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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')
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