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translate.py
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translate.py
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import torch
import argparse
from tqdm import tqdm
import os
from misc.run import get_loader, run_eval
from misc.logger import CsvLogger
from config import Constants
from misc.utils import load_model_and_opt, get_dict_mapping
from tensorboardX import SummaryWriter
import shutil
from misc.crit import get_criterion_during_evaluation
def prepare_collect_config(option, opt):
if not os.path.exists(opt.collect_path):
os.makedirs(opt.collect_path)
names = [option['dataset'], option['method'], opt.evaluation_mode]
if opt.not_only_best_candidate:
names.insert(0, 'nobc')
if option['decoding_type'] == 'ARFormer':
parameter = 'bs%d_topk%d.pkl' % (option['beam_size'], option['topk'])
else:
names.append(('%s' % ('CT' if option['use_ct'] else '')) + option['paradigm'])
if option['paradigm'] == 'mp':
parameter = 'i%db%da%03d.pkl' % (
option['iterations'],
option['length_beam_size'],
int(100*option['beam_alpha'])
)
else:
parameter = 'q%dqi%db%da%03d.pkl' % (
option['q'],
option['q_iterations'],
option['length_beam_size'],
int(100*option['beam_alpha'])
)
filename = '_'.join(names + [parameter])
opt.collect_path = os.path.join(opt.collect_path, filename)
def main():
'''Main Function'''
parser = argparse.ArgumentParser(description='translate.py')
parser.add_argument('-df', '--default', default=False, action='store_true')
parser.add_argument('-method', '--method', default='ARB', type=str)
parser.add_argument('-dataset', '--dataset', default='MSRVTT', type=str)
parser.add_argument('--default_model_name', default='best.pth.tar', type=str)
parser.add_argument('-scope', '--scope', default='', type=str)
parser.add_argument('-record', '--record', default=False, action='store_true')
parser.add_argument('-field', '--field', nargs='+', type=str, default=['seed'])
parser.add_argument('-val_and_test', '--val_and_test', default=False, action='store_true')
parser.add_argument('-model_path', '--model_path', type=str)
parser.add_argument('-teacher_path', '--teacher_path', type=str)
parser.add_argument('-bs', '--beam_size', type=int, default=5, help='Beam size')
parser.add_argument('-ba', '--beam_alpha', type=float, default=1.0)
parser.add_argument('-topk', '--topk', type=int, default=1)
# NA decoding
parser.add_argument('-i', '--iterations', type=int, default=5)
parser.add_argument('-lbs', '--length_beam_size', type=int, default=6)
parser.add_argument('-q', '--q', type=int, default=1)
parser.add_argument('-qi', '--q_iterations', type=int, default=1)
parser.add_argument('-paradigm', '--paradigm', type=str, default='mp')
parser.add_argument('-use_ct', '--use_ct', default=False, action='store_true')
parser.add_argument('-md', '--masking_decision', default=False, action='store_true')
parser.add_argument('-ncd', '--no_candidate_decision', default=False, action='store_true')
parser.add_argument('--algorithm_print_sent', default=False, action='store_true')
parser.add_argument('-batch_size', '--batch_size', type=int, default=128)
parser.add_argument('-no_cuda', action='store_true')
parser.add_argument('-em', '--evaluation_mode', type=str, default='test')
parser.add_argument('-print_sent', action='store_true')
parser.add_argument('-json_path', type=str, default='')
parser.add_argument('-json_name', type=str, default='')
parser.add_argument('-ns', '--no_score', default=False, action='store_true')
parser.add_argument('-analyze', default=False, action='store_true')
parser.add_argument('-latency', default=False, action='store_true')
