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biotine_unpaired/dynamic/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.35.2",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 3000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": true,
+  "steps_offset": 0,
+  "thresholding": true,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

chromalive/dynamic/scheduler_config.json

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+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.35.2",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 3000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": true,
+  "steps_offset": 0,
+  "thresholding": true,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

chromalive/my_conf/dataset/BBBC021/BBBC021_196_docetaxel.yaml

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+name: BBBC021_196_docetaxel
+path: /projects/static2dynamic/datasets/BBBC021/196x196/docetaxel
+data_shape: [3, 196, 196]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8

chromalive/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_fully_ordered_inference.py

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+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_fully_ordered_inference import dataset
+
+dataset = replace(
+    dataset,
+    name=dataset.name + "_docetaxel",
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/docetaxel",
+    path_to_single_parquet="/projects/static2dynamic/datasets/BBBC021/196x196/BBBC021_196_docetaxel__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)

chromalive/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_hard_aug_inference.py

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+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_hard_aug_inference import dataset
+
+dataset = replace(
+    dataset,
+    name=dataset.name + "_docetaxel",
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/docetaxel_hard_augmented",
+)

chromalive/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_inference.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_inference import dataset
+
+dataset = replace(
+    dataset,
+    name=dataset.name + "_docetaxel",
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/docetaxel",
+)

chromalive/my_conf/dataset/BBBC021/BBBC021_196_nocodazole_fully_ordered_inference.py

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+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_fully_ordered_inference import dataset
+
+# Nocodazole classes + DMSO
+CLASSES_IN_ORDER = (
+    "DMSO",
+    "nocodazole_0.001",
+    "nocodazole_0.003",
+    "nocodazole_0.01",
+    "nocodazole_0.03",
+    "nocodazole_0.1",
+    "nocodazole_0.3",
+    "nocodazole_1.0",
+    "nocodazole_3.0",
+)
+assert dataset.dataset_params is not None
+ds_params = replace(dataset.dataset_params, sorting_func=lambda subdir: CLASSES_IN_ORDER.index(subdir.name))
+
+# Path and name
+dataset = replace(
+    dataset,
+    name=dataset.name + "_nocodazole",
+    dataset_params=ds_params,
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/nocodazole",
+    path_to_single_parquet="/projects/static2dynamic/datasets/BBBC021/196x196/BBBC021_196_nocodazole__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)

chromalive/my_conf/dataset/BBBC048/bbbc048.yaml

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+name: BBBC048_fully_ordered
+
+path: /projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed_full_circle_augmented
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/BBBC048/BBBC048__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 1, 48, 48 ]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+  - _target_: torchvision.transforms.v2.ToDtype
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+    scale: true
+  - _target_: torchvision.transforms.v2.Normalize
+    mean: [ 0.5 ]
+    std: [ 0.5 ]
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true

chromalive/my_conf/dataset/BBBC048/bbbc048_inference.py

@@ -0,0 +1,7 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC048.bbbc048_inference import dataset
+
+dataset = replace(
+    dataset, path="/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed_full_circle_augmented"
+)

chromalive/my_conf/dataset/BBBC048/bbbc048_inference_fully_ordered.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC048.bbbc048_inference_fully_ordered import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed_full_circle_augmented",
+    path_to_single_parquet="/projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/BBBC048/BBBC048__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)

chromalive/my_conf/dataset/ChromaLive6h/ChromaLive6h_3ch_png.yaml

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+name: chromaLive6h_3ch_png_patches_380px
+path: /projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ] # move to [-1:1]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+selected_dists:

chromalive/my_conf/dataset/ChromaLive6h/ChromaLive6h_3ch_png_high_doses_fully_ordered.yaml

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+name: chromaLive6h_3ch_png_patches_380px_doses_10_11_combined_fully_ordered
+
+path: /projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/patches_fc_many_augs
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/heldout_test_trajs_2_doses_10_11_combined__chromaLive6h_3ch_png_patches_380px_fc_many_augs/heldout_test_trajs_2_doses_10_11_combined__chromaLive6h_3ch_png_patches_380px_fc_many_augs__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 3, 128, 128 ]
+
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ] # move to [-1:1]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+selected_dists:
+
+fully_ordered: true

chromalive/my_conf/dataset/ChromaLive6h/ChromaLive6h_4ch_tif.yaml

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+name: chromaLive6h_4ch_tif_patches_380px
+path: /projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches
+data_shape: [ 4, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5, 0.5 ] # move to [-1:1]
+    std: [ 0.5, 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 65536 ]
+expected_dtype: torch.uint16
+selected_dists: [ 'time_1', 'time_3', 'time_5', 'time_7', 'time_9', 'time_11', 'time_13' ]

chromalive/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fc_augs_inference.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset = replace(
+    dataset,
+    name="chromaLive6h_3ch_png_patches_380px_fc_augs",
+    path="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/patches_fc_augs",
+)

chromalive/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fc_many_augs_inference.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset = replace(
+    dataset,
+    name="chromaLive6h_3ch_png_patches_380px_fc_many_augs",
+    path="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/patches_fc_many_augs",
+)

chromalive/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fully_ordered_inference.py

