I am training an Unet arch on Medical image semantic segmentation, supervised learning - images/masks and I am getting weird numbers for DICE loss
Vanila_UNet
Epoch [0]
Mean loss on train: -140.31943819224836
Mean DICE on train: 1.7142934219089918
Mean DICE on validation: 1.8950854703170916
Epoch [1]
Mean loss on train: -154.01165542602538
Mean DICE on train: 1.8439450739097656
Mean DICE on validation: 1.923283325048502
Epoch [2]
Mean loss on train: -155.57704811096193
Mean DICE on train: 1.8617926383475962
Mean DICE on validation: 1.9318473889899364
Epoch [3]
Mean loss on train: -156.61962712605794
Mean DICE on train: 1.8733720566917649
Mean DICE on validation: 1.933697909810023
Epoch [4]
Mean loss on train: -157.22541224161785
Mean DICE on train: 1.8788127825940564
Mean DICE on validation: 1.9533974303968433
I am using argumentation for normalisation. However, my mask stays min 0 and max 255. Image is normalised. In this scenario I get some numbers and the network is trying to actually do something, see image https://imgur.com/a/WqRhbaM
If I change when loading the data to dataloader and if I divide mask with 255., my predictions are all Mean DICE on train and Mean DICE on validation are 0, mean loss still negative but small negative ~ -0.025. The final NN prediction is blank image.
I assume the problem is in data loading:
class DukePeopleDataset(Dataset):
def __init__(self, df, img_w, img_h):
self.IMG_SIZE_W = img_w
self.IMG_SIZE_H = img_h
self.df = df
self.in_channels = 3
self.out_channels = 1
self.transforms = self.define_transorms()
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
image = cv2.resize(cv2.imread(self.df.iloc[idx, 0]), (self.IMG_SIZE_W, self.IMG_SIZE_H))
mask = cv2.resize(cv2.imread(self.df.iloc[idx, 1],0), (self.IMG_SIZE_W, self.IMG_SIZE_H))
# mask = mask/255.
augmented = self.transforms(image = image,
mask = mask)
image = augmented['image']
mask = augmented['mask']
mask = mask.unsqueeze(0)
return image, mask
def get_dataframe(self):
return self.df
def define_transorms(self):
transforms = A.Compose([
A.HorizontalFlip(p=0.5),
A.Normalize(p=1.0),
ToTensorV2(),
])
return transforms