Repository: SweetyTian/efficientdet
Branch: master
Commit: f7c1051ba46a
Files: 18
Total size: 124.1 KB

Directory structure:
gitextract_6idj_8bv/

├── README.md
├── backbones/
│   ├── efficientnet.py
│   └── geffnet/
│       ├── __init__.py
│       ├── activations/
│       │   ├── __init__.py
│       │   ├── activations.py
│       │   ├── activations_autofn.py
│       │   └── activations_jit.py
│       ├── config.py
│       ├── conv2d_layers.py
│       ├── efficientnet_builder.py
│       ├── gen_efficientnet.py
│       ├── helpers.py
│       ├── mobilenetv3.py
│       ├── model_factory.py
│       └── version.py
├── configs/
│   ├── efficientdet_d2_bifpn_1x.py
│   └── efficientdet_d4_bifpn_1x.py
└── necks/
    └── bifpn.py

================================================
FILE CONTENTS
================================================

================================================
FILE: README.md
================================================
# efficientdet
BiFPN and Modified BiFPN.

effcientNet backbones and pretrained weights from @rwightman(https://github.com/rwightman/gen-efficientnet-pytorch)

# TODO
train and test


================================================
FILE: backbones/efficientnet.py
================================================
import torch.nn as nn

from torch.nn.modules.batchnorm import _BatchNorm
from ..registry import BACKBONES
import sys
sys.path.append('./mmdet/models/backbones')
import geffnet

@BACKBONES.register_module
class EfficientNet(nn.Module):
    """EfficientNet backbone and pretrained from https://github.com/rwightman/gen-efficientnet-pytorch

    Args:
        model_name (string): tf_efficientnet_b0-b7.
        pretrained (bool) : load pretrained weights, must be True.
        out_indices (Sequence[int]): Output from which stages. Should be (2, 3, 4, 5, 6) in EfficientDet.
        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
            layer is the 3x3 conv layer, otherwise the stride-two layer is
            the first 1x1 conv layer. Not used.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.

    Example:
        >>> from mmdet.models import EfficientNet
        >>> import torch
        >>> self = EfficientNet(model_name='tf_efficientnet_b2', pretrained=False)
        >>> self.eval()
        >>> inputs = torch.rand(1,3,768,768)
        >>> level_outputs = self(inputs)
        >>> for level_out in level_outputs:
        ...     print(tuple(level_out.shape))
        (1, 48, 96, 96)
        (1, 88, 48, 48)
        (1, 120, 24, 24)
        (1, 208, 12, 12)
        (1, 352, 6, 6)
    """

    def __init__(self,
                 model_name,
                 pretrained=True,
                 out_indices=(2, 3, 4, 5, 6),
                 style='pytorch',
                 frozen_stages=-1,
                 norm_eval=True):
        super(EfficientNet, self).__init__()
        self.out_indices = out_indices
        self.style = style
        self.frozen_stages = frozen_stages
        self.norm_eval = norm_eval
        self.model = geffnet.create_model(model_name,pretrained=pretrained)

        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.eval()
            for param in self.parameters():
                param.requires_grad = False

    def init_weights(self, pretrained=None):
        return

    def forward(self, x):
        feature_map = self.model(x)
        outs=[]
        for i in self.out_indices:
            outs.append(feature_map[i])
        return tuple(outs)

    def train(self, mode=True): #need modify
        super(EfficientNet, self).train(mode)
        self._freeze_stages()
        if mode and self.norm_eval:
            for m in self.modules():
                # trick: eval have effect on BatchNorm only
                if isinstance(m, _BatchNorm):
                    m.eval()


================================================
FILE: backbones/geffnet/__init__.py
================================================
from .gen_efficientnet import *
from .mobilenetv3 import *
from .model_factory import create_model
from .config import is_exportable, is_scriptable, set_exportable, set_scriptable
from .activations import *

================================================
FILE: backbones/geffnet/activations/__init__.py
================================================
from geffnet import config
from geffnet.activations.activations_autofn import *
from geffnet.activations.activations_jit import *
from geffnet.activations.activations import *


_ACT_FN_DEFAULT = dict(
    swish=swish,
    mish=mish,
    relu=F.relu,
    relu6=F.relu6,
    sigmoid=sigmoid,
    tanh=tanh,
    hard_sigmoid=hard_sigmoid,
    hard_swish=hard_swish,
)

_ACT_FN_AUTO = dict(
    swish=swish_auto,
    mish=mish_auto,
)

_ACT_FN_JIT = dict(
    swish=swish_jit,
    mish=mish_jit,
    #hard_swish=hard_swish_jit,
    #hard_sigmoid_jit=hard_sigmoid_jit,
)

_ACT_LAYER_DEFAULT = dict(
    swish=Swish,
    mish=Mish,
    relu=nn.ReLU,
    relu6=nn.ReLU6,
    sigmoid=Sigmoid,
    tanh=Tanh,
    hard_sigmoid=HardSigmoid,
    hard_swish=HardSwish,
)

_ACT_LAYER_AUTO = dict(
    swish=SwishAuto,
    mish=MishAuto,
)

_ACT_LAYER_JIT = dict(
    swish=SwishJit,
    mish=MishJit,
    #hard_swish=HardSwishJit,
    #hard_sigmoid=HardSigmoidJit
)

_OVERRIDE_FN = dict()
_OVERRIDE_LAYER = dict()


def add_override_act_fn(name, fn):
    global _OVERRIDE_FN
    _OVERRIDE_FN[name] = fn


def update_override_act_fn(overrides):
    assert isinstance(overrides, dict)
    global _OVERRIDE_FN
    _OVERRIDE_FN.update(overrides)


def clear_override_act_fn():
    global _OVERRIDE_FN
    _OVERRIDE_FN = dict()


def add_override_act_layer(name, fn):
    _OVERRIDE_LAYER[name] = fn


def update_override_act_layer(overrides):
    assert isinstance(overrides, dict)
    global _OVERRIDE_LAYER
    _OVERRIDE_LAYER.update(overrides)


def clear_override_act_layer():
    global _OVERRIDE_LAYER
    _OVERRIDE_LAYER = dict()


def get_act_fn(name='relu'):
    """ Activation Function Factory
    Fetching activation fns by name with this function allows export or torch script friendly
    functions to be returned dynamically based on current config.
    """
    if name in _OVERRIDE_FN:
        return _OVERRIDE_FN[name]
    if not config.is_exportable() and not config.is_scriptable():
        # If not exporting or scripting the model, first look for a JIT optimized version
        # of our activation, then a custom autograd.Function variant before defaulting to
        # a Python or Torch builtin impl
        if name in _ACT_FN_JIT:
            return _ACT_FN_JIT[name]
        if name in _ACT_FN_AUTO:
            return _ACT_FN_AUTO[name]
    return _ACT_FN_DEFAULT[name]


def get_act_layer(name='relu'):
    """ Activation Layer Factory
    Fetching activation layers by name with this function allows export or torch script friendly
    functions to be returned dynamically based on current config.
    """
    if name in _OVERRIDE_LAYER:
        return _OVERRIDE_LAYER[name]
    if not config.is_exportable() and not config.is_scriptable():
        if name in _ACT_LAYER_JIT:
            return _ACT_LAYER_JIT[name]
        if name in _ACT_LAYER_AUTO:
            return _ACT_LAYER_AUTO[name]
    return _ACT_LAYER_DEFAULT[name]




================================================
FILE: backbones/geffnet/activations/activations.py
================================================
from torch import nn as nn
from torch.nn import functional as F


def swish(x, inplace: bool = False):
    """Swish - Described in: https://arxiv.org/abs/1710.05941
    """
    return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())


class Swish(nn.Module):
    def __init__(self, inplace: bool = False):
        super(Swish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return swish(x, self.inplace)


def mish(x, inplace: bool = False):
    """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
    """
    return x.mul(F.softplus(x).tanh())


class Mish(nn.Module):
    def __init__(self, inplace: bool = False):
        super(Mish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return mish(x, self.inplace)


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


# PyTorch has this, but not with a consistent inplace argmument interface
class Sigmoid(nn.Module):
    def __init__(self, inplace: bool = False):
        super(Sigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x.sigmoid_() if self.inplace else x.sigmoid()


def tanh(x, inplace: bool = False):
    return x.tanh_() if inplace else x.tanh()


# PyTorch has this, but not with a consistent inplace argmument interface
class Tanh(nn.Module):
    def __init__(self, inplace: bool = False):
        super(Tanh, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x.tanh_() if self.inplace else x.tanh()


def hard_swish(x, inplace: bool = False):
    inner = F.relu6(x + 3.).div_(6.)
    return x.mul_(inner) if inplace else x.mul(inner)


class HardSwish(nn.Module):
    def __init__(self, inplace: bool = False):
        super(HardSwish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return hard_swish(x, self.inplace)


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


class HardSigmoid(nn.Module):
    def __init__(self, inplace: bool = False):
        super(HardSigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return hard_sigmoid(x, self.inplace)




================================================
FILE: backbones/geffnet/activations/activations_autofn.py
================================================
import torch
from torch import nn as nn
from torch.nn import functional as F


__all__ = ['swish_auto', 'SwishAuto', 'mish_auto', 'MishAuto']


class SwishAutoFn(torch.autograd.Function):
    """Swish - Described in: https://arxiv.org/abs/1710.05941
    Memory efficient variant from:
     https://medium.com/the-artificial-impostor/more-memory-efficient-swish-activation-function-e07c22c12a76
    """
    @staticmethod
    def forward(ctx, x):
        result = x.mul(torch.sigmoid(x))
        ctx.save_for_backward(x)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        x = ctx.saved_tensors[0]
        x_sigmoid = torch.sigmoid(x)
        return grad_output.mul(x_sigmoid * (1 + x * (1 - x_sigmoid)))


def swish_auto(x, inplace=False):
    # inplace ignored
    return SwishAutoFn.apply(x)


class SwishAuto(nn.Module):
    def __init__(self, inplace: bool = False):
        super(SwishAuto, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return SwishAutoFn.apply(x)


class MishAutoFn(torch.autograd.Function):
    """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
    Experimental memory-efficient variant
    """

