text: # 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.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """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
  
      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)
  
      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:
        x = self.bn(x)
      x = self._activation_fn(x)
      return x
  
    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)
  