@ -148,6 +148,49 @@ default_cfgs = {
' swsl_resnext101_32x16d ' : _cfg (
url = ' https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth ' ) ,
# Efficient Channel Attention ResNets
' ecaresnet26t ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' , input_size = ( 3 , 256 , 256 ) , pool_size = ( 8 , 8 ) ,
crop_pct = 0.95 , test_input_size = ( 3 , 320 , 320 ) ) ,
' ecaresnetlight ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth ' ,
interpolation = ' bicubic ' ) ,
' ecaresnet50d ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet50d_pruned ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet50t ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' , input_size = ( 3 , 256 , 256 ) , pool_size = ( 8 , 8 ) ,
crop_pct = 0.95 , test_input_size = ( 3 , 320 , 320 ) ) ,
' ecaresnet101d ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
' ecaresnet101d_pruned ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet200d ' : _cfg (
url = ' ' ,
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 = ( 10 , 10 ) ,
crop_pct = 1.0 , test_input_size = ( 3 , 352 , 352 ) ) ,
# Efficient Channel Attention ResNeXts
' ecaresnext26t_32x4d ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
' ecaresnext50t_32x4d ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
# Squeeze-Excitation ResNets, to eventually replace the models in senet.py
' seresnet18 ' : _cfg (
url = ' ' ,
@ -180,7 +223,6 @@ default_cfgs = {
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' , input_size = ( 3 , 256 , 256 ) , crop_pct = 0.94 , pool_size = ( 8 , 8 ) ) ,
# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
' seresnext26d_32x4d ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth ' ,
@ -199,55 +241,16 @@ default_cfgs = {
' seresnext101_32x8d ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101_32x8d_ah-e6bc4c0a.pth ' ,
interpolation = ' bicubic ' , test_input_size = ( 3 , 288 , 288 ) , crop_pct = 1.0 ) ,
' seresnext101d_32x8d ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101d_32x8d_ah-191d7b94.pth ' ,
interpolation = ' bicubic ' , test_input_size = ( 3 , 288 , 288 ) , crop_pct = 1.0 ) ,
' senet154 ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
# Efficient Channel Attention ResNets
' ecaresnet26t ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' , input_size = ( 3 , 256 , 256 ) , pool_size = ( 8 , 8 ) ,
crop_pct = 0.95 , test_input_size = ( 3 , 320 , 320 ) ) ,
' ecaresnetlight ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth ' ,
interpolation = ' bicubic ' ) ,
' ecaresnet50d ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet50d_pruned ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet50t ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' , input_size = ( 3 , 256 , 256 ) , pool_size = ( 8 , 8 ) ,
crop_pct = 0.95 , test_input_size = ( 3 , 320 , 320 ) ) ,
' ecaresnet101d ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
' ecaresnet101d_pruned ' : _cfg (
url = ' https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth ' ,
interpolation = ' bicubic ' ,
first_conv = ' conv1.0 ' ) ,
' ecaresnet200d ' : _cfg (
url = ' ' ,
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 = ( 10 , 10 ) ,
crop_pct = 1.0 , test_input_size = ( 3 , 352 , 352 ) ) ,
# Efficient Channel Attention ResNeXts
' ecaresnext26t_32x4d ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
' ecaresnext50t_32x4d ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
# ResNets with anti-aliasing blur pool
# ResNets with anti-aliasing / blur pool
' resnetblur18 ' : _cfg (
interpolation = ' bicubic ' ) ,
' resnetblur50 ' : _cfg (
@ -268,6 +271,9 @@ default_cfgs = {
' seresnetaa50d ' : _cfg (
url = ' ' ,
interpolation = ' bicubic ' , first_conv = ' conv1.0 ' ) ,
' seresnextaa101d_32x8d ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnextaa101d_32x8d_ah-83c8ae12.pth ' ,
interpolation = ' bicubic ' , test_input_size = ( 3 , 288 , 288 ) , crop_pct = 1.0 ) ,
# ResNet-RS models
' resnetrs50 ' : _cfg (
@ -1157,98 +1163,6 @@ def ecaresnet50d(pretrained=False, **kwargs):
return _create_resnet ( ' ecaresnet50d ' , pretrained , * * model_args )
@register_model
def resnetrs50 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-50 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs50 ' , pretrained , * * model_args )
@register_model
def resnetrs101 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-101 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs101 ' , pretrained , * * model_args )
@register_model
def resnetrs152 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-152 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 8 , 36 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs152 ' , pretrained , * * model_args )
@register_model
def resnetrs200 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-200 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 24 , 36 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs200 ' , pretrained , * * model_args )
@register_model
def resnetrs270 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-270 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 29 , 53 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs270 ' , pretrained , * * model_args )
@register_model
def resnetrs350 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-350 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 36 , 72 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs350 ' , pretrained , * * model_args )
@register_model
def resnetrs420 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-420 model
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 44 , 87 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs420 ' , pretrained , * * model_args )
@register_model
def ecaresnet50d_pruned ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50-D model pruned with eca.
