_, _, h, w = x.size() AttributeError: 'list' object has no attribute 'size'
时间: 2023-12-15 21:33:28 浏览: 27
这个错误是因为你将一个列表对象传递给了一个期望是张量的函数。张量是PyTorch中的一种数据结构,它是一个多维数组,可以用于表示向量、矩阵和更高维度的数组。因此,你需要将列表转换为张量,然后才能使用size()函数。
以下是一个将列表转换为张量并使用size()函数的例子:
```python
import torch
x = [1, 2, 3, 4]
x_tensor = torch.tensor(x)
_, _, h, w = x_tensor.size()
print(h, w) # 输出:1 4
```
相关问题
n = out.size AttributeError: 'list' object has no attribute 'size'
这个错误提示是因为你使用了一个列表对象,而列表对象没有 `size()` 方法。在 Python 中,列表对象有一个内置的 `len()` 函数来获取列表的长度,你可以尝试将 `size()` 替换为 `len()`,例如:
```
n = len(out)
```
这样就可以获得列表 `out` 的长度了。
model=model.module AttributeError: 'list' object has no attribute 'module'
This error occurs when you try to access the 'module' attribute of a list object. It means that you are trying to call a method or attribute that is not defined for a list.
To fix this error, you need to check your code and make sure that you are calling the 'module' attribute on the correct object. It's possible that you are passing a list object to a function that expects a model object.
If you are working with a PyTorch model, make sure that you have defined it correctly and that you are calling the 'module' attribute on the right object. The 'module' attribute is used to access the underlying model when using DataParallel.
Here's an example of how to fix this error when working with a PyTorch model:
```python
import torch.nn as nn
import torch.optim as optim
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 16 * 16, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = x.view(-1, 64 * 16 * 16)
x = self.fc1(x)
return x
model = MyModel()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Train the model
for epoch in range(10):
for data in dataloader:
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Access the underlying model when using DataParallel
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.module.training = False
# Test the model
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
In this example, we define a simple PyTorch model and train it using an SGD optimizer. After training, we check if there are multiple GPUs available and wrap the model with DataParallel if necessary. Finally, we use the 'module' attribute to access the underlying model when running inference.