调整晋升要求，添加评估接口

douzero/dmc/models.py

@@ -12,9 +12,6 @@ from onnxruntime.datasets import get_example
 from torch import nn
 import torch.nn.functional as F
 
-def to_numpy(tensor):
-    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
-
 class LandlordLstmModel(nn.Module):
     def __init__(self):
         super().__init__()

douzero/evaluation/deep_agent.py

@@ -6,7 +6,7 @@ from onnxruntime.datasets import get_example
 
 from douzero.env.env import get_obs
 
-def _load_model(position, model_path, model_type, use_legacy, use_lite):
+def _load_model(position, model_path, model_type, use_legacy, use_lite, use_onnx=False):
     from douzero.dmc.models import model_dict_new, model_dict_new_lite, model_dict, model_dict_legacy, model_dict_lite
     model = None
     if model_type == "general":
@@ -34,9 +34,9 @@ def _load_model(position, model_path, model_type, use_legacy, use_lite):
     if torch.cuda.is_available():
         model.cuda()
     model.eval()
-    onnx_params = model.get_onnx_params(torch.device('cpu'))
     model_path = model_path + '.onnx'
-    if not os.path.exists(model_path):
+    if use_onnx and not os.path.exists(model_path):
+        onnx_params = model.get_onnx_params(torch.device('cpu'))
         torch.onnx.export(
             model,
             onnx_params['args'],
@@ -53,28 +53,42 @@ def _load_model(position, model_path, model_type, use_legacy, use_lite):
 
 class DeepAgent:
 
-    def __init__(self, position, model_path):
+    def __init__(self, position, model_path, use_onnx=False):
         self.use_legacy = True if "legacy" in model_path else False
         self.lite_model = True if "lite" in model_path else False
         self.model_type = "general" if "resnet" in model_path else "old"
-        self.model = _load_model(position, model_path, self.model_type, self.use_legacy, self.lite_model)
+        self.model = _load_model(position, model_path, self.model_type, self.use_legacy, self.lite_model, use_onnx=use_onnx)
-        self.onnx_model = onnxruntime.InferenceSession(get_example(os.path.abspath(model_path + '.onnx')), providers=['CPUExecutionProvider'])
+        if use_onnx:
+            self.onnx_model = onnxruntime.InferenceSession(get_example(os.path.abspath(model_path + '.onnx')), providers=['CPUExecutionProvider'])
+        else:
+            self.onnx_model = None
         self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
                             8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
                             13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
-    def act(self, infoset):
+    def act(self, infoset, with_confidence = False):
-        if len(infoset.legal_actions) == 1:
+        if not with_confidence and len(infoset.legal_actions) == 1:
             return infoset.legal_actions[0]
 
         obs = get_obs(infoset, self.model_type == "general", self.use_legacy, self.lite_model)
 
-        # z_batch = torch.from_numpy(obs['z_batch']).float()
+        if self.onnx_model is None:
-        # x_batch = torch.from_numpy(obs['x_batch']).float()
+            z_batch = torch.from_numpy(obs['z_batch']).float()
-        # if torch.cuda.is_available():
+            x_batch = torch.from_numpy(obs['x_batch']).float()
-        #     z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
+            if torch.cuda.is_available():
-        # y_pred = self.model.forward(z_batch, x_batch)['values']
+                z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
-        # y_pred = y_pred.detach().cpu().numpy()
+            y_pred = self.model.forward(z_batch, x_batch)['values']
-        y_pred = self.onnx_model.run(None, {'z_batch': obs['z_batch'], 'x_batch': obs['x_batch']})[0]
+            y_pred = y_pred.detach().cpu().numpy()
+        else:
+            y_pred = self.onnx_model.run(None, {'z_batch': obs['z_batch'], 'x_batch': obs['x_batch']})[0]
+
+        if with_confidence:
+            y_pred = y_pred.flatten()
+            size = min(3, len(y_pred))
+            best_action_index = np.argpartition(y_pred, -size)[-size:]
+            best_action_confidence = y_pred[best_action_index]
+            best_action = [infoset.legal_actions[index] for index in best_action_index]
+
+            return best_action, best_action_confidence
 
