def main():
single_image_applier = YOLOSingleImageApplier()
default_inference_mode_config = InfModeFullImage.make_default_config(model_result_suffix='_yolo')
dataset_applier = BatchInferenceApplier(single_image_inference=single_image_applier,
default_inference_mode_config=default_inference_mode_config,
config_validator=YoloJsonConfigValidator())
dataset_applier.run_inference()
def main():
# Initially instantiate an inference applier without loading the weights.
# This will parse, merge and validate all the configs and let us know
# the number of inference processes to use.
single_image_applier = YOLOSingleImageApplier(_load_model_weights=False)
num_processes = single_image_applier._config[NUM_PROCESSES]
default_inference_mode_config = InfModeFullImage.make_default_config(model_result_suffix='_yolo')
config_validator = common.YoloJsonConfigValidator()
if num_processes == 1:
# Use the usual fully serial inference logic if only one inference process was requested.
single_image_applier._construct_and_fill_model()
dataset_applier = BatchInferenceApplier(single_image_inference=single_image_applier,
default_inference_mode_config=default_inference_mode_config,
config_validator=config_validator)
else:
# More than one inference process was requested - use the new parallelized logic.
def yolo_initializer():
return YOLOSingleImageApplier()
dataset_applier = BatchInferenceMultiprocessApplier(single_image_inference_initializer=yolo_initializer,
num_processes=num_processes,
default_inference_mode_config=default_inference_mode_config,
config_validator=config_validator)
dataset_applier.run_inference()
def __init__(self, logger, model_applier: SingleImageInferenceInterface,
conn_config, cache):
self.logger = logger
self.model_applier = model_applier
self._default_inference_mode_config = InfModeFullImage.make_default_config(
model_result_suffix=MODEL_RESULT_SUFFIX)
self.logger.info('Created InactiveRPCServicer for internal usage',
extra=conn_config)
def main():
# Bring up the model for inference (process class mapping configs; load model weights to the GPU).
single_image_applier = PytorchSegmentationApplier(model_factory_fn=model_factory_fn)
# By default simply use the full image as model input. Other inference modes are possible, see
# supervisely_lib/nn/hosted/inference_modes.py
default_inference_mode_config = InfModeFullImage.make_default_config(
model_result_suffix='_pytorch_segm_example')
# IO wrapper to read inputs and save results in supervisely format within the context of a supervisely
# agent task.
dataset_applier = BatchInferenceApplier(single_image_inference=single_image_applier,
default_inference_mode_config=default_inference_mode_config)
# Process the input images and write out results.
dataset_applier.run_inference()
def __init__(self, logger, model_applier: SingleImageInferenceInterface, conn_config, cache):
self.logger = logger
self.api = AgentAPI(token=conn_config['token'],
server_address=conn_config['server_address'],
ext_logger=self.logger)
self.api.add_to_metadata('x-task-id', conn_config['task_id'])
self.model_applier = model_applier
self.thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=10)
self.download_queue = Queue(maxsize=self.QUEUE_MAX_SIZE)
self.final_processing_queue = Queue(maxsize=self.QUEUE_MAX_SIZE)
self.image_cache = cache
self._default_inference_mode_config = InfModeFullImage.make_default_config(
model_result_suffix=MODEL_RESULT_SUFFIX)
self.logger.info('Created AgentRPCServicer', extra=conn_config)
