style: use configparser to manage config file

sample.py

@@ -4,17 +4,23 @@ from ddpm import DDPM
 from unet import Unet
 import sys
 import os
-
-BATCH_SIZE = 256
-ITERATION = 500
-TIME_EMB_DIM = 128
-DEVICE = torch.device('cuda')
+import configparser
 
 if __name__ == '__main__':
     if len(sys.argv) != 2:
         print("Usage: python sample.py [pic_num]")
         exit()
     
+    # read config file
+    config = configparser.ConfigParser()
+    config.read('training.ini')
+
+    BATCH_SIZE      =   int(config['unet']['batch_size'])
+    ITERATION       =   int(config['ddpm']['iteration'])
+    TIME_EMB_DIM    =   int(config['unet']['time_emb_dim'])
+    DEVICE          =   torch.device(config['unet']['device'])
+
+    # start sampling
     model = Unet(TIME_EMB_DIM, DEVICE).to(DEVICE)
     ddpm = DDPM(int(sys.argv[1]), ITERATION, 1e-4, 2e-2, DEVICE)
 

train.py

@@ -7,15 +7,18 @@ import matplotlib.pyplot as plt
 from tqdm import tqdm
 from ddpm import DDPM
 from unet import Unet
+import configparser
 
-BATCH_SIZE = 256
-ITERATION = 500
-TIME_EMB_DIM = 128
-DEVICE = torch.device('cuda')
-EPOCH_NUM = 500
-LEARNING_RATE = 1e-4
+def getMnistLoader(config):
+    '''
+        Get MNIST dataset's loader
 
-def getMnistLoader():
+        Inputs:
+            config (configparser.ConfigParser)
+        Outputs:
+            loader (nn.utils.data.DataLoader)
+    '''
+    BATCH_SIZE      =   int(config['unet']['batch_size'])
 
     transform = torchvision.transforms.Compose([
         torchvision.transforms.ToTensor()
@@ -25,18 +28,33 @@ def getMnistLoader():
     loader = DataLoader(data, batch_size=BATCH_SIZE, shuffle=True)
     return loader
 
-def train(loader, device, epoch_num, lr):
-    model = Unet(TIME_EMB_DIM, DEVICE).to(device)
-    ddpm = DDPM(BATCH_SIZE, ITERATION, 1e-4, 2e-2, device)
+def train(config):
+    '''
+        Start Unet Training
+
+        Inputs:
+            config (configparser.ConfigParser)
+        Outputs:
+            None
+    '''
+    BATCH_SIZE      =   int(config['unet']['batch_size'])
+    ITERATION       =   int(config['ddpm']['iteration'])
+    TIME_EMB_DIM    =   int(config['unet']['time_emb_dim'])
+    DEVICE          =   torch.device(config['unet']['device'])
+    EPOCH_NUM       =   int(config['unet']['epoch_num'])
+    LEARNING_RATE   =   float(config['unet']['learning_rate'])
+
+    # training
+    model = Unet(TIME_EMB_DIM, DEVICE).to(DEVICE)
+    ddpm = DDPM(BATCH_SIZE, ITERATION, 1e-4, 2e-2, DEVICE)
 
     criterion = nn.MSELoss()
-    optimzer = torch.optim.Adam(model.parameters(), lr=lr)
+    optimzer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
 
     min_loss = 99
 
-    for epoch in range(epoch_num):
+    for epoch in range(EPOCH_NUM):
         loss_sum = 0
-        # progress = tqdm(total=len(loader))
         for x, y in loader:
             optimzer.zero_grad()
 
@@ -51,12 +69,18 @@ def train(loader, device, epoch_num, lr):
             
             loss.backward()
             optimzer.step()
-            # progress.update(1)
+        
         print("Epoch {}/{}: With lr={}, batch_size={}, iteration={}. The best loss: {} - loss: {}".format(epoch, EPOCH_NUM, LEARNING_RATE, BATCH_SIZE, ITERATION, min_loss, loss_sum/len(loader)))
         if loss_sum/len(loader) < min_loss:
             min_loss = loss_sum/len(loader)
             print("save model: the best loss is {}".format(min_loss))
             torch.save(model.state_dict(), 'unet.pth')
 
-loader = getMnistLoader()
-train(loader, DEVICE, EPOCH_NUM, LEARNING_RATE)
+if __name__ == '__main__':
+    # read config file
+    config = configparser.ConfigParser()
+    config.read('training.ini')
+
+    # start training
+    loader = getMnistLoader(config)
+    train(config)

training.ini

@@ -0,0 +1,9 @@
+[unet]
+batch_size = 256
+time_emb_dim = 128
+device = cuda
+epoch_num = 500
+learning_rate = 1e-4
+
+[ddpm]
+iteration = 500