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data_util.py
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data_util.py
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import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import os
import nltk
import numpy as np
import csv
def cap2imgid(opt, data_split):
dpath = os.path.join(opt.data_path, opt.data_name)
imgcapId = []
with open(dpath + '/' + '%s_ids.txt' % data_split, 'rb') as f:
for line in f:
imgcapId.append(line.strip())
return imgcapId
def cap_preprocessing(cap, vocab):
# Convert caption (string) to word ids.
tokens = nltk.tokenize.word_tokenize(
str(cap).lower().decode('utf-8'))
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
target = torch.Tensor(caption)
return target
def get_ref_capstrings(opt):
dpath = os.path.join(opt.data_path, opt.data_name)
loc = dpath + '/'
captions = []
with open(loc+'%s_caps.txt' % opt.split_name, 'rb') as f:
for line in f:
captions.append(line.strip())
return captions
def flickr8k_imgname2idx(data_path, data_name, split_name):
ref_imgids = []
ref_imgnames = []
with(open(os.path.join(data_path, data_name, split_name)+'_ids.txt')) as f1:
for line in f1:
imgid = line.replace('\n', '')
ref_imgids.append(imgid)
with(open(os.path.join(data_path, data_name, split_name)+'_names.txt')) as f2:
for line in f2:
imgname = line.replace('\n', '')
ref_imgnames.append(imgname)
return ref_imgids, ref_imgnames
def read_composite(vocab, opt, source):
imgids = []
caps = {'h':{}, 'm1':{}, 'm2':{}}
for sys in caps.keys():
caps[sys]['tensor'] = []
caps[sys]['string'] = []
caps[sys]['eval'] = []
if source == '8k':
ref_imgids, ref_imgnames = flickr8k_imgname2idx(opt.data_path, opt.data_name, opt.split_name)
loc = opt.candidate_path+'/composite/'+source+'_correctness.csv'
with open(loc) as csv_file:
csv_reader = csv.reader(csv_file, delimiter = ';')
line_count = 0
for row in csv_reader:
if line_count == 0 or row[0] == '':
line_count += 1
continue
else:
line_count += 1
if source == 'coco':
item = row[0].split('_')
elif source == '30k' or source == '8k':
item = row[0].split('/')
imgid = item[len(item)-1].replace('.jpg', '').lstrip('0')
imgids.append(imgid)
for k in range(1,4):
target = cap_preprocessing(row[k], vocab)
if k == 1:
sys = 'h'
elif k == 2:
sys = 'm1'
elif k == 3:
sys = 'm2'
caps[sys]['tensor'].append(target)
caps[sys]['string'].append(row[k])
caps[sys]['eval'].append(row[k+3])
for sys,val in caps.items():
sys_imgids = imgids
# Sort a data list by caption length
data = zip(sys_imgids, val['tensor'], val['string'], val['eval'])
data.sort(key=lambda x: len(x[1]), reverse=True)
sys_imgids, sys_caps_tensor, sys_caps_string, sys_caps_evals = zip(*data)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
caps_lengths = [len(cap) for cap in sys_caps_tensor]
targets = torch.zeros(len(sys_caps_tensor), max(caps_lengths)).long()
for i, cap in enumerate(sys_caps_tensor):
end = caps_lengths[i]
targets[i, :end] = cap[:end]
caps[sys]['imgid'] = sys_imgids
caps[sys]['tensor'] = targets
caps[sys]['string'] = sys_caps_string
caps[sys]['length'] = caps_lengths
caps[sys]['eval'] = sys_caps_evals
return caps
def read_usecase(vocab, opt):
imgids = []
caps_tensor = []
caps_string = []
with open(os.path.join(opt.candidate_path, 'usecase', 'output_cap.txt')) as f:
for line in f:
item = line.strip().split('\t')
imgid = item[0]
cstr = item[1]
target = cap_preprocessing(cstr, vocab)
