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

""" Generic EfficientNets

A generic class with building blocks to support a variety of models with efficient architectures:
* EfficientNet (B0-B5)
* MixNet (Small, Medium, and Large)
* MnasNet B1, A1 (SE), Small
* MobileNet V1, V2, and V3
* FBNet-C (TODO A & B)
* ChamNet (TODO still guessing at architecture definition)
* Single-Path NAS Pixel1
* And likely more...

TODO not all combinations and variations have been tested. Currently working on training hyper-params...

Hacked together by Ross Wightman
"""

import math
import re
import logging
from copy import deepcopy

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

from .registry import register_model
from .helpers import load_pretrained
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .conv2d_helpers import select_conv2d
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


__all__ = ['GenEfficientNet']


def _cfg(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': 'conv_stem', 'classifier': 'classifier',
        **kwargs
    }


default_cfgs = {
    'mnasnet_050': _cfg(url=''),
    'mnasnet_075': _cfg(url=''),
    'mnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'),
    'mnasnet_140': _cfg(url=''),
    'semnasnet_050': _cfg(url=''),
    'semnasnet_075': _cfg(url=''),
    'semnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
    'semnasnet_140': _cfg(url=''),
    'mnasnet_small': _cfg(url=''),
    'mobilenetv1_100': _cfg(url=''),
    'mobilenetv2_100': _cfg(url=''),
    'mobilenetv3_050': _cfg(url=''),
    'mobilenetv3_075': _cfg(url=''),
    'mobilenetv3_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth'),
    'chamnetv1_100': _cfg(url=''),
    'chamnetv2_100': _cfg(url=''),
    'fbnetc_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
        interpolation='bilinear'),
    'spnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
        interpolation='bilinear'),
    'efficientnet_b0': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth'),
    'efficientnet_b1': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
    'efficientnet_b2': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2-cf78dc4d.pth',
        input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
    'efficientnet_b3': _cfg(
        url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
    'efficientnet_b4': _cfg(
        url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
    'efficientnet_b5': _cfg(
        url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
    'tf_efficientnet_b0': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
        input_size=(3, 224, 224)),
    'tf_efficientnet_b1': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
    'tf_efficientnet_b2': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
        input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
    'tf_efficientnet_b3': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
    'tf_efficientnet_b4': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4-74ee3bed.pth',
        input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
    'tf_efficientnet_b5': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5-c6949ce9.pth',
        input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
    'mixnet_s': _cfg(url=''),
    'mixnet_m': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'),
    'mixnet_l': _cfg(url=''),
    'tf_mixnet_s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
    'tf_mixnet_m': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
    'tf_mixnet_l': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
}


_DEBUG = False

# Default args for PyTorch BN impl
_BN_MOMENTUM_PT_DEFAULT = 0.1
_BN_EPS_PT_DEFAULT = 1e-5
_BN_ARGS_PT = dict(momentum=_BN_MOMENTUM_PT_DEFAULT, eps=_BN_EPS_PT_DEFAULT)

# Defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
# papers and TF reference implementations. PT momentum equiv for TF decay is (1 - TF decay)
# NOTE: momentum varies btw .99 and .9997 depending on source
# .99 in official TF TPU impl
# .9997 (/w .999 in search space) for paper
_BN_MOMENTUM_TF_DEFAULT = 1 - 0.99
_BN_EPS_TF_DEFAULT = 1e-3
_BN_ARGS_TF = dict(momentum=_BN_MOMENTUM_TF_DEFAULT, eps=_BN_EPS_TF_DEFAULT)


def _resolve_bn_args(kwargs):
    bn_args = _BN_ARGS_TF.copy() if kwargs.pop('bn_tf', False) else _BN_ARGS_PT.copy()
    bn_momentum = kwargs.pop('bn_momentum', None)
    if bn_momentum is not None:
        bn_args['momentum'] = bn_momentum
    bn_eps = kwargs.pop('bn_eps', None)
    if bn_eps is not None:
        bn_args['eps'] = bn_eps
    return bn_args


def _round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):
    """Round number of filters based on depth multiplier."""
    if not multiplier:
        return channels

    channels *= multiplier
    channel_min = channel_min or divisor
    new_channels = max(
        int(channels + divisor / 2) // divisor * divisor,
        channel_min)
    # Make sure that round down does not go down by more than 10%.
    if new_channels < 0.9 * channels:
        new_channels += divisor
    return new_channels


def _parse_ksize(ss):
    if ss.isdigit():
        return int(ss)
    else:
        return [int(k) for k in ss.split('.')]


def _decode_block_str(block_str, depth_multiplier=1.0):
    """ Decode block definition string

    Gets a list of block arg (dicts) through a string notation of arguments.
    E.g. ir_r2_k3_s2_e1_i32_o16_se0.25_noskip

    All args can exist in any order with the exception of the leading string which
    is assumed to indicate the block type.

