Merge branch 'unified' of ssh://git@git.zaneyork.cn:2222/douzero/Douzero_Resnet.git into main

douzero/dmc/arguments.py

@@ -31,6 +31,8 @@ parser.add_argument('--load_model', action='store_true',
                     help='Load an existing model')
 parser.add_argument('--old_model', action='store_true',
                     help='Use vanilla model')
+parser.add_argument('--unified_model', action='store_true',
+                    help='Use unified model')
 parser.add_argument('--lite_model', action='store_true',
                     help='Use lite card model')
 parser.add_argument('--lagacy_model', action='store_true',

douzero/dmc/dmc.py

@@ -13,8 +13,8 @@ from torch import nn
 import douzero.dmc.models
 import douzero.env.env
 from .file_writer import FileWriter
-from .models import Model, OldModel
+from .models import Model, OldModel, UnifiedModel
-from .utils import get_batch, log, create_env, create_optimizers, act, infer_logic
+from .utils import get_batch, log, create_optimizers, act, infer_logic
 import psutil
 import shutil
 import requests
@@ -87,6 +87,8 @@ def train(flags):
     # Initialize actor models
     if flags.old_model:
         actor_model = OldModel(device="cpu", flags = flags, lite_model = flags.lite_model)
+    elif flags.unified_model:
+        actor_model = UnifiedModel(device="cpu", flags = flags, lite_model = flags.lite_model)
     else:
         actor_model = Model(device="cpu", flags = flags, lite_model = flags.lite_model)
     actor_model.eval()
@@ -100,6 +102,8 @@ def train(flags):
     # Learner model for training
     if flags.old_model:
         learner_model = OldModel(device=flags.training_device, lite_model = flags.lite_model)
+    elif flags.unified_model:
+        learner_model = UnifiedModel(device=flags.training_device, lite_model = flags.lite_model)
     else:
         learner_model = Model(device=flags.training_device, lite_model = flags.lite_model)
 
@@ -116,13 +120,18 @@ def train(flags):
     ]
     frames, stats = 0, {k: 0 for k in stat_keys}
     position_frames = {'landlord':0, 'landlord_up':0, 'landlord_front':0, 'landlord_down':0}
+    if flags.unified_model:
+        lock = threading.Lock() 
+        position_locks = {'landlord': lock, 'landlord_up': lock, 'landlord_front': lock, 'landlord_down': lock, 'uni': lock}
+    else:
         position_locks = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
 
     def sync_onnx_model(frames):
         p_path = '%s/%s' % (flags.onnx_model_path, flags.xpid)
         if not os.path.exists(p_path):
             os.makedirs(p_path)
-        for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
+        positions = ['uni'] if flags.unified_model else ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
+        for position in positions:
             if flags.enable_onnx:
                 model_path = '%s/%s/model_%s.onnx' % (flags.onnx_model_path, flags.xpid, position)
                 onnx_params = learner_model.get_model(position)\
@@ -149,6 +158,12 @@ def train(flags):
         checkpoint_states = torch.load(
             checkpointpath, map_location=("cuda:"+str(flags.training_device) if flags.training_device != "cpu" else "cpu")
         )
+        if flags.unified_model:
+            learner_model.get_model('uni').load_state_dict(checkpoint_states["model_state_dict"]['uni'])
+            optimizers['uni'].load_state_dict(checkpoint_states["optimizer_state_dict"]['uni'])
+            if not flags.enable_onnx:
+                actor_model.get_model('uni').load_state_dict(checkpoint_states["model_state_dict"]['uni'])
+        else:
             for k in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']: # ['landlord', 'landlord_up', 'landlord_down']
                 learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
                 optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k])
@@ -206,7 +221,7 @@ def train(flags):
         while frames < flags.total_frames:
             batch = get_batch(batch_queues, position, flags, local_lock)
             _stats = learn(position, actor_model, learner_model.get_model(position), batch,
-                optimizers[position], flags, position_lock)
+                optimizers['uni'], flags, position_lock)
             with lock:
                 for k in _stats:
                     stats[k] = _stats[k]
@@ -244,7 +259,7 @@ def train(flags):
         }, checkpointpath + '.new')
 
