def __init__(self, global_conf, prefix=default_extr_check_prefix, pid=None):
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Need to be build from extraction type and detection input + "_processed"
self.extr_family_column = "ext"
tmp_extr_family_column = self.get_param("extr_family_column")
if tmp_extr_family_column:
self.extr_family_column = tmp_extr_family_column
# Max delay
self.max_delay = 3600
# self.max_delay = 600
max_delay = self.get_param("max_delay")
if max_delay:
self.max_delay = int(max_delay)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
# Beware, the self.extr_family_column should be added to the indexer families parameter in get_create_table...
# TODO: should we add the 'ad' column family too here by default
self.tablesha1_col_families = {'info': dict(), self.extr_family_column: dict()}
self.list_extr_prefix = [self.featurizer_type, "feat", self.detector_type, self.input_type]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# Need to be build from extraction type and extraction input + "_batchid"
self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer and ingester
self.indexer = HBaseIndexerMinimal(self.global_conf,
prefix=self.get_required_param("indexer_prefix"))
self.ingester = GenericKafkaProcessor(self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
# This will not be set for HBase processing, but checker would keep dying here...
try:
self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
except Exception as inst:
print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
time.sleep(self.max_delay)
raise(inst)
self.ingester.pp = "ec"
if self.pid:
self.ingester.pp += str(self.pid)
def __init__(self, global_conf, prefix=default_extr_proc_prefix):
self.extractor = None
self.nb_empt = 0
self.nb_err = 0
self.max_proc_time = 1200 # in seconds. Increased for sbcmdline...
self.url_input = True
super(ExtractionProcessor, self).__init__(global_conf, prefix)
self.input_type = self.get_required_param("input_type")
self.nb_threads = self.get_required_param("nb_threads")
self.featurizer_type = self.get_required_param("featurizer_type")
self.featurizer_prefix = self.get_required_param("featurizer_prefix")
self.detector_type = self.get_required_param("detector_type")
# Means we extract feature from the whole image
if self.detector_type == "full":
self.detector = None
self.verbose = 0
verbose = self.get_param("verbose")
if verbose:
self.verbose = int(verbose)
self.ingestion_input = "kafka"
ingestion_input = self.get_param("ingestion_input")
if ingestion_input:
self.ingestion_input = ingestion_input
file_input = self.get_param("file_input")
print("[{}.ExtractionProcessor: log] file_input: {}".format(self.pp, file_input))
if file_input:
self.url_input = False
print("[{}.ExtractionProcessor: log] url_input: {}".format(self.pp, self.url_input))
if self.url_input:
self.img_column = img_URL_column
else:
self.img_column = img_path_column
print("[{}.ExtractionProcessor: log] img_column: {}".format(self.pp, self.img_column))
# Need to be build from extraction type and detection input + "_processed"
self.extr_family_column = "ext"
tmp_extr_family_column = self.get_param("extr_family_column")
if tmp_extr_family_column:
self.extr_family_column = tmp_extr_family_column
self.push_back = False
push_back = self.get_param("push_back")
if push_back:
self.push_back = True
self.extr_prefix = build_extr_str(self.featurizer_type, self.detector_type, self.input_type)
self.set_pp()
# Initialize queues
self.init_queues()
# Initialize extractors only once (just one first)
self.extractors = []
#for i in range(self.nb_threads):
# self.extractors.append(GenericExtractor(self.detector_type, self.featurizer_type, self.input_type,
# self.extr_family_column, self.featurizer_prefix, self.global_conf))
self.extractors.append(GenericExtractor(self.detector_type, self.featurizer_type,
self.input_type, self.extr_family_column,
self.featurizer_prefix, self.global_conf))
# Beware, the self.extr_family_column should be added to the indexer families parameter in get_create_table...
# What if the table has some other column families?...
self.tablesha1_col_families = {'info': dict(), self.extr_family_column: dict()}
# Initialize indexer
self.indexer = HBaseIndexerMinimal(self.global_conf, prefix=self.get_required_param("indexer_prefix"))
self.last_update_date_id = ''
# Initialize ingester
self.ingester = GenericKafkaProcessor(self.global_conf, prefix=self.get_required_param("proc_ingester_prefix"))
self.ingester.pp = "ep"
class ExtractionProcessor(ConfReader):
def __init__(self, global_conf, prefix=default_extr_proc_prefix):
self.extractor = None
self.nb_empt = 0
self.nb_err = 0
self.max_proc_time = 1200 # in seconds. Increased for sbcmdline...
self.url_input = True
super(ExtractionProcessor, self).__init__(global_conf, prefix)
self.input_type = self.get_required_param("input_type")
self.nb_threads = self.get_required_param("nb_threads")
self.featurizer_type = self.get_required_param("featurizer_type")
self.featurizer_prefix = self.get_required_param("featurizer_prefix")
self.detector_type = self.get_required_param("detector_type")
# Means we extract feature from the whole image
if self.detector_type == "full":
self.detector = None
self.verbose = 0
verbose = self.get_param("verbose")
if verbose:
self.verbose = int(verbose)
self.ingestion_input = "kafka"
ingestion_input = self.get_param("ingestion_input")
if ingestion_input:
self.ingestion_input = ingestion_input
file_input = self.get_param("file_input")
print("[{}.ExtractionProcessor: log] file_input: {}".format(self.pp, file_input))
if file_input:
self.url_input = False
print("[{}.ExtractionProcessor: log] url_input: {}".format(self.pp, self.url_input))
if self.url_input:
self.img_column = img_URL_column
else:
self.img_column = img_path_column
print("[{}.ExtractionProcessor: log] img_column: {}".format(self.pp, self.img_column))
# Need to be build from extraction type and detection input + "_processed"
self.extr_family_column = "ext"
tmp_extr_family_column = self.get_param("extr_family_column")
if tmp_extr_family_column:
self.extr_family_column = tmp_extr_family_column
self.push_back = False
push_back = self.get_param("push_back")
if push_back:
self.push_back = True
self.extr_prefix = build_extr_str(self.featurizer_type, self.detector_type, self.input_type)
self.set_pp()
# Initialize queues
self.init_queues()
# Initialize extractors only once (just one first)
self.extractors = []
#for i in range(self.nb_threads):
# self.extractors.append(GenericExtractor(self.detector_type, self.featurizer_type, self.input_type,
# self.extr_family_column, self.featurizer_prefix, self.global_conf))
self.extractors.append(GenericExtractor(self.detector_type, self.featurizer_type,
self.input_type, self.extr_family_column,
self.featurizer_prefix, self.global_conf))
# Beware, the self.extr_family_column should be added to the indexer families parameter in get_create_table...
# What if the table has some other column families?...
self.tablesha1_col_families = {'info': dict(), self.extr_family_column: dict()}
# Initialize indexer
self.indexer = HBaseIndexerMinimal(self.global_conf, prefix=self.get_required_param("indexer_prefix"))
self.last_update_date_id = ''
# Initialize ingester
self.ingester = GenericKafkaProcessor(self.global_conf, prefix=self.get_required_param("proc_ingester_prefix"))
self.ingester.pp = "ep"
def set_pp(self):
self.pp = "ExtractionProcessor"
if self.extractor:
self.pp += "_"+self.extr_prefix
def init_queues(self):
from multiprocessing import JoinableQueue
self.q_in = []
self.q_out = []
for i in range(self.nb_threads):
self.q_in.append(JoinableQueue(0))
self.q_out.append(JoinableQueue(0))
def get_batch_hbase(self):
# legacy implementation: better to have a kafka topic for batches to be processed to allow
# safe parallelization on different machines
try:
for updates in self.indexer.get_unprocessed_updates_from_date(self.last_update_date_id,
extr_type=self.extr_prefix):
for update_id, update_cols in updates:
if self.extr_prefix in update_id:
# double check update has not been processed somewhere else
if self.is_update_unprocessed(update_id):
# double check update was not marked as started recently i.e. by another process
if self.is_update_notstarted(update_id, max_delay=TIME_ELAPSED_FAILED):
list_sha1s = update_cols[column_list_sha1s].split(',')
log_msg = "[{}.get_batch_hbase: log] Update {} has {} images."
print(log_msg.format(self.pp, update_id, len(list_sha1s)))
# also get 'ext:' to check if extraction was already processed?
rows_batch = self.indexer.get_columns_from_sha1_rows(list_sha1s,
columns=[img_buffer_column,
self.img_column])
# print "rows_batch", rows_batch
if rows_batch:
yield rows_batch, update_id
self.last_update_date_id = '_'.join(update_id.split('_')[-2:])
else:
log_msg = "[{}.get_batch_hbase: log] Did not get any image buffer for update: {}"
print(log_msg.format(self.pp, update_id))
else:
log_msg = "[{}.get_batch_hbase: log] Skipping update started recently: {}"
print(log_msg.format(self.pp, update_id))
continue
else:
log_msg = "[{}.get_batch_hbase: log] Skipping already processed update: {}"
print(log_msg.format(self.pp, update_id))
continue
else:
if self.verbose > 6:
log_msg = "[{}.get_batch_hbase: log] Skipping update {} from another extraction type."
print(log_msg.format(self.pp, update_id))
else:
print("[{}.get_batch_hbase: log] No unprocessed update found.".format(self.pp))
# Look for updates that have some unprocessed images
# TODO: wether we do that or not could be specified by a parameter
# as this induces slow down during update...
for updates in self.indexer.get_missing_extr_updates_from_date("1970-01-01",
extr_type=self.extr_prefix):
for update_id, update_cols in updates:
if self.extr_prefix in update_id:
if column_list_sha1s in update_cols:
list_sha1s = update_cols[column_list_sha1s].split(',')
log_msg = "[{}.get_batch_hbase: log] Update {} has {} images missing extractions."
print(log_msg.format(self.pp, update_id, len(list_sha1s)))
sys.stdout.flush()
# also get 'ext:' to check if extraction was already processed?
rows_batch = self.indexer.get_columns_from_sha1_rows(list_sha1s,
columns=[img_buffer_column,
self.img_column])
if rows_batch:
yield rows_batch, update_id
else:
log_msg = "[{}.get_batch_hbase: log] Did not get any image buffer for update: {}"
print(log_msg.format(self.pp, update_id))
else:
log_msg = "[{}.get_batch_hbase: log] Update {} has no images list."
print(log_msg.format(self.pp, update_id))
else:
log_msg = "[{}.get_batch_hbase: log] Skipping update {} from another extraction type."
print(log_msg.format(self.pp, update_id))
else:
log_msg = "[{}.get_batch_hbase: log] No updates with missing extractions found."
print(log_msg.format(self.pp))
sys.stdout.flush()
except Exception as inst:
full_trace_error("[{}.get_batch_hbase: error] {}".format(self.pp, inst))
def is_update_unprocessed(self, update_id):
update_rows = self.indexer.get_rows_by_batch([update_id],
table_name=self.indexer.table_updateinfos_name)
if update_rows:
for row in update_rows:
if info_column_family+":"+update_str_processed in row[1]:
return False
return True
def is_update_notstarted(self, update_id, max_delay=None):
"""Check if an update was not started yet.
