82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class ResidualBlock(nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(channels)
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self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(channels)
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def forward(self, x):
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residual = x
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x = F.relu(self.bn1(self.conv1(x)))
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x = self.bn2(self.conv2(x))
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return F.relu(x + residual)
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class UltimateTicTacToeModel(nn.Module):
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def __init__(self, board_size, action_size, device, channels=64, num_blocks=6):
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super().__init__()
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self.action_size = action_size
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self.input_shape = board_size
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self.input_channels = board_size[0]
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self.board_height = board_size[1]
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self.board_width = board_size[2]
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self.device = torch.device(device)
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self.stem = nn.Sequential(
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nn.Conv2d(self.input_channels, channels, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(channels),
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nn.ReLU(inplace=True),
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)
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self.residual_tower = nn.Sequential(*(ResidualBlock(channels) for _ in range(num_blocks)))
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self.policy_head = nn.Sequential(
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nn.Conv2d(channels, 32, kernel_size=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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)
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self.policy_fc = nn.Linear(32 * self.board_height * self.board_width, self.action_size)
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self.value_head = nn.Sequential(
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nn.Conv2d(channels, 32, kernel_size=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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)
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self.value_fc1 = nn.Linear(32 * self.board_height * self.board_width, 128)
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self.value_fc2 = nn.Linear(128, 1)
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self.to(self.device)
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def forward(self, x):
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x = x.view(-1, *self.input_shape)
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x = self.stem(x)
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x = self.residual_tower(x)
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policy = self.policy_head(x)
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policy = torch.flatten(policy, 1)
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policy = self.policy_fc(policy)
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value = self.value_head(x)
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value = torch.flatten(value, 1)
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value = F.relu(self.value_fc1(value))
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value = torch.tanh(self.value_fc2(value))
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return F.softmax(policy, dim=1), value
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def predict(self, board):
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board = torch.as_tensor(board, dtype=torch.float32, device=self.device)
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board = board.view(1, *self.input_shape)
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self.eval()
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with torch.no_grad():
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pi, v = self.forward(board)
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return pi.detach().cpu().numpy()[0], float(v.item())
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