def __init__(self,
yaml_file,
tokenizer=None,
add_od_labels=True,
max_img_seq_length=50,
max_seq_length=70,
max_seq_a_length=40,
is_train=True,
mask_prob=0.15,
max_masked_tokens=3,
add_conf=False,
**kwargs):
"""Constructor.
Args:
yaml file with all required data (image feature, caption, labels, etc)
tokenizer: tokenizer for text processing.
add_od_labels: whether to add labels from yaml file to BERT.
max_img_seq_length: max image sequence length.
max_seq_length: max text sequence length.
max_seq_a_length: max caption sequence length.
is_train: train or test mode.
mask_prob: probability to mask a input token.
max_masked_tokens: maximum number of tokens to be masked in one sentence.
kwargs: other arguments.
"""
self.yaml_file = yaml_file
self.cfg = load_from_yaml_file(yaml_file)
self.root = op.dirname(yaml_file)
self.label_file = find_file_path_in_yaml(self.cfg['label'], self.root)
self.feat_file = find_file_path_in_yaml(self.cfg['feature'], self.root)
self.caption_file = find_file_path_in_yaml(self.cfg.get('caption'),
self.root)
assert op.isfile(self.feat_file)
if add_od_labels: assert op.isfile(self.label_file)
if is_train:
assert op.isfile(self.caption_file) and tokenizer is not None
self.label_tsv = None if not self.label_file else TSVFile(
self.label_file)
self.feat_tsv = TSVFile(self.feat_file)
if self.caption_file and op.isfile(self.caption_file):
with open(self.caption_file, 'r') as f:
self.captions = json.load(f)
self.tokenizer = tokenizer
self.tensorizer = CaptionTensorizer(self.tokenizer,
max_img_seq_length,
max_seq_length,
max_seq_a_length,
mask_prob,
max_masked_tokens,
is_train=is_train)
self.add_od_labels = add_od_labels
self.is_train = is_train
self.kwargs = kwargs
self.image_keys = self.prepare_image_keys()
self.key2index = self.prepare_image_key_to_index()
self.key2captions = self.prepare_image_key_to_captions()
self.add_conf = add_conf
def check_img_label_file(self):
if self.img_label_file is None:
self.img_label_file = {}
self.img_qa_file = {}
for dataset_name in self.datasets_names:
img_label_file_path = os.path.join(
self.image_label_path[dataset_name], 'predictions_gt.tsv')
img_qa_file_path = os.path.join(
self.image_label_path[dataset_name], 'QA_fileB.tsv')
t_s = time.time()
self.img_label_file[dataset_name] = TSVFile(
img_label_file_path)
if os.path.exists(img_qa_file_path):
self.img_qa_file[dataset_name] = TSVFile(img_qa_file_path)
t_e = time.time()
logging.info("Open image label file {}, time: {}".format(
img_label_file_path, (t_e - t_s)))
def check_img_feature_file(self):
if self.img_feature_file is None:
# self.img_feature_file = [] # original
self.img_feature_file = {}
self.img_feat_offset_map = {}
for dataset_name in self.datasets_names:
logging.info("* Loading dataset {}".format(dataset_name))
if dataset_name in self.datasets_with_splits:
self.img_feature_file[dataset_name] = {}
self.img_feat_offset_map[dataset_name] = {}
chunk_list = []
if self.chunk_list is not None:
chunk_list = self.chunk_list
chunk_file_list = []
for chunk_fp_id in chunk_list:
chunk_file_list.append(
os.path.join(
self.image_feature_path[dataset_name],
chunk_fp_id, self.image_file_name))
if dataset_name == 'googlecc':
for i, (chunk_fp_id, chunk_fp) in enumerate(
zip(chunk_list, chunk_file_list)):
assert os.path.exists(
chunk_file_list[i]
), "Chunk file {} does not exists!".format(
chunk_fp)
else:
chunk_file_list = glob.glob(
self.image_feature_path[dataset_name] +
"/*/{}".format(self.image_file_name))
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
chunk_list.append(chunk_fp_id)
logging.info("* Load Image Chunks {}".format(
len(chunk_list)))
t_s_total = time.time()
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
t_s = time.time()
self.img_feature_file[dataset_name][
chunk_fp_id] = TSVFile(chunk_fp)
chunk_offsetmap = os.path.join(
os.path.dirname(chunk_fp), 'imageid2idx.json')
assert os.path.isfile(
chunk_offsetmap
), "Imageid2idx file {} does not exists!".format(
chunk_offsetmap)
self.img_feat_offset_map[dataset_name][
chunk_fp_id] = json.load(open(
chunk_offsetmap, 'r'))
t_e = time.time()
logging.info("Open image chunk {}, time: {}".format(
chunk_fp_id, (t_e - t_s)))
t_e_total = time.time()
logging.info("Open total {} image chunks, time: {}".format(
len(chunk_list), (t_e_total - t_s_total)))
logging.info("Image chunk info: {}".format(
'\n'.join(chunk_file_list)))
elif dataset_name in self.datasets_with_onesplit:
t_s = time.time()
chunk_fp = os.path.join(
self.image_feature_path[dataset_name],
self.image_file_name)
self.img_feature_file[dataset_name] = TSVFile(chunk_fp)
chunk_offsetmap = os.path.join(os.path.dirname(chunk_fp),
'imageid2idx.json')
assert os.path.isfile(
chunk_offsetmap
), "Imageid2idx file {} does not exists!".format(
chunk_offsetmap)
self.img_feat_offset_map[dataset_name] = json.load(
open(chunk_offsetmap, 'r'))
t_e = time.time()
logging.info("Open dataset {}, time: {}".format(
chunk_fp, (t_e - t_s)))
else:
raise ValueError(
"Not supported dataset: {}".format(dataset_name))
def __init__(self,
yaml_file,
args=None,
tokenizer=None,
seq_len=35,
encoding="utf-8",
corpus_lines=None,
on_memory=True,
**kwargs):
self.cfg = load_from_yaml_file(yaml_file)
self.root = os.path.dirname(yaml_file)
self.vocab = tokenizer.vocab
self.tokenizer = tokenizer
self.seq_len = seq_len
self.on_memory = on_memory
self.corpus_lines = corpus_lines # number of non-empty lines in input corpus
self.corpus_tsvfile = TSVFile(
os.path.join(self.root, self.cfg['corpus_file']))
if 'textb_sample_mode' in kwargs:
self.textb_sample_mode = kwargs['textb_sample_mode']
else:
self.textb_sample_mode = args.textb_sample_mode
self.datasets_names = self.cfg['corpus'].split('_')
self.datasets_with_splits = [
'googlecc', 'sbu', 'oi', 'objects365', 'tagoi'
]
self.datasets_with_onesplit = ['coco', 'flickr30k', 'gqa']
logging.info('Datasets: {}'.format(','.join(self.datasets_names)))
self.image_label_path = self.cfg['image_label_path']
for key, val in self.image_label_path.items():
# get the absolute path
if key in self.datasets_names:
self.image_label_path[key] = os.path.join(self.root, val)
self.image_feature_path = self.cfg['image_feature_path']
self.image_file_name = 'features.tsv'
if args.data_dir is not None:
for key, val in self.image_feature_path.items():
# get the absolute path
if key in self.datasets_names:
self.image_feature_path[key] = os.path.join(
args.data_dir, val)
else:
logging.info("Data {} with path {} is not used in the "
"training.".format(key, val))
self.encoding = encoding
self.current_doc = 0 # to avoid random sentence from same doc
self.current_img = '' # to avoid random sentence from same image
self.args = args
# for loading samples directly from file
self.sample_counter = 0 # used to keep track of full epochs on file
self.line_buffer = None # keep second sentence of a pair in memory and use as first sentence in next pair
# for loading samples in memory
self.current_random_doc = 0
self.num_docs = 0
self.sample_to_doc = [] # map sample index to doc and line
self.chunk_list = None
if 0 <= args.chunk_start_id <= args.chunk_end_id and args.chunk_end_id >= 0:
self.chunk_list = [
str(c_i)
for c_i in range(args.chunk_start_id, args.chunk_end_id)
]
logging.info('Chunk list: {}'.format(','.join(self.chunk_list)))
# load image tags and features
t_start = time.time()
self.img_label_file = None
self.img_qa_file = None
self.img_label_offset_map = None
self.img_qa_offset_map = None
self.img_feature_file = None
self.img_feat_offset_map = None
self.load_img_labels()
self.load_img_tsv_features()
t_end = time.time()
logging.info(
'Info: loading img features using {} secs'.format(t_end - t_start))
# load samples into memory
if on_memory:
self.all_docs = []
self.all_qa_docs = []
self.imgid2labels = {}
self.corpus_lines = 0
max_tokens = 0
for line_no in tqdm(range(len(self.corpus_tsvfile))):
doc = []
row = self.corpus_tsvfile.seek(line_no)
img_info = row[0].split('_')
label_info = row[1].split('_')
assert img_info[0] == label_info[
0], "Dataset names for image and label do not match!"