         # Save the weights for evaluation purpose
-        for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
+        for position in ['uni'] if flags.unified_model else ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
             model_weights_dir = os.path.expandvars(os.path.expanduser(
                 '%s/%s/%s' % (flags.savedir, flags.xpid, "general_" + position + '_' + str(frames) + '.ckpt')))
             torch.save(learner_model.get_model(position).state_dict(), model_weights_dir)
@@ -255,13 +270,18 @@ def train(flags):
                     type = ''
                 if flags.old_model:
                     type += 'vanilla'
+                elif flags.unified_model:
+                    type += 'unified'
                 else:
                     type += 'resnet'
+                try:
                     requests.post(flags.upload_url, data={
                         'type': type,
                         'position': position,
                         'frame': frames
                     }, files = {'model_file':('model.ckpt', open(model_weights_dir, 'rb'))})
+                except:
+                    print("模型上传失败")
                 os.remove(model_weights_dir)
         shutil.move(checkpointpath + '.new', checkpointpath)
 

douzero/dmc/models.py

@@ -400,6 +400,74 @@ class GeneralModelLite(nn.Module):
         return dict(values=out)
 
 
+
+class UnifiedModelLite(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.in_planes = 30
+        #input 1*69*15
+        self.conv1 = nn.Conv1d(15, 30, kernel_size=(3,),
+                               stride=(2,), padding=1, bias=False) #1*35*30
+
+        self.bn1 = nn.BatchNorm1d(30)
+
+        self.layer1 = self._make_layer(BasicBlock, 30, 2, stride=2)#1*18*30
+        self.layer2 = self._make_layer(BasicBlock, 60, 2, stride=2)#1*9*60
+        self.layer3 = self._make_layer(BasicBlock, 120, 2, stride=2)#1*5*120
+        # self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.lstm = nn.LSTM(276, 128, batch_first=True)
+
+        self.linear1 = nn.Linear(120 * BasicBlock.expansion * 5 + 128, 2048)
+        self.linear2 = nn.Linear(2048, 1024)
+        self.linear3 = nn.Linear(1024, 512)
+        self.linear4 = nn.Linear(512, 256)
+        self.linear5 = nn.Linear(256, 1)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def get_onnx_params(self, device=None):
+        return {
+            'args': (
+                torch.randn(1, 15, 69, requires_grad=True, device=device),
+                torch.randn(1, 24, 276, requires_grad=True, device=device),
+            ),
+            'input_names': ['z_batch','x_batch'],
+            'output_names': ['values'],
+            'dynamic_axes': {
+                'z_batch': {
+                    0: "batch_size"
+                },
+                'x_batch': {
+                    0: "batch_size"
+                },
+                'values': {
+                    0: "batch_size"
+                }
+            }
+        }
+
+    def forward(self, z, x):
+        out = F.relu(self.bn1(self.conv1(z)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = out.flatten(1,2)
+        lstm_out, (h_n, _) = self.lstm(x)
+        lstm_out = lstm_out[:,-1,:]
+        out = torch.cat([lstm_out,out], dim=1)
+        out = F.leaky_relu_(self.linear1(out))
+        out = F.leaky_relu_(self.linear2(out))
+        out = F.leaky_relu_(self.linear3(out))
+        out = F.leaky_relu_(self.linear4(out))
+        out = F.leaky_relu_(self.linear5(out))
+        return dict(values=out)
+
 class GeneralModel(nn.Module):
     def __init__(self):
         super().__init__()
@@ -492,6 +560,11 @@ model_dict_new_lite['landlord'] = GeneralModelLite
 model_dict_new_lite['landlord_up'] = GeneralModelLite
 model_dict_new_lite['landlord_front'] = GeneralModelLite
 model_dict_new_lite['landlord_down'] = GeneralModelLite
+model_dict_uni_lite = {}
+model_dict_uni_lite['landlord'] = UnifiedModelLite
+model_dict_uni_lite['landlord_up'] = UnifiedModelLite
+model_dict_uni_lite['landlord_front'] = UnifiedModelLite
+model_dict_uni_lite['landlord_down'] = UnifiedModelLite
 
 def forward_logic(self_model, position, z, x, device='cpu', return_value=False, flags=None):
     legal_count = len(z)
@@ -675,3 +748,64 @@ class Model:
     def get_models(self):
         return self.models
 
