def load_combined_anns(cls, data_dir, data_split):
data_dir = to_path(data_dir)
ann_file = data_dir.joinpath(f'{cls.name}/{data_split}_ann.pt')
if ann_file.exists():
anns_t = torch.load(ann_file)
else:
sys_logger.info(f'Creating combined annotation files {ann_file.name}')
q_vocab, a_vocab = cls.load_combined_vocab(data_dir)
if data_split in ('valB_30k', 'valB_120k'):
q_anns = json.load(data_dir.joinpath('questions', f'CLEVR_valB_questions.json').open())['questions']
if data_split == 'valB_30k':
q_anns = q_anns[:30000]
else:
q_anns = q_anns[30000:]
else:
q_anns = json.load(data_dir.joinpath('questions', f'CLEVR_{data_split}_questions.json').open())['questions']
anns = defaultdict(list)
for q_item in tqdm(q_anns, desc=f'Creating {ann_file.name}'):
anns['q_ids'].append(q_item['question_index'])
anns['img_ids'].append(q_item['image_index'])
anns['img_names'].append(q_item['image_filename'])
anns['q_tokens'].append(cls.q_tokenize(q_item['question']))
anns['q_types'].append(q_item['question_family_index'])
anns['a_tokens'].append(q_item['answer'])
anns['q_lens'].append(len(anns['q_tokens'][-1]))
q_label = [q_vocab[token] for token in anns['q_tokens'][-1]]
q_label_t = torch.empty(47).fill_(q_vocab.padding_idx).long()
q_label_t[:len(q_label)] = torch.tensor(q_label).long()
anns['q_labels'].append(q_label_t)
anns['a_labels'].append(a_vocab[q_item['answer']])
anns_t = {name: to_tensor(value) for name, value in anns.items()}
torch.save(anns_t, ann_file)
return anns_t
def load_combined_anns(cls,
data_dir: str = 'work_dir/data/gqa',
data_split: str = 'train',
q_max=30):
data_dir = to_path(data_dir).joinpath(cls.name)
ann_file = data_dir.joinpath(f'{data_split}_ann.pt')
if ann_file.exists():
return torch.load(ann_file)
logger.info(f'Creating combined annotation files {ann_file.name}')
q_vocab, a_vocab = cls.load_combined_vocab(data_dir.parent)
if data_split in ('train', 'val'):
q_file = data_dir.parent.joinpath(
f'questions/{data_split}_balanced_questions.json')
obj_infos = json.load(
data_dir.parent.joinpath(
'objects/gqa_objects_info.json').open())
# scene_file = data_dir.joinpath(f'sceneGraphs/{data_split}_sceneGraphs.json')
q_anns = json.load(q_file.open())
else:
raise NotImplementedError()
anns = collections.defaultdict(list)
pbar = tqdm.tqdm(
range(len(q_anns)),
desc=f'Creating combined annotation files {ann_file.name}')
for q_id, q_ann in q_anns.items():
anns['splits'].append(data_split)
anns['q_ids'].append(q_id)
anns['img_ids'].append(q_ann['imageId'])
anns['q_tokens'].append(cls.q_tokenize(q_ann['question']))
q_label = [
q_vocab[token] for token in anns['q_tokens'][-1][:q_max]
]
anns['q_lens'].append(len(q_label))
q_label_t = torch.empty(q_max).fill_(q_vocab.padding_idx).long()
q_label_t[:len(q_label)] = torch.tensor(q_label).long()
anns['q_labels'].append(q_label_t)
obj_info = obj_infos[q_ann['imageId']]
anns['img_shapes'].append(
torch.tensor((obj_info['width'], obj_info['height'])).float())
anns['img_obj_nums'].append(torch.tensor(obj_info['objectsNum']))
if 'answer' in q_ann:
anns['a_tokens'].append(q_ann['answer'])
anns['a_labels'].append(torch.tensor(a_vocab[q_ann['answer']]))
pbar.update(1)
anns_t = {key: to_tensor(value) for key, value in anns.items()}
torch.save(anns_t, ann_file, pickle_module=dill)
return anns_t
def process_sample(self, sample, non_blocking=False):
sample = to_cuda(sample, self.master_device, non_blocking)
sample = {key: to_tensor(value) for key, value in sample.items()}
return sample
def load_combined_anns(
cls,
data_dir: str = 'data/vqa2',
data_split: str = 'train',
):
data_dir = to_path(data_dir)
ann_file = data_dir.joinpath(f'{cls.name}/{data_split}_ann.pt')
if ann_file.exists():
anns_t = torch.load(ann_file)
if 'splits' not in anns_t:
anns_t['splits'] = [data_split] * len(anns_t['q_ids'])
torch.save(anns_t, ann_file)
else:
logger.info(f'Creating combined annotation files {ann_file.name}')
q_vocab, a_vocab = cls.load_combined_vocab(data_dir)
img_ann_dict = {}
for split in ('train', 'val'):
ins_anns = json.load(
data_dir.joinpath(
f'annotations/instances_{split}2014.json').open())
img_anns = {ann['id']: ann for ann in ins_anns['images']}
img_ann_dict[split] = img_anns
if data_split in ('train', 'test'):
origin_anns = json.load(
data_dir.joinpath(
f'annotations/vqacp_v2_{data_split}_annotations.json').
