From 5f9aff395c224492e9e44248b15f44b5cc095d9c Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Sat, 13 Feb 2021 16:58:51 -0800 Subject: [PATCH] Fix stem width in NFNet-F models, add some more comments, add some 'light' NFNet models for testing. --- timm/models/nfnet.py | 199 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 162 insertions(+), 37 deletions(-) diff --git a/timm/models/nfnet.py b/timm/models/nfnet.py index 1f83f6df..b43ee5ef 100644 --- a/timm/models/nfnet.py +++ b/timm/models/nfnet.py @@ -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) \ No newline at end of file + """ Normalization-Free ECA-ResNet101 + """ + return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)