mmdetection-2.26.0/tools/deployment/test.py

# 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)