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) # 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) model = ONNXRuntimeDetector(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))
def onnx2tensorrt(onnx_file, trt_file, input_config, verify=False, show=False, workspace_size=1, verbose=False): import tensorrt as trt onnx_model = onnx.load(onnx_file) max_shape = input_config['max_shape'] min_shape = input_config['min_shape'] opt_shape = input_config['opt_shape'] fp16_mode = False # create trt engine and wraper opt_shape_dict = {'input': [min_shape, opt_shape, max_shape]} max_workspace_size = get_GiB(workspace_size) trt_engine = onnx2trt( onnx_model, opt_shape_dict, log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR, fp16_mode=fp16_mode, max_workspace_size=max_workspace_size) save_dir, _ = osp.split(trt_file) if save_dir: os.makedirs(save_dir, exist_ok=True) save_trt_engine(trt_engine, trt_file) print(f'Successfully created TensorRT engine: {trt_file}') if verify: # prepare input one_img, one_meta = preprocess_example_input(input_config) img_list, img_meta_list = [one_img], [[one_meta]] img_list = [_.cuda().contiguous() for _ in img_list] # wrap ONNX and TensorRT model onnx_model = ONNXRuntimeDetector(onnx_file, CLASSES, device_id=0) trt_model = TensorRTDetector(trt_file, CLASSES, device_id=0) # inference with wrapped model with torch.no_grad(): onnx_results = onnx_model(img_list, img_metas=img_meta_list, return_loss=False)[0] trt_results = trt_model(img_list, img_metas=img_meta_list, return_loss=False)[0] if show: out_file_ort, out_file_trt = None, None else: out_file_ort, out_file_trt = 'show-ort.png', 'show-trt.png' show_img = one_meta['show_img'] score_thr = 0.3 onnx_model.show_result(show_img, onnx_results, score_thr=score_thr, show=True, win_name='ONNXRuntime', out_file=out_file_ort) trt_model.show_result(show_img, trt_results, score_thr=score_thr, show=True, win_name='TensorRT', out_file=out_file_trt) with_mask = trt_model.with_masks # compare a part of result if with_mask: compare_pairs = list(zip(onnx_results, trt_results)) else: compare_pairs = [(onnx_results, trt_results)] err_msg = 'The numerical values are different between Pytorch' + \ ' and ONNX, but it does not necessarily mean the' + \ ' exported ONNX model is problematic.' # check the numerical value for onnx_res, pytorch_res in compare_pairs: for o_res, p_res in zip(onnx_res, pytorch_res): np.testing.assert_allclose(o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg) print('The numerical values are the same between Pytorch and ONNX')
def pytorch2onnx(model, input_img, input_shape, normalize_cfg, opset_version=11, show=False, output_file='tmp.onnx', verify=False, test_img=None, do_simplify=False, dynamic_export=None, skip_postprocess=False): input_config = { 'input_shape': input_shape, 'input_path': input_img, 'normalize_cfg': normalize_cfg } # prepare input one_img, one_meta = preprocess_example_input(input_config) img_list, img_meta_list = [one_img], [[one_meta]] if skip_postprocess: warnings.warn('Not all models support export onnx without post ' 'process, especially two stage detectors!') model.forward = model.forward_dummy torch.onnx.export(model, one_img, output_file, input_names=['input'], export_params=True, keep_initializers_as_inputs=True, do_constant_folding=True, verbose=show, opset_version=opset_version) print(f'Successfully exported ONNX model without ' f'post process: {output_file}') return # replace original forward function origin_forward = model.forward model.forward = partial(model.forward, img_metas=img_meta_list, return_loss=False, rescale=False) output_names = ['dets', 'labels'] if model.with_mask: output_names.append('masks') input_name = 'input' dynamic_axes = None if dynamic_export: dynamic_axes = { input_name: { 0: 'batch', 2: 'width', 3: 'height' }, 'dets': { 0: 'batch', 1: 'num_dets', }, 'labels': { 0: 'batch', 1: 'num_dets', }, } if model.with_mask: dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'} torch.onnx.export(model, img_list, output_file, input_names=[input_name], output_names=output_names, export_params=True, keep_initializers_as_inputs=True, do_constant_folding=True, verbose=show, opset_version=opset_version, dynamic_axes=dynamic_axes) model.forward = origin_forward # 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.') if do_simplify: import onnxsim from mmdet import digit_version min_required_version = '0.3.0' assert digit_version(onnxsim.__version__) >= digit_version( min_required_version ), f'Requires to install onnx-simplify>={min_required_version}' input_dic = {'input': img_list[0].detach().cpu().numpy()} onnxsim.simplify(output_file, input_data=input_dic, custom_lib=ort_custom_op_path) print(f'Successfully exported ONNX model: {output_file}') if verify: # check by onnx onnx_model = onnx.load(output_file) onnx.checker.check_model(onnx_model) # wrap onnx model onnx_model = ONNXRuntimeDetector(output_file, model.CLASSES, 0) if dynamic_export: # scale up to test dynamic shape h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]] h, w = min(1344, h), min(1344, w) input_config['input_shape'] = (1, 3, h, w) if test_img is None: input_config['input_path'] = input_img # prepare input once again one_img, one_meta = preprocess_example_input(input_config) img_list, img_meta_list = [one_img], [[one_meta]] # get pytorch output with torch.no_grad(): pytorch_results = model(img_list, img_metas=img_meta_list, return_loss=False, rescale=True)[0] img_list = [_.cuda().contiguous() for _ in img_list] if dynamic_export: img_list = img_list + [_.flip(-1).contiguous() for _ in img_list] img_meta_list = img_meta_list * 2 # get onnx output onnx_results = onnx_model(img_list, img_metas=img_meta_list, return_loss=False)[0] # visualize predictions score_thr = 0.3 if show: out_file_ort, out_file_pt = None, None else: out_file_ort, out_file_pt = 'show-ort.png', 'show-pt.png' show_img = one_meta['show_img'] model.show_result(show_img, pytorch_results, score_thr=score_thr, show=True, win_name='PyTorch', out_file=out_file_pt) onnx_model.show_result(show_img, onnx_results, score_thr=score_thr, show=True, win_name='ONNXRuntime', out_file=out_file_ort) # compare a part of result if model.with_mask: compare_pairs = list(zip(onnx_results, pytorch_results)) else: compare_pairs = [(onnx_results, pytorch_results)] err_msg = 'The numerical values are different between Pytorch' + \ ' and ONNX, but it does not necessarily mean the' + \ ' exported ONNX model is problematic.' # check the numerical value for onnx_res, pytorch_res in compare_pairs: for o_res, p_res in zip(onnx_res, pytorch_res): np.testing.assert_allclose(o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg) print('The numerical values are the same between Pytorch and ONNX')
custom_lib=ort_custom_op_path, dynamic_input_shape=dynamic_export) if check_ok: onnx.save(model_opt, output_file) print(f'Successfully simplified ONNX model: {output_file}') else: warnings.warn('Failed to simplify ONNX model.') print(f'Successfully exported ONNX model: {output_file}') if verify: # check by onnx onnx_model = onnx.load(output_file) onnx.checker.check_model(onnx_model) # wrap onnx model onnx_model = ONNXRuntimeDetector(output_file, model.CLASSES, 0) if dynamic_export: # scale up to test dynamic shape h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]] h, w = min(1344, h), min(1344, w) input_config['input_shape'] = (1, 3, h, w) if test_img is None: input_config['input_path'] = input_img # prepare input once again one_img, one_meta = preprocess_example_input(input_config) img_list, img_meta_list = [one_img], [[one_meta]] # get pytorch output with torch.no_grad():