def run_inference(self):
inference_mode = InferenceModeFactory.create(
self._inference_mode_config, self._in_project.meta,
self._single_image_inference)
out_project = Project(
os.path.join(TaskPaths.RESULTS_DIR, self._in_project.name),
OpenMode.CREATE)
out_project.set_meta(inference_mode.out_meta)
progress_bar = Progress('Model applying: ',
self._in_project.total_items)
for in_dataset in self._in_project:
out_dataset = out_project.create_dataset(in_dataset.name)
for in_item_name in in_dataset:
# Use output project meta so that we get an annotation that is already in the context of the output
# project (with added object classes etc).
in_item_paths = in_dataset.get_item_paths(in_item_name)
in_img = sly_image.read(in_item_paths.img_path)
in_ann = Annotation.load_json_file(in_item_paths.ann_path,
inference_mode.out_meta)
logger.trace('Will process image',
extra={
'dataset_name': in_dataset.name,
'image_name': in_item_name
})
inference_annotation = inference_mode.infer_annotate(
in_img, in_ann)
out_dataset.add_item_file(in_item_name,
in_item_paths.img_path,
ann=inference_annotation)
progress_bar.iter_done_report()
report_inference_finished()
def run_inference(self):
progress_report_thread = Thread(target=progress_report_thread_fn,
args=(self._in_project,
self._progress_report_queue),
daemon=True)
progress_report_thread.start()
feed_status = populate_inference_requests_queue(
self._in_project, self._inference_processes,
self._inference_request_queue)
for _ in self._inference_processes:
self._inference_request_queue.put(None)
out_meta_json = self._result_meta_queue.get()
self._out_project.set_meta(ProjectMeta.from_json(out_meta_json))
for p in self._inference_processes:
p.join()
if not feed_status or not all(p.exitcode == 0
for p in self._inference_processes):
raise RuntimeError(
'One of the inference processes encountered an error.')
self._progress_report_queue.put(None)
progress_report_thread.join()
report_inference_finished()
def run_inference(self):
result_writer_thread = Thread(target=result_writer_thread_fn,
args=(self._in_project,
self._inference_result_queue),
daemon=True)
result_writer_thread.start()
feed_status = populate_inference_requests_queue(
self._in_project, self._inference_processes,
self._inference_request_queue)
for _ in self._inference_processes:
self._inference_request_queue.put(None)
for p in self._inference_processes:
p.join()
if not feed_status or not all(p.exitcode == 0
for p in self._inference_processes):
raise RuntimeError(
'One of the inference processes encountered an error.')
self._inference_result_queue.put(None)
result_writer_thread.join()
report_inference_finished()