open())
q_anns = json.load(
data_dir.joinpath(
f'annotations/vqacp_v2_{data_split}_questions.json').
open())
else:
raise NotImplementedError()
anns = collections.defaultdict(list)
for idx, q in enumerate(tqdm.tqdm(q_anns)):
anns['splits'].append(q['coco_split'][:-4])
anns['q_ids'].append(q['question_id'])
anns['img_ids'].append(q['image_id'])
anns['q_tokens'].append(cls.q_tokenize(q['question']))
anns['q_lens'].append(len(anns['q_tokens'][-1]))
q_label = [q_vocab[token] for token in anns['q_tokens'][-1]]
q_label_t = torch.empty(24).fill_(q_vocab.padding_idx).long()
q_label_t[:len(q_label)] = torch.tensor(q_label).long()
anns['q_labels'].append(q_label_t)
img_anns = img_ann_dict[anns['splits'][-1]]
img_ann = img_anns[q['image_id']]
anns['img_names'].append(img_ann['file_name'])
anns['img_shapes'].append(
(img_ann['width'], img_ann['height']))
if origin_anns is None:
continue
assert q['question_id'] == origin_anns[idx]['question_id']
ori_ann = origin_anns[idx]
anns['a_tokens'].append(
cls.a_tokenize(ori_ann['multiple_choice_answer']))
answers = []
for ans in ori_ann['answers']:
answ = cls.a_tokenize(ans['answer'])
if answ in a_vocab.words:
answers.append(answ)
anns['a_counts'].append(
collections.Counter(answers).most_common())
accepted_answers = sum(
[ans[1] for ans in anns['a_counts'][-1]])
anns['a_scores'].append([(ans[0], ans[1] / accepted_answers)
for ans in anns['a_counts'][-1]])
anns_t = {key: to_tensor(value) for key, value in anns.items()}
torch.save(anns_t, ann_file)
return anns_t
def load_combined_anns(
cls,
data_dir: str = 'data/vqa2',
data_split: str = 'train',
):
data_dir = to_path(data_dir)
ann_file = data_dir.joinpath(f'{cls.name}/{data_split}_ann.pt')
if ann_file.exists():
anns_t = torch.load(ann_file)
if 'splits' not in anns_t:
anns_t['splits'] = [data_split] * len(anns_t['q_ids'])
torch.save(anns_t, ann_file)
else:
logger.info(f'Creating combined annotation files {ann_file.name}')
q_vocab, a_vocab = cls.load_combined_vocab(data_dir)
if data_split in ('train', 'val'):
origin_anns = json.load(
data_dir.joinpath(
f'annotations/v2_mscoco_{data_split}2014_annotations.json'
).open())['annotations']
q_anns = json.load(
data_dir.joinpath(
f'annotations/v2_OpenEnded_mscoco_{data_split}2014_questions.json'
).open())['questions']
ins_anns = json.load(
data_dir.joinpath(
f'annotations/instances_{data_split}2014.json').open())
img_anns = {ann['id']: ann for ann in ins_anns['images']}
elif data_split == 'test':
origin_anns = None
q_anns = json.load(
data_dir.joinpath(
f'annotations/v2_OpenEnded_mscoco_{data_split}2015_questions.json'
).open())['questions']
ins_anns = json.load(
data_dir.joinpath(
f'annotations/image_info_{data_split}2015.json').open(
))
img_anns = {ann['id']: ann for ann in ins_anns['images']}
elif data_split == 'train_val':
train_anns, val_anns = [
cls.load_combined_anns(data_dir, split)
for split in ('train', 'val')
]
anns_t = {}
for key in train_anns.keys():
if torch.is_tensor(train_anns[key]):
anns_t[key] = torch.cat(
(train_anns[key], val_anns[key]), dim=0)
elif isinstance(train_anns[key], (list, tuple)):
anns_t[key] = list(train_anns[key]) + list(
val_anns[key])
else:
raise NotImplementedError()
return anns_t
else:
raise NotImplementedError()
anns = collections.defaultdict(list)
for idx, q in enumerate(tqdm.tqdm(q_anns)):
anns['splits'].append(data_split)
anns['q_ids'].append(q['question_id'])
anns['img_ids'].append(q['image_id'])
anns['q_tokens'].append(cls.q_tokenize(q['question']))
anns['q_lens'].append(len(anns['q_tokens'][-1]))
q_label = [q_vocab[token] for token in anns['q_tokens'][-1]]
q_label_t = torch.empty(24).fill_(q_vocab.padding_idx).long()
q_label_t[:len(q_label)] = torch.tensor(q_label).long()
anns['q_labels'].append(q_label_t)
img_ann = img_anns[q['image_id']]
