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Depthwise 2-D convolution.
tf.compat.v2.nn.depthwise_conv2d(
input, filter, strides, padding, data_format=None, dilations=None, name=None
)
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]
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 with shape according to data_format.
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filter
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4-D with shape
[filter_height, filter_width, in_channels, channel_multiplier].
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strides
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1-D of size 4. The stride of the sliding window for each
dimension of input.
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padding
|
A string, either 'VALID' or 'SAME'. The padding algorithm.
See the "returns" section of tf.nn.convolution for details.
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data_format
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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.
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name
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A name for this operation (optional). |
Returns | |
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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].
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