pytorch stride
时间: 2023-11-18 22:02:20 浏览: 219
PyTorch中的stride(步长)是指在卷积操作中滑动窗口的步长大小。它决定了每次滑动窗口移动的距离,从而影响输出特征图的尺寸。在PyTorch中,stride可以通过设置卷积层的参数来指定。
例如,对于一个二维卷积层,可以使用torch.nn.Conv2d类,并在初始化时设置stride参数。例如:
```python
import torch
import torch.nn as nn
# 输入特征图大小为 (batch_size, channels, height, width)
input_size = (1, 3, 32, 32)
# 创建一个卷积层,设置stride为2
conv_layer = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2)
# 输入特征图
input_tensor = torch.randn(input_size)
# 进行卷积操作
output_tensor = conv_layer(input_tensor)
print(output_tensor.size()) # 输出特征图大小
```
在上述代码中,stride参数被设置为2,这意味着滑动窗口每次移动的距离为2个像素。输出特征图的尺寸会相应地减小,具体减小的规则可以根据输入特征图大小、卷积核大小和stride来计算。
相关问题
no, i mean pytorch tensor stride
I see, sorry for the confusion earlier. In PyTorch, a tensor's stride is a tuple of integers representing the number of elements that need to be skipped in memory to go to the next element in each dimension. The stride values depend on the layout of the tensor in memory and are used to calculate the memory address of each element.
For example, consider a 2D tensor with shape (3, 4) stored in row-major order (default in PyTorch). This means that the elements in the same row are stored next to each other in memory. If we access tensor[1, 2], we need to skip 1 element in the first dimension and 2 elements in the second dimension to get to the desired element. The stride values for this tensor would be (4, 1), indicating that we need to skip 4 elements in memory to go to the next row and 1 element to go to the next column.
You can access the stride values of a PyTorch tensor using the `stride()` method. For example:
```
import torch
x = torch.randn(3, 4)
print(x.stride()) # prints (4, 1)
```
pytorch中的stride参数
在PyTorch中,stride参数是指卷积操作中卷积核在输入数据上移动的步长。它控制了输出的大小,因为步长越大,输出的尺寸就越小。Stride可以设置为一个整数值,表示在每个方向上的移动步长相同,也可以设置为一个元组,表示在每个方向上的移动步长可以不同。例如,如果stride设置为2,则卷积核每次在输入数据上沿着水平和垂直方向移动2个像素。如果stride设置为(2,1),则水平方向上的移动步长为2,垂直方向上的移动步长为1。