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eval_utils.py
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eval_utils.py
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# encoding=utf8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import importlib
importlib.reload(sys)
####
# reload(sys)
# sys.setdefaultencoding('utf8')
import os
import json
import hashlib
import pandas as pd
import time
from vist_eval.album_eval import AlbumEvaluator
import logging
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from torch.utils.data import DataLoader
import misc.utils as utils
class CocoResFormat:
def __init__(self):
self.res = []
self.caption_dict = {}
def read_multiple_files(self, filelist, hash_img_name):
for filename in filelist:
# print('In file %s\n' % filename)
self.read_file(filename, hash_img_name)
def read_file(self, filename, hash_img_name):
count = 0
with open(filename, 'r') as opfd:
for line in opfd:
count += 1
id_sent = line.split('\t')
if len(id_sent) > 2:
id_sent = id_sent[-2:]
assert len(id_sent) == 2
sent = id_sent[1].strip()
if hash_img_name:
img_id = int(int(hashlib.sha256(id_sent[0].encode('utf8')).hexdigest(),
16) % sys.maxsize)
else:
img_id = id_sent[0]
imgid_sent = {}
if img_id in self.caption_dict:
print(img_id)
assert self.caption_dict[img_id] == sent
else:
self.caption_dict[img_id] = sent
imgid_sent['image_id'] = img_id
imgid_sent['caption'] = sent
self.res.append(imgid_sent)
def dump_json(self, outfile):
with open(outfile, 'w') as fd:
json.dump(self.res, fd, ensure_ascii=False, sort_keys=True,
indent=2, separators=(',', ': '))
class Evaluator:
def __init__(self, opt, mode='val'):
if opt.task == 'story_telling' or opt.task == 'story_telling_with_caption':
ref_json_path = "data/reference/{}_reference.json".format(mode)
else:
ref_json_path = "data/reference/{}_desc_reference.json".format(mode)
self.reference = json.load(open(ref_json_path))
print("loading file {}".format(ref_json_path))
self.save_dir = os.path.join(opt.checkpoint_path, opt.id)
self.prediction_file = os.path.join(self.save_dir, 'prediction_{}'.format(mode))
self.eval = AlbumEvaluator()
def measure(self):
json_prediction_file = '{}.json'.format(self.prediction_file)
predictions = {}
with open(self.prediction_file) as f:
for line in f:
vid, seq = line.strip().split('\t')
if vid not in predictions:
predictions[vid] = [seq]
self.eval.evaluate(self.reference, predictions)
with open(json_prediction_file, 'w') as f:
json.dump(predictions, f)
return self.eval.eval_overall
def eval_story(self, model, crit, dataset, loader, opt, side_model=None):
# Make sure in the evaluation mode
logging.info("Evaluating...")
start = time.time()
model.eval()
dataset.val()
loss_sum = 0
loss_evals = 0
predictions = {}
prediction_txt = open(self.prediction_file, 'w') # open the file to store the predictions
count = 0
for iter, batch in enumerate(loader):
iter_start = time.time()
feature_fc = Variable(batch['feature_fc'], volatile=True).cuda()
target = Variable(batch['split_story'], volatile=True).cuda()
conv_feature = Variable(batch['feature_conv'], volatile=True).cuda() if 'feature_conv' in batch else None
count += feature_fc.size(0)
if side_model is not None:
story, _ = side_model.predict(feature_fc.view(-1, feature_fc.shape[2]), 1)
story = Variable(story).cuda()
if conv_feature is not None:
output = model(feature_fc, target, story, conv_feature)
else:
output = model(feature_fc, target, story)
else:
if conv_feature is not None:
output = model(feature_fc, target, conv_feature)
else:
output = model(feature_fc, target)
loss = crit(output, target).data[0]
loss_sum += loss
loss_evals += 1
# forward the model to also get generated samples for each video
if side_model is not None:
if conv_feature is not None:
results, _ = model.decode(feature_fc, story, conv_feature, beam_size=opt.beam_size)
else:
results, _ = model.decode(feature_fc, conv_feature, beam_size=opt.beam_size)
else:
if conv_feature is not None:
results, _ = model.decode(feature_fc, conv_feature, beam_size=opt.beam_size)
else:
results, _ = model.decode(feature_fc, beam_size=opt.beam_size)
stories = utils.decode_story(dataset.get_vocab(), results)
indexes = batch['index'].numpy()
for j, story in enumerate(stories):
vid, _ = dataset.get_id(indexes[j])
if vid not in predictions: # only predict one story for an album
# write into txt file for evaluate metrics like Cider
prediction_txt.write('{}\t {}\n'.format(vid, story))
# save into predictions
predictions[vid] = story
logging.info("Evaluate iter {}/{} {:04.2f}%. Time used: {}".format(iter,
len(loader),
iter * 100.0 / len(loader),
time.time() - iter_start))
prediction_txt.close()
metrics = self.measure() # compute all the language metrics
# Switch back to training mode
model.train()
dataset.train()
logging.info("Evaluation finished. Evaluated {} samples. Time used: {}".format(count, time.time() - start))
return loss_sum / loss_evals, predictions, metrics
def test_story(self, model, dataset, loader, opt):
logging.info("Evaluating...")
