329 lines
13 KiB
C
329 lines
13 KiB
C
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// Copyright 2015-2019 Espressif Systems (Shanghai) PTE LTD
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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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// 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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#ifndef DL_LIB_H
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#define DL_LIB_H
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#include "dl_lib_matrix.h"
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#include "dl_lib_matrixq.h"
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typedef int padding_state;
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/**
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* @brief Does a fast version of the exp() operation on a floating point number.
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*
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* As described in https://codingforspeed.com/using-faster-exponential-approximation/
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* Should be good til an input of 5 or so with a steps factor of 8.
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*
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* @param in Floating point input
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* @param steps Approximation steps. More is more precise. 8 or 10 should be good enough for most purposes.
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* @return Exp()'ed output
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*/
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fptp_t fast_exp(double x, int steps);
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/**
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* @brief Does a softmax operation on a matrix.
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_softmax(const dl_matrix2d_t *in, dl_matrix2d_t *out);
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/**
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* @brief Does a softmax operation on a quantized matrix.
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_softmax_q(const dl_matrix2dq_t *in, dl_matrix2dq_t *out);
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/**
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* @brief Does a sigmoid operation on a floating point number
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*
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* @param in Floating point input
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* @return Sigmoid output
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*/
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fptp_t dl_sigmoid_op(fptp_t in);
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/**
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* @brief Does a sigmoid operation on a matrix.
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_sigmoid(const dl_matrix2d_t *in, dl_matrix2d_t *out);
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/**
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* @brief Does a tanh operation on a floating point number
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*
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* @param in Floating point input number
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* @return Tanh value
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*/
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fptp_t dl_tanh_op(fptp_t v);
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/**
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* @brief Does a tanh operation on a matrix.
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_tanh(const dl_matrix2d_t *in, dl_matrix2d_t *out);
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/**
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* @brief Does a relu (Rectifier Linear Unit) operation on a floating point number
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*
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* @param in Floating point input
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* @param clip If value is higher than this, it will be clipped to this value
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* @return Relu output
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*/
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fptp_t dl_relu_op(fptp_t in, fptp_t clip);
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/**
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* @brief Does a ReLu operation on a matrix.
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*
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* @param in Input matrix
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* @param clip If values are higher than this, they will be clipped to this value
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_relu(const dl_matrix2d_t *in, fptp_t clip, dl_matrix2d_t *out);
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/**
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* @brief Fully connected layer operation
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*
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* @param in Input vector
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* @param weight Weights of the neurons
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* @param bias Biases for the neurons. Can be NULL if a bias of 0 is required.
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* @param out Output array. Outputs are placed here. Needs to be an initialized, weight->w by in->h in size, matrix.
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*/
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void dl_fully_connect_layer(const dl_matrix2d_t *in, const dl_matrix2d_t *weight, const dl_matrix2d_t *bias, dl_matrix2d_t *out);
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/**
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* @brief Pre-calculate the sqrtvari variable for the batch_normalize function.
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* The sqrtvari matrix depends on the variance and epsilon values, which normally are constant. Hence,
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* this matrix only needs to be calculated once. This function does that.
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*
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* @param
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* @return
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*/
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void dl_batch_normalize_get_sqrtvar(const dl_matrix2d_t *variance, fptp_t epsilon, dl_matrix2d_t *out);
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/**
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* @brief Batch-normalize a matrix
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*
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* @param m The matrix to normalize
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* @param offset Offset matrix
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* @param scale Scale matrix
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* @param mean Mean matrix
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* @param sqrtvari Matrix precalculated using dl_batch_normalize_get_sqrtvar
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* @return
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*/
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void dl_batch_normalize(dl_matrix2d_t *m, const dl_matrix2d_t *offset, const dl_matrix2d_t *scale,
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const dl_matrix2d_t *mean, const dl_matrix2d_t *sqrtvari);
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/**
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* @brief Do a basic LSTM layer pass.
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*
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* @warning Returns state_h pointer, so do not free result.
