# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Contains common building blocks for yolo neural networks."""
from typing import Callable, List, Tuple

import tensorflow as tf

from official.modeling import tf_utils
from official.vision.beta.ops import spatial_transform_ops


@tf.keras.utils.register_keras_serializable(package='yolo')
class Identity(tf.keras.layers.Layer):

  def call(self, inputs):
    return inputs


@tf.keras.utils.register_keras_serializable(package='yolo')
class ConvBN(tf.keras.layers.Layer):
  """ConvBN block.

  Modified Convolution layer to match that of the Darknet Library.
  The Layer is a standards combination of Conv BatchNorm Activation,
  however, the use of bias in the conv is determined by the use of batch
  normalization.
  Cross Stage Partial networks (CSPNets) were proposed in:
  [1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu,
        Ping-Yang Chen, Jun-Wei Hsieh
      CSPNet: A New Backbone that can Enhance Learning Capability of CNN.
        arXiv:1911.11929
  """

  def __init__(self,
               filters=1,
               kernel_size=(1, 1),
               strides=(1, 1),
               padding='same',
               dilation_rate=(1, 1),
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_separable_conv=False,
               use_bn=True,
               use_sync_bn=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               activation='leaky',
               leaky_alpha=0.1,
               **kwargs):
    """ConvBN initializer.

    Args:
      filters: integer for output depth, or the number of features to learn.
      kernel_size: integer or tuple for the shape of the weight matrix or kernel
        to learn.
      strides: integer of tuple how much to move the kernel after each kernel
        use.
      padding: string 'valid' or 'same', if same, then pad the image, else do
        not.
      dilation_rate: tuple to indicate how much to modulate kernel weights and
        how many pixels in a feature map to skip.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      use_separable_conv: `bool` wether to use separable convs.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics
        of all batch norm layers to the models global statistics
        (across all input batches).
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      activation: string or None for activation function to use in layer,
        if None activation is replaced by linear.
      leaky_alpha: float to use as alpha if activation function is leaky.
      **kwargs: Keyword Arguments.
    """

    # convolution params
    self._filters = filters
    self._kernel_size = kernel_size
    self._strides = strides
    self._padding = padding
    self._dilation_rate = dilation_rate

    if kernel_initializer == 'VarianceScaling':
      # to match pytorch initialization method
      self._kernel_initializer = tf.keras.initializers.VarianceScaling(
          scale=1 / 3, mode='fan_in', distribution='uniform')
    else:
      self._kernel_initializer = kernel_initializer

    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer

    self._bias_regularizer = bias_regularizer

    # batch normalization params
    self._use_bn = use_bn
    self._use_separable_conv = use_separable_conv
    self._use_sync_bn = use_sync_bn
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon

    ksize = self._kernel_size
    if not isinstance(ksize, List) and not isinstance(ksize, Tuple):
      ksize = [ksize]
    if use_separable_conv and not all([a == 1 for a in ksize]):
      self._conv_base = tf.keras.layers.SeparableConv2D
    else:
      self._conv_base = tf.keras.layers.Conv2D

    if use_sync_bn:
      self._bn_base = tf.keras.layers.experimental.SyncBatchNormalization
    else:
      self._bn_base = tf.keras.layers.BatchNormalization

    if tf.keras.backend.image_data_format() == 'channels_last':
      # format: (batch_size, height, width, channels)
      self._bn_axis = -1
    else:
      # format: (batch_size, channels, width, height)
      self._bn_axis = 1

    # activation params
    self._activation = activation
    self._leaky_alpha = leaky_alpha
    self._fuse = False

    super().__init__(**kwargs)

  def build(self, input_shape):
    use_bias = not self._use_bn

    self.conv = self._conv_base(
        filters=self._filters,
        kernel_size=self._kernel_size,
        strides=self._strides,
        padding=self._padding,
        dilation_rate=self._dilation_rate,
        use_bias=use_bias,
        kernel_initializer=self._kernel_initializer,
        bias_initializer=self._bias_initializer,
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer)

    if self._use_bn:
      self.bn = self._bn_base(
          momentum=self._norm_momentum,
          epsilon=self._norm_epsilon,
          axis=self._bn_axis)
    else:
      self.bn = None

    if self._activation == 'leaky':
      self._activation_fn = tf.keras.layers.LeakyReLU(alpha=self._leaky_alpha)
    elif self._activation == 'mish':
      self._activation_fn = lambda x: x * tf.math.tanh(tf.math.softplus(x))
    else:
      self._activation_fn = tf_utils.get_activation(self._activation)

  def call(self, x):
    x = self.conv(x)
    if self._use_bn and not self._fuse:
      x = self.bn(x)
    x = self._activation_fn(x)
    return x

  def fuse(self):
    if self.bn is not None and not self._use_separable_conv:
      # Fuse convolution and batchnorm, gives me +2 to 3 FPS 2ms latency.
      # layers: https://tehnokv.com/posts/fusing-batchnorm-and-conv/
      if self._fuse:
        return

      self._fuse = True
      conv_weights = self.conv.get_weights()[0]
      gamma, beta, moving_mean, moving_variance = self.bn.get_weights()

      self.conv.use_bias = True
      infilters = conv_weights.shape[-2]
      self.conv.build([None, None, None, infilters])

      base = tf.sqrt(self._norm_epsilon + moving_variance)
      w_conv_base = tf.transpose(conv_weights, perm=(3, 2, 0, 1))
      w_conv = tf.reshape(w_conv_base, [conv_weights.shape[-1], -1])

      w_bn = tf.linalg.diag(gamma / base)
      w_conv = tf.reshape(tf.matmul(w_bn, w_conv), w_conv_base.get_shape())
      w_conv = tf.transpose(w_conv, perm=(2, 3, 1, 0))

      b_bn = beta - gamma * moving_mean / base

      self.conv.set_weights([w_conv, b_bn])
      del self.bn

      self.trainable = False
      self.conv.trainable = False
      self.bn = None
    return

  def get_config(self):
    # used to store/share parameters to reconstruct the model
    layer_config = {
        'filters': self._filters,
        'kernel_size': self._kernel_size,
        'strides': self._strides,
        'padding': self._padding,
        'dilation_rate': self._dilation_rate,
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'kernel_regularizer': self._kernel_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._activation,
        'leaky_alpha': self._leaky_alpha
    }
    layer_config.update(super().get_config())
    return layer_config


