UDCP算法Python

UDCP算法（Uniformly Distributed Cloning and Pruning）是一种用于神经网络模型压缩和加速的算法。它通过克隆和剪枝网络中的参数来减少模型的大小和计算量，同时保持模型性能。以下是UDCP算法的Python实现示例：

import torch
import torch.nn as nn
import torch.optim as optim

def udcp(model, input_size, output_size, compression_ratio):
    # 克隆原始模型
    cloned_model = type(model)()
    cloned_model.load_state_dict(model.state_dict())

    # 计算要剪枝的参数数量
    total_params = sum(p.numel() for p in cloned_model.parameters())
    target_params = int(total_params * (1 - compression_ratio))

    # 剪枝
    params = []
    for name, param in cloned_model.named_parameters():
        if 'weight' in name:
            params.append(param)

    pruner = nn.utils.prune.L1Unstructured(amount=target_params)
    pruner.prune(params)

    # 替换最后一层为正确的输出大小
    cloned_model.fc = nn.Linear(input_size, output_size)

    return cloned_model

# 示例用法
model = MyModel()  # 自定义模型
input_size = 784  # 输入大小
output_size = 10  # 输出大小
compression_ratio = 0.5  # 压缩比例

compressed_model = udcp(model, input_size, output_size, compression_ratio)

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