python - 获取（torch.cuda.FloatTensor）和权重类型（torch.FloatTensor）应该相同

问题描述

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels, down=True, use_act=True, **kwargs):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, padding_mode="reflect", **kwargs)
            if down
            else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
            nn.InstanceNorm2d(out_channels),
            nn.ReLU(inplace=True) if use_act else nn.Identity()
        )
        
    def forward(self, x):
        return self.conv(x)

class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.block = nn.Sequential(
            ConvBlock(channels, channels, kernel_size=3, padding=1),
            ConvBlock(channels, channels, use_act=False, kernel_size=3, padding=1),
        )
        
    def forward(self, x):
        return x + self.block(x)

class Generator(nn.Module):
    def __init__(self, image_channels, num_features= 64, num_residuals=9):
        super().__init__()
        self.initial = nn.Sequential(
            nn.Conv2d(image_channels, num_features, kernel_size=7, stride=1, padding=3, padding_mode="reflect"),
            nn.ReLU(inplace=True)
        )
        self.down_blocks = nn.ModuleList = ([
            ConvBlock(num_features, num_features*2, kernel_size=3, stride=2, padding=1),
            ConvBlock(num_features*2, num_features*4, kernel_size=3, stride=2, padding=1),
        ])
        
        self.residual_blocks = nn.Sequential(
            *[ResidualBlock(num_features*4) for _ in range(num_residuals)]
        )
        
        self.up_blocks = nn.ModuleList = ([
            ConvBlock(num_features*4, num_features*2, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
            ConvBlock(num_features*2, num_features, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
        ])
        
        self.last = nn.Conv2d(num_features, image_channels, kernel_size=7, stride=1, padding=3, padding_mode="reflect")
        
    def forward(self, x):
        x = self.initial(x)
        
        for layer in self.down_blocks:
            x = layer(x)
            
        x = self.residual_blocks(x)
        
        for layer in self.up_blocks:
            x = layer(x)
            
        return torch.tanh(self.last(x))

img_channels = 3
img_size = 256
x = torch.randn((2, img_channels, img_size, img_size))
x = x.to(DEVICE)
gen = Generator(img_channels, 9).to(DEVICE)
print(gen(x).shape)

我已经为 Cycle GAN 实现了这个模型。此处使用的输入仅用于演示目的以缩短代码，但实际输入会引发相同的错误。代码在 CPU 上运行良好，但是当我将其转移到 GPU 时，出现以下错误：

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-30-a56669856674> in <module>
      4 x = x.to(DEVICE)
      5 gen = Generator(img_channels, 9).to(DEVICE)
----> 6 print(gen(x).shape)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

<ipython-input-25-f9a41f6c9d12> in forward(self, x)
     26 
     27         for layer in self.down_blocks:
---> 28             x = layer(x)
     29 
     30         x = self.residual_blocks(x)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

<ipython-input-23-e139087b9df4> in forward(self, x)
     11 
     12     def forward(self, x):
---> 13         return self.conv(x)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
    115     def forward(self, input):
    116         for module in self:
--> 117             input = module(input)
    118         return input
    119 

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
    421 
    422     def forward(self, input: Tensor) -> Tensor:
--> 423         return self._conv_forward(input, self.weight)
    424 
    425 class Conv3d(_ConvNd):

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
    416             return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    417                             weight, self.bias, self.stride,
--> 418                             _pair(0), self.dilation, self.groups)
    419         return F.conv2d(input, weight, self.bias, self.stride,
    420                         self.padding, self.dilation, self.groups)

RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

我的模型正在更改为设备（CUDA）和输入。我不知道现在是什么问题

标签： pythonpytorchgenerative-adversarial-network

python - 获取（torch.cuda.FloatTensor）和权重类型（torch.FloatTensor）应该相同

问题描述

解决方案

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