def execute(self, slot, subindex, roi, result):
featMatrix=[]
labelsMatrix=[]
for i,labels in enumerate(self.inputs["Labels"]):
if labels.meta.shape is not None:
labels=labels[:].allocate().wait()
indexes=numpy.nonzero(labels[...,0].view(numpy.ndarray))
#Maybe later request only part of the region?
image=self.inputs["Images"][i][:].allocate().wait()
features=image[indexes]
labels=labels[indexes]
featMatrix.append(features)
labelsMatrix.append(labels)
featMatrix=numpy.concatenate(featMatrix,axis=0)
labelsMatrix=numpy.concatenate(labelsMatrix,axis=0)
# train and store self._forest_count forests in parallel
pool = Pool()
for i in range(self._forest_count):
def train_and_store(number):
result[number] = vigra.learning.RandomForest(self._tree_count)
result[number].learnRF(featMatrix.astype(numpy.float32),labelsMatrix.astype(numpy.uint32))
req = pool.request(partial(train_and_store, i))
pool.wait()
return result
def execute(self, slot, subindex, roi, result):
t1 = time.time()
key = roi.toSlice()
nlabels=self.inputs["LabelsCount"].value
traceLogger.debug("OpPredictRandomForest: Requesting classifier. roi={}".format(roi))
forests=self.inputs["Classifier"][:].wait()
if forests is None:
# Training operator may return 'None' if there was no data to train with
return numpy.zeros(numpy.subtract(roi.stop, roi.start), dtype=numpy.float32)[...]
traceLogger.debug("OpPredictRandomForest: Got classifier")
#assert RF.labelCount() == nlabels, "ERROR: OpPredictRandomForest, labelCount differs from true labelCount! %r vs. %r" % (RF.labelCount(), nlabels)
newKey = key[:-1]
newKey += (slice(0,self.inputs["Image"].meta.shape[-1],None),)
res = self.inputs["Image"][newKey].wait()
shape=res.shape
prod = numpy.prod(shape[:-1])
res.shape = (prod, shape[-1])
features=res
predictions = [0]*len(forests)
def predict_forest(number):
predictions[number] = forests[number].predictProbabilities(numpy.asarray(features, dtype=numpy.float32))
t2 = time.time()
# predict the data with all the forests in parallel
pool = Pool()
for i,f in enumerate(forests):
req = pool.request(partial(predict_forest, i))
pool.wait()
pool.clean()
prediction=numpy.dstack(predictions)
prediction = numpy.average(prediction, axis=2)
prediction.shape = shape[:-1] + (forests[0].labelCount(),)
#prediction = prediction.reshape(*(shape[:-1] + (forests[0].labelCount(),)))
# If our LabelsCount is higher than the number of labels in the training set,
# then our results aren't really valid. FIXME !!!
# Duplicate the last label's predictions
for c in range(result.shape[-1]):
result[...,c] = prediction[...,min(c+key[-1].start, prediction.shape[-1]-1)]
t3 = time.time()
# logger.info("Predict took %fseconds, actual RF time was %fs, feature time was %fs" % (t3-t1, t3-t2, t2-t1))
return result
def execute(self, slot, subindex, roi, result):
progress = 0
self.progressSignal(progress)
numImages = len(self.Images)
key = roi.toSlice()
featMatrix=[]
labelsMatrix=[]
for i,labels in enumerate(self.inputs["Labels"]):
if labels.meta.shape is not None:
#labels=labels[:].allocate().wait()
blocks = self.inputs["nonzeroLabelBlocks"][i][0].allocate().wait()
progress += 10/numImages
self.progressSignal(progress)
reqlistlabels = []
reqlistfeat = []
traceLogger.debug("Sending requests for {} non-zero blocks (labels and data)".format( len(blocks[0])) )
for b in blocks[0]:
request = labels[b].allocate()
featurekey = list(b)
featurekey[-1] = slice(None, None, None)
request2 = self.inputs["Images"][i][featurekey].allocate()
reqlistlabels.append(request)
reqlistfeat.append(request2)
traceLogger.debug("Requests prepared")
numLabelBlocks = len(reqlistlabels)
progress_outer = [progress] # Store in list for closure access
if numLabelBlocks > 0:
progressInc = (80-10)/numLabelBlocks/numImages
def progressNotify(req):
# Note: If we wanted perfect progress reporting, we could use lock here
# to protect the progress from being incremented simultaneously.
# But that would slow things down and imperfect reporting is okay for our purposes.
progress_outer[0] += progressInc/2
self.progressSignal(progress_outer[0])
for ir, req in enumerate(reqlistfeat):
image = req.notify(progressNotify)
for ir, req in enumerate(reqlistlabels):
labblock = req.notify(progressNotify)
traceLogger.debug("Requests fired")
for ir, req in enumerate(reqlistlabels):
traceLogger.debug("Waiting for a label block...")