:param update_id: update id
:param max_delay: delay (in seconds) between marked start time and now to consider update failed
:return: boolean
"""
update_rows = self.indexer.get_rows_by_batch([update_id],
table_name=self.indexer.table_updateinfos_name)
if update_rows:
for row in update_rows:
if info_column_family+":"+update_str_started in row[1]:
if max_delay:
# check that started was mark recently, if not it may mean the update processing failed
start_str = row[1][info_column_family+":"+update_str_started]
# start time format is '%Y-%m-%d:%H.%M.%S'
start_dt = datetime.strptime(start_str, '%Y-%m-%d:%H.%M.%S')
now_dt = datetime.now()
diff_dt = now_dt - start_dt
if diff_dt.total_seconds() > max_delay:
return True
else:
return False
else:
return False
return True
def get_batch_kafka(self):
# Read from a kafka topic to allow safer parallelization on different machines
try:
# Needs to read topic to get update_id and list of sha1s
for msg in self.ingester.consumer:
msg_dict = json.loads(msg.value)
update_id = msg_dict.keys()[0]
# NB: Try to get update info and check it was really not processed yet.
if self.is_update_unprocessed(update_id):
str_list_sha1s = msg_dict[update_id]
list_sha1s = str_list_sha1s.split(',')
print("[{}.get_batch_kafka: log] Update {} has {} images.".format(self.pp, update_id, len(list_sha1s)))
# NB: we could also get 'ext:' of images to double check if extraction was already processed
#rows_batch = self.indexer.get_columns_from_sha1_rows(list_sha1s, columns=["info:img_buffer"])
if self.verbose > 3:
print("[{}.get_batch_kafka: log] Looking for colums: {}".format(self.pp, [img_buffer_column, self.img_column]))
rows_batch = self.indexer.get_columns_from_sha1_rows(list_sha1s, columns=[img_buffer_column, self.img_column])
#print "rows_batch", rows_batch
if rows_batch:
if self.verbose > 4:
print("[{}.get_batch_kafka: log] Yielding for update: {}".format(self.pp, update_id))
yield rows_batch, update_id
self.ingester.consumer.commit()
if self.verbose > 4:
print("[{}.get_batch_kafka: log] After yielding for update: {}".format(self.pp, update_id))
self.last_update_date_id = '_'.join(update_id.split('_')[-2:])
# Should we try to commit offset only at this point?
else:
print("[{}.get_batch_kafka: log] Did not get any image buffers for the update: {}".format(self.pp, update_id))
else:
print("[{}.get_batch_kafka: log] Skipping already processed update: {}".format(self.pp, update_id))
else:
print("[{}.get_batch_kafka: log] No update found.".format(self.pp))
# Fall back to checking HBase for unstarted/unfinished updates
for rows_batch, update_id in self.get_batch_hbase():
yield rows_batch, update_id
except Exception as inst:
full_trace_error("[{}.get_batch_kafka: error] {}".format(self.pp, inst))
def get_batch(self):
if self.ingestion_input == "hbase":
for rows_batch, update_id in self.get_batch_hbase():
yield rows_batch, update_id
else:
for rows_batch, update_id in self.get_batch_kafka():
yield rows_batch, update_id
def process_batch(self):
# Get a new update batch
for rows_batch, update_id in self.get_batch():
try:
start_update = time.time()
print("[{}] Processing update {} of {} rows.".format(self.pp, update_id, len(rows_batch)))
sys.stdout.flush()
# Initialize
self.nb_empt = 0
self.init_queues()
threads = []
# If we deleted an extractor at some point or for first batch
nb_extr_to_create = self.nb_threads - len(self.extractors)
if nb_extr_to_create:
start_create_extractor = time.time()
while len(self.extractors) < self.nb_threads:
self.extractors.append(GenericExtractor(self.detector_type, self.featurizer_type, self.input_type,
self.extr_family_column, self.featurizer_prefix,
self.global_conf))
buff_msg = "[{}] Created {} extractors in {}s."
create_extr_time = time.time() - start_create_extractor
print(buff_msg.format(self.pp, nb_extr_to_create, create_extr_time))
# Mark batch as started to be process
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
dict_val = {info_column_family + ':' + update_str_started: now_str}
update_started_dict = {update_id: dict_val}
self.indexer.push_dict_rows(dict_rows=update_started_dict,
table_name=self.indexer.table_updateinfos_name)
# Push images to queue
list_in = []
# For parallelized downloading...
from Queue import Queue
nb_imgs_dl = 0
q_in_dl = Queue(0)
q_out_dl = Queue(0)
start_get_buffer = time.time()
for img in rows_batch:
# should decode base64
if img_buffer_column in img[1]:
tup = (img[0], img[1][img_buffer_column], False)
list_in.append(tup)
else:
# need to re-download, accumulate a list of URLs to download
# Deal with img_path_column for local_images_kafka_pusher
if self.img_column in img[1]:
q_in_dl.put((img[0], img[1][self.img_column], self.push_back))
nb_imgs_dl += 1
else:
print("[{}: warning] No buffer and no URL/path for image {} !".format(self.pp, img[0]))
continue
# Download missing images
if nb_imgs_dl > 0:
threads_dl = []
for i in range(min(self.nb_threads, nb_imgs_dl)):
# should read (url, obj_pos) from self.q_in
# and push (url, obj_pos, buffer, img_info, start_process, end_process) to self.q_out
thread = ThreadedDownloaderBufferOnly(q_in_dl, q_out_dl, url_input=self.url_input)
thread.start()
threads_dl.append(thread)
q_in_dl.join()
# Push downloaded images to list_in too
nb_dl = 0
while nb_dl < nb_imgs_dl:
# This can block?
#sha1, buffer, push_back, inst = q_out_dl.get()
try:
sha1, buffer, push_back, inst = q_out_dl.get(True, 10)
except Exception as queue_err:
warn_msg = "[{}: error] Download queue out timed out: {}"
print(warn_msg.format(self.pp, queue_err))
break
nb_dl += 1
if inst:
if self.verbose > 0:
log_msg = "[{}: log] Could not download image {}, error was: {}"
print(log_msg.format(self.pp, sha1, inst))
else:
if buffer:
list_in.append((sha1, buffer, push_back))
else:
# Is that possible?
err_msg = "[{}: error] No error but no buffer either for image {}"
print(err_msg.format(self.pp, sha1))
get_buffer_time = time.time() - start_get_buffer
buff_msg = "[{}] Got {}/{} image buffers for update {} in {}s."
print(buff_msg.format(self.pp, len(list_in), len(rows_batch), update_id, get_buffer_time))
sys.stdout.flush()
q_batch_size = int(math.ceil(float(len(list_in))/self.nb_threads))
for i, q_batch in enumerate(build_batch(list_in, q_batch_size)):
self.q_in[i].put(q_batch)
q_in_size = []
q_in_size_tot = 0
for i in range(self.nb_threads):
q_in_size.append(self.q_in[i].qsize())
q_in_size_tot += q_in_size[i]
if self.verbose > 5:
print("[{}] Total input queues sizes is: {}".format(self.pp, q_in_size_tot))
# Start daemons...
thread_creation_failed = [0]*self.nb_threads
for i in range(self.nb_threads):
# one per non empty input queue
if q_in_size[i] > 0:
try:
thread = DaemonBatchExtractor(self.extractors[i], self.q_in[i], self.q_out[i], verbose=self.verbose)
# Could get a 'Cannot allocate memory' if we are using too many threads...
thread.start()
threads.append(thread)
except OSError as inst:
# Should we try to push self.q_in[i] data to some other thread?
print("[{}.process_batch: error] Could not start thread #{}: {}".format(self.pp, i+1, inst))
thread_creation_failed[i] = 1
time.sleep(10*sum(thread_creation_failed))
if sum(thread_creation_failed) == self.nb_threads:
raise ValueError("Could not start any thread...")
nb_threads_running = len(threads)
start_process = time.time()
stop = time.time() + self.max_proc_time
# Wait for all tasks to be marked as done
threads_finished = [0] * nb_threads_running
deleted_extr = [0] * nb_threads_running
thread_msg = "[{}] Thread {}/{} (pid: {}) "
while sum(threads_finished) < nb_threads_running:
for i in range(nb_threads_running):
if sum(threads_finished) == nb_threads_running:
sys.stdout.flush()
break
if threads_finished[i] == 1:
continue
i_q_in = i + sum(thread_creation_failed[:i + 1])
if q_in_size[i_q_in] > 0:
# This seems to block forever sometimes, if subprocess crashed?...
#self.q_in[i].join()
# Manual join with timeout...
# https://github.com/python/cpython/blob/3.6/Lib/multiprocessing/queues.py
if not self.q_in[i_q_in]._unfinished_tasks._semlock._is_zero() and time.time() < stop:
time.sleep(1)
else:
if self.q_in[i_q_in]._unfinished_tasks._semlock._is_zero():
if self.verbose > 5:
msg = thread_msg+"marked as finished because processing seems finished"
print(msg.format(self.pp, i+1, nb_threads_running, threads[i].pid))
else:
if self.verbose > 0:
# In this cases does this happen...
msg = thread_msg+"force marked task as done as max_proc_time ({}) has passed."
print(msg.format(self.pp, i+1, nb_threads_running, threads[i].pid,
self.max_proc_time))
sys.stdout.flush()
# Try to delete corresponding extractor to free memory?
# And reduce number of threads at the end of the loop
try:
self.q_in[i_q_in].task_done()
if deleted_extr[i] == 0:
# we pushed the extractor as self.extractors[i] in a loop of self.nb_threads
# we use i_q_in
del self.extractors[i_q_in]
deleted_extr[i] = 1
except Exception:
pass
threads_finished[i] = 1
else:
if self.verbose > 2:
# We actually never gave something to process...
msg = thread_msg+"marked as finished because no data was passed to it"
print(msg.format(self.pp, i+1, nb_threads_running, threads[i].pid))
threads_finished[i] = 1
# Cleanup threads to free memory before getting data back
# Daemon may still be running...
# and will actually be deleted only when they exit after not getting a batch
del threads
# Gather results
q_out_size = []
q_out_size_tot = 0
for i in range(self.nb_threads):
q_out_size.append(self.q_out[i].qsize())
q_out_size_tot += q_out_size[i]
if self.verbose > 5:
print("[{}: log] Total output queues size is: {}".format(self.pp, q_out_size_tot))
sys.stdout.flush()
# Can get stuck here?
dict_imgs = dict()
for i in range(self.nb_threads):
if self.verbose > 4:
print("[{}] Thread {} q_out_size: {}".format(self.pp, i+1, q_out_size[i]))
sys.stdout.flush()
while q_out_size[i]>0 and not self.q_out[i].empty():
if self.verbose > 6:
print("[{}] Thread {} q_out is not empty.".format(self.pp, i + 1))
sys.stdout.flush()
try:
batch_out = self.q_out[i].get(True, 10)
if self.verbose > 4:
msg = "[{}] Got batch of {} features from thread {} q_out."
print(msg.format(self.pp, len(batch_out), i + 1))
sys.stdout.flush()
for sha1, dict_out in batch_out:
dict_imgs[sha1] = dict_out
except:
if self.verbose > 1:
print("[{}] Thread {} failed to get from q_out: {}".format(self.pp, i+1))
sys.stdout.flush()
pass
if self.verbose > 4:
print("[{}] Marking task done in q_out of thread {}.".format(self.pp, i + 1))
sys.stdout.flush()
self.q_out[i].task_done()
#if self.verbose > 0:
print_msg = "[{}] Got features for {}/{} images in {}s."
proc_time = time.time() - start_process
print(print_msg.format(self.pp, len(dict_imgs.keys()), len(list_in), proc_time))
sys.stdout.flush()
# Push them
self.indexer.push_dict_rows(dict_rows=dict_imgs, table_name=self.indexer.table_sha1infos_name)
# Mark batch as processed
update_processed_dict = {update_id: {info_column_family + ':' + update_str_processed: datetime.now().strftime('%Y-%m-%d:%H.%M.%S')}}
self.indexer.push_dict_rows(dict_rows=update_processed_dict, table_name=self.indexer.table_updateinfos_name)
# Mark as completed if all rows had an extraction
if len(rows_batch) == len(dict_imgs.keys()):
update_completed_dict = {update_id: {info_column_family + ':' + update_str_completed: str(1)}}
self.indexer.push_dict_rows(dict_rows=update_completed_dict,
table_name=self.indexer.table_updateinfos_name)
# Cleanup
del self.q_in
del self.q_out
# To try to adjust a too optimistic nb_threads setting
# if (sum(thread_creation_failed) > 0 or sum(deleted_extr) > 0) and self.nb_threads > 2:
# self.nb_threads -= 1
print_msg = "[{}] Completed update {} in {}s."
print(print_msg.format(self.pp, update_id, time.time() - start_update))
sys.stdout.flush()
self.nb_err = 0
# Force garbage collection?
gc.collect()
# Should we just raise an Exception and restart clean?
if sum(thread_creation_failed) > 0 or sum(deleted_extr) > 0:
raise ValueError("Something went wrong. Trying to restart clean")
except Exception as inst:
exc_type, exc_obj, exc_tb = sys.exc_info()
fulltb = traceback.format_tb(exc_tb)
raise type(inst)(" {} ({})".format(inst, ''.join(fulltb)))
def run(self):
self.nb_empt = 0
self.nb_err = 0
while True:
self.process_batch()
print("[ExtractionProcessor: log] Nothing to process at: {}".format(datetime.now().strftime('%Y-%m-%d:%H.%M.%S')))
sys.stdout.flush()
time.sleep(10*self.nb_empt)
self.nb_empt += 1
"/home/ubuntu/caffe_cpu/build/tools/extract_nfeatures",
"HBI_host": "10.1.94.57",
"HBI_table_sha1infos": "escorts_images_sha1_infos_from_ts"
}
pyconf = {
"SBPYCAFFEIMGFEAT_sbcaffe_path":
"./data/caffe_sentibank_train_iter_250000",
"SBPYCAFFEIMGFEAT_imgmean_path": "./data/imagenet_mean.npy",
}
diffs = []
rows = []
if list_sha1s[0]:
hbi = HBaseIndexerMinimal(conf, prefix="HBI_")
rows = hbi.get_columns_from_sha1_rows(
list_sha1s, columns=["info:featnorm_cu", "info:s3_url"])
sbclif = SentiBankCmdLineImgFeaturizer(conf)
sbpcif = SentiBankPyCaffeImgFeaturizer(pyconf)
for row in rows:
feat_hbase_b64 = featB64decode(row[1]["info:featnorm_cu"])
#print feat_hbase_b64.shape
img_url = row[1]["info:s3_url"]
start_extr = time.time()
img_buffer = get_buffer_from_URL(img_url)
feat, data = sbclif.featurize(img_buffer, sha1=row[0])
img_buffer.seek(0)
pydata = sbpcif.preprocess_img(img_buffer)
fpydata = pydata.flatten()
def __init__(self, global_conf, prefix=DEFAULT_EXTR_PROC_PREFIX):
"""ExtractionProcessor constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
"""
self.extractor = None
self.nb_empt = 0
self.nb_err = 0
self.max_proc_time = 1200 # in seconds. Increased for sbcmdline...
