Compute Library
 22.08
FusedConvolutionBatchNormalizationWithPostOpsFunction.h
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24 
25 #ifndef ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H
26 #define ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H
27 
28 #include "arm_compute/core/Types.h"
31 
32 namespace arm_compute
33 {
34 namespace graph
35 {
36 namespace backends
37 {
38 /** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}ConvolutionLayer with the modified weights */
39 template <typename TargetInfo, typename FusedLayerTypes>
41 {
42 public:
44  using TensorConcreteType = typename TargetInfo::TensorConcreteType;
45 
46  FusedConvolutionBatchNormalizationWithPostOpsFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr)
47  : _conv_layer(memory_manager), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false)
48  {
49  }
50 
51  /** Set the input and output tensors.
52  *
53  * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
54  * while every optional dimension from 4 and above represent a batch of inputs.
55  * Data types supported: QASYMM8/F16/F32.
56  * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
57  * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
58  * Data type supported: Should match @p input data type.
59  * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
60  * Data types supported: Same as @p input.
61  * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
62  * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
63  * @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
64  * @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
65  * @param[in] epsilon Small value to avoid division with zero. Default value is 0.001f.
66  * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
67  * @param[in] num_groups Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
68  * @param[in] fast_math Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
69  * available which may introduce a drop of accuracy as well. Default is false
70  * @param[in] post_ops A sequence of post operations that are performed after the main operation.
71  *
72  */
74  TensorType *weights,
76  TensorType *output,
77  const TensorType *mean,
78  const TensorType *var,
79  const TensorType *beta,
80  const TensorType *gamma,
81  float epsilon, const PadStrideInfo &conv_info, unsigned int num_groups, bool fast_math,
83  {
84  // We don't run any validate, as we assume that the layers have been already validated
85  const bool has_bias = (bias != nullptr);
86  const TensorType *bias_to_use;
87 
88  // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
89  // as batch normalization might end up with a bias != 0
90  if(has_bias)
91  {
92  _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon);
93  bias_to_use = bias;
94  }
95  else
96  {
97  _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon);
98  bias_to_use = &_fused_bias;
99  }
100 
101  ActivationLayerInfo fused_act = ActivationLayerInfo(); // Passing an empty ActivationLayerInfo.
102  _conv_layer.configure(input, weights, bias_to_use, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups, post_ops);
103 
104  if(!has_bias)
105  {
106  _fused_bias.allocator()->allocate();
107  }
108  }
109 
110  // Inherited methods overridden:
111  void run()
112  {
113  prepare();
114  _conv_layer.run();
115  }
116 
117  void prepare()
118  {
119  if(!_is_prepared)
120  {
121  _fused_batch_norm_layer.run();
122  _is_prepared = true;
123  }
124  }
125 
126 private:
127  typename FusedLayerTypes::ConvolutionLayer _conv_layer;
128  typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer;
129  TensorConcreteType _fused_bias;
130  bool _is_prepared;
131 };
132 } // namespace backends
133 } // namespace graph
134 } // namespace arm_compute
135 
136 #endif /* ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H */
Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE...
FusedConvolutionBatchNormalizationWithPostOpsFunction(std::shared_ptr< IMemoryManager > memory_manager=nullptr)
experimental::PostOpList< ITensorInfo * > post_ops
Base class for all functions.
Definition: IFunction.h:30
TensorType
Memory type.
Definition: Types.h:38
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
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, const arm_compute::experimental::PostOpList< TensorType *> &post_ops=experimental::PostOpList< TensorType *> {})
Set the input and output tensors.
typename TargetInfo::TensorType TensorType
Class for specifying the size of an image or rectangle.
Definition: Size2D.h:34
void run()
Run the kernels contained in the function.
typename TargetInfo::TensorConcreteType TensorConcreteType
A sequence of PostOps that can be appended to the end of other operators.
Definition: IPostOp.h:119
void prepare()
Prepare the function for executing.
const int32_t * bias