1222 lines
75 KiB
C
1222 lines
75 KiB
C
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/*
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* Copyright (c) 2012-2017 The Khronos Group Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef _VX_KHR_NN_INTERNAL_H_
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#define _VX_KHR_NN_INTERNAL_H_
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/*!
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* \file
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* \brief The Khronos Extension for Deep Convolutional Networks Functions.
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*
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* \defgroup group_cnn Extension: Deep Convolutional Networks API
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* \brief Convolutional Network Nodes.
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*/
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#define OPENVX_KHR_NN_INTERNAL "vx_khr_nn_internal"
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#include <VX/vx.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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/*TODO: check it for OpenVX 1.2*/
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//#if defined(OPENVX_CNN_1_0)
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//#undef OPENVX_CNN_1_1
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//#endif
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/*! \brief [Graph] Creates a Convolutional Network Convolution and Activation(Relu) and pooling Layer Node.
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* \details This function implement Convolutional Network Convolution and Activation(Relu) and pooling layer.
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* \param [in] graph The handle to the graph.
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* \param [in] inputs The input tensor data. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
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* The dimension order is [width, height, #IFM, #batches]. \n
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* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.\n
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* \param [in] pad_x [static] Number of elements added at each side in the x dimension of the input.
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* \param [in] pad_y [static] Number of elements added at each side in the y dimension of the input. In fully connected layers this input is ignored.
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* \param [in] accumulator_bits [static] Is the total number of bits used during intermediate accumulation.
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* \param [in] overflow_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_convert_policy_e</tt> enumeration.
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* \param [in] rounding_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
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* \param [in] down_scale_size_rounding [static] Rounding method for calculating output dimensions. See <tt>\ref vx_convolutional_network_rounding_type_e</tt>
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* \param [in] enable_relu [static] If true, enable vxActivationLayer's relu function
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* \param [in] pool_type [static] if neither max pooling nor average pooling, disable pooling function. (see <tt>\ref vx_convolutional_network_pooling_type_e</tt>).
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* \param [in] pool_size_x [static] Size of the pooling region in the x dimension
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* \param [in] pool_size_y [static] Size of the pooling region in the y dimension.
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* \param [out] outputs The output tensor data. Output will have the same number and structure of dimensions as input.
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* \return <tt> vx_node</tt>.
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* \retval 0 Node could not be created.
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* \retval * Node handle.
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* \ingroup group_cnn
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*/
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VX_API_ENTRY vx_node VX_API_CALL vxConvolutionReluPoolingLayer(
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vx_graph graph,
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vx_tensor inputs,
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vx_weights_biases_parameter weights_biases,
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vx_uint32 pad_x,
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vx_uint32 pad_y,
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vx_uint8 accumulator_bits,
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vx_enum overflow_policy,
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vx_enum rounding_policy,
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vx_enum down_scale_size_rounding,
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vx_bool enable_relu,
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vx_enum pool_type,
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vx_uint32 pool_size_x,
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vx_uint32 pool_size_y,
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vx_tensor outputs
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);
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/*! \brief [Graph] Creates a Convolutional Network Convolution and Activation(Relu) Layer Node.
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* \details This function implement Convolutional Network Convolution and Activation(Relu) layer.
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* \param [in] graph The handle to the graph.
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* \param [in] inputs The input tensor data. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
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* The dimension order is [width, height, #IFM, #batches]. \n
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* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.
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* \param [in] pad_x [static] Number of elements added at each side in the x dimension of the input.
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* \param [in] pad_y [static] Number of elements added at each side in the y dimension of the input. In fully connected layers this input is ignored.
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* \param [in] accumulator_bits [static] Is the total number of bits used during intermediate accumulation.
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* \param [in] overflow_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_convert_policy_e</tt> enumeration.
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* \param [in] rounding_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
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* \param [in] down_scale_size_rounding [static] Rounding method for calculating output dimensions. See <tt>\ref vx_convolutional_network_rounding_type_e</tt>
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* \param [in] enable_relu [static] If true, enable vxActivationLayer's relu function.
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* \param [out] outputs The output tensor data. Output will have the same number and structure of dimensions as input.
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* \return <tt> vx_node</tt>.
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* \retval 0 Node could not be created.
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* \retval * Node handle.
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* \ingroup group_cnn
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*/
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VX_API_ENTRY vx_node VX_API_CALL vxConvolutionReluLayer(
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vx_graph graph,
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vx_tensor inputs,
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vx_weights_biases_parameter weights_biases,
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vx_uint32 pad_x,
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vx_uint32 pad_y,
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vx_uint8 accumulator_bits,
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vx_enum overflow_policy,
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vx_enum rounding_policy,
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vx_enum down_scale_size_rounding,
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vx_bool enable_relu,
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vx_tensor outputs
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);
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/*! \brief [Graph] Creates a Fully connected and Activation(Relu) Convolutional Network Layer Node.
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* \details This function implement Fully connected and Activation(Relu) Convolutional Network layers.
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* \param [in] graph The handle to the graph.
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* \param [in] inputs The input tensor data. There two possible input layouts:
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* 1. [#IFM, #batches]. See <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>.
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* 2. [width, height, #IFM, #batches]. See <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>\n
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* In both cases number of batches are optional and may be multidimensional.
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* The second option is a special case to deal with convolution layer followed by fully connected.
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* The dimension order is [#IFM, #batches]. See <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>. Note that batch may be multidimensional.
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* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.\n
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* \param [in] pad [static] Number of elements added at each side in the input.
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* \param [in] accumulator_bits [static] Is the total number of bits used during intermediate accumulation.
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* \param [in] overflow_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_convert_policy_e</tt> enumeration.
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* \param [in] rounding_policy [static] A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
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* \param [in] down_scale_size_rounding [static] Rounding method for calculating output dimensions. See <tt>\ref vx_convolutional_network_rounding_type_e</tt>
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* \param [in] enable_relu [static] If true, enable vxActivationLayer's relu function.
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* \param [out] outputs The output tensor data. Output dimension layout is [#OFM,#batches]. See <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>, where #batches may be multidimensional.
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* \return <tt> vx_node</tt>.
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* \retval 0 Node could not be created.
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* \retval * Node handle.
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* \ingroup group_cnn
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*/
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VX_API_ENTRY vx_node VX_API_CALL vxFullyConnectedReluLayer(
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vx_graph graph,
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vx_tensor inputs,
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vx_weights_biases_parameter weights_biases,
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vx_uint32 pad,
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vx_uint8 accumulator_bits,
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vx_enum overflow_policy,
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vx_enum rounding_policy,
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vx_enum down_scale_size_rounding,
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vx_bool enable_relu,
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vx_tensor outputs
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);
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/*! \brief Input parameter for convolutionReluPooling2
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* \ingroup group_cnn
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*/
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typedef struct _vx_nn_convolution_relu_pooling_params_t
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{
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vx_size dilation_x; /*!< \brief "inflate" the kernel by inserting zeros between the kernel elements in the x direction.
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The value is the number of zeros to insert. */
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vx_size dilation_y; /*!< \brief "inflate" the kernel by inserting zeros between the kernel elements in the y direction.
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The value is the number of zeros to insert. */
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vx_uint32 pad_x_left; /*!< \brief Number of elements added at each side in the left of x dimension of the input. */
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vx_uint32 pad_x_right; /*!< \brief Number of elements added at each side in the right of x dimension of the input. */
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vx_uint32 pad_y_top; /*!< \brief Number of elements added at each side in the top of y dimension of the input. */
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vx_uint32 pad_y_bottom; /*!< \brief Number of elements added at each side in the bottom of y dimension of the input. */
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vx_uint8 accumulator_bits; /*!< \brief Is the total number of bits used during intermediate accumulation. */
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vx_enum overflow_policy; /*!< \brief A VX_TYPE_ENUM of the vx_convert_policy_e enumeration. */
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vx_enum rounding_policy; /*!< \brief A VX_TYPE_ENUM of the vx_round_policy_e enumeration. */
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vx_enum down_scale_size_rounding; /*!< \brief Rounding method for calculating output dimensions. See vx_convolutional_network_rounding_type_e */
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vx_bool enable_relu; /*!< \brief Enable Relu layer function or not. */
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vx_enum pool_type; /*!< \brief neither max pooling nor average pooling, disable pooling function (see vx_convolutional_network_pooling_type_e). */
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vx_uint32 pool_size_x; /*!< \brief Size of the pooling region in the x dimension */
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vx_uint32 pool_size_y; /*!< \brief Size of the pooling region in the y dimension. */
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vx_enum pad_mode; /*!< \brief A VX_TYPE_ENUM of the <tt> \ref vx_pad_mode_e </tt> enumeration. */
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vx_scalar pad_const; /*!< \brief The order const value if setting pad mode to const, the const value is base value, not quantized value. */
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} vx_nn_convolution_relu_pooling_params_t, * vx_nn_convolution_relu_pooling_params;
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/*! \brief Extended input parameter for a convolutionReluPooling2 operation.
