def inference(self, pl):
t0 = time.time()
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(time.time() - t0))
t1 = time.time()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(time.time() - t1))
t2 = time.time()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(time.time() - t2))
classes = self.engine.classes
labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes), labels))
def inference(self, pl):
duration = lambda t: (datetime.now() - t).microseconds / 1000
t = datetime.now()
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(duration(t)))
#classes = self.engine.classes
#labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
#self.result_hook(
# draw_bb(bgr_array,
# self.generalize_result(jpg_json, model_outputs),
# generate_class_color(class_num=classes),
# labels))
self.result_hook(self.generalize_result(jpg_json, model_outputs))
def inference(self, pl):
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
bgr_array = payload.jpg2bgr(jpg_bytes)
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
self.result_hook(self.generalize_result(jpg_json, model_outputs))
def inference(self, pl):
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
bgr_array = payload.jpg2bgr(jpg_bytes)
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
classes = self.engine.classes
labels = self.engine.labels
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes), labels))
def dl_inference(self, pl):
def empty_inference_result(count):
return [{'channel': i, 'annotations': []} for i in range(count)]
t = datetime.now()
base_name = None
logger.debug('counter #{}'.format(self.counter))
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
# Unify the type of input payload to a list, so that
# bnpipeline can process the input in the same way.
#
# If the payload is
# - a list of items: keep the list
# - a single item: convert to a list with an item
mqtt_payload = payload.deserialize_payload(pl.decode('utf-8'))
if isinstance(mqtt_payload, list):
jpg_json = mqtt_payload
else:
jpg_json = [mqtt_payload]
logger.info('Convert input type from {0} to {1}'.format(
type(mqtt_payload), type(jpg_json)))
jpg_bytes_list = [
payload.destringify_jpg(img['bytes']) for img in jpg_json
]
metas = [img.get('meta', {}) for img in jpg_json]
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_arrays = [
payload.jpg2bgr(jpg_bytes) for jpg_bytes in jpg_bytes_list
]
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
# FIXME: Galaxy pipeline may or may not use a list as input, so we
# check the length here and then choose whether to send a list or not.
# We may drop it when Galaxy Pipline unite their input.
if len(bgr_arrays) > 1:
image_data = self.engine.process_input(bgr_arrays)
else:
image_data = self.engine.process_input(bgr_arrays[0])
# FIXME: Galaxy pipeline doesn't support multiple metadata for multiple
# images at the moment (which will be needed), so we provide the first
# metadata here. This commit should be revert when Galaxy pipeline
# support it: https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/120
meta_data = metas[0]
try:
logger.debug(meta_data)
output = self.engine.inference(image_data,
meta=meta_data,
base_name=base_name)
model_outputs = self.engine.process_output(output)
except IndexError as e:
# FIXME: workaround for pipeline
# Pipeline throw IndexError when there's no results, see:
# https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/86
# So we catch the exception, and produce a dummy result
# to hook. This workaround should be removed after the issue
# has been fixed.
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'pipeline raised IndexError'))
if model_outputs is None:
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'severe error happened in pipeline'))
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} ms'.format(duration(t)))
# self.engine.cache_data('model_output', model_outputs)
# self.engine.cache_data('model_output_filepath', output_name)
# self.engine.save_cache()
self.send_result(self.generalize_result(jpg_json, model_outputs))
self.counter += 1
def dl_inference(self, pl):
def empty_inference_result(count):
return [{'channel': i, 'annotations': []} for i in range(count)]
t = datetime.now()
base_name = None
logger.debug('counter #{}'.format(self.counter))
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes_list = [
payload.destringify_jpg(img['bytes']) for img in jpg_json
]
metas = [img.get('meta', {}) for img in jpg_json]
logger.debug('destringify_jpg: {} ms'.format(duration(t)))
t = datetime.now()
bgr_arrays = [
payload.jpg2bgr(jpg_bytes) for jpg_bytes in jpg_bytes_list
]
logger.debug('jpg2bgr: {} ms'.format(duration(t)))
t = datetime.now()
image_data = self.engine.process_input(bgr_arrays)
# FIXME: Galaxy pipeline doesn't support multiple metadata for multiple
# images at the moment (which will be needed), so we provide the first
# metadata here. This commit should be revert when Galaxy pipeline
# support it: https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/120
meta_data = metas[0]
try:
logger.debug(meta_data)
output = self.engine.inference(image_data,
meta=meta_data,
base_name=base_name)
model_outputs = self.engine.process_output(output)
except IndexError as e:
# FIXME: workaround for pipeline
# Pipeline throw IndexError when there's no results, see:
# https://gitlab.com/DT42/galaxy42/dt42-trainer/issues/86
# So we catch the exeception, and produce a dummy result
# to hook. This workaround should be removed after the issue
# has been fixed.
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'pipeline raised IndexError'))
if model_outputs is None:
model_outputs = empty_inference_result(len(jpg_json))
logger.warning(('inference results are empty because '
'severe error happened in pipeline'))
if isinstance(model_outputs, dict):
model_outputs = [model_outputs]
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Classification takes {} ms'.format(duration(t)))
# self.engine.cache_data('model_output', model_outputs)
# self.engine.cache_data('model_output_filepath', output_name)
# self.engine.save_cache()
self.send_result(self.generalize_result(jpg_json, model_outputs),
self.output_mqtt_topic)
self.counter += 1