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#!/usr/bin/env python3
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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 numpy as np
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import onnxruntime
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import paddle
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import os
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import pickle
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def parse_args():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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'--input_file',
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type=str,
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default="static_ds2online_inputs.pickle",
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help="ds2 input pickle file.",
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)
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parser.add_argument(
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'--model_dir',
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type=str,
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default=".",
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help="paddle model dir."
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)
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parser.add_argument(
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'--model_prefix',
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type=str,
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default="avg_1.jit",
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help="paddle model prefix."
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)
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parser.add_argument(
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'--onnx_model',
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type=str,
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default='./model.old.onnx',
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help="onnx model."
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)
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return parser.parse_args()
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if __name__ == '__main__':
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FLAGS = parse_args()
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# input and output
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with open(FLAGS.input_file, 'rb') as f:
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iodict = pickle.load(f)
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print(iodict.keys())
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audio_chunk = iodict['audio_chunk']
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audio_chunk_lens = iodict['audio_chunk_lens']
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chunk_state_h_box = iodict['chunk_state_h_box']
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chunk_state_c_box = iodict['chunk_state_c_bos']
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# paddle
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model = paddle.jit.load(os.path.join(FLAGS.model_dir, FLAGS.model_prefix))
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res_chunk, res_lens, chunk_state_h, chunk_state_c = model(
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paddle.to_tensor(audio_chunk),
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paddle.to_tensor(audio_chunk_lens),
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paddle.to_tensor(chunk_state_h_box),
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paddle.to_tensor(chunk_state_c_box),
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)
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# onnxruntime
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options = onnxruntime.SessionOptions()
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options.enable_profiling=True
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sess = onnxruntime.InferenceSession(FLAGS.onnx_model, sess_options=options)
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ort_res_chunk, ort_res_lens, ort_chunk_state_h, ort_chunk_state_c = sess.run(
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['softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'],
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{"audio_chunk": audio_chunk,
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"audio_chunk_lens":audio_chunk_lens,
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"chunk_state_h_box": chunk_state_h_box,
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"chunk_state_c_box":chunk_state_c_box})
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print(sess.end_profiling())
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# assert paddle equal ort
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print(np.allclose(ort_res_chunk, res_chunk, atol=1e-6))
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print(np.allclose(ort_res_lens, res_lens, atol=1e-6))
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print(np.allclose(ort_chunk_state_h, chunk_state_h, atol=1e-6))
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print(np.allclose(ort_chunk_state_c, chunk_state_c, atol=1e-6))
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