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* \ingroup group_cnn
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*\version 0.3
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*/
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typedef struct _vx_nn_convolution_relu_pooling_params_ext_t
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{
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vx_nn_convolution_relu_pooling_params_t base; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt> */
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vx_uint32 stride_x; /*!< \brief skip x jump for down scale. */
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vx_uint32 stride_y; /*!< \brief skip y jump for down scale. */
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} vx_nn_convolution_relu_pooling_params_ext_t, * vx_nn_convolution_relu_pooling_params_ext;
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/*! \brief The 2nd version of extended input parameter for a convolutionReluPooling2 operation.
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*\ingroup group_cnn
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*\version 0.4
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*/
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typedef struct _vx_nn_convolution_relu_pooling_params_ext2_t
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{
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vx_nn_convolution_relu_pooling_params_ext_t ext; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params__ext_t</tt> */
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vx_int32 depth_multiplier; /*!< \brief specifying the depthwise multiplier for depthwise convolution. */
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vx_enum src_rank_mode; /*!< \brief source rank mode A VX_TYPE_ENUM of the <tt> \ref vx_tensor_rank_type_e </tt> enumeration. */
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vx_enum convert_dst_format; /*!< \brief The convert target format. */
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} vx_nn_convolution_relu_pooling_params_ext2_t, * vx_nn_convolution_relu_pooling_params_ext2;
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#define MERGED_NODE_COUNT_MAX 4
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typedef struct _vx_nn_convolution_relu_pooling_params_ext3_t
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{
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vx_nn_convolution_relu_pooling_params_ext2_t ext2; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params__ext_t</tt> */
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vx_uint32 mergedNodeCount;
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vx_float32* interScale; /*!< \brief specifying the depthwise multiplier for depthwise convolution. */
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vx_int32* interZeroPoint;
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vx_enum* interDataType;
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} vx_nn_convolution_relu_pooling_params_ext3_t, * vx_nn_convolution_relu_pooling_params_ext3;
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typedef struct _vx_nn_convolution_relu_pooling_params_ext4_t
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{
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vx_nn_convolution_relu_pooling_params_ext3_t ext3; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params__ext_t</tt> */
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vx_uint32 poolingStrideX;
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vx_uint32 poolingStrideY;
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vx_uint32 poolingPadLeft;
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vx_uint32 poolingPadRight;
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vx_uint32 poolingPadTop;
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vx_uint32 poolingPadBottom;
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vx_bool enable_nn_tensor_add_relu; /*!< \brief Enable Relu function after tensor add. */
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} vx_nn_convolution_relu_pooling_params_ext4_t, * vx_nn_convolution_relu_pooling_params_ext4;
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typedef struct _vx_nn_convolution_relu_pooling_params_ext5_t
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{
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vx_nn_convolution_relu_pooling_params_ext4_t ext4; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params_ext_t</tt> */
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vx_object_array inputs_list;
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vx_object_array outputs_list;
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vx_spinst spinst_obj;
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} vx_nn_convolution_relu_pooling_params_ext5_t, * vx_nn_convolution_relu_pooling_params_ext5;
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typedef struct _vx_nn_convolution_relu_pooling_params_ext6_t
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{
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vx_nn_convolution_relu_pooling_params_ext5_t ext5; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params_ext_t</tt> */
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vx_uint32 depth2space_block_x; /*!< \brief hw limitation: value between 2 and 16. 2, 16 included. */
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vx_uint32 depth2space_block_y; /*!< \brief hw limitation: equals value of depth2space_block_x. */
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} vx_nn_convolution_relu_pooling_params_ext6_t, * vx_nn_convolution_relu_pooling_params_ext6;;
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typedef struct _vx_nn_convolution_relu_pooling_params_ext7_t
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{
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vx_nn_convolution_relu_pooling_params_ext6_t ext6; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params_ext_t</tt> */
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vx_bool isSub;
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} vx_nn_convolution_relu_pooling_params_ext7_t, * vx_nn_convolution_relu_pooling_params_ext7;
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typedef struct _vx_nn_fused_sp_params_t
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{
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vx_enum multi_sp_kernel_type;
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/*!<for mul>*/
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vx_scalar mul_scale;
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/*!<for sp>*/
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union
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{
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struct
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{
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vx_scalar linear_a, linear_b;
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} linear;
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struct
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{
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vx_scalar tanh_a, tanh_b;
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float a_v, b_v;
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} tanh_linear;
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struct
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{
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vx_scalar hsigmoid_a, hsigmoid_b;
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} hsigmoid;
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struct
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{
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vx_scalar clip_a, clip_b;
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} clip;
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struct
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{
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vx_scalar scalar_a, scalar_b, scalar_c, scalar_d;
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} params;
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} scalar_params;
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/*!<for other kernel>*/
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} vx_nn_fused_sp_params_t, * vx_nn_fused_sp_params;
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typedef struct _vx_nn_convolution_relu_pooling_params_sp_ext_t
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{
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|
vx_nn_convolution_relu_pooling_params_ext4_t ext4; /*!< \brief convolution relu pooling params <tt>\ref vx_nn_convolution_relu_pooling_params_ext_t</tt> */
|
||
|
|
vx_object_array inputs_list;
|
||
|
|
vx_object_array outputs_list;
|
||
|
|
vx_nn_fused_sp_params_t sp_param;
|
||
|
|
|
||
|
|
} vx_nn_convolution_relu_pooling_params_sp_ext_t, * vx_nn_convolution_relu_pooling_params_sp_ext;
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Convolution and Activation(Relu) and Pooling Layer Node, this fucntion match kronos NN Extension 1.2 verion.
|
||
|
|
* \details This function implement Convolutional Network Convolution and Activation(Relu) and Pooling layer.
|
||
|
|
* For fixed-point data types, a fixed point calculation is performed with round and saturate according to the number of accumulator bits. The number of the accumulator bits are implementation defined,
|
||
|
|
* and should be at least 16.\n
|
||
|
|
* round: rounding according the <tt>vx_round_policy_e</tt> enumeration. \n
|
||
|
|
* saturate: A saturation according the <tt>vx_convert_policy_e</tt> enumeration.
|
||
|
|
* The following equation is implemented: \n
|
||
|
|
* \f$ outputs[j,k,i] = saturate(round(\sum_{l} (\sum_{m,n} inputs[j-m,k-n,l] \times weights[m,n,l,i])+biasses[j,k,i])) \f$\n
|
||
|
|
* Where \f$m,n\f$ are indexes on the convolution matrices. \f$ l\f$ is an index on all the convolutions per input.\f$ i\f$ is an index per output.
|
||
|
|
* \f$ j,k \f$ are the inputs/outputs spatial indexes.
|
||
|
|
* Convolution is done on the width and height dimensions of the <tt>\ref vx_tensor</tt>. Therefore, we use here the term x for index along the width dimension and y for index along the height dimension.\n
|
||
|
|
* before the Convolution is done, a padding with zeros of the width and height input dimensions is performed.
|
||
|
|
* Then down scale is done by picking the results according to a skip jump. The skip in the x and y is determined by the output size dimensions.
|
||
|
|
* The relation between input to output is as follows: \n
|
||
|
|
* \f$ width_{output} = round(\frac{(width_{input} + paddingleft_x + paddingright_x - kernel_x - (kernel_x -1) * dilation_x)}{skip_x} + 1) \f$\n
|
||
|
|
* and \n
|
||
|
|
* \f$ height_{output} = round(\frac{(height + paddingtop_y + paddingbottom_y - kernel_y - (kernel_y -1) * dilation_y)}{skip_y} + 1) \f$\n
|
||
|
|
* where \f$width\f$ is the size of the input width dimension. \f$height\f$ is the size of the input height dimension.
