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Merge branch 'main' into update-tcia-prostatex-model-notebook
2 parents f3d172e + 071eb1a commit 90d0a9f

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-419
lines changed

active_learning/liver_tumor_al/active_learning.py

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -54,7 +54,7 @@
5454
parser = argparse.ArgumentParser(description="Active Learning Setting")
5555

5656
# Directory & Json & Seed
57-
parser.add_argument("--base_dir", default="/home/vishwesh/experiments/al_sanity_test_apr27_2023", type=str)
57+
parser.add_argument("--base_dir", default="./experiments/al_sanity_test_apr27_2023", type=str)
5858
parser.add_argument("--data_root", default="/scratch_2/data_2021/68111", type=str)
5959
parser.add_argument("--json_path", default="/scratch_2/data_2021/68111/dataset_val_test_0_debug.json", type=str)
6060
parser.add_argument("--seed", default=102, type=int)
@@ -155,7 +155,7 @@ def main():
155155
# Model Definition
156156
device = torch.device("cuda:0")
157157
network = UNet(
158-
dimensions=3,
158+
spatial_dims=3,
159159
in_channels=1,
160160
out_channels=3,
161161
channels=(16, 32, 64, 128, 256),
@@ -187,7 +187,7 @@ def main():
187187
b_max=1.0,
188188
clip=True,
189189
),
190-
CropForegroundd(keys=["image", "label"], source_key="image"),
190+
CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True),
191191
SpatialPadd(keys=["image", "label"], spatial_size=(96, 96, 96)),
192192
RandCropByPosNegLabeld(
193193
keys=["image", "label"],
@@ -225,7 +225,7 @@ def main():
225225
b_max=1.0,
226226
clip=True,
227227
),
228-
CropForegroundd(keys=["image", "label"], source_key="image"),
228+
CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True),
229229
EnsureTyped(keys=["image", "label"]),
230230
]
231231
)
@@ -240,7 +240,7 @@ def main():
240240
mode=("bilinear"),
241241
),
242242
ScaleIntensityRanged(keys="image", a_min=-21, a_max=189, b_min=0.0, b_max=1.0, clip=True),
243-
CropForegroundd(keys=("image"), source_key="image"),
243+
CropForegroundd(keys=("image"), source_key="image", allow_smaller=True),
244244
EnsureTyped(keys=["image"]),
245245
]
246246
)
@@ -315,7 +315,7 @@ def main():
315315
unl_loader = DataLoader(unl_ds, batch_size=1)
316316

317317
# Calculation of Epochs based on steps
318-
max_epochs = np.int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
318+
max_epochs = int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
319319
print("Epochs Estimated are {} for Active Iter {} with {} Vols".format(max_epochs, active_iter, len(train_d)))
320320

321321
# Model Training begins for one active iteration
@@ -393,7 +393,7 @@ def main():
393393
prev_best_ckpt = os.path.join(active_model_dir, "model.pt")
394394

395395
device = torch.device("cuda:0")
396-
ckpt = torch.load(prev_best_ckpt)
396+
ckpt = torch.load(prev_best_ckpt, weights_only=True)
397397
network.load_state_dict(ckpt)
398398
network.to(device=device)
399399

@@ -487,16 +487,16 @@ def main():
487487

488488
variance_dims = np.shape(variance)
489489
score_list.append(np.nanmean(variance))
490-
name_list.append(unl_data["image_meta_dict"]["filename_or_obj"][0])
490+
name_list.append(unl_data["image"].meta["filename_or_obj"][0])
491491
print(
492492
"Variance for image: {} is: {}".format(
493-
unl_data["image_meta_dict"]["filename_or_obj"][0], np.nanmean(variance)
493+
unl_data["image"].meta["filename_or_obj"][0], np.nanmean(variance)
494494
)
495495
)
496496

497497
# Plot with matplotlib and save all slices
498498
plt.figure(1)
499-
plt.imshow(np.squeeze(variance[:, :, np.int(variance_dims[2] / 2)]))
499+
plt.imshow(np.squeeze(variance[:, :, int(variance_dims[2] / 2)]))
500500
plt.colorbar()
501501
plt.title("Dropout Uncertainty")
502502
fig_path = os.path.join(fig_base_dir, "active_{}_file_{}.png".format(active_iter, counter))

active_learning/liver_tumor_al/results_uncertainty_analysis.ipynb

Lines changed: 10 additions & 20 deletions
Large diffs are not rendered by default.

active_learning/tool_tracking_al/active_learning.py

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -47,7 +47,7 @@
4747
parser = argparse.ArgumentParser(description="Active Learning Settings")
4848

4949
# Directory & Json & Seed
50-
parser.add_argument("--base_dir", default="/home/vishwesh/experiments/robo_tool_experiments/variance_sanity", type=str)
50+
parser.add_argument("--base_dir", default="./experiments/robo_tool_experiments/variance_sanity", type=str)
5151
parser.add_argument("--data_root", default="/scratch_2/robo_tool_dataset_2023", type=str)
5252
parser.add_argument("--json_path", default="/scratch_2/robo_tool_dataset_2023/data_list.json", type=str)
5353
parser.add_argument("--seed", default=120, type=int)
@@ -281,7 +281,7 @@ def main():
281281
unl_loader = DataLoader(unl_ds, batch_size=1)
282282

283283
# Calculation of Epochs based on steps
284-
max_epochs = np.int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
284+
max_epochs = int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
285285
print("Epochs Estimated are {} for Active Iter {} with {} Vols".format(max_epochs, active_iter, len(train_d)))
286286

287287
# Keep track of Best_metric, it is being used as IoU and not Dice
@@ -379,7 +379,7 @@ def main():
379379
prev_best_ckpt = os.path.join(active_model_dir, "model.pt")
380380

381381
device = torch.device("cuda:0")
382-
ckpt = torch.load(prev_best_ckpt)
382+
ckpt = torch.load(prev_best_ckpt, weights_only=True)
383383
network.load_state_dict(ckpt)
384384
network.to(device=device)
385385

@@ -469,10 +469,10 @@ def main():
469469
variance = np.sum(np.nanvar(vol_input, axis=0), axis=0)
470470

471471
score_list.append(np.nanmean(variance))
472-
name_list.append(unl_data["image_meta_dict"]["filename_or_obj"][0])
472+
name_list.append(unl_data["image"].meta["filename_or_obj"][0])
473473
print(
474474
"Variance for image: {} is: {}".format(
475-
unl_data["image_meta_dict"]["filename_or_obj"][0], np.nanmean(variance)
475+
unl_data["image"].meta["filename_or_obj"][0], np.nanmean(variance)
476476
)
477477
)
478478

active_learning/tool_tracking_al/results_uncertainty_analysis.ipynb

Lines changed: 37 additions & 34 deletions
Large diffs are not rendered by default.

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