""" Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` - https://arxiv.org/abs/2101.08692 Paper: `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets Status: * These models are a work in progress, experiments ongoing. * Pretrained weights for two models so far, more to come. * Model details updated to closer match official JAX code now that it's released * NF-ResNet, NF-RegNet-B, and NFNet-F models supported Hacked together by / copyright Ross Wightman, 2021. """ from collections import OrderedDict from dataclasses import dataclass, replace from functools import partial from typing import Tuple, Optional import torch import torch.nn as nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.layers import ClassifierHead, DropPath, AvgPool2dSame, ScaledStdConv2d, ScaledStdConv2dSame, \ get_act_layer, get_act_fn, get_attn, make_divisible from ._builder import build_model_with_cfg from ._features_fx import register_notrace_module from ._manipulate import checkpoint_seq from ._registry import register_model __all__ = ['NormFreeNet', 'NfCfg'] # model_registry will add each entrypoint fn to this def _dcfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), 'crop_pct': 0.9, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'stem.conv1', 'classifier': 'head.fc', **kwargs } default_cfgs = dict( dm_nfnet_f0=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f0-604f9c3a.pth', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), crop_pct=.9), dm_nfnet_f1=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f1-fc540f82.pth', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), crop_pct=0.91), dm_nfnet_f2=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f2-89875923.pth', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), crop_pct=0.92), dm_nfnet_f3=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f3-d74ab3aa.pth', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), crop_pct=0.94), dm_nfnet_f4=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f4-0ac5b10b.pth', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), crop_pct=0.951), dm_nfnet_f5=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f5-ecb20ab1.pth', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), crop_pct=0.954), dm_nfnet_f6=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-dnf-weights/dm_nfnet_f6-e0f12116.pth', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), crop_pct=0.956), nfnet_f0=_dcfg( url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256)), nfnet_f1=_dcfg( url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320)), nfnet_f2=_dcfg( url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352)), nfnet_f3=_dcfg( url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416)), nfnet_f4=_dcfg( url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512)), nfnet_f5=_dcfg( url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544)), nfnet_f6=_dcfg( url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576)), nfnet_f7=_dcfg( url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608)), nfnet_l0=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nfnet_l0_ra2-45c6688d.pth', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0), eca_nfnet_l0=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l0_ra2-e3e9ac50.pth', hf_hub_id='timm/eca_nfnet_l0', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 288, 288), crop_pct=1.0), eca_nfnet_l1=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l1_ra2-7dce93cd.pth', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 320, 320), crop_pct=1.0), eca_nfnet_l2=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l2_ra3-da781a61.pth', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), crop_pct=1.0), eca_nfnet_l3=_dcfg( url='', pool_size=(11, 11), input_size=(3, 352, 352), test_input_size=(3, 448, 448), crop_pct=1.0), nf_regnet_b0=_dcfg( url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv'), nf_regnet_b1=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_regnet_b1_256_ra2-ad85cfef.pth', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), first_conv='stem.conv'), # NOT to paper spec nf_regnet_b2=_dcfg( url='', pool_size=(8, 8), input_size=(3, 240, 240), test_input_size=(3, 272, 272), first_conv='stem.conv'), nf_regnet_b3=_dcfg( url='', pool_size=(9, 9), input_size=(3, 288, 288), test_input_size=(3, 320, 320), first_conv='stem.conv'), nf_regnet_b4=_dcfg( url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), first_conv='stem.conv'), nf_regnet_b5=_dcfg( url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 456, 