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* @param in Input vector
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* @param state_c Internal state of the LSTM network
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* @param state_h Internal state (previous output values) of the LSTM network
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* @param weights Weights for the neurons
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* @param bias Bias for the neurons. Can be NULL if no bias is required
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* @return Output values of the neurons
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*/
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dl_matrix2d_t *dl_basic_lstm_layer(const dl_matrix2d_t *in, dl_matrix2d_t *state_c, dl_matrix2d_t *state_h,
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const dl_matrix2d_t *weight, const dl_matrix2d_t *bias);
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/**
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* @brief Do a basic LSTM layer pass, partial quantized version.
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* This LSTM function accepts 16-bit fixed-point weights and 32-bit float-point bias.
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*
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* @warning Returns state_h pointer, so do not free result.
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* @param in Input vector
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* @param state_c Internal state of the LSTM network
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* @param state_h Internal state (previous output values) of the LSTM network
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* @param weights Weights for the neurons, need to be quantised
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* @param bias Bias for the neurons. Can be NULL if no bias is required
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* @return Output values of the neurons
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*/
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dl_matrix2dq_t *dl_basic_lstm_layer_quantised_weights(const dl_matrix2d_t *in, dl_matrix2d_t *state_c, dl_matrix2d_t *state_h,
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const dl_matrix2dq_t *weight, const dl_matrix2d_t *bias);
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/**
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* @brief Do a fully-connected layer pass, fully-quantized version.
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*
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* @param in Input vector
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* @param weight Weights of the neurons
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* @param bias Bias values of the neurons. Can be NULL if no bias is needed.
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* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
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* @return Output values of the neurons
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*/
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void dl_fully_connect_layer_q(const dl_matrix2dq_t *in, const dl_matrix2dq_t *weight, const dl_matrix2dq_t *bias, dl_matrix2dq_t *out, int shift);
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/**
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* @brief Do a basic LSTM layer pass, fully-quantized version
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*
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* @warning Returns state_h pointer, so do not free result.
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* @param in Input vector
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* @param state_c Internal state of the LSTM network
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* @param state_h Internal state (previous output values) of the LSTM network
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* @param weights Weights for the neurons
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* @param bias Bias for the neurons. Can be NULL if no bias is required
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* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
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* @return Output values of the neurons
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*/
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dl_matrix2dq_t *dl_basic_lstm_layer_q(const dl_matrix2dq_t *in, dl_matrix2dq_t *state_c, dl_matrix2dq_t *state_h,
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const dl_matrix2dq_t *weight, const dl_matrix2dq_t *bias, int shift);
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/**
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* @brief Batch-normalize a matrix, fully-quantized version
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*
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* @param m The matrix to normalize
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* @param offset Offset matrix
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* @param scale Scale matrix
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* @param mean Mean matrix
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* @param sqrtvari Matrix precalculated using dl_batch_normalize_get_sqrtvar
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* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
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* @return
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*/
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void dl_batch_normalize_q(dl_matrix2dq_t *m, const dl_matrix2dq_t *offset, const dl_matrix2dq_t *scale,
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const dl_matrix2dq_t *mean, const dl_matrix2dq_t *sqrtvari, int shift);
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/**
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* @brief Does a relu (Rectifier Linear Unit) operation on a fixed-point number
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* This accepts and returns fixed-point 32-bit number with the last 15 bits being the bits after the decimal
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* point. (Equivalent to a mantissa in a quantized matrix with exponent -15.)
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*
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* @param in Fixed-point input
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* @param clip If value is higher than this, it will be clipped to this value
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* @return Relu output
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*/
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qtp_t dl_relu_q_op(qtp_t in, qtp_t clip);
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/**
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* @brief Does a ReLu operation on a matrix, quantized version
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*
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* @param in Input matrix
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* @param clip If values are higher than this, they will be clipped to this value
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_relu_q(const dl_matrix2dq_t *in, fptp_t clip, dl_matrix2dq_t *out);
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/**
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* @brief Does a sigmoid operation on a fixed-point number.
