Deep learning PyTorch运行时错误：参数0无效：除维度1外，张量的大小必须匹配
我有一个PyTorch模型，我正试图通过向前传球来测试它。代码如下：Deep learning PyTorch运行时错误：参数0无效：除维度1外，张量的大小必须匹配,deep-learning,pytorch,Deep Learning,Pytorch,我有一个PyTorch模型，我正试图通过向前传球来测试它。代码如下： class ResBlock(nn.Module): def __init__(self, inplanes, planes, stride=1): super(ResBlock, self).__init__() self.conv1x1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False)
class ResBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1):
super(ResBlock, self).__init__()
self.conv1x1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
#batch normalization
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.stride = stride
def forward(self, x):
residual = self.conv1x1(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
#adding the skip connection
out += residual
out = self.relu(out)
return out
class ResUnet (nn.Module):
def __init__(self, in_shape, num_classes):
super(ResUnet, self).__init__()
in_channels, height, width = in_shape
#
#self.L1 = IncResBlock(in_channels,64)
self.e1 = nn.Sequential(
nn.Conv2d(in_channels, 64, kernel_size=4, stride=2,padding=1),
ResBlock(64,64))
self.e2 = nn.Sequential(
nn.LeakyReLU(0.2,),
nn.Conv2d(64, 128, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(128),
ResBlock(128,128))
#
self.e2add = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(128))
#
##
self.e3 = nn.Sequential(
nn.LeakyReLU(0.2,inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2,),
nn.Conv2d(128,256, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(256),
ResBlock(256,256))
self.e4 = nn.Sequential(
nn.LeakyReLU(0.2,),
nn.Conv2d(256,512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
ResBlock(512,512))
#
self.e4add = nn.Sequential(
nn.Conv2d(512,512, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(512))
#
self.e5 = nn.Sequential(
nn.LeakyReLU(0.2,inplace=True),
nn.Conv2d(512,512, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2,),
nn.Conv2d(512,512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
ResBlock(512,512))
#
#
self.e6 = nn.Sequential(
nn.LeakyReLU(0.2,),
nn.Conv2d(512,512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
ResBlock(512,512))
#
self.e6add = nn.Sequential(
nn.Conv2d(512,512, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(512))
#
self.e7 = nn.Sequential(
nn.LeakyReLU(0.2,inplace=True),
nn.Conv2d(512,512, kernel_size=3, stride=1,padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2,),
nn.Conv2d(512,512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
ResBlock(512,512))
#
self.e8 = nn.Sequential(
nn.LeakyReLU(0.2,),
nn.Conv2d(512,512, kernel_size=4, stride=2,padding=1))
#nn.BatchNorm2d(512))
self.d1 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
nn.Dropout(p=0.5),
ResBlock(512,512))
#
self.d2 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
nn.Dropout(p=0.5),
ResBlock(512,512))
#
self.d3 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
nn.Dropout(p=0.5),
ResBlock(512,512))
#
self.d4 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(512),
ResBlock(512,512))
#
self.d5 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(1024, 256, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(256),
ResBlock(256,256))
#
self.d6 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(512, 128, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(128),
ResBlock(128,128))
#
self.d7 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(256, 64, kernel_size=4, stride=2,padding=1),
nn.BatchNorm2d(64),
ResBlock(64,64))
#
self.d8 = nn.Sequential(
nn.ReLU(),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2,padding=1))
#nn.BatchNorm2d(64),
#nn.ReLU())
self.out_l = nn.Sequential(
nn.Conv2d(64,num_classes,kernel_size=1,stride=1))
#nn.ReLU())
def forward(self, x):
#Image Encoder
#### Encoder #####
en1 = self.e1(x)
en2 = self.e2(en1)
en2add = self.e2add(en2)
en3 = self.e3(en2add)
en4 = self.e4(en3)
en4add = self.e4add(en4)
en5 = self.e5(en4add)
en6 = self.e6(en5)
en6add = self.e6add(en6)
en7 = self.e7(en6add)
en8 = self.e8(en7)
#### Decoder ####
de1_ = self.d1(en8)
de1 = torch.cat([en7,de1_],1)
de2_ = self.d2(de1)
de2 = torch.cat([en6add,de2_],1)
de3_ = self.d3(de2)
de3 = torch.cat([en5,de3_],1)
de4_ = self.d4(de3)
de4 = torch.cat([en4add,de4_],1)
de5_ = self.d5(de4)
de5 = torch.cat([en3,de5_],1)
de6_ = self.d6(de5)
de6 = torch.cat([en2add,de6_],1)
de7_ = self.d7(de6)
de7 = torch.cat([en1,de7_],1)
de8 = self.d8(de7)
out_l_mask = self.out_l(de8)
return out_l_mask
modl = ResUnet((1,512,512), 1)
x = torch.rand(1, 1, 512, 512)
modl(x)
modl = ResUnet((1,320,320), 1)
x = torch.rand(1, 1, 320, 320)
modl(x)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-46-4ddc821c365b> in <module>
----> 1 modl(x)
~/.conda/envs/torch0.4/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)
<ipython-input-36-f9eeefa3c0b8> in forward(self, x)
221 de2_ = self.d2(de1)
222 #print de2_.size()
--> 223 de2 = torch.cat([en6add,de2_],1)
224 #print de2.size()
225
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 5 and 4 in dimension 2 at /opt/conda/conda-bld/pytorch_1535491974311/work/aten/src/TH/generic/THTensorMath.cpp:3616
我认为问题是由输入尺寸不是2的幂引起的，但我不确定如何针对给定的输入尺寸320、320对其进行校正。此问题源于下采样编码器路径和上采样解码器路径中变量之间的尺寸失配。您的代码庞大且难以理解，但通过插入print语句，我们可以检查这一点 en6add的大小为[1,512,5,5] en7是[1，512，2，2] en8是[1，512，1，1] 然后上采样以二的幂进行：de1_u是[1,512,2,2] de1[1，1024，2，2] de2_u2;[1,512,4,4]
在这一点上，您尝试将其与en6add连接起来，因此显然创建de2_u的代码没有足够的上采样。我的猜测是，您需要注意的是的output_padding参数，并可能在几个地方将其设置为1。我会尝试为您修复此错误，但该示例远未出现，因此我无法对其进行全面分析。此问题源于下采样编码器路径和上采样解码器路径中变量的大小不匹配。您的代码庞大且难以理解，但通过插入print语句，我们可以检查这一点 en6add的大小为[1,512,5,5] en7是[1，512，2，2] en8是[1，512，1，1] 然后上采样以二的幂进行：de1_u是[1,512,2,2] de1[1，1024，2，2] de2_u2;[1,512,4,4] 在这一点上，您尝试将其与en6add连接起来，因此显然创建de2_u的代码没有足够的上采样。我的猜测是，您需要注意的是的output_padding参数，并可能在几个地方将其设置为1。我会尝试为您修复此错误，但该示例远未出现，因此我无法对整个错误进行概括。谢谢，我刚刚在self.d2中将输出填充设置为1，结果似乎很好。谢谢，我刚刚在self.d2中将输出填充设置为1，结果似乎很好。