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

""" Bring-Your-Own-Blocks Network

A flexible network w/ dataclass based config for stacking those NN blocks.

This model is currently used to implement the following networks:

GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
Code and weights: https://github.com/idstcv/GPU-Efficient-Networks, licensed Apache 2.0

RepVGG - repvgg_*
Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
Code and weights: https://github.com/DingXiaoH/RepVGG, licensed MIT

In all cases the models have been modified to fit within the design of ByobNet. I've remapped
the original weights and verified accuracies.

For GPU Efficient nets, I used the original names for the blocks since they were for the most part
the same as original residual blocks in ResNe(X)t, DarkNet, and other existing models. Note also some
changes introduced in RegNet were also present in the stem and bottleneck blocks for this model.

A significant number of different network archs can be implemented here, including variants of the
above nets that include attention.

Hacked together by / copyright Ross Wightman, 2021.
"""
import math
from dataclasses import dataclass, field, replace
from typing import Tuple, List, Dict, Optional, Union, Any, Callable, Sequence
from functools import partial

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import ClassifierHead, ConvBnAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
    create_conv2d, get_act_layer, convert_norm_act, get_attn, make_divisible, to_2tuple
from .registry import register_model

__all__ = ['ByobNet', 'ByoModelCfg', 'ByoBlockCfg', 'create_byob_stem', 'create_block']


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bilinear',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.conv', 'classifier': 'head.fc',
        **kwargs
    }


default_cfgs = {
    # GPU-Efficient (ResNet) weights
    'gernet_s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_s-756b4751.pth'),
    'gernet_m': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth'),
    'gernet_l': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth',
        input_size=(3, 256, 256), pool_size=(8, 8)),

    # RepVGG weights
    'repvgg_a2': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_a2-c1ee6d2b.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b0': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b0-80ac3f1b.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b1': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b1-77ca2989.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b1g4': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b1g4-abde5d92.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b2': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b2-25b7494e.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b2g4': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b2g4-165a85f2.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b3': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b3-199bc50d.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
    'repvgg_b3g4': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b3g4-73c370bf.pth',
        first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),

    # experimental configs
    'resnet51q': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
        first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
        test_input_size=(3, 288, 288), crop_pct=1.0),
    'resnet61q': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),

    'gcresnext26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
    'seresnext26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
    'eca_resnext26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
    'bat_resnext26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
        min_input_size=(3, 256, 256)),

    'gcresnet26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
    'seresnet26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
    'eca_resnet26ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),

    'gcresnet50t': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),

    'gcresnext50ts': _cfg(
        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
}


@dataclass
class ByoBlockCfg:
    type: Union[str, nn.Module]
    d: int  # block depth (number of block repeats in stage)
    c: int  # number of output channels for each block in stage
    s: int = 2  # stride of stage (first block)
    gs: Optional[Union[int, Callable]] = None  # group-size of blocks in stage, conv is depthwise if gs == 1
    br: float = 1.  # bottleneck-ratio of blocks in stage

    # NOTE: these config items override the model cfgs that are applied to all blocks by default
    attn_layer: Optional[str] = None
    attn_kwargs: Optional[Dict[str, Any]] = None
    self_attn_layer: Optional[str] = None
    self_attn_kwargs: Optional[Dict[str, Any]] = None
    block_kwargs: Optional[Dict[str, Any]] = None


@dataclass
class ByoModelCfg:
    blocks: Tuple[Union[ByoBlockCfg, Tuple[ByoBlockCfg, ...]], ...]
    downsample: str = 'conv1x1'
    stem_type: str = '3x3'
    stem_pool: Optional[str] = 'maxpool'
    stem_chs: int = 32
    width_factor: float = 1.0
    num_features: int = 0  # num out_channels for final conv, no final 1x1 conv if 0
    zero_init_last_bn: bool = True
    fixed_input_size: bool = False  # model constrained to a fixed-input size / img_size must be provided on creation

    act_layer: str = 'relu'
    norm_layer: str = 'batchnorm'

