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Runtime error
| import torch | |
| import torch.nn as nn | |
| __all__ = ['vgg11_bn', 'vgg13_bn', 'vgg16_bn', 'vgg19_bn'] | |
| class VGG(nn.Module): | |
| def __init__(self, features, num_channel_out=512, init_weights=True): | |
| super(VGG, self).__init__() | |
| self.features = features | |
| self.num_out_features = 512 | |
| self.lastlayer = nn.Sequential( | |
| nn.Conv2d(self.num_out_features, num_channel_out, kernel_size=1, stride=1, padding=0, groups=32, bias=False), | |
| nn.BatchNorm2d(num_channel_out), | |
| nn.ReLU(inplace=True), | |
| ) | |
| if init_weights: | |
| self._initialize_weights() | |
| def forward(self, x): | |
| x = self.features(x) | |
| x = self.lastlayer(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, down_sample=8, batch_norm=False): | |
| layers = [] | |
| in_channels = 3 | |
| for v in cfg: | |
| if v == 'M': | |
| layers += [nn.MaxPool2d(kernel_size=2, stride=2)] | |
| elif isinstance(v, dict): | |
| cur_size = v[down_sample] | |
| layers += [nn.MaxPool2d(kernel_size=cur_size, stride=cur_size)] | |
| else: | |
| 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 = { | |
| 'A': [64, 'M', 128, 'M', 256, 256, {4: (2, 1), 8: (2, 2)}, 512, 512, {4: (2, 1), 8: (2, 1)}, 512, 512, | |
| {4: (2, 1), 8: (2, 1)}], | |
| 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, {4: (2, 1), 8: (2, 2)}, 512, 512, {4: (2, 1), 8: (2, 1)}, 512, 512, | |
| {4: (2, 1), 8: (2, 1)}, ], | |
| 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, {4: (2, 1), 8: (2, 2)}, 512, 512, 512, {4: (2, 1), 8: (2, 1)}, 512, | |
| 512, 512, {4: (2, 1), 8: (2, 1)}, ], | |
| 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, {4: (2, 1), 8: (2, 2)}, 512, 512, 512, 512, | |
| {4: (2, 1), 8: (2, 1)}, 512, 512, 512, 512, {4: (2, 1), 8: (2, 1)}, ], | |
| } | |
| def _vgg(model_path, cfg, batch_norm, pretrained, progress, num_channel_out, down_sample, **kwargs): | |
| if pretrained: | |
| kwargs['init_weights'] = False | |
| model = VGG(make_layers(cfgs[cfg], down_sample, batch_norm=batch_norm), num_channel_out, **kwargs) | |
| if model_path and pretrained: | |
| state_dict = torch.load(model_path) | |
| model.load_state_dict(state_dict, strict=False) | |
| return model | |
| def vgg11_bn(model_path='', num_channel_out=512, down_sample=8, pretrained=True, progress=True, **kwargs): | |
| return _vgg(model_path, 'A', True, pretrained, progress, num_channel_out, down_sample, **kwargs) | |
| def vgg13_bn(model_path='', num_channel_out=512, down_sample=8, pretrained=True, progress=True, **kwargs): | |
| return _vgg(model_path, 'B', True, pretrained, progress, num_channel_out, down_sample, **kwargs) | |
| def vgg16_bn(model_path='', num_channel_out=512, down_sample=8, pretrained=True, progress=True, **kwargs): | |
| return _vgg(model_path, 'D', True, pretrained, progress, num_channel_out, down_sample, **kwargs) | |
| def vgg19_bn(model_path='', num_channel_out=512, down_sample=8, pretrained=True, progress=True, **kwargs): | |
| return _vgg(model_path, 'E', True, pretrained, progress, num_channel_out, down_sample, **kwargs) |