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- # -*- coding: utf-8 -*-
- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- #
- # Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- #
- # Unless required by applicable law or agreed to in writing,
- # software distributed under the License is distributed on an
- # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- from typing import Tuple, Union
-
- from ..functional import local_response_norm
- from .module import Module
-
-
- class LocalResponseNorm(Module):
- r"""
- Apply local response normalization to the input tensor.
-
- Args:
- kernel_size: the size of the kernel to apply LRN on.
- k: hyperparameter k. The default vaule is 2.0.
- alpha: hyperparameter alpha. The default value is 1e-4.
- beta: hyperparameter beta. The default value is 0.75.
-
- Example:
- >>> import numpy as np
- >>> inp = Tensor(np.arange(25, dtype=np.float32).reshape(1,1,5,5))
- >>> GT = np.array([[[[ 0., 0.999925, 1.9994003, 2.9979765, 3.9952066],
- ... [ 4.9906454, 5.983851, 6.974385, 7.961814, 8.945709 ],
- ... [ 9.925651, 10.90122, 11.872011, 12.837625, 13.7976675],
- ... [14.751757, 15.699524, 16.640602, 17.574642, 18.501305 ],
- ... [19.420258, 20.331186, 21.233786, 22.127764, 23.012836 ]]]])
- >>> op = M.LocalResponseNorm(kernel_size=3, k=1.0, alpha=1e-4, beta=0.75)
- >>> out = op(inp)
- >>> np.testing.assert_allclose(GT, out.numpy(), rtol=1e-6, atol=1e-6)
- >>> print('pass')
- pass
- """
-
- def __init__(
- self,
- kernel_size: int = 5,
- k: float = 2.0,
- alpha: float = 1e-4,
- beta: float = 0.75,
- **kwargs
- ):
- super(LocalResponseNorm, self).__init__(**kwargs)
- self.kernel_size = kernel_size
- self.k = k
- self.alpha = alpha
- self.beta = beta
-
- def forward(self, inp):
- return local_response_norm(inp, self.kernel_size, self.k, self.alpha, self.beta)
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