chainer.functions.convolution_nd

chainer.functions.convolution_nd(x, W, b=None, stride=1, pad=0, cover_all=False, dilate=1, groups=1)

N-dimensional convolution function.

This is an implementation of N-dimensional convolution which is generalized two-dimensional convolution in ConvNets. It takes three variables: the input x, the filter weight W and the bias vector b.

Notation: here is a notation for dimensionalities.

• $N$ is the number of spatial dimensions.

• $n$ is the batch size.

• $c_I$ and $c_O$ are the number of the input and output channels, respectively.

• $d_1, d_2, ..., d_N$ are the size of each axis of the input’s spatial dimensions, respectively.

• $k_1, k_2, ..., k_N$ are the size of each axis of the filters, respectively.

• $l_1, l_2, ..., l_N$ are the size of each axis of the output’s spatial dimensions, respectively.

• $p_1, p_2, ..., p_N$ are the size of each axis of the spatial padding size, respectively.

Then the convolution_nd function computes correlations between filters and patches of size $(k_1, k_2, ..., k_N)$ in x. Note that correlation here is equivalent to the inner product between expanded tensors. Patches are extracted at positions shifted by multiples of stride from the first position (-p_1, -p_2, ..., -p_N) for each spatial axis.

Let $(s_1, s_2, ..., s_N)$ be the stride of filter application. Then, the output size $(l_1, l_2, ..., l_N)$ is determined by the following equations:

$$l_n = (d_n + 2p_n - k_n) / s_n + 1 \ \ (n = 1, ..., N)$$

If cover_all option is True, the filter will cover the all spatial locations. So, if the last stride of filter does not cover the end of spatial locations, an addtional stride will be applied to the end part of spatial locations. In this case, the output size is determined by the following equations:

$$l_n = (d_n + 2p_n - k_n + s_n - 1) / s_n + 1 \ \ (n = 1, ..., N)$$

Parameters

• x (Variable or N-dimensional array) – Input variable of shape $(n, c_I, d_1, d_2, ..., d_N)$.

• W (Variable or N-dimensional array) – Weight variable of shape $(c_O, c_I, k_1, k_2, ..., k_N)$.

• b (None or Variable or N-dimensional array) – One-dimensional bias variable with length $c_O$ (optional).

• stride (int or tuple of int s) – Stride of filter applications $(s_1, s_2, ..., s_N)$. stride=s is equivalent to (s, s, ..., s).

• pad (int or tuple of int s) – Spatial padding width for input arrays $(p_1, p_2, ..., p_N)$. pad=p is equivalent to (p, p, ..., p).

• cover_all (bool) – If True, all spatial locations are convoluted into some output pixels. It may make the output size larger. cover_all needs to be False if you want to use cuDNN.

• dilate (int or tuple of int s) – Dilation factor of filter applications. dilate=d and dilate=(d, d, ..., d) are equivalent.

• groups (int) – The number of groups to use grouped convolution. The default is one, where grouped convolution is not used.

Returns

Output variable of shape $(n, c_O, l_1, l_2, ..., l_N)$.

Return type

Variable

Note

This function uses cuDNN implementation for its forward and backward computation if ALL of the following conditions are satisfied:

• cuda.cudnn_enabled is True

• chainer.config.use_cudnn is 'always' or 'auto'

• The number of spatial dimensions is more than one.

• cover_all is False

• The input’s dtype is equal to the filter weight’s.

• The dtype is FP16, FP32 or FP64. (FP16 is only available when cuDNN version $\geq$ v3.)

Convolution links can use a feature of cuDNN called autotuning, which selects the most efficient CNN algorithm for images of fixed-size, can provide a significant performance boost for fixed neural nets. To enable, set chainer.using_config(‘autotune’, True)

See also

ConvolutionND, convolution_2d()

Example

>>> n = 10
>>> c_i, c_o = 3, 1
>>> d1, d2, d3 = 30, 40, 50
>>> k1, k2, k3 = 10, 10, 10
>>> p1, p2, p3 = 5, 5, 5
>>> x = np.random.uniform(0, 1, (n, c_i, d1, d2, d3)).astype(np.float32)
>>> x.shape
(10, 3, 30, 40, 50)
>>> W = np.random.uniform(0, 1, (c_o, c_i, k1, k2, k3)).astype(np.float32)
>>> W.shape
(1, 3, 10, 10, 10)
>>> b = np.random.uniform(0, 1, (c_o)).astype(np.float32)
>>> b.shape
(1,)
>>> s1, s2, s3 = 2, 4, 6
>>> y = F.convolution_nd(x, W, b, stride=(s1, s2, s3), pad=(p1, p2, p3))
>>> y.shape
(10, 1, 16, 11, 9)
>>> l1 = int((d1 + 2 * p1 - k1) / s1 + 1)
>>> l2 = int((d2 + 2 * p2 - k2) / s2 + 1)
>>> l3 = int((d3 + 2 * p3 - k3) / s3 + 1)
>>> y.shape == (n, c_o, l1, l2, l3)
True
>>> y = F.convolution_nd(x, W, b, stride=(s1, s2, s3), pad=(p1, p2, p3), cover_all=True)
>>> y.shape == (n, c_o, l1, l2, l3 + 1)
True