Question

我正在尝试使用此code作为从我自己的224x224图像的图像数据集构建GAN的起点。图像大小包含在默认参数中（例如--imageSize 224）。

以下是模型的代码段：

class _netG(nn.Module):
def __init__(self, ngpu):
    super(_netG, self).__init__()
    self.ngpu = ngpu
    self.main = nn.Sequential(
        # input is Z, going into a convolution
        nn.ConvTranspose2d(     nz, ngf * 8, 4, 1, 0, bias=False),
        nn.BatchNorm2d(ngf * 8),
        nn.ReLU(True),
        # state size. (ngf*8) x 4 x 4
        nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ngf * 4),
        nn.ReLU(True),
        # state size. (ngf*4) x 8 x 8
        nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ngf * 2),
        nn.ReLU(True),
        # state size. (ngf*2) x 16 x 16
        nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ngf),
        nn.ReLU(True),
        # state size. (ngf) x 32 x 32
        nn.ConvTranspose2d(    ngf,      nc, 4, 2, 1, bias=False),
        nn.Tanh()
        # state size. (nc) x 64 x 64
    )

def forward(self, input):
    if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
    else:
        output = self.main(input)
    return output

class _netD(nn.Module):
def __init__(self, ngpu):
    super(_netD, self).__init__()
    self.ngpu = ngpu
    self.main = nn.Sequential(
        # input is (nc) x 64 x 64
        nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
        nn.LeakyReLU(0.2, inplace=True),
        # state size. (ndf) x 32 x 32
        nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ndf * 2),
        nn.LeakyReLU(0.2, inplace=True),
        # state size. (ndf*2) x 16 x 16
        nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ndf * 4),
        nn.LeakyReLU(0.2, inplace=True),
        # state size. (ndf*4) x 8 x 8
        nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
        nn.BatchNorm2d(ndf * 8),
        nn.LeakyReLU(0.2, inplace=True),
        # state size. (ndf*8) x 4 x 4
        nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
        nn.Sigmoid()
    )

def forward(self, input):
    if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
        output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
    else:
        output = self.main(input)

    return output.view(-1, 1).squeeze(1)

如果我更改任何默认参数（例如--imageSize 224），我会收到以下错误：

Traceback (most recent call last):
   File "main.py", line 209, in <module>
      errD_real = criterion(output, label)
   File "/opt/python/lib/python3.6/site-
      packages/torch/nn/modules/module.py", line 210, in __call__
        result = self.forward(*input, **kwargs)
     File "/opt/python/lib/python3.6/site-
    packages/torch/nn/modules/loss.py", line 36, in forward
     return backend_fn(self.size_average, weight=self.weight)(input, 
   target)
    File "/opt/python/lib/python3.6/site-
      packages/torch/nn/_functions/thnn/loss.py", line 22, in forward
      assert input.nelement() == target.nelement()
     AssertionError

我最初被告知尝试其他型号，但我也认为尝试解决这个问题会很好。我已经尝试了这个github thread中的解决方案，但它仍然不起作用。我还在学习PyTorch，所以在上述消息之前吐出的网络架构还没有给我很多直觉。我感谢您提供的任何指示！

Pytorch DCGAN示例不适用于不同的图像大小

0 个答案: