Deep learning RuntimeError:三维权重[64512,1]应为三维输入,但得到的却是大小为[4512]的二维输入
下面是我正在尝试运行的pytorch模型。但这是一个错误。我也发布了错误跟踪。它运行得非常好,除非我添加了卷积层。我对深入学习和学习仍然是新手。所以,如果这是一个愚蠢的问题,我道歉。我使用的是conv1d,为什么conv1d需要三维输入,而它也需要二维输入,这也很奇怪Deep learning RuntimeError:三维权重[64512,1]应为三维输入,但得到的却是大小为[4512]的二维输入,deep-learning,pytorch,cnn,Deep Learning,Pytorch,Cnn,下面是我正在尝试运行的pytorch模型。但这是一个错误。我也发布了错误跟踪。它运行得非常好,除非我添加了卷积层。我对深入学习和学习仍然是新手。所以,如果这是一个愚蠢的问题,我道歉。我使用的是conv1d,为什么conv1d需要三维输入,而它也需要二维输入,这也很奇怪 class Net(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(C
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, out_channels=64, kernel_size=1, stride=2)
self.fc3 = nn.Linear(64, 256)
self.conv1d3 = nn.Conv1d(in_channels=256, out_channels=64, kernel_size=1, 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, *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)
/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.dilation, 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
---------------------------------------------------------------------------
运行时错误回溯(上次最近调用)
在里面
5 y=数据['landmarks']。to(设备)#y是一批目标。
6 optimizer.zero_grad()#在损失计算之前将梯度设置为0。您可能会在每一步都这样做。
---->7输出=净(X.view(-1,裁剪尺寸*裁剪尺寸*3))#通过重塑批次
8#打印(np.argmax(输出))
9#打印(y)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/modules.py in____________(self,*input,**kwargs)
548结果=self.\u slow\u forward(*输入,**kwargs)
549其他:
-->550结果=自转发(*输入,**kwargs)
551用于钩住自身。\u向前\u钩住.values():
552钩子结果=钩子(自身、输入、结果)
前进中(自我,x)
16
17 def前进档(自身,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/modules.py in____________(self,*input,**kwargs)
548结果=self.\u slow\u forward(*输入,**kwargs)
549其他:
-->550结果=自转发(*输入,**kwargs)
551用于钩住自身。\u向前\u钩住.values():
552钩子结果=钩子(自身、输入、结果)
/前进中的opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py(自我,输入)
210_单个(0),自膨胀,自组)
211返回F.conv1d(输入、自身重量、自身偏差、自身步幅、,
-->212自填充、自膨胀、自组)
213
214
RuntimeError:三维权重[64512,1]应为三维输入,但得到的却是大小为[4512]的二维输入
您应该学习卷积是如何工作的(例如,请参见)和一些神经网络基础知识()
基本上,Conv1d
需要输入shape[批次、频道、功能]
(其中功能可以是一些时间步,并且可以变化,请参见示例)
nn.Linear
需要形状[批次,特征]
,因为它是完全连接的,并且每个输入特征都连接到每个输出特征
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, channels, timesteps]
layer(data).shape # [64, 10, 15], [batch, out_features]
layer(torch.randn(32, 20, 25)).shape # [32, 10, 25]
顺便说一句。在处理图像时,您应该使用torch.nn.Conv
2d
。大多数Pytorch函数处理批数据,即它们接受大小(批大小、形状)
@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unqueze和sqeeze函数来添加和删除额外维度
以下是示例代码:
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)