Pytorch：张量a（24）的大小必须与非单维3的张量b（48）的大小匹配
Pytorch: The size of tensor a (24) must match the size of tensor b (48) at non-singleton dimension 3
下面的代码工作正常并生成正确的结果。
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules import ConvLSTMCell, Sign
class EncoderCell(nn.Module):
def __init__(self):
super(EncoderCell, self).__init__()
self.conv = nn.Conv2d(
3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.rnn1 = ConvLSTMCell(
64,
256,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
256,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
def forward(self, input, hidden1, hidden2, hidden3):
x = self.conv(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
return x, hidden1, hidden2, hidden3
class Binarizer(nn.Module):
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
self.sign = Sign()
def forward(self, input):
feat = self.conv(input)
x = F.tanh(feat)
return self.sign(x)
class DecoderCell(nn.Module):
def __init__(self):
super(DecoderCell, self).__init__()
self.conv1 = nn.Conv2d(
32, 512, kernel_size=1, stride=1, padding=0, bias=False)
self.rnn1 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
128,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
128,
256,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.rnn4 = ConvLSTMCell(
64,
128,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.conv2 = nn.Conv2d(
32, 3, kernel_size=1, stride=1, padding=0, bias=False)
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
x = self.conv1(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
现在我已经在 self.con 中进行了更改，并添加了预训练的 resent 层。现在它在训练后显示张量不匹配错误。所有的事情都是一样的，只需在代码中添加这一行。我把 ** 放在那些行
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from modules import ConvLSTMCell, Sign
class EncoderCell(nn.Module):
def __init__(self):
super(EncoderCell, self).__init__()
#self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
**resConv = models.resnet50(pretrained=True)
resConv.layer4 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.conv = resConv.layer4**
self.rnn1 = ConvLSTMCell(
64,
256,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
256,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
def forward(self, input, hidden1, hidden2, hidden3):
x = self.conv(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
return x, hidden1, hidden2, hidden3
class Binarizer(nn.Module):
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
self.sign = Sign()
def forward(self, input):
feat = self.conv(input)
x = F.tanh(feat)
return self.sign(x)
class DecoderCell(nn.Module):
def __init__(self):
super(DecoderCell, self).__init__()
**resConv = models.resnet50(pretrained=True)
resConv.layer4 = nn.Conv2d(32, 512, kernel_size=3, stride=2, padding=1, bias=False)
self.conv1 = resConv.layer4**
self.rnn1 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
128,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
128,
256,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.rnn4 = ConvLSTMCell(
64,
128,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
**resConv2 = models.resnet50(pretrained=True)
resConv2.layer4 = nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False)
self.conv2 = resConv2.layer4**
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
x = self.conv1(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
你做错了，一些解释是，
**resConv = models.resnet50(pretrained=True) # you are reading a model
现在您正在用新初始化的层替换该模型中的层。其次，resnet50 中的 layer4 是一个包含多个层的顺序块。使用 print 查看模型中的确切图层。
resConv.layer4 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