@@ -0,0 +1,9 @@
+from dataclass import replace
+
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_fully_ordered_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches",
+    path_to_single_parquet="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/chromaLive6h_3ch_png_patches_380px__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)

chromalive/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_hard_aug_inference.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset = replace(
+    dataset,
+    name="chromaLive6h_3ch_png_patches_380px_hard_aug",
+    path="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches_hard_augmented",
+)

chromalive/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_inference.py

@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches",
+)

chromalive/my_conf/dataset/DeepCycle/deepcycle_brightfield_to_3D_inference.py

@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_brightfield_to_3D_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"

chromalive/my_conf/dataset/DeepCycle/deepcycle_hoechst_brightfield_to_3D_inference.py

@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_hoechst_brightfield_to_3D_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"

chromalive/my_conf/dataset/DeepCycle/deepcycle_inference.py

@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"

chromalive/my_conf/dataset/DeepCycle/deepcycle_markers_inference.py

@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_markers_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"

chromalive/my_conf/dataset/Jurkat/Jurkat.yaml

@@ -0,0 +1,16 @@
+name: Jurkat
+path: /projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles
+data_shape: [3, 66, 66]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8

chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_inference.py

@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_brightfield_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed"

chromalive/my_conf/dataset/Jurkat/Jurkat_fully_ordered.yaml

@@ -0,0 +1,18 @@
+name: Jurkat_fully_ordered_dinov2_regs_giant_ds_preproc
+path: /projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles
+path_to_single_parquet: /projects/static2dynamic/datasets/Jurkat/Jurkat__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 66, 66 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+fully_ordered: true

chromalive/my_conf/dataset/Jurkat/Jurkat_inference.py

@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles",
+)

chromalive/my_conf/dataset/NASH_fibrosis/NASH_fibrosis.yaml

@@ -0,0 +1,24 @@
+name: NASH_fibrosis
+path: /projects/static2dynamic/datasets/NASH/prepared_data/fibrosis
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  # Convert to float32 (and normalize to [0, 1]) before resizing
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  # Random crop from 299x299 to 192x192, then resize to 128x128
+  - _target_: torchvision.transforms.RandomCrop
+    size: 192
+  - _target_: torchvision.transforms.Resize
+    size: 128
+  # Normalize to [-1, 1]
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  # Random 8x square augmentations
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]

chromalive/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_fully_ordered_inference.py

@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.NASH_fibrosis.NASH_fibrosis_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/fibrosis"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/NASH/prepared_data/NASH_fibrosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"

chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered.yaml

@@ -0,0 +1,26 @@
+name: NASH_steatosis_fully_ordered_dinov2_regs_giant_ds_preproc
+path: /projects/static2dynamic/datasets/NASH/prepared_data/steatosis
+path_to_single_parquet: /projects/static2dynamic/datasets/NASH/prepared_data/NASH_steatosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  # Convert to float32 (and normalize to [0, 1]) before resizing
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  # Random crop from 299x299 to 192x192, then resize to 128x128
+  - _target_: torchvision.transforms.RandomCrop
+    size: 192
+  - _target_: torchvision.transforms.Resize
+    size: 128
+  # Normalize to [-1, 1]
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  # Random 8x square augmentations
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
+fully_ordered: true

chromalive/my_conf/dataset/biotine/biotine_png_128.yaml

@@ -0,0 +1,19 @@
+name: biotine_png
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]

chromalive/my_conf/dataset/biotine/biotine_png_128_fully_ordered.yaml

@@ -0,0 +1,21 @@
+name: biotine_png_fully_ordered
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255
+path_to_single_parquet: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/biotine_png__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
+fully_ordered: true

chromalive/my_conf/dataset/biotine/biotine_png_128_fully_ordered_inference.py

@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.biotine.biotine_png_128_inference import dataset
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+assert dataset.dataset_params is not None
+updated_ds_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255",
+    path_to_single_parquet="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/biotine_png__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=updated_ds_params,
+)

chromalive/my_conf/dataset/biotine/biotine_png_128_hard_aug.yaml

@@ -0,0 +1,16 @@
+name: biotine_png_hard_aug
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255_hard_augmented
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]

chromalive/my_conf/dataset/biotine/biotine_png_128_hard_aug_inference.py

@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.biotine.biotine_png_128_hard_aug_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255_hard_augmented",
+)