parser.add_argument('-specific', default=-1, type=int)
parser.add_argument('-collect_path', type=str, default='./collected_captions')
parser.add_argument('-collect', default=False, action='store_true')
parser.add_argument('-nobc', '--not_only_best_candidate', default=False, action='store_true')
opt = parser.parse_args()
device = torch.device('cuda' if not opt.no_cuda else 'cpu')
teacher_model = None
dict_mapping = {}
if opt.default:
if opt.dataset.lower() == 'msvd':
opt.dataset = 'Youtube2Text'
opt.model_path = os.path.join(
Constants.base_checkpoint_path,
opt.dataset,
opt.method,
opt.scope,
opt.default_model_name
)
if opt.method in ['NAB', 'NACF']:
opt.teacher_path = os.path.join(
Constants.base_checkpoint_path,
opt.dataset,
'ARB',
opt.scope,
opt.default_model_name
)
assert os.path.exists(opt.teacher_path)
else:
assert opt.model_path and os.path.exists(opt.model_path)
model, option, other_info = load_model_and_opt(opt.model_path, device, return_other_info=True)
if getattr(opt, 'teacher_path', None) is not None:
print('Loading teacher model from %s' % opt.teacher_path)
teacher_model, teacher_option = load_model_and_opt(opt.teacher_path, device)
dict_mapping = get_dict_mapping(option, teacher_option)
option['reference'] = option['reference'].replace('msvd_refs.pkl', 'refs.pkl')
option['info_corpus'] = option['info_corpus'].replace('info_corpus_0.pkl', 'info_corpus.pkl')
if not opt.default:
_ = option['dataset']
option.update(vars(opt))
option['dataset'] = _
else:
if option['decoding_type'] != 'NARFormer':
option['topk'] = opt.topk
option['beam_size'] = 5
option['beam_alpha'] = 1.0
else:
option['algorithm_print_sent'] = opt.algorithm_print_sent
option['paradigm'] = opt.paradigm
option['iterations'] = 5
option['length_beam_size'] = 6
option['beam_alpha'] = 1.35 if opt.dataset == 'MSRVTT' else 1.0
option['q'] = 1
option['q_iterations'] = 1 if opt.use_ct else 0
option['use_ct'] = opt.use_ct
if opt.collect:
prepare_collect_config(option, opt)
if opt.latency:
opt.batch_size = 1
option['batch_size'] = 1
if opt.val_and_test:
modes = ['validate', 'test']
csv_filenames = ['validation_record.csv', 'testing_record.csv']
else:
modes = [opt.evaluation_mode]
csv_filenames = ['validation_record.csv' if opt.evaluation_mode == 'validate' else 'testing_record.csv']
crit = get_criterion_during_evaluation(option)
for mode, csv_filename in zip(modes, csv_filenames):
loader = get_loader(option, mode=mode, print_info=True, specific=opt.specific, batch_size=opt.batch_size)
vocab = loader.dataset.get_vocab()
if opt.record:
summarywriter = SummaryWriter(os.path.join(option['checkpoint_path'], mode))
else:
summarywriter = None
metric = run_eval(option, model, crit, loader, vocab, device,
teacher_model=teacher_model,
dict_mapping=dict_mapping,
json_path=opt.json_path,
json_name=opt.json_name,
print_sent=opt.print_sent,
no_score=opt.no_score,
analyze=True if opt.record else opt.analyze,
collect_best_candidate_iterative_results=True if opt.collect else False,
collect_path=opt.collect_path,
summarywriter=summarywriter,
global_step=option['seed']
)
print(metric)
if opt.record:
fieldsnames = ['Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4',
'METEOR', 'ROUGE_L', 'CIDEr', 'Sum',
'ave_length', 'novel', 'unique', 'usage']
if crit is not None:
fieldsnames += crit.get_fieldsnames()
logger = CsvLogger(filepath=option['checkpoint_path'], filename=csv_filename,
fieldsnames=fieldsnames + opt.field)
if 'loss' in metric:
metric.pop('loss')
for key in opt.field:
metric[key] = option[key]
logger.write(metric)
if __name__ == "__main__":
main()