print('Current user:'******'Dataset directory:', data_dir)
print('PyTorch Version:', torch.__version__)
print('# CPUs:', multiprocessing.cpu_count())
print('# GPUs:', torch.cuda.device_count())
print('Current cuda device:', torch.cuda.current_device())
print('Device name:', torch.cuda.get_device_name(0))
print('Seed:', config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed(config.seed)
np.random.seed(config.seed)
random.seed(config.seed)
train_loader, val_loader, test_loader, word2id, id2word = get_loader(
config, data_dir)
PAD_ID, UNK_ID = word2id['<pad>'], word2id['<unk>']
vocab_size = len(word2id)
print('Loaded data loaders & Vocab!')
print('Vocab Size:', vocab_size)
model = build_model(config, word2id, id2word)
print('#===== Parameters =====#')
for name, p in model.named_parameters():
print(name, '\t', list(p.size()))
print('#==== Weight Initialization ====#')
if config.model == 'NQG':
for name, p in model.named_parameters():
return metric_result, hypotheses, best_hypothesis, hyp_focus, hyp_attention
if __name__ == '__main__':
from pathlib import Path
current_dir = Path(__file__).resolve().parent
import configs
config = configs.get_config()
print(config)
from build_utils import get_loader, build_model, get_ckpt_name
# Build Data Loader
data_dir = current_dir.joinpath(config.data + '_out')
_, _, test_loader, word2id, id2word = get_loader(config, data_dir)
# Build Model
model = build_model(config, word2id, id2word)
model.to(device)
# Load Model from checkpoint
ckpt_dir = Path(f"./ckpt/{config.model}/").resolve()
filename = get_ckpt_name(config)
filename += f"_epoch{config.load_ckpt}.pkl"
ckpt_path = ckpt_dir.joinpath(filename)
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt['model'])
print('Loaded model from', ckpt_path)
# Run Evaluation