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy.yaml

@@ -0,0 +1,18 @@
+name: diabetic_retinopathy
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train
+data_shape: [3, 256, 256]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 256 # single int => image resized to (size * aspect_ratio, size) or (size, size * aspect_ratio) with aspect_ratio >= 1 preserved
+  - _target_: torchvision.transforms.v2.CenterCrop
+    size: 256 # square centered crop
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_fully_ordered.yaml

@@ -0,0 +1,25 @@
+name: diabetic_retinopathy_2048_crop_fully_ordered
+
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/balanced_classes__diabetic_retinopathy_2048_crop/balanced_classes__diabetic_retinopathy_2048_crop__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [3, 256, 256]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+    - _target_: torchvision.transforms.v2.CenterCrop
+      size: 2048
+    - _target_: torchvision.transforms.v2.Resize
+      size: 256
+    - _target_: torchvision.transforms.v2.ToDtype # this also scales to [0; 1]
+      dtype: ${torch_dtype:float32}
+      scale: true
+    - _target_: torchvision.transforms.v2.Normalize
+      mean: [0.5, 0.5, 0.5]
+      std: [0.5, 0.5, 0.5]
+
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
+
+fully_ordered: true

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_fully_ordered.yaml

@@ -0,0 +1,21 @@
+name: diabetic_retinopathy_2048_precrop_fully_ordered
+
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented_2048_crop
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/diabetic_retinopathy_full_circle_augs_2048_precrop/diabetic_retinopathy_full_circle_augs_2048_precrop__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 3, 256, 256 ]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+  - _target_: torchvision.transforms.v2.ToDtype
+    dtype: ${torch_dtype:float32}
+    scale: true
+  - _target_: torchvision.transforms.v2.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_fully_ordered_inference.py

@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_full_circle_augs_2048_precrop_inference import (
+    dataset,
+)
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+assert dataset.dataset_params is not None
+ds_params = replace(dataset.dataset_params, dataset_class=ContinuousTimeImageDataset)
+
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented_2048_crop",
+    path_to_single_parquet="/projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/diabetic_retinopathy_full_circle_augs_2048_precrop/diabetic_retinopathy_full_circle_augs_2048_precrop__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    fully_ordered=True,
+    dataset_params=ds_params,
+)

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_fully_ordered_inference.py

@@ -0,0 +1,15 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_inference import dataset
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+assert dataset.dataset_params is not None
+updated_ds_params = replace(dataset.dataset_params, dataset_class=ContinuousTimeImageDataset)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train",
+    path_to_single_parquet="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/diabetic_retinopathy__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=updated_ds_params,
+)

chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_inference.py

@@ -0,0 +1,5 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_inference import dataset
+
+dataset = replace(dataset, path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train")

chromalive/my_conf/dataset/ependymal_context/ependymal_context_inference.py

@@ -0,0 +1,32 @@
+from torch import float32
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize
+
+from GaussianProxy.conf.training_conf import DataSet, DatasetParams
+from GaussianProxy.utils.data import ImageDataset
+
+DEFINITION = 256
+NUMBER_OF_CHANNELS = 3
+
+transforms = Compose(
+    transforms=[
+        ConvertImageDtype(float32),
+        Normalize(mean=[0.5] * NUMBER_OF_CHANNELS, std=[0.5] * NUMBER_OF_CHANNELS),
+    ]
+)
+
+ds_params = DatasetParams(
+    file_extension="png",
+    key_transform=str,
+    sorting_func=lambda subdir: int(subdir.name),
+    dataset_class=ImageDataset,
+)
+
+dataset = DataSet(
+    name="ependymal_context",
+    data_shape=(NUMBER_OF_CHANNELS, DEFINITION, DEFINITION),
+    transforms=transforms,
+    expected_initial_data_range=(0, 255),
+    dataset_params=ds_params,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context",
+    selected_dists=["0", "3", "5", "7", "9", "16", "30"],  # ignore .REMOVED_IMAGES/16 (imaging artifacts)
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_fc_augs_sep_gt_inference.py