@tf.keras.utils.register_keras_serializable(package='yolo')
class DarkResidual(tf.keras.layers.Layer):
  """Darknet block with Residual connection for Yolo v3 Backbone."""

  def __init__(self,
               filters=1,
               filter_scale=2,
               dilation_rate=1,
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               kernel_regularizer=None,
               bias_regularizer=None,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               activation='leaky',
               leaky_alpha=0.1,
               sc_activation='linear',
               downsample=False,
               **kwargs):
    """Dark Residual initializer.

    Args:
      filters: integer for output depth, or the number of features to learn.
      filter_scale: `int` for filter scale.
      dilation_rate: tuple to indicate how much to modulate kernel weights and
        how many pixels in a feature map to skip.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics.
        of all batch norm layers to the models global statistics
        (across all input batches).
      use_separable_conv: `bool` wether to use separable convs.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      activation: string or None for activation function to use in layer,
        if None activation is replaced by linear.
      leaky_alpha: float to use as alpha if activation function is leaky.
      sc_activation: string for activation function to use in layer.
      downsample: boolean for if image input is larger than layer output, set
        downsample to True so the dimensions are forced to match.
      **kwargs: Keyword Arguments.
    """

    # downsample
    self._downsample = downsample

    # ConvBN params
    self._filters = filters
    self._filter_scale = filter_scale
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._bias_regularizer = bias_regularizer
    self._use_bn = use_bn
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._kernel_regularizer = kernel_regularizer

    # normal params
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon
    self._dilation_rate = dilation_rate if isinstance(dilation_rate,
                                                      int) else dilation_rate[0]

    # activation params
    self._conv_activation = activation
    self._leaky_alpha = leaky_alpha
    self._sc_activation = sc_activation

    super().__init__(**kwargs)

  def build(self, input_shape):
    dark_conv_args = {
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._conv_activation,
        'kernel_regularizer': self._kernel_regularizer,
        'leaky_alpha': self._leaky_alpha
    }
    if self._downsample:
      if self._dilation_rate > 1:
        dilation_rate = 1
        if self._dilation_rate // 2 > 0:
          dilation_rate = self._dilation_rate // 2
        down_stride = 1
      else:
        dilation_rate = 1
        down_stride = 2

      self._dconv = ConvBN(
          filters=self._filters,
          kernel_size=(3, 3),
          strides=down_stride,
          dilation_rate=dilation_rate,
          padding='same',
          **dark_conv_args)

    self._conv1 = ConvBN(
        filters=self._filters // self._filter_scale,
        kernel_size=(1, 1),
        strides=(1, 1),
        padding='same',
        **dark_conv_args)

    self._conv2 = ConvBN(
        filters=self._filters,
        kernel_size=(3, 3),
        strides=(1, 1),
        dilation_rate=self._dilation_rate,
        padding='same',
        **dark_conv_args)

    self._shortcut = tf.keras.layers.Add()
    if self._sc_activation == 'leaky':
      self._activation_fn = tf.keras.layers.LeakyReLU(alpha=self._leaky_alpha)
    elif self._sc_activation == 'mish':
      self._activation_fn = lambda x: x * tf.math.tanh(tf.math.softplus(x))
    else:
      self._activation_fn = tf_utils.get_activation(self._sc_activation)
    super().build(input_shape)

  def call(self, inputs, training=None):
    if self._downsample:
      inputs = self._dconv(inputs)
    x = self._conv1(inputs)
    x = self._conv2(x)
    x = self._shortcut([x, inputs])
    return self._activation_fn(x)

  def get_config(self):
    # used to store/share parameters to reconstruct the model
    layer_config = {
        'filters': self._filters,
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'dilation_rate': self._dilation_rate,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._conv_activation,
        'leaky_alpha': self._leaky_alpha,
        'sc_activation': self._sc_activation,
        'downsample': self._downsample,
    }
    layer_config.update(super().get_config())
    return layer_config


@tf.keras.utils.register_keras_serializable(package='yolo')
class CSPTiny(tf.keras.layers.Layer):
  """CSP Tiny layer.

  A Small size convolution block proposed in the CSPNet. The layer uses
  shortcuts, routing(concatnation), and feature grouping in order to improve
  gradient variablity and allow for high efficency, low power residual learning
  for small networtf.keras.
  Cross Stage Partial networks (CSPNets) were proposed in:
  [1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu,
        Ping-Yang Chen, Jun-Wei Hsieh
      CSPNet: A New Backbone that can Enhance Learning Capability of CNN.
        arXiv:1911.11929
  """

  def __init__(self,
               filters=1,
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_bn=True,
               dilation_rate=1,
               use_sync_bn=False,
               use_separable_conv=False,
               group_id=1,
               groups=2,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               activation='leaky',
               downsample=True,
               leaky_alpha=0.1,
               **kwargs):
    """Initializer for CSPTiny block.

