pytorch-image-models/timm/models/nfnet.py

""" 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.
"""
import math
from dataclasses import dataclass, field
from collections import OrderedDict
from typing import Tuple, Optional
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .registry import register_model
from .layers import ClassifierHead, DropPath, AvgPool2dSame, ScaledStdConv2d, get_act_layer, get_attn, make_divisible, get_act_fn


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.conv', 'classifier': 'head.fc',
        **kwargs
    }


default_cfgs = dict(
    nfnet_f0=_dcfg(
        url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
    nfnet_f1=_dcfg(
        url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), first_conv='stem.conv1'),
    nfnet_f2=_dcfg(
        url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), first_conv='stem.conv1'),
    nfnet_f3=_dcfg(
        url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), first_conv='stem.conv1'),
    nfnet_f4=_dcfg(
        url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), first_conv='stem.conv1'),
    nfnet_f5=_dcfg(
        url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), first_conv='stem.conv1'),
    nfnet_f6=_dcfg(
        url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), first_conv='stem.conv1'),
    nfnet_f7=_dcfg(
        url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608), first_conv='stem.conv1'),

    nfnet_f0s=_dcfg(
        url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv1'),
    nfnet_f1s=_dcfg(
        url='', pool_size=(7, 7), input_size=(3, 224, 224), test_input_size=(3, 320, 320), first_conv='stem.conv1'),
    nfnet_f2s=_dcfg(
        url='', pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 352, 352), first_conv='stem.conv1'),
    nfnet_f3s=_dcfg(
        url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 416, 416), first_conv='stem.conv1'),
    nfnet_f4s=_dcfg(
        url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 512, 512), first_conv='stem.conv1'),
    nfnet_f5s=_dcfg(
        url='', pool_size=(13, 13), input_size=(3, 416, 416), test_input_size=(3, 544, 544), first_conv='stem.conv1'),
    nfnet_f6s=_dcfg(
        url='', pool_size=(14, 14), input_size=(3, 448, 448), test_input_size=(3, 576, 576), first_conv='stem.conv1'),
    nfnet_f7s=_dcfg(
        url='', pool_size=(15, 15), input_size=(3, 480, 480), test_input_size=(3, 608, 608), 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',
        pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 288, 288)),  # NOT to paper spec
    nf_regnet_b2=_dcfg(url='', pool_size=(8, 8), input_size=(3, 240, 240), test_input_size=(3, 272, 272)),
    nf_regnet_b3=_dcfg(url='', pool_size=(9, 9), input_size=(3, 288, 288), test_input_size=(3, 320, 320)),
    nf_regnet_b4=_dcfg(url='', pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384)),
    nf_regnet_b5=_dcfg(url='', pool_size=(12, 12), input_size=(3, 384, 384), test_input_size=(3, 456, 456)),

    nf_resnet26=_dcfg(url=''),
    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),
    nf_resnet101=_dcfg(url=''),

    nf_seresnet26=_dcfg(url=''),
    nf_seresnet50=_dcfg(url=''),
    nf_seresnet101=_dcfg(url=''),

    nf_ecaresnet26=_dcfg(url=''),
    nf_ecaresnet50=_dcfg(url=''),
    nf_ecaresnet101=_dcfg(url=''),
)


@dataclass
class NfCfg:
    depths: Tuple[int, int, int, int]
    channels: Tuple[int, int, int, int]
    alpha: float = 0.2
    gamma_in_act: bool = False
    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
    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(reduction_ratio=0.5, divisor=8)
    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, 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)
    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)
    return cfg


model_cfgs = dict(
    # 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)),

    # NFNet-F models w/ SiLU (much faster in PyTorch)
    nfnet_f0s=_nfnet_cfg(depths=(1, 2, 6, 3), act_layer='silu'),
    nfnet_f1s=_nfnet_cfg(depths=(2, 4, 12, 6), act_layer='silu'),
    nfnet_f2s=_nfnet_cfg(depths=(3, 6, 18, 9), act_layer='silu'),
    nfnet_f3s=_nfnet_cfg(depths=(4, 8, 24, 12), act_layer='silu'),
    nfnet_f4s=_nfnet_cfg(depths=(5, 10, 30, 15), act_layer='silu'),
    nfnet_f5s=_nfnet_cfg(depths=(6, 12, 36, 18), 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'),

    # 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(reduction_ratio=1/16)),
    nf_seresnet50=_nfres_cfg(depths=(3, 4, 6, 3), attn_layer='se', attn_kwargs=dict(reduction_ratio=1/16)),
    nf_seresnet101=_nfres_cfg(depths=(3, 4, 23, 3), attn_layer='se', attn_kwargs=dict(reduction_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.gamma * self.act_fn(x, inplace=self.inplace)


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))


