Add CI for windows

.github/workflows/build.yml

@@ -224,3 +224,50 @@ jobs:
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.8]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade --user
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install MMCV
+        run: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+      - name: Install unittest dependencies
+        run: |
+          python -V
+          python -m pip install pycocotools
+          python -m pip install -r requirements/tests.txt -r requirements/optional.txt
+          python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          python -m pip install git+https://github.com/cocodataset/panopticapi.git
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Show pip list
+        run: pip list
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests
+        run: coverage run --branch --source mmdet -m pytest tests -sv
+      - name: Generate coverage report
+        run: |
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false

README.md

@@ -320,3 +320,4 @@ If you use this toolbox or benchmark in your research, please cite this project.
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
 - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab Model Compression Toolbox and Benchmark.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.

README_zh-CN.md

@@ -323,6 +323,7 @@ MMDetection 是一款由来自不同高校和企业的研发人员共同参与
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
 - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
 
 ## 欢迎加入 OpenMMLab 社区
 

docs/en/get_started.md

@@ -134,7 +134,7 @@ Or you can still install MMDetection manually:
     # for LVIS dataset
     pip install git+https://github.com/lvis-dataset/lvis-api.git
     # for albumentations
-    pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+    pip install -r requirements/albu.txt
     ```
 
 **Note:**
@@ -148,9 +148,12 @@ you can install it before installing MMCV.
 c. Some dependencies are optional. Simply running `pip install -v -e .` will
  only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
 
-d. If you would like to use `albumentations`, we suggest using
-`pip install albumentations>=0.3.2 --no-binary imgaug,albumentations`. If you simply use
-`pip install albumentations>=0.3.2`, it will install `opencv-python-headless` simultaneously (even though you have already installed `opencv-python`). We should not allow `opencv-python` and `opencv-python-headless` installed at the same time, because it might cause unexpected issues. Please refer to [official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) for more details.
+d. If you would like to use `albumentations`, we suggest using `pip install -r requirements/albu.txt` or
+`pip install -U albumentations --no-binary qudida,albumentations`. If you simply use `pip install albumentations>=0.3.2`,
+it will install `opencv-python-headless` simultaneously (even though you have already
+installed `opencv-python`). We recommended checking the environment after installing `albumentation` to
+ensure that `opencv-python` and `opencv-python-headless` are not installed at the same time, because it might cause unexpected issues if they both installed. Please refer
+to [official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) for more details.
 
 ### Install without GPU support
 

docs/zh_cn/get_started.md

@@ -142,7 +142,7 @@ MIM 能够自动地安装 OpenMMLab 的项目以及对应的依赖包。
     # 安装 LVIS 数据集依赖
     pip install git+https://github.com/lvis-dataset/lvis-api.git
     # 安装 albumentations 依赖
-    pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+    pip install -r requirements/albu.txt
     ```
 
 **注意：**
@@ -153,7 +153,7 @@ MIM 能够自动地安装 OpenMMLab 的项目以及对应的依赖包。
 
 (3) 一些安装依赖是可以选择的。例如只需要安装最低运行要求的版本，则可以使用 `pip install -v -e .` 命令。如果希望使用可选择的像 `albumentations` 和 `imagecorruptions` 这种依赖项，可以使用 `pip install -r requirements/optional.txt` 进行手动安装，或者在使用 `pip` 时指定所需的附加功能（例如 `pip install -v -e .[optional]`），支持附加功能的有效键值包括  `all`、`tests`、`build` 以及 `optional` 。
 
-(4) 如果希望使用 `albumentations`，我们建议使用 `pip install albumentations>=0.3.2 --no-binary imgaug,albumentations` 进行安装。 如果简单地使用 `pip install albumentations>=0.3.2` 进行安装，则会同时安装 `opencv-python-headless`（即便已经安装了 `opencv-python` 也会再次安装）。我们不允许同时安装 `opencv-python` 和 `opencv-python-headless`，因为这样可能会导致一些问题。更多细节请参考[官方文档](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)。
+(4) 如果希望使用 `albumentations`，我们建议使用 `pip install -r requirements/albu.txt` 或者 `pip install -U albumentations --no-binary qudida,albumentations` 进行安装。 如果简单地使用 `pip install albumentations>=0.3.2` 进行安装，则会同时安装 `opencv-python-headless`（即便已经安装了 `opencv-python` 也会再次安装）。我们建议在安装 `albumentations` 后检查环境，以确保没有同时安装 `opencv-python` 和 `opencv-python-headless`，因为同时安装可能会导致一些问题。更多细节请参考[官方文档](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)。
 
 ### 只在 CPU 安装
 

mmdet/core/bbox/assigners/assign_result.py

@@ -164,7 +164,7 @@ def random(cls, **kwargs):
             true_idxs = np.arange(num_gts)
             rng.shuffle(true_idxs)
             true_idxs = torch.from_numpy(true_idxs)
-            gt_inds[is_assigned] = true_idxs[:n_assigned]
+            gt_inds[is_assigned] = true_idxs[:n_assigned].long()
 
             gt_inds = torch.from_numpy(
                 rng.randint(1, num_gts + 1, size=num_preds))

mmdet/core/bbox/samplers/sampling_result.py

@@ -42,7 +42,7 @@ def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
             if len(gt_bboxes.shape) < 2:
                 gt_bboxes = gt_bboxes.view(-1, 4)
 
