登录
View source on GitHub
|
Computes a 2-D depthwise convolution.
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
|
Controls how to pad the image before applying the convolution. Can
be the string "SAME" or "VALID" indicating the type of padding
algorithm to use, or a list indicating the explicit paddings at the start
and end of each dimension. When explicit padding is used and data_format
is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom],
[pad_left, pad_right], [0, 0]]. When explicit padding used and
data_format is "NCHW", this should be in the form [[0, 0], [0, 0],
[pad_top, pad_bottom], [pad_left, pad_right]].
|
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.
|