    Args:
      filters: integer for output depth, or the number of features to learn.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      use_bn: boolean for whether to use batch normalization.
      dilation_rate: `int`, dilation rate for conv layers.
      use_sync_bn: boolean for whether sync batch normalization statistics
        of all batch norm layers to the models global statistics
        (across all input batches).
      use_separable_conv: `bool` wether to use separable convs.
      group_id: integer for which group of features to pass through the csp
        tiny stack.
      groups: integer for how many splits there should be in the convolution
        feature stack output.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      activation: string or None for activation function to use in layer,
        if None activation is replaced by linear.
      downsample: boolean for if image input is larger than layer output, set
        downsample to True so the dimensions are forced to match.
      leaky_alpha: float to use as alpha if activation function is leaky.
      **kwargs: Keyword Arguments.
    """

    # ConvBN params
    self._filters = filters
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._bias_regularizer = bias_regularizer
    self._use_bn = use_bn
    self._dilation_rate = dilation_rate
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._kernel_regularizer = kernel_regularizer
    self._groups = groups
    self._group_id = group_id
    self._downsample = downsample

    # normal params
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon

    # activation params
    self._conv_activation = activation
    self._leaky_alpha = leaky_alpha

    super().__init__(**kwargs)

  def build(self, input_shape):
    dark_conv_args = {
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._conv_activation,
        'leaky_alpha': self._leaky_alpha
    }
    self._convlayer1 = ConvBN(
        filters=self._filters,
        kernel_size=(3, 3),
        strides=(1, 1),
        padding='same',
        **dark_conv_args)

    self._convlayer2 = ConvBN(
        filters=self._filters // 2,
        kernel_size=(3, 3),
        strides=(1, 1),
        padding='same',
        **dark_conv_args)

    self._convlayer3 = ConvBN(
        filters=self._filters // 2,
        kernel_size=(3, 3),
        strides=(1, 1),
        padding='same',
        **dark_conv_args)

    self._convlayer4 = ConvBN(
        filters=self._filters,
        kernel_size=(1, 1),
        strides=(1, 1),
        padding='same',
        **dark_conv_args)

    if self._downsample:
      self._maxpool = tf.keras.layers.MaxPool2D(
          pool_size=2, strides=2, padding='same', data_format=None)

    super().build(input_shape)

  def call(self, inputs, training=None):
    x1 = self._convlayer1(inputs)
    x1_group = tf.split(x1, self._groups, axis=-1)[self._group_id]
    x2 = self._convlayer2(x1_group)  # grouping
    x3 = self._convlayer3(x2)
    x4 = tf.concat([x3, x2], axis=-1)  # csp partial using grouping
    x5 = self._convlayer4(x4)
    x = tf.concat([x1, x5], axis=-1)  # csp connect
    if self._downsample:
      x = self._maxpool(x)
    return x, x5


@tf.keras.utils.register_keras_serializable(package='yolo')
class CSPRoute(tf.keras.layers.Layer):
  """CSPRoute block.

  Down sampling layer to take the place of down sampleing done in Residual
  networks. This is the first of 2 layers needed to convert any Residual Network
  model to a CSPNet. At the start of a new level change, this CSPRoute layer
  creates a learned identity that will act as a cross stage connection,
  that is used to inform the inputs to the next stage. It is called cross stage
  partial because the number of filters required in every intermitent Residual
  layer is reduced by half. The sister layer will take the partial generated by
  this layer and concatnate it with the output of the final residual layer in
  the stack to create a fully feature level output. This concatnation merges the
  partial blocks of 2 levels as input to the next allowing the gradients of each
  level to be more unique, and reducing the number of parameters required by
  each level by 50% while keeping accuracy consistent.

  Cross Stage Partial networks (CSPNets) were proposed in:
  [1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu,
        Ping-Yang Chen, Jun-Wei Hsieh
      CSPNet: A New Backbone that can Enhance Learning Capability of CNN.
        arXiv:1911.11929
  """

  def __init__(self,
               filters,
               filter_scale=2,
               activation='mish',
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               dilation_rate=1,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               downsample=True,
               leaky_alpha=0.1,
               **kwargs):
    """CSPRoute layer initializer.

    Args:
      filters: integer for output depth, or the number of features to learn
      filter_scale: integer dictating (filters//2) or the number of filters in
        the partial feature stack.
      activation: string for activation function to use in layer.
      kernel_initializer: string to indicate which function to use to
        initialize weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      dilation_rate: dilation rate for conv layers.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics
        of all batch norm layers to the models global statistics
        (across all input batches).
      use_separable_conv: `bool` wether to use separable convs.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      downsample: down_sample the input.
      leaky_alpha: `float`, for leaky alpha value.
      **kwargs: Keyword Arguments.
    """

    super().__init__(**kwargs)
    # layer params
    self._filters = filters
    self._filter_scale = filter_scale
    self._activation = activation

    # convoultion params
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._dilation_rate = dilation_rate
    self._use_bn = use_bn
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon
    self._downsample = downsample
    self._leaky_alpha = leaky_alpha

  def build(self, input_shape):
    dark_conv_args = {
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._activation,
        'kernel_regularizer': self._kernel_regularizer,
        'leaky_alpha': self._leaky_alpha,
    }
    if self._downsample:
      if self._dilation_rate > 1:
        dilation_rate = 1
        if self._dilation_rate // 2 > 0:
          dilation_rate = self._dilation_rate // 2
        down_stride = 1
      else:
        dilation_rate = 1
        down_stride = 2

      self._conv1 = ConvBN(
          filters=self._filters,
          kernel_size=(3, 3),
          strides=down_stride,
          dilation_rate=dilation_rate,
          **dark_conv_args)

    self._conv2 = ConvBN(
        filters=self._filters // self._filter_scale,
        kernel_size=(1, 1),
        strides=(1, 1),
        **dark_conv_args)

    self._conv3 = ConvBN(
        filters=self._filters // self._filter_scale,
        kernel_size=(1, 1),
        strides=(1, 1),
        **dark_conv_args)

  def call(self, inputs, training=None):
    if self._downsample:
      inputs = self._conv1(inputs)
    y = self._conv2(inputs)
    x = self._conv3(inputs)
    return (x, y)


@tf.keras.utils.register_keras_serializable(package='yolo')
class CSPConnect(tf.keras.layers.Layer):
  """CSPConnect block.

