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51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
# Copyright (c) ONNX Project Contributors
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
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class LabelEncoder(OpRunAiOnnxMl):
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def _run(
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self,
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x,
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default_float=None,
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default_int64=None,
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default_string=None,
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default_tensor=None,
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keys_floats=None,
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keys_int64s=None,
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keys_strings=None,
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values_floats=None,
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values_int64s=None,
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values_strings=None,
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keys_tensor=None,
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values_tensor=None,
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):
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keys = keys_floats or keys_int64s or keys_strings or keys_tensor
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values = values_floats or values_int64s or values_strings or values_tensor
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classes = dict(zip(keys, values, strict=False))
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if values is values_tensor:
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defval = default_tensor.item()
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otype = default_tensor.dtype
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elif values is values_floats:
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defval = default_float
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otype = np.float32
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elif values is values_int64s:
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defval = default_int64
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otype = np.int64
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elif values is values_strings:
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defval = default_string
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otype = np.str_
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if not isinstance(defval, str):
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defval = ""
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lookup_func = np.vectorize(lambda x: classes.get(x, defval), otypes=[otype])
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output = lookup_func(x)
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if output.dtype == object:
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output = output.astype(np.str_)
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return (output,)
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