| from typing import Union, List, Dict, Any, cast | |
| import torch | |
| import torch.nn as nn | |
| from torch.utils.model_zoo import load_url as 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/vgg11-8a719046.pth', | |
| 'vgg13': 'https://download.pytorch.org/models/vgg13-19584684.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: nn.Module, | |
| num_classes: int = 1000, | |
| init_weights: bool = True | |
| ) -> None: | |
| 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: torch.Tensor): | |
| feat = self.features(x) | |
| x = self.avgpool(feat) | |
| x = torch.flatten(x, 1) | |
| x = self.classifier(x) | |
| return feat, x | |
| def _initialize_weights(self) -> None: | |
| 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: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential: | |
| layers: List[nn.Module] = [] | |
| in_channels = 3 | |
| for v in cfg: | |
| if v == 'M': | |
| layers += [nn.MaxPool2d(kernel_size=2, stride=2)] | |
| else: | |
| v = cast(int, v) | |
| 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: Dict[str, List[Union[str, int]]] = { | |
| '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: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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: bool = False, progress: bool = True, **kwargs: Any) -> VGG: | |
| 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) | |