    # NOTE: these config items will be overridden by the block cfg (per-block) if they are set there
    attn_layer: Optional[str] = None
    attn_kwargs: dict = field(default_factory=lambda: dict())
    self_attn_layer: Optional[str] = None
    self_attn_kwargs: dict = field(default_factory=lambda: dict())
    block_kwargs: Dict[str, Any] = field(default_factory=lambda: dict())


def _rep_vgg_bcfg(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0):
    c = (64, 128, 256, 512)
    group_size = 0
    if groups > 0:
        group_size = lambda chs, idx: chs // groups if (idx + 1) % 2 == 0 else 0
    bcfg = tuple([ByoBlockCfg(type='rep', d=d, c=c * wf, gs=group_size) for d, c, wf in zip(d, c, wf)])
    return bcfg


def interleave_blocks(
        types: Tuple[str, str], every: Union[int, List[int]], d, first: bool = False, **kwargs
) -> Tuple[ByoBlockCfg]:
    """ interleave 2 block types in stack
    """
    assert len(types) == 2
    if isinstance(every, int):
        every = list(range(0 if first else every, d, every))
        if not every:
            every = [d - 1]
    set(every)
    blocks = []
    for i in range(d):
        block_type = types[1] if i in every else types[0]
        blocks += [ByoBlockCfg(type=block_type, d=1, **kwargs)]
    return tuple(blocks)


model_cfgs = dict(
    gernet_l=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
            ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
            ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
            ByoBlockCfg(type='bottle', d=5, c=640, s=2, gs=1, br=3.),
            ByoBlockCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
        ),
        stem_chs=32,
        stem_pool=None,
        num_features=2560,
    ),
    gernet_m=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
            ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
            ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
            ByoBlockCfg(type='bottle', d=4, c=640, s=2, gs=1, br=3.),
            ByoBlockCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
        ),
        stem_chs=32,
        stem_pool=None,
        num_features=2560,
    ),
    gernet_s=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='basic', d=1, c=48, s=2, gs=0, br=1.),
            ByoBlockCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
            ByoBlockCfg(type='bottle', d=7, c=384, s=2, gs=0, br=1 / 4),
            ByoBlockCfg(type='bottle', d=2, c=560, s=2, gs=1, br=3.),
            ByoBlockCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
        ),
        stem_chs=13,
        stem_pool=None,
        num_features=1920,
    ),

    repvgg_a2=ByoModelCfg(
        blocks=_rep_vgg_bcfg(d=(2, 4, 14, 1), wf=(1.5, 1.5, 1.5, 2.75)),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b0=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(1., 1., 1., 2.5)),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b1=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.)),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b1g4=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.), groups=4),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b2=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.)),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b2g4=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.), groups=4),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b3=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.)),
        stem_type='rep',
        stem_chs=64,
    ),
    repvgg_b3g4=ByoModelCfg(
        blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.), groups=4),
        stem_type='rep',
        stem_chs=64,
    ),

    # WARN: experimental, may vanish/change
    resnet51q=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
        ),
        stem_chs=128,
        stem_type='quad2',
        stem_pool=None,
        num_features=2048,
        act_layer='silu',
    ),

    resnet61q=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='edge', d=1, c=256, s=1, gs=0, br=1.0, block_kwargs=dict()),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
        ),
        stem_chs=128,
        stem_type='quad',
        stem_pool=None,
        num_features=2048,
        act_layer='silu',
        block_kwargs=dict(extra_conv=True),
    ),

    # WARN: experimental, may vanish/change
    geresnet50t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='edge', d=3, c=256, s=1, br=0.25),
            ByoBlockCfg(type='edge', d=4, c=512, s=2, br=0.25),
            ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool=None,
        attn_layer='ge',
        attn_kwargs=dict(extent=8, extra_params=True),
    ),

    # A series of ResNeXt-26 models w/ one of GC, SE, ECA, BAT attn, group size 32, SiLU act,
    # and a tiered stem w/ maxpool
    gcresnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        attn_layer='gca',
    ),
    seresnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='relu',
        attn_layer='se',
    ),
    eca_resnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        attn_layer='eca',
    ),
    bat_resnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        attn_layer='bat',
        attn_kwargs=dict(block_size=8)
    ),

    # A series of ResNet-26 models w/ one of GC, SE, ECA attn, no groups, SiLU act, 1280 feat fc
    # and a tiered stem w/ no maxpool
    gcresnet26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='',
        num_features=1280,
        act_layer='silu',
        attn_layer='gca',
    ),
    seresnet26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='',
        num_features=1280,
        act_layer='silu',
        attn_layer='se',
    ),
    eca_resnet26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='',
        num_features=1280,
        act_layer='silu',
        attn_layer='eca',
    ),

    gcresnet50t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, br=0.25),
            ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool=None,
        attn_layer='gca',
    ),

    gcresnext50ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
            ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        # stem_pool=None,
        act_layer='silu',
        attn_layer='gca',
    ),
)


