| import torch | |
| import torch.nn as nn | |
| from torch.hub import load_state_dict_from_url | |
| from typing import Union, List, Dict, Any, cast | |
| __all__ = ['get_vgg'] | |
| 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, | |
| num_classes, | |
| out_keys, | |
| output_make_layers, | |
| init_weights: bool = True, | |
| **kwargs | |
| ) -> None: | |
| super(VGG, self).__init__() | |
| self.stage_id = output_make_layers[0] | |
| self.features = output_make_layers[1] | |
| self.num_classes = num_classes | |
| self.out_keys = out_keys | |
| if num_classes is not None: | |
| 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): | |
| out_blocks = dict() | |
| stage = 0 | |
| out_blocks['block%d' % stage] = x | |
| for idx, op in enumerate(self.features): | |
| if idx in self.stage_id: | |
| stage += 1 | |
| x = op(x) | |
| out_blocks['block%d' % stage] = x | |
| continue | |
| x = op(x) | |
| if self.num_classes is not None: | |
| x = self.avgpool(x) | |
| x = torch.flatten(x, 1) | |
| x = self.classifier(x) | |
| if self.out_keys is not None: | |
| out_blocks = {key: out_blocks[key] for key in self.out_keys} | |
| return x, out_blocks | |
| 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(in_channels, out_keys, cfg: List[Union[str, int]], batch_norm: bool = False): | |
| layer_list = [] | |
| idx = 0 | |
| stage_ids = [] | |
| for v in cfg: | |
| if isinstance(v, int) and v in [1, 2, 3, 4, 5]: | |
| if v > int(out_keys[-1].replace('block', '')): | |
| break | |
| continue | |
| if v == 'M': | |
| layer_list += [nn.MaxPool2d(kernel_size=2, stride=2)] | |
| stage_ids += [idx] | |
| idx += 1 | |
| else: | |
| v = cast(int, v) | |
| conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) | |
| if batch_norm: | |
| layer_list += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] | |
| idx += 3 | |
| else: | |
| layer_list += [conv2d, nn.ReLU(inplace=True)] | |
| idx += 2 | |
| in_channels = v | |
| return stage_ids, nn.Sequential(*layer_list) | |
| 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': [1, 64, 64, 'M', 2, 128, 128, 'M', 3, 256, 256, 256, 'M', 4, 512, 512, 512, 'M', 5, 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(in_channels, num_classes, out_keys, arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG: | |
| if pretrained: | |
| kwargs['init_weights'] = False | |
| stage_id, ops = make_layers(in_channels, out_keys, cfgs[cfg], batch_norm=batch_norm) | |
| model = VGG(num_classes, out_keys, (stage_id, ops), **kwargs) | |
| if pretrained: | |
| state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) | |
| if in_channels != 3: | |
| keys = state_dict.keys() | |
| keys = [x for x in keys if 'features.0.' in x] | |
| for key in keys: | |
| del state_dict[key] | |
| if num_classes != 1000: | |
| keys = state_dict.keys() | |
| keys = [x for x in keys if 'classifier' in x] | |
| for key in keys: | |
| del state_dict[key] | |
| if 'block5' not in out_keys: | |
| keys = list(state_dict.keys()) | |
| for key in keys: | |
| key_layer_id = int(key.split('.')[1]) | |
| if key_layer_id >= stage_id[-1]: | |
| del state_dict[key] | |
| 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(in_channels, num_classes, out_keys, 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(in_channels, num_classes, out_keys,'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) | |
| def get_vgg(name='vgg16_bn', pretrained=True, progress=True, num_classes=None, out_keys=None, in_channels=3, **kwargs): | |
| if pretrained and num_classes != 1000: | |
| print('warning: num_class is not equal to 1000, which will cause some parameters to fail to load!') | |
| if pretrained and in_channels != 3: | |
| print('warning: in_channels is not equal to 3, which will cause some parameters to fail to load!') | |
| if name == 'vgg16_bn': | |
| return vgg16_bn(in_channels=in_channels, num_classes=num_classes, | |
| out_keys=out_keys, pretrained=pretrained, progress=progress, **kwargs) | |
| elif name == 'resnet50': | |
| return _resnet50(name=name, pretrained=pretrained, progress=progress, | |
| num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs) | |
| elif name == 'resnet101': | |
| return _resnet101(name=name, pretrained=pretrained, progress=progress, | |
| num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs) | |
| elif name == 'resnet152': | |
| return _resnet152(name=name, pretrained=pretrained, progress=progress, | |
| num_classes=num_classes, out_keys=out_keys, in_channels=in_channels, **kwargs) | |
| else: | |
| raise NotImplementedError(r'''{0} is not an available values. \ | |
| Please choose one of the available values in | |
| [resnet18, reset50, resnet101, resnet152]'''.format(name)) | |
| if __name__ == '__main__': | |
| model = get_vgg('vgg16_bn', pretrained=True, num_classes=None, in_channels=4, out_keys=['block3']) | |
| x = torch.rand([2, 3, 512, 512]) | |
| x = model(x) | |
| torch.save(model.state_dict(), '../../vgg16bns4.pth') | |