def resnet152(pretrained=True, root='~/.gluoncvth/models', **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: d = torch.load(get_model_file('resnet101', root=root)) d = warp_dict_fn(d) try: model.load_state_dict(d, strict=True) except Exception as e: print(e) print( "try load with strict = True failed , load with strict = False" ) model.load_state_dict(d, strict=False) return model
def resnet101(pretrained=True, root='~/.gluoncvth/models', **kwargs): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ se_kwargs = kwargs.pop('se_kwargs') block = partial(Bottleneck, se_kwargs=se_kwargs) block.expansion = Bottleneck.expansion model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) if pretrained: d = torch.load(get_model_file('resnet101', root=root)) d = warp_dict_fn(d) try: model.load_state_dict(d, strict=True) except Exception as e: print(e) print( "try load with strict = True failed , load with strict = False" ) model.load_state_dict(d, strict=False) return model