132924d0468c72ba081900ba97013b8353a85f3b,sonnet/python/modules/conv.py,SeparableConv2D,_build,#SeparableConv2D#Any#,2638

Before Change

                              self._input_channels, self._channel_multiplier)
    pointwise_input_size = self._channel_multiplier * self._input_channels
    pointwise_weight_shape = (1, 1, pointwise_input_size, self._output_channels)
    bias_shape = (self._output_channels,)

    if "w_dw" not in self._initializers:
      fan_in_shape = depthwise_weight_shape[:2]
      self._initializers["w_dw"] = create_weight_initializer(fan_in_shape,
                                                             dtype=inputs.dtype)

    if "w_pw" not in self._initializers:
      fan_in_shape = pointwise_weight_shape[:3]
      self._initializers["w_pw"] = create_weight_initializer(fan_in_shape,
                                                             dtype=inputs.dtype)

    if "b" not in self._initializers and self._use_bias:
      self._initializers["b"] = create_bias_initializer(bias_shape,
                                                        dtype=inputs.dtype)

    self._w_dw = tf.get_variable(
        "w_dw",
        shape=depthwise_weight_shape,
        dtype=inputs.dtype,
        initializer=self._initializers["w_dw"],
        partitioner=self._partitioners.get("w_dw", None),
        regularizer=self._regularizers.get("w_dw", None))
    self._w_pw = tf.get_variable(
        "w_pw",
        shape=pointwise_weight_shape,
        dtype=inputs.dtype,
        initializer=self._initializers["w_pw"],
        partitioner=self._partitioners.get("w_pw", None),
        regularizer=self._regularizers.get("w_pw", None))

    outputs = tf.nn.separable_conv2d(inputs,
                                     self._w_dw,
                                     self._w_pw,
                                     strides=self._stride,
                                     padding=self._padding,
                                     data_format=self._data_format)

    if self._use_bias:
      self._b = tf.get_variable("b",
                                shape=bias_shape,
                                dtype=inputs.dtype,
                                initializer=self._initializers["b"],
                                partitioner=self._partitioners.get("b", None),
                                regularizer=self._regularizers.get("b", None))
      outputs = tf.nn.bias_add(outputs, self._b, data_format=self._data_format)

    return outputs

  @property

After Change

        partitioner=self._partitioners.get("w_pw", None),
        regularizer=self._regularizers.get("w_pw", None))

    outputs = tf.nn.separable_conv2d(inputs,
                                     self._w_dw,
                                     self._w_pw,
                                     strides=self._stride,
                                     padding=self._padding,
                                     data_format=self._data_format)

    if self._use_bias:
      self._b, outputs = _apply_bias(
          inputs, outputs, self._channel_index, self._data_format,
          self._output_channels, self._initializers, self._partitioners,
          self._regularizers)

    return outputs

  @property

Instances