python中权重剪枝，低秩分解，量化技术 代码

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

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.fc1 = nn.Linear(10*12*12, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 初始化模型
model = SimpleNet()

# 应用权重剪枝，剪去权重绝对值最小的20%
prune.l1_unstructured(model.conv1, name='weight', amount=0.2)

# 微调剪枝后的模型
# 这里省略了微调的代码实现，但通常包括继续训练模型以恢复性能

import torch
import torch.nn as nn

class LowRankDecomposition(nn.Module):
    def __init__(self, in_features, out_features, rank):
        super(LowRankDecomposition, self).__init__()
        self.A = nn.Parameter(torch.Tensor(rank, in_features))
        self.B = nn.Parameter(torch.Tensor(out_features, rank))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.A, a=math.sqrt(5))
        nn.init.kaiming_uniform_(self.B, a=math.sqrt(5))

    def forward(self, x):
        return torch.matmul(torch.matmul(x, self.A), self.B)

# 使用低秩分解替换全连接层
# 假设原始权重矩阵为W，现在将其替换为两个低秩矩阵的乘积
rank = 10  # 假设我们希望分解后的秩为10
low_rank_layer = LowRankDecomposition(in_features=100, out_features=200, rank=rank)

import torch
import torch.nn as nn
import torch.quantization

# 定义一个简单的网络
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.fc1 = nn.Linear(10*12*12, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 初始化模型
model = SimpleNet()

# 准备模型进行量化
model.qconfig = torch.quantization.default_qat_qconfig
torch.quantization.prepare_qat(model, inplace=True)

# 模拟训练过程
# 这里省略了模拟训练的代码实现

# 转换模型为量化版本
torch.quantization.convert(model, inplace=True)