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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import warnings | |
| import mmcv | |
| from mmcv import Config, DictAction | |
| from mmcv.parallel import MMDataParallel | |
| from mmdet.apis import single_gpu_test | |
| from mmdet.datasets import (build_dataloader, build_dataset, | |
| replace_ImageToTensor) | |
| from mmdet.utils import compat_cfg | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='MMDet test (and eval) an ONNX model using ONNXRuntime') | |
| parser.add_argument('config', help='test config file path') | |
| parser.add_argument('model', help='Input model file') | |
| parser.add_argument('--out', help='output result file in pickle format') | |
| parser.add_argument( | |
| '--format-only', | |
| action='store_true', | |
| help='Format the output results without perform evaluation. It is' | |
| 'useful when you want to format the result to a specific format and ' | |
| 'submit it to the test server') | |
| parser.add_argument( | |
| '--backend', | |
| required=True, | |
| choices=['onnxruntime', 'tensorrt'], | |
| help='Backend for input model to run. ') | |
| parser.add_argument( | |
| '--eval', | |
| type=str, | |
| nargs='+', | |
| help='evaluation metrics, which depends on the dataset, e.g., "bbox",' | |
| ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') | |
| parser.add_argument('--show', action='store_true', help='show results') | |
| parser.add_argument( | |
| '--show-dir', help='directory where painted images will be saved') | |
| parser.add_argument( | |
| '--show-score-thr', | |
| type=float, | |
| default=0.3, | |
| help='score threshold (default: 0.3)') | |
| parser.add_argument( | |
| '--cfg-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='override some settings in the used config, the key-value pair ' | |
| 'in xxx=yyy format will be merged into config file. If the value to ' | |
| 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' | |
| 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' | |
| 'Note that the quotation marks are necessary and that no white space ' | |
| 'is allowed.') | |
| parser.add_argument( | |
| '--eval-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='custom options for evaluation, the key-value pair in xxx=yyy ' | |
| 'format will be kwargs for dataset.evaluate() function') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| assert args.out or args.eval or args.format_only or args.show \ | |
| or args.show_dir, \ | |
| ('Please specify at least one operation (save/eval/format/show the ' | |
| 'results / save the results) with the argument "--out", "--eval"' | |
| ', "--format-only", "--show" or "--show-dir"') | |
| if args.eval and args.format_only: | |
| raise ValueError('--eval and --format_only cannot be both specified') | |
| if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): | |
| raise ValueError('The output file must be a pkl file.') | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| cfg = compat_cfg(cfg) | |
| # in case the test dataset is concatenated | |
| samples_per_gpu = 1 | |
| if isinstance(cfg.data.test, dict): | |
| cfg.data.test.test_mode = True | |
| samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) | |
| if samples_per_gpu > 1: | |
| # Replace 'ImageToTensor' to 'DefaultFormatBundle' | |
| cfg.data.test.pipeline = replace_ImageToTensor( | |
| cfg.data.test.pipeline) | |
| elif isinstance(cfg.data.test, list): | |
| for ds_cfg in cfg.data.test: | |
| ds_cfg.test_mode = True | |
| samples_per_gpu = max( | |
| [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) | |
| if samples_per_gpu > 1: | |
| for ds_cfg in cfg.data.test: | |
| ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) | |
| # build the dataloader | |
| dataset = build_dataset(cfg.data.test) | |
| data_loader = build_dataloader( | |
| dataset, | |
| samples_per_gpu=samples_per_gpu, | |
| workers_per_gpu=cfg.data.workers_per_gpu, | |
| dist=False, | |
| shuffle=False) | |
| if args.backend == 'onnxruntime': | |
| from mmdet.core.export.model_wrappers import ONNXRuntimeDetector | |
| model = ONNXRuntimeDetector( | |
| args.model, class_names=dataset.CLASSES, device_id=0) | |
| elif args.backend == 'tensorrt': | |
| from mmdet.core.export.model_wrappers import TensorRTDetector | |
| model = TensorRTDetector( | |
| args.model, class_names=dataset.CLASSES, device_id=0) | |
| model = MMDataParallel(model, device_ids=[0]) | |
| outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, | |
| args.show_score_thr) | |
| if args.out: | |
| print(f'\nwriting results to {args.out}') | |
| mmcv.dump(outputs, args.out) | |
| kwargs = {} if args.eval_options is None else args.eval_options | |
| if args.format_only: | |
| dataset.format_results(outputs, **kwargs) | |
| if args.eval: | |
| eval_kwargs = cfg.get('evaluation', {}).copy() | |
| # hard-code way to remove EvalHook args | |
| for key in [ | |
| 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', | |
| 'rule' | |
| ]: | |
| eval_kwargs.pop(key, None) | |
| eval_kwargs.update(dict(metric=args.eval, **kwargs)) | |
| print(dataset.evaluate(outputs, **eval_kwargs)) | |
| if __name__ == '__main__': | |
| main() | |
| # Following strings of text style are from colorama package | |
| bright_style, reset_style = '\x1b[1m', '\x1b[0m' | |
| red_text, blue_text = '\x1b[31m', '\x1b[34m' | |
| white_background = '\x1b[107m' | |
| msg = white_background + bright_style + red_text | |
| msg += 'DeprecationWarning: This tool will be deprecated in future. ' | |
| msg += blue_text + 'Welcome to use the unified model deployment toolbox ' | |
| msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy' | |
| msg += reset_style | |
| warnings.warn(msg) | |