def __init__(self,file_list, modification_pipeline=None, use_menpo_type=False):
videos=OrderedDict()
file_list_filtered=[]
video_getter_pool=ThreadPool(nodes=8)
mpio_obj_list = video_getter_pool.map(menpo_import_video_verbose,file_list)
for vp,mpio_obj in zip(file_list,mpio_obj_list):
if not mpio_obj is None:
videos[vp]=mpio_obj
file_list_filtered.append(vp)
#for vp in file_list:
#print('menpo.io.import_video importing %s' % vp)
#try:
#mpio_obj=menpo.io.import_video(vp, exact_frame_count=False, normalise=False)
#videos[vp]=mpio_obj
#except Exception as err:
#print('menpo.io.import_video could not import %s' % vp)
#print(err)
self.file_list = file_list_filtered
self.videos=videos
if not modification_pipeline is None:
self.modification_pipeline=modification_pipeline
self.fps=mpio_obj.fps
self.use_menpo_type=use_menpo_type
def __init__(self,
image_generators=[],
fps=None,
modification_pipelines=None,
structured_modification_pipelines=None,
use_menpo_type=False,
opts={}):
self.video_lists=[]
self.file_lists=[]
print('GroupedVideoGenerator3()')
for i in range(0,len(image_generators)):
if(isinstance(image_generators[i], VideoGenerator3)):
assert(isinstance(image_generators[i].videos, OrderedDict))
self.file_lists.append(image_generators[i].file_list);
self.video_lists.append(image_generators[i].videos)
fps=image_generators[i].fps
elif(isinstance(image_generators[i],list)):
video_getter_pool=ThreadPool(nodes=8)
tmp_mpio_obj_list=video_getter_pool.map(menpo_import_video_verbose, image_generators[i])
tmp_mpio_obj_list = [x for x in tmp_mpio_obj_list if x is not None]
safe_idxs = [idx for idx,x in enumerate(tmp_mpio_obj_list) if x is not None]
tmp_file_list=image_generators[i]
self.file_lists.append([tmp_file_list[safe_idx] for safe_idx in safe_idxs])
self.video_lists.append(tmp_mpio_obj_list)
else:
raise TypeError('You can only make a GroupedImageGenerator2 '+\
'from a list of string-lists or ImageGenerator2s')
self.file_lists[i] = [tmp for tmp in self.file_lists[i] if not tmp is None]
self.fps=fps
self.modification_pipelines= \
modification_pipelines if not modification_pipelines is None else {}
self.structured_modification_pipelines= \
structured_modification_pipelines if not structured_modification_pipelines is None else {}
self.io_pool = None
self.enable_caching = 0
self.enable_pp_caching = 0
self.img_pp_cache = {}
self.img_pp_oo_cache = {}
self.img_cache = {}
self.opts = opts
self.use_menpo_type = use_menpo_type
def init_thread_pool(self):
self.io_pool=ThreadPool(nodes=4)
class GroupedVideoGenerator2:
def __init__(self, image_generators,fps=None,modification_pipelines=None,structured_modification_pipelines=None):
self.video_lists=[]
self.file_lists=[]
for i in range(0,len(image_generators)):
if(isinstance(image_generators[i], VideoGenerator)):
assert(isinstance(image_generators[i].videos, OrderedDict))
self.file_lists.append(image_generators[i].file_list);
self.video_lists.append(image_generators[i].videos)
fps=image_generators[i].fps
elif(isinstance(image_generators[i],list)):
self.file_lists.append(image_generators[i]);
tmp_mpio_obj_list=map(lambda vp:menpo.io.import_video(vp, exact_frame_count=False, normalise=False), image_generators[i])
self.video_lists.append(tmp_mpio_obj_list)
else:
raise TypeError('You can only make a GroupedImageGenerator2 from a list of string-lists or ImageGenerator2s')
self.file_lists[i] = filter(None,self.file_lists[i]);
self.fps=fps
self.modification_pipelines=modification_pipelines if not modification_pipelines is None else {}
self.structured_modification_pipelines=structured_modification_pipelines if not structured_modification_pipelines is None else {}
self.io_pool = None
self.enable_caching=0;
self.enable_pp_caching=0;
self.img_pp_cache={}
self.img_pp_oo_cache={}
self.img_cache={}
self.opts={}
def group_lists(self,get_key_callback):
new_file_lists=sbpy_utils.core.sets.group_by(self.file_lists,get_key_callback);
self.file_lists=new_file_lists;
reordered_video_lists=[]
old_video_lists=self.video_lists
for view_idx,file_list in enumerate(new_file_lists):
