def vgg16_bn(pretrained=True, **kwargs):
kwargs['lconf'] = [2, 2, 3, 3, 3]
return get_net(VGG,
pretrained=pretrained,
pretrain_url=urls.vgg16_bn,
fname='vgg16_bn',
kwargs_net=kwargs,
attr='classifier',
inn=25088)
def vgg19_bn(pretrained=False, **kwargs):
if pretrained: raise Exception('No pretrained models avaialble!')
kwargs['lconf'] = [2, 2, 4, 4, 4]
return get_net(VGG,
pretrained=pretrained,
pretrain_url=None,
fname='vgg19_bn',
kwargs_net=kwargs,
attr='classifier',
inn=25088)
def vgg19(pretrained=True, **kwargs):
kwargs['lconf'] = [2, 2, 4, 4, 4]
kwargs['norm'] = False
return get_net(VGG,
pretrained=pretrained,
pretrain_url=urls.vgg19,
fname='vgg19',
kwargs_net=kwargs,
attr='classifier',
inn=25088)
def googlenet_paper(pretrained=False, **kwargs):
"""
GoogLeNet Model as given in the official Paper.
"""
kwargs['aux'] = True if 'aux' not in kwargs else kwargs['aux']
kwargs['replace5x5with3x3'] = False if 'replace5x5with3x3' not in kwargs \
else kwargs['replace5x5with3x3']
return get_net(GoogLeNet, pretrained=pretrained, pretrain_url=None,
fname='googlenet', kwargs_net=kwargs, attr='classifier',
inn=1024)
def googlenet(pretrained=True, **kwargs):
"""
GoogLeNet Model with weights as given by the officials who
trained it on TensorFlow.
"""
kwargs['aux'] = False if 'aux' not in kwargs else kwargs['aux']
kwargs['replace5x5with3x3'] = True if 'replace5x5with3x3' not in kwargs \
else kwargs['replace5x5with3x3']
return get_net(GoogLeNet, pretrained=pretrained, fname='googlenet',
kwargs_net=kwargs, attr='classifier', inn=1024,
pretrain_url=urls.googlenet_url)