  Sister Layer to the CSPRoute layer. Merges the partial feature stacks
  generated by the CSPDownsampling layer, and the finaly output of the
  residual stack. Suggested in the CSPNet paper.
  Cross Stage Partial networks (CSPNets) were proposed in:
  [1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu,
        Ping-Yang Chen, Jun-Wei Hsieh
      CSPNet: A New Backbone that can Enhance Learning Capability of CNN.
        arXiv:1911.11929
  """

  def __init__(self,
               filters,
               filter_scale=2,
               drop_final=False,
               drop_first=False,
               activation='mish',
               kernel_size=(1, 1),
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               dilation_rate=1,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               leaky_alpha=0.1,
               **kwargs):
    """Initializer for CSPConnect block.

    Args:
      filters: integer for output depth, or the number of features to learn.
      filter_scale: integer dictating (filters//2) or the number of filters in
        the partial feature stack.
      drop_final: `bool`, whether to drop final conv layer.
      drop_first: `bool`, whether to drop first conv layer.
      activation: string for activation function to use in layer.
      kernel_size: `Tuple`, kernel size for conv layers.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      dilation_rate: `int`, dilation rate for conv layers.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics
        of all batch norm layers to the models global
        statistics (across all input batches).
      use_separable_conv: `bool` wether to use separable convs.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      leaky_alpha: `float`, for leaky alpha value.
      **kwargs: Keyword Arguments.
    """

    super().__init__(**kwargs)
    # layer params
    self._filters = filters
    self._filter_scale = filter_scale
    self._activation = activation

    # convoultion params
    self._kernel_size = kernel_size
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._use_bn = use_bn
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon
    self._drop_final = drop_final
    self._drop_first = drop_first
    self._leaky_alpha = leaky_alpha

  def build(self, input_shape):
    dark_conv_args = {
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._activation,
        'kernel_regularizer': self._kernel_regularizer,
        'leaky_alpha': self._leaky_alpha,
    }
    if not self._drop_first:
      self._conv1 = ConvBN(
          filters=self._filters // self._filter_scale,
          kernel_size=self._kernel_size,
          strides=(1, 1),
          **dark_conv_args)
    self._concat = tf.keras.layers.Concatenate(axis=-1)

    if not self._drop_final:
      self._conv2 = ConvBN(
          filters=self._filters,
          kernel_size=(1, 1),
          strides=(1, 1),
          **dark_conv_args)

  def call(self, inputs, training=None):
    x_prev, x_csp = inputs
    if not self._drop_first:
      x_prev = self._conv1(x_prev)
    x = self._concat([x_prev, x_csp])

    # skipped if drop final is true
    if not self._drop_final:
      x = self._conv2(x)
    return x


class CSPStack(tf.keras.layers.Layer):
  """CSP Stack layer.

  CSP full stack, combines the route and the connect in case you dont want to
  jsut quickly wrap an existing callable or list of layers to
  make it a cross stage partial. Added for ease of use. you should be able
  to wrap any layer stack with a CSP independent of wether it belongs
  to the Darknet family. if filter_scale = 2, then the blocks in the stack
  passed into the the CSP stack should also have filters = filters/filter_scale
  Cross Stage Partial networks (CSPNets) were proposed in:

  [1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu,
        Ping-Yang Chen, Jun-Wei Hsieh
      CSPNet: A New Backbone that can Enhance Learning Capability of CNN.
        arXiv:1911.11929
  """

  def __init__(self,
               filters,
               model_to_wrap=None,
               filter_scale=2,
               activation='mish',
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               downsample=True,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               **kwargs):
    """CSPStack layer initializer.

    Args:
      filters: filter size for conv layers.
      model_to_wrap: callable Model or a list of callable objects that will
        process the output of CSPRoute, and be input into CSPConnect. list will
        be called sequentially.
      filter_scale: integer dictating (filters//2) or the number of filters in
        the partial feature stack.
      activation: string for activation function to use in layer.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      downsample: down_sample the input.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics of
        all batch norm layers to the models global statistics (across all input
        batches).
      use_separable_conv: `bool` wether to use separable convs.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      **kwargs: Keyword Arguments.

    Raises:
      TypeError: model_to_wrap is not a layer or a list of layers
    """

    super().__init__(**kwargs)
    # layer params
    self._filters = filters
    self._filter_scale = filter_scale
    self._activation = activation
    self._downsample = downsample

    # convoultion params
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._use_bn = use_bn
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon

    if model_to_wrap is None:
      self._model_to_wrap = []
    elif isinstance(model_to_wrap, Callable):
      self._model_to_wrap = [model_to_wrap]
    elif isinstance(model_to_wrap, List):
      self._model_to_wrap = model_to_wrap
    else:
      raise TypeError(
          'the input to the CSPStack must be a list of layers that we can' +
          'iterate through, or \n a callable')

  def build(self, input_shape):
    dark_conv_args = {
        'filters': self._filters,
        'filter_scale': self._filter_scale,
        'activation': self._activation,
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'kernel_regularizer': self._kernel_regularizer,
    }
    self._route = CSPRoute(downsample=self._downsample, **dark_conv_args)
    self._connect = CSPConnect(**dark_conv_args)

  def call(self, inputs, training=None):
    x, x_route = self._route(inputs)
    for layer in self._model_to_wrap:
      x = layer(x)
    x = self._connect([x, x_route])
    return x


@tf.keras.utils.register_keras_serializable(package='yolo')
class PathAggregationBlock(tf.keras.layers.Layer):
  """Path Aggregation block."""

  def __init__(self,
               filters=1,
               drop_final=True,
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               inverted=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               activation='leaky',
               leaky_alpha=0.1,
               downsample=False,
               upsample=False,
               upsample_size=2,
               **kwargs):
    """Initializer for path aggregation block.

