chvlyl / isic2018 Goto Github PK

image_path="/kaggle/input/isic2018-200-pics/ISIC2018_Task1-2_Training_Input100/"
output_path="/kaggle/working/data"
temp_path="/kaggle/working/temp"
model_weight="/kaggle/input/isicmodelunet/model.pt"
model='UNet16'
model = UNet16(num_classes=5, pretrained='vgg')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model = nn.DataParallel(model)
state = torch.load(model_weight)
model.load_state_dict(state['model'])

print('load model weight')

image_ids = sorted([fname.split('/')[-1].split('.')[0] for fname in glob.glob(os.path.join(image_path, '*.jpg'))])
data_set = TestDataset(image_ids, image_path)
test_loader = DataLoader(data_set, batch_size=1, shuffle=False, num_workers=10, pin_memory=False)
for img_id, test_image, W, H in test_loader:
      test_image = test_image.to(device)  # [N, 1, H, W]
      test_image = test_image.permute(0, 3, 1, 2)
      outputs, outputs_mask_ind1, outputs_mask_ind2 = model(test_image)
      break

load model weight
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-58-cd870e1e196b> in <module>
     19       test_image = test_image.to(device)  # [N, 1, H, W]
     20       test_image = test_image.permute(0, 3, 1, 2)
---> 21       outputs, outputs_mask_ind1, outputs_mask_ind2 = model(test_image)
     22       break
     23 

/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

/opt/conda/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py in forward(self, *inputs, **kwargs)
    148         inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
    149         if len(self.device_ids) == 1:
--> 150             return self.module(*inputs[0], **kwargs[0])
    151         replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
    152         outputs = self.parallel_apply(replicas, inputs, kwargs)

/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-44-863006fd5294> in forward(self, x)
     65         conv2 = self.conv2(self.pool(conv1))
     66         conv3 = self.conv3(self.pool(conv2))
---> 67         conv4 = self.conv4(self.pool(conv3))
     68         conv5 = self.conv5(self.pool(conv4))
     69         center = self.center(self.pool(conv5))

/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

/opt/conda/lib/python3.6/site-packages/torch/nn/modules/pooling.py in forward(self, input)
    139         return F.max_pool2d(input, self.kernel_size, self.stride,
    140                             self.padding, self.dilation, self.ceil_mode,
--> 141                             self.return_indices)
    142 
    143 

/opt/conda/lib/python3.6/site-packages/torch/_jit_internal.py in fn(*args, **kwargs)
    179             return if_true(*args, **kwargs)
    180         else:
--> 181             return if_false(*args, **kwargs)
    182 
    183     if if_true.__doc__ is None and if_false.__doc__ is not None:

/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py in _max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)
    486         stride = torch.jit.annotate(List[int], [])
    487     return torch.max_pool2d(
--> 488         input, kernel_size, stride, padding, dilation, ceil_mode)
    489 
    490 max_pool2d = boolean_dispatch(

RuntimeError: max_pool2d_with_indices_out_cuda_frame failed with error code 0
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