456), first_conv='stem.conv'), nf_resnet26=_dcfg(url='', first_conv='stem.conv'), nf_resnet50=_dcfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nf_resnet50_ra2-9f236009.pth', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288), crop_pct=0.94, first_conv='stem.conv'), nf_resnet101=_dcfg(url='', first_conv='stem.conv'), nf_seresnet26=_dcfg(url='', first_conv='stem.conv'), nf_seresnet50=_dcfg(url='', first_conv='stem.conv'), nf_seresnet101=_dcfg(url='', first_conv='stem.conv'), nf_ecaresnet26=_dcfg(url='', first_conv='stem.conv'), nf_ecaresnet50=_dcfg(url='', first_conv='stem.conv'), nf_ecaresnet101=_dcfg(url='', first_conv='stem.conv'), ) @dataclass class NfCfg: depths: Tuple[int, int, int, int] channels: Tuple[int, int, int, int] alpha: float = 0.2 stem_type: str = '3x3' stem_chs: Optional[int] = None group_size: Optional[int] = None attn_layer: Optional[str] = None attn_kwargs: dict = None attn_gain: float = 2.0 # NF correction gain to apply if attn layer is used width_factor: float = 1.0 bottle_ratio: float = 0.5 num_features: int = 0 # num out_channels for final conv, no final_conv if 0 ch_div: int = 8 # round channels % 8 == 0 to keep tensor-core use optimal reg: bool = False # enables EfficientNet-like options used in RegNet variants, expand from in_chs, se in middle extra_conv: bool = False # extra 3x3 bottleneck convolution for NFNet models gamma_in_act: bool = False same_padding: bool = False std_conv_eps: float = 1e-5 skipinit: bool = False # disabled by default, non-trivial performance impact zero_init_fc: bool = False act_layer: str = 'silu' def _nfres_cfg( depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kwargs=None, ): attn_kwargs = attn_kwargs or {} cfg = NfCfg( depths=depths, channels=channels, stem_type='7x7_pool', stem_chs=64, bottle_ratio=0.25, group_size=group_size, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs, ) return cfg def _nfreg_cfg(depths, channels=(48, 104, 208, 440)): num_features = 1280 * channels[-1] // 440 attn_kwargs = dict(rd_ratio=0.5) cfg = NfCfg( depths=depths, channels=channels, stem_type='3x3', group_size=8, width_factor=0.75, bottle_ratio=2.25, num_features=num_features, reg=True, attn_layer='se', attn_kwargs=attn_kwargs, ) return cfg 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(rd_ratio=0.5) cfg = NfCfg( 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 def _dm_nfnet_cfg( depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True, ): cfg = NfCfg( depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=128, bottle_ratio=0.5, extra_conv=True, gamma_in_act=True, same_padding=True, skipinit=skipinit, num_features=int(channels[-1] * 2.0), act_layer=act_layer, attn_layer='se', attn_kwargs=dict(rd_ratio=0.5), ) return cfg model_cfgs = dict( # NFNet-F models w/ GELU compatible with DeepMind weights dm_nfnet_f0=_dm_nfnet_cfg(depths=(1, 2, 6, 3)), dm_nfnet_f1=_dm_nfnet_cfg(depths=(2, 4, 12, 6)), dm_nfnet_f2=_dm_nfnet_cfg(depths=(3, 6, 18, 9)), dm_nfnet_f3=_dm_nfnet_cfg(depths=(4, 8, 24, 12)), dm_nfnet_f4=_dm_nfnet_cfg(depths=(5, 10, 30, 15)), dm_nfnet_f5=_dm_nfnet_cfg(depths=(6, 12, 36, 18)), dm_nfnet_f6=_dm_nfnet_cfg(depths=(7, 14, 42, 21)), # NFNet-F models w/ GELU nfnet_f0=_nfnet_cfg(depths=(1, 2, 6, 3)), nfnet_f1=_nfnet_cfg(depths=(2, 4, 12, 6)), nfnet_f2=_nfnet_cfg(depths=(3, 6, 18, 9)), nfnet_f3=_nfnet_cfg(depths=(4, 8, 24, 12)), nfnet_f4=_nfnet_cfg(depths=(5, 10, 30, 15)), nfnet_f5=_nfnet_cfg(depths=(6, 12, 36, 18)), nfnet_f6=_nfnet_cfg(depths=(7, 14, 42, 21)), nfnet_f7=_nfnet_cfg(depths=(8, 16, 48, 24)), # Experimental 'light' versions of NFNet-F that are little leaner nfnet_l0=_nfnet_cfg( depths=(1, 2, 6, 3), feat_mult=1.5, group_size=64, bottle_ratio=0.25, attn_kwargs=dict(rd_ratio=0.25, rd_divisor=8), act_layer='silu'), eca_nfnet_l0=_nfnet_cfg( 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'), eca_nfnet_l2=_nfnet_cfg( depths=(3, 6, 18, 9), feat_mult=2, group_size=64, bottle_ratio=0.25, attn_layer='eca', attn_kwargs=dict(), act_layer='silu'), eca_nfnet_l3=_nfnet_cfg( depths=(4, 8, 24, 12), feat_mult=2, 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)), nf_regnet_b1=_nfreg_cfg(depths=(2, 4, 7, 7)), nf_regnet_b2=_nfreg_cfg(depths=(2, 4, 8, 8), channels=(56, 112, 232, 488)), nf_regnet_b3=_nfreg_cfg(depths=(2, 5, 9, 9), channels=(56, 128, 248, 528)), nf_regnet_b4=_nfreg_cfg(depths=(2, 6, 11, 11), channels=(64, 144, 288, 616)), nf_regnet_b5=_nfreg_cfg(depths=(3, 7, 14, 14), channels=(80, 168, 336, 704)), # FIXME add B6-B8 # ResNet (preact, D style deep stem/avg down) defs nf_resnet26=_nfres_cfg(depths=(2, 2, 2, 2)), nf_resnet50=_nfres_cfg(depths=(3, 4, 6, 3)), nf_resnet101=_nfres_cfg(depths=(3, 4, 23, 3)), nf_seresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)), nf_seresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)), nf_seresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='se', attn_kwargs=dict(rd_ratio=1/16)), nf_ecaresnet26=_nfres_cfg(depths=(2, 2, 2, 2), attn_layer='eca', attn_kwargs=dict()), nf_ecaresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='eca', attn_kwargs=dict()), nf_ecaresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='eca', attn_kwargs=dict()), ) class GammaAct(nn.Module): def __init__(self, act_type='relu', gamma: float = 1.0, inplace=False): super().