Merge branch 'main' into main

Project-MONAI · Sep 25, 2024 · 4b631e8 · 4b631e8
2 parents ca1cdfa + 203a999
commit 4b631e8
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Showing 16 changed files with 292 additions and 258 deletions.
diff --git a/active_learning/liver_tumor_al/active_learning.py b/active_learning/liver_tumor_al/active_learning.py
@@ -155,7 +155,7 @@ def main():
     # Model Definition
     device = torch.device("cuda:0")
     network = UNet(
-        dimensions=3,
+        spatial_dims=3,
         in_channels=1,
         out_channels=3,
         channels=(16, 32, 64, 128, 256),
@@ -187,7 +187,7 @@ def main():
                 b_max=1.0,
                 clip=True,
             ),
-            CropForegroundd(keys=["image", "label"], source_key="image"),
+            CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True),
             SpatialPadd(keys=["image", "label"], spatial_size=(96, 96, 96)),
             RandCropByPosNegLabeld(
                 keys=["image", "label"],
@@ -225,7 +225,7 @@ def main():
                 b_max=1.0,
                 clip=True,
             ),
-            CropForegroundd(keys=["image", "label"], source_key="image"),
+            CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True),
             EnsureTyped(keys=["image", "label"]),
         ]
     )
@@ -240,7 +240,7 @@ def main():
                 mode=("bilinear"),
             ),
             ScaleIntensityRanged(keys="image", a_min=-21, a_max=189, b_min=0.0, b_max=1.0, clip=True),
-            CropForegroundd(keys=("image"), source_key="image"),
+            CropForegroundd(keys=("image"), source_key="image", allow_smaller=True),
             EnsureTyped(keys=["image"]),
         ]
     )
@@ -315,7 +315,7 @@ def main():
         unl_loader = DataLoader(unl_ds, batch_size=1)
 
         # Calculation of Epochs based on steps
-        max_epochs = np.int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
+        max_epochs = int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
         print("Epochs Estimated are {} for Active Iter {} with {} Vols".format(max_epochs, active_iter, len(train_d)))
 
         # Model Training begins for one active iteration
@@ -393,7 +393,7 @@ def main():
         prev_best_ckpt = os.path.join(active_model_dir, "model.pt")
 
         device = torch.device("cuda:0")
-        ckpt = torch.load(prev_best_ckpt)
+        ckpt = torch.load(prev_best_ckpt, weights_only=True)
         network.load_state_dict(ckpt)
         network.to(device=device)
 
@@ -487,16 +487,16 @@ def main():
 
                     variance_dims = np.shape(variance)
                     score_list.append(np.nanmean(variance))
-                    name_list.append(unl_data["image_meta_dict"]["filename_or_obj"][0])
+                    name_list.append(unl_data["image"].meta["filename_or_obj"][0])
                     print(
                         "Variance for image: {} is: {}".format(
-                            unl_data["image_meta_dict"]["filename_or_obj"][0], np.nanmean(variance)
+                            unl_data["image"].meta["filename_or_obj"][0], np.nanmean(variance)
                         )
                     )
 
                     # Plot with matplotlib and save all slices
                     plt.figure(1)
-                    plt.imshow(np.squeeze(variance[:, :, np.int(variance_dims[2] / 2)]))
+                    plt.imshow(np.squeeze(variance[:, :, int(variance_dims[2] / 2)]))
                     plt.colorbar()
                     plt.title("Dropout Uncertainty")
                     fig_path = os.path.join(fig_base_dir, "active_{}_file_{}.png".format(active_iter, counter))

diff --git a/active_learning/liver_tumor_al/results_uncertainty_analysis.ipynb b/active_learning/liver_tumor_al/results_uncertainty_analysis.ipynb
diff --git a/active_learning/tool_tracking_al/active_learning.py b/active_learning/tool_tracking_al/active_learning.py
@@ -281,7 +281,7 @@ def main():
         unl_loader = DataLoader(unl_ds, batch_size=1)
 
         # Calculation of Epochs based on steps
-        max_epochs = np.int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
+        max_epochs = int(args.steps / (np.ceil(len(train_d) / args.batch_size)))
         print("Epochs Estimated are {} for Active Iter {} with {} Vols".format(max_epochs, active_iter, len(train_d)))
 
         # Keep track of Best_metric, it is being used as IoU and not Dice
@@ -379,7 +379,7 @@ def main():
         prev_best_ckpt = os.path.join(active_model_dir, "model.pt")
 
         device = torch.device("cuda:0")
-        ckpt = torch.load(prev_best_ckpt)
+        ckpt = torch.load(prev_best_ckpt, weights_only=True)
         network.load_state_dict(ckpt)
         network.to(device=device)
 

diff --git a/active_learning/tool_tracking_al/results_uncertainty_analysis.ipynb b/active_learning/tool_tracking_al/results_uncertainty_analysis.ipynb
diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb
diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb
@@ -364,25 +364,25 @@
     "device = torch.device(\"cuda\")\n",
     "\n",
     "autoencoder = define_instance(args, \"autoencoder_def\").to(device)\n",
-    "checkpoint_autoencoder = torch.load(args.trained_autoencoder_path)\n",
+    "checkpoint_autoencoder = torch.load(args.trained_autoencoder_path, weights_only=True)\n",
     "autoencoder.load_state_dict(checkpoint_autoencoder)\n",
     "\n",
     "diffusion_unet = define_instance(args, \"diffusion_unet_def\").to(device)\n",
-    "checkpoint_diffusion_unet = torch.load(args.trained_diffusion_path)\n",
+    "checkpoint_diffusion_unet = torch.load(args.trained_diffusion_path, weights_only=False)\n",
     "diffusion_unet.load_state_dict(checkpoint_diffusion_unet[\"unet_state_dict\"], strict=True)\n",
     "scale_factor = checkpoint_diffusion_unet[\"scale_factor\"].to(device)\n",
     "\n",
     "controlnet = define_instance(args, \"controlnet_def\").to(device)\n",
-    "checkpoint_controlnet = torch.load(args.trained_controlnet_path)\n",
+    "checkpoint_controlnet = torch.load(args.trained_controlnet_path, weights_only=False)\n",
     "monai.networks.utils.copy_model_state(controlnet, diffusion_unet.state_dict())\n",
     "controlnet.load_state_dict(checkpoint_controlnet[\"controlnet_state_dict\"], strict=True)\n",
     "\n",
     "mask_generation_autoencoder = define_instance(args, \"mask_generation_autoencoder_def\").to(device)\n",
-    "checkpoint_mask_generation_autoencoder = torch.load(args.trained_mask_generation_autoencoder_path)\n",
+    "checkpoint_mask_generation_autoencoder = torch.load(args.trained_mask_generation_autoencoder_path, weights_only=True)\n",
     "mask_generation_autoencoder.load_state_dict(checkpoint_mask_generation_autoencoder)\n",
     "\n",
     "mask_generation_diffusion_unet = define_instance(args, \"mask_generation_diffusion_def\").to(device)\n",
-    "checkpoint_mask_generation_diffusion_unet = torch.load(args.trained_mask_generation_diffusion_path)\n",
+    "checkpoint_mask_generation_diffusion_unet = torch.load(args.trained_mask_generation_diffusion_path, weights_only=True)\n",
     "mask_generation_diffusion_unet.load_state_dict(checkpoint_mask_generation_diffusion_unet[\"unet_state_dict\"])\n",
     "mask_generation_scale_factor = checkpoint_mask_generation_diffusion_unet[\"scale_factor\"]\n",
     "\n",