dataset_name = label_info[0]
if dataset_name == 'cc':
dataset_name = 'googlecc'
if dataset_name not in self.datasets_names:
continue
if dataset_name in self.datasets_with_splits:
chunk_id = img_info[-2]
if self.chunk_list is not None and chunk_id not in self.chunk_list:
continue
else:
img_feat_offset_map = self.img_feat_offset_map[
dataset_name][chunk_id]
else:
img_feat_offset_map = self.img_feat_offset_map[
dataset_name]
assert img_info[
-1] in img_feat_offset_map, "{}: Image id {} cannot be found in image feature imageid_to_index file!".format(
row[0], img_info[-1])
# append id info
doc.append('%s|%s' % (row[0], row[1]))
# append text_a info
self.corpus_lines = self.corpus_lines + 1
sample = {"doc_id": len(self.all_docs), "line": len(doc)}
self.sample_to_doc.append(sample)
assert len(row[2]) != 0, "Text_a is empty in {} : {}"\
.format(dataset_name, row[0])
doc.append(row[2])
# append text_b info
self.corpus_lines = self.corpus_lines + 1
label_id = label_info[-1]
if 'qa' in label_info:
assert img_info[-1] == label_info[
-2], "Image ids for image and qa do not match!"
label_line_no = self.img_qa_offset_map[dataset_name][
label_id]
rowb = self.img_qa_file[dataset_name].seek(label_line_no)
else:
assert img_info[-1] == label_info[
-1], "Image ids for image and label do not match!"
label_line_no = self.img_label_offset_map[dataset_name][
label_id]
rowb = self.img_label_file[dataset_name].seek(
label_line_no)
assert label_id == rowb[0]
results = json.loads(rowb[1])
if 'qa' not in label_info: # more intuitively, should be if 'qa' not in label_info:
objects = results['objects']
if row[0] not in self.imgid2labels:
self.imgid2labels[row[0]] = {
"image_h": results["image_h"],
"image_w": results["image_w"],
"boxes": None
}
else:
assert results["image_h"] == self.imgid2labels[row[0]][
"image_h"], "Image_h does not match in image {}!".format(
row[0])
assert results["image_w"] == self.imgid2labels[row[0]][
"image_w"], "Image_w does not match in image {}!".format(
row[0])
if args.use_gtlabels and 'gt_objects' in results:
# use ground-truth tags for text_b
textb = ' '.join([
cur_d['class'] for cur_d in results["gt_objects"]
])
else:
textb = ' '.join([cur_d['class'] for cur_d in objects])
else:
tag_label_line_no = self.img_label_offset_map[
dataset_name][img_info[-1]]
tag_rowb = self.img_label_file[dataset_name].seek(
tag_label_line_no)
tag_results = json.loads(tag_rowb[1])
if row[0] not in self.imgid2labels:
self.imgid2labels[row[0]] = {
"image_h": tag_results["image_h"],
"image_w": tag_results["image_w"],
"boxes": None
}
else:
assert tag_results["image_h"] == self.imgid2labels[row[0]][
"image_h"], "Image_h does not match in image {}!".format(
row[0])
assert tag_results["image_w"] == self.imgid2labels[row[0]][
"image_w"], "Image_w does not match in image {}!".format(
row[0])
textb = ' '.join(results['labels'])
assert len(textb) != 0, "Text_b is empty in {} : {}".format(
dataset_name, row[1])
doc.append(textb)
# add to all_docs
max_tokens = max(
max_tokens,
len(doc[1].split(' ')) + len(doc[2].split(' ')))
if 'qa' in label_info:
self.all_qa_docs.append({
"doc": doc,
"doc_id": len(self.all_docs)
})
self.all_docs.append(doc)
self.num_docs = len(self.all_docs)
logging.info("Max_tokens: {}".format(max_tokens))
# load samples later lazily from disk
else:
raise ValueError("on_memory = False Not supported yet!")
