|
|
|
@ -217,7 +217,7 @@ default_cfgs = {
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
|
|
|
|
|
'ecaresnet269d': _cfg(
|
|
|
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(8, 8),
|
|
|
|
|
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(10, 10),
|
|
|
|
|
crop_pct=1.0, test_input_size=(3, 352, 352)),
|
|
|
|
|
|
|
|
|
|
# Efficient Channel Attention ResNeXts
|
|
|
|
@ -1029,14 +1029,6 @@ def swsl_resnext101_32x16d(pretrained=True, **kwargs):
|
|
|
|
|
return _create_resnet('swsl_resnext101_32x16d', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet18(pretrained=False, **kwargs):
|
|
|
|
|
""" Constructs an ECA-ResNet-18 model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet18', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet26t(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an ECA-ResNeXt-26-T model.
|
|
|
|
@ -1049,14 +1041,6 @@ def ecaresnet26t(pretrained=False, **kwargs):
|
|
|
|
|
return _create_resnet('ecaresnet26t', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet50(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an ECA-ResNet-50 model.
|
|
|
|
|
"""
|
|
|
|
|
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='eca'), **kwargs)
|
|
|
|
|
return _create_resnet('ecaresnet50', pretrained, **model_args)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@register_model
|
|
|
|
|
def ecaresnet50d(pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50-D model with eca.
|
|
|
|
|