--- a
+++ b/U-Net/evaluate.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+from utils.dice_score import multiclass_dice_coeff, dice_coeff
+
+
+@torch.inference_mode()
+def evaluate(net, dataloader, device, amp):
+    net.eval()
+    num_val_batches = len(dataloader)
+    dice_score = 0
+
+    # iterate over the validation set
+    with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):
+        for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False):
+            image, mask_true = batch['image'], batch['mask']
+
+            # move images and labels to correct device and type
+            image = image.to(device=device, dtype=torch.float32, memory_format=torch.channels_last)
+            mask_true = mask_true.to(device=device, dtype=torch.long)
+
+            # predict the mask
+            mask_pred = net(image)
+
+            if net.n_classes == 1:
+                assert mask_true.min() >= 0 and mask_true.max() <= 1, 'True mask indices should be in [0, 1]'
+                mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
+                # compute the Dice score
+                dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)
+            else:
+                assert mask_true.min() >= 0 and mask_true.max() < net.n_classes, 'True mask indices should be in [0, n_classes['
+                # convert to one-hot format
+                mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
+                mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()
+                # compute the Dice score, ignoring background
+                dice_score += multiclass_dice_coeff(mask_pred[:, 1:], mask_true[:, 1:], reduce_batch_first=False)
+
+    net.train()
+    return dice_score / max(num_val_batches, 1)