+
+class UnifiedModel:
+    """
+    The wrapper for the three models. We also wrap several
+    interfaces such as share_memory, eval, etc.
+    """
+    def __init__(self, device=0, flags=None, lite_model = False):
+        self.onnx_models = {}
+        self.model = None
+        self.models = {}
+        self.flags = flags
+        if not device == "cpu":
+            device = 'cuda:' + str(device)
+        self.device = torch.device(device)
+        positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
+        if flags is not None and flags.enable_onnx:
+            self.model = None
+        else:
+            if lite_model:
+                self.model = UnifiedModelLite().to(self.device)
+                for position in positions:
+                    self.models[position] = self.model
+            else:
+                self.model = GeneralModel().to(self.device)
+            self.onnx_model = None
+
+    def set_onnx_model(self, device='cpu'):
+        model_path = os.path.abspath('%s/%s/model_uni.onnx' % (self.flags.onnx_model_path, self.flags.xpid))
+        positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
+        if device == 'cpu':
+            self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
+        else:
+            self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
+        for position in positions:
+            self.onnx_models[position] = self.onnx_model
+
+    def get_onnx_params(self, position):
+        self.model.get_onnx_params(self.device)
+
+    def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
+        return forward_logic(self, position, z, x, device, return_value, flags)
+
+    def share_memory(self):
+        if self.model is not None:
+            self.model.share_memory()
+
+    def eval(self):
+        if self.model is not None:
+            self.model.eval()
+
+    def parameters(self, position):
+        return self.model.parameters()
+
+    def get_model(self, position):
+        return self.model
+
+    def get_models(self):
+        return {
+            'uni' : self.model
+        }
+

douzero/dmc/utils.py

@@ -73,7 +73,7 @@ log.setLevel(logging.INFO)
 Buffers = typing.Dict[str, typing.List[torch.Tensor]]
 
 def create_env(flags):
-    return Env(flags.objective, flags.old_model, flags.lagacy_model, flags.lite_model)
+    return Env(flags.objective, flags.old_model, flags.lagacy_model, flags.lite_model, flags.unified_model)
 
 def get_batch(b_queues, position, flags, lock):
     """
@@ -104,6 +104,14 @@ def create_optimizers(flags, learner_model):
     """
     positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
     optimizers = {}
+    if flags.unified_model:
+        position = 'uni'
+        optimizer = RAdam(
+            learner_model.parameters(position),
+            lr=flags.learning_rate,
+            eps=flags.epsilon)
+        optimizers[position] = optimizer
+    else:
         for position in positions:
             optimizer = RAdam(
                 learner_model.parameters(position),
@@ -116,6 +124,9 @@ def create_optimizers(flags, learner_model):
 def infer_logic(i, device, infer_queues, model, flags, onnx_frame):
     positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
     if not flags.enable_onnx:
+        if flags.unified_model:
+            model.model.to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
+        else:
             for pos in positions:
                 model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
     last_onnx_frame = -1

douzero/env/env.py

@@ -49,7 +49,47 @@ NumOnesJoker2ArrayCompressed = {0: np.array([0, 0, 0, 0, 0]),
                                 12: np.array([0, 0, 1, 1, 0]),
                                 13: np.array([1, 0, 1, 1, 0]),
                                 15: np.array([1, 1, 1, 1, 0])}
+PositionInfoArray = {
+    'landlord': np.array([1, 0, 0, 0]),
+    'landlord_down': np.array([0, 1, 0, 0]),
+    'landlord_front': np.array([0, 0, 1, 0]),
+    'landlord_up': np.array([0, 0, 0, 1]),
+}
 
