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@ -35,6 +35,16 @@ def _cfg(url='', **kwargs):
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default_cfgs = {
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# ResNet and Wide ResNet
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'resnet10t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet10t_176_c3-f3215ab1.pth',
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input_size=(3, 176, 176), pool_size=(6, 6),
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test_crop_pct=0.95, test_input_size=(3, 224, 224),
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first_conv='conv1.0'),
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'resnet14t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet14t_176_c3-c4ed2c37.pth',
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input_size=(3, 176, 176), pool_size=(6, 6),
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test_crop_pct=0.95, test_input_size=(3, 224, 224),
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first_conv='conv1.0'),
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'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
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'resnet18d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
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@ -262,6 +272,10 @@ default_cfgs = {
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'resnetblur101d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0'),
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'resnetaa50': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth',
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test_input_size=(3, 288, 288), test_crop_pct=1.0,
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interpolation='bicubic', first_conv='conv1.0'),
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'resnetaa50d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0'),
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@ -1454,6 +1468,14 @@ def resnetblur101d(pretrained=False, **kwargs):
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return _create_resnet('resnetblur101d', pretrained, **model_args)
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@register_model
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def resnetaa50(pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model with avgpool anti-aliasing
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, **kwargs)
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return _create_resnet('resnetaa50', pretrained, **model_args)
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@register_model
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def resnetaa50d(pretrained=False, **kwargs):
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"""Constructs a ResNet-50-D model with avgpool anti-aliasing
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