type or paste code here
model = MyModel()
model_path='/home/pracheta2/semi-supervised/model_best_covid_original_500.pth.tar'
model.load_state_dict(torch.load(model_path)['state_dict'])
# load input images and prepare data
datum_l_cc = utils.load_images(parameters['image_path'], 'L-CC')
datum_r_cc = utils.load_images(parameters['image_path'], 'R-CC')
datum_l_mlo = utils.load_images(parameters['image_path'], 'L-MLO')
datum_r_mlo = utils.load_images(parameters['image_path'], 'R-MLO')
x = {
"L-CC": torch.Tensor(datum_l_cc).permute(0, 3, 1, 2).to(device),
"L-MLO": torch.Tensor(datum_l_mlo).permute(0, 3, 1, 2).to(device),
"R-CC": torch.Tensor(datum_r_cc).permute(0, 3, 1, 2).to(device),
"R-MLO": torch.Tensor(datum_r_mlo).permute(0, 3, 1, 2).to(device),
}
transform = transforms.Compose([transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(
(.485, .456, .406), (.229, .224, .225))
])
# run prediction:
features, prediction_birads = model(x)
print(prediction_birads)
Traceback (most recent call last):
File “birads.py”, line 103, in
inference(parameters_)
File “birads.py”, line 80, in inference
features, prediction_birads = model(x)
File “/home/pracheta2/anaconda3/envs/oldpy/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 493, in call
result = self.forward(*input, **kwargs)
File “birads.py”, line 20, in forward
x_feature = self.model_resnet(x)
File “/home/pracheta2/anaconda3/envs/oldpy/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 493, in call
result = self.forward(*input, **kwargs)
File “/home/pracheta2/anaconda3/envs/oldpy/lib/python3.6/site-packages/torchvision/models/resnet.py”, line 192, in forward
x = self.conv1(x)
File “/home/pracheta2/anaconda3/envs/oldpy/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 493, in call
result = self.forward(*input, **kwargs)
File “/home/pracheta2/anaconda3/envs/oldpy/lib/python3.6/site-packages/torch/nn/modules/conv.py”, line 338, in forward
self.padding, self.dilation, self.groups)