mirror of
https://github.com/tesseract-ocr/tesseract.git
synced 2024-12-26 18:08:18 +08:00
189 lines
8.3 KiB
C++
189 lines
8.3 KiB
C++
///////////////////////////////////////////////////////////////////////
|
|
// File: weightmatrix.h
|
|
// Description: Hides distinction between float/int implementations.
|
|
// Author: Ray Smith
|
|
// Created: Tue Jun 17 09:05:39 PST 2014
|
|
//
|
|
// (C) Copyright 2014, Google Inc.
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
///////////////////////////////////////////////////////////////////////
|
|
|
|
#ifndef TESSERACT_LSTM_WEIGHTMATRIX_H_
|
|
#define TESSERACT_LSTM_WEIGHTMATRIX_H_
|
|
|
|
#include "genericvector.h"
|
|
#include "matrix.h"
|
|
#include "tprintf.h"
|
|
|
|
namespace tesseract {
|
|
|
|
// Convenience instantiation of GENERIC_2D_ARRAY<double> with additional
|
|
// operations to write a strided vector, so the transposed form of the input
|
|
// is memory-contiguous.
|
|
class TransposedArray : public GENERIC_2D_ARRAY<double> {
|
|
public:
|
|
// Copies the whole input transposed, converted to double, into *this.
|
|
void Transpose(const GENERIC_2D_ARRAY<double>& input);
|
|
// Writes a vector of data representing a timestep (gradients or sources).
|
|
// The data is assumed to be of size1 in size (the strided dimension).
|
|
void WriteStrided(int t, const float* data) {
|
|
int size1 = dim1();
|
|
for (int i = 0; i < size1; ++i) put(i, t, data[i]);
|
|
}
|
|
void WriteStrided(int t, const double* data) {
|
|
int size1 = dim1();
|
|
for (int i = 0; i < size1; ++i) put(i, t, data[i]);
|
|
}
|
|
// Prints the first and last num elements of the un-transposed array.
|
|
void PrintUnTransposed(int num) {
|
|
int num_features = dim1();
|
|
int width = dim2();
|
|
for (int y = 0; y < num_features; ++y) {
|
|
for (int t = 0; t < width; ++t) {
|
|
if (num == 0 || t < num || t + num >= width) {
|
|
tprintf(" %g", (*this)(y, t));
|
|
}
|
|
}
|
|
tprintf("\n");
|
|
}
|
|
}
|
|
}; // class TransposedArray
|
|
|
|
// Generic weight matrix for network layers. Can store the matrix as either
|
|
// an array of floats or inT8. Provides functions to compute the forward and
|
|
// backward steps with the matrix and updates to the weights.
|
|
class WeightMatrix {
|
|
public:
|
|
WeightMatrix() : int_mode_(false), use_adam_(false) {}
|
|
// Sets up the network for training. Initializes weights using weights of
|
|
// scale `range` picked according to the random number generator `randomizer`.
|
|
// Note the order is outputs, inputs, as this is the order of indices to
|
|
// the matrix, so the adjacent elements are multiplied by the input during
|
|
// a forward operation.
|
|
int InitWeightsFloat(int no, int ni, bool use_adam, float weight_range,
|
|
TRand* randomizer);
|
|
// Changes the number of outputs to the size of the given code_map, copying
|
|
// the old weight matrix entries for each output from code_map[output] where
|
|
// non-negative, and uses the mean (over all outputs) of the existing weights
|
|
// for all outputs with negative code_map entries. Returns the new number of
|
|
// weights.
|
|
int RemapOutputs(const std::vector<int>& code_map);
|
|
|
|
// Converts a float network to an int network. Each set of input weights that
|
|
// corresponds to a single output weight is converted independently:
|
|
// Compute the max absolute value of the weight set.
|
|
// Scale so the max absolute value becomes MAX_INT8.
|
|
// Round to integer.
|
|
// Store a multiplicative scale factor (as a float) that will reproduce
|
|
// the original value, subject to rounding errors.
|
|
void ConvertToInt();
|
|
|
|
// Accessors.
|
|
bool is_int_mode() const {
|
|
return int_mode_;
|
|
}
|
|
int NumOutputs() const { return int_mode_ ? wi_.dim1() : wf_.dim1(); }
|
|
// Provides one set of weights. Only used by peep weight maxpool.
|
|
const double* GetWeights(int index) const { return wf_[index]; }
|
|
// Provides access to the deltas (dw_).
|
|
double GetDW(int i, int j) const { return dw_(i, j); }
|
|
|
|
// Allocates any needed memory for running Backward, and zeroes the deltas,
|
|
// thus eliminating any existing momentum.
|
|
void InitBackward();
|
|
|
|
// Writes to the given file. Returns false in case of error.
