pytorch-image-models/timm/models/resnetv2.py

"""Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.

A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfoer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their original .NPZ checkpoints.

Additionally, supports non pre-activation bottleneck for use as a backbone for Vision Transfomers (ViT) and
extra padding support to allow porting of official Hybrid ResNet pretrained weights from
https://github.com/google-research/vision_transformer

Thanks to the Google team for the above two repositories and associated papers:
* Big Transfer (BiT): General Visual Representation Learning - https://arxiv.org/abs/1912.11370
* An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929

Original copyright of Google code below, modifications by Ross Wightman, Copyright 2020.
"""
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from collections import OrderedDict  # pylint: disable=g-importing-member

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg
from .registry import register_model
from .layers import get_padding, GroupNormAct, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, conv2d_same


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 480, 480), 'pool_size': (7, 7),
        'crop_pct': 1.0, 'interpolation': 'bilinear',
        'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
        'first_conv': 'stem.conv', 'classifier': 'head.fc',
        **kwargs
    }


default_cfgs = {
    # pretrained on imagenet21k, finetuned on imagenet1k
    'resnetv2_50x1_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz'),
    'resnetv2_50x3_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz'),
    'resnetv2_101x1_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz'),
    'resnetv2_101x3_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz'),
    'resnetv2_152x2_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz'),
    'resnetv2_152x4_bitm': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz'),

    # trained on imagenet-21k
    'resnetv2_50x1_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R50x1.npz',
        num_classes=21843),
    'resnetv2_50x3_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R50x3.npz',
        num_classes=21843),
    'resnetv2_101x1_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R101x1.npz',
        num_classes=21843),
    'resnetv2_101x3_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R101x3.npz',
        num_classes=21843),
    'resnetv2_152x2_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R152x2.npz',
        num_classes=21843),
    'resnetv2_152x4_bitm_in21k': _cfg(
        url='https://storage.googleapis.com/bit_models/BiT-M-R152x4.npz',
        num_classes=21843),


    # trained on imagenet-1k, NOTE not overly interesting set of weights, leaving disabled for now
    # 'resnetv2_50x1_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R50x1.npz'),
    # 'resnetv2_50x3_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R50x3.npz'),
    # 'resnetv2_101x1_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R101x3.npz'),
    # 'resnetv2_101x3_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R101x3.npz'),
    # 'resnetv2_152x2_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R152x2.npz'),
    # 'resnetv2_152x4_bits': _cfg(
    #     url='https://storage.googleapis.com/bit_models/BiT-S-R152x4.npz'),
}


def make_div(v, divisor=8):
    min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class StdConv2d(nn.Conv2d):

    def __init__(
            self, in_channel, out_channels, kernel_size, stride=1, dilation=1, bias=False, groups=1, eps=1e-5):
        padding = get_padding(kernel_size, stride, dilation)
        super().__init__(
            in_channel, out_channels, kernel_size, stride=stride,
            padding=padding, dilation=dilation, bias=bias, groups=groups)
        self.eps = eps

    def forward(self, x):
        w = self.weight
        v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
        w = (w - m) / (torch.sqrt(v) + self.eps)
        x = F.conv2d(x, w, self.bias, self.stride, self.padding, self.dilation, self.groups)
        return x


class StdConv2dSame(nn.Conv2d):
    """StdConv2d w/ TF compatible SAME padding. Used for ViT Hybrid model.
    """
    def __init__(
            self, in_channel, out_channels, kernel_size, stride=1, dilation=1, bias=False, groups=1, eps=1e-5):
        padding = get_padding(kernel_size, stride, dilation)
        super().__init__(
            in_channel, out_channels, kernel_size, stride=stride,
            padding=padding, dilation=dilation, bias=bias, groups=groups)
        self.eps = eps

    def forward(self, x):
        w = self.weight
        v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
        w = (w - m) / (torch.sqrt(v) + self.eps)
        x = conv2d_same(x, w, self.bias, self.stride, self.padding, self.dilation, self.groups)
        return x


def tf2th(conv_weights):
    """Possibly convert HWIO to OIHW."""
    if conv_weights.ndim == 4:
        conv_weights = conv_weights.transpose([3, 2, 0, 1])
    return torch.from_numpy(conv_weights)


class PreActBottleneck(nn.Module):
    """Pre-activation (v2) bottleneck block.

