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import argparse |
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import os |
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import os.path as osp |
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import warnings |
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import mmcv |
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import numpy as np |
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import onnxruntime as ort |
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import torch |
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from mmcv.parallel import MMDataParallel |
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from mmcv.runner import get_dist_info |
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from mmcv.utils import DictAction |
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from mmseg.apis import single_gpu_test |
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from mmseg.datasets import build_dataloader, build_dataset |
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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): |
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super(ONNXRuntimeSegmentor, self).__init__() |
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ort_custom_op_path = '' |
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try: |
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from mmcv.ops import get_onnxruntime_op_path |
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ort_custom_op_path = get_onnxruntime_op_path() |
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except (ImportError, ModuleNotFoundError): |
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warnings.warn('If input model has custom op from mmcv, \ |
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you may have to build mmcv with ONNXRuntime from source.') |
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session_options = ort.SessionOptions() |
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if osp.exists(ort_custom_op_path): |
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session_options.register_custom_ops_library(ort_custom_op_path) |
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sess = ort.InferenceSession(onnx_file, session_options) |
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providers = ['CPUExecutionProvider'] |
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options = [{}] |
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is_cuda_available = ort.get_device() == 'GPU' |
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if is_cuda_available: |
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providers.insert(0, 'CUDAExecutionProvider') |
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options.insert(0, {'device_id': device_id}) |
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sess.set_providers(providers, options) |
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self.sess = sess |
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self.device_id = device_id |
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self.io_binding = sess.io_binding() |
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self.output_names = [_.name for _ in sess.get_outputs()] |
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for name in self.output_names: |
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self.io_binding.bind_output(name) |
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self.cfg = cfg |
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self.test_mode = cfg.model.test_cfg.mode |
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def extract_feat(self, imgs): |
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raise NotImplementedError('This method is not implemented.') |
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def encode_decode(self, img, img_metas): |
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raise NotImplementedError('This method is not implemented.') |
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def forward_train(self, imgs, img_metas, **kwargs): |
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raise NotImplementedError('This method is not implemented.') |
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def simple_test(self, img, img_meta, **kwargs): |
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device_type = img.device.type |
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self.io_binding.bind_input( |
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name='input', |
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device_type=device_type, |
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device_id=self.device_id, |
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element_type=np.float32, |
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shape=img.shape, |
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buffer_ptr=img.data_ptr()) |
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self.sess.run_with_iobinding(self.io_binding) |
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seg_pred = self.io_binding.copy_outputs_to_cpu()[0] |
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ori_shape = img_meta[0]['ori_shape'] |
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if not (ori_shape[0] == seg_pred.shape[-2] |
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and ori_shape[1] == seg_pred.shape[-1]): |
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seg_pred = torch.from_numpy(seg_pred).float() |
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seg_pred = torch.nn.functional.interpolate( |
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seg_pred, size=tuple(ori_shape[:2]), mode='nearest') |
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seg_pred = seg_pred.long().detach().cpu().numpy() |
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seg_pred = seg_pred[0] |
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seg_pred = list(seg_pred) |
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return seg_pred |
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def aug_test(self, imgs, img_metas, **kwargs): |
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raise NotImplementedError('This method is not implemented.') |
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def parse_args(): |
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parser = argparse.ArgumentParser( |
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description='mmseg onnxruntime backend test (and eval) a model') |
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parser.add_argument('config', help='test config file path') |
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parser.add_argument('model', help='Input model file') |
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parser.add_argument('--out', help='output result file in pickle format') |
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parser.add_argument( |
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'--format-only', |
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action='store_true', |
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help='Format the output results without perform evaluation. It is' |
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'useful when you want to format the result to a specific format and ' |
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'submit it to the test server') |
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parser.add_argument( |
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'--eval', |
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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., "mIoU"' |
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' for generic datasets, and "cityscapes" for Cityscapes') |
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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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parser.add_argument( |
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'--options', nargs='+', action=DictAction, help='custom options') |
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parser.add_argument( |
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'--eval-options', |
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nargs='+', |
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action=DictAction, |
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help='custom options for evaluation') |
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parser.add_argument( |
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'--opacity', |
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type=float, |
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default=0.5, |
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help='Opacity of painted segmentation map. In (0, 1] range.') |
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parser.add_argument('--local_rank', type=int, default=0) |
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args = parser.parse_args() |
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if 'LOCAL_RANK' not in os.environ: |
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os.environ['LOCAL_RANK'] = str(args.local_rank) |
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return args |
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def main(): |
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args = parse_args() |
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assert args.out or args.eval or args.format_only or args.show \ |
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or args.show_dir, \ |
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('Please specify at least one operation (save/eval/format/show the ' |
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'results / save the results) with the argument "--out", "--eval"' |
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', "--format-only", "--show" or "--show-dir"') |
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if args.eval and args.format_only: |
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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')): |
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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.options is not None: |
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cfg.merge_from_dict(args.options) |
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cfg.model.pretrained = None |
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cfg.data.test.test_mode = True |
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distributed = False |
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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=1, |
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workers_per_gpu=cfg.data.workers_per_gpu, |
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dist=distributed, |
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shuffle=False) |
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cfg.model.train_cfg = None |
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model = ONNXRuntimeSegmentor(args.model, cfg=cfg, device_id=0) |
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model.CLASSES = dataset.CLASSES |
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model.PALETTE = dataset.PALETTE |
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efficient_test = False |
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if args.eval_options is not None: |
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efficient_test = args.eval_options.get('efficient_test', False) |
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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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efficient_test, args.opacity) |
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rank, _ = get_dist_info() |
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if rank == 0: |
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if args.out: |
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print(f'\nwriting results to {args.out}') |
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mmcv.dump(outputs, args.out) |
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kwargs = {} if args.eval_options is None else args.eval_options |
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if args.format_only: |
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dataset.format_results(outputs, **kwargs) |
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if args.eval: |
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dataset.evaluate(outputs, args.eval, **kwargs) |
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if __name__ == '__main__': |
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main() |
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