reordered_video_list=OrderedDict()
old_video_list=old_video_lists[view_idx]
for g in file_list:
reordered_video_list[g]=old_video_list[g]
def dynamic_func(self,key,*args):
fhandle = self.opts[key];
out = fhandle(*args);
return out
def get_sample_dims(self, pipeline_mask=[], invert_pipeline_mask=True):
modified_img,_=self.get(0,0,pipeline_mask, invert_pipeline_mask)
all_dims=[]
for view in modified_img:
dims = view[0].shape;
all_dims.append(dims)
return all_dims
def structured_get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=True)
def get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=False)
def get_num_samples(self):
return len(self.file_lists[0]);
def _get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True,collect_pipeline_multi_outputs=False):
segments=[]
metadatas=[]
all_oos=[]
if isinstance(time_idxs, int):
time_idxs=[time_idxs]
for i in range(0,len(self.file_lists)):
vid_path = self.video_lists[i].keys()[idx]
video_obj = self.video_lists[i][vid_path]
safe_time_idxs=[safe_idx for safe_idx in time_idxs if safe_idx <len(video_obj)]
sliced_frame_list=video_obj[safe_time_idxs]
if self.io_pool is None:
processed_imgs=[]
pipeline_oos=[]
for frame in sliced_frame_list:
if collect_pipeline_multi_outputs:
processed_img,oo=apply_func_pipeline_masked_wrapped(menpo_grayscale_to_rgb(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
pipeline_oos.append(oo)
processed_imgs.append(processed_img)
else:
processed_img = apply_func_pipeline_masked(menpo_grayscale_to_rgb(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs.append(processed_img)
else:
if collect_pipeline_multi_outputs:
frame_proc = lambda frame: apply_func_pipeline_masked_wrapped(menpo_grayscale_to_rgb(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs_oos = self.io_pool.map(frame_proc,sliced_frame_list)
processed_imgs,pipeline_oos=zip(*processed_imgs_oos)
else:
frame_proc = lambda frame: apply_func_pipeline_masked(menpo_grayscale_to_rgb(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs = self.io_pool.map(frame_proc,sliced_frame_list)
metadata={'video_path':vid_path,'time_idxs':time_idxs}
#if(self.enable_caching and (mij_path in self.img_cache)):
#img = self.img_cache[mij_path];
##print(mij_path+'(using cache) ; ')
#elif(self.enable_caching and (not mij_path in self.img_cache)):
#img = self.imreader_callback(mij_path);
#self.img_cache[mij_path]=img
##print(mij_path + '(caching) ; ');
#else:
#img = self.imreader_callback(mij_path);
##print(mij_path+' ; ');
#pp_cache_key=stringman.sanitize_string(mij_path+str(pipeline_mask)+str(invert_pipeline_mask));
#if(self.enable_pp_caching and kv_haskey(pp_cache_key,self.img_pp_cache)):
#modified_img = self.img_pp_cache[pp_cache_key];
##print(mij_path+'(using img_pp_cache) ; ');
#elif(self.enable_pp_caching and not (pp_cache_key in self.img_pp_cache)):
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
#self.img_pp_cache = kv_set(pp_cache_key,modified_img,self.img_pp_cache);
##print([mij_path,'(caching to img_pp_cache) ; ']);
#else:
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
##print([mij_path,' ; ']);
segments.append(processed_imgs);
metadatas.append(metadata);
all_oos.append(pipeline_oos)
ret_val=(segments, metadatas)
if collect_pipeline_multi_outputs:
ret_val+=(all_oos,)
return ret_val
def start_fill(self,queuesize=10,pipeline_mask=[],invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
self.read_queue = Queue(maxsize=queuesize)
self.worker_threads_events=[]
self.worker_threads=[]
def structured_get_loop(queue_obj,event_obj):
while True:
print('fetching sequence')
data_obj,metadatas,data_obj_oos = self.get_random_segment_group(pipeline_mask, invert_pipeline_mask,min_length,max_length,alpha,beta)
queue_obj.put( {'data_obj':data_obj,
'data_paths':metadatas,
'data_obj_oos':data_obj_oos,
'metadatas':metadatas} )
event_is_set = event_obj.wait()
num_threads=1
for i in range(num_threads):
worker_event = threading.Event()
worker_event.set()
worker = threading.Thread(target=structured_get_loop, args=(self.read_queue,worker_event))