    Follows the implementation of "Identity Mappings in Deep Residual Networks":
    https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua

    Except it puts the stride on 3x3 conv when available.
    """

    def __init__(
            self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
            act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
        super().__init__()
        first_dilation = first_dilation or dilation
        conv_layer = conv_layer or StdConv2d
        norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
        out_chs = out_chs or in_chs
        mid_chs = make_div(out_chs * bottle_ratio)

        if proj_layer is not None:
            self.downsample = proj_layer(
                in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, preact=True,
                conv_layer=conv_layer, norm_layer=norm_layer)
        else:
            self.downsample = None

        self.norm1 = norm_layer(in_chs)
        self.conv1 = conv_layer(in_chs, mid_chs, 1)
        self.norm2 = norm_layer(mid_chs)
        self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
        self.norm3 = norm_layer(mid_chs)
        self.conv3 = conv_layer(mid_chs, out_chs, 1)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()

    def forward(self, x):
        x_preact = self.norm1(x)

        # shortcut branch
        shortcut = x
        if self.downsample is not None:
            shortcut = self.downsample(x_preact)

        # residual branch
        x = self.conv1(x_preact)
        x = self.conv2(self.norm2(x))
        x = self.conv3(self.norm3(x))
        x = self.drop_path(x)
        return x + shortcut


class Bottleneck(nn.Module):
    """Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
    """
    def __init__(
            self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
            act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
        super().__init__()
        first_dilation = first_dilation or dilation
        act_layer = act_layer or nn.ReLU
        conv_layer = conv_layer or StdConv2d
        norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
        out_chs = out_chs or in_chs
        mid_chs = make_div(out_chs * bottle_ratio)

        if proj_layer is not None:
            self.downsample = proj_layer(
                in_chs, out_chs, stride=stride, dilation=dilation, preact=False,
                conv_layer=conv_layer, norm_layer=norm_layer)
        else:
            self.downsample = None

        self.conv1 = conv_layer(in_chs, mid_chs, 1)
        self.norm1 = norm_layer(mid_chs)
        self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
        self.norm2 = norm_layer(mid_chs)
        self.conv3 = conv_layer(mid_chs, out_chs, 1)
        self.norm3 = norm_layer(out_chs, apply_act=False)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
        self.act3 = act_layer(inplace=True)

    def forward(self, x):
        # shortcut branch
        shortcut = x
        if self.downsample is not None:
            shortcut = self.downsample(x)

        # residual
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.conv2(x)
        x = self.norm2(x)
        x = self.conv3(x)
        x = self.norm3(x)
        x = self.act3(x + shortcut)
        return x


class DownsampleConv(nn.Module):
    def __init__(
            self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, preact=True,
            conv_layer=None, norm_layer=None):
        super(DownsampleConv, self).__init__()
        self.conv = conv_layer(in_chs, out_chs, 1, stride=stride)
        self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)

    def forward(self, x):
        return self.norm(self.conv(x))


class DownsampleAvg(nn.Module):
    def __init__(
            self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None,
            preact=True, conv_layer=None, norm_layer=None):
        """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
        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)
        self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)

    def forward(self, x):
        return self.norm(self.conv(self.pool(x)))


class ResNetStage(nn.Module):
    """ResNet Stage."""
    def __init__(self, in_chs, out_chs, stride, dilation, depth, bottle_ratio=0.25, groups=1,
                 avg_down=False, block_dpr=None, block_fn=PreActBottleneck,
                 act_layer=None, conv_layer=None, norm_layer=None, **block_kwargs):
        super(ResNetStage, self).__init__()
        first_dilation = 1 if dilation in (1, 2) else 2
        layer_kwargs = dict(act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer)
        proj_layer = DownsampleAvg if avg_down else DownsampleConv
        prev_chs = in_chs
        self.blocks = nn.Sequential()
        for block_idx in range(depth):
            drop_path_rate = block_dpr[block_idx] if block_dpr else 0.
            stride = stride if block_idx == 0 else 1
            self.blocks.add_module(str(block_idx), block_fn(
                prev_chs, out_chs, stride=stride, dilation=dilation, bottle_ratio=bottle_ratio, groups=groups,
                first_dilation=first_dilation, proj_layer=proj_layer, drop_path_rate=drop_path_rate,
                **layer_kwargs, **block_kwargs))
            prev_chs = out_chs
            first_dilation = dilation
            proj_layer = None

    def forward(self, x):
        x = self.blocks(x)
        return x


def create_stem(in_chs, out_chs, stem_type='', preact=True, conv_layer=None, norm_layer=None):
    stem = OrderedDict()
    assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same')