    @staticmethod
    def forward(ctx, x):
        ctx.save_for_backward(x)
        y = x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
        return y

    @staticmethod
    def backward(ctx, grad_output):
        x = ctx.saved_tensors[0]
        x_sigmoid = torch.sigmoid(x)
        x_tanh_sp = F.softplus(x).tanh()
        return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))


def mish_auto(x, inplace=False):
    # inplace ignored
    return MishAutoFn.apply(x)


class MishAuto(nn.Module):
    def __init__(self, inplace: bool = False):
        super(MishAuto, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return MishAutoFn.apply(x)



================================================
FILE: backbones/geffnet/activations/activations_jit.py
================================================
import torch
from torch import nn as nn
from torch.nn import functional as F


__all__ = ['swish_jit', 'SwishJit', 'mish_jit', 'MishJit']
           #'hard_swish_jit', 'HardSwishJit', 'hard_sigmoid_jit', 'HardSigmoidJit']


@torch.jit.script
def swish_jit_fwd(x):
    return x.mul(torch.sigmoid(x))


@torch.jit.script
def swish_jit_bwd(x, grad_output):
    x_sigmoid = torch.sigmoid(x)
    return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))


class SwishJitAutoFn(torch.autograd.Function):
    """ torch.jit.script optimised Swish
    Inspired by conversation btw Jeremy Howard & Adam Pazske
    https://twitter.com/jeremyphoward/status/1188251041835315200
    """
    @staticmethod
    def forward(ctx, x):
        ctx.save_for_backward(x)
        return swish_jit_fwd(x)

    @staticmethod
    def backward(ctx, grad_output):
        x = ctx.saved_tensors[0]
        return swish_jit_bwd(x, grad_output)


def swish_jit(x, inplace=False):
    # inplace ignored
    return SwishJitAutoFn.apply(x)


class SwishJit(nn.Module):
    def __init__(self, inplace: bool = False):
        super(SwishJit, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return SwishJitAutoFn.apply(x)


@torch.jit.script
def mish_jit_fwd(x):
    return x.mul(torch.tanh(F.softplus(x)))


@torch.jit.script
def mish_jit_bwd(x, grad_output):
    x_sigmoid = torch.sigmoid(x)
    x_tanh_sp = F.softplus(x).tanh()
    return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))


class MishJitAutoFn(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        ctx.save_for_backward(x)
        return mish_jit_fwd(x)

    @staticmethod
    def backward(ctx, grad_output):
        x = ctx.saved_tensors[0]
        return mish_jit_bwd(x, grad_output)


def mish_jit(x, inplace=False):
    # inplace ignored
    return MishJitAutoFn.apply(x)


class MishJit(nn.Module):
    def __init__(self, inplace: bool = False):
        super(MishJit, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return MishJitAutoFn.apply(x)


# @torch.jit.script
# def hard_swish_jit(x, inplac: bool = False):
#     return x.mul(F.relu6(x + 3.).mul_(1./6.))
#
#
# class HardSwishJit(nn.Module):
#     def __init__(self, inplace: bool = False):
#         super(HardSwishJit, self).__init__()
#
#     def forward(self, x):
#         return hard_swish_jit(x)
#
#
# @torch.jit.script
# def hard_sigmoid_jit(x, inplace: bool = False):
#     return F.relu6(x + 3.).mul(1./6.)
#
#
# class HardSigmoidJit(nn.Module):
#     def __init__(self, inplace: bool = False):
#         super(HardSigmoidJit, self).__init__()
#
#     def forward(self, x):
#         return hard_sigmoid_jit(x)


================================================
FILE: backbones/geffnet/config.py
================================================
""" Global Config and Constants
"""

__all__ = ['is_exportable', 'is_scriptable', 'set_exportable', 'set_scriptable']

# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False

# Set to True if wanting to use torch.jit.script on a model
_SCRIPTABLE = False


def is_exportable():
    return _EXPORTABLE


def set_exportable(value):
    global _EXPORTABLE
    _EXPORTABLE = value


def is_scriptable():
    return _SCRIPTABLE


def set_scriptable(value):
    global _SCRIPTABLE
    _SCRIPTABLE = value



================================================
FILE: backbones/geffnet/conv2d_layers.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._six import container_abcs

from itertools import repeat
from functools import partial
from typing import Union, List, Tuple, Optional, Callable
import numpy as np
import math

from .config import *


def _ntuple(n):
    def parse(x):
        if isinstance(x, container_abcs.Iterable):
            return x
        return tuple(repeat(x, n))
    return parse


_single = _ntuple(1)
_pair = _ntuple(2)
_triple = _ntuple(3)
_quadruple = _ntuple(4)


def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0


def _get_padding(kernel_size, stride=1, dilation=1, **_):
    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
    return padding


def _calc_same_pad(i: int, k: int, s: int, d: int):
    return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)


def _same_pad_arg(input_size, kernel_size, stride, dilation):
    ih, iw = input_size
    kh, kw = kernel_size
    pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
    pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
    return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]


def _split_channels(num_chan, num_groups):
    split = [num_chan // num_groups for _ in range(num_groups)]
    split[0] += num_chan - sum(split)
    return split


def conv2d_same(
        x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1),
        padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1):
    ih, iw = x.size()[-2:]
    kh, kw = weight.size()[-2:]
    pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
    pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
    return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)


class Conv2dSame(nn.Conv2d):
    """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions
    """

    # pylint: disable=unused-argument
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        super(Conv2dSame, self).__init__(
            in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)

    def forward(self, x):
        return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


class Conv2dSameExport(nn.Conv2d):
    """ ONNX export friendly Tensorflow like 'SAME' convolution wrapper for 2D convolutions

    NOTE: This does not currently work with torch.jit.script
    """

    # pylint: disable=unused-argument
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        super(Conv2dSameExport, self).__init__(
            in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
        self.pad = None
        self.pad_input_size = (0, 0)

    def forward(self, x):
        input_size = x.size()[-2:]
        if self.pad is None:
            pad_arg = _same_pad_arg(input_size, self.weight.size()[-2:], self.stride, self.dilation)
            self.pad = nn.ZeroPad2d(pad_arg)
            self.pad_input_size = input_size
        else:
            assert self.pad_input_size == input_size

        x = self.pad(x)
        return F.conv2d(
            x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


def get_padding_value(padding, kernel_size, **kwargs):
    dynamic = False
    if isinstance(padding, str):
        # for any string padding, the padding will be calculated for you, one of three ways
        padding = padding.lower()
        if padding == 'same':
            # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
            if _is_static_pad(kernel_size, **kwargs):
                # static case, no extra overhead
                padding = _get_padding(kernel_size, **kwargs)
            else:
                # dynamic padding
                padding = 0
                dynamic = True
        elif padding == 'valid':
            # 'VALID' padding, same as padding=0
            padding = 0
        else:
            # Default to PyTorch style 'same'-ish symmetric padding
            padding = _get_padding(kernel_size, **kwargs)
    return padding, dynamic


def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
    padding = kwargs.pop('padding', '')
    kwargs.setdefault('bias', False)
    padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
    if is_dynamic:
        if is_exportable():
            assert not is_scriptable()
            return Conv2dSameExport(in_chs, out_chs, kernel_size, **kwargs)
        else:
            return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
    else:
        return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)


class MixedConv2d(nn.Module):
    """ Mixed Grouped Convolution
    Based on MDConv and GroupedConv in MixNet impl:
      https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py

    NOTE: This does not currently work with torch.jit.script
    """

    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding='', dilation=1, depthwise=False, **kwargs):
        super(MixedConv2d, self).__init__()

        kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
        num_groups = len(kernel_size)
        in_splits = _split_channels(in_channels, num_groups)
        out_splits = _split_channels(out_channels, num_groups)
        for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
            conv_groups = out_ch if depthwise else 1
            # use add_module to keep key space clean
            self.add_module(
                str(idx),
                create_conv2d_pad(
                    in_ch, out_ch, k, stride=stride,
                    padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
            )
        self.splits = in_splits

    def forward(self, x):
        x_split = torch.split(x, self.splits, 1)
        x_out = [c(x) for x, c in zip(x_split, self._modules.values())]
        x = torch.cat(x_out, 1)
        return x


def get_condconv_initializer(initializer, num_experts, expert_shape):
    def condconv_initializer(weight):
        """CondConv initializer function."""
        num_params = np.prod(expert_shape)
        if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
                weight.shape[1] != num_params):
            raise (ValueError(
                'CondConv variables must have shape [num_experts, num_params]'))
        for i in range(num_experts):
            initializer(weight[i].view(expert_shape))
    return condconv_initializer


class CondConv2d(nn.Module):
    """ Conditional Convolution
    Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py

    Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
    https://github.com/pytorch/pytorch/issues/17983
    """
    __constants__ = ['bias', 'in_channels', 'out_channels', 'dynamic_padding']

