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Diffstat (limited to 'arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h')
| -rw-r--r-- | arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h | 136 |
1 files changed, 0 insertions, 136 deletions
diff --git a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h deleted file mode 100644 index 10f2e5c25e..0000000000 --- a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h +++ /dev/null @@ -1,136 +0,0 @@ -/* - * Copyright (c) 2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#ifndef ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H -#define ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H - -#include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/IPostOp.h" -#include "arm_compute/runtime/IFunction.h" - -namespace arm_compute -{ -namespace graph -{ -namespace backends -{ -/** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}ConvolutionLayer with the modified weights */ -template <typename TargetInfo, typename FusedLayerTypes> -class FusedConvolutionBatchNormalizationWithPostOpsFunction : public IFunction -{ -public: - using TensorType = typename TargetInfo::TensorType; - using TensorConcreteType = typename TargetInfo::TensorConcreteType; - - FusedConvolutionBatchNormalizationWithPostOpsFunction(std::shared_ptr<IMemoryManager> memory_manager = nullptr) - : _conv_layer(memory_manager), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false) - { - } - - /** Set the input and output tensors. - * - * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: QASYMM8/F16/F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. - * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. - * Data type supported: Should match @p input data type. - * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. - * Data types supported: Same as @p input. - * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input - * @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 - * @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 - * @param[in] epsilon Small value to avoid division with zero. Default value is 0.001f. - * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. - * @param[in] num_groups Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout - * @param[in] fast_math Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation - * available which may introduce a drop of accuracy as well. Default is false - * @param[in] post_ops A sequence of post operations that are performed after the main operation. - * - */ - 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 *> {}) - { - // We don't run any validate, as we assume that the layers have been already validated - const bool has_bias = (bias != nullptr); - const TensorType *bias_to_use; - - // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one - // as batch normalization might end up with a bias != 0 - if(has_bias) - { - _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon); - bias_to_use = bias; - } - else - { - _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon); - bias_to_use = &_fused_bias; - } - - ActivationLayerInfo fused_act = ActivationLayerInfo(); // Passing an empty ActivationLayerInfo. - _conv_layer.configure(input, weights, bias_to_use, output, conv_info, WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups, post_ops); - - if(!has_bias) - { - _fused_bias.allocator()->allocate(); - } - } - - // Inherited methods overridden: - void run() - { - prepare(); - _conv_layer.run(); - } - - void prepare() - { - if(!_is_prepared) - { - _fused_batch_norm_layer.run(); - _is_prepared = true; - } - } - -private: - typename FusedLayerTypes::ConvolutionLayer _conv_layer; - typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer; - TensorConcreteType _fused_bias; - bool _is_prepared; -}; -} // namespace backends -} // namespace graph -} // namespace arm_compute - -#endif /* ARM_COMPUTE_GRAPH_BACKENDS_FUSED_CONVOLUTION_BATCH_NORMAZLIZATION_WITH_POST_OPS_FUNCTION_H */ |