worker.setDaemon(True)
self.worker_threads.append(worker)
self.worker_threads_events.append(worker_event)
worker.start()
def get_random_segment_group_from_q(self,num_items=1):
return self.structured_gets_from_q(num_items)
def structured_gets_from_q(self,num_items=1):
items=[]
for i in range(0,num_items):
res=self.read_queue.get()
item_consumed_handle = self.read_queue.task_done
items.append((res,item_consumed_handle))
return items
def structured_get_from_q(self):
return self.structured_gets_from_q()
def pause_fill(self):
for event_obj in self.worker_threads_events:
event_obj.clear()
with self.read_queue.mutex:
self.read_queue.queue.clear()
def resume_fill(self):
for event_obj in self.worker_threads_events:
event_obj.set()
def gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx,time_idxs: self.get(idx, time_idxs,pipeline_mask, invert_pipeline_mask)
data_objs = io_pool.map(f,idxs,time_idxs_s)
return data_objs
def structured_gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx, time_idx: self.structured_get(idx, time_idxs, pipeline_mask, invert_pipeline_mask)
data_objs_tmp = io_pool.map(f,idxs,time_idxs_s)
data_obj_tmp,data_paths_tmp,data_obj_oos_tmp = [list(c) for c in zip(*data_objs_tmp)]
data_obj,data_paths,data_obj_oos=[list(x) for x in zip(*data_obj_tmp)],[list(x) for x in zip(*data_paths_tmp)],[list(x) for x in zip(*data_obj_oos_tmp)]
return (data_obj,data_paths,data_obj_oos)
def get_random_segment_group(self,pipeline_mask=[], invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
file_group_idx=random.randint(0,len(self.video_lists[0].keys())-1)
vid_paths=[vlist[file_group_idx] for vlist in self.file_lists]
vid_lengths=map(lambda vlist,path: len(vlist[path]), self.video_lists, vid_paths)
vid_length=min(vid_lengths)
start_idx=random.randint(0, vid_length-min_length)
segment_length=min(max_length,max(random.gammavariate(alpha,beta),min_length))*self.fps
end_idx=int(np.round(start_idx+segment_length))
time_idxs=range(start_idx,end_idx)
processed_segment = self.structured_get(file_group_idx,time_idxs,pipeline_mask,invert_pipeline_mask)
return processed_segment
def set_image_modifiers(self,modification_pipelines):
self.modification_pipelines=modification_pipelines;
def set_structured_image_modifiers(self,structured_modification_pipelines):
self.structured_modification_pipelines=structured_modification_pipelines;
def get_func_pipeline(self):
return copy.deepcopy(self.modification_pipelines);
def get_structured_func_pipeline(self):
return copy.deepcopy(self.structured_modification_pipelines)
def get_num_groups(self):
return len(self.file_lists);
def init_thread_pool(self):
self.io_pool=ThreadPool(nodes=4)
def close_thread_pool(self):
self.io_pool.close()
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2019 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/pathos/blob/master/LICENSE
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
if __name__ == '__main__':
from pathos.pools import ThreadPool as TPool
tpool = TPool()
print("Evaluate 10 items on 1 thread")
tpool.nthreads = 1
res3 = tpool.map(host, range(10))
print(tpool)
print('\n'.join(res3))
print('')
print("Evaluate 10 items on 2 threads")
tpool.nthreads = 2
res5 = tpool.map(host, range(10))
print(tpool)
print('\n'.join(res5))
print('')
# build from inner function
add_me = adder(5)
# build from lambda functions
squ = lambda x:x**2
if __name__ is '__main__':
from pathos.helpers import freeze_support
freeze_support()
from pathos.pools import ProcessPool as Pool
from pathos.pools import ThreadPool as TPool
pool = Pool()
tpool = TPool()
# test 'dilled' multiprocessing for inner
print("Evaluate 10 items on 2 proc:")
pool.ncpus = 2
print(pool)
print(pool.map(add_me, range(10)))
print('')
# test 'dilled' multiprocessing for lambda
print("Evaluate 10 items on 4 proc:")
pool.ncpus = 4
print(pool)
print(pool.map(squ, range(10)))
print('')
xp = np.arange(N * nodes, dtype=np.float64)[::-1]
print("Input: %s\n" % x)
# map sin_diff to the workers, then print to screen
print("Running serial python ...")
