Function separable_conv2d_v2

tensorflow/python/ops/nn_impl.py:1084–1147

2-D convolution with separable filters. Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions `[1, 2]` and `3`, not spatial separability between dimensions `1` and `2`.

(
    input,
    depthwise_filter,
    pointwise_filter,
    strides,
    padding,
    data_format=None,
    dilations=None,
    name=None,
)

@tf_export("nn.separable_conv2d", v1=[])
def separable_conv2d_v2(
    input,
    depthwise_filter,
    pointwise_filter,
    strides,
    padding,
    data_format=None,
    dilations=None,
    name=None,
):
  """2-D convolution with separable filters.

  Performs a depthwise convolution that acts separately on channels followed by
  a pointwise convolution that mixes channels.  Note that this is separability
  between dimensions `[1, 2]` and `3`, not spatial separability between
  dimensions `1` and `2`.

  In detail, with the default NHWC format,

      output[b, i, j, k] = sum_{di, dj, q, r}
          input[b, strides[1] * i + di, strides[2] * j + dj, q] *
          depthwise_filter[di, dj, q, r] *
          pointwise_filter[0, 0, q * channel_multiplier + r, k]

  `strides` controls the strides for the depthwise convolution only, since
  the pointwise convolution has implicit strides of `[1, 1, 1, 1]`.  Must have
  `strides[0] = strides[3] = 1`.  For the most common case of the same
  horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
  If any value in `rate` is greater than 1, we perform atrous depthwise
  convolution, in which case all values in the `strides` tensor must be equal
  to 1.

  Args:
    input: 4-D `Tensor` with shape according to `data_format`.
    depthwise_filter: 4-D `Tensor` with shape `[filter_height, filter_width,
      in_channels, channel_multiplier]`. Contains `in_channels` convolutional
      filters of depth 1.
    pointwise_filter: 4-D `Tensor` with shape `[1, 1, channel_multiplier *
      in_channels, out_channels]`.  Pointwise filter to mix channels after
      `depthwise_filter` has convolved spatially.
    strides: 1-D of size 4.  The strides for the depthwise convolution for each
      dimension of `input`.
    padding: A string, either `'VALID'` or `'SAME'`.  The padding algorithm. See
      the "returns" section of `tf.nn.convolution` for details.
    data_format: The data format for input. Either "NHWC" (default) or "NCHW".
    dilations: 1-D of size 2. The dilation rate in which we sample input values
      across the `height` and `width` dimensions in atrous convolution. If it is
      greater than 1, then all values of strides must be 1.
    name: A name for this operation (optional).

  Returns:
    A 4-D `Tensor` with shape according to 'data_format'. For
      example, with data_format="NHWC", shape is [batch, out_height,
      out_width, out_channels].
  """
  return separable_conv2d(
      input,
      depthwise_filter,