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197 lines
6.2 KiB
C
Executable File
197 lines
6.2 KiB
C
Executable File
/**
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******************************************************************************
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* @file layers_conv2d.h
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* @author AST Embedded Analytics Research Platform
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* @date 18-Apr-2018
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* @brief header file of AI platform conv2d layers datatypes
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******************************************************************************
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* @attention
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*
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* <h2><center>© Copyright (c) 2018 STMicroelectronics.
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* All rights reserved.</center></h2>
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*
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* This software component is licensed by ST under Ultimate Liberty license
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* SLA0044, the "License"; You may not use this file except in compliance with
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* the License. You may obtain a copy of the License at:
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* www.st.com/SLA0044
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*
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******************************************************************************
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*/
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#ifndef __LAYERS_CONV2D_H_
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#define __LAYERS_CONV2D_H_
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#pragma once
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#include "layers_nl.h"
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#include "layers_pool.h"
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#define AI_LAYER_CONV2D_FIELDS_DECLARE \
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AI_LAYER_COMMON_FIELDS_DECLARE \
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ai_u32 groups; /*!< groups for separable convolution */ \
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AI_CONST ai_array* nl_params; /*!< array pointer to non linear parameters */ \
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func_nl nl_func; /*!< function pointer to non linear transform */ \
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ai_shape_2d filter_stride; /*!< filter stride, how much the filter moves */ \
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ai_shape_2d dilation; /*!< dilation value along axis of the filter */ \
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ai_shape filter_pad; /*!< filter pad 4d */
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/*!
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* @defgroup layers_conv2d Convolutive Layers Definitions
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* @brief definition
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*
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*/
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AI_API_DECLARE_BEGIN
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/*!
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* @struct ai_layer_dense
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* @ingroup layers_conv2d
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* @brief Dense (fully connected) layer
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*/
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typedef ai_layer ai_layer_dense;
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/*!
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* @struct ai_layer_gemm
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* @ingroup layers_conv2d
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* @brief layer for General Matrix Multiplication
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*
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* Layer for General Matrix Multiplication (GEMM):
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* \f{equation}{ Y = \alpha A \cdot B + \beta C \f}
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* \f$\alpha\f$ and \f$\beta\f$ are paramaters, A and B are matrices,
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* C is a matrix or an array. Size checks for A, B, C, and Y are performed and
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* broadcast is applied on C if necessary.
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* This is a sequential layer (see @ref ai_layer).
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*/
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typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_gemm_ {
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AI_LAYER_COMMON_FIELDS_DECLARE
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ai_float alpha; /*!< alpha coefficient */
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ai_float beta; /*!< beta coefficient */
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ai_u8 tA; /*!< transpose A flag */
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ai_u8 tB; /*!< transpose B flag */
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} ai_layer_gemm;
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/*!
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* @struct ai_layer_conv2d
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* @ingroup layers_conv2d
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* @brief 2D convolutional layer with strides and pads
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*/
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typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_conv2d_ {
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AI_LAYER_CONV2D_FIELDS_DECLARE
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} ai_layer_conv2d;
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/*!
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* @struct ai_layer_conv2d_nl_pool
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* @ingroup layers_conv2d
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* @brief 2D convolutional layer + nl + pooling with strides and pads
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*/
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typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_conv2d_nl_pool_ {
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AI_LAYER_CONV2D_FIELDS_DECLARE
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ai_shape_2d pool_size; /*!< pooling size */
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ai_shape_2d pool_stride; /*!< pooling stride */
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ai_shape pool_pad; /*!< pooling pad */
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func_pool pool_func; /*!< function pointer to pooling transform */
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} ai_layer_conv2d_nl_pool;
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AI_INTERNAL_API
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void ai_dict8_dot_array_f32(ai_handle out, ai_ptr_const data0, ai_ptr_const lut,
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const ai_float* data1, const ai_size data_size);
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AI_INTERNAL_API
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void ai_dict4_dot_array_f32(ai_handle out, ai_ptr_const data0, ai_ptr_const lut,
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const ai_float* data1, const ai_size data_size);/******************************************************************************/
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/* Forward Functions Section */
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/******************************************************************************/
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/*!
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* @brief Computes the activations of a 2D convolutional layer.
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* @ingroup layers_conv2d
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* @param layer the convolutional (conv) layer
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*/
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AI_INTERNAL_API
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void forward_conv2d(ai_layer* layer);
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/*!
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* @brief Computes the activations of a @ref ai_layer_conv2d_nl_pool layer
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* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
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* layer + optional pooling / nonlinearity (average, max, softmax)
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* @ingroup layers_conv2d
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* @param layer see @ai_layer_conv2d_nl_pool
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*/
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AI_INTERNAL_API
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void forward_conv2d_nl_pool(ai_layer* layer);
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/*!
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* @brief Computes the activations of a GEMM layer.
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* @ingroup layers
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* @param layer the layer including output and input tensors
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*/
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AI_INTERNAL_API
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void forward_gemm(ai_layer* layer);
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/*!
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* @brief Computes matmul layer, intended as numpy.matmul(A,B).
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* @ingroup layers
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* @param layer the layer including output and input tensors
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*/
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AI_INTERNAL_API
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void forward_matmul(ai_layer* layer);
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/*!
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* @brief Computes the activations of a dense (fully connected) layer.
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* @ingroup layers_conv2d
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* @param layer the dense layer
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*/
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AI_INTERNAL_API
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void forward_dense(ai_layer* layer);
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/*!
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* @brief Computes the activations of a fixed point 2D convolutional layer.
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* @ingroup layers_conv2d
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* @param layer the convolutional (conv) layer
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*/
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AI_INTERNAL_API
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void forward_conv2d_fixed(ai_layer *pLayer);
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/*!
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* @brief Computes the activations of a fixed point @ref ai_layer_conv2d_nl_pool
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* layer.
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* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
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* layer + optional pooling / nonlinearity (average, max)
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* @ingroup layers_conv2d
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* @param layer see @ai_layer_conv2d_nl_pool
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*/
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AI_INTERNAL_API
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void forward_conv2d_nl_pool_fixed(ai_layer *pLayer);
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/*!
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* @brief Computes the activations of a integer quantized 2D convolutional layer.
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* @ingroup layers_conv2d
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* @param layer the convolutional (conv) layer
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*/
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AI_INTERNAL_API
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void forward_conv2d_integer(ai_layer *pLayer);
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/*!
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* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer.
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* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
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* layer + optional pooling / nonlinearity (average, max)
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* @ingroup layers_conv2d
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* @param layer see @ai_layer_conv2d_nl_pool
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*/
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AI_INTERNAL_API
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void forward_conv2d_nl_pool_integer(ai_layer *pLayer);
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/*!
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* @brief Computes the activations of a integer dense (fully connected) layer.
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* @ingroup layers_dense
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* @param layer the dense layer
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*/
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AI_INTERNAL_API
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void forward_dense_integer(ai_layer *pLayer);
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AI_API_DECLARE_END
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#endif /*__LAYERS_CONV2D_H_*/
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