labblock = req.wait()
traceLogger.debug("Waiting for an image block...")
image = reqlistfeat[ir].wait()
indexes=numpy.nonzero(labblock[...,0].view(numpy.ndarray))
features=image[indexes]
labbla=labblock[indexes]
featMatrix.append(features)
labelsMatrix.append(labbla)
progress = progress_outer[0]
traceLogger.debug("Requests processed")
self.progressSignal(80/numImages)
if len(featMatrix) == 0 or len(labelsMatrix) == 0:
# If there was no actual data for the random forest to train with, we return None
result[:] = None
else:
featMatrix=numpy.concatenate(featMatrix,axis=0)
labelsMatrix=numpy.concatenate(labelsMatrix,axis=0)
maxLabel = self.inputs["MaxLabel"].value
labelList = range(1, maxLabel+1) if maxLabel > 0 else list()
try:
logger.debug("Learning with Vigra...")
# train and store self._forest_count forests in parallel
pool = Pool()
for i in range(self._forest_count):
def train_and_store(number):
result[number] = vigra.learning.RandomForest(self._tree_count, labels=labelList)
result[number].learnRF( numpy.asarray(featMatrix, dtype=numpy.float32),
numpy.asarray(labelsMatrix, dtype=numpy.uint32))
req = pool.request(partial(train_and_store, i))
pool.wait()
pool.clean()
logger.debug("Vigra finished")
except:
logger.error( "ERROR: could not learn classifier" )
logger.error( "featMatrix shape={}, max={}, dtype={}".format(featMatrix.shape, featMatrix.max(), featMatrix.dtype) )
logger.error( "labelsMatrix shape={}, max={}, dtype={}".format(labelsMatrix.shape, labelsMatrix.max(), labelsMatrix.dtype ) )
raise
finally:
self.progressSignal(100)
return result
def _extract(self, image, labels):
assert len(image.shape) == len(labels.shape) == 3, "Images must be 3D. Shapes were: {} and {}".format( image.shape, labels.shape )
xAxis = image.axistags.index('x')
yAxis = image.axistags.index('y')
zAxis = image.axistags.index('z')
image = numpy.asarray(image, dtype=numpy.float32)
labels = numpy.asarray(labels, dtype=numpy.uint32)
feature_names_first = [feat for feat in self._vigraFeatureNames if feat in self.OffsetSensitiveFeatures]
feature_names_second = [feat for feat in self._vigraFeatureNames if not feat in self.OffsetSensitiveFeatures]
if not "Coord<Minimum>" in feature_names_first:
feature_names_first.append("Coord<Minimum>")
if not "Coord<Maximum>" in feature_names_first:
feature_names_first.append("Coord<Maximum>")
if not "Count" in feature_names_first:
feature_names_first.append("Count")
features_first = vigra.analysis.extractRegionFeatures(image, labels, feature_names_first, ignoreLabel=0)
feature_dict = {}
for key in features_first.keys():
feature_dict[key] = features_first[key]
mins = features_first["Coord<Minimum>"]
maxs = features_first["Coord<Maximum>"]
counts = features_first["Count"]
nobj = mins.shape[0]
features_obj = [None] #don't compute for the 0-th object (the background)
features_incl = [None]
features_excl = [None]
first_good = 1
pool = Pool()
otherFeatures_dict = {}
if len(self._otherFeatureNames)>0:
#there are non-vigra features. let's make room for them
#we can't do that for vigra features, because vigra computes more than
#we specify in featureNames and we want to keep that
otherFeatures_dict = {}
for key in self._otherFeatureNames:
otherFeatures_dict[key]=[None]
for i in range(1,nobj):
print "processing object ", i
#find the bounding box
minx = max(mins[i][xAxis]-self.MARGIN, 0)
miny = max(mins[i][yAxis]-self.MARGIN, 0)
minz = max(mins[i][zAxis], 0)
# Coord<Minimum> and Coord<Maximum> give us the [min,max]
# coords of the object, but we want the bounding box: [min,max), so add 1
maxx = min(maxs[i][xAxis]+1+self.MARGIN, image.shape[xAxis])
maxy = min(maxs[i][yAxis]+1+self.MARGIN, image.shape[yAxis])
