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vgg.py
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vgg.py
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import torch
import torch.nn as nn
from utils import my_sequential
__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',
}
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 = my_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()
self.num_feature = features.num_feature + self.classifier.num_feature
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def feature_forward(self, x):
features = []
x, feature = self.features(x, get_features=True)
features.extend(feature)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x, feature = self.classifier(x, get_features=True)
features.extend(feature)
return x, features
def get_feature(self, x, feature_index):
features = []
if feature_index < self.features.num_feature:
return self.features(x, get_features=True, return_index=feature_index)
else:
x, feature = self.features(x, get_features=True)
features.extend(feature)
x = self.avgpool(x)
x = torch.flatten(x, 1)
return self.classifier(x, get_features=True, \
return_index=feature_index - self.features.num_feature)
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):
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:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return my_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, **kwargs):
kwargs['init_weights'] = False
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
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, **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 _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)
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)
class Test_CLS(torch.nn.Module):
def __init__(self, num_classes, arch, load_pretrain=True):
super(Test_CLS, self).__init__()
model = vgg16(num_classes=2)
self.model = model
def forward(self, x, get_features=False):
if get_features:
return self.model(x, True)
else:
return self.model(x)
if __name__ == "__main__":
from network import Cls_Net
from saver import Saver
import argparse
import os
parser = argparse.ArgumentParser(description='Training Code')
parser.add_argument('--root_dir', metavar='DIR', default='/apdcephfs/share_1290796/qingsongyao/SecureMedIA/dataset/aptos2019/',
help='path to dataset')
parser.add_argument('-n', '--run_name', default='vgg16')
parser.add_argument('-a', '--arch', metavar='ARCH', default='vgg16')
args = parser.parse_args()
saver = Saver(args.run_name)
src_model = Cls_Net(2, args.arch, load_pretrain=False).cuda()
src_model, ckpt = saver.load_model(src_model, args.run_name, get_ckpt=True)
src_model.eval()
test = Test_CLS(2, 'vgg16').cuda()
test.load_state_dict(ckpt)
test.eval()
test_input = torch.rand([2,3,299,299]).cuda()
print(test(test_input) == src_model(test_input))
x, features = test.model.feature_forward(test_input)
import ipdb; ipdb.set_trace()