Computes a 2-D depthwise convolution given 4-D input and filter tensors.
tf.raw_ops.DepthwiseConv2dNative(
input,
filter,
strides,
padding,
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1],
name=None
)
Given an input tensor of shape [batch, in_height, in_width, in_channels]
and a filter / kernel 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. Thus, the output has in_channels * channel_multiplier channels.
for k in 0..in_channels-1
for q in 0..channel_multiplier-1
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 vertices strides, strides = [1, stride, stride, 1].
Returns | |
|---|---|
A Tensor. Has the same type as input.
|