def __init__(self):
logger.info('Will init all required to inference.')
self.helper = sly.TaskHelperInference()
self._determine_settings()
self._load_train_config()
self._construct_and_fill_model()
self._determine_input_data()
self.debug_copy_images = os.getenv('DEBUG_COPY_IMAGES') is not None
logger.info('Model is ready to inference.')
def main():
# Please note that auxiliary methods from sly (supervisely_lib) use supervisely_lib.logger to format output.
# So don't replace formatters or handlers of the logger.
# One may use other loggers or simple prints for other output, but it's recommended to use supervisely_lib.logger.
logger.info('Hello ML world')
print('Glad to see u')
# TaskHelperTrain contains almost all needed to run inference as Supervisely task,
# including task settings and paths to data and model.
task_helper = sly.TaskHelperInference()
# All settings and parameters are passed to task in json file.
# Content of the file is entirely dependent on model implementation.
inference_settings = task_helper.task_settings
logger.info('Task settings are read',
extra={'task_settings': inference_settings})
# Let's imitate loading training model weights. Task acquires directory with the weights.
# And content of the directory is entirely dependent on model implementation.
model_dir = task_helper.paths.model_dir
model = load_fake_model(model_dir)
logger.info('Model weights are loaded', extra={'model_dir': model_dir})
# We are going to read input project with data for inference.
project_meta = task_helper.in_project_meta # Project meta contains list of project classes.
project_dir = task_helper.paths.project_dir
project_fs = sly.ProjectFS.from_disk_dir_project(project_dir)
# ProjectFS enlists all samples (image/annotation pairs) in input project.
# We are to write inference results as sly project with same structure into provided results dir.
# There is no need to save images, only annotations and project meta are required.
results_dir = task_helper.paths.results_dir
out_project_fs = sly.ProjectFS(results_dir, project_fs.pr_structure)
# It's necessary to write project meta (with list of classes) for output project.
out_meta = sly.ProjectMeta([{
'title': 'hat',
'shape': 'point',
'color': '#FF0000'
}]) # create meta
out_meta.to_dir(out_project_fs.project_path) # and save
# We are to report progress of task over sly.ProgressCounter if we want to observe the progress in web panel.
# In fact one task may report progress for some sequential (not nested) subtasks,
# but here we will report inference progress only.
ia_cnt = out_project_fs.pr_structure.image_cnt
progress = sly.progress_counter_inference(cnt_imgs=ia_cnt)
# Iterating over samples (image/annotation pairs) in input project.
for item_descr in project_fs:
logger.info('Processing input sample',
extra={
'dataset': item_descr.ds_name,
'image_name': item_descr.image_name
})
# Open some image...
img = cv2.imread(item_descr.img_path)
logger.info('Read image from input project',
extra={
'width': img.shape[1],
'height': img.shape[0]
})
# And read corresponding annotation...
ann_packed = sly.json_load(item_descr.ann_path)
ann = sly.Annotation.from_packed(ann_packed, project_meta)
logger.info('Read annotation from input project',
extra={'tags': ann['tags']})
logger.info('Some forward pass...')
time.sleep(1)
# Let's imitate inference output.
# We are to save results as sly Figures in annotation.
ann['objects'] = [sly.FigurePoint.from_pt('hat', (800, 800))
] # imagine that our model found the point
out_ann_path = out_project_fs.ann_path(item_descr.ds_name,
item_descr.image_name)
sly.ensure_base_path(out_ann_path) # create intermediate directories
sly.json_dump(ann.pack(), out_ann_path) # and save annotation
# Note that there is no need to save image.
progress.iter_done_report(
) # call it after every iteration to report progress
# It's necessary to report that the inference task is finished.
sly.report_inference_finished()
# Thank you for your patience.
logger.info('Applying finished.')