0.基础知识
0.1. torch.nn.init.kaiming_normal_(m.weight, mode=‘fan_out’, nonlinearity=‘relu’)
- 用正态分布来，填充输入张量
0.2.torch.nn.init.constant_(m.weight, 1)
用常量1，来填充输入张量
0.3.nn.BatchNorm2d
- 2维数据归一化层（把数据按比例缩放，使之落入一个小小的特定区间）
使得数据在进行Relu之前不会因为数据过大而导致网络性能的不稳定
0.4.torch.nn.init.normal_(tensor, mean=0, std=1),
- 用服从正态分布N（mean，std）的数据来填充张量tensor
1.VGG16结构图
1.1.导包
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import torch import torch.nn as nn from hub import * __all__=[ 'VGG','vgg11','vgg11_bn','vgg13','vgg13_bn','vgg16','vgg16_bn', 'vgg_19','vgg19_bn', ] model_urls={<!-- --> 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth', 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth', 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth', 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth', 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth', } |
1.2.定义VGG类方法
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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | class VGG(nn.Module): def __init__(self, features, num_classes=1000, init_weights=True): super(VGG, self).__init__() self.features = features self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) if init_weights: self._initialize_weights() def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): #如果m是2维卷积层 if isinstance(m, nn.Conv2d): #按照《深入整流器:在ImageNet分类上超越人类水平的性能》中描述的方法，用正态分布填充输入张量 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) #如果m，是2维数据归一化层（把数据按比例缩放，使之落入一个小小的特定区间） #使得数据在进行Relu之前不会因为数据过大而导致网络性能的不稳定 elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) #如果m是线性层 elif isinstance(m, nn.Linear): #torch.nn.init.normal_(tensor, mean=0, std=1),用服从正态分布N（mean，std）的数据来填充张量tensor nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) |
1.3.神经网络层合成函数
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 30 31 32 33 34 35 36 | def make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) cfgs = {<!-- --> 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } #print(cfgs['A']) def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs): if pretrained: kwargs['init_weights'] = False model = VGG(make_layers(cfgs[cfg],batch_norm=batch_norm), **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) model.load_state_dict(state_dict) return model |
1.4.调用合成vgg16
1 2 3 4 5 6 7 8 | def vgg16(pretrained=False,progress=True,**kwargs): return _vgg('vgg16','D',False,pretrained,progress,**kwargs) r"""VGG 16-layer model (configuration "D") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ |
1.5.输出vgg16网络层参数
1 2 | VGG_16_Net =vgg16() print(VGG_16_Net) |