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)
        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:
            # this really slows things down for some reason, TBD
            out = out * self.skipinit_gain
        out = out * self.alpha + shortcut
        return out


def create_stem(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None):
    stem_stride = 2
    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 'quad' in stem_type:
            assert not 'pool' in stem_type
            stem_chs = (16, 32, 64, out_chs)
            strides = (2, 1, 1, 2)
            stem_stride = 4
            stem_feature = dict(num_chs=64, 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
            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)
            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


_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 ResNets and RegNets

    As described in `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
        - https://arxiv.org/abs/2101.08692

    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.):
        super().__init__()
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})."
        if cfg.gamma_in_act:
            act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer])
            conv_layer = partial(ScaledStdConv2d, bias=True, gain=True)
        else:
            act_layer = get_act_layer(cfg.act_layer)
            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)
        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] if stem_stride == 4 else []
        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 stride == 2:
                self.feature_info += [dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}.0.act1')]
            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,  # NOTE: beta used as multiplier in block
                    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
            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)
        else:
            self.num_features = prev_chs
            self.final_conv = nn.Identity()
        self.final_act = act_layer(inplace=cfg.num_features > 0)
        self.feature_info += [dict(num_chs=self.num_features, reduction=net_stride, module='final_act')]

        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):
                # as per discussion with paper authors, original in haiku is
                # hk.initializers.VarianceScaling(1.0, 'fan_in', 'normal')' w/ zero'd bias
                nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='linear')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

    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)
        x = self.stages(x)
        x = self.final_conv(x)
        x = self.final_act(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def _create_normfreenet(variant, pretrained=False, **kwargs):
    model_cfg = model_cfgs[variant]
    feature_cfg = dict(flatten_sequential=True)
    feature_cfg['feature_cls'] = 'hook'  # pre-act models need hooks to grab feat from act1 in bottleneck blocks
    if 'pool' in model_cfg.stem_type and 'deep' not in model_cfg.stem_type:
        feature_cfg['out_indices'] = (1, 2, 3, 4)  # no stride 2 feat for stride 4, 1 layer maxpool stems

    return build_model_with_cfg(
        NormFreeNet, variant, pretrained,
        default_cfg=default_cfgs[variant],
        model_cfg=model_cfg,
        feature_cfg=feature_cfg,
        **kwargs)


@register_model
def nfnet_f0(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f0', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f1(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f1', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f2(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f2', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f3(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f3', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f4(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f4', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f5(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f5', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f6(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f6', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f7(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f7', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f0s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f0s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f1s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f1s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f2s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f2s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f3s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f3s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f4s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f4s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f5s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f5s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f6s(pretrained=False, **kwargs):
    return _create_normfreenet('nfnet_f6s', pretrained=pretrained, **kwargs)


@register_model
def nfnet_f7s(pretrained=False, **kwargs):
    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)


@register_model
def nf_regnet_b4(pretrained=False, **kwargs):
    return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)


@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
    return _create_normfreenet('nf_regnet_b5', 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)


@register_model
def nf_regnet_b4(pretrained=False, **kwargs):
    return _create_normfreenet('nf_regnet_b4', pretrained=pretrained, **kwargs)


@register_model
def nf_regnet_b5(pretrained=False, **kwargs):
    return _create_normfreenet('nf_regnet_b5', pretrained=pretrained, **kwargs)


@register_model
def nf_resnet26(pretrained=False, **kwargs):
    return _create_normfreenet('nf_resnet26', pretrained=pretrained, **kwargs)


@register_model
def nf_resnet50(pretrained=False, **kwargs):
    return _create_normfreenet('nf_resnet50', pretrained=pretrained, **kwargs)


@register_model
def nf_resnet101(pretrained=False, **kwargs):
    return _create_normfreenet('nf_resnet101', pretrained=pretrained, **kwargs)


@register_model
def nf_seresnet26(pretrained=False, **kwargs):
    return _create_normfreenet('nf_seresnet26', pretrained=pretrained, **kwargs)


@register_model
def nf_seresnet50(pretrained=False, **kwargs):
    return _create_normfreenet('nf_seresnet50', pretrained=pretrained, **kwargs)


@register_model
def nf_seresnet101(pretrained=False, **kwargs):
    return _create_normfreenet('nf_seresnet101', pretrained=pretrained, **kwargs)


@register_model
def nf_ecaresnet26(pretrained=False, **kwargs):
    return _create_normfreenet('nf_ecaresnet26', pretrained=pretrained, **kwargs)


@register_model
def nf_ecaresnet50(pretrained=False, **kwargs):
    return _create_normfreenet('nf_ecaresnet50', pretrained=pretrained, **kwargs)


@register_model
def nf_ecaresnet101(pretrained=False, **kwargs):
    return _create_normfreenet('nf_ecaresnet101', pretrained=pretrained, **kwargs)