Python Pytorch DCGAN示例不'；无法处理不同的图像大小
我试图以此为起点，从我自己的224x224图像的图像数据集构建GAN。图像大小包含在默认参数中（例如--imageSize 224） 以下是模型的代码片段：Python Pytorch DCGAN示例不'；无法处理不同的图像大小,python,neural-network,deep-learning,pytorch,generative-art,Python,Neural Network,Deep Learning,Pytorch,Generative Art,我试图以此为起点，从我自己的224x224图像的图像数据集构建GAN。图像大小包含在默认参数中（例如--imageSize 224） 以下是模型的代码片段： 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
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)
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
回溯（最近一次呼叫最后一次）：
文件“main.py”，第209行，在
errD_real=标准（输出、标签）
文件“/opt/python/lib/python3.6/site-
程序包/torch/nn/modules/module.py”，第210行，输入调用__
结果=自我转发（*输入，**kwargs）
文件“/opt/python/lib/python3.6/site-
包裹/火炬/nn/modules/loss.py”，第36行，向前
返回后端\u fn（self.size\u average，weight=self.weight）（输入，
（目标）
文件“/opt/python/lib/python3.6/site-
包裹/torch/nn/_功能/thnn/loss.py”，第22行，向前
断言输入.neelement（）==target.neelement（）
断言错误