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Update Pathology Pipelines for MetaTensor Changes #905

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Merged
bhashemian merged 6 commits into from
Sep 13, 2022
Merged

Update Pathology Pipelines for MetaTensor Changes #905

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30f9417
Update tumor detection pipeline
bhashemian Sep 8, 2022
5d9fde2
Merge branch 'main' into update-for-metatensor
bhashemian Sep 8, 2022
6906650
Update MIL pipeline for MetaTensor changes
bhashemian Sep 9, 2022
2858388
Merge branch 'main' into update-for-metatensor
bhashemian Sep 9, 2022
4388855
Merge branch 'update-for-metatensor' of github.com:drbeh/tutorials in…
bhashemian Sep 9, 2022
2d06d2f
Merge branch 'main' into update-for-metatensor
bhashemian Sep 13, 2022
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6 changes: 5 additions & 1 deletion pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
RandGridPatchd,
RandRotate90d,
ScaleIntensityRanged,
SplitDimd,
ToTensord,
)
from sklearn.metrics import cohen_kappa_score
Expand Down Expand Up @@ -235,9 +236,10 @@ def list_data_collate(batch: collections.abc.Sequence):
"""

for i, item in enumerate(batch):
# print(f"{i} = {item['image'].shape=} >> {item['image'].keys=}")
data = item[0]
data["image"] = torch.stack([ix["image"] for ix in item], dim=0)
data["patch_location"] = torch.stack([ix["patch_location"] for ix in item], dim=0)
# data["patch_location"] = torch.stack([ix["patch_location"] for ix in item], dim=0)
batch[i] = data
return default_collate(batch)

Expand Down Expand Up @@ -289,6 +291,7 @@ def main_worker(gpu, args):
pad_mode=None,
constant_values=255,
),
SplitDimd(keys=["image"], dim=0, keepdim=False, list_output=True),
RandFlipd(keys=["image"], spatial_axis=0, prob=0.5),
RandFlipd(keys=["image"], spatial_axis=1, prob=0.5),
RandRotate90d(keys=["image"], prob=0.5),
Expand All @@ -308,6 +311,7 @@ def main_worker(gpu, args):
pad_mode=None,
constant_values=255,
),
SplitDimd(keys=["image"], dim=0, keepdim=False, list_output=True),
ScaleIntensityRanged(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)),
ToTensord(keys=["image", "label"]),
]
Expand Down
4 changes: 2 additions & 2 deletions pathology/tumor_detection/ignite/camelyon_train_evaluate.py
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Expand Up @@ -115,7 +115,7 @@ def train(cfg):
# Create MONAI dataset
train_data_list = CSVDataset(
cfg["train_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand All @@ -129,7 +129,7 @@ def train(cfg):

valid_data_list = CSVDataset(
cfg["valid_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand Down
4 changes: 2 additions & 2 deletions pathology/tumor_detection/ignite/camelyon_train_evaluate_nvtx_profiling.py
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Expand Up @@ -117,7 +117,7 @@ def train(cfg):
# Create MONAI dataset
train_data_list = CSVDataset(
cfg["train_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand All @@ -131,7 +131,7 @@ def train(cfg):

valid_data_list = CSVDataset(
cfg["valid_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand Down
6 changes: 3 additions & 3 deletions pathology/tumor_detection/torch/camelyon_train_evaluate_pytorch_gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -308,7 +308,7 @@ def main(cfg):
# Create train dataset and dataloader
train_data_list = CSVDataset(
cfg["train_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand All @@ -327,7 +327,7 @@ def main(cfg):
if not cfg["no_validate"]:
valid_data_list = CSVDataset(
cfg["valid_file"],
col_groups={"image": 0, "patch_location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
col_groups={"image": 0, "location": [2, 1], "label": [3, 6, 9, 4, 7, 10, 5, 8, 11]},
kwargs_read_csv={"header": None},
transform=Lambdad("image", lambda x: os.path.join(cfg["root"], "training/images", x + ".tif")),
)
Expand Down Expand Up @@ -361,7 +361,7 @@ def main(cfg):
# Deep Learning Model and Configurations
# -------------------------------------------------------------------------
# Initialize model
model = TorchVisionFCModel("resnet18", n_classes=1, use_conv=True, pretrained=cfg["pretrain"])
model = TorchVisionFCModel("resnet18", num_classes=1, use_conv=True, pretrained=cfg["pretrain"])
model = model.to(device)

# Loss function
Expand Down