[Pytorch][转载]VGG模型实现

本文源自Pytoch官方：https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py import torch

import torch.nn as nn

from .utils import load_state_dict_from_url

__all__ = [

'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',

'vgg19_bn', 'vgg19',

]

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',

}

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():

if isinstance(m, nn.Conv2d):

nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

if m.bias is not None:

nn.init.constant_(m.bias, 0)

elif isinstance(m, nn.BatchNorm2d):

nn.init.constant_(m.weight, 1)

nn.init.constant_(m.bias, 0)

elif isinstance(m, nn.Linear):

nn.init.normal_(m.weight, 0, 0.01)

nn.init.constant_(m.bias, 0)

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'],

}

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

def vgg11(pretrained=False, progress=True, **kwargs):

r"""VGG 11-layer model (configuration "A") from

`"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

"""

return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)

def vgg11_bn(pretrained=False, progress=True, **kwargs):

r"""VGG 11-layer model (configuration "A") with batch normalization

`"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

"""

return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs)

def vgg13(pretrained=False, progress=True, **kwargs):

r"""VGG 13-layer model (configuration "B")

`"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

"""

return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)

def vgg13_bn(pretrained=False, progress=True, **kwargs):

r"""VGG 13-layer model (configuration "B") with batch normalization

`"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

"""

return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs)

def vgg16(pretrained=False, progress=True, **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

"""

return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)

def vgg16_bn(pretrained=False, progress=True, **kwargs):

r"""VGG 16-layer model (configuration "D") with batch normalization

`"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

"""

return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)

def vgg19(pretrained=False, progress=True, **kwargs):

r"""VGG 19-layer model (configuration "E")

`"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

"""

return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)

def vgg19_bn(pretrained=False, progress=True, **kwargs):

r"""VGG 19-layer model (configuration 'E') with batch normalization

`"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

"""

return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)