|
||
|
|
* \f$width_{output}\f$ is the size of the output width dimension. \f$height_{output}\f$ is the size of the output height dimension.
|
||
|
|
* \f$kernel_x\f$ and \f$kernel_y\f$ are the convolution sizes in width and height dimensions.
|
||
|
|
* skip is calculated by the relation between input and output.
|
||
|
|
* rounding is done according to <tt>\ref vx_convolutional_network_rounding_type_e</tt>.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs The input tensor data. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
|
||
|
|
* The dimension order is [width, height, #IFM, #batches]. \n
|
||
|
|
* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.
|
||
|
|
* \param [in] convolution_relu_pooling_params [static] Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params [static] Size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [out] outputs The output tensor data. Output will have the same number and structure of dimensions as input.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvolutionReluPoolingLayer2(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor inputs,
|
||
|
|
vx_weights_biases_parameter weights_biases,
|
||
|
|
const vx_nn_convolution_relu_pooling_params_t * convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_tensor outputs);
|
||
|
|
|
||
|
|
/*! \brief The optimization direvative for weights_biases_parameter create.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
typedef struct _vx_weights_biases_parameter_optimizations_t {
|
||
|
|
vx_int8 zrl; /*!< \brief The zero run length. Set negtive value to disable*/
|
||
|
|
vx_enum outputFormat; /*!< \brief The output format. */
|
||
|
|
vx_int32 inputZeroPoint; /*!< \brief zero point of input. A 32 bit integer, in range [0, 255], Set zero value to disable */
|
||
|
|
} vx_weights_biases_parameter_optimizations_t;
|
||
|
|
|
||
|
|
typedef struct _vx_weights_biases_parameter_optimizations_ext_t {
|
||
|
|
vx_int8 zrl; /*!< \brief The zero run length. Set negtive value to disable*/
|
||
|
|
vx_enum outputFormat; /*!< \brief The output format. */
|
||
|
|
vx_int32 inputZeroPoint; /*!< \brief zero point of input. A 32 bit integer, in range [0, 255], Set zero value to disable */
|
||
|
|
vx_uint32 num_of_input_dims; /*< \brief The input dimesion number*/
|
||
|
|
vx_uint32 num_of_output_dims; /*!< \brief The output dimesion number*/
|
||
|
|
} vx_weights_biases_parameter_optimizations_ext_t;
|
||
|
|
|
||
|
|
|
||
|
|
typedef struct _vx_weights_biases_parameter_optimizations_ext2_t {
|
||
|
|
vx_weights_biases_parameter_optimizations_ext_t ext;
|
||
|
|
vx_float32 inputScale;
|
||
|
|
vx_float32 outputScale;
|
||
|
|
vx_enum inputFormat;
|
||
|
|
vx_int32 output_ZP_dw; /*depthwise conv output ZP*/
|
||
|
|
vx_float32 output_scale_dw; /*depthwise conv output scale*/
|
||
|
|
vx_int8 output_fpp_dw; /*depthwise conv output fix-point*/
|
||
|
|
} vx_weights_biases_parameter_optimizations_ext2_t;
|
||
|
|
|
||
|
|
#if VX_VA40_EXT_SUPPORT
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to a vx_weights_biases_parameter opaque object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] pad_x The number of elements subtracted at each side in the x dimension of the input.
|
||
|
|
* \param [in] pad_y The number of elements subtracted at each side in the y dimension of the input.
|
||
|
|
* \param [in] pooling_size_x The size of the pooling region in the x dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] pooling_size_y The size of the pooling region in the y dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] down_scale_size_rounding A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL
|
||
|
|
vxCreateWeightsBiasesParameterFromTensors(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_size num_of_dims,
|
||
|
|
vx_size * inputs_dims,
|
||
|
|
vx_uint32 pad_x,
|
||
|
|
vx_uint32 pad_y,
|
||
|
|
vx_uint32 pooling_size_x,
|
||
|
|
vx_uint32 pooling_size_y,
|
||
|
|
vx_enum down_scale_size_rounding,
|
||
|
|
vx_size * convolution_outputs_dims,
|
||
|
|
vx_size * pool_outputs_dims,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an opaque vx_weights_biases_parameter object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] output_format The output tensor element type.
|
||
|
|
* \param [in] convolution_relu_pooling_params The convolution_relu_pooling_params Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params The size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL vxCreateWeightsBiasesParameterFromTensors2(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_size num_of_dims,
|
||
|
|
vx_size * inputs_dims,
|
||
|
|
vx_size * convolution_outputs_dims,
|
||
|
|
vx_size * pool_outputs_dims,
|
||
|
|
vx_enum output_format,
|
||
|
|
const vx_nn_convolution_relu_pooling_params convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an opaque vx_weights_biases_parameter object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] convolution_relu_pooling_params The convolution_relu_pooling_params Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params The size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] size_of_optimizations The size in bytes of optimizations.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL vxCreateWeightsBiasesParameterFromTensors3(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_size * inputs_dims,
|
||
|
|
vx_size * convolution_outputs_dims,
|
||
|
|
vx_size * pool_outputs_dims,
|
||
|
|
const vx_nn_convolution_relu_pooling_params convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_size size_of_optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an vx_weights_biases_parameter object.
|
||
|
|
* \param [in] context The OpenVX context object.
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] pad_x The number of elements subtracted at each side in the x dimension of the input.
|
||
|
|
* \param [in] pad_y The number of elements subtracted at each side in the y dimension of the input.
|
||
|
|
* \param [in] pooling_size_x The size of the pooling region in the x dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] pooling_size_y The size of the pooling region in the y dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] down_scale_size_rounding A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] weights_num_of_dims The dimention number of weights tensor.
|
||
|
|
* \param [in] weights_dims The dimention size of weights tensor.
|
||
|
|
* \param [in] weights_data_format The format of weights tensor.
|
||
|
|
* \param [in] weights_fixed_point_pos The fixed point position when the weights element type is int16/int8, if 0 calculations are performed in integer math.
|
||
|
|
* \param [in] biases_num_of_dims The dimention number of biases tensor.
|
||
|
|
* \param [in] biases_dims The dimention size of biases tensor.
|
||
|
|
* \param [in] biases_data_format The format of biases tensor.
|
||
|
|
* \param [in] biases_fixed_point_pos The fixed point position when the biases element type is int16/int8, if 0 calculations are performed in integer math.
|
||
|
|
* \param [in] raw_data_size The data size of compressed data.
|
||
|
|
*
|
||
|
|
* \returns A weightsbiases reference without compressed kernel data <tt>vx_weights_biases_parameter</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL
|
||
|
|
vxCreateWeightsBiasesParameter(
|
||
|
|
vx_context context,
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_size num_of_dims,
|
||
|
|
vx_size * inputs_dims,
|
||
|
|
vx_uint32 pad_x,
|
||
|
|
vx_uint32 pad_y,
|
||
|
|
vx_uint32 pooling_size_x,
|
||
|
|
vx_uint32 pooling_size_y,
|
||
|
|
vx_enum down_scale_size_rounding,
|
||
|
|
vx_size * convolution_outputs_dims,
|
||
|
|
vx_size * pool_outputs_dims,
|
||
|
|
vx_size weights_num_of_dims,
|
||
|
|
vx_size * weights_dims,
|
||
|
|
vx_enum weights_data_format,
|
||
|
|
vx_int8 weights_fixed_point_pos,
|
||
|
|
vx_size biases_num_of_dims,
|
||
|
|
vx_size * biases_dims,
|
||
|
|
vx_enum biases_data_format,
|
||
|
|
vx_int8 biases_fixed_point_pos,
|
||
|
|
vx_uint32 raw_data_size
|
||
|
|
);
|
||
|
|
#else
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to a vx_weights_biases_parameter opaque object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] pad_x The number of elements subtracted at each side in the x dimension of the input.
|
||
|
|
* \param [in] pad_y The number of elements subtracted at each side in the y dimension of the input.
