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Minor clean up and testing using a low-end GPU

This commit is contained in:
Jerome Kieffer 2012-06-14 23:32:36 +02:00
parent 22b7745cb6
commit 70a13047bf
8 changed files with 7559 additions and 5578 deletions
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4
.gitignore vendored
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@ -25,3 +25,7 @@ pip-log.txt
#Mr Developer
.mr.developer.cfg
#Shared libraries
*.so

1
openCL/ocl_azim.c
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@ -1 +0,0 @@
#error Do not use this file, it is the result of a failed Cython compilation.

2926
openCL/ocl_azim.cpp
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49
openCL/ocl_azim.pyx
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@ -36,6 +36,8 @@ cimport numpy
import numpy
import threading
import cython
from stdlib cimport free, malloc
from libc.string cimport memcpy
@ -45,9 +47,11 @@ cdef class Integrator1d:
"""
cdef ocl_xrpd1d.ocl_xrpd1D_fullsplit* cpp_integrator
cdef char* _devicetype
cdef int _nBins, _nData, _platformid, _devid, _tth_id, _dtth_id, _mask_id, _solid_angle_id
cdef char* filename
cdef int _nBins, _nData, _platformid, _devid,
cdef cpp_bool _useFp64
cdef float tth_min, tth_max,_tth_min, _tth_max
cdef float* ctth_out
def __cinit__(self, filename=None):
"""
@ -55,28 +59,26 @@ cdef class Integrator1d:
"""
self._nBins = -1
self._nData = -1
self._platformid = -1
self._devid = -1
self._useFp64 = False
self._tth_id = 0
self._dtth_id = 0
self._mask_id = 0
self._solid_angle_id = 0
self._devicetype = "gpu"
if filename is None:
self.cpp_integrator = new ocl_xrpd1d.ocl_xrpd1D_fullsplit()
else:
name = str(filename)
self.cpp_integrator = new ocl_xrpd1d.ocl_xrpd1D_fullsplit(name)
def __init__(self,filename=None):
"""
Python constructor
"""
self.filename = filename
self.tth_out = None
self.cpp_integrator = new ocl_xrpd1d.ocl_xrpd1D_fullsplit(self.filename)
def __dealloc__(self):
if self.ctth_out:
free(self.ctth_out)
del self.cpp_integrator
def __repr__(self):
return os.linesep.join(["Cython wrapper for ocl_xrpd1d.ocl_xrpd1D_fullsplit C++ class. Logging in %s"%self.filename,
"device: %s, platform %s device %s 64bits:%s image size: %s histogram size: %s"%(self._devicetype,self._platformid,self._devid, self._useFp64, self._nData,self._nBins),
",\t ".join(["%s: %s"%(k,v) for k,v in self.get_status().items()])])
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
@ -86,12 +88,15 @@ cdef class Integrator1d:
"""
self.tth_min = lower
self.tth_max = upper
cdef numpy.ndarray[numpy.float32_t, ndim = 1] tth_out = numpy.empty(self._nBins,dtype=numpy.float32)
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] tth_out = numpy.empty(self._nBins,dtype=numpy.float32)
if self.ctth_out:
free(self.ctth_out)
self.ctth_out= <float*> malloc(self._nBins*sizeof(float))
cdef float delta = (upper - lower ) / (< float > (self._nBins))
with nogil:
for i in range(self._nBins):
tth_out[i] = self.tth_min + (0.5 +< float > i) * delta
self.tth_out = tth_out
self.ctth_out[i] = <float>( self.tth_min + (0.5 +< float > i) * delta)
# self.tth_out = tth_out
def getConfiguration(self, int Nimage, int Nbins, useFp64=None):
@ -244,17 +249,19 @@ cdef class Integrator1d:
set / unset and loadTth methods have a direct impact on the execute() method.
All the rest of the methods will require at least a new configuration via configure()"""
cdef int rc,i
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cimage, histogram, bins
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cimage, histogram, bins,tth_out
cimage = numpy.ascontiguousarray(image.ravel(),dtype=numpy.float32)
outPos = numpy.zeros(self._nBins,dtype=numpy.float32)
histogram = numpy.zeros(self._nBins,dtype=numpy.float32)
bins = numpy.zeros(self._nBins,dtype=numpy.float32)
histogram = numpy.empty(self._nBins,dtype=numpy.float32)
bins = numpy.empty(self._nBins,dtype=numpy.float32)
tth_out = numpy.empty(self._nBins,dtype=numpy.float32)
assert cimage.size == self._nData
with nogil:
rc = self.cpp_integrator.execute(<float*> cimage.data, <float*> histogram.data, <float*> bins.data)
if rc!=0:
raise RuntimeError("OpenCL integrator failed with RC=%s"%rc)
return self.tth_out,histogram,bins
memcpy(tth_out.data,self.ctth_out,self._nBins*sizeof(float))
return tth_out,histogram,bins
def clean(self, int preserve_context=0):
"""Free OpenCL related resources.

