LeNet剪枝

2021年11月20日 阅读数:3
这篇文章主要向大家介绍LeNet剪枝,主要内容包括基础应用、实用技巧、原理机制等方面,希望对大家有所帮助。

LeNet剪枝

import torch
import torch.nn as nn
from torchsummary import summary
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#新建一个简单的LeNet
class LeNet(nn.Module):
def __init__(self, num_classes=10):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2)
self.conv3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, stride=1, padding=1)
self.fc = nn.Linear(in_features=16 * 16 * 24, out_features=num_classes)
def forward(self, input):
output = self.conv1(input)
output = nn.ReLU()(output)
output = self.conv2(output)
output = nn.ReLU()(output)
output = self.pool(output)
output = self.conv3(output)
output = nn.ReLU()(output)
output = self.conv4(output)
output = nn.ReLU()(output)
output = output.view(-1, 16 * 16 * 24)
output = self.fc(output)
return output

model = LeNet().to(device=device)

用prune剪枝

import torch.nn.utils.prune as prune

parameters_to_prune = (
(model.conv1, 'weight'),
(model.conv2, 'weight'),
(model.conv4, 'weight'),
(model.fc, 'weight'),
)

prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=0.2,
)