RuntimeError: conv2d的预期3D (未批处理)或4D (批处理)输入，但输入的大小为：[64，2]
RuntimeError: conv2d的预期3D (未批处理)或4D (批处理)输入，但输入的大小为：[64，2]
提问于 2022-08-07 06:22:59
我正在尝试使用PyTorch创建一个定制的CNN模型，用于对RGB图像进行二值图像分类，但是我一直收到一个运行时错误，它说我原来的输入形状64，3,128被输出为64,2。我已经试着修复它2天了，但是我仍然不知道代码有什么问题。
以下是网络的代码：
class MyCNN(nn.Module):
def __init__(self):
super(MyCNN, self).__init__()
self.network = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32, 64, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(in_features=25088, out_features=2048),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 2),
)
def forward(self, x):
return self.network(x)这里叫它：
for epoch in range(num_epochs):
for images, labels in data_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
loss.backward()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))下面是堆栈跟踪：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-30-fb9ee290e1d6> in <module>()
7
8 # Forward pass
----> 9 outputs = model(images)
10 loss = criterion(outputs, labels)
11
6 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-29-09c58015e865> in forward(self, x)
27 x = layer(x)
28 print(x.shape)
---> 29 return self.network(x)
30
31 model = MyCNN()
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
141
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in forward(self, input)
455
456 def forward(self, input: Tensor) -> Tensor:
--> 457 return self._conv_forward(input, self.weight, self.bias)
458
459 class Conv3d(_ConvNd):
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
452 _pair(0), self.dilation, self.groups)
453 return F.conv2d(input, weight, bias, self.stride,
--> 454 self.padding, self.dilation, self.groups)
455
456 def forward(self, input: Tensor) -> Tensor:
RuntimeError: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [64, 2]我真的很感激你的帮助。如果解决办法很简单，我很抱歉，但我并不容易看到。干杯。
回答 1
Stack Overflow用户
发布于 2022-08-07 12:36:29
数据似乎已经改变，因为图像的大小是(64，3,512,512)，标签是(64,2)。如果形状合适的话，它就能正常工作。这是我的密码。
代码：
import torch
import torch.nn as nn
import torch.optim as optim
class MyCNN(nn.Module):
def __init__(self):
super(MyCNN, self).__init__()
self.network = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(32, 64, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(in_features=25088, out_features=2048),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 2),
)
def forward(self, x):
return self.network(x)
model = MyCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr = 0.001)
optimizer.zero_grad()
# Forward pass
images = torch.randn(64, 3, 128, 128)
labels = torch.randn(64, 2)
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
loss.backward()
optimizer.step()我建议更改这一行
for images, labels in data_loader:
images, labels = images.to(device), labels.to(device)到这个
for labels, images in data_loader:
images, labels = images.to(device), labels.to(device)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73265333
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