imgids.append(imgid)
caps_tensor.append(target)
caps_string.append(cstr)
# Sort a data list by caption length
data = zip(imgids, caps_tensor, caps_string)
data.sort(key=lambda x: len(x[1]), reverse=True)
imgids, caps_tensor, caps_string = zip(*data)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
caps_lengths = [len(cap) for cap in caps_tensor]
targets = torch.zeros(len(caps_tensor), max(caps_lengths)).long()
for i, cap in enumerate(caps_tensor):
end = caps_lengths[i]
targets[i, :end] = cap[:end]
return imgids, caps_string, targets, caps_lengths
def read_flickr8k(vocab, opt):
imgids = []
caps_id = []
caps_tensor = []
caps_string = []
caps_evals = []
caps_idxs = []
all_caps = {}
with open(os.path.join(opt.candidate_path, 'flickr8k', 'Flickr8k.token.txt'), 'rb') as f:
for line in f:
item = line.split('\t')
all_caps[item[0]] = item[1].replace('\n', '')
with open(os.path.join(opt.candidate_path, 'flickr8k', 'ExpertAnnotations.txt')) as f:
for line in f:
item = line.split('\t')
imgid = item[0]
cid = item[1]
##remove candidates that are actually belonged to the target image
if(cid.split('#')[0] == imgid):
continue
cstr = all_caps[cid]
ceva = []
ceva.append(item[2])
ceva.append(item[3])
ceva.append(item[4].replace('\n', ''))
target = cap_preprocessing(cstr, vocab)
#imgids.append(sudoid)
imgids.append(imgid)
caps_id.append(cid)
caps_tensor.append(target)
caps_string.append(cstr)
caps_evals.append(ceva)
caps_idxs.append(caps_string.index(cstr))
# Sort a data list by caption length
data = zip(imgids, caps_tensor, caps_string, caps_evals, caps_id, caps_idxs)
data.sort(key=lambda x: len(x[1]), reverse=True)
imgids, caps_tensor, caps_string, caps_evals, caps_id, caps_idxs = zip(*data)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
caps_lengths = [len(cap) for cap in caps_tensor]
targets = torch.zeros(len(caps_tensor), max(caps_lengths)).long()
for i, cap in enumerate(caps_tensor):
end = caps_lengths[i]
targets[i, :end] = cap[:end]
return imgids, caps_string, targets, caps_lengths, caps_evals, caps_id, caps_idxs
def read_pascal(vocab, opt):
imgids = []
caps_tensor = []
caps_string = []
caps_type = []
caps_idxs = []
caps_pairevals = []
caps_pairtype = []
caps_pairid = []
with open(os.path.join(opt.candidate_path, 'pascal', 'pascal_test.txt')) as f:
for line in f:
item = line.replace('\n', '').split('\t')
for k in range(2,4):
if k == 2:
cstr = item[k]
ctype = 'B'
if k == 3:
cstr = item[k]
ctype = 'C'
imgids.append(item[1])
target = cap_preprocessing(cstr, vocab)
caps_tensor.append(target)
caps_string.append(cstr)
caps_type.append(ctype)
caps_idxs.append(caps_string.index(cstr))
caps_pairid.append(int(item[0]))
caps_pairevals.append(item[4])
caps_pairtype.append(item[5])
# Sort a data list by caption length
data = zip(imgids, caps_tensor, caps_string, caps_type, caps_idxs, caps_pairevals, caps_pairtype, caps_pairid)
data.sort(key=lambda x: len(x[1]), reverse=True)
imgids, caps_tensor, caps_string, caps_type, caps_idxs, caps_pairevals, caps_pairtype, caps_pairid = zip(*data)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
caps_lengths = [len(cap) for cap in caps_tensor]
targets = torch.zeros(len(caps_tensor), max(caps_lengths)).long()
for i, cap in enumerate(caps_tensor):
end = caps_lengths[i]
targets[i, :end] = cap[:end]
return imgids, caps_string, targets, caps_lengths, caps_type, caps_idxs, caps_pairevals, caps_pairtype, caps_pairid