def deepbase_resnet101(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def resnet34(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def resnet50(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type,
width_multiplier=kwargs["width_multiplier"])
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model