def vgg19(pretrained=False, **kwargs):
"""VGG 19-layer model (configuration "E")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGG_FCN(make_layers(cfg['E']), **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['vgg19'])
model = model_helper.get_not_fc_para(model, pretrained_dict)
# model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet152'])
model = model_helper.get_not_fc_para(model, pretrained_dict)
# model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def vgg13_bn(pretrained=False, **kwargs):
"""VGG 13-layer model (configuration "B") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['vgg13_bn'])
model = model_helper.get_not_fc_para(model, pretrained_dict)
# model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))
return model
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet18'])
model = model_helper.get_not_fc_para(model, pretrained_dict)
# model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def inception_v3(pretrained=False, **kwargs):
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
if 'transform_input' not in kwargs:
kwargs['transform_input'] = True
model = Inception3(**kwargs)
pretrained_dict = model_zoo.load_url(model_urls['inception_v3_google'])
model = model_helper.get_not_fc_para(model, pretrained_dict)
# model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
return model
return Inception3(**kwargs)
