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Adding the attn series weights, tweaking model names, comments...

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pull/821/head
Ross Wightman 3 years ago
parent 0639d9a591
commit 484e61648d
2 changed files with 78 additions and 25 deletions
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20
timm/models/byoanet.py
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@ -33,16 +33,26 @@ def _cfg(url='', **kwargs):
default_cfgs = {
# GPU-Efficient (ResNet) weights
'botnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
'botnet26t_256': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/botnet26t_256-a0e6c3b1.pth',
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)),
'eca_botnext26ts_256': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_botnext26ts_256-fb3bf984.pth',
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)),
'halonet26t': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
'halonet26t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet26t_256-9b4bf0b3.pth',
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)),
'eca_halonext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_halonext26ts_256-1e55880b.pth',
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_resnet26t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_256-b040fce6.pth',
min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
}

83
timm/models/byobnet.py
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@ -93,33 +93,49 @@ default_cfgs = {
first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
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'),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet61q_ra2-6afc536c.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8),
test_input_size=(3, 288, 288), crop_pct=1.0, interpolation='bicubic'),
'resnext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256-df727fca.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'seresnext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'eca_resnext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'bat_resnext26ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/bat_resnext26ts_256-fa6fd595.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
min_input_size=(3, 256, 256)),
'resnet26tfs': _cfg(
'resnet32ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'resnet33ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet26tfs': _cfg(
'gcresnet33ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'seresnet26tfs': _cfg(
'seresnet33ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'eca_resnet26tfs': _cfg(
'eca_resnet33ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet50t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnext50ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
}
@ -270,7 +286,8 @@ model_cfgs = dict(
stem_chs=64,
),
# WARN: experimental, may vanish/change
# 4 x conv stem w/ 2 act, no maxpool, 2,4,6,4 repeats, group size 32 in first 3 blocks
# DW convs in last block, 2048 pre-FC, silu act
resnet51q=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
@ -285,6 +302,8 @@ model_cfgs = dict(
act_layer='silu',
),
# 4 x conv stem w/ 4 act, no maxpool, 1,4,6,4 repeats, edge block first, group size 32 in next 2 blocks
# DW convs in last block, 4 conv for each bottle block, 2048 pre-FC, silu act
resnet61q=ByoModelCfg(
blocks=(
ByoBlockCfg(type='edge', d=1, c=256, s=1, gs=0, br=1.0, block_kwargs=dict()),
@ -368,9 +387,8 @@ model_cfgs = dict(
attn_kwargs=dict(block_size=8)
),
# A series of ResNet-26 models w/ one of none, GC, SE, ECA attn, no groups, SiLU act, 1280 feat fc
# and a tiered stem w/ no maxpool
resnet26tfs=ByoModelCfg(
# ResNet-32 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, no pre-fc feat layer, tiered stem w/o maxpool
resnet32ts=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),
@ -383,7 +401,25 @@ model_cfgs = dict(
num_features=0,
act_layer='silu',
),
gcresnet26tfs=ByoModelCfg(
# ResNet-33 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, 1280 pre-FC feat, tiered stem w/o maxpool
resnet33ts=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',
),
# A series of ResNet-33 (2, 3, 3, 2) models w/ one of GC, SE, ECA attn, no groups, SiLU act, 1280 pre-FC feat
# and a tiered stem w/ no maxpool
gcresnet33ts=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),
@ -397,7 +433,7 @@ model_cfgs = dict(
act_layer='silu',
attn_layer='gca',
),
seresnet26tfs=ByoModelCfg(
seresnet33ts=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),
@ -411,7 +447,7 @@ model_cfgs = dict(
act_layer='silu',
attn_layer='se',
),
eca_resnet26tfs=ByoModelCfg(
eca_resnet33ts=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),
@ -594,31 +630,38 @@ def bat_resnext26ts(pretrained=False, **kwargs):
@register_model
def resnet26tfs(pretrained=False, **kwargs):
def resnet32ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('resnet32ts', pretrained=pretrained, **kwargs)
@register_model
def resnet33ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('resnet26tfs', pretrained=pretrained, **kwargs)
return _create_byobnet('resnet33ts', pretrained=pretrained, **kwargs)
@register_model
def gcresnet26tfs(pretrained=False, **kwargs):
def gcresnet33ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('gcresnet26tfs', pretrained=pretrained, **kwargs)
return _create_byobnet('gcresnet33ts', pretrained=pretrained, **kwargs)
@register_model
def seresnet26tfs(pretrained=False, **kwargs):
def seresnet33ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('seresnet26tfs', pretrained=pretrained, **kwargs)
return _create_byobnet('seresnet33ts', pretrained=pretrained, **kwargs)
@register_model
def eca_resnet26tfs(pretrained=False, **kwargs):
def eca_resnet33ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('eca_resnet26tfs', pretrained=pretrained, **kwargs)
return _create_byobnet('eca_resnet33ts', pretrained=pretrained, **kwargs)
@register_model

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