@ -1346,72 +1260,6 @@ def ecaresnext50t_32x4d(pretrained=False, **kwargs):
return _create_resnet ( ' ecaresnext50t_32x4d ' , pretrained , * * model_args )
@register_model
def resnetblur18 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-18 model with blur anti-aliasing
"""
model_args = dict ( block = BasicBlock , layers = [ 2 , 2 , 2 , 2 ] , aa_layer = BlurPool2d , * * kwargs )
return _create_resnet ( ' resnetblur18 ' , pretrained , * * model_args )
@register_model
def resnetblur50 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50 model with blur anti-aliasing
"""
model_args = dict ( block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = BlurPool2d , * * kwargs )
return _create_resnet ( ' resnetblur50 ' , pretrained , * * model_args )
@register_model
def resnetblur50d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50-D model with blur anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = BlurPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetblur50d ' , pretrained , * * model_args )
@register_model
def resnetblur101d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-101-D model with blur anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , aa_layer = BlurPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetblur101d ' , pretrained , * * model_args )
@register_model
def resnetaa50d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetaa50d ' , pretrained , * * model_args )
@register_model
def resnetaa101d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-101-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetaa101d ' , pretrained , * * model_args )
@register_model
def seresnetaa50d ( pretrained = False , * * kwargs ) :
""" Constructs a SE=ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
return _create_resnet ( ' seresnetaa50d ' , pretrained , * * model_args )
@register_model
def seresnet18 ( pretrained = False , * * kwargs ) :
model_args = dict ( block = BasicBlock , layers = [ 2 , 2 , 2 , 2 ] , block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
@ -1535,9 +1383,187 @@ def seresnext101_32x8d(pretrained=False, **kwargs):
return _create_resnet ( ' seresnext101_32x8d ' , pretrained , * * model_args )
@register_model
def seresnext101d_32x8d ( pretrained = False , * * kwargs ) :
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , cardinality = 32 , base_width = 8 ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True ,
block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
return _create_resnet ( ' seresnext101d_32x8d ' , pretrained , * * model_args )
@register_model
def senet154 ( pretrained = False , * * kwargs ) :
model_args = dict (
block = Bottleneck , layers = [ 3 , 8 , 36 , 3 ] , cardinality = 64 , base_width = 4 , stem_type = ' deep ' ,
down_kernel_size = 3 , block_reduce_first = 2 , block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
return _create_resnet ( ' senet154 ' , pretrained , * * model_args )
@register_model
def resnetblur18 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-18 model with blur anti-aliasing
"""
model_args = dict ( block = BasicBlock , layers = [ 2 , 2 , 2 , 2 ] , aa_layer = BlurPool2d , * * kwargs )
return _create_resnet ( ' resnetblur18 ' , pretrained , * * model_args )
@register_model
def resnetblur50 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50 model with blur anti-aliasing
"""
model_args = dict ( block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = BlurPool2d , * * kwargs )
return _create_resnet ( ' resnetblur50 ' , pretrained , * * model_args )
@register_model
def resnetblur50d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50-D model with blur anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = BlurPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetblur50d ' , pretrained , * * model_args )
@register_model
def resnetblur101d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-101-D model with blur anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , aa_layer = BlurPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetblur101d ' , pretrained , * * model_args )
@register_model
def resnetaa50d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetaa50d ' , pretrained , * * model_args )
@register_model
def resnetaa101d ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-101-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , * * kwargs )
return _create_resnet ( ' resnetaa101d ' , pretrained , * * model_args )
@register_model
def seresnetaa50d ( pretrained = False , * * kwargs ) :
""" Constructs a SE=ResNet-50-D model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , aa_layer = nn . AvgPool2d ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
return _create_resnet ( ' seresnetaa50d ' , pretrained , * * model_args )
@register_model
def seresnextaa101d_32x8d ( pretrained = False , * * kwargs ) :
""" Constructs a SE=ResNeXt-101-D 32x8d model with avgpool anti-aliasing
"""
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , cardinality = 32 , base_width = 8 ,
stem_width = 32 , stem_type = ' deep ' , avg_down = True , aa_layer = nn . AvgPool2d ,
block_args = dict ( attn_layer = ' se ' ) , * * kwargs )
return _create_resnet ( ' seresnextaa101d_32x8d ' , pretrained , * * model_args )
@register_model
def resnetrs50 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-50 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 6 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs50 ' , pretrained , * * model_args )
@register_model
def resnetrs101 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-101 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 4 , 23 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs101 ' , pretrained , * * model_args )
@register_model
def resnetrs152 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-152 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 8 , 36 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs152 ' , pretrained , * * model_args )
@register_model
def resnetrs200 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-200 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 3 , 24 , 36 , 3 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs200 ' , pretrained , * * model_args )
@register_model
def resnetrs270 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-270 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 29 , 53 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs270 ' , pretrained , * * model_args )
@register_model
def resnetrs350 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-350 model.
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 36 , 72 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs350 ' , pretrained , * * model_args )
@register_model
def resnetrs420 ( pretrained = False , * * kwargs ) :
""" Constructs a ResNet-RS-420 model
Paper : Revisiting ResNets - https : / / arxiv . org / abs / 2103.07579
Pretrained weights from https : / / github . com / tensorflow / tpu / tree / bee9c4f6 / models / official / resnet / resnet_rs
"""
attn_layer = partial ( get_attn ( ' se ' ) , rd_ratio = 0.25 )
model_args = dict (
block = Bottleneck , layers = [ 4 , 44 , 87 , 4 ] , stem_width = 32 , stem_type = ' deep ' , replace_stem_pool = True ,
avg_down = True , block_args = dict ( attn_layer = attn_layer ) , * * kwargs )
return _create_resnet ( ' resnetrs420 ' , pretrained , * * model_args )
Ross Wightman
2 years ago