Exemple #1
0
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
Exemple #2
0
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
Exemple #4
0
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
Exemple #5
0
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
Exemple #6
0
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
Exemple #7
0
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
Exemple #10
0
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