@register_model
def gernet_l(pretrained=False, **kwargs):
    """ GEResNet-Large (GENet-Large from official impl)
    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
    """
    return _create_byobnet('gernet_l', pretrained=pretrained, **kwargs)


@register_model
def gernet_m(pretrained=False, **kwargs):
    """ GEResNet-Medium (GENet-Normal from official impl)
    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
    """
    return _create_byobnet('gernet_m', pretrained=pretrained, **kwargs)


@register_model
def gernet_s(pretrained=False, **kwargs):
    """ EResNet-Small (GENet-Small from official impl)
    `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
    """
    return _create_byobnet('gernet_s', pretrained=pretrained, **kwargs)


@register_model
def repvgg_a2(pretrained=False, **kwargs):
    """ RepVGG-A2
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_a2', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b0(pretrained=False, **kwargs):
    """ RepVGG-B0
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b0', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b1(pretrained=False, **kwargs):
    """ RepVGG-B1
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b1', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b1g4(pretrained=False, **kwargs):
    """ RepVGG-B1g4
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b1g4', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b2(pretrained=False, **kwargs):
    """ RepVGG-B2
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b2', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b2g4(pretrained=False, **kwargs):
    """ RepVGG-B2g4
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b2g4', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b3(pretrained=False, **kwargs):
    """ RepVGG-B3
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b3', pretrained=pretrained, **kwargs)


@register_model
def repvgg_b3g4(pretrained=False, **kwargs):
    """ RepVGG-B3g4
    `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
    """
    return _create_byobnet('repvgg_b3g4', pretrained=pretrained, **kwargs)


@register_model
def resnet51q(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('resnet51q', pretrained=pretrained, **kwargs)


@register_model
def resnet61q(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('resnet61q', pretrained=pretrained, **kwargs)


@register_model
def gcresnext26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs)


@register_model
def seresnext26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('seresnext26ts', pretrained=pretrained, **kwargs)


@register_model
def eca_resnext26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('eca_resnext26ts', pretrained=pretrained, **kwargs)


@register_model
def bat_resnext26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)


@register_model
def gcresnet26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('gcresnet26ts', pretrained=pretrained, **kwargs)


@register_model
def seresnet26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('seresnet26ts', pretrained=pretrained, **kwargs)


@register_model
def eca_resnet26ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('eca_resnet26ts', pretrained=pretrained, **kwargs)


@register_model
def gcresnet50t(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)


@register_model
def gcresnext50ts(pretrained=False, **kwargs):
    """
    """
    return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)


def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
    if not isinstance(stage_blocks_cfg, Sequence):
        stage_blocks_cfg = (stage_blocks_cfg,)
    block_cfgs = []
    for i, cfg in enumerate(stage_blocks_cfg):
        block_cfgs += [replace(cfg, d=1) for _ in range(cfg.d)]
    return block_cfgs


def num_groups(group_size, channels):
    if not group_size:  # 0 or None
        return 1  # normal conv with 1 group
    else:
        # NOTE group_size == 1 -> depthwise conv
        assert channels % group_size == 0
        return channels // group_size


@dataclass
class LayerFn:
    conv_norm_act: Callable = ConvBnAct
    norm_act: Callable = BatchNormAct2d
    act: Callable = nn.ReLU
    attn: Optional[Callable] = None
    self_attn: Optional[Callable] = None


class DownsampleAvg(nn.Module):
    def __init__(self, in_chs, out_chs, stride=1, dilation=1, apply_act=False, layers: LayerFn = None):
        """ AvgPool Downsampling as in 'D' ResNet variants."""
        super(DownsampleAvg, self).__init__()
        layers = layers or LayerFn()
        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 = layers.conv_norm_act(in_chs, out_chs, 1, apply_act=apply_act)

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


def create_downsample(downsample_type, layers: LayerFn, **kwargs):
    if downsample_type == 'avg':
        return DownsampleAvg(**kwargs)
    else:
        return layers.conv_norm_act(kwargs.pop('in_chs'), kwargs.pop('out_chs'), kernel_size=1, **kwargs)


class BasicBlock(nn.Module):
    """ ResNet Basic Block - kxk + kxk
    """

    def __init__(
            self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), group_size=None, bottle_ratio=1.0,
            downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
            drop_path_rate=0.):
        super(BasicBlock, self).__init__()
        layers = layers or LayerFn()
        mid_chs = make_divisible(out_chs * bottle_ratio)
        groups = num_groups(group_size, mid_chs)