maxz = min(maxs[i][zAxis]+1, image.shape[zAxis])
#FIXME: there must be a better way
key = 3*[None]
key[xAxis] = slice(minx, maxx, None)
key[yAxis] = slice(miny, maxy, None)
key[zAxis] = slice(minz, maxz, None)
key = tuple(key)
ccbbox = labels[key]
rawbbox = image[key]
ccbboxobject = numpy.where(ccbbox==i, 1, 0)
#find the context area around the object
bboxshape = 3*[None]
bboxshape[xAxis] = maxx-minx
bboxshape[yAxis] = maxy-miny
bboxshape[zAxis] = maxz-minz
bboxshape = tuple(bboxshape)
passed = numpy.zeros(bboxshape, dtype=bool)
for iz in range(maxz-minz):
#FIXME: shoot me, axistags
bboxkey = 3*[None]
bboxkey[xAxis] = slice(None, None, None)
bboxkey[yAxis] = slice(None, None, None)
bboxkey[zAxis] = iz
bboxkey = tuple(bboxkey)
#TODO: Ulli once mentioned that distance transform can be made anisotropic in 3D
dt = vigra.filters.distanceTransform2D( numpy.asarray(ccbbox[bboxkey], dtype=numpy.float32) )
passed[bboxkey] = dt<self.MARGIN
ccbboxexcl = passed-ccbboxobject
if "bad_slices" in self._otherFeatureNames:
#compute the quality score of an object -
#count the number of fully black slices inside its bbox
#FIXME: the interpolation part is not tested at all...
nbadslices = 0
badslices = []
area = rawbbox.shape[xAxis]*rawbbox.shape[yAxis]
bboxkey = 3*[None]
bboxkey[xAxis] = slice(None, None, None)
bboxkey[yAxis] = slice(None, None, None)
for iz in range(maxz-minz):
bboxkey[zAxis] = iz
nblack = numpy.sum(rawbbox[tuple(bboxkey)]==0)
if nblack>0.5*area:
nbadslices = nbadslices+1
badslices.append(iz)
otherFeatures_dict["bad_slices"].append(numpy.array([nbadslices]))
labeled_bboxes = [passed, ccbboxexcl, ccbboxobject]
feats = [None, None, None]
for ibox, bbox in enumerate(labeled_bboxes):
def extractObjectFeatures(ibox):
feats[ibox] = vigra.analysis.extractRegionFeatures(numpy.asarray(rawbbox, dtype=numpy.float32), \
numpy.asarray(labeled_bboxes[ibox], dtype=numpy.uint32), \
feature_names_second, \
histogramRange=[0, 255], \
binCount = 10,\
ignoreLabel=0)
req = pool.request(partial(extractObjectFeatures, ibox))
pool.wait()
features_incl.append(feats[0])
features_excl.append(feats[1])
features_obj.append(feats[2])
if "lbp" in self._otherFeatureNames:
#FIXME: there is a mess about which of the lbp features are computed (obj, excl or incl)
#compute lbp features
import skimage.feature as ft
P=8
R=1
lbp_total = numpy.zeros(passed.shape)
for iz in range(maxz-minz):
#an lbp image
bboxkey = 3*[None]
bboxkey[xAxis] = slice(None, None, None)
bboxkey[yAxis] = slice(None, None, None)
bboxkey[zAxis] = iz
bboxkey = tuple(bboxkey)
lbp_total[bboxkey] = ft.local_binary_pattern(rawbbox[bboxkey], P, R, "uniform")
#extract relevant parts
#print "computed lbp for volume:", lbp_total.shape,
#print "extracting pieces:", passed.shape, ccbboxexcl.shape, ccbboxobject.shape
lbp_incl = lbp_total[passed]
lbp_excl = lbp_total[ccbboxexcl.astype(bool)]
lbp_obj = lbp_total[ccbboxobject.astype(bool)]
#print "extracted pieces", lbp_incl.shape, lbp_excl.shape, lbp_obj.shape
lbp_hist_incl, _ = numpy.histogram(lbp_incl, normed=True, bins=P+2, range=(0, P+2))
lbp_hist_excl, _ = numpy.histogram(lbp_excl, normed=True, bins=P+2, range=(0, P+2))
lbp_hist_obj, _ = numpy.histogram(lbp_obj, normed=True, bins=P+2, range=(0, P+2))
#print "computed histogram"
otherFeatures_dict["lbp_incl"].append(lbp_hist_incl)
otherFeatures_dict["lbp_excl"].append(lbp_hist_excl)
otherFeatures_dict["lbp"].append(lbp_hist_obj)
if "lapl" in self._otherFeatureNames:
#compute mean and variance of laplacian in the object and its neighborhood
lapl = None
try:
lapl = vigra.filters.laplacianOfGaussian(rawbbox)
except RuntimeError:
#kernel longer than line. who cares?
