Hello,
I have created a Class for the Dataset, (Code Below)
`Preformatted text`class CustomDataset(Dataset):
    def __init__(self, csv_file, id_col, target_col, root_dir, sufix=None, transform=None):
        """
        Args:
            csv_file   (string):             Path to the csv file with annotations.
            root_dir   (string):             Directory with all the images.
            id_col     (string):             csv id column name.
            target_col (string):             csv target column name.
            sufix      (string, optional):   Optional sufix for samples.
            transform  (callable, optional): Optional transform to be applied on a sample.
        """
        self.data      = pd.read_csv(csv_file)
        self.id        = id_col
        self.target    = target_col
        self.root      = root_dir
        self.sufix     = sufix
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        # get the image name at the different idx
        img_name = self.data.loc[idx, self.id]
        
        # if there is not sufic, nothing happened. in this case sufix is '.jpg'
        if self.sufix is not None:
            img_name = img_name + self.sufix
        
        # it opens the image of the img_name at the specific idx
        image = Image.open(os.path.join(self.root, img_name))
        
        # if there is not transform nothing happens, here we defined below two transforms for train and for test
        if self.transform is not None:
            image = self.transform(image)
        
        # define the label based on the idx
        label = pd.read_csv(csv_file).loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
        label = torch.from_numpy(label.astype(np.int8))
        #label = label.unsqueeze(-1)
        
        return image, label

It returns a label shape: torch.size([4])
and then
train_dataset = CustomDataset(csv_file=data_dir+'train.csv', root_dir=data_dir+'images', **params)

train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4)

But I have this message Error:" ValueError: Expected input batch_size (4) to match target batch_size (16)."
for idx, (data, target) in enumerate(loaders):

            ## find the loss and update the model parameters accordingly
            ## record the average training loss, using something like
            ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
            optimizer.zero_grad()
            # forward pass: compute predicted outputs by passing inputs to the model
            #print(data)
            output = model(data)
            # calculate the batch loss
            #torch.max(target, 1)[1]
            print('output shape:  ', output.shape)
            #target = target.view(-1)
            print('target shape:  ', target.shape)
            
            loss = criterion(output, target)

I can see that the

Output shape is: torch.size([4, 133])
Target shape is: torch.size([4, 4])

I know that my target should be ([4]) and as the label shape of the dataset is this shape I don’t understand why it changed to [(4, 4)]).
I don’t understand what I missed and how can I get the target shape to be ([4])
Look forward to reading your clarifications