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* This accepts and returns a fixed-point 32-bit number with the last 15 bits being the bits after the decimal
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* point. (Equivalent to a mantissa in a quantized matrix with exponent -15.)
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*
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* @param in Fixed-point input
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* @return Sigmoid output
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*/
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int dl_sigmoid_op_q(const int in);
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/**
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* @brief Does a sigmoid operation on a matrix, quantized version
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_sigmoid_q(const dl_matrix2dq_t *in, dl_matrix2dq_t *out);
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/**
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* @brief Does a tanh operation on a matrix, quantized version
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*
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* @param in Input matrix
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_tanh_q(const dl_matrix2dq_t *in, dl_matrix2dq_t *out);
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/**
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* @brief Does a tanh operation on a fixed-point number.
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* This accepts and returns a fixed-point 32-bit number with the last 15 bits being the bits after the decimal
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* point. (Equivalent to a mantissa in a quantized matrix with exponent -15.)
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*
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* @param in Fixed-point input
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* @return tanh output
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*/
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int dl_tanh_op_q(int v);
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/**
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* @brief Filter out the number greater than clip in the matrix, quantized version
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*
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* @param in Input matrix
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* @param clip If values are higher than this, they will be clipped to this value
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_minimum(const dl_matrix2d_t *in, fptp_t clip, dl_matrix2d_t *out);
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/**
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* @brief Filter out the number greater than clip in the matrix, float version
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*
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* @param in Input matrix
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* @param clip If values are higher than this, they will be clipped to this value
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* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
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*/
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void dl_minimum_q(const dl_matrix2dq_t *in, fptp_t clip, dl_matrix2dq_t *out);
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/**
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* @brief Do a basic CNN layer pass.
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*
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* @Warning This just supports the single channel input image, and the output is single row matrix.
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That is to say, the height of output is 1, and the weight of output is out_channels*out_image_width*out_image_height
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*
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* @param in Input single channel image
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* @param weight Weights of the neurons, weight->w = out_channels, weight->h = filter_width*filter_height
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* @param bias Bias for the CNN layer.
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* @param filter_height The height of convolution kernel
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* @param filter_width The width of convolution kernel
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* @param out_channels The number of output channels of convolution kernel
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param pad One of `"VALID"` or `"SAME"`, 0 is "VALID" and the other is "SAME"
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* @param out The result of CNN layer, out->h=1.
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* @return The result of CNN layer.
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*/
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dl_matrix2d_t *dl_basic_conv_layer(const dl_matrix2d_t *in, const dl_matrix2d_t *weight, const dl_matrix2d_t *bias, int filter_width, int filter_height,
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const int out_channels, const int stride_x, const int stride_y, padding_state pad, const dl_matrix2d_t* out);
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/**
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* @brief Do a basic CNN layer pass, quantised wersion.
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*
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* @Warning This just supports the single channel input image, and the output is single row matrix.
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That is to say, the height of output is 1, and the weight of output is out_channels*out_image_width*out_image_height
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*
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* @param in Input single channel image
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* @param weight Weights of the neurons, weight->w = out_channels, weight->h = filter_width*filter_height,
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* @param bias Bias of the neurons.
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* @param filter_height The height of convolution kernel
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* @param filter_width The width of convolution kernel
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* @param out_channels The number of output channels of convolution kernel
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param pad One of `"VALID"` or `"SAME"`, 0 is "VALID" and the other is "SAME"
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* @param out The result of CNN layer, out->h=1
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* @return The result of CNN layer
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*/
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dl_matrix2d_t *dl_basic_conv_layer_quantised_weight(const dl_matrix2d_t *in, const dl_matrix2dq_t *weight, const dl_matrix2d_t *bias, int filter_width, int filter_height,
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const int out_channels, const int stride_x, const int stride_y, padding_state pad, const dl_matrix2d_t* out);
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#endif
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