def main():
parser = argparse.ArgumentParser(description="")
parser.add_argument("--net", type=str, help="path to a .nnue net")
parser.add_argument("--engine", type=str, help="path to stockfish")
parser.add_argument("--data",
type=str,
help="path to a .bin or .binpack dataset")
parser.add_argument(
"--checkpoint",
type=str,
help="Optional checkpoint (used instead of nnue for local eval)")
parser.add_argument("--count",
type=int,
default=100,
help="number of datapoints to process")
features.add_argparse_args(parser)
args = parser.parse_args()
batch_size = 1000
feature_set = features.get_feature_set_from_name(args.features)
if args.checkpoint:
model = NNUE.load_from_checkpoint(args.checkpoint,
feature_set=feature_set)
else:
model = read_model(args.net, feature_set)
model.eval()
model.cuda()
fen_batch_provider = make_fen_batch_provider(args.data, batch_size)
model_evals = []
engine_evals = []
done = 0
print('Processed {} positions.'.format(done))
while done < args.count:
fens = filter_fens(next(fen_batch_provider))
b = nnue_dataset.make_sparse_batch_from_fens(feature_set, fens,
[0] * len(fens),
[1] * len(fens),
[0] * len(fens))
model_evals += eval_model_batch(model, b)
nnue_dataset.destroy_sparse_batch(b)
engine_evals += eval_engine_batch(args.engine, args.net, fens)
done += len(fens)
print('Processed {} positions.'.format(done))
compute_correlation(engine_evals, model_evals)
def commit_batch():
nonlocal fens
nonlocal results
nonlocal scores
nonlocal plies
nonlocal model_evals
nonlocal engine_evals
if len(fens) == 0:
return
b = nnue_dataset.make_sparse_batch_from_fens(feature_set, fens, scores, plies, results)
model_evals += eval_model_batch(model, b)
nnue_dataset.destroy_sparse_batch(b)
engine_evals += eval_engine_batch(args.engine, args.net, fens)
fens = []
results = []
scores = []
plies = []