__init__() self.act_fn = get_act_fn(act_type) self.gamma = gamma self.inplace = inplace def forward(self, x): return self.act_fn(x, inplace=self.inplace).mul_(self.gamma) def act_with_gamma(act_type, gamma: float = 1.): def _create(inplace=False): return GammaAct(act_type, gamma=gamma, inplace=inplace) return _create class DownsampleAvg(nn.Module): def __init__( self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, conv_layer=ScaledStdConv2d, ): """ AvgPool Downsampling as in 'D' ResNet variants. Support for dilation.""" super(DownsampleAvg, self).__init__() avg_stride = stride if dilation == 1 else 1 if stride > 1 or dilation > 1: avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False) else: self.pool = nn.Identity() self.conv = conv_layer(in_chs, out_chs, 1, stride=1) def forward(self, x): return self.conv(self.pool(x)) @register_notrace_module # reason: mul_ causes FX to drop a relevant node. https://github.com/pytorch/pytorch/issues/68301 class NormFreeBlock(nn.Module): """Normalization-Free pre-activation block. """ def __init__( self, in_chs, out_chs=None, stride=1, dilation=1, first_dilation=None, alpha=1.0, beta=1.0, bottle_ratio=0.25, group_size=None, ch_div=1, reg=True, extra_conv=False, skipinit=False, attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0., ): super().__init__() first_dilation = first_dilation or dilation out_chs = out_chs or in_chs # RegNet variants scale bottleneck from in_chs, otherwise scale from out_chs like ResNet mid_chs = make_divisible(in_chs * bottle_ratio if reg else out_chs * bottle_ratio, ch_div) groups = 1 if not group_size else mid_chs // group_size if group_size and group_size % ch_div == 0: mid_chs = group_size * groups # correct mid_chs if group_size divisible by ch_div, otherwise error self.alpha = alpha self.beta = beta self.attn_gain = attn_gain if in_chs != out_chs or stride != 1 or dilation != first_dilation: self.downsample = DownsampleAvg( in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, conv_layer=conv_layer, ) else: self.downsample = None self.act1 = act_layer() self.conv1 = conv_layer(in_chs, mid_chs, 1) self.act2 = act_layer(inplace=True) self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups) if extra_conv: self.act2b = act_layer(inplace=True) self.conv2b = conv_layer(mid_chs, mid_chs, 3, stride=1, dilation=dilation, groups=groups) else: self.act2b = None self.conv2b = None if reg and attn_layer is not None: self.attn = attn_layer(mid_chs) # RegNet blocks apply attn btw conv2 & 3 else: self.attn = None self.act3 = act_layer() self.conv3 = conv_layer(mid_chs, out_chs, 1, gain_init=1. if skipinit else 0.) if not reg and attn_layer is not None: self.attn_last = attn_layer(out_chs) # ResNet blocks apply attn after conv3 else: self.attn_last = None self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity() self.skipinit_gain = nn.Parameter(torch.tensor(0.)) if skipinit else None def forward(self, x): out = self.act1(x) * self.beta # shortcut branch shortcut = x if self.downsample is not None: shortcut = self.downsample(out) # residual branch out = self.conv1(out) out = self.conv2(self.act2(out)) if self.conv2b is not None: out = self.conv2b(self.act2b(out)) if self.attn is not None: out = self.attn_gain * self.attn(out) out = self.conv3(self.act3(out)) if self.attn_last is not None: out = self.attn_gain * self.attn_last(out) out = self.drop_path(out) if self.skipinit_gain is not None: out.mul_(self.skipinit_gain) # this slows things down more than expected, TBD out = out * self.alpha + shortcut return out def create_stem(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None, preact_feature=True): stem_stride = 2 stem_feature = dict(num_chs=out_chs, reduction=2, module='stem.conv') stem = OrderedDict() assert stem_type in ('', 'deep', 'deep_tiered', 'deep_quad', '3x3', '7x7', 'deep_pool', '3x3_pool', '7x7_pool') 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 = (out_chs // 8, out_chs // 4, out_chs // 2, out_chs) strides = (2, 1, 1, 2) stem_stride = 4 