| |
| """ |
| Mostly copy-paste from torchvision references. |
| """ |
| import torch |
| import torch.nn as nn |
|
|
|
|
| __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', |
| } |
|
|
|
|
| model_paths = { |
| 'vgg16_bn': '/apdcephfs/private_changanwang/checkpoints/vgg16_bn-6c64b313.pth', |
| 'vgg16': '/apdcephfs/private_changanwang/checkpoints/vgg16-397923af.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, sync=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: |
| if sync: |
| print('use sync backbone') |
| layers += [conv2d, nn.SyncBatchNorm(v), nn.ReLU(inplace=True)] |
| else: |
| 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, sync=False, **kwargs): |
| if pretrained: |
| kwargs['init_weights'] = False |
| model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, sync=sync), **kwargs) |
| if pretrained: |
| state_dict = torch.load(model_paths[arch]) |
| 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, sync=False, **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 torch.hub.load("pytorch/vision", "vgg16_bn", pretrained=True) |
| |
|
|
|
|
| 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) |
|
|