    leading string - block type (
      ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct)
    r - number of repeat blocks,
    k - kernel size,
    s - strides (1-9),
    e - expansion ratio,
    c - output channels,
    se - squeeze/excitation ratio
    n - activation fn ('re', 'r6', 'hs', or 'sw')
    Args:
        block_str: a string representation of block arguments.
    Returns:
        A list of block args (dicts)
    Raises:
        ValueError: if the string def not properly specified (TODO)
    """
    assert isinstance(block_str, str)
    ops = block_str.split('_')
    block_type = ops[0]  # take the block type off the front
    ops = ops[1:]
    options = {}
    noskip = False
    for op in ops:
        # string options being checked on individual basis, combine if they grow
        if op == 'noskip':
            noskip = True
        elif op.startswith('n'):
            # activation fn
            key = op[0]
            v = op[1:]
            if v == 're':
                value = F.relu
            elif v == 'r6':
                value = F.relu6
            elif v == 'hs':
                value = hard_swish
            elif v == 'sw':
                value = swish
            else:
                continue
            options[key] = value
        else:
            # all numeric options
            splits = re.split(r'(\d.*)', op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

    # if act_fn is None, the model default (passed to model init) will be used
    act_fn = options['n'] if 'n' in options else None
    exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1
    pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1

    num_repeat = int(options['r'])
    # each type of block has different valid arguments, fill accordingly
    if block_type == 'ir':
        block_args = dict(
            block_type=block_type,
            dw_kernel_size=_parse_ksize(options['k']),
            exp_kernel_size=exp_kernel_size,
            pw_kernel_size=pw_kernel_size,
            out_chs=int(options['c']),
            exp_ratio=float(options['e']),
            se_ratio=float(options['se']) if 'se' in options else None,
            stride=int(options['s']),
            act_fn=act_fn,
            noskip=noskip,
        )
    elif block_type == 'ds' or block_type == 'dsa':
        block_args = dict(
            block_type=block_type,
            dw_kernel_size=_parse_ksize(options['k']),
            pw_kernel_size=pw_kernel_size,
            out_chs=int(options['c']),
            se_ratio=float(options['se']) if 'se' in options else None,
            stride=int(options['s']),
            act_fn=act_fn,
            pw_act=block_type == 'dsa',
            noskip=block_type == 'dsa' or noskip,
        )
    elif block_type == 'cn':
        block_args = dict(
            block_type=block_type,
            kernel_size=int(options['k']),
            out_chs=int(options['c']),
            stride=int(options['s']),
            act_fn=act_fn,
        )
    else:
        assert False, 'Unknown block type (%s)' % block_type

    # return a list of block args expanded by num_repeat and
    # scaled by depth_multiplier
    num_repeat = int(math.ceil(num_repeat * depth_multiplier))
    return [deepcopy(block_args) for _ in range(num_repeat)]


def _decode_arch_def(arch_def, depth_multiplier=1.0):
    arch_args = []
    for stack_idx, block_strings in enumerate(arch_def):
        assert isinstance(block_strings, list)
        stack_args = []
        for block_str in block_strings:
            assert isinstance(block_str, str)
            stack_args.extend(_decode_block_str(block_str, depth_multiplier))
        arch_args.append(stack_args)
    return arch_args


def swish(x, inplace=False):
    if inplace:
        return x.mul_(x.sigmoid())
    else:
        return x * x.sigmoid()


def sigmoid(x, inplace=False):
    return x.sigmoid_() if inplace else x.sigmoid()


def hard_swish(x, inplace=False):
    if inplace:
        return x.mul_(F.relu6(x + 3.) / 6.)
    else:
        return x * F.relu6(x + 3.) / 6.


def hard_sigmoid(x, inplace=False):
    if inplace:
        return x.add_(3.).clamp_(0., 6.).div_(6.)
    else:
        return F.relu6(x + 3.) / 6.


class _BlockBuilder:
    """ Build Trunk Blocks

    This ended up being somewhat of a cross between
    https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
    and
    https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py

    """
    def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
                 pad_type='', act_fn=None, se_gate_fn=sigmoid, se_reduce_mid=False,
                 bn_args=_BN_ARGS_PT, drop_connect_rate=0., verbose=False):
        self.channel_multiplier = channel_multiplier
        self.channel_divisor = channel_divisor
        self.channel_min = channel_min
        self.pad_type = pad_type
        self.act_fn = act_fn
        self.se_gate_fn = se_gate_fn
        self.se_reduce_mid = se_reduce_mid
        self.bn_args = bn_args
        self.drop_connect_rate = drop_connect_rate
        self.verbose = verbose