y = map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
if HAS_PYINA:
# map sin_diff to the workers, then print to screen
print("Running mpi4py on %d cores..." % nodes)
y = MpiPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running multiprocesing on %d processors..." % nodes)
y = ProcessPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running multiprocesing on %d threads..." % nodes)
y = ThreadPool(nodes).map(sins_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# map sin_diff to the workers, then print to screen
print("Running parallelpython on %d cpus..." % nodes)
y = ParallelPool(nodes).map(sin_diff, x, xp)
print("Output: %s\n" % np.asarray(y))
# EOF
print("Running serial python ...")
y = map(sin2, x)
print("Output: %s\n" % np.asarray(y))
if HAS_PYINA:
# map sin2 to the workers, then print to screen
print("Running mpi4py on %d cores..." % nodes)
y = MpiPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running multiprocesing on %d processors..." % nodes)
y = ProcessPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running multiprocesing on %d threads..." % nodes)
y = ThreadPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# map sin2 to the workers, then print to screen
print("Running parallelpython on %d cpus..." % nodes)
y = ParallelPool(nodes).map(sin2, x)
print("Output: %s\n" % np.asarray(y))
# EOF
def f(x, y):
return x * y
x = range(10)
y = range(5)
if __name__ == '__main__':
from pathos.helpers import freeze_support, shutdown
freeze_support()
from pathos.pools import ProcessPool, ThreadPool
amap = ProcessPool().amap
tmap = ThreadPool().map
print(amap(f, [h(x), h(x), h(x), h(x), h(x)], y).get())
def _f(m, g, x, y):
return sum(m(g, x)) * y
print(amap(_f, [tmap] * len(y), [g] * len(y), [x] * len(y), y).get())
from math import sin, cos
print(amap(tmap, [sin, cos], [x, x]).get())
shutdown()
# EOF
def __init__(self):
self.num_partitions = self.num_cores
self.pool = ThreadPool(self.num_cores)
def threadcompute(self, xs):
pool = ThreadPool(4)
results = pool.map(self.compute, xs)
return results
def orm_extract(args):
"""
Function for the ORMExtractParser
:param args: Namespace
:return: nothing
"""
# Load database
Base = databaseManage.WebsiteBase(args.database[0])
Base.create_tables()
if type(args.thread) is list:
args.thread = args.thread[0]
# Load data
URLs = list(importData.csv_to_list(args.path[0])[1].keys())
# ---------------------
# Filter the results already in database
# ---------------------
alreadyIn = []
for url in Base.session.query(Base.__getattribute__(args.table[0])).all():
alreadyIn.append(url.url)
for url in URLs:
if "http://" in url[:7]:
URLs[URLs.index(url)] = url[7:]
elif "https://" in url[:8]:
URLs[URLs.index(url)] = url[8:]
URLs = set(URLs)
for url in alreadyIn:
try:
URLs.remove(url)
except KeyError:
pass
logger.info("{} websites will be added to the database".format(len(URLs)))
itera = iter(URLs)
URLs = zip(*[itera] * args.thread)
# ---------------------
# Add to the database
# --------------------
dBase = databaseManage.NormalizationBase("DB/norm.db")
normDict = {}
for norm in dBase.session.query(dBase.Normalization).all():
normDict[norm.feature] = {"data": norm.data, "normalizer": norm.normalizer, "scaler": norm.scaler}
i = 1
for url in URLs:
logger.debug(str(i))
logger.info("Add : {}".format(url))
i += args.thread
# Create URL object
result1 = ThreadPool().map(Website.website, url)
result2 = []
tmp = []
for web in result1:
if web.html is None:
result2.append(web)
# result1.remove(web)
else:
tmp.append(web)
if args.extraction:
# Extract features
fct = partial(Website.website.features_extraction, normDict=normDict)
ThreadPool().map(fct, tmp)
result2 += tmp
for web in result2:
print(web)
# Add in database
Base.adding(web, args.table[0])
else:
for web in result1:
# Add in database
Base.adding(web, args.table[0])
if i % ((50 // args.thread) * args.thread) == 1 and i != 1:
# Get new identity with tor
with Controller.from_port(port=9051) as controller:
controller.authenticate()
controller.signal(Signal.NEWNYM)
class GroupedVideoGenerator3:
def __init__(self,
