def process(options, collection):
overwrite = options.overwrite
rootpath = options.rootpath
threshold = options.threshold
encoding = options.encoding
language = options.language
vocab_file = os.path.join(rootpath, collection, 'TextData', 'vocab',
'%s_%d.pkl' % (encoding, threshold))
count_file = os.path.join(os.path.dirname(vocab_file),
'%s_%d.txt' % (encoding, threshold))
if checkToSkip(vocab_file, overwrite):
return 0
cap_file = os.path.join(rootpath, collection, 'TextData',
'%s.caption.txt' % collection)
vocab, word_counts = build_vocab(cap_file,
encoding,
threshold=threshold,
lang=language)
makedirsforfile(vocab_file)
with open(vocab_file, 'wb') as fw:
pickle.dump(vocab, fw, pickle.HIGHEST_PROTOCOL)
logger.info("Saved vocabulary of %d words to %s", len(vocab), vocab_file)
with open(count_file, 'w') as fw:
fw.write('\n'.join(['%s %d' % x for x in word_counts]))
logger.info("Saved word-counts to %s", count_file)
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
topk = options.topk
label_file = os.path.join(rootpath, collection, 'TextData',
'%s.imglabel.txt' % collection)
vocab_file = os.path.join(rootpath, collection, 'Annotations',
'concepts%s%d.txt' % (collection, topk))
if checkToSkip(vocab_file, overwrite):
return 0
tag2count = {}
for line in open(label_file):
elems = line.strip().split()
del elems[0] # because this is imgid
for x in elems:
tag2count[x] = tag2count.get(x, 0) + 1
taglist = sorted(tag2count.iteritems(),
key=lambda v: (v[1], v[0]),
reverse=True)
assert (len(taglist) >= topk)
taglist = taglist[:topk]
makedirsforfile(vocab_file)
fw = open(vocab_file, 'w')
fw.write('\n'.join([x[0] for x in taglist]))
fw.close()
logger.info('A vocabulary of %d labels at %s', len(taglist), vocab_file)
def process(options, collection, src_file, res_file):
rootpath = options.rootpath
overwrite = options.overwrite
imset_file = os.path.join(rootpath, collection, 'ImageSets',
'%s.txt' % collection)
imset = set(map(str.strip, open(imset_file).readlines()))
if checkToSkip(res_file, overwrite):
return 0
cached = set()
obtained = 0
makedirsforfile(res_file)
fw = open(res_file, 'w')
for line in open(src_file):
imgid = line.split()[0]
if imgid in cached:
continue
cached.add(imgid)
if imgid in imset:
fw.write(line)
obtained += 1
fw.close()
logger.info('%d wanted, %d obtained', len(imset), obtained)
def process(options, collection):
rootpath = options.rootpath
lang = options.lang
overwrite = options.overwrite
res_file = os.path.join(rootpath, collection, 'TextData', '%s.imglabel.txt' % collection)
if checkToSkip(res_file, overwrite):
return 0
pos_method = 'stanford' if 'en' == lang else 'boson'
pos_sent_file = os.path.join(rootpath, collection, 'TextData', '%s.%spos.txt' % (collection,pos_method))
dict_img = {}
for line in open(pos_sent_file):
elems = line.strip().split()
imgid = elems[0].split('#')[0]
if imgid not in dict_img:
dict_img[imgid] = {}
for x in elems[1:]:
if len(x.split(':')) != 2:
#logger.error('invalid format %s', x)
continue
word, pos = x.split(':')
if pos[0] in POS_SET[lang] and word not in STOPWORDS:
if 'en' == lang:
if pos[0] == 'N':
word = lemmatizer.lemmatize(word)
elif pos[0] == 'V':
word = lemmatizer.lemmatize(word, pos='v')
else:
continue
else:
if pos[0] != 'n' and len(word)<=3:
#print (len(word), word)
continue
dict_img[imgid][word] = dict_img[imgid].get(word, 0) + 1
fw = open(res_file, 'w')
label_stat = []
zero = 0
for imgid, word2count in dict_img.iteritems():
labels = [word for word,c in word2count.iteritems() if c>= 2]
fw.write(' '.join([imgid] + labels) + '\n')
