1 /*
2  * Copyright (c) 2019 Arm Limited.
3  *
4  * SPDX-License-Identifier: MIT
5  *
6  * Permission is hereby granted, free of charge, to any person obtaining a copy
7  * of this software and associated documentation files (the "Software"), to
8  * deal in the Software without restriction, including without limitation the
9  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10  * sell copies of the Software, and to permit persons to whom the Software is
11  * furnished to do so, subject to the following conditions:
12  *
13  * The above copyright notice and this permission notice shall be included in all
14  * copies or substantial portions of the Software.
15  *
16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22  * SOFTWARE.
23  */
24 
25 #ifndef ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H
26 #define ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H
27 
28 #include "arm_compute/core/Types.h"
29 #include "arm_compute/runtime/IFunction.h"
30 
31 namespace arm_compute
32 {
33 namespace graph
34 {
35 namespace backends
36 {
37 /** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}DepthwiseConvolutionLayer with the modified weights */
38 template <typename TargetInfo, typename FusedLayerTypes>
39 class FusedDepthwiseConvolutionBatchNormalizationFunction : public IFunction
40 {
41 public:
42     using TensorType         = typename TargetInfo::TensorType;
43     using TensorConcreteType = typename TargetInfo::TensorConcreteType;
44 
45     FusedDepthwiseConvolutionBatchNormalizationFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr)
_depth_conv_layer(memory_manager)46         : _depth_conv_layer(memory_manager), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false)
47     {
48     }
49 
50     /** Set the input and output tensors.
51      *
52      * @param[in]  input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
53      *                              while every optional dimension from 4 and above represent a batch of inputs.
54      *                              Data types supported: F16/F32.
55      * @param[in]  weights          Weights tensor.  These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input.
56      * @param[in]  bias             Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [IFM].
57      *                              Data type supported: Should match @p input data type.
58      * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
59      *                              Data types supported: Same as @p input.
60      * @param[in]  mean             Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
61      * @param[in]  var              Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
62      * @param[in]  beta             Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input
63      * @param[in]  gamma            Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input
64      * @param[in]  epsilon          Small value to avoid division with zero. Default value is 0.001f.
65      * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
66      * @param[in]  depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
67      * @param[in]  fused_act        Activation layer information in case of a fused activation.
68      *
69      */
configure(TensorType * input,TensorType * weights,TensorType * bias,TensorType * output,const TensorType * mean,const TensorType * var,const TensorType * beta,const TensorType * gamma,float epsilon,const PadStrideInfo & conv_info,unsigned int depth_multiplier,ActivationLayerInfo const & fused_act)70     void configure(TensorType       *input,
71                    TensorType       *weights,
72                    TensorType       *bias,
73                    TensorType       *output,
74                    const TensorType *mean,
75                    const TensorType *var,
76                    const TensorType *beta,
77                    const TensorType *gamma,
78                    float epsilon, const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo const &fused_act)
79     {
80         // We don't run any validate, as we assume that the layers have been already validated
81         const bool        has_bias = (bias != nullptr);
82         const TensorType *bias_to_use;
83 
84         // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
85         // as batch normalization might end up with a bias != 0
86         if(has_bias)
87         {
88             _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
89             bias_to_use = bias;
90         }
91         else
92         {
93             _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
94             bias_to_use = &_fused_bias;
95         }
96 
97         _depth_conv_layer.configure(input, weights, bias_to_use, output, conv_info, depth_multiplier, fused_act.enabled() ? fused_act : ActivationLayerInfo());
98 
99         if(!has_bias)
100         {
101             _fused_bias.allocator()->allocate();
102         }
103     }
104 
105     // Inherited methods overridden:
run()106     void run()
107     {
108         prepare();
109         _depth_conv_layer.run();
110     }
111 
prepare()112     void prepare()
113     {
114         if(!_is_prepared)
115         {
116             _fused_batch_norm_layer.run();
117             _is_prepared = true;
118         }
119     }
120 
121 private:
122     typename FusedLayerTypes::DepthwiseConvolutionLayer _depth_conv_layer;
123     typename FusedLayerTypes::FuseBatchNormalization    _fused_batch_norm_layer;
124     TensorConcreteType                                  _fused_bias;
125     bool                                                _is_prepared;
126 };
127 } // namespace backends
128 } // namespace graph
129 } // namespace arm_compute
130 
131 #endif /* ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H */
132