image_generators=[],
fps=None,
modification_pipelines=None,
structured_modification_pipelines=None,
use_menpo_type=False,
opts={}):
self.video_lists=[]
self.file_lists=[]
print('GroupedVideoGenerator3()')
for i in range(0,len(image_generators)):
if(isinstance(image_generators[i], VideoGenerator3)):
assert(isinstance(image_generators[i].videos, OrderedDict))
self.file_lists.append(image_generators[i].file_list);
self.video_lists.append(image_generators[i].videos)
fps=image_generators[i].fps
elif(isinstance(image_generators[i],list)):
video_getter_pool=ThreadPool(nodes=8)
tmp_mpio_obj_list=video_getter_pool.map(menpo_import_video_verbose, image_generators[i])
tmp_mpio_obj_list = [x for x in tmp_mpio_obj_list if x is not None]
safe_idxs = [idx for idx,x in enumerate(tmp_mpio_obj_list) if x is not None]
tmp_file_list=image_generators[i]
self.file_lists.append([tmp_file_list[safe_idx] for safe_idx in safe_idxs])
self.video_lists.append(tmp_mpio_obj_list)
else:
raise TypeError('You can only make a GroupedImageGenerator2 '+\
'from a list of string-lists or ImageGenerator2s')
self.file_lists[i] = [tmp for tmp in self.file_lists[i] if not tmp is None]
self.fps=fps
self.modification_pipelines= \
modification_pipelines if not modification_pipelines is None else {}
self.structured_modification_pipelines= \
structured_modification_pipelines if not structured_modification_pipelines is None else {}
self.io_pool = None
self.enable_caching = 0
self.enable_pp_caching = 0
self.img_pp_cache = {}
self.img_pp_oo_cache = {}
self.img_cache = {}
self.opts = opts
self.use_menpo_type = use_menpo_type
def copy3(self):
ret_val=GroupedVideoGenerator3([],
fps=self.fps,
modification_pipelines=self.modification_pipelines,
structured_modification_pipelines=self.structured_modification_pipelines,
use_menpo_type=self.use_menpo_type,
opts=self.opts)
ret_val.file_lists = copy.copy(self.file_lists)
ret_val.video_lists = copy.copy(self.video_lists)
return ret_val
def group_lists(self,get_key_callback):
new_file_lists=sbpy_utils.core.sets.group_by(self.file_lists,get_key_callback);
self.file_lists=new_file_lists;
reordered_video_lists=[]
old_video_lists=self.video_lists
for view_idx,file_list in enumerate(new_file_lists):
reordered_video_list=OrderedDict()
old_video_list=old_video_lists[view_idx]
for g in file_list:
reordered_video_list[g]=old_video_list[g]
def dynamic_func(self,key,*args):
fhandle = self.opts[key];
out = fhandle(*args);
return out
def get_sample_dims(self, pipeline_mask=[], invert_pipeline_mask=True):
vidgroup_obj=self.get(0,0,pipeline_mask, invert_pipeline_mask)
return vidgroup_obj.shape()
def structured_get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=True)
def get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=False)
def get_num_samples(self):
return len(self.file_lists[0]);
def _get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True,collect_pipeline_multi_outputs=False):
segments=[]
metadatas=[]
all_oos=[]
if isinstance(time_idxs, int):
time_idxs=[time_idxs]
img_type_converter = (lambda x:x) if self.use_menpo_type else menpo_to_rgb
for i in range(0,len(self.file_lists)):
vid_path = self.video_lists[i].keys()[idx]
video_obj = self.video_lists[i][vid_path]
safe_time_idxs=[safe_idx for safe_idx in time_idxs if safe_idx <len(video_obj)]
sliced_frame_list=video_obj[safe_time_idxs]
if self.io_pool is None:
processed_imgs=[]
pipeline_oos=[]
for frame in sliced_frame_list:
if collect_pipeline_multi_outputs:
processed_img,oo=apply_func_pipeline_masked_wrapped(img_type_converter(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
pipeline_oos.append(oo)
processed_imgs.append(processed_img)
else:
processed_img = apply_func_pipeline_masked(img_type_converter(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs.append(processed_img)
else:
if collect_pipeline_multi_outputs:
frame_proc = lambda frame: apply_func_pipeline_masked_wrapped(img_type_converter(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs_oos = self.io_pool.map(frame_proc,sliced_frame_list)
processed_imgs,pipeline_oos=zip(*processed_imgs_oos)
