Function depthwise_conv2d

tensorflow/python/keras/backend.py:4961–5006

2D convolution with separable filters. Arguments: x: input tensor depthwise_kernel: convolution kernel for the depthwise convolution. strides: strides tuple (length 2). padding: string, `"same"` or `"valid"`. data_format: string, `"channels_last"` or `"channels_first

(x,
                     depthwise_kernel,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None,
                     dilation_rate=(1, 1))

def depthwise_conv2d(x,
                     depthwise_kernel,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None,
                     dilation_rate=(1, 1)):
  """2D convolution with separable filters.

  Arguments:
      x: input tensor
      depthwise_kernel: convolution kernel for the depthwise convolution.
      strides: strides tuple (length 2).
      padding: string, `"same"` or `"valid"`.
      data_format: string, `"channels_last"` or `"channels_first"`.
      dilation_rate: tuple of integers,
          dilation rates for the separable convolution.

  Returns:
      Output tensor.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format: ' + str(data_format))

  x, tf_data_format = _preprocess_conv2d_input(x, data_format)
  padding = _preprocess_padding(padding)
  if tf_data_format == 'NHWC':
    strides = (1,) + strides + (1,)
  else:
    strides = (1, 1) + strides

  x = nn.depthwise_conv2d(
      x,
      depthwise_kernel,
      strides=strides,
      padding=padding,
      rate=dilation_rate,
      data_format=tf_data_format)
  if data_format == 'channels_first' and tf_data_format == 'NHWC':
    x = array_ops.transpose(x, (0, 3, 1, 2))  # NHWC -> NCHW
  return x


@keras_export('keras.backend.conv3d')