Set random seed for reproducibility

Train_doublePhoton.py

@@ -6,6 +6,11 @@ from models import *
 from datasets import *
 import torch.optim as optim
 from tqdm import tqdm
+from torchinfo import summary
+
+### random seed for reproducibility
+torch.manual_seed(0)
+np.random.seed(0)
 
 modelVersion = '251001_2'  # '250910' or '251001'
 TrainLosses, ValLosses = [], []
@@ -45,6 +50,8 @@ elif modelVersion == '251001_2':
     loss_fn = min_matching_loss
     model = doublePhotonNet_251001_2().cuda()
 
+# summary(model, input_size=(128, 1, 6, 6)) ### print model summary
+
 def train(model, trainLoader, optimizer):
     model.train()
     batchLoss = 0
@@ -142,7 +149,7 @@ scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.7, p
 #         test(model, valLoader)
 #         scheduler.step(TrainLosses[-1])
 
-model.load_state_dict(torch.load('doublePhotonNet_251001_2.pth', weights_only=True))
+model.load_state_dict(torch.load(f'doublePhotonNet_{modelVersion}.pth', weights_only=True))
 test(model, testLoader)
 torch.save(model.state_dict(), f'doublePhotonNet_{modelVersion}.pth')