72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
# -*- coding: utf-8 -*-
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# Created by: Vincentzyx
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import os
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from torch.utils.data.dataset import Dataset
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import time
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def EnvToOnehot(cards):
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Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
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cards = [Env2IdxMap[i] for i in cards]
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Onehot = torch.zeros((4,15))
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for i in range(0, 15):
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Onehot[:cards.count(i),i] = 1
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return Onehot
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def RealToOnehot(cards):
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RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
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'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
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'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
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cards = [RealCard2EnvCard[c] for c in cards]
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Onehot = torch.zeros((4,15))
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for i in range(0, 15):
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Onehot[:cards.count(i),i] = 1
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return Onehot
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(60, 512)
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self.fc2 = nn.Linear(512, 512)
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self.fc3 = nn.Linear(512, 512)
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self.fc4 = nn.Linear(512, 512)
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self.fc5 = nn.Linear(512, 512)
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self.fc6 = nn.Linear(512, 1)
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self.dropout5 = nn.Dropout(0.5)
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self.dropout3 = nn.Dropout(0.3)
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self.dropout1 = nn.Dropout(0.1)
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def forward(self, input):
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x = self.fc1(input)
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x = torch.relu(self.dropout1(self.fc2(x)))
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x = torch.relu(self.dropout3(self.fc3(x)))
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x = torch.relu(self.dropout5(self.fc4(x)))
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x = torch.relu(self.dropout5(self.fc5(x)))
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x = self.fc6(x)
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return x
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UseGPU = False
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device = torch.device('cuda:0')
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net = Net()
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net.eval()
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if UseGPU:
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net = net.to(device)
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if os.path.exists("./bid_weights.pkl"):
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if torch.cuda.is_available():
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net.load_state_dict(torch.load('./bid_weights.pkl'))
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else:
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net.load_state_dict(torch.load('./bid_weights.pkl', map_location=torch.device("cpu")))
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def predict(cards):
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input = RealToOnehot(cards)
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if UseGPU:
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input = input.to(device)
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input = torch.flatten(input)
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win_rate = net(input)
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return win_rate[0].item() * 100 |