logging.info("Total docs - Corpus_lines: {}-{}".format(
self.num_docs, self.corpus_lines))
logging.info("Total QA docs - Corpus_lines: {}".format(
len(self.all_qa_docs)))
class OscarTSVDataset(Dataset):
def __init__(self,
yaml_file,
args=None,
tokenizer=None,
seq_len=35,
encoding="utf-8",
corpus_lines=None,
on_memory=True,
**kwargs):
self.cfg = load_from_yaml_file(yaml_file)
self.root = os.path.dirname(yaml_file)
self.vocab = tokenizer.vocab
self.tokenizer = tokenizer
self.seq_len = seq_len
self.on_memory = on_memory
self.corpus_lines = corpus_lines # number of non-empty lines in input corpus
self.corpus_tsvfile = TSVFile(
os.path.join(self.root, self.cfg['corpus_file']))
if 'textb_sample_mode' in kwargs:
self.textb_sample_mode = kwargs['textb_sample_mode']
else:
self.textb_sample_mode = args.textb_sample_mode
self.datasets_names = self.cfg['corpus'].split('_')
self.datasets_with_splits = [
'googlecc', 'sbu', 'oi', 'objects365', 'tagoi'
]
self.datasets_with_onesplit = ['coco', 'flickr30k', 'gqa']
logging.info('Datasets: {}'.format(','.join(self.datasets_names)))
self.image_label_path = self.cfg['image_label_path']
for key, val in self.image_label_path.items():
# get the absolute path
if key in self.datasets_names:
self.image_label_path[key] = os.path.join(self.root, val)
self.image_feature_path = self.cfg['image_feature_path']
self.image_file_name = 'features.tsv'
if args.data_dir is not None:
for key, val in self.image_feature_path.items():
# get the absolute path
if key in self.datasets_names:
self.image_feature_path[key] = os.path.join(
args.data_dir, val)
else:
logging.info("Data {} with path {} is not used in the "
"training.".format(key, val))
self.encoding = encoding
self.current_doc = 0 # to avoid random sentence from same doc
self.current_img = '' # to avoid random sentence from same image
self.args = args
# for loading samples directly from file
self.sample_counter = 0 # used to keep track of full epochs on file
self.line_buffer = None # keep second sentence of a pair in memory and use as first sentence in next pair
# for loading samples in memory
self.current_random_doc = 0
self.num_docs = 0
self.sample_to_doc = [] # map sample index to doc and line
self.chunk_list = None
if 0 <= args.chunk_start_id <= args.chunk_end_id and args.chunk_end_id >= 0:
self.chunk_list = [
str(c_i)
for c_i in range(args.chunk_start_id, args.chunk_end_id)
]
logging.info('Chunk list: {}'.format(','.join(self.chunk_list)))
# load image tags and features
t_start = time.time()
self.img_label_file = None
self.img_qa_file = None
self.img_label_offset_map = None
self.img_qa_offset_map = None
self.img_feature_file = None
self.img_feat_offset_map = None
self.load_img_labels()
self.load_img_tsv_features()
t_end = time.time()
logging.info(
'Info: loading img features using {} secs'.format(t_end - t_start))
# load samples into memory
if on_memory:
self.all_docs = []
self.all_qa_docs = []
self.imgid2labels = {}
self.corpus_lines = 0
max_tokens = 0
for line_no in tqdm(range(len(self.corpus_tsvfile))):
doc = []
row = self.corpus_tsvfile.seek(line_no)
img_info = row[0].split('_')
label_info = row[1].split('_')
assert img_info[0] == label_info[
0], "Dataset names for image and label do not match!"
dataset_name = label_info[0]
if dataset_name == 'cc':
dataset_name = 'googlecc'
if dataset_name not in self.datasets_names:
continue
if dataset_name in self.datasets_with_splits:
chunk_id = img_info[-2]
if self.chunk_list is not None and chunk_id not in self.chunk_list:
continue
else:
img_feat_offset_map = self.img_feat_offset_map[
dataset_name][chunk_id]
else:
img_feat_offset_map = self.img_feat_offset_map[
dataset_name]
assert img_info[
-1] in img_feat_offset_map, "{}: Image id {} cannot be found in image feature imageid_to_index file!".format(
row[0], img_info[-1])
# append id info
doc.append('%s|%s' % (row[0], row[1]))
# append text_a info
self.corpus_lines = self.corpus_lines + 1
sample = {"doc_id": len(self.all_docs), "line": len(doc)}
self.sample_to_doc.append(sample)
assert len(row[2]) != 0, "Text_a is empty in {} : {}"\
.format(dataset_name, row[0])
doc.append(row[2])
# append text_b info
self.corpus_lines = self.corpus_lines + 1
label_id = label_info[-1]
if 'qa' in label_info:
assert img_info[-1] == label_info[
-2], "Image ids for image and qa do not match!"
label_line_no = self.img_qa_offset_map[dataset_name][
label_id]
rowb = self.img_qa_file[dataset_name].seek(label_line_no)
else:
assert img_info[-1] == label_info[
-1], "Image ids for image and label do not match!"
label_line_no = self.img_label_offset_map[dataset_name][
label_id]
rowb = self.img_label_file[dataset_name].seek(
label_line_no)
assert label_id == rowb[0]
results = json.loads(rowb[1])
if 'qa' not in label_info: # more intuitively, should be if 'qa' not in label_info:
objects = results['objects']
if row[0] not in self.imgid2labels:
self.imgid2labels[row[0]] = {
"image_h": results["image_h"],
"image_w": results["image_w"],
"boxes": None
}
else:
assert results["image_h"] == self.imgid2labels[row[0]][
"image_h"], "Image_h does not match in image {}!".format(
row[0])
assert results["image_w"] == self.imgid2labels[row[0]][
"image_w"], "Image_w does not match in image {}!".format(
row[0])
if args.use_gtlabels and 'gt_objects' in results:
# use ground-truth tags for text_b
textb = ' '.join([
cur_d['class'] for cur_d in results["gt_objects"]
])
else:
textb = ' '.join([cur_d['class'] for cur_d in objects])
else:
tag_label_line_no = self.img_label_offset_map[
dataset_name][img_info[-1]]
tag_rowb = self.img_label_file[dataset_name].seek(
tag_label_line_no)
tag_results = json.loads(tag_rowb[1])
if row[0] not in self.imgid2labels:
self.imgid2labels[row[0]] = {
"image_h": tag_results["image_h"],
"image_w": tag_results["image_w"],
"boxes": None
}
else:
assert tag_results["image_h"] == self.imgid2labels[row[0]][
"image_h"], "Image_h does not match in image {}!".format(
row[0])
assert tag_results["image_w"] == self.imgid2labels[row[0]][
"image_w"], "Image_w does not match in image {}!".format(
row[0])
textb = ' '.join(results['labels'])
assert len(textb) != 0, "Text_b is empty in {} : {}".format(
dataset_name, row[1])
doc.append(textb)
# add to all_docs
max_tokens = max(
max_tokens,
len(doc[1].split(' ')) + len(doc[2].split(' ')))
if 'qa' in label_info:
self.all_qa_docs.append({
"doc": doc,
"doc_id": len(self.all_docs)
})
self.all_docs.append(doc)
self.num_docs = len(self.all_docs)
logging.info("Max_tokens: {}".format(max_tokens))
# load samples later lazily from disk
else:
raise ValueError("on_memory = False Not supported yet!")
logging.info("Total docs - Corpus_lines: {}-{}".format(
self.num_docs, self.corpus_lines))
logging.info("Total QA docs - Corpus_lines: {}".format(
len(self.all_qa_docs)))
def __len__(self):
# last line of doc won't be used, because there's no "nextSentence".
return self.corpus_lines - self.num_docs
def get_img_info(self, idx):
sample = self.sample_to_doc[idx]
# img_id = self.all_docs[sample["doc_id"]][0].strip() # original
img_id = self.all_docs[sample["doc_id"]][0].strip().split('|')[0]
imgid2labels = self.imgid2labels[img_id]
return {
"height": imgid2labels["image_h"],
"width": imgid2labels["image_w"]
}
def __getitem__(self, item):
cur_id = self.sample_counter
self.sample_counter += 1
if not self.on_memory:
# after one epoch we start again from beginning of file
if cur_id != 0 and (cur_id % len(self) == 0):
raise ValueError("on_memory = False Not supported yet!")
img_id, t1, t2, is_next_label, is_img_match = self.random_sent(item)
# tokenize
tokens_a = self.tokenizer.tokenize(t1)
if self.args.use_b:
tokens_b = self.tokenizer.tokenize(t2)
else:
tokens_b = None
# combine to one sample
cur_example = InputExample(guid=cur_id,
tokens_a=tokens_a,
tokens_b=tokens_b,
is_next=is_next_label,
img_id=img_id,
is_img_match=is_img_match)
# get image feature
img_feat = self.get_img_feature(img_id)
if img_feat.shape[0] >= self.args.max_img_seq_length:
img_feat = img_feat[0:self.args.max_img_seq_length, ]
img_feat_len = img_feat.shape[0]
else:
img_feat_len = img_feat.shape[0]
padding_matrix = torch.zeros(
(self.args.max_img_seq_length - img_feat.shape[0],
img_feat.shape[1]))
img_feat = torch.cat((img_feat, padding_matrix), 0)
# transform sample to features
cur_features = convert_example_to_features(self.args, cur_example,
self.seq_len,
self.tokenizer,
img_feat_len)
return img_feat, (
torch.tensor(cur_features.input_ids, dtype=torch.long),
torch.tensor(cur_features.input_mask, dtype=torch.long),
torch.tensor(cur_features.segment_ids, dtype=torch.long),
torch.tensor(cur_features.lm_label_ids, dtype=torch.long),
torch.tensor(cur_features.is_next),
torch.tensor(cur_features.is_img_match),
), item
# return cur_tensors
def random_sent(self, index):
"""
Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences
from one doc. With 50% the second sentence will be a random one from another doc.
