tesseract/lstm/plumbing.cpp
2017-09-08 10:24:00 +01:00

248 lines
8.8 KiB
C++

///////////////////////////////////////////////////////////////////////
// File: plumbing.cpp
// Description: Base class for networks that organize other networks
// eg series or parallel.
// Author: Ray Smith
// Created: Mon May 12 08:17:34 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.
///////////////////////////////////////////////////////////////////////
#include "plumbing.h"
namespace tesseract {
// ni_ and no_ will be set by AddToStack.
Plumbing::Plumbing(const STRING& name)
: Network(NT_PARALLEL, name, 0, 0) {
}
Plumbing::~Plumbing() {
}
// Suspends/Enables training by setting the training_ flag. Serialize and
// DeSerialize only operate on the run-time data if state is false.
void Plumbing::SetEnableTraining(TrainingState state) {
Network::SetEnableTraining(state);
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->SetEnableTraining(state);
}
// Sets flags that control the action of the network. See NetworkFlags enum
// for bit values.
void Plumbing::SetNetworkFlags(uinT32 flags) {
Network::SetNetworkFlags(flags);
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->SetNetworkFlags(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.
int Plumbing::InitWeights(float range, TRand* randomizer) {
num_weights_ = 0;
for (int i = 0; i < stack_.size(); ++i)
num_weights_ += stack_[i]->InitWeights(range, randomizer);
return num_weights_;
}
// Recursively searches the network for softmaxes with old_no outputs,
// and remaps their outputs according to code_map. See network.h for details.
int Plumbing::RemapOutputs(int old_no, const std::vector<int>& code_map) {
num_weights_ = 0;
for (int i = 0; i < stack_.size(); ++i) {
num_weights_ += stack_[i]->RemapOutputs(old_no, code_map);
}
return num_weights_;
}
// Converts a float network to an int network.
void Plumbing::ConvertToInt() {
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->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.
void Plumbing::SetRandomizer(TRand* randomizer) {
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->SetRandomizer(randomizer);
}
// Adds the given network to the stack.
void Plumbing::AddToStack(Network* network) {
if (stack_.empty()) {
ni_ = network->NumInputs();
no_ = network->NumOutputs();
} else if (type_ == NT_SERIES) {
// ni is input of first, no output of last, others match output to input.
ASSERT_HOST(no_ == network->NumInputs());
no_ = network->NumOutputs();
} else {
// All parallel types. Output is sum of outputs, inputs all match.
ASSERT_HOST(ni_ == network->NumInputs());
no_ += network->NumOutputs();
}
stack_.push_back(network);
}
// Sets needs_to_backprop_ to needs_backprop and calls on sub-network
// according to needs_backprop || any weights in this network.
bool Plumbing::SetupNeedsBackprop(bool needs_backprop) {
if (IsTraining()) {
needs_to_backprop_ = needs_backprop;
bool retval = needs_backprop;
for (int i = 0; i < stack_.size(); ++i) {
if (stack_[i]->SetupNeedsBackprop(needs_backprop)) retval = true;
}
return retval;
}
// Frozen networks don't do backprop.
needs_to_backprop_ = false;
return false;
}
// Returns an integer reduction factor that the network applies to the
// time sequence. Assumes that any 2-d is already eliminated. Used for
// scaling bounding boxes of truth data.
// 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.
int Plumbing::XScaleFactor() const {
return stack_[0]->XScaleFactor();
}
// Provides the (minimum) x scale factor to the network (of interest only to
// input units) so they can determine how to scale bounding boxes.
void Plumbing::CacheXScaleFactor(int factor) {
for (int i = 0; i < stack_.size(); ++i) {
stack_[i]->CacheXScaleFactor(factor);
}
}
// Provides debug output on the weights.
void Plumbing::DebugWeights() {
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->DebugWeights();
}
// Returns a set of strings representing the layer-ids of all layers below.
void Plumbing::EnumerateLayers(const STRING* prefix,
GenericVector<STRING>* layers) const {
for (int i = 0; i < stack_.size(); ++i) {
STRING layer_name;
if (prefix) layer_name = *prefix;
layer_name.add_str_int(":", i);
if (stack_[i]->IsPlumbingType()) {
Plumbing* plumbing = static_cast<Plumbing*>(stack_[i]);
plumbing->EnumerateLayers(&layer_name, layers);
} else {
layers->push_back(layer_name);
}
}
}
// Returns a pointer to the network layer corresponding to the given id.
Network* Plumbing::GetLayer(const char* id) const {
char* next_id;
int index = strtol(id, &next_id, 10);
if (index < 0 || index >= stack_.size()) return NULL;
if (stack_[index]->IsPlumbingType()) {
Plumbing* plumbing = static_cast<Plumbing*>(stack_[index]);
ASSERT_HOST(*next_id == ':');
return plumbing->GetLayer(next_id + 1);
}
return stack_[index];
}
// Returns a pointer to the learning rate for the given layer id.
float* Plumbing::LayerLearningRatePtr(const char* id) const {
char* next_id;
int index = strtol(id, &next_id, 10);
if (index < 0 || index >= stack_.size()) return NULL;
if (stack_[index]->IsPlumbingType()) {
Plumbing* plumbing = static_cast<Plumbing*>(stack_[index]);
ASSERT_HOST(*next_id == ':');
return plumbing->LayerLearningRatePtr(next_id + 1);
}
if (index < 0 || index >= learning_rates_.size()) return NULL;
return &learning_rates_[index];
}
// Writes to the given file. Returns false in case of error.
bool Plumbing::Serialize(TFile* fp) const {
if (!Network::Serialize(fp)) return false;
inT32 size = stack_.size();
// Can't use PointerVector::Serialize here as we need a special DeSerialize.
if (fp->FWrite(&size, sizeof(size), 1) != 1) return false;
for (int i = 0; i < size; ++i)
if (!stack_[i]->Serialize(fp)) return false;
if ((network_flags_ & NF_LAYER_SPECIFIC_LR) &&
!learning_rates_.Serialize(fp)) {
return false;
}
return true;
}
// Reads from the given file. Returns false in case of error.
bool Plumbing::DeSerialize(TFile* fp) {
stack_.truncate(0);
no_ = 0; // We will be modifying this as we AddToStack.
inT32 size;
if (fp->FReadEndian(&size, sizeof(size), 1) != 1) return false;
for (int i = 0; i < size; ++i) {
Network* network = CreateFromFile(fp);
if (network == NULL) return false;
AddToStack(network);
}
if ((network_flags_ & NF_LAYER_SPECIFIC_LR) &&
!learning_rates_.DeSerialize(fp)) {
return false;
}
return true;
}
// 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.
void Plumbing::Update(float learning_rate, float momentum, float adam_beta,
int num_samples) {
for (int i = 0; i < stack_.size(); ++i) {
if (network_flags_ & NF_LAYER_SPECIFIC_LR) {
if (i < learning_rates_.size())
learning_rate = learning_rates_[i];
else
learning_rates_.push_back(learning_rate);
}
if (stack_[i]->IsTraining()) {
stack_[i]->Update(learning_rate, momentum, adam_beta, 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.
void Plumbing::CountAlternators(const Network& other, double* same,
double* changed) const {
ASSERT_HOST(other.type() == type_);
const Plumbing* plumbing = static_cast<const Plumbing*>(&other);
ASSERT_HOST(plumbing->stack_.size() == stack_.size());
for (int i = 0; i < stack_.size(); ++i)
stack_[i]->CountAlternators(*plumbing->stack_[i], same, changed);
}
} // namespace tesseract.