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105 lines (88 loc) · 3.53 KB
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/*
This file is part of Leela Zero.
Copyright (C) 2017-2019 Gian-Carlo Pascutto and contributors
Leela Zero is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Leela Zero is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Leela Zero. If not, see <http://www.gnu.org/licenses/>.
Additional permission under GNU GPL version 3 section 7
If you modify this Program, or any covered work, by linking or
combining it with NVIDIA Corporation's libraries from the
NVIDIA CUDA Toolkit and/or the NVIDIA CUDA Deep Neural
Network library and/or the NVIDIA TensorRT inference library
(or a modified version of those libraries), containing parts covered
by the terms of the respective license agreement, the licensors of
this Program grant you additional permission to convey the resulting
work.
*/
#ifndef TRAINING_H_INCLUDED
#define TRAINING_H_INCLUDED
#include "config.h"
#include <bitset>
#include <cstddef>
#include <string>
#include <utility>
#include <vector>
#include "GameState.h"
#include "Network.h"
#include "UCTNode.h"
class TimeStep {
public:
using BoardPlane = std::bitset<NUM_INTERSECTIONS>;
using NNPlanes = std::vector<BoardPlane>;
NNPlanes planes;
std::vector<float> probabilities;
int to_move;
float net_winrate;
float root_uct_winrate;
float child_uct_winrate;
int bestmove_visits;
};
std::ostream& operator<< (std::ostream& stream, const TimeStep& timestep);
std::istream& operator>> (std::istream& stream, TimeStep& timestep);
class OutputChunker {
public:
OutputChunker(const std::string& basename, bool compress = false);
~OutputChunker();
void append(const std::string& str);
// Group this many games in a batch.
static constexpr size_t CHUNK_SIZE = 32;
private:
std::string gen_chunk_name() const;
void flush_chunks();
size_t m_game_count{0};
size_t m_chunk_count{0};
std::string m_buffer;
std::string m_basename;
bool m_compress{false};
};
class Training {
public:
static void clear_training();
static void dump_training(int winner_color,
const std::string& out_filename);
static void dump_debug(const std::string& out_filename);
static void record(Network & network, GameState& state, UCTNode& node);
static void dump_supervised(const std::string& sgf_file,
const std::string& out_filename);
static void save_training(const std::string& filename);
static void load_training(const std::string& filename);
private:
static TimeStep::NNPlanes get_planes(const GameState* const state);
static void process_game(GameState& state, size_t& train_pos, int who_won,
const std::vector<int>& tree_moves,
OutputChunker& outchunker);
static void dump_training(int winner_color,
OutputChunker& outchunker);
static void dump_debug(OutputChunker& outchunker);
static void save_training(std::ofstream& out);
static void load_training(std::ifstream& in);
static std::vector<TimeStep> m_data;
};
#endif