104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
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import argparse
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import warnings
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import mmcv
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import numpy as np
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from mmcv import DictAction
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from mmcv.parallel import MMDataParallel
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from mmcls.apis import single_gpu_test
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from mmcls.core.export import ONNXRuntimeClassifier
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from mmcls.datasets import build_dataloader, build_dataset
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Test (and eval) an ONNX model using ONNXRuntime.')
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parser.add_argument('config', help='model config file')
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parser.add_argument('model', help='filename of the input ONNX model')
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parser.add_argument(
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'--out', type=str, help='output result file in pickle format')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file.')
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parser.add_argument(
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'--metrics',
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type=str,
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nargs='+',
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help='evaluation metrics, which depends on the dataset, e.g., '
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'"accuracy", "precision", "recall", "f1_score", "support" for single '
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'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
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'multi-label dataset')
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parser.add_argument(
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'--metric-options',
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nargs='+',
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action=DictAction,
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default={},
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help='custom options for evaluation, the key-value pair in xxx=yyy '
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'format will be parsed as a dict metric_options for dataset.evaluate()'
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' function.')
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parser.add_argument('--show', action='store_true', help='show results')
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parser.add_argument(
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'--show-dir', help='directory where painted images will be saved')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
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raise ValueError('The output file must be a pkl file.')
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cfg = mmcv.Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# build dataset and dataloader
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dataset = build_dataset(cfg.data.test)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=cfg.data.samples_per_gpu,
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workers_per_gpu=cfg.data.workers_per_gpu,
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shuffle=False,
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round_up=False)
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# build onnxruntime model and run inference.
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model = ONNXRuntimeClassifier(
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args.model, class_names=dataset.CLASSES, device_id=0)
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model = MMDataParallel(model, device_ids=[0])
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outputs = single_gpu_test(model, data_loader, args.show, args.show_dir)
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if args.metrics:
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results = dataset.evaluate(outputs, args.metrics, args.metric_options)
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for k, v in results.items():
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print(f'\n{k} : {v:.2f}')
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else:
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warnings.warn('Evaluation metrics are not specified.')
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scores = np.vstack(outputs)
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pred_score = np.max(scores, axis=1)
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pred_label = np.argmax(scores, axis=1)
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pred_class = [dataset.CLASSES[lb] for lb in pred_label]
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results = {
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'pred_score': pred_score,
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'pred_label': pred_label,
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'pred_class': pred_class
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}
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if not args.out:
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print('\nthe predicted result for the first element is '
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f'pred_score = {pred_score[0]:.2f}, '
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f'pred_label = {pred_label[0]} '
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f'and pred_class = {pred_class[0]}. '
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'Specify --out to save all results to files.')
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if args.out:
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print(f'\nwriting results to {args.out}')
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mmcv.dump(results, args.out)
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if __name__ == '__main__':
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main()
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