Line data Source code
1 : /**
2 : * Copyright (C) 2020 Samsung Electronics Co., Ltd. All Rights Reserved.
3 : *
4 : * Licensed under the Apache License, Version 2.0 (the "License");
5 : * you may not use this file except in compliance with the License.
6 : * You may obtain a copy of the License at
7 : * http://www.apache.org/licenses/LICENSE-2.0
8 : * Unless required by applicable law or agreed to in writing, software
9 : * distributed under the License is distributed on an "AS IS" BASIS,
10 : * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 : * See the License for the specific language governing permissions and
12 : * limitations under the License.
13 : *
14 : * @file bn_layer.h
15 : * @date 14 May 2020
16 : * @brief This is Batch Normalization Layer Class of Neural Network
17 : * @see https://github.com/nnstreamer/nntrainer
18 : * @author Jijoong Moon <jijoong.moon@samsung.com>
19 : * @bug No known bugs except for NYI items
20 : *
21 : */
22 :
23 : #ifndef __BN_LAYER_H__
24 : #define __BN_LAYER_H__
25 : #ifdef __cplusplus
26 :
27 : #include <array>
28 : #include <functional>
29 : #include <vector>
30 :
31 : #include <common_properties.h>
32 : #include <layer_devel.h>
33 :
34 : namespace nntrainer {
35 :
36 : /**
37 : * @class BatchNormalizationLayer
38 : * @brief Batch Noramlization Layer
39 : */
40 : class BatchNormalizationLayer : public Layer {
41 : public:
42 : /**
43 : * @brief Constructor of Batch Normalization Layer
44 : */
45 : BatchNormalizationLayer();
46 :
47 : /**
48 : * @brief Destructor of BatchNormalizationLayer
49 : */
50 56 : ~BatchNormalizationLayer() {}
51 :
52 : /**
53 : * @brief Move constructor of Pooling 2D Layer.
54 : * @param[in] BatchNormalization &&
55 : */
56 : BatchNormalizationLayer(BatchNormalizationLayer &&rhs) noexcept = default;
57 :
58 : /**
59 : * @brief Move assignment operator.
60 : * @parma[in] rhs BatchNormalizationLayer to be moved.
61 : */
62 : BatchNormalizationLayer &operator=(BatchNormalizationLayer &&rhs) = default;
63 :
64 : /**
65 : * @copydoc Layer::finalize(InitLayerContext &context)
66 : */
67 : void finalize(InitLayerContext &context) override;
68 :
69 : /**
70 : * @copydoc Layer::forwarding(RunLayerContext &context, bool training)
71 : */
72 : void forwarding(RunLayerContext &context, bool training) override;
73 :
74 : /**
75 : * @copydoc Layer::calcDerivative(RunLayerContext &context)
76 : */
77 : void calcDerivative(RunLayerContext &context) override;
78 :
79 : /**
80 : * @copydoc Layer::calcGradient(RunLayerContext &context)
81 : */
82 : void calcGradient(RunLayerContext &context) override;
83 :
84 : /**
85 : * @copydoc Layer::exportTo(Exporter &exporter, ml::train::ExportMethods
86 : * method)
87 : */
88 : void exportTo(Exporter &exporter,
89 : const ml::train::ExportMethods &method) const override;
90 :
91 : /**
92 : * @copydoc Layer::getType()
93 : */
94 862 : const std::string getType() const override {
95 862 : return BatchNormalizationLayer::type;
96 : };
97 :
98 : /**
99 : * @copydoc Layer::supportBackwarding()
100 : */
101 39 : bool supportBackwarding() const override { return true; }
102 :
103 : /**
104 : * @brief Initialize the in-place settings of the layer
105 : * @return InPlaceType
106 : */
107 18 : InPlaceType initializeInPlace() final {
108 18 : is_inplace = true;
109 18 : return InPlaceType::NON_RESTRICTING;
110 : }
111 :
112 : using Layer::setProperty;
113 :
114 : /**
115 : * @copydoc Layer::setProperty(const PropertyType type, const std::string
116 : * &value)
117 : */
118 : void setProperty(const std::vector<std::string> &values) override;
119 :
120 : /**
121 : * @copydoc Layer::setBatch(RunLayerContext &context, unsigned int batch)
122 : */
123 : void setBatch(RunLayerContext &context, unsigned int batch) override;
124 :
125 : static constexpr const char *type = "batch_normalization";
126 :
127 : /**
128 : * @copydoc Layer::save(std::ofstream &file,
129 : * RunLayerContext &run_context,
130 : * bool opt_var,
131 : * ml::train::ExecutionMode mode,
132 : * bool trainable,
133 : * TensorDim::DataType definedWeightDataType)
134 : */
135 : void save(std::ofstream &file, RunLayerContext &run_context, bool opt_var,
136 : ml::train::ExecutionMode mode, bool trainable,
137 : TensorDim::DataType definedWeightDataType) const override;
138 :
139 : /**
140 : * @copydoc Layer::read(std::ifstream &file, RunLayerContext &context, bool
141 : * opt_var, ml::train::ExecutionMode mode, bool trainable, TensorDim::DataType
142 : * definedWeightDataType, bool fsu)
143 : */
144 : void read(std::ifstream &file, RunLayerContext &context, bool opt_var,
145 : ml::train::ExecutionMode mode, bool trainable,
146 : TensorDim::DataType definedWeightDataType, bool fsu = false,
147 : size_t start_offset = 0, bool read_from_offset = false,
148 : int file_fd = -1) override;
149 :
150 : /**
151 : * @copydoc Layer::read(ReadSource src, RunLayerContext &context, bool
152 : * opt_var, ml::train::ExecutionMode mode, bool trainable, TensorDim::DataType
153 : * definedWeightDataType, bool fsu)
154 : */
155 : void read(ReadSource src, RunLayerContext &context, bool opt_var,
156 : ml::train::ExecutionMode mode, bool trainable,
157 : TensorDim::DataType definedWeightDataType, bool fsu = false,
158 : size_t start_offset = 0, bool read_from_offset = false) override;
159 :
160 : private:
161 : float divider; /**< size of the axes of the reduced */
162 :
163 : std::vector<unsigned int> axes_to_reduce; /**< target axes to reduce */
164 : std::array<unsigned int, 11>
165 : wt_idx; /**< indices of the weights and tensors */
166 : std::tuple<props::Epsilon, props::MuInitializer, props::VarInitializer,
167 : props::BetaInitializer, props::GammaInitializer, props::Momentum,
168 : props::Axis, props::WeightDecay, props::BiasDecay>
169 : bn_props;
170 : };
171 :
172 : } // namespace nntrainer
173 :
174 : #endif /* __cplusplus */
175 : #endif /* __BN_LAYER_H__ */
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