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Update attention / self-attn based models from a series of experiments: 

* remove dud attention, involution + my swin attention adaptation don't seem worth keeping
* add or update several new 26/50 layer ResNe(X)t variants that were used in experiments
* remove models associated with dead-end or uninteresting experiment results
* weights coming soon...
pull/821/head
Ross Wightman 2021-08-20 16:13:11 -07:00
parent acd6c687fd
commit 925e102982
6 changed files with 166 additions and 486 deletions
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195
timm/models/byoanet.py
View File

@ -38,21 +38,11 @@ default_cfgs = {
'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='', input_size=(3, 256, 256), pool_size=(8, 8)),
'rednet50ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
}
@ -121,20 +111,6 @@ model_cfgs = dict(
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=3),
),
halonet_h1_c4c5=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=0, br=1.0),
ByoBlockCfg(type='bottle', d=3, c=128, s=2, gs=0, br=1.0),
ByoBlockCfg(type='self_attn', d=10, c=256, s=2, gs=0, br=1.0),
ByoBlockCfg(type='self_attn', d=3, c=512, s=2, gs=0, br=1.0),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
self_attn_layer='halo',
self_attn_kwargs=dict(block_size=8, halo_size=3),
),
halonet26t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
@ -193,117 +169,7 @@ model_cfgs = dict(
stem_pool='maxpool',
num_features=0,
self_attn_layer='lambda',
self_attn_kwargs=dict()
),
lambda_resnet50t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=3, d=6, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
self_attn_layer='lambda',
self_attn_kwargs=dict()
),
eca_lambda_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoBlockCfg(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=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
fixed_input_size=True,
self_attn_layer='swin',
self_attn_kwargs=dict(win_size=8)
),
swinnet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=4, c=512, s=2, gs=0, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
fixed_input_size=True,
act_layer='silu',
self_attn_layer='swin',
self_attn_kwargs=dict(win_size=8)
),
eca_swinnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=16, br=0.25),
interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
ByoBlockCfg(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,
fixed_input_size=True,
act_layer='silu',
attn_layer='eca',
self_attn_layer='swin',
self_attn_kwargs=dict(win_size=8)
),
rednet26t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='self_attn', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered', # FIXME RedNet uses involution in middle of stem
stem_pool='maxpool',
num_features=0,
self_attn_layer='involution',
self_attn_kwargs=dict()
),
rednet50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='self_attn', d=3, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=4, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=2, c=1024, s=2, gs=0, br=0.25),
ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
self_attn_layer='involution',
self_attn_kwargs=dict()
self_attn_kwargs=dict(r=9)
),
)
@ -350,13 +216,6 @@ def halonet_h1(pretrained=False, **kwargs):
return _create_byoanet('halonet_h1', pretrained=pretrained, **kwargs)
@register_model
def halonet_h1_c4c5(pretrained=False, **kwargs):
""" HaloNet-H1 config w/ attention in last two stages.
"""
return _create_byoanet('halonet_h1_c4c5', pretrained=pretrained, **kwargs)
@register_model
def halonet26t(pretrained=False, **kwargs):
""" HaloNet w/ a ResNet26-t backbone, Hallo attention in final stage
@ -383,55 +242,3 @@ def lambda_resnet26t(pretrained=False, **kwargs):
""" Lambda-ResNet-26T. Lambda layers in one C4 stage and all C5.
"""
return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)
@register_model
def lambda_resnet50t(pretrained=False, **kwargs):
""" Lambda-ResNet-50T. Lambda layers in one C4 stage and all C5.
"""
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):
"""
"""
kwargs.setdefault('img_size', 256)
return _create_byoanet('swinnet26t_256', 'swinnet26t', pretrained=pretrained, **kwargs)
@register_model
def swinnet50ts_256(pretrained=False, **kwargs):
"""
"""
kwargs.setdefault('img_size', 256)
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):
"""
"""
return _create_byoanet('rednet26t', pretrained=pretrained, **kwargs)
@register_model
def rednet50ts(pretrained=False, **kwargs):
"""
"""
return _create_byoanet('rednet50ts', pretrained=pretrained, **kwargs)