stem_feature = dict(num_chs=out_chs // 2, reduction=2, module='stem.conv3') else: if 'tiered' in stem_type: 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.conv2') 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) if i != last_idx: stem[f'act{i + 2}'] = act_layer(inplace=True) in_chs = c elif '3x3' in stem_type: # 3x3 stem conv as in RegNet stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=3, stride=2) else: # 7x7 stem conv as in ResNet stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2) if 'pool' in stem_type: stem['pool'] = nn.MaxPool2d(3, stride=2, padding=1) stem_stride = 4 return nn.Sequential(stem), stem_stride, stem_feature # from https://github.com/deepmind/deepmind-research/tree/master/nfnets _nonlin_gamma = dict( identity=1.0, celu=1.270926833152771, elu=1.2716004848480225, gelu=1.7015043497085571, leaky_relu=1.70590341091156, log_sigmoid=1.9193484783172607, log_softmax=1.0002083778381348, relu=1.7139588594436646, relu6=1.7131484746932983, selu=1.0008515119552612, sigmoid=4.803835391998291, silu=1.7881293296813965, softsign=2.338853120803833, softplus=1.9203323125839233, tanh=1.5939117670059204, ) class NormFreeNet(nn.Module): """ Normalization-Free Network As described in : `Characterizing signal propagation to close the performance gap in unnormalized ResNets` - https://arxiv.org/abs/2101.08692 and `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 This model aims to cover both the NFRegNet-Bx models as detailed in the paper's code snippets and the (preact) ResNet models described earlier in the paper. There are a few differences: * channels are rounded to be divisible by 8 by default (keep tensor core kernels happy), this changes channel dim and param counts slightly from the paper models * activation correcting gamma constants are moved into the ScaledStdConv as it has less performance impact in PyTorch when done with the weight scaling there. This likely wasn't a concern in the JAX impl. * a config option `gamma_in_act` can be enabled to not apply gamma in StdConv as described above, but apply it in each activation. This is slightly slower, numerically different, but matches official impl. * skipinit is disabled by default, it seems to have a rather drastic impact on GPU memory use and throughput for what it is/does. Approx 8-10% throughput loss. """ def __init__( self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32, drop_rate=0., drop_path_rate=0., **kwargs, ): """ Args: cfg (NfCfg): Model architecture configuration num_classes (int): Number of classifier classes (default: 1000) in_chans (int): Number of input channels (default: 3) global_pool (str): Global pooling type (default: 'avg') output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) drop_rate (float): Dropout rate (default: 0.) drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) kwargs (dict): Extra kwargs overlayed onto cfg """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate self.grad_checkpointing = False cfg = replace(cfg, **kwargs) assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})." conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d if cfg.gamma_in_act: act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer]) conv_layer = partial(conv_layer, eps=cfg.std_conv_eps) else: act_layer = get_act_layer(cfg.act_layer) conv_layer = partial(conv_layer, gamma=_nonlin_gamma[cfg.act_layer], eps=cfg.std_conv_eps) attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None 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, ) self.feature_info = [stem_feat] drop_path_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)] prev_chs = stem_chs net_stride = stem_stride dilation = 1 expected_var = 1.0 stages = [] for stage_idx, stage_depth in enumerate(cfg.depths): stride = 1 if stage_idx == 0 and stem_stride > 2 else 2 if net_stride >= output_stride and stride > 1: dilation *= stride stride = 1 net_stride *= stride first_dilation = 1 if dilation in (1, 2) else 2 blocks = [] for block_idx in range(cfg.depths[stage_idx]): first_block = block_idx == 0 and stage_idx == 0 out_chs = make_divisible(cfg.channels[stage_idx] * cfg.width_factor, cfg.ch_div) blocks += [NormFreeBlock( in_chs=prev_chs, out_chs=out_chs, alpha=cfg.alpha, beta=1. / expected_var ** 0.5, stride=stride if block_idx == 0 else 1, dilation=dilation, first_dilation=first_dilation, group_size=cfg.group_size, bottle_ratio=1. if cfg.reg and first_block else cfg.bottle_ratio, ch_div=cfg.ch_div, reg=cfg.reg, extra_conv=cfg.extra_conv, skipinit=cfg.skipinit, attn_layer=attn_layer, attn_gain=cfg.attn_gain, act_layer=act_layer, conv_layer=conv_layer, drop_path_rate=drop_path_rates[stage_idx][block_idx], )] if