def load_leela_network():
global net, nn
if network_id is not None:
net = load_network(backend='net_client', network_id=network_id)
else:
net = load_network(backend='pytorch_cuda', filename=weights)
nn = search.NeuralNet(net=net, lru_size=min(5000, nodes))
def load_leela_network():
global net, nn
if network_id is not None:
net = load_network(backend='net_client', network_id=network_id, policy_softmax_temp=2.2)
else:
net = load_network(backend='pytorch_cuda', filename=weights, policy_softmax_temp=2.2)
nn = search.NeuralNet(net=net, lru_size=max(5000, nodes))
def load(self):
if dummy:
self.model = None
self.batch_processor = DummyBatchProcessor(self.context,
self.model,
self.network_id,
self.batch_queue,
self.response_queue)
else:
with BatchProcessor._cuda_lock:
net = load_network(backend='pytorch_cuda',
filename=self.weights_file,
half=self.half)
self.model = net.model
self.batch_processor = BatchProcessor(self.context, self.model,
self.network_id,
self.batch_queue,
self.response_queue)
"--black",
help="the engine to use for black",
choices=search.engines.keys(),
default=default_engine)
parser.add_argument("-n",
"--nodes",
help="the engine to use for black",
type=int,
default=800)
parser.add_argument("-v", "--verbosity", action="count", default=0)
args = parser.parse_args()
backend = 'pytorch_cuda' if os.path.exists(
'/opt/bin/nvidia-smi') else 'pytorch_cpu'
net = load_network(backend=backend,
filename=args.weights,
policy_softmax_temp=2.2)
nn = search.NeuralNet(net=net)
board = LeelaBoard()
players = [{
'engine': args.white,
'root': None,
'resets': 0
}, {
'engine': args.black,
'root': None,
'resets': 0
}]
turn = 0
import chess.pgn
import time
import json
import collections
# NOTE: Policy values seem to be a tiny bit off from lc0...
# The reason for this seems to be usage of src/mcts/node.cc::Edge::SetP in lc0
# This function applies some rounding to policy values
#
# Changing tolerance from 0.00006 to 0.0005
TOLERANCE = 0.0005
engine = LC0Engine()
board = lcztools.LeelaBoard()
# engine.evaluate(board())
net = lcztools.load_network()
def fix_policy_float(policy):
'''Numpy to normal python float, for json dumps'''
return collections.OrderedDict((k, float(v)) for k, v in policy.items())
g_max_policy_error = 0
g_max_value_error = 0
g_mse_policy = 0
g_mse_value = 0
g_se_policy_sum = 0
g_se_value_sum = 0
g_policy_samples = 0
g_value_samples = 0
# For now, if using the tensorflow backend, tfprocess is imported to build the network,
# so training/tf has to be in the Python path
sys.path.append(os.path.expanduser('~/git/leela-chess/training/tf'))
weights_file = os.path.expanduser('~/git/leela-chess/release/weights.txt.gz')
import lcztools
def json_default(obj):
# Serialize numpy floats
if isinstance(obj, np.floating):
return float(obj)
raise TypeError
print("Test pytorch")
lcz_net = lcztools.load_network('pytorch', weights_file)
lcz_board = lcztools.LeelaBoard()
print(lcz_board)
policy, value = lcz_net.evaluate(lcz_board)
print('Policy: {}'.format(json.dumps(policy, default=json_default, indent=3)))
print('Value: {}'.format(value))
lcz_board.push_uci('e2e4')
print(lcz_board)
policy, value = lcz_net.evaluate(lcz_board)
print('Policy: {}'.format(json.dumps(policy, default=json_default, indent=3)))
print('Value: {}'.format(value))
print("Test tensorflow")
lcz_net = lcztools.load_network('tensorflow', weights_file)
import os
import sys
sys.path.append(os.path.expanduser('~/git/lczero_tools/src/'))
import lcztools
from lcztools.testing.leela_engine import LCZEngine
import numpy as np
import chess.pgn
import time
engine_path = os.path.expanduser('~/git/leela-chess/release/lczero')
weights_file = os.path.expanduser('~/git/leela-chess/release/weights.txt.gz')
engine = LCZEngine(engine_path, weights_file)
board = lcztools.LeelaBoard()
engine.evaluate(board.lcz_to_uci_engine_board())
net = lcztools.load_network('pytorch', weights_file)
def eval_equal(neteval, engineeval, tolerance=.00006):
npol, nv = neteval
epol, ev = engineeval
for uci in npol:
if abs(npol[uci] - epol[uci]) > tolerance:
return False
if (ev is not None) and (abs(nv - ev) > tolerance):
return False
return True
net_eval_time = 0
engine_eval_time = 0
from lcztools import load_network, LeelaBoard
import search
import chess
import chess.pgn
import sys
import datetime
weights = sys.argv[1]
board = LeelaBoard()
net = load_network(backend='pytorch_cuda',
filename=weights,
policy_softmax_temp=2.2)
nn = search.NeuralNet(net=net)
NODES = 10000
def do_nn():
best, node = search.UCT_search(board, NODES, net=net, C=3.4)
start = datetime.datetime.now()
do_nn()
fini = datetime.datetime.now()
diff = fini - start
msecs = diff.microseconds + (diff.seconds * 1000000.0)
nps = round(NODES * 1000000.0 / (msecs))
print("{} nps".format(nps))
