also, tried this and got error
transformed_dataset = MothLandmarksDataset(csv_file='moth_gt.csv',
                                           root_dir='.',
                                           transform=transforms.Compose([
                                               Rescale(256),
                                               RandomCrop(224),
                                               transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
                                                        std = [ 0.229, 0.224, 0.225 ]),
                                               ToTensor()
                                           ]))

for i in range(len(transformed_dataset)):
    sample = transformed_dataset[i]

    print(i, sample['image'].size(), sample['landmarks'].size())

    if i == 3:
        break

error is:
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-81-eb8dc46e0284> in <module>
     10 
     11 for i in range(len(transformed_dataset)):
---> 12     sample = transformed_dataset[i]
     13 
     14     print(i, sample['image'].size(), sample['landmarks'].size())

<ipython-input-48-9d04158922fb> in __getitem__(self, idx)
     30 
     31         if self.transform:
---> 32             sample = self.transform(sample)
     33 
     34         return sample

~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, img)
     59     def __call__(self, img):
     60         for t in self.transforms:
---> 61             img = t(img)
     62         return img
     63 

~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
    210             Tensor: Normalized Tensor image.
    211         """
--> 212         return F.normalize(tensor, self.mean, self.std, self.inplace)
    213 
    214     def __repr__(self):

~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
    278     """
    279     if not torch.is_tensor(tensor):
--> 280         raise TypeError('tensor should be a torch tensor. Got {}.'.format(type(tensor)))
    281 
    282     if tensor.ndimension() != 3:

TypeError: tensor should be a torch tensor. Got <class 'dict'>.