Merge pull request #77 from AllenCell/npm1_64_res

Npm1 64 res

configs/data/npm1_64_res/classical_image_sdf.yaml

@@ -0,0 +1,40 @@
+_target_: cyto_dl.datamodules.DataframeDatamodule
+_convert_: all
+path: ./morphology_appropriate_representation_learning/preprocessed_data/npm1_64_res/manifest.csv
+split_column: split
+batch_size: 64
+num_workers: 4
+transforms:
+  - _target_: cyto_dl.dataframe.transforms.GroupCols
+    groups:
+      image: sdf_path
+      cell_id: CellId
+  - _target_: cyto_dl.image.io.ReadNumpyFile
+    keys:
+      - image
+    remote: false
+    unsqueeze: True
+  - _target_: monai.transforms.ThresholdIntensityd
+    keys:
+      - image
+    threshold: 2
+    above: false
+    cval: 2
+  - _target_: monai.transforms.ThresholdIntensityd
+    keys:
+      - image
+    threshold: -2
+    above: true
+    cval: -2
+  - _target_: monai.transforms.SpatialPadd
+    keys:
+      - image
+    spatial_size:
+      - 67
+      - 67
+      - 67
+    value: 2
+  - _target_: monai.transforms.ToTensord
+    keys:
+      - image
+    track_meta: false

configs/data/npm1_64_res/classical_image_seg.yaml

@@ -0,0 +1,28 @@
+_target_: cyto_dl.datamodules.DataframeDatamodule
+_convert_: all
+path: ./morphology_appropriate_representation_learning/preprocessed_data/npm1_64_res/manifest.csv
+split_column: split
+batch_size: 64
+num_workers: 4
+transforms:
+  - _target_: cyto_dl.dataframe.transforms.GroupCols
+    groups:
+      image: seg_path
+      cell_id: CellId
+  - _target_: cyto_dl.image.io.ReadNumpyFile
+    keys:
+      - image
+    remote: false
+    unsqueeze: True
+  - _target_: monai.transforms.SpatialPadd
+    keys:
+      - image
+    spatial_size:
+      - 67
+      - 67
+      - 67
+    value: 0
+  - _target_: monai.transforms.ToTensord
+    keys:
+      - image
+    track_meta: false

configs/data/npm1_64_res/pc.yaml

@@ -0,0 +1,24 @@
+_target_: pointcloudutils.datamodules.ShapenetDataModule
+dataset_folder: ./morphology_appropriate_representation_learning/preprocessed_data/npm1_64_res/pointcloud/
+method: shapenet_dfnet
+dataset_type: pointcloud
+train_split: train
+val_split: val
+test_split: test
+points_subsample: 20000
+input_type: pointcloud
+points_file: points.npz
+points_iou_file: points.npz
+pointcloud_n: 8192
+pointcloud_noise: 0.005
+pointcloud_file: pointcloud.npz
+part_ratio: 0.5
+partial_type: centery_random
+return_idx: true
+train_rotate: true
+train_translate: false
+train_single_trans: false
+batch_size: 4
+num_workers: 16
+multi_files: null
+x_label: pcloud

configs/data/npm1_64_res/so3_image_sdf.yaml

@@ -0,0 +1,47 @@
+_target_: cyto_dl.datamodules.DataframeDatamodule
+_convert_: all
+path: ./morphology_appropriate_representation_learning/preprocessed_data/npm1_64_res/manifest.csv
+split_column: split
+batch_size: 64
+num_workers: 4
+transforms:
+  - _target_: cyto_dl.dataframe.transforms.GroupCols
+    groups:
+      image: sdf_path
+      cell_id: CellId
+  - _target_: cyto_dl.image.io.ReadNumpyFile
+    keys:
+      - image
+    remote: false
+    unsqueeze: True
+  - _target_: monai.transforms.ThresholdIntensityd
+    keys:
+      - image
+    threshold: 2
+    above: false
+    cval: 2
+  - _target_: monai.transforms.ThresholdIntensityd
+    keys:
+      - image
+    threshold: -2
+    above: true
+    cval: -2
+  - _target_: monai.transforms.SpatialPadd
+    keys:
+      - image
+    spatial_size:
+      - 67
+      - 67
+      - 67
+    value: 2
+  - _target_: cyto_dl.image.transforms.RotationMaskd
+    keys:
+      - image
+    group: so3
+    spatial_dims: 3
+    mask_side: 67
+    background: 2
+  - _target_: monai.transforms.ToTensord
+    keys:
+      - image
+    track_meta: false

