def resnet18_llsq(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_name = sys._getframe().f_code.co_name
quan_type = model_name.split('_')[-1]
quan_factory = my_nn.QuantizationFactory(quan_type, **kwargs)
model = _ResNetQ(_BasicBlockQ, [2, 2, 2, 2], qf=quan_factory)
if pretrained:
load_pre_state_dict(model, model_zoo.load_url(model_urls['resnet18']),
'{}_map.json'.format(model_name))
return model
def cifar10_vggsmall_llsq(pretrained=False, **kwargs):
"""VGG small model (configuration "A")
Please use [dataset]_[architecture]_[quan_type] as the function name
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model_name = sys._getframe().f_code.co_name
quan_type = model_name.split('_')[-1]
quan_factory = my_nn.QuantizationFactory(quan_type, **kwargs)
model = _VGGQ('VGG7', quan_factory)
if pretrained:
load_pre_state_dict(model, model_zoo.load_url(model_urls['vgg7']),
'{}_map.json'.format(model_name))
return model