         best_action_index = np.argmax(y_pred, axis=0)[0]
         best_action = infoset.legal_actions[best_action_index]

douzero/evaluation/simulation.py

@@ -19,7 +19,7 @@ def load_card_play_models(card_play_model_path_dict):
             players[position] = RandomAgent()
         else:
             from .deep_agent import DeepAgent
-            players[position] = DeepAgent(position, card_play_model_path_dict[position])
+            players[position] = DeepAgent(position, card_play_model_path_dict[position], use_onnx=True)
     return players
 
 def mp_simulate(card_play_data_list, card_play_model_path_dict, q, output, title):

douzero/server/battle.py

@@ -45,16 +45,16 @@ def battle_logic(baseline : Baseline, battle : Battle):
 
     challenge_success = False
     if battle.challenger_position == 'landlord':
-        if baseline.landlord_wp == 0 or landlord_wp / float(baseline.landlord_wp) > 1.2:
+        if baseline.landlord_wp == 0 or landlord_wp / float(baseline.landlord_wp) > 1.15:
             landlord_wp, farmer_wp, landlord_adp, farmer_adp = \
                 _second_eval(landlord_wp, farmer_wp, landlord_adp, farmer_adp)
-            if baseline.landlord_wp == 0 or landlord_wp / float(baseline.landlord_wp) > 1.2:
+            if baseline.landlord_wp == 0 or landlord_wp / float(baseline.landlord_wp) > 1.15:
                 challenge_success = True
     else:
-        if baseline.farmer_wp == 0 or farmer_wp / float(baseline.farmer_wp) > 1.2:
+        if baseline.farmer_wp == 0 or farmer_wp / float(baseline.farmer_wp) > 1.05:
             landlord_wp, farmer_wp, landlord_adp, farmer_adp = \
                 _second_eval(landlord_wp, farmer_wp, landlord_adp, farmer_adp)
-            if baseline.farmer_wp == 0 or farmer_wp / float(baseline.farmer_wp) > 1.2:
+            if baseline.farmer_wp == 0 or farmer_wp / float(baseline.farmer_wp) > 1.05:
                 challenge_success = True
     if challenge_success:
         challenger_baseline['rank'] = baseline.rank + 1

evaluate_server.py

@@ -1,3 +1,5 @@
+import itertools
+
 from douzero.server.orm import Model, Battle, Baseline
 from douzero.server.battle import tick
 from flask import Flask, jsonify, request
@@ -5,6 +7,10 @@ from flask_cors import CORS
 from datetime import datetime
 from concurrent.futures import ThreadPoolExecutor
 
+from douzero.env.move_generator import MovesGener
+from douzero.env import move_detector as md, move_selector as ms
+from douzero.evaluation.deep_agent import DeepAgent
+
 app = Flask(__name__)
 CORS(app)
 
@@ -12,9 +18,29 @@ Model.create_table()
 Battle.create_table()
 Baseline.create_table()
 positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
+idx_position = {0: 'landlord', 1: 'landlord_down', 2: 'landlord_front', 3:'landlord_up'}
 
 threadpool = ThreadPoolExecutor(1)
 
+
+EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
+                    8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
+                    13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
+
+RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
+                    '8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
+                    'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}
+
+baselines = Baseline.select().order_by(Baseline.rank.desc()).limit(1)
+if len(baselines) >= 1:
+    baseline = baselines[0]
+    players = [
+        DeepAgent('landlord', str(baseline.landlord_path), use_onnx=True),
+        DeepAgent('landlord_down', str(baseline.landlord_down_path), use_onnx=True),
+        DeepAgent('landlord_front', str(baseline.landlord_front_path), use_onnx=True),
+        DeepAgent('landlord_up', str(baseline.landlord_up_path), use_onnx=True)
+    ]
+
 @app.route('/upload', methods=['POST'])
 def upload():
     type = request.form.get('type')
@@ -71,6 +97,241 @@ def metrics():
         metrics[baseline.rank] = baseline_metric
     return jsonify({'status': 0, 'message': 'success', 'result': metrics})
 
