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Depthwise 2-D convolution.

Given a 4D input tensor ('NHWC' or 'NCHW' data formats) 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, with the default NHWC format,

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


input 4-D with shape according to data_format.
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.
name A name for this operation (optional).
dilations Alias of rate.

A 4-D Tensor with shape according to data_format. E.g., for "NHWC" format, shape is [batch, out_height, out_width, in_channels * channel_multiplier].