spikingjelly.clock_driven.ann2snn.examples.model_sample.cifar10.vgg 源代码

'''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn


cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


[文档]class VGG(nn.Module): def __init__(self, vgg_name): super(VGG, self).__init__() self.features = self._make_layers(cfg[vgg_name]) self.classifier = nn.Linear(512, 10)
[文档] def forward(self, x): out = self.features(x) out = out.view(out.size(0), -1) out = self.classifier(out) return out
def _make_layers(self, cfg): layers = [] in_channels = 3 for x in cfg: if x == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1), nn.BatchNorm2d(x), nn.ReLU(inplace=True)] in_channels = x layers += [nn.AvgPool2d(kernel_size=1, stride=1)] return nn.Sequential(*layers)
[文档]class VGG_no_bias_bn(nn.Module): def __init__(self, vgg_name): super(VGG_no_bias_bn, self).__init__() self.features = self._make_layers(cfg[vgg_name]) self.classifier = nn.Linear(512, 10,bias=False)
[文档] def forward(self, x): out = self.features(x) out = out.view(out.size(0), -1) out = self.classifier(out) return out
def _make_layers(self, cfg): layers = [] in_channels = 3 for x in cfg: if x == 'M': layers += [nn.AvgPool2d(kernel_size=2, stride=2)] else: layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1,bias=False), nn.ReLU(inplace=True)] in_channels = x layers += [nn.AvgPool2d(kernel_size=1, stride=1)] return nn.Sequential(*layers)
[文档]def test(): net = VGG('VGG11') x = torch.randn(2,3,32,32) y = net(x) print(y.size())