    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
        super(CondConv2d, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        padding_val, is_padding_dynamic = get_padding_value(
            padding, kernel_size, stride=stride, dilation=dilation)
        self.dynamic_padding = is_padding_dynamic  # if in forward to work with torchscript
        self.padding = _pair(padding_val)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.num_experts = num_experts

        self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
        weight_num_param = 1
        for wd in self.weight_shape:
            weight_num_param *= wd
        self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))

        if bias:
            self.bias_shape = (self.out_channels,)
            self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        init_weight = get_condconv_initializer(
            partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
        init_weight(self.weight)
        if self.bias is not None:
            fan_in = np.prod(self.weight_shape[1:])
            bound = 1 / math.sqrt(fan_in)
            init_bias = get_condconv_initializer(
                partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
            init_bias(self.bias)

    def forward(self, x, routing_weights):
        B, C, H, W = x.shape
        weight = torch.matmul(routing_weights, self.weight)
        new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
        weight = weight.view(new_weight_shape)
        bias = None
        if self.bias is not None:
            bias = torch.matmul(routing_weights, self.bias)
            bias = bias.view(B * self.out_channels)
        # move batch elements with channels so each batch element can be efficiently convolved with separate kernel
        x = x.view(1, B * C, H, W)
        if self.dynamic_padding:
            out = conv2d_same(
                x, weight, bias, stride=self.stride, padding=self.padding,
                dilation=self.dilation, groups=self.groups * B)
        else:
            out = F.conv2d(
                x, weight, bias, stride=self.stride, padding=self.padding,
                dilation=self.dilation, groups=self.groups * B)
        out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])

        # Literal port (from TF definition)
        # x = torch.split(x, 1, 0)
        # weight = torch.split(weight, 1, 0)
        # if self.bias is not None:
        #     bias = torch.matmul(routing_weights, self.bias)
        #     bias = torch.split(bias, 1, 0)
        # else:
        #     bias = [None] * B
        # out = []
        # for xi, wi, bi in zip(x, weight, bias):
        #     wi = wi.view(*self.weight_shape)
        #     if bi is not None:
        #         bi = bi.view(*self.bias_shape)
        #     out.append(self.conv_fn(
        #         xi, wi, bi, stride=self.stride, padding=self.padding,
        #         dilation=self.dilation, groups=self.groups))
        # out = torch.cat(out, 0)
        return out


def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
    assert 'groups' not in kwargs  # only use 'depthwise' bool arg
    if isinstance(kernel_size, list):
        assert 'num_experts' not in kwargs  # MixNet + CondConv combo not supported currently
        # We're going to use only lists for defining the MixedConv2d kernel groups,
        # ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
        m = MixedConv2d(in_chs, out_chs, kernel_size, **kwargs)
    else:
        depthwise = kwargs.pop('depthwise', False)
        groups = out_chs if depthwise else 1
        if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
            m = CondConv2d(in_chs, out_chs, kernel_size, groups=groups, **kwargs)
        else:
            m = create_conv2d_pad(in_chs, out_chs, kernel_size, groups=groups, **kwargs)
    return m


================================================
FILE: backbones/geffnet/efficientnet_builder.py
================================================
import re
from copy import deepcopy

from .conv2d_layers import *
from geffnet.activations import *


# 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
#
# PyTorch defaults are momentum = .1, eps = 1e-5
#
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 get_bn_args_tf():
    return _BN_ARGS_TF.copy()


def resolve_bn_args(kwargs):
    bn_args = get_bn_args_tf() if kwargs.pop('bn_tf', False) else {}
    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


_SE_ARGS_DEFAULT = dict(
    gate_fn=sigmoid,
    act_layer=None,  # None == use containing block's activation layer
    reduce_mid=False,
    divisor=1)


def resolve_se_args(kwargs, in_chs, act_layer=None):
    se_kwargs = kwargs.copy() if kwargs is not None else {}
    # fill in args that aren't specified with the defaults
    for k, v in _SE_ARGS_DEFAULT.items():
        se_kwargs.setdefault(k, v)
    # some models, like MobilNetV3, calculate SE reduction chs from the containing block's mid_ch instead of in_ch
    if not se_kwargs.pop('reduce_mid'):
        se_kwargs['reduced_base_chs'] = in_chs
    # act_layer override, if it remains None, the containing block's act_layer will be used
    if se_kwargs['act_layer'] is None:
        assert act_layer is not None
        se_kwargs['act_layer'] = act_layer
    return se_kwargs


def resolve_act_layer(kwargs, default='relu'):
    act_layer = kwargs.pop('act_layer', default)
    if isinstance(act_layer, str):
        act_layer = get_act_layer(act_layer)
    return act_layer


def make_divisible(v: int, divisor: int = 8, min_value: int = None):
    min_value = min_value or divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:  # ensure round down does not go down by more than 10%.
        new_v += divisor
    return new_v


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
    return make_divisible(channels, divisor, channel_min)


def drop_connect(inputs, training: bool = False, drop_connect_rate: float = 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 SqueezeExcite(nn.Module):
    __constants__ = ['gate_fn']

    def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None, act_layer=nn.ReLU, gate_fn=sigmoid, divisor=1):
        super(SqueezeExcite, self).__init__()
        self.gate_fn = gate_fn
        reduced_chs = make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
        self.act1 = act_layer(inplace=True)
        self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)

    def forward(self, x):
        # tensor.view + mean bad for ONNX export (produces mess of gather ops that break TensorRT)
        x_se = self.avg_pool(x)
        x_se = self.conv_reduce(x_se)
        x_se = self.act1(x_se)
        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_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
        super(ConvBnAct, self).__init__()
        assert stride in [1, 2]
        norm_kwargs = norm_kwargs or {}
        self.conv = select_conv2d(in_chs, out_chs, kernel_size, stride=stride, padding=pad_type)
        self.bn1 = norm_layer(out_chs, **norm_kwargs)
        self.act1 = act_layer(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn1(x)
        x = self.act1(x)
        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_layer=nn.ReLU, noskip=False,
                 pw_kernel_size=1, pw_act=False, se_ratio=0., se_kwargs=None,
                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_connect_rate=0.):
        super(DepthwiseSeparableConv, self).__init__()
        assert stride in [1, 2]
        norm_kwargs = norm_kwargs or {}
        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.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 = norm_layer(in_chs, **norm_kwargs)
        self.act1 = act_layer(inplace=True)

        # Squeeze-and-excitation
        if self.has_se:
            se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
            self.se = SqueezeExcite(in_chs, se_ratio=se_ratio, **se_kwargs)
        else:
            self.se = nn.Identity()

        self.conv_pw = select_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn2 = norm_layer(out_chs, **norm_kwargs)
        self.act2 = act_layer(inplace=True) if pw_act else nn.Identity()

    def forward(self, x):
        residual = x

        x = self.conv_dw(x)
        x = self.bn1(x)
        x = self.act1(x)

        x = self.se(x)

        x = self.conv_pw(x)
        x = self.bn2(x)
        x = self.act2(x)

        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_layer=nn.ReLU, noskip=False,
                 exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
                 se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
                 conv_kwargs=None, drop_connect_rate=0.):
        super(InvertedResidual, self).__init__()
        norm_kwargs = norm_kwargs or {}
        conv_kwargs = conv_kwargs or {}
        mid_chs: int = make_divisible(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.drop_connect_rate = drop_connect_rate

        # Point-wise expansion
        self.conv_pw = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type, **conv_kwargs)
        self.bn1 = norm_layer(mid_chs, **norm_kwargs)
        self.act1 = act_layer(inplace=True)

        # Depth-wise convolution
        self.conv_dw = select_conv2d(
            mid_chs, mid_chs, dw_kernel_size, stride=stride, padding=pad_type, depthwise=True, **conv_kwargs)
        self.bn2 = norm_layer(mid_chs, **norm_kwargs)
        self.act2 = act_layer(inplace=True)

        # Squeeze-and-excitation
        if self.has_se:
            se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
            self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio, **se_kwargs)
        else:
            self.se = nn.Identity()  # for jit.script compat

        # Point-wise linear projection
        self.conv_pwl = select_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
        self.bn3 = norm_layer(out_chs, **norm_kwargs)

    def forward(self, x):
        residual = x

        # Point-wise expansion
        x = self.conv_pw(x)
        x = self.bn1(x)
        x = self.act1(x)

        # Depth-wise convolution
        x = self.conv_dw(x)
        x = self.bn2(x)
        x = self.act2(x)

        # Squeeze-and-excitation
        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
        return x


class CondConvResidual(InvertedResidual):
    """ Inverted residual block w/ CondConv routing"""

    def __init__(self, in_chs, out_chs, dw_kernel_size=3,
                 stride=1, pad_type='', act_layer=nn.ReLU, noskip=False,
                 exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
                 se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
                 num_experts=0, drop_connect_rate=0.):

        self.num_experts = num_experts
        conv_kwargs = dict(num_experts=self.num_experts)

        super(CondConvResidual, self).__init__(
            in_chs, out_chs, dw_kernel_size=dw_kernel_size, stride=stride, pad_type=pad_type,
            act_layer=act_layer, noskip=noskip, exp_ratio=exp_ratio, exp_kernel_size=exp_kernel_size,
            pw_kernel_size=pw_kernel_size, se_ratio=se_ratio, se_kwargs=se_kwargs,
            norm_layer=norm_layer, norm_kwargs=norm_kwargs, conv_kwargs=conv_kwargs,
            drop_connect_rate=drop_connect_rate)

        self.routing_fn = nn.Linear(in_chs, self.num_experts)

    def forward(self, x):
        residual = x

        # CondConv routing
        pooled_inputs = F.adaptive_avg_pool2d(x, 1).flatten(1)
        routing_weights = torch.sigmoid(self.routing_fn(pooled_inputs))