|
||
|
|
* \param [in] pooling_size_x The size of the pooling region in the x dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] pooling_size_y The size of the pooling region in the y dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] down_scale_size_rounding A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL
|
||
|
|
vxCreateWeightsBiasesParameterFromTensors(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_uint32 num_of_dims,
|
||
|
|
vx_uint32 * inputs_dims,
|
||
|
|
vx_uint32 pad_x,
|
||
|
|
vx_uint32 pad_y,
|
||
|
|
vx_uint32 pooling_size_x,
|
||
|
|
vx_uint32 pooling_size_y,
|
||
|
|
vx_enum down_scale_size_rounding,
|
||
|
|
vx_uint32 * convolution_outputs_dims,
|
||
|
|
vx_uint32 * pool_outputs_dims,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an opaque vx_weights_biases_parameter object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] output_format The output tensor element type.
|
||
|
|
* \param [in] convolution_relu_pooling_params The convolution_relu_pooling_params Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params The size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL vxCreateWeightsBiasesParameterFromTensors2(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_uint32 num_of_dims,
|
||
|
|
vx_uint32 * inputs_dims,
|
||
|
|
vx_uint32 * convolution_outputs_dims,
|
||
|
|
vx_uint32 * pool_outputs_dims,
|
||
|
|
vx_enum output_format,
|
||
|
|
const vx_nn_convolution_relu_pooling_params convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an opaque vx_weights_biases_parameter object.
|
||
|
|
*
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] convolution_relu_pooling_params The convolution_relu_pooling_params Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params The size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] optimizations A optional param for <tt>\ref vx_weights_biases_parameter_optimizations_t</tt>.
|
||
|
|
* \param [in] size_of_optimizations The size in bytes of optimizations.
|
||
|
|
* \param [in] weights The weights tensor which need be compressed.
|
||
|
|
* \param [in] biases The biases tensor which need be compressed.
|
||
|
|
*
|
||
|
|
* \returns An opaque vx_weights_biases_parameter reference with compressed kernel data. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL vxCreateWeightsBiasesParameterFromTensors3(
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_uint32 * inputs_dims,
|
||
|
|
vx_uint32 * convolution_outputs_dims,
|
||
|
|
vx_uint32 * pool_outputs_dims,
|
||
|
|
const vx_nn_convolution_relu_pooling_params convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_weights_biases_parameter_optimizations_t *optimizations,
|
||
|
|
vx_size size_of_optimizations,
|
||
|
|
vx_tensor weights,
|
||
|
|
vx_tensor biases);
|
||
|
|
|
||
|
|
/*!
|
||
|
|
* \brief Creates a reference to an vx_weights_biases_parameter object.
|
||
|
|
* \param [in] context The OpenVX context object.
|
||
|
|
* \param [in] layer_type The network type of objects to hold. Types allowed are:
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_CONVOLUTION_LAYER for convolution layer.
|
||
|
|
* \arg VX_CONVOLUTIONAL_NETWORK_FULLYCONNECTED_LAYER for fullyconnected layer.
|
||
|
|
* \param [in] num_of_dims The dimention number of input & output image tensor.
|
||
|
|
* \param [in] inputs_dims The input tensor's dimension size.
|
||
|
|
* \param [in] pad_x The number of elements subtracted at each side in the x dimension of the input.
|
||
|
|
* \param [in] pad_y The number of elements subtracted at each side in the y dimension of the input.
|
||
|
|
* \param [in] pooling_size_x The size of the pooling region in the x dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] pooling_size_y The size of the pooling region in the y dimension, 0 means no pooling operation.
|
||
|
|
* \param [in] down_scale_size_rounding A <tt> VX_TYPE_ENUM</tt> of the <tt> vx_round_policy_e</tt> enumeration.
|
||
|
|
* \param [in] convolution_outputs_dims The output's dimension size after covolution operation.
|
||
|
|
* \param [in] pool_outputs_dims The output's dimension size after pooling operation.
|
||
|
|
* \param [in] weights_num_of_dims The dimention number of weights tensor.
|
||
|
|
* \param [in] weights_dims The dimention size of weights tensor.
|
||
|
|
* \param [in] weights_data_format The format of weights tensor.
|
||
|
|
* \param [in] weights_fixed_point_pos The fixed point position when the weights element type is int16/int8, if 0 calculations are performed in integer math.
|
||
|
|
* \param [in] biases_num_of_dims The dimention number of biases tensor.
|
||
|
|
* \param [in] biases_dims The dimention size of biases tensor.
|
||
|
|
* \param [in] biases_data_format The format of biases tensor.
|
||
|
|
* \param [in] biases_fixed_point_pos The fixed point position when the biases element type is int16/int8, if 0 calculations are performed in integer math.
|
||
|
|
* \param [in] raw_data_size The data size of compressed data.
|
||
|
|
*
|
||
|
|
* \returns A weightsbiases reference without compressed kernel data <tt>vx_weights_biases_parameter</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_weights_biases_parameter VX_API_CALL
|
||
|
|
vxCreateWeightsBiasesParameter(
|
||
|
|
vx_context context,
|
||
|
|
vx_enum layer_type,
|
||
|
|
vx_uint32 num_of_dims,
|
||
|
|
vx_uint32 * inputs_dims,
|
||
|
|
vx_uint32 pad_x,
|
||
|
|
vx_uint32 pad_y,
|
||
|
|
vx_uint32 pooling_size_x,
|
||
|
|
vx_uint32 pooling_size_y,
|
||
|
|
vx_enum down_scale_size_rounding,
|
||
|
|
vx_uint32 * convolution_outputs_dims,
|
||
|
|
vx_uint32 * pool_outputs_dims,
|
||
|
|
vx_uint32 weights_num_of_dims,
|
||
|
|
vx_uint32 * weights_dims,
|
||
|
|
vx_enum weights_data_format,
|
||
|
|
vx_int8 weights_fixed_point_pos,
|
||
|
|
vx_uint32 biases_num_of_dims,
|
||
|
|
vx_uint32 * biases_dims,
|
||
|
|
vx_enum biases_data_format,
|
||
|
|
vx_int8 biases_fixed_point_pos,
|
||
|
|
vx_uint32 raw_data_size
|
||
|
|
);
|
||
|
|
#endif
|
||
|
|
|
||
|
|
/*! \brief Releases the OpenVX object vx_weights_biases_parameter.
|
||
|
|
* \param [in] weights_bias The pointer to the reference to the vx_weights_biases_parameter.
|
||
|
|
* \post After returning from this function the reference is zeroed.
|
||
|
|
* \return A <tt>\ref vx_status_e</tt> enumeration.
|
||
|
|
* \retval VX_SUCCESS No errors.
|
||
|
|
* \retval VX_ERROR_INVALID_REFERENCE If weights_bias is not a <tt> vx_weights_biases_parameter</tt>.
|
||
|
|
* \pre <tt>\ref vxCreateWeightsBiasesParameterFromTensors / vxCreateWeightsBiasesParameterFromTensors2/ vxCreateWeightsBiasesParameter / vxCreateWeightsBiasesParameterFromStream</tt>
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_status VX_API_CALL vxReleaseWeightsBiasesParameter(vx_weights_biases_parameter *weights_bias);
|
||
|
|
/*! \brief Input parameters for a gru operation.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
typedef struct _vx_nn_gru_params_t
|
||
|
|
{
|
||
|
|
vx_tensor reset2input_weights; /*!< \brief [static] Weight matrix for the reset gate with input. A 2-D tensor of type T, of shape [input_size, cell_size]. where "cell_size" corresponds to the number of cell units.*/
|
||
|
|
vx_tensor update2input_weights; /*!< \brief [static] Weight matrix for the update gate with input. A 2-D tensor of type T, of shape [input_size, cell_size]. */
|
||
|
|
vx_tensor reset2recurrent_weights; /*!< \brief [static] Weight matrix for the reset gate with recurrent(h_prev). A 2-D tensor of type T, of shape [cell_size, cell_size]. */
|
||
|
|
vx_tensor update2recurrent_weights; /*!< \brief [static] Weight matrix for the update gate with recurrent(h_prev). A 2-D tensor of type T, of shape [cell_size, cell_size]. */
|
||
|
|
|
||
|
|
vx_tensor connection2input_weights; /*!< \brief [static] Weight matrix for the cell connection gate with input. A 2-D tensor of type T, of shape [input_size, cell_size]. */
|
||
|
|
vx_tensor connection2recurrent_weights; /*!< \brief [static] Weight matrix for the cell connection gate with recurrent(h_prev). A 2-D tensor of type T, of shape [cell_size, cell_size]. */
|
||
|
|
|
||
|
|
vx_tensor gate_input_bias; /*!< \brief [static] Bias vector for the reset and update gate for input. A 1-D tensor of type T, of shape [cell_size].*/
|
||
|
|
vx_tensor gate_recurrent_bias; /*!< \brief [static] Bias vector for the reset and update gate for recurrent. A 1-D tensor of type T, of shape [cell_size].*/
|
||
|
|
|
||
|
|
vx_tensor connection_bias; /*!< \brief [static] Bias vector for the cell connection gate. A 1-D tensor of type T, of shape [cell_size].*/
|
||
|
|
|
||
|
|
} vx_nn_gru_params_t;
|
||
|
|
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Long short-term memory unit (gru) Unit Networks Layer Node. not implement yet.
