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Fix stem width in NFNet-F models, add some more comments, add some 'light' NFNet models for testing.

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pull/437/head
Ross Wightman 3 years ago
parent 4df513c68f
commit 5f9aff395c
1 changed files with 162 additions and 37 deletions
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199
timm/models/nfnet.py
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@ -78,6 +78,13 @@ default_cfgs = dict(
nfnet_f7s=_dcfg(
url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608), first_conv='stem.conv1'),
nfnet_l0a=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
nfnet_l0b=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
nfnet_l0c=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
nf_regnet_b0=_dcfg(url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256)),
nf_regnet_b1=_dcfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_regnet_b1_256_ra2-ad85cfef.pth',
@ -144,13 +151,15 @@ def _nfreg_cfg(depths, channels=(48, 104, 208, 440)):
return cfg
def _nfnet_cfg(depths, act_layer='gelu', attn_layer='se', attn_kwargs=None):
channels = (256, 512, 1536, 1536)
num_features = channels[-1] * 2
attn_kwargs = attn_kwargs or dict(reduction_ratio=0.5, divisor=8)
def _nfnet_cfg(
depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2.,
act_layer='gelu', attn_layer='se', attn_kwargs=None):
num_features = int(channels[-1] * feat_mult)
attn_kwargs = attn_kwargs if attn_kwargs is not None else dict(reduction_ratio=0.5, divisor=8)
cfg = NfCfg(
depths=depths, channels=channels, stem_type='deep_quad', group_size=128, bottle_ratio=0.5, extra_conv=True,
num_features=num_features, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs)
depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=group_size,
bottle_ratio=bottle_ratio, extra_conv=True, num_features=num_features, act_layer=act_layer,
attn_layer=attn_layer, attn_kwargs=attn_kwargs)
return cfg
@ -175,6 +184,17 @@ model_cfgs = dict(
nfnet_f6s=_nfnet_cfg(depths=(7, 14, 42, 21), 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
nfnet_l0a=_nfnet_cfg(
depths=(1, 2, 6, 3), channels=(256, 512, 1280, 1536), 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'),
nfnet_l0c=_nfnet_cfg(
depths=(1, 2, 6, 3), channels=(256, 512, 1536, 1536), feat_mult=1.5, group_size=64, bottle_ratio=0.25,
attn_layer='eca', attn_kwargs=dict(), act_layer='silu'),
# EffNet influenced RegNet defs.
# NOTE: These aren't quite the official ver, ch_div=1 must be set for exact ch counts. I round to ch_div=8.
nf_regnet_b0=_nfreg_cfg(depths=(1, 3, 6, 6)),
@ -316,26 +336,26 @@ def create_stem(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None):
stem_feature = dict(num_chs=out_chs, reduction=2, module='')
stem = OrderedDict()
assert stem_type in ('', 'deep', 'deep_tiered', 'deep_quad', '3x3', '7x7', 'deep_pool', '3x3_pool', '7x7_pool')
if 'deep' in stem_type or 'nff' in stem_type:
# 3 deep 3x3 conv stack as in ResNet V1D models. NOTE: doesn't work as well here
if 'deep' in stem_type:
if 'quad' in stem_type:
# 4 deep conv stack as in NFNet-F models
assert not 'pool' in stem_type
stem_chs = (16, 32, 64, out_chs)
stem_chs = (out_chs // 8, out_chs // 4, out_chs // 2, out_chs)
strides = (2, 1, 1, 2)
stem_stride = 4
stem_feature = dict(num_chs=64, reduction=2, module='stem.act4')
stem_feature = dict(num_chs=out_chs // 2, reduction=2, module='stem.act4')
else:
if 'tiered' in stem_type:
stem_chs = (3 * out_chs // 8, out_chs // 2, out_chs) # like 'T' resnets in resnet.py
stem_chs = (3 * out_chs // 8, out_chs // 2, out_chs) # 'T' resnets in resnet.py
else:
stem_chs = (out_chs // 2, out_chs // 2, out_chs) # 'D' ResNets
strides = (2, 1, 1)
stem_feature = dict(num_chs=out_chs // 2, reduction=2, module='stem.act3')
last_idx = len(stem_chs) - 1
for i, (c, s) in enumerate(zip(stem_chs, strides)):
stem[f'conv{i+1}'] = conv_layer(in_chs, c, kernel_size=3, stride=s)
stem[f'conv{i + 1}'] = conv_layer(in_chs, c, kernel_size=3, stride=s)
if i != last_idx:
stem[f'act{i+2}'] = act_layer(inplace=True)
stem[f'act{i + 2}'] = act_layer(inplace=True)
in_chs = c
elif '3x3' in stem_type:
# 3x3 stem conv as in RegNet
@ -407,8 +427,7 @@ class NormFreeNet(nn.Module):
conv_layer = partial(ScaledStdConv2d, bias=True, gain=True, gamma=_nonlin_gamma[cfg.act_layer])
attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
stem_chs = cfg.stem_chs or cfg.channels[0]
stem_chs = make_divisible(stem_chs * cfg.width_factor, cfg.ch_div)
stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div)
self.stem, stem_stride, stem_feat = create_stem(
in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer, act_layer=act_layer)
@ -521,184 +540,290 @@ def _create_normfreenet(variant, pretrained=False, **kwargs):
@register_model
def nfnet_f0(pretrained=False, **kwargs):
""" NFNet-F0
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f0', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f1(pretrained=False, **kwargs):
""" NFNet-F1
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f1', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f2(pretrained=False, **kwargs):
""" NFNet-F2
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f2', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f3(pretrained=False, **kwargs):
""" NFNet-F3
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f3', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f4(pretrained=False, **kwargs):
""" NFNet-F4