        # updated during build
        self.in_chs = None
        self.block_idx = 0
        self.block_count = 0

    def _round_channels(self, chs):
        return _round_channels(chs, self.channel_multiplier, self.channel_divisor, self.channel_min)

    def _make_block(self, ba):
        bt = ba.pop('block_type')
        ba['in_chs'] = self.in_chs
        ba['out_chs'] = self._round_channels(ba['out_chs'])
        ba['bn_args'] = self.bn_args
        ba['pad_type'] = self.pad_type
        # block act fn overrides the model default
        ba['act_fn'] = ba['act_fn'] if ba['act_fn'] is not None else self.act_fn
        assert ba['act_fn'] is not None
        if bt == 'ir':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_gate_fn'] = self.se_gate_fn
            ba['se_reduce_mid'] = self.se_reduce_mid
            if self.verbose:
                logging.info('  InvertedResidual {}, Args: {}'.format(self.block_idx, str(ba)))
            block = InvertedResidual(**ba)
        elif bt == 'ds' or bt == 'dsa':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            if self.verbose:
                logging.info('  DepthwiseSeparable {}, Args: {}'.format(self.block_idx, str(ba)))
            block = DepthwiseSeparableConv(**ba)
        elif bt == 'cn':
            if self.verbose:
                logging.info('  ConvBnAct {}, Args: {}'.format(self.block_idx, str(ba)))
            block = ConvBnAct(**ba)
        else:
            assert False, 'Uknkown block type (%s) while building model.' % bt
        self.in_chs = ba['out_chs']  # update in_chs for arg of next block

        return block

    def _make_stack(self, stack_args):
        blocks = []
        # each stack (stage) contains a list of block arguments
        for i, ba in enumerate(stack_args):
            if self.verbose:
                logging.info(' Block: {}'.format(i))
            if i >= 1:
                # only the first block in any stack can have a stride > 1
                ba['stride'] = 1
            block = self._make_block(ba)
            blocks.append(block)
            self.block_idx += 1  # incr global idx (across all stacks)
        return nn.Sequential(*blocks)

    def __call__(self, in_chs, block_args):
        """ Build the blocks
        Args:
            in_chs: Number of input-channels passed to first block
            block_args: A list of lists, outer list defines stages, inner
                list contains strings defining block configuration(s)
        Return:
             List of block stacks (each stack wrapped in nn.Sequential)
        """
        if self.verbose:
            logging.info('Building model trunk with %d stages...' % len(block_args))
        self.in_chs = in_chs
        self.block_count = sum([len(x) for x in block_args])
        self.block_idx = 0
        blocks = []
        # outer list of block_args defines the stacks ('stages' by some conventions)
        for stack_idx, stack in enumerate(block_args):
            if self.verbose:
                logging.info('Stack: {}'.format(stack_idx))
            assert isinstance(stack, list)
            stack = self._make_stack(stack)
            blocks.append(stack)
        return blocks


def _initialize_weight_goog(m):
    # weight init as per Tensorflow Official impl
    # https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
    if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels  # fan-out
        m.weight.data.normal_(0, math.sqrt(2.0 / n))
        if m.bias is not None:
            m.bias.data.zero_()
    elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1.0)
        m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        n = m.weight.size(0)  # fan-out
        init_range = 1.0 / math.sqrt(n)
        m.weight.data.uniform_(-init_range, init_range)
        m.bias.data.zero_()


def _initialize_weight_default(m):
    if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
    elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1.0)
        m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='linear')


def drop_connect(inputs, training=False, drop_connect_rate=0.):
    """Apply drop connect."""
    if not training:
        return inputs

    keep_prob = 1 - drop_connect_rate
    random_tensor = keep_prob + torch.rand(
        (inputs.size()[0], 1, 1, 1), dtype=inputs.dtype, device=inputs.device)
    random_tensor.floor_()  # binarize
    output = inputs.div(keep_prob) * random_tensor
    return output


class ChannelShuffle(nn.Module):
    # FIXME haven't used yet
    def __init__(self, groups):
        super(ChannelShuffle, self).__init__()
        self.groups = groups

    def forward(self, x):
        """Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
        N, C, H, W = x.size()
        g = self.groups
        assert C % g == 0, "Incompatible group size {} for input channel {}".format(
            g, C
        )
        return (
            x.view(N, g, int(C / g), H, W)
            .permute(0, 2, 1, 3, 4)
            .contiguous()
            .view(N, C, H, W)
        )


class SqueezeExcite(nn.Module):
    def __init__(self, in_chs, reduce_chs=None, act_fn=F.relu, gate_fn=sigmoid):
        super(SqueezeExcite, self).__init__()
        self.act_fn = act_fn
        self.gate_fn = gate_fn
        reduced_chs = reduce_chs or in_chs
        self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
        self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)

    def forward(self, x):
        # NOTE adaptiveavgpool can be used here, but seems to cause issues with NVIDIA AMP performance
        x_se = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
        x_se = self.conv_reduce(x_se)
        x_se = self.act_fn(x_se, inplace=True)
        x_se = self.conv_expand(x_se)
        x = x * self.gate_fn(x_se)
        return x


class ConvBnAct(nn.Module):
    def __init__(self, in_chs, out_chs, kernel_size,
                 stride=1, pad_type='', act_fn=F.relu, bn_args=_BN_ARGS_PT):
        super(ConvBnAct, self).__init__()
        assert stride in [1, 2]
        self.act_fn = act_fn
        self.conv = select_conv2d(in_chs, out_chs, kernel_size, stride=stride, padding=pad_type)
        self.bn1 = nn.BatchNorm2d(out_chs, **bn_args)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn1(x)
        x = self.act_fn(x, inplace=True)
        return x


