Merge genboard

python/board.py

@@ -2,6 +2,9 @@
 import random
 import numpy as np
 
+class IllegalMoveError(ValueError):
+  pass
+
 #Implements legal moves without superko
 class Board:
   EMPTY = 0
@@ -290,9 +293,9 @@ def is_on_board(self,loc):
   #Set a given location with error checking. Suicide setting allowed.
   def set_stone(self,pla,loc):
     if pla != Board.EMPTY and pla != Board.BLACK and pla != Board.WHITE:
-      raise ValueError("Invalid pla for board.set")
+      raise IllegalMoveError("Invalid pla for board.set")
     if not self.is_on_board(loc):
-      raise ValueError("Invalid loc for board.set")
+      raise IllegalMoveError("Invalid loc for board.set")
 
     if self.board[loc] == pla:
       pass
@@ -312,17 +315,17 @@ def set_stone(self,pla,loc):
   #Single stone suicide is disallowed but suicide is allowed, to support rule sets and sgfs that have suicide
   def play(self,pla,loc):
     if pla != Board.BLACK and pla != Board.WHITE:
-      raise ValueError("Invalid pla for board.play")
+      raise IllegalMoveError("Invalid pla for board.play")
 
     if loc != Board.PASS_LOC:
       if not self.is_on_board(loc):
-        raise ValueError("Invalid loc for board.set")
+        raise IllegalMoveError("Invalid loc for board.set")
       if self.board[loc] != Board.EMPTY:
-        raise ValueError("Location is nonempty")
+        raise IllegalMoveError("Location is nonempty")
       if self.would_be_single_stone_suicide(pla,loc):
-        raise ValueError("Move would be illegal single stone suicide")
+        raise IllegalMoveError("Move would be illegal single stone suicide")
       if loc == self.simple_ko_point:
-        raise ValueError("Move would be illegal simple ko recapture")
+        raise IllegalMoveError("Move would be illegal simple ko recapture")
 
     self.playUnsafe(pla,loc)
 

python/data.py

@@ -5,13 +5,14 @@
 from board import Board
 
 class Metadata:
-  def __init__(self, size, bname, wname, brank, wrank, komi):
+  def __init__(self, size, bname, wname, brank, wrank, komi, handicap):
     self.size = size
     self.bname = bname
     self.wname = wname
     self.brank = brank
     self.wrank = wrank
     self.komi = komi
+    self.handicap = handicap
 
 #Returns (metadata, list of setup stones, list of move stones)
 #Setup and move stones are both pairs of (pla,loc)
@@ -89,6 +90,7 @@ def load_sgf_moves_exn(path):
   wrank = (root.get("WR") if root.has_property("WR") else None)
   komi = (root.get("KM") if root.has_property("KM") else None)
   rulesstr = (root.get("RU") if root.has_property("RU") else None)
+  handicap = (root.get("HA") if root.has_property("HA") else None)
 
   rules = None
   if rulesstr is not None:
@@ -157,5 +159,5 @@ def load_sgf_moves_exn(path):
       else:
         raise Exception("Could not parse rules: " + origrulesstr)
 
-  metadata = Metadata(size, bname, wname, brank, wrank, komi)
+  metadata = Metadata(size, bname, wname, brank, wrank, komi, handicap)
   return metadata, setup, moves, rules

