def generalize_result(self, eng_inputs, eng_outputs):
# Pipeline returns None if any error happened
if eng_outputs is None:
eng_outputs = [{}]
if len(eng_inputs) != len(eng_outputs):
logger.warning('Input length != output length: {} != {}'.format(
len(eng_inputs), len(eng_outputs)))
# We guarantee len of inputs will always be 1 (at least now), so
# it's safer to access eng_inputs by index than to eng_outputs
c_id = int(eng_inputs[0]['meta']['channel_id'])
eng_outputs = [eng_outputs[c_id]]
# FIXME: Workaround for spec incompatibility
# DLBox spec use 'image_blob', but BerryNet use 'bytes', so we have to
# do a convert here
for eng_in, eng_out in list(zip(eng_inputs, eng_outputs)):
if isinstance(eng_out, np.ndarray):
r, result_img = cv2.imencode('.jpg', eng_out)
eng_in['bytes'] = payload.stringify_jpg(result_img)
else:
try:
eng_in.update(eng_out)
except KeyError as e:
logger.exception('{} ({}): {}'.format(
e.__class__, e.__doc__, e))
eng_in['image_blob'] = eng_in.pop('bytes')
return eng_inputs
def process_input(self, tensor):
"""Resize tensor (if needed) and change layout from HWC to CHW.
Args:
tensor: Input BGR tensor (OpenCV convention)
Returns:
Resized and transposed tensor
"""
if tensor.shape[:-1] != (self.h, self.w):
logger.warning("Input tensor is resized from {} to {}".format(
tensor.shape[:-1], (self.h, self.w)))
tensor = cv2.resize(tensor, (self.w, self.h))
tensor = tensor.transpose((2, 0, 1)) # Change data layout from HWC to CHW
return tensor
def get_dl_component_config(self, pipeline_config):
"""Get pipeline def list containing only DL components
Args:
pipeline_config: pipeline config JSON object
Returns:
List of DL components definitions
"""
dl_comp_config = []
try:
pipeline_def = pipeline_config['pipeline_def']
except KeyError:
logger.warning('Invalid pipeline config')
pipeline_def = []
for comp_config in pipeline_def:
if ('classifier' in comp_config['name']
or 'detector' in comp_config['name']):
dl_comp_config.append(comp_config)
return dl_comp_config
def handleConfig(self, pl):
payload_json = ""
try:
id = pl.decode('utf-8')
if (id in self.idlist):
configFilename = self.idlist[id]
f = open(configFilename)
payload_json = payload.deserialize_payload(f.read())
self.sendConfig(payload.serialize_payload(payload_json))
else:
logger.warning("ID %s is not in idlist" % (id))
return
except Exception as e:
logger.info(e)
# output config file
with open(self.comm_config['configfile'], 'w') as configfile:
configfile.write(payload.serialize_payload(payload_json))
configfile.close()
# restart service
subprocess.run(["supervisorctl", "restart", "bnpipeline-bndyda"])
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