216
timm/models/byobnet.py
View File

@ -94,18 +94,29 @@ default_cfgs = {
test_input_size=(3, 288, 288), crop_pct=1.0),
'resnet61q': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'geresnet50t': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet50t': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet26ts': _cfg(
'seresnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'eca_resnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'bat_resnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
min_input_size=(3, 256, 256)),
'gcresnet26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'seresnet26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'eac_resnet26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet50t': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnext50ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
}
@ -298,54 +309,52 @@ model_cfgs = dict(
stem_pool=None,
attn_layer='ge',
attn_kwargs=dict(extent=8, extra_params=True),
#attn_kwargs=dict(extent=8),
#block_kwargs=dict(attn_last=True)
),
# WARN: experimental, may vanish/change
gcresnet50t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool=None,
attn_layer='gc'
),
# A series of ResNeXt-26 models w/ one of GC, SE, ECA, BAT attn, group size 32, SiLU act,
# and a tiered stem w/ maxpool
gcresnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='gc',
attn_layer='gca',
),
gcresnet26ts=ByoModelCfg(
seresnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='gc',
stem_pool='maxpool',
num_features=0,
act_layer='relu',
attn_layer='se',
),
eca_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='eca',
),
bat_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
@ -361,6 +370,79 @@ model_cfgs = dict(
attn_layer='bat',
attn_kwargs=dict(block_size=8)
),
# A series of ResNet-26 models w/ one of GC, SE, ECA attn, no groups, SiLU act, 1280 feat fc
# and a tiered stem w/ no maxpool
gcresnet26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='gca',
),
seresnet26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='se',
),
eca_resnet26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='',
num_features=1280,
act_layer='silu',
attn_layer='eca',
),
gcresnet50t=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool=None,
attn_layer='gca',
),
gcresnext50ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
# stem_pool=None,
act_layer='silu',
attn_layer='gca',
),
)
@ -466,20 +548,6 @@ def resnet61q(pretrained=False, **kwargs):
return _create_byobnet('resnet61q', pretrained=pretrained, **kwargs)
@register_model
def geresnet50t(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('geresnet50t', pretrained=pretrained, **kwargs)
@register_model
def gcresnet50t(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)
@register_model
def gcresnext26ts(pretrained=False, **kwargs):
"""
@ -487,6 +555,27 @@ def gcresnext26ts(pretrained=False, **kwargs):
return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs)
@register_model
def seresnext26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('seresnext26ts', pretrained=pretrained, **kwargs)
@register_model
def eca_resnext26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('eca_resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def bat_resnext26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnet26ts(pretrained=False, **kwargs):
"""
@ -495,10 +584,31 @@ def gcresnet26ts(pretrained=False, **kwargs):
@register_model
def bat_resnext26ts(pretrained=False, **kwargs):
def seresnet26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)
return _create_byobnet('seresnet26ts', pretrained=pretrained, **kwargs)
@register_model
def eca_resnet26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('eca_resnet26ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnet50t(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)
@register_model
def gcresnext50ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:

1
timm/models/layers/__init__.py
View File

@ -19,7 +19,6 @@ from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible
from .inplace_abn import InplaceAbn
from .involution import Involution
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp

8
timm/models/layers/create_attn.py
View File

@ -11,13 +11,11 @@ from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .involution import Involution
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
from .swin_attn import WindowAttention
def get_attn(attn_type):
@ -43,6 +41,8 @@ def get_attn(attn_type):
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
@ -65,10 +65,6 @@ def get_attn(attn_type):
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'swin':
return WindowAttention
elif attn_type == 'involution':
return Involution
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':