        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
            self.shortcut = create_downsample(
                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
                apply_act=False, layers=layers)
        else:
            self.shortcut = nn.Identity()

        self.conv1_kxk = layers.conv_norm_act(in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0])
        self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
        self.conv2_kxk = layers.conv_norm_act(
            mid_chs, out_chs, kernel_size, dilation=dilation[1], groups=groups, drop_block=drop_block, apply_act=False)
        self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.act = nn.Identity() if linear_out else layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        if zero_init_last_bn:
            nn.init.zeros_(self.conv2_kxk.bn.weight)
        for attn in (self.attn, self.attn_last):
            if hasattr(attn, 'reset_parameters'):
                attn.reset_parameters()

    def forward(self, x):
        shortcut = self.shortcut(x)

        # residual path
        x = self.conv1_kxk(x)
        x = self.conv2_kxk(x)
        x = self.attn(x)
        x = self.drop_path(x)

        x = self.act(x + shortcut)
        return x


class BottleneckBlock(nn.Module):
    """ ResNet-like Bottleneck Block - 1x1 - kxk - 1x1
    """

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
                 downsample='avg', attn_last=False, linear_out=False, extra_conv=False, layers: LayerFn = None,
                 drop_block=None, drop_path_rate=0.):
        super(BottleneckBlock, self).__init__()
        layers = layers or LayerFn()
        mid_chs = make_divisible(out_chs * bottle_ratio)
        groups = num_groups(group_size, mid_chs)

        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
            self.shortcut = create_downsample(
                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
                apply_act=False, layers=layers)
        else:
            self.shortcut = nn.Identity()

        self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
        self.conv2_kxk = layers.conv_norm_act(
            mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
            groups=groups, drop_block=drop_block)
        self.conv2_kxk = layers.conv_norm_act(
            mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
            groups=groups, drop_block=drop_block)
        if extra_conv:
            self.conv2b_kxk = layers.conv_norm_act(
                mid_chs, mid_chs, kernel_size, dilation=dilation[1], groups=groups, drop_block=drop_block)
        else:
            self.conv2b_kxk = nn.Identity()
        self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
        self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
        self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.act = nn.Identity() if linear_out else layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        if zero_init_last_bn:
            nn.init.zeros_(self.conv3_1x1.bn.weight)
        for attn in (self.attn, self.attn_last):
            if hasattr(attn, 'reset_parameters'):
                attn.reset_parameters()

    def forward(self, x):
        shortcut = self.shortcut(x)

        x = self.conv1_1x1(x)
        x = self.conv2_kxk(x)
        x = self.conv2b_kxk(x)
        x = self.attn(x)
        x = self.conv3_1x1(x)
        x = self.attn_last(x)
        x = self.drop_path(x)

        x = self.act(x + shortcut)
        return x


class DarkBlock(nn.Module):
    """ DarkNet-like (1x1 + 3x3 w/ stride) block

    The GE-Net impl included a 1x1 + 3x3 block in their search space. It was not used in the feature models.
    This block is pretty much a DarkNet block (also DenseNet) hence the name. Neither DarkNet or DenseNet
    uses strides within the block (external 3x3 or maxpool downsampling is done in front of the block repeats).

    If one does want to use a lot of these blocks w/ stride, I'd recommend using the EdgeBlock (3x3 /w stride + 1x1)
    for more optimal compute.
    """

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
                 downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
                 drop_path_rate=0.):
        super(DarkBlock, self).__init__()
        layers = layers or LayerFn()
        mid_chs = make_divisible(out_chs * bottle_ratio)
        groups = num_groups(group_size, mid_chs)

        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
            self.shortcut = create_downsample(
                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
                apply_act=False, layers=layers)
        else:
            self.shortcut = nn.Identity()

        self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
        self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
        self.conv2_kxk = layers.conv_norm_act(
            mid_chs, out_chs, kernel_size, stride=stride, dilation=dilation[0],
            groups=groups,  drop_block=drop_block, apply_act=False)
        self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.act = nn.Identity() if linear_out else layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        if zero_init_last_bn:
            nn.init.zeros_(self.conv2_kxk.bn.weight)
        for attn in (self.attn, self.attn_last):
            if hasattr(attn, 'reset_parameters'):
                attn.reset_parameters()

    def forward(self, x):
        shortcut = self.shortcut(x)

        x = self.conv1_1x1(x)
        x = self.attn(x)
        x = self.conv2_kxk(x)
        x = self.attn_last(x)
        x = self.drop_path(x)
        x = self.act(x + shortcut)
        return x


class EdgeBlock(nn.Module):
    """ EdgeResidual-like (3x3 + 1x1) block

    A two layer block like DarkBlock, but with the order of the 3x3 and 1x1 convs reversed.
    Very similar to the EfficientNet Edge-Residual block but this block it ends with activations, is
    intended to be used with either expansion or bottleneck contraction, and can use DW/group/non-grouped convs.