+FaceUpLevelArray = {
+    0x00: np.array([0, 0, 0, 0, 0, 0, 0, 0, 0]),
+    0x01: np.array([1, 0, 0, 0, 0, 0, 0, 0, 0]),
+    0x02: np.array([0, 1, 0, 0, 0, 0, 0, 0, 0]),
+    0x03: np.array([1, 1, 0, 0, 0, 0, 0, 0, 0]),
+    0x04: np.array([0, 0, 1, 0, 0, 0, 0, 0, 0]),
+    0x05: np.array([1, 0, 1, 0, 0, 0, 0, 0, 0]),
+    0x06: np.array([0, 1, 1, 0, 0, 0, 0, 0, 0]),
+    0x07: np.array([1, 1, 1, 0, 0, 0, 0, 0, 0]),
+    0x08: np.array([0, 0, 0, 1, 0, 0, 0, 0, 0]),
+    0x09: np.array([1, 0, 0, 1, 0, 0, 0, 0, 0]),
+    0x0A: np.array([0, 1, 0, 1, 0, 0, 0, 0, 0]),
+    0x0B: np.array([1, 1, 0, 1, 0, 0, 0, 0, 0]),
+    0x0C: np.array([0, 0, 1, 1, 0, 0, 0, 0, 0]),
+    0x0D: np.array([1, 0, 1, 1, 0, 0, 0, 0, 0]),
+    0x0E: np.array([0, 1, 1, 1, 0, 0, 0, 0, 0]),
+    0x0F: np.array([1, 1, 1, 1, 0, 0, 0, 0, 0]),
+    0x10: np.array([0, 0, 0, 0, 1, 0, 0, 0, 0]),
+    0x11: np.array([1, 0, 0, 0, 1, 0, 0, 0, 0]),
+    0x12: np.array([0, 1, 0, 0, 1, 0, 0, 0, 0]),
+    0x13: np.array([1, 1, 0, 0, 1, 0, 0, 0, 0]),
+    0x14: np.array([0, 0, 1, 0, 1, 0, 0, 0, 0]),
+    0x15: np.array([1, 0, 1, 0, 1, 0, 0, 0, 0]),
+    0x16: np.array([0, 1, 1, 0, 1, 0, 0, 0, 0]),
+    0x17: np.array([1, 1, 1, 0, 1, 0, 0, 0, 0]),
+    0x18: np.array([0, 0, 0, 1, 1, 0, 0, 0, 0]),
+    0x19: np.array([1, 0, 0, 1, 1, 0, 0, 0, 0]),
+    0x1A: np.array([0, 1, 0, 1, 1, 0, 0, 0, 0]),
+    0x1B: np.array([1, 1, 0, 1, 1, 0, 0, 0, 0]),
+    0x1C: np.array([0, 0, 1, 1, 1, 0, 0, 0, 0]),
+    0x1D: np.array([1, 0, 1, 1, 1, 0, 0, 0, 0]),
+    0x1E: np.array([0, 1, 1, 1, 1, 0, 0, 0, 0]),
+    0x1F: np.array([1, 1, 1, 1, 1, 0, 0, 0, 0]),
+}
 
 deck = []
 for i in range(3, 15):
@@ -63,7 +103,7 @@ class Env:
     Doudizhu multi-agent wrapper
     """
 
-    def __init__(self, objective, old_model, legacy_model=False, lite_model = False):
+    def __init__(self, objective, old_model, legacy_model=False, lite_model = False, unified_model = False):
         """
         Objective is wp/adp/logadp. It indicates whether considers
         bomb in reward calculation. Here, we use dummy agents.
@@ -77,7 +117,8 @@ class Env:
         """
         self.objective = objective
         self.use_legacy = legacy_model
-        self.use_general = not old_model
+        self.use_unified = unified_model
+        self.use_general = not old_model and not unified_model
         self.lite_model = lite_model
 
         # Initialize players
@@ -107,6 +148,8 @@ class Env:
                           'landlord_up': _deck[33:58],
                           'landlord_front': _deck[58:83],
                           'landlord_down': _deck[83:108],
+                          'three_landlord_cards': _deck[25:33],
+                          'three_landlord_cards_all': _deck[25:33],
                           }
         for key in card_play_data:
             card_play_data[key].sort()
@@ -124,7 +167,7 @@ class Env:
             self._env.info_sets[pos].player_id = pid
         self.infoset = self._game_infoset
 
-        return get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model)
+        return get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model, self.use_unified)
 
     def step(self, action):
         """
@@ -152,7 +195,7 @@ class Env:
             }
             obs = None
         else:
-            obs = get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model)
+            obs = get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model, self.use_unified)
         return obs, reward, done, {}
 
     def _get_reward(self, pos):
@@ -250,7 +293,7 @@ class DummyAgent(object):
         self.action = action
 
