mixup.randomize() - result = dict(data) - for k in self.keys: - result[k] = self.mixup.apply(data[k]) - return result + d = dict(data) + # all the keys share the same random state + self.mixup.randomize(None) + for k in self.key_iterator(d): + d[k] = self.mixup(data[k], randomize=False) + return d -class CutMixd(MapTransform): +class CutMixd(MapTransform, RandomizableTransform): """ Dictionary-based version :py:class:`monai.transforms.CutMix`. @@ -63,17 +76,27 @@ def __init__( self.mixer = CutMix(batch_size, alpha) self.label_keys = ensure_tuple(label_keys) if label_keys is not None else [] - def __call__(self, data): - self.mixer.randomize() - result = dict(data) - for k in self.keys: - result[k] = self.mixer.apply(data[k]) - for k in self.label_keys: - result[k] = self.mixer.apply_on_labels(data[k]) - return result - + def set_random_state(self, seed: int | None = None, state: np.random.RandomState | None = None) -> CutMixd: + super().set_random_state(seed, state) + self.mixer.set_random_state(seed, state) + return self -class CutOutd(MapTransform): + def __call__(self, data): + d = dict(data) + first_key: Hashable = self.first_key(d) + if first_key == (): + out: dict[Hashable, NdarrayOrTensor] = convert_to_tensor(d, track_meta=get_track_meta()) + return out + self.mixer.randomize(d[first_key]) + for key, label_key in self.key_iterator(d, self.label_keys): + ret = self.mixer(data[key], data.get(label_key, None), randomize=False) + d[key] = ret[0] + if label_key in d: + d[label_key] = ret[1] + return d + + +class CutOutd(MapTransform, RandomizableTransform): """ Dictionary-based version :py:class:`monai.transforms.CutOut`. @@ -84,12 +107,21 @@ def __init__(self, keys: KeysCollection, batch_size: int, allow_missing_keys: bo super().__init__(keys, allow_missing_keys) self.cutout = CutOut(batch_size) + def set_random_state(self, seed: int | None = None, state: np.random.RandomState | None = None) -> CutOutd: + super().set_random_state(seed, state) + self.cutout.set_random_state(seed, state) + return self + def __call__(self, data): - result = dict(data) - self.cutout.randomize() - for k in self.keys: - result[k] = self.cutout(data[k]) - return result + d = dict(data) + first_key: Hashable = self.first_key(d) + if first_key == (): + out: dict[Hashable, NdarrayOrTensor] = convert_to_tensor(d, track_meta=get_track_meta()) + return out + self.cutout.randomize(d[first_key]) + for k in self.key_iterator(d): + d[k] = self.cutout(data[k], randomize=False) + return d MixUpD = MixUpDict = MixUpd diff --git a/tests/test_regularization.py b/tests/test_regularization.py index 32df2f7b41..12d64637d5 100644 --- a/tests/test_regularization.py +++ b/tests/test_regularization.py @@ -13,29 +13,31 @@ import unittest +import numpy as np import torch -from monai.transforms import CutMix, CutMixd, CutOut, MixUp, MixUpd -from monai.utils import set_determinism +from monai.transforms import CutMix, CutMixd, CutOut, CutOutd, MixUp, MixUpd +from tests.utils import assert_allclose -@unittest.skip("Mixup is non-deterministic. Skip it temporarily") class TestMixup(unittest.TestCase): - def setUp(self) -> None: - set_determinism(seed=0) - - def tearDown(self) -> None: - set_determinism(None) - def test_mixup(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims sample = torch.rand(*shape, dtype=torch.float32) mixup = MixUp(6, 1.0) + mixup.set_random_state(seed=0) output = mixup(sample) + np.random.seed(0) + # simulate the randomize() of transform + np.random.random() + weight = torch.from_numpy(np.random.beta(1.0, 1.0, 6)).type(torch.float32) + perm = np.random.permutation(6) self.assertEqual(output.shape, sample.shape) - self.assertTrue(any(not torch.allclose(sample, mixup(sample)) for _ in range(10))) + mixweight = weight[(Ellipsis,) + (None,) * (dims + 1)] + expected = mixweight * sample + (1 - mixweight) * sample[perm, ...] + assert_allclose(output, expected, type_test=False, atol=1e-7) with self.assertRaises(ValueError): MixUp(6, -0.5) @@ -53,27 +55,32 @@ def test_mixupd(self): t = torch.rand(*shape, dtype=torch.float32) sample = {"a": t, "b": t} mixup = MixUpd(["a", "b"], 6) + mixup.set_random_state(seed=0) output = mixup(sample) - self.assertTrue(torch.allclose(output["a"], output["b"])) + np.random.seed(0) + # simulate the randomize() of transform + np.random.random() + weight = torch.from_numpy(np.random.beta(1.0, 1.0, 6)).type(torch.float32) + perm = np.random.permutation(6) + self.assertEqual(output["a"].shape, sample["a"].shape) + mixweight = weight[(Ellipsis,) + (None,) * (dims + 1)] + expected = mixweight * sample["a"] + (1 - mixweight) * sample["a"][perm, ...] + assert_allclose(output["a"], expected, type_test=False, atol=1e-7) + assert_allclose(output["a"], output["b"], type_test=False, atol=1e-7) + # self.assertTrue(torch.allclose(output["a"], output["b"])) with self.assertRaises(ValueError): MixUpd(["k1", "k2"], 6, -0.5) -@unittest.skip("CutMix is non-deterministic. Skip it temporarily") class TestCutMix(unittest.TestCase): - def setUp(self) -> None: - set_determinism(seed=0) - - def tearDown(self) -> None: - set_determinism(None) - def test_cutmix(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims sample = torch.rand(*shape, dtype=torch.float32) cutmix = CutMix(6, 1.0) + cutmix.set_random_state(seed=0) output = cutmix(sample) self.assertEqual(output.shape, sample.shape) self.assertTrue(any(not torch.allclose(sample, cutmix(sample)) for _ in range(10))) @@ -85,30 +92,50 @@ def test_cutmixd(self): label = torch.randint(0, 1, shape) sample = {"a": t, "b": t, "lbl1": label, "lbl2": label} cutmix = CutMixd(["a", "b"], 6, label_keys=("lbl1", "lbl2")) + cutmix.set_random_state(seed=123) output = cutmix(sample) - # croppings are different on each application - self.assertTrue(not torch.allclose(output["a"], output["b"])) # but mixing of labels is not affected by it self.assertTrue(torch.allclose(output["lbl1"], output["lbl2"])) -@unittest.skip("CutOut is non-deterministic. Skip it temporarily") class TestCutOut(unittest.TestCase): - def setUp(self) -> None: - set_determinism(seed=0) - - def tearDown(self) -> None: - set_determinism(None) - def test_cutout(self): for dims in [2, 3]: shape = (6, 3) + (32,) * dims sample = torch.rand(*shape, dtype=torch.float32) cutout = CutOut(6, 1.0) + cutout.set_random_state(seed=123) output = cutout(sample) + np.random.seed(123) + # simulate the randomize() of transform + np.random.random() + weight = torch.from_numpy(np.random.beta(1.0, 1.0, 6)).type(torch.float32) + perm = np.random.permutation(6) + coords = [torch.from_numpy(np.random.randint(0, d, size=(1,))) for d in sample.shape[2:]] + assert_allclose(weight, cutout._params[0]) + assert_allclose(perm, cutout._params[1]) + self.assertSequenceEqual(coords, cutout._params[2]) self.assertEqual(output.shape, sample.shape) - self.assertTrue(any(not torch.allclose(sample, cutout(sample)) for _ in range(10))) + + def test_cutoutd(self): + for dims in [2, 3]: + shape = (6, 3) + (32,) * dims + t = torch.rand(*shape, dtype=torch.float32) + sample = {"a": t, "b": t} + cutout = CutOutd(["a", "b"], 6, 1.0) + cutout.set_random_state(seed=123) + output = cutout(sample) + np.random.seed(123) + # simulate the randomize() of transform + np.random.random() + weight = torch.from_numpy(np.random.beta(1.0, 1.0, 6)).type(torch.float32) + perm = np.random.permutation(6) + coords = [torch.from_numpy(np.random.randint(0, d, size=(1,))) for d in t.shape[2:]] + assert_allclose(weight, cutout.cutout._params[0]) + assert_allclose(perm, cutout.cutout._params[1]) + self.assertSequenceEqual(coords, cutout.cutout._params[2]) + self.assertEqual(output["a"].shape, sample["a"].shape) if __name__ == "__main__": From 4029c422f1da84cdf5e4ce546dbff33245cbe025 Mon Sep 17 00:00:00 2001 From: Suraj Pai Date: Thu, 30 May 2024 23:26:46 -0400 Subject: [PATCH 130/136] Refactor Dataset to use Compose for transforms (#7784) Fixes #7646 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Suraj Pai Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Ben Murray --- monai/data/dataset.py | 52 +++++++++-------------------- tests/test_arraydataset.py | 2 +- tests/test_dataset.py | 68 +++++++++++++++++++++++++++++++++++++- tests/test_profiling.py | 4 ++- 4 files changed, 86 insertions(+), 40 deletions(-) diff --git a/monai/data/dataset.py b/monai/data/dataset.py index 79e066303e..871b523289 100644 --- a/monai/data/dataset.py +++ b/monai/data/dataset.py @@ -36,15 +36,7 @@ from monai.data.meta_tensor import MetaTensor from monai.data.utils import SUPPORTED_PICKLE_MOD, convert_tables_to_dicts, pickle_hashing -from monai.transforms import ( - Compose, - Randomizable, - RandomizableTrait, - Transform, - apply_transform, - convert_to_contiguous, - reset_ops_id, -) +from monai.transforms import Compose, Randomizable, RandomizableTrait, Transform, convert_to_contiguous, reset_ops_id from monai.utils import MAX_SEED, convert_to_tensor, get_seed, look_up_option, min_version, optional_import from monai.utils.misc import first @@ -77,15 +69,19 @@ class Dataset(_TorchDataset): }, }, }] """ - def __init__(self, data: Sequence, transform: Callable | None = None) -> None: + def __init__(self, data: Sequence, transform: Sequence[Callable] | Callable | None = None) -> None: """ Args: data: input data to load and transform to generate dataset for model. - transform: a callable data transform on input data. - + transform: a callable, sequence of callables or None. If transform is not + a `Compose` instance, it will be wrapped in a `Compose` instance. Sequences + of callables are applied in order and if `None` is passed, the data is returned as is. """ self.data = data - self.transform: Any = transform + try: + self.transform = Compose(transform) if not isinstance(transform, Compose) else transform + except Exception as e: + raise ValueError("`transform` must be a callable or a list of callables that is Composable") from e def __len__(self) -> int: return len(self.data) @@ -95,7 +91,7 @@ def _transform(self, index: int): Fetch single data item from `self.data`. """ data_i = self.data[index] - return apply_transform(self.transform, data_i) if self.transform is not None else data_i + return self.transform(data_i) def __getitem__(self, index: int | slice | Sequence[int]): """ @@ -264,8 +260,6 @@ def __init__( using the cached content and with re-created transform instances. """ - if not isinstance(transform, Compose): - transform = Compose(transform) super().__init__(data=data, transform=transform) self.cache_dir = Path(cache_dir) if cache_dir is not None else None self.hash_func = hash_func @@ -323,9 +317,6 @@ def _pre_transform(self, item_transformed): random transform object """ - if not isinstance(self.transform, Compose): - raise ValueError("transform must be an instance of monai.transforms.Compose.") - first_random = self.transform.get_index_of_first( lambda t: isinstance(t, RandomizableTrait) or not isinstance(t, Transform) ) @@ -346,9 +337,6 @@ def _post_transform(self, item_transformed): the transformed element through the random transforms """ - if not isinstance(self.transform, Compose): - raise ValueError("transform must be an instance of monai.transforms.Compose.") - first_random = self.transform.get_index_of_first( lambda t: isinstance(t, RandomizableTrait) or not isinstance(t, Transform) ) @@ -501,9 +489,6 @@ def _pre_transform(self, item_transformed): Returns: the transformed element up to the N transform object """ - if not isinstance(self.transform, Compose): - raise ValueError("transform must be an instance of monai.transforms.Compose.") - item_transformed = self.transform(item_transformed, end=self.cache_n_trans, threading=True) reset_ops_id(item_transformed) @@ -519,9 +504,6 @@ def _post_transform(self, item_transformed): Returns: the final transformed result """ - if not isinstance(self.transform, Compose): - raise ValueError("transform must be an instance of monai.transforms.Compose.") - return self.transform(item_transformed, start=self.cache_n_trans) @@ -809,8 +791,6 @@ def __init__( Not following these recommendations may lead to runtime errors or duplicated cache across processes. """ - if not isinstance(transform, Compose): - transform = Compose(transform) super().__init__(data=data, transform=transform) self.set_num = cache_num # tracking the user-provided `cache_num` option self.set_rate = cache_rate # tracking the user-provided `cache_rate` option @@ -1282,8 +1262,10 @@ def to_list(x): data = [] for dataset in self.data: data.extend(to_list(dataset[index])) + if self.transform is not None: - data = apply_transform(self.transform, data, map_items=False) # transform the list data + self.transform.map_items = False # Compose object map_items to false so transform is applied to list + data = self.transform(data) # use tuple instead of list as the default collate_fn callback of MONAI DataLoader flattens nested lists return tuple(data) @@ -1432,15 +1414,11 @@ def __len__(self): def _transform(self, index: int): data = {k: v[index] for k, v in self.arrays.items()} - - if not self.transform: - return data - - result = apply_transform(self.transform, data) + result = self.transform(data) if self.transform is not None else data if isinstance(result, dict) or (isinstance(result, list) and isinstance(result[0], dict)): return result - raise AssertionError("With a dict supplied to apply_transform, should return a dict or a list of dicts.") + raise AssertionError("With a dict supplied to Compose, should return a dict or a list of dicts.") class CSVDataset(Dataset): diff --git a/tests/test_arraydataset.py b/tests/test_arraydataset.py index efc014a267..b61b3c139c 100644 --- a/tests/test_arraydataset.py +++ b/tests/test_arraydataset.py @@ -41,7 +41,7 @@ class TestCompose(Compose): - def __call__(self, input_, lazy): + def __call__(self, input_, lazy=False): img = self.transforms[0](input_) metadata = img.meta img = self.transforms[1](img) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 1398009c63..0d37ae2efd 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -23,7 +23,7 @@ from parameterized import parameterized from monai.data import Dataset -from monai.transforms import Compose, LoadImaged, SimulateDelayd +from monai.transforms import Compose, Lambda, LoadImage, LoadImaged, SimulateDelay, SimulateDelayd from tests.test_compose import