Created demo notebooks

LucianoDeben · Jun 11, 2024 · 970a999 · 970a999
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diff --git a/Demo-notebooks/DRR.ipynb b/Demo-notebooks/DRR.ipynb
diff --git a/Demo-notebooks/Evaluation.ipynb b/Demo-notebooks/Evaluation.ipynb
diff --git a/Demo-notebooks/Training.ipynb b/Demo-notebooks/Training.ipynb
diff --git a/notebooks/diffdrr.ipynb → Deprecated-notebooks/diffdrr.ipynb b/notebooks/diffdrr.ipynb → Deprecated-notebooks/diffdrr.ipynb
diff --git a/notebooks/evaluate.ipynb → Deprecated-notebooks/evaluate.ipynb b/notebooks/evaluate.ipynb → Deprecated-notebooks/evaluate.ipynb
diff --git a/notebooks/pipeline.ipynb → Deprecated-notebooks/pipeline.ipynb b/notebooks/pipeline.ipynb → Deprecated-notebooks/pipeline.ipynb
diff --git a/notebooks/preprocessing.ipynb → Deprecated-notebooks/preprocessing.ipynb b/notebooks/preprocessing.ipynb → Deprecated-notebooks/preprocessing.ipynb
diff --git a/notebooks/speed.ipynb → Deprecated-notebooks/speed.ipynb b/notebooks/speed.ipynb → Deprecated-notebooks/speed.ipynb
diff --git a/models/TACEnet_vessel_enhancement_deformations_06062024.pth b/models/TACEnet_vessel_enhancement_deformations_06062024.pth
diff --git a/models/TACEnet_vessel_enhancement_deformations_30052024.pth b/models/TACEnet_vessel_enhancement_deformations_30052024.pth
diff --git a/notebooks/training.ipynb b/notebooks/training.ipynb
diff --git a/src/preprocessing.py b/src/preprocessing.py
@@ -117,7 +117,7 @@ def get_datasets(
 
     # Create the corresponding validation dataset
     val_dataset = TciaDataset(
-        root_dir="../data",
+        root_dir="../data081",
         collection=collection,
         section="validation",
         transform=transform,

diff --git a/src/training.py b/src/training.py
@@ -6,6 +6,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from timeit import default_timer as timer
+from diffdrr.data import read
 
 
 def loadData():
@@ -59,20 +60,26 @@ def trainSingleEpoch(model, criterion, optimizer, Volume, Target, device, length
 
     model.train()
     for i in range(length):
-        rotation = random.uniform(-40,40)
+        rotation = random.uniform(-5,5)
         # Create raw DRR
-        drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+        subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+
+        drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
             rotations=torch.tensor([[rotation, 0.0, 0.0]]),
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
+        )
 
 
         enhancement_factor = random.uniform(0.4,0.6)
@@ -92,19 +99,25 @@ def testModel(model, criterion, Volume, Target, device):
     loss = []
 
     for i in range(5):
-        rotation = (i*20-40)
-        drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+        rotation = random.uniform(-5,5)
+        subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+
+        drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
             rotations=torch.tensor([[rotation, 0.0, 0.0]]),
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
+        )
         drr_body = drr_raw[:,0,:,:]
         drr_vessels = drr_raw[:,1,:,:]
 
@@ -159,18 +172,24 @@ def testInferenceSpeed(path):
 
     model.load_state_dict(torch.load(path))
 
-    drr_raw = create_drr(
-            volume[0],
-            target[0],
+    subject = read(
+            tensor=volume[0],
+            label_tensor=target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+
+    drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
-            rotations=torch.tensor([[0.0, 0.0, 0.0]]),
+            rotations=torch.tensor([[0, 0.0, 0.0]]),
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
+        )
 
     drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.6, device=device)
 
@@ -187,19 +206,25 @@ def testInferenceSpeed(path):
 
 
 def visualizeModel(model, Volume, Target, device):
-    rotation = random.uniform(-40,40)
-    drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+    rotation = random.uniform(-5,5)
+    subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+
+    drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
             rotations=torch.tensor([[rotation, 0.0, 0.0]]),
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
+        )
 
     drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.5, device=device)
 
@@ -244,19 +269,25 @@ def drr_to_input(drr_raw, enhancement_factor, device):
 def visualizationResult(model, Volume, Target, device):
     model.eval()
 
-    rotation = random.uniform(-40,40)
-    drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+    rotation = random.uniform(-5,5)
+    subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+
+    drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
             rotations=torch.tensor([[rotation, 0.0, 0.0]]),
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
+        )
     drr_body = drr_raw[:,0,:,:].to(device)
     fig, axs = plt.subplots(2, 4, figsize=(16, 8))
     axs = axs.flatten()
@@ -306,25 +337,31 @@ def rotationSweep(model, Volume, Target, device, criterion):
         loss = []
         #Rotation sweep
         for i in range(80):
+            subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
+            bone_attenuation_multiplier=5.0,
+            )
+
+
             drr_raw = create_drr(
-                Volume[0],
-                Target[0],
-                bone_attenuation_multiplier=5.0,
-                sdd=1020,
-                height=256,
-                width=256,
-                rotations=torch.tensor([[i-40, 0.0, 0.0]]),
-                translations=torch.tensor([[0.0, 850.0, 0.0]]),
-                mask_to_channels=True,
-                device="cpu",
-                )
+            subject,
+            sdd=1020,
+            height=256,
+            width=256,
+            rotations=torch.tensor([[i-40, 0.0, 0.0]]),
+            translations=torch.tensor([[0.0, 850.0, 0.0]]),
+            mask_to_channels=True,
+            device="cpu",
+            )
 
             drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=ef, device=device)
             prediction, latent_representation = model(Target.to(device), drr_input)
             loss.append(criterion(prediction, drr_vessels).item())
-            print(f"loop: {j}, angle: {i}")
+            #print(f"loop: {j}, angle: {i}")
 
-        print(f"loss of loop {j}: {np.mean(loss)}")
+        #print(f"loss of loop {j}: {np.mean(loss)}")
         total_loss.append(np.mean(loss))