    FIXME is there a more common 3x3 + 1x1 conv block to name this after?
    """

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
                 downsample='avg', attn_last=False, linear_out=False, layers: LayerFn = None,
                 drop_block=None, drop_path_rate=0.):
        super(EdgeBlock, self).__init__()
        layers = layers or LayerFn()
        mid_chs = make_divisible(out_chs * bottle_ratio)
        groups = num_groups(group_size, mid_chs)

        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
            self.shortcut = create_downsample(
                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
                apply_act=False, layers=layers)
        else:
            self.shortcut = nn.Identity()

        self.conv1_kxk = layers.conv_norm_act(
            in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
            groups=groups,  drop_block=drop_block)
        self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
        self.conv2_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
        self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.act = nn.Identity() if linear_out else layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        if zero_init_last_bn:
            nn.init.zeros_(self.conv2_1x1.bn.weight)
        for attn in (self.attn, self.attn_last):
            if hasattr(attn, 'reset_parameters'):
                attn.reset_parameters()

    def forward(self, x):
        shortcut = self.shortcut(x)

        x = self.conv1_kxk(x)
        x = self.attn(x)
        x = self.conv2_1x1(x)
        x = self.attn_last(x)
        x = self.drop_path(x)
        x = self.act(x + shortcut)
        return x


class RepVggBlock(nn.Module):
    """ RepVGG Block.

    Adapted from impl at https://github.com/DingXiaoH/RepVGG

    This version does not currently support the deploy optimization. It is currently fixed in 'train' mode.
    """

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
                 downsample='', layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
        super(RepVggBlock, self).__init__()
        layers = layers or LayerFn()
        groups = num_groups(group_size, in_chs)

        use_ident = in_chs == out_chs and stride == 1 and dilation[0] == dilation[1]
        self.identity = layers.norm_act(out_chs, apply_act=False) if use_ident else None
        self.conv_kxk = layers.conv_norm_act(
            in_chs, out_chs, kernel_size, stride=stride, dilation=dilation[0],
            groups=groups, drop_block=drop_block, apply_act=False)
        self.conv_1x1 = layers.conv_norm_act(in_chs, out_chs, 1, stride=stride, groups=groups, apply_act=False)
        self.attn = nn.Identity() if layers.attn is None else layers.attn(out_chs)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. and use_ident else nn.Identity()
        self.act = layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        # NOTE this init overrides that base model init with specific changes for the block type
        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d):
                nn.init.normal_(m.weight, .1, .1)
                nn.init.normal_(m.bias, 0, .1)
        if hasattr(self.attn, 'reset_parameters'):
            self.attn.reset_parameters()

    def forward(self, x):
        if self.identity is None:
            x = self.conv_1x1(x) + self.conv_kxk(x)
        else:
            identity = self.identity(x)
            x = self.conv_1x1(x) + self.conv_kxk(x)
            x = self.drop_path(x)  # not in the paper / official impl, experimental
            x = x + identity
        x = self.attn(x)  # no attn in the paper / official impl, experimental
        x = self.act(x)
        return x


class SelfAttnBlock(nn.Module):
    """ ResNet-like Bottleneck Block - 1x1 - optional kxk - self attn - 1x1
    """

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
                 downsample='avg', extra_conv=False, linear_out=False, post_attn_na=True, feat_size=None,
                 layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
        super(SelfAttnBlock, self).__init__()
        assert layers is not None
        mid_chs = make_divisible(out_chs * bottle_ratio)
        groups = num_groups(group_size, mid_chs)