self.url_input = True
super(ExtractionProcessor, self).__init__(global_conf, prefix)
# TODO: move that to self.read_conf()
# Get required parameters
self.input_type = self.get_required_param("input_type")
self.nb_threads = self.get_required_param("nb_threads")
self.featurizer_type = self.get_required_param("featurizer_type")
self.featurizer_prefix = self.get_required_param("featurizer_prefix")
self.detector_type = self.get_required_param("detector_type")
# Get optional parameters
self.verbose = int(self.get_param("verbose", default=0))
self.ingestion_input = self.get_param("ingestion_input",
default="kafka")
self.push_back = self.get_param("push_back", default=False)
file_input = self.get_param("file_input")
print("[{}.ExtractionProcessor: log] file_input: {}".format(
self.pp, file_input))
if file_input:
self.url_input = False
print("[{}.ExtractionProcessor: log] url_input: {}".format(
self.pp, self.url_input))
# Means we extract feature from the whole image
if self.detector_type == "full":
self.detector = None
self.extr_prefix = build_extr_str(self.featurizer_type,
self.detector_type, self.input_type)
self.set_pp()
# Initialize queues
self.init_queues()
# Initialize indexer
# We now have two indexers:
# - one "in_indexer" for TF table with buffer, img URLs etc...
# - one "out_indexer" for our table with extractions etc
# NB: they could be the same if tables are merged...
self.out_indexer = HBaseIndexerMinimal(
self.global_conf, prefix=self.get_required_param("indexer_prefix"))
prefix_in_indexer = self.get_param("in_indexer_prefix", default=False)
if prefix_in_indexer:
self.in_indexer = HBaseIndexerMinimal(self.global_conf,
prefix=prefix_in_indexer)
insha1tab = self.in_indexer.table_sha1infos_name
insha1cfs = self.in_indexer.get_dictcf_sha1_table()
print("[{}] 'in_indexer' sha1 table {} columns are: {}".format(
self.pp, insha1tab, insha1cfs))
else:
print(
"[{}] empty 'in_indexer_prefix', using out_indexer as in_indexer too."
.format(self.pp))
self.in_indexer = self.out_indexer
# Initialize extractors only once (just one first)
self.extractors = []
# DONE: use 'out_indexer'
self.extractors.append(
GenericExtractor(self.detector_type, self.featurizer_type,
self.input_type, self.out_indexer.extrcf,
self.featurizer_prefix, self.global_conf))
# DONE: use 'in_indexer'
if self.url_input:
self.img_column = self.in_indexer.get_col_imgurl()
else:
self.img_column = self.in_indexer.get_col_imgpath()
img_cols = [
self.in_indexer.get_col_imgbuff(),
self.in_indexer.get_col_imgurlbak(), self.img_column
]
print("[{}.ExtractionProcessor: log] img_cols: {}".format(
self.pp, img_cols))
self.last_update_date_id = ''
# Initialize ingester
self.ingester = GenericKafkaProcessor(
self.global_conf,
prefix=self.get_required_param("proc_ingester_prefix"))
self.ingester.pp = "ep"
class ExtractionProcessor(ConfReader):
"""ExtractionProcessor class
"""
def __init__(self, global_conf, prefix=DEFAULT_EXTR_PROC_PREFIX):
"""ExtractionProcessor constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
"""
self.extractor = None
self.nb_empt = 0
self.nb_err = 0
self.max_proc_time = 900 # in seconds. Increased for sbcmdline...
self.url_input = True
super(ExtractionProcessor, self).__init__(global_conf, prefix)
# TODO: move that to self.read_conf()
# Get required parameters
self.input_type = self.get_required_param("input_type")
self.nb_threads = self.get_required_param("nb_threads")
self.featurizer_type = self.get_required_param("featurizer_type")
self.featurizer_prefix = self.get_required_param("featurizer_prefix")
self.detector_type = self.get_required_param("detector_type")
# Get optional parameters
self.verbose = int(self.get_param("verbose", default=0))
self.maxucme = int(
self.get_param("max_up_check_miss_extr",
default=MAX_UP_CHECK_MISS_EXTR))
self.ingestion_input = self.get_param("ingestion_input",
default="kafka")
self.push_back = self.get_param("push_back", default=False)
file_input = self.get_param("file_input")
print("[{}.ExtractionProcessor: log] file_input: {}".format(
self.pp, file_input))
if file_input:
self.url_input = False
print("[{}.ExtractionProcessor: log] url_input: {}".format(
self.pp, self.url_input))
# Means we extract feature from the whole image
if self.detector_type == "full":
self.detector = None
self.extr_prefix = build_extr_str(self.featurizer_type,
self.detector_type, self.input_type)
self.set_pp()
# Initialize queues
self.init_queues()
# Initialize indexer
# We now have two indexers:
# - one "in_indexer" for TF table with buffer, img URLs etc...
# - one "out_indexer" for our table with extractions etc
# NB: they could be the same if tables are merged...
self.out_indexer = HBaseIndexerMinimal(
self.global_conf, prefix=self.get_required_param("indexer_prefix"))
self.out_indexer.pp = "ProcOutHBase"
prefix_in_indexer = self.get_param("in_indexer_prefix", default=False)
if prefix_in_indexer:
self.in_indexer = HBaseIndexerMinimal(self.global_conf,
prefix=prefix_in_indexer)
self.in_indexer.pp = "ProcInHBase"
insha1tab = self.in_indexer.table_sha1infos_name
insha1cfs = self.in_indexer.get_dictcf_sha1_table()
print("[{}] 'in_indexer' sha1 table {} columns are: {}".format(
self.pp, insha1tab, insha1cfs))
else:
print(
"[{}] empty 'in_indexer_prefix', using out_indexer as in_indexer too."
.format(self.pp))
self.in_indexer = self.out_indexer
self.in_indexer.pp = "ProcInOutHBase"
# Initialize extractors only once (just one first)
self.extractors = []
# DONE: use 'out_indexer'
self.extractors.append(
GenericExtractor(self.detector_type, self.featurizer_type,
self.input_type, self.out_indexer.extrcf,
self.featurizer_prefix, self.global_conf))
# DONE: use 'in_indexer'
if self.url_input:
self.img_column = self.in_indexer.get_col_imgurl()
else:
self.img_column = self.in_indexer.get_col_imgpath()
img_cols = [
self.in_indexer.get_col_imgbuff(),
self.in_indexer.get_col_imgurlbak(), self.img_column
]
print("[{}.ExtractionProcessor: log] img_cols: {}".format(
self.pp, img_cols))
self.last_update_date_id = "1970-01-01"
self.last_missing_extr_date = "1970-01-01"
# Initialize ingester
self.ingester = GenericKafkaProcessor(
self.global_conf,
prefix=self.get_required_param("proc_ingester_prefix"))
self.ingester.pp = "ep"
def set_pp(self, pp="ExtractionProcessor"):
"""Set pretty name
:param pp: pretty name prefix
:type pp: str
"""
self.pp = pp
if self.extractor:
self.pp += "_" + self.extr_prefix
def init_queues(self):
"""Initialize queues list ``self.q_in`` and ``self.q_out``
"""
from multiprocessing import JoinableQueue
self.q_in = []
self.q_out = []
for _ in range(self.nb_threads):
self.q_in.append(JoinableQueue(0))
self.q_out.append(JoinableQueue(0))
# Should these two methods be in indexer?
def is_update_unprocessed(self, update_id):
"""Check if an update was not processed yet
:param update_id: update id
:type update_id: str
:return: boolean indicated if update ``update_id`` is unprocessed
:rtype: bool
"""
# DONE: use out_indexer
update_rows = self.out_indexer.get_rows_by_batch(
[update_id], table_name=self.out_indexer.table_updateinfos_name)
if update_rows:
for row in update_rows:
if self.out_indexer.get_col_upproc() in row[1]:
return False
return True
def is_update_notstarted(self, update_id, max_delay=None):
"""Check if an update was not started yet
:param update_id: update id
:type update_id: str
:param max_delay: delay (in seconds) between marked start time and now to consider update failed
:type max_delay: int
:return: boolean
:rtype: bool
"""
# DONE: use out_indexer
update_rows = self.out_indexer.get_rows_by_batch(
[update_id], table_name=self.out_indexer.table_updateinfos_name)
if update_rows:
for row in update_rows:
# changed to: self.column_update_started
#if info_column_family+":"+update_str_started in row[1]:
#if self.column_update_started in row[1]:
# DONE: use out_indexer
if self.out_indexer.get_col_upstart() in row[1]:
if max_delay:
start_str = row[1][self.out_indexer.get_col_upstart()]
# start time format is '%Y-%m-%d:%H.%M.%S'
start_dt = datetime.strptime(start_str,
'%Y-%m-%d:%H.%M.%S')
now_dt = datetime.now()
diff_dt = now_dt - start_dt
if diff_dt.total_seconds() > max_delay:
return True
return False
return True
def get_batch_hbase(self):
"""Get one batch of images from HBase
:yield: tuple (rows_batch, update_id)
"""
# legacy implementation: better to have a kafka topic for batches to be processed to allow
# safe and efficient parallelization on different machines
# DONE: use in_indexer
img_cols = [
self.in_indexer.get_col_imgbuff(),
self.in_indexer.get_col_imgurlbak(), self.img_column
]
try:
# DONE: use out_indexer
for updates in self.out_indexer.get_unprocessed_updates_from_date(
self.last_update_date_id, extr_type=self.extr_prefix):
for update_id, update_cols in updates:
if self.extr_prefix in update_id:
# double check update has not been processed somewhere else
if self.is_update_unprocessed(update_id):
# double check update was not marked as started recently i.e. by another process
if self.is_update_notstarted(
update_id, max_delay=TIME_ELAPSED_FAILED):
# DONE: use out_indexer
list_sha1s = update_cols[
self.out_indexer.get_col_listsha1s(
)].split(',')
msg = "[{}.get_batch_hbase: log] Update {} has {} images."
print(
msg.format(self.pp, update_id,
len(list_sha1s)))
# We should time that, it seems slow i.e. 2/3 minutes per update.
try:
rows_batch = self.in_indexer.get_columns_from_sha1_rows(
list_sha1s,
rbs=BATCH_SIZE_IMGBUFFER,
columns=img_cols)
except Exception:
msg = "[{}.get_batch_hbase: warning] Failed retrieving images data for update: {}"
print(msg.format(self.pp, update_id))
# flush?
sys.stdout.flush()
# Update self.last_update_date_id ?
#self.last_update_date_id = '_'.join(update_id.split('_')[-2:])
continue
# print "rows_batch", rows_batch
if rows_batch:
yield rows_batch, update_id
else:
msg = "[{}.get_batch_hbase: log] Did not get any image buffer for update: {}"
print(msg.format(self.pp, update_id))
#msg = "[{}.get_batch_hbase: log] Was trying to read columns {} from table {} for rows {}"
#print(msg.format(self.pp, img_cols, self.in_indexer.table_sha1infos_name, list_sha1s))
# Store last update id
self.last_update_date_id = '_'.join(
update_id.split('_')[-2:])
else:
msg = "[{}.get_batch_hbase: log] Skipping update started recently: {}"
print(msg.format(self.pp, update_id))
continue
else:
msg = "[{}.get_batch_hbase: log] Skipping already processed update: {}"
print(msg.format(self.pp, update_id))
continue
else:
if self.verbose > 6:
msg = "[{}.get_batch_hbase: log] Skipping update {} from another extraction type."
print(msg.format(self.pp, update_id))
else:
print("[{}.get_batch_hbase: log] No unprocessed update found.".
format(self.pp))
# Should we reinitialized self.last_update_date_id?
# Look for updates that have some unprocessed images
# TODO: wether we do that or not could be specified by a parameter
# as this induces slow down during update...
# DONE: use out_indexer
count_ucme = 0
stop_cme = False
for updates in self.out_indexer.get_missing_extr_updates_from_date(
self.last_missing_extr_date,
extr_type=self.extr_prefix):
for update_id, update_cols in updates:
if self.extr_prefix in update_id:
# DONE: use out_indexer
if self.out_indexer.get_col_listsha1s(
) in update_cols:
list_sha1s = update_cols[
self.out_indexer.get_col_listsha1s(
)].split(',')
msg = "[{}.get_batch_hbase: log] Update {} has {} images missing extractions."
