一维卷积剪枝pytorch代码实现

以下是使用PyTorch实现一维卷积剪枝的示例代码：

import torch
import torch.nn as nn
import numpy as np

class PrunedConv1d(nn.Conv1d):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(PrunedConv1d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
        self.mask = nn.Parameter(torch.ones_like(self.weight), requires_grad=False)
    
    def forward(self, input):
        return nn.functional.conv1d(input, self.weight * self.mask, self.bias, self.stride, self.padding, self.dilation, self.groups)
    
    def prune(self, percent):
        with torch.no_grad():
            weight = self.weight.abs().numpy()
            cutoff = np.percentile(weight, percent)
            self.mask.data[weight <= cutoff] = 0
            
    def get_sparsity(self):
        return 1.0 - self.mask.sum().item() / self.mask.numel()

在这个示例中，我们建立了一个继承自`nn.Conv1d`的剪枝卷积层。该层在初始化时创建了一个与权重形状相同的剪枝掩码，并将其设置为全1。当调用`forward()`方法时，权重会乘以这个剪枝掩码，以实现剪枝。`prune`方法使用`numpy.percentile`函数计算权重的阈值，并将剪枝掩码中小于该阈值的所有元素设置为零。`get_sparsity`方法返回剪枝掩码的稀疏度。