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tester.py
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import os
import sys
import json
import torch
import pickle
import logging
import argparse
import evaluation
from model import get_model
from validate import norm_score, cal_perf
import util.tag_data_provider as data
from util.text2vec import get_text_encoder
import util.metrics as metrics
from util.vocab import Vocabulary
from basic.util import read_dict, log_config
from basic.constant import ROOT_PATH
from basic.bigfile import BigFile
from basic.common import makedirsforfile, checkToSkip
def parse_args():
# Hyper Parameters
parser = argparse.ArgumentParser()
parser.add_argument('--rootpath', type=str, default=ROOT_PATH, help='path to datasets. (default: %s)' % ROOT_PATH)
parser.add_argument('--testCollection', type=str, help='test collection')
parser.add_argument('--collectionStrt', type=str, default='single', help='collection structure (single|multiple)')
parser.add_argument('--split', default='test', type=str,
help='split, only for single-folder collection structure (val|test)')
parser.add_argument('--overwrite', type=int, default=0, choices=[0, 1], help='overwrite existed file. (default: 0)')
parser.add_argument('--log_step', default=100, type=int, help='Number of steps to print and record the log.')
parser.add_argument('--batch_size', default=128, type=int, help='Size of a training mini-batch.')
parser.add_argument('--workers', default=5, type=int, help='Number of data loader workers.')
parser.add_argument('--logger_name', default='runs', help='Path to save the model and Tensorboard log.')
parser.add_argument('--cv_name', default='cv_2021')
parser.add_argument('--checkpoint_name', default='model_best.pth.tar', type=str,
help='name of checkpoint (default: model_best.pth.tar)')
args = parser.parse_args()
return args
def main():
opt = parse_args()
print(json.dumps(vars(opt), indent=2))
rootpath = opt.rootpath
collectionStrt = opt.collectionStrt
resume = os.path.join(opt.logger_name, opt.checkpoint_name)
if not os.path.exists(resume):
logging.info(resume + ' not exists.')
sys.exit(0)
checkpoint = torch.load(resume)
start_epoch = checkpoint['epoch']
best_rsum = checkpoint['best_rsum']
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
.format(resume, start_epoch, best_rsum))
options = checkpoint['opt']
# collection setting
testCollection = opt.testCollection
collections_pathname = options.collections_pathname
collections_pathname['test'] = testCollection
# train
for key in collections_pathname:
if collections_pathname[key] == 'o_tgif':
collections_pathname[key] = 'tgif_chen'
elif collections_pathname[key] == 'tgif':
collections_pathname[key] = 'tgif_li'
print(collections_pathname)
trainCollection = options.trainCollection
output_dir = resume.replace(trainCollection, testCollection)
if 'checkpoints' in output_dir:
output_dir = output_dir.replace('/checkpoints/', '/results/')
else:
output_dir = output_dir.replace('/%s/' % opt.cv_name,
'/results/%s/%s/' % (opt.cv_name, trainCollection))
result_pred_sents = os.path.join(output_dir, 'id.sent.score.txt')
pred_error_matrix_file = os.path.join(output_dir, 'pred_errors_matrix.pth.tar')
if checkToSkip(pred_error_matrix_file, opt.overwrite):
sys.exit(0)
makedirsforfile(pred_error_matrix_file)
log_config(output_dir)
logging.info(json.dumps(vars(opt), indent=2))
# data loader prepare
test_cap = os.path.join(rootpath, collections_pathname['test'], 'TextData', '%s.caption.txt' % testCollection)
if collectionStrt == 'single':
test_cap = os.path.join(rootpath, collections_pathname['test'], 'TextData',
'%s%s.caption.txt' % (testCollection, opt.split))
elif collectionStrt == 'multiple':
test_cap = os.path.join(rootpath, collections_pathname['test'], 'TextData', '%s.caption.txt' % testCollection)
else:
raise NotImplementedError('collection structure %s not implemented' % collectionStrt)