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if request.method == 'POST':
+        try:
+            # Player postion
+            player_position = request.form.get('player_position')
+            if player_position not in ['0', '1', '2', '3']:
+                return jsonify({'status': 1, 'message': 'player_position must be 0, 1, 2 or 3'})
+            player_position = int(player_position)
+
+            # Player hand cards
+            player_hand_cards = [RealCard2EnvCard[c] for c in request.form.get('player_hand_cards')]
+            if player_position == 0:
+                if len(player_hand_cards) < 1 or len(player_hand_cards) > 33:
+                    return jsonify({'status': 2, 'message': 'the number of hand cards should be 1-33'})
+            else:
+                if len(player_hand_cards) < 1 or len(player_hand_cards) > 25:
+                    return jsonify({'status': 3, 'message': 'the number of hand cards should be 1-25'})
+
+            # Number cards left
+            num_cards_left = [
+                int(request.form.get('num_cards_left_landlord')),
+                int(request.form.get('num_cards_left_landlord_down')),
+                int(request.form.get('num_cards_left_landlord_front')),
+                int(request.form.get('num_cards_left_landlord_up'))
+            ]
+            if num_cards_left[player_position] != len(player_hand_cards):
+                return jsonify({'status': 4, 'message': 'the number of cards left do not align with hand cards'})
+            if num_cards_left[0] < 0 or num_cards_left[1] < 0 or num_cards_left[2] < 0 or num_cards_left[3] < 0 \
+                or num_cards_left[0] > 33 or num_cards_left[1] > 25 or num_cards_left[2] > 25 or num_cards_left[2] > 25:
+                return jsonify({'status': 5, 'message': 'the number of cards left not in range'})
+
+            # Card play sequence
+            if request.form.get('card_play_action_seq') == '':
+                card_play_action_seq = []
+            else:
+                card_play_action_seq = [['', [RealCard2EnvCard[c] for c in cards]] for cards in request.form.get('card_play_action_seq').split(',')]
+
+            # Other hand cards
+            other_hand_cards = [RealCard2EnvCard[c] for c in request.form.get('other_hand_cards')]
+            if len(other_hand_cards) != sum(num_cards_left) - num_cards_left[player_position]:
+                return jsonify({'status': 7, 'message': 'the number of the other hand cards do not align with the number of cards left'})
+
+            # Last moves
+            last_moves = []
+            for field in ['last_move_landlord', 'last_move_landlord_down', 'last_move_landlord_front', 'last_move_landlord_up']:
+                last_moves.append([RealCard2EnvCard[c] for c in request.form.get(field)])
+
+            # Played cards
+            played_cards = {}
+            for idx, field in enumerate(['played_cards_landlord', 'played_cards_landlord_down', 'played_cards_landlord_front', 'played_cards_landlord_up']):
+                played_cards[idx_position[idx]] = [RealCard2EnvCard[c] for c in request.form.get(field)]
+
+            # Bomb Num
+            bomb_num = request.form.get('bomb_num')
+
+            # InfoSet
+            info_set = InfoSet()
+            info_set.player_position = idx_position[player_position]
+            info_set.player_hand_cards = player_hand_cards
+            info_set.num_cards_left = num_cards_left
+            info_set.card_play_action_seq = card_play_action_seq
+            info_set.other_hand_cards = other_hand_cards
+            info_set.last_moves = last_moves
+            info_set.played_cards = played_cards
+            info_set.bomb_num = [int(x) for x in str.split(bomb_num, ',')]
