取回legacy_mode参数代码

douzero/dmc/models.py

@@ -113,6 +113,104 @@ class FarmerLstmModel(nn.Module):
         x = self.dense6(x)
         return dict(values=x)
 
+class LandlordLstmModelLegacy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.lstm = nn.LSTM(432, 128, batch_first=True)
+        self.dense1 = nn.Linear(860 + 128, 1024)
+        self.dense2 = nn.Linear(1024, 1024)
+        self.dense3 = nn.Linear(1024, 768)
+        self.dense4 = nn.Linear(768, 512)
+        self.dense5 = nn.Linear(512, 256)
+        self.dense6 = nn.Linear(256, 1)
+
+    def get_onnx_params(self, device):
+        return {
+            'args': (
+                torch.randn(1, 5, 432, requires_grad=True, device=device),
+                torch.randn(1, 887, 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):
+        lstm_out, (h_n, _) = self.lstm(z)
+        lstm_out = lstm_out[:,-1,:]
+        x = torch.cat([lstm_out,x], dim=-1)
+        x = self.dense1(x)
+        x = torch.relu(x)
+        x = self.dense2(x)
+        x = torch.relu(x)
+        x = self.dense3(x)
+        x = torch.relu(x)
+        x = self.dense4(x)
+        x = torch.relu(x)
+        x = self.dense5(x)
+        x = torch.relu(x)
+        x = self.dense6(x)
+        return dict(values=x)
+
+class FarmerLstmModelLegacy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.lstm = nn.LSTM(432, 128, batch_first=True)
+        self.dense1 = nn.Linear(1192 + 128, 1024)
+        self.dense2 = nn.Linear(1024, 1024)
+        self.dense3 = nn.Linear(1024, 768)
+        self.dense4 = nn.Linear(768, 512)
+        self.dense5 = nn.Linear(512, 256)
+        self.dense6 = nn.Linear(256, 1)
+
+    def get_onnx_params(self, device):
+        return {
+            'args': (
+                torch.randn(1, 5, 432, requires_grad=True, device=device),
+                torch.randn(1, 1219, requires_grad=True, device=device),
+            ),
+            'input_names': ['z_batch','x_batch'],
+            'output_names': ['values'],
+            'dynamic_axes': {
+                'z_batch': {
+                    0: "legal_actions"
+                },
+                'x_batch': {
+                    0: "legal_actions"
+                },
+                'values': {
+                    0: "batch_size"
+                }
+            }
+        }
+
+    def forward(self, z, x):
+        lstm_out, (h_n, _) = self.lstm(z)
+        lstm_out = lstm_out[:,-1,:]
+        x = torch.cat([lstm_out,x], dim=-1)
+        x = self.dense1(x)
+        x = torch.relu(x)
+        x = self.dense2(x)
+        x = torch.relu(x)
+        x = self.dense3(x)
+        x = torch.relu(x)
+        x = self.dense4(x)
+        x = torch.relu(x)
+        x = self.dense5(x)
+        x = torch.relu(x)
+        x = self.dense6(x)
+        return dict(values=x)
+
 # 用于ResNet18和34的残差块，用的是2个3x3的卷积
 class BasicBlock(nn.Module):
     expansion = 1
@@ -214,6 +312,11 @@ model_dict['landlord'] = LandlordLstmModel
 model_dict['landlord_up'] = FarmerLstmModel
 model_dict['landlord_front'] = FarmerLstmModel
 model_dict['landlord_down'] = FarmerLstmModel
+model_dict_legacy = {}
+model_dict_legacy['landlord'] = LandlordLstmModelLegacy
+model_dict_legacy['landlord_up'] = FarmerLstmModelLegacy
+model_dict_legacy['landlord_front'] = FarmerLstmModelLegacy
+model_dict_legacy['landlord_down'] = FarmerLstmModelLegacy
 model_dict_new = {}
 model_dict_new['landlord'] = GeneralModel
 model_dict_new['landlord_up'] = GeneralModel

douzero/dmc/utils.py

@@ -41,7 +41,7 @@ log.setLevel(logging.INFO)
 Buffers = typing.Dict[str, typing.List[torch.Tensor]]
 
 def create_env(flags):
-    return Env(flags.objective, flags.old_model)
+    return Env(flags.objective, flags.old_model, flags.lagacy_model)
 
 def get_batch(b_queues, position, flags, lock):
     """

douzero/env/env.py

@@ -33,7 +33,7 @@ class Env:
     Doudizhu multi-agent wrapper
     """
 
-    def __init__(self, objective, old_model):
+    def __init__(self, objective, old_model, legacy_model=False):
         """
         Objective is wp/adp/logadp. It indicates whether considers
         bomb in reward calculation. Here, we use dummy agents.
@@ -108,7 +108,7 @@ class Env:
                 self._env.info_sets[pos].player_id = pid
             self.infoset = self._game_infoset
 
-            return get_obs(self.infoset, self.use_general)
+            return get_obs(self.infoset, self.use_general, self.use_legacy)
 
     def step(self, action):
         """
@@ -234,7 +234,7 @@ class DummyAgent(object):
         self.action = action
 
 
-def get_obs(infoset, use_general=True):
+def get_obs(infoset, use_general=True, use_legacy = False):
     """
     This function obtains observations with imperfect information
     from the infoset. It has three branches since we encode
@@ -264,13 +264,13 @@ def get_obs(infoset, use_general=True):
         return _get_obs_general(infoset, infoset.player_position)
     else:
         if infoset.player_position == 'landlord':
-            return _get_obs_landlord(infoset)
+            return _get_obs_landlord(infoset, use_legacy)
         elif infoset.player_position == 'landlord_up':
-            return _get_obs_landlord_up(infoset)
+            return _get_obs_landlord_up(infoset, use_legacy)
         elif infoset.player_position == 'landlord_front':
-            return _get_obs_landlord_front(infoset)
+            return _get_obs_landlord_front(infoset, use_legacy)
         elif infoset.player_position == 'landlord_down':
-            return _get_obs_landlord_down(infoset)
+            return _get_obs_landlord_down(infoset, use_legacy)
         else:
             raise ValueError('')
 