label_stat.append(len(labels))
if len(labels) == 0:
logger.info('image %s has no label', imgid)
zero += 1
fw.close()
logger.info('number of images with zero label: %d', zero)
logger.info('number of labels per image: min (%d), max (%d), mean (%.1f)', min(label_stat), max(label_stat), sum(label_stat)/float(len(label_stat)))
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
resultfile = os.path.join(rootpath, collection, "id.imagepath.txt")
if checkToSkip(resultfile, overwrite):
return 0
imageFolders = [os.path.join(rootpath, collection, 'ImageData')]
filenames = []
imageSet = set()
for imageFolder in imageFolders:
for r, d, f in os.walk(imageFolder):
for filename in f:
name, ext = os.path.splitext(filename)
if ext not in FILTER_SET:
continue
if name in imageSet:
print("id %s exists, ignore %s" %
(name, os.path.join(r, filename)))
continue
imageSet.add(name)
filenames.append("%s %s" % (name, os.path.join(r, filename)))
try:
os.makedirs(os.path.split(resultfile)[0])
except:
pass
fout = open(resultfile, "w")
fout.write("\n".join(filenames) + "\n")
fout.close()
idfile = os.path.join(rootpath, collection, "ImageSets",
'%s.txt' % collection)
try:
os.makedirs(os.path.split(idfile)[0])
except:
pass
fout = open(idfile, "w")
fout.write("\n".join(sorted(list(imageSet))) + "\n")
fout.close()
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
resultfile = os.path.join(rootpath, collection, "id.imagepath.txt")
if checkToSkip(resultfile, overwrite):
return 0
imageFolders = [os.path.join(rootpath, collection, 'ImageData')]
filenames = []
imageSet = set()
for imageFolder in imageFolders:
for r,d,f in os.walk(imageFolder):
for filename in f:
name,ext = os.path.splitext(filename)
if ext not in FILTER_SET:
continue
if name in imageSet:
print ("id %s exists, ignore %s" % (name, os.path.join(r,filename)))
continue
imageSet.add(name)
filenames.append("%s %s" % (name, os.path.join(r, filename)))
try:
os.makedirs(os.path.split(resultfile)[0])
except:
pass
fout = open(resultfile, "w")
fout.write("\n".join(filenames) + "\n")
fout.close()
idfile = os.path.join(rootpath, collection, "ImageSets", '%s.txt' % collection)
try:
os.makedirs(os.path.split(idfile)[0])
except:
pass
fout = open(idfile, "w")
fout.write("\n".join(sorted(list(imageSet))) + "\n")
fout.close()
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
newsize = options.newsize
imagepathFilename = os.path.join(rootpath, collection, "id.imagepath.txt")
if not os.path.exists(imagepathFilename):
print('%s does not exist' % imagepathFilename)
return 0
try:
os.makedirs(os.path.split(resultFilename)[0])
except:
pass
done = 0
for line in open(imagepathFilename):
if not line.strip():
continue
name, fullpath = str.split(line.strip())
resultfile = fullpath.replace('ImageData',
'ImageData%dx%d' % (newsize, newsize))
if checkToSkip(resultfile, overwrite):
continue
try:
os.makedirs(os.path.split(resultfile)[0])
except:
pass
# The '>' flag is to only apply the resize to images 'greater than' the size given
cmd = "convert %s -resize '%d>x%d>' %s" % (fullpath, newsize, newsize,
resultfile)
os.system(cmd)
done += 1
if done % 100 == 0:
printMessage("info", "image_rescale", "%d done" % done)
# done
printMessage("info", "image_rescale", "%d done" % done)
return done
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
newsize = options.newsize
imagepathFilename = os.path.join(rootpath, collection, "id.imagepath.txt")
if not os.path.exists(imagepathFilename):
print ('%s does not exist' % imagepathFilename)
return 0
try:
os.makedirs(os.path.split(resultFilename)[0])
