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):
# def resnet50(num_classes=1000, pretrained=None, norm_type='encsync_batchnorm', **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
# """
# print("entered")
# input()
model = ResNet(Bottleneck, [3, 4, 6, 3],
num_classes=num_classes,
deep_base=False,
norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model