File size: 2,328 Bytes
d341ccf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """Compact ResNet policy/value network for the AChess distillation dataset."""
from __future__ import annotations
import torch
from torch import nn
from torch.nn import functional as F
class ResBlock(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.bn1 = nn.BatchNorm2d(channels)
self.conv1 = nn.Conv2d(channels, channels, 3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(channels)
self.conv2 = nn.Conv2d(channels, channels, 3, padding=1, bias=False)
nn.init.zeros_(self.bn2.weight)
def forward(self, x):
h = F.relu(self.bn1(x), inplace=True)
h = self.conv1(h)
h = self.bn2(h)
h = self.conv2(h)
return F.relu(x + h, inplace=True)
class ChessNet(nn.Module):
def __init__(self, channels: int = 128, blocks: int = 10, planes: int = 20):
super().__init__()
self.stem = nn.Sequential(
nn.Conv2d(planes, channels, 3, padding=1, bias=False),
nn.BatchNorm2d(channels),
nn.ReLU(inplace=True),
)
self.body = nn.Sequential(*[ResBlock(channels) for _ in range(blocks)])
# 64 output planes x 64 squares = 4096 (from * 64 + to).
self.policy = nn.Conv2d(channels, 64, 1)
self.value_conv = nn.Conv2d(channels, 1, 1)
self.value_bn = nn.BatchNorm2d(1)
self.value_fc = nn.Linear(64, 256)
self.value_out = nn.Linear(256, 1)
self.wdl_out = nn.Linear(256, 3)
self._init_weights()
def _init_weights(self):
for module in self.modules():
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
elif isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
nn.init.zeros_(module.bias)
def forward(self, x, legal_mask=None):
h = self.body(self.stem(x))
logits = self.policy(h).flatten(1)
if legal_mask is not None:
logits = logits.masked_fill(~legal_mask, -1e4)
v = F.relu(self.value_bn(self.value_conv(h)), inplace=True).flatten(1)
v = F.relu(self.value_fc(v), inplace=True)
value = torch.tanh(self.value_out(v)).squeeze(-1)
wdl = self.wdl_out(v)
return logits, value, wdl
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