|
||
|
|
* \details
|
||
|
|
* The implementation is based on: http://arxiv.org/abs/1406.1078
|
||
|
|
* Computes the GRU cell forward propagation for 1 time step.
|
||
|
|
* This kernel op implements the following mathematical equations:
|
||
|
|
* Biases are initialized with:
|
||
|
|
* * `b_ru` - constant_initializer(1.0)
|
||
|
|
* * `b_c` - constant_initializer(0.0)
|
||
|
|
*
|
||
|
|
* x_h_prev = [x, h_prev]
|
||
|
|
* [r_bar u_bar] = x_h_prev * w_ru + b_ru
|
||
|
|
* r = sigmoid(r_bar)
|
||
|
|
* u = sigmoid(u_bar)
|
||
|
|
* h_prevr = h_prev x r
|
||
|
|
* x_h_prevr = [x h_prevr]
|
||
|
|
* c_bar = x_h_prevr * w_c + b_c
|
||
|
|
* c = tanh(c_bar)
|
||
|
|
* h = (1-u) x c + u x h_prev
|
||
|
|
*
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input A 2-D tensor of type T, of shape [input_size, batch_size], where
|
||
|
|
* "batch_size" corresponds to the batching dimension, and "input_size"
|
||
|
|
* is the size of the input.
|
||
|
|
* \param [in] h_prev A 2-D tensor of type T, of shape [cell_size, batch_size].
|
||
|
|
* \param [in] gru_params gru paraments <tt>\ref vx_nn_gru_params_t </tt>.
|
||
|
|
* \param [in] size_of_gru_params [static] The size of the gru_params.
|
||
|
|
* \param [out] output A 2-D tensor of type T, of shape [cell_size, batch_size].
|
||
|
|
* This is effectively the same as the current "output_state" value.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxGRUUnitLayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor input,
|
||
|
|
vx_tensor h_prev,
|
||
|
|
const vx_nn_gru_params_t * gru_params,
|
||
|
|
vx_size size_of_gru_params,
|
||
|
|
vx_tensor output);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Long short-term memory layer (gru) Networks Layer Node. not implement yet.
|
||
|
|
* \details
|
||
|
|
*
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input A 3-D tensor of type T, of shape [input_size, batch_size, time_step], where
|
||
|
|
* "input_size" corresponds to the size of the input, and "batch_size"
|
||
|
|
* is the batching dimension, time_step means time length actually used by the input.
|
||
|
|
* \param [in] h_prev optional, A 2-D tensor of type T, of shape [cell_size, batch_size], where
|
||
|
|
* "input_size" corresponds to the size of the input, and "batch_size"
|
||
|
|
* is the batching dimension.
|
||
|
|
* \param [in] vx_nn_gru_params gru paraments <tt>\ref vx_nn_gru_params_t </tt>.
|
||
|
|
* \param [in] size_of_gru_layer_params [static] The size of the vx_nn_gru_params.
|
||
|
|
* \param [out] output A 2-D tensor of type T, of shape [cell_size, batch_size].
|
||
|
|
* This is effectively the same as the current "output_state" value.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxGRULayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor input,
|
||
|
|
vx_tensor h_prev,
|
||
|
|
const vx_nn_gru_params_t * gru_layer_params,
|
||
|
|
vx_size size_of_gru_layer_params,
|
||
|
|
vx_tensor output
|
||
|
|
);
|
||
|
|
|
||
|
|
|
||
|
|
/*! \brief Input parameters for a convolution lstm operation.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
typedef struct _vx_nn_convlstm_params_t
|
||
|
|
{
|
||
|
|
vx_tensor input2input_weight; /*!< \brief Optional A 2-D tensor of type T, of shape [num_units, input_size]. where "num_units" corresponds to the number of cell units.*/
|
||
|
|
vx_tensor input2forget_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, input_size].*/
|
||
|
|
vx_tensor input2cell_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, input_size].*/
|
||
|
|
vx_tensor input2output_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, input_size].*/
|
||
|
|
|
||
|
|
vx_tensor recurrent2input_weight; /*!< \brief Optional A 2-D tensor of type T, of shape [num_units, output_size]. where "output_size" corresponds to either the number of cell units (i.e., "num_units"), or the second dimension of the "projection_weights", if defined.*/
|
||
|
|
vx_tensor recurrent2forget_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, output_size].*/
|
||
|
|
vx_tensor recurrent2cell_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, output_size].*/
|
||
|
|
vx_tensor recurrent2output_weight; /*!< \brief A 2-D tensor of type T, of shape [num_units, output_size].*/
|
||
|
|
|
||
|
|
vx_tensor input_gate_bias; /*!< \brief Optional A 1-D tensor of type T, of shape [num_units].*/
|
||
|
|
vx_tensor forget_gate_bias; /*!< \brief A 1-D tensor of type T, of shape [num_units].*/
|
||
|
|
vx_tensor cell_bias; /*!< \brief A 1-D tensor of type T, of shape [num_units].*/
|
||
|
|
vx_tensor output_gate_bias; /*!< \brief A 1-D tensor of type T, of shape [num_units].*/
|
||
|
|
|
||
|
|
vx_tensor activation; /*!< \brief Optional. An ActivationFunctionType indicating the activation function. If "NONE" is specified then it results in a linear activation.If "NONE" is specified then it results in a linear activation.*/
|
||
|
|
|
||
|
|
vx_float32 forget_bias; /*!< \brief Float32[static] A bias for the forget gate. If set to 0.0f(by default) then bias is ignored.*/
|
||
|
|
vx_bool skip_connection; /*< \brief If set to `vx_true_e`, concatenate the input to the output of the conv LSTM. Default: `vx_false_e`.*/
|
||
|
|
|
||
|
|
} vx_nn_convlstm_params_t;
|
||
|
|
|
||
|
|
/*! \brief input parameters for a convolution lstm layer operation.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
typedef struct _vx_nn_convlstm_layer_params_t
|
||
|
|
{
|
||
|
|
vx_nn_convlstm_params_t convlstm_param; /*!< \brief convolution lstm input param <tt>\ref vx_nn_convlstm_params_t</tt>.*/
|
||
|
|
vx_enum convlstm_layer_type; /*!< \brief convolution lstm layer type.*/
|
||
|
|
} vx_nn_convlstm_layer_params_t;
|
||
|
|
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolution Long short-term memory unit (ConvLSTM) Unit Networks Layer Node. not implement yet.
|
||
|
|
* \details
|
||
|
|
*
|
||
|
|
* https://arxiv.org/pdf/1506.04214v1.pdf
|
||
|
|
*
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input A 2-D tensor of type T, of shape [input_size, batch_size], where
|
||
|
|
* "batch_size" corresponds to the batching dimension, and "input_size"
|
||
|
|
* is the size of the input.
|
||
|
|
* \param [in] output_state_in A 2-D tensor of type T, of shape [output_size, batch_size].
|
||
|
|
* \param [in] cell_state_in A 2-D tensor of type T, of shape [num_units, batch_size].
|
||
|
|
* \param [in] convlstm_params LSTM paraments <tt>\ref vx_nn_convlstm_params_t </tt>.
|
||
|
|
* \param [in] size_of_convlstm_params [static] The size of the convlstm_params.
|
||
|
|
* \param [out] scratch A 3-D tensor of type T, of shape [num_cell, 4, batch_size].
|
||
|
|
* \param [out] output_state_out A 2-D tensor of type T, of shape [output_size, batch_size].
|
||
|
|
* \param [out] cell_state_out A 2-D tensor of type T, of shape [num_units, batch_size].