        # Point-wise expansion
        x = self.conv_pw(x, routing_weights)
        x = self.bn1(x)
        x = self.act1(x)

        # Depth-wise convolution
        x = self.conv_dw(x, routing_weights)
        x = self.bn2(x)
        x = self.act2(x)

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

        # Point-wise linear projection
        x = self.conv_pwl(x, routing_weights)
        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
        return x


class EdgeResidual(nn.Module):
    """ EdgeTPU Residual block with expansion convolution followed by pointwise-linear w/ stride"""

    def __init__(self, in_chs, out_chs, exp_kernel_size=3, exp_ratio=1.0, fake_in_chs=0,
                 stride=1, pad_type='', act_layer=nn.ReLU, noskip=False, pw_kernel_size=1,
                 se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_connect_rate=0.):
        super(EdgeResidual, self).__init__()
        norm_kwargs = norm_kwargs or {}
        mid_chs = make_divisible(fake_in_chs * exp_ratio) if fake_in_chs > 0 else make_divisible(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.drop_connect_rate = drop_connect_rate

        # Expansion convolution
        self.conv_exp = select_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
        self.bn1 = norm_layer(mid_chs, **norm_kwargs)
        self.act1 = act_layer(inplace=True)

        # Squeeze-and-excitation
        if self.has_se:
            se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
            self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio, **se_kwargs)
        else:
            self.se = nn.Identity()

        # Point-wise linear projection
        self.conv_pwl = select_conv2d(mid_chs, out_chs, pw_kernel_size, stride=stride, padding=pad_type)
        self.bn2 = nn.BatchNorm2d(out_chs, **norm_kwargs)

    def forward(self, x):
        residual = x

        # Expansion convolution
        x = self.conv_exp(x)
        x = self.bn1(x)
        x = self.act1(x)

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

        # Point-wise linear projection
        x = self.conv_pwl(x)
        x = self.bn2(x)

        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 EfficientNetBuilder:
    """ Build Trunk Blocks for Efficient/Mobile Networks

    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_layer=None, se_kwargs=None,
                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_connect_rate=0.):
        self.channel_multiplier = channel_multiplier
        self.channel_divisor = channel_divisor
        self.channel_min = channel_min
        self.pad_type = pad_type
        self.act_layer = act_layer
        self.se_kwargs = se_kwargs
        self.norm_layer = norm_layer
        self.norm_kwargs = norm_kwargs
        self.drop_connect_rate = drop_connect_rate

        # 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'])
        if 'fake_in_chs' in ba and ba['fake_in_chs']:
            # FIXME this is a hack to work around mismatch in origin impl input filters for EdgeTPU
            ba['fake_in_chs'] = self._round_channels(ba['fake_in_chs'])
        ba['norm_layer'] = self.norm_layer
        ba['norm_kwargs'] = self.norm_kwargs
        ba['pad_type'] = self.pad_type
        # block act fn overrides the model default
        ba['act_layer'] = ba['act_layer'] if ba['act_layer'] is not None else self.act_layer
        assert ba['act_layer'] is not None
        if bt == 'ir':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            if ba.get('num_experts', 0) > 0:
                block = CondConvResidual(**ba)
            else:
                block = InvertedResidual(**ba)
        elif bt == 'ds' or bt == 'dsa':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            block = DepthwiseSeparableConv(**ba)
        elif bt == 'er':
            ba['drop_connect_rate'] = self.drop_connect_rate * self.block_idx / self.block_count
            ba['se_kwargs'] = self.se_kwargs
            block = EdgeResidual(**ba)
        elif bt == 'cn':
            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 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)
        """
        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):
            assert isinstance(stack, list)
            stack = self._make_stack(stack)
            blocks.append(stack)
        return blocks


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):
    """ 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 = get_act_layer('relu')
            elif v == 'r6':
                value = get_act_layer('relu6')
            elif v == 'hs':
                value = get_act_layer('hard_swish')
            elif v == 'sw':
                value = get_act_layer('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_layer is None, the model default (passed to model init) will be used
    act_layer = 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
    fake_in_chs = int(options['fc']) if 'fc' in options else 0  # FIXME hack to deal with in_chs issue in TPU def

    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_layer=act_layer,
            noskip=noskip,
        )
        if 'cc' in options:
            block_args['num_experts'] = int(options['cc'])
    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_layer=act_layer,
            pw_act=block_type == 'dsa',
            noskip=block_type == 'dsa' or noskip,
        )
    elif block_type == 'er':
        block_args = dict(
            block_type=block_type,
            exp_kernel_size=_parse_ksize(options['k']),
            pw_kernel_size=pw_kernel_size,
            out_chs=int(options['c']),
            exp_ratio=float(options['e']),
            fake_in_chs=fake_in_chs,
            se_ratio=float(options['se']) if 'se' in options else None,
            stride=int(options['s']),
            act_layer=act_layer,
            noskip=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_layer=act_layer,
        )
    else:
        assert False, 'Unknown block type (%s)' % block_type

    return block_args, num_repeat


def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='ceil'):
    """ Per-stage depth scaling
    Scales the block repeats in each stage. This depth scaling impl maintains
    compatibility with the EfficientNet scaling method, while allowing sensible
    scaling for other models that may have multiple block arg definitions in each stage.
    """

    # We scale the total repeat count for each stage, there may be multiple
    # block arg defs per stage so we need to sum.
    num_repeat = sum(repeats)
    if depth_trunc == 'round':
        # Truncating to int by rounding allows stages with few repeats to remain
        # proportionally smaller for longer. This is a good choice when stage definitions
        # include single repeat stages that we'd prefer to keep that way as long as possible
        num_repeat_scaled = max(1, round(num_repeat * depth_multiplier))
    else:
        # The default for EfficientNet truncates repeats to int via 'ceil'.
        # Any multiplier > 1.0 will result in an increased depth for every stage.
        num_repeat_scaled = int(math.ceil(num_repeat * depth_multiplier))

    # Proportionally distribute repeat count scaling to each block definition in the stage.
    # Allocation is done in reverse as it results in the first block being less likely to be scaled.
    # The first block makes less sense to repeat in most of the arch definitions.
    repeats_scaled = []
    for r in repeats[::-1]:
        rs = max(1, round((r / num_repeat * num_repeat_scaled)))
        repeats_scaled.append(rs)
        num_repeat -= r
        num_repeat_scaled -= rs
    repeats_scaled = repeats_scaled[::-1]

    # Apply the calculated scaling to each block arg in the stage
    sa_scaled = []
    for ba, rep in zip(stack_args, repeats_scaled):
        sa_scaled.extend([deepcopy(ba) for _ in range(rep)])
    return sa_scaled


def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1):
    arch_args = []
    for stack_idx, block_strings in enumerate(arch_def):
        assert isinstance(block_strings, list)
        stack_args = []
        repeats = []
        for block_str in block_strings:
            assert isinstance(block_str, str)
            ba, rep = _decode_block_str(block_str)
            if ba.get('num_experts', 0) > 0 and experts_multiplier > 1:
                ba['num_experts'] *= experts_multiplier
            stack_args.append(ba)
            repeats.append(rep)
        arch_args.append(_scale_stage_depth(stack_args, repeats, depth_multiplier, depth_trunc))
    return arch_args


def initialize_weight_goog(m, n=''):
    # weight init as per Tensorflow Official impl
    # https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
    if isinstance(m, CondConv2d):
        fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        init_weight_fn = get_condconv_initializer(
            lambda w: w.data.normal_(0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape)
        init_weight_fn(m.weight)
        if m.bias is not None:
            m.bias.data.zero_()
    elif isinstance(m, nn.Conv2d):
        fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        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.BatchNorm2d):
        m.weight.data.fill_(1.0)
        m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        fan_out = m.weight.size(0)  # fan-out
        fan_in = 0
        if 'routing_fn' in n:
            fan_in = m.weight.size(1)
        init_range = 1.0 / math.sqrt(fan_in + fan_out)
        m.weight.data.uniform_(-init_range, init_range)
        m.bias.data.zero_()


def initialize_weight_default(m, n=''):
    if isinstance(m, CondConv2d):
        init_fn = get_condconv_initializer(partial(
            nn.init.kaiming_normal_, mode='fan_out', nonlinearity='relu'), m.num_experts, m.weight_shape)
        init_fn(m.weight)
    elif 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')


================================================
FILE: backbones/geffnet/gen_efficientnet.py
================================================
""" Generic Efficient Networks

A generic MobileNet class with building blocks to support a variety of models:

* EfficientNet (B0-B8 + Tensorflow pretrained AutoAug/RandAug/AdvProp ports)
  - EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946
  - CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971
  - Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665

* MixNet (Small, Medium, and Large)
  - MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595

* MNasNet B1, A1 (SE), Small
  - MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626

* FBNet-C
  - FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443

* Single-Path NAS Pixel1
  - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877

* And likely more...