+            info_set.num_cards_left_dict['landlord'] = num_cards_left[0]
+            info_set.num_cards_left_dict['landlord_down'] = num_cards_left[1]
+            info_set.num_cards_left_dict['landlord_front'] = num_cards_left[2]
+            info_set.num_cards_left_dict['landlord_up'] = num_cards_left[3]
+
+            # Get rival move and legal_actions
+            rival_move = []
+            if len(card_play_action_seq) != 0:
+                if len(card_play_action_seq[-1][1]) == 0:
+                    if len(card_play_action_seq[-2][1]) == 0:
+                        rival_move = card_play_action_seq[-3][1]
+                    else:
+                        rival_move = card_play_action_seq[-2][1]
+                else:
+                    rival_move = card_play_action_seq[-1][1]
+            info_set.rival_move = rival_move
+            info_set.legal_actions = _get_legal_card_play_actions(player_hand_cards, rival_move)
+
+            # Prediction
+            actions, actions_confidence = players[player_position].act(info_set, True)
+            actions = [''.join([EnvCard2RealCard[a] for a in action]) for action in actions]
+            result = {}
+            win_rates = {}
+            for i in range(len(actions)):
+                # Here, we calculate the win rate
+                win_rate = max(actions_confidence[i], -1)
+                win_rate = min(win_rate, 1)
+                win_rates[actions[i]] = str(round((win_rate + 1) / 2, 4))
+                result[actions[i]] = str(round(actions_confidence[i], 6))
+
+            ############## DEBUG ################
+            if app.debug:
+                print('--------------- DEBUG START --------------')
+                command = 'curl --data "'
+                parameters = []
+                for key in request.form:
+                    parameters.append(key+'='+request.form.get(key))
+                    print(key+':', request.form.get(key))
+                command += '&'.join(parameters)
+                command += '" "http://127.0.0.1:5000/predict"'
+                print('Command:', command)
+                print('Rival Move:', rival_move)
+                print('legal_actions:', info_set.legal_actions)
+                print('Result:', result)
+                print('--------------- DEBUG END --------------')
+            ############## DEBUG ################
+            return jsonify({'status': 0, 'message': 'success', 'result': result, 'win_rates': win_rates})
+        except:
+            import traceback
+            traceback.print_exc()
+            return jsonify({'status': -1, 'message': 'unkown error'})
+
+@app.route('/legal', methods=['POST'])
+def legal():
+    if request.method == 'POST':
+        try:
+            player_hand_cards = [RealCard2EnvCard[c] for c in request.form.get('player_hand_cards')]
+            rival_move = [RealCard2EnvCard[c] for c in request.form.get('rival_move')]
+            legal_actions = _get_legal_card_play_actions(player_hand_cards, rival_move)
+            legal_actions = ','.join([''.join([EnvCard2RealCard[a] for a in action]) for action in legal_actions])
+            return jsonify({'status': 0, 'message': 'success', 'legal_action': legal_actions})
+        except:
+            import traceback
+            traceback.print_exc()
+            return jsonify({'status': -1, 'message': 'unkown error'})
+
+class InfoSet(object):
+
+    def __init__(self):
+        self.player_position = None
+        self.player_hand_cards = None
+        self.num_cards_left = None
+        self.num_cards_left_dict = {}
+        self.all_handcards = {}
+        # self.three_landlord_cards = None
+        self.card_play_action_seq = None
+        self.other_hand_cards = None
+        self.legal_actions = None
