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Add weights for SE NFNet-L0 model, rename nfnet_l0b -> nfnet_l0. 82.75 top-1 @ 288. Add nfnet_l1 model def for training.

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Ross Wightman 2021-04-01 17:22:27 -07:00
parent c468c47a9c
commit 9071568f0e
1 changed files with 25 additions and 23 deletions
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48
timm/models/nfnet.py
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@ -100,14 +100,16 @@ default_cfgs = dict(
nfnet_f7s=_dcfg( nfnet_f7s=_dcfg(
url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608)), url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608)),
nfnet_l0a=_dcfg( nfnet_l0=_dcfg(
url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288)), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nfnet_l0_ra2-45c6688d.pth',
nfnet_l0b=_dcfg( pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0),
url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288)),
eca_nfnet_l0=_dcfg( eca_nfnet_l0=_dcfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l0_ra2-e3e9ac50.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l0_ra2-e3e9ac50.pth',
hf_hub='timm/eca_nfnet_l0', hf_hub='timm/eca_nfnet_l0',
pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0), pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0),
eca_nfnet_l1=_dcfg(
url='',
pool_size=(7, 7), input_size=(3, 256, 256), test_input_size=(3, 320, 320), crop_pct=1.0),
nf_regnet_b0=_dcfg( nf_regnet_b0=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv'), url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv'),
@ -232,15 +234,15 @@ model_cfgs = dict(
nfnet_f6s=_nfnet_cfg(depths=(7, 14, 42, 21), act_layer='silu'), nfnet_f6s=_nfnet_cfg(depths=(7, 14, 42, 21), act_layer='silu'),
nfnet_f7s=_nfnet_cfg(depths=(8, 16, 48, 24), act_layer='silu'), nfnet_f7s=_nfnet_cfg(depths=(8, 16, 48, 24), act_layer='silu'),
# Experimental 'light' versions of nfnet-f that are little leaner # Experimental 'light' versions of NFNet-F that are little leaner
nfnet_l0a=_nfnet_cfg( nfnet_l0=_nfnet_cfg(
depths=(1, 2, 6, 3), channels=(256, 512, 1280, 1536), feat_mult=1.5, group_size=64, bottle_ratio=0.25, depths=(1, 2, 6, 3), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
attn_kwargs=dict(reduction_ratio=0.25, divisor=8), act_layer='silu'),
nfnet_l0b=_nfnet_cfg(
depths=(1, 2, 6, 3), channels=(256, 512, 1536, 1536), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
attn_kwargs=dict(reduction_ratio=0.25, divisor=8), act_layer='silu'), attn_kwargs=dict(reduction_ratio=0.25, divisor=8), act_layer='silu'),
eca_nfnet_l0=_nfnet_cfg( eca_nfnet_l0=_nfnet_cfg(
depths=(1, 2, 6, 3), channels=(256, 512, 1536, 1536), feat_mult=1.5, group_size=64, bottle_ratio=0.25, depths=(1, 2, 6, 3), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
eca_nfnet_l1=_nfnet_cfg(
depths=(2, 4, 12, 6), feat_mult=2, group_size=64, bottle_ratio=0.25,
attn_layer='eca', attn_kwargs=dict(), act_layer='silu'), attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
# EffNet influenced RegNet defs. # EffNet influenced RegNet defs.
@ -789,29 +791,29 @@ def nfnet_f7s(pretrained=False, **kwargs):
@register_model @register_model
def nfnet_l0a(pretrained=False, **kwargs): def nfnet_l0(pretrained=False, **kwargs):
""" NFNet-L0a w/ SiLU
My experimental 'light' model w/ 1280 width stage 3, 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio
"""
return _create_normfreenet('nfnet_l0a', pretrained=pretrained, **kwargs)
@register_model
def nfnet_l0b(pretrained=False, **kwargs):
""" NFNet-L0b w/ SiLU """ NFNet-L0b w/ SiLU
My experimental 'light' model w/ 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio My experimental 'light' model w/ F0 repeats, 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio
""" """
return _create_normfreenet('nfnet_l0b', pretrained=pretrained, **kwargs) return _create_normfreenet('nfnet_l0', pretrained=pretrained, **kwargs)
@register_model @register_model
def eca_nfnet_l0(pretrained=False, **kwargs): def eca_nfnet_l0(pretrained=False, **kwargs):
""" ECA-NFNet-L0 w/ SiLU """ ECA-NFNet-L0 w/ SiLU
My experimental 'light' model w/ 1.5x final_conv mult, 64 group_size, .25 bottleneck & ECA attn My experimental 'light' model w/ F0 repeats, 1.5x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
""" """
return _create_normfreenet('eca_nfnet_l0', pretrained=pretrained, **kwargs) return _create_normfreenet('eca_nfnet_l0', pretrained=pretrained, **kwargs)
@register_model
def eca_nfnet_l1(pretrained=False, **kwargs):
""" ECA-NFNet-L1 w/ SiLU
My experimental 'light' model w/ F1 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
"""
return _create_normfreenet('eca_nfnet_l1', pretrained=pretrained, **kwargs)
@register_model @register_model
def nf_regnet_b0(pretrained=False, **kwargs): def nf_regnet_b0(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B0 """ Normalization-Free RegNet-B0