PyTorch UNet semantic segmentation dice score more than 1-CodePudding

I'm trying to develop a program that finds road lanes using semantic segmentation with UNet backend. But while training the model, it's giving me dice score more than 1. Why is this happening?

Batch size: 16

Num_workers: 2

Epochs: 50

IMAGE_HEIGHT = 80

IMAGE_WIDTH = 120

PIN_MEMORY = True

Here's my accuracy function:

def check_accuracy(loader, model, device="cpu"):
    num_correct = 0
    num_pixels = 0
    dice_score = 0
    model.eval()

    with torch.no_grad():
        for x, y in loader:
            x = x.to(device)
            y = y.to(device).unsqueeze(1)
            preds = torch.sigmoid(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-8)

        print(f"Got {num_correct/num_pixels} with accuracy {num_correct/num_pixels*100:.2f}")
        print(f"Dice score: {dice_score/len(loader)}")
        model.train()

x's are images, and y's are ground truths.

Here's an example for images and ground truths:

Image:

Ground truth:

Here's my dataset's __getitem__ method:

    def __getitem__(self, index):
        img_path = os.path.join(self.root_dir, self.images[index])
        mask_path = os.path.join(self.val_dir, self.images[index])
        image = np.array(Image.open(img_path).convert("RGB"))
        mask = np.array(Image.open(mask_path).convert("L"), dtype=np.float32)
        mask[mask == 255.0] = 1.0

        if self.transform is not None:
            augmentations = self.transform(image=image, mask=mask)
            image = augmentations["image"]
            mask = augmentations["mask"]

        return image, mask

Here's my transformations:

    train_transform = A.Compose(
        [
            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
            A.Rotate(limit=35, p=1.0),
            A.HorizontalFlip(p=0.5),
            A.VerticalFlip(p=0.1),
            A.Normalize(
                mean=[0.0, 0.0, 0.0],
                std=[1.0, 1.0, 1.0],
                max_pixel_value=255.0,
            ),
            ToTensorV2(),
        ],
    )

    val_transforms = A.Compose(
        [
            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
            A.Normalize(
                mean=[0.0, 0.0, 0.0],
                std=[1.0, 1.0, 1.0],
                max_pixel_value=255.0,
            ),
            ToTensorV2(),
        ],
    )

And these are the accuracy metrics:

Epoch: 1/50

100%|██████████| 1/1 [00:00<00:00, 1.99it/s, loss=1.13]

=> Saving checkpoint 

Epoch: 2/50

100%|██████████| 1/1 [00:00<00:00, 1.81it/s, loss=1.13]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 3/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=0.7]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 4/50

100%|██████████| 1/1 [00:00<00:00, 1.87it/s, loss=0.354]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 5/50

100%|██████████| 1/1 [00:00<00:00, 1.91it/s, loss=-.094]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 6/50

100%|██████████| 1/1 [00:00<00:00, 1.94it/s, loss=-.419]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 7/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-.914]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 8/50

100%|██████████| 1/1 [00:00<00:00, 1.92it/s, loss=-.7]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 9/50

100%|██████████| 1/1 [00:00<00:00, 1.87it/s, loss=-1.26]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 10/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-1.6]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 11/50

100%|██████████| 1/1 [00:00<00:00, 1.96it/s, loss=-2.04]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 12/50

100%|██████████| 1/1 [00:00<00:00, 1.89it/s, loss=-2.53]

=> Saving checkpoint Got 0.9816353917121887 with accuracy 98.16 Dice score: 0.0 

Epoch: 13/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-2.77]

=> Saving checkpoint Got 0.9814687371253967 with accuracy 98.15 Dice score: 0.0 

Epoch: 14/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-3.14]

=> Saving checkpoint Got 0.9801874756813049 with accuracy 98.02 Dice score: 0.0 

Epoch: 15/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-3.77]

=> Saving checkpoint Got 0.9771562218666077 with accuracy 97.72 Dice score: 0.0 

Epoch: 16/50

100%|██████████| 1/1 [00:00<00:00, 1.85it/s, loss=-3.95]