    Args:
      filters: integer for output depth, or the number of features to learn.
      drop_final: do not create the last convolution block.
      kernel_initializer: string to indicate which function to use to initialize
        weights.
      bias_initializer: string to indicate which function to use to initialize
        bias.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      use_bn: boolean for whether to use batch normalization.
      use_sync_bn: boolean for whether sync batch normalization statistics
        of all batch norm layers to the models global statistics
        (across all input batches).
      use_separable_conv: `bool` wether to use separable convs.
      inverted: boolean for inverting the order of the convolutions.
      norm_momentum: float for moment to use for batch normalization.
      norm_epsilon: float for batch normalization epsilon.
      activation: string or None for activation function to use in layer,
        if None activation is replaced by linear.
      leaky_alpha: float to use as alpha if activation function is leaky.
      downsample: `bool` for whehter to downwample and merge.
      upsample: `bool` for whehter to upsample and merge.
      upsample_size: `int` how much to upsample in order to match shapes.
      **kwargs: Keyword Arguments.
    """

    # Darkconv params
    self._filters = filters
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._bias_regularizer = bias_regularizer
    self._kernel_regularizer = kernel_regularizer
    self._use_bn = use_bn
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv

    # Normal params
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon

    # Activation params
    self._conv_activation = activation
    self._leaky_alpha = leaky_alpha
    self._downsample = downsample
    self._upsample = upsample
    self._upsample_size = upsample_size
    self._drop_final = drop_final

    # Block params
    self._inverted = inverted

    super().__init__(**kwargs)

  def _build_regular(self, input_shape, kwargs):
    if self._downsample:
      self._conv = ConvBN(
          filters=self._filters,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding='same',
          **kwargs)
    else:
      self._conv = ConvBN(
          filters=self._filters,
          kernel_size=(1, 1),
          strides=(1, 1),
          padding='same',
          **kwargs)

    if not self._drop_final:
      self._conv_concat = ConvBN(
          filters=self._filters,
          kernel_size=(1, 1),
          strides=(1, 1),
          padding='same',
          **kwargs)

  def _build_reversed(self, input_shape, kwargs):
    if self._downsample:
      self._conv_prev = ConvBN(
          filters=self._filters,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding='same',
          **kwargs)
    else:
      self._conv_prev = ConvBN(
          filters=self._filters,
          kernel_size=(1, 1),
          strides=(1, 1),
          padding='same',
          **kwargs)

    self._conv_route = ConvBN(
        filters=self._filters,
        kernel_size=(1, 1),
        strides=(1, 1),
        padding='same',
        **kwargs)

    if not self._drop_final:
      self._conv_sync = ConvBN(
          filters=self._filters,
          kernel_size=(1, 1),
          strides=(1, 1),
          padding='same',
          **kwargs)

  def build(self, input_shape):
    dark_conv_args = {
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_bn': self._use_bn,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'activation': self._conv_activation,
        'kernel_regularizer': self._kernel_regularizer,
        'leaky_alpha': self._leaky_alpha,
    }

    if self._inverted:
      self._build_reversed(input_shape, dark_conv_args)
    else:
      self._build_regular(input_shape, dark_conv_args)

    self._concat = tf.keras.layers.Concatenate()
    super().build(input_shape)

  def _call_regular(self, inputs, training=None):
    input_to_convolve, input_to_concat = inputs
    x_prev = self._conv(input_to_convolve)
    if self._upsample:
      x_prev = spatial_transform_ops.nearest_upsampling(x_prev,
                                                        self._upsample_size)
    x = self._concat([x_prev, input_to_concat])

    # used in csp conversion
    if not self._drop_final:
      x = self._conv_concat(x)
    return x_prev, x

  def _call_reversed(self, inputs, training=None):
    x_route, x_prev = inputs
    x_prev = self._conv_prev(x_prev)
    if self._upsample:
      x_prev = spatial_transform_ops.nearest_upsampling(x_prev,
                                                        self._upsample_size)
    x_route = self._conv_route(x_route)
    x = self._concat([x_route, x_prev])
    if not self._drop_final:
      x = self._conv_sync(x)
    return x_prev, x

  def call(self, inputs, training=None):
    # done this way to prevent confusion in the auto graph
    if self._inverted:
      return self._call_reversed(inputs, training=training)
    else:
      return self._call_regular(inputs, training=training)


@tf.keras.utils.register_keras_serializable(package='yolo')
class SPP(tf.keras.layers.Layer):
  """Spatial Pyramid Pooling.

  A non-agregated SPP layer that uses Pooling.
  """

  def __init__(self, sizes, **kwargs):
    self._sizes = list(reversed(sizes))
    if not sizes:
      raise ValueError('More than one maxpool should be specified in SSP block')
    super().__init__(**kwargs)

  def build(self, input_shape):
    maxpools = []
    for size in self._sizes:
      maxpools.append(
          tf.keras.layers.MaxPool2D(
              pool_size=(size, size),
              strides=(1, 1),
              padding='same',
              data_format=None))
    self._maxpools = maxpools
    super().build(input_shape)

  def call(self, inputs, training=None):
    outputs = []
    for maxpool in self._maxpools:
      outputs.append(maxpool(inputs))
    outputs.append(inputs)
    concat_output = tf.keras.layers.concatenate(outputs)
    return concat_output

  def get_config(self):
    layer_config = {'sizes': self._sizes}
    layer_config.update(super().get_config())
    return layer_config


class SAM(tf.keras.layers.Layer):
  """Spatial Attention Model.