        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
            self.shortcut = create_downsample(
                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
                apply_act=False, layers=layers)
        else:
            self.shortcut = nn.Identity()

        self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
        if extra_conv:
            self.conv2_kxk = layers.conv_norm_act(
                mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
                groups=groups, drop_block=drop_block)
            stride = 1  # striding done via conv if enabled
        else:
            self.conv2_kxk = nn.Identity()
        opt_kwargs = {} if feat_size is None else dict(feat_size=feat_size)
        # FIXME need to dilate self attn to have dilated network support, moop moop
        self.self_attn = layers.self_attn(mid_chs, stride=stride, **opt_kwargs)
        self.post_attn = layers.norm_act(mid_chs) if post_attn_na else nn.Identity()
        self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
        self.act = nn.Identity() if linear_out else layers.act(inplace=True)

    def init_weights(self, zero_init_last_bn: bool = False):
        if zero_init_last_bn:
            nn.init.zeros_(self.conv3_1x1.bn.weight)
        if hasattr(self.self_attn, 'reset_parameters'):
            self.self_attn.reset_parameters()

    def forward(self, x):
        shortcut = self.shortcut(x)

        x = self.conv1_1x1(x)
        x = self.conv2_kxk(x)
        x = self.self_attn(x)
        x = self.post_attn(x)
        x = self.conv3_1x1(x)
        x = self.drop_path(x)

        x = self.act(x + shortcut)
        return x


_block_registry = dict(
    basic=BasicBlock,
    bottle=BottleneckBlock,
    dark=DarkBlock,
    edge=EdgeBlock,
    rep=RepVggBlock,
    self_attn=SelfAttnBlock,
)


def register_block(block_type:str, block_fn: nn.Module):
    _block_registry[block_type] = block_fn


def create_block(block: Union[str, nn.Module], **kwargs):
    if isinstance(block, (nn.Module, partial)):
        return block(**kwargs)
    assert block in _block_registry, f'Unknown block type ({block}'
    return _block_registry[block](**kwargs)


class Stem(nn.Sequential):

    def __init__(self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool',
                 num_rep=3, num_act=None, chs_decay=0.5, layers: LayerFn = None):
        super().__init__()
        assert stride in (2, 4)
        layers = layers or LayerFn()

        if isinstance(out_chs, (list, tuple)):
            num_rep = len(out_chs)
            stem_chs = out_chs
        else:
            stem_chs = [round(out_chs * chs_decay ** i) for i in range(num_rep)][::-1]

        self.stride = stride
        self.feature_info = []  # track intermediate features
        prev_feat = ''
        stem_strides = [2] + [1] * (num_rep - 1)
        if stride == 4 and not pool:
            # set last conv in stack to be strided if stride == 4 and no pooling layer
            stem_strides[-1] = 2

        num_act = num_rep if num_act is None else num_act
        # if num_act < num_rep, first convs in stack won't have bn + act
        stem_norm_acts = [False] * (num_rep - num_act) + [True] * num_act
        prev_chs = in_chs
        curr_stride = 1
        for i, (ch, s, na) in enumerate(zip(stem_chs, stem_strides, stem_norm_acts)):
            layer_fn = layers.conv_norm_act if na else create_conv2d
            conv_name = f'conv{i + 1}'
            if i > 0 and s > 1:
                self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
            self.add_module(conv_name, layer_fn(prev_chs, ch, kernel_size=kernel_size, stride=s))
            prev_chs = ch
            curr_stride *= s
            prev_feat = conv_name

        if pool and 'max' in pool.lower():
            self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
            self.add_module('pool', nn.MaxPool2d(3, 2, 1))
            curr_stride *= 2
            prev_feat = 'pool'

        self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
        assert curr_stride == stride


def create_byob_stem(in_chs, out_chs, stem_type='', pool_type='', feat_prefix='stem', layers: LayerFn = None):
    layers = layers or LayerFn()
    assert stem_type in ('', 'quad', 'quad2', 'tiered', 'deep', 'rep', '7x7', '3x3')
    if 'quad' in stem_type:
        # based on NFNet stem, stack of 4 3x3 convs
        num_act = 2 if 'quad2' in stem_type else None
        stem = Stem(in_chs, out_chs, num_rep=4, num_act=num_act, pool=pool_type, layers=layers)
    elif 'tiered' in stem_type:
        # 3x3 stack of 3 convs as in my ResNet-T
        stem = Stem(in_chs, (3 * out_chs // 8, out_chs // 2, out_chs), pool=pool_type, layers=layers)
    elif 'deep' in stem_type:
        # 3x3 stack of 3 convs as in ResNet-D
        stem = Stem(in_chs, out_chs, num_rep=3, chs_decay=1.0, pool=pool_type, layers=layers)
    elif 'rep' in stem_type:
        stem = RepVggBlock(in_chs, out_chs, stride=2, layers=layers)
    elif '7x7' in stem_type:
        # 7x7 stem conv as in ResNet
        if pool_type:
            stem = Stem(in_chs, out_chs, 7, num_rep=1, pool=pool_type, layers=layers)
        else:
            stem = layers.conv_norm_act(in_chs, out_chs, 7, stride=2)
    else:
        # 3x3 stem conv as in RegNet is the default
        if pool_type:
            stem = Stem(in_chs, out_chs, 3, num_rep=1, pool=pool_type, layers=layers)
        else:
            stem = layers.conv_norm_act(in_chs, out_chs, 3, stride=2)

