bug fixes for i/o and low val set

Former-commit-id: bdcad8300e3c930b43b976ccd2562f27c9867892

README.md

@@ -154,7 +154,7 @@ You can also download it using the helper script:
 bash scripts/download_data.sh
 ```
 
-The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively. For Carvana, images are RGB and masks are black and white.
+The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively (note that the `imgs` and `masks` folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white.
 
 You can use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`.
 

evaluate.py

@@ -35,4 +35,8 @@ def evaluate(net, dataloader, device):
            
 
     net.train()
+
+    # Fixes a potential division by zero error
+    if num_val_batches == 0:
+        return dice_score
     return dice_score / num_val_batches

train.py

@@ -111,7 +111,9 @@ def train_net(net,
                 pbar.set_postfix(**{'loss (batch)': loss.item()})
 
                 # Evaluation round
-                if global_step % (n_train // (10 * batch_size)) == 0:
+                division_step = (n_train // (10 * batch_size))
+                if  division_step > 0:
+                    if global_step % division_step == 0:
                         histograms = {}
                         for tag, value in net.named_parameters():
                             tag = tag.replace('/', '.')