print(
msg.format(self.pp, update_id,
len(list_sha1s)))
sys.stdout.flush()
# also get 'ext:' to check if extraction was already processed?
# DONE: use in_indexer
rows_batch = self.in_indexer.get_columns_from_sha1_rows(
list_sha1s,
rbs=BATCH_SIZE_IMGBUFFER,
columns=img_cols)
if rows_batch:
yield rows_batch, update_id
self.last_missing_extr_date = '_'.join(
update_id.split('_')[-2:])
count_ucme += 1
if count_ucme >= self.maxucme:
stop_cme = True
break
else:
msg = "[{}.get_batch_hbase: log] Did not get any image buffer for update: {}"
print(msg.format(self.pp, update_id))
else:
msg = "[{}.get_batch_hbase: log] Update {} has no images list."
print(msg.format(self.pp, update_id))
else:
msg = "[{}.get_batch_hbase: log] Skipping update {} from another extraction type."
print(msg.format(self.pp, update_id))
# We have reached maximum number of check for missing extractions in one call
if stop_cme:
break
else:
if stop_cme:
msg = "[{}.get_batch_hbase: log] Stopped checking updates with missing extractions"
msg += "after founding {}/{}."
print(
msg.format(self.pp, count_ucme, self.maxucme,
self.last_missing_extr_date))
msg = "[{}.get_batch_hbase: log] Will restart next time from: {}"
print(msg.format(self.pp, self.last_missing_extr_date))
sys.stdout.flush()
else:
msg = "[{}.get_batch_hbase: log] No updates with missing extractions found."
print(msg.format(self.pp))
sys.stdout.flush()
# Re-initialize dates just to make sure we don't miss anything
self.last_update_date_id = "1970-01-01"
self.last_missing_extr_date = "1970-01-01"
except Exception as inst:
# If we reach this point it is really a succession of failures
full_trace_error("[{}.get_batch_hbase: error] {}".format(
self.pp, inst))
# Raise Exception to restart process or docker
raise inst
def get_batch_kafka(self):
"""Get one batch of images from Kafka
:yield: tuple (rows_batch, update_id)
"""
# Read from a kafka topic to allow safer parallelization on different machines
# DONE: use in_indexer
img_cols = [
self.in_indexer.get_col_imgbuff(),
self.in_indexer.get_col_imgurlbak(), self.img_column
]
try:
# Needs to read topic to get update_id and list of sha1s
if self.ingester.consumer:
for msg in self.ingester.consumer:
msg_dict = json.loads(msg.value)
update_id = msg_dict.keys()[0]
# NB: Try to get update info and check it was really not processed yet.
if self.is_update_unprocessed(update_id):
str_list_sha1s = msg_dict[update_id]
list_sha1s = str_list_sha1s.split(',')
msg = "[{}.get_batch_kafka: log] Update {} has {} images."
print(msg.format(self.pp, update_id, len(list_sha1s)))
if self.verbose > 3:
msg = "[{}.get_batch_kafka: log] Looking for columns: {}"
print(msg.format(self.pp, img_cols))
# DONE: use in_indexer
#rows_batch = self.in_indexer.get_columns_from_sha1_rows(list_sha1s, columns=img_cols)
rows_batch = self.in_indexer.get_columns_from_sha1_rows(
list_sha1s,
rbs=BATCH_SIZE_IMGBUFFER,
columns=img_cols)
#print "rows_batch", rows_batch
if rows_batch:
if self.verbose > 4:
msg = "[{}.get_batch_kafka: log] Yielding for update: {}"
print(msg.format(self.pp, update_id))
yield rows_batch, update_id
self.ingester.consumer.commit()
if self.verbose > 4:
msg = "[{}.get_batch_kafka: log] After yielding for update: {}"
print(msg.format(self.pp, update_id))
self.last_update_date_id = '_'.join(
update_id.split('_')[-2:])
# Should we try to commit offset only at this point?
else:
msg = "[{}.get_batch_kafka: log] Did not get any image buffers for the update: {}"
print(msg.format(self.pp, update_id))
else:
msg = "[{}.get_batch_kafka: log] Skipping already processed update: {}"
print(msg.format(self.pp, update_id))
else:
print("[{}.get_batch_kafka: log] No update found.".format(
self.pp))
# Fall back to checking HBase for unstarted/unfinished updates
for rows_batch, update_id in self.get_batch_hbase():
yield rows_batch, update_id
else:
print("[{}.get_batch_kafka: log] No consumer found.".format(
self.pp))
# Fall back to checking HBase for unstarted/unfinished updates
for rows_batch, update_id in self.get_batch_hbase():
yield rows_batch, update_id
except Exception as inst:
# If we reach this point it is really a succession of failures
full_trace_error("[{}.get_batch_kafka: error] {}".format(
self.pp, inst))
# Raise Exception to restart process or docker
raise inst
def get_batch(self):
"""Get one batch of images
:yield: tuple (rows_batch, update_id)
"""
if self.ingestion_input == "hbase":
for rows_batch, update_id in self.get_batch_hbase():
yield rows_batch, update_id
else:
for rows_batch, update_id in self.get_batch_kafka():
yield rows_batch, update_id
def process_batch(self):
"""Process one batch of images
:raises Exception: if something goes really wrong
"""
# Get a new update batch
try:
for rows_batch, update_id in self.get_batch():
start_update = time.time()
img_list_size = len(rows_batch)
print("[{}] Processing update {} of {} rows.".format(
self.pp, update_id, img_list_size))
sys.stdout.flush()
# Initialize
self.nb_empt = 0
self.init_queues()
threads = []
# If we have deleted an extractor at some point or for first batch
nb_extr_to_create = self.nb_threads - len(self.extractors)
if nb_extr_to_create:
start_create_extractor = time.time()
while len(self.extractors) < min(self.nb_threads,
img_list_size):
# DONE: use 'out_indexer'
self.extractors.append(
GenericExtractor(self.detector_type,
self.featurizer_type,
self.input_type,
self.out_indexer.extrcf,
self.featurizer_prefix,
self.global_conf))
msg = "[{}] Created {} extractors in {}s."
create_extr_time = time.time() - start_create_extractor
print(
msg.format(self.pp, len(self.extractors),
create_extr_time))
# Mark batch as started to be process
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
# changed to: self.column_update_started
#dict_val = {info_column_family + ':' + update_str_started: now_str}
#dict_val = {self.column_update_started: now_str}
# DONE: use out_indexer
dict_val = {self.out_indexer.get_col_upstart(): now_str}
update_started_dict = {update_id: dict_val}
# DONE: use out_indexer
self.out_indexer.push_dict_rows(
dict_rows=update_started_dict,
table_name=self.out_indexer.table_updateinfos_name)
# TODO: define a get_buffer_images method
# --------
# Push images to queue
list_in = []
# For parallelized downloading...
from Queue import Queue
nb_imgs_dl = 0
q_in_dl = Queue(0)
q_out_dl = Queue(0)
start_get_buffer = time.time()
# DONE: use in_indexer in all this scope
# How could we transfer URL from in table to out table if they are different?...
for img in rows_batch:
# should decode base64
#if img_buffer_column in img[1]:
if self.in_indexer.get_col_imgbuff() in img[1]:
# That's messy...
# b64buffer = buffer_to_B64(cStringIO.StringIO(img[1][self.in_indexer.get_col_imgbuff()]))
# use img[1].pop(self.in_indexer.get_col_imgbuff())
b64buffer = buffer_to_B64(
cStringIO.StringIO(img[1].pop(
self.in_indexer.get_col_imgbuff())))
tup = (img[0], b64buffer, False)
list_in.append(tup)
else:
# need to re-download, accumulate a list of URLs to download
if self.verbose > 5:
msg = "[{}: log] Will try to download image {} without buffer"
print(msg.format(self.pp, img[0]))
# Deal with img_path_column for local_images_kafka_pusher
if self.img_column in img[1]:
q_in_dl.put((img[0], img[1][self.img_column],
self.push_back))
nb_imgs_dl += 1
elif self.in_indexer.get_col_imgurlbak() in img[1]:
q_in_dl.put(
(img[0],
img[1][self.in_indexer.get_col_imgurlbak()],
self.push_back))
nb_imgs_dl += 1
else:
msg = "[{}: warning] No buffer and no URL/path for image {} !"
print(msg.format(self.pp, img[0]))
continue
# At this point we can delete rows_batch
del rows_batch
gc.collect()
# Download missing images
nb_dl = 0
nb_dl_failed = 0
if nb_imgs_dl > 0:
threads_dl = []
for i in range(min(self.nb_threads, nb_imgs_dl)):
# should read (url, obj_pos) from self.q_in
# and push (url, obj_pos, buffer, img_info, start_process, end_process) to self.q_out
thread = ThreadedDownloaderBufferOnly(
q_in_dl, q_out_dl, url_input=self.url_input)
thread.start()
threads_dl.append(thread)
q_in_dl.join()
# Push downloaded images to list_in too
while nb_dl < nb_imgs_dl:
# This can block?
#sha1, buffer, push_back, inst = q_out_dl.get()
try:
sha1, buffer, push_back, inst = q_out_dl.get(
True, 10)
except Exception as queue_err:
msg = "[{}: error] Download queue out timed out: {}"
print(msg.format(self.pp, queue_err))
break
nb_dl += 1
if inst:
if self.verbose > 6:
msg = "[{}: log] Could not download image {}, error was: {}"
print(msg.format(self.pp, sha1, inst))
nb_dl_failed += 1
else:
if buffer:
list_in.append((sha1, buffer, push_back))
else:
# Is that even possible?
msg = "[{}: error] No error but no buffer either for image {}"
print(msg.format(self.pp, sha1))
get_buffer_time = time.time() - start_get_buffer
buff_list_size = len(list_in)
msg = "[{}] Got {}/{} image buffers ({}/{} downloaded) for update {} in {}s."
print(
msg.format(self.pp, buff_list_size, img_list_size,
nb_dl - nb_dl_failed, nb_dl, update_id,
get_buffer_time))
sys.stdout.flush()
# --------
# if buff_list_size == 0, we shouldn't try to process anything, just mark update as processed
if buff_list_size != 0:
# TODO: define a get_features method
# --------
q_batch_size = int(
math.ceil(float(buff_list_size) / self.nb_threads))
for i, q_batch in enumerate(
build_batch(list_in, q_batch_size)):
self.q_in[i].put(q_batch)
# At this point we can delete list_in
del list_in
gc.collect()
q_in_size = []
q_in_size_tot = 0
for i in range(self.nb_threads):
q_in_size.append(self.q_in[i].qsize())
q_in_size_tot += q_in_size[i]
if self.verbose > 5:
print("[{}] Total input queues sizes is: {}".format(
self.pp, q_in_size_tot))
# Start daemons...
thread_creation_failed = [0] * self.nb_threads
for i in range(self.nb_threads):
# one per non empty input queue
if q_in_size[i] > 0:
try:
thread = DaemonBatchExtractor(
self.extractors[i],
self.q_in[i],
self.q_out[i],
verbose=self.verbose)
# Could get a 'Cannot allocate memory' if we are using too many threads...
thread.start()
threads.append(thread)
except OSError as inst:
# Should we try to push self.q_in[i] data to some other thread?
msg = "[{}.process_batch: error] Could not start thread #{}: {}"
print(msg.format(self.pp, i + 1, inst))
thread_creation_failed[i] = 1
time.sleep(sum(thread_creation_failed))
if sum(thread_creation_failed) == self.nb_threads:
# We are in trouble...
raise RuntimeError("Could not start any thread...")
nb_threads_running = len(threads)
deleted_extr = [0] * nb_threads_running
start_process = time.time()
stop = time.time() + self.max_proc_time
# Wait for all tasks to be marked as done
threads_finished = [0] * nb_threads_running
thread_msg = "[{}] Thread {}/{} (pid: {}) "
while sum(threads_finished) < nb_threads_running:
for i in range(nb_threads_running):
if sum(threads_finished) == nb_threads_running:
sys.stdout.flush()
break
if threads_finished[i] == 1:
continue
i_q_in = i + sum(thread_creation_failed[:i + 1])
if q_in_size[i_q_in] > 0:
# This seems to block forever sometimes, if subprocess crashed?...
#self.q_in[i].join()
# Manual join with timeout...
# https://github.com/python/cpython/blob/3.6/Lib/multiprocessing/queues.py
if not self.q_in[
i_q_in]._unfinished_tasks._semlock._is_zero(
) and time.time() < stop:
time.sleep(1)
else:
if self.q_in[
i_q_in]._unfinished_tasks._semlock._is_zero(
):
if self.verbose > 5:
msg = thread_msg + "marked as finished because processing seems finished"
print(
msg.format(
self.pp, i + 1,
nb_threads_running,
threads[i].pid))
else:
# Try to stop processing
threads[i].killed = True
if self.verbose > 0:
# In this cases does this happen...
msg = thread_msg + "killed as max_proc_time ({}) has passed."
