Add 320x320 model default cfgs for 101D and 152D ResNets. Add SEResNet-152D weights and 320x320 cfg.

pull/352/head
Ross Wightman 4 years ago
parent 0167f749d3
commit 4e2533db77

@ -59,10 +59,16 @@ default_cfgs = {
'resnet101d': _cfg( 'resnet101d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'resnet101d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
'resnet152': _cfg(url='', interpolation='bicubic'), 'resnet152': _cfg(url='', interpolation='bicubic'),
'resnet152d': _cfg( 'resnet152d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'resnet152d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
'resnet200': _cfg(url='', interpolation='bicubic'), 'resnet200': _cfg(url='', interpolation='bicubic'),
'resnet200d': _cfg( 'resnet200d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
@ -151,8 +157,12 @@ default_cfgs = {
url='', url='',
interpolation='bicubic'), interpolation='bicubic'),
'seresnet152d': _cfg( 'seresnet152d': _cfg(
url='', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'seresnet152d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py # Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
'seresnext26_32x4d': _cfg( 'seresnext26_32x4d': _cfg(
@ -710,6 +720,14 @@ def resnet101d(pretrained=False, **kwargs):
return _create_resnet('resnet101d', pretrained, **model_args) return _create_resnet('resnet101d', pretrained, **model_args)
@register_model
def resnet101d_320(pretrained=False, **kwargs):
"""Constructs a ResNet-101-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet101d_320', pretrained, **model_args)
@register_model @register_model
def resnet152(pretrained=False, **kwargs): def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model. """Constructs a ResNet-152 model.
@ -727,6 +745,15 @@ def resnet152d(pretrained=False, **kwargs):
return _create_resnet('resnet152d', pretrained, **model_args) return _create_resnet('resnet152d', pretrained, **model_args)
@register_model
def resnet152d_320(pretrained=False, **kwargs):
"""Constructs a ResNet-152-D model.
"""
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet152d_320', pretrained, **model_args)
@register_model @register_model
def resnet200(pretrained=False, **kwargs): def resnet200(pretrained=False, **kwargs):
"""Constructs a ResNet-200 model. """Constructs a ResNet-200 model.
@ -1171,6 +1198,14 @@ def seresnet152d(pretrained=False, **kwargs):
return _create_resnet('seresnet152d', pretrained, **model_args) return _create_resnet('seresnet152d', pretrained, **model_args)
@register_model
def seresnet152d_320(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnet152d_320', pretrained, **model_args)
@register_model @register_model
def seresnext26_32x4d(pretrained=False, **kwargs): def seresnext26_32x4d(pretrained=False, **kwargs):
model_args = dict( model_args = dict(

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