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :]
+            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :]
 
         if assign_result.labels is not None:
             self.pos_gt_labels = assign_result.labels[pos_inds]

mmdet/models/backbones/csp_darknet.py

@@ -128,9 +128,9 @@ class CSPDarknet(BaseModule):
         arch (str): Architecture of CSP-Darknet, from {P5, P6}.
             Default: P5.
         deepen_factor (float): Depth multiplier, multiply number of
-            channels in each layer by this amount. Default: 1.0.
-        widen_factor (float): Width multiplier, multiply number of
             blocks in CSP layer by this amount. Default: 1.0.
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
         out_indices (Sequence[int]): Output from which stages.
             Default: (2, 3, 4).
         frozen_stages (int): Stages to be frozen (stop grad and set eval

requirements/albu.txt

@@ -0,0 +1 @@
+albumentations>=0.3.2 --no-binary qudida,albumentations

tests/test_data/test_datasets/test_common.py

@@ -3,6 +3,8 @@
 import logging
 import os
 import os.path as osp
+import platform
+import shutil
 import tempfile
 from unittest.mock import MagicMock, patch
 
@@ -107,8 +109,11 @@ def _create_dummy_results():
 def test_dataset_init(config_path):
     use_symlink = False
     if not os.path.exists('./data'):
-        os.symlink('./tests/data', './data')
-        use_symlink = True
+        if platform.system() != 'Windows':
+            os.symlink('./tests/data', './data')
+            use_symlink = True
+        else:
+            shutil.copytree('./tests/data', './data')
     data_config = mmcv.Config.fromfile(config_path)
     if 'data' not in data_config:
         return
@@ -119,6 +124,8 @@ def test_dataset_init(config_path):
         dataset[0]
     if use_symlink:
         os.unlink('./data')
+    else:
+        shutil.rmtree('./data')
 
 
 def test_dataset_evaluation():

tests/test_data/test_datasets/test_custom_dataset.py

@@ -75,16 +75,16 @@ def test_custom_classes_override_default(dataset):
 
     # Test sending file path
     import tempfile
-    tmp_file = tempfile.NamedTemporaryFile()
-    with open(tmp_file.name, 'w') as f:
-        f.write('bus\ncar\n')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('bus\ncar\n')
     custom_dataset = dataset_class(
         ann_file=MagicMock(),
         pipeline=[],
-        classes=tmp_file.name,
+        classes=path,
         test_mode=True,
         img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
-    tmp_file.close()
 
     assert custom_dataset.CLASSES != original_classes
     assert custom_dataset.CLASSES == ['bus', 'car']
@@ -113,7 +113,7 @@ def test_data_infos__default_db_directories(self):
 
         self.assertListEqual([{
             'id': '000001',
-            'filename': 'JPEGImages/000001.jpg',
+            'filename': f'JPEGImages{os.sep}000001.jpg',
             'width': 353,
             'height': 500
         }], custom_ds.data_infos)
@@ -133,7 +133,7 @@ def test_data_infos__overridden_db_subdirectories(self):
 
         self.assertListEqual([{
             'id': '000001',
-            'filename': 'images/000001.jpg',
+            'filename': f'images{os.sep}000001.jpg',
             'width': 353,
             'height': 500
         }], custom_ds.data_infos)

tests/test_utils/test_misc.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import os
 import tempfile
 
 import numpy as np
@@ -172,32 +173,32 @@ def test_find_latest_checkpoint():
         # There are no checkpoints in the path.
         assert latest is None
 
-        path = tmpdir + '/none'
+        path = os.path.join(tmpdir, 'none')
         latest = find_latest_checkpoint(path)
         # The path does not exist.
         assert latest is None
 
     with tempfile.TemporaryDirectory() as tmpdir:
-        with open(tmpdir + '/latest.pth', 'w') as f:
+        with open(os.path.join(tmpdir, 'latest.pth'), 'w') as f:
             f.write('latest')
         path = tmpdir
         latest = find_latest_checkpoint(path)
-        assert latest == tmpdir + '/latest.pth'
+        assert latest == os.path.join(tmpdir, 'latest.pth')
 
     with tempfile.TemporaryDirectory() as tmpdir:
-        with open(tmpdir + '/iter_4000.pth', 'w') as f:
+        with open(os.path.join(tmpdir, 'iter_4000.pth'), 'w') as f:
             f.write('iter_4000')
-        with open(tmpdir + '/iter_8000.pth', 'w') as f:
+        with open(os.path.join(tmpdir, 'iter_8000.pth'), 'w') as f:
             f.write('iter_8000')
         path = tmpdir
         latest = find_latest_checkpoint(path)
-        assert latest == tmpdir + '/iter_8000.pth'
+        assert latest == os.path.join(tmpdir, 'iter_8000.pth')
 
     with tempfile.TemporaryDirectory() as tmpdir:
-        with open(tmpdir + '/epoch_1.pth', 'w') as f:
+        with open(os.path.join(tmpdir, 'epoch_1.pth'), 'w') as f:
             f.write('epoch_1')
-        with open(tmpdir + '/epoch_2.pth', 'w') as f:
+        with open(os.path.join(tmpdir, 'epoch_2.pth'), 'w') as f:
             f.write('epoch_2')
         path = tmpdir
         latest = find_latest_checkpoint(path)
-        assert latest == tmpdir + '/epoch_2.pth'
+        assert latest == os.path.join(tmpdir, 'epoch_2.pth')

tools/deployment/onnx2tensorrt.py

@@ -201,7 +201,7 @@ def parse_args():
         parsed directly from config file and are deprecated and will be \
         removed in future releases.')
     if not args.input_img:
-        args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.jpg')
+        args.input_img = osp.join(osp.dirname(__file__), '../../demo/demo.jpg')
 
     cfg = Config.fromfile(args.config)