def make_data_loaders(train_filename, val_filename, feature_set, num_workers,
batch_size, filtered, random_fen_skipping, main_device):
# Epoch and validation sizes are arbitrary
epoch_size = 100000000
val_size = 1000000
features_name = feature_set.name
train_infinite = nnue_dataset.SparseBatchDataset(
features_name,
train_filename,
batch_size,
num_workers=num_workers,
filtered=filtered,
random_fen_skipping=random_fen_skipping,
device=main_device)
val_infinite = nnue_dataset.SparseBatchDataset(
features_name,
val_filename,
batch_size,
filtered=filtered,
random_fen_skipping=random_fen_skipping,
device=main_device)
# num_workers has to be 0 for sparse, and 1 for dense
# it currently cannot work in parallel mode but it shouldn't need to
train = DataLoader(nnue_dataset.FixedNumBatchesDataset(
train_infinite, (epoch_size + batch_size - 1) // batch_size),
batch_size=None,
batch_sampler=None)
val = DataLoader(nnue_dataset.FixedNumBatchesDataset(
val_infinite, (val_size + batch_size - 1) // batch_size),
batch_size=None,
batch_sampler=None)
return train, val
def data_loader_cc(train_filename, val_filename, num_workers, batch_size,
filtered, random_fen_skipping, main_device):
# Epoch and validation sizes are arbitrary
epoch_size = 100000000
val_size = 2000000
train_infinite = nnue_dataset.SparseBatchDataset(
train_filename,
batch_size,
num_workers=num_workers,
filtered=filtered,
random_fen_skipping=random_fen_skipping,
device=main_device)
val_infinite = nnue_dataset.SparseBatchDataset(
val_filename,
batch_size,
filtered=filtered,
random_fen_skipping=random_fen_skipping,
device=main_device)
train = DataLoader(nnue_dataset.FixedNumBatchesDataset(
train_infinite, (epoch_size + batch_size - 1) // batch_size),
batch_size=None,
batch_sampler=None)
val = DataLoader(nnue_dataset.FixedNumBatchesDataset(
val_infinite, (val_size + batch_size - 1) // batch_size),
batch_size=None,
batch_sampler=None)
return train, val
def data_loader_cc(train_filename, val_filename, data_name, num_workers, batch_size, filtered): # Epoch and validation sizes are arbitrary epoch_size = int(3e7) val_size = epoch_size // 20 train_infinite = nnue_dataset.SparseBatchDataset(data_name, train_filename, batch_size, num_workers=num_workers, filtered=filtered) val_infinite = nnue_dataset.SparseBatchDataset(data_name, val_filename, batch_size, filtered=filtered) # num_workers has to be 0 for sparse, and 1 for dense # it currently cannot work in parallel mode but it shouldn't need to train = DataLoader(nnue_dataset.FixedNumBatchesDataset(train_infinite, (epoch_size + batch_size - 1) // batch_size), batch_size=None, batch_sampler=None) val = DataLoader(nnue_dataset.FixedNumBatchesDataset(val_infinite, (val_size + batch_size - 1) // batch_size), batch_size=None, batch_sampler=None) return train, val
def main():
parser = argparse.ArgumentParser(
description="Runs evaluation for a model.")
parser.add_argument("model",
help="Source file (can be .ckpt, .pt or .nnue)")
parser.add_argument("--dataset",
default="d8_128000_21865.binpack",
help="Dataset to evaluate on (.bin)")
args = parser.parse_args()
if args.model.endswith(".pt"):
nnue = torch.load(args.model, map_location=torch.device('cpu'))
else:
nnue = M.NNUE.load_from_checkpoint(args.model)
val_infinite = nnue_dataset.SparseBatchDataset(halfkp.NAME, args.dataset,
8000)
data = nnue_dataset.FixedNumBatchesDataset(val_infinite, 16)
trainer = pl.Trainer(progress_bar_refresh_rate=0)
for i in range(21):
nnue.lambda_ = i / 20.0
loss = trainer.test(nnue, data, verbose=False)
print(nnue.lambda_, ",", loss[0]['test_loss'])