Hacked together by Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F

from .helpers import load_pretrained
from .efficientnet_builder import *

__all__ = ['GenEfficientNet', 'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140',
           'semnasnet_050', 'semnasnet_075', 'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small',
           'fbnetc_100', 'spnasnet_100', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2',  'efficientnet_b3',
           'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'efficientnet_b8',
           'efficientnet_es', 'efficientnet_em', 'efficientnet_el',
           'efficientnet_cc_b0_4e', 'efficientnet_cc_b0_8e', 'efficientnet_cc_b1_8e',
           'tf_efficientnet_b0', 'tf_efficientnet_b1', 'tf_efficientnet_b2', 'tf_efficientnet_b3',
           'tf_efficientnet_b4', 'tf_efficientnet_b5', 'tf_efficientnet_b6', 'tf_efficientnet_b7',
           'tf_efficientnet_b0_ap', 'tf_efficientnet_b1_ap', 'tf_efficientnet_b2_ap', 'tf_efficientnet_b3_ap',
           'tf_efficientnet_b4_ap', 'tf_efficientnet_b5_ap', 'tf_efficientnet_b6_ap', 'tf_efficientnet_b7_ap',
           'tf_efficientnet_b8_ap', 'tf_efficientnet_es', 'tf_efficientnet_em', 'tf_efficientnet_el',
           'tf_efficientnet_cc_b0_4e', 'tf_efficientnet_cc_b0_8e', 'tf_efficientnet_cc_b1_8e',
           'mixnet_s', 'mixnet_m', 'mixnet_l', 'mixnet_xl', 'tf_mixnet_s', 'tf_mixnet_m', 'tf_mixnet_l']


model_urls = {
    'mnasnet_050': None,
    'mnasnet_075': None,
    'mnasnet_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
    'mnasnet_140': None,
    'semnasnet_050': None,
    'semnasnet_075': None,
    'semnasnet_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth',
    'semnasnet_140': None,
    'mnasnet_small': None,
    'fbnetc_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
    'spnasnet_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
    'efficientnet_b0':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth',
    'efficientnet_b1':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
    'efficientnet_b2':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2-cf78dc4d.pth',
    'efficientnet_b3': None,
    'efficientnet_b4': None,
    'efficientnet_b5': None,
    'efficientnet_b6': None,
    'efficientnet_b7': None,
    'efficientnet_b8': None,
    'efficientnet_es': None,
    'efficientnet_em': None,
    'efficientnet_el': None,
    'efficientnet_cc_b0_4e': None,
    'efficientnet_cc_b0_8e': None,
    'efficientnet_cc_b1_8e': None,
    'tf_efficientnet_b0':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
    'tf_efficientnet_b1':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth',
    'tf_efficientnet_b2':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth',
    'tf_efficientnet_b3':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth',
    'tf_efficientnet_b4':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth',
    'tf_efficientnet_b5':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth',
    'tf_efficientnet_b6':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth',
    'tf_efficientnet_b7':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth',
    'tf_efficientnet_b0_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth',
    'tf_efficientnet_b1_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth',
    'tf_efficientnet_b2_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth',
    'tf_efficientnet_b3_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth',
    'tf_efficientnet_b4_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth',
    'tf_efficientnet_b5_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth',
    'tf_efficientnet_b6_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth',
    'tf_efficientnet_b7_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth',
    'tf_efficientnet_b8_ap':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth',
    'tf_efficientnet_es':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
    'tf_efficientnet_em':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth',
    'tf_efficientnet_el':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth',
    'tf_efficientnet_cc_b0_4e':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth',
    'tf_efficientnet_cc_b0_8e':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth',
    'tf_efficientnet_cc_b1_8e':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth',
    'mixnet_s': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth',
    'mixnet_m': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth',
    'mixnet_l': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth',
    'mixnet_xl': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl-ac5fbe8d.pth',
    'tf_mixnet_s':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth',
    'tf_mixnet_m':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth',
    'tf_mixnet_l':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth',
}


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

    An implementation of mobile optimized networks that covers:
      * EfficientNet (B0-B8, CondConv, EdgeTPU)
      * MixNet (Small, Medium, and Large, XL)
      * MNASNet A1, B1, and small
      * FBNet C
      * Single-Path NAS Pixel1
    """

    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_layer=nn.ReLU, drop_rate=0., drop_connect_rate=0.,
                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
                 weight_init='goog'):
        super(GenEfficientNet, self).__init__()
        self.drop_rate = drop_rate

        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 = norm_layer(stem_size, **norm_kwargs)
        self.act1 = act_layer(inplace=True)
        in_chs = stem_size

        builder = EfficientNetBuilder(
            channel_multiplier, channel_divisor, channel_min,
            pad_type, act_layer, se_kwargs, norm_layer, norm_kwargs, drop_connect_rate)
        # modulelist = builder(in_chs, block_args)
        self.blocks = nn.ModuleList()
        for block in builder(in_chs,block_args):
            self.blocks.append(block)

        in_chs = builder.in_chs

        self.conv_head = select_conv2d(in_chs, num_features, 1, padding=pad_type)
        self.bn2 = norm_layer(num_features, **norm_kwargs)
        self.act2 = act_layer(inplace=True)
        self.global_pool = nn.AdaptiveAvgPool2d(1)
        self.classifier = nn.Linear(num_features, num_classes)

        for n, m in self.named_modules():
            if weight_init == 'goog':
                initialize_weight_goog(m, n)
            else:
                initialize_weight_default(m, n)

    def features(self, x):
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act1(x)
        outs=[]
        for block in self.blocks:
            x = block(x)
            outs.append(x)
        # x = self.blocks(x)
        # x = self.conv_head(x)
        # x = self.bn2(x)
        # x = self.act2(x)
        return outs

    def as_sequential(self):
        layers = [self.conv_stem, self.bn1, self.act1]
        layers.extend(self.blocks)
        layers.extend([
            self.conv_head, self.bn2, self.act2,
            self.global_pool, nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
        return nn.Sequential(*layers)

    def forward(self, x):
        # x = self.features(x)
        # x = self.global_pool(x)
        # x = x.flatten(1)
        # if self.drop_rate > 0.:
        #     x = F.dropout(x, p=self.drop_rate, training=self.training)
        return self.features(x)

def _create_model(model_kwargs, variant, pretrained=False):
    as_sequential = model_kwargs.pop('as_sequential', False)
    model = GenEfficientNet(**model_kwargs)
    if pretrained and model_urls[variant]:
        load_pretrained(model, model_urls[variant])
    if as_sequential:
        model = model.as_sequential()
    return model


def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        stem_size=8,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        stem_size=16,
        num_features=1984,  # paper suggests this, but is not 100% clear
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **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),
    'efficientnet-b8': (2.2, 3.6, 672, 0.5),

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

    """
    # s1->s2
    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_s2_e6_c112_se0.25'],
        ['ir_r4_k5_s2_e6_c192_se0.25'],
        ['ir_r1_k3_s2_e6_c320_se0.25'],
    ]
    model_kwargs = dict(
        block_args=decode_arch_def(arch_def, depth_multiplier),
        num_features=round_channels(1280, channel_multiplier, 8, None),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'swish'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs,
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
    arch_def = [
        # NOTE `fc` is present to override a mismatch between stem channels and in chs not
        # present in other models
        ['er_r1_k3_s1_e4_c24_fc24_noskip'],
        ['er_r2_k3_s2_e8_c32'],
        ['er_r4_k3_s2_e8_c48'],
        ['ir_r5_k5_s2_e8_c96'],
        ['ir_r4_k5_s1_e8_c144'],
        ['ir_r2_k5_s2_e8_c192'],
    ]
    model_kwargs = dict(
        block_args=decode_arch_def(arch_def, depth_multiplier),
        num_features=round_channels(1280, channel_multiplier, 8, None),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs,
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_efficientnet_condconv(
        variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
    """Creates an efficientnet-condconv model."""
    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_cc4'],
      ['ir_r4_k5_s2_e6_c192_se0.25_cc4'],
      ['ir_r1_k3_s1_e6_c320_se0.25_cc4'],
    ]
    model_kwargs = dict(
        block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
        num_features=round_channels(1280, channel_multiplier, 8, None),
        stem_size=32,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'swish'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs,
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        num_features=1536,
        stem_size=16,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **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_kwargs = dict(
        block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
        num_features=1536,
        stem_size=24,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'relu'),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def mnasnet_050(pretrained=False, **kwargs):
    """ MNASNet B1, depth multiplier of 0.5. """
    model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs)
    return model


def mnasnet_075(pretrained=False, **kwargs):
    """ MNASNet B1, depth multiplier of 0.75. """
    model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def mnasnet_100(pretrained=False, **kwargs):
    """ MNASNet B1, depth multiplier of 1.0. """
    model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def mnasnet_b1(pretrained=False, **kwargs):
    """ MNASNet B1, depth multiplier of 1.0. """
    return mnasnet_100(pretrained, **kwargs)


def mnasnet_140(pretrained=False, **kwargs):
    """ MNASNet B1,  depth multiplier of 1.4 """
    model = _gen_mnasnet_b1('mnasnet_140', 1.4, pretrained=pretrained, **kwargs)
    return model


def semnasnet_050(pretrained=False, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 0.5 """
    model = _gen_mnasnet_a1('semnasnet_050', 0.5, pretrained=pretrained, **kwargs)
    return model


def semnasnet_075(pretrained=False, **kwargs):
    """ MNASNet A1 (w/ SE),  depth multiplier of 0.75. """
    model = _gen_mnasnet_a1('semnasnet_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def semnasnet_100(pretrained=False, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """
    model = _gen_mnasnet_a1('semnasnet_100', 1.0, pretrained=pretrained, **kwargs)
    return model