    # NOTE conv padding mode can be changed by overriding the conv_layer def
    if 'deep' in stem_type:
        # A 3 deep 3x3  conv stack as in ResNet V1D models
        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)
    else:
        # The usual 7x7 stem conv
        stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)

    if not preact:
        stem['norm'] = norm_layer(out_chs)

    if 'fixed' in stem_type:
        # 'fixed' SAME padding approximation that is used in BiT models
        stem['pad'] = nn.ConstantPad2d(1, 0)
        stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
    elif 'same' in stem_type:
        # full, input size based 'SAME' padding, used in ViT Hybrid model
        stem['pool'] = create_pool2d('max', kernel_size=3, stride=2, padding='same')
    else:
        # the usual PyTorch symmetric padding
        stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    return nn.Sequential(stem)


class ResNetV2(nn.Module):
    """Implementation of Pre-activation (v2) ResNet mode.
    """

    def __init__(self, layers, channels=(256, 512, 1024, 2048),
                 num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
                 width_factor=1, stem_chs=64, stem_type='', avg_down=False, preact=True,
                 act_layer=nn.ReLU, conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32),
                 drop_rate=0., drop_path_rate=0.):
        super().__init__()
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        wf = width_factor

        self.feature_info = []
        stem_chs = make_div(stem_chs * wf)
        self.stem = create_stem(in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
        # NOTE no, reduction 2 feature if preact
        self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module='' if preact else 'stem.norm'))

        prev_chs = stem_chs
        curr_stride = 4
        dilation = 1
        block_fn = PreActBottleneck if preact else Bottleneck
        self.stages = nn.Sequential()
        for stage_idx, (d, c) in enumerate(zip(layers, channels)):
            out_chs = make_div(c * wf)
            stride = 1 if stage_idx == 0 else 2
            if curr_stride >= output_stride:
                dilation *= stride
                stride = 1
            stage = ResNetStage(
                prev_chs, out_chs, stride=stride, dilation=dilation, depth=d, avg_down=avg_down,
                act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer, block_fn=block_fn)
            prev_chs = out_chs
            curr_stride *= stride
            feat_name = f'stages.{stage_idx}'
            if preact:
                feat_name = f'stages.{stage_idx + 1}.blocks.0.norm1' if (stage_idx + 1) != len(channels) else 'norm'
            self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=feat_name)]
            self.stages.add_module(str(stage_idx), stage)

        self.num_features = prev_chs
        self.norm = norm_layer(self.num_features) if preact else nn.Identity()
        self.head = ClassifierHead(
            self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, use_conv=True)

        for n, m in self.named_modules():
            if isinstance(m, nn.Linear) or ('.fc' in n and isinstance(m, nn.Conv2d)):
                nn.init.normal_(m.weight, mean=0.0, std=0.01)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    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, use_conv=True)

    def forward_features(self, x):
        x = self.stem(x)
        x = self.stages(x)
        x = self.norm(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        if not self.head.global_pool.is_identity():
            x = x.flatten(1)  # conv classifier, flatten if pooling isn't pass-through (disabled)
        return x