except:
pass
done = 0
for line in open(imagepathFilename):
if not line.strip():
continue
name,fullpath = str.split(line.strip())
resultfile = fullpath.replace('ImageData', 'ImageData%dx%d'%(newsize,newsize))
if checkToSkip(resultfile, overwrite):
continue
try:
os.makedirs(os.path.split(resultfile)[0])
except:
pass
# The '>' flag is to only apply the resize to images 'greater than' the size given
cmd = "convert %s -resize '%d>x%d>' %s" % (fullpath, newsize, newsize, resultfile)
os.system(cmd)
done += 1
if done % 100 == 0:
printMessage("info", "image_rescale", "%d done" % done)
# done
printMessage("info", "image_rescale", "%d done" % done)
return done
def process(options, collection):
rootpath = options.rootpath
overwrite = options.overwrite
imagepathFilename = os.path.join(rootpath, collection, "id.imagepath.txt")
if not os.path.exists(imagepathFilename):
print ('%s does not exist' % imagepathFilename)
return 0
resultFilename = os.path.join(rootpath, collection, "FeatureData", "color64", "id.feature.txt")
if checkToSkip(resultFilename, overwrite):
return 0
try:
os.makedirs(os.path.split(resultFilename)[0])
except:
pass
done = 0
fout = open(resultFilename, "w")
for line in open(imagepathFilename):
if not line.strip():
continue
name,fullpath = str.split(line.strip())
feature = extractColor64(fullpath)
if feature:
output = name + " " + " ".join([str(x) for x in feature])
fout.write(output + "\n")
done += 1
if done % 100 == 0:
printMessage("info", "docolor64", "%d done" % done)
# done
fout.close()
printMessage("info", "docolor64", "%d done" % done)
return done
def main():
opt = parse_args()
print(json.dumps(vars(opt), indent=2))
rootpath = opt.rootpath
testCollection = opt.testCollection
resume_file = os.path.join(opt.model_path)
if not os.path.exists(resume_file):
logging.info(resume_file + ' not exists.')
sys.exit(0)
# Load checkpoint
logger.info('loading model...')
checkpoint = torch.load(resume_file)
epoch = checkpoint['epoch']
best_perf = checkpoint['best_perf']
config = checkpoint['config']
if hasattr(config, 't2v_w2v'):
w2v_feature_file = os.path.join(rootpath, 'word2vec', 'flickr',
'vec500flickr30m', 'feature.bin')
config.t2v_w2v.w2v.binary_file = w2v_feature_file
# Construct the model
model = get_model('w2vvpp')(config)
print(model.vis_net)
print(model.txt_net)
model.load_state_dict(checkpoint['model'])
print("=> loaded checkpoint '{}' (epoch {}, best_perf {})".format(
resume_file, epoch, best_perf))
vis_feat_file = BigFile(
os.path.join(rootpath, testCollection, 'FeatureData', config.vid_feat))
vis_ids = map(
str.strip,
open(
os.path.join(rootpath, testCollection, 'VideoSets',
testCollection + '.txt')))
vis_loader = data.vis_provider({
'vis_feat': vis_feat_file,
'vis_ids': vis_ids,
'pin_memory': True,
'batch_size': opt.batch_size,
'num_workers': opt.num_workers
})
vis_embs = None
for query_set in opt.query_sets.split(','):
output_dir = os.path.join(rootpath, testCollection, 'SimilarityIndex',
query_set, opt.sim_name)
pred_result_file = os.path.join(output_dir, 'id.sent.score.txt')
if util.checkToSkip(pred_result_file, opt.overwrite):
continue
util.makedirs(output_dir)
if vis_embs is None:
logger.info('Encoding videos')
vis_embs, vis_ids = evaluation.encode_vis(model, vis_loader)
capfile = os.path.join(rootpath, testCollection, 'TextData', query_set)
# load text data
txt_loader = data.txt_provider({
'capfile': capfile,
'pin_memory': True,
'batch_size': opt.batch_size,
'num_workers': opt.num_workers
})
logger.info('Encoding %s captions' % query_set)