class DepthwiseSeparableConv(nn.Module):
    """ DepthwiseSeparable block
    Used for DS convs in MobileNet-V1 and in the place of IR blocks with an expansion
    factor of 1.0. This is an alternative to having a IR with optional first pw conv.
    """
    def __init__(self, in_chs, out_chs, dw_kernel_size=3,
                 stride=1, pad_type='', act_fn=F.relu, noskip=False,
                 pw_kernel_size=1, pw_act=False,
                 se_ratio=0., se_gate_fn=sigmoid,
                 bn_args=_BN_ARGS_PT, drop_connect_rate=0.):
        super(DepthwiseSeparableConv, self).__init__()
        assert stride in [1, 2]
        self.has_se = se_ratio is not None and se_ratio > 0.
        self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
        self.has_pw_act = pw_act  # activation after point-wise conv
        self.act_fn = act_fn
        self.drop_connect_rate = drop_connect_rate

        self.conv_dw = select_conv2d(
            in_chs, in_chs, dw_kernel_size, stride=stride, padding=pad_type, depthwise=True)
        self.bn1 = nn.BatchNorm2d(in_chs, **bn_args)

        # Squeeze-and-excitation
        if self.has_se:
            self.se = SqueezeExcite(
                in_chs, reduce_chs=max(1, int(in_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)

        self.conv_pw = select_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn2 = nn.BatchNorm2d(out_chs, **bn_args)

    def forward(self, x):
        residual = x

        x = self.conv_dw(x)
        x = self.bn1(x)
        x = self.act_fn(x, inplace=True)

        if self.has_se:
            x = self.se(x)

        x = self.conv_pw(x)
        x = self.bn2(x)
        if self.has_pw_act:
            x = self.act_fn(x, inplace=True)

        if self.has_residual:
            if self.drop_connect_rate > 0.:
                x = drop_connect(x, self.training, self.drop_connect_rate)
            x += residual
        return x


class InvertedResidual(nn.Module):
    """ Inverted residual block w/ optional SE"""

    def __init__(self, in_chs, out_chs, dw_kernel_size=3,
                 stride=1, pad_type='', act_fn=F.relu, noskip=False,
                 exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
                 se_ratio=0., se_reduce_mid=False, se_gate_fn=sigmoid,
                 shuffle_type=None, bn_args=_BN_ARGS_PT, drop_connect_rate=0.):
        super(InvertedResidual, self).__init__()
        mid_chs = int(in_chs * exp_ratio)
        self.has_se = se_ratio is not None and se_ratio > 0.
        self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
        self.act_fn = act_fn
        self.drop_connect_rate = drop_connect_rate

        # Point-wise expansion
        self.conv_pw = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
        self.bn1 = nn.BatchNorm2d(mid_chs, **bn_args)

        self.shuffle_type = shuffle_type
        if shuffle_type is not None and isinstance(exp_kernel_size, list):
            self.shuffle = ChannelShuffle(len(exp_kernel_size))

        # Depth-wise convolution
        self.conv_dw = select_conv2d(
            mid_chs, mid_chs, dw_kernel_size, stride=stride, padding=pad_type, depthwise=True)
        self.bn2 = nn.BatchNorm2d(mid_chs, **bn_args)

        # Squeeze-and-excitation
        if self.has_se:
            se_base_chs = mid_chs if se_reduce_mid else in_chs
            self.se = SqueezeExcite(
                mid_chs, reduce_chs=max(1, int(se_base_chs * se_ratio)), act_fn=act_fn, gate_fn=se_gate_fn)

        # Point-wise linear projection
        self.conv_pwl = select_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn3 = nn.BatchNorm2d(out_chs, **bn_args)

    def forward(self, x):
        residual = x

        # Point-wise expansion
        x = self.conv_pw(x)
        x = self.bn1(x)
        x = self.act_fn(x, inplace=True)

        # FIXME haven't tried this yet
        # for channel shuffle when using groups with pointwise convs as per FBNet variants
        if self.shuffle_type == "mid":
            x = self.shuffle(x)

        # Depth-wise convolution
        x = self.conv_dw(x)
        x = self.bn2(x)
        x = self.act_fn(x, inplace=True)

        # Squeeze-and-excitation
        if self.has_se:
            x = self.se(x)

        # Point-wise linear projection
        x = self.conv_pwl(x)
        x = self.bn3(x)

        if self.has_residual:
            if self.drop_connect_rate > 0.:
                x = drop_connect(x, self.training, self.drop_connect_rate)
            x += residual