:param index: int, index of sample.
:return: (str, str, int), sentence 1, sentence 2, isNextSentence Label
"""
img_id, t1, t2 = self.get_corpus_line(index)
rand_dice = random.random()
if rand_dice > 0.5:
label = 0
random_img_id = img_id
elif rand_dice > self.args.texta_false_prob and t2 != "":
# wrong qa triplets
random_img_id, t2 = self.get_random_line()
label = 1
else:
# wrong retrieval triplets
random_img_id, t1 = self.get_random_texta()
# args.num_contrast_classes = 3 if args.texta_false_prob<0.5 and (args.texta_false_prob>0 or not args.use_b) else 2
label = self.args.num_contrast_classes - 1
img_match_label = 0
if img_id != random_img_id: img_match_label = 1
assert len(t1) > 0
assert len(t2) > 0 or not self.args.use_b
return img_id, t1, t2, label, img_match_label
def get_corpus_line(self, item):
"""
Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.
:param item: int, index of sample.
:return: (str, str), two subsequent sentences from corpus
"""
assert item < self.corpus_lines
if self.on_memory:
sample = self.sample_to_doc[item]
# img_id = self.all_docs[sample["doc_id"]][0].strip() # original
img_id = self.all_docs[sample["doc_id"]][0].strip().split('|')[0]
t1 = self.all_docs[sample["doc_id"]][sample["line"]]
t2 = self.all_docs[sample["doc_id"]][sample["line"] + 1]
# used later to avoid random nextSentence from same doc
self.current_doc = sample["doc_id"]
self.current_img = img_id
assert t1 != ""
if self.args.use_b or 'qa' in self.all_docs[
sample["doc_id"]][0].split('_'):
assert t2 != ""
else:
t2 = ""
return img_id, t1, t2
else:
raise ValueError("on_memory = False Not supported yet!")
def get_random_line(self):
"""
Get random line from another document for nextSentence task.
:return: str, content of one line
"""
# Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the document we're processing.
if self.on_memory:
if self.textb_sample_mode in [0, 1]:
# sample from all docs
for _ in range(10):
rand_doc_idx = random.randrange(0, len(self.all_docs))
img_id = self.all_docs[rand_doc_idx][0].split('|')[0]
# check if our picked random line is really from another image like we want it to be
if img_id != self.current_img:
break
rand_doc = self.all_docs[rand_doc_idx]
else:
# sample from all qa docs
for _ in range(10):
rand_doc_idx = random.randrange(0, len(self.all_qa_docs))
# check if our picked random line is really from another doc like we want it to be % no need to be different image here
if self.all_qa_docs[rand_doc_idx][
"doc_id"] != self.current_doc:
break
rand_doc = self.all_qa_docs[rand_doc_idx]["doc"]
# img_id = rand_doc[0] # original
img_id = rand_doc[0].split('|')[0]
if self.textb_sample_mode == 0:
# default oscar sample mode
line = rand_doc[random.randrange(1, len(rand_doc))]
else:
# only sample text_b
line = rand_doc[2]
return img_id, line
else:
raise ValueError("on_memory = False Not supported yet!")
def get_random_texta(self):
"""
Get random text_a from another document for nextSentence task.
:return: str, content of one line
"""
# Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the document we're processing.
if self.on_memory:
for _ in range(10):
rand_doc_idx = random.randrange(0, len(self.all_docs))
img_id = self.all_docs[rand_doc_idx][0].split('|')[0]
# check if our picked random line is really from another image like we want it to be
if img_id != self.current_img:
break
rand_doc = self.all_docs[rand_doc_idx]
# img_id = rand_doc[0] # original
img_id = rand_doc[0].split('|')[0]
line = rand_doc[1] # we want the text_a
return img_id, line
else:
raise ValueError("on_memory = False Not supported yet!")
# tsv image labels
def load_img_labels(self):
self.check_img_label_file()
self.check_img_label_offset_map()
def check_img_label_file(self):
if self.img_label_file is None:
self.img_label_file = {}
self.img_qa_file = {}
for dataset_name in self.datasets_names:
img_label_file_path = os.path.join(
self.image_label_path[dataset_name], 'predictions_gt.tsv')
img_qa_file_path = os.path.join(
self.image_label_path[dataset_name], 'QA_fileB.tsv')
t_s = time.time()
self.img_label_file[dataset_name] = TSVFile(
img_label_file_path)
if os.path.exists(img_qa_file_path):
self.img_qa_file[dataset_name] = TSVFile(img_qa_file_path)
t_e = time.time()
logging.info("Open image label file {}, time: {}".format(
img_label_file_path, (t_e - t_s)))
def check_img_label_offset_map(self):
if self.img_label_offset_map is None:
self.img_label_offset_map = {}
self.img_qa_offset_map = {}
for dataset_name in self.datasets_names:
img_label_offset_map_path = os.path.join(
self.image_label_path[dataset_name], 'imageid2idx.json')
img_qa_offset_map_path = os.path.join(
self.image_label_path[dataset_name], 'QA_qaid2idx.json')
t_s = time.time()
self.img_label_offset_map[dataset_name] = json.load(
open(img_label_offset_map_path))
if os.path.exists(img_qa_offset_map_path):
self.img_qa_offset_map[dataset_name] = json.load(
open(img_qa_offset_map_path))
t_e = time.time()
logging.info("Load img label offset map: {}, time: {}".format(
img_label_offset_map_path, (t_e - t_s)))
def get_img_labels(self, image_id):
""" decode the image labels: read the image label from the img_label.tsv """
self.check_img_label_file()
self.check_img_label_offset_map()
if image_id in self.img_label_offset_map:
img_offset = self.img_label_offset_map[image_id]
self.img_label_file.seek(img_offset, 0)
arr = [
s.strip() for s in self.img_label_file.readline().split('\t')
]
eles = json.loads(arr[1])
labels = eles['labels']
return labels
return None
# tsv feature loading
def load_img_tsv_features(self):
self.check_img_feature_file()
self.check_img_feature_offset_map()
