train/model.py

from torch import nn


class BasicBlock(nn.Module):
    def __init__(self, in_channels, channels, bias, k=3, p=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, channels, k, stride=1, padding=p, bias=bias)
        self.bn1 = nn.BatchNorm2d(channels)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(channels, channels, k, stride=1, padding=p, bias=bias)
        self.bn2 = nn.BatchNorm2d(channels)
        self.relu2 = nn.ReLU()

    def forward(self, x):
        y = self.conv1(x)
        y = self.bn1(y)
        y = self.relu1(y)
        y = self.conv2(y)
        y = self.bn2(y)
        x = x + y
        x = self.relu2(x)
        return x


class Bottleneck(nn.Module):
    def __init__(self, in_channels, channels, bias):
        super().__init__()
        mid_channels = channels // 2
        self.conv1 = nn.Conv2d(in_channels, mid_channels, 1, 1, bias=bias)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, padding=1, bias=bias)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.relu2 = nn.ReLU()
        self.conv3 = nn.Conv2d(mid_channels, channels, 1, 1, bias=bias)
        self.bn3 = nn.BatchNorm2d(channels)
        self.relu3 = nn.ReLU()

    def forward(self, x):
        y = self.conv1(x)
        y = self.bn1(y)
        y = self.relu1(y)
        y = self.conv2(y)
        y = self.bn2(y)
        y = self.relu2(y)
        y = self.conv3(y)
        y = self.bn3(y)
        x = x + y
        x = self.relu3(x)
        return x


class Bottlenest(nn.Module):
    def __init__(self, in_channels, channels, bias):
        super().__init__()
        mid_channels = channels // 2
        self.conv0 = nn.Conv2d(in_channels, mid_channels, 1, 1, bias=bias)
        self.bn0 = nn.BatchNorm2d(mid_channels)
        self.conv1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, padding=1, bias=bias)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, padding=1, bias=bias)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.relu2 = nn.ReLU()
        self.conv3 = nn.Conv2d(mid_channels, mid_channels, 3, 1, padding=1, bias=bias)
        self.bn3 = nn.BatchNorm2d(mid_channels)
        self.relu3 = nn.ReLU()
        self.conv4 = nn.Conv2d(mid_channels, mid_channels, 3, 1, padding=1, bias=bias)
        self.bn4 = nn.BatchNorm2d(mid_channels)
        self.relu4 = nn.ReLU()
        self.conv5 = nn.Conv2d(mid_channels, channels, 1, 1, bias=bias)
        self.bn5 = nn.BatchNorm2d(channels)
        self.relu5 = nn.ReLU()

    def forward(self, x):
        y = self.conv0(x)
        y = self.bn0(y)
        z = self.conv1(y)
        z = self.bn1(z)
        z = self.relu1(z)
        z = self.conv2(z)
        z = self.bn2(z)
        y = y + z
        y = self.relu2(y)
        z = self.conv3(y)
        z = self.bn3(z)
        z = self.relu3(z)
        z = self.conv4(z)
        z = self.bn4(z)
        y = y + z
        y = self.relu4(y)
        y = self.conv5(y)
        y = self.bn5(y)
        x = x + y
        x = self.relu5(x)
        return x


class ResNet(nn.Module):
    def __init__(self, block, in_channels, layers, channels, bias):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels, channels, kernel_size=5, stride=1, padding=2, bias=bias
            ),
            nn.BatchNorm2d(channels),
            nn.ReLU(),
        )
        self.convs = nn.ModuleList(
            [block(channels, channels, bias) for _ in range(layers)]
        )

    def forward(self, x):
        x = self.conv1(x)
        for conv in self.convs:
            x = conv(x)
        return x


class AlphaZero(nn.Module):
    def __init__(
        self,
        in_channels,
        layers,
        channels,
        moves,
        board_size,
        value_heads=1,
        bias=False,
        block=BasicBlock,
    ):
        super().__init__()
        self.board_size = board_size
        self.resnet = ResNet(block, in_channels, layers, channels, bias)
        # policy head
        self.policy_head_front = nn.Sequential(
            nn.Conv2d(channels, 2, 1),
            nn.BatchNorm2d(2),
            nn.ReLU(),
        )
        self.policy_head_end = nn.Linear(2 * board_size, moves)
        # value head
        self.value_head_front = nn.Sequential(
            nn.Conv2d(channels, 1, 1),
            nn.BatchNorm2d(1),
            nn.ReLU(),
        )
        self.value_head_end = nn.Sequential(
            nn.Linear(board_size, channels),
            nn.ReLU(),
            nn.Linear(channels, value_heads),
            nn.Tanh(),
        )

    def forward(self, x):
        x = self.resnet(x)
        # policy head
        p = self.policy_head_front(x)
        p = p.view(-1, 2 * self.board_size)
        p = self.policy_head_end(p)
        # value head
        v = self.value_head_front(x)
        v = v.view(-1, self.board_size)
        v = self.value_head_end(v)
        return p, v