RuntimeError：3维权重[64，512，1]的3维输入，但尺寸为[4，512]的2维输入

问题描述

您好，下面是我尝试运行的pytorch模型。但是越来越错误。我也发布了错误跟踪。除非添加卷积层，否则它运行得很好。我对深度学习和Pytorch还是陌生的。因此，我很抱歉这是一个愚蠢的问题。我使用的是conv1d，为什么conv1d为什么要期待3D输入，并且还得到了2d输入，所以也很奇怪。

class Net(nn.Module):
        def __init__(self):
            super().__init__()
            self.fc1 = nn.Linear(CROP_SIZE*CROP_SIZE*3,512)
            self.conv1d1 = nn.Conv1d(in_channels=512,out_channels=64,kernel_size=1,stride=2)
            self.fc2 = nn.Linear(64,128)
            self.conv1d2 = nn.Conv1d(in_channels=128,stride=2)
            self.fc3 = nn.Linear(64,256)
            self.conv1d3 = nn.Conv1d(in_channels=256,stride=2)
            self.fc4 = nn.Linear(64,256)
            self.fc4 = nn.Linear(256,128)
            self.fc5 = nn.Linear(128,64)
            self.fc6 = nn.Linear(64,32)
            self.fc7 = nn.Linear(32,64)
   

     self.fc8 = nn.Linear(64,frame['landmark_id'].nunique())

    def forward(self,x):
        x = F.relu(self.conv1d1(self.fc1(x)))
        x = F.relu(self.conv1d2(self.fc2(x)))
        x = F.relu(self.conv1d3(self.fc3(x)))
        x = F.relu(self.fc4(x))
        x = F.relu(self.fc5(x))
        x = F.relu(self.fc6(x))
        x = F.relu(self.fc7(x))
        x = self.fc8(x)
        return F.log_softmax(x,dim=1)


net = Net()

import torch.optim as optim

loss_function = nn.CrossEntropyLoss()
net.to(torch.device('cuda:0'))
for epoch in range(3): # 3 full passes over the data
    optimizer = optim.Adam(net.parameters(),lr=0.001)
    for data in tqdm(train_loader):  # `data` is a batch of data
        X = data['image'].to(device)  # X is the batch of features
        y = data['landmarks'].to(device) # y is the batch of targets.
        optimizer.zero_grad()  # sets gradients to 0 before loss calc. You will do this likely every step.
        output = net(X.view(-1,CROP_SIZE*CROP_SIZE*3))  # pass in the reshaped batch
#         print(np.argmax(output))
#         print(y)
        loss = F.nll_loss(output,y)  # calc and grab the loss value
        loss.backward()  # apply this loss backwards thru the network's parameters
        optimizer.step()  # attempt to optimize weights to account for loss/gradients

    print(loss)  # print loss. We hope loss (a measure of wrong-ness) declines! 

错误跟踪

    ---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-42-f5ed7999ce57> in <module>
      5         y = data['landmarks'].to(device) # y is the batch of targets.
      6         optimizer.zero_grad()  # sets gradients to 0 before loss calc. You will do this likely every step.
----> 7         output = net(X.view(-1,CROP_SIZE*CROP_SIZE*3))  # pass in the reshaped batch
      8 #         print(np.argmax(output))
      9 #         print(y)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self,*input,**kwargs)
    548             result = self._slow_forward(*input,**kwargs)
    549         else:
--> 550             result = self.forward(*input,**kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self,input,result)

<ipython-input-37-6d3e34d425a0> in forward(self,x)
     16 
     17     def forward(self,x):
---> 18         x = F.relu(self.conv1d1(self.fc1(x)))
     19         x = F.relu(self.conv1d2(self.fc2(x)))
     20         x = F.relu(self.conv1d3(self.fc3(x)))

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self,result)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self,input)
    210                             _single(0),self.dilation,self.groups)
    211         return F.conv1d(input,self.weight,self.bias,self.stride,--> 212                         self.padding,self.groups)
    213 
    214 

RuntimeError: Expected 3-dimensional input for 3-dimensional weight [64,512,1],but got 2-dimensional input of size [4,512] instead

解决方法

您应该了解卷积的工作原理（例如，参见this answer）和一些神经网络基础知识（this tutorial from PyTorch）。

基本上，Conv1d期望输入形状为[batch,channels,features]（其中features可能是一些时间步长，并且可能有所不同，请参见示例）。

nn.Linear的形状为[batch,features]，因为它已完全连接并且每个输入要素都已连接到每个输出要素。

对于torch.nn.Linear，您可以自己验证这些形状：

import torch

layer = torch.nn.Linear(20,10)
data = torch.randn(64,20)  # [batch,in_features]
layer(data).shape  # [64,10],[batch,out_features]

对于Conv1d：

layer = torch.nn.Conv1d(in_channels=20,out_channels=10,kernel_size=3,padding=1)
data = torch.randn(64,20,15)  # [batch,timesteps]
layer(data).shape  # [64,10,15],out_features]

layer(torch.randn(32,25)).shape  # [32,25]

顺便说一句。。在处理图像时，应改用torch.nn.Conv 2 d。

,

大多数 Pytorch 函数可处理批处理数据，即它们接受大小为(batch_size,shape)的输入。 @Szymon Maszke已经发布了与此相关的答案。

因此，在您的情况下，可以使用取消挤压和 sqeeze 功能来添加和删除多余的尺寸。

这是示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(100,512)
        self.conv1d1 = nn.Conv1d(in_channels=512,out_channels=64,kernel_size=1,stride=2)
        self.fc2 = nn.Linear(64,128)

    def forward(self,x):
        x = self.fc1(x)
        x = x.unsqueeze(dim=2)
        x = F.relu(self.conv1d1(x))
        x = x.squeeze()

        x = self.fc2(x)

        return x


net = Net()

bsize = 4
inp   = torch.randn((bsize,100))

out = net(inp)
print(out.shape)