start = time.time()
model.eval()
dataset.test()
predictions = {}
prediction_txt = open(self.prediction_file, 'w') # open the file to store the predictions
for iter, batch in enumerate(loader):
iter_start = time.time()
feature_fc = Variable(batch['feature_fc'], volatile=True).cuda()
feature_conv = Variable(batch['feature_conv'], volatile=True).cuda() if 'feature_conv' in batch else None
if feature_conv is not None:
results, _ = model.predict(feature_fc, feature_conv, beam_size=opt.beam_size)
else:
results, _ = model.predict(feature_fc, beam_size=opt.beam_size)
sents = utils.decode_story(dataset.get_vocab(), results)
indexes = batch['index'].numpy()
for j, story in enumerate(sents):
vid, _ = dataset.get_id(indexes[j])
if vid not in predictions: # only predict one story for an album
# write into txt file for evaluate metrics like Cider
prediction_txt.write('{}\t {}\n'.format(vid, story))
# save into predictions
predictions[vid] = story
print("Evaluate iter {}/{} {:04.2f}%. Time used: {}".format(iter,
len(loader),
iter * 100.0 / len(loader),
time.time() - iter_start))
prediction_txt.close()
metrics = self.measure() # compute all the language metrics
json.dump(metrics, open(os.path.join(self.save_dir, 'test_scores.json'), 'w'))
# Switch back to training mode
print("Test finished. Time used: {}".format(time.time() - start))
return predictions, metrics
def test_challange(self, model, dataset, loader, opt, side_model=None):
# Make sure in the evaluation mode
logging.info("Evaluating...")
start = time.time()
model.eval()
dataset.test()
predictions = {"team_name": "", "evaluation_info": {"additional_description": ""}, "output_stories": []}
prediction_txt = open(self.prediction_file, 'w') # open the file to store the predictions
count = 0
finished_flickr_ids = []
for iter, batch in enumerate(loader):
iter_start = time.time()
feature_fc = Variable(batch['feature_fc'], volatile=True).cuda()
conv_feature = Variable(batch['feature_conv'], volatile=True).cuda() if 'feature_conv' in batch else None
count += feature_fc.size(0)
if conv_feature is not None:
results, _ = model.predict(feature_fc, conv_feature, beam_size=opt.beam_size)
else:
results, _ = model.predict(feature_fc, beam_size=opt.beam_size)
stories = utils.decode_story(dataset.get_vocab(), results)
indexes = batch['index'].numpy()
for j, story in enumerate(stories):
album_id, flickr_id = dataset.get_all_id(indexes[j])
concat_flickr_id = "-".join(flickr_id)
if concat_flickr_id not in finished_flickr_ids:
# if vid not in predictions: # only predict one story for an album
# write into txt file for evaluate metrics like Cider
prediction_txt.write('{}\t {}\n'.format(album_id, story))
# save into predictions
predictions['output_stories'].append(
{'album_id': album_id, 'photo_sequence': flickr_id, 'story_text_normalized': story})
finished_flickr_ids.append(concat_flickr_id)
logging.info("Evaluate iter {}/{} {:04.2f}%. Time used: {}".format(iter,
len(loader),
iter * 100.0 / len(loader),
time.time() - iter_start))
prediction_txt.close()
json_prediction_file = os.path.join(self.save_dir, 'challenge.json')
with open(json_prediction_file, 'w') as f:
json.dump(predictions, f)
logging.info("Evaluation finished. Evaluated {} samples. Time used: {}".format(count, time.time() - start))
return predictions