b233d46552
* add ctcloss training scripts. * update compute_det_ctc * fix typo. * add fsmn model, can use pretrained kws model from modelscope. * Add streaming detection of CTC model. Add CTC model onnx export. Add CTC model's result in README; For now CTC model runtime is not supported yet. * QA run.sh, maxpooling training scripts is compatible. Ready to PR. * Add a streaming kws demo, support fsmn online forward * fix typo. * Align Stream FSMN and Non-Stream FSMN, both in feature extraction and model forward. * fix repeat activation, add a interval restrict. * fix timestamp when subsampling!=1. * fix flake8, update training script and README, give pretrained ckpt. * fix quickcheck and flake8 * Add realtime CTC-KWS demo in README. --------- Co-authored-by: dujing <dujing@xmov.ai>
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
# Copyright (c) 2021 Binbin Zhang(binbzha@qq.com)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import torch
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import yaml
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import onnx
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import onnxruntime as ort
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from wekws.model.kws_model import init_model
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from wekws.utils.checkpoint import load_checkpoint
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def get_args():
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parser = argparse.ArgumentParser(description='export to onnx model')
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parser.add_argument('--config', required=True, help='config file')
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parser.add_argument('--onnx_model',
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required=True,
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help='output onnx model')
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parser.add_argument('--checkpoint', required=True, help='checkpoint model')
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args = parser.parse_args()
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return args
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def main():
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args = get_args()
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with open(args.config, 'r') as fin:
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configs = yaml.load(fin, Loader=yaml.FullLoader)
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feature_dim = configs['model']['input_dim']
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model = init_model(configs['model'])
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if configs['training_config'].get('criterion', 'max_pooling') == 'ctc':
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# if we use ctc_loss, the logits need to be convert into probs
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model.forward = model.forward_softmax
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print(model)
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load_checkpoint(model, args.checkpoint)
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model.eval()
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# dummy_input: (batch, time, feature_dim)
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dummy_input = torch.randn(1, 100, feature_dim, dtype=torch.float)
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cache = torch.zeros(1,
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model.hdim,
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model.backbone.padding,
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dtype=torch.float)
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torch.onnx.export(model, (dummy_input, cache),
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args.onnx_model,
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input_names=['input', 'cache'],
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output_names=['output', 'r_cache'],
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dynamic_axes={
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'input': {
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1: 'T'
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},
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'output': {
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1: 'T'
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}},
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opset_version=13,
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verbose=False,
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do_constant_folding=True)
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# Add hidden dim and cache size
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onnx_model = onnx.load(args.onnx_model)
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meta = onnx_model.metadata_props.add()
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meta.key, meta.value = 'cache_dim', str(model.hdim)
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meta = onnx_model.metadata_props.add()
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meta.key, meta.value = 'cache_len', str(model.backbone.padding)
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onnx.save(onnx_model, args.onnx_model)
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# Verify onnx precision
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torch_output = model(dummy_input, cache)
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ort_sess = ort.InferenceSession(args.onnx_model)
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onnx_output = ort_sess.run(None, {
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'input': dummy_input.numpy(),
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'cache': cache.numpy()
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})
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if torch.allclose(torch_output[0],
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torch.tensor(onnx_output[0]), atol=1e-6) and \
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torch.allclose(torch_output[1],
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torch.tensor(onnx_output[1]), atol=1e-6):
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print('Export to onnx succeed!')
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else:
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print('''Export to onnx succeed, but pytorch/onnx have different
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outputs when given the same input, please check!!!''')
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if __name__ == '__main__':
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main()
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