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101 lines (84 loc) · 3.71 KB
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// Copyright 2018 Google LLC
//
// 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 CC_DUAL_NET_H_
#define CC_DUAL_NET_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "cc/constants.h"
#include "cc/position.h"
#include "cc/random.h"
#include "cc/symmetries.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/public/session.h"
namespace minigo {
// The input features to the DualNet neural network have 17 binary feature
// planes. 8 feature planes X_t indicate the presence of the current player's
// stones at time t. A further 8 feature planes Y_t indicate the presence of
// the opposing player's stones at time t. The final feature plane C holds all
// 1s if black is to play, or 0s if white is to play. The planes are
// concatenated together to give input features:
// [X_t, Y_t, X_t-1, Y_t-1, ..., X_t-7, Y_t-7, C].
class DualNet {
public:
// Number of features per stone.
static constexpr int kNumStoneFeatures = 17;
// Index of the per-stone feature that describes whether the black or white
// player is to play next.
static constexpr int kPlayerFeature = 16;
// Total number of features for the board.
static constexpr int kNumBoardFeatures = kN * kN * kNumStoneFeatures;
using StoneFeatures = std::array<float, kNumStoneFeatures>;
using BoardFeatures = std::array<float, kNumBoardFeatures>;
// Initializes the input features so that the C feature plane is taken from
// position.to_play(), and position.stones() are copied into all X and Y
// feature planes (that is: X_t .. X_t-7 are identical and Y_t .. Y_t-7 are
// identical).
static void InitializeFeatures(const Position& position,
BoardFeatures* features);
// Updates the input features after the move position.previous_move() was
// played.
// old_features holds the input features for the network prior to
// position.previous_move() being played.
// position.stones() holds the board state after position.previous_move() was
// played.
// The updated input features for the network are written to new_features.
// old_features and new_features are allowed to be the same object.
static void UpdateFeatures(const BoardFeatures& old_features,
const Position& position,
BoardFeatures* new_features);
struct Output {
std::array<float, kNumMoves> policy;
float value;
};
DualNet();
virtual ~DualNet();
void Initialize(const std::string& graph_path);
// Runs the model on a batch of input features.
// If rnd != nullptr, the features will be randomly rotated and mirrored
// before running the model, then the inverse transform applied to the
// returned policy array.
virtual void RunMany(absl::Span<const BoardFeatures* const> features,
absl::Span<Output> outputs, Random* rnd = nullptr);
private:
std::unique_ptr<tensorflow::Session> session_;
std::vector<std::pair<std::string, tensorflow::Tensor>> inputs_;
std::vector<std::string> output_names_;
std::vector<tensorflow::Tensor> outputs_;
std::vector<symmetry::Symmetry> symmetries_used_;
};
} // namespace minigo
#endif // CC_DUAL_NET_H_