 
-def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False):
+def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False, use_unified=False):
     """
     This function obtains observations with imperfect information
     from the infoset. It has three branches since we encode
@@ -278,6 +321,8 @@ def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False):
         if infoset.player_position not in ["landlord", "landlord_up", "landlord_front", "landlord_down"]:
             raise ValueError('')
         return _get_obs_general(infoset, infoset.player_position, lite_model)
+    elif use_unified:
+        return _get_obs_unified(infoset, infoset.player_position, lite_model)
     else:
         if infoset.player_position == 'landlord':
             return _get_obs_landlord(infoset, use_legacy, lite_model)
@@ -312,6 +357,12 @@ def _get_one_hot_array(num_left_cards, max_num_cards, compress_size = 0):
     return one_hot
 
 
+def _cards2noise(list_cards, compressed_form = False):
+    if compressed_form:
+        return np.random.randint(0, 2, 69, dtype=np.int8)
+    else:
+        return np.random.randint(0, 2, 108, dtype=np.int8)
+
 def _cards2array(list_cards, compressed_form = False):
     """
     A utility function that transforms the actions, i.e.,
@@ -381,7 +432,7 @@ def _cards2array(list_cards, compressed_form = False):
 #     # action_seq_array = action_seq_array.reshape(5, 162)
 #     return action_seq_array
 
-def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = False):
+def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = False, use_unified = False):
     """
     A utility function to encode the historical moves.
     We encode the historical 20 actions. If there is
@@ -404,6 +455,19 @@ def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = Fa
             for row, list_cards in enumerate(action_seq_list):
                 if list_cards != []:
                     action_seq_array[row, :108] = _cards2array(list_cards[1], compressed_form)
+    elif use_unified:
+        if compressed_form:
+            action_seq_array = np.zeros((len(action_seq_list), 69))
+            for row, list_cards in enumerate(action_seq_list):
+                if list_cards != []:
+                    action_seq_array[row, :] = _cards2array(list_cards[1], compressed_form)
+            action_seq_array = action_seq_array.reshape(24, 276)
+        else:
+            action_seq_array = np.zeros((len(action_seq_list), 108))
+            for row, list_cards in enumerate(action_seq_list):
+                if list_cards != []:
+                    action_seq_array[row, :] = _cards2array(list_cards[1], compressed_form)
+            action_seq_array = action_seq_array.reshape(24, 432)
     else:
         if compressed_form:
             action_seq_array = np.zeros((len(action_seq_list), 69))
@@ -442,7 +506,7 @@ def _process_action_seq(sequence, length=20, new_model=True):
     return sequence
 