otherFeatures_dict["lapl_incl"].append(None)
otherFeatures_dict["lapl_excl"].append(None)
otherFeatures_dict["lapl"].append(None)
else:
lapl_incl = lapl[passed]
lapl_excl = lapl[ccbboxexcl.astype(bool)]
lapl_obj = lapl[ccbboxobject.astype(bool)]
lapl_mean_incl = numpy.mean(lapl_incl)
lapl_var_incl = numpy.var(lapl_incl)
lapl_mean_excl = numpy.mean(lapl_excl)
lapl_var_excl = numpy.var(lapl_excl)
lapl_mean_obj = numpy.mean(lapl_obj)
lapl_var_obj = numpy.var(lapl_obj)
otherFeatures_dict["lapl_incl"].append(numpy.array([lapl_mean_incl, lapl_var_incl]))
otherFeatures_dict["lapl_excl"].append(numpy.array([lapl_mean_excl, lapl_var_excl]))
otherFeatures_dict["lapl"].append(numpy.array([lapl_mean_obj, lapl_var_obj]))
feature_keys = features_incl[first_good].keys()
#copy over non-vigra features and turn them into numpy arrays
for key in otherFeatures_dict.keys():
#print otherFeatures_dict[key]
#find the number of channels
feature = otherFeatures_dict[key]
nchannels = feature[first_good].shape[0]
for irow, row in enumerate(feature):
if row is None:
#print "NaNs in row", irow
feature[irow]=numpy.zeros((nchannels,))
feature_dict[key]=numpy.vstack(otherFeatures_dict[key])
assert feature_dict[key].shape[0]==nobj, "didn't compute features for all objects {}".format(key)
#print key, feature_dict[key].shape
for key in feature_keys:
if key in feature_names_first:
continue
nchannels = 0
#we always have two objects, background is first
#unless, of course, it's a global measurement, and then it's just one element, grrrh
#sometimes, vigra returns one-dimensional features as (nobj, 1) and sometimes as (nobj,)
#the following try-except is for this case
try:
nchannels = len(features_incl[first_good][key][0])
except TypeError:
nchannels = 1
#print "assembling key:", key, "nchannels:", nchannels
#print "feature arrays:", len(features_incl), len(features_excl), len(features_obj)
#FIXME: find the maximum number of channels and pre-allocate
feature_obj = numpy.zeros((nobj, nchannels))
feature_incl = numpy.zeros((nobj, nchannels))
feature_excl = numpy.zeros((nobj, nchannels))
for i in range(nobj):
if features_obj[i] is not None:
try:
feature_obj[i] = features_obj[i][key][1]
feature_incl[i] = features_incl[i][key][1]
feature_excl[i] = features_excl[i][key][1]
except:
#global number, not a list, haha
feature_obj[i] = features_obj[i][key]
feature_incl[i] = features_incl[i][key]
feature_excl[i] = features_excl[i][key]
feature_dict[key]=feature_obj
feature_dict[key+"_incl"]=feature_incl
feature_dict[key+"_excl"]=feature_excl
#print key, feature_obj.shape, feature_incl.shape, feature_excl.shape
end1 = time.clock()
#print "computed the following features:", feature_dict.keys()
return feature_dict
def execute(self, slot, subindex, rroi, result):
assert slot == self.Features or slot == self.Output
if slot == self.Features:
key = roiToSlice(rroi.start, rroi.stop)
index = subindex[0]
subslot = self.Features[index]
key = list(key)
channelIndex = self.Input.meta.axistags.index('c')