print(
msg.format(
self.pp, i + 1,
nb_threads_running,
threads[i].pid,
self.max_proc_time))
sys.stdout.flush()
# Try to delete corresponding extractor to free memory?
# And reduce number of threads at the end of the loop
try:
# This can block?
#self.q_in[i_q_in].task_done()
if deleted_extr[i] == 0:
# we pushed the extractor as self.extractors[i] in a loop of self.nb_threads
# we use i_q_in
#del self.extractors[i_q_in]
deleted_extr[i] = 1
del self.extractors[
i_q_in -
sum(deleted_extr[:i + 1])]
#del threads[i - sum(deleted_extr[:i+1])]
except Exception:
pass
threads_finished[i] = 1
else:
if self.verbose > 2:
# We actually never gave something to process...
msg = thread_msg + "marked as finished because no data was passed to it"
print(
msg.format(self.pp, i + 1,
nb_threads_running,
threads[i].pid))
threads_finished[i] = 1
# Cleanup threads to free memory before getting data back
# Daemon may still be running...
# and will actually be deleted only when they exit after not getting a batch
del threads
# Gather results
q_out_size = []
q_out_size_tot = 0
for i in range(self.nb_threads):
q_out_size.append(self.q_out[i].qsize())
q_out_size_tot += q_out_size[i]
if self.verbose > 5:
print(
"[{}: log] Total output queues size is: {}".format(
self.pp, q_out_size_tot))
sys.stdout.flush()
# Can get stuck here?
dict_imgs = dict()
if q_out_size_tot > 0:
for i in range(self.nb_threads):
if self.verbose > 4:
print("[{}] Thread {} q_out_size: {}".format(
self.pp, i + 1, q_out_size[i]))
sys.stdout.flush()
while q_out_size[i] > 0 and not self.q_out[
i].empty():
if self.verbose > 6:
print("[{}] Thread {} q_out is not empty.".
format(self.pp, i + 1))
sys.stdout.flush()
try:
# This can still block forever?
#batch_out = self.q_out[i].get(True, 10)
batch_out = self.q_out[i].get_nowait()
if self.verbose > 4:
msg = "[{}] Got batch of {} features from thread {} q_out."
print(
msg.format(self.pp, len(batch_out),
i + 1))
sys.stdout.flush()
for sha1, dict_out in batch_out:
dict_imgs[sha1] = dict_out
except:
if self.verbose > 1:
print(
"[{}] Thread {} failed to get from q_out: {}"
.format(self.pp, i + 1))
sys.stdout.flush()
#pass
if self.verbose > 4:
print(
"[{}] Marking task done in q_out of thread {}."
.format(self.pp, i + 1))
sys.stdout.flush()
self.q_out[i].task_done()
#if self.verbose > 0:
print_msg = "[{}] Got features for {}/{} images in {}s."
proc_time = time.time() - start_process
print(
print_msg.format(self.pp, len(dict_imgs.keys()),
buff_list_size, proc_time))
sys.stdout.flush()
# --------
if len(dict_imgs.keys()) > 0:
# Push computed features
self.out_indexer.push_dict_rows(
dict_rows=dict_imgs,
table_name=self.out_indexer.table_sha1infos_name)
else:
msg = "[{}: Warning] Could not get any image buffer (out of {} requested) for update {}"
print(msg.format(self.pp, img_list_size, update_id))
dict_imgs = dict()
nb_threads_running = len(threads)
thread_creation_failed = [0] * self.nb_threads
deleted_extr = [0] * nb_threads_running
# Mark batch as processed
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
# DONE: use out_indexer
update_processed_dict = {
update_id: {
self.out_indexer.get_col_upproc(): now_str
}
}
self.out_indexer.push_dict_rows(
dict_rows=update_processed_dict,
table_name=self.out_indexer.table_updateinfos_name)
# Mark as completed if all rows had an extraction
if img_list_size == len(dict_imgs.keys()):
# DONE: use out_indexer
update_completed_dict = {
update_id: {
self.out_indexer.get_col_upcomp(): str(1)
}
}
self.out_indexer.push_dict_rows(
dict_rows=update_completed_dict,
table_name=self.out_indexer.table_updateinfos_name)
# Cleanup
del self.q_in
del self.q_out
# To try to adjust a too optimistic nb_threads setting
# if (sum(thread_creation_failed) > 0 or sum(deleted_extr) > 0) and self.nb_threads > 2:
# self.nb_threads -= 1
msg = "[{}] Completed update {} in {}s."
print(
msg.format(self.pp, update_id,
time.time() - start_update))
sys.stdout.flush()
self.nb_err = 0
# Force garbage collection
gc.collect()
# Should we just raise an Exception and restart clean?
if sum(thread_creation_failed) > 0 or sum(deleted_extr) > 0:
# To try to adjust a too optimistic nb_threads setting
if self.nb_threads > 1:
self.nb_threads -= 1
self.extractors = []
gc.collect()
else:
raise RuntimeError(
"Processed failed with a single thread...")
except Exception as inst:
#exc_type, exc_obj, exc_tb = sys.exc_info()
#fulltb = traceback.format_tb(exc_tb)
print("[{}.process_batch: ERROR] {}".format(self.pp, inst))
#print("[{}] {} ({})".format(self.pp, inst, ''.join(fulltb)))
# Things are likely to be very bad at that point... Docker should be restarted
#if self.nb_threads == 2:
raise inst
#raise type(inst)(" {} ({})".format(inst, ''.join(fulltb)))
def run(self):
"""Run processor
"""
self.nb_empt = 0
self.nb_err = 0
while True:
self.process_batch()
msg = "[ExtractionProcessor: log] Nothing to process at: {}"
print(msg.format(datetime.now().strftime('%Y-%m-%d:%H.%M.%S')))
sys.stdout.flush()
time.sleep(10 * min(self.nb_empt, 60))
self.nb_empt += 1
def __init__(self, global_conf, prefix=DEFAULT_EXTR_CHECK_PREFIX, pid=None):
"""ExtractionChecker constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
:param pid: process id
:type pid: int
"""
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Max delay
self.max_delay = int(self.get_param("max_delay", default=3600))
self.list_extr_prefix = [self.featurizer_type, "feat", self.detector_type, self.input_type]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.batch_check_column = None
self.check_columns = []
# changed to: get column family from indexer in set_check_columns
# Need to be build from extraction type and detection input + "_processed"
#self.extr_family_column = self.get_param("extr_family_column", default="ext")
# self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
# self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# # Need to be build from extraction type and extraction input + "_batchid"
# self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
# self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer
self.indexer = HBaseIndexerMinimal(self.global_conf,
prefix=self.get_required_param("indexer_prefix"))
self.indexer.pp = "CheckerHBase"
print(self.get_required_param("indexer_prefix"), self.indexer.get_dictcf_sha1_table())
self.set_check_columns()
print(self.check_columns)
# Initialize ingester
try:
self.ingester = GenericKafkaProcessor(self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("[{}: ERROR] Could not start ingester.".format(self.pp, inst))
raise inst
# This will not be set for HBase processing, but checker would keep dying here...
self.updates_out_topic = None
try:
self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("{}. Will write only to HBase.".format(inst))
self.ingester.pp = "ec"
if self.pid:
self.ingester.pp += str(self.pid)
class ExtractionChecker(ConfReader):
"""ExtractionChecker class
"""
def __init__(self, global_conf, prefix=DEFAULT_EXTR_CHECK_PREFIX, pid=None):
"""ExtractionChecker constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
:param pid: process id
:type pid: int
"""
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Max delay
self.max_delay = int(self.get_param("max_delay", default=3600))
self.list_extr_prefix = [self.featurizer_type, "feat", self.detector_type, self.input_type]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.batch_check_column = None
self.check_columns = []
# changed to: get column family from indexer in set_check_columns
# Need to be build from extraction type and detection input + "_processed"
#self.extr_family_column = self.get_param("extr_family_column", default="ext")
# self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
# self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# # Need to be build from extraction type and extraction input + "_batchid"
# self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
# self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer
self.indexer = HBaseIndexerMinimal(self.global_conf,
prefix=self.get_required_param("indexer_prefix"))
self.indexer.pp = "CheckerHBase"
print(self.get_required_param("indexer_prefix"), self.indexer.get_dictcf_sha1_table())
self.set_check_columns()
print(self.check_columns)
# Initialize ingester
try:
self.ingester = GenericKafkaProcessor(self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("[{}: ERROR] Could not start ingester.".format(self.pp, inst))
raise inst
# This will not be set for HBase processing, but checker would keep dying here...
self.updates_out_topic = None
try:
self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("{}. Will write only to HBase.".format(inst))
self.ingester.pp = "ec"
if self.pid:
self.ingester.pp += str(self.pid)
def set_check_columns(self):
"""Set columns to be checked in indexer
"""
# changed to: get column family from indexer
extr_prefix_base_column_name = self.indexer.extrcf + ":" + self.extr_prefix
extr_check_column = extr_prefix_base_column_name + "_processed"
# Need to be build from extraction type and extraction input + "_batchid"
self.batch_check_column = extr_prefix_base_column_name + "_updateid"
self.check_columns = [extr_check_column, self.batch_check_column]
#print(self.check_columns)
def set_pp(self, pp=""):
"""Set pretty name
"""
self.pp = "ExtractionChecker"
self.pp += "-".join(self.list_extr_prefix)
if self.pid:
self.pp += "." + str(self.pid)
def store_img_infos(self, msg):
"""Store information about the images of ``msg`` in ``self.dict_sha1_infos``
:param msg: Kafka record
:type msg: collections.namedtuple
"""
# msg is technically a ConsumerRecord that is a collections.namedtuple, see:
# https://github.com/dpkp/kafka-python/blob/master/kafka/consumer/fetcher.py#L30
strk = str(msg['sha1'])
self.dict_sha1_infos[strk] = dict()
for key in msg:
# dumps json of 'img_info'
# We actually need that only for DIG...
if key == "img_info":
self.dict_sha1_infos[strk][key] = json.dumps(msg[key])
else:
# discard 'img_buffer' (if it exists?...), and 'sha1'
# if k != "img_buffer" and k != "sha1":
# self.dict_sha1_infos[strk][k] = msg[k]
# discard 'sha1'
if key != "sha1":
self.dict_sha1_infos[strk][key] = msg[key]
def cleanup_dict_infos(self, list_del_sha1s):
"""Remove images ``list_del_sha1s`` from ``self.dict_sha1_infos``
:param list_del_sha1s: list of images sha1 to remove
:type list_del_sha1s: list
"""
for sha1 in list_del_sha1s:
try:
del self.dict_sha1_infos[str(sha1)]
except:
# could happen when cleaning up duplicates or image processed by another process
pass
def get_dict_push(self, list_get_sha1s, daemon=False):
"""Get dictionary to be pushed to HBase for images in ``list_get_sha1s``
:param list_get_sha1s: list of images
:type list_get_sha1s: list
:param daemon: whether the checker is running in daemon mode
:type daemon: bool
:return: (dict_push, update_id)
:rtype: tuple
"""
#TODO: is this needed for every get_dict_push call?