 
-def _get_one_hot_bomb(bomb_num, use_legacy = False):
+def _get_one_hot_bomb(bomb_num, use_legacy = False, compressed_form = False):
     """
     A utility function to encode the number of bombs
     into one-hot representation.
@@ -451,7 +515,7 @@ def _get_one_hot_bomb(bomb_num, use_legacy = False):
         one_hot = np.zeros(29)
         one_hot[bomb_num[0] + bomb_num[1]] = 1
     else:
-        one_hot = np.zeros(56) # 14 + 15 + 27
+        one_hot = np.zeros(56 if compressed_form else 95) # 14 + 15 + 27
         one_hot[bomb_num[0]] = 1
         one_hot[14 + bomb_num[1]] = 1
         one_hot[29 + bomb_num[2]] = 1
@@ -1000,3 +1064,134 @@ def _get_obs_general(infoset, position, compressed_form = False):
         'z': z.astype(np.int8),
     }
     return obs
+
+'''
+    face_up_level   0x01: three_landlord_cards, 0x02: landlord, 0x04: landlord_up, 0x08: landlord_front, 0x10: landlord_down
+'''
+def _get_obs_unified(infoset, position, compressed_form = True, face_up_level = 0):
+    num_legal_actions = len(infoset.legal_actions)
+    my_handcards = _cards2array(infoset.player_hand_cards, compressed_form)
+    my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
+                                   num_legal_actions, axis=0)
+
+    other_handcards = _cards2array(infoset.other_hand_cards, compressed_form)
+
+    my_action_batch = np.zeros(my_handcards_batch.shape)
+    for j, action in enumerate(infoset.legal_actions):
+        my_action_batch[j, :] = _cards2array(action, compressed_form)
+
+    landlord_num_cards_left = _get_one_hot_array(
+        infoset.num_cards_left_dict['landlord'], 33, 15 if compressed_form else 0)
+
+    landlord_up_num_cards_left = _get_one_hot_array(
+        infoset.num_cards_left_dict['landlord_up'], 25, 8 if compressed_form else 0)
+
+    landlord_front_num_cards_left = _get_one_hot_array(
+        infoset.num_cards_left_dict['landlord_front'], 25, 8 if compressed_form else 0)
+
+    landlord_down_num_cards_left = _get_one_hot_array(
+        infoset.num_cards_left_dict['landlord_down'], 25, 8 if compressed_form else 0)
+
+    landlord_played_cards = _cards2array(
+        infoset.played_cards['landlord'], compressed_form)
+
+    landlord_up_played_cards = _cards2array(
+        infoset.played_cards['landlord_up'], compressed_form)
+
+    landlord_front_played_cards = _cards2array(
+        infoset.played_cards['landlord_front'], compressed_form)
+
+    landlord_down_played_cards = _cards2array(
+        infoset.played_cards['landlord_down'], compressed_form)
+
+    if (face_up_level & 0x01) > 0:
+        three_landlord_cards = _cards2array(
+            infoset.three_landlord_cards, compressed_form)
+
+        three_landlord_cards_all = _cards2array(
+            infoset.three_landlord_cards_all, compressed_form)
+    else:
+        three_landlord_cards = _cards2noise(
+            infoset.three_landlord_cards, compressed_form)
+
+        three_landlord_cards_all = _cards2noise(
+            infoset.three_landlord_cards_all, compressed_form)
+
+    if (face_up_level & 0x02) > 0:
+        landlord_cards = _cards2array(
+            infoset.all_handcards['landlord'], compressed_form)
+    else:
+        landlord_cards = _cards2noise(
+            infoset.all_handcards['landlord'], compressed_form)
+
+    if (face_up_level & 0x04) > 0:
+        landlord_up_cards = _cards2array(
+            infoset.all_handcards['landlord_up'], compressed_form)
+    else:
+        landlord_up_cards = _cards2noise(
+            infoset.all_handcards['landlord_up'], compressed_form)
+
+    if (face_up_level & 0x08) > 0:
+        landlord_front_cards = _cards2array(
+            infoset.all_handcards['landlord_front'], compressed_form)
+    else:
+        landlord_front_cards = _cards2noise(
+            infoset.all_handcards['landlord_front'], compressed_form)
+
+    if (face_up_level & 0x10) > 0:
+        landlord_down_cards = _cards2array(
+            infoset.all_handcards['landlord_down'], compressed_form)
+    else:
+        landlord_down_cards = _cards2noise(
+            infoset.all_handcards['landlord_down'], compressed_form)
+
+    bomb_num = _get_one_hot_bomb(
+        infoset.bomb_num, compressed_form=compressed_form) # 56/95
+    base_info = np.hstack((
+        PositionInfoArray[position], # 4
+        FaceUpLevelArray[face_up_level], # 9
+        bomb_num, #56
+    ))
+    num_cards_left = np.hstack((
+                         landlord_num_cards_left,  # 33/18
+                         landlord_up_num_cards_left,  # 25/17
+                         landlord_front_num_cards_left,  # 25/17
+                         landlord_down_num_cards_left))  # 25/17
+
+    x_no_action = _action_seq_list2array(_process_action_seq(infoset.card_play_action_seq, 96), False, compressed_form, True) # 24*276 / 24*432
+
+    x_batch = np.repeat(
+        x_no_action[np.newaxis, :],
+        num_legal_actions, axis=0)
+
+    z =np.vstack((
+                  base_info, # 69
+                  num_cards_left,   # 108 / 18+17*3=69
+                  my_handcards,  # 108/69
+                  other_handcards,  # 108/69
+                  landlord_played_cards,  # 108/69
+                  landlord_up_played_cards,  # 108/69
+                  landlord_front_played_cards,  # 108/69
+                  landlord_down_played_cards,  # 108/69
+                  landlord_cards,  # 108/69
+                  landlord_up_cards,  # 108/69
+                  landlord_front_cards,  # 108/69
+                  landlord_down_cards,  # 108/69
+                  three_landlord_cards,  # 108/69
+                  three_landlord_cards_all,  # 108/69
+                  ))
+
+    _z_batch = np.repeat(
+        z[np.newaxis, :, :],
+        num_legal_actions, axis=0)
+    my_action_batch = my_action_batch[:,np.newaxis,:]
+    z_batch = np.concatenate((my_action_batch, _z_batch), axis=1)
+    obs = {
+        'position': position,
+        'x_batch': x_batch.astype(np.float32),
+        'z_batch': z_batch.astype(np.float32),
+        'legal_actions': infoset.legal_actions,
+        'x_no_action': x_no_action.astype(np.int8),
+        'z': z.astype(np.int8),
+    }
+    return obs

