Compute Library
 21.02
FusedDepthwiseConvolutionBatchNormalizationFunction.h
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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"
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>
40 {
41 public:
43  using TensorConcreteType = typename TargetInfo::TensorConcreteType;
44 
45  FusedDepthwiseConvolutionBatchNormalizationFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr)
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  */
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:
106  void run()
107  {
108  prepare();
109  _depth_conv_layer.run();
110  }
111 
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 */
Base class for all functions.
Definition: IFunction.h:30
bool enabled() const
Check if initialised.
Definition: Types.h:1600
TensorType
Memory type.
Definition: Types.h:38
Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE...
Activation Layer Information class.
Definition: Types.h:1550
Copyright (c) 2017-2021 Arm Limited.
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 depth_multiplier, ActivationLayerInfo const &fused_act)
Set the input and output tensors.
Padding and stride information class.
Definition: Types.h:722