self.set_check_columns()
# TODO: also pass current update_id, and move the creation of update id out of this method
# this method should actually be used to 'claim' an image as soon as we can.
dict_push = dict()
# append processid to 'update_id' for safe use with multiple consumers, even after restart
# /!\ beware, it should not contain underscores
tmp_update_id, _ = self.indexer.get_next_update_id(today=None, extr_type=self.extr_prefix)
update_id = tmp_update_id + '-' + self.ingester.pp + '-' + str(time.time())
for sha1 in list_get_sha1s:
dict_push[str(sha1)] = dict()
try:
tmp_dict = self.dict_sha1_infos[str(sha1)]
except:
# This would mean the image has been marked as part of another batch by another process,
# and thus deleted in a previous 'get_unprocessed_rows' call
# This is also only relevant if we run on Daemon mode...
# TODO: for transition we won't really have any info to push except the update_id...
if daemon:
del dict_push[str(sha1)]
continue
# build column names properly i.e. appending 'info:'
for key in tmp_dict:
# changed to: use column_family from indexer
# But the use of 'key' here also means we rely on the input to define column name...
#dict_push[str(sha1)]['info:' + key] = tmp_dict[key]
dict_push[str(sha1)][self.indexer.imginfocf + ':' + key] = tmp_dict[key]
dict_push[str(sha1)][self.batch_check_column] = update_id
return dict_push, update_id
def get_unprocessed_rows(self, list_check_sha1s):
"""Get the subset of the list of sha1s ``list_check_sha1s`` that have not been processed yet
:param list_check_sha1s: list of images sha1 to check
:type list_check_sha1s: list
:return: set of unprocessed images
:rtype: set
"""
# TODO: also pass current update_id and only delete if != from current update...
unprocessed_rows = set(list_check_sha1s)
if list_check_sha1s:
# Check if the selected sha1 rows in HBase table 'sha1infos' have those check_column
# This call will only return rows that DO have those check_column
fam = self.indexer.get_dictcf_sha1_table()
try:
sha1s_rows = self.indexer.get_columns_from_sha1_rows(list_check_sha1s, self.check_columns,
families=fam)
except Exception as inst:
print("[{}.get_unprocessed_rows: log] fam: {}".format(self.pp, fam))
raise inst
#families=self.tablesha1_col_families)
if sha1s_rows:
# TODO: only delete if really previously processed, i.e. if != from current update...
found_sha1_rows = set([str(row[0]) for row in sha1s_rows])
# Clean up 'dict_sha1_infos' deleting found_sha1_rows
self.cleanup_dict_infos(found_sha1_rows)
set_list_check_sha1s = set(list_check_sha1s)
# TODO: but we should not re-add them, so we should discard them from unprocessed_rows
unprocessed_rows = set_list_check_sha1s - found_sha1_rows
return unprocessed_rows
def run(self, daemon=False):
"""Run extraction checker
:param daemon: whether we are running in daemon mode
:type daemon: bool
:raises Exception: if check fails
"""
i = 0
try:
list_sha1s_to_process = []
# TODO: create update_id here
while True:
list_check_sha1s = []
try:
# Accumulate images infos
for msg_json in self.ingester.consumer:
msg = json.loads(msg_json.value)
# i += 1
# print((i, len(list_check_sha1s), msg))
# msg could now contain keys 'sha1' or 'list_sha1s'
# should we check that we can't have both or other keys?...
if 'sha1' in msg:
list_check_sha1s.append(str(msg['sha1']))
# Store other fields to be able to push them too
self.store_img_infos(msg)
elif 'list_sha1s' in msg:
for sha1 in msg['list_sha1s']:
list_check_sha1s.append(str(sha1))
# We won't have any additional infos no?
# But we should still build a dict for each sample for consistency...
tmp_dict = dict()
tmp_dict['sha1'] = str(sha1)
# will basically push an empty dict to self.dict_sha1_infos, so self.get_dict_push
# works properly later on...
self.store_img_infos(tmp_dict)
else:
print('Unknown keys in msg: {}'.format(msg.keys()))
if len(list_check_sha1s) >= self.indexer.batch_update_size:
break
except Exception as inst:
# trying to use 'consumer_timeout_ms' to raise timeout and get last samples
msg = "[{}: warning] At {}, caught {} {} in consumer loop"
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
print(msg.format(self.pp, now_str, type(inst), inst))
sys.stdout.flush()
if not list_check_sha1s:
# TODO: should we fallback to scanning Hbase table here?
continue
# Check which images have not been processed (or pushed in an update) yet
unprocessed_rows = self.get_unprocessed_rows(list_check_sha1s)
self.nb_imgs_check += len(list_check_sha1s)
push_delay = (time.time() - self.last_push) > self.max_delay / 60
if push_delay and self.nb_imgs_unproc_lastprint != self.nb_imgs_unproc:
msg = "[{}: log] Found {}/{} unprocessed images"
print(msg.format(self.pp, self.nb_imgs_unproc, self.nb_imgs_check))
self.nb_imgs_unproc_lastprint = self.nb_imgs_unproc
# TODO: we should mark those images as being 'owned' by the update we are constructing
# (only important if we are running multiple threads i.e. daemon is True)
# otherwise another update running at the same time could also claim it (in another ad)
# could be handle when adding data to the searcher but duplicates in extraction process...
# Push sha1s to be processed
for sha1 in unprocessed_rows:
list_sha1s_to_process.append(sha1)
# Remove potential duplicates
list_sha1s_to_process = list(set(list_sha1s_to_process))
if list_sha1s_to_process:
# Push them to HBase by batch of 'batch_update_size'
push_delay = (time.time() - self.last_push) > self.max_delay
full_batch = len(list_sha1s_to_process) >= self.indexer.batch_update_size
if full_batch or (push_delay and list_sha1s_to_process):
# Trim here to push exactly a batch of 'batch_update_size'
list_push = list_sha1s_to_process[:min(self.indexer.batch_update_size,
len(list_sha1s_to_process))]
# TODO: this should be done before,
# to 'claim' the images as soon as we plan to process them for this update
# Gather corresponding sha1 infos
dict_push, update_id = self.get_dict_push(list_push, daemon=daemon)
if dict_push:
self.nb_imgs_unproc += len(dict_push.keys())
msg = "[{}: at {}] Pushing update {} of {} images."
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
print(msg.format(self.pp, now_str, update_id, len(dict_push.keys())))
sys.stdout.flush()
# Push images
fam = self.indexer.get_dictcf_sha1_table()
if self.verbose > 4:
msg = "[{}] Pushing images for update {} with fam {}"
print(msg.format(self.pp, update_id, fam))
sha1s_table = self.indexer.table_sha1infos_name
self.indexer.push_dict_rows(dict_push, sha1s_table, families=fam)
# Build HBase updates dict
dict_updates_db = dict()
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
list_sha1s_col = self.indexer.get_col_listsha1s()
dict_updates_db[update_id] = {list_sha1s_col: ','.join(dict_push.keys()),
self.indexer.get_col_upcreate(): now_str}
# Push it
fam = self.indexer.get_dictcf_update_table()
if self.verbose > 4:
msg = "[{}] Pushing update {} info with fam {}"
print(msg.format(self.pp, update_id, fam))
self.indexer.push_dict_rows(dict_updates_db, self.indexer.table_updateinfos_name,
families=fam)
# Build HBase updates dict
if self.updates_out_topic is not None:
dict_updates_kafka = dict()
dict_updates_kafka[update_id] = ','.join(dict_push.keys())
# Push it
self.ingester.producer.send(self.updates_out_topic, json.dumps(dict_updates_kafka))
# Gather any remaining sha1s and clean up infos
if len(list_sha1s_to_process) > self.indexer.batch_update_size:
list_sha1s_to_process = list_sha1s_to_process[self.indexer.batch_update_size:]
else:
list_sha1s_to_process = []
# if duplicates wrt list_push, remove them. Can this still happen?
list_sha1s_to_process = [sh1 for sh1 in list_sha1s_to_process if sh1 not in list_push]
self.cleanup_dict_infos(list_push)
else:
msg = "[{}: at {}] Nothing to push for update {}"
print(msg.format(self.pp, datetime.now().strftime('%Y-%m-%d:%H.%M.%S'), update_id))
sys.stdout.flush()
self.last_push = time.time()
# TODO: we should create a new update_id here,
# and let it claim the potential remaining images in 'list_sha1s_to_process'
# sanity check that len(list_sha1s_to_process) == len(self.dict_sha1_infos) ?
except Exception as inst:
exc_type, exc_obj, exc_tb = sys.exc_info()
fulltb = traceback.format_tb(exc_tb)
raise type(inst)(" {} ({})".format(inst, ''.join(fulltb)))
def __init__(self,
global_conf,
prefix=DEFAULT_EXTR_CHECK_PREFIX,
pid=None):
"""ExtractionChecker constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
:param pid: process id
:type pid: int
"""
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Max delay
self.max_delay = int(
self.get_param("max_delay", default=DEFAULT_MAX_DELAY))
self.min_len_check = int(
self.get_param("min_len_check", default=DEFAULT_MIN_LENGTH_CHECK))
self.list_extr_prefix = [
self.featurizer_type, "feat", self.detector_type, self.input_type
]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.batch_check_column = None
self.check_columns = []
# changed to: get column family from indexer in set_check_columns
# Need to be build from extraction type and detection input + "_processed"
#self.extr_family_column = self.get_param("extr_family_column", default="ext")
# self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
# self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# # Need to be build from extraction type and extraction input + "_batchid"
# self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
# self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer
self.indexer = HBaseIndexerMinimal(
self.global_conf, prefix=self.get_required_param("indexer_prefix"))
self.indexer.pp = "CheckerHBase"
print(self.get_required_param("indexer_prefix"),
self.indexer.get_dictcf_sha1_table())
self.set_check_columns()
print(self.check_columns)
# Initialize ingester, that could now be Kafka or Kinesis. Should we have a default?
ingester_type = self.get_required_param("image_ingestion_type")
try:
if ingester_type == "kafka":
self.ingester = KafkaIngester(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif ingester_type == "kinesis":
self.ingester = KinesisIngester(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
else:
raise ValueError(
"Unknown 'ingester_type': {}".format(ingester_type))
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("[{}: ERROR] Could not start ingester.".format(
self.pp, inst))
raise inst
# Initialize producer
# TODO: also check for 'update_ingestion_type' as producer_type?
producer_type = self.get_param("update_ingestion_type",
DEFAULT_UPDATE_INGESTION_TYPE)
# TODO: create a producer if 'update_ingestion_type' is Kinesis or Kafka
# if producer_type != "hbase":
# self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
if producer_type == "kafka":
self.pusher = KafkaPusher(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif producer_type == "kinesis":
self.pusher = KinesisPusher(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif producer_type == "hbase":
self.pusher = None
print("[{}: log] Will write updates only to HBase.".format(
self.pp))
else:
raise ValueError(
"Unknown 'producer_type': {}".format(producer_type))
class ExtractionChecker(ConfReader):
"""ExtractionChecker class
"""
def __init__(self,
global_conf,
prefix=DEFAULT_EXTR_CHECK_PREFIX,
pid=None):
"""ExtractionChecker constructor
:param global_conf_in: configuration file or dictionary
:type global_conf_in: str, dict
:param prefix: prefix in configuration
:type prefix: str
:param pid: process id
:type pid: int
"""
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Max delay
self.max_delay = int(
self.get_param("max_delay", default=DEFAULT_MAX_DELAY))
self.min_len_check = int(
self.get_param("min_len_check", default=DEFAULT_MIN_LENGTH_CHECK))
self.list_extr_prefix = [
self.featurizer_type, "feat", self.detector_type, self.input_type
]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.batch_check_column = None
self.check_columns = []
# changed to: get column family from indexer in set_check_columns
# Need to be build from extraction type and detection input + "_processed"
#self.extr_family_column = self.get_param("extr_family_column", default="ext")
# self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
# self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# # Need to be build from extraction type and extraction input + "_batchid"
# self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
# self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer
self.indexer = HBaseIndexerMinimal(
self.global_conf, prefix=self.get_required_param("indexer_prefix"))
self.indexer.pp = "CheckerHBase"
print(self.get_required_param("indexer_prefix"),
self.indexer.get_dictcf_sha1_table())
self.set_check_columns()
print(self.check_columns)
# Initialize ingester, that could now be Kafka or Kinesis. Should we have a default?
ingester_type = self.get_required_param("image_ingestion_type")
try:
if ingester_type == "kafka":
self.ingester = KafkaIngester(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif ingester_type == "kinesis":
self.ingester = KinesisIngester(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
else:
raise ValueError(
"Unknown 'ingester_type': {}".format(ingester_type))
except Exception as inst:
# print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
# time.sleep(self.max_delay)
# raise(inst)
#print("Could not initialize 'updates_out_topic' ({}). Will write only to HBase.".format(inst))
print("[{}: ERROR] Could not start ingester.".format(
self.pp, inst))
raise inst
# Initialize producer
# TODO: also check for 'update_ingestion_type' as producer_type?
producer_type = self.get_param("update_ingestion_type",
DEFAULT_UPDATE_INGESTION_TYPE)
# TODO: create a producer if 'update_ingestion_type' is Kinesis or Kafka
# if producer_type != "hbase":
# self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
if producer_type == "kafka":
self.pusher = KafkaPusher(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif producer_type == "kinesis":
self.pusher = KinesisPusher(
self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
elif producer_type == "hbase":
self.pusher = None
print("[{}: log] Will write updates only to HBase.".format(
self.pp))
else:
raise ValueError(
"Unknown 'producer_type': {}".format(producer_type))
#self.ingester.pp = self.get_param("pp", "ImageIngester")
