--- a
+++ b/Utils/utils.py
@@ -0,0 +1,53 @@
+import torch
+import torchvision
+import os
+
+
+def save_checkpoint(state, filename='tmp/checkpoint.pth.tar'):
+    print('[INFO] Saving checkpoint')
+    torch.save(state, filename)
+
+
+def load_checkpoint(checkpoint, model):
+    print('[INFO] Loading checkpoint')
+    model.load_state_dict(checkpoint['state_dict'])
+
+
+def check_accuracy(loader, model, device):
+    num_correct = 0
+    num_pixels = 0
+    dice_score = 0
+
+    with torch.no_grad():
+        for x, y in loader:
+            x = x.to(device)
+            y = y.to(device)
+
+            preds = model(x)
+            preds = (preds > 0.5).float()
+
+            num_correct += (preds == y).sum()
+            num_pixels += torch.numel(preds)
+            dice_score += (2 * (preds * y).sum()) / ((preds + y).sum() + 1e-9)
+            print('Got {}/{} with acc {:2f}'.format(num_correct, num_pixels, num_correct / num_pixels * 100))
+            print('Dice score {}'.format(dice_score / len(loader)))
+            # wandb.log({"dice": dice_score})
+            # wandb.log({"acc": (num_correct, num_pixels, num_correct / num_pixels * 100)})
+
+            model.train()
+
+
+def save_predictions_as_imgs(loader, model, device, folder='tmp/'):
+    model.eval()
+
+    for idx, (x, y) in enumerate(loader):
+        x = x.to(device)
+
+        with torch.no_grad():
+            preds = model(x)
+            preds = (preds > 0.5).float()
+
+        torchvision.utils.save_image(preds, os.path.join(folder, 'pred_{}.png'.format(idx)))
+        # torchvision.utils.save_image(y, os.path.join(folder, '{}.png'.format(idx)))
+
+    model.train()