Apply sigmoid before calc dice in eval_net()

Former-commit-id: 0da18fda34f29c81968425715e19c5dc76c9ec46

eval.py

@@ -5,28 +5,28 @@ from tqdm import tqdm
 from dice_loss import dice_coeff
 
 
-def eval_net(net, loader, device, n_val):
+def eval_net(net, loader, device):
     """Evaluation without the densecrf with the dice coefficient"""
     net.eval()
+    mask_type = torch.float32 if net.n_classes == 1 else torch.long
+    n_val = len(loader)  # the number of batch
     tot = 0
 
-    with tqdm(total=n_val, desc='Validation round', unit='img', leave=False) as pbar:
+    with tqdm(total=n_val, desc='Validation round', unit='batch', leave=False) as pbar:
         for batch in loader:
-            imgs = batch['image']
-            true_masks = batch['mask']
-
+            imgs, true_masks = batch['image'], batch['mask']
             imgs = imgs.to(device=device, dtype=torch.float32)
-            mask_type = torch.float32 if net.n_classes == 1 else torch.long
             true_masks = true_masks.to(device=device, dtype=mask_type)
 
-            mask_pred = net(imgs)
+            with torch.no_grad():
+                mask_pred = net(imgs)
 
-            for true_mask, pred in zip(true_masks, mask_pred):
+            if net.n_classes > 1:
+                tot += F.cross_entropy(mask_pred, true_masks).item()
+            else:
+                pred = torch.sigmoid(mask_pred)
                 pred = (pred > 0.5).float()
-                if net.n_classes > 1:
-                    tot += F.cross_entropy(pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
-                else:
-                    tot += dice_coeff(pred, true_mask.squeeze(dim=1)).item()
-            pbar.update(imgs.shape[0])
+                tot += dice_coeff(pred, true_masks).item()
+            pbar.update()
 
     return tot / n_val

train.py

@@ -88,7 +88,7 @@ def train_net(net,
                 pbar.update(imgs.shape[0])
                 global_step += 1
                 if global_step % (len(dataset) // (10 * batch_size)) == 0:
-                    val_score = eval_net(net, val_loader, device, n_val)
+                    val_score = eval_net(net, val_loader, device)
                     if net.n_classes > 1:
                         logging.info('Validation cross entropy: {}'.format(val_score))
                         writer.add_scalar('Loss/test', val_score, global_step)