else:
frame_proc = lambda frame: apply_func_pipeline_masked(img_type_converter(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs = self.io_pool.map(frame_proc,sliced_frame_list)
#if(self.enable_caching and (mij_path in self.img_cache)):
#img = self.img_cache[mij_path];
##print(mij_path+'(using cache) ; ')
#elif(self.enable_caching and (not mij_path in self.img_cache)):
#img = self.imreader_callback(mij_path);
#self.img_cache[mij_path]=img
##print(mij_path + '(caching) ; ');
#else:
#img = self.imreader_callback(mij_path);
##print(mij_path+' ; ');
#pp_cache_key=stringman.sanitize_string(mij_path+str(pipeline_mask)+str(invert_pipeline_mask));
#if(self.enable_pp_caching and kv_haskey(pp_cache_key,self.img_pp_cache)):
#modified_img = self.img_pp_cache[pp_cache_key];
##print(mij_path+'(using img_pp_cache) ; ');
#elif(self.enable_pp_caching and not (pp_cache_key in self.img_pp_cache)):
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
#self.img_pp_cache = kv_set(pp_cache_key,modified_img,self.img_pp_cache);
##print([mij_path,'(caching to img_pp_cache) ; ']);
#else:
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
##print([mij_path,' ; ']);
if collect_pipeline_multi_outputs:
per_frame_extra_data=pipeline_oos
else:
per_frame_extra_data=None
new_vid_obj=Video(processed_imgs, video_path=vid_path, frame_idxs=safe_time_idxs, per_frame_extra_data=per_frame_extra_data)
segments.append(new_vid_obj);
return VideoGroup(segments)
def start_fill(self,queuesize=10,pipeline_mask=[],invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
self.read_queue = Queue(maxsize=queuesize)
self.worker_threads_events=[]
self.worker_threads=[]
def structured_get_loop(queue_obj,event_obj):
while True:
grouped_video_lists = self.get_random_segment_group(pipeline_mask, invert_pipeline_mask,min_length,max_length,alpha,beta)
queue_obj.put( grouped_video_lists )
event_is_set = event_obj.wait()
num_threads=1
for i in range(num_threads):
worker_event = threading.Event()
worker_event.set()
worker = threading.Thread(target=structured_get_loop, args=(self.read_queue,worker_event))
worker.setDaemon(True)
self.worker_threads.append(worker)
self.worker_threads_events.append(worker_event)
worker.start()
def get_random_segment_group_from_q(self,num_items=1):
return self.structured_gets_from_q(num_items)
def structured_gets_from_q(self,num_items=1):
video_groups=[]
for i in range(0,num_items):
video_group=self.read_queue.get()
video_groups.append(video_group)
return (VideoGroupList(video_groups).as_GroupedVideoLists(),[self.read_queue.task_done]*num_items)
def structured_get_from_q(self):
return self.structured_gets_from_q()
def pause_fill(self):
for event_obj in self.worker_threads_events:
event_obj.clear()
with self.read_queue.mutex:
self.read_queue.queue.clear()
def resume_fill(self):
for event_obj in self.worker_threads_events:
event_obj.set()
def gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx,time_idxs: self.get(idx, time_idxs,pipeline_mask, invert_pipeline_mask)
data_objs = io_pool.map(f,idxs,time_idxs_s)
return data_objs
def structured_gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx, time_idx: self.structured_get(idx, time_idxs, pipeline_mask, invert_pipeline_mask)
video_group_list = io_pool.map(f,idxs,time_idxs_s)
return video_group_list.as_GroupedVideoLists()
def get_random_segment_group(self,pipeline_mask=[], invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
file_group_idx=random.randint(0,len(self.video_lists[0].keys())-1)
vid_paths=[vlist[file_group_idx] for vlist in self.file_lists]
vid_lengths=map(lambda vlist,path: len(vlist[path]), self.video_lists, vid_paths)
vid_length=min(vid_lengths)
start_idx=random.randint(0, vid_length-min_length)
segment_length=min(max_length,max(random.gammavariate(alpha,beta),min_length))*self.fps
end_idx=int(np.round(start_idx+segment_length))
time_idxs=range(start_idx,end_idx)
#print('intended_segment length: '+str(len(time_idxs)))
processed_segment = self.structured_get(file_group_idx,time_idxs,pipeline_mask,invert_pipeline_mask)
print('processed_segment length: '+str(len(processed_segment)))