configs/data/npm1_64_res/so3_image_seg.yaml

@@ -0,0 +1,35 @@
+_target_: cyto_dl.datamodules.DataframeDatamodule
+_convert_: all
+path: ./morphology_appropriate_representation_learning/preprocessed_data/npm1_64_res/manifest.csv
+split_column: split
+batch_size: 64
+num_workers: 4
+transforms:
+  - _target_: cyto_dl.dataframe.transforms.GroupCols
+    groups:
+      image: seg_path
+      cell_id: CellId
+  - _target_: cyto_dl.image.io.ReadNumpyFile
+    keys:
+      - image
+    remote: false
+    unsqueeze: True
+  - _target_: monai.transforms.SpatialPadd
+    keys:
+      - image
+    spatial_size:
+      - 67
+      - 67
+      - 67
+    value: 0
+  - _target_: cyto_dl.image.transforms.RotationMaskd
+    keys:
+      - image
+    group: so3
+    spatial_dims: 3
+    mask_side: 67
+    background: 0
+  - _target_: monai.transforms.ToTensord
+    keys:
+      - image
+    track_meta: false

configs/experiment/npm1/image_sdf_classical.yaml

@@ -4,7 +4,7 @@
 # python train.py experiment=example
 
 defaults:
-  - override /data: other_polymorphic/classical_image_sdf.yaml
+  - override /data: npm1/classical_image_sdf.yaml
   - override /model: image/classical_sdf_35.yaml
   - override /callbacks: default.yaml
   - override /trainer: default.yaml

configs/experiment/npm1_64_res/image_sdf_classical.yaml

@@ -0,0 +1,54 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: npm1_64_res/classical_image_sdf.yaml
+  - override /model: image/classical_sdf_64.yaml
+  - override /callbacks: default.yaml
+  - override /trainer: default.yaml
+  - override /logger: csv.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+experiment_name: npm1_variance
+tags: ["equivariance"]
+
+seed: 42
+
+data:
+  batch_size: 4
+
+model:
+  x_label: image
+
+trainer:
+  check_val_every_n_epoch: 1
+  min_epochs: 400
+  max_epochs: 2000
+  accelerator: gpu
+  devices: [0]
+
+callbacks:
+  early_stopping:
+    monitor: val/loss
+
+  model_checkpoint:
+    dirpath: ./npm1_variance/ckpts
+    monitor: val/loss
+    save_top_k: 2
+    every_n_epochs: 1
+
+logger:
+  csv:
+    save_dir: ./npm1_variance_64_res
+    name: "classical_sdf"
+    prefix:
+
+##### ONLY USE WITH A100s
+extras:
+  precision:
+    _target_: torch.set_float32_matmul_precision
+    precision: medium

configs/experiment/npm1_64_res/image_sdf_so3.yaml

@@ -0,0 +1,55 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: npm1_64_res/classical_image_sdf.yaml
+  - override /model: image/classical_sdf_64.yaml
+  - override /callbacks: default.yaml
+  - override /trainer: default.yaml
+  - override /logger: csv.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+experiment_name: npm1_variance
+tags: ["equivariance"]
+
+seed: 42
+
+data:
+  batch_size: 4
+
+model:
+  x_label: image
+  group: so3
+
+trainer:
+  check_val_every_n_epoch: 1
+  min_epochs: 400
+  max_epochs: 2000
+  accelerator: gpu
+  devices: [0]
+
+callbacks:
+  early_stopping:
+    monitor: val/loss
+
+  model_checkpoint:
+    dirpath: ./npm1_variance/ckpts
+    monitor: val/loss
+    save_top_k: 2
+    every_n_epochs: 1
+
+logger:
+  csv:
+    save_dir: ./npm1_variance_64_res
+    name: "so3_sdf"
+    prefix:
+
+##### ONLY USE WITH A100s
+extras:
+  precision:
+    _target_: torch.set_float32_matmul_precision
+    precision: medium

configs/experiment/npm1_64_res/image_seg_classical.yaml

@@ -0,0 +1,54 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: npm1_64_res/classical_image_seg.yaml
+  - override /model: image/classical_seg_64.yaml
+  - override /callbacks: default.yaml
+  - override /trainer: default.yaml
+  - override /logger: csv.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+experiment_name: npm1_variance
+tags: ["equivariance"]
+
+seed: 42
+
+data:
+  batch_size: 4
+
+model:
+  x_label: image
+
+trainer:
+  check_val_every_n_epoch: 1
+  min_epochs: 400
+  max_epochs: 2000
+  accelerator: gpu
+  devices: [0]
+
+callbacks:
+  early_stopping:
+    monitor: val/loss
+
+  model_checkpoint:
+    dirpath: ./npm1_variance/ckpts
+    monitor: val/loss
+    save_top_k: 2
+    every_n_epochs: 1
+
+logger:
+  csv:
+    save_dir: ./npm1_variance_64_res
+    name: "classical_seg"
+    prefix:
+
+##### ONLY USE WITH A100s
+extras:
+  precision:
+    _target_: torch.set_float32_matmul_precision
+    precision: medium