        # NOTE maskrcnn_benchmark building blocks have an SE module defined here for some variants

        return x


class GenEfficientNet(nn.Module):
    """ Generic EfficientNet

    An implementation of efficient network architectures, in many cases mobile optimized networks:
      * MobileNet-V1
      * MobileNet-V2
      * MobileNet-V3
      * MnasNet A1, B1, and small
      * FBNet A, B, and C
      * ChamNet (arch details are murky)
      * Single-Path NAS Pixel1
      * EfficientNet B0-B5
      * MixNet S, M, L
    """

    def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
                 channel_multiplier=1.0, channel_divisor=8, channel_min=None,
                 pad_type='', act_fn=F.relu, drop_rate=0., drop_connect_rate=0.,
                 se_gate_fn=sigmoid, se_reduce_mid=False, bn_args=_BN_ARGS_PT,
                 global_pool='avg', head_conv='default', weight_init='goog'):
        super(GenEfficientNet, self).__init__()
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        self.act_fn = act_fn
        self.num_features = num_features

        stem_size = _round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
        self.conv_stem = select_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
        self.bn1 = nn.BatchNorm2d(stem_size, **bn_args)
        in_chs = stem_size

        builder = _BlockBuilder(
            channel_multiplier, channel_divisor, channel_min,
            pad_type, act_fn, se_gate_fn, se_reduce_mid,
            bn_args, drop_connect_rate, verbose=_DEBUG)
        self.blocks = nn.Sequential(*builder(in_chs, block_args))
        in_chs = builder.in_chs

        if not head_conv or head_conv == 'none':
            self.efficient_head = False
            self.conv_head = None
            assert in_chs == self.num_features
        else:
            self.efficient_head = head_conv == 'efficient'
            self.conv_head = select_conv2d(in_chs, self.num_features, 1, padding=pad_type)
            self.bn2 = None if self.efficient_head else nn.BatchNorm2d(self.num_features, **bn_args)

        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)

        for m in self.modules():
            if weight_init == 'goog':
                _initialize_weight_goog(m)
            else:
                _initialize_weight_default(m)

    def get_classifier(self):
        return self.classifier

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.num_classes = num_classes
        del self.classifier
        if num_classes:
            self.classifier = nn.Linear(
                self.num_features * self.global_pool.feat_mult(), num_classes)
        else:
            self.classifier = None

    def forward_features(self, x, pool=True):
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act_fn(x, inplace=True)
        x = self.blocks(x)
        if self.efficient_head:
            # efficient head, currently only mobilenet-v3 performs pool before last 1x1 conv
            x = self.global_pool(x)  # always need to pool here regardless of flag
            x = self.conv_head(x)
            # no BN
            x = self.act_fn(x, inplace=True)
            if pool:
                # expect flattened output if pool is true, otherwise keep dim
                x = x.view(x.size(0), -1)
        else:
            if self.conv_head is not None:
                x = self.conv_head(x)
                x = self.bn2(x)
            x = self.act_fn(x, inplace=True)
            if pool:
                x = self.global_pool(x)
                x = x.view(x.size(0), -1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        if self.drop_rate > 0.:
            x = F.dropout(x, p=self.drop_rate, training=self.training)
        return self.classifier(x)


def _gen_mnasnet_a1(channel_multiplier, num_classes=1000, **kwargs):
    """Creates a mnasnet-a1 model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
    Paper: https://arxiv.org/pdf/1807.11626.pdf.

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_e1_c16_noskip'],
        # stage 1, 112x112 in
        ['ir_r2_k3_s2_e6_c24'],
        # stage 2, 56x56 in
        ['ir_r3_k5_s2_e3_c40_se0.25'],
        # stage 3, 28x28 in
        ['ir_r4_k3_s2_e6_c80'],
        # stage 4, 14x14in
        ['ir_r2_k3_s1_e6_c112_se0.25'],
        # stage 5, 14x14in
        ['ir_r3_k5_s2_e6_c160_se0.25'],
        # stage 6, 7x7 in
        ['ir_r1_k3_s1_e6_c320'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_mnasnet_b1(channel_multiplier, num_classes=1000, **kwargs):
    """Creates a mnasnet-b1 model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
    Paper: https://arxiv.org/pdf/1807.11626.pdf.

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_c16_noskip'],
        # stage 1, 112x112 in
        ['ir_r3_k3_s2_e3_c24'],
        # stage 2, 56x56 in
        ['ir_r3_k5_s2_e3_c40'],
        # stage 3, 28x28 in
        ['ir_r3_k5_s2_e6_c80'],
        # stage 4, 14x14in
        ['ir_r2_k3_s1_e6_c96'],
        # stage 5, 14x14in
        ['ir_r4_k5_s2_e6_c192'],
        # stage 6, 7x7 in
        ['ir_r1_k3_s1_e6_c320_noskip']
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_mnasnet_small(channel_multiplier, num_classes=1000, **kwargs):
    """Creates a mnasnet-b1 model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
    Paper: https://arxiv.org/pdf/1807.11626.pdf.

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        ['ds_r1_k3_s1_c8'],
        ['ir_r1_k3_s2_e3_c16'],
        ['ir_r2_k3_s2_e6_c16'],
        ['ir_r4_k5_s2_e6_c32_se0.25'],
        ['ir_r3_k3_s1_e6_c32_se0.25'],
        ['ir_r3_k5_s2_e6_c88_se0.25'],
        ['ir_r1_k3_s1_e6_c144']
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=8,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_mobilenet_v1(channel_multiplier, num_classes=1000, **kwargs):
    """ Generate MobileNet-V1 network
    Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
    Paper: https://arxiv.org/abs/1801.04381
    """
    arch_def = [
        ['dsa_r1_k3_s1_c64'],
        ['dsa_r2_k3_s2_c128'],
        ['dsa_r2_k3_s2_c256'],
        ['dsa_r6_k3_s2_c512'],
        ['dsa_r2_k3_s2_c1024'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        num_features=1024,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=F.relu6,
        head_conv='none',
        **kwargs
        )
    return model


