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43 lines
1.2 KiB
Python
43 lines
1.2 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.op_run import OpRun
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def _argmax(data, axis=0, keepdims=True):
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result = np.argmax(data, axis=axis)
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if keepdims and len(result.shape) < len(data.shape):
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result = np.expand_dims(result, axis)
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return result.astype(np.int64)
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def _argmax_use_numpy_select_last_index(data, axis=0, keepdims=True):
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data = np.flip(data, axis)
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result = np.argmax(data, axis=axis)
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result = data.shape[axis] - result - 1
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if keepdims:
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result = np.expand_dims(result, axis)
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return result.astype(np.int64)
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class _ArgMax(OpRun):
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def _run(self, data, axis=None, keepdims=None):
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return (_argmax(data, axis=axis, keepdims=keepdims),)
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class ArgMax_1(_ArgMax):
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pass
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class ArgMax_12(_ArgMax):
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def _run(self, data, axis=None, keepdims=None, select_last_index=None):
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if select_last_index == 0:
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return _ArgMax._run(self, data, axis=axis, keepdims=keepdims)
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return (
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_argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims),
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)
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