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Pytorch遇到的bug:
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RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'mat2'
1
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #4 'mat1
1
TraceBack
RuntimeError Traceback (most recent call last)
<ipython-input-1-a2020579d495> in <module>
297 for epoch in range(START,EPOCHS):
298 # train for one epoch
--> 299 train(train_loader, model, criterion, optimizer, epoch, scheduler)
300 # evaluate on validation set
301 F1 = validate(val_loader, model, criterion,epoch)
<ipython-input-1-a2020579d495> in train(train_loader, model, criterion, optimizer, epoch, scheduler)
90 #print(input_var.type())
91 # compute output
---> 92 output = model(input_var)
93 loss = criterion(output, target_var)
94 #print(output.data)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/model/BALL/t_cnn.py in forward(self, x)
91 #print(e.size())
92
---> 93 x = self.fc1_layer(e)
94 #print(x.size())
95 x = self.classifier(x)
~/model/BALL/t_cnn.py in fc1_layer(self, x)
79 fc = nn.Linear(x_size[1],4096)
80 #print(x.type())
---> 81 out = fc(x)
82 return out
83 def forward(self,x):
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input)
53
54 def forward(self, input):
---> 55 return F.linear(input, self.weight, self.bias)
56
57 def extra_repr(self):
~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1022 if input.dim() == 2 and bias is not None:
1023 # fused op is marginally faster
-> 1024 return torch.addmm(bias, input, weight.t())
1025
1026 output = input.matmul(weight.t())
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #4 'mat1'
以上两个bug，都是因为输入和模型参数不在一块导致的，输入在GPU，但是模型参数在CPU，因为mat1表示input，mat2表示weight。所以解决方案当然是把他们都放在GPU上。（第一个是没解决时的错误，第二个是解决了想复现错误时报的错误，有点区别但是本质一样）
这个错误网上很多博客都说类似的问题是因为输入没有在GPU，所有只要给输入的变量input加上.cuda()就可以了。但是在这里输出input的类型其实它早就是torch.cuda.FloatTensor类型了，所以问题可能不是这里。
所以很可能就是模型没有加载到GPU，但是代码中又写了model = model.cuda()，而且从trace back看出只有一层出现了问题，那是不是因为这一层没有加载呢。
class TCNN_Alex(AlexNet):
def __init__(self,num_classes = 1000):
super(TCNN_Alex,self).__init__()
def energy_layer(self,x):
x_size = x.size()
avgpool = nn.AvgPool2d(kernel_size = x_size[2],stride = 1)
out = avgpool(x)
return out
def fc1_layer(self,x):
x_size = x.size()
x = x.view(x.size(0), x_size[1])
fc = nn.Linear(x_size[1],4096)
#print(x.type())
out = fc(x)
return out
def forward(self,x):
x = self.conv1(x)
#print(x.size())
x = self.conv2(x)
#print(x.size())
x = self.conv3(x)
#print(x.size())
e = self.energy_layer(x)
#print(e.size())
x = self.fc1_layer(e)
#print(x.size())
x = self.classifier(x)
#print(x.size())
return x
从上面的代码可以看出，Linear层是我自己定义的，并不是模型的属性，所以那就是说明它还在CPU中，于是尝试加上cuda()，还有上面energy_layer中的AvgPool2d为了保险起见也加上（实际上这里没有报错，也是迷醉了）。
class TCNN_Alex(AlexNet):
def __init__(self,num_classes = 1000):
super(TCNN_Alex,self).__init__()
def energy_layer(self,x):
x_size = x.size()
avgpool = nn.AvgPool2d(kernel_size = x_size[2],stride = 1).cuda()
out = avgpool(x)
return out
def fc1_layer(self,x):
x_size = x.size()
x = x.view(x.size(0), x_size[1])
fc = nn.Linear(x_size[1],4096).cuda()
#print(x.type())
out = fc(x)
return out
def forward(self,x):
x = self.conv1(x)
#print(x.size())
x = self.conv2(x)
#print(x.size())
x = self.conv3(x)
#print(x.size())
e = self.energy_layer(x)
#print(e.size())
x = self.fc1_layer(e)
#print(x.size())
x = self.classifier(x)
#print(x.size())
return x
再次运行果然可以了！
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作者：Rayfong_Kwong
