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| import onnxruntime
| import numpy as np
|
|
| if __name__ == '__main__':
| onnx_path = "./export/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/model.onnx"
| sess = onnxruntime.InferenceSession(onnx_path)
| input_name = [nd.name for nd in sess.get_inputs()]
| output_name = [nd.name for nd in sess.get_outputs()]
|
| def _get_feed_dict(text_length):
| return {'inputs': np.ones((1, text_length), dtype=np.int64),
| 'text_lengths': np.array([text_length,], dtype=np.int32),
| 'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32),
| 'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
| }
|
| def _run(feed_dict):
| output = sess.run(output_name, input_feed=feed_dict)
| for name, value in zip(output_name, output):
| print('{}: {}'.format(name, value))
| _run(_get_feed_dict(10))
|
|