这里你使用的是新图层。
self.conv = resConv.layer4**
根据您关于使用预训练层的查询，您应该这样做，
resConv = models.resnet50(pretrained=True)
print(resConv) #see the layer which you want to use
self.conv = resConv.conv1 # replace conv1 with that layer
# note: conv1 is the name of first conv layer in resnet
除此之外，我还建议在对象初始化之外获取和添加该层（或权重和偏差）。类似于：
enc = EncoderCell()
resnet50 = models.resnet50(pretrained=True)
然后
enc.conv = resnet50.conv1
或更理想
enc.conv.load_state_dict(resnet50.layer1.state_dict())
原因是，在 nn.Module class 上调用 state_dict() 创建参数的克隆（在本例中为权重和偏差），可以通过 nn.Module.load_state_dict() 方法只要 nn.Module 的两个实例共享相同的形状。所以你得到了预训练的权重，它们完全脱离了预训练模型。然后你可以摆脱预训练模型，因为它的内存可能相当大。
del resnet50
我提交了对其他答案的潜在改进，但为了解决您遇到的错误，我也在这里回答。如果代码在您编辑之前运行，并且您尝试更改的图层与前一个图层的形状相同，那么我的猜测是它可能与通过创建 resnet50 对象形成的计算图有关。我会推荐我在对其他答案的编辑中提到的方法，但我会在这里再次声明（注意，这假设您保持代码原样）：
# instantiate you encoder (repeat these steps with the decoder as well)
enc = EncoderCell()
# get the pretrained model
resnet = models.resnet50(pretrained=True)
# load the state dict into the regular conv layer
enc.conv.load_state_dict(resnet50.layer4.state_dict())
这应该将来自 resnet50 模型的预训练权重和偏差加载到您的转换层，这可以对解码器转换层完成，只要它们都共享相同的形状。
要对不匹配错误进行更多测试，我建议在模型的 forward() 方法中使用调试器或打印语句，以查看应用每一层后张量的形状，就像这样
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
print(x.size())
x = self.conv1(input)
print(x.size())
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
当然，您可以将 print 语句放在 forward 方法中的任何其他位置。我也强烈推荐调试器； pycharm 使这变得非常容易，并且还可以在它提供的 python 控制台旁边以科学模式轻松查看变量的状态。在变量通过某些层（如卷积层）后，可能值得寻找计算变量大小的方法。这是很好理解的，并且存在根据初始大小、过滤器大小、步幅宽度和填充计算维度大小的公式。
下面的代码工作正常并生成正确的结果。
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules import ConvLSTMCell, Sign
class EncoderCell(nn.Module):
def __init__(self):
super(EncoderCell, self).__init__()
self.conv = nn.Conv2d(
3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.rnn1 = ConvLSTMCell(
64,
256,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
256,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
def forward(self, input, hidden1, hidden2, hidden3):
x = self.conv(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
return x, hidden1, hidden2, hidden3
class Binarizer(nn.Module):
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
self.sign = Sign()
def forward(self, input):
feat = self.conv(input)
x = F.tanh(feat)
return self.sign(x)
class DecoderCell(nn.Module):
def __init__(self):
super(DecoderCell, self).__init__()
self.conv1 = nn.Conv2d(
32, 512, kernel_size=1, stride=1, padding=0, bias=False)
self.rnn1 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
128,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
128,
256,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.rnn4 = ConvLSTMCell(
64,
128,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.conv2 = nn.Conv2d(
32, 3, kernel_size=1, stride=1, padding=0, bias=False)
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
x = self.conv1(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
现在我已经在 self.con 中进行了更改，并添加了预训练的 resent 层。现在它在训练后显示张量不匹配错误。所有的事情都是一样的，只需在代码中添加这一行。我把 ** 放在那些行
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from modules import ConvLSTMCell, Sign
class EncoderCell(nn.Module):
def __init__(self):
super(EncoderCell, self).__init__()
#self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
**resConv = models.resnet50(pretrained=True)
resConv.layer4 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.conv = resConv.layer4**
self.rnn1 = ConvLSTMCell(
64,
256,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
