Hi,
I’m using transforms.RandomHorizontalFlip for data augmentation. However it throws the following error.

  File "/home/usr/Courses/Projects/Iceberg_id/load_data.py", line 129, in __getitem__
    imgs = self.transform(imgs)

  File "/home/usr/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/transforms.py", line 34, in __call__
    img = t(img)

  File "/home/usr/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/transforms.py", line 326, in __call__
    return img.transpose(Image.FLIP_LEFT_RIGHT)

  TypeError: transpose received an invalid combination of arguments - got (int), but expected (int dim0, int dim1)

My Transform is is follows:
    txfm = transforms.Compose([Resize((80,80)), ToTensor(),\
            transforms.RandomHorizontalFlip(),\
            transforms.Normalize(mn,std)])

Here Resize and ToTensor were defined by me, the other two are from transforms library.
These transforms are implemented as follows:
class req_data(Dataset):
    '''
    Creates the dataset for the data
    initiates the __len__ and __getitem__
    '''
    def __init__(self,images,labels, transform = None):
        self.images = images
        self.labels = labels
        self.transform = transform
    
    def __len__(self):
        return len(self.labels)
    
    def __getitem__(self,indx):
        imgs = self.images[indx]
        labls = self.labels[indx]
        
        if self.transform:
            imgs = self.transform(imgs)
        return (imgs,labls)
    

# Create the transforms

class Resize(object):
    '''
    Inputs: a tuple of the form (h,w)
    Returns: a resized image of the given dimensions
    '''
    def __init__(self,output_size):
        self.output_size = output_size
    
    def __call__(self,inpt):
        image = inpt
        new_h, new_w = self.output_size
        image = image.transpose((1,2,0))
        image = transform.resize(image,self.output_size)
        image = image.transpose((2,0,1))
        return image
    

class ToTensor(object):
    '''Convert ndarrays in sample to Tensors
        We don't need to make a tensor out of labels
        as it is only 1 dim array
        '''

    def __call__(self, inpt):

        image = inpt
        return torch.from_numpy(image).float()

The transform in transform.resize(image,self.output_size) is from skimage, not PyTorch.
My PyTorch is installed from source.
Could someone please help me out?