张量中指定卷积核为[[0, 1, 0], [1, -4, 1], [0, 1, 0]]的卷积代码pytorch
时间: 2023-10-05 22:06:56 浏览: 38
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
相关问题
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
```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)
```
张量中指定卷积核为[[0, 1, 0], [1, -4, 1], [0, 1, 0]]与输入张量(1,16,208,208)的卷积代码pytorch
import torch
# 创建输入张量
input_tensor = torch.randn(1, 16, 208, 208)
# 创建卷积核张量
kernel_tensor = torch.tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
# 添加通道维度
kernel_tensor = kernel_tensor.unsqueeze(0).unsqueeze(0)
# 创建卷积层并进行卷积操作
conv_layer = torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1, bias=False)
conv_layer.weight.data = kernel_tensor.float()
output_tensor = conv_layer(input_tensor)
# 打印输出张量的形状
print(output_tensor.shape) # (1, 16, 208, 208)