张量下指定卷积核为([0, 1, 0], [1, -4, 1], [0, 1, 0])的depthwise convolution 的 PyTorch 代码
时间: 2023-10-02 22:05:32 浏览: 27
```python
import torch
import torch.nn as nn
# 创建输入张量
input_tensor = torch.randn(1, 3, 5, 5)
# 定义depthwise convolution层
depthwise_conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1, groups=3, bias=False)
# 指定卷积核为([0, 1, 0], [1, -4, 1], [0, 1, 0])
depthwise_conv.weight.data = torch.tensor([[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]],
[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]],
[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]]], dtype=torch.float)
# 对输入张量进行depthwise convolution操作
output_tensor = depthwise_conv(input_tensor)
print(output_tensor.shape) # (1, 3, 5, 5)
```
相关问题
张量中指定卷积核为[[0, 1, 0], [1, -4, 1], [0, 1, 0]]的卷积代码pytorch
import torch.nn.functional as F
# assuming input_tensor is the input tensor to be convolved
kernel = torch.tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=torch.float32)
input_tensor = input_tensor.unsqueeze(0).unsqueeze(0) # add batch and channel dimension
output_tensor = F.conv2d(input_tensor, kernel.unsqueeze(0).unsqueeze(0)) # convolve the tensor with the kernel
output_tensor = output_tensor.squeeze() # remove batch and channel dimension
张量时指定卷积核为([0, 1, 0], [1, -4, 1], [0, 1, 0])的depthwise convolution 的 PyTorch 代码
import torch.nn as nn
# define the depthwise convolution layer
depthwise_conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1, groups=3, bias=False)
# set the convolution kernel
kernel = torch.Tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
depthwise_conv.weight.data = torch.stack([kernel, kernel, kernel], dim=0).unsqueeze(1)
# apply the depthwise convolution to a tensor
x = torch.randn(1, 3, 224, 224)
output = depthwise_conv(x)