TEST_COMPOSE_LAZY_ON_CALL_LOGGING_TEST_CASES, data_from_keys TEST_CASE_1 = [(128, 128, 128)] @@ -99,6 +99,72 @@ def test_dataset_lazy_on_call(self): data[0, 0:2, 0:2] = 1 +class TestTupleDataset(unittest.TestCase): + + @parameterized.expand([TEST_CASE_1]) + def test_shape(self, expected_shape): + test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4)) + with tempfile.TemporaryDirectory() as tempdir: + nib.save(test_image, os.path.join(tempdir, "test_image1.nii.gz")) + nib.save(test_image, os.path.join(tempdir, "test_label1.nii.gz")) + nib.save(test_image, os.path.join(tempdir, "test_image2.nii.gz")) + nib.save(test_image, os.path.join(tempdir, "test_label2.nii.gz")) + test_data = [ + (os.path.join(tempdir, "test_image1.nii.gz"), os.path.join(tempdir, "test_label1.nii.gz")), + (os.path.join(tempdir, "test_image2.nii.gz"), os.path.join(tempdir, "test_label2.nii.gz")), + ] + + test_transform = Compose([LoadImage(), SimulateDelay(delay_time=1e-5)]) + + # Here test_transform is applied element by element for the tuple. + dataset = Dataset(data=test_data, transform=test_transform) + data1 = dataset[0] + data2 = dataset[1] + + # Output is a list/tuple + self.assertTrue(isinstance(data1, (list, tuple))) + self.assertTrue(isinstance(data2, (list, tuple))) + + # Number of elements are 2 + self.assertEqual(len(data1), 2) + self.assertEqual(len(data2), 2) + + # Output shapes are as expected + self.assertTupleEqual(data1[0].shape, expected_shape) + self.assertTupleEqual(data1[1].shape, expected_shape) + self.assertTupleEqual(data2[0].shape, expected_shape) + self.assertTupleEqual(data2[1].shape, expected_shape) + + # Here test_transform is applied to the tuple as a whole. + test_transform = Compose( + [ + # LoadImage creates a channel-stacked image when applied to a tuple + LoadImage(), + # Get the channel-stacked image and the label + Lambda(func=lambda x: (x[0].permute(2, 1, 0), x[1])), + ], + map_items=False, + ) + + dataset = Dataset(data=test_data, transform=test_transform) + data1 = dataset[0] + data2 = dataset[1] + + # Output is a list/tuple + self.assertTrue(isinstance(data1, (list, tuple))) + self.assertTrue(isinstance(data2, (list, tuple))) + + # Number of elements are 2 + self.assertEqual(len(data1), 2) + self.assertEqual(len(data2), 2) + + # Output shapes are as expected + self.assertTupleEqual(data1[0].shape, expected_shape) + self.assertTupleEqual(data1[1].shape, expected_shape) + self.assertTupleEqual(data2[0].shape, expected_shape) + self.assertTupleEqual(data2[1].shape, expected_shape) + + class TestDatsesetWithLazy(unittest.TestCase): LOGGER_NAME = "a_logger_name" diff --git a/tests/test_profiling.py b/tests/test_profiling.py index 6bee7ba262..649d980ebf 100644 --- a/tests/test_profiling.py +++ b/tests/test_profiling.py @@ -35,6 +35,7 @@ def setUp(self): self.scale = mt.ScaleIntensity() self.scale_call_name = "ScaleIntensity.__call__" + self.compose_call_name = "Compose.__call__" self.test_comp = mt.Compose([mt.ScaleIntensity(), mt.RandAxisFlip(0.5)]) self.test_image = torch.rand(1, 16, 16, 16) self.pid = os.getpid() @@ -82,7 +83,7 @@ def test_profile_multithread(self): self.assertSequenceEqual(batch.shape, (4, 1, 16, 16, 16)) results = wp.get_results() - self.assertSequenceEqual(list(results), [self.scale_call_name]) + self.assertSequenceEqual(list(results), [self.scale_call_name, self.compose_call_name]) prs = results[self.scale_call_name] @@ -98,6 +99,7 @@ def test_profile_context(self): self.scale(self.test_image) results = wp.get_results() + self.assertSequenceEqual(set(results), {"ScaleIntensity.__call__", "context"}) prs = results["context"] From 7449c4d23137e6fd264b35cc71c5396e2fce03c7 Mon Sep 17 00:00:00 2001 From: YanxuanLiu <104543031+YanxuanLiu@users.noreply.github.com> Date: Thu, 13 Jun 2024 17:08:06 +0800 Subject: [PATCH 131/136] change blossom-ci to ACL security format (#7843) Fixes # . ### Description Requested by security to prevent DDOS. The new format is provided by blossom team. ### Types of changes - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: YanxuanLiu --- .github/workflows/blossom-ci.yml | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/.github/workflows/blossom-ci.yml b/.github/workflows/blossom-ci.yml index 1d6ee8a46c..bf507bab3b 100644 --- a/.github/workflows/blossom-ci.yml +++ b/.github/workflows/blossom-ci.yml @@ -29,13 +29,15 @@ jobs: args: ${{ env.args }} # This job only runs for pull request comments - if: contains('\ - Nic-Ma,\ - wyli,\ - pxLi,\ - YanxuanLiu,\ - KumoLiu,\ - ', format('{0},', github.actor)) && github.event.comment.body == '/build' + if: | + github.event.comment.body == '/build' && + ( + github.actor == 'Nic-Ma' || + github.actor == 'wyli' || + github.actor == 'pxLi' || + github.actor == 'YanxuanLiu' || + github.actor == 'KumoLiu' + ) steps: - name: Check if comment is issued by authorized person run: blossom-ci From 5855fa4ef25299aa7d6bebc7880ce71cd0229f76 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Sun, 30 Jun 2024 15:42:22 +0100 Subject: [PATCH 132/136] Minor fixes Signed-off-by: Eric Kerfoot --- monai/networks/blocks/selfattention.py | 2 +- monai/transforms/regularization/array.py | 1 - tests/test_clip_intensity_percentilesd.py | 2 +- tests/test_pad_collation.py | 6 +++--- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py index 049ff08933..7b410b1a7c 100644 --- a/monai/networks/blocks/selfattention.py +++ b/monai/networks/blocks/selfattention.py @@ -81,4 +81,4 @@ def forward(self, x): x = self.out_rearrange(x) x = self.out_proj(x) x = self.drop_output(x) - return x \ No newline at end of file + return x diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py index 38eb74d7ee..a7436bda84 100644 --- a/monai/transforms/regularization/array.py +++ b/monai/transforms/regularization/array.py @@ -190,4 +190,3 @@ def __call__(self, data: torch.Tensor, randomize=True): if randomize: self.randomize(data) return convert_to_dst_type(self.apply(data_t), dst=data)[0] - \ No newline at end of file diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py index ea0bfd4fa9..3e06b18418 100644 --- a/tests/test_clip_intensity_percentilesd.py +++ b/tests/test_clip_intensity_percentilesd.py @@ -193,4 +193,4 @@ def test_channel_wise(self, p): if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/test_pad_collation.py b/tests/test_pad_collation.py index 17f49611df..9d5012c9a3 100644 --- a/tests/test_pad_collation.py +++ b/tests/test_pad_collation.py @@ -89,7 +89,7 @@ def tearDown(self) -> None: @parameterized.expand(TESTS) def test_pad_collation(self, t_type, collate_method, transform): - if t_type == dict: + if t_type is dict: dataset = CacheDataset(self.dict_data, transform, progress=False) else: dataset = _Dataset(self.list_data, self.list_labels, transform) @@ -104,7 +104,7 @@ def test_pad_collation(self, t_type, collate_method, transform): loader = DataLoader(dataset, batch_size=10, collate_fn=collate_method) # check collation in forward direction for data in loader: - if t_type == dict: + if t_type is dict: shapes = [] decollated_data = decollate_batch(data) for d in decollated_data: @@ -113,7 +113,7 @@ def