a800e9b12cb38767fc9815868b4f86f4d0380205,official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py,CSPTiny,build,#CSPTiny#Any#,432

Before Change

    super().__init__(**kwargs)

  def build(self, input_shape):
    self._convlayer1 = 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,
                                kernel_regularizer=self._kernel_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,
                                leaky_alpha=self._leaky_alpha)

    self._convlayer2 = DarkConv(filters=self._filters // 2,
                                kernel_size=(3, 3),
                                strides=(1, 1),

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
                      }
    self._convlayer1 = DarkConv(filters=self._filters,
                                kernel_size=(3, 3),
                                strides=(1, 1),
                                padding="same",
                                **_dark_conv_args)

    self._convlayer2 = DarkConv(filters=self._filters // 2,
                                kernel_size=(3, 3),
                                strides=(1, 1),
                                padding="same",
                                use_bias=self._use_bias,
                                groups = self._groups,
                                group_id = self._group_id,
                                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,
                                norm_momentum=self._norm_moment,
                                norm_epsilon=self._norm_epsilon,
                                activation=self._conv_activation,
                                leaky_alpha=self._leaky_alpha)

    self._convlayer3 = DarkConv(filters=self._filters // 2,
                                kernel_size=(3, 3),
                                strides=(1, 1),
                                padding="same",
                                **_dark_conv_args)

    self._convlayer4 = DarkConv(filters=self._filters,
                                kernel_size=(1, 1),
                                strides=(1, 1),
                                padding="same",
                                **_dark_conv_args)

    self._maxpool = tf.keras.layers.MaxPool2D(pool_size=2,
                                              strides=2,
                                              padding="same",

Instances