# Only if daemon mode, as we may have multiple ingesters
# But for Kinesis the `shard_infos_filename` may not be re-used...
#if self.pid:
# self.ingester.pp += str(self.pid)
def set_check_columns(self):
"""Set columns to be checked in indexer
"""
# changed to: get column family from indexer
# TODO: get the suffixes as global variables maybe from common.defaults
extr_prefix_base_column_name = self.indexer.extrcf + ":" + self.extr_prefix
extr_check_column = extr_prefix_base_column_name + "_processed"
# Need to be build from extraction type and extraction input + "_batchid"
self.batch_check_column = extr_prefix_base_column_name + "_updateid"
self.check_columns = [extr_check_column, self.batch_check_column]
#print(self.check_columns)
def set_pp(self, pp=""):
"""Set pretty name
"""
self.pp = "ExtractionChecker"
self.pp += "-".join(self.list_extr_prefix)
if self.pid:
self.pp += "." + str(self.pid)
def store_img_infos(self, msg):
"""Store information about the images of ``msg`` in ``self.dict_sha1_infos``
:param msg: message
:type msg: dict
"""
strk = str(msg['sha1']).upper()
self.dict_sha1_infos[strk] = dict()
for key in msg:
# dumps json of 'img_info'
# We actually need that only for DIG...
if key == "img_info":
self.dict_sha1_infos[strk][key] = json.dumps(msg[key])
else:
# discard 'img_buffer' (if it exists?...), and 'sha1'
# if k != "img_buffer" and k != "sha1":
# self.dict_sha1_infos[strk][k] = msg[k]
# discard 'sha1'
if key != "sha1":
self.dict_sha1_infos[strk][key] = msg[key]
def cleanup_dict_infos(self, list_del_sha1s):
"""Remove images ``list_del_sha1s`` from ``self.dict_sha1_infos``
:param list_del_sha1s: list of images sha1 to remove
:type list_del_sha1s: list
"""
for sha1 in list_del_sha1s:
try:
del self.dict_sha1_infos[str(sha1)]
except:
# could happen when cleaning up duplicates or image processed by another process
pass
def get_dict_push(self, list_get_sha1s, daemon=False):
"""Get dictionary to be pushed to HBase for images in ``list_get_sha1s``
:param list_get_sha1s: list of images
:type list_get_sha1s: list
:param daemon: whether the checker is running in daemon mode
:type daemon: bool
:return: (dict_push, update_id)
:rtype: tuple
"""
#TODO: is this needed for every get_dict_push call?
self.set_check_columns()
# TODO: also pass current update_id, and move the creation of update id out of this method
# this method should actually be used to 'claim' an image as soon as we can.
dict_push = dict()
# append processid to 'update_id' for safe use with multiple consumers, even after restart
# /!\ beware, it should not contain underscores
tmp_update_id, _ = self.indexer.get_next_update_id(
today=None, extr_type=self.extr_prefix)
update_id = tmp_update_id + '-' + self.ingester.pp + '-' + str(
time.time())
for sha1 in list_get_sha1s:
dict_push[str(sha1)] = dict()
try:
tmp_dict = self.dict_sha1_infos[str(sha1)]
except:
# This would mean the image has been marked as part of another batch by another process,
# and thus deleted in a previous 'get_unprocessed_rows' call
# This is also only relevant if we run on Daemon mode...
# TODO: for transition we won't really have any info to push except the update_id...
if daemon:
del dict_push[str(sha1)]
continue
# build column names properly i.e. appending 'info:'
for key in tmp_dict:
# changed to: use column_family from indexer
# But the use of 'key' here also means we rely on the input to define column name...
#dict_push[str(sha1)]['info:' + key] = tmp_dict[key]
dict_push[str(sha1)][self.indexer.imginfocf + ':' +
key] = tmp_dict[key]
dict_push[str(sha1)][self.batch_check_column] = update_id
return dict_push, update_id
def get_unprocessed_rows(self, list_check_sha1s):
"""Get the subset of the list of sha1s ``list_check_sha1s`` that have not been processed yet
:param list_check_sha1s: list of images sha1 to check
:type list_check_sha1s: list
:return: set of unprocessed images
:rtype: set
"""
# TODO: also pass current update_id and only delete if != from current update...
unprocessed_rows = set(list_check_sha1s)
if list_check_sha1s:
# Check if the selected sha1 rows in HBase table 'sha1infos' have those check_column
# This call will only return rows that DO have those check_column
fam = self.indexer.get_dictcf_sha1_table()
try:
sha1s_rows = self.indexer.get_columns_from_sha1_rows(
list_check_sha1s, self.check_columns, families=fam)
except Exception as inst:
print("[{}.get_unprocessed_rows: log] fam: {}".format(
self.pp, fam))
raise inst
#families=self.tablesha1_col_families)
if sha1s_rows:
# TODO: only delete if really previously processed, i.e. if != from current update...
found_sha1_rows = set([str(row[0]) for row in sha1s_rows])
# Clean up 'dict_sha1_infos' deleting found_sha1_rows
self.cleanup_dict_infos(found_sha1_rows)
set_list_check_sha1s = set(list_check_sha1s)
# TODO: but we should not re-add them, so we should discard them from unprocessed_rows
unprocessed_rows = set_list_check_sha1s - found_sha1_rows
return unprocessed_rows
def run(self, daemon=False):
"""Run extraction checker
:param daemon: whether we are running in daemon mode
:type daemon: bool
:raises Exception: if check fails
"""
# import inspect
# if not inspect.isgeneratorfunction(self.ingester.get_msg_json()):
# msg = "[{}: Warning] Ingester {} function `get_msg_json` is not a generator"
# print(msg.format(self.pp, type(self.ingester)))
try:
list_sha1s_to_process = []
list_check_sha1s = []
# TODO: create update_id here
if self.verbose > 1:
msg = "[{}: log] Start run main loop"
msg.format(self.pp)
while True:
try:
# Accumulate images infos
#while len(list_check_sha1s) < self.indexer.batch_update_size:
#while len(list_check_sha1s) < self.min_len_check:
for msg in self.ingester.get_msg_json():
try:
# Fix if input was JSON dumped twice?
if not isinstance(msg, dict):
msg = json.loads(msg)
# msg could now contain keys 'sha1' or 'list_sha1s'
if 'sha1' in msg:
list_check_sha1s.append(
str(msg['sha1']).upper())
# Store other fields to be able to push them too
self.store_img_infos(msg)
elif 'list_sha1s' in msg:
for sha1 in msg['list_sha1s']:
list_check_sha1s.append(str(sha1).upper())
# We won't have any additional infos no?
# But we should still build a dict for each sample for consistency...
tmp_dict = dict()
tmp_dict['sha1'] = str(sha1).upper()
# will basically push a dict with just the sha1 to self.dict_sha1_infos, so self.get_dict_push
# works properly later on...
self.store_img_infos(tmp_dict)
else:
raise ValueError(
'Unknown keys in msg: {}'.format(
msg.keys()))
# This is dangerous, as it assumes the self.ingester.get_msg_json() generator
# would restart from the next point... Is this the case for Kafka?
prev_len = len(list_check_sha1s)
list_check_sha1s = list(set(list_check_sha1s))
if len(list_check_sha1s) < prev_len:
msg = "[{}: log] Removed {} duplicate from `list_check_sha1s`"
print(
msg.format(
self.pp,
prev_len - len(list_check_sha1s)))
if len(list_check_sha1s
) >= self.indexer.batch_update_size:
break
except Exception as inst:
pr_msg = "[{}: ERROR] Could not process message: {}. {}"
print(pr_msg.format(self.pp, msg, inst))
except Exception as inst:
pr_msg = "[{}: at {} ERROR] Caught {} {} in consumer loop"
now_str = datetime.now().strftime('%Y-%m-%d:%H.%M.%S')
print(pr_msg.format(self.pp, now_str, type(inst), inst))
if msg is not None:
print(msg)
sys.stdout.flush()
if self.verbose > 3:
msg = "[{}: log] Gathered {} images to check so far"
msg = msg.format(self.pp, len(list_check_sha1s))
msg2 = ""
if len(list_check_sha1s) > 0:
msg2 = " (first: {}, last: {})"
msg2 = msg2.format(list_check_sha1s[0],
list_check_sha1s[-1])
print(msg + msg2)
# To be able to push one (non empty) update every max_delay
#if not list_check_sha1s and (time.time() - self.last_push) < self.max_delay:
if len(list_check_sha1s) < self.indexer.batch_update_size and (
time.time() - self.last_push) < self.max_delay:
time.sleep(1)
continue
self.nb_imgs_check += len(list_check_sha1s)
push_delay = (time.time() - self.last_push) > max(
int(self.max_delay / 60), 10)
if push_delay and self.nb_imgs_unproc_lastprint != self.nb_imgs_unproc:
msg = "[{}: log] Pushed {} unprocessed images so far"
print(
msg.format(self.pp, self.nb_imgs_unproc,
self.nb_imgs_check))
self.nb_imgs_unproc_lastprint = self.nb_imgs_unproc
if list_check_sha1s:
# Check which images have not been processed (or pushed in an update) yet
# This seems slow
start_check = time.time()
unprocessed_rows = self.get_unprocessed_rows(
list_check_sha1s)
msg = "[{}: log] Found {}/{} unprocessed images in {:.2f}s"
print(
msg.format(self.pp, len(unprocessed_rows),
len(list_check_sha1s),
time.time() - start_check))
if len(unprocessed_rows) != len(
list_check_sha1s) and self.verbose > 5:
already_processed = list(
set(list_check_sha1s) - set(unprocessed_rows))
msg = "[{}: log] Images ".format(self.pp)
for ap in already_processed:
msg += "{} ".format(ap)
msg += "were already processed."
print(msg)
#unprocessed_rows = self.get_unprocessed_rows(list_check_sha1s)
# TODO: we should mark those images as being 'owned' by the update we are constructing
# (only important if we are running multiple threads i.e. daemon is True)
# otherwise another update running at the same time could also claim it (in another ad)
# could be handle when adding data to the searcher but duplicates in extraction process...
# Push sha1s to be processed
for sha1 in unprocessed_rows:
list_sha1s_to_process.append(sha1)
# Remove potential duplicates
list_sha1s_to_process = list(set(list_sha1s_to_process))
list_check_sha1s = []
if list_sha1s_to_process:
# Push them to HBase by batch of 'batch_update_size'
push_delay = (time.time() -
self.last_push) > self.max_delay
full_batch = len(list_sha1s_to_process
) >= self.indexer.batch_update_size
if full_batch or (push_delay and list_sha1s_to_process):
# Trim here to push exactly a batch of 'batch_update_size'
list_push = list_sha1s_to_process[:min(
self.indexer.
batch_update_size, len(list_sha1s_to_process))]
# TODO: this should be done before,
# to 'claim' the images as soon as we plan to process them for this update
# Gather corresponding sha1 infos
dict_push, update_id = self.get_dict_push(
list_push, daemon=daemon)
if dict_push:
self.nb_imgs_unproc += len(dict_push.keys())
msg = "[{}: at {}] Pushing update {} of {} images."
now_str = datetime.now().strftime(
'%Y-%m-%d:%H.%M.%S')
print(
msg.format(self.pp, now_str, update_id,
len(dict_push.keys())))
sys.stdout.flush()
# Push images
fam = self.indexer.get_dictcf_sha1_table()
if self.verbose > 5:
msg = "[{}] Pushing images for update {} with fam {}"
print(msg.format(self.pp, update_id, fam))
sha1s_table = self.indexer.table_sha1infos_name
self.indexer.push_dict_rows(dict_push,
sha1s_table,
families=fam)
# Build HBase updates dict
dict_updates_db = dict()
now_str = datetime.now().strftime(
'%Y-%m-%d:%H.%M.%S')
list_sha1s_col = self.indexer.get_col_listsha1s()
dict_updates_db[update_id] = {
list_sha1s_col: ','.join(dict_push.keys()),
self.indexer.get_col_upcreate(): now_str
}
# Push it
fam = self.indexer.get_dictcf_update_table()
if self.verbose > 5:
msg = "[{}] Pushing update {} info with fam {}"
print(msg.format(self.pp, update_id, fam))
self.indexer.push_dict_rows(
dict_updates_db,
self.indexer.table_updateinfos_name,
families=fam)
# Build pusher updates dict
if self.pusher is not None:
dict_updates_kafka = dict()
dict_updates_kafka[update_id] = ','.join(
dict_push.keys())
# Push it
#self.ingester.producer.send(self.updates_out_topic, json.dumps(dict_updates_kafka))
#self.pusher.send(self.updates_out_topic, dict_updates_kafka)
self.pusher.send(dict_updates_kafka)
# Gather any remaining sha1s and clean up infos
if len(list_sha1s_to_process
) > self.indexer.batch_update_size:
list_sha1s_to_process = list_sha1s_to_process[
self.indexer.batch_update_size:]
else:
list_sha1s_to_process = []
# if duplicates wrt list_push, remove them. Can this still happen?
list_sha1s_to_process = [
sh1 for sh1 in list_sha1s_to_process
if sh1 not in list_push
]
self.cleanup_dict_infos(list_push)
else:
msg = "[{}: at {}] Nothing to push for update {}"
print(
msg.format(
self.pp,
datetime.now().strftime(
'%Y-%m-%d:%H.%M.%S'), update_id))
sys.stdout.flush()
self.last_push = time.time()
# TODO: we should create a new update_id here,
# and let it claim the potential remaining images in 'list_sha1s_to_process'
# sanity check that len(list_sha1s_to_process) == len(self.dict_sha1_infos) ?
else:
if self.verbose > 3:
msg = "[{}: at {}] Gathered {} images so far..."