def check_img_feature_file(self):
if self.img_feature_file is None:
# self.img_feature_file = [] # original
self.img_feature_file = {}
self.img_feat_offset_map = {}
for dataset_name in self.datasets_names:
logging.info("* Loading dataset {}".format(dataset_name))
if dataset_name in self.datasets_with_splits:
self.img_feature_file[dataset_name] = {}
self.img_feat_offset_map[dataset_name] = {}
chunk_list = []
if self.chunk_list is not None:
chunk_list = self.chunk_list
chunk_file_list = []
for chunk_fp_id in chunk_list:
chunk_file_list.append(
os.path.join(
self.image_feature_path[dataset_name],
chunk_fp_id, self.image_file_name))
if dataset_name == 'googlecc':
for i, (chunk_fp_id, chunk_fp) in enumerate(
zip(chunk_list, chunk_file_list)):
assert os.path.exists(
chunk_file_list[i]
), "Chunk file {} does not exists!".format(
chunk_fp)
else:
chunk_file_list = glob.glob(
self.image_feature_path[dataset_name] +
"/*/{}".format(self.image_file_name))
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
chunk_list.append(chunk_fp_id)
logging.info("* Load Image Chunks {}".format(
len(chunk_list)))
t_s_total = time.time()
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
t_s = time.time()
self.img_feature_file[dataset_name][
chunk_fp_id] = TSVFile(chunk_fp)
chunk_offsetmap = os.path.join(
os.path.dirname(chunk_fp), 'imageid2idx.json')
assert os.path.isfile(
chunk_offsetmap
), "Imageid2idx file {} does not exists!".format(
chunk_offsetmap)
self.img_feat_offset_map[dataset_name][
chunk_fp_id] = json.load(open(
chunk_offsetmap, 'r'))
t_e = time.time()
logging.info("Open image chunk {}, time: {}".format(
chunk_fp_id, (t_e - t_s)))
t_e_total = time.time()
logging.info("Open total {} image chunks, time: {}".format(
len(chunk_list), (t_e_total - t_s_total)))
logging.info("Image chunk info: {}".format(
'\n'.join(chunk_file_list)))
elif dataset_name in self.datasets_with_onesplit:
t_s = time.time()
chunk_fp = os.path.join(
self.image_feature_path[dataset_name],
self.image_file_name)
self.img_feature_file[dataset_name] = TSVFile(chunk_fp)
chunk_offsetmap = os.path.join(os.path.dirname(chunk_fp),
'imageid2idx.json')
assert os.path.isfile(
chunk_offsetmap
), "Imageid2idx file {} does not exists!".format(
chunk_offsetmap)
self.img_feat_offset_map[dataset_name] = json.load(
open(chunk_offsetmap, 'r'))
t_e = time.time()
logging.info("Open dataset {}, time: {}".format(
chunk_fp, (t_e - t_s)))
else:
raise ValueError(
"Not supported dataset: {}".format(dataset_name))
def check_img_feature_offset_map(self):
""" load the image feature offset map """
if self.img_feat_offset_map is None:
self.img_feat_offset_map = {}
for dataset_name in self.datasets_names:
logging.info(
"* Loading imageid2idx_map {}".format(dataset_name))
if dataset_name in self.datasets_with_splits:
chunk_list = []
chunk_file_list = glob.glob(
self.image_feature_path[dataset_name] +
"/*/imageid2idx.json")
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
chunk_list.append(chunk_fp_id)
logging.info("* Load Image Chunks {}".format(
len(chunk_list)))
t_s_total = time.time()
for chunk_fp in chunk_file_list:
chunk_fp_id = chunk_fp.split('/')[-2]
t_s = time.time()
self.img_feat_offset_map[dataset_name][
chunk_fp_id] = json.load(open(chunk_fp))
t_e = time.time()
logging.info("Open image chunk {}, time: {}".format(
chunk_fp_id, (t_e - t_s)))
t_e_total = time.time()
logging.info("Open total {} image chunks, time: {}".format(
len(chunk_list), (t_e_total - t_s_total)))
elif dataset_name in self.datasets_with_onesplit:
t_s = time.time()
chunk_fp = self.image_feature_path[
dataset_name] + "/imageid2idx.json"
self.img_feat_offset_map[dataset_name] = json.load(
open(chunk_fp))
t_e = time.time()
logging.info("Open dataset {}, time: {}".format(
chunk_fp, (t_e - t_s)))
else:
raise ValueError(
"Not supported dataset: {}".format(dataset_name))
def get_img_feature(self, image_id):
""" decode the image feature: read the image feature from the right chunk id """
self.check_img_feature_file()
self.check_img_feature_offset_map()
img_infos = image_id.split('_')
dataset_name = img_infos[0]
if dataset_name == 'cc':
dataset_name = 'googlecc'
img_id = img_infos[-1]
if dataset_name in self.datasets_with_splits:
chunk_id = img_infos[-2]
img_feat_offset_map = self.img_feat_offset_map[dataset_name][
chunk_id]
img_feature_file = self.img_feature_file[dataset_name][chunk_id]
else:
img_feat_offset_map = self.img_feat_offset_map[dataset_name]
img_feature_file = self.img_feature_file[dataset_name]
if img_id in img_feat_offset_map:
img_offset = img_feat_offset_map[img_id]
arr = img_feature_file.seek(img_offset)
num_boxes = int(arr[1])
feat = np.frombuffer(base64.b64decode(arr[-1]),
dtype=np.float32).reshape(
(num_boxes, self.args.img_feature_dim))
feat = torch.from_numpy(feat)
return feat
return None
class CaptionTSVDataset(Dataset):
def __init__(self,
yaml_file,
tokenizer=None,
add_od_labels=True,
disable_img_features=False,
keep_top_percentage_tag_conf_threshold=0.3,
keep_top_percentage_tag=1,
max_img_seq_length=50,
max_seq_length=70,
max_seq_a_length=40,
is_train=True,
mask_prob=0.15,
max_masked_tokens=3,
**kwargs):
"""Constructor.
Args:
yaml file with all required data (image feature, caption, labels, etc)
tokenizer: tokenizer for text processing.
add_od_labels: whether to add labels from yaml file to BERT.
max_img_seq_length: max image sequence length.
max_seq_length: max text sequence length.
max_seq_a_length: max caption sequence length.
is_train: train or test mode.
mask_prob: probability to mask a input token.
max_masked_tokens: maximum number of tokens to be masked in one sentence.
kwargs: other arguments.
