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| """Demo based on ModelAPI.""" | |
| # Copyright (C) 2021-2022 Intel Corporation | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| import os | |
| import sys | |
| from argparse import SUPPRESS, ArgumentParser | |
| from pathlib import Path | |
| os.environ["FEATURE_FLAGS_OTX_ACTION_TASKS"] = "1" | |
| # pylint: disable=no-name-in-module, import-error | |
| from otx.api.usecases.exportable_code.demo.demo_package import ( | |
| AsyncExecutor, | |
| ChainExecutor, | |
| ModelContainer, | |
| SyncExecutor, | |
| create_visualizer, | |
| ) | |
| def build_argparser(): | |
| """Parses command line arguments.""" | |
| parser = ArgumentParser(add_help=False) | |
| args = parser.add_argument_group("Options") | |
| args.add_argument( | |
| "-h", | |
| "--help", | |
| action="help", | |
| default=SUPPRESS, | |
| help="Show this help message and exit.", | |
| ) | |
| args.add_argument( | |
| "-i", | |
| "--input", | |
| required=True, | |
| help="Required. An input to process. The input must be a single image, " | |
| "a folder of images, video file or camera id.", | |
| ) | |
| args.add_argument( | |
| "-m", | |
| "--models", | |
| help="Optional. Path to directory with trained model and configuration file. " | |
| "If you provide several models you will start the task chain pipeline with " | |
| "the provided models in the order in which they were specified. Default value " | |
| "points to deployed model folder '../model'.", | |
| nargs="+", | |
| default=[Path("../model")], | |
| type=Path, | |
| ) | |
| args.add_argument( | |
| "-it", | |
| "--inference_type", | |
| help="Optional. Type of inference for single model.", | |
| choices=["sync", "async"], | |
| default="sync", | |
| type=str, | |
| ) | |
| args.add_argument( | |
| "-l", | |
| "--loop", | |
| help="Optional. Enable reading the input in a loop.", | |
| default=False, | |
| action="store_true", | |
| ) | |
| args.add_argument( | |
| "--no_show", | |
| help="Optional. Disables showing inference results on UI.", | |
| default=False, | |
| action="store_true", | |
| ) | |
| args.add_argument( | |
| "-d", | |
| "--device", | |
| help="Optional. Device to infer the model.", | |
| choices=["CPU", "GPU"], | |
| default="CPU", | |
| type=str, | |
| ) | |
| args.add_argument( | |
| "--output", | |
| default=None, | |
| type=str, | |
| help="Optional. Output path to save input data with predictions.", | |
| ) | |
| return parser | |
| EXECUTORS = { | |
| "sync": SyncExecutor, | |
| "async": AsyncExecutor, | |
| "chain": ChainExecutor, | |
| } | |
| def get_inferencer_class(type_inference, models): | |
| """Return class for inference of models.""" | |
| if len(models) > 1: | |
| type_inference = "chain" | |
| print("You started the task chain pipeline with the provided models in the order in which they were specified") | |
| return EXECUTORS[type_inference] | |
| def main(): | |
| """Main function that is used to run demo.""" | |
| args = build_argparser().parse_args() | |
| if args.loop and args.output: | |
| raise ValueError("--loop and --output cannot be both specified") | |
| # create models | |
| models = [] | |
| for model_dir in args.models: | |
| model = ModelContainer(model_dir, device=args.device) | |
| models.append(model) | |
| inferencer = get_inferencer_class(args.inference_type, models) | |
| # create visualizer | |
| visualizer = create_visualizer(models[-1].task_type, no_show=args.no_show, output=args.output) | |
| if len(models) == 1: | |
| models = models[0] | |
| # create inferencer and run | |
| demo = inferencer(models, visualizer) | |
| demo.run(args.input, args.loop and not args.no_show) | |
| if __name__ == "__main__": | |
| sys.exit(main() or 0) | |