    if isinstance(stem, Stem):
        feature_info = [dict(f, module='.'.join([feat_prefix, f['module']])) for f in stem.feature_info]
    else:
        feature_info = [dict(num_chs=out_chs, reduction=2, module=feat_prefix)]
    return stem, feature_info


def reduce_feat_size(feat_size, stride=2):
    return None if feat_size is None else tuple([s // stride for s in feat_size])


def override_kwargs(block_kwargs, model_kwargs):
    """ Override model level attn/self-attn/block kwargs w/ block level

    NOTE: kwargs are NOT merged across levels, block_kwargs will fully replace model_kwargs
    for the block if set to anything that isn't None.

    i.e. an empty block_kwargs dict will remove kwargs set at model level for that block
    """
    out_kwargs = block_kwargs if block_kwargs is not None else model_kwargs
    return out_kwargs or {}  # make sure None isn't returned


def update_block_kwargs(block_kwargs: Dict[str, Any], block_cfg: ByoBlockCfg, model_cfg: ByoModelCfg, ):
    layer_fns = block_kwargs['layers']

    # override attn layer / args with block local config
    if block_cfg.attn_kwargs is not None or block_cfg.attn_layer is not None:
        # override attn layer config
        if not block_cfg.attn_layer:
            # empty string for attn_layer type will disable attn for this block
            attn_layer = None
        else:
            attn_kwargs = override_kwargs(block_cfg.attn_kwargs, model_cfg.attn_kwargs)
            attn_layer = block_cfg.attn_layer or model_cfg.attn_layer
            attn_layer = partial(get_attn(attn_layer), *attn_kwargs) if attn_layer is not None else None
        layer_fns = replace(layer_fns, attn=attn_layer)

    # override self-attn layer / args with block local cfg
    if block_cfg.self_attn_kwargs is not None or block_cfg.self_attn_layer is not None:
        # override attn layer config
        if not block_cfg.self_attn_layer:
            # empty string for self_attn_layer type will disable attn for this block
            self_attn_layer = None
        else:
            self_attn_kwargs = override_kwargs(block_cfg.self_attn_kwargs, model_cfg.self_attn_kwargs)
            self_attn_layer = block_cfg.self_attn_layer or model_cfg.self_attn_layer
            self_attn_layer = partial(get_attn(self_attn_layer), *self_attn_kwargs) \
                if self_attn_layer is not None else None
        layer_fns = replace(layer_fns, self_attn=self_attn_layer)

    block_kwargs['layers'] = layer_fns

    # add additional block_kwargs specified in block_cfg or model_cfg, precedence to block if set
    block_kwargs.update(override_kwargs(block_cfg.block_kwargs, model_cfg.block_kwargs))


def create_byob_stages(
        cfg: ByoModelCfg, drop_path_rate: float, output_stride: int, stem_feat: Dict[str, Any],
        feat_size: Optional[int] = None,
        layers: Optional[LayerFn] = None,
        block_kwargs_fn: Optional[Callable] = update_block_kwargs):

