CE-net based module, pause for a while

UnetBasedMethod.py

@@ -82,13 +82,13 @@ def get_args():
     return parser.parse_args()
 
 
-if __name__ == '__main__':
+def cli_main():
     args = get_args()
 
     path = [(y, x) for y in filter(lambda x: x != '.DS_Store', os.listdir('data/imgs')) for x in filter(
         lambda x: x.endswith('.tif') and not x.endswith('dc.tif') and not x.endswith('DC.tif') and not x.endswith(
             'dc .tif'), os.listdir('data/imgs/' + y))]
-    seperate_path = divide_list(path,args.process)
+    seperate_path = divide_list(path, args.process)
 
     if args.step == 1:
         net = UNet(n_channels = 1, n_classes = 1)
@@ -107,10 +107,13 @@ if __name__ == '__main__':
 
     elif args.step == 2:
         dir = [x for x in filter(lambda x: x != '.DS_Store', os.listdir('data/imgs/'))]
-        sep_dir = divide_list(dir,args.process)
+        sep_dir = divide_list(dir, args.process)
 
         pool = Pool(args.process)
         for i, list in enumerate(sep_dir):
             pool.apply_async(step_2, args = (list, i))
         pool.close()
         pool.join()
+
+if __name__ == '__main__':
+    cli_main()
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CE-Unet/__init__.py → ceunet/__init__.py

 #!/usr/bin/env python
-# -*- coding:utf-8 -*-
\ No newline at end of file
+# -*- coding:utf-8 -*-
+from .ceunet_model import CEUnet
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CE-Unet/ceunet_model.py → ceunet/ceunet_model.py

@@ -15,11 +15,14 @@ class CEUnet(nn.Module):
         resnet = models.resnet34(pretrained = True)
         weight = resnet.conv1.weight
 
+        self.n_channels = n_channels
+        self.n_classes = n_classes
+
         self.inc = nn.Conv2d(in_channels = n_channels, out_channels = 64, kernel_size = 7, stride = 2, padding = 1)
         self.inc.weight = nn.Parameter(weight[:, :1, :, :])
 
         self.bn1 = resnet.bn1
-        self.relu1 = resnet.relu1
+        self.relu = resnet.relu
         self.maxpool1 = resnet.maxpool
 
         self.encoder1 = resnet.layer1
@@ -44,7 +47,7 @@ class CEUnet(nn.Module):
     def forward(self,x):
         x = self.inc(x)
         x = self.bn1(x)
-        x = self.relu1(x)
+        x = self.relu(x)
         x = self.maxpool1(x)
 
         e1 = self.encoder1(x)

train.py

@@ -57,11 +57,10 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0
             for batch in train_loader:
                 imgs = batch['image']
                 true_masks = batch['mask']
-                assert imgs.shape[
-                           1] == net.n_channels, f'Network has been defined with {net.n_channels} input channels, ' \
-                                                 f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
-                                                 f'' \
-                                                 'the images are loaded correctly.'
+                assert imgs.shape[1] == net.n_channels, \
+                    f'Network has been defined with {net.n_channels} input channels, ' \
+                    f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
+                    'the images are loaded correctly.'
 
                 imgs = imgs.to(device = device, dtype = torch.float32)
                 mask_type = torch.float32 if net.n_classes == 1 else torch.long
@@ -153,7 +152,7 @@ if __name__ == '__main__':
     # cudnn.benchmark = True
 
     try:
-        train_net(net = net, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr, device = device,
+        train_net(net = net, device=device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr,
                   img_scale = args.scale, val_percent = args.val / 100)
     except KeyboardInterrupt:
         torch.save(net.state_dict(), 'INTERRUPTED.pth')

trainCE-Net.py

+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+import argparse
+import logging
+import os
+import sys
+
+import torch
+from ceunet import CEUnet
+from train import get_args,train_net
+
+dir_img = 'data/train_imgs/'
+dir_mask = 'data/train_masks/'
+dir_checkpoint = 'ce_checkpoints/'
+
+
+if __name__ == '__main__':
+    logging.basicConfig(level = logging.INFO, format = '%(levelname)s: %(message)s')
+    args = get_args()
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    logging.info(f'Using device {device}')
+
+    net = CEUnet(n_channels = 1, n_classes = 1)
+    logging.info(f'Network:\n'
+                 f'\t{net.n_channels} input channels\n'
+                 f'\t{net.n_classes} output channels (classes)\n')
+
+    if args.load:
+        net.load_state_dict(torch.load(args.load, map_location = device))
+        logging.info(f'Model loaded from {args.load}')
+
+    net.to(device = device)
+
+    try:
+        train_net(net = net, device=device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr,
+                  img_scale = args.scale, val_percent = args.val / 100)
+    except KeyboardInterrupt:
+        torch.save(net.state_dict(), 'INTERRUPTED.pth')
+        logging.info('Saved interrupt')
+        try:
+            sys.exit(0)
+        except SystemExit:
+            os._exit(0)
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