"""
self.yaml_file = yaml_file
self.cfg = load_from_yaml_file(yaml_file)
self.root = op.dirname(yaml_file)
self.label_file = find_file_path_in_yaml(self.cfg['label'], self.root)
self.feat_file = find_file_path_in_yaml(self.cfg['feature'], self.root)
self.caption_file = find_file_path_in_yaml(self.cfg.get('caption'),
self.root)
assert op.isfile(self.feat_file)
if add_od_labels: assert op.isfile(self.label_file)
if is_train:
assert op.isfile(self.caption_file) and tokenizer is not None
self.label_tsv = None if not self.label_file else TSVFile(
self.label_file)
self.feat_tsv = TSVFile(self.feat_file)
if self.caption_file and op.isfile(self.caption_file):
with open(self.caption_file, 'r') as f:
self.captions = json.load(f)
self.tokenizer = tokenizer
self.tensorizer = CaptionTensorizer(self.tokenizer,
max_img_seq_length,
max_seq_length,
max_seq_a_length,
mask_prob,
max_masked_tokens,
is_train=is_train)
self.add_od_labels = add_od_labels
self.disable_img_features = disable_img_features
self.keep_top_percentage_tag_conf_threshold = keep_top_percentage_tag_conf_threshold
self.keep_top_percentage_tag = keep_top_percentage_tag
self.is_train = is_train
self.kwargs = kwargs
self.image_keys = self.prepare_image_keys()
self.key2index = self.prepare_image_key_to_index()
self.key2captions = self.prepare_image_key_to_captions()
def get_valid_tsv(self):
# based on the order of file size
if self.label_tsv:
return self.label_tsv
if self.feat_tsv:
return self.feat_tsv
def prepare_image_keys(self):
tsv = self.get_valid_tsv()
return [tsv.seek(i)[0] for i in range(tsv.num_rows())]
def prepare_image_key_to_index(self):
tsv = self.get_valid_tsv()
return {tsv.seek(i)[0]: i for i in range(tsv.num_rows())}
def prepare_image_key_to_captions(self):
if self.is_train:
key2captions = {key: [] for key in self.image_keys}
for cap in self.captions:
key2captions[cap['image_id']].append(cap['caption'])
return key2captions
def get_image_index(self, idx):
if self.is_train:
img_cap_pair = self.captions[idx]
img_key = img_cap_pair['image_id']
return self.key2index[img_key]
return idx
def get_image_key(self, idx):
img_idx = self.get_image_index(idx)
return self.image_keys[img_idx]
def get_image_features(self, img_idx):
feat_info = json.loads(self.feat_tsv.seek(img_idx)[1])
num_boxes = feat_info['num_boxes']
tmp = np.frombuffer(base64.b64decode(feat_info['features']),
np.float32).reshape((num_boxes, -1))
# remove image features from fine-tuning stage
if self.disable_img_features:
features = tmp.copy()
features.fill(0)
else:
features = tmp
return torch.Tensor(features)
def get_caption(self, idx):
if self.is_train:
img_cap_pair = self.captions[idx]
return img_cap_pair['caption']
return ""
def get_od_labels(self, img_idx):
od_labels = None
if self.add_od_labels:
label_info = json.loads(self.label_tsv.seek(img_idx)[1])
if self.keep_top_percentage_tag == 1:
od_labels = " ".join([l['class'] for l in label_info])
else:
# only keep label that have >= keep_top_percentage_tag_conf_threshold confidence
label_over_threshold = [
l for l in label_info
if l['conf'] >= self.keep_top_percentage_tag_conf_threshold
]
sorted_by_conf = sorted(label_over_threshold,
key=lambda i: i['conf'],
reverse=True)
# top keep_top_percentage_tag% of object tag that is above the confidence threshold
label_info_sort_by_conf = sorted_by_conf[:int(
len(sorted_by_conf) * self.keep_top_percentage_tag)]
od_labels = " ".join(
[l['class'] for l in label_info_sort_by_conf])
return od_labels
def get_caption_file_in_coco_format(self):
cap_file = op.splitext(self.caption_file)[0] + '_coco_format.json'
return cap_file
def get_captions_by_key(self, key):
assert self.is_train, "cannot get captions for inference"
return self.key2captions[key]
def __getitem__(self, idx):
img_idx = self.get_image_index(idx)
img_key = self.image_keys[img_idx]
features = self.get_image_features(img_idx)
caption = self.get_caption(idx)
od_labels = self.get_od_labels(img_idx)
example = self.tensorizer.tensorize_example(caption,
features,
text_b=od_labels)
return img_key, example
def __len__(self):
if self.is_train:
return len(self.captions)
return self.get_valid_tsv().num_rows()
def __init__(self, tokenizer, args, split='train', is_train=True):
"""
tokenizer: tokenizer to process caption text.
args: configureation parameters including max_seq_length, etc.
split: used to infer the data used for training or testing.
All files are in .pt format of a dictionary with image keys and
image features (pytorch tensors), captions (list of str, support multiple
captions per image), labels (list of dictionary or str of all labels),
"""
super(RetrievalDataset, self).__init__()
self.img_file = args.img_feat_file
caption_file = op.join(args.data_dir, '{}_captions.pt'.format(split))
self.img_tsv = TSVFile(self.img_file)
self.captions = torch.load(caption_file)
self.img_keys = list(self.captions.keys()) # img_id as int
if not type(self.captions[self.img_keys[0]]) == list:
self.captions = {
k: json.loads(self.captions[k])
for k in self.img_keys
}
# get the image image_id to index map
imgid2idx_file = op.join(op.dirname(self.img_file), 'imageid2idx.json')
self.image_id2idx = json.load(open(imgid2idx_file)) # img_id as string
if args.add_od_labels:
label_data_dir = op.dirname(self.img_file)
label_file = os.path.join(label_data_dir, "predictions.tsv")
self.label_tsv = TSVFile(label_file)
self.labels = {}
for line_no in range(self.label_tsv.num_rows()):
row = self.label_tsv.seek(line_no)
image_id = row[0]
if int(image_id) in self.img_keys:
results = json.loads(row[1])
objects = results['objects'] if type(
results) == dict else results
self.labels[int(image_id)] = {
"image_h":
results["image_h"] if type(results) == dict else 600,
"image_w":
results["image_w"] if type(results) == dict else 800,
"class": [cur_d['class'] for cur_d in objects],
"boxes":
np.array([cur_d['rect'] for cur_d in objects],
dtype=np.float32)
}
self.label_tsv._fp.close()
self.label_tsv._fp = None
if is_train:
self.num_captions_per_img = args.num_captions_per_img_train
else:
self.num_captions_per_img = args.num_captions_per_img_val
if args.eval_img_keys_file:
# select a subset of image keys for evaluation. eg. COCO 1k and 5k
# eval_img_keys_file is a list of image keys saved in tsv file
with open(op.join(args.data_dir, args.eval_img_keys_file),
'r') as f:
img_keys = f.readlines()
self.img_keys = [int(k.strip()) for k in img_keys]
self.captions = {k: self.captions[k] for k in self.img_keys}
if args.add_od_labels:
self.labels = {k: self.labels[k] for k in self.img_keys}
if args.eval_caption_index_file:
# hard negative image/caption indexs for retrieval re-rank setting.
# useful for mini val set to monitor the performance during training.
# However, it cannot be used together with cross image evaluation.
self.has_caption_indexs = True
assert not args.cross_image_eval
caption_index_file = op.join(args.data_dir,
args.eval_caption_index_file)
self.caption_indexs = torch.load(caption_index_file)
if not type(self.caption_indexs[self.img_keys[0]]) == list:
self.caption_indexs = {
k: json.loads(self.caption_indexs[k])
for k in self.img_keys
}
else:
self.has_caption_indexs = False
self.is_train = is_train
self.output_mode = args.output_mode
self.tokenizer = tokenizer
self.max_seq_len = args.max_seq_length
self.max_img_seq_len = args.max_img_seq_length
self.args = args
class RetrievalDataset(Dataset):
""" Image/Text Retrieval Dataset"""
def __init__(self, tokenizer, args, split='train', is_train=True):
"""
tokenizer: tokenizer to process caption text.
args: configureation parameters including max_seq_length, etc.
split: used to infer the data used for training or testing.
