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 single_inference_process_fn(inference_initializer, inference_mode_config,
in_project_meta_json, request_queue,
response_queue):
"""Loads a separate model, processes requests from request_queue, results go to result_queue.
None request signals the process to finish.
"""
single_image_inference = inference_initializer()
inference_mode = InferenceModeFactory.create(
inference_mode_config, ProjectMeta.from_json(in_project_meta_json),
single_image_inference)
out_meta_json = inference_mode.out_meta.to_json()
req = ''
while req is not None:
req = request_queue.get()
if req is not None:
in_img = sly_image.read(req.item_paths.img_path)
in_ann = Annotation.load_json_file(req.item_paths.ann_path,
inference_mode.out_meta)
ann = inference_mode.infer_annotate(in_img, in_ann)
resp = InferenceResponse(ds_name=req.ds_name,
item_name=req.item_name,
item_paths=req.item_paths,
ann_json=ann.to_json(),
meta_json=out_meta_json)
response_queue.put(resp)
request_queue.task_done()
def single_inference_process_fn(inference_initializer, inference_mode_config,
in_project_meta_json, request_queue,
result_meta_queue, progress_queue, project):
"""Loads a separate model, processes requests from request_queue, results go to result_queue.
None request signals the process to finish.
"""
single_image_inference = inference_initializer()
inference_mode = InferenceModeFactory.create(
inference_mode_config, ProjectMeta.from_json(in_project_meta_json),
single_image_inference)
project_meta_sent = False
req = ''
while req is not None:
req = request_queue.get()
if req is not None:
# Send the resulting project meta to the parent project to make sure we only write the meta JSON once.
if not project_meta_sent:
try:
result_meta_queue.put(inference_mode.out_meta.to_json(),
block=False)
except queue.Full:
pass
project_meta_sent = True
in_ann = Annotation.load_json_file(req.item_paths.ann_path,
inference_mode.out_meta)
ann = inference_mode.infer_annotate_image_file(
req.item_paths.img_path, in_ann)
out_dataset = project.datasets.get(req.ds_name)
out_dataset.add_item_file(req.item_name,
req.item_paths.img_path,
ann=ann,
_validate_img=False,
_use_hardlink=True)
progress_queue.put(1)
def _make_inference_mode(self, msg_inference_mode_config, in_project_meta):
inference_mode_config = get_effective_inference_mode_config(
msg_inference_mode_config,
deepcopy(self._default_inference_mode_config))
return InferenceModeFactory.create(inference_mode_config,
in_project_meta, self.model_applier)