|
||
|
|
* \param [out] output A 2-D tensor of type T, of shape [output_size, batch_size].
|
||
|
|
* This is effectively the same as the current "output_state" value.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvLSTMUnitLayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor input,
|
||
|
|
vx_tensor output_state_in,
|
||
|
|
vx_tensor cell_state_in,
|
||
|
|
const vx_nn_convlstm_params_t * convlstm_params,
|
||
|
|
vx_size size_of_convlstm_params,
|
||
|
|
vx_tensor output_state_out,
|
||
|
|
vx_tensor cell_state_out,
|
||
|
|
vx_tensor output);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Long short-term memory layer (LSTM) Networks Layer Node. not implement yet.
|
||
|
|
* \details
|
||
|
|
*
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input A 3-D tensor of type T, of shape [input_size, batch_size, time_step], where
|
||
|
|
* "input_size" corresponds to the size of the input, and "batch_size"
|
||
|
|
* is the batching dimension, time_step means time length actually used by the input.
|
||
|
|
* \param [in] static_input optional, A 2-D tensor of type T, of shape [input_size, batch_size], where
|
||
|
|
* "input_size" corresponds to the size of the input, and "batch_size"
|
||
|
|
* is the batching dimension.
|
||
|
|
* \param [in] cont optional, A 2-D tensor of type T, of shape [input_size, batch_size], where
|
||
|
|
* "input_size" corresponds to the size of the input, and "batch_size"
|
||
|
|
* is the batching dimension.
|
||
|
|
* \param [in] convlstm_layer_params LSTM paraments <tt>\ref vx_nn_convlstm_layer_params_t </tt>.
|
||
|
|
* \param [in] size_of_convlstm_layer_params [static] The size of the convlstm_layer_params.
|
||
|
|
* \param [out] output A 2-D tensor of type T, of shape [output_size, batch_size].
|
||
|
|
* This is effectively the same as the current "output_state" value.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \version 0.5
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvLSTMLayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor input,
|
||
|
|
vx_tensor static_input,
|
||
|
|
vx_tensor cont,
|
||
|
|
const vx_nn_convlstm_layer_params_t * convlstm_layer_params,
|
||
|
|
vx_size size_of_convlstm_layer_params,
|
||
|
|
vx_tensor output
|
||
|
|
);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Pooling Layer Node.
|
||
|
|
* \details Pooling is done on the first 2 dimensions or the <tt>\ref vx_tensor</tt>. Therefore, we use here the term x for the first dimension and y for the second.\n
|
||
|
|
* Pooling operation is a function operation over a rectangle size and then a nearest neighbour down scale.
|
||
|
|
* Here we use pool_size_x and pool_size_y to specify the rectangle size on which the operation
|
||
|
|
* is performed. \n
|
||
|
|
* before the operation is done (average or maximum value). the data is padded in the first 2D with zeros.
|
||
|
|
* The down scale is done by picking the results according to a skip jump. The skip in the x and y dimension is determined by the output size dimensions.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs The input tensor data. 3 lower dimensions represent a single input, 4th dimension for batch of inputs is optional.Dimension layout is [width, height, #IFM, #batches].
|
||
|
|
* See <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>
|
||
|
|
* \param [in] pool_type [static] Either max pooling or average pooling (see <tt>\ref vx_convolutional_network_pooling_type_e</tt>).
|
||
|
|
* \param [in] pool_size_x [static] Size of the pooling region in the x dimension
|
||
|
|
* \param [in] pool_size_y [static] Size of the pooling region in the y dimension.
|
||
|
|
* \param [in] pool_pad_x [static] Padding size in the x dimension.
|
||
|
|
* \param [in] pool_pad_y [static] Padding size in the y dimension.
|
||
|
|
* \param [in] rounding [static] The rounding method for calculating output dimensions. See <tt>\ref vx_convolutional_network_rounding_type_e</tt>
|
||
|
|
* \param [out] outputs The output tensor data. Output will have the same number of dimensions as input.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \retval 0 Node could not be created.
|
||
|
|
* \retval * Node handle.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxPoolingLayer(vx_graph graph, vx_tensor inputs, vx_enum pooling_type,
|
||
|
|
vx_size pooling_size_x,
|
||
|
|
vx_size pooling_size_y,
|
||
|
|
vx_size pooling_padding_x,
|
||
|
|
vx_size pooling_padding_y,
|
||
|
|
vx_enum rounding,
|
||
|
|
vx_tensor outputs);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Softmax Layer Node.
|
||
|
|
* \details the softmax function, is a generalization of the logistic function that "squashes" a K-dimensional vector \f$ z \f$ of arbitrary real values to a K-dimensional vector
|
||
|
|
* \f$ \sigma(z) \f$ of real values in the range (0, 1) that add up to 1. The function is given by:
|
||
|
|
* \f$ \sigma(z) = \frac{\exp^z}{\sum_i \exp^{z_i}} \f$
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs The input tensor, with the number of dimensions according to the following scheme.
|
||
|
|
* In case IFM dimension is 1. Softmax is be calculated on that dimension.
|
||
|
|
* In case IFM dimension is 2. Softmax is be calculated on the first dimension. The second dimension is batching.
|
||
|
|
* In case IFM dimension is 3. Dimensions are [Width, Height, Classes]. And Softmax is calculated on the third dimension.
|
||
|
|
* In case IFM dimension is 4. Dimensions are [Width, Height, Classes, batching]. Softmax is calculated on the third dimension.
|
||
|
|
* Regarding the layout specification, see <tt>\ref vxCreateTensor</tt> and <tt>\ref vxCreateVirtualTensor</tt>.
|
||
|
|
* \param [out] outputs The output tensor. Output will have the same number of dimensions as input. Output tensor data type must be same as the inputs.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxSoftmaxLayer(vx_graph graph, vx_tensor inputs, vx_tensor outputs);
|
||
|
|
|
||
|
|
/* vxCopyTensorPatchForNN11 is for back compatibility with spec 1.1, which is used in nn*/
|
||
|
|
VX_API_ENTRY vx_status VX_API_CALL vxCopyTensorPatchForNN11(
|
||
|
|
vx_tensor tensor,
|
||
|
|
vx_tensor_view view,
|
||
|
|
vx_tensor_addressing user_addr,
|
||
|
|
void *user_ptr,
|
||
|
|
vx_enum usage,
|
||
|
|
vx_enum user_mem_type
|
||
|
|
);
|
||
|
|
|
||
|
|
/* vxCreateTensorForNN11 is for back compatibility with spec 1.1, which is used in nn*/
|
||
|
|
VX_API_ENTRY vx_tensor VX_API_CALL
|
||
|
|
vxCreateTensorForNN11(
|
||
|
|
vx_context context,
|
||
|
|
vx_uint32 num_of_dims,
|
||
|
|
vx_uint32 *sizes,
|
||
|
|
vx_enum data_format,
|
||
|
|
vx_int8 fixed_point_pos
|
||
|
|
);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Normalization Layer Node.
|
||
|
|
* \details Normalizing over local input regions. Each input value is divided by \f$ (1+\frac{\alpha}{n}\sum_i x^2_i)^\beta \f$ , where n is the number of elements to normalize across.
|
||
|
|
* and the sum is taken over the region centred at that value (zero padding is added where necessary).
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs The input tensor data. 3 lower dimensions represent a single input, 4th dimension for batch of inputs is optional.Dimension layout is [width, height, IFM, #batches].
|
||
|
|
* See <tt>\ref vxCreateTensor2</tt> and <tt>\ref vxCreateVirtualTensor2</tt>.
|
||
|
|
* \param [in] type [static] Either same map or across maps (see vx_convolutional_network_norm_type_e).
|
||
|
|
* \param [in] norm_size [static] Number of elements to normalize across.
|
||
|
|
* \param [in] alpha [static] Alpha parameter in the normalization equation.
|
||
|
|
* \param [in] beta [static ] Beta parameter in the normalization equation.
|
||
|
|
* \param [out] outputs The output tensor data. Output will have the same number of dimensions as input.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \retval 0 Node could not be created.
|
||
|
|
* \retval * Node handle.
