During the test, cifar10, the output data structure is incorrect.

[xk88372527]

test code：

print('%s :Accuracy of the model on the test images: %d %%' % (datetime.now(),100 * correct / total))

# print('Accuracy of the model on the test images:', correct.item()/total)
# print(correct.item())
# print(total)
# for i in range(10):
#     print('%s :Accuracy of %5s : %2d %%' % (
#         datetime.now(),classes[i],  class_correct[i].item() / class_total[i]))
#     print(class_correct[i].item())
#     print(class_total[i])
# return correct / total

out：
D:\Microsoft Visual Studio\Shared\Anaconda3_64\envs\xk\lib\site-packages\torch\nn\modules\upsampling.py:129: UserWarning: nn.UpsamplingBilinear2d is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.{} is deprecated. Use nn.functional.interpolate instead.".format(self.name))
2020-03-31 15:32:25.001979 :Accuracy of the model on the test images: 95 %
Accuracy of the model on the test images: 0.954
9540
10000
2020-03-31 15:32:25.002979 :Accuracy of plane : 0 %
194
1000.0
2020-03-31 15:32:25.002979 :Accuracy of car : 0 %
206
1000.0
2020-03-31 15:32:25.002979 :Accuracy of bird : 0 %
169
1000.0
2020-03-31 15:32:25.002979 :Accuracy of cat : 0 %
136
1000.0
2020-03-31 15:32:25.002979 :Accuracy of deer : 0 %
187
1000.0
2020-03-31 15:32:25.002979 :Accuracy of dog : 0 %
159
1000.0
2020-03-31 15:32:25.003980 :Accuracy of frog : 0 %
204
1000.0
2020-03-31 15:32:25.003980 :Accuracy of horse : 0 %
197
1000.0
2020-03-31 15:32:25.003980 :Accuracy of ship : 0 %
205
1000.0
2020-03-31 15:32:25.003980 :Accuracy of truck : 0 %
203
1000.0

If don't add ‘.item()’
The output will become：
D:\Microsoft Visual Studio\Shared\Anaconda3_64\envs\xk\lib\site-packages\torch\nn\modules\upsampling.py:129: UserWarning: nn.UpsamplingBilinear2d is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.{} is deprecated. Use nn.functional.interpolate instead.".format(self.name))
2020-03-31 15:38:02.784257 :Accuracy of the model on the test images: 95 %
Accuracy of the model on the test images: tensor(0, device='cuda:0')
9540
10000
2020-03-31 15:38:02.785258 :Accuracy of plane : 0 %
tensor(194, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.786259 :Accuracy of car : 0 %
tensor(206, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.786259 :Accuracy of bird : 0 %
tensor(169, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.787259 :Accuracy of cat : 0 %
tensor(136, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.787259 :Accuracy of deer : 0 %
tensor(187, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.788261 :Accuracy of dog : 0 %
tensor(159, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.789261 :Accuracy of frog : 0 %
tensor(204, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.789261 :Accuracy of horse : 0 %
tensor(197, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.789261 :Accuracy of ship : 0 %
tensor(205, device='cuda:0', dtype=torch.uint8)
1000.0
2020-03-31 15:38:02.790264 :Accuracy of truck : 0 %
tensor(203, device='cuda:0', dtype=torch.uint8)
1000.0

I hope to get your help. thanks

[Ixuanzhang]

将train.py中的test函数中：
c = (predicted == labels.data).squeeze()
改为 c = (predicted == labels.data).squeeze().tolist() 即可