mirror of https://github.com/silx-kit/pyFAI.git
170 lines
5.0 KiB
Python
170 lines
5.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 07 09:52:51 2014
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@author: ashiotis
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"""
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from __future__ import absolute_import
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from __future__ import print_function
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import sys, numpy, time
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from . import utilstest
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import fabio
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import pyopencl as cl
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from pylab import *
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print("#"*50)
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pyFAI = sys.modules["pyFAI"]
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from pyFAI import splitPixelFullLUT
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from pyFAI import ocl_hist_pixelsplit
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from pyFAI.third_party import six
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# from pyFAI import splitBBoxLUT
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# from pyFAI import splitBBoxCSR
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# logger = utilstest.getLogger("profile")
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ai = pyFAI.load("testimages/halfccd.poni")
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data = fabio.open("testimages/halfccd.edf").data
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workgroup_size = 256
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bins = 1000
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pos_in = ai.array_from_unit(data.shape, "corner", unit="2th_deg", scale=False)
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pos = pos_in.reshape(pos_in.size / 8, 4, 2)
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pos_size = pos.size
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# size = data.size
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size = pos_size / 8
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ctx = cl.create_some_context()
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queue = cl.CommandQueue(ctx)
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mf = cl.mem_flags
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d_pos = cl.array.to_device(queue, pos)
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d_preresult = cl.array.empty(queue, (4 * workgroup_size,), dtype=numpy.float32)
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d_minmax = cl.array.empty(queue, (4,), dtype=numpy.float32)
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with open("../openCL/ocl_lut_pixelsplit.cl", "r") as kernelFile:
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kernel_src = kernelFile.read()
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compile_options = "-D BINS=%i -D NIMAGE=%i -D WORKGROUP_SIZE=%i -D EPS=%e" % \
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(bins, size, workgroup_size, numpy.finfo(numpy.float32).eps)
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print(compile_options)
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program = cl.Program(ctx, kernel_src).build(options=compile_options)
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program.reduce1(queue, (workgroup_size * workgroup_size,), (workgroup_size,), d_pos.data, numpy.uint32(pos_size), d_preresult.data)
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program.reduce2(queue, (workgroup_size,), (workgroup_size,), d_preresult.data, d_minmax.data)
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min0 = pos[:, :, 0].min()
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max0 = pos[:, :, 0].max()
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min1 = pos[:, :, 1].min()
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max1 = pos[:, :, 1].max()
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minmax = (min0, max0, min1, max1)
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print(minmax)
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print(d_minmax)
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memset_size = (bins + workgroup_size - 1) & ~(workgroup_size - 1),
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d_outMax = cl.array.empty(queue, (bins,), dtype=numpy.int32)
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program.memset_out_int(queue, memset_size, (workgroup_size,), d_outMax.data)
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global_size = (size + workgroup_size - 1) & ~(workgroup_size - 1),
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program.lut1(queue, global_size, (workgroup_size,), d_pos.data, d_minmax.data, numpy.uint32(size), d_outMax.data)
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outMax_1 = numpy.copy(d_outMax)
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d_idx_ptr = cl.array.empty(queue, (bins + 1,), dtype=numpy.int32)
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d_lutsize = cl.array.empty(queue, (1,), dtype=numpy.int32)
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program.lut2(queue, (1,), (1,), d_outMax.data, d_idx_ptr.data, d_lutsize.data)
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lutsize = numpy.ndarray(1, dtype=numpy.int32)
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cl.enqueue_copy(queue, lutsize, d_lutsize.data)
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print(lutsize)
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lut_size = int(lutsize[0])
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d_indices = cl.array.empty(queue, (lut_size,), dtype=numpy.int32)
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d_data = cl.array.empty(queue, (lut_size,), dtype=numpy.float32)
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# d_check_atomics = cl.Buffer(ctx, mf.READ_WRITE, 4*lut_size)
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program.memset_out_int(queue, memset_size, (workgroup_size,), d_outMax.data)
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d_outData = cl.array.empty(queue, (bins,), dtype=numpy.float32)
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d_outCount = cl.array.empty(queue, (bins,), dtype=numpy.float32)
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d_outMerge = cl.array.empty(queue, (bins,), dtype=numpy.float32)
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program.lut3(queue, global_size, (workgroup_size,), d_pos.data, d_minmax.data, numpy.uint32(size), d_outMax.data, d_idx_ptr.data, d_indices.data, d_data.data)
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outMax_2 = numpy.copy(d_outMax)
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# check_atomics = numpy.ndarray(lut_size, dtype=numpy.int32)
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# cl.enqueue_copy(queue, check_atomics, d_check_atomics)
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program.memset_out(queue, memset_size, (workgroup_size,), d_outData.data, d_outCount.data, d_outMerge.data)
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d_image = cl.array.to_device(queue, data)
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d_image_float = cl.array.empty(queue, (size,), dtype=numpy.float32)
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# program.s32_to_float(queue, global_size, (workgroup_size,), d_image.data, d_image_float) # Pilatus1M
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program.u16_to_float(queue, global_size, (workgroup_size,), d_image.data, d_image_float.data) # halfccd
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program.csr_integrate(queue, (bins * workgroup_size,), (workgroup_size,), d_image_float.data, d_data.data, d_indices.data, d_idx_ptr.data, d_outData.data, d_outCount.data, d_outMerge.data)
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# outData = numpy.ndarray(bins, dtype=numpy.float32)
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# outCount = numpy.ndarray(bins, dtype=numpy.float32)
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outMerge = numpy.ndarray(bins, dtype=numpy.float32)
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# cl.enqueue_copy(queue,outData, d_outData)
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# cl.enqueue_copy(queue,outCount, d_outCount)
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cl.enqueue_copy(queue, outMerge, d_outMerge.data)
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# program.integrate2(queue, (1024,), (workgroup_size,), d_outData, d_outCount, d_outMerge)
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# cl.enqueue_copy(queue,outData, d_outData)
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# cl.enqueue_copy(queue,outCount, d_outCount)
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# cl.enqueue_copy(queue,outMerge, d_outMerge)
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# ref = ai.integrate1d(data,bins,unit="2th_deg", correctSolidAngle=False, method="splitpixelfull")
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ref = splitPixelFullLUT.HistoLUT1dFullSplit(pos, bins, unit="2th_deg")
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# assert(numpy.allclose(ref,outMerge))
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# #plot(ref[0],outMerge, label="ocl_lut_merge")
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# ##plot(ref[0],outData, label="ocl_lut_data")
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# ##plot(ref[0],outCount, label="ocl_lut_count")
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# plot(ref[1], label="ref_merge")
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# ##plot(ref[0], ref[2], label="ref_data")
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# ##plot(ref[0], ref[3], label="ref_count")
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####plot(abs(ref-outMerge)/outMerge, label="ocl_csr_fullsplit")
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# legend()
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# show()
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# six.moves.input()
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