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxNormalizationLayer(vx_graph graph, vx_tensor inputs, vx_enum type,
|
||
|
|
vx_size normalization_size,
|
||
|
|
vx_float32 alpha,
|
||
|
|
vx_float32 beta,
|
||
|
|
vx_tensor outputs);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Reorgnization Layer Node.
|
||
|
|
* \details Reorganize the layer. Picking up pixels from input tensor according to the rule \n
|
||
|
|
* dimension 1: i * stride + (k / out_c) % stride \n
|
||
|
|
* dimension 2: j * stride + (k / out_c) / stride \n
|
||
|
|
* dimension 3: k % out_c \n
|
||
|
|
* out_c = input_c / (stride * stride), i is in range (0, input_w-1), j is in range (0, input_h-1), k is in range (0, input_c-1)
|
||
|
|
* Output value is in order sequence.
|
||
|
|
* \param [in] graph The reference to the parent graph.
|
||
|
|
* \param [in] inputs The input tensor data to reorg.
|
||
|
|
* \param [in] stride [static] Delta size of two pixels in each dimensions to do a reorg operation.
|
||
|
|
* \param [out] outputs The output tensor data. Output will have different number of each dimensions as input.
|
||
|
|
* \returns <tt> vx_node</tt>.
|
||
|
|
* \retval 0 Node could not be created.
|
||
|
|
* \retval * Node handle.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxReorgLayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor inputs,
|
||
|
|
vx_uint32 stride,
|
||
|
|
vx_tensor outputs
|
||
|
|
);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network L2Normalize Layer Node.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs The input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Dimension layout is [width, height, #IFM, #batches].
|
||
|
|
* See <tt>\ref vxCreateTensor2</tt> and <tt>\ref vxCreateVirtualTensor2</tt>.
|
||
|
|
* \param [out] outputs The output tensor data. Output will have the same number of dimensions as input.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \retval 0 Node could not be created.
|
||
|
|
* \retval * Node handle.
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxL2NormalizeLayer(vx_graph graph, vx_tensor inputs, vx_tensor outputs);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Convolution and Activation(Relu) and Pooling and Add Layer Node.
|
||
|
|
* \details This function implement Convolutional Network Convolution and Activation(Relu) and Pooling and Add layer.
|
||
|
|
* For fixed-point data types, a fixed point calculation is performed with round and saturate according to the number of accumulator bits. The number of the accumulator bits are implementation defined,
|
||
|
|
* and should be at least 16.\n
|
||
|
|
* round: rounding according the <tt>vx_round_policy_e</tt> enumeration. \n
|
||
|
|
* saturate: A saturation according the <tt>vx_convert_policy_e</tt> enumeration.
|
||
|
|
* The following equation is implemented: \n
|
||
|
|
* \f$ outputs[j,k,i] = saturate(round(\sum_{l} (\sum_{m,n} inputs[j-m,k-n,l] \times weights[m,n,l,i])+biasses[j,k,i])) \f$\n
|
||
|
|
* Where \f$m,n\f$ are indexes on the convolution matrices. \f$ l\f$ is an index on all the convolutions per input.\f$ i\f$ is an index per output.
|
||
|
|
* \f$ j,k \f$ are the inputs/outputs spatial indexes.
|
||
|
|
* Convolution is done on the width and height dimensions of the <tt>\ref vx_tensor</tt>. Therefore, we use here the term x for index along the width dimension and y for index along the height dimension.\n
|
||
|
|
* before the Convolution is done, a padding with zeros of the width and height input dimensions is performed.
|
||
|
|
* Then down scale is done by picking the results according to a skip jump. The skip in the x and y is determined by the output size dimensions.
|
||
|
|
* The relation between input to output is as follows: \n
|
||
|
|
* \f$ width_{output} = round(\frac{(width_{input} + paddingleft_x + paddingright_x - kernel_x - (kernel_x -1) * dilation_x)}{skip_x} + 1) \f$\n
|
||
|
|
* and \n
|
||
|
|
* \f$ height_{output} = round(\frac{(height + paddingtop_y + paddingbottom_y - kernel_y - (kernel_y -1) * dilation_y)}{skip_y} + 1) \f$\n
|
||
|
|
* where \f$width\f$ is the size of the input width dimension. \f$height\f$ is the size of the input height dimension.
|
||
|
|
* \f$width_{output}\f$ is the size of the output width dimension. \f$height_{output}\f$ is the size of the output height dimension.
|
||
|
|
* \f$kernel_x\f$ and \f$kernel_y\f$ are the convolution sizes in width and height dimensions.
|
||
|
|
* skip is calculated by the relation between input and output.
|
||
|
|
* rounding is done according to <tt>\ref vx_convolutional_network_rounding_type_e</tt>.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs_conv The input tensor data for convolution. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
|
||
|
|
* \param [in] inputs_add The input tensor data for add. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
|
||
|
|
* The dimension order is [width, height, #IFM, #batches]. \n
|
||
|
|
* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.
|
||
|
|
* \param [in] convolution_relu_pooling_params [static] Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params [static] Size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] outputs_conv The convolution output tensor data. Output will have the same number and structure of dimensions as inputs_conv.
|
||
|
|
* We uses this tensor to provide format information of convolution output data to hardware, don't really return convolution output data.
|
||
|
|
* \param [out] outputs_add The final add output tensor data. Output will have the same number and structure of dimensions as input.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvolutionReluPoolingAddLayer2(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor inputs_conv,
|
||
|
|
vx_tensor inputs_add,
|
||
|
|
vx_weights_biases_parameter weights_biases,
|
||
|
|
const vx_nn_convolution_relu_pooling_params_t * convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_tensor outputs_conv,
|
||
|
|
vx_tensor outputs_add);
|
||
|
|
|
||
|
|
/*! \brief [Graph] Creates a Convolutional Network Convolution and Activation(Relu) and Pooling and Multiply Layer Node.
|
||
|
|
* \details This function implement Convolutional Network Convolution and Activation(Relu) and Pooling and Multiply layer.
|
||
|
|
* For fixed-point data types, a fixed point calculation is performed with round and saturate according to the number of accumulator bits. The number of the accumulator bits are implementation defined,
|
||
|
|
* and should be at least 16.\n
|
||
|
|
* round: rounding according the <tt>vx_round_policy_e</tt> enumeration. \n
|
||
|
|
* saturate: A saturation according the <tt>vx_convert_policy_e</tt> enumeration.
|
||
|
|
* The following equation is implemented: \n
|
||
|
|
* \f$ outputs[j,k,i] = saturate(round(\sum_{l} (\sum_{m,n} inputs[j-m,k-n,l] \times weights[m,n,l,i])+biasses[j,k,i])) \f$\n
|
||
|
|
* Where \f$m,n\f$ are indexes on the convolution matrices. \f$ l\f$ is an index on all the convolutions per input.\f$ i\f$ is an index per output.
|
||
|
|
* \f$ j,k \f$ are the inputs/outputs spatial indexes.
|
||
|
|
* Convolution is done on the width and height dimensions of the <tt>\ref vx_tensor</tt>. Therefore, we use here the term x for index along the width dimension and y for index along the height dimension.\n
|
||
|
|
* before the Convolution is done, a padding with zeros of the width and height input dimensions is performed.
|
||
|
|
* Then down scale is done by picking the results according to a skip jump. The skip in the x and y is determined by the output size dimensions.
|
||
|
|
* The relation between input to output is as follows: \n
|
||
|
|
* \f$ width_{output} = round(\frac{(width_{input} + paddingleft_x + paddingright_x - kernel_x - (kernel_x -1) * dilation_x)}{skip_x} + 1) \f$\n
|
||
|
|
* and \n
|
||
|
|
* \f$ height_{output} = round(\frac{(height + paddingtop_y + paddingbottom_y - kernel_y - (kernel_y -1) * dilation_y)}{skip_y} + 1) \f$\n
|
||
|
|
* where \f$width\f$ is the size of the input width dimension. \f$height\f$ is the size of the input height dimension.
|
||
|
|
* \f$width_{output}\f$ is the size of the output width dimension. \f$height_{output}\f$ is the size of the output height dimension.
|
||
|
|
* \f$kernel_x\f$ and \f$kernel_y\f$ are the convolution sizes in width and height dimensions.
|
||
|
|
* skip is calculated by the relation between input and output.
|
||
|
|
* rounding is done according to <tt>\ref vx_convolutional_network_rounding_type_e</tt>.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs_conv The input tensor data for convolution. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
|
||
|
|
* \param [in] inputs_mul The input tensor data for mul. 3 lower dimensions represent a single input, all following dimensions represent number of batches, possibly nested.
|
||
|
|
* The dimension order is [width, height, #IFM, #batches]. \n
|
||
|
|
* \param [in] scale A non-negative <tt>\ref VX_TYPE_FLOAT32</tt> multiplied to each product before overflow handling.
|
||
|
|
* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.
|
||
|
|
* \param [in] convolution_relu_pooling_params [static] Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params [static] Size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] outputs_conv The convolution output tensor data. Output will have the same number and structure of dimensions as inputs_conv.
|
||
|
|
* We uses this tensor to provide format information of convolution output data to hardware, don't really return convolution output data.
|
||
|
|
* \param [out] outputs_mul The final mul output tensor data. Output will have the same number and structure of dimensions as input.