  [1] Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon
  CBAM: Convolutional Block Attention Module. arXiv:1807.06521

  implementation of the Spatial Attention Model (SAM)
  """

  def __init__(self,
               use_pooling=False,
               filter_match=False,
               filters=1,
               kernel_size=(1, 1),
               strides=(1, 1),
               padding='same',
               dilation_rate=(1, 1),
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_bn=True,
               use_sync_bn=True,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               activation='sigmoid',
               output_activation=None,
               leaky_alpha=0.1,
               **kwargs):

    # use_pooling
    self._use_pooling = use_pooling
    self._filters = filters
    self._output_activation = output_activation
    self._leaky_alpha = leaky_alpha

    self.dark_conv_args = {
        'kernel_size': kernel_size,
        'strides': strides,
        'padding': padding,
        'dilation_rate': dilation_rate,
        'kernel_initializer': kernel_initializer,
        'bias_initializer': bias_initializer,
        'bias_regularizer': bias_regularizer,
        'use_bn': use_bn,
        'use_sync_bn': use_sync_bn,
        'use_separable_conv': use_separable_conv,
        'norm_momentum': norm_momentum,
        'norm_epsilon': norm_epsilon,
        'activation': activation,
        'kernel_regularizer': kernel_regularizer,
        'leaky_alpha': leaky_alpha
    }

    super().__init__(**kwargs)

  def build(self, input_shape):
    if self._filters == -1:
      self._filters = input_shape[-1]
    self._conv = ConvBN(filters=self._filters, **self.dark_conv_args)
    if self._output_activation == 'leaky':
      self._activation_fn = tf.keras.layers.LeakyReLU(alpha=self._leaky_alpha)
    elif self._output_activation == 'mish':
      self._activation_fn = lambda x: x * tf.math.tanh(tf.math.softplus(x))
    else:
      self._activation_fn = tf_utils.get_activation(self._output_activation)

  def call(self, inputs, training=None):
    if self._use_pooling:
      depth_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
      depth_avg = tf.reduce_mean(inputs, axis=-1, keepdims=True)
      input_maps = tf.concat([depth_avg, depth_max], axis=-1)
    else:
      input_maps = inputs

    attention_mask = self._conv(input_maps)
    return self._activation_fn(inputs * attention_mask)


class CAM(tf.keras.layers.Layer):
  """Channel Attention Model.

  [1] Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon
  CBAM: Convolutional Block Attention Module. arXiv:1807.06521

  Implementation of the Channel Attention Model (CAM)
  """

  def __init__(self,
               reduction_ratio=1.0,
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_bn=False,
               use_sync_bn=False,
               use_bias=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               mlp_activation='linear',
               activation='sigmoid',
               leaky_alpha=0.1,
               **kwargs):

    self._reduction_ratio = reduction_ratio

    # use_pooling
    if use_sync_bn:
      self._bn = tf.keras.layers.experimental.SyncBatchNormalization
    else:
      self._bn = tf.keras.layers.BatchNormalization

    if not use_bn:
      self._bn = Identity
      self._bn_args = {}
    else:
      self._bn_args = {
          'momentum': norm_momentum,
          'epsilon': norm_epsilon,
      }

    self._mlp_args = {
        'use_bias': use_bias,
        'kernel_initializer': kernel_initializer,
        'bias_initializer': bias_initializer,
        'bias_regularizer': bias_regularizer,
        'activation': mlp_activation,
        'kernel_regularizer': kernel_regularizer,
    }

    self._leaky_alpha = leaky_alpha
    self._activation = activation

    super().__init__(**kwargs)

  def build(self, input_shape):
    self._filters = input_shape[-1]

    self._mlp = tf.keras.Sequential([
        tf.keras.layers.Dense(self._filters, **self._mlp_args),
        self._bn(**self._bn_args),
        tf.keras.layers.Dense(
            int(self._filters * self._reduction_ratio), **self._mlp_args),
        self._bn(**self._bn_args),
        tf.keras.layers.Dense(self._filters, **self._mlp_args),
        self._bn(**self._bn_args),
    ])

    if self._activation == 'leaky':
      self._activation_fn = tf.keras.layers.LeakyReLU(alpha=self._leaky_alpha)
    elif self._activation == 'mish':
      self._activation_fn = lambda x: x * tf.math.tanh(tf.math.softplus(x))
    else:
      self._activation_fn = tf_utils.get_activation(self._activation)

  def call(self, inputs, training=None):
    depth_max = self._mlp(tf.reduce_max(inputs, axis=(1, 2)))
    depth_avg = self._mlp(tf.reduce_mean(inputs, axis=(1, 2)))
    channel_mask = self._activation_fn(depth_avg + depth_max)

    channel_mask = tf.expand_dims(channel_mask, axis=1)
    attention_mask = tf.expand_dims(channel_mask, axis=1)

    return inputs * attention_mask


class CBAM(tf.keras.layers.Layer):
  """Convolutional Block Attention Module.