=> Saving checkpoint Got 0.972906231880188 with accuracy 97.29 Dice score: 0.013821512460708618 

Epoch: 17/50

100%|██████████| 1/1 [00:00<00:00, 1.92it/s, loss=-4.83]

=> Saving checkpoint Got 0.9672812223434448 with accuracy 96.73 Dice score: 0.09691906720399857 

Epoch: 18/50

100%|██████████| 1/1 [00:00<00:00, 1.96it/s, loss=-4.96]

=> Saving checkpoint Got 0.9596353769302368 with accuracy 95.96 Dice score: 0.2153720110654831 

Epoch: 19/50

100%|██████████| 1/1 [00:00<00:00, 1.86it/s, loss=-5.43]

=> Saving checkpoint Got 0.9514479041099548 with accuracy 95.14 Dice score: 0.34086111187934875 

Epoch: 20/50

100%|██████████| 1/1 [00:00<00:00, 1.98it/s, loss=-5.81]

=> Saving checkpoint Got 0.9496874809265137 with accuracy 94.97 Dice score: 0.385390967130661 

Epoch: 21/50

100%|██████████| 1/1 [00:00<00:00, 1.91it/s, loss=-5.86]

=> Saving checkpoint Got 0.9429270625114441 with accuracy 94.29 Dice score: 0.4814487397670746 

Epoch: 22/50

100%|██████████| 1/1 [00:00<00:00, 1.98it/s, loss=-6.34]

=> Saving checkpoint Got 0.9388750195503235 with accuracy 93.89 Dice score: 0.5995486974716187 

Epoch: 23/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-6.92]

=> Saving checkpoint Got 0.9380520582199097 with accuracy 93.81 Dice score: 0.7058220505714417 

Epoch: 24/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-7.42]

=> Saving checkpoint Got 0.9415416717529297 with accuracy 94.15 Dice score: 0.8273581266403198 

Epoch: 25/50

100%|██████████| 1/1 [00:00<00:00, 1.94it/s, loss=-7.84]

=> Saving checkpoint Got 0.9451770782470703 with accuracy 94.52 Dice score: 0.9627659916877747 

Epoch: 26/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-8.32]

=> Saving checkpoint Got 0.9467916488647461 with accuracy 94.68 Dice score: 1.1096603870391846 

Epoch: 27/50

100%|██████████| 1/1 [00:00<00:00, 1.84it/s, loss=-8.75]

=> Saving checkpoint Got 0.9468228816986084 with accuracy 94.68 Dice score: 1.2025865316390991 

Epoch: 28/50

100%|██████████| 1/1 [00:00<00:00, 1.92it/s, loss=-8.6]

=> Saving checkpoint Got 0.942562460899353 with accuracy 94.26 Dice score: 1.3215676546096802 

Epoch: 29/50

100%|██████████| 1/1 [00:00<00:00, 1.95it/s, loss=-9.3]

=> Saving checkpoint Got 0.9366874694824219 with accuracy 93.67 Dice score: 1.4410816431045532 

Epoch: 30/50

100%|██████████| 1/1 [00:00<00:00, 1.85it/s, loss=-9.37]

=> Saving checkpoint Got 0.9291979074478149 with accuracy 92.92 Dice score: 1.5965386629104614 

Epoch: 31/50

100%|██████████| 1/1 [00:00<00:00, 1.89it/s, loss=-9.29]

=> Saving checkpoint Got 0.9251145720481873 with accuracy 92.51 Dice score: 1.7157979011535645 

Epoch: 32/50

100%|██████████| 1/1 [00:00<00:00, 1.89it/s, loss=-9]

=> Saving checkpoint Got 0.9198125004768372 with accuracy 91.98 Dice score: 1.7378650903701782 

Epoch: 33/50

100%|██████████| 1/1 [00:00<00:00, 1.88it/s, loss=-9.82]

=> Saving checkpoint Got 0.9136666655540466 with accuracy 91.37 Dice score: 1.758676290512085 

