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evaluate.py
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evaluate.py
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from socket import NI_MAXHOST
import sys
sys.path.append('caption-eval')
# sys.path.insert(0, './caption-eval')
import torch
import pickle
import models
from utils.utils import Vocabulary
from utils.data import get_eval_loader
from cocoeval import COCOScorer, suppress_stdout_stderr
import h5py
from utils.opt import parse_opt
from tqdm import tqdm
import os
import torchtext
import json
from sklearn.cluster import MiniBatchKMeans
import models
from models.encoder import Encoder
from models.decoder import Decoder
from models.capmodel import CapModel
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def convert_data_to_coco_scorer_format(reference):
reference_json = {}
non_ascii_count = 0
with open(reference, 'r') as f:
lines = f.readlines()
for line in lines:
vid = line.split('\t')[0]
sent = line.split('\t')[1].strip()
try:
sent.encode('ascii', 'ignore').decode('ascii')
except UnicodeDecodeError:
non_ascii_count += 1
continue
if vid in reference_json:
reference_json[vid].append({u'video_id': vid, u'cap_id': len(reference_json[vid]),
u'caption': sent.encode('ascii', 'ignore').decode('ascii')})
else:
reference_json[vid] = []
reference_json[vid].append({u'video_id': vid, u'cap_id': len(reference_json[vid]),
u'caption': sent.encode('ascii', 'ignore').decode('ascii')})
if non_ascii_count:
print("=" * 20 + "\n" + "non-ascii: " + str(non_ascii_count) + "\n" + "=" * 20)
return reference_json
def convert_prediction(prediction):
prediction_json = {}
with open(prediction, 'r') as f:
lines = f.readlines()
for line in lines:
vid = line.split('\t')[0]
sent = line.split('\t')[1].strip()
prediction_json[vid] = [{u'video_id': vid, u'caption': sent}]
return prediction_json
def evaluate(opt, net, eval_range, prediction_txt_path, reference):
eval_loader = get_eval_loader(eval_range, opt.feature_h5_path, opt.test_batch_size)
result = {}
for i, (frames, video_ids) in tqdm(enumerate(eval_loader)):
frames = frames.to(DEVICE)
outputs, _ = net(frames, None)
for (tokens, vid) in zip(outputs, video_ids):
if opt.use_multi_gpu:
s = net.module.decoder.decode_tokens(tokens.data)
else:
s = net.decoder.decode_tokens(tokens.data)
result[vid] = s
with open(prediction_txt_path, 'w') as f:
for vid, s in result.items():
f.write('%d\t%s\n' % (vid, s))
prediction_json = convert_prediction(prediction_txt_path)
############### visualization ###############
# vis = []
# for k,v in prediction_json.items():
# vis.append(v[-1])
# json.dump(vis, open(os.path.join(opt.result_dir,'visualize.json'),'w'))
#############################################
# compute scores
scorer = COCOScorer()
with suppress_stdout_stderr():
scores, sub_category_score = scorer.score(reference, prediction_json, prediction_json.keys())
for metric, score in scores.items():
print('%s: %.6f' % (metric, score * 100))
if sub_category_score is not None:
print('Sub Category Score in Spice:')
for category, score in sub_category_score.items():
print('%s: %.6f' % (category, score * 100))
return scores
if __name__ == '__main__':
opt = parse_opt()
filed = torchtext.legacy.data.Field(sequential=True, tokenize="spacy",
eos_token="<eos>",
include_lengths=True,
batch_first=True,
tokenizer_language='en_core_web_sm',
fix_length=opt.max_words,
lower=True,
)
if opt.min_freq == 4:
filed.vocab = pickle.load(open(opt.vocab_pkl_path, 'rb'))
elif opt.min_freq == 2:
filed.vocab = pickle.load(open(opt.vocab_pkl_path, 'rb'))
vocab_size = len(filed.vocab)
# build model
encoder = Encoder(opt)
decoder = Decoder(opt, filed)
net = CapModel(encoder, decoder)
if opt.use_multi_gpu:
net = torch.nn.DataParallel(net)
if not opt.eval_metric:
net.load_state_dict(torch.load(opt.model_pth_path))
elif opt.eval_metric == 'METEOR':
net.load_state_dict(torch.load(opt.best_meteor_pth_path))
elif opt.eval_metric == 'CIDEr':
net.load_state_dict(torch.load(opt.best_cider_pth_path))
else:
raise ValueError('Please choose the metric from METEOR|CIDEr')
net.to(DEVICE)
net.eval()
# reference = convert_data_to_coco_scorer_format(opt.train_reference_txt_path)
# metrics = evaluate(opt, net, opt.train_range, opt.train_prediction_txt_path, reference)
reference = convert_data_to_coco_scorer_format(opt.test_reference_txt_path)
metrics = evaluate(opt, net, opt.test_range, opt.test_prediction_txt_path, reference)
with open(opt.test_score_txt_path, 'a') as f:
f.write('\nBEST ' + str(opt.eval_metric) + '(beam size = {}):\n'.format(opt.beam_size))
for k, v in metrics.items():
f.write('\t%s: %.2f\n' % (k, 100 * v))