a800e9b12cb38767fc9815868b4f86f4d0380205,official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py,DarkResidual,build,#DarkResidual#Any#,268

Before Change

                             kernel_size=(3, 3),
                             strides=(2, 2),
                             padding="same",
                             use_bias=self._use_bias,
                             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,
                             norm_momentum=self._norm_moment,
                             norm_epsilon=self._norm_epsilon,
                             activation=self._conv_activation,
                             kernel_regularizer=self._kernel_regularizer,
                             leaky_alpha=self._leaky_alpha)
    else:
      self._dconv = Identity()

    self._conv1 = DarkConv(filters=self._filters // self._filter_scale,
                           kernel_size=(1, 1),
                           strides=(1, 1),
                           padding="same",
                           use_bias=self._use_bias,
                           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,
                           norm_momentum=self._norm_moment,
                           norm_epsilon=self._norm_epsilon,
                           activation=self._conv_activation,
                           kernel_regularizer=self._kernel_regularizer,
                           leaky_alpha=self._leaky_alpha)
    self._conv2 = DarkConv(filters=self._filters,
                           kernel_size=(3, 3),
                           strides=(1, 1),
                           padding="same",
                           use_bias=self._use_bias,
                           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,
                           norm_momentum=self._norm_moment,
                           norm_epsilon=self._norm_epsilon,
                           activation=self._conv_activation,
                           kernel_regularizer=self._kernel_regularizer,
                           leaky_alpha=self._leaky_alpha)

    self._shortcut = ks.layers.Add()
    // self._activation_fn = ks.layers.Activation(activation=self._sc_activation)
    if self._sc_activation == "leaky":

After Change

    super().__init__(**kwargs)

  def build(self, input_shape):
    _dark_conv_args = {"use_bias" : self._use_bias,
                       "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,
                       "norm_momentum" : self._norm_moment,
                       "norm_epsilon" : self._norm_epsilon,
                       "activation" : self._conv_activation,
                       "kernel_regularizer" : self._kernel_regularizer,
                       "leaky_alpha" : self._leaky_alpha
                      }
    if self._downsample:
      self._dconv = DarkConv(filters=self._filters,
                             kernel_size=(3, 3),
                             strides=(2, 2),
                             padding="same",
                             **_dark_conv_args)
    else:
      self._dconv = Identity()

    self._conv1 = DarkConv(filters=self._filters // self._filter_scale,
                           kernel_size=(1, 1),
                           strides=(1, 1),
                           padding="same",
                           **_dark_conv_args)

    self._conv2 = DarkConv(filters=self._filters,
                           kernel_size=(3, 3),
                           strides=(1, 1),
                           padding="same",
                           **_dark_conv_args)

    self._shortcut = ks.layers.Add()
    // self._activation_fn = ks.layers.Activation(activation=self._sc_activation)
    if self._sc_activation == "leaky":

Instances