import torch import torch.nn as nn from .utils import load_state_dict_from_url from typing import Union, List, Dict, Any, cast __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: 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) -> torch.Tensor: x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return 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" `._ 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" `._ 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" `._ 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" `._ 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" `._ 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" `._ 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" `._ 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" `._ 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)