Errors when testing on CPU

layer_name: <class 'torch.nn.modules.conv.Conv2d'>, total_params: 15121584, total_traina_params: 15121584, n_layers: 39
device:  cpu
Traceback (most recent call last):
  File "main.py", line 208, in <module>
    test(epoch)
  File "main.py", line 189, in test
    outputs = net(inputs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Repos/pytorch-cifar/models/dla_simple.py", line 106, in forward
    out = self.base(x)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/container.py", line 117, in forward
    input = module(input)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 423, in forward
    return self._conv_forward(input, self.weight)
  File "/home/brcao/Apps/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 419, in _conv_forward
    return F.conv2d(input, weight, self.bias, self.stride,
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _thnn_conv2d_forward

[bryanbocao]

@bryanbocao I started off with the first approach and just added a resize transform, but that loses a lot of information. For datasets like ImageNet, this doesn’t give accuracy above 50%. So I was thinking maybe if I try to make the models accept images of any size, it might give me better results, taking more time training of course.

The codebase on the repo has a lot of hardcoded elements. I combated the 10 output classes by adding an argument for number of classes in every model class. But I don’t have the knowledge to know what’s going on in the complex models to modify them to be able to accept images of any size.

P.S, I’m using a Jupyter notebook instead of my main.py

@Phillibob55 Sounds good. If you would like to create an easy-to-use repo that we can just change some arguments to train and test many different models, I am happy to contribute in my spare time. I have forked you repo to https://github.com/bryanbocao/image-classification

[Phillibob55]

I’ve created a repo for it here