diff options
Diffstat (limited to 'silx/opencl/convolution.py')
| -rw-r--r-- | silx/opencl/convolution.py | 124 |
1 files changed, 6 insertions, 118 deletions
diff --git a/silx/opencl/convolution.py b/silx/opencl/convolution.py index 18232f4..b8dd8f6 100644 --- a/silx/opencl/convolution.py +++ b/silx/opencl/convolution.py @@ -29,99 +29,14 @@ from __future__ import absolute_import, print_function, with_statement, division __authors__ = ["P. Paleo"] __license__ = "MIT" -__date__ = "11/02/2019" +__date__ = "01/08/2019" import numpy as np -from math import ceil from copy import copy # python2 from .common import pyopencl as cl import pyopencl.array as parray from .processing import OpenclProcessing, EventDescription - - -class ConvolutionInfos(object): - allowed_axes = { - "1D": [None], - "separable_2D_1D_2D": [None, (0, 1), (1, 0)], - "batched_1D_2D": [(0,), (1,)], - "separable_3D_1D_3D": [ - None, - (0, 1, 2), - (1, 2, 0), - (2, 0, 1), - (2, 1, 0), - (1, 0, 2), - (0, 2, 1) - ], - "batched_1D_3D": [(0,), (1,), (2,)], - "batched_separable_2D_1D_3D": [(0,), (1,), (2,)], # unsupported (?) - "2D": [None], - "batched_2D_3D": [(0,), (1,), (2,)], - "separable_3D_2D_3D": [ - (1, 0), - (0, 1), - (2, 0), - (0, 2), - (1, 2), - (2, 1), - ], - "3D": [None], - } - use_cases = { - (1, 1): { - "1D": { - "name": "1D convolution on 1D data", - "kernels": ["convol_1D_X"], - }, - }, - (2, 2): { - "2D": { - "name": "2D convolution on 2D data", - "kernels": ["convol_2D_XY"], - }, - }, - (3, 3): { - "3D": { - "name": "3D convolution on 3D data", - "kernels": ["convol_3D_XYZ"], - }, - }, - (2, 1): { - "separable_2D_1D_2D": { - "name": "Separable (2D->1D) convolution on 2D data", - "kernels": ["convol_1D_X", "convol_1D_Y"], - }, - "batched_1D_2D": { - "name": "Batched 1D convolution on 2D data", - "kernels": ["convol_1D_X", "convol_1D_Y"], - }, - }, - (3, 1): { - "separable_3D_1D_3D": { - "name": "Separable (3D->1D) convolution on 3D data", - "kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"], - }, - "batched_1D_3D": { - "name": "Batched 1D convolution on 3D data", - "kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"], - }, - "batched_separable_2D_1D_3D": { - "name": "Batched separable (2D->1D) convolution on 3D data", - "kernels": ["convol_1D_X", "convol_1D_Y", "convol_1D_Z"], - }, - }, - (3, 2): { - "separable_3D_2D_3D": { - "name": "Separable (3D->2D) convolution on 3D data", - "kernels": ["convol_2D_XY", "convol_2D_XZ", "convol_2D_YZ"], - }, - "batched_2D_3D": { - "name": "Batched 2D convolution on 3D data", - "kernels": ["convol_2D_XY", "convol_2D_XZ", "convol_2D_YZ"], - }, - }, - } - +from .utils import ConvolutionInfos class Convolution(OpenclProcessing): """ @@ -262,7 +177,8 @@ class Convolution(OpenclProcessing): # Allocate arrays for option_name, array_name in option_array_names.items(): if self.extra_options[option_name]: - value = parray.zeros(self.queue, self.shape, "f") + value = parray.empty(self.queue, self.shape, np.float32) + value.fill(np.float32(0.0)) else: value = None setattr(self, array_name, value) @@ -320,11 +236,9 @@ class Convolution(OpenclProcessing): str("-DIMAGE_DIMS=%d" % self.data_ndim), str("-DFILTER_DIMS=%d" % self.kernel_ndim), ]) - data_in_ref = self.data_in_tex d_kernel_ref = self.d_kernel_tex else: kernel_files = ["convolution.cl"] - data_in_ref = self.data_in.data d_kernel_ref = self.d_kernel.data self.compile_kernels( kernel_files=kernel_files, @@ -335,8 +249,8 @@ class Convolution(OpenclProcessing): kernel_args = [ self.queue, self.ndrange, self.wg, - data_in_ref, - self.data_out.data, + None, + None, d_kernel_ref, np.int32(self.kernel.shape[0]), self.Nx, self.Ny, self.Nz @@ -527,29 +441,3 @@ class Convolution(OpenclProcessing): __call__ = convolve -def gaussian_kernel(sigma, cutoff=4, force_odd_size=False): - """ - Generates a Gaussian convolution kernel. - - :param sigma: Standard Deviation of the Gaussian curve. - :param cutoff: Parameter tuning the truncation of the Gaussian. - The higher cutoff, the biggest the array will be (and the closest to - a "true" Gaussian function). - :param force_odd_size: when set to True, the resulting array will always - have an odd size, regardless of the values of "sigma" and "cutoff". - :return: a numpy.ndarray containing the truncated Gaussian function. - The array size is 2*c*s+1 where c=cutoff, s=sigma. - - Nota: due to the quick decay of the Gaussian function, small values of the - "cutoff" parameter are usually fine. The energy difference between a - Gaussian truncated to [-c, c] and a "true" one is - erfc(c/(sqrt(2)*s)) - so choosing cutoff=4*sigma keeps the truncation error below 1e-4. - """ - size = int(ceil(2 * cutoff * sigma + 1)) - if force_odd_size and size % 2 == 0: - size += 1 - x = np.arange(size) - (size - 1.0) / 2.0 - g = np.exp(-(x / sigma) ** 2 / 2.0) - g /= g.sum() - return g |