    def load_pretrained(self, checkpoint_path, prefix='resnet/'):
        import numpy as np
        weights = np.load(checkpoint_path)
        with torch.no_grad():
            self.stem.conv.weight.copy_(tf2th(weights[f'{prefix}root_block/standardized_conv2d/kernel']))
            self.norm.weight.copy_(tf2th(weights[f'{prefix}group_norm/gamma']))
            self.norm.bias.copy_(tf2th(weights[f'{prefix}group_norm/beta']))
            self.head.fc.weight.copy_(tf2th(weights[f'{prefix}head/conv2d/kernel']))
            self.head.fc.bias.copy_(tf2th(weights[f'{prefix}head/conv2d/bias']))
            for i, (sname, stage) in enumerate(self.stages.named_children()):
                for j, (bname, block) in enumerate(stage.blocks.named_children()):
                    convname = 'standardized_conv2d'
                    block_prefix = f'{prefix}block{i + 1}/unit{j + 1:02d}/'
                    block.conv1.weight.copy_(tf2th(weights[f'{block_prefix}a/{convname}/kernel']))
                    block.conv2.weight.copy_(tf2th(weights[f'{block_prefix}b/{convname}/kernel']))
                    block.conv3.weight.copy_(tf2th(weights[f'{block_prefix}c/{convname}/kernel']))
                    block.norm1.weight.copy_(tf2th(weights[f'{block_prefix}a/group_norm/gamma']))
                    block.norm2.weight.copy_(tf2th(weights[f'{block_prefix}b/group_norm/gamma']))
                    block.norm3.weight.copy_(tf2th(weights[f'{block_prefix}c/group_norm/gamma']))
                    block.norm1.bias.copy_(tf2th(weights[f'{block_prefix}a/group_norm/beta']))
                    block.norm2.bias.copy_(tf2th(weights[f'{block_prefix}b/group_norm/beta']))
                    block.norm3.bias.copy_(tf2th(weights[f'{block_prefix}c/group_norm/beta']))
                    if block.downsample is not None:
                        w = weights[f'{block_prefix}a/proj/{convname}/kernel']
                        block.downsample.conv.weight.copy_(tf2th(w))


def _create_resnetv2(variant, pretrained=False, **kwargs):
    # FIXME feature map extraction is not setup properly for pre-activation mode right now
    preact = kwargs.get('preact', True)
    feature_cfg = dict(flatten_sequential=True)
    if preact:
        feature_cfg['feature_cls'] = 'hook'
        feature_cfg['out_indices'] = (1, 2, 3, 4)  # no stride 2, 0 level feat for preact

    return build_model_with_cfg(
        ResNetV2, variant, pretrained, default_cfg=default_cfgs[variant], pretrained_custom_load=True,
        feature_cfg=feature_cfg, **kwargs)


@register_model
def resnetv2_50x1_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_50x1_bitm', pretrained=pretrained,
        layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)


@register_model
def resnetv2_50x3_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_50x3_bitm', pretrained=pretrained,
        layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)


@register_model
def resnetv2_101x1_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_101x1_bitm', pretrained=pretrained,
        layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)


@register_model
def resnetv2_101x3_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_101x3_bitm', pretrained=pretrained,
        layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)


@register_model
def resnetv2_152x2_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_152x2_bitm', pretrained=pretrained,
        layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)


@register_model
def resnetv2_152x4_bitm(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_152x4_bitm', pretrained=pretrained,
        layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)


@register_model
def resnetv2_50x1_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_50x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)


@register_model
def resnetv2_50x3_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_50x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)


@register_model
def resnetv2_101x1_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_101x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)


@register_model
def resnetv2_101x3_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_101x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)


@register_model
def resnetv2_152x2_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_152x2_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)


@register_model
def resnetv2_152x4_bitm_in21k(pretrained=False, **kwargs):
    return _create_resnetv2(
        'resnetv2_152x4_bitm_in21k', pretrained=pretrained, num_classes=kwargs.get('num_classes', 21843),
        layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)


# NOTE the 'S' versions of the model weights arent as interesting as original 21k or transfer to 1K M.

# @register_model
# def resnetv2_50x1_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_50x1_bits', pretrained=pretrained,
#         layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_50x3_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_50x3_bits', pretrained=pretrained,
#         layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_101x1_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_101x1_bits', pretrained=pretrained,
#         layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_101x3_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_101x3_bits', pretrained=pretrained,
#         layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_152x2_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_152x2_bits', pretrained=pretrained,
#         layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_152x4_bits(pretrained=False, **kwargs):
#     return _create_resnetv2(
#         'resnetv2_152x4_bits', pretrained=pretrained,
#         layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)
#