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generate_robust_split.py
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generate_robust_split.py
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# import _init_paths
import copy
import json
import operator
from random import seed, shuffle
import numpy as np
from six.moves import xrange
from pycocotools.coco import COCO
def get_det_word(bbox_ann, captions, wtoi, wtod, dtoi, wtol, ngram=2):
# get the present category.
pcats = [box['label'] for box in bbox_ann]
# get the orginial form of the caption.
indicator = []
stem_caption = []
for s in captions:
tmp = []
for w in s:
if w in wtol:
tmp.append(wtol[w])
else:
tmp.append(w)
stem_caption.append(tmp)
indicator.append([(0, 0, 0)]*len(s)) # category class, binary class, fine-grain class.
ngram_indicator = {i+1:copy.deepcopy(indicator) for i in range(ngram)}
# get the 2 gram of the caption.
for n in range(ngram,0,-1):
for i, s in enumerate(stem_caption):
for j in xrange(len(s)-n+1):
ng = ' '.join(s[j:j+n])
# if the n-gram exist in word_to_detection dictionary.
if ng in wtod and indicator[i][j][0] == 0 and wtod[ng] in pcats: # make sure that larger gram not overwright with lower gram.
bn = (ng != ' '.join(captions[i][j:j+n])) + 1
fg = dtoi[ng]
ngram_indicator[n][i][j] = (wtod[ng], bn, fg)
indicator[i][j:j+n] = [(wtod[ng], bn, fg)] * n
return ngram_indicator
def get_stats(imgs, wtoi, wtod, dtoi, wtol, ctol, coco_det_train, coco_det_val):
train_matrix = np.zeros((len(wtod),len(wtod)))
test_matrix = np.zeros((len(wtod),len(wtod)))
test_num = 0
coco_stats = []
for idx, img in enumerate(imgs):
image_id = info['images'][idx]['id']
file_path = info['images'][idx]['file_path'].split('/')[0]
if file_path == 'train2014':
coco = coco_det_train
else:
coco = coco_det_val
bbox_ann_ids = coco.getAnnIds(imgIds=image_id)
bbox_ann = [{'label': ctol[i['category_id']], 'bbox': i['bbox']} for i in coco.loadAnns(bbox_ann_ids)]
captions = []
for sent in img['sentences']:
captions.append(sent['tokens'])
det_indicator = get_det_word(bbox_ann, captions, wtoi, wtod, dtoi, wtol)
present_clss = []
for i, caption in enumerate(captions):
for j in range(len(caption)):
for n in range(2, 0, -1):
if det_indicator[n][i][j][0] != 0 and det_indicator[n][i][j][0] not in present_clss:
present_clss.append(det_indicator[n][i][j][0])
coco_stats.append({'pclss':present_clss, 'image_id':image_id})
return coco_stats
imgs = json.load(open('data/robust_coco_creation/dataset_coco.json', 'r'))
det_train_path = 'data/robust_coco_creation/annotations/instances_train2014.json'
det_val_path = 'data/robust_coco_creation/annotations/instances_val2014.json'
coco_det_train = COCO(det_train_path)
coco_det_val = COCO(det_val_path)
info = json.load(open('data/robust_coco_creation/dic_coco.json', 'r'))
itow = info['ix_to_word']
wtoi = {w:i for i,w in itow.items()}
wtod = {w:i+1 for w,i in info['wtod'].items()} # word to detection
dtoi = {w:i+1 for i,w in enumerate(wtod.keys())} # detection to index
wtol = info['wtol']
ctol = {c:i+1 for i, c in enumerate(coco_det_train.cats.keys())}
imgs = imgs['images']
coco_stats = get_stats(imgs, wtoi, wtod, dtoi, wtol, ctol, coco_det_train, coco_det_val)
class_total = np.zeros(80)
# get the sum for each category.
for img in coco_stats:
img['pclss'] = [i-1 for i in img['pclss']]
for idx in img['pclss']:
class_total[idx] += 1
json.dump(coco_stats, open('coco_obj_stats.json', 'w'))
pair_list = {}
for img in coco_stats:
for i in range(len(img['pclss'])):
for j in range(len(img['pclss'])):
if i != j:
idx_i = img['pclss'][i]
idx_j = img['pclss'][j]
if idx_i < idx_j:
idx_ij = (idx_i, idx_j)
else:
idx_ij = (idx_j, idx_i)
if idx_ij not in pair_list:
pair_list[idx_ij] = 0
else:
pair_list[idx_ij] += 1
pair_list_sort = sorted(pair_list.items(), key=operator.itemgetter(1))
pair_list = []
for pair in pair_list_sort:
pair_list.append([pair[0][0], pair[0][1], pair[1]])
# for each pair, go throughall the images
testing_total = np.zeros(80)
test_pair = []
count = 0
test_img_num = 0
for pair in pair_list:
tmp_num = 0
testing_total_copy = copy.deepcopy(testing_total)
for img in coco_stats:
if pair[0] in img['pclss'] and pair[1] in img['pclss']:
# also accumulate other class.
for idx in img['pclss']:
testing_total_copy[idx] += 1
tmp_num += 1
# if the testing data exceed half of the total data, don't count this pair.
drop_flag = False
for i in range(80):
if testing_total_copy[i] > (class_total[i] / 2):
drop_flag = True
print("drop pair " + str(pair[0]) + '_' + str(pair[1]))
break
if drop_flag == False:
test_pair.append(pair)
testing_total = copy.deepcopy(testing_total_copy)
test_img_num += tmp_num
count += 1
print(count, test_img_num)
if test_img_num > 15000:
break
print('saving the test pair list....')
json.dump(test_pair, open('test_pair_list.json', 'w'))
test_pair_dic = {}
for pair in test_pair:
test_pair_dic[str(pair[0])+'_'+str(pair[1])] = 0
train_img_id = []
test_img_id = []
for img in coco_stats:
present_clss = img['pclss']
# generate the pair.
tmp = []
for i in range(len(present_clss)):
for j in range(len(present_clss)):
if i != j:
tmp.append(str(present_clss[i]) + '_' + str(present_clss[j]))
test_flag = False
for i in tmp:
if i in test_pair_dic:
test_flag = True
if test_flag == True:
test_img_id.append({'img_id': img['image_id']})
else:
train_img_id.append({'img_id': img['image_id']})
seed(123) # make reproducible
shuffle(test_img_id) # shuffle the order
num_val = int(0.3 * len(test_img_id))
train_id = train_img_id
val_id = test_img_id[:num_val]
test_id = test_img_id[num_val:]
print("train, val, test", len(train_id), len(val_id), len(test_id))
robust_split = {'train_id':train_id, 'val_id':val_id, 'test_id':test_id}
json.dump(robust_split, open('split_robust_coco.json', 'w'))