tesseract/lstm/network.h
2017-09-08 10:24:00 +01:00

321 lines
14 KiB
C++

///////////////////////////////////////////////////////////////////////
// File: network.h
// Description: Base class for neural network implementations.
// Author: Ray Smith
// Created: Wed May 01 16:38:06 PST 2013
//
// (C) Copyright 2013, 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_NETWORK_H_
#define TESSERACT_LSTM_NETWORK_H_
#include <stdio.h>
#include <cmath>
#include "genericvector.h"
#include "helpers.h"
#include "matrix.h"
#include "networkio.h"
#include "serialis.h"
#include "static_shape.h"
#include "tprintf.h"
struct Pix;
class ScrollView;
class TBOX;
namespace tesseract {
class ImageData;
class NetworkScratch;
// Enum to store the run-time type of a Network. Keep in sync with kTypeNames.
enum NetworkType {
NT_NONE, // The naked base class.
NT_INPUT, // Inputs from an image.
// Plumbing networks combine other networks or rearrange the inputs.
NT_CONVOLVE, // Duplicates inputs in a sliding window neighborhood.
NT_MAXPOOL, // Chooses the max result from a rectangle.
NT_PARALLEL, // Runs networks in parallel.
NT_REPLICATED, // Runs identical networks in parallel.
NT_PAR_RL_LSTM, // Runs LTR and RTL LSTMs in parallel.
NT_PAR_UD_LSTM, // Runs Up and Down LSTMs in parallel.
NT_PAR_2D_LSTM, // Runs 4 LSTMs in parallel.
NT_SERIES, // Executes a sequence of layers.
NT_RECONFIG, // Scales the time/y size but makes the output deeper.
NT_XREVERSED, // Reverses the x direction of the inputs/outputs.
NT_YREVERSED, // Reverses the y-direction of the inputs/outputs.
NT_XYTRANSPOSE, // Transposes x and y (for just a single op).
// Functional networks actually calculate stuff.
NT_LSTM, // Long-Short-Term-Memory block.
NT_LSTM_SUMMARY, // LSTM that only keeps its last output.
NT_LOGISTIC, // Fully connected logistic nonlinearity.
NT_POSCLIP, // Fully connected rect lin version of logistic.
NT_SYMCLIP, // Fully connected rect lin version of tanh.
NT_TANH, // Fully connected with tanh nonlinearity.
NT_RELU, // Fully connected with rectifier nonlinearity.
NT_LINEAR, // Fully connected with no nonlinearity.
NT_SOFTMAX, // Softmax uses exponential normalization, with CTC.
NT_SOFTMAX_NO_CTC, // Softmax uses exponential normalization, no CTC.
// The SOFTMAX LSTMs both have an extra softmax layer on top, but inside, with
// the outputs fed back to the input of the LSTM at the next timestep.
// The ENCODED version binary encodes the softmax outputs, providing log2 of
// the number of outputs as additional inputs, and the other version just
// provides all the softmax outputs as additional inputs.
NT_LSTM_SOFTMAX, // 1-d LSTM with built-in fully connected softmax.
NT_LSTM_SOFTMAX_ENCODED, // 1-d LSTM with built-in binary encoded softmax.
// A TensorFlow graph encapsulated as a Tesseract network.
NT_TENSORFLOW,
NT_COUNT // Array size.
};
// Enum of Network behavior flags. Can in theory be set for each individual
// network element.
enum NetworkFlags {
// Network forward/backprop behavior.
NF_LAYER_SPECIFIC_LR = 64, // Separate learning rate for each layer.
NF_ADAM = 128, // Weight-specific learning rate.
};
// State of training and desired state used in SetEnableTraining.
enum TrainingState {
// Valid states of training_.
TS_DISABLED, // Disabled permanently.
TS_ENABLED, // Enabled for backprop and to write a training dump.
// Re-enable from ANY disabled state.
TS_TEMP_DISABLE, // Temporarily disabled to write a recognition dump.
// Valid only for SetEnableTraining.
TS_RE_ENABLE, // Re-Enable from TS_TEMP_DISABLE, but not TS_DISABLED.