All files are in .pt format of a dictionary with image keys and
image features (pytorch tensors), captions (list of str, support multiple
captions per image), labels (list of dictionary or str of all labels),
"""
super(RetrievalDataset, self).__init__()
self.img_file = args.img_feat_file
caption_file = op.join(args.data_dir, '{}_captions.pt'.format(split))
self.img_tsv = TSVFile(self.img_file)
self.captions = torch.load(caption_file)
self.img_keys = list(self.captions.keys()) # img_id as int
if not type(self.captions[self.img_keys[0]]) == list:
self.captions = {
k: json.loads(self.captions[k])
for k in self.img_keys
}
# get the image image_id to index map
imgid2idx_file = op.join(op.dirname(self.img_file), 'imageid2idx.json')
self.image_id2idx = json.load(open(imgid2idx_file)) # img_id as string
if args.add_od_labels:
label_data_dir = op.dirname(self.img_file)
label_file = os.path.join(label_data_dir, "predictions.tsv")
self.label_tsv = TSVFile(label_file)
self.labels = {}
for line_no in range(self.label_tsv.num_rows()):
row = self.label_tsv.seek(line_no)
image_id = row[0]
if int(image_id) in self.img_keys:
results = json.loads(row[1])
objects = results['objects'] if type(
results) == dict else results
self.labels[int(image_id)] = {
"image_h":
results["image_h"] if type(results) == dict else 600,
"image_w":
results["image_w"] if type(results) == dict else 800,
"class": [cur_d['class'] for cur_d in objects],
"boxes":
np.array([cur_d['rect'] for cur_d in objects],
dtype=np.float32)
}
self.label_tsv._fp.close()
self.label_tsv._fp = None
if is_train:
self.num_captions_per_img = args.num_captions_per_img_train
else:
self.num_captions_per_img = args.num_captions_per_img_val
if args.eval_img_keys_file:
# select a subset of image keys for evaluation. eg. COCO 1k and 5k
# eval_img_keys_file is a list of image keys saved in tsv file
with open(op.join(args.data_dir, args.eval_img_keys_file),
'r') as f:
img_keys = f.readlines()
self.img_keys = [int(k.strip()) for k in img_keys]
self.captions = {k: self.captions[k] for k in self.img_keys}
if args.add_od_labels:
self.labels = {k: self.labels[k] for k in self.img_keys}
if args.eval_caption_index_file:
# hard negative image/caption indexs for retrieval re-rank setting.
# useful for mini val set to monitor the performance during training.
# However, it cannot be used together with cross image evaluation.
self.has_caption_indexs = True
assert not args.cross_image_eval
caption_index_file = op.join(args.data_dir,
args.eval_caption_index_file)
self.caption_indexs = torch.load(caption_index_file)
if not type(self.caption_indexs[self.img_keys[0]]) == list:
self.caption_indexs = {
k: json.loads(self.caption_indexs[k])
for k in self.img_keys
}
else:
self.has_caption_indexs = False
self.is_train = is_train
self.output_mode = args.output_mode
self.tokenizer = tokenizer
self.max_seq_len = args.max_seq_length
self.max_img_seq_len = args.max_img_seq_length
self.args = args
def get_image_caption_index(self, index):
# return img_idx to access features and [img_key, cap_idx] to access caption
if not self.is_train and self.args.cross_image_eval:
img_idx = index // (self.num_captions_per_img * len(self.img_keys))
cap_idx = index % (self.num_captions_per_img * len(self.img_keys))
img_idx1 = cap_idx // self.num_captions_per_img
cap_idx1 = cap_idx % self.num_captions_per_img
return img_idx, [self.img_keys[img_idx1], cap_idx1]
if not self.is_train and self.has_caption_indexs:
img_idx = index // self.num_captions_per_img
cap_idx = index % self.num_captions_per_img
img_key1, cap_idx1 = self.caption_indexs[
self.img_keys[img_idx]][cap_idx]
return img_idx, [img_key1, cap_idx1]
img_idx = index // self.num_captions_per_img
cap_idx = index % self.num_captions_per_img
return img_idx, [self.img_keys[img_idx], cap_idx]
def get_label(self, index):
img_idx, cap_idx = self.get_image_caption_index(index)
return 1 if self.img_keys[img_idx] == cap_idx[0] else 0
def get_od_labels(self, img_key):
if self.args.add_od_labels:
if type(self.labels[img_key]) == str:
od_labels = self.labels[img_key]
else:
od_labels = ' '.join(self.labels[img_key]['class'])
return od_labels
def tensorize_example(self,
text_a,
img_feat,
text_b=None,
cls_token_segment_id=0,
pad_token_segment_id=0,
sequence_a_segment_id=0,
sequence_b_segment_id=1):
tokens_a = self.tokenizer.tokenize(text_a)
if len(tokens_a) > self.args.max_seq_length - 2:
tokens_a = tokens_a[:(self.args.max_seq_length - 2)]
tokens = [self.tokenizer.cls_token
] + tokens_a + [self.tokenizer.sep_token]
segment_ids = [cls_token_segment_id
] + [sequence_a_segment_id] * (len(tokens_a) + 1)
seq_a_len = len(tokens)
if text_b:
tokens_b = self.tokenizer.tokenize(text_b)
if len(tokens_b) > self.max_seq_len - len(tokens) - 1:
tokens_b = tokens_b[:(self.max_seq_len - len(tokens) - 1)]
tokens += tokens_b + [self.tokenizer.sep_token]
segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
seq_len = len(tokens)
seq_padding_len = self.max_seq_len - seq_len
tokens += [self.tokenizer.pad_token] * seq_padding_len
segment_ids += [pad_token_segment_id] * seq_padding_len
input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
# image features
img_len = img_feat.shape[0]
if img_len > self.max_img_seq_len:
img_feat = img_feat[0:self.max_img_seq_len, :]
img_len = img_feat.shape[0]
img_padding_len = 0
else:
img_padding_len = self.max_img_seq_len - img_len
padding_matrix = torch.zeros((img_padding_len, img_feat.shape[1]))
img_feat = torch.cat((img_feat, padding_matrix), 0)
# generate attention_mask
att_mask_type = self.args.att_mask_type
if att_mask_type == "CLR":
attention_mask = [1] * seq_len + [0] * seq_padding_len + \
[1] * img_len + [0] * img_padding_len
else:
# use 2D mask to represent the attention
max_len = self.max_seq_len + self.max_img_seq_len
attention_mask = torch.zeros((max_len, max_len), dtype=torch.long)
# full attention of C-C, L-L, R-R
c_start, c_end = 0, seq_a_len
l_start, l_end = seq_a_len, seq_len
r_start, r_end = self.max_seq_len, self.max_seq_len + img_len
attention_mask[c_start:c_end, c_start:c_end] = 1
attention_mask[l_start:l_end, l_start:l_end] = 1
attention_mask[r_start:r_end, r_start:r_end] = 1
if att_mask_type == 'CL':
attention_mask[c_start:c_end, l_start:l_end] = 1
attention_mask[l_start:l_end, c_start:c_end] = 1
elif att_mask_type == 'CR':
attention_mask[c_start:c_end, r_start:r_end] = 1
attention_mask[r_start:r_end, c_start:c_end] = 1
elif att_mask_type == 'LR':
attention_mask[l_start:l_end, r_start:r_end] = 1
attention_mask[r_start:r_end, l_start:l_end] = 1
else:
raise ValueError(
"Unsupported attention mask type {}".format(att_mask_type))
input_ids = torch.tensor(input_ids, dtype=torch.long)
attention_mask = torch.tensor(attention_mask, dtype=torch.long)
segment_ids = torch.tensor(segment_ids, dtype=torch.long)
return (input_ids, attention_mask, segment_ids, img_feat)
def __getitem__(self, index):
if self.is_train:
img_idx, cap_idxs = self.get_image_caption_index(index)
img_key = self.img_keys[img_idx]
feature = self.get_image(img_key)
caption = self.captions[cap_idxs[0]][cap_idxs[1]]
od_labels = self.get_od_labels(img_key)
example = self.tensorize_example(caption,
feature,
text_b=od_labels)
# select a negative pair
neg_img_indexs = list(range(0, img_idx)) + list(
range(img_idx + 1, len(self.img_keys)))
img_idx_neg = random.choice(neg_img_indexs)
if random.random() <= 0.5:
