1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
|
/*
* Copyright (c) 2018-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_NEFUSEBATCHNORMALIZATIONKERNEL_H
#define ARM_COMPUTE_NEFUSEBATCHNORMALIZATIONKERNEL_H
#include "src/core/NEON/INEKernel.h"
namespace arm_compute
{
// Forward declarations
class ITensor;
/** OpenNE kernel to fuse the batch normalization node to a preceding convolution node */
class NEFuseBatchNormalizationKernel : public INEKernel
{
public:
const char *name() const override
{
return "NEFuseBatchNormalizationKernel";
}
/** Default constructor */
NEFuseBatchNormalizationKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFuseBatchNormalizationKernel(const NEFuseBatchNormalizationKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFuseBatchNormalizationKernel &operator=(const NEFuseBatchNormalizationKernel &) = delete;
/** Allow instances of this class to be moved */
NEFuseBatchNormalizationKernel(NEFuseBatchNormalizationKernel &&) = default;
/** Allow instances of this class to be moved */
NEFuseBatchNormalizationKernel &operator=(NEFuseBatchNormalizationKernel &&) = default;
/** Default destructor */
~NEFuseBatchNormalizationKernel() = default;
/** Set the source, destination of the kernel
*
* @param[in] input_weights Input weights tensor for convolution or depthwise convolution layer. Data type supported: F16/F32. Data layout supported: NCHW, NHWC
* @param[in] bn_mean Batch normalization layer mean tensor. Same as @p input_weights
* @param[in] bn_var Batch normalization layer variance tensor. Same as @p input_weights
* @param[out] fused_weights (Optional) Output fused weights tensor. It can be a nullptr in case of in-place computation. Same as @p input_weights
* @param[out] fused_bias (Optional) Output fused bias tensor. It can be a nullptr in case of in-place computation and input_bias != nullptr. Same as @p input_weights
* @param[in] input_bias (Optional) Input bias tensor for convolution or depthwise convolution layer. It can be a nullptr in case the bias tensor is not required. Same as @p input_weights
* @param[in] bn_beta (Optional) Batch normalization layer beta tensor. It can be a nullptr in case the beta tensor is not required. Same as @p input_weights
* @note if nullptr, bn_beta is set to 0.0
* @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor. It can be a nullptr in case the gamma tensor is not required. Same as @p input_weights
* @note if nullptr, bn_gamma is set to 1.0
* @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f.
* @param[in] fbn_type (Optional) Fused batch normalization type. Defaults to CONVOLUTION.
*/
void configure(const ITensor *input_weights,
const ITensor *bn_mean,
const ITensor *bn_var,
ITensor *fused_weights,
ITensor *fused_bias,
const ITensor *input_bias = nullptr,
const ITensor *bn_beta = nullptr,
const ITensor *bn_gamma = nullptr,
float epsilon = 0.001f,
FuseBatchNormalizationType fbn_type = FuseBatchNormalizationType::CONVOLUTION);
/** Static function to check if given info will lead to a valid configuration of @ref NEFuseBatchNormalizationKernel
*
* @param[in] input_weights Input weights tensor info for convolution or depthwise convolution layer. Data type supported: F16/F32. Data layout supported: NCHW, NHWC
* @param[in] bn_mean Batch normalization layer mean tensor info. Same as @p input_weights
* @param[in] bn_var Batch normalization layer variance tensor info. Same as @p input_weights
* @param[in] fused_weights (Optional) Output fused weights tensor info. It can be a nullptr in case of in-place computation. Same as @p input_weights
* @param[in] fused_bias (Optional) Output fused bias tensor info. It can be a nullptr in case of in-place computation and input_bias != nullptr. Same as @p input_weights
* @param[in] input_bias (Optional) Input bias tensor info for convolution or depthwise convolution layer. It can be a nullptr in case the bias tensor is not required. Same as @p input_weights
* @param[in] bn_beta (Optional) Batch normalization layer beta tensor info. It can be a nullptr in case the beta tensor is not required. Same as @p input_weights
* @note if nullptr, bn_beta is set to 0.0
* @param[in] bn_gamma (Optional) Batch normalization layer gamma tensor info. It can be a nullptr in case the gamma tensor is not required. Same as @p input_weights
* @note if nullptr, bn_gamma is set to 1.0
* @param[in] epsilon (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f.
* @param[in] fbn_type (Optional) Fused batch normalization type. Defaults to CONVOLUTION.
*
* @return a status
*/
static Status validate(const ITensorInfo *input_weights,
const ITensorInfo *bn_mean,
const ITensorInfo *bn_var,
const ITensorInfo *fused_weights,
const ITensorInfo *fused_bias,
const ITensorInfo *input_bias = nullptr,
const ITensorInfo *bn_beta = nullptr,
const ITensorInfo *bn_gamma = nullptr,
float epsilon = 0.001f,
FuseBatchNormalizationType fbn_type = FuseBatchNormalizationType::CONVOLUTION);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
private:
const ITensor *_input_weights;
const ITensor *_input_bias;
const ITensor *_bn_mean;
const ITensor *_bn_var;
const ITensor *_bn_gamma;
const ITensor *_bn_beta;
ITensor *_fused_weights;
ITensor *_fused_bias;
float _epsilon;
bool _run_in_place_weights;
bool _run_in_place_bias;
using FuseBatchNormFunction = void(const ITensor *input_weights,
const ITensor *input_bias,
ITensor *fused_weights,
ITensor *fused_bias,
const ITensor *bn_mean,
const ITensor *bn_var,
const ITensor *bn_beta,
const ITensor *bn_gamma,
float epsilon,
const Window &window);
FuseBatchNormFunction *_func;
};
} // namespace arm_compute
#endif /*ARM_COMPUTE_NEFUSEBATCHNORMALIZATIONKERNEL_H */
|