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


def semnasnet_140(pretrained=False, **kwargs):
    """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """
    model = _gen_mnasnet_a1('semnasnet_140', 1.4, pretrained=pretrained, **kwargs)
    return model


def mnasnet_small(pretrained=False, **kwargs):
    """ MNASNet Small,  depth multiplier of 1.0. """
    model = _gen_mnasnet_small('mnasnet_small', 1.0, pretrained=pretrained, **kwargs)
    return model


def fbnetc_100(pretrained=False, **kwargs):
    """ FBNet-C """
    if pretrained:
        # pretrained model trained with non-default BN epsilon
        kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    model = _gen_fbnetc('fbnetc_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def spnasnet_100(pretrained=False, **kwargs):
    """ Single-Path NAS Pixel1"""
    model = _gen_spnasnet('spnasnet_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b0(pretrained=False, **kwargs):
    """ EfficientNet-B0 """
    # NOTE for train set drop_rate=0.2, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b1(pretrained=False, **kwargs):
    """ EfficientNet-B1 """
    # NOTE for train set drop_rate=0.2, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b2(pretrained=False, **kwargs):
    """ EfficientNet-B2 """
    # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b3(pretrained=False, **kwargs):
    """ EfficientNet-B3 """
    # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b4(pretrained=False, **kwargs):
    """ EfficientNet-B4 """
    # NOTE for train set drop_rate=0.4, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b5(pretrained=False, **kwargs):
    """ EfficientNet-B5 """
    # NOTE for train set drop_rate=0.4, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b6(pretrained=False, **kwargs):
    """ EfficientNet-B6 """
    # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b7(pretrained=False, **kwargs):
    """ EfficientNet-B7 """
    # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
    return model


def efficientnet_b8(pretrained=False, **kwargs):
    """ EfficientNet-B8 """
    # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2
    model = _gen_efficientnet(
        'efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
    return model


def efficientnet_es(pretrained=False, **kwargs):
    """ EfficientNet-Edge Small. """
    model = _gen_efficientnet_edge(
        'efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def efficientnet_em(pretrained=False, **kwargs):
    """ EfficientNet-Edge-Medium. """
    model = _gen_efficientnet_edge(
        'efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
    return model


def efficientnet_el(pretrained=False, **kwargs):
    """ EfficientNet-Edge-Large. """
    model = _gen_efficientnet_edge(
        'efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
    return model


def efficientnet_cc_b0_4e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B0 w/ 8 Experts """
    # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2
    model = _gen_efficientnet_condconv(
        'efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def efficientnet_cc_b0_8e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B0 w/ 8 Experts """
    # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2
    model = _gen_efficientnet_condconv(
        'efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
        pretrained=pretrained, **kwargs)
    return model


def efficientnet_cc_b1_8e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B1 w/ 8 Experts """
    # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2
    model = _gen_efficientnet_condconv(
        'efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
        pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b0(pretrained=False, **kwargs):
    """ EfficientNet-B0. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b1(pretrained=False, **kwargs):
    """ EfficientNet-B1. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b2(pretrained=False, **kwargs):
    """ EfficientNet-B2. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
    return model


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


def tf_efficientnet_b4(pretrained=False, **kwargs):
    """ EfficientNet-B4. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b5(pretrained=False, **kwargs):
    """ EfficientNet-B5. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b6(pretrained=False, **kwargs):
    """ EfficientNet-B6. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b7(pretrained=False, **kwargs):
    """ EfficientNet-B7. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b0_ap(pretrained=False, **kwargs):
    """ EfficientNet-B0 AdvProp. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b0_ap', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b1_ap(pretrained=False, **kwargs):
    """ EfficientNet-B1 AdvProp. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b1_ap', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b2_ap(pretrained=False, **kwargs):
    """ EfficientNet-B2 AdvProp. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b2_ap', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b3_ap(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ EfficientNet-B3 AdvProp. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b3_ap', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b4_ap(pretrained=False, **kwargs):
    """ EfficientNet-B4 AdvProp. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b4_ap', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b5_ap(pretrained=False, **kwargs):
    """ EfficientNet-B5 AdvProp. Tensorflow compatible variant """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b5_ap', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b6_ap(pretrained=False, **kwargs):
    """ EfficientNet-B6 AdvProp. Tensorflow compatible variant """
    # NOTE for train, drop_rate should be 0.5
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b6_ap', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b7_ap(pretrained=False, **kwargs):
    """ EfficientNet-B7 AdvProp. Tensorflow compatible variant """
    # NOTE for train, drop_rate should be 0.5
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b7_ap', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_b8_ap(pretrained=False, **kwargs):
    """ EfficientNet-B8 AdvProp. Tensorflow compatible variant """
    # NOTE for train, drop_rate should be 0.5
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet(
        'tf_efficientnet_b8_ap', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_es(pretrained=False, **kwargs):
    """ EfficientNet-Edge Small. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_edge(
        'tf_efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_em(pretrained=False, **kwargs):
    """ EfficientNet-Edge-Medium. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_edge(
        'tf_efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_el(pretrained=False, **kwargs):
    """ EfficientNet-Edge-Large. Tensorflow compatible variant  """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_edge(
        'tf_efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B0 w/ 4 Experts """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_condconv(
        'tf_efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model



def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B0 w/ 8 Experts """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_condconv(
        'tf_efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2,
        pretrained=pretrained, **kwargs)
    return model


def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
    """ EfficientNet-CondConv-B1 w/ 8 Experts """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_efficientnet_condconv(
        'tf_efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2,
        pretrained=pretrained, **kwargs)
    return model


def mixnet_s(pretrained=False, **kwargs):
    """Creates a MixNet Small model.
    """
    # NOTE for train set drop_rate=0.2
    model = _gen_mixnet_s(
        'mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def mixnet_m(pretrained=False, **kwargs):
    """Creates a MixNet Medium model.
    """
    # NOTE for train set drop_rate=0.25
    model = _gen_mixnet_m(
        'mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def mixnet_l(pretrained=False, **kwargs):
    """Creates a MixNet Large model.
    """
    # NOTE for train set drop_rate=0.25
    model = _gen_mixnet_m(
        'mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
    return model


def mixnet_xl(pretrained=False, **kwargs):
    """Creates a MixNet Extra-Large model.
    Not a paper spec, experimental def by RW w/ depth scaling.
    """
    # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2
    model = _gen_mixnet_m(
        'mixnet_xl', channel_multiplier=1.6, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
    return model


def mixnet_xxl(pretrained=False, **kwargs):
    """Creates a MixNet Double Extra Large model.
    Not a paper spec, experimental def by RW w/ depth scaling.
    """
    # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2
    model = _gen_mixnet_m(
        'mixnet_xxl', channel_multiplier=2.4, depth_multiplier=1.3, pretrained=pretrained, **kwargs)
    return model


def tf_mixnet_s(pretrained=False, **kwargs):
    """Creates a MixNet Small model. Tensorflow compatible variant
    """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_s(
        'tf_mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mixnet_m(pretrained=False, **kwargs):
    """Creates a MixNet Medium model. Tensorflow compatible variant
    """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_m(
        'tf_mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mixnet_l(pretrained=False, **kwargs):
    """Creates a MixNet Large model. Tensorflow compatible variant
    """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mixnet_m(
        'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
    return model


================================================
FILE: backbones/geffnet/helpers.py
================================================
import torch
import os
from collections import OrderedDict
try:
    from torch.hub import load_state_dict_from_url
except ImportError:
    from torch.utils.model_zoo import load_url as load_state_dict_from_url


def load_checkpoint(model, checkpoint_path):
    if checkpoint_path and os.path.isfile(checkpoint_path):
        print("=> Loading checkpoint '{}'".format(checkpoint_path))
        checkpoint = torch.load(checkpoint_path)
        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
            new_state_dict = OrderedDict()
            for k, v in checkpoint['state_dict'].items():
                if k.startswith('module'):
                    name = k[7:]  # remove `module.`
                else:
                    name = k
                new_state_dict[name] = v
            model.load_state_dict(new_state_dict)
        else:
            model.load_state_dict(checkpoint)
        print("=> Loaded checkpoint '{}'".format(checkpoint_path))
    else:
        print("=> Error: No checkpoint found at '{}'".format(checkpoint_path))
        raise FileNotFoundError()


def load_pretrained(model, url, filter_fn=None, strict=True):
    state_dict = load_state_dict_from_url(url, progress=False, map_location='cpu')

    input_conv = 'conv_stem'
    classifier = 'classifier'
    in_chans = getattr(model, input_conv).weight.shape[1]
    num_classes = getattr(model, classifier).weight.shape[0]

    input_conv_weight = input_conv + '.weight'
    pretrained_in_chans = state_dict[input_conv_weight].shape[1]
    if in_chans != pretrained_in_chans:
        if in_chans == 1:
            print('=> Converting pretrained input conv {} from {} to 1 channel'.format(
                input_conv_weight, pretrained_in_chans))
            conv1_weight = state_dict[input_conv_weight]
            state_dict[input_conv_weight] = conv1_weight.sum(dim=1, keepdim=True)
        else:
            print('=> Discarding pretrained input conv {} since input channel count != {}'.format(
                input_conv_weight, pretrained_in_chans))
            del state_dict[input_conv_weight]
            strict = False

    classifier_weight = classifier + '.weight'
    pretrained_num_classes = state_dict[classifier_weight].shape[0]
    if num_classes != pretrained_num_classes:
        print('=> Discarding pretrained classifier since num_classes != {}'.format(pretrained_num_classes))
        del state_dict[classifier_weight]
        del state_dict[classifier + '.bias']
        strict = False

    if filter_fn is not None:
        state_dict = filter_fn(state_dict)

    model.load_state_dict(state_dict, strict=strict)


================================================
FILE: backbones/geffnet/mobilenetv3.py
================================================
""" MobileNet-V3

A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.

Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244

Hacked together by Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F

from .helpers import load_pretrained
from .efficientnet_builder import *

__all__ = ['mobilenetv3_rw', 'mobilenetv3_large_075', 'mobilenetv3_large_100', 'mobilenetv3_large_minimal_100',
           'mobilenetv3_small_075', 'mobilenetv3_small_100', 'mobilenetv3_small_minimal_100',
           'tf_mobilenetv3_large_075', 'tf_mobilenetv3_large_100', 'tf_mobilenetv3_large_minimal_100',
           'tf_mobilenetv3_small_075', 'tf_mobilenetv3_small_100', 'tf_mobilenetv3_small_minimal_100']

model_urls = {
    'mobilenetv3_rw':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
    'mobilenetv3_large_075': None,
    'mobilenetv3_large_100': None,
    'mobilenetv3_large_minimal_100': None,
    'mobilenetv3_small_075': None,
    'mobilenetv3_small_100': None,
    'mobilenetv3_small_minimal_100': None,
    'tf_mobilenetv3_large_075':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
    'tf_mobilenetv3_large_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth',
    'tf_mobilenetv3_large_minimal_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth',
    'tf_mobilenetv3_small_075':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth',
    'tf_mobilenetv3_small_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth',
    'tf_mobilenetv3_small_minimal_100':
        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
}


class MobileNetV3(nn.Module):
    """ MobileNet-V3

    A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the
    head convolution without a final batch-norm layer before the classifier.

    Paper: https://arxiv.org/abs/1905.02244
    """

    def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
                 channel_multiplier=1.0, pad_type='', act_layer=HardSwish, drop_rate=0., drop_connect_rate=0.,
                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, weight_init='goog'):
        super(MobileNetV3, self).__init__()
        self.drop_rate = drop_rate

        stem_size = round_channels(stem_size, channel_multiplier)
        self.conv_stem = select_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
        self.bn1 = nn.BatchNorm2d(stem_size, **norm_kwargs)
        self.act1 = act_layer(inplace=True)
        in_chs = stem_size

        builder = EfficientNetBuilder(
            channel_multiplier, pad_type=pad_type, act_layer=act_layer, se_kwargs=se_kwargs,
            norm_layer=norm_layer, norm_kwargs=norm_kwargs, drop_connect_rate=drop_connect_rate)
        self.blocks = nn.Sequential(*builder(in_chs, block_args))
        in_chs = builder.in_chs

        self.global_pool = nn.AdaptiveAvgPool2d(1)
        self.conv_head = select_conv2d(in_chs, num_features, 1, padding=pad_type, bias=head_bias)
        self.act2 = act_layer(inplace=True)
        self.classifier = nn.Linear(num_features, num_classes)

        for m in self.modules():
            if weight_init == 'goog':
                initialize_weight_goog(m)
            else:
                initialize_weight_default(m)

    def as_sequential(self):
        layers = [self.conv_stem, self.bn1, self.act1]
        layers.extend(self.blocks)
        layers.extend([
            self.global_pool, self.conv_head, self.act2,
            nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
        return nn.Sequential(*layers)

    def features(self, x):
        x = self.conv_stem(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.blocks(x)
        x = self.global_pool(x)
        x = self.conv_head(x)
        x = self.act2(x)
        return x

    def forward(self, x):
        x = self.features(x)
        x = x.flatten(1)
        if self.drop_rate > 0.:
            x = F.dropout(x, p=self.drop_rate, training=self.training)
        return self.classifier(x)


def _create_model(model_kwargs, variant, pretrained=False):
    as_sequential = model_kwargs.pop('as_sequential', False)
    model = MobileNetV3(**model_kwargs)
    if pretrained and model_urls[variant]:
        load_pretrained(model, model_urls[variant])
    if as_sequential:
        model = model.as_sequential()
    return model


def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
    """Creates a MobileNet-V3 model (RW variant).

    Paper: https://arxiv.org/abs/1905.02244

    This was my first attempt at reproducing the MobileNet-V3 from paper alone. It came close to the
    eventual Tensorflow reference impl but has a few differences:
    1. This model has no bias on the head convolution
    2. This model forces no residual (noskip) on the first DWS block, this is different than MnasNet
    3. This model always uses ReLU for the SE activation layer, other models in the family inherit their act layer
       from their parent block
    4. This model does not enforce divisible by 8 limitation on the SE reduction channel count

    Overall the changes are fairly minor and result in a very small parameter count difference and no
    top-1/5

    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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        head_bias=False,  # one of my mistakes
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, 'hard_swish'),
        se_kwargs=dict(gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs,
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
    """Creates a MobileNet-V3 large/small/minimal models.

    Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v3.py
    Paper: https://arxiv.org/abs/1905.02244

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    if 'small' in variant:
        num_features = 1024
        if 'minimal' in variant:
            act_layer = 'relu'
            arch_def = [
                # stage 0, 112x112 in
                ['ds_r1_k3_s2_e1_c16'],
                # stage 1, 56x56 in
                ['ir_r1_k3_s2_e4.5_c24', 'ir_r1_k3_s1_e3.67_c24'],
                # stage 2, 28x28 in
                ['ir_r1_k3_s2_e4_c40', 'ir_r2_k3_s1_e6_c40'],
                # stage 3, 14x14 in
                ['ir_r2_k3_s1_e3_c48'],
                # stage 4, 14x14in
                ['ir_r3_k3_s2_e6_c96'],
                # stage 6, 7x7 in
                ['cn_r1_k1_s1_c576'],
            ]
        else:
            act_layer = 'hard_swish'
            arch_def = [
                # stage 0, 112x112 in
                ['ds_r1_k3_s2_e1_c16_se0.25_nre'],  # relu
                # stage 1, 56x56 in
                ['ir_r1_k3_s2_e4.5_c24_nre', 'ir_r1_k3_s1_e3.67_c24_nre'],  # relu
                # stage 2, 28x28 in
                ['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r2_k5_s1_e6_c40_se0.25'],  # hard-swish
                # stage 3, 14x14 in
                ['ir_r2_k5_s1_e3_c48_se0.25'],  # hard-swish
                # stage 4, 14x14in
                ['ir_r3_k5_s2_e6_c96_se0.25'],  # hard-swish
                # stage 6, 7x7 in
                ['cn_r1_k1_s1_c576'],  # hard-swish
            ]
    else:
        num_features = 1280
        if 'minimal' in variant:
            act_layer = 'relu'
            arch_def = [
                # stage 0, 112x112 in
                ['ds_r1_k3_s1_e1_c16'],
                # stage 1, 112x112 in
                ['ir_r1_k3_s2_e4_c24', 'ir_r1_k3_s1_e3_c24'],
                # stage 2, 56x56 in
                ['ir_r3_k3_s2_e3_c40'],
                # stage 3, 28x28 in
                ['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],
                # stage 4, 14x14in
                ['ir_r2_k3_s1_e6_c112'],
                # stage 5, 14x14in
                ['ir_r3_k3_s2_e6_c160'],
                # stage 6, 7x7 in
                ['cn_r1_k1_s1_c960'],
            ]
        else:
            act_layer = 'hard_swish'
            arch_def = [
                # stage 0, 112x112 in
                ['ds_r1_k3_s1_e1_c16_nre'],  # 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_kwargs = dict(
        block_args=decode_arch_def(arch_def),
        num_features=num_features,
        stem_size=16,
        channel_multiplier=channel_multiplier,
        act_layer=resolve_act_layer(kwargs, act_layer),
        se_kwargs=dict(
            act_layer=get_act_layer('relu'), gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True, divisor=8),
        norm_kwargs=resolve_bn_args(kwargs),
        **kwargs,
    )
    model = _create_model(model_kwargs, variant, pretrained)
    return model


def mobilenetv3_rw(pretrained=False, **kwargs):
    """ MobileNet-V3 RW
    Attn: See note in gen function for this variant.
    """
    # NOTE for train set drop_rate=0.2
    if pretrained:
        # pretrained model trained with non-default BN epsilon
        kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_large_075(pretrained=False, **kwargs):
    """ MobileNet V3 Large 0.75"""
    # NOTE for train set drop_rate=0.2
    model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_large_100(pretrained=False, **kwargs):
    """ MobileNet V3 Large 1.0 """
    # NOTE for train set drop_rate=0.2
    model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
    """ MobileNet V3 Large (Minimalistic) 1.0 """
    # NOTE for train set drop_rate=0.2
    model = _gen_mobilenet_v3('mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_small_075(pretrained=False, **kwargs):
    """ MobileNet V3 Small 0.75 """
    model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_small_100(pretrained=False, **kwargs):
    """ MobileNet V3 Small 1.0 """
    model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
    """ MobileNet V3 Small (Minimalistic) 1.0 """
    model = _gen_mobilenet_v3('mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_large_075(pretrained=False, **kwargs):
    """ MobileNet V3 Large 0.75. Tensorflow compat variant. """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_large_100(pretrained=False, **kwargs):
    """ MobileNet V3 Large 1.0. Tensorflow compat variant. """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
    """ MobileNet V3 Large Minimalistic 1.0. Tensorflow compat variant. """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_small_075(pretrained=False, **kwargs):
    """ MobileNet V3 Small 0.75. Tensorflow compat variant. """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_small_100(pretrained=False, **kwargs):
    """ MobileNet V3 Small 1.0. Tensorflow compat variant."""
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
    return model


def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
    """ MobileNet V3 Small Minimalistic 1.0. Tensorflow compat variant. """
    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
    kwargs['pad_type'] = 'same'
    model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
    return model


================================================
FILE: backbones/geffnet/model_factory.py
================================================
from .mobilenetv3 import *
from .gen_efficientnet import *
from .helpers import load_checkpoint


def create_model(
        model_name='mnasnet_100',
        pretrained=None,
        num_classes=1000,
        in_chans=3,
        checkpoint_path='',
        **kwargs):

    margs = dict(num_classes=num_classes, in_chans=in_chans, pretrained=pretrained)

    if model_name in globals():
        create_fn = globals()[model_name]
        model = create_fn(**margs, **kwargs)
    else:
        raise RuntimeError('Unknown model (%s)' % model_name)

    if checkpoint_path and not pretrained:
        load_checkpoint(model, checkpoint_path)

    return model


================================================
FILE: backbones/geffnet/version.py
================================================
__version__ = '0.9.5'


