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vgg_features.py
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vgg_features.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
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_dir = './pretrained_models'
cfg = {
'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'],
}
class VGG_features(nn.Module):
def __init__(self, cfg, batch_norm=False, init_weights=True):
super(VGG_features, self).__init__()
self.batch_norm = batch_norm
self.kernel_sizes = []
self.strides = []
self.paddings = []
self.features = self._make_layers(cfg, batch_norm)
if init_weights:
self._initialize_weights()
def forward(self, x):
x = self.features(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(self, cfg, batch_norm):
self.n_layers = 0
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
self.kernel_sizes.append(2)
self.strides.append(2)
self.paddings.append(0)
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)]
self.n_layers += 1
self.kernel_sizes.append(3)
self.strides.append(1)
self.paddings.append(1)
in_channels = v
return nn.Sequential(*layers)
def conv_info(self):
return self.kernel_sizes, self.strides, self.paddings
def num_layers(self):
'''
the number of conv layers in the network
'''
return self.n_layers
def __repr__(self):
template = 'VGG{}, batch_norm={}'
return template.format(self.num_layers() + 3,
self.batch_norm)
def vgg11_features(pretrained=False, **kwargs):
"""VGG 11-layer model (configuration "A")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['A'], batch_norm=False, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg11'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg11_bn_features(pretrained=False, **kwargs):
"""VGG 11-layer model (configuration "A") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['A'], batch_norm=True, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg11_bn'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg13_features(pretrained=False, **kwargs):
"""VGG 13-layer model (configuration "B")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['B'], batch_norm=False, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg13'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg13_bn_features(pretrained=False, **kwargs):
"""VGG 13-layer model (configuration "B") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['B'], batch_norm=True, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg13_bn'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg16_features(pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['D'], batch_norm=False, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg16'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg16_bn_features(pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['D'], batch_norm=True, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg16_bn'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg19_features(pretrained=False, **kwargs):
"""VGG 19-layer model (configuration "E")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['E'], batch_norm=False, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg19'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
def vgg19_bn_features(pretrained=False, **kwargs):
"""VGG 19-layer model (configuration 'E') with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG_features(cfg['E'], batch_norm=True, **kwargs)
if pretrained:
my_dict = model_zoo.load_url(model_urls['vgg19_bn'], model_dir=model_dir)
keys_to_remove = set()
for key in my_dict:
if key.startswith('classifier'):
keys_to_remove.add(key)
for key in keys_to_remove:
del my_dict[key]
model.load_state_dict(my_dict, strict=False)
return model
if __name__ == '__main__':
vgg11_f = vgg11_features(pretrained=True)
print(vgg11_f)
vgg11_bn_f = vgg11_bn_features(pretrained=True)
print(vgg11_bn_f)
vgg13_f = vgg13_features(pretrained=True)
print(vgg13_f)
vgg13_bn_f = vgg13_bn_features(pretrained=True)
print(vgg13_bn_f)
vgg16_f = vgg16_features(pretrained=True)
print(vgg16_f)
vgg16_bn_f = vgg16_bn_features(pretrained=True)
print(vgg16_bn_f)
vgg19_f = vgg19_features(pretrained=True)
print(vgg19_f)
vgg19_bn_f = vgg19_bn_features(pretrained=True)
print(vgg19_bn_f)