+        self.rival_move = None
+        self.last_moves = None
+        self.played_cards = None
+        self.bomb_num = None
+
+def _get_legal_card_play_actions(player_hand_cards, rival_move):
+    mg = MovesGener(player_hand_cards)
+
+    rival_type = md.get_move_type(rival_move)
+    rival_move_type = rival_type['type']
+    rival_move_len = rival_type.get('len', 1)
+    moves = list()
+
+    if rival_move_type == md.TYPE_0_PASS:
+        moves = mg.gen_moves()
+
+    elif rival_move_type == md.TYPE_1_SINGLE:
+        all_moves = mg.gen_type_1_single()
+        moves = ms.filter_type_1_single(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_2_PAIR:
+        all_moves = mg.gen_type_2_pair()
+        moves = ms.filter_type_2_pair(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_3_TRIPLE:
+        all_moves = mg.gen_type_3_triple()
+        moves = ms.filter_type_3_triple(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_4_BOMB:
+        all_moves = mg.gen_type_4_bomb(4)
+        moves = ms.filter_type_4_bomb(all_moves, rival_move)
+        moves += mg.gen_type_4_bomb(5) + mg.gen_type_4_bomb(6) + mg.gen_type_4_bomb(7) + mg.gen_type_4_bomb(8) + mg.gen_type_5_king_bomb()
+
+    elif rival_move_type == md.TYPE_4_BOMB5:
+        all_moves = mg.gen_type_4_bomb(5)
+        moves = ms.filter_type_4_bomb(all_moves, rival_move)
+        moves += mg.gen_type_4_bomb(6) + mg.gen_type_4_bomb(7) + mg.gen_type_4_bomb(8) + mg.gen_type_5_king_bomb()
+
+    elif rival_move_type == md.TYPE_4_BOMB6:
+        all_moves = mg.gen_type_4_bomb(6)
+        moves = ms.filter_type_4_bomb(all_moves, rival_move)
+        moves += mg.gen_type_4_bomb(7) + mg.gen_type_4_bomb(8) + mg.gen_type_5_king_bomb()
+
+    elif rival_move_type == md.TYPE_4_BOMB7:
+        all_moves = mg.gen_type_4_bomb(7)
+        moves = ms.filter_type_4_bomb(all_moves, rival_move)
+        moves += mg.gen_type_4_bomb(8) + mg.gen_type_5_king_bomb()
+
+    elif rival_move_type == md.TYPE_4_BOMB8:
+        all_moves = mg.gen_type_4_bomb(8)
+        moves = ms.filter_type_4_bomb(all_moves, rival_move)
+        moves += mg.gen_type_5_king_bomb()
+
+    elif rival_move_type == md.TYPE_5_KING_BOMB:
+        moves = []
+
+    elif rival_move_type == md.TYPE_7_3_2:
+        all_moves = mg.gen_type_7_3_2()
+        moves = ms.filter_type_7_3_2(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_8_SERIAL_SINGLE:
+        all_moves = mg.gen_type_8_serial_single(repeat_num=rival_move_len)
+        moves = ms.filter_type_8_serial_single(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_9_SERIAL_PAIR:
+        all_moves = mg.gen_type_9_serial_pair(repeat_num=rival_move_len)
+        moves = ms.filter_type_9_serial_pair(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_10_SERIAL_TRIPLE:
+        all_moves = mg.gen_type_10_serial_triple(repeat_num=rival_move_len)
+        moves = ms.filter_type_10_serial_triple(all_moves, rival_move)
+
+    elif rival_move_type == md.TYPE_12_SERIAL_3_2:
+        all_moves = mg.gen_type_12_serial_3_2(repeat_num=rival_move_len)
+        moves = ms.filter_type_12_serial_3_2(all_moves, rival_move)
+
+    if rival_move_type != md.TYPE_0_PASS and rival_move_type < md.TYPE_4_BOMB:
+        moves = moves + mg.gen_type_4_bomb(4) + mg.gen_type_4_bomb(5) + mg.gen_type_4_bomb(6) + mg.gen_type_4_bomb(7) + mg.gen_type_4_bomb(8) + mg.gen_type_5_king_bomb()
+
+    if len(rival_move) != 0:  # rival_move is not 'pass'
+        moves = moves + [[]]
+
+    for m in moves:
+        m.sort()
+
+    moves.sort()
+    moves = list(move for move,_ in itertools.groupby(moves))
+
+    return moves
+
 if __name__ == '__main__':
     import argparse
     parser = argparse.ArgumentParser(description='DouZero evaluation backend')