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

""" Normalizer Free RegNet / ResNet (pre-activation) Models

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

NOTE: These models are a work in progress, no pretrained weights yet but I'm currently training some.
Details may change, especially once the paper authors release their official models.

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': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.conv', 'classifier': 'head.fc',
        **kwargs
    }

# FIXME finish
default_cfgs = {
    'nf_regnet_b0': _dcfg(url=''),
    'nf_regnet_b1': _dcfg(url='', input_size=(3, 240, 240), pool_size=(8, 8)),
    'nf_regnet_b2': _dcfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
    'nf_regnet_b3': _dcfg(url='', input_size=(3, 272, 272), pool_size=(9, 9)),
    'nf_regnet_b4': _dcfg(url='', input_size=(3, 320, 320), pool_size=(10, 10)),
    'nf_regnet_b5': _dcfg(url='', input_size=(3, 384, 384), pool_size=(12, 12)),

    'nf_resnet26': _dcfg(url='', first_conv='stem.conv'),
    'nf_resnet50': _dcfg(url='', 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
    gamma_in_act: bool = False
    stem_type: str = '3x3'
    stem_chs: Optional[int] = None
    group_size: Optional[int] = 8
    attn_layer: Optional[str] = 'se'
    attn_kwargs: dict = field(default_factory=lambda: dict(reduction_ratio=0.5, divisor=8))
    attn_gain: float = 2.0  # NF correction gain to apply if attn layer is used
    width_factor: float = 0.75
    bottle_ratio: float = 2.25
    efficient: bool = True  # enables EfficientNet-like options that are used in paper 'nf_regnet_b*' models
    num_features: int = 1280  # num out_channels for final conv (when enabled in efficient mode)
    ch_div: int = 8  # round channels % 8 == 0 to keep tensor-core use optimal
    skipinit: bool = False
    act_layer: str = 'silu'


model_cfgs = dict(
    # EffNet influenced RegNet defs
    nf_regnet_b0=NfCfg(depths=(1, 3, 6, 6), channels=(48, 104, 208, 440), num_features=1280),
    nf_regnet_b1=NfCfg(depths=(2, 4, 7, 7), channels=(48, 104, 208, 440), num_features=1280),
    nf_regnet_b2=NfCfg(depths=(2, 4, 8, 8), channels=(56, 112, 232, 488), num_features=1416),
    nf_regnet_b3=NfCfg(depths=(2, 5, 9, 9), channels=(56, 128, 248, 528), num_features=1536),
    nf_regnet_b4=NfCfg(depths=(2, 6, 11, 11), channels=(64, 144, 288, 616), num_features=1792),
    nf_regnet_b5=NfCfg(depths=(3, 7, 14, 14), channels=(80, 168, 336, 704), num_features=2048),

    # ResNet (preact, D style deep stem/avg down) defs
    nf_resnet26=NfCfg(
        depths=(2, 2, 2, 2), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer=None,),
    nf_resnet50=NfCfg(
        depths=(3, 4, 6, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer=None),
    nf_resnet101=NfCfg(
        depths=(3, 4, 23, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer=None),


    nf_seresnet26=NfCfg(
        depths=(2, 2, 2, 2), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),
    nf_seresnet50=NfCfg(
        depths=(3, 4, 6, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),
    nf_seresnet101=NfCfg(
        depths=(3, 4, 23, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer='se', attn_kwargs=dict(reduction_ratio=0.25)),


    nf_ecaresnet26=NfCfg(
        depths=(2, 2, 2, 2), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer='eca', attn_kwargs=dict()),
    nf_ecaresnet50=NfCfg(
        depths=(3, 4, 6, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', attn_layer='eca', attn_kwargs=dict()),
    nf_ecaresnet101=NfCfg(
        depths=(3, 4, 23, 3), channels=(256, 512, 1024, 2048),
        stem_type='7x7_pool', stem_chs=64, width_factor=1.0, bottle_ratio=0.25, efficient=False, group_size=None,
        act_layer='relu', 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 NormalizationFreeBlock(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, efficient=True, ch_div=1, group_size=None,
            attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0., skipinit=False):
        super().__init__()
        first_dilation = first_dilation or dilation
        out_chs = out_chs or in_chs
        # EfficientNet-like models scale bottleneck from in_chs, otherwise scale from out_chs like ResNet
        mid_chs = make_divisible(in_chs * bottle_ratio if efficient else out_chs * bottle_ratio, ch_div)
        groups = 1 if group_size is None 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 attn_layer is not None:
            self.attn = attn_layer(mid_chs)
        else:
            self.attn = None
        self.act3 = act_layer()
        self.conv3 = conv_layer(mid_chs, out_chs, 1)
        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.attn is not None:
            out = self.attn_gain * self.attn(out)
        out = self.conv3(self.act3(out))
        out = self.drop_path(out)
        if self.skipinit_gain is None:
            out = out * self.alpha + shortcut
        else:
            # this really slows things down for some reason, TBD
            out = out * self.alpha * self.skipinit_gain + shortcut
        return out


def create_stem(in_chs, out_chs, stem_type='', conv_layer=None):
    stem_stride = 2
    stem = OrderedDict()
    assert stem_type in ('', 'deep', '3x3', '7x7', 'deep_pool', '3x3_pool', '7x7_pool')
    if 'deep' in stem_type:
        # 3 deep 3x3  conv stack as in ResNet V1D models. NOTE: doesn't work as well here
        mid_chs = out_chs // 2
        stem['conv1'] = conv_layer(in_chs, mid_chs, kernel_size=3, stride=2)
        stem['conv2'] = conv_layer(mid_chs, mid_chs, kernel_size=3, stride=1)
        stem['conv3'] = conv_layer(mid_chs, out_chs, kernel_size=3, stride=1)
    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


_nonlin_gamma = dict(
    silu=1./.5595,
    relu=(0.5 * (1. - 1. / math.pi)) ** -0.5,
    identity=1.0
)


class NormalizerFreeNet(nn.Module):
    """ Normalizer-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, and yields slightly different results.
        * 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 = create_stem(in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer)

        self.feature_info = []  # NOTE: there will be no stride == 2 feature if stem_stride == 4
        dpr = [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
            self.feature_info += [dict(
                num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}.0.act1' if stride == 2 else '')]
            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 += [NormalizationFreeBlock(
                    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.efficient and first_block else cfg.bottle_ratio,
                    efficient=cfg.efficient,
                    ch_div=cfg.ch_div,
                    attn_layer=attn_layer,
                    attn_gain=cfg.attn_gain,
                    act_layer=act_layer,
                    conv_layer=conv_layer,
                    drop_path_rate=dpr[stage_idx][block_idx],
                    skipinit=cfg.skipinit,
                )]
                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.efficient and 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()
        # FIXME not 100% clear on gamma subtleties final conv/final act in case where it's in stdconv
        self.final_act = act_layer()
        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):
                nn.init.zeros_(m.weight)
                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:
        feature_cfg['out_indices'] = (1, 2, 3, 4)  # no stride 2, 0 level feat for stride 4 maxpool stems in ResNet

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