191 lines
7.6 KiB
Python
191 lines
7.6 KiB
Python
import torch
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import torch.nn as nn
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from .utils import load_state_dict_from_url
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from typing import Union, List, Dict, Any, cast
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__all__ = [
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'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
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'vgg19_bn', 'vgg19',
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]
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model_urls = {
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'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
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'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
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'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
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'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
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'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
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'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
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'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
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'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
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}
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class VGG(nn.Module):
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def __init__(
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self,
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features: nn.Module,
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num_classes: int = 1000,
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init_weights: bool = True
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) -> None:
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super(VGG, self).__init__()
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self.features = features
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self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
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self.classifier = nn.Sequential(
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nn.Linear(512 * 7 * 7, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, num_classes),
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)
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if init_weights:
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self._initialize_weights()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.features(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.classifier(x)
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return x
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def _initialize_weights(self) -> None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.constant_(m.bias, 0)
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def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential:
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layers: List[nn.Module] = []
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in_channels = 3
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for v in cfg:
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if v == 'M':
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layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
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else:
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v = cast(int, v)
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conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
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else:
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layers += [conv2d, nn.ReLU(inplace=True)]
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in_channels = v
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return nn.Sequential(*layers)
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cfgs: Dict[str, List[Union[str, int]]] = {
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'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
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'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
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}
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def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG:
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if pretrained:
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kwargs['init_weights'] = False
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model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls[arch],
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progress=progress)
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model.load_state_dict(state_dict)
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return model
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def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 11-layer model (configuration "A") from
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)
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def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 11-layer model (configuration "A") with batch normalization
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs)
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def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 13-layer model (configuration "B")
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)
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def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 13-layer model (configuration "B") with batch normalization
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs)
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def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 16-layer model (configuration "D")
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)
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def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 16-layer model (configuration "D") with batch normalization
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)
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def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 19-layer model (configuration "E")
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)
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def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
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r"""VGG 19-layer model (configuration 'E') with batch normalization
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)
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