anns['img_names'].append(img_ann['file_name'])
anns['img_shapes'].append(
(img_ann['width'], img_ann['height']))
if origin_anns is None:
continue
assert q['question_id'] == origin_anns[idx]['question_id']
ori_ann = origin_anns[idx]
anns['a_tokens'].append(
cls.a_tokenize(ori_ann['multiple_choice_answer']))
answers = []
for ans in ori_ann['answers']:
answ = cls.a_tokenize(ans['answer'])
if answ in a_vocab.words:
answers.append(answ)
anns['a_counts'].append(
collections.Counter(answers).most_common())
# a_label_count = torch.empty(len(a_vocab)).fill_(a_vocab.padding_idx).float()
# for a_token, a_count in anns['a_counts'][-1]:
# a_label_count[a_vocab[a_token]] = a_count
# anns['a_label_counts'].append(a_label_count)
accepted_answers = sum(
[ans[1] for ans in anns['a_counts'][-1]])
anns['a_scores'].append([(ans[0], ans[1] / accepted_answers)
for ans in anns['a_counts'][-1]])
# a_label_score = torch.empty(len(a_vocab)).fill_(a_vocab.padding_idx).float()
# for a_token, a_score in anns['a_scores'][-1]:
# a_label_score[a_vocab[a_token]] = a_score
# anns['a_label_scores'].append(a_label_score)
anns_t = {key: to_tensor(value) for key, value in anns.items()}
torch.save(anns_t, ann_file)
return anns_t
def load_combined_anns(
cls,
data_dir: str = 'data/vqa2',
split: str = 'train',
):
data_dir = utils.to_path(data_dir)
ann_file = data_dir.joinpath(f'{cls.name}/{split}_ann.pt')
if ann_file.exists():
anns_t = torch.load(ann_file)
else:
logger.info(f'Creating combined annotation files {ann_file.name}')
q_vocab, a_vocab = cls.load_combined_vocab(data_dir)
if split in ('train', 'val'):
json_anns = json.load(
data_dir.joinpath(cls.name,
f'vqa_{split}_final_3000.json').open())
ins_anns = json.load(
data_dir.joinpath(
f'annotations/instances_{split}2014.json').open())
img_anns = {ann['id']: ann for ann in ins_anns['images']}
elif split == 'test':
json_anns = json.load(
data_dir.joinpath(cls.name, f'vqa_test_toked.json').open())
q_anns = json.load(
data_dir.joinpath(
f'annotations/v2_OpenEnded_mscoco_{split}2015_questions.json'
).open())['questions']
ins_anns = json.load(
data_dir.joinpath(
f'annotations/image_info_{split}2015.json').open())
img_anns = {ann['id']: ann for ann in ins_anns['images']}
elif split == 'train_val':
train_anns, val_anns = [
cls.load_combined_anns(data_dir, split)
for split in ('train', 'val')
]
anns_t = {}
for key in train_anns.keys():
if torch.is_tensor(train_anns[key]):
anns_t[key] = torch.cat(
(train_anns[key], val_anns[key]), dim=0)
elif isinstance(train_anns[key], (list, tuple)):
anns_t[key] = list(train_anns[key]) + list(
val_anns[key])
else:
raise NotImplementedError()
return anns_t
else:
raise NotImplementedError()
anns = collections.defaultdict(list)
for idx, json_ann in enumerate(tqdm(json_anns)):
anns['splits'].append(split)
anns['q_ids'].append(json_ann['question_id'])
anns['img_ids'].append(int(json_ann['image_id']))
anns['q_tokens'].append(json_ann['question_toked'])
anns['q_lens'].append(len(anns['q_tokens'][-1]))
q_label = [q_vocab[token] for token in anns['q_tokens'][-1]]
q_label_t = torch.empty(30).fill_(q_vocab.padding_idx).long()
q_label_t[:len(q_label)] = torch.tensor(q_label).long()
anns['q_labels'].append(q_label_t)
img_ann = img_anns[int(json_ann['image_id'])]
anns['img_names'].append(img_ann['file_name'])
anns['img_shapes'].append(
(img_ann['width'], img_ann['height']))
if split == 'test':
continue
anns['a_counts'].append([
a_count for a_count in json_ann['answers']
if a_count[0] in a_vocab.word2idx
])
anns['a_tokens'].append(json_ann['answer'])
anns['a_scores'].append([
a_score for a_score in json_ann['answers_w_scores']
if a_score[0] in a_vocab.word2idx
])
anns_t = {
key: utils.to_tensor(value)
for key, value in anns.items()
}
torch.save(anns_t, ann_file)
return anns_t
