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44 lines
1.2 KiB
Python
44 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.ops.op_pool_common import CommonPool
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class LpPool(CommonPool):
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def _run(
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self,
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x,
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auto_pad=None,
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ceil_mode=None,
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dilations=None,
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kernel_shape=None,
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p=2,
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pads=None,
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strides=None,
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count_include_pad=None,
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):
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# utilize AvgPool the same fashion Pytorch does. Note that there is a difference in computation.
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# it needs another PR to address.
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# https://github.com/pytorch/pytorch/blob/f58ba553b78db7f88477f9ba8c9333bd1590e30a/torch/nn/functional.py#L1015
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power_average = CommonPool._run(
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self,
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"AVG",
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count_include_pad,
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np.power(np.absolute(x), p),
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auto_pad=auto_pad,
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ceil_mode=ceil_mode,
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dilations=dilations,
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kernel_shape=kernel_shape,
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pads=pads,
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strides=strides,
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)
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kernel_element_count = np.prod(kernel_shape, dtype=np.int64)
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return (
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np.power(kernel_element_count * power_average[0], 1.0 / p).astype(x.dtype),
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)
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