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')
def onnx2tensorrt(onnx_file, trt_file, input_config, verify=False, show=False, dataset='coco', workspace_size=1, verbose=False): import tensorrt as trt onnx_model = onnx.load(onnx_file) input_shape = input_config['input_shape'] # create trt engine and wraper opt_shape_dict = {'input': [input_shape, input_shape, input_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=False, 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: one_img, one_meta = preprocess_example_input(input_config) input_img_cpu = one_img.detach().cpu().numpy() input_img_cuda = one_img.cuda() img = one_meta['show_img'] # Get results from ONNXRuntime ort_custom_op_path = get_onnxruntime_op_path() session_options = ort.SessionOptions() if osp.exists(ort_custom_op_path): session_options.register_custom_ops_library(ort_custom_op_path) sess = ort.InferenceSession(onnx_file, session_options) output_names = [_.name for _ in sess.get_outputs()] ort_outputs = sess.run(None, { 'input': input_img_cpu, }) with_mask = len(output_names) == 3 ort_outputs = [_.squeeze(0) for _ in ort_outputs] ort_dets, ort_labels = ort_outputs[:2] ort_masks = ort_outputs[2] if with_mask else None ort_shapes = [_.shape for _ in ort_outputs] print(f'ONNX Runtime output names: {output_names}, \ output shapes: {ort_shapes}') # Get results from TensorRT trt_model = TRTWraper(trt_file, ['input'], output_names) with torch.no_grad(): trt_outputs = trt_model({'input': input_img_cuda}) trt_outputs = [ trt_outputs[_].detach().cpu().numpy().squeeze(0) for _ in output_names ] trt_dets, trt_labels = trt_outputs[:2] trt_shapes = [_.shape for _ in trt_outputs] print(f'TensorRT output names: {output_names}, \ output shapes: {trt_shapes}') trt_masks = trt_outputs[2] if with_mask else None # Show detection outputs if show: CLASSES = get_classes(dataset) score_thr = 0.35 imshow_det_bboxes(img.copy(), trt_dets, trt_labels, segms=trt_masks, class_names=CLASSES, score_thr=score_thr, win_name='TensorRT') imshow_det_bboxes(img.copy(), ort_dets, ort_labels, segms=ort_masks, class_names=CLASSES, score_thr=score_thr, win_name='ONNXRuntime') # Compare results np.testing.assert_allclose(ort_dets, trt_dets, rtol=1e-03, atol=1e-05) np.testing.assert_allclose(ort_labels, trt_labels) if with_mask: np.testing.assert_allclose(ort_masks, trt_masks, rtol=1e-03, atol=1e-05) print('The numerical values are the same ' + 'between ONNXRuntime and TensorRT')
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(config_path, checkpoint_path, input_img, input_shape, opset_version=11, show=False, output_file='tmp.onnx', verify=False, normalize_cfg=None, dataset='coco', test_img=None, do_simplify=False, cfg_options=None, dynamic_export=None): input_config = { 'input_shape': input_shape, 'input_path': input_img, 'normalize_cfg': normalize_cfg } # prepare original model and meta for verifying the onnx model orig_model = build_model_from_cfg(config_path, checkpoint_path, cfg_options=cfg_options) one_img, one_meta = preprocess_example_input(input_config) model, tensor_data = generate_inputs_and_wrap_model( config_path, checkpoint_path, input_config, cfg_options=cfg_options) output_names = ['dets', 'labels'] if model.with_mask: output_names.append('masks') dynamic_axes = None if dynamic_export: dynamic_axes = { 'input': { 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, tensor_data, output_file, input_names=['input'], 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 = orig_model.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: from mmdet import digit_version import onnxsim 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': one_img.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: from mmdet.core import get_classes, bbox2result from mmdet.apis import show_result_pyplot model.CLASSES = get_classes(dataset) num_classes = len(model.CLASSES) # check by onnx onnx_model = onnx.load(output_file) onnx.checker.check_model(onnx_model) if dynamic_export: # scale up to test dynamic shape h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]] input_config['input_shape'] = (1, 3, h, w) if test_img is not None: input_config['input_path'] = test_img one_img, one_meta = preprocess_example_input(input_config) tensor_data = [one_img] # get pytorch output pytorch_results = model(tensor_data, [[one_meta]], return_loss=False) pytorch_results = pytorch_results[0] # get onnx output input_all = [node.name for node in onnx_model.graph.input] input_initializer = [ node.name for node in onnx_model.graph.initializer ] net_feed_input = list(set(input_all) - set(input_initializer)) assert (len(net_feed_input) == 1) session_options = rt.SessionOptions() # register custom op for ONNX Runtime if osp.exists(ort_custom_op_path): session_options.register_custom_ops_library(ort_custom_op_path) feed_input_img = one_img.detach().numpy() if dynamic_export: # test batch with two input images feed_input_img = np.vstack([feed_input_img, feed_input_img]) sess = rt.InferenceSession(output_file, session_options) onnx_outputs = sess.run(None, {net_feed_input[0]: feed_input_img}) output_names = [_.name for _ in sess.get_outputs()] output_shapes = [_.shape for _ in onnx_outputs] print(f'ONNX Runtime output names: {output_names}, \ output shapes: {output_shapes}') # get last image's outputs onnx_outputs = [_[-1] for _ in onnx_outputs] ort_dets, ort_labels = onnx_outputs[:2] onnx_results = bbox2result(ort_dets, ort_labels, num_classes) if model.with_mask: segm_results = onnx_outputs[2] cls_segms = [[] for _ in range(num_classes)] for i in range(ort_dets.shape[0]): cls_segms[ort_labels[i]].append(segm_results[i]) onnx_results = (onnx_results, cls_segms) # visualize predictions if show: show_result_pyplot(model, one_meta['show_img'], pytorch_results, title='Pytorch') show_result_pyplot(model, one_meta['show_img'], onnx_results, title='ONNXRuntime') # 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')
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,