@@ -377,19 +377,23 @@ def _process_action_seq(sequence, length=20, new_model=True):
     return sequence
 
 
-def _get_one_hot_bomb(bomb_num):
+def _get_one_hot_bomb(bomb_num, use_legacy = False):
     """
     A utility function to encode the number of bombs
     into one-hot representation.
     """
-    one_hot = np.zeros(56) # 14 + 15 + 27
+    if use_legacy:
-    one_hot[bomb_num[0]] = 1
+        one_hot = np.zeros(29)
-    one_hot[14 + bomb_num[1]] = 1
+        one_hot[bomb_num[0] + bomb_num[1]] = 1
-    one_hot[29 + bomb_num[2]] = 1
+    else:
+        one_hot = np.zeros(56) # 14 + 15 + 27
+        one_hot[bomb_num[0]] = 1
+        one_hot[14 + bomb_num[1]] = 1
+        one_hot[29 + bomb_num[2]] = 1
     return one_hot
 
 
-def _get_obs_landlord(infoset):
+def _get_obs_landlord(infoset, use_legacy = False):
     """
     Obttain the landlord features. See Table 4 in
     https://arxiv.org/pdf/2106.06135.pdf
@@ -448,7 +452,7 @@ def _get_obs_landlord(infoset):
         num_legal_actions, axis=0)
 
     bomb_num = _get_one_hot_bomb(
-        infoset.bomb_num)
+        infoset.bomb_num, use_legacy)
     bomb_num_batch = np.repeat(
         bomb_num[np.newaxis, :],
         num_legal_actions, axis=0)
@@ -489,7 +493,7 @@ def _get_obs_landlord(infoset):
           }
     return obs
 
-def _get_obs_landlord_up(infoset):
+def _get_obs_landlord_up(infoset, use_legacy = False):
     """
     Obttain the landlord_up features. See Table 5 in
     https://arxiv.org/pdf/2106.06135.pdf
@@ -563,7 +567,7 @@ def _get_obs_landlord_up(infoset):
         num_legal_actions, axis=0)
 
     bomb_num = _get_one_hot_bomb(
-        infoset.bomb_num)
+        infoset.bomb_num, use_legacy)
     bomb_num_batch = np.repeat(
         bomb_num[np.newaxis, :],
         num_legal_actions, axis=0)
@@ -610,7 +614,7 @@ def _get_obs_landlord_up(infoset):
           }
     return obs
 
-def _get_obs_landlord_front(infoset):
+def _get_obs_landlord_front(infoset, use_legacy = False):
     """
     Obttain the landlord_front features. See Table 5 in
     https://arxiv.org/pdf/2106.06135.pdf
@@ -684,7 +688,7 @@ def _get_obs_landlord_front(infoset):
         num_legal_actions, axis=0)
 
     bomb_num = _get_one_hot_bomb(
-        infoset.bomb_num)
+        infoset.bomb_num, use_legacy)
     bomb_num_batch = np.repeat(
         bomb_num[np.newaxis, :],
         num_legal_actions, axis=0)
@@ -731,7 +735,7 @@ def _get_obs_landlord_front(infoset):
           }
     return obs
 
-def _get_obs_landlord_down(infoset):
+def _get_obs_landlord_down(infoset, use_legacy = False):
     """
     Obttain the landlord_down features. See Table 5 in
     https://arxiv.org/pdf/2106.06135.pdf
@@ -805,7 +809,7 @@ def _get_obs_landlord_down(infoset):
         num_legal_actions, axis=0)
 
     bomb_num = _get_one_hot_bomb(
-        infoset.bomb_num)
+        infoset.bomb_num, use_legacy)
     bomb_num_batch = np.repeat(
         bomb_num[np.newaxis, :],
         num_legal_actions, axis=0)

douzero/evaluation/deep_agent.py

@@ -3,13 +3,16 @@ import numpy as np
 
 from douzero.env.env import get_obs
 
-def _load_model(position, model_path, model_type):
+def _load_model(position, model_path, model_type, use_legacy):
-    from douzero.dmc.models import model_dict_new, model_dict, model_dict_new_legacy, model_dict_legacy
+    from douzero.dmc.models import model_dict_new, model_dict, model_dict_legacy
     model = None
     if model_type == "general":
         model = model_dict_new[position]()
     else:
-        model = model_dict[position]()
+        if use_legacy:
+            model = model_dict_legacy[position]()
+        else:
+            model = model_dict[position]()
     model_state_dict = model.state_dict()
     if torch.cuda.is_available():
         pretrained = torch.load(model_path, map_location='cuda:0')
@@ -27,8 +30,9 @@ def _load_model(position, model_path, model_type):
 class DeepAgent:
 
     def __init__(self, position, model_path):
+        self.use_legacy = True if "legacy" 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.model = _load_model(position, model_path, self.model_type, self.use_legacy)
         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'}
@@ -36,7 +40,7 @@ class DeepAgent:
         if len(infoset.legal_actions) == 1:
             return infoset.legal_actions[0]
 
-        obs = get_obs(infoset, self.model_type == "general")
+        obs = get_obs(infoset, self.model_type == "general", self.use_legacy)
 
         z_batch = torch.from_numpy(obs['z_batch']).float()
         x_batch = torch.from_numpy(obs['x_batch']).float()