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* add pytorch2onnx part * Update according to the latest mmcv * add docstring * update docs * update docs Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,198 @@ | ||
| import argparse | ||
| from functools import partial | ||
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| import mmcv | ||
| import numpy as np | ||
| import onnxruntime as rt | ||
| import torch | ||
| import torch._C | ||
| import torch.serialization | ||
| from mmcv.onnx import register_extra_symbolics | ||
| from mmcv.runner import load_checkpoint | ||
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| from mmseg.models import build_segmentor | ||
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| torch.manual_seed(3) | ||
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| def _convert_batchnorm(module): | ||
| module_output = module | ||
| if isinstance(module, torch.nn.SyncBatchNorm): | ||
| module_output = torch.nn.BatchNorm2d(module.num_features, module.eps, | ||
| module.momentum, module.affine, | ||
| module.track_running_stats) | ||
| if module.affine: | ||
| module_output.weight.data = module.weight.data.clone().detach() | ||
| module_output.bias.data = module.bias.data.clone().detach() | ||
| # keep requires_grad unchanged | ||
| module_output.weight.requires_grad = module.weight.requires_grad | ||
| module_output.bias.requires_grad = module.bias.requires_grad | ||
| module_output.running_mean = module.running_mean | ||
| module_output.running_var = module.running_var | ||
| module_output.num_batches_tracked = module.num_batches_tracked | ||
| for name, child in module.named_children(): | ||
| module_output.add_module(name, _convert_batchnorm(child)) | ||
| del module | ||
| return module_output | ||
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| def _demo_mm_inputs(input_shape, num_classes): | ||
| """Create a superset of inputs needed to run test or train batches. | ||
| Args: | ||
| input_shape (tuple): | ||
| input batch dimensions | ||
| num_classes (int): | ||
| number of semantic classes | ||
| """ | ||
| (N, C, H, W) = input_shape | ||
| rng = np.random.RandomState(0) | ||
| imgs = rng.rand(*input_shape) | ||
| segs = rng.randint( | ||
| low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8) | ||
| img_metas = [{ | ||
| 'img_shape': (H, W, C), | ||
| 'ori_shape': (H, W, C), | ||
| 'pad_shape': (H, W, C), | ||
| 'filename': '<demo>.png', | ||
| 'scale_factor': 1.0, | ||
| 'flip': False, | ||
| } for _ in range(N)] | ||
| mm_inputs = { | ||
| 'imgs': torch.FloatTensor(imgs).requires_grad_(True), | ||
| 'img_metas': img_metas, | ||
| 'gt_semantic_seg': torch.LongTensor(segs) | ||
| } | ||
| return mm_inputs | ||
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| def pytorch2onnx(model, | ||
| input_shape, | ||
| opset_version=11, | ||
| show=False, | ||
| output_file='tmp.onnx', | ||
| verify=False): | ||
| """Export Pytorch model to ONNX model and verify the outputs are same | ||
| between Pytorch and ONNX. | ||
| Args: | ||
| model (nn.Module): Pytorch model we want to export. | ||
| input_shape (tuple): Use this input shape to construct | ||
| the corresponding dummy input and execute the model. | ||
| opset_version (int): The onnx op version. Default: 11. | ||
| show (bool): Whether print the computation graph. Default: False. | ||
| output_file (string): The path to where we store the output ONNX model. | ||
| Default: `tmp.onnx`. | ||
| verify (bool): Whether compare the outputs between Pytorch and ONNX. | ||
| Default: False. | ||
| """ | ||
| model.cpu().eval() | ||
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| num_classes = model.decode_head.num_classes | ||
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| mm_inputs = _demo_mm_inputs(input_shape, num_classes) | ||
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| imgs = mm_inputs.pop('imgs') | ||
| img_metas = mm_inputs.pop('img_metas') | ||
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| img_list = [img[None, :] for img in imgs] | ||
| img_meta_list = [[img_meta] for img_meta in img_metas] | ||
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| # replace original forward function | ||
| origin_forward = model.forward | ||
| model.forward = partial( | ||
| model.forward, img_metas=img_meta_list, return_loss=False) | ||
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| register_extra_symbolics(opset_version) | ||
| with torch.no_grad(): | ||
| torch.onnx.export( | ||
| model, (img_list, ), | ||
| output_file, | ||
| export_params=True, | ||
| keep_initializers_as_inputs=True, | ||
| verbose=show, | ||
| opset_version=opset_version) | ||
| print(f'Successfully exported ONNX model: {output_file}') | ||
| model.forward = origin_forward | ||
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| if verify: | ||
| # check by onnx | ||
| import onnx | ||
| onnx_model = onnx.load(output_file) | ||
| onnx.checker.check_model(onnx_model) | ||
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| # check the numerical value | ||
| # get pytorch output | ||
| pytorch_result = model(img_list, img_meta_list, return_loss=False)[0] | ||
|
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| # 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) | ||
| sess = rt.InferenceSession(output_file) | ||
| onnx_result = sess.run( | ||
| None, {net_feed_input[0]: img_list[0].detach().numpy()})[0] | ||
| if not np.allclose(pytorch_result, onnx_result): | ||
| raise ValueError( | ||
| 'The outputs are different between Pytorch and ONNX') | ||
| print('The outputs are same between Pytorch and ONNX') | ||
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| def parse_args(): | ||
| parser = argparse.ArgumentParser(description='Convert MMDet to ONNX') | ||
| parser.add_argument('config', help='test config file path') | ||
| parser.add_argument('--checkpoint', help='checkpoint file', default=None) | ||
| parser.add_argument('--show', action='store_true', help='show onnx graph') | ||
| parser.add_argument( | ||
| '--verify', action='store_true', help='verify the onnx model') | ||
| parser.add_argument('--output-file', type=str, default='tmp.onnx') | ||
| parser.add_argument('--opset-version', type=int, default=11) | ||
| parser.add_argument( | ||
| '--shape', | ||
| type=int, | ||
| nargs='+', | ||
| default=[256, 256], | ||
| help='input image size') | ||
| args = parser.parse_args() | ||
| return args | ||
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| if __name__ == '__main__': | ||
| args = parse_args() | ||
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| if 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') | ||
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| cfg = mmcv.Config.fromfile(args.config) | ||
| cfg.model.pretrained = None | ||
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| # build the model and load checkpoint | ||
| segmentor = build_segmentor( | ||
| cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) | ||
| # convert SyncBN to BN | ||
| segmentor = _convert_batchnorm(segmentor) | ||
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| num_classes = segmentor.decode_head.num_classes | ||
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| if args.checkpoint: | ||
| checkpoint = load_checkpoint( | ||
| segmentor, args.checkpoint, map_location='cpu') | ||
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| # conver model to onnx file | ||
| pytorch2onnx( | ||
| segmentor, | ||
| input_shape, | ||
| opset_version=args.opset_version, | ||
| show=args.show, | ||
| output_file=args.output_file, | ||
| verify=args.verify) |