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35 lines
1.1 KiB
Python
35 lines
1.1 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._op import OpRunReduceNumpy
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class ReduceLogSum_1(OpRunReduceNumpy):
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def _run(self, data, axes=None, keepdims=True):
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tax = tuple(axes) if axes is not None else None
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if data.size == 0:
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return self.reduce_constant(data, -np.inf, tax, keepdims)
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res = np.sum(data, axis=tax, keepdims=keepdims) # type: ignore[arg-type]
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if len(res.shape) > 0:
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return (np.log(res, out=res),)
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return (np.log(res),)
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class ReduceLogSum_18(OpRunReduceNumpy):
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def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0):
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axes = self.handle_axes(axes, noop_with_empty_axes)
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keepdims = keepdims != 0
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if data.size == 0:
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return self.reduce_constant(data, -np.inf, axes, keepdims)
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res = np.sum(data, axis=axes, keepdims=keepdims)
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if len(res.shape) > 0:
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return (np.log(res, out=res),)
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return (np.log(res),)
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