Computes a 2-D depthwise convolution given 4-D input
and filter
tensors.
tf.compat.v1.nn.depthwise_conv2d_native(
input, filter, strides, padding, 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]
.
Args | |
---|---|
input
|
A Tensor . Must be one of the following types: half , bfloat16 , float32 , float64 .
|
filter
|
A Tensor . Must have the same type as input .
|
strides
|
A list of ints .
1-D of length 4. The stride of the sliding window for each dimension
of input .
|
padding
|
A string from: "SAME", "VALID" .
The type of padding algorithm to use.
|
data_format
|
An optional string from: "NHWC", "NCHW" . Defaults to "NHWC" .
Specify the data format of the input and output data. With the
default format "NHWC", the data is stored in the order of:
[batch, height, width, channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, channels, height, width].
|
dilations
|
An optional list of ints . Defaults to [1, 1, 1, 1] .
1-D tensor of length 4. The dilation factor for each dimension of
input . If set to k > 1, there will be k-1 skipped cells between each filter
element on that dimension. The dimension order is determined by the value of
data_format , see above for details. Dilations in the batch and depth
dimensions must be 1.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as input .
|