|
|
bool Serialize(bool training, TFile* fp) const;
|
|
// Reads from the given file. Returns false in case of error.
|
|
bool DeSerialize(bool training, TFile* fp);
|
|
// As DeSerialize, but reads an old (float) format WeightMatrix for
|
|
// backward compatibility.
|
|
bool DeSerializeOld(bool training, TFile* fp);
|
|
|
|
// Computes matrix.vector v = Wu.
|
|
// u is of size W.dim2() - 1 and the output v is of size W.dim1().
|
|
// u is imagined to have an extra element at the end with value 1, to
|
|
// implement the bias, but it doesn't actually have it.
|
|
// Asserts that the call matches what we have.
|
|
void MatrixDotVector(const double* u, double* v) const;
|
|
void MatrixDotVector(const inT8* u, double* v) const;
|
|
// MatrixDotVector for peep weights, MultiplyAccumulate adds the
|
|
// component-wise products of *this[0] and v to inout.
|
|
void MultiplyAccumulate(const double* v, double* inout);
|
|
// Computes vector.matrix v = uW.
|
|
// u is of size W.dim1() and the output v is of size W.dim2() - 1.
|
|
// The last result is discarded, as v is assumed to have an imaginary
|
|
// last value of 1, as with MatrixDotVector.
|
|
void VectorDotMatrix(const double* u, double* v) const;
|
|
// Fills dw_[i][j] with the dot product u[i][] . v[j][], using elements
|
|
// from u and v, starting with u[i][offset] and v[j][offset].
|
|
// Note that (matching MatrixDotVector) v[last][] is missing, presumed 1.0.
|
|
// Runs parallel if requested. Note that inputs must be transposed.
|
|
void SumOuterTransposed(const TransposedArray& u, const TransposedArray& v,
|
|
bool parallel);
|
|
// Updates the weights using the given learning rate, momentum and adam_beta.
|
|
// num_samples is used in the Adam correction factor.
|
|
void Update(double learning_rate, double momentum, double adam_beta,
|
|
int num_samples);
|
|
// Adds the dw_ in other to the dw_ is *this.
|
|
void AddDeltas(const WeightMatrix& other);
|
|
// Sums the products of weight updates in *this and other, splitting into
|
|
// positive (same direction) in *same and negative (different direction) in
|
|
// *changed.
|
|
void CountAlternators(const WeightMatrix& other, double* same,
|
|
double* changed) const;
|
|
|
|
void Debug2D(const char* msg);
|
|
|
|
// Computes and returns the dot product of the two n-vectors u and v.
|
|
static double DotProduct(const double* u, const double* v, int n);
|
|
// Utility function converts an array of float to the corresponding array
|
|
// of double.
|
|
static void FloatToDouble(const GENERIC_2D_ARRAY<float>& wf,
|
|
GENERIC_2D_ARRAY<double>* wd);
|
|
|
|
private:
|
|
// Computes matrix.vector v = Wu.
|
|
// u is of size starts.back()+extents.back() and the output v is of size
|
|
// starts.size().
|
|
// The weight matrix w, is of size starts.size()xMAX(extents)+add_bias_fwd.
|
|
// If add_bias_fwd, an extra element at the end of w[i] is the bias weight
|
|
// and is added to v[i].
|
|
static void MatrixDotVectorInternal(const GENERIC_2D_ARRAY<double>& w,
|
|
bool add_bias_fwd, bool skip_bias_back,
|
|
const double* u, double* v);
|
|
|
|
private:
|
|
// Choice between float and 8 bit int implementations.
|
|
GENERIC_2D_ARRAY<double> wf_;
|
|
GENERIC_2D_ARRAY<inT8> wi_;
|
|
// Transposed copy of wf_, used only for Backward, and set with each Update.
|
|
TransposedArray wf_t_;
|
|
// Which of wf_ and wi_ are we actually using.
|
|
bool int_mode_;
|
|
// True if we are running adam in this weight matrix.
|
|
bool use_adam_;
|
|
// If we are using wi_, then scales_ is a factor to restore the row product
|
|
// with a vector to the correct range.
|
|
GenericVector<double> scales_;
|
|
// Weight deltas. dw_ is the new delta, and updates_ the momentum-decaying
|
|
// amount to be added to wf_/wi_.
|
|
GENERIC_2D_ARRAY<double> dw_;
|
|
GENERIC_2D_ARRAY<double> updates_;
|
|
// Iff use_adam_, the sum of squares of dw_. The number of samples is
|
|
// given to Update(). Serialized iff use_adam_.
|
|
GENERIC_2D_ARRAY<double> dw_sq_sum_;
|
|
};
|
|
|
|
} // namespace tesseract.
|
|
|
|
#endif // TESSERACT_LSTM_WEIGHTMATRIX_H_
|