  [1] Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon
  CBAM: Convolutional Block Attention Module. arXiv:1807.06521

  implementation of the Convolution Block Attention Module (CBAM)
  """

  def __init__(self,
               use_pooling=False,
               filters=1,
               reduction_ratio=1.0,
               kernel_size=(1, 1),
               strides=(1, 1),
               padding='same',
               dilation_rate=(1, 1),
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_bn=True,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               mlp_activation=None,
               activation='sigmoid',
               leaky_alpha=0.1,
               **kwargs):

    # use_pooling

    self._sam_args = {
        'use_pooling': use_pooling,
        'filters': filters,
        'kernel_size': kernel_size,
        'strides': strides,
        'padding': padding,
        'dilation_rate': dilation_rate,
        'use_separable_conv': use_separable_conv,
    }

    self._cam_args = {
        'reduction_ratio': reduction_ratio,
        'mlp_activation': mlp_activation
    }

    self._common_args = {
        'kernel_initializer': kernel_initializer,
        'bias_initializer': bias_initializer,
        'bias_regularizer': bias_regularizer,
        'use_bn': use_bn,
        'use_sync_bn': use_sync_bn,
        'norm_momentum': norm_momentum,
        'norm_epsilon': norm_epsilon,
        'activation': activation,
        'kernel_regularizer': kernel_regularizer,
        'leaky_alpha': leaky_alpha
    }

    self._cam_args.update(self._common_args)
    self._sam_args.update(self._common_args)
    super().__init__(**kwargs)

  def build(self, input_shape):
    self._cam = CAM(**self._cam_args)
    self._sam = SAM(**self._sam_args)

  def call(self, inputs, training=None):
    return self._sam(self._cam(inputs))


@tf.keras.utils.register_keras_serializable(package='yolo')
class DarkRouteProcess(tf.keras.layers.Layer):
  """Dark Route Process block.

  Process darknet outputs and connect back bone to head more generalizably
  Abstracts repetition of DarkConv objects that is common in YOLO.

  It is used like the following:

  x = ConvBN(1024, (3, 3), (1, 1))(x)
  proc = DarkRouteProcess(filters = 1024,
                          repetitions = 3,
                          insert_spp = False)(x)
  """

  def __init__(self,
               filters=2,
               repetitions=2,
               insert_spp=False,
               insert_sam=False,
               insert_cbam=False,
               csp_stack=0,
               csp_scale=2,
               kernel_initializer='VarianceScaling',
               bias_initializer='zeros',
               bias_regularizer=None,
               kernel_regularizer=None,
               use_sync_bn=False,
               use_separable_conv=False,
               norm_momentum=0.99,
               norm_epsilon=0.001,
               block_invert=False,
               activation='leaky',
               leaky_alpha=0.1,
               spp_keys=None,
               **kwargs):
    """DarkRouteProcess initializer.

    Args:
      filters: the number of filters to be used in all subsequent layers
        filters should be the depth of the tensor input into this layer,
        as no downsampling can be done within this layer object.
      repetitions: number of times to repeat the processign nodes.
        for tiny: 1 repition, no spp allowed.
        for spp: insert_spp = True, and allow for 6 repetitions.
        for regular: insert_spp = False, and allow for 6 repetitions.
      insert_spp: bool if true add the spatial pyramid pooling layer.
      insert_sam: bool if true add spatial attention module to path.
      insert_cbam: bool if true add convolutional block attention
        module to path.
      csp_stack: int for the number of sequential layers from 0
        to <value> you would like to convert into a Cross Stage
        Partial(csp) type.
      csp_scale: int for how much to down scale the number of filters
        only for the csp layers in the csp section of the processing
        path. A value 2 indicates that each layer that is int eh CSP
        stack will have filters = filters/2.
      kernel_initializer: method to use to initialize kernel weights.
      bias_initializer: method to use to initialize the bias of the conv
        layers.
      bias_regularizer: string to indicate which function to use to regularizer
        bias.
      kernel_regularizer: string to indicate which function to use to
        regularizer weights.
      use_sync_bn: bool if true use the sync batch normalization.
      use_separable_conv: `bool` wether to use separable convs.
      norm_momentum: batch norm parameter see Tensorflow documentation.
      norm_epsilon: batch norm parameter see Tensorflow documentation.
      block_invert: bool use for switching between the even and odd
        repretions of layers. usually the repetition is based on a
        3x3 conv with filters, followed by a 1x1 with filters/2 with
        an even number of repetitions to ensure each 3x3 gets a 1x1
        sqeeze. block invert swaps the 3x3/1 1x1/2 to a 1x1/2 3x3/1
        ordering typically used when the model requires an odd number
        of repetiitions. All other peramters maintain their affects
      activation: activation function to use in processing.
      leaky_alpha: if leaky acitivation function, the alpha to use in
        processing the relu input.
      spp_keys: List[int] of the sampling levels to be applied by
        the Spatial Pyramid Pooling Layer. By default it is
        [5, 9, 13] inidicating a 5x5 pooling followed by 9x9
        followed by 13x13 then followed by the standard concatnation
        and convolution.
      **kwargs: Keyword Arguments.
    """

    super().__init__(**kwargs)
    # darkconv params
    self._filters = filters
    self._use_sync_bn = use_sync_bn
    self._use_separable_conv = use_separable_conv
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._bias_regularizer = bias_regularizer
    self._kernel_regularizer = kernel_regularizer

    # normal params
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon

    # activation params
    self._activation = activation
    self._leaky_alpha = leaky_alpha

    repetitions += (2 * int(insert_spp))
    if repetitions == 1:
      block_invert = True

    self._repetitions = repetitions
    self.layer_list, self.outputs = self._get_base_layers()

    if csp_stack > 0:
      self._csp_scale = csp_scale
      csp_stack += (2 * int(insert_spp))
      self._csp_filters = lambda x: x // csp_scale
      self._convert_csp(self.layer_list, self.outputs, csp_stack)
      block_invert = False

    self._csp_stack = csp_stack

    if block_invert:
      self._conv1_filters = lambda x: x
      self._conv2_filters = lambda x: x // 2
      self._conv1_kernel = (3, 3)
      self._conv2_kernel = (1, 1)
    else:
      self._conv1_filters = lambda x: x // 2
      self._conv2_filters = lambda x: x
      self._conv1_kernel = (1, 1)
      self._conv2_kernel = (3, 3)

