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Depthwise 2-D convolution.
tf.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 + dilations[0] * di,
strides[2] * j + dilations[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 dilations
is greater than 1, we perform atrous depthwise
convolution, in which case all values in the strides
tensor must be equal
to 1.
Usage Example:
x = np.array([
[1., 2.],
[3., 4.],
[5., 6.]
], dtype=np.float32).reshape((1, 3, 2, 1))
kernel = np.array([
[1., 2.],
[3., 4]
], dtype=np.float32).reshape((2, 1, 1, 2))
tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1],
padding='VALID').numpy()
array([[[[10., 14.],
[14., 20.]],
[[18., 26.],
[22., 32.]]]], dtype=float32)
tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1],
padding=[[0, 0], [1, 0], [1, 0], [0, 0]]).numpy()
array([[[[ 0., 0.],
[ 3., 4.],
[ 6., 8.]],
[[ 0., 0.],
[10., 14.],
[14., 20.]],
[[ 0., 0.],
[18., 26.],
[22., 32.]]]], dtype=float32)
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
|
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. See
here
for more information. 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
|
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.
|
name
|
A name for this operation (optional). |
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].
|