now_str = datetime.now().strftime(
'%Y-%m-%d:%H.%M.%S')
print(
msg.format(self.pp, now_str,
len(list_sha1s_to_process)))
except Exception as inst:
exc_type, exc_obj, exc_tb = sys.exc_info()
fulltb = traceback.format_tb(exc_tb)
raise type(inst)(" {} ({})".format(inst, ''.join(fulltb)))
class ExtractionChecker(ConfReader):
def __init__(self, global_conf, prefix=default_extr_check_prefix, pid=None):
self.list_extr_prefix = []
self.pid = pid
self.dict_sha1_infos = dict()
super(ExtractionChecker, self).__init__(global_conf, prefix)
self.featurizer_type = self.get_required_param("featurizer_type")
self.detector_type = self.get_required_param("detector_type")
self.input_type = self.get_required_param("input_type")
# Need to be build from extraction type and detection input + "_processed"
self.extr_family_column = "ext"
tmp_extr_family_column = self.get_param("extr_family_column")
if tmp_extr_family_column:
self.extr_family_column = tmp_extr_family_column
# Max delay
self.max_delay = 3600
# self.max_delay = 600
max_delay = self.get_param("max_delay")
if max_delay:
self.max_delay = int(max_delay)
self.last_push = time.time()
self.nb_imgs_check = 0
self.nb_imgs_unproc = 0
self.nb_imgs_unproc_lastprint = 0
# Beware, the self.extr_family_column should be added to the indexer families parameter in get_create_table...
# TODO: should we add the 'ad' column family too here by default
self.tablesha1_col_families = {'info': dict(), self.extr_family_column: dict()}
self.list_extr_prefix = [self.featurizer_type, "feat", self.detector_type, self.input_type]
self.extr_prefix = "_".join(self.list_extr_prefix)
self.extr_prefix_base_column_name = self.extr_family_column + ":" + self.extr_prefix
self.extr_check_column = self.extr_prefix_base_column_name + "_processed"
# Need to be build from extraction type and extraction input + "_batchid"
self.batch_check_column = self.extr_prefix_base_column_name + "_updateid"
self.check_columns = [self.extr_check_column, self.batch_check_column]
self.set_pp()
# Initialize indexer and ingester
self.indexer = HBaseIndexerMinimal(self.global_conf,
prefix=self.get_required_param("indexer_prefix"))
self.ingester = GenericKafkaProcessor(self.global_conf,
prefix=self.get_required_param("check_ingester_prefix"))
# This will not be set for HBase processing, but checker would keep dying here...
try:
self.updates_out_topic = self.ingester.get_required_param("producer_updates_out_topic")
except Exception as inst:
print "Could not initialize checker, sleeping for {}s.".format(self.max_delay)
time.sleep(self.max_delay)
raise(inst)
self.ingester.pp = "ec"
if self.pid:
self.ingester.pp += str(self.pid)
def set_pp(self):
self.pp = "ExtractionChecker."
self.pp += "-".join(self.list_extr_prefix)
if self.pid:
self.pp += "." + str(self.pid)
def store_img_infos(self, msg):
strk = str(msg['sha1'])
self.dict_sha1_infos[strk] = dict()
for k in msg:
# dumps json of 'img_info'
if k == "img_info":
self.dict_sha1_infos[strk][k] = json.dumps(msg[k])
else:
# discard 'img_buffer' (if it exists?...), and 'sha1'
# if k != "img_buffer" and k != "sha1":
# self.dict_sha1_infos[strk][k] = msg[k]
# discard 'sha1'
if k != "sha1":
self.dict_sha1_infos[strk][k] = msg[k]
def cleanup_dict_infos(self, list_del_sha1s):
for sha1 in list_del_sha1s:
try:
del self.dict_sha1_infos[str(sha1)]
except:
# could happen when cleaning up duplicates or image processed by another process
pass
def get_dict_push(self, list_get_sha1s):
# TODO: also pass current update_id, and move the creation of update id out of this method
# this method should actually be used to 'claim' an image as soon as we can.
dict_push = dict()
# append unique processid to 'update_id' to make it safe to use with multiple consumers, even after a restart.
# /!\ beware, it should not contain underscores
tmp_update_id, _ = self.indexer.get_next_update_id(today=None, extr_type=self.extr_prefix)
update_id = tmp_update_id + '-' + self.ingester.pp + '-' + str(time.time())
for sha1 in list_get_sha1s:
dict_push[str(sha1)] = dict()
try:
tmp_dict = self.dict_sha1_infos[str(sha1)]
except:
# This would mean the image has been marked as part of another batch by another process,
# and thus deleted in a previous 'get_unprocessed_rows' call
del dict_push[str(sha1)]
continue
# build column names properly i.e. appending 'info:'
for k in tmp_dict:
dict_push[str(sha1)]['info:' + k] = tmp_dict[k]
dict_push[str(sha1)][self.batch_check_column] = update_id
return dict_push, update_id
def get_unprocessed_rows(self, list_sha1s_to_check):
# TODO: also pass current update_id and only delete if != from current update...
unprocessed_rows = set(list_sha1s_to_check)
if list_sha1s_to_check:
# Check if the selected sha1 rows in HBase table 'sha1infos' have those check_column
# This call will only return rows that DO have those check_column
sha1s_rows = self.indexer.get_columns_from_sha1_rows(list_sha1s_to_check, self.check_columns,
families=self.tablesha1_col_families)
if sha1s_rows:
# TODO: only delete if really previously processed, i.e. if != from current update...
found_sha1_rows = set([str(row[0]) for row in sha1s_rows])
# Clean up 'dict_sha1_infos' deleting found_sha1_rows
self.cleanup_dict_infos(found_sha1_rows)
set_list_sha1s_to_check = set(list_sha1s_to_check)
# TODO: but we should not re-add them, so we should discard them from unprocessed_rows
unprocessed_rows = set_list_sha1s_to_check - found_sha1_rows
return unprocessed_rows
def run(self):
try:
list_sha1s_to_process = []
# TODO: create update_id here
while True:
list_sha1s_to_check = []
try:
# Accumulate images infos
for msg_json in self.ingester.consumer:
msg = json.loads(msg_json.value)
list_sha1s_to_check.append(str(msg['sha1']))
# Store other fields to be able to push them too
self.store_img_infos(msg)
if len(list_sha1s_to_check) >= self.indexer.batch_update_size:
break
except Exception as inst:
# trying to use 'consumer_timeout_ms' to raise timeout and get last samples
warn_msg = "[{}: warning] At {}, caught {} {} in consumer loop"
print warn_msg.format(self.pp, datetime.now().strftime('%Y-%m-%d:%H.%M.%S'), type(inst), inst)
sys.stdout.flush()
# Check which images have not been processed (or pushed in an update) yet
unprocessed_rows = self.get_unprocessed_rows(list_sha1s_to_check)
self.nb_imgs_check += len(list_sha1s_to_check)
if (
time.time() - self.last_push) > self.max_delay / 60 and self.nb_imgs_unproc_lastprint != self.nb_imgs_unproc:
msg_log = "[{}: log] Found {}/{} unprocessed images"
print msg_log.format(self.pp, self.nb_imgs_unproc, self.nb_imgs_check)
self.nb_imgs_unproc_lastprint = self.nb_imgs_unproc
# TODO: we should mark those images as being 'owned' by the update we are constructing
# (only reallyimportant if we are running multiple threads...)
# otherwise another update running at the same time could also claim it (if it appears in another ad)
# this can be handle when adding data to the searcher but induces duplicates in extraction process...
# Push sha1s to be processed
for sha1 in unprocessed_rows:
list_sha1s_to_process.append(sha1)
# Remove potential duplicates
list_sha1s_to_process = list(set(list_sha1s_to_process))
if list_sha1s_to_process:
# Push them to HBase by batch of 'batch_update_size'
if len(list_sha1s_to_process) >= self.indexer.batch_update_size or (
(time.time() - self.last_push) > self.max_delay and len(list_sha1s_to_process) > 0):
# Trim here to push exactly a batch of 'batch_update_size'
list_push = list_sha1s_to_process[:min(self.indexer.batch_update_size, len(list_sha1s_to_process))]
# TODO: this should be done before, to 'claim' the images as soon as we plan to process them for this update
# Gather corresponding sha1 infos
dict_push, update_id = self.get_dict_push(list_push)
if dict_push:
self.nb_imgs_unproc += len(dict_push.keys())
push_msg = "[{}: at {}] Pushing update {} of {} images."
print push_msg.format(self.pp, datetime.now().strftime('%Y-%m-%d:%H.%M.%S'), update_id,
len(dict_push.keys()))
sys.stdout.flush()
# Push images
self.indexer.push_dict_rows(dict_push, self.indexer.table_sha1infos_name,
families=self.tablesha1_col_families)
# Build updates dict
dict_updates_db = dict()
dict_updates_kafka = dict()
dict_updates_db[update_id] = {self.indexer.column_list_sha1s: ','.join(dict_push.keys()),
'info:' + update_str_created: datetime.now().strftime('%Y-%m-%d:%H.%M.%S')}
dict_updates_kafka[update_id] = ','.join(dict_push.keys())
# Push them
self.indexer.push_dict_rows(dict_updates_db, self.indexer.table_updateinfos_name)
self.ingester.producer.send(self.updates_out_topic, json.dumps(dict_updates_kafka))
# Gather any remaining sha1s and clean up infos
if len(list_sha1s_to_process) > self.indexer.batch_update_size:
list_sha1s_to_process = list_sha1s_to_process[self.indexer.batch_update_size:]
else:
list_sha1s_to_process = []
# if duplicates wrt list_push, remove them. Can this still happen?
list_sha1s_to_process = [sha1 for sha1 in list_sha1s_to_process if sha1 not in list_push]
self.cleanup_dict_infos(list_push)
else:
no_push_msg = "[{}: at {}] Nothing to push for update {}"
print no_push_msg.format(self.pp, datetime.now().strftime('%Y-%m-%d:%H.%M.%S'), update_id)
sys.stdout.flush()
self.last_push = time.time()
# TODO: we should create a new update_id here, and let it claim the potential remaining images in 'list_sha1s_to_process'
# sanity check that len(list_sha1s_to_process) == len(self.dict_sha1_infos) ?
except Exception as inst:
exc_type, exc_obj, exc_tb = sys.exc_info()
fulltb = traceback.format_tb(exc_tb)
raise type(inst)(" {} ({})".format(inst, ''.join(fulltb)))
from cufacesearch.indexer.hbase_indexer_minimal import HBaseIndexerMinimal
from cufacesearch.detector.utils import show_bbox_from_URL
import numpy as np
import base64
hbim = HBaseIndexerMinimal('../conf/global_conf_test_get_face_hbase.json')
list_sha1s = ['000000D29139258BD3716C94A68CFF54A8A7C033', '000001BF13372B9665A89ED25E8948FC7F99F7F1']
# TODO: use column_family and column_name from indexer
rows = hbim.get_columns_from_sha1_rows(list_sha1s, ['face', 'info:s3_url'])
for sha1, data in rows:
print sha1, data
url = data['info:s3_url']
for key in data:
if key.startswith('face:'):
face_bbox = key.split('face:dlib_feat_dlib_face_')[-1].split('_')
feat_b64 = np.frombuffer(base64.b64decode(data[key]), dtype=np.float32)
print feat_b64.shape, feat_b64
show_bbox_from_URL(url, map(int, face_bbox), close_after=1)
# This should be run in the docker
from __future__ import print_function
from cufacesearch.indexer.hbase_indexer_minimal import HBaseIndexerMinimal
import sys
start_row = '0' * 40
# Change that
hbim = HBaseIndexerMinimal(
'../conf/generated/conf_extraction_lfw_local_dlib.json')
#hbim.get_updates_from_date()
nb_face = 0
nb_image = 0
prev_row = '~'
curr_row = start_row
#print(curr_row)
file_names = []
sha1s = []
#print('Scanning', end='', flush=True)
sys.stdout.write('Scanning')
sys.stdout.flush()
while prev_row != curr_row + '~':
prev_row = curr_row
#if prev_row != start_row:
prev_row += '~'
#print(prev_row)
#print('.', end='', flush=True)
sys.stdout.write('.')
sys.stdout.flush()
for row in hbim.scan_from_row(hbim.table_sha1infos_name,
row_start=prev_row,