};
// Base class for network types. Not quite an abstract base class, but almost.
// Most of the time no isolated Network exists, except prior to
// deserialization.
class Network {
public:
Network();
Network(NetworkType type, const STRING& name, int ni, int no);
virtual ~Network();
// Accessors.
NetworkType type() const {
return type_;
}
bool IsTraining() const { return training_ == TS_ENABLED; }
bool needs_to_backprop() const {
return needs_to_backprop_;
}
int num_weights() const { return num_weights_; }
int NumInputs() const {
return ni_;
}
int NumOutputs() const {
return no_;
}
// Returns the required shape input to the network.
virtual StaticShape InputShape() const {
StaticShape result;
return result;
}
// Returns the shape output from the network given an input shape (which may
// be partially unknown ie zero).
virtual StaticShape OutputShape(const StaticShape& input_shape) const {
StaticShape result(input_shape);
result.set_depth(no_);
return result;
}
const STRING& name() const {
return name_;
}
virtual STRING spec() const {
return "?";
}
bool TestFlag(NetworkFlags flag) const {
return (network_flags_ & flag) != 0;
}
// Initialization and administrative functions that are mostly provided
// by Plumbing.
// Returns true if the given type is derived from Plumbing, and thus contains
// multiple sub-networks that can have their own learning rate.
virtual bool IsPlumbingType() const { return false; }
// Suspends/Enables/Permanently disables training by setting the training_
// flag. Serialize and DeSerialize only operate on the run-time data if state
// is TS_DISABLED or TS_TEMP_DISABLE. Specifying TS_TEMP_DISABLE will
// temporarily disable layers in state TS_ENABLED, allowing a trainer to
// serialize as if it were a recognizer.
// TS_RE_ENABLE will re-enable layers that were previously in any disabled
// state. If in TS_TEMP_DISABLE then the flag is just changed, but if in
// TS_DISABLED, the deltas in the weight matrices are reinitialized so that a
// recognizer can be converted back to a trainer.
virtual void SetEnableTraining(TrainingState state);
// Sets flags that control the action of the network. See NetworkFlags enum
// for bit values.
virtual void SetNetworkFlags(uinT32 flags);
// Sets up the network for training. Initializes weights using weights of
// scale `range` picked according to the random number generator `randomizer`.
// Note that randomizer is a borrowed pointer that should outlive the network
// and should not be deleted by any of the networks.
// Returns the number of weights initialized.
virtual int InitWeights(float range, TRand* randomizer);
// Changes the number of outputs to the outside world to the size of the given
// code_map. Recursively searches the entire network for Softmax layers that
// have exactly old_no outputs, and operates only on those, leaving all others
// unchanged. This enables networks with multiple output layers to get all
// their softmaxes updated, but if an internal layer, uses one of those
// softmaxes for input, then the inputs will effectively be scrambled.
// TODO(rays) Fix this before any such network is implemented.
// The softmaxes are resized by 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.
virtual int RemapOutputs(int old_no, const std::vector<int>& code_map) {
return 0;
}
// Converts a float network to an int network.
virtual void ConvertToInt() {}
// Provides a pointer to a TRand for any networks that care to use it.
// Note that randomizer is a borrowed pointer that should outlive the network
// and should not be deleted by any of the networks.
virtual void SetRandomizer(TRand* randomizer);
// Sets needs_to_backprop_ to needs_backprop and returns true if
// needs_backprop || any weights in this network so the next layer forward
// can be told to produce backprop for this layer if needed.
virtual bool SetupNeedsBackprop(bool needs_backprop);
// Returns the most recent reduction factor that the network applied to the
// time sequence. Assumes that any 2-d is already eliminated. Used for
// scaling bounding boxes of truth data and calculating result bounding boxes.
// WARNING: if GlobalMinimax is used to vary the scale, this will return
// the last used scale factor. Call it before any forward, and it will return
// the minimum scale factor of the paths through the GlobalMinimax.
virtual int XScaleFactor() const {
return 1;
}
// Provides the (minimum) x scale factor to the network (of interest only to
// input units) so they can determine how to scale bounding boxes.
virtual void CacheXScaleFactor(int factor) {}
// Provides debug output on the weights.
virtual void DebugWeights() {
tprintf("Must override Network::DebugWeights for type %d\n", type_);
}
// Writes to the given file. Returns false in case of error.
