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[Feature] add onnxruntime test tool (#498)
* add onnxruntime test tool, update pytorch2onnx to support slice export * onnx convert with custom output shape, update test code * update pytorch2onnx, add rescale_shape support, add document * update doc for lint error fixing * remove cpu flag in ort_test.py * change class name, fix cuda error * remote comment * fix bug of torch2onnx * mIOU to mIoU
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,191 @@ | ||
| import argparse | ||
| import os | ||
| import os.path as osp | ||
| import warnings | ||
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| import mmcv | ||
| import numpy as np | ||
| import onnxruntime as ort | ||
| import torch | ||
| from mmcv.parallel import MMDataParallel | ||
| from mmcv.runner import get_dist_info | ||
| from mmcv.utils import DictAction | ||
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| from mmseg.apis import single_gpu_test | ||
| from mmseg.datasets import build_dataloader, build_dataset | ||
| from mmseg.models.segmentors.base import BaseSegmentor | ||
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| class ONNXRuntimeSegmentor(BaseSegmentor): | ||
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| def __init__(self, onnx_file, cfg, device_id): | ||
| super(ONNXRuntimeSegmentor, self).__init__() | ||
| # get the custom op path | ||
| ort_custom_op_path = '' | ||
| try: | ||
| from mmcv.ops import get_onnxruntime_op_path | ||
| ort_custom_op_path = get_onnxruntime_op_path() | ||
| except (ImportError, ModuleNotFoundError): | ||
| warnings.warn('If input model has custom op from mmcv, \ | ||
| you may have to build mmcv with ONNXRuntime from source.') | ||
| session_options = ort.SessionOptions() | ||
| # register custom op for onnxruntime | ||
| if osp.exists(ort_custom_op_path): | ||
| session_options.register_custom_ops_library(ort_custom_op_path) | ||
| sess = ort.InferenceSession(onnx_file, session_options) | ||
| providers = ['CPUExecutionProvider'] | ||
| options = [{}] | ||
| is_cuda_available = ort.get_device() == 'GPU' | ||
| if is_cuda_available: | ||
| providers.insert(0, 'CUDAExecutionProvider') | ||
| options.insert(0, {'device_id': device_id}) | ||
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| sess.set_providers(providers, options) | ||
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| self.sess = sess | ||
| self.device_id = device_id | ||
| self.io_binding = sess.io_binding() | ||
| self.output_names = [_.name for _ in sess.get_outputs()] | ||
| for name in self.output_names: | ||
| self.io_binding.bind_output(name) | ||
| self.cfg = cfg | ||
| self.test_mode = cfg.model.test_cfg.mode | ||
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| def extract_feat(self, imgs): | ||
| raise NotImplementedError('This method is not implemented.') | ||
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| def encode_decode(self, img, img_metas): | ||
| raise NotImplementedError('This method is not implemented.') | ||
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| def forward_train(self, imgs, img_metas, **kwargs): | ||
| raise NotImplementedError('This method is not implemented.') | ||
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| def simple_test(self, img, img_meta, **kwargs): | ||
| device_type = img.device.type | ||
| self.io_binding.bind_input( | ||
| name='input', | ||
| device_type=device_type, | ||
| device_id=self.device_id, | ||
| element_type=np.float32, | ||
| shape=img.shape, | ||
| buffer_ptr=img.data_ptr()) | ||
| self.sess.run_with_iobinding(self.io_binding) | ||
| seg_pred = self.io_binding.copy_outputs_to_cpu()[0] | ||
| # whole might support dynamic reshape | ||
| ori_shape = img_meta[0]['ori_shape'] | ||
| if not (ori_shape[0] == seg_pred.shape[-2] | ||
| and ori_shape[1] == seg_pred.shape[-1]): | ||
| seg_pred = torch.from_numpy(seg_pred).float() | ||
| seg_pred = torch.nn.functional.interpolate( | ||
| seg_pred, size=tuple(ori_shape[:2]), mode='nearest') | ||
| seg_pred = seg_pred.long().detach().cpu().numpy() | ||
| seg_pred = seg_pred[0] | ||
| seg_pred = list(seg_pred) | ||
| return seg_pred | ||
|
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| def aug_test(self, imgs, img_metas, **kwargs): | ||
| raise NotImplementedError('This method is not implemented.') | ||
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| def parse_args(): | ||
| parser = argparse.ArgumentParser( | ||
| description='mmseg onnxruntime backend test (and eval) a model') | ||
| 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( | ||
| '--eval', | ||
| type=str, | ||
| nargs='+', | ||
| help='evaluation metrics, which depends on the dataset, e.g., "mIoU"' | ||
| ' for generic datasets, and "cityscapes" for Cityscapes') | ||
| 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( | ||
| '--options', nargs='+', action=DictAction, help='custom options') | ||
| parser.add_argument( | ||
| '--eval-options', | ||
| nargs='+', | ||
| action=DictAction, | ||
| help='custom options for evaluation') | ||
| parser.add_argument( | ||
| '--opacity', | ||
| type=float, | ||
| default=0.5, | ||
| help='Opacity of painted segmentation map. In (0, 1] range.') | ||
| parser.add_argument('--local_rank', type=int, default=0) | ||
| args = parser.parse_args() | ||
| if 'LOCAL_RANK' not in os.environ: | ||
| os.environ['LOCAL_RANK'] = str(args.local_rank) | ||
| return args | ||
|
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| def main(): | ||
| args = parse_args() | ||
|
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| 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"') | ||
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| if args.eval and args.format_only: | ||
| raise ValueError('--eval and --format_only cannot be both specified') | ||
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| if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): | ||
| raise ValueError('The output file must be a pkl file.') | ||
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| cfg = mmcv.Config.fromfile(args.config) | ||
| if args.options is not None: | ||
| cfg.merge_from_dict(args.options) | ||
| cfg.model.pretrained = None | ||
| cfg.data.test.test_mode = True | ||
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| # init distributed env first, since logger depends on the dist info. | ||
| distributed = False | ||
|
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| # build the dataloader | ||
| # TODO: support multiple images per gpu (only minor changes are needed) | ||
| dataset = build_dataset(cfg.data.test) | ||
| data_loader = build_dataloader( | ||
| dataset, | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=cfg.data.workers_per_gpu, | ||
| dist=distributed, | ||
| shuffle=False) | ||
|
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| # load onnx config and meta | ||
| cfg.model.train_cfg = None | ||
| model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0) | ||
| model.CLASSES = dataset.CLASSES | ||
| model.PALETTE = dataset.PALETTE | ||
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| efficient_test = False | ||
| if args.eval_options is not None: | ||
| efficient_test = args.eval_options.get('efficient_test', False) | ||
|
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| model = MMDataParallel(model, device_ids=[0]) | ||
| outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, | ||
| efficient_test, args.opacity) | ||
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| rank, _ = get_dist_info() | ||
| if rank == 0: | ||
| 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: | ||
| dataset.evaluate(outputs, args.eval, **kwargs) | ||
|
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|
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| if __name__ == '__main__': | ||
| main() |
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