Epoch: 34/50

100%|██████████| 1/1 [00:00<00:00, 1.85it/s, loss=-9.84]

=> Saving checkpoint Got 0.9044270515441895 with accuracy 90.44 Dice score: 1.7790669202804565 

Epoch: 35/50

100%|██████████| 1/1 [00:00<00:00, 1.92it/s, loss=-10.4]

=> Saving checkpoint Got 0.8995833396911621 with accuracy 89.96 Dice score: 1.7764993906021118 

Epoch: 36/50

100%|██████████| 1/1 [00:00<00:00, 1.95it/s, loss=-10.7]

=> Saving checkpoint Got 0.8992916345596313 with accuracy 89.93 Dice score: 1.775970697402954 

Epoch: 37/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-10.8]

=> Saving checkpoint Got 0.8976249694824219 with accuracy 89.76 Dice score: 1.77357017993927 

Epoch: 38/50

100%|██████████| 1/1 [00:00<00:00, 1.95it/s, loss=-10.5]

=> Saving checkpoint Got 0.8952499628067017 with accuracy 89.52 Dice score: 1.782752513885498 

Epoch: 39/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-10.8]

=> Saving checkpoint Got 0.8901249766349792 with accuracy 89.01 Dice score: 1.7879419326782227 

Epoch: 40/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-11]

=> Saving checkpoint Got 0.8890937566757202 with accuracy 88.91 Dice score: 1.7865288257598877 

Epoch: 41/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-11.2]

=> Saving checkpoint Got 0.8915520906448364 with accuracy 89.16 Dice score: 1.7899266481399536 

Epoch: 42/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-11.1]

=> Saving checkpoint Got 0.8923645615577698 with accuracy 89.24 Dice score: 1.7993781566619873 

Epoch: 43/50

100%|██████████| 1/1 [00:00<00:00, 1.96it/s, loss=-10.9]

=> Saving checkpoint Got 0.887333333492279 with accuracy 88.73 Dice score: 1.804529070854187 

Epoch: 44/50

100%|██████████| 1/1 [00:00<00:00, 1.85it/s, loss=-11.9]

=> Saving checkpoint Got 0.8823854327201843 with accuracy 88.24 Dice score: 1.7975029945373535 

Epoch: 45/50

100%|██████████| 1/1 [00:00<00:00, 1.91it/s, loss=-11.2]

=> Saving checkpoint Got 0.8859270811080933 with accuracy 88.59 Dice score: 1.802530288696289 

Epoch: 46/50

100%|██████████| 1/1 [00:00<00:00, 1.92it/s, loss=-11.6]

=> Saving checkpoint Got 0.890500009059906 with accuracy 89.05 Dice score: 1.8090462684631348 

Epoch: 47/50

100%|██████████| 1/1 [00:00<00:00, 1.90it/s, loss=-11.7]

=> Saving checkpoint Got 0.9020833373069763 with accuracy 90.21 Dice score: 1.8257639408111572 

Epoch: 48/50

100%|██████████| 1/1 [00:00<00:00, 1.81it/s, loss=-12.1]

=> Saving checkpoint Got 0.9160521030426025 with accuracy 91.61 Dice score: 1.8463400602340698 

Epoch: 49/50

100%|██████████| 1/1 [00:00<00:00, 1.91it/s, loss=-12]

=> Saving checkpoint Got 0.925125002861023 with accuracy 92.51 Dice score: 1.859954833984375 

Epoch: 50/50

100%|██████████| 1/1 [00:00<00:00, 1.93it/s, loss=-11.4]

=> Saving checkpoint Got 0.933968722820282 with accuracy 93.40 Dice score: 1.873431921005249

As you can see 50th dice score is 1.873431921005249. Why dice score tis more than 1?

CodePudding user response：

The equation you are using to calculate the dice coefficient is wrong. This will work.

dice_score  = (2 * (preds * y).sum()) / (2 * (preds * y).sum()  ((preds*y)<1).sum())

You can interpret it as 2 x correct_classified/(2 x correct_classified wrong_classified). Note that this only works in the binary case.