def _gen_mobilenet_v2(channel_multiplier, num_classes=1000, **kwargs):
    """ Generate MobileNet-V2 network
    Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
    Paper: https://arxiv.org/abs/1801.04381
    """
    arch_def = [
        ['ds_r1_k3_s1_c16'],
        ['ir_r2_k3_s2_e6_c24'],
        ['ir_r3_k3_s2_e6_c32'],
        ['ir_r4_k3_s2_e6_c64'],
        ['ir_r3_k3_s1_e6_c96'],
        ['ir_r3_k3_s2_e6_c160'],
        ['ir_r1_k3_s1_e6_c320'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=F.relu6,
        **kwargs
    )
    return model


def _gen_mobilenet_v3(channel_multiplier, num_classes=1000, **kwargs):
    """Creates a MobileNet-V3 model.

    Ref impl: ?
    Paper: https://arxiv.org/abs/1905.02244

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_e1_c16_nre_noskip'],  # relu
        # stage 1, 112x112 in
        ['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'],  # relu
        # stage 2, 56x56 in
        ['ir_r3_k5_s2_e3_c40_se0.25_nre'],  # relu
        # stage 3, 28x28 in
        ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],  # hard-swish
        # stage 4, 14x14in
        ['ir_r2_k3_s1_e6_c112_se0.25'],  # hard-swish
        # stage 5, 14x14in
        ['ir_r3_k5_s2_e6_c160_se0.25'],  # hard-swish
        # stage 6, 7x7 in
        ['cn_r1_k1_s1_c960'],  # hard-swish
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=16,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=hard_swish,
        se_gate_fn=hard_sigmoid,
        se_reduce_mid=True,
        head_conv='efficient',
        **kwargs
    )
    return model


def _gen_chamnet_v1(channel_multiplier, num_classes=1000, **kwargs):
    """ Generate Chameleon Network (ChamNet)

    Paper: https://arxiv.org/abs/1812.08934
    Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py

    FIXME: this a bit of an educated guess based on trunkd def in maskrcnn_benchmark
    """
    arch_def = [
        ['ir_r1_k3_s1_e1_c24'],
        ['ir_r2_k7_s2_e4_c48'],
        ['ir_r5_k3_s2_e7_c64'],
        ['ir_r7_k5_s2_e12_c56'],
        ['ir_r5_k3_s1_e8_c88'],
        ['ir_r4_k3_s2_e7_c152'],
        ['ir_r1_k3_s1_e10_c104'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        num_features=1280,  # no idea what this is? try mobile/mnasnet default?
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_chamnet_v2(channel_multiplier, num_classes=1000, **kwargs):
    """ Generate Chameleon Network (ChamNet)

    Paper: https://arxiv.org/abs/1812.08934
    Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py

    FIXME: this a bit of an educated guess based on trunk def in maskrcnn_benchmark
    """
    arch_def = [
        ['ir_r1_k3_s1_e1_c24'],
        ['ir_r4_k5_s2_e8_c32'],
        ['ir_r6_k7_s2_e5_c48'],
        ['ir_r3_k5_s2_e9_c56'],
        ['ir_r6_k3_s1_e6_c56'],
        ['ir_r6_k3_s2_e2_c152'],
        ['ir_r1_k3_s1_e6_c112'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        num_features=1280,  # no idea what this is? try mobile/mnasnet default?
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_fbnetc(channel_multiplier, num_classes=1000, **kwargs):
    """ FBNet-C

        Paper: https://arxiv.org/abs/1812.03443
        Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py

        NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper,
        it was used to confirm some building block details
    """
    arch_def = [
        ['ir_r1_k3_s1_e1_c16'],
        ['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'],
        ['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'],
        ['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'],
        ['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'],
        ['ir_r4_k5_s2_e6_c184'],
        ['ir_r1_k3_s1_e6_c352'],
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=16,
        num_features=1984,  # paper suggests this, but is not 100% clear
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_spnasnet(channel_multiplier, num_classes=1000, **kwargs):
    """Creates the Single-Path NAS model from search targeted for Pixel1 phone.

    Paper: https://arxiv.org/abs/1904.02877

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_c16_noskip'],
        # stage 1, 112x112 in
        ['ir_r3_k3_s2_e3_c24'],
        # stage 2, 56x56 in
        ['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'],
        # stage 3, 28x28 in
        ['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'],
        # stage 4, 14x14in
        ['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'],
        # stage 5, 14x14in
        ['ir_r4_k5_s2_e6_c192'],
        # stage 6, 7x7 in
        ['ir_r1_k3_s1_e6_c320_noskip']
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=32,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        **kwargs
    )
    return model


def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=1000, **kwargs):
    """Creates an EfficientNet model.

    Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
    Paper: https://arxiv.org/abs/1905.11946

    EfficientNet params
    name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
    'efficientnet-b0': (1.0, 1.0, 224, 0.2),
    'efficientnet-b1': (1.0, 1.1, 240, 0.2),
    'efficientnet-b2': (1.1, 1.2, 260, 0.3),
    'efficientnet-b3': (1.2, 1.4, 300, 0.3),
    'efficientnet-b4': (1.4, 1.8, 380, 0.4),
    'efficientnet-b5': (1.6, 2.2, 456, 0.4),
    'efficientnet-b6': (1.8, 2.6, 528, 0.5),
    'efficientnet-b7': (2.0, 3.1, 600, 0.5),

    Args:
      channel_multiplier: multiplier to number of channels per layer
      depth_multiplier: multiplier to number of repeats per stage

    """
    arch_def = [
        ['ds_r1_k3_s1_e1_c16_se0.25'],
        ['ir_r2_k3_s2_e6_c24_se0.25'],
        ['ir_r2_k5_s2_e6_c40_se0.25'],
        ['ir_r3_k3_s2_e6_c80_se0.25'],
        ['ir_r3_k5_s1_e6_c112_se0.25'],
        ['ir_r4_k5_s2_e6_c192_se0.25'],
        ['ir_r1_k3_s1_e6_c320_se0.25'],
    ]
    # NOTE: other models in the family didn't scale the feature count
    num_features = _round_channels(1280, channel_multiplier, 8, None)
    model = GenEfficientNet(
        _decode_arch_def(arch_def, depth_multiplier),
        num_classes=num_classes,
        stem_size=32,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        num_features=num_features,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=swish,
        **kwargs
    )
    return model


def _gen_mixnet_s(channel_multiplier=1.0, num_classes=1000, **kwargs):
    """Creates a MixNet Small model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
    Paper: https://arxiv.org/abs/1907.09595
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_e1_c16'],  # relu
        # stage 1, 112x112 in
        ['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'],  # relu
        # stage 2, 56x56 in
        ['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'],  # swish
        # stage 3, 28x28 in
        ['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'],  # swish
        # stage 4, 14x14in
        ['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'],  # swish
        # stage 5, 14x14in
        ['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'],  # swish
        # 7x7
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=16,
        num_features=1536,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=F.relu,
        **kwargs
    )
    return model


def _gen_mixnet_m(channel_multiplier=1.0, num_classes=1000, **kwargs):
    """Creates a MixNet Medium-Large model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
    Paper: https://arxiv.org/abs/1907.09595
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_e1_c24'],  # relu
        # stage 1, 112x112 in
        ['ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32', 'ir_r1_k3_a1.1_p1.1_s1_e3_c32'],  # relu
        # stage 2, 56x56 in
        ['ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'],  # swish
        # stage 3, 28x28 in
        ['ir_r1_k3.5.7_s2_e6_c80_se0.25_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nsw'],  # swish
        # stage 4, 14x14in
        ['ir_r1_k3_s1_e6_c120_se0.5_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'],  # swish
        # stage 5, 14x14in
        ['ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nsw', 'ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'],  # swish
        # 7x7
    ]
    model = GenEfficientNet(
        _decode_arch_def(arch_def),
        num_classes=num_classes,
        stem_size=24,
        num_features=1536,
        channel_multiplier=channel_multiplier,
        channel_divisor=8,
        channel_min=None,
        bn_args=_resolve_bn_args(kwargs),
        act_fn=F.relu,
        **kwargs
    )
    return model


@register_model
def mnasnet_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet B1, depth multiplier of 0.5. """
    default_cfg = default_cfgs['mnasnet_050']
    model = _gen_mnasnet_b1(0.5, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mnasnet_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet B1, depth multiplier of 0.75. """
    default_cfg = default_cfgs['mnasnet_075']
    model = _gen_mnasnet_b1(0.75, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet B1, depth multiplier of 1.0. """
    default_cfg = default_cfgs['mnasnet_100']
    model = _gen_mnasnet_b1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mnasnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet B1, depth multiplier of 1.0. """
    return mnasnet_100(pretrained, num_classes, in_chans, **kwargs)


@register_model
def mnasnet_140(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet B1,  depth multiplier of 1.4 """
    default_cfg = default_cfgs['mnasnet_140']
    model = _gen_mnasnet_b1(1.4, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def semnasnet_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 0.5 """
    default_cfg = default_cfgs['semnasnet_050']
    model = _gen_mnasnet_a1(0.5, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def semnasnet_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet A1 (w/ SE),  depth multiplier of 0.75. """
    default_cfg = default_cfgs['semnasnet_075']
    model = _gen_mnasnet_a1(0.75, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def semnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """
    default_cfg = default_cfgs['semnasnet_100']
    model = _gen_mnasnet_a1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mnasnet_a1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """
    return semnasnet_100(pretrained, num_classes, in_chans, **kwargs)


