Rename single photon files

Infer_1Photon.py

@@ -38,7 +38,7 @@ task = 'SiemenStarLowerLeft'
 config = configs[task]
 
 BinningFactor = 10
-numberOfAugOps = 1
+numberOfAugOps = 8
 flag_normalize = False
 Roi = config['roi']
 X_st, X_ed, Y_st, Y_ed = Roi
@@ -76,7 +76,7 @@ def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) ->
 
 if __name__ == "__main__":
     model = models.get_model_class(config['modelVersion'])().cuda()
-    modelName = f'singlePhoton{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_aug1'
+    modelName = f'singlePhoton{config["modelVersion"]}_{config["energy"]}keV_Noise{config["noise"]}keV_E500_aug1'
     if flag_normalize:
         modelName += '_normalized'
     model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/{modelName}.pth', weights_only=True))

Train_1Photon.py

@@ -23,7 +23,7 @@ TestLoss = -1
 model = models.get_model_class(modelVersion)().cuda()
 # summary(model, input_size=(128, 1, 3, 3))
 LearningRate = 1e-3
-Noise = 0.13 # in keV
+Noise = 0.23 # in keV
 NoiseThreshold = 0 * Noise  # in keV, set values below this threshold to zero
 numberOfAugOps = 1 # 1 (no augmentation) or (1,8] (with augmentation)
 flag_normalize = False
@@ -141,27 +141,8 @@ testLoader = torch.utils.data.DataLoader(
 
 optimizer = torch.optim.Adam(model.parameters(), lr=LearningRate)
 scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.7, patience = 3)
-if __name__ == "__main__":
-    for epoch in tqdm(range(1, 1001)):
-        train(model, trainLoader, optimizer)
-        test(model, valLoader)
-        scheduler.step(ValLosses[-1])
-        print(f"Learning Rate: {optimizer.param_groups[0]['lr']}")
-        if epoch in [20, 30, 50, 100, 200, 300, 500, 750, 1000]:
-            modelName = f'singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}_aug{numberOfAugOps}'
-            if flag_normalize == True:
-                modelName += '_normalized'
-            torch.save(model.state_dict(), f'Models/{modelName}.pth')
-            print(f"Saved model checkpoint: {modelName}.pth")
 
-test(model, testLoader)
+def plot_loss_curve(TrainLosses, ValLosses, modelVersion, TestLoss=0):
-modelName = f'singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}_aug{numberOfAugOps}'
-if flag_normalize == True:
-    modelName += '_normalized'
-torch.save(model.state_dict(), f'Models/{modelName}.pth')
-print(f"Saved final model checkpoint: {modelName}.pth")
-
-def plot_loss_curve(TrainLosses, ValLosses, TestLoss, modelVersion):
     import matplotlib.pyplot as plt
     plt.figure(figsize=(8,6))
     plt.plot(TrainLosses, label='Train Loss', color='blue')
@@ -177,4 +158,25 @@ def plot_loss_curve(TrainLosses, ValLosses, TestLoss, modelVersion):
     if flag_normalize:
         plotName += '_normalized'
     plt.savefig(f'Results/{plotName}.png')
-plot_loss_curve(TrainLosses, ValLosses, TestLoss, modelVersion=modelVersion)
+
+if __name__ == "__main__":
+    for epoch in tqdm(range(1, 151)):
+        train(model, trainLoader, optimizer)
+        test(model, valLoader)
+        scheduler.step(ValLosses[-1])
+        print(f"Learning Rate: {optimizer.param_groups[0]['lr']}")
+        if epoch in [20, 30, 50, 100, 150]:
+            modelName = f'singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}_aug{numberOfAugOps}'
+            if flag_normalize == True:
+                modelName += '_normalized'
+            torch.save(model.state_dict(), f'Models/{modelName}.pth')
+            print(f"Saved model checkpoint: {modelName}.pth")
+            plot_loss_curve(TrainLosses, ValLosses, modelVersion=modelVersion)
+
+test(model, testLoader)
+modelName = f'singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}_aug{numberOfAugOps}'
+if flag_normalize == True:
+    modelName += '_normalized'
+torch.save(model.state_dict(), f'Models/{modelName}.pth')
+print(f"Saved final model checkpoint: {modelName}.pth")
+plot_loss_curve(TrainLosses, ValLosses, modelVersion=modelVersion, TestLoss=TestLoss)