256,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=2,
padding=1,
hidden_kernel_size=1,
bias=False)
def forward(self, input, hidden1, hidden2, hidden3):
x = self.conv(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
return x, hidden1, hidden2, hidden3
class Binarizer(nn.Module):
def __init__(self):
super(Binarizer, self).__init__()
self.conv = nn.Conv2d(512, 32, kernel_size=1, bias=False)
self.sign = Sign()
def forward(self, input):
feat = self.conv(input)
x = F.tanh(feat)
return self.sign(x)
class DecoderCell(nn.Module):
def __init__(self):
super(DecoderCell, self).__init__()
**resConv = models.resnet50(pretrained=True)
resConv.layer4 = nn.Conv2d(32, 512, kernel_size=3, stride=2, padding=1, bias=False)
self.conv1 = resConv.layer4**
self.rnn1 = ConvLSTMCell(
512,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn2 = ConvLSTMCell(
128,
512,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=1,
bias=False)
self.rnn3 = ConvLSTMCell(
128,
256,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
self.rnn4 = ConvLSTMCell(
64,
128,
kernel_size=3,
stride=1,
padding=1,
hidden_kernel_size=3,
bias=False)
**resConv2 = models.resnet50(pretrained=True)
resConv2.layer4 = nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False)
self.conv2 = resConv2.layer4**
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
x = self.conv1(input)
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
你做错了，一些解释是，
**resConv = models.resnet50(pretrained=True) # you are reading a model
现在您正在用新初始化的层替换该模型中的层。其次，resnet50 中的 layer4 是一个包含多个层的顺序块。使用 print 查看模型中的确切图层。
resConv.layer4 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
这里你使用的是新图层。
self.conv = resConv.layer4**
根据您关于使用预训练层的查询，您应该这样做，
resConv = models.resnet50(pretrained=True)
print(resConv) #see the layer which you want to use
self.conv = resConv.conv1 # replace conv1 with that layer
# note: conv1 is the name of first conv layer in resnet
除此之外，我还建议在对象初始化之外获取和添加该层（或权重和偏差）。类似于：
enc = EncoderCell()
resnet50 = models.resnet50(pretrained=True)
然后
enc.conv = resnet50.conv1
或更理想
enc.conv.load_state_dict(resnet50.layer1.state_dict())
原因是，在 nn.Module class 上调用 state_dict() 创建参数的克隆（在本例中为权重和偏差），可以通过 nn.Module.load_state_dict() 方法只要 nn.Module 的两个实例共享相同的形状。所以你得到了预训练的权重，它们完全脱离了预训练模型。然后你可以摆脱预训练模型，因为它的内存可能相当大。
del resnet50
我提交了对其他答案的潜在改进，但为了解决您遇到的错误，我也在这里回答。如果代码在您编辑之前运行，并且您尝试更改的图层与前一个图层的形状相同，那么我的猜测是它可能与通过创建 resnet50 对象形成的计算图有关。我会推荐我在对其他答案的编辑中提到的方法，但我会在这里再次声明（注意，这假设您保持代码原样）：
# instantiate you encoder (repeat these steps with the decoder as well)
enc = EncoderCell()
# get the pretrained model
resnet = models.resnet50(pretrained=True)
# load the state dict into the regular conv layer
enc.conv.load_state_dict(resnet50.layer4.state_dict())
这应该将来自 resnet50 模型的预训练权重和偏差加载到您的转换层，这可以对解码器转换层完成，只要它们都共享相同的形状。
要对不匹配错误进行更多测试，我建议在模型的 forward() 方法中使用调试器或打印语句，以查看应用每一层后张量的形状，就像这样
def forward(self, input, hidden1, hidden2, hidden3, hidden4):
print(x.size())
x = self.conv1(input)
print(x.size())
hidden1 = self.rnn1(x, hidden1)
x = hidden1[0]
x = F.pixel_shuffle(x, 2)
hidden2 = self.rnn2(x, hidden2)
x = hidden2[0]
x = F.pixel_shuffle(x, 2)
hidden3 = self.rnn3(x, hidden3)
x = hidden3[0]
x = F.pixel_shuffle(x, 2)
hidden4 = self.rnn4(x, hidden4)
x = hidden4[0]
x = F.pixel_shuffle(x, 2)
x = F.tanh(self.conv2(x)) / 2
return x, hidden1, hidden2, hidden3, hidden4
当然，您可以将 print 语句放在 forward 方法中的任何其他位置。我也强烈推荐调试器； pycharm 使这变得非常容易，并且还可以在它提供的 python 控制台旁边以科学模式轻松查看变量的状态。在变量通过某些层（如卷积层）后，可能值得寻找计算变量大小的方法。这是很好理解的，并且存在根据初始大小、过滤器大小、步幅宽度和填充计算维度大小的公式。