Technology sharing

한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina

Compilatio exemplaris de punctis cognitionis putationis

putatio estalta doctrina exemplumDuae artes communes ad optimizationem adhibentur ad exemplar multiplicitatem reducendam et celeritas illationem meliorem, et ad res culturas coactas resource aptas.

Putatio

Putatio est methodus reducendi exemplar magnitudinis et nisus computationis tollendo parametros minimos vel redundantes in exemplari. Putatio plerumque dividitur in genera sequentes;

1. Pondus Putatio

Pondus putationis numerum parametri exemplaris minuit, removendo elementa prope nulla in pondere matricis. Communia modi sunt:

• Putatio informis: Aufer parva pondera in matrice pondus singillatim.
• Exstructus Putatio: Aufer pondera per certa structuram (ut totos ordines vel columnas integras).

Exemplum:

import torch

# 假设有一个全连接层
fc = torch.nn.Linear(100, 100)

# 获取权重矩阵
weights = fc.weight.data.abs()

# 设定剪枝阈值
threshold = 0.01

# 应用剪枝
mask = weights > threshold
fc.weight.data *= mask
1
2
3
4
5
6
7
8
9
10
11
12
13
14

2. Channel Pruning

Channel putatio maxime usus estneural network convolution reducendo summam computationis removendo canales minimos in strato convolutionis. Communia modi sunt:

• Secundum momenti ustulo: Calculare momentum score uniuscuiusque canalis et canales removere cum inferioribus ustulo.
• Ex sparsity: adjectis terminis sparsis regularizationis, nonnullae canales naturaliter sparsae erunt in processu disciplinae et deinde amputatae.

import torch
import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        return x

model = ConvNet()

# 获取卷积层的权重
weights = model.conv1.weight.data.abs()

# 计算每个通道的L1范数
channel_importance = torch.sum(weights, dim=[1, 2, 3])

# 设定剪枝阈值
threshold = torch.topk(channel_importance, k=32, largest=True).values[-1]

# 应用剪枝
mask = channel_importance > threshold
model.conv1.weight.data *= mask.view(-1, 1, 1, 1)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

3. Layer Pruning

Iacuit putatio totius retis strata removet ad exemplar profunditatem computationalem reducere. Accessio haec magis radicalis est ac saepe coniungitur cum Exemplar Architecturae Investigationis (NAS).

import torch.nn as nn

class LayerPrunedNet(nn.Module):
    def __init__(self, use_layer=True):
        super(LayerPrunedNet, self).__init__()
        self.use_layer = use_layer
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
    
    def forward(self, x):
        x = self.conv1(x)
        if self.use_layer:
            x = self.conv2(x)
        return x

# 初始化网络，选择是否使用第二层
model = LayerPrunedNet(use_layer=False)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18