2
pyFAI-src/azimuthalIntegrator.py
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@ -389,7 +389,7 @@ class AzimuthalIntegrator(Geometry):
with self._ocl_sem:
if self._ocl is None:
size = data.size
fd, tmpfile = tempfile.mkstemp(".log", "pyfai-opencl")
fd, tmpfile = tempfile.mkstemp(".log", "pyfai-opencl-")
os.close(fd)
integr = ocl_azim.Integrator1d(tmpfile)
if platformid and deviceid:

4542
src/splitBBox.c
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5329
src/splitBBox.html
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86
src/splitBBox.pyx
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@ -79,22 +79,21 @@ def histoBBox1d(numpy.ndarray weights not None,
assert pos0.size == size
assert delta_pos0.size == size
assert bins > 1
print pos0Range
cdef long bin0_max, bin0_min, bin = 0
cdef float data, deltaR, deltaL, deltaA,p1, epsilon = 1e-10, cdummy, ddummy
cdef float pos0_min, pos0_max, pos0_maxin, pos1_min, pos1_max, pos1_maxin, min0, max0, fbin0_min, fbin0_max
cdef int checkpos1 = 0, check_mask = 0, check_dummy = 0
cdef long checkpos1 = 0, check_mask = 0, check_dummy = 0
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cdata = numpy.ascontiguousarray(weights.ravel(),dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cdata = numpy.ascontiguousarray(weights.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0, dpos0, cpos1, dpos1,cpos0_lower, cpos0_upper
cpos0 = numpy.ascontiguousarray(pos0.ravel(), dtype="float32")
dpos0 = numpy.ascontiguousarray(delta_pos0.ravel(), dtype="float32")
cpos0 = numpy.ascontiguousarray(pos0.ravel(), dtype=numpy.float32)
dpos0 = numpy.ascontiguousarray(delta_pos0.ravel(), dtype=numpy.float32)
cdef numpy.ndarray[numpy.float64_t, ndim = 1] outData = numpy.zeros(bins, dtype="float64")
cdef numpy.ndarray[numpy.float64_t, ndim = 1] outCount = numpy.zeros(bins, dtype="float64")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] outMerge = numpy.zeros(bins, dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] outPos = numpy.zeros(bins, dtype="float32")
cdef numpy.ndarray[numpy.float64_t, ndim = 1] outData = numpy.zeros(bins, dtype=numpy.float64)
cdef numpy.ndarray[numpy.float64_t, ndim = 1] outCount = numpy.zeros(bins, dtype=numpy.float64)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] outMerge = numpy.zeros(bins, dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] outPos = numpy.zeros(bins, dtype=numpy.float32)
cdef numpy.ndarray[numpy.int8_t, ndim = 1] cmask
if mask is not None:
@ -111,8 +110,8 @@ def histoBBox1d(numpy.ndarray weights not None,
else:
cdummy=0.0
cpos0_lower = numpy.zeros(size, dtype="float32")
cpos0_upper = numpy.zeros(size, dtype="float32")
cpos0_lower = numpy.zeros(size, dtype=numpy.float32)
cpos0_upper = numpy.zeros(size, dtype=numpy.float32)
pos0_min=cpos0[0]
pos0_max=cpos0[0]
with nogil:
@ -138,8 +137,8 @@ def histoBBox1d(numpy.ndarray weights not None,
assert pos1.size == size
assert delta_pos1.size == size
checkpos1 = 1
cpos1 = numpy.ascontiguousarray(pos1.ravel(),dtype="float32")
dpos1 = numpy.ascontiguousarray(delta_pos1.ravel(),dtype="float32")
cpos1 = numpy.ascontiguousarray(pos1.ravel(),dtype=numpy.float32)
dpos1 = numpy.ascontiguousarray(delta_pos1.ravel(),dtype=numpy.float32)
pos1_min = min(pos1Range)
pos1_maxin = max(pos1Range)
pos1_max = pos1_maxin * (1 + numpy.finfo(numpy.float32).eps)
@ -255,32 +254,54 @@ def histoBBox2d(numpy.ndarray weights not None,
bins0 = 1
if bins1 <= 0:
bins1 = 1
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cdata = numpy.ascontiguousarray(weights.ravel(),dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0 = numpy.ascontiguousarray(pos0.ravel(),dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] dpos0 = numpy.ascontiguousarray(delta_pos0.ravel(),dtype="float32")
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0_inf = cpos0 - dpos0
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0_sup = cpos0 + dpos0
