How to change the picture size in PyTorch

Question

I'm trying to convert CNN Keras model for Emotion Recognition using FER2013 dataset to PyTorch model and I have following error:

Traceback (most recent call last):
  File "VGG.py", line 112, in <module>
    transfer.keras_to_pytorch(keras_network, pytorch_network)
  File "/home/eorg/NeuralNetworks/user/Project/model/nntransfer.py", line 121, in keras_to_pytorch
    pytorch_model.load_state_dict(state_dict)
  File "/home/eorg/.local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 334, in load_state_dict
    own_state[name].copy_(param)
RuntimeError: inconsistent tensor size at /b/wheel/pytorch-src/torch/lib/TH/generic/THTensorCopy.c:51

I understood that the error is related to the shape of images. In Keras the input size is defined to be 48 by 48.

And my question is how to define in PyTorch models that of my pictures are the shape of 48x48? I couldn't find such function in the documentation and examples.

Any help would be useful!

Answer 1

In order to automatically resize your input images you need to define a preprocessing pipeline all your images go through. This can be done with torchvision.transforms.Compose() (Compose docs). To resize Images you can use torchvision.transforms.Scale() (Scale docs) from the torchvision package.

See the documentation: Note, in the documentation it says that .Scale() is deprecated and .Resize() should be used instead. Resize docs

This would be a minimal working example:

import torch
from torchvision import transforms

p = transforms.Compose([transforms.Scale((48,48))])

from PIL import Image

img = Image.open('img.jpg')

img.size
# (224, 224) <-- This will be the original dimensions of your image

p(img).size
# (48, 48) <-- This will be the rescaled/resized dimensions of your image