Python tensorflow.python.ops.nn_ops.depthwise_conv2d_native() Examples

def depthwise_conv2d(input, filter, strides, padding, name=None):
  """Depthwise 2-D convolution.

  Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
  and a filter tensor of shape
  `[filter_height, filter_width, in_channels, channel_multiplier]`
  containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
  applies a different filter to each input channel (expanding from 1 channel
  to `channel_multiplier` channels for each), then concatenates the results
  together.  The output has `in_channels * channel_multiplier` channels.

  In detail,

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

  Must have `strides[0] = strides[3] = 1`.  For the most common case of the
  same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.

  Args:
    input: 4-D with shape `[batch, in_height, in_width, in_channels]`.
    filter: 4-D with shape
      `[filter_height, filter_width, in_channels, channel_multiplier]`.
    strides: 1-D of size 4.  The stride of the sliding window for each
      dimension of `input`.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      See the [comment
        here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
    name: A name for this operation (optional).

  Returns:
    A 4-D `Tensor` of shape
    `[batch, out_height, out_width, in_channels * channel_multiplier].`
  """
  with ops.name_scope(name, "depthwise", [input, filter]) as name:
    input = ops.convert_to_tensor(input, name="tensor_in")
    filter = ops.convert_to_tensor(filter, name="filter_in")

    return nn_ops.depthwise_conv2d_native(
        input, filter, strides, padding, name=name)
# pylint: enable=redefined-builtin


# pylint: disable=redefined-builtin,line-too-long 

def _test_convolution(opname, tensor_in_sizes, filter_in_sizes,
                      dilations, strides, padding, data_format,
                      deconv_output_shape=[]):
    """ One iteration of convolution with given shapes and attributes """

    total_size_1 = np.prod(tensor_in_sizes)
    total_size_2 = np.prod(filter_in_sizes)
    # Initializes the input tensor with array containing incrementing
    # numbers from 1.
    data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
    filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]

    with tf.Graph().as_default():
        in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
        in_filter = constant_op.constant(
            filter_array, shape=filter_in_sizes, dtype='float32')
        if data_format == 'NHWC':
            strides = [1] + strides + [1]
            dilations = [1] + dilations + [1]
        else:
            strides = [1, 1] + strides
            dilations = [1, 1] + dilations

        if opname == 'conv':
            nn_ops.conv2d(in_data,
                          in_filter,
                          strides=strides,
                          dilations=dilations,
                          padding=padding,
                          data_format=data_format)

            compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
                                'Placeholder:0', 'Conv2D:0')
        elif opname == 'conv_transpose':
            nn_ops.conv2d_transpose(in_data,
                                    in_filter,
                                    output_shape=deconv_output_shape,
                                    strides=strides,
                                    padding=padding,
                                    data_format=data_format)

            compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
                                'Placeholder:0', 'conv2d_transpose:0')
        else:
            nn_ops.depthwise_conv2d_native(in_data,
                                           in_filter,
                                           strides=strides,
                                           dilations=dilations,
                                           padding=padding,
                                           data_format=data_format)

            compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
                                'Placeholder:0', 'DepthwiseConv2dNative:0')