imagenet/resnext/timm/models/resnet.py
def harm_se_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a HarmSeResNeXt-101 64x4d model.
    """
    default_cfg = default_cfgs['harm_se_resnext101_64x4d']
    model = ResNet(
        Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
        num_classes=num_classes, in_chans=in_chans, use_se=True,
        avg_down=True, zero_init_residual=True, harmonic=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model
Constructs a HarmSeResNeXt-101 64x4d model.
    
Constructs a HarmSeResNeXt-101 64x4d model.
Constructs a HarmSeResNeXt - 101 64x4d model .
def harm_se_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    default_cfg = default_cfgs['harm_se_resnext101_64x4d']
    model = ResNet(
        Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
        num_classes=num_classes, in_chans=in_chans, use_se=True,
        avg_down=True, zero_init_residual=True, harmonic=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model
def harm_se_resnext101_64x4d ( pretrained = False , num_classes = 1000 , in_chans = 3 , ** kwargs ) : default_cfg = default_cfgs [ 'harm_se_resnext101_64x4d' ] model = ResNet ( Bottleneck , [ 3 , 4 , 23 , 3 ] , cardinality = 64 , base_width = 4 , num_classes = num_classes , in_chans = in_chans , use_se = True , avg_down = True , zero_init_residual = True , harmonic = True , ** kwargs ) model . default_cfg = default_cfg if pretrained : load_pretrained ( model , default_cfg , num_classes , in_chans ) return model
Constructs a HarmSeResNeXt-101 64x4d model.
Constructs a HarmSeResNeXt - 101 64x4d model .
"""Constructs a HarmSeResNeXt-101 64x4d model.
    """
pretrained
num_classes
in_chans