block_idx == 0: expected_var = 1. # expected var is reset after first block of each stage expected_var += cfg.alpha ** 2 # Even if reset occurs, increment expected variance first_dilation = dilation prev_chs = out_chs self.feature_info += [dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')] stages += [nn.Sequential(*blocks)] self.stages = nn.Sequential(*stages) if cfg.num_features: # The paper NFRegNet models have an EfficientNet-like final head convolution. self.num_features = make_divisible(cfg.width_factor * cfg.num_features, cfg.ch_div) self.final_conv = conv_layer(prev_chs, self.num_features, 1) self.feature_info[-1] = dict(num_chs=self.num_features, reduction=net_stride, module=f'final_conv') else: self.num_features = prev_chs self.final_conv = nn.Identity() self.final_act = act_layer(inplace=cfg.num_features > 0) self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate) for n, m in self.named_modules(): if 'fc' in n and isinstance(m, nn.Linear): if cfg.zero_init_fc: nn.init.zeros_(m.weight) else: nn.init.normal_(m.weight, 0., .01) if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='linear') if m.bias is not None: nn.init.zeros_(m.bias) @torch.jit.ignore def group_matcher(self, coarse=False): matcher = dict( stem=r'^stem', blocks=[ (r'^stages\.(\d+)' if coarse else r'^stages\.(\d+)\.(\d+)', None), (r'^final_conv', (99999,)) ] ) return matcher @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable @torch.jit.ignore def get_classifier(self): return self.head.fc def reset_classifier(self, num_classes, global_pool='avg'): self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate) def forward_features(self, x): x = self.stem(x) if self.grad_checkpointing and not torch.jit.is_scripting(): x = checkpoint_seq(self.stages, x) else: x = self.stages(x) x = self.final_conv(x) x = self.final_act(x) return x def forward_head(self, x): return self.head(x) def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x def _create_normfreenet(variant, pretrained=False, **kwargs): model_cfg = model_cfgs[variant] feature_cfg = dict(flatten_sequential=True) return build_model_with_cfg( NormFreeNet, variant, pretrained, model_cfg=model_cfg, feature_cfg=feature_cfg, **kwargs) @register_model def dm_nfnet_f0(pretrained=False, **kwargs): """ NFNet-F0 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f0', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f1(pretrained=False, **kwargs): """ NFNet-F1 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f1', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f2(pretrained=False, **kwargs): """ NFNet-F2 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f2', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f3(pretrained=False, **kwargs): """ NFNet-F3 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f3', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f4(pretrained=False, **kwargs): """ NFNet-F4 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f4', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f5(pretrained=False, **kwargs): """ NFNet-F5 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f5', pretrained=pretrained, **kwargs) @register_model def dm_nfnet_f6(pretrained=False, **kwargs): """ NFNet-F6 (DeepMind weight compatible) `High-Performance Large-Scale Image Recognition Without Normalization` - https://arxiv.org/abs/2102.06171 """ return _create_normfreenet('dm_nfnet_f6', pretrained=pretrained, **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_l0(pretrained=False, **kwargs): """ NFNet-L0b w/ SiLU My experimental 'light' model w/ F0 repeats, 1.5x final_conv mult, 64 group_size, .25 bottleneck & SE ratio """ return _create_normfreenet('nfnet_l0', pretrained=pretrained, **kwargs) @register_model def eca_nfnet_l0(pretrained=False, **kwargs): """ ECA-NFNet-L0 w/ SiLU 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) @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 def eca_nfnet_l2(pretrained=False, **kwargs): """ ECA-NFNet-L2 w/ SiLU My experimental 'light' model w/ F2 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn """ return _create_normfreenet('eca_nfnet_l2', pretrained=pretrained, **kwargs) @register_model def eca_nfnet_l3(pretrained=False, **kwargs): """ ECA-NFNet-L3 w/ SiLU My experimental 'light' model w/ F3 repeats, 2.0x final_conv mult, 64 group_size, .25 bottleneck & ECA attn """ return _create_normfreenet('eca_nfnet_l3', 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): """ Normalization-Free ECA-ResNet101 """ return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)