================================================
FILE: configs/efficientdet_d2_bifpn_1x.py
================================================
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
    type='RetinaNet',
    backbone=dict(
        type='EfficientNet',
        model_name='tf_efficientnet_b2'),
    neck=dict(
        type='BIFPN',
        in_channels=[48, 88, 120, 208, 352],
        out_channels=112,
        start_level=0,
        stack=4,
        add_extra_convs=True,
        num_outs=5,
        norm_cfg=dict(type='BN', requires_grad=False),
        activation='relu'),
    bbox_head=dict(
        type='RetinaHead',
        num_classes=81,
        in_channels=112,#256->112
        stacked_convs=3, #4->3
        feat_channels=112,#256->112
        octave_base_scale=4,
        scales_per_octave=3,
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[8, 16, 32, 64, 128],
        target_means=[.0, .0, .0, .0],
        target_stds=[1.0, 1.0, 1.0, 1.0],
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5, #2->1.5
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
    assigner=dict(
        type='MaxIoUAssigner',
        pos_iou_thr=0.5,
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type='nms', iou_thr=0.5),
    max_per_img=100)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(768, 768), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=128),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(768, 768),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=128),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=4e-5) #wd 0.0001->4e-5
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/efficient_d2_bifpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]


================================================
FILE: configs/efficientdet_d4_bifpn_1x.py
================================================
# model settings
model = dict(
    type='RetinaNet',
    backbone=dict(
        type='EfficientNet',
        model_name='tf_efficientnet_b4'),
    neck=dict(
        type='BIFPN',
        in_channels=[56, 112, 160, 272, 448],
        out_channels=224,
        start_level=0,
        stack=6,
        add_extra_convs=True,
        num_outs=5,
        norm_cfg=dict(type='BN', requires_grad=False),
        activation='relu'),
    bbox_head=dict(
        type='RetinaHead',
        num_classes=81,
        in_channels=224,#256->224
        stacked_convs=4,
        feat_channels=224,#256->224
        octave_base_scale=4,
        scales_per_octave=3,
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[8, 16, 32, 64, 128],
        target_means=[.0, .0, .0, .0],
        target_stds=[1.0, 1.0, 1.0, 1.0],
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=1.5, #2->1.5
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
    assigner=dict(
        type='MaxIoUAssigner',
        pos_iou_thr=0.5,
        neg_iou_thr=0.4,
        min_pos_iou=0,
        ignore_iof_thr=-1),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type='nms', iou_thr=0.5),
    max_per_img=100)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1024, 1024), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=128),#32->128
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1024, 1024),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=128),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=4e-5) #wd 0.0001->4e-5
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/efficient_d4_bifpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]


================================================
FILE: necks/bifpn.py
================================================
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init

from mmdet.core import auto_fp16
from ..registry import NECKS
from ..utils import ConvModule
import torch
eps=0.0001

@NECKS.register_module
class BIFPN(nn.Module):

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_outs,
                 start_level=0,
                 end_level=-1,
                 stack=1,
                 add_extra_convs=False,
                 extra_convs_on_inputs=True,
                 relu_before_extra_convs=False,
                 no_norm_on_lateral=False,
                 conv_cfg=None,
                 norm_cfg=None,
                 activation=None):
        super(BIFPN, self).__init__()
        assert isinstance(in_channels, list)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_ins = len(in_channels)
        self.num_outs = num_outs
        self.activation = activation
        self.relu_before_extra_convs = relu_before_extra_convs
        self.no_norm_on_lateral = no_norm_on_lateral
        self.fp16_enabled = False
        self.stack = stack

        if end_level == -1:
            self.backbone_end_level = self.num_ins
            assert num_outs >= self.num_ins - start_level
        else:
            # if end_level < inputs, no extra level is allowed
            self.backbone_end_level = end_level
            assert end_level <= len(in_channels)
            assert num_outs == end_level - start_level
        self.start_level = start_level
        self.end_level = end_level
        self.add_extra_convs = add_extra_convs
        self.extra_convs_on_inputs = extra_convs_on_inputs

        self.lateral_convs = nn.ModuleList()
        self.fpn_convs = nn.ModuleList()
        self.stack_bifpn_convs = nn.ModuleList()

        for i in range(self.start_level, self.backbone_end_level):
            l_conv = ConvModule(
                in_channels[i],
                out_channels,
                1,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
                activation=self.activation,
                inplace=False)
            self.lateral_convs.append(l_conv)

        for ii in range(stack):
            self.stack_bifpn_convs.append(BiFPNModule(channels=out_channels,
                                                      levels= self.backbone_end_level-self.start_level,
                                                      conv_cfg=conv_cfg,
                                                      norm_cfg=norm_cfg,
                                                      activation=activation))
        # add extra conv layers (e.g., RetinaNet)
        extra_levels = num_outs - self.backbone_end_level + self.start_level
        if add_extra_convs and extra_levels >= 1:
            for i in range(extra_levels):
                if i == 0 and self.extra_convs_on_inputs:
                    in_channels = self.in_channels[self.backbone_end_level - 1]
                else:
                    in_channels = out_channels
                extra_fpn_conv = ConvModule(
                    in_channels,
                    out_channels,
                    3,
                    stride=2,
                    padding=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    activation=self.activation,
                    inplace=False)
                self.fpn_convs.append(extra_fpn_conv)

    # default init_weights for conv(msra) and norm in ConvModule
    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                xavier_init(m, distribution='uniform')

    @auto_fp16()
    def forward(self, inputs):
        assert len(inputs) == len(self.in_channels)

        # build laterals
        laterals = [
            lateral_conv(inputs[i + self.start_level])
            for i, lateral_conv in enumerate(self.lateral_convs)
        ]
        # part 1: build top-down and down-top path with stack
        used_backbone_levels = len(laterals)
        for bifpn_module in self.stack_bifpn_convs:
            laterals = bifpn_module(laterals)
        outs = laterals
        # part 2: add extra levels
        if self.num_outs > len(outs):
            # use max pool to get more levels on top of outputs
            # (e.g., Faster R-CNN, Mask R-CNN)
            if not self.add_extra_convs:
                for i in range(self.num_outs - used_backbone_levels):
                    outs.append(F.max_pool2d(outs[-1], 1, stride=2))
            # add conv layers on top of original feature maps (RetinaNet)
            else:
                if self.extra_convs_on_inputs:
                    orig = inputs[self.backbone_end_level - 1]
                    outs.append(self.fpn_convs[0](orig))
                else:
                    outs.append(self.fpn_convs[0](outs[-1]))
                for i in range(1, self.num_outs - used_backbone_levels):
                    if self.relu_before_extra_convs:
                        outs.append(self.fpn_convs[i](F.relu(outs[-1])))
                    else:
                        outs.append(self.fpn_convs[i](outs[-1]))
        return tuple(outs)


class BiFPNModule(nn.Module):
    def __init__(self,
                 channels,
                 levels,
                 init=0.5,
                 conv_cfg=None,
                 norm_cfg=None,
                 activation=None):
        super(BiFPNModule, self).__init__()
        self.activation = activation
        self.levels = levels
        self.bifpn_convs =nn.ModuleList()
        #weighted
        self.w1 = nn.Parameter(torch.Tensor(2, levels).fill_(init))
        self.relu1 = nn.ReLU()
        self.w2 = nn.Parameter(torch.Tensor(3, levels - 2).fill_(init))
        self.relu2 = nn.ReLU()
        for jj in range(2):
            for i in range(self.levels-1):  # 1,2,3
                fpn_conv = nn.Sequential(
                    ConvModule(
                        channels,
                        channels,
                        3,
                        padding=1,
                        groups=channels,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        activation=self.activation,
                        inplace=False),
                    ConvModule(
                        channels,
                        channels,
                        1,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        activation=self.activation,
                        inplace=False))
                self.bifpn_convs.append(fpn_conv)

    # default init_weights for conv(msra) and norm in ConvModule
    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                xavier_init(m, distribution='uniform')

    @auto_fp16()
    def forward(self, inputs):
        assert len(inputs) == self.levels
        # build top-down and down-top path with stack
        levels = self.levels
        #w relu
        w1 = self.relu1(self.w1)
        w1 /= torch.sum(w1, dim=0) + eps #normalize
        w2 = self.relu2(self.w2)
        w2 /= torch.sum(w2, dim=0) + eps
        # build top-down
        kk=0
        pathtd = inputs
        inputs_clone=[]
        for in_tensor in inputs:
            inputs_clone.append(in_tensor.clone())
        for i in range(levels - 1, 0, -1):
            pathtd[i - 1] = w1[0,kk]*pathtd[i - 1] + w1[1,kk]*F.interpolate(
                pathtd[i], scale_factor=2, mode='nearest')
            pathtd[i - 1] = self.bifpn_convs[kk](pathtd[i - 1])
            kk=kk+1
        jj=kk
        # build down-top
        for i in range(0, levels - 2, 1):
            pathtd[i + 1] = w2[0, i] * pathtd[i + 1] + w2[1, i] * F.max_pool2d(pathtd[i], kernel_size=2) + w2[2, i] * \
                            inputs_clone[i + 1]
            pathtd[i + 1] = self.bifpn_convs[jj](pathtd[i + 1])
            jj=jj+1

        pathtd[levels - 1] = w1[0, kk] * pathtd[levels - 1] + w1[1, kk] * F.max_pool2d(pathtd[levels - 2],
                                                                                       kernel_size=2)
        pathtd[levels - 1] = self.bifpn_convs[jj](pathtd[levels - 1])
        return pathtd