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1 = numpy.ascontiguousarray(pos1.ravel(),dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] dpos1 = numpy.ascontiguousarray(delta_pos1.ravel(),dtype="float32")
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1_inf = cpos1 - dpos1
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1_sup = cpos1 + dpos1
cdef numpy.ndarray[numpy.float64_t, ndim = 2] outData = numpy.zeros((bins0, bins1), dtype="float64")
cdef numpy.ndarray[numpy.float64_t, ndim = 2] outCount = numpy.zeros((bins0, bins1), dtype="float64")
cdef numpy.ndarray[numpy.float32_t, ndim = 2] outMerge = numpy.zeros((bins0, bins1), dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] edges0 = numpy.zeros(bins0, dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] edges1 = numpy.zeros(bins1, dtype="float32")
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cdata = numpy.ascontiguousarray(weights.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0 = numpy.ascontiguousarray(pos0.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] dpos0 = numpy.ascontiguousarray(delta_pos0.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1 = numpy.ascontiguousarray(pos1.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] dpos1 = numpy.ascontiguousarray(delta_pos1.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0_upper = numpy.zeros(size, dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0_lower = numpy.zeros(size, dtype=numpy.float32)
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1_upper = numpy.zeros(size, dtype=numpy.float32)
# cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos1_lower = numpy.zeros(size, dtype=numpy.float32)
cdef numpy.ndarray[numpy.float64_t, ndim = 2] outData = numpy.zeros((bins0, bins1), dtype=numpy.float64)
cdef numpy.ndarray[numpy.float64_t, ndim = 2] outCount = numpy.zeros((bins0, bins1), dtype=numpy.float64)
cdef numpy.ndarray[numpy.float32_t, ndim = 2] outMerge = numpy.zeros((bins0, bins1), dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] edges0 = numpy.zeros(bins0, dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] edges1 = numpy.zeros(bins1, dtype=numpy.float32)
cdef float min0, max0, min1, max1, deltaR, deltaL, deltaU, deltaD, deltaA, tmp, delta0, delta1
cdef float pos0_min, pos0_max, pos1_min, pos1_max, pos0_maxin, pos1_maxin
cdef float fbin0_min, fbin0_max, fbin1_min, fbin1_max, data, epsilon = 1e-10
cdef long bin0_max, bin0_min, bin1_max, bin1_min
pos0_min=cpos0[0]
pos0_max=cpos0[0]
with nogil:
for idx in range(size):
min0 = cpos0[idx] - dpos0[idx]
max0 = cpos0[idx] + dpos0[idx]
cpos0_upper[idx]=max0
cpos0_lower[idx]=min0
if max0>pos0_max:
pos0_max=max0
if min0<pos0_min:
pos0_min=min0
# for idx in range(size):
# min1 = cpos1[idx] - dpos1[idx]
# max1 = cpos1[idx] + dpos1[idx]
# cpos1_upper[idx]=max1
# cpos1_lower[idx]=min1
# if max1>pos1_max:
# pos1_max=max1
# if min1<pos1_min:
# pos1_min=min1
if (pos0Range is not None) and (len(pos0Range) == 2):
pos0_min = min(pos0Range)
pos0_maxin = max(pos0Range)
else:
pos0_min = cpos0.min()
pos0_maxin = cpos0.max()
pos0_min = pos0_min
pos0_maxin = pos0_max
if pos0_min<0:
pos0_min=0
pos0_max = pos0_maxin * (1 + numpy.finfo(numpy.float32).eps)
@ -289,7 +310,6 @@ def histoBBox2d(numpy.ndarray weights not None,
pos1_min = min(pos1Range)
pos1_maxin = max(pos1Range)
else:
# tmp = cdelta_pos1.min()
pos1_min = cpos1.min()
pos1_maxin = cpos1.max()
pos1_max = pos1_maxin * (1 + numpy.finfo(numpy.float32).eps)
@ -305,8 +325,8 @@ def histoBBox2d(numpy.ndarray weights not None,
for idx in range(size):
data = cdata[idx]
min0 = cpos0[idx] - dpos0[idx]
max0 = cpos0[idx] + dpos0[idx]
min0 = cpos0_lower[idx]
max0 = cpos0_upper[idx]
min1 = cpos1[idx] - dpos1[idx]
max1 = cpos1[idx] + dpos1[idx]
@ -404,7 +424,7 @@ def histoBBox2d(numpy.ndarray weights not None,
for i in range(bins0):
for j in range(bins1):
if outCount[i, j] > epsilon:
outMerge[i, j] = outData[i, j] / outCount[i, j]
outMerge[i, j] = <float> (outData[i, j] / outCount[i, j])
else:
outMerge[i, j] = dummy
return outMerge.T, edges0, edges1, outData.T, outCount.T