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Another set of byoanet models w/ ECA channel + SA + groups

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pull/637/head
Ross Wightman 4 years ago
parent d53e91218e
commit 9a3ae97311
2 changed files with 102 additions and 1 deletions
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101
timm/models/byoanet.py
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@ -47,17 +47,21 @@ default_cfgs = {
# GPU-Efficient (ResNet) weights
'botnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'botnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'eca_botnext26ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'halonet_h1': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'halonet_h1_c4c5': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'halonet26t': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'halonet50ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'eca_halonext26ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'lambda_resnet26t': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
'lambda_resnet50t': _cfg(url='', min_input_size=(3, 128, 128)),
'eca_lambda_resnext26ts': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
'swinnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'swinnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'eca_swinnext26ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'rednet26t': _cfg(url='', fixed_input_size=False, input_size=(3, 256, 256), pool_size=(8, 8)),
'rednet50ts': _cfg(url='', fixed_input_size=False, input_size=(3, 256, 256), pool_size=(8, 8)),
@ -129,6 +133,23 @@ model_cfgs = dict(
self_attn_fixed_size=True,
self_attn_kwargs=dict()
),
eca_botnext26ts=ByoaCfg(
blocks=(
ByoaBlocksCfg(type='bottle', d=3, c=256, s=1, gs=16, br=0.25),
ByoaBlocksCfg(type='bottle', d=4, c=512, s=2, gs=16, br=0.25),
interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoaBlocksCfg(type='self_attn', d=3, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='eca',
self_attn_layer='bottleneck',
self_attn_fixed_size=True,
self_attn_kwargs=dict()
),
halonet_h1=ByoaCfg(
blocks=(
@ -187,6 +208,22 @@ model_cfgs = dict(
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=2)
),
eca_halonext26ts=ByoaCfg(
blocks=(
ByoaBlocksCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
ByoaBlocksCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoaBlocksCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='eca',
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=2) # intended for 256x256 res
),
lambda_resnet26t=ByoaCfg(
blocks=(
@ -216,6 +253,22 @@ model_cfgs = dict(
self_attn_layer='lambda',
self_attn_kwargs=dict()
),
eca_lambda_resnext26ts=ByoaCfg(
blocks=(
ByoaBlocksCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
ByoaBlocksCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoaBlocksCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='eca',
self_attn_layer='lambda',
self_attn_kwargs=dict()
),
swinnet26t=ByoaCfg(
blocks=(
@ -248,6 +301,24 @@ model_cfgs = dict(
self_attn_fixed_size=True,
self_attn_kwargs=dict(win_size=8)
),
eca_swinnext26ts=ByoaCfg(
blocks=(
ByoaBlocksCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=16, br=0.25),
interleave_attn(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoaBlocksCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='eca',
self_attn_layer='swin',
self_attn_fixed_size=True,
self_attn_kwargs=dict(win_size=8)
),
rednet26t=ByoaCfg(
blocks=(
@ -454,6 +525,14 @@ def botnet50ts_256(pretrained=False, **kwargs):
return _create_byoanet('botnet50ts_256', 'botnet50ts', pretrained=pretrained, **kwargs)
@register_model
def eca_botnext26ts_256(pretrained=False, **kwargs):
""" Bottleneck Transformer w/ ResNet26-T backbone. Bottleneck attn in final stage.
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('eca_botnext26ts_256', 'eca_botnext26ts', pretrained=pretrained, **kwargs)
@register_model
def halonet_h1(pretrained=False, **kwargs):
""" HaloNet-H1. Halo attention in all stages as per the paper.
@ -484,6 +563,13 @@ def halonet50ts(pretrained=False, **kwargs):
return _create_byoanet('halonet50ts', pretrained=pretrained, **kwargs)
@register_model
def eca_halonext26ts(pretrained=False, **kwargs):
""" HaloNet w/ a ResNet26-t backbone, Hallo attention in final stage
"""
return _create_byoanet('eca_halonext26ts', pretrained=pretrained, **kwargs)
@register_model
def lambda_resnet26t(pretrained=False, **kwargs):
""" Lambda-ResNet-26T. Lambda layers in one C4 stage and all C5.
@ -498,6 +584,13 @@ def lambda_resnet50t(pretrained=False, **kwargs):
return _create_byoanet('lambda_resnet50t', pretrained=pretrained, **kwargs)
@register_model
def eca_lambda_resnext26ts(pretrained=False, **kwargs):
""" Lambda-ResNet-26T. Lambda layers in one C4 stage and all C5.
"""
return _create_byoanet('eca_lambda_resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def swinnet26t_256(pretrained=False, **kwargs):
"""
@ -514,6 +607,14 @@ def swinnet50ts_256(pretrained=False, **kwargs):
return _create_byoanet('swinnet50ts_256', 'swinnet50ts', pretrained=pretrained, **kwargs)
@register_model
def eca_swinnext26ts_256(pretrained=False, **kwargs):
"""
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('eca_swinnext26ts_256', 'eca_swinnext26ts', pretrained=pretrained, **kwargs)
@register_model
def rednet26t(pretrained=False, **kwargs):
"""

2
timm/models/byobnet.py
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@ -98,7 +98,7 @@ class BlocksCfg:
s: int = 2 # stride of stage (first block)
gs: Optional[Union[int, Callable]] = None # group-size of blocks in stage, conv is depthwise if gs == 1
br: float = 1. # bottleneck-ratio of blocks in stage
no_attn: bool = True # disable channel attn (ie SE) when layer is set for model
no_attn: bool = False # disable channel attn (ie SE) when layer is set for model
@dataclass

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