50
timm/models/layers/involution.py
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@ -1,50 +0,0 @@
""" PyTorch Involution Layer
Official impl: https://github.com/d-li14/involution/blob/main/cls/mmcls/models/utils/involution_naive.py
Paper: `Involution: Inverting the Inherence of Convolution for Visual Recognition` - https://arxiv.org/abs/2103.06255
"""
import torch.nn as nn
from .conv_bn_act import ConvBnAct
from .create_conv2d import create_conv2d
class Involution(nn.Module):
def __init__(
self,
channels,
kernel_size=3,
stride=1,
group_size=16,
rd_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(Involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.channels = channels
self.group_size = group_size
self.groups = self.channels // self.group_size
self.conv1 = ConvBnAct(
in_channels=channels,
out_channels=channels // rd_ratio,
kernel_size=1,
norm_layer=norm_layer,
act_layer=act_layer)
self.conv2 = self.conv = create_conv2d(
in_channels=channels // rd_ratio,
out_channels=kernel_size**2 * self.groups,
kernel_size=1,
stride=1)
self.avgpool = nn.AvgPool2d(stride, stride) if stride == 2 else nn.Identity()
self.unfold = nn.Unfold(kernel_size, 1, (kernel_size-1)//2, stride)
def forward(self, x):
weight = self.conv2(self.conv1(self.avgpool(x)))
B, C, H, W = weight.shape
KK = int(self.kernel_size ** 2)
weight = weight.view(B, self.groups, KK, H, W).unsqueeze(2)
out = self.unfold(x).view(B, self.groups, self.group_size, KK, H, W)
out = (weight * out).sum(dim=3).view(B, self.channels, H, W)
return out

182
timm/models/layers/swin_attn.py
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@ -1,182 +0,0 @@
""" Shifted Window Attn
This is a WIP experiment to apply windowed attention from the Swin Transformer
to a stand-alone module for use as an attn block in conv nets.
Based on original swin window code at https://github.com/microsoft/Swin-Transformer
Swin Transformer paper: https://arxiv.org/pdf/2103.14030.pdf
"""
from typing import Optional
import torch
import torch.nn as nn
from .drop import DropPath
from .helpers import to_2tuple
from .weight_init import trunc_normal_
def window_partition(x, win_size: int):
"""
Args:
x: (B, H, W, C)
win_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // win_size, win_size, W // win_size, win_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_size, win_size, C)
return windows
def window_reverse(windows, win_size: int, H: int, W: int):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
win_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / win_size / win_size))
x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
win_size (int): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, win_size=8, shift_size=None, num_heads=8,
qkv_bias=True, attn_drop=0.):
super().__init__()
self.dim_out = dim_out or dim
self.feat_size = to_2tuple(feat_size)
self.win_size = win_size
self.shift_size = shift_size or win_size // 2
if min(self.feat_size) <= win_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.win_size = min(self.feat_size)
assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-window_size"
self.num_heads = num_heads
head_dim = self.dim_out // num_heads
self.scale = head_dim ** -0.5
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.feat_size
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (
slice(0, -self.win_size),
slice(-self.win_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (
slice(0, -self.win_size),
slice(-self.win_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.win_size) # num_win, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.win_size * self.win_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
# 2 * Wh - 1 * 2 * Ww - 1, nH
torch.zeros((2 * self.win_size - 1) * (2 * self.win_size - 1), num_heads))
trunc_normal_(self.relative_position_bias_table, std=.02)
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.win_size)
coords_w = torch.arange(self.win_size)
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.win_size - 1 # shift to start from 0
relative_coords[:, :, 1] += self.win_size - 1
relative_coords[:, :, 0] *= 2 * self.win_size - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, self.dim_out * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.softmax = nn.Softmax(dim=-1)
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
def reset_parameters(self):
trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5)
trunc_normal_(self.relative_position_bias_table, std=.02)
def forward(self, x):
B, C, H, W = x.shape
x = x.permute(0, 2, 3, 1)
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
win_size_sq = self.win_size * self.win_size
x_windows = window_partition(shifted_x, self.win_size) # num_win * B, window_size, window_size, C
x_windows = x_windows.view(-1, win_size_sq, C) # num_win * B, window_size*window_size, C
BW, N, _ = x_windows.shape
qkv = self.qkv(x_windows)
qkv = qkv.reshape(BW, N, 3, self.num_heads, self.dim_out // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)].view(win_size_sq, win_size_sq, -1)
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh * Ww, Wh * Ww
attn = attn + relative_position_bias.unsqueeze(0)
if self.attn_mask is not None:
num_win = self.attn_mask.shape[0]
attn = attn.view(B, num_win, self.num_heads, N, N) + self.attn_mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(BW, N, self.dim_out)
# merge windows
x = x.view(-1, self.win_size, self.win_size, self.dim_out)
shifted_x = window_reverse(x, self.win_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
x = x.view(B, H, W, self.dim_out).permute(0, 3, 1, 2)
x = self.pool(x)
return x