test_pad_collation(self, t_type, collate_method, transform): self.assertTrue(len(output["image"].applied_operations), len(dataset.transform.transforms)) self.assertTrue(len(set(shapes)) > 1) # inverted shapes must be different because of random xforms - if t_type == dict: + if t_type is dict: batch_inverse = BatchInverseTransform(dataset.transform, loader) for data in loader: output = batch_inverse(data) From 81175cc361ff38173d316796f4037a078fe665ba Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Sun, 30 Jun 2024 16:37:06 +0100 Subject: [PATCH 133/136] Minor fixes Signed-off-by: Eric Kerfoot --- monai/data/torchscript_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/monai/data/torchscript_utils.py b/monai/data/torchscript_utils.py index cabf06ce89..507cf411d6 100644 --- a/monai/data/torchscript_utils.py +++ b/monai/data/torchscript_utils.py @@ -116,7 +116,7 @@ def load_net_with_metadata( Returns: Triple containing loaded object, metadata dict, and extra files dict containing other file data if present """ - extra_files = {f: "" for f in more_extra_files} + extra_files = dict.fromkeys(more_extra_files, "") extra_files[METADATA_FILENAME] = "" jit_obj = torch.jit.load(filename_prefix_or_stream, map_location, extra_files) From e3a75afe9135363d0cedb274dad1bd09dd628090 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Mon, 1 Jul 2024 03:35:05 +0100 Subject: [PATCH 134/136] Updates to remove undeclared variable errors from pylint Signed-off-by: Eric Kerfoot --- monai/apps/detection/utils/anchor_utils.py | 4 +- monai/apps/pathology/transforms/post/array.py | 1 + monai/bundle/utils.py | 1 + monai/data/dataset_summary.py | 1 + monai/data/utils.py | 5 +- monai/networks/blocks/selfattention.py | 84 ++++++++++-- monai/networks/blocks/spatialattention.py | 2 +- monai/networks/nets/quicknat.py | 1 + monai/networks/nets/swin_unetr.py | 7 +- monai/networks/schedulers/ddim.py | 4 + monai/transforms/regularization/array.py | 7 + .../transforms/utils_create_transform_ims.py | 6 +- tests/hvd_evenly_divisible_all_gather.py | 8 +- tests/test_controlnet_inferers.py | 21 +++ tests/test_ensure_channel_first.py | 7 +- tests/test_ensure_channel_firstd.py | 7 +- .../test_evenly_divisible_all_gather_dist.py | 8 +- tests/test_handler_metrics_saver_dist.py | 4 +- tests/test_hilbert_transform.py | 121 +++--------------- tests/test_integration_unet_2d.py | 1 + tests/test_latent_diffusion_inferer.py | 16 +++ tests/test_reg_loss_integration.py | 3 + tests/test_synthetic.py | 2 +- tests/test_vis_cam.py | 3 + tests/test_vis_gradcam.py | 2 + 25 files changed, 187 insertions(+), 139 deletions(-) diff --git a/monai/apps/detection/utils/anchor_utils.py b/monai/apps/detection/utils/anchor_utils.py index 283169b653..cbde3ebae9 100644 --- a/monai/apps/detection/utils/anchor_utils.py +++ b/monai/apps/detection/utils/anchor_utils.py @@ -189,7 +189,7 @@ def generate_anchors( w_ratios = 1 / area_scale h_ratios = area_scale # if 3d, w:h:d = 1:aspect_ratios[:,0]:aspect_ratios[:,1] - elif self.spatial_dims == 3: + else: area_scale = torch.pow(aspect_ratios_t[:, 0] * aspect_ratios_t[:, 1], 1 / 3.0) w_ratios = 1 / area_scale h_ratios = aspect_ratios_t[:, 0] / area_scale @@ -199,7 +199,7 @@ def generate_anchors( hs = (h_ratios[:, None] * scales_t[None, :]).view(-1) if self.spatial_dims == 2: base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2.0 - elif self.spatial_dims == 3: + else: # elif self.spatial_dims == 3: ds = (d_ratios[:, None] * scales_t[None, :]).view(-1) base_anchors = torch.stack([-ws, -hs, -ds, ws, hs, ds], dim=1) / 2.0 diff --git a/monai/apps/pathology/transforms/post/array.py b/monai/apps/pathology/transforms/post/array.py index 99e94f89c0..0f57fb41cb 100644 --- a/monai/apps/pathology/transforms/post/array.py +++ b/monai/apps/pathology/transforms/post/array.py @@ -379,6 +379,7 @@ def _generate_contour_coord(self, current: np.ndarray, previous: np.ndarray) -> """ p_delta = (current[0] - previous[0], current[1] - previous[1]) + row, col = -1, -1 if p_delta in ((0.0, 1.0), (0.5, 0.5), (1.0, 0.0)): row = int(current[0] + 0.5) diff --git a/monai/bundle/utils.py b/monai/bundle/utils.py index a0f39d236f..0f17422ba5 100644 --- a/monai/bundle/utils.py +++ b/monai/bundle/utils.py @@ -221,6 +221,7 @@ def load_bundle_config(bundle_path: str, *config_names: str, **load_kw_args: Any raise ValueError(f"Cannot find config file '{full_cname}'") ardata = archive.read(full_cname) + cdata = {} if full_cname.lower().endswith("json"): cdata = json.loads(ardata, **load_kw_args) diff --git a/monai/data/dataset_summary.py b/monai/data/dataset_summary.py index 769ae33b46..5b9e32afca 100644 --- a/monai/data/dataset_summary.py +++ b/monai/data/dataset_summary.py @@ -84,6 +84,7 @@ def collect_meta_data(self): """ for data in self.data_loader: + meta_dict = {} if isinstance(data[self.image_key], MetaTensor): meta_dict = data[self.image_key].meta elif self.meta_key in data: diff --git a/monai/data/utils.py b/monai/data/utils.py index 585f02ec9e..8b988c78d2 100644 --- a/monai/data/utils.py +++ b/monai/data/utils.py @@ -56,6 +56,8 @@ if pytorch_after(1, 13): # import private code for reuse purposes, comment in case things break in the future from torch.utils.data._utils.collate import collate_tensor_fn, default_collate_fn_map +else: + collate_tensor_fn = default_collate pd, _ = optional_import("pandas") DataFrame, _ = optional_import("pandas", name="DataFrame") @@ -454,8 +456,7 @@ def collate_meta_tensor_fn(batch, *, collate_fn_map=None): Collate a sequence of meta tensor into a single batched metatensor. This is called by `collage_meta_tensor` and so should not be used as a collate function directly in dataloaders. """ - collate_fn = collate_tensor_fn if pytorch_after(1, 13) else default_collate - collated = collate_fn(batch) # type: ignore + collated = collate_tensor_fn(batch) # type: ignore meta_dicts = [i.meta or TraceKeys.NONE for i in batch] common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)]) if common_: diff --git a/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py index 7b410b1a7c..370ad38595 100644 --- a/monai/networks/blocks/selfattention.py +++ b/monai/networks/blocks/selfattention.py @@ -11,9 +11,12 @@ from __future__ import annotations +from typing import Optional, Tuple + import torch import torch.nn as nn +from monai.networks.layers.utils import get_rel_pos_embedding_layer from monai.utils import optional_import Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange") @@ -23,6 +26,7 @@ class SABlock(nn.Module): """ A self-attention block, based on: "Dosovitskiy et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale " + One can setup relative positional embedding as described in """ def __init__( @@ -30,18 +34,32 @@ def __init__( hidden_size: int, num_heads: int, dropout_rate: float = 0.0, + hidden_input_size: int | None = None, + dim_head: int | None = None, qkv_bias: bool = False, save_attn: bool = False, - dim_head: int | None = None, + causal: bool = False, + sequence_length: int | None = None, + rel_pos_embedding: Optional[str] = None, + input_size: Optional[Tuple] = None, + attention_dtype: Optional[torch.dtype] = None, ) -> None: """ Args: hidden_size (int): dimension of hidden layer. num_heads (int): number of attention heads. dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0. + hidden_input_size (int, optional): dimension of the input tensor. Defaults to hidden_size. + dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads. qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False. save_attn (bool, optional): to make accessible the attention matrix. Defaults to False. - dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads. + causal: whether to use causal attention (see https://arxiv.org/abs/1706.03762). + sequence_length: if causal is True, it is necessary to specify the sequence length. + rel_pos_embedding (str, optional): Add relative positional embeddings to the attention map. + For now only "decomposed" is supported (see https://arxiv.org/abs/2112.01526). 