    layers = layers or LayerFn()
    feature_info = []
    block_cfgs = [expand_blocks_cfg(s) for s in cfg.blocks]
    depths = [sum([bc.d for bc in stage_bcs]) for stage_bcs in block_cfgs]
    dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
    dilation = 1
    net_stride = stem_feat['reduction']
    prev_chs = stem_feat['num_chs']
    prev_feat = stem_feat
    stages = []
    for stage_idx, stage_block_cfgs in enumerate(block_cfgs):
        stride = stage_block_cfgs[0].s
        if stride != 1 and prev_feat:
            feature_info.append(prev_feat)
        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, block_cfg in enumerate(stage_block_cfgs):
            out_chs = make_divisible(block_cfg.c * cfg.width_factor)
            group_size = block_cfg.gs
            if isinstance(group_size, Callable):
                group_size = group_size(out_chs, block_idx)
            block_kwargs = dict(  # Blocks used in this model must accept these arguments
                in_chs=prev_chs,
                out_chs=out_chs,
                stride=stride if block_idx == 0 else 1,
                dilation=(first_dilation, dilation),
                group_size=group_size,
                bottle_ratio=block_cfg.br,
                downsample=cfg.downsample,
                drop_path_rate=dpr[stage_idx][block_idx],
                layers=layers,
            )
            if block_cfg.type in ('self_attn',):
                # add feat_size arg for blocks that support/need it
                block_kwargs['feat_size'] = feat_size
            block_kwargs_fn(block_kwargs, block_cfg=block_cfg, model_cfg=cfg)
            blocks += [create_block(block_cfg.type, **block_kwargs)]
            first_dilation = dilation
            prev_chs = out_chs
            if stride > 1 and block_idx == 0:
                feat_size = reduce_feat_size(feat_size, stride)

        stages += [nn.Sequential(*blocks)]
        prev_feat = dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')

    feature_info.append(prev_feat)
    return nn.Sequential(*stages), feature_info


def get_layer_fns(cfg: ByoModelCfg):
    act = get_act_layer(cfg.act_layer)
    norm_act = convert_norm_act(norm_layer=cfg.norm_layer, act_layer=act)
    conv_norm_act = partial(ConvBnAct, norm_layer=cfg.norm_layer, act_layer=act)
    attn = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
    self_attn = partial(get_attn(cfg.self_attn_layer), **cfg.self_attn_kwargs) if cfg.self_attn_layer else None
    layer_fn = LayerFn(conv_norm_act=conv_norm_act, norm_act=norm_act, act=act, attn=attn, self_attn=self_attn)
    return layer_fn


class ByobNet(nn.Module):
    """ 'Bring-your-own-blocks' Net

    A flexible network backbone that allows building model stem + blocks via
    dataclass cfg definition w/ factory functions for module instantiation.

    Current assumption is that both stem and blocks are in conv-bn-act order (w/ block ending in act).
    """
    def __init__(self, cfg: ByoModelCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
                 zero_init_last_bn=True, img_size=None, drop_rate=0., drop_path_rate=0.):
        super().__init__()
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        layers = get_layer_fns(cfg)
        if cfg.fixed_input_size:
            assert img_size is not None, 'img_size argument is required for fixed input size model'
        feat_size = to_2tuple(img_size) if img_size is not None else None

        self.feature_info = []
        stem_chs = int(round((cfg.stem_chs or cfg.blocks[0].c) * cfg.width_factor))
        self.stem, stem_feat = create_byob_stem(in_chans, stem_chs, cfg.stem_type, cfg.stem_pool, layers=layers)
        self.feature_info.extend(stem_feat[:-1])
        feat_size = reduce_feat_size(feat_size, stride=stem_feat[-1]['reduction'])

        self.stages, stage_feat = create_byob_stages(
            cfg, drop_path_rate, output_stride, stem_feat[-1], layers=layers, feat_size=feat_size)
        self.feature_info.extend(stage_feat[:-1])

        prev_chs = stage_feat[-1]['num_chs']
        if cfg.num_features:
            self.num_features = int(round(cfg.width_factor * cfg.num_features))
            self.final_conv = layers.conv_norm_act(prev_chs, self.num_features, 1)
        else:
            self.num_features = prev_chs
            self.final_conv = nn.Identity()
        self.feature_info += [
            dict(num_chs=self.num_features, reduction=stage_feat[-1]['reduction'], module='final_conv')]

        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)

        for n, m in self.named_modules():
            _init_weights(m, n)
        for m in self.modules():
            # call each block's weight init for block-specific overrides to init above
            if hasattr(m, 'init_weights'):
                m.init_weights(zero_init_last_bn=zero_init_last_bn)

    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)
        return x

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


def _init_weights(m, n=''):
    if isinstance(m, nn.Conv2d):
        fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        fan_out //= m.groups
        m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
        if m.bias is not None:
            m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        nn.init.normal_(m.weight, mean=0.0, std=0.01)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.ones_(m.weight)
        nn.init.zeros_(m.bias)


def _create_byobnet(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(
        ByobNet, variant, pretrained,
        default_cfg=default_cfgs[variant],
        model_cfg=model_cfgs[variant],
        feature_cfg=dict(flatten_sequential=True),
        **kwargs)