这是我的评估单元：
start_time = time.time()
with torch.no_grad():
best_network = Network()
best_network.cuda()
best_network.load_state_dict(torch.load('../moth_landmarks.pth'))
best_network.eval()
batch = next(iter(train_loader))
images, landmarks = batch['image'], batch['landmarks']
#images = images.unsqueeze_(1)
images = torch.cat((images,images,images),1)
images = images.cuda()
norm_image = transforms.Normalize(0.3812, 0.1123)
for image in images:
image = image.float()
##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
image = norm_image(image)
landmarks = (landmarks + 0.5) * 224
##[8, 600, 800] --> [8,3,600,800]
images = images.unsqueeze(1)
images = torch.cat((images, images, images), 1)
predictions = (best_network(images).cpu() + 0.5) * 224
predictions = predictions.view(-1,4,2)
plt.figure(figsize=(10,40))
for img_num in range(8):
plt.subplot(8,1,img_num+1)
plt.imshow(images[img_num].cpu().numpy().transpose(1,2,0).squeeze(), cmap='gray')
plt.scatter(predictions[img_num,:,0], predictions[img_num,:,1], c = 'r')
plt.scatter(landmarks[img_num,:,0], landmarks[img_num,:,1], c = 'g')
print('Total number of test images: {}'.format(len(test_dataset)))
end_time = time.time()
print("Elapsed Time : {}".format(end_time - start_time))
我该如何解决以下错误？
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-59-e4aa0ace8c75> in <module>
19 image = image.float()
20 ##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
---> 21 image = norm_image(image)
22 landmarks = (landmarks + 0.5) * 224
23
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
210 Tensor: Normalized Tensor image.
211 """
--> 212 return F.normalize(tensor, self.mean, self.std, self.inplace)
213
214 def __repr__(self):
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
282 if tensor.ndimension() != 3:
283 raise ValueError('Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = '
--> 284 '{}.'.format(tensor.size()))
285
286 if not inplace:
ValueError: Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = torch.Size([1800, 800]).
如果我删除以下行：
images = torch.cat((images,images,images),1)
我会收到这个新错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-61-3e892b69015c> in <module>
19 image = image.float()
20 ##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
---> 21 image = norm_image(image)
22 landmarks = (landmarks + 0.5) * 224
23
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
210 Tensor: Normalized Tensor image.
211 """
--> 212 return F.normalize(tensor, self.mean, self.std, self.inplace)
213
214 def __repr__(self):
~/anaconda3/lib/python3.7/site-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
282 if tensor.ndimension() != 3:
283 raise ValueError('Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = '
--> 284 '{}.'.format(tensor.size()))
285
286 if not inplace:
ValueError: Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = torch.Size([600, 800]).
答案 0 :(得分：0)
以下是正确的：
start_time = time.time()
with torch.no_grad():
best_network = Network()
best_network.cuda()
best_network.load_state_dict(torch.load('../moth_landmarks.pth'))
best_network.eval()
batch = next(iter(train_loader))
images, landmarks = batch['image'], batch['landmarks']
landmarks = landmarks.view(landmarks.size(0),-1).cuda()
print(landmarks.shape)
for i in range(8):
if(i%2==0):
landmarks[:,i] = landmarks[:,i]/800
else:
landmarks[:,i] = landmarks[:,i]/600
landmarks [landmarks != landmarks] = 0
#landmarks = landmarks.unsqueeze_(0)
images = images.cuda()
print('*, ', landmarks.shape)
norm_image = transforms.Normalize(0.3812, 0.1123)
print('images shape: ', images.shape)
for image in images:
image = image.unsqueeze_(1)
#images = torch.cat((images,images,images),1)
image = image.float()
##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
image = norm_image(image)
print('max: ', torch.max(landmarks))
print('min: ', torch.min(landmarks))
##landmarks = (landmarks + 0.5) * 224 #?? chera?
print('**')
print(images.shape, landmarks.shape)
##[8, 600, 800] --> [8,3,600,800]
images = images.unsqueeze(1)
images = torch.cat((images, images, images), 1)
#predictions = (best_network(images).cpu() + 0.5) * 224
predictions = best_network(images).cpu()
print('****', predictions.shape)
for i in range(8):
if(i%2==0):
predictions[:,i] = predictions[:,i]*800
else:
predictions[:,i] = predictions[:,i]*600
predictions = predictions.view(-1,4,2)
print('****', predictions.shape)
for i in range(8):
if(i%2==0):
landmarks[:,i] = landmarks[:,i]*800
else:
landmarks[:,i] = landmarks[:,i]*600
landmarks = landmarks.view(-1,4,2)
plt.figure(figsize=(10,40))
landmarks = landmarks.cpu()
print(type(landmarks), landmarks.shape)
for img_num in range(8):
plt.subplot(8,1,img_num+1)
plt.imshow(images[img_num].cpu().numpy().transpose(1,2,0).squeeze(), cmap='gray')
plt.scatter(predictions[img_num,:,0], predictions[img_num,:,1], c = 'r')
plt.scatter(landmarks[img_num,:,0], landmarks[img_num,:,1], c = 'g')
print('Total number of test images: {}'.format(len(test_dataset)))
end_time = time.time()
print("Elapsed Time : {}".format(end_time - start_time))