diff --git a/timm/models/byoanet.py b/timm/models/byoanet.py index 73c6811b..b11e7d52 100644 --- a/timm/models/byoanet.py +++ b/timm/models/byoanet.py @@ -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) diff --git a/timm/models/byobnet.py b/timm/models/byobnet.py index 4c891ea5..af790584 100644 --- a/timm/models/byobnet.py +++ b/timm/models/byobnet.py @@ -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'), } @@ -298,39 +309,70 @@ 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( + # 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, 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), + 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=None, - attn_layer='gc' + stem_pool='maxpool', + num_features=0, + act_layer='silu', + attn_layer='gca', ), - - gcresnext26ts=ByoModelCfg( + seresnext26ts=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='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), + 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='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), @@ -343,23 +385,63 @@ model_cfgs = dict( stem_pool='', num_features=1280, act_layer='silu', - attn_layer='gc', + 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', ), - bat_resnext26ts=ByoModelCfg( + gcresnet50t=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), + 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', - num_features=0, + # stem_pool=None, act_layer='silu', - attn_layer='bat', - attn_kwargs=dict(block_size=8) + attn_layer='gca', ), ) @@ -467,24 +549,31 @@ def resnet61q(pretrained=False, **kwargs): @register_model -def geresnet50t(pretrained=False, **kwargs): +def gcresnext26ts(pretrained=False, **kwargs): """ """ - return _create_byobnet('geresnet50t', pretrained=pretrained, **kwargs) + return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs) @register_model -def gcresnet50t(pretrained=False, **kwargs): +def seresnext26ts(pretrained=False, **kwargs): """ """ - return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs) + return _create_byobnet('seresnext26ts', pretrained=pretrained, **kwargs) @register_model -def gcresnext26ts(pretrained=False, **kwargs): +def eca_resnext26ts(pretrained=False, **kwargs): """ """ - return _create_byobnet('gcresnext26ts', pretrained=pretrained, **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 @@ -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]: diff --git a/timm/models/layers/__init__.py b/timm/models/layers/__init__.py index 77d1026e..e9a5f18f 100644 --- a/timm/models/layers/__init__.py +++ b/timm/models/layers/__init__.py @@ -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 diff --git a/timm/models/layers/create_attn.py b/timm/models/layers/create_attn.py index 3fed646b..028c0f75 100644 --- a/timm/models/layers/create_attn.py +++ b/timm/models/layers/create_attn.py @@ -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': diff --git a/timm/models/layers/involution.py b/timm/models/layers/involution.py deleted file mode 100644 index ccdeefcb..00000000 --- a/timm/models/layers/involution.py +++ /dev/null @@ -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 diff --git a/timm/models/layers/swin_attn.py b/timm/models/layers/swin_attn.py deleted file mode 100644 index 02131bbc..00000000 --- a/timm/models/layers/swin_attn.py +++ /dev/null @@ -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 - -