Augment for single photon

Train_SinglePhoton.py

@@ -24,6 +24,7 @@ model = models.get_model_class(modelVersion)().cuda()
 # summary(model, input_size=(128, 1, 3, 3))
 LearningRate = 1e-3
 Noise = 0.13 # in keV
+numberOfAugOps = 8 # 1 (no augmentation) or (1,8] (with augmentation)
 
 TrainLosses, TestLosses = [], []
 def weighted_loss(pred, target, alpha=7.0):
@@ -87,12 +88,14 @@ trainDataset = singlePhotonDataset(
     sampleRatio=1.0,
     datasetName='Train',
     noiseKeV = Noise,
+    numberOfAugOps=numberOfAugOps,
     )
 valDataset = singlePhotonDataset(
     [f'{sampleFolder}/{Energy}_Moench040_150V_{i}.npz' for i in range(13,14)],
     sampleRatio=1.0,
     datasetName='Val',
     noiseKeV = Noise,
+    numberOfAugOps=numberOfAugOps,
     )
 testDataset = singlePhotonDataset(
     [f'{sampleFolder}/{Energy}_Moench040_150V_{i}.npz' for i in range(15,16)],
@@ -104,37 +107,37 @@ trainLoader = torch.utils.data.DataLoader(
     trainDataset,
     batch_size=4096,
     shuffle=True,
-    num_workers=16,
+    num_workers=32,
     pin_memory=True,
     )
 valLoader = torch.utils.data.DataLoader(
     valDataset,
     batch_size=1024,
     shuffle=False,
-    num_workers=16,
+    num_workers=32,
     pin_memory=True,
     )
 testLoader = torch.utils.data.DataLoader(
     testDataset,
     batch_size=1024,
     shuffle=4096,
-    num_workers=16,
+    num_workers=32,
     pin_memory=True,
     )
 
 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, 301)):
+    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, 50, 100, 200, 300, 500]:
-            torch.save(model.state_dict(), f'Models/singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}.pth')
+        if epoch in [20, 50, 100, 200, 300, 500, 750, 1000]:
+            torch.save(model.state_dict(), f'Models/singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}_aug{numberOfAugOps}.pth')
 
 test(model, testLoader)
-torch.save(model.state_dict(), f'Models/singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV.pth')
+torch.save(model.state_dict(), f'Models/singlePhoton{modelVersion}_{Energy}_Noise{Noise}keV_aug{numberOfAugOps}.pth')
 
 def plot_loss_curve(TrainLosses, ValLosses, TestLoss, modelVersion):
     import matplotlib.pyplot as plt