Compute Library
 22.08
FusedConvolutionBatchNormalizationFunction.h
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24 
25 #ifndef ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H
26 #define ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H
27 
28 #include "arm_compute/core/Types.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}ConvolutionLayer with the modified weights */
38 template <typename TargetInfo, typename FusedLayerTypes>
40 {
41 public:
43  using TensorConcreteType = typename TargetInfo::TensorConcreteType;
44 
45  FusedConvolutionBatchNormalizationFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr)
46  : _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: QASYMM8/F16/F32.
55  * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
56  * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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] num_groups Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
67  * @param[in] fast_math Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
68  * available which may introduce a drop of accuracy as well. Default is false
69  * @param[in] fused_act Activation layer information in case of a fused activation.
70  *
71  */
73  TensorType *weights,
75  TensorType *output,
76  const TensorType *mean,
77  const TensorType *var,
78  const TensorType *beta,
79  const TensorType *gamma,
80  float epsilon, const PadStrideInfo &conv_info, unsigned int num_groups, bool fast_math, ActivationLayerInfo const &fused_act)
81  {
82  // We don't run any validate, as we assume that the layers have been already validated
83  const bool has_bias = (bias != nullptr);
84  const TensorType *bias_to_use;
85 
86  // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
87  // as batch normalization might end up with a bias != 0
88  if(has_bias)
89  {
90  _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon);
91  bias_to_use = bias;
92  }
93  else
94  {
95  _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon);
96  bias_to_use = &_fused_bias;
97  }
98 
99  _conv_layer.configure(input, weights, bias_to_use, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups);
100 
101  if(!has_bias)
102  {
103  _fused_bias.allocator()->allocate();
104  }
105  }
106 
107  // Inherited methods overridden:
108  void run()
109  {
110  prepare();
111  _conv_layer.run();
112  }
113 
114  void prepare()
115  {
116  if(!_is_prepared)
117  {
118  _fused_batch_norm_layer.run();
119  _is_prepared = true;
120  }
121  }
122 
123 private:
124  typename FusedLayerTypes::ConvolutionLayer _conv_layer;
125  typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer;
126  TensorConcreteType _fused_bias;
127  bool _is_prepared;
128 };
129 } // namespace backends
130 } // namespace graph
131 } // namespace arm_compute
132 
133 #endif /* ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H */
Base class for all functions.
Definition: IFunction.h:30
TensorType
Memory type.
Definition: Types.h:38
Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE...
FusedConvolutionBatchNormalizationFunction(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
Activation Layer Information class.
Definition: Types.h:1625
Copyright (c) 2017-2022 Arm Limited.
Convolution Layer Weights Information class.
Definition: Types.h:2006
const unsigned int num_groups
Definition: Im2Col.cpp:153
Padding and stride information class.
Definition: Types.h:669
Class for specifying the size of an image or rectangle.
Definition: Size2D.h:34
void 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 num_groups, bool fast_math, ActivationLayerInfo const &fused_act)
Set the input and output tensors.
const int32_t * bias