# Translate channel slice to the correct location for the output slot.
key[channelIndex] = slice(self.featureOutputChannels[index][0] + key[channelIndex].start,
self.featureOutputChannels[index][0] + key[channelIndex].stop)
rroi = SubRegion(subslot, pslice=key)
# Get output slot region for this channel
return self.execute(self.Output, (), rroi, result)
elif slot == self.outputs["Output"]:
key = rroi.toSlice()
cnt = 0
written = 0
assert (rroi.stop<=self.outputs["Output"].meta.shape).all()
flag = 'c'
channelAxis=self.inputs["Input"].meta.axistags.index('c')
axisindex = channelAxis
oldkey = list(key)
oldkey.pop(axisindex)
inShape = self.inputs["Input"].meta.shape
shape = self.outputs["Output"].meta.shape
axistags = self.inputs["Input"].meta.axistags
result = result.view(vigra.VigraArray)
result.axistags = copy.copy(axistags)
hasTimeAxis = self.inputs["Input"].meta.axistags.axisTypeCount(vigra.AxisType.Time)
timeAxis=self.inputs["Input"].meta.axistags.index('t')
subkey = popFlagsFromTheKey(key,axistags,'c')
subshape=popFlagsFromTheKey(shape,axistags,'c')
at2 = copy.copy(axistags)
at2.dropChannelAxis()
subshape=popFlagsFromTheKey(subshape,at2,'t')
subkey = popFlagsFromTheKey(subkey,at2,'t')
oldstart, oldstop = roi.sliceToRoi(key, shape)
start, stop = roi.sliceToRoi(subkey,subkey)
maxSigma = max(0.7,self.maxSigma)
# The region of the smoothed image we need to give to the feature filter (in terms of INPUT coordinates)
vigOpSourceStart, vigOpSourceStop = roi.extendSlice(start, stop, subshape, 0.7, window = 2)
# The region of the input that we need to give to the smoothing operator (in terms of INPUT coordinates)
newStart, newStop = roi.extendSlice(vigOpSourceStart, vigOpSourceStop, subshape, maxSigma, window = 3.5)
# Translate coordinates (now in terms of smoothed image coordinates)
vigOpSourceStart = roi.TinyVector(vigOpSourceStart - newStart)
vigOpSourceStop = roi.TinyVector(vigOpSourceStop - newStart)
readKey = roi.roiToSlice(newStart, newStop)
writeNewStart = start - newStart
writeNewStop = writeNewStart + stop - start
treadKey=list(readKey)
if hasTimeAxis:
if timeAxis < channelAxis:
treadKey.insert(timeAxis, key[timeAxis])
else:
treadKey.insert(timeAxis-1, key[timeAxis])
if self.inputs["Input"].meta.axistags.axisTypeCount(vigra.AxisType.Channels) == 0:
treadKey = popFlagsFromTheKey(treadKey,axistags,'c')
else:
treadKey.insert(channelAxis, slice(None,None,None))
treadKey=tuple(treadKey)
req = self.inputs["Input"][treadKey].allocate()
sourceArray = req.wait()
req.result = None
req.destination = None
if sourceArray.dtype != numpy.float32:
sourceArrayF = sourceArray.astype(numpy.float32)
sourceArray.resize((1,), refcheck = False)
del sourceArray
sourceArray = sourceArrayF
sourceArrayV = sourceArray.view(vigra.VigraArray)
sourceArrayV.axistags = copy.copy(axistags)
dimCol = len(self.scales)
dimRow = self.matrix.shape[0]
sourceArraysForSigmas = [None]*dimCol
#connect individual operators
for j in range(dimCol):
hasScale = False
for i in range(dimRow):
if self.matrix[i,j]:
hasScale = True
if not hasScale:
continue
destSigma = 1.0
if self.scales[j] > destSigma:
tempSigma = math.sqrt(self.scales[j]**2 - destSigma**2)
else:
destSigma = 0.0
tempSigma = self.scales[j]
vigOpSourceShape = list(vigOpSourceStop - vigOpSourceStart)
if hasTimeAxis:
if timeAxis < channelAxis:
vigOpSourceShape.insert(timeAxis, ( oldstop - oldstart)[timeAxis])
else:
vigOpSourceShape.insert(timeAxis-1, ( oldstop - oldstart)[timeAxis])
vigOpSourceShape.insert(channelAxis, inShape[channelAxis])
sourceArraysForSigmas[j] = numpy.ndarray(tuple(vigOpSourceShape),numpy.float32)
for i,vsa in enumerate(sourceArrayV.timeIter()):
droi = (tuple(vigOpSourceStart._asint()), tuple(vigOpSourceStop._asint()))
tmp_key = getAllExceptAxis(len(sourceArraysForSigmas[j].shape),timeAxis, i)
sourceArraysForSigmas[j][tmp_key] = vigra.filters.gaussianSmoothing(vsa,tempSigma, roi = droi, window_size = 3.5 )
else:
droi = (tuple(vigOpSourceStart._asint()), tuple(vigOpSourceStop._asint()))
#print droi, sourceArray.shape, tempSigma,self.scales[j]
sourceArraysForSigmas[j] = vigra.filters.gaussianSmoothing(sourceArrayV, sigma = tempSigma, roi = droi, window_size = 3.5)
#sourceArrayForSigma = sourceArrayForSigma.view(numpy.ndarray)
del sourceArrayV
try:
sourceArray.resize((1,), refcheck = False)
except ValueError:
# Sometimes this fails, but that's okay.
logger.debug("Failed to free array memory.")