@@ -0,0 +1,19 @@
+from dataclasses import replace
+from pathlib import Path
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+# note the data organization is special with separate ground truth folder
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    sorting_func=lambda subdir: str(subdir.name) if isinstance(subdir, Path) else str(subdir),
+)  # subdirs are "ground_truth" and "all_imgs" now...
+
+dataset = replace(
+    dataset,
+    name="ependymal_cutout_01_noised_full_circle_augs",
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.1_crop_full_circle_augmented",
+    separate_gt_starting_class_path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.1_crop_full_circle_augmented/ground_truths/1",
+    dataset_params=dataset_params,
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_separate_gt_fully_ordered_inference.py

@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+from my_conf.dataset.ependymal_cutout.ependymal_cutout_01_noised_separate_gt_inference import dataset
+
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/ependymal_cutout_01_noised__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=dataset_params,
+    selected_dists=["all_imgs"],
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_03_noised_separate_gt_inference.py

@@ -0,0 +1,17 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+# note the data organization is special with separate ground truth folder
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params, sorting_func=lambda subdir: str(subdir.name)
+)  # subdirs are "ground_truth" and "all_imgs" now...
+
+dataset = replace(
+    dataset,
+    name="ependymal_cutout_03_noised",
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.3_crop/",
+    dataset_params=dataset_params,
+    separate_gt_starting_class_path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.3_crop/ground_truths/1",
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_fully_ordered_inference.py

@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_fully_ordered_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_crop",
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/ependymal_cutout__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_inference.py

@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_crop",
+)

chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_white_bg_separate_gt_fully_ordered_inference.py

@@ -0,0 +1,19 @@
+from dataclasses import replace
+
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+from my_conf.dataset.ependymal_cutout.ependymal_cutout_white_bg_separate_gt_inference import dataset
+
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/ependymal_cutout_white_bg__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=dataset_params,
+    selected_dists=["all_imgs"],
+    separate_gt_starting_class_path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_white_bg_crop/ground_truths/1",
+)

chromalive/my_conf/dataset/human_embryo/human_embryo_inference.py

@@ -0,0 +1,5 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.human_embryo.human_embryo_inference import dataset
+
+dataset = replace(dataset, path="/projects/imagesets3/2022_Gomez/reformated_phases/phases")

chromalive/my_conf/dataset/imagenet_n01917289_hard_aug_inference.py

@@ -0,0 +1,36 @@
+from torch import float32
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize, Resize
+from torchvision.transforms.v2 import CenterCrop
+
+from GaussianProxy.conf.training_conf import DataSet, DatasetParams
+from GaussianProxy.utils.data import ImageDataset
+
+DEFINITION = 128
+NUMBER_OF_CHANNELS = 3
+
+transforms = Compose(
+    transforms=[
+        # single int => image resized to (size * aspect_ratio, size) or (size, size * aspect_ratio) with aspect_ratio >= 1 preserved
+        Resize(DEFINITION),
+        CenterCrop(DEFINITION),  # pyright: ignore[reportAttributeAccessIssue]
+        ConvertImageDtype(float32),
+        Normalize(mean=[0.5] * NUMBER_OF_CHANNELS, std=[0.5] * NUMBER_OF_CHANNELS),
+    ]
+)
+
+ds_params = DatasetParams(
+    file_extension="JPEG",
+    key_transform=int,
+    sorting_func=lambda subdir: int(subdir.name),
+    dataset_class=ImageDataset,
+)
+
+dataset = DataSet(
+    path="/localtmp/tboyer/augmented_imagenet_n01917289",
+    name="imagenet_n01917289_hard_aug_inference",
+    data_shape=(NUMBER_OF_CHANNELS, DEFINITION, DEFINITION),
+    transforms=transforms,
+    selected_dists=None,  # not used
+    expected_initial_data_range=(0, 255),
+    dataset_params=ds_params,
+)

chromalive/my_conf/hydra/job_logging/custom.yaml

@@ -0,0 +1,35 @@
+version: 1
+formatters:
+  simple:
+    format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+  colorlog:
+    (): colorlog.ColoredFormatter
+    format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s] - %(message)s'
+    log_colors:
+      DEBUG: purple
+      INFO: green
+      WARNING: yellow
+      ERROR: red
+      CRITICAL: red
+handlers:
+  console:
+    class: logging.StreamHandler
+    formatter: colorlog
+    stream: ext://sys.stdout
+    level: INFO
+  file:
+    class: logging.FileHandler
+    formatter: simple
+    filename: ${hydra:run.dir}/logs.log # unify logs from launcher and script
+    level: DEBUG
+root:
+  level: DEBUG
+  handlers:
+  - console
+  - file
+logger:
+  matplotlib:
+    level: INFO
+  PIL:
+    level: INFO
+disable_existing_loggers: true