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f4', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f5(pretrained=False, **kwargs):
""" NFNet-F5
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f5', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f6(pretrained=False, **kwargs):
""" NFNet-F6
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f6', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f7(pretrained=False, **kwargs):
""" NFNet-F7
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f7', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f0s(pretrained=False, **kwargs):
""" NFNet-F0 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f0s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f1s(pretrained=False, **kwargs):
""" NFNet-F1 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f1s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f2s(pretrained=False, **kwargs):
""" NFNet-F2 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f2s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f3s(pretrained=False, **kwargs):
""" NFNet-F3 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f3s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f4s(pretrained=False, **kwargs):
""" NFNet-F4 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f4s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f5s(pretrained=False, **kwargs):
""" NFNet-F5 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f5s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f6s(pretrained=False, **kwargs):
""" NFNet-F6 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f6s', pretrained=pretrained, **kwargs)
@register_model
def nfnet_f7s(pretrained=False, **kwargs):
""" NFNet-F7 w/ SiLU
`High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/abs/2102.06171
"""
return _create_normfreenet('nfnet_f7s', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b0(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b0', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b1(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b1', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b2(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b2', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b3(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b3', pretrained=pretrained, **kwargs)
def nfnet_l0a(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 nf_regnet_b4(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)
def nfnet_l0b(pretrained=False, **kwargs):
""" NFNet-L0b w/ SiLU
My experimental 'light' model w/ 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio
"""
return _create_normfreenet('nfnet_l0b', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)
def nfnet_l0c(pretrained=False, **kwargs):
""" NFNet-L0c w/ SiLU
My experimental 'light' model w/ 1.5x final_conv mult, 64 group_size, .25 bottleneck & ECA attn
"""
return _create_normfreenet('nfnet_l0c', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b0(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B0
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b0', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b1(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B1
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b1', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b2(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B2
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b2', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b3(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B3
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b3', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b4(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B4
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)
@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
""" Normalization-Free RegNet-B5
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet26(pretrained=False, **kwargs):
""" Normalization-Free ResNet-26
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_resnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet50(pretrained=False, **kwargs):
""" Normalization-Free ResNet-50
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_resnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_resnet101(pretrained=False, **kwargs):
""" Normalization-Free ResNet-101
`Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
"""
return _create_normfreenet('nf_resnet101', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet26(pretrained=False, **kwargs):
""" Normalization-Free SE-ResNet26
"""
return _create_normfreenet('nf_seresnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet50(pretrained=False, **kwargs):
""" Normalization-Free SE-ResNet50
"""
return _create_normfreenet('nf_seresnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_seresnet101(pretrained=False, **kwargs):
""" Normalization-Free SE-ResNet101
"""
return _create_normfreenet('nf_seresnet101', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet26(pretrained=False, **kwargs):
""" Normalization-Free ECA-ResNet26
"""
return _create_normfreenet('nf_ecaresnet26', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet50(pretrained=False, **kwargs):
""" Normalization-Free ECA-ResNet50
"""
return _create_normfreenet('nf_ecaresnet50', pretrained=pretrained, **kwargs)
@register_model
def nf_ecaresnet101(pretrained=False, **kwargs):
return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)
""" Normalization-Free ECA-ResNet101
"""
return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)

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