# Copyright (c) OpenMMLab. All rights reserved. import argparse import os.path as osp import warnings from functools import partial import sys sys.path.append("/home/shanwei-luo/userdata/mmdetection") import numpy as np #import onnx import torch from mmcv import Config, DictAction from mmdet.core.export import build_model_from_cfg, preprocess_example_input from mmdet.core.export.model_wrappers import ONNXRuntimeDetector import onnxruntime as ort import onnx print(f"onnxruntime device: {ort.get_device()}") # output: GPU print(f'ort avail providers: {ort.get_available_providers()}') # output: ['CUDAExecutionProvider', 'CPUExecutionProvider'] 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: 'height', 3: 'width' }, '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()} model_opt, check_ok = onnxsim.simplify( output_file, input_data=input_dic, 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(): 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 print(input_config['input_shape']) print(one_img) print(len(onnx_results)) print(len(pytorch_results)) print(onnx_results) print(pytorch_results) for i in range(len(onnx_results)): print(onnx_results[i].shape) for i in range(len(pytorch_results)): print(pytorch_results[i].shape) 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 parse_normalize_cfg(test_pipeline): transforms = None for pipeline in test_pipeline: if 'transforms' in pipeline: transforms = pipeline['transforms'] break assert transforms is not None, 'Failed to find `transforms`' norm_config_li = [_ for _ in transforms if _['type'] == 'Normalize'] assert len(norm_config_li) == 1, '`norm_config` should only have one' norm_config = norm_config_li[0] return norm_config def parse_args(): parser = argparse.ArgumentParser( description='Convert MMDetection models to ONNX') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') parser.add_argument('--input-img', type=str, help='Images for input') parser.add_argument( '--show', action='store_true', help='Show onnx graph and detection outputs') parser.add_argument('--output-file', type=str, default='tmp.onnx') parser.add_argument('--opset-version', type=int, default=11) parser.add_argument( '--test-img', type=str, default=None, help='Images for test') parser.add_argument( '--dataset', type=str, default='coco', help='Dataset name. This argument is deprecated and will be removed \ in future releases.') parser.add_argument( '--verify', action='store_true', help='verify the onnx model output against pytorch output') parser.add_argument( '--simplify', action='store_true', help='Whether to simplify onnx model.') parser.add_argument( '--shape', type=int, nargs='+', default=[800, 1216], help='input image size') parser.add_argument( '--mean', type=float, nargs='+', default=[123.675, 116.28, 103.53], help='mean value used for preprocess input data.This argument \ is deprecated and will be removed in future releases.') parser.add_argument( '--std', type=float, nargs='+', default=[58.395, 57.12, 57.375], help='variance value used for preprocess input data. ' 'This argument is deprecated and will be removed in future releases.') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='Override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') parser.add_argument( '--dynamic-export', action='store_true', help='Whether to export onnx with dynamic axis.') parser.add_argument( '--skip-postprocess', action='store_true', help='Whether to export model without post process. Experimental ' 'option. We do not guarantee the correctness of the exported ' 'model.') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() warnings.warn('Arguments like `--mean`, `--std`, `--dataset` would be \ parsed directly from config file and are deprecated and \ will be removed in future releases.') assert args.opset_version == 11, 'MMDet only support opset 11 now' try: from mmcv.onnx.symbolic import register_extra_symbolics except ModuleNotFoundError: raise NotImplementedError('please update mmcv to version>=v1.0.4') register_extra_symbolics(args.opset_version) cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) if args.shape is None: img_scale = cfg.test_pipeline[1]['img_scale'] input_shape = (1, 3, img_scale[1], img_scale[0]) elif len(args.shape) == 1: input_shape = (1, 3, args.shape[0], args.shape[0]) elif len(args.shape) == 2: input_shape = (1, 3) + tuple(args.shape) else: raise ValueError('invalid input shape') # build the model and load checkpoint model = build_model_from_cfg(args.config, args.checkpoint, args.cfg_options) if not args.input_img: args.input_img = osp.join(osp.dirname(__file__), '../../demo/demo.jpg') normalize_cfg = parse_normalize_cfg(cfg.test_pipeline) # convert model to onnx file pytorch2onnx( model, args.input_img, input_shape, normalize_cfg, opset_version=args.opset_version, show=args.show, output_file=args.output_file, verify=args.verify, test_img=args.test_img, do_simplify=args.simplify, dynamic_export=args.dynamic_export, skip_postprocess=args.skip_postprocess)