def resnet18_l05_w05(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [1, 1, 1, 1], scale_factor=2, **kwargs)
model.name = 'ResNet18(length=05 width=05)'
return model
def resnet36_l2_w2(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [4, 4, 4, 4], scale_factor=0.5, **kwargs)
model.name = 'ResNet18(length=2 width=2)'
return model
def resnet18_nr3_234(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], noskip_by_layer=[False, True, True, True], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18_NR3_234'
return model
def resnet152(pretrained=False, **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:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
model.name = 'ResNet152'
return model
def resnet18_thin(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [4, 4, 4, 4], scale_factor=2, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18_Thin'
return model
def resnet18_late(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [5, 1, 1, 1], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18_Late'
return model
def resnet18nodownsample(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], nodownsampling=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18NoDownsampling'
return model
def resnet18_dp00_dspl2(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [4, 4, None, None], disable_early_pooling=True, disable_early_downsampling=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18_dp00_DSPL2'
return model
def resnet18_ep0(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], disable_early_pooling=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18_EP0'
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
model.name = 'ResNet34'
return model
def resnet18noskip_dspl3(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
kwargs["noskip"] = True
model = ResNet(BasicBlock, [2, 3, 3, None], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18NoSkip_DSPL3'
return model
def resnet18noskip(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if "noskip" in kwargs:
kwargs.pop("noskip")
model = ResNet(BasicBlock, [2, 2, 2, 2], noskip=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
model.name = 'ResNet18NoSkip'
return model
def resnet50noskip(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if "noskip" in kwargs:
kwargs.pop("noskip")
model = ResNet(Bottleneck, [3, 4, 6, 3], noskip=True, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
model.name = 'ResNet50NoSkip'
return model