# randomly select a negative caption from a different image.
cap_idx_neg = random.randint(0, self.num_captions_per_img - 1)
caption_neg = self.captions[
self.img_keys[img_idx_neg]][cap_idx_neg]
example_neg = self.tensorize_example(caption_neg,
feature,
text_b=od_labels)
else:
# randomly select a negative image
feature_neg = self.get_image(self.img_keys[img_idx_neg])
od_labels_neg = self.get_od_labels(self.img_keys[img_idx_neg])
example_neg = self.tensorize_example(caption,
feature_neg,
text_b=od_labels_neg)
example_pair = tuple(list(example) + [1] + list(example_neg) + [0])
return index, example_pair
else:
img_idx, cap_idxs = self.get_image_caption_index(index)
img_key = self.img_keys[img_idx]
feature = self.get_image(img_key)
caption = self.captions[cap_idxs[0]][cap_idxs[1]]
od_labels = self.get_od_labels(img_key)
example = self.tensorize_example(caption,
feature,
text_b=od_labels)
label = 1 if img_key == cap_idxs[0] else 0
return index, tuple(list(example) + [label])
def get_image(self, image_id):
image_idx = self.image_id2idx[str(image_id)]
row = self.img_tsv.seek(image_idx)
num_boxes = int(row[1])
features = np.frombuffer(base64.b64decode(row[-1]),
dtype=np.float32).reshape((num_boxes, -1))
t_features = torch.from_numpy(features)
return t_features
def __len__(self):
if not self.is_train and self.args.cross_image_eval:
return len(self.img_keys)**2 * self.num_captions_per_img
return len(self.img_keys) * self.num_captions_per_img
class CaptionTSVDataset(Dataset):
def __init__(self,
yaml_file,
tokenizer=None,
add_od_labels=True,
max_img_seq_length=50,
max_seq_length=70,
max_seq_a_length=40,
is_train=True,
mask_prob=0.15,
max_masked_tokens=3,
add_conf=False,
**kwargs):
"""Constructor.
Args:
yaml file with all required data (image feature, caption, labels, etc)
tokenizer: tokenizer for text processing.
add_od_labels: whether to add labels from yaml file to BERT.
max_img_seq_length: max image sequence length.
max_seq_length: max text sequence length.
max_seq_a_length: max caption sequence length.
is_train: train or test mode.
mask_prob: probability to mask a input token.
max_masked_tokens: maximum number of tokens to be masked in one sentence.
kwargs: other arguments.
"""
self.yaml_file = yaml_file
self.cfg = load_from_yaml_file(yaml_file)
self.root = op.dirname(yaml_file)
self.label_file = find_file_path_in_yaml(self.cfg['label'], self.root)
self.feat_file = find_file_path_in_yaml(self.cfg['feature'], self.root)
self.caption_file = find_file_path_in_yaml(self.cfg.get('caption'),
self.root)
assert op.isfile(self.feat_file)
if add_od_labels: assert op.isfile(self.label_file)
if is_train:
assert op.isfile(self.caption_file) and tokenizer is not None
self.label_tsv = None if not self.label_file else TSVFile(
self.label_file)
self.feat_tsv = TSVFile(self.feat_file)
if self.caption_file and op.isfile(self.caption_file):
with open(self.caption_file, 'r') as f:
self.captions = json.load(f)
self.tokenizer = tokenizer
self.tensorizer = CaptionTensorizer(self.tokenizer,
max_img_seq_length,
max_seq_length,
max_seq_a_length,
mask_prob,
max_masked_tokens,
is_train=is_train)
self.add_od_labels = add_od_labels
self.is_train = is_train
self.kwargs = kwargs
self.image_keys = self.prepare_image_keys()
self.key2index = self.prepare_image_key_to_index()
self.key2captions = self.prepare_image_key_to_captions()
self.add_conf = add_conf
def get_valid_tsv(self):
# based on the order of file size
if self.label_tsv:
return self.label_tsv
if self.feat_tsv:
return self.feat_tsv
def prepare_image_keys(self):
tsv = self.get_valid_tsv()
return [tsv.seek(i)[0] for i in range(tsv.num_rows())]
def prepare_image_key_to_index(self):
tsv = self.get_valid_tsv()
return {tsv.seek(i)[0]: i for i in range(tsv.num_rows())}
def prepare_image_key_to_captions(self):
if self.is_train:
key2captions = {key: [] for key in self.image_keys}
for cap in self.captions:
key2captions[cap['image_id']].append(cap['caption'])
return key2captions
def get_image_index(self, idx):
if self.is_train:
img_cap_pair = self.captions[idx]
img_key = img_cap_pair['image_id']
return self.key2index[img_key]
return idx
def get_image_key(self, idx):
img_idx = self.get_image_index(idx)
return self.image_keys[img_idx]
def get_image_features(self, img_idx):
feat_info = json.loads(self.feat_tsv.seek(img_idx)[1])
num_boxes = feat_info['num_boxes']
features = np.frombuffer(base64.b64decode(feat_info['features']),
np.float32).reshape((num_boxes, -1))
return torch.Tensor(features)
def get_caption(self, idx):
if self.is_train:
img_cap_pair = self.captions[idx]
return img_cap_pair['caption']
return ""
def get_od_labels(self, img_idx):
od_labels = None
if self.add_od_labels:
label_info = json.loads(self.label_tsv.seek(img_idx)[1])
od_labels = " ".join([l['class'] for l in label_info])
return od_labels
def get_od_confidence(self, img_idx):
od_confs = None
od_labels = None
if self.add_conf:
label_info = json.loads(self.label_tsv.seek(img_idx)[1])
od_confs = []
# we need to repeat conf because some labels have spaces
for idx, info in enumerate(label_info):
repeats = len(info["class"].strip().split(" "))
od_confs.extend([info["conf"]] * repeats)
return od_confs
def get_caption_file_in_coco_format(self):
cap_file = op.splitext(self.caption_file)[0] + '_coco_format.json'
return cap_file
def get_captions_by_key(self, key):
assert self.is_train, "cannot get captions for inference"
return self.key2captions[key]
def __getitem__(self, idx):
img_idx = self.get_image_index(idx)
img_key = self.image_keys[img_idx]
features = self.get_image_features(img_idx)
caption = self.get_caption(idx)
od_labels = self.get_od_labels(img_idx)
od_confs = self.get_od_confidence(img_idx)
example = self.tensorizer.tensorize_example(caption,
features,
text_b=od_labels,
confs=od_confs)
return img_key, example
def __len__(self):
if self.is_train:
return len(self.captions)
return self.get_valid_tsv().num_rows()