txt_embs, txt_ids = evaluation.encode_txt(model, txt_loader)
t2i_matrix = evaluation.compute_sim(txt_embs,
vis_embs,
measure=config.measure)
inds = np.argsort(t2i_matrix, axis=1)
if testCollection == 'msrvtt10ktest':
label_matrix = np.zeros(inds.shape)
for index in range(inds.shape[0]):
ind = inds[index][::-1]
label_matrix[index][np.where(
np.array(vis_ids)[ind] == txt_ids[index].split('#')[0])
[0]] = 1
(r1, r5, r10, medr, meanr, mir,
mAP) = evaluation.eval(label_matrix)
sum_recall = r1 + r5 + r10
tempStr = " * Text to video:\n"
tempStr += " * r_1_5_10: {}\n".format(
[round(r1, 3), round(r5, 3),
round(r10, 3)])
tempStr += " * medr, meanr, mir: {}\n".format(
[round(medr, 3),
round(meanr, 3),
round(mir, 3)])
tempStr += " * mAP: {}\n".format(round(mAP, 3))
tempStr += " * " + '-' * 10
print(tempStr)
open(os.path.join(output_dir, 'perf.txt'), 'w').write(tempStr)
start = time.time()
with open(pred_result_file, 'w') as fout:
for index in range(inds.shape[0]):
ind = inds[index][::-1]
fout.write(txt_ids[index] + ' ' + ' '.join(
[vis_ids[i] + ' %s' % t2i_matrix[index][i]
for i in ind]) + '\n')
print('writing result into file time: %.3f seconds\n' %
(time.time() - start))
def main():
opt = parse_args()
print(json.dumps(vars(opt), indent = 2))
rootpath = opt.rootpath
trainCollection = opt.trainCollection
valCollection = opt.valCollection
val_set = opt.val_set
config = load_config('configs.%s' % opt.config_name)
model_path = os.path.join(rootpath, trainCollection, 'w2vvpp_train', valCollection, val_set, opt.config_name, opt.model_prefix)
if util.checkToSkip(os.path.join(model_path, 'model_best.pth.tar'), opt.overwrite):
sys.exit(0)
util.makedirs(model_path)
global writer
writer = SummaryWriter(log_dir=model_path, flush_secs=5)
collections = {'train': trainCollection, 'val': valCollection}
capfiles = {'train': '%s.caption.txt', 'val': os.path.join(val_set, '%s.caption.txt')}
cap_file_paths = {x: os.path.join(rootpath, collections[x], 'TextData', capfiles[x]%collections[x]) for x in collections}
vis_feat_files = {x: BigFile(os.path.join(rootpath, collections[x], 'FeatureData', config.vid_feat)) for x in collections}
config.vis_fc_layers = map(int, config.vis_fc_layers.split('-'))
config.vis_fc_layers[0] = vis_feat_files['train'].ndims
bow_encoding, w2v_encoding, rnn_encoding = config.text_encoding.split('@')
rnn_encoding, config.pooling = rnn_encoding.split('_', 1)
bow_vocab_file = os.path.join(rootpath, trainCollection, 'TextData', 'vocab', '%s_%d.pkl'%(bow_encoding, config.threshold))
config.t2v_bow = get_txt2vec(bow_encoding)(bow_vocab_file, norm=config.bow_norm)
w2v_data_path = os.path.join(rootpath, 'word2vec', 'flickr', 'vec500flickr30m')
config.t2v_w2v = get_txt2vec(w2v_encoding)(w2v_data_path)
rnn_vocab_file = os.path.join(rootpath, trainCollection, 'TextData', 'vocab', '%s_%d.pkl'%(rnn_encoding, config.threshold))
config.t2v_idx = get_txt2vec('idxvec')(rnn_vocab_file)
if config.we_dim == 500:
config.we = get_we(config.t2v_idx.vocab, w2v_data_path)
config.txt_fc_layers = map(int, config.txt_fc_layers.split('-'))
if config.pooling == 'mean_last':
config.txt_fc_layers[0] = config.rnn_size*2 + config.t2v_w2v.ndims + config.t2v_bow.ndims
else:
config.txt_fc_layers[0] = config.rnn_size + config.t2v_w2v.ndims + config.t2v_bow.ndims
# Construct the model
model = get_model('w2vvpp')(config)
print(model.vis_net)
print(model.txt_net)
data_loaders = {x: data.pair_provider({'vis_feat':vis_feat_files[x], 'capfile':cap_file_paths[x], 'pin_memory': True,
'batch_size':opt.batch_size, 'num_workers':opt.workers,'shuffle':(x=='train')})
