134 lines
6.4 KiB
C++
134 lines
6.4 KiB
C++
/*
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* Copyright (c) 2019 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H
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#define ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/IFunction.h"
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namespace arm_compute
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{
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namespace graph
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{
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namespace backends
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{
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/** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}ConvolutionLayer with the modified weights */
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template <typename TargetInfo, typename FusedLayerTypes>
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class FusedConvolutionBatchNormalizationFunction : public IFunction
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{
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public:
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using TensorType = typename TargetInfo::TensorType;
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using TensorConcreteType = typename TargetInfo::TensorConcreteType;
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FusedConvolutionBatchNormalizationFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr)
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: _conv_layer(memory_manager), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false)
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{
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}
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/** Set the input and output tensors.
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*
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
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* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type.
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* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
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* @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input
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* @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
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* @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
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* @param[in] epsilon Small value to avoid division with zero. Default value is 0.001f.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] num_groups Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
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* @param[in] fast_math Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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* @param[in] fused_act Activation layer information in case of a fused activation.
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*
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*/
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void configure(TensorType *input,
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TensorType *weights,
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TensorType *bias,
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TensorType *output,
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const TensorType *mean,
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const TensorType *var,
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const TensorType *beta,
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const TensorType *gamma,
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float epsilon, const PadStrideInfo &conv_info, unsigned int num_groups, bool fast_math, ActivationLayerInfo const &fused_act)
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{
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// We don't run any validate, as we assume that the layers have been already validated
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const bool has_bias = (bias != nullptr);
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const TensorType *bias_to_use;
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// We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
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// as batch normalization might end up with a bias != 0
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if(has_bias)
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{
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_fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon);
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bias_to_use = bias;
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}
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else
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{
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_fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon);
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bias_to_use = &_fused_bias;
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}
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_conv_layer.configure(input, weights, bias_to_use, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups);
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if(!has_bias)
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{
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_fused_bias.allocator()->allocate();
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}
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}
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// Inherited methods overridden:
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void run()
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{
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prepare();
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_conv_layer.run();
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}
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void prepare()
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{
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if(!_is_prepared)
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{
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_fused_batch_norm_layer.run();
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_is_prepared = true;
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}
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}
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private:
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typename FusedLayerTypes::ConvolutionLayer _conv_layer;
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typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer;
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TensorConcreteType _fused_bias;
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bool _is_prepared;
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};
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} // namespace backends
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} // namespace graph
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_FUNCTION_H */
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