return processed_segment
def set_image_modifiers(self,modification_pipelines):
self.modification_pipelines=modification_pipelines;
def set_structured_image_modifiers(self,structured_modification_pipelines):
self.structured_modification_pipelines=structured_modification_pipelines;
def get_func_pipeline(self):
return copy.deepcopy(self.modification_pipelines);
def get_structured_func_pipeline(self):
return copy.deepcopy(self.structured_modification_pipelines)
def get_num_groups(self):
return len(self.file_lists);
def init_thread_pool(self):
self.io_pool=ThreadPool(nodes=4)
def close_thread_pool(self):
self.io_pool.close()
def threadcompute(self, xs):
pool = ThreadPool(4)
results = pool.map(self.compute, xs)
return results
# build from inner function
add_me = adder(5)
# build from lambda functions
squ = lambda x: x**2
if __name__ == '__main__':
from pathos.helpers import freeze_support, shutdown
freeze_support()
from pathos.pools import ProcessPool as Pool
from pathos.pools import ThreadPool as TPool
pool = Pool()
tpool = TPool()
# test 'dilled' multiprocessing for inner
print("Evaluate 10 items on 2 proc:")
pool.ncpus = 2
print(pool)
print(pool.map(add_me, range(10)))
print('')
# test 'dilled' multiprocessing for lambda
print("Evaluate 10 items on 4 proc:")
pool.ncpus = 4
print(pool)
print(pool.map(squ, range(10)))
print('')
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2015 California Institute of Technology.
# License: 3-clause BSD. The full license text is available at:
# - http://trac.mystic.cacr.caltech.edu/project/pathos/browser/pathos/LICENSE
def host(id):
import socket
return "Rank: %d -- %s" % (id, socket.gethostname())
if __name__ == '__main__':
from pathos.pools import ThreadPool as TPool
tpool = TPool()
print "Evaluate 10 items on 1 thread"
tpool.nthreads = 1
res3 = tpool.map(host, range(10))
print tpool
print '\n'.join(res3)
print ''
print "Evaluate 10 items on 2 threads"
tpool.nthreads = 2
res5 = tpool.map(host, range(10))
print tpool
print '\n'.join(res5)
print ''
print "Evaluate 10 items on ? threads"
def df_apply(df, f, pool=None, n_cpus=None, return_df=True):
"""Apply the function `f` to each row in `df` in a parallel fashion.
"""
if pool is None:
if n_cpus is None:
n_cpus = cpu_count()
pool = ThreadPool(n_cpus)
class RecordProxy:
"""A proxy object to wrap a `DataFrame.iat[row_i, col_i]` access model and
provide a dictionary style interface.
"""
__df = df
__field_names = list(df.columns)
@classmethod
def _field_i(cls, name):
try:
return cls.__field_names.index(name)
except ValueError as e:
raise KeyError(
f"key '{name}' not found on record. Available keys are: {cls.__field_names}"
)
@classmethod
def wrap_map_func(cls, f):
"""Wraps the given function to be passed to a map() style function.
Returns a function that expects to be called with an index value and it will call
the given function passing it an object with a python dictionary style interface to the row.
"""
return lambda row_i: f(cls(row_i))
@property
def index(self):
return self.__row_i
def __init__(self, row_i):
self.__row_i = row_i
def __getitem__(self, key):
i = self._field_i(key)
return self.__df.iat[self.__row_i, i]
def __setitem__(self, key, value):
i = self._field_i(key)
self.__df.iat[self.__row_i, i] = value
def get(self, key, value=None):
try:
i = self._field_i(key)
return self.__df.iat[self.__row_i, i]
except KeyError:
return value
def __str__(self):
parts = ["Record({"]
fields_repr = []
for field_name in self.__field_names:
field_repr = self.__getitem__(field_name).__repr__()
fields_repr.append(f"'{field_name}': {field_repr}")
parts.extend(",".join(fields_repr))
parts.append("})")
return "".join(parts)
def dict(self, keys=None):
if keys is None:
keys = self.__field_names
return {
key: self.__df.iat[self.__row_i, i]
for i, key in enumerate(self.__field_names) if key in keys
}
def __iter__(self):
return (self.__df.iat[self.__row_i, i]
for i in range(len(self.__field_names)))
results = pool.map(RecordProxy.wrap_map_func(f), range(df.shape[0]))
if return_df:
return df
else:
return results