douzero/env/game.py

@@ -127,7 +127,8 @@ class GameEnv(object):
 
         self.card_play_action_seq = []
 
-        # self.three_landlord_cards = None
+        self.three_landlord_cards = None
+        self.three_landlord_cards_all = None
         self.game_over = False
 
         self.acting_player_position = None
@@ -185,7 +186,12 @@ class GameEnv(object):
                 card_play_data['landlord_front']
             self.info_sets['landlord_down'].player_hand_cards = \
                 card_play_data['landlord_down']
-            # self.three_landlord_cards = card_play_data['three_landlord_cards']
+            if 'three_landlord_cards' not in card_play_data.keys():
+                self.three_landlord_cards = card_play_data['landlord'][25:33]
+                self.three_landlord_cards_all = card_play_data['landlord'][25:33]
+            else:
+                self.three_landlord_cards = card_play_data['three_landlord_cards'][:]
+                self.three_landlord_cards_all = card_play_data['three_landlord_cards'][:]
         self.get_acting_player_position()
         self.game_infoset = self.get_infoset()
 
@@ -253,15 +259,15 @@ class GameEnv(object):
 
         self.played_cards[self.acting_player_position] += action
 
-        # if self.acting_player_position == 'landlord' and \
+        if self.acting_player_position == 'landlord' and \
-        #         len(action) > 0 and \
+                len(action) > 0 and \
-        #         len(self.three_landlord_cards) > 0:
+                len(self.three_landlord_cards) > 0:
-        #     for card in action:
+            for card in action:
-        #         if len(self.three_landlord_cards) > 0:
+                if len(self.three_landlord_cards) > 0:
-        #             if card in self.three_landlord_cards:
+                    if card in self.three_landlord_cards:
-        #                 self.three_landlord_cards.remove(card)
+                        self.three_landlord_cards.remove(card)
-        #         else:
+                else:
-        #             break
+                    break
 
         self.game_done()
         if not self.game_over:
@@ -333,7 +339,8 @@ class GameEnv(object):
     def reset(self):
         self.card_play_action_seq = []
 
-        # self.three_landlord_cards = None
+        self.three_landlord_cards = None
+        self.three_landlord_cards_all = None
         self.game_over = False
 
         self.acting_player_position = None
@@ -397,8 +404,10 @@ class GameEnv(object):
 
         self.info_sets[self.acting_player_position].played_cards = \
             self.played_cards
-        # self.info_sets[self.acting_player_position].three_landlord_cards = \
+        self.info_sets[self.acting_player_position].three_landlord_cards = \
-        #     self.three_landlord_cards
+            self.three_landlord_cards
+        self.info_sets[self.acting_player_position].three_landlord_cards_all = \
+            self.three_landlord_cards_all
         self.info_sets[self.acting_player_position].card_play_action_seq = \
             self.card_play_action_seq
 
@@ -424,7 +433,8 @@ class InfoSet(object):
         # The number of cards left for each player. It is a dict with str-->int 
         self.num_cards_left_dict = None
         # The three landload cards. A list.
-        # self.three_landlord_cards = None
+        self.three_landlord_cards = None
+        self.three_landlord_cards_all = None
         # The historical moves. It is a list of list
         self.card_play_action_seq = None
         # The union of the hand cards of the other two players for the current player 

