chainer.functions.depthwise_convolution_2d¶
-
chainer.functions.
depthwise_convolution_2d
(x, W, b=None, stride=1, pad=0)[source]¶ Two-dimensional depthwise convolution function.
This is an implementation of two-dimensional depthwise convolution. It takes two or three variables: the input image
x
, the filter weightW
, and optionally, the bias vectorb
.Notation: here is a notation for dimensionalities.
\(n\) is the batch size.
\(c_I\) is the number of the input.
\(c_M\) is the channel multiplier.
\(h\) and \(w\) are the height and width of the input image, respectively.
\(h_O\) and \(w_O\) are the height and width of the output image, respectively.
\(k_H\) and \(k_W\) are the height and width of the filters, respectively.
- Parameters
x (
Variable
or N-dimensional array) – Input variable of shape \((n, c_I, h, w)\).W (
Variable
or N-dimensional array) – Weight variable of shape \((c_M, c_I, k_H, k_W)\).b (
Variable
or N-dimensional array) – Bias variable of length \(c_M * c_I\) (optional).stride (int or pair of ints) – Stride of filter applications.
stride=s
andstride=(s, s)
are equivalent.pad (int or pair of ints) – Spatial padding width for input arrays.
pad=p
andpad=(p, p)
are equivalent.
- Returns
Output variable. Its shape is \((n, c_I * c_M, h_O, w_O)\).
- Return type
Like
Convolution2D
,DepthwiseConvolution2D
function computes correlations between filters and patches of size \((k_H, k_W)\) inx
. But unlikeConvolution2D
,DepthwiseConvolution2D
does not add up input channels of filters but concatenates them. For that reason, the shape of outputs of depthwise convolution are \((n, c_I * c_M, h_O, w_O)\), \(c_M\) is called channel_multiplier.\((h_O, w_O)\) is determined by the equivalent equation of
Convolution2D
.If the bias vector is given, then it is added to all spatial locations of the output of convolution.
See: L. Sifre. Rigid-motion scattering for image classification
See also
Example
>>> x = np.random.uniform(0, 1, (2, 3, 4, 7)) >>> W = np.random.uniform(0, 1, (2, 3, 3, 3)) >>> b = np.random.uniform(0, 1, (6,)) >>> y = F.depthwise_convolution_2d(x, W, b) >>> y.shape (2, 6, 2, 5)