// Should be overridden by subclasses, but called by their Serialize.
virtual bool Serialize(TFile* fp) const;
// Reads from the given file. Returns false in case of error.
// Should be overridden by subclasses, but NOT called by their DeSerialize.
virtual bool DeSerialize(TFile* fp);
// Updates the weights using the given learning rate, momentum and adam_beta.
// num_samples is used in the adam computation iff use_adam_ is true.
virtual void Update(float learning_rate, float momentum, float adam_beta,
int num_samples) {}
// Sums the products of weight updates in *this and other, splitting into
// positive (same direction) in *same and negative (different direction) in
// *changed.
virtual void CountAlternators(const Network& other, double* same,
double* changed) const {}
// Reads from the given file. Returns NULL in case of error.
// Determines the type of the serialized class and calls its DeSerialize
// on a new object of the appropriate type, which is returned.
static Network* CreateFromFile(TFile* fp);
// Runs forward propagation of activations on the input line.
// Note that input and output are both 2-d arrays.
// The 1st index is the time element. In a 1-d network, it might be the pixel
// position on the textline. In a 2-d network, the linearization is defined
// by the stride_map. (See networkio.h).
// The 2nd index of input is the network inputs/outputs, and the dimension
// of the input must match NumInputs() of this network.
// The output array will be resized as needed so that its 1st dimension is
// always equal to the number of output values, and its second dimension is
// always NumOutputs(). Note that all this detail is encapsulated away inside
// NetworkIO, as are the internals of the scratch memory space used by the
// network. See networkscratch.h for that.
// If input_transpose is not NULL, then it contains the transpose of input,
// and the caller guarantees that it will still be valid on the next call to
// backward. The callee is therefore at liberty to save the pointer and
// reference it on a call to backward. This is a bit ugly, but it makes it
// possible for a replicating parallel to calculate the input transpose once
// instead of all the replicated networks having to do it.
virtual void Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
tprintf("Must override Network::Forward for type %d\n", type_);
}
// Runs backward propagation of errors on fwdX_deltas.
// Note that fwd_deltas and back_deltas are both 2-d arrays as with Forward.
// Returns false if back_deltas was not set, due to there being no point in
// propagating further backwards. Thus most complete networks will always
// return false from Backward!
virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
NetworkScratch* scratch,
NetworkIO* back_deltas) {
tprintf("Must override Network::Backward for type %d\n", type_);
return false;
}
// === Debug image display methods. ===
// Displays the image of the matrix to the forward window.
void DisplayForward(const NetworkIO& matrix);
// Displays the image of the matrix to the backward window.
void DisplayBackward(const NetworkIO& matrix);
// Creates the window if needed, otherwise clears it.
static void ClearWindow(bool tess_coords, const char* window_name,
int width, int height, ScrollView** window);
// Displays the pix in the given window. and returns the height of the pix.
// The pix is pixDestroyed.
static int DisplayImage(Pix* pix, ScrollView* window);
protected:
// Returns a random number in [-range, range].
double Random(double range);
protected:
NetworkType type_; // Type of the derived network class.
TrainingState training_; // Are we currently training?
bool needs_to_backprop_; // This network needs to output back_deltas.
inT32 network_flags_; // Behavior control flags in NetworkFlags.
inT32 ni_; // Number of input values.
inT32 no_; // Number of output values.
inT32 num_weights_; // Number of weights in this and sub-network.
STRING name_; // A unique name for this layer.
// NOT-serialized debug data.
ScrollView* forward_win_; // Recognition debug display window.
ScrollView* backward_win_; // Training debug display window.
TRand* randomizer_; // Random number generator.
// Static serialized name/type_ mapping. Keep in sync with NetworkType.
static char const* const kTypeNames[NT_COUNT];
};
} // namespace tesseract.
#endif // TESSERACT_LSTM_NETWORK_H_