2D and 3D are supported. + input_size (tuple(spatial_dim), optional): Input resolution for calculating the relative + positional parameter size. + attention_dtype: cast attention operations to this dtype. """ @@ -50,27 +68,71 @@ def __init__( if not (0 <= dropout_rate <= 1): raise ValueError("dropout_rate should be between 0 and 1.") - if hidden_size % num_heads != 0: - raise ValueError("hidden size should be divisible by num_heads.") + if dim_head: + inner_dim = num_heads * dim_head + self.dim_head = dim_head + else: + if hidden_size % num_heads != 0: + raise ValueError("hidden size should be divisible by num_heads.") + inner_dim = hidden_size + self.dim_head = hidden_size // num_heads - self.num_heads = num_heads - self.dim_head = hidden_size // num_heads if dim_head is None else dim_head - self.inner_dim = self.dim_head * num_heads + if causal and sequence_length is None: + raise ValueError("sequence_length is necessary for causal attention.") - self.out_proj = nn.Linear(self.inner_dim, hidden_size) - self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias) + self.num_heads = num_heads + self.hidden_input_size = hidden_input_size if hidden_input_size else hidden_size + self.out_proj = nn.Linear(inner_dim, self.hidden_input_size) + self.qkv = nn.Linear(self.hidden_input_size, inner_dim * 3, bias=qkv_bias) self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads) self.out_rearrange = Rearrange("b h l d -> b l (h d)") self.drop_output = nn.Dropout(dropout_rate) self.drop_weights = nn.Dropout(dropout_rate) self.scale = self.dim_head**-0.5 self.save_attn = save_attn + self.attention_dtype = attention_dtype + self.causal = causal + self.sequence_length = sequence_length + + if causal and sequence_length is not None: + # causal mask to ensure that attention is only applied to the left in the input sequence + self.register_buffer( + "causal_mask", + torch.tril(torch.ones(sequence_length, sequence_length)).view(1, 1, sequence_length, sequence_length), + ) + self.causal_mask: torch.Tensor + self.att_mat = torch.Tensor() + self.rel_positional_embedding = ( + get_rel_pos_embedding_layer(rel_pos_embedding, input_size, self.dim_head, self.num_heads) + if rel_pos_embedding is not None + else None + ) + self.input_size = input_size - def forward(self, x): + def forward(self, x: torch.Tensor): + """ + Args: + x (torch.Tensor): input tensor. B x (s_dim_1 * ... * s_dim_n) x C + + Return: + torch.Tensor: B x (s_dim_1 * ... * s_dim_n) x C + """ output = self.input_rearrange(self.qkv(x)) q, k, v = output[0], output[1], output[2] - att_mat = (torch.einsum("blxd,blyd->blxy", q, k) * self.scale).softmax(dim=-1) + if self.attention_dtype is not None: + q = q.to(self.attention_dtype) + k = k.to(self.attention_dtype) + att_mat = torch.einsum("blxd,blyd->blxy", q, k) * self.scale + + # apply relative positional embedding if defined + att_mat = self.rel_positional_embedding(x, att_mat, q) if self.rel_positional_embedding is not None else att_mat + + if self.causal: + att_mat = att_mat.masked_fill(self.causal_mask[:, :, : x.shape[1], : x.shape[1]] == 0, float("-inf")) + + att_mat = att_mat.softmax(dim=-1) + if self.save_attn: # no gradients and new tensor; # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html diff --git a/monai/networks/blocks/spatialattention.py b/monai/networks/blocks/spatialattention.py index 020d8d23fd..75319853d9 100644 --- a/monai/networks/blocks/spatialattention.py +++ b/monai/networks/blocks/spatialattention.py @@ -68,7 +68,7 @@ def forward(self, x: torch.Tensor): h, w = x.shape[2], x.shape[3] rearrange_input = Rearrange("b c h w -> b (h w) c") rearrange_output = Rearrange("b (h w) c -> b c h w", h=h, w=w) - if self.spatial_dims == 3: + else: h, w, d = x.shape[2], x.shape[3], x.shape[4] rearrange_input = Rearrange("b c h w d -> b (h w d) c") rearrange_output = Rearrange("b (h w d) c -> b c h w d", h=h, w=w, d=d) diff --git a/monai/networks/nets/quicknat.py b/monai/networks/nets/quicknat.py index cbcccf24d7..b77ea2bd11 100644 --- a/monai/networks/nets/quicknat.py +++ b/monai/networks/nets/quicknat.py @@ -168,6 +168,7 @@ def _get_layer(self, in_channels, out_channels, dilation): def forward(self, input, _): i = 0 result = input + result1 = None for l in self.children(): # ignoring the max (un-)pool and droupout already added in the initial initialization step if isinstance(l, (nn.MaxPool2d, nn.MaxUnpool2d, nn.Dropout2d)): diff --git a/monai/networks/nets/swin_unetr.py b/monai/networks/nets/swin_unetr.py index 6f96dfd291..3900c866b3 100644 --- a/monai/networks/nets/swin_unetr.py +++ b/monai/networks/nets/swin_unetr.py @@ -347,7 +347,7 @@ def window_partition(x, window_size): x: input tensor. window_size: local window size. """ - x_shape = x.size() + x_shape = x.size() # length 4 or 5 only if len(x_shape) == 5: b, d, h, w, c = x_shape x = x.view( @@ -363,10 +363,11 @@ def window_partition(x, window_size): windows = ( x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, window_size[0] * window_size[1] * window_size[2], c) ) - elif len(x_shape) == 4: + else: # if len(x_shape) == 4: b, h, w, c = x.shape x = x.view(b, h // window_size[0], window_size[0], w // window_size[1], window_size[1], c) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0] * window_size[1], c) + return windows @@ -613,7 +614,7 @@ def forward_part1(self, x, mask_matrix): _, dp, hp, wp, _ = x.shape dims = [b, dp, hp, wp] - elif len(x_shape) == 4: + else: # elif len(x_shape) == 4 b, h, w, c = x.shape window_size, shift_size = get_window_size((h, w), self.window_size, self.shift_size) pad_l = pad_t = 0 diff --git a/monai/networks/schedulers/ddim.py b/monai/networks/schedulers/ddim.py index 19e24d94b8..a24e40dc9b 100644 --- a/monai/networks/schedulers/ddim.py +++ b/monai/networks/schedulers/ddim.py @@ -184,6 +184,8 @@ def step( beta_prod_t = 1 - alpha_prod_t + pred_original_sample = pred_epsilon = None # satisfies pylint + # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf if self.prediction_type == DDIMPredictionType.EPSILON: @@ -258,6 +260,8 @@ def reversed_step( beta_prod_t = 1 - alpha_prod_t + pred_original_sample = pred_epsilon = None # satisfies pylint + # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py index a7436bda84..da7247b6ed 100644 --- a/monai/transforms/regularization/array.py +++ b/monai/transforms/regularization/array.py @@ -87,12 +87,15 @@ def apply(self, data: