Removed dense_crf and small fixes

predict.py

@@ -4,22 +4,20 @@
 
 import numpy as np
 import torch
+import torch.nn.functional as F
 from PIL import Image
 from torchvision import transforms
-import torch.nn.functional as F
 
 from unet import UNet
 from utils.data_vis import plot_img_and_mask
 from utils.dataset import BasicDataset
-from utils.crf import dense_crf
 
 
 def predict_img(net,
                 full_img,
                 device,
                 scale_factor=1,
-                out_threshold=0.5,
-                use_dense_crf=False):
+                out_threshold=0.5):
     net.eval()
 
     img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor))
@@ -40,17 +38,14 @@ def predict_img(net,
         tf = transforms.Compose(
             [
                 transforms.ToPILImage(),
-                transforms.Resize(full_img.shape[1]),
+                transforms.Resize(full_img.size[1]),
                 transforms.ToTensor()
             ]
         )
 
         probs = tf(probs.cpu())
         full_mask = probs.squeeze().cpu().numpy()
 
-    if use_dense_crf:
-        full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
-
     return full_mask > out_threshold
 
 
@@ -127,7 +122,6 @@ def mask_to_image(mask):
                            full_img=img,
                            scale_factor=args.scale,
                            out_threshold=args.mask_threshold,
-                           use_dense_crf=False,
                            device=device)
 
         if not args.no_save:

train.py

@@ -162,18 +162,18 @@ def get_args():
     # faster convolutions, but more memory
     # cudnn.benchmark = True
 
-try:
-    train_net(net=net,
-              epochs=args.epochs,
-              batch_size=args.batchsize,
-              lr=args.lr,
-              device=device,
-              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)
+        train_net(net=net,
+                  epochs=args.epochs,
+                  batch_size=args.batchsize,
+                  lr=args.lr,
+                  device=device,
+                  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)

utils/dataset.py

@@ -15,7 +15,7 @@ def __init__(self, imgs_dir, masks_dir, scale=1):
         self.scale = scale
         assert 0 < scale <= 1, 'Scale must be between 0 and 1'
 
-        self.ids = [splitext(file)[0] for file in listdir(imgs_dir) 
+        self.ids = [splitext(file)[0] for file in listdir(imgs_dir)
                     if not file.startswith('.')]
         logging.info(f'Creating dataset with {len(self.ids)} examples')