import argparse import os import os.path as osp import warnings 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 from mmseg.apis import single_gpu_test from mmseg.datasets import build_dataloader, build_dataset from mmseg.models.segmentors.base import BaseSegmentor class ONNXRuntimeSegmentor(BaseSegmentor): 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}) sess.set_providers(providers, options) 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 def extract_feat(self, imgs): raise NotImplementedError('This method is not implemented.') def encode_decode(self, img, img_metas): raise NotImplementedError('This method is not implemented.') def forward_train(self, imgs, img_metas, **kwargs): raise NotImplementedError('This method is not implemented.') 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 def aug_test(self, imgs, img_metas, **kwargs): raise NotImplementedError('This method is not implemented.') 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 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 = 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 # init distributed env first, since logger depends on the dist info. distributed = False # 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) # 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 efficient_test = False if args.eval_options is not None: efficient_test = args.eval_options.get('efficient_test', False) model = MMDataParallel(model, device_ids=[0]) outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, efficient_test, args.opacity) 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) if __name__ == '__main__': main()