caption_files = {'test': test_cap}
img_feat_path = os.path.join(rootpath, collections_pathname['test'], 'FeatureData', options.visual_feature)
visual_feats = {'test': BigFile(img_feat_path)}
assert options.visual_feat_dim == visual_feats['test'].ndims
video2frames = {'test': read_dict(
os.path.join(rootpath, collections_pathname['test'], 'FeatureData', options.visual_feature,
'video2frames.txt'))}
# set bow vocabulary and encoding
bow_vocab_file = os.path.join(rootpath, collections_pathname['train'], 'TextData', 'vocabulary', 'bow',
options.vocab + '.pkl')
bow_vocab = pickle.load(open(bow_vocab_file, 'rb'))
bow2vec = get_text_encoder('bow')(bow_vocab)
options.bow_vocab_size = len(bow_vocab)
# set rnn vocabulary
rnn_vocab_file = os.path.join(rootpath, collections_pathname['train'], 'TextData', 'vocabulary', 'rnn',
options.vocab + '.pkl')
rnn_vocab = pickle.load(open(rnn_vocab_file, 'rb'))
options.vocab_size = len(rnn_vocab)
# Construct the model
model = get_model(options.model)(options)
model.load_state_dict(checkpoint['model'])
model.Eiters = checkpoint['Eiters']
model.val_start()
# set data loader
video_ids_list = data.read_video_ids(caption_files['test'])
vid_data_loader = data.get_vis_data_loader(visual_feats['test'], opt.batch_size, opt.workers, video2frames['test'],
video_ids=video_ids_list)
text_data_loader = data.get_txt_data_loader(options, caption_files['test'], rnn_vocab, bow2vec, options.bert_file, opt.batch_size, opt.workers)
# mapping
if options.space == 'hybrid':
video_embs, video_tag_probs, video_locals_embs, video_local_tag_probs, video_ids = evaluation.encode_hybrid(
model.embed_vis,
vid_data_loader)
cap_embs, cap_tag_probs, cap_locals_embs, cap_local_tag_probs, caption_ids = evaluation.encode_hybrid(
model.embed_txt,
text_data_loader)
else:
video_embs_global, video_embs_local, video_ids = evaluation.encode_vid(model.embed_vis, vid_data_loader)
cap_embs, caption_ids = evaluation.encode_text(model.embed_txt, text_data_loader)
v2t_gt, t2v_gt = metrics.get_gt(video_ids, caption_ids)
logging.info("write into: %s" % output_dir)
if options.space != 'latent':
tag_vocab_path = os.path.join(rootpath, collections_pathname['train'], 'TextData', 'tags', 'video_label_th_1',
'tag_vocab_%d.json' % options.tag_vocab_size)
evaluation.pred_tag(video_tag_probs, video_ids, tag_vocab_path, os.path.join(output_dir, 'video'))
evaluation.pred_tag(cap_tag_probs, caption_ids, tag_vocab_path, os.path.join(output_dir, 'text'))
if options.space in ['hybrid']:
t2v_all_errors_global = evaluation.cal_error(video_embs, cap_embs, options.measure)
t2v_all_errors_local = evaluation.cal_error(video_locals_embs, cap_locals_embs, options.measure)
t2v_all_errors_global_tag = evaluation.cal_error_batch(video_tag_probs, cap_tag_probs, options.measure_2)
t2v_all_errors_local_tag = evaluation.cal_error_batch(video_local_tag_probs, cap_local_tag_probs, options.measure_2)
t2v_all_errors_global = norm_score(t2v_all_errors_global)
t2v_all_errors_local = norm_score(t2v_all_errors_local)
t2v_all_errors_global_tag = norm_score(t2v_all_errors_global_tag)
t2v_all_errors_local_tag = norm_score(t2v_all_errors_local_tag)
t2v_all_errors = 0.6 * (t2v_all_errors_global + t2v_all_errors_local) + 0.4 * (t2v_all_errors_global_tag + t2v_all_errors_local_tag)
cal_perf(t2v_all_errors, v2t_gt, t2v_gt)
torch.save({'errors': t2v_all_errors, 'videos': video_ids, 'captions': caption_ids}, pred_error_matrix_file)
logging.info("write into: %s" % pred_error_matrix_file)
elif options.space in ['latent']:
t2v_all_errors_1 = evaluation.cal_error(video_embs_global, cap_embs[0], options.measure)
t2v_all_errors_1 += evaluation.cal_error(video_embs_local, cap_embs[1], options.measure)
cal_perf(t2v_all_errors_1, v2t_gt, t2v_gt)
torch.save({'errors': t2v_all_errors_1, 'videos': video_ids, 'captions': caption_ids}, pred_error_matrix_file)
logging.info("write into: %s" % pred_error_matrix_file)
if __name__ == '__main__':
main()