Update 2Photon training

Train_DoublePhoton.py

@@ -12,13 +12,15 @@ from torchinfo import summary
 torch.manual_seed(0)
 np.random.seed(0)
 
-modelVersion = '251001_2'  # '250910' or '251001'
+modelVersion = '251124'  # '250910' or '251001' or '251124'
 model = models.get_double_photon_model_class(modelVersion)().cuda()
-Energy = '15.3keV'
+Energy = '12keV'
 TrainLosses, ValLosses = [], []
 TestLoss = -1
 LearningRate = 1e-3
 Noise = 0.13 # in keV
+NoiseThreshold = 3*Noise
+Normalize = False
 
 def two_point_set_loss_l2(pred_xy, gt_xy):
     def pair_cost_l2sq(p, q):  # p,q: (...,2)
@@ -118,6 +120,9 @@ trainDataset = doublePhotonDataset(
     datasetName='Train',
     reuselFactor=1,
     noiseKeV = Noise,
+    nSize=7,
+    noiseThreshold = NoiseThreshold,
+    normalize = Normalize
     )
 valDataset = doublePhotonDataset(
     [f'{sampleFolder}/{Energy}_Moench040_150V_{i}.npz' for i in range(13,14)],
@@ -125,6 +130,9 @@ valDataset = doublePhotonDataset(
     datasetName='Val',
     reuselFactor=1,
     noiseKeV = Noise,
+    nSize=7,
+    noiseThreshold = NoiseThreshold,
+    normalize = Normalize
     )
 testDataset = doublePhotonDataset(
     [f'{sampleFolder}/{Energy}_Moench040_150V_{i}.npz' for i in range(15,16)],
@@ -132,6 +140,9 @@ testDataset = doublePhotonDataset(
     datasetName='Test',
     reuselFactor=1,
     noiseKeV = Noise,
+    nSize=7,
+    noiseThreshold = NoiseThreshold,
+    normalize = Normalize
     )
 trainLoader = torch.utils.data.DataLoader(
     trainDataset, 
@@ -161,10 +172,12 @@ if __name__ == "__main__":
         scheduler.step(ValLosses[-1])
         print(f"Learning Rate: {optimizer.param_groups[0]['lr']}")
         if epoch in [20, 50, 100, 200, 300, 500, 750, 1000]:
-            torch.save(model.state_dict(), f'Models/doublePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}.pth')
+            modelName = f'doublePhoton{modelVersion}_{Energy}_Noise{Noise}keV_E{epoch}'
+            if Normalize:
+                modelName += '_normalized'
+            torch.save(model.state_dict(), f'Models/{modelName}.pth')
 
 test(model, testLoader)
-torch.save(model.state_dict(), f'Models/doublePhotonNet_{modelVersion}.pth')
 
 def plot_loss_curve(TrainLosses, ValLosses, TestLoss, modelVersion):
     import matplotlib.pyplot as plt