Thank you. I proceeded this way:
'class val_Dataset(Dataset):
def __init__(self, imgarray_val, labelarray_val, transform=None):

    
    self.images = imgarray_val
    self.labels = labelarray_val
    self.transform = transform

    
def __getitem__(self, index):
   
    img = self.images[index]
    if self.transform is not None:
        img = self.transform(img)
        
    
    Img = Image.fromarray(self.labels[index]) #numpy array to PIL
    PilImg = Img.resize((224, 224),Image.NEAREST) #resize the PIL image
    label = torch.from_numpy(np.asarray(PilImg)) # convert PIL to numpy and then convert the numpyarray to tensor
    
    #trnsf1 = transforms.RandomResizedCrop(input_size)
    #trnsf2 = transforms.ToTensor()
    #label = trnsf2(trnsf1(self.masks[index]))
          
    print(img.size())
    print(label.size()) 
          
    return img, label

def __len__(self):
    return len(self.images) '

Now I encounter this error:
‘RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 231 and 228 in dimension 3 at …\aten\src\TH/generic/THTensor.cpp:711’
what I get as output is this:
'Epoch 0/0
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
image shape: torch.Size([4, 3, 224, 224])
label shape: torch.Size([4, 224, 224])
squeezed label shape:torch.Size([4, 224, 224])
model output shape:torch.Size([4, 4, 224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
image shape: torch.Size([4, 3, 224, 224])
label shape: torch.Size([4, 224, 224])
squeezed label shape:torch.Size([4, 224, 224])
model output shape:torch.Size([4, 4, 224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
image shape: torch.Size([4, 3, 224, 224])
label shape: torch.Size([4, 224, 224])
squeezed label shape:torch.Size([4, 224, 224])
model output shape:torch.Size([4, 4, 224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
torch.Size([3, 224, 224]) torch.Size([224, 224])
image shape: torch.Size([4, 3, 224, 224])
label shape: torch.Size([4, 224, 224])
squeezed label shape:torch.Size([4, 224, 224])
model output shape:torch.Size([4, 4, 224, 224])
train Loss: 1.7062 Acc: 29483.5625
torch.Size([3, 224, 231])
torch.Size([224, 224])
torch.Size([3, 224, 228])
torch.Size([224, 224])
torch.Size([3, 224, 232])
torch.Size([224, 224])
torch.Size([3, 224, 229])
torch.Size([224, 224])
’
and the training function is as follows:
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, has_aux = True):
since = time.time()
val_acc_history = []

best_model_wts = copy.deepcopy(model.state_dict())#??????
best_acc = 0.0

for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs - 1))
    print('-' * 10)

    # Each epoch has a training and validation phase
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()  # Set model to training mode
        else:
            model.eval()   # Set model to evaluate mode

        running_loss = 0.0
        running_corrects = 0

        # Iterate over data.
        
        for inputs, labels in dataloaders[phase]:
            inputs, labels = Variable(inputs), Variable(labels)
            inputs = inputs.to('cpu')
            labels = labels.to('cpu')
            
            print('image shape: {}'.format(inputs.shape))
            print('label shape: {}'.format(labels.shape))
            #labels = labels.squeeze(1)
            #print('squeezed label shape:{}'.format(labels.shape))

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward
            # track history if only in train
            with torch.set_grad_enabled(phase == 'train'):
                # Get model outputs and calculate loss
                # Special case for inception because in training it has an auxiliary output. In train
                #   mode we calculate the loss by summing the final output and the auxiliary output
                #   but in testing we only consider the final output.
                if has_aux and phase == 'train':
                    # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                    outputs = model(inputs)
                    print('model output shape:{}'.format(outputs['out'].shape))
                    loss1 = criterion(outputs['out'], labels.long())
                    loss2 = criterion(outputs['aux'], labels.long())
                    #loss2 = output['aux']
                    loss = loss1 + 0.4*loss2
                #else:
                 #   outputs = model(inputs)
                  #  loss = criterion(outputs, labels.long())

                _, preds = torch.max(outputs['out'], 1)

                # backward + optimize only if in training phase
                if phase == 'train':
                    loss.backward()
                    optimizer.step()

            # statistics
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data.long())

        epoch_loss = running_loss / len(dataloaders[phase].dataset)
        epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

        print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

        # deep copy the model
        if phase == 'val' and epoch_acc > best_acc:
            best_acc = epoch_acc
            best_model_wts = copy.deepcopy(model.state_dict())
        if phase == 'val':
            val_acc_history.append(epoch_acc)

    print()

time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))

# load best model weights
model.load_state_dict(best_model_wts)
return model, val_acc_history

What I understood so far is that transformation works fine for the train images and not the validation images but I am checking my code and I cannot find the error!
this is the  data set function and data loaders:

data_transforms = {
‘train’: transforms.Compose([
transforms.Resize(input_size, input_size),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
‘val’: transforms.Compose([
transforms.Resize(input_size, input_size),
#transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
print(“Initializing Datasets and Dataloaders…”)


class Train_Dataset(Dataset):
def __init__(self, imgarray, labelarray, transform=None):

    
    self.images = imgarray
    self.labels = labelarray
    self.transform = transform

    
def __getitem__(self, index):
    
    img = self.images[index]
    if self.transform is not None:
        img = self.transform(img)  
    
   
    Img = Image.fromarray(self.labels[index]) #numpy array to PIL
    PilImg = Img.resize((224, 224), Image.NEAREST ) #resize the PIL image
    label = torch.from_numpy(np.asarray(PilImg)) # convert PIL to numpy and then convert the numpyarray to tensor
    

    print(img.size(), label.size())
    return img, label

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

class val_Dataset(Dataset):
def __init__(self, imgarray_val, labelarray_val, transform=None):

    
    self.images = imgarray_val
    self.labels = labelarray_val
    self.transform = transform

    
def __getitem__(self, index):
   
    img = self.images[index]
    if self.transform is not None:
        img = self.transform(img)
        
    
    Img = Image.fromarray(self.labels[index]) #numpy array to PIL
    PilImg = Img.resize((224, 224),Image.NEAREST) #resize the PIL image
    label = torch.from_numpy(np.asarray(PilImg)) # convert PIL to numpy and then convert the numpyarray to tensor
    
    #trnsf1 = transforms.RandomResizedCrop(input_size)
    #trnsf2 = transforms.ToTensor()
    #label = trnsf2(trnsf1(self.masks[index]))
          
    print(img.size(), label.size())

          
    return img, label

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


I markeddown two lines of code in transformation code block:
for train set
#transforms.RandomHorizontalFlip(),
and for val set:
#transforms.CenterCrop(input_size),
might it be this is the problem?