CPU Support

BidHelper.py

@@ -1,36 +0,0 @@
-# -*- coding: utf-8 -*-
-# Created by: Vincentzyx
-from douzero.env.game import GameEnv
-from douzero.evaluation.deep_agent import DeepAgent
-
-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}
-
-card_play_model_path_dict = {
-    'landlord': "baselines/douzero_WP/landlord.ckpt",
-    'landlord_up': "baselines/douzero_WP/landlord_up.ckpt",
-    'landlord_down': "baselines/douzero_WP/landlord_down.ckpt"
-}
-
-user_position = "landlord"  # 玩家角色代码：0-地主上家, 1-地主, 2-地主下家
-ai_players = [0, 0]
-ai_players[0] = user_position
-ai_players[1] = DeepAgent(user_position, card_play_model_path_dict[user_position])
-
-env = GameEnv(ai_players)
-card_play_data_list = {}
-
-def GetWinRate(cards):
-    env.reset()
-    card_play_data_list.update({
-        'three_landlord_cards': [RealCard2EnvCard[i] for i in "333"],
-        'landlord': [RealCard2EnvCard[i] for i in cards],
-        'landlord_up': [RealCard2EnvCard[i] for i in "33333333333333333"],
-        'landlord_down': [RealCard2EnvCard[i] for i in "33333333333333333"]
-    })
-
-    env.card_play_init(card_play_data_list)
-    action_message = env.step(user_position)
-    win_rate = float(action_message["win_rate"].replace("%",""))
-    return win_rate

BidModel.py

@@ -58,7 +58,10 @@ net.eval()
 if UseGPU:
     net = net.to(device)
 if os.path.exists("bid_weights.pkl"):
+    if torch.cuda.is_available():
         net.load_state_dict(torch.load('bid_weights.pkl'))
+    else:
+        net.load_state_dict(torch.load('bid_weights.pkl', map_location=torch.device("cpu")))
 
 def predict(cards):
     input = RealToOnehot(cards)

FarmerModel.py

@@ -54,10 +54,16 @@ class Net(nn.Module):
 
 Nets = {"up": Net(), "down": Net()}
 if os.path.exists("landlord_up_weights.pkl"):
+    if torch.cuda.is_available():
         Nets["up"].load_state_dict(torch.load("landlord_up_weights.pkl"))
+    else:
+        Nets["up"].load_state_dict(torch.load("landlord_up_weights.pkl", map_location=torch.device("cpu")))
     Nets["up"].eval()
 if os.path.exists("landlord_down_weights.pkl"):
-    Nets["down"].load_state_dict(torch.load("landlord_down_weights.pkl"))
+    if torch.cuda.is_available():
+        Nets["up"].load_state_dict(torch.load("landlord_down_weights.pkl"))
+    else:
+        Nets["up"].load_state_dict(torch.load("landlord_down_weights.pkl", map_location=torch.device("cpu")))
     Nets["down"].eval()
 
 def predict(cards, llc, type="up"):

LandlordModel.py

@@ -54,7 +54,10 @@ class Net(nn.Module):
 net = Net()
 net.eval()
 if os.path.exists("landlord_weights.pkl"):
+    if torch.cuda.is_available():
         net.load_state_dict(torch.load('landlord_weights.pkl'))
+    else:
+        net.load_state_dict(torch.load('landlord_weights.pkl', map_location=torch.device("cpu")))
 else:
     print("landlord_weights.pkl not found")
 

requirements.txt

@@ -1,7 +1,10 @@
-torch>=1.6.0
-GitPython>=3.0.5
-gitdb2>=2.0.6
-PyAutoGUI>=0.9.50
-PyQt5>=5.13.0
-PyQt5-sip>=12.8.1
+torch==1.6.0
+GitPython==3.0.5
+gitdb2==2.0.6
+PyAutoGUI==0.9.50
+PyQt5==5.13.0
+PyQt5-sip==12.8.1
 rlcard
+pywin32
+matplotlib
+opencv-python