torch.Tensor): def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): data_t = convert_to_tensor(data, track_meta=get_track_meta()) + labels_t = None + if labels is not None: labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) if randomize: self.randomize() if labels is None: return convert_to_dst_type(self.apply(data_t), dst=data)[0] + return ( convert_to_dst_type(self.apply(data_t), dst=data)[0], convert_to_dst_type(self.apply(labels_t), dst=labels)[0], @@ -149,13 +152,17 @@ def apply_on_labels(self, labels: torch.Tensor): def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): data_t = convert_to_tensor(data, track_meta=get_track_meta()) + augmented_label = None + if labels is not None: labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) if randomize: self.randomize(data) augmented = convert_to_dst_type(self.apply(data_t), dst=data)[0] + if labels is not None: augmented_label = convert_to_dst_type(self.apply(labels_t), dst=labels)[0] + return (augmented, augmented_label) if labels is not None else augmented diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py index 4b5990abd3..a29fd4dbf9 100644 --- a/monai/transforms/utils_create_transform_ims.py +++ b/monai/transforms/utils_create_transform_ims.py @@ -269,11 +269,9 @@ def update_docstring(code_path, transform_name): def pre_process_data(data, ndim, is_map, is_post): - """If transform requires 2D data, then convert to 2D""" + """If transform requires 2D data, then convert to 2D by selecting the middle of the last dimension.""" if ndim == 2: - for k in keys: - data[k] = data[k][..., data[k].shape[-1] // 2] - + data = {k: v[..., v.shape[-1] // 2] for k, v in data.items()} if is_map: return data return data[CommonKeys.LABEL] if is_post else data[CommonKeys.IMAGE] diff --git a/tests/hvd_evenly_divisible_all_gather.py b/tests/hvd_evenly_divisible_all_gather.py index 78c6ca06bc..732ad13b83 100644 --- a/tests/hvd_evenly_divisible_all_gather.py +++ b/tests/hvd_evenly_divisible_all_gather.py @@ -30,10 +30,10 @@ def test_data(self): self._run() def _run(self): - if hvd.rank() == 0: - data1 = torch.tensor([[1, 2], [3, 4]]) - data2 = torch.tensor([[1.0, 2.0]]) - data3 = torch.tensor(7) + # if hvd.rank() == 0: + data1 = torch.tensor([[1, 2], [3, 4]]) + data2 = torch.tensor([[1.0, 2.0]]) + data3 = torch.tensor(7) if hvd.rank() == 1: data1 = torch.tensor([[5, 6]]) diff --git a/tests/test_controlnet_inferers.py b/tests/test_controlnet_inferers.py index 96e707acb5..e3b0aeb5a2 100644 --- a/tests/test_controlnet_inferers.py +++ b/tests/test_controlnet_inferers.py @@ -663,6 +663,8 @@ def test_prediction_shape( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -730,6 +732,8 @@ def test_sample_shape( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -793,6 +797,8 @@ def test_sample_intermediates( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -835,6 +841,10 @@ def test_sample_intermediates( controlnet=controlnet, cn_cond=mask, ) + + # TODO: this isn't correct, should the above produce intermediates as well? + # This test has always passed so is this branch not being used? + intermediates = None else: sample, intermediates = inferer.sample( input_noise=noise, @@ -846,6 +856,7 @@ def test_sample_intermediates( controlnet=controlnet, cn_cond=mask, ) + self.assertEqual(len(intermediates), 10) self.assertEqual(intermediates[0].shape, input_shape) @@ -861,6 +872,8 @@ def test_get_likelihoods( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -929,6 +942,8 @@ def test_resample_likelihoods( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -999,6 +1014,8 @@ def test_prediction_shape_conditioned_concat( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -1080,6 +1097,8 @@ def test_sample_shape_conditioned_concat( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -1156,6 +1175,8 @@ def test_sample_shape_different_latents( input_shape, latent_shape, ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": diff --git a/tests/test_ensure_channel_first.py b/tests/test_ensure_channel_first.py index 0c9ad5869e..fe046a4cdf 100644 --- a/tests/test_ensure_channel_first.py +++ b/tests/test_ensure_channel_first.py @@ -50,9 +50,10 @@ class TestEnsureChannelFirst(unittest.TestCase): @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6]) @unittest.skipUnless(has_itk, "itk not installed") def test_load_nifti(self, input_param, filenames, original_channel_dim): - if original_channel_dim is None: - test_image = np.random.rand(8, 8, 8) - elif original_channel_dim == -1: + # if original_channel_dim is None + test_image = np.random.rand(8, 8, 8) + + if original_channel_dim == -1: test_image = np.random.rand(8, 8, 8, 1) with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_ensure_channel_firstd.py b/tests/test_ensure_channel_firstd.py index 63a437894b..e9effad951 100644 --- a/tests/test_ensure_channel_firstd.py +++ b/tests/test_ensure_channel_firstd.py @@ -35,9 +35,10 @@ class TestEnsureChannelFirstd(unittest.TestCase): @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_load_nifti(self, input_param, filenames, original_channel_dim): - if original_channel_dim is None: - test_image = np.random.rand(8, 8, 8) - elif original_channel_dim == -1: + # if original_channel_dim is None: + test_image = np.random.rand(8, 8, 8) + + if original_channel_dim == -1: test_image = np.random.rand(8, 8, 8, 1) with tempfile.TemporaryDirectory() as tempdir: diff --git a/tests/test_evenly_divisible_all_gather_dist.py b/tests/test_evenly_divisible_all_gather_dist.py index d6d26c7e23..f1d45ba48f 100644 --- a/tests/test_evenly_divisible_all_gather_dist.py +++ b/tests/test_evenly_divisible_all_gather_dist.py @@ -27,10 +27,10 @@ def test_data(self): self._run() def _run(self): - if dist.get_rank() == 0: - data1 = torch.tensor([[1, 2], [3, 4]]) - data2 = torch.tensor([[1.0, 2.0]]) - data3 = torch.tensor(7) + # if dist.get_rank() == 0 + data1 = torch.tensor([[1, 2], [3, 4]]) + data2 = torch.tensor([[1.0, 2.0]]) + data3 = torch.tensor(7) if dist.get_rank() == 1: data1 = torch.tensor([[5, 6]]) diff --git a/tests/test_handler_metrics_saver_dist.py b/tests/test_handler_metrics_saver_dist.py index 46c9ad27d7..2e12b08aa9 100644 --- a/tests/test_handler_metrics_saver_dist.py +++ b/tests/test_handler_metrics_saver_dist.py @@ -51,8 +51,10 @@ def _val_func(engine, batch): engine = Engine(_val_func) + # define here to ensure symbol always exists regardless of the following if conditions + data = [{PostFix.meta("image"): {"filename_or_obj": [fnames[0]]}}] + if my_rank == 0: - data = [{PostFix.meta("image"): {"filename_or_obj": [fnames[0]]}}] @engine.on(Events.EPOCH_COMPLETED) def _save_metrics0(engine): diff --git a/tests/test_hilbert_transform.py b/tests/test_hilbert_transform.py index 879a74969d..49416ab7b8 100644 --- a/tests/test_hilbert_transform.py +++ b/tests/test_hilbert_transform.py @@ -23,7 +23,7 @@ def create_expected_numpy_output(input_datum, **kwargs): - x = np.fft.fft(input_datum.cpu().numpy() if input_datum.device.type == "cuda" else input_datum.numpy(), **kwargs) + x = np.fft.fft(input_datum.cpu().numpy(), **kwargs) f = np.fft.fftfreq(x.shape[kwargs["axis"]]) u = np.heaviside(f, 0.5) new_dims_before = kwargs["axis"] @@ -44,19 +44,15 @@ def create_expected_numpy_output(input_datum, **kwargs): # CPU TEST DATA cpu_input_data = {} -cpu_input_data["1D"] = torch.as_tensor(hann_windowed_sine, device=cpu).unsqueeze(0).unsqueeze(0) -cpu_input_data["2D"] = ( - torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu).unsqueeze(0).unsqueeze(0) -) -cpu_input_data["3D"] = ( - torch.as_tensor(np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=cpu) - .unsqueeze(0) - .unsqueeze(0) -) -cpu_input_data["1D 2CH"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu).unsqueeze(0) +cpu_input_data["1D"] = torch.as_tensor(hann_windowed_sine, device=cpu)[None, None] +cpu_input_data["2D"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu)[None, None] +cpu_input_data["3D"] = torch.as_tensor( + np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=cpu +)[None, None] +cpu_input_data["1D 2CH"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=cpu)[None] cpu_input_data["2D 2CH"] = torch.as_tensor( np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=cpu -).unsqueeze(0) +)[None] # SINGLE-CHANNEL CPU VALUE TESTS @@ -97,64 +93,21 @@ def create_expected_numpy_output(input_datum, **kwargs): 1e-5, # absolute tolerance ] +TEST_CASES_CPU = [ + TEST_CASE_1D_SINE_CPU, + TEST_CASE_2D_SINE_CPU, + TEST_CASE_3D_SINE_CPU, + TEST_CASE_1D_2CH_SINE_CPU, + TEST_CASE_2D_2CH_SINE_CPU, +] + # GPU TEST DATA if torch.cuda.is_available(): gpu = torch.device("cuda") - - gpu_input_data = {} - gpu_input_data["1D"] = torch.as_tensor(hann_windowed_sine, device=gpu).unsqueeze(0).unsqueeze(0) - gpu_input_data["2D"] = ( - torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=gpu).unsqueeze(0).unsqueeze(0) - ) - gpu_input_data["3D"] = ( - torch.as_tensor(np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=gpu) - .unsqueeze(0) - .unsqueeze(0) - ) - gpu_input_data["1D 2CH"] = torch.as_tensor(np.stack([hann_windowed_sine] * 10, axis=1), device=gpu).unsqueeze(0) - gpu_input_data["2D 2CH"] = torch.as_tensor( - np.stack([np.stack([hann_windowed_sine] * 10, axis=1)] * 10, axis=2), device=gpu - ).unsqueeze(0) - - # SINGLE CHANNEL GPU VALUE TESTS - - TEST_CASE_1D_SINE_GPU = [ - {}, # args (empty, so use default) - gpu_input_data["1D"], # Input data: Random 1D signal - create_expected_numpy_output(gpu_input_data["1D"], axis=2), # Expected output: FFT of signal - 1e-5, # absolute tolerance - ] - - TEST_CASE_2D_SINE_GPU = [ - {}, # args (empty, so use default) - gpu_input_data["2D"], # Input data: Random 1D signal - create_expected_numpy_output(gpu_input_data["2D"], axis=2), # Expected output: FFT of signal - 1e-5, # absolute tolerance - ] - - TEST_CASE_3D_SINE_GPU = [ - {}, # args (empty, so use default) - gpu_input_data["3D"], # Input data: Random 1D signal - create_expected_numpy_output(gpu_input_data["3D"], axis=2), # Expected output: FFT of signal - 1e-5, # absolute tolerance - ] - - # MULTICHANNEL GPU VALUE TESTS, PROCESS ALONG FIRST SPATIAL AXIS - - TEST_CASE_1D_2CH_SINE_GPU = [ - {}, # args (empty, so use default) - gpu_input_data["1D 2CH"], # Input data: Random 1D signal - create_expected_numpy_output(gpu_input_data["1D 2CH"], axis=2), - 1e-5, # absolute tolerance - ] - - TEST_CASE_2D_2CH_SINE_GPU = [ - {}, # args (empty, so use default) - gpu_input_data["2D 2CH"], # Input data: Random 1D signal - create_expected_numpy_output(gpu_input_data["2D 2CH"], axis=2), - 1e-5, # absolute tolerance - ] + TEST_CASES_GPU = [[args, image.to(gpu), exp_data, atol] for args, image, exp_data, atol in TEST_CASES_CPU] +else: + TEST_CASES_GPU = [] # TESTS CHECKING PADDING, AXIS SELECTION ETC ARE COVERED BY test_detect_envelope.py @@ -162,42 +115,10 @@ def create_expected_numpy_output(input_datum, **kwargs): @SkipIfNoModule("torch.fft") class TestHilbertTransformCPU(unittest.TestCase): - @parameterized.expand( - [ - TEST_CASE_1D_SINE_CPU, - TEST_CASE_2D_SINE_CPU, - TEST_CASE_3D_SINE_CPU, - TEST_CASE_1D_2CH_SINE_CPU, - TEST_CASE_2D_2CH_SINE_CPU, - ] - ) - def test_value(self, arguments, image, expected_data, atol): - result = HilbertTransform(**arguments)(image) - result = result.squeeze(0).squeeze(0).numpy() - np.testing.assert_allclose(result, expected_data.squeeze(), atol=atol) - - -@skip_if_no_cuda -@SkipIfNoModule("torch.fft") -class TestHilbertTransformGPU(unittest.TestCase): - - @parameterized.expand( - ( - [] - if not torch.cuda.is_available() - else [ - TEST_CASE_1D_SINE_GPU, - TEST_CASE_2D_SINE_GPU, - TEST_CASE_3D_SINE_GPU, - TEST_CASE_1D_2CH_SINE_GPU, - TEST_CASE_2D_2CH_SINE_GPU, - ] - ), - skip_on_empty=True, - ) + @parameterized.expand(TEST_CASES_CPU + TEST_CASES_GPU) def test_value(self, arguments, image, expected_data, atol): result = HilbertTransform(**arguments)(image) - result = result.squeeze(0).squeeze(0).cpu().numpy() + result = np.squeeze(result.cpu().numpy()) np.testing.assert_allclose(result, expected_data.squeeze(), atol=atol) diff --git a/tests/test_integration_unet_2d.py b/tests/test_integration_unet_2d.py index 918190775c..3b40682de0 100644 --- a/tests/test_integration_unet_2d.py +++ b/tests/test_integration_unet_2d.py @@ -35,6 +35,7 @@ def __getitem__(self, _unused_id): def __len__(self): return train_steps + net = None if net_name == "basicunet": net = BasicUNet(spatial_dims=2, in_channels=1, out_channels=1, features=(4, 8, 8, 16, 16, 32)) elif net_name == "unet": diff --git a/tests/test_latent_diffusion_inferer.py b/tests/test_latent_diffusion_inferer.py index 065ebafd95..2e04ad6c5c 100644 --- a/tests/test_latent_diffusion_inferer.py +++ b/tests/test_latent_diffusion_inferer.py @@ -320,6 +320,8 @@ class TestDiffusionSamplingInferer(unittest.TestCase): def test_prediction_shape( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -368,6 +370,8 @@ def test_prediction_shape( def test_sample_shape( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -413,6 +417,8 @@ def test_sample_shape( def test_sample_intermediates( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -468,6 +474,8 @@ def test_sample_intermediates( def test_get_likelihoods( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -521,6 +529,8 @@ def test_get_likelihoods( def test_resample_likelihoods( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -576,6 +586,8 @@ def test_resample_likelihoods( def test_prediction_shape_conditioned_concat( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -642,6 +654,8 @@ def test_prediction_shape_conditioned_concat( def test_sample_shape_conditioned_concat( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": @@ -703,6 +717,8 @@ def test_sample_shape_conditioned_concat( def test_sample_shape_different_latents( self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape ): + stage_1 = None + if ae_model_type == "AutoencoderKL": stage_1 = AutoencoderKL(**autoencoder_params) if ae_model_type == "VQVAE": diff --git a/tests/test_reg_loss_integration.py b/tests/test_reg_loss_integration.py index 1fb81689e6..8afc2da6ad 100644 --- a/tests/test_reg_loss_integration.py +++ b/tests/test_reg_loss_integration.py @@ -83,6 +83,9 @@ def forward(self, x): # initialize a SGD optimizer optimizer = optim.Adam(net.parameters(), lr=learning_rate) + # declare first for pylint + init_loss = None + # train the network for it in range(max_iter): # set the gradient to zero diff --git a/tests/test_synthetic.py b/tests/test_synthetic.py index 7db3c3e77a..4ab2144568 100644 --- a/tests/test_synthetic.py +++ b/tests/test_synthetic.py @@ -47,7 +47,7 @@ def test_create_test_image(self, dim, input_param, expected_img, expected_seg, e set_determinism(seed=0) if dim == 2: img, seg = create_test_image_2d(**input_param) - elif dim == 3: + else: # dim == 3 img, seg = create_test_image_3d(**input_param) self.assertEqual(img.shape, expected_shape) self.assertEqual(seg.max(), expected_max_cls) diff --git a/tests/test_vis_cam.py b/tests/test_vis_cam.py index b641599af2..68b12de2f8 100644 --- a/tests/test_vis_cam.py +++ b/tests/test_vis_cam.py @@ -70,6 +70,8 @@ class TestClassActivationMap(unittest.TestCase): @parameterized.expand([TEST_CASE_0, TEST_CASE_1, TEST_CASE_2, TEST_CASE_3]) def test_shape(self, input_data, expected_shape): + model = None + if input_data["model"] == "densenet2d": model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3) if input_data["model"] == "densenet3d": @@ -80,6 +82,7 @@ def test_shape(self, input_data, expected_shape): model = SEResNet50(spatial_dims=2, in_channels=3, num_classes=4) if input_data["model"] == "senet3d": model = SEResNet50(spatial_dims=3, in_channels=3, num_classes=4) + device = "cuda:0" if torch.cuda.is_available() else "cpu" model.to(device) model.eval() diff --git a/tests/test_vis_gradcam.py b/tests/test_vis_gradcam.py index 325b74b3ce..f77d916a5b 100644 --- a/tests/test_vis_gradcam.py +++ b/tests/test_vis_gradcam.py @@ -153,6 +153,8 @@ class TestGradientClassActivationMap(unittest.TestCase): @parameterized.expand(TESTS) def test_shape(self, cam_class, input_data, expected_shape): + model = None + if input_data["model"] == "densenet2d": model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=3) elif input_data["model"] == "densenet2d_bin": From d4af01efd5d5b81c9fbc5bd399537c994242ae8a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 1 Jul 2024 02:39:48 +0000 Subject: [PATCH 135/136] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/test_hilbert_transform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_hilbert_transform.py b/tests/test_hilbert_transform.py index 49416ab7b8..b91ba3f6b7 100644 --- a/tests/test_hilbert_transform.py +++ b/tests/test_hilbert_transform.py @@ -19,7 +19,7 @@ from monai.networks.layers import HilbertTransform from monai.utils import OptionalImportError -from tests.utils import SkipIfModule, SkipIfNoModule, skip_if_no_cuda +from tests.utils import SkipIfModule, SkipIfNoModule def create_expected_numpy_output(input_datum, **kwargs): From cc1a79b2fa6e7499f765de0caef1fa1c24c16356 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Mon, 1 Jul 2024 04:45:48 +0100 Subject: [PATCH 136/136] Fixes Signed-off-by: Eric Kerfoot --- monai/data/utils.py | 16 +++++++++------- monai/networks/nets/quicknat.py | 3 ++- monai/networks/schedulers/ddim.py | 8 ++++++-- monai/transforms/regularization/array.py | 2 +- tests/test_hilbert_transform.py | 2 +- 5 files changed, 19 insertions(+), 12 deletions(-) diff --git a/monai/data/utils.py b/monai/data/utils.py index 8b988c78d2..7a08300abb 100644 --- a/monai/data/utils.py +++ b/monai/data/utils.py @@ -53,12 +53,6 @@ pytorch_after, ) -if pytorch_after(1, 13): - # import private code for reuse purposes, comment in case things break in the future - from torch.utils.data._utils.collate import collate_tensor_fn, default_collate_fn_map -else: - collate_tensor_fn = default_collate - pd, _ = optional_import("pandas") DataFrame, _ = optional_import("pandas", name="DataFrame") nib, _ = optional_import("nibabel") @@ -456,7 +450,13 @@ def collate_meta_tensor_fn(batch, *, collate_fn_map=None): Collate a sequence of meta tensor into a single batched metatensor. This is called by `collage_meta_tensor` and so should not be used as a collate function directly in dataloaders. """ - collated = collate_tensor_fn(batch) # type: ignore + if pytorch_after(1, 13): + from torch.utils.data._utils.collate import collate_tensor_fn # imported here for pylint/mypy issues + + collated = collate_tensor_fn(batch) + else: + collated = default_collate(batch) + meta_dicts = [i.meta or TraceKeys.NONE for i in batch] common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)]) if common_: @@ -497,6 +497,8 @@ def list_data_collate(batch: Sequence): if pytorch_after(1, 13): # needs to go here to avoid circular import + from torch.utils.data._utils.collate import default_collate_fn_map + from monai.data.meta_tensor import MetaTensor default_collate_fn_map.update({MetaTensor: collate_meta_tensor_fn}) diff --git a/monai/networks/nets/quicknat.py b/monai/networks/nets/quicknat.py index b77ea2bd11..bbc4e7e490 100644 --- a/monai/networks/nets/quicknat.py +++ b/monai/networks/nets/quicknat.py @@ -168,7 +168,8 @@ def _get_layer(self, in_channels, out_channels, dilation): def forward(self, input, _): i = 0 result = input - result1 = None + result1 = input # this will not stay this value, needed here for pylint/mypy + for l in self.children(): # ignoring the max (un-)pool and droupout already added in the initial initialization step if isinstance(l, (nn.MaxPool2d, nn.MaxUnpool2d, nn.Dropout2d)): diff --git a/monai/networks/schedulers/ddim.py b/monai/networks/schedulers/ddim.py index a24e40dc9b..2a0121d063 100644 --- a/monai/networks/schedulers/ddim.py +++ b/monai/networks/schedulers/ddim.py @@ -184,7 +184,9 @@ def step( beta_prod_t = 1 - alpha_prod_t - pred_original_sample = pred_epsilon = None # satisfies pylint + # predefinitions satisfy pylint/mypy, these values won't be ultimately used + pred_original_sample = sample + pred_epsilon = model_output # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf @@ -260,7 +262,9 @@ def reversed_step( beta_prod_t = 1 - alpha_prod_t - pred_original_sample = pred_epsilon = None # satisfies pylint + # predefinitions satisfy pylint/mypy, these values won't be ultimately used + pred_original_sample = sample + pred_epsilon = model_output # 3. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf diff --git a/monai/transforms/regularization/array.py b/monai/transforms/regularization/array.py index da7247b6ed..9186a5c46f 100644 --- a/monai/transforms/regularization/array.py +++ b/monai/transforms/regularization/array.py @@ -87,7 +87,7 @@ def apply(self, data: torch.Tensor): def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): data_t = convert_to_tensor(data, track_meta=get_track_meta()) - labels_t = None + labels_t = data_t # will not stay this value, needed to satisfy pylint/mypy if labels is not None: labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) diff --git a/tests/test_hilbert_transform.py b/tests/test_hilbert_transform.py index 49416ab7b8..b91ba3f6b7 100644 --- a/tests/test_hilbert_transform.py +++ b/tests/test_hilbert_transform.py @@ -19,7 +19,7 @@ from monai.networks.layers import HilbertTransform from monai.utils import OptionalImportError -from tests.utils import SkipIfModule, SkipIfNoModule, skip_if_no_cuda +from tests.utils import SkipIfModule, SkipIfNoModule def create_expected_numpy_output(input_datum, **kwargs):