    # insert SPP will always add to the total nuber of layer, never replace
    if insert_spp:
      self._spp_keys = spp_keys if spp_keys is not None else [5, 9, 13]
      self.layer_list = self._insert_spp(self.layer_list)

    if repetitions > 1:
      self.outputs[-2] = True

    if insert_sam:
      self.layer_list = self._insert_sam(self.layer_list, self.outputs)
      self._repetitions += 1
    self.outputs[-1] = True

  def _get_base_layers(self):
    layer_list = []
    outputs = []
    for i in range(self._repetitions):
      layers = ['conv1'] * ((i + 1) % 2) + ['conv2'] * (i % 2)
      layer_list.extend(layers)
      outputs = [False] + outputs
    return layer_list, outputs

  def _insert_spp(self, layer_list):
    if len(layer_list) <= 3:
      layer_list[1] = 'spp'
    else:
      layer_list[3] = 'spp'
    return layer_list

  def _convert_csp(self, layer_list, outputs, csp_stack_size):
    layer_list[0] = 'csp_route'
    layer_list.insert(csp_stack_size - 1, 'csp_connect')
    outputs.insert(csp_stack_size - 1, False)
    return layer_list, outputs

  def _insert_sam(self, layer_list, outputs):
    if len(layer_list) >= 2 and layer_list[-2] != 'spp':
      layer_list.insert(-2, 'sam')
      outputs.insert(-1, True)
    else:
      layer_list.insert(-1, 'sam')
      outputs.insert(-1, False)
    return layer_list

  def _conv1(self, filters, kwargs, csp=False):
    if csp:
      filters_ = self._csp_filters
    else:
      filters_ = self._conv1_filters

    x1 = ConvBN(
        filters=filters_(filters),
        kernel_size=self._conv1_kernel,
        strides=(1, 1),
        padding='same',
        use_bn=True,
        **kwargs)
    return x1

  def _conv2(self, filters, kwargs, csp=False):
    if csp:
      filters_ = self._csp_filters
    else:
      filters_ = self._conv2_filters

    x1 = ConvBN(
        filters=filters_(filters),
        kernel_size=self._conv2_kernel,
        strides=(1, 1),
        padding='same',
        use_bn=True,
        **kwargs)
    return x1

  def _csp_route(self, filters, kwargs):
    x1 = CSPRoute(
        filters=filters,
        filter_scale=self._csp_scale,
        downsample=False,
        **kwargs)
    return x1

  def _csp_connect(self, filters, kwargs):
    x1 = CSPConnect(filters=filters, drop_final=True, drop_first=True, **kwargs)
    return x1

  def _spp(self, filters, kwargs):
    x1 = SPP(self._spp_keys)
    return x1

  def _sam(self, filters, kwargs):
    x1 = SAM(filters=-1, use_pooling=False, use_bn=True, **kwargs)
    return x1

  def build(self, input_shape):
    dark_conv_args = {
        'activation': self._activation,
        'kernel_initializer': self._kernel_initializer,
        'bias_initializer': self._bias_initializer,
        'bias_regularizer': self._bias_regularizer,
        'use_sync_bn': self._use_sync_bn,
        'use_separable_conv': self._use_separable_conv,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon,
        'kernel_regularizer': self._kernel_regularizer,
        'leaky_alpha': self._leaky_alpha,
    }

    csp = False
    self.layers = []
    for layer in self.layer_list:
      if layer == 'csp_route':
        self.layers.append(self._csp_route(self._filters, dark_conv_args))
        csp = True
      elif layer == 'csp_connect':
        self.layers.append(self._csp_connect(self._filters, dark_conv_args))
        csp = False
      elif layer == 'conv1':
        self.layers.append(self._conv1(self._filters, dark_conv_args, csp=csp))
      elif layer == 'conv2':
        self.layers.append(self._conv2(self._filters, dark_conv_args, csp=csp))
      elif layer == 'spp':
        self.layers.append(self._spp(self._filters, dark_conv_args))
      elif layer == 'sam':
        self.layers.append(self._sam(-1, dark_conv_args))

    self._lim = len(self.layers)
    super().build(input_shape)

  def _call_regular(self, inputs, training=None):
    # check efficiency
    x = inputs
    x_prev = x
    output_prev = True

    for (layer, output) in zip(self.layers, self.outputs):
      if output_prev:
        x_prev = x
      x = layer(x)
      output_prev = output
    return x_prev, x

  def _call_csp(self, inputs, training=None):
    # check efficiency
    x = inputs
    x_prev = x
    output_prev = True
    x_route = None

    for i, (layer, output) in enumerate(zip(self.layers, self.outputs)):
      if output_prev:
        x_prev = x
      if i == 0:
        x, x_route = layer(x)
      elif i == self._csp_stack - 1:
        x = layer([x, x_route])
      else:
        x = layer(x)
      output_prev = output
    return x_prev, x

  def call(self, inputs, training=None):
    if self._csp_stack > 0:
      return self._call_csp(inputs, training=training)
    else:
      return self._call_regular(inputs)


class Reorg(tf.keras.layers.Layer):
  """Splits a high resolution image into 4 lower resolution images.

  Used in YOLOR to process very high resolution inputs efficiently.
  for example an input image of [1280, 1280, 3] will become [640, 640, 12],
  the images are sampled in such a way that the spatial resoltion is
  retained.
  """

  def call(self, x, training=None):
    return tf.concat([
        x[..., ::2, ::2, :], x[..., 1::2, ::2, :], x[..., ::2, 1::2, :],
        x[..., 1::2, 1::2, :]
    ],
                     axis=-1)