@register_model
def semnasnet_140(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """
    default_cfg = default_cfgs['semnasnet_140']
    model = _gen_mnasnet_a1(1.4, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mnasnet_small(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MNASNet Small,  depth multiplier of 1.0. """
    default_cfg = default_cfgs['mnasnet_small']
    model = _gen_mnasnet_small(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mobilenetv1_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MobileNet V1 """
    default_cfg = default_cfgs['mobilenetv1_100']
    model = _gen_mobilenet_v1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mobilenetv2_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MobileNet V2 """
    default_cfg = default_cfgs['mobilenetv2_100']
    model = _gen_mobilenet_v2(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mobilenetv3_050(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MobileNet V3 """
    default_cfg = default_cfgs['mobilenetv3_050']
    model = _gen_mobilenet_v3(0.5, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mobilenetv3_075(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MobileNet V3 """
    default_cfg = default_cfgs['mobilenetv3_075']
    model = _gen_mobilenet_v3(0.75, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mobilenetv3_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ MobileNet V3 """
    default_cfg = default_cfgs['mobilenetv3_100']
    if pretrained:
        # pretrained model trained with non-default BN epsilon
        kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    model = _gen_mobilenet_v3(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def fbnetc_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ FBNet-C """
    default_cfg = default_cfgs['fbnetc_100']
    if pretrained:
        # pretrained model trained with non-default BN epsilon
        kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    model = _gen_fbnetc(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def chamnetv1_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ ChamNet """
    default_cfg = default_cfgs['chamnetv1_100']
    model = _gen_chamnet_v1(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def chamnetv2_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ ChamNet """
    default_cfg = default_cfgs['chamnetv2_100']
    model = _gen_chamnet_v2(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def spnasnet_100(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ Single-Path NAS Pixel1"""
    default_cfg = default_cfgs['spnasnet_100']
    model = _gen_spnasnet(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b0(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B0 """
    default_cfg = default_cfgs['efficientnet_b0']
    # NOTE for train, drop_rate should be 0.2
    #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
    model = _gen_efficientnet(
        channel_multiplier=1.0, depth_multiplier=1.0,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B1 """
    default_cfg = default_cfgs['efficientnet_b1']
    # NOTE for train, drop_rate should be 0.2
    #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
    model = _gen_efficientnet(
        channel_multiplier=1.0, depth_multiplier=1.1,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B2 """
    default_cfg = default_cfgs['efficientnet_b2']
    # NOTE for train, drop_rate should be 0.3
    #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
    model = _gen_efficientnet(
        channel_multiplier=1.1, depth_multiplier=1.2,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B3 """
    default_cfg = default_cfgs['efficientnet_b3']
    # NOTE for train, drop_rate should be 0.3
    #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
    model = _gen_efficientnet(
        channel_multiplier=1.2, depth_multiplier=1.4,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b4(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B4 """
    default_cfg = default_cfgs['efficientnet_b4']
    # NOTE for train, drop_rate should be 0.4
    #kwargs['drop_connect_rate'] = 0.2  #  set when training, TODO add as cmd arg
    model = _gen_efficientnet(
        channel_multiplier=1.4, depth_multiplier=1.8,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B5 """
    # NOTE for train, drop_rate should be 0.4
    #kwargs['drop_connect_rate'] = 0.2  # set when training, TODO add as cmd arg
    default_cfg = default_cfgs['efficientnet_b5']
    model = _gen_efficientnet(
        channel_multiplier=1.6, depth_multiplier=2.2,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b0(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B0. Tensorflow compatible variant  """
    default_cfg = default_cfgs['tf_efficientnet_b0']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.0, depth_multiplier=1.0,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b1(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B1. Tensorflow compatible variant  """
    default_cfg = default_cfgs['tf_efficientnet_b1']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.0, depth_multiplier=1.1,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B2. Tensorflow compatible variant  """
    default_cfg = default_cfgs['tf_efficientnet_b2']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.1, depth_multiplier=1.2,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B3. Tensorflow compatible variant """
    default_cfg = default_cfgs['tf_efficientnet_b3']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.2, depth_multiplier=1.4,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b4(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B4. Tensorflow compatible variant """
    default_cfg = default_cfgs['tf_efficientnet_b4']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.4, depth_multiplier=1.8,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_efficientnet_b5(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B5. Tensorflow compatible variant """
    default_cfg = default_cfgs['tf_efficientnet_b5']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        channel_multiplier=1.6, depth_multiplier=2.2,
        num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mixnet_s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Small model.
    """
    default_cfg = default_cfgs['mixnet_m']
    model = _gen_mixnet_s(
        channel_multiplier=1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    #if pretrained:
    #    load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mixnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Medium model.
    """
    default_cfg = default_cfgs['mixnet_m']
    model = _gen_mixnet_m(
        channel_multiplier=1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Large model.
    """
    default_cfg = default_cfgs['mixnet_l']
    model = _gen_mixnet_m(
        channel_multiplier=1.3, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_mixnet_s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Small model. Tensorflow compatible variant
    """
    default_cfg = default_cfgs['tf_mixnet_s']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_s(
        channel_multiplier=1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_mixnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Medium model. Tensorflow compatible variant
    """
    default_cfg = default_cfgs['tf_mixnet_m']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_m(
        channel_multiplier=1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def tf_mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Creates a MixNet Large model. Tensorflow compatible variant
    """
    default_cfg = default_cfgs['tf_mixnet_l']
    kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_m(
        channel_multiplier=1.3, num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


def gen_efficientnet_model_names():
    return set(_models)