python/genboard_common.py

@@ -0,0 +1,131 @@
+import traceback
+import json
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ResBlock(nn.Module):
+  def __init__(self,num_channels,scale_init):
+    super(ResBlock, self).__init__()
+    kernel_size = 3
+    self.biasa = nn.Parameter(torch.zeros(num_channels,1,1))
+    self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False)
+    torch.nn.init.normal_(self.conva.weight,std=math.sqrt(2.0 / num_channels / kernel_size / kernel_size)*scale_init)
+    self.biasb = nn.Parameter(torch.zeros(num_channels,1,1))
+    self.scalb = nn.Parameter(torch.ones(num_channels,1,1))
+    self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False)
+    torch.nn.init.zeros_(self.convb.weight)
+
+  def forward(self, trunk):
+    x = F.relu(trunk+self.biasa)
+    x = self.conva(x)
+    x = F.relu(x*self.scalb+self.biasb)
+    x = self.convb(x)
+    return trunk+x
+
+class GPoolResBlock(nn.Module):
+  def __init__(self,num_channels,scale_init):
+    super(GPoolResBlock, self).__init__()
+    kernel_size = 3
+    self.biasa = nn.Parameter(torch.zeros(num_channels,1,1))
+    self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False)
+    torch.nn.init.normal_(self.conva.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*scale_init)
+    self.convg = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False)
+    torch.nn.init.normal_(self.convg.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*math.sqrt(scale_init))
+    self.matg = nn.Parameter(torch.zeros(num_channels,num_channels))
+    torch.nn.init.normal_(self.matg,std=math.sqrt(1.0 / num_channels)*math.sqrt(scale_init))
+    self.biasb = nn.Parameter(torch.zeros(num_channels,1,1))
+    self.scalb = nn.Parameter(torch.ones(num_channels,1,1))
+    self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False)
+    torch.nn.init.zeros_(self.convb.weight)
+
+  def forward(self, trunk):
+    x = F.relu(trunk+self.biasa)
+    x = self.conva(x)
+    g = self.convg(x)
+    gsize = g.size()
+    g = torch.sum(g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc
+    g = torch.matmul(g,self.matg)
+    g = g.view(gsize[0],gsize[1],1,1)
+    x = x + g
+    x = F.relu(x*self.scalb+self.biasb)
+    x = self.convb(x)
+    return trunk+x
+
+
+class Model(nn.Module):
+  def __init__(self, num_channels, num_blocks):
+    super(Model, self).__init__()
+    # Channel 0: Next inference point
+    # Channel 1: On-board
+    # Channel 2: Black
+    # Channel 3: White
+    # Channel 4: Unknown
+    # Channel 5: Turn number / 100
+    # Channel 6: Noise stdev in turn number / 50
+    # Channel 7: Source
+
+    self.inference_channel = 0
+    self.num_channels = num_channels
+    self.num_blocks = num_blocks
+    self.conv0 = nn.Conv2d(in_channels=8, out_channels=self.num_channels, kernel_size=3, padding=1, bias=False)
+
+    self.blocks = nn.ModuleList([])
+    self.fixup_scale_init = 1.0 / math.sqrt(self.num_blocks)
+    self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init))
+    self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init))
+
+    next_is_gpool = True
+    for b in range(num_blocks-2):
+      if next_is_gpool:
+        self.blocks.append(GPoolResBlock(self.num_channels,self.fixup_scale_init))
+      else:
+        self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init))
+      next_is_gpool = not next_is_gpool
+
+    assert(len(self.blocks) == self.num_blocks)
+
+    self.endtrunk_bias_focus = nn.Parameter(torch.zeros(self.num_channels,1,1))
+    self.endtrunk_bias_g = nn.Parameter(torch.zeros(self.num_channels,1,1))
+    self.convg = nn.Conv2d(in_channels=self.num_channels, out_channels=self.num_channels, kernel_size=1, padding=0, bias=False)
+
+    self.fc1 = nn.Linear(self.num_channels*2, self.num_channels)
+    self.fc2 = nn.Linear(self.num_channels,3)
+    self.convaux = nn.Conv2d(in_channels=self.num_channels, out_channels=3, kernel_size=1, padding=0, bias=True)
+
+  def forward(self, inputs):
+    trunk = self.conv0(inputs)
+    for i in range(self.num_blocks):
+      trunk = self.blocks[i](trunk)
+
+    head_focus = F.relu(trunk+self.endtrunk_bias_focus)
+    head_g = F.relu(trunk+self.endtrunk_bias_g)
+    aux = self.convaux(head_focus)
+    gsize = head_g.size()
+
+    x = torch.sum(head_focus * inputs[:,self.inference_channel:self.inference_channel+1,:,:],(2,3))
+    g = torch.sum(head_g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc
+
+    x = torch.cat((x,g),dim=1)
+
+    x = F.relu(self.fc1(x))
+    x = self.fc2(x)
+    return x,aux
+
+  def save_to_file(self, filename):
+    state_dict = self.state_dict()
+    data = {}
+    data["num_channels"] = self.num_channels
+    data["num_blocks"] = self.num_blocks
+    data["state_dict"] = state_dict
+    torch.save(data, filename)
+
+  @staticmethod
+  def load_from_file(filename):
+    data = torch.load(filename)
+    model = Model(data["num_channels"], data["num_blocks"])
+    model.load_state_dict(data["state_dict"])
+    return model
+
+