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Depthwise 2-D convolution.
tf.compat.v1.nn.depthwise_conv2d(
input, filter, strides, padding, rate=None, name=None, data_format=None,
dilations=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 .
|
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 "returns" section of tf.nn.convolution for details.
|
rate
|
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). |
data_format
|
The data format for input. Either "NHWC" (default) or "NCHW". |
dilations
|
Alias of rate. |
Returns | |
---|---|
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].
|