print(f"Skipping step {step}")
continue
print(idx, "translating", fname)
model = os.path.join(mdl_dir, fname)
output_pfx = f'bms{args.bms}.blk{args.blk}.bsz{args.bsz}'
if args.weights:
output_pfx += f'.wgt{args.weights}'.replace(' ', '')
output_pfx += f'.{"small" if args.small else "full"}'
output = os.path.join(gns_dir, f'{output_pfx}-step_{step}.txt')
log_file = os.path.join(exp_dir, 'translate-log.txt')
cmd_args = [
f'-model {model}',
f'-src {src}',
f'-tgt {tgt}',
f'-output {output}',
f'-beam_size {args.bms}',
f'-block_ngram_repeat {args.blk}',
f'-batch_size {args.bsz}',
f'-gpu {args.gpu}',
f'-log_file {log_file}'
]
if args.weights:
weights = ' '.join([str(w) for w in args.weights])
cmd_args.append(f'-rnn_weights {weights}')
translate(f'--config translate.cfg {" ".join(cmd_args)}')
from onmt.bin.train import main as train
if __name__ == '__main__':
parser = ArgumentParser()
# Simply add an argument for preprocess, train, translate
mode = parser.add_mutually_exclusive_group()
mode.add_argument("--preprocess",
dest='preprocess',
action='store_true',
help="Activate to preprocess with OpenNMT")
mode.add_argument("--train",
dest='train',
action='store_true',
help="Activate to train with OpenNMT")
mode.add_argument("--translate",
dest='translate',
action='store_true',
help="Activate to translate with OpenNMT")
mode, remaining_args = parser.parse_known_args()
if mode.preprocess:
preprocess(remaining_args)
elif mode.train:
train(remaining_args)
elif mode.translate:
args = translate(remaining_args)
# TODO compute scores directly after the translation is done
avg = sys.argv[5] == "True"
test = sys.argv[6] == "True"
sys.argv = [
sys.argv[0], "--config",
"config-seed-{}/translate_{}.cfg".format(seed, exp)
]
logger.info("seed-{} exp-{} start-{} end-{}".format(seed, exp, start, end))
midstr = "_avg" if avg else ""
gens = "test" if test else "valid"
data = "test" if test else "validation"
for i in range(start, end + 1):
steps = i * 1000
parser = _get_parser()
opt = parser.parse_args()
opt.src = "data/{}_{}_data.txt".format(
exp if "S4" in exp else exp[2:4], data)
opt.output = "experiments/exp-seed-{}/exp-{}/gens/{}/predictions{}_{}.txt".format(
seed, exp, gens, midstr, steps)
opt.models = [
"experiments/exp-seed-{}/exp-{}/models/model{}_step_{}.pt".format(
seed, exp, midstr, steps)
]
opt.log_file = "experiments/exp-seed-{}/exp-{}/translation{}-log.txt".format(
seed, exp, midstr, steps)
tag = prepare_model(seed, exp, i, avg, i == end)
if tag:
translate(opt)
clear_translate_model(seed, exp, i, avg)
else:
logger.info("translate error n={}".format(i))