conv(x, w, b=None, stride=1, pad=0, cover_all=False)¶
This is an implementation of N-dimensional convolution which is generalized two-dimensional convolution in ConvNets. It takes three arrays: the input
x, the filter weight
wand the bias vector
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.
convfunction 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
stridefrom 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:\[l_n = (d_n + 2p_n - k_n) / s_n + 1 \ \ (n = 1, ..., N)\]
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 additional stride will be applied to the end part of spatial locations. In this case, the output size is determined by the following equations:\[l_n = (d_n + 2p_n - k_n + s_n - 1) / s_n + 1 \ \ (n = 1, ..., N)\]
ndarray) – Input array of shape \((n, c_I, d_1, d_2, ..., d_N)\).
ndarray) – Weight array of shape \((c_O, c_I, k_1, k_2, ..., k_N)\).
b (None or
ndarray) – One-dimensional bias array with length \(c_O\) (optional).
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
Falseif you want to use
Output array of shape \((n, c_O, l_1, l_2, ..., l_N)\).
- Return type
cudabackend, this function uses cuDNN implementation for its forward and backward computation.
cudabackend, this function has following limitations yet:
cover_all=Trueoption is not supported yet.
float16is not supported yet.)
During backpropagation, this function propagates the gradient of the output array to input arrays
>>> 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 = chainerx.random.uniform(0, 1, (n, c_i, d1, d2, d3)).astype(np.float32) >>> x.shape (10, 3, 30, 40, 50) >>> w = chainerx.random.uniform(0, 1, (c_o, c_i, k1, k2, k3)).astype(np.float32) >>> w.shape (1, 3, 10, 10, 10) >>> b = chainerx.random.uniform(0, 1, (c_o)).astype(np.float32) >>> b.shape (1,) >>> s1, s2, s3 = 2, 4, 6 >>> y = chainerx.conv(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 = chainerx.conv(x, w, b, stride=(s1, s2, s3), pad=(p1, p2, p3), cover_all=True) >>> y.shape == (n, c_o, l1, l2, l3 + 1) True