del sourceArray
closures = []
#connect individual operators
for i in range(dimRow):
for j in range(dimCol):
val=self.matrix[i,j]
if val:
vop= self.featureOps[i][j]
oslot = vop.outputs["Output"]
req = None
inTagKeys = [ax.key for ax in oslot.meta.axistags]
if flag in inTagKeys:
slices = oslot.meta.shape[axisindex]
if cnt + slices >= rroi.start[axisindex] and rroi.start[axisindex]-cnt<slices and rroi.start[axisindex]+written<rroi.stop[axisindex]:
begin = 0
if cnt < rroi.start[axisindex]:
begin = rroi.start[axisindex] - cnt
end = slices
if cnt + end > rroi.stop[axisindex]:
end -= cnt + end - rroi.stop[axisindex]
key_ = copy.copy(oldkey)
key_.insert(axisindex, slice(begin, end, None))
reskey = [slice(None, None, None) for x in range(len(result.shape))]
reskey[axisindex] = slice(written, written+end-begin, None)
destArea = result[tuple(reskey)]
roi_ = SubRegion(self.Input, pslice=key_)
closure = partial(oslot.operator.execute, oslot, (), roi_, destArea, sourceArray = sourceArraysForSigmas[j])
closures.append(closure)
written += end - begin
cnt += slices
else:
if cnt>=rroi.start[axisindex] and rroi.start[axisindex] + written < rroi.stop[axisindex]:
reskey = copy.copy(oldkey)
reskey.insert(axisindex, written)
#print "key: ", key, "reskey: ", reskey, "oldkey: ", oldkey
#print "result: ", result.shape, "inslot:", inSlot.shape
destArea = result[tuple(reskey)]
logger.debug(oldkey, destArea.shape, sourceArraysForSigmas[j].shape)
oldroi = SubRegion(self.Input, pslice=oldkey)
closure = partial(oslot.operator.execute, oslot, (), oldroi, destArea, sourceArray = sourceArraysForSigmas[j])
closures.append(closure)
written += 1
cnt += 1
pool = Pool()
for c in closures:
r = pool.request(c)
pool.wait()
pool.clean()
for i in range(len(sourceArraysForSigmas)):
if sourceArraysForSigmas[i] is not None:
try:
sourceArraysForSigmas[i].resize((1,))
except:
sourceArraysForSigmas[i] = None
req = Request(functools.partial(empty_func, b = 11))
req.submit()
for r in requests:
r.wait()
# Make sure this test occurs entirely within greenlets.
req = Request( functools.partial( lots_of_work ) )
req.submit()
req.wait()
t2 = time.time()
print "\n\n"
print "LAZYFLOW REQUEST WAIT: %f seconds for %d iterations" % (t2-t1,mcount)
print " %0.3fms latency" % ((t2-t1)*1e3/mcount,)
t1 = time.time()
pool = Pool()
for i in range(50000):
pool.request(functools.partial(empty_func, b = 11))
pool.wait()
t2 = time.time()
print "\n\n"
print "LAZYFLOW POOL WAIT: %f seconds for %d iterations" % (t2-t1,mcount)
print " %0.3fms latency" % ((t2-t1)*1e3/mcount,)
t1 = time.time()
requests = []
for i in range(50000):
req = Request(empty_func, b = 11)
req.submit()
for r in requests:
r.wait()
t2 = time.time()
print "\n\n"
print "LAZYFLOW REQUEST WAIT: %f seconds for %d iterations" % (t2-t1,mcount)
print " %0.3fms latency" % ((t2-t1)*1e3/mcount,)
t1 = time.time()
pool = Pool()
for i in range(50000):
pool.request(empty_func, b = 11)
pool.wait()
t2 = time.time()
print "\n\n"
print "LAZYFLOW POOL WAIT: %f seconds for %d iterations" % (t2-t1,mcount)
print " %0.3fms latency" % ((t2-t1)*1e3/mcount,)