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \returns A node reference <tt>\ref vx_node</tt>. Any possible errors preventing a
|
||
|
|
* successful creation should be checked using <tt>\ref vxGetStatus</tt>.
|
||
|
|
* \ingroup group_cnn
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvolutionReluPoolingMultiplyLayer2(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor inputs_conv,
|
||
|
|
vx_tensor inputs_mul,
|
||
|
|
vx_float32 input_scale,
|
||
|
|
vx_weights_biases_parameter weights_biases,
|
||
|
|
const vx_nn_convolution_relu_pooling_params_t * convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_tensor outputs_conv,
|
||
|
|
vx_tensor outputs_mul);
|
||
|
|
/*! \brief [Graph] Performs LUT on element values in the input tensor data's.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input input tensor data.
|
||
|
|
* \param [in] InLut The look-up table of x value, of type <tt>\ref vx_lut</tt>.
|
||
|
|
* \param [in] OutLut The look-up table of y value, of type <tt>\ref vx_lut</tt>.
|
||
|
|
* \param [out] output The output tensor data with the same dimensions as the input tensor data's.
|
||
|
|
* \ingroup group_tensor
|
||
|
|
* \return <tt> vx_node</tt>.
|
||
|
|
* \retval 0 Node could not be created.
|
||
|
|
* \retval * Node handle.
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxTensorTableLookupLayer(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor input,
|
||
|
|
vx_lut InLut,
|
||
|
|
vx_lut OutLut,
|
||
|
|
vx_tensor output);
|
||
|
|
|
||
|
|
typedef struct _vx_nn_gemm_relu_pooling_params_t
|
||
|
|
{
|
||
|
|
vx_bool enable_relu; /*!< \brief Enable Relu layer function or not. */
|
||
|
|
vx_bool enable_leaky_relu; /*!< \brief Enable LeakyRelu layer function or not. */
|
||
|
|
vx_float32 alpha; /*!< \brief Alpha value for Activation */
|
||
|
|
vx_float32 beta; /*!< \brief Beta value for Activation */
|
||
|
|
vx_uint32 node_count; /*!< \brief node count to merge */
|
||
|
|
vx_float32 merged_scale[MERGED_NODE_COUNT_MAX]; /*!< \brief scale of merged node output */
|
||
|
|
vx_int32 merged_zero_point[MERGED_NODE_COUNT_MAX]; /*!< \brief zero point of merged node output */
|
||
|
|
vx_enum merged_data_type[MERGED_NODE_COUNT_MAX]; /*!< \brief data type of merged node output */
|
||
|
|
vx_enum act_func; /*!< \brief nn activation function */
|
||
|
|
vx_lut lut_in; /*!< \brief LUT in */
|
||
|
|
vx_lut lut_out; /*!< \brief LUT out */
|
||
|
|
vx_bool enbale_const_multiplier; /*!< \brief tensor mul with one of inputs as a single pixel const tensor */
|
||
|
|
vx_float32 const_multiplier; /*!< \brief const multiplier */
|
||
|
|
} vx_nn_gemm_relu_pooling_params_t, * vx_nn_gemm_relu_pooling_params;
|
||
|
|
|
||
|
|
/*! \brief Create a batch gemm node, the calcution formula is output = matrix_a * matrix_b + matrix_c.
|
||
|
|
* \param [in] graph The reference to the graph.
|
||
|
|
* \param [in] matrix_a The first input tensor.
|
||
|
|
* \param [in] matrix_b The second input tensor. Must be in the same data type and batch count as first input tensor.
|
||
|
|
* \param [in] matrix_c The third input tensor. Must be in the same data type and batch count as first input tensor. [optional]
|
||
|
|
* \param [in] trans_a If true, the matrix_a has been transposed before calcution.
|
||
|
|
* \param [in] trans_b If true, the matrix_b has been transposed before calcution.
|
||
|
|
* \param [in] trans_c If true, the matrix_c has been transposed before calcution. [optional]
|
||
|
|
* \param [in] merge_param the parameters for gemm + op merging
|
||
|
|
* \param [out] output The output tensor. Output dimension must agree the formula in the description.
|
||
|
|
* \return <tt>\ref vx_node</tt>.
|
||
|
|
* \retval vx_node A node reference. Any possible errors preventing a successful creation
|
||
|
|
* should be checked using <tt>\ref vxGetStatus</tt>
|
||
|
|
* \ingroup group_vision_function_gemm
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxBatchGemmReluPoolingLayer(vx_graph graph,
|
||
|
|
vx_tensor matrix_a,
|
||
|
|
vx_tensor matrix_b,
|
||
|
|
vx_tensor matrix_c,
|
||
|
|
vx_scalar trans_a,
|
||
|
|
vx_scalar trans_b,
|
||
|
|
vx_scalar trans_c,
|
||
|
|
const vx_nn_gemm_relu_pooling_params merge_param,
|
||
|
|
vx_tensor output);
|
||
|
|
|
||
|
|
/*! \brief Create a fuse stream process node.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] input_list input tensor list.
|
||
|
|
* \param [in] input_count input tensor number.
|
||
|
|
* \param [in] output_list output tensor list.
|
||
|
|
* \param [in] output_count output tensor number.
|
||
|
|
* \param [in] params the parameters for multi streamprocessor merging.
|
||
|
|
* \return <tt>\ref vx_node</tt>.
|
||
|
|
* \retval vx_node A node reference. Any possible errors preventing a successful creation
|
||
|
|
* should be checked using <tt>\ref vxGetStatus</tt>
|
||
|
|
* \ingroup group_vision_function_sp
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxFusedSpNode(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor* input_list,
|
||
|
|
vx_uint32 input_count,
|
||
|
|
vx_tensor* output_list,
|
||
|
|
vx_uint32 output_count,
|
||
|
|
const vx_nn_fused_sp_params_t * params
|
||
|
|
);
|
||
|
|
|
||
|
|
/*! \brief Create a conv fuse stream process node.
|
||
|
|
* \param [in] graph The handle to the graph.
|
||
|
|
* \param [in] inputs input tensor.
|
||
|
|
* \param [in] weights_biases [static] Point to WeightBiasesParameter data, vx_weights_biases_parameter is an opaque reference.
|
||
|
|
* \param [in] convolution_relu_pooling_params [static] Pointer to parameters of type <tt>\ref vx_nn_convolution_relu_pooling_params_t</tt>
|
||
|
|
* \param [in] size_of_convolution_relu_pooling_params [static] Size in bytes of convolution_relu_pooling_params.
|
||
|
|
* \param [in] outputs output tensor.
|
||
|
|
* \return <tt>\ref vx_node</tt>.
|
||
|
|
* \retval vx_node A node reference. Any possible errors preventing a successful creation
|
||
|
|
* should be checked using <tt>\ref vxGetStatus</tt>
|
||
|
|
* \ingroup group_vision_function_sp
|
||
|
|
*/
|
||
|
|
VX_API_ENTRY vx_node VX_API_CALL vxConvSpNode(
|
||
|
|
vx_graph graph,
|
||
|
|
vx_tensor inputs,
|
||
|
|
vx_weights_biases_parameter weights_biases,
|
||
|
|
const vx_nn_convolution_relu_pooling_params_t * convolution_relu_pooling_params,
|
||
|
|
vx_size size_of_convolution_relu_pooling_params,
|
||
|
|
vx_tensor outputs
|
||
|
|
);
|
||
|
|
|
||
|
|
#ifdef __cplusplus
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
|
||
|
|
|
||
|
|
#endif
|