for x in collections}
# Train the Model
best_perf = 0
no_impr_counter = 0
val_perf_hist_fout = open(os.path.join(model_path, 'val_perf_hist.txt'), 'w')
for epoch in range(opt.num_epochs):
print('Epoch[{0} / {1}] LR: {2}'.format(epoch, opt.num_epochs, model.learning_rate))
print('-'*10)
writer.add_scalar('train/learning_rate', model.learning_rate[0], epoch)
# train for one epoch
train(model, data_loaders['train'], epoch)
# evaluate on validation set
cur_perf = validate(model, data_loaders['val'], epoch, measure=config.measure, metric=opt.metric)
model.lr_step(val_value=cur_perf)
print(' * Current perf: {}\n * Best perf: {}\n'.format(cur_perf, best_perf))
val_perf_hist_fout.write('epoch_%d:\nText2Video(%s): %f\n' % (epoch, opt.metric, cur_perf))
val_perf_hist_fout.flush()
# remember best performance and save checkpoint
is_best = cur_perf > best_perf
best_perf = max(cur_perf, best_perf)
save_checkpoint({'epoch': epoch+1, 'model': model.state_dict(), 'best_perf': best_perf,
'config': config, 'opt': opt}, is_best, logdir=model_path, only_best=True,
filename='checkpoint_epoch_%s.pth.tar'%epoch)
if is_best:
no_impr_counter = 0
else:
no_impr_counter += 1
if no_impr_counter > 10:
print('Early stopping happended.\n')
break
val_perf_hist_fout.close()
message = 'best performance on validation:\n Text to video({}): {}'.format(opt.metric, best_perf)
print(message)
with open(os.path.join(model_path, 'val_perf.txt'), 'w') as fout:
fout.write(message)
def process(options, trainCollection, feature, testCollection):
rootpath = options.rootpath
tpp = options.tpp
distance = options.distance
k = options.k
r = options.r
donefile = options.donefile
overwrite = options.overwrite
numjobs = options.numjobs
job = options.job
blocksize = options.blocksize
if options.testset is None:
testset = testCollection
test_tag_file = os.path.join(rootpath, testCollection, "TextData",
"id.userid.%stags.txt" % tpp)
try:
testStore = RecordStore(test_tag_file)
resultName = "tagrel"
except:
testStore = None
printStatus(
INFO,
"Failed to load %s, will do image auto-tagging" % test_tag_file)
resultName = "autotagging"
nnName = distance + "knn"
resultfile = os.path.join(rootpath, testCollection, resultName, testset,
trainCollection,
"%s,%s,%d,%s" % (feature, nnName, k, tpp),
"id.tagvotes.txt")
if numjobs > 1:
resultfile += ".%d.%d" % (numjobs, job)
if checkToSkip(resultfile, overwrite):
return 0
if donefile:
doneset = set([x.split()[0] for x in open(donefile).readlines()[:-1]])
else:
doneset = set()
printStatus(
INFO, "%d images have been done already, and they will be ignored" %
len(doneset))
test_imset = readImageSet(testCollection, testset, rootpath)
test_imset = [x for x in test_imset if x not in doneset]
test_imset = [
test_imset[i] for i in range(len(test_imset))
if (i % numjobs + 1) == job
]
test_feat_dir = os.path.join(rootpath, testCollection, 'FeatureData',
feature)
test_feat_file = BigFile(test_feat_dir)
learner = TagrelLearner(trainCollection,
feature,
distance,
tpp=tpp,
rootpath=rootpath)
learner.set_nr_neighbors(k)
learner.set_nr_autotags(r)
printStatus(
INFO, "working on %d-%d, %d test images -> %s" %
(numjobs, job, len(test_imset), resultfile))
done = 0
makedirsforfile(resultfile)
fw = open(resultfile, "w")
read_time = 0
test_time = 0
start = 0
while start < len(test_imset):
end = min(len(test_imset), start + blocksize)
printStatus(INFO, 'processing images from %d to %d' % (start, end - 1))
s_time = time.time()
renamed, vectors = test_feat_file.read(test_imset[start:end])
