pruning_filters源码pytorch版本源码

以下是PyTorch版本的`pruning_filters`源码：

import torch
import torch.nn as nn
import torch.nn.utils.prune as prune


class PrunedConv2d(nn.Module):
    def __init__(self, conv):
        super(PrunedConv2d, self).__init__()
        self.conv = conv

    def forward(self, x):
        return self.conv(x)


def pruning_filters(model, layer_name, amount):
    """
    对指定层进行卷积核剪枝
    :param model: 待剪枝的模型
    :param layer_name: 指定层的名称
    :param amount: 剪枝比例，即需要删除的卷积核所占的比例
    :return:
    """
    layer = dict(model.named_modules())[layer_name]
    if isinstance(layer, PrunedConv2d):
        layer = layer.conv
    if not isinstance(layer, nn.Conv2d):
        raise TypeError("Only 'nn.Conv2d' layer type can be pruned")

    # 获取卷积核数量
    num_filters = layer.weight.shape[0]
    # 计算需要删除的卷积核数量
    num_pruned_filters = int(num_filters * amount)

    # 构建剪枝对象
    prune_params = (
        prune.get_parameter(module=layer, name="weight"),
        prune.L1Unstructured,
        prune.Criterion.L1,
        (num_pruned_filters,)
    )

    # 执行剪枝操作
    prune.remove(*prune_params)

    # 创建新的PrunedConv2d层替换原有的层
    new_layer = PrunedConv2d(layer)
    setattr(model, layer_name, new_layer)

此代码中定义了一个`PrunedConv2d`类，用于将剪枝后的卷积层替换原有的卷积层。`pruning_filters`函数接收一个待剪枝的模型、需要剪枝的层的名称以及剪枝比例，然后使用PyTorch内置的剪枝函数`prune.remove`对指定层进行卷积核剪枝，并使用`PrunedConv2d`类创建一个新的剪枝后的卷积层替换原有的层。