douzero/evaluation/deep_agent.py

@@ -6,8 +6,8 @@ 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, use_onnx=False):
+def _load_model(position, model_path, model_type, use_legacy, use_lite, use_unified, use_onnx=False):
-    from douzero.dmc.models import model_dict_new, model_dict_new_lite, model_dict, model_dict_legacy, model_dict_lite
+    from douzero.dmc.models import model_dict_new, model_dict_new_lite, model_dict, model_dict_legacy, model_dict_lite, model_dict_uni_lite
     model_path_onnx = model_path + '.onnx'
     if use_onnx and os.path.exists(model_path_onnx):
         return None
@@ -20,6 +20,11 @@ def _load_model(position, model_path, model_type, use_legacy, use_lite, use_onnx
     else:
         if use_legacy:
             model = model_dict_legacy[position]()
+        elif use_unified:
+            if use_lite:
+                model = model_dict_uni_lite[position]()
+            else:
+                model = model_dict[position]()
         else:
             if use_lite:
                 model = model_dict_lite[position]()
@@ -52,9 +57,10 @@ class DeepAgent:
 
     def __init__(self, position, model_path, use_onnx=False):
         self.use_legacy = True if "legacy" in model_path else False
+        self.use_unified = True if "uni" 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, use_onnx=use_onnx)
+        self.model = _load_model(position, model_path, self.model_type, self.use_legacy, self.lite_model, self.use_unified, use_onnx=use_onnx)
         self.onnx_model_path = os.path.abspath(model_path + '.onnx')
         self.use_onnx = use_onnx
         self.onnx_model = None
@@ -71,7 +77,7 @@ class DeepAgent:
         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)
+        obs = get_obs(infoset, self.model_type == "general", self.use_legacy, self.lite_model, self.use_unified)
 
         if self.onnx_model is None:
             z_batch = torch.from_numpy(obs['z_batch']).float()

evaluate.py

@@ -58,15 +58,15 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         'Dou Dizhu Evaluation')
     parser.add_argument('--landlord', type=str,
-                        default='baselines/douzero_12/landlord_weights_39762328900.ckpt')
+                        default='baselines/lite_uni_5001600.ckpt')
     parser.add_argument('--landlord_up', type=str,
-                        default='baselines/douzero_12/landlord_up_weights_39762328900.ckpt')
+                        default='baselines/lite_uni_5001600.ckpt')
     parser.add_argument('--landlord_front', type=str,
-                        default='baselines/douzero_12/landlord_front_weights_39762328900.ckpt')
+                        default='baselines/lite_uni_5001600.ckpt')
     parser.add_argument('--landlord_down', type=str,
-                        default='baselines/douzero_12/landlord_down_weights_39762328900.ckpt')
+                        default='baselines/lite_uni_5001600.ckpt')
     parser.add_argument('--eval_data', type=str,
-                        default='eval_data_200.pkl')
+                        default='eval_data_200_r.pkl')
     parser.add_argument('--num_workers', type=int, default=3)
     parser.add_argument('--gpu_device', type=str, default='0')
     parser.add_argument('--output', type=bool, default=True)
@@ -80,6 +80,17 @@ if __name__ == '__main__':
     os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
     os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_device
 
+    landlord_wp, farmer_wp, landlord_adp, farmer_adp =  evaluate(args.landlord,
+             args.landlord_up,
+             args.landlord_front,
+             args.landlord_down,
+             args.eval_data,
+             args.num_workers,
+             args.output,
+             args.title)
+    print(landlord_wp, farmer_wp, landlord_adp, farmer_adp)
+    os._exit(0)
+
     baselines = [
         {'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 736107200},
         {'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 479412800},

evaluate_server.py

@@ -28,7 +28,7 @@ RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
 @app.route('/upload', methods=['POST'])
 def upload():
     type = request.form.get('type')
-    if type not in ['lite_resnet', 'lite_vanilla', 'legacy_vanilla']:
+    if type not in ['lite_resnet', 'lite_vanilla', 'legacy_vanilla', 'lite_unified']:
         return jsonify({'status': -1, 'message': 'illegal type'})
     position = request.form.get("position")
     if position not in positions:

generate_eval_data.py

@@ -21,7 +21,8 @@ def generate():
                       'landlord_up': _deck[33:58],
                       'landlord_front': _deck[58:83],
                       'landlord_down': _deck[83:108],
-                    #   'three_landlord_cards': _deck[25:33],
+                      'three_landlord_cards': _deck[25:33],
+                      'three_landlord_cards_all': _deck[25:33],
                       }
     for key in card_play_data:
         card_play_data[key].sort()