read_time += time.time() - s_time
nr_images = len(renamed)
#assert(len(test_imset[start:end]) == nr_images) # some images may have no visual features available
s_time = time.time()
output = [None] * nr_images
for i in range(nr_images):
if testStore:
(qry_userid, qry_tags) = testStore.lookup(renamed[i])
else:
qry_userid = None
qry_tags = None
tagvotes = learner.estimate(vectors[i], qry_tags, qry_userid)
output[i] = '%s %s\n' % (renamed[i], " ".join([
"%s %s" % (tag, niceNumber(vote, 8))
for (tag, vote) in tagvotes
]))
test_time += time.time() - s_time
start = end
fw.write(''.join(output))
fw.flush()
done += len(output)
# done
printStatus(
INFO, "%d done. read time %g seconds, test_time %g seconds" %
(done, read_time, test_time))
fw.close()
return 1
def process(options, trainCollection, feature, testCollection):
rootpath = options.rootpath
tpp = options.tpp
distance = options.distance
k = options.k
r = options.r
donefile = options.donefile
overwrite = options.overwrite
numjobs = options.numjobs
job = options.job
blocksize = options.blocksize
if options.testset is None:
testset = testCollection
test_tag_file = os.path.join(rootpath, testCollection, "TextData", "id.userid.%stags.txt"%tpp)
try:
testStore = RecordStore(test_tag_file)
resultName = "tagrel"
except:
testStore = None
printStatus(INFO, "Failed to load %s, will do image auto-tagging" % test_tag_file)
resultName = "autotagging"
nnName = distance + "knn"
resultfile = os.path.join(rootpath, testCollection,resultName,testset,trainCollection,"%s,%s,%d,%s" % (feature,nnName,k,tpp), "id.tagvotes.txt")
if numjobs>1:
resultfile += ".%d.%d" % (numjobs,job)
if checkToSkip(resultfile, overwrite):
return 0
if donefile:
doneset = set([x.split()[0] for x in open(donefile).readlines()[:-1]])
else:
doneset = set()
printStatus(INFO, "%d images have been done already, and they will be ignored" % len(doneset))
test_imset = readImageSet(testCollection, testset, rootpath)
test_imset = [x for x in test_imset if x not in doneset]
test_imset = [test_imset[i] for i in range(len(test_imset)) if (i%numjobs+1) == job]
test_feat_dir = os.path.join(rootpath, testCollection, 'FeatureData', feature)
test_feat_file = BigFile(test_feat_dir)
learner = TagrelLearner(trainCollection, feature, distance, tpp=tpp, rootpath=rootpath)
learner.set_nr_neighbors(k)
learner.set_nr_autotags(r)
printStatus(INFO, "working on %d-%d, %d test images -> %s" % (numjobs,job,len(test_imset),resultfile))
done = 0
makedirsforfile(resultfile)
fw = open(resultfile, "w")
read_time = 0
test_time = 0
start = 0
while start < len(test_imset):
end = min(len(test_imset), start + blocksize)
printStatus(INFO, 'processing images from %d to %d' % (start, end-1))
s_time = time.time()
renamed, vectors = test_feat_file.read(test_imset[start:end])
read_time += time.time() - s_time
nr_images = len(renamed)
#assert(len(test_imset[start:end]) == nr_images) # some images may have no visual features available
s_time = time.time()
output = [None] * nr_images
for i in range(nr_images):
if testStore:
(qry_userid, qry_tags) = testStore.lookup(renamed[i])
else:
qry_userid = None
qry_tags = None
tagvotes = learner.estimate(vectors[i], qry_tags, qry_userid)
output[i] = '%s %s\n' % (renamed[i], " ".join(["%s %s" % (tag, niceNumber(vote,8)) for (tag,vote) in tagvotes]))
test_time += time.time() - s_time
start = end
fw.write(''.join(output))
fw.flush()
done += len(output)
# done
printStatus(INFO, "%d done. read time %g seconds, test_time %g seconds" % (done, read_time, test_time))
fw.close()
return 1
