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import json
import re
from ..root import DATASETS, IMAGE_PLACEHOLDER, BOXES_PLACEHOLDER, QUESTION_PLACEHOLDER, METRICS
from ..utils.flickr30k_entities_utils import PHRASE_ST_PLACEHOLDER, PHRASE_ED_PLACEHOLDER
from ..utils import MInstrDataset, BaseComputeMetrics
REFID_PAT = re.compile(r'(\s\((?:(?:\d+(?:,\d+)*)|-)\)\s?)')
ANS_EXTRACT_PAT = re.compile(r'(?:(?:(?:(?:(?:So t)|(?:T)|(?:t))he answer is)|(?:Answer:)) (.+))')
@DATASETS.register_module()
class GQADataset(MInstrDataset):
def __init__(
self,
*args,
scene_graph_file,
scene_graph_index,
version,
question_box_prob=0.5,
**kwargs
):
super().__init__(*args, **kwargs, placeholders=(IMAGE_PLACEHOLDER, QUESTION_PLACEHOLDER))
self.scene_graph_file = scene_graph_file
self.scene_graph_index = scene_graph_index
self.version = version
self.question_box_prob = question_box_prob
qtype, atype = version.split('-')
assert qtype in ['q', 'qb', 'qbp']
assert atype in ['a', 'c', 'bc', 's', 'bs', 'l', 'bl']
self.qtype = qtype
self.atype = atype
assert bool(scene_graph_file) == bool(scene_graph_index)
if scene_graph_file is not None and scene_graph_index is not None:
self.scene_graph = [line for line in open(scene_graph_file, 'r', encoding='utf8')]
self.scene_index = json.load(open(scene_graph_index, 'r', encoding='utf8'))
else:
self.scene_graph = None
self.scene_index = None
def get_raw_item(self, index):
question = json.loads(self.data[index])
if self.scene_graph is None:
return question, None
scene = json.loads(self.scene_graph[self.scene_index[question['imageId']]])
return question, scene
def __getitem__(self, index):
question, scene = self.get_raw_item(index)
img_path = f"{question['imageId']}.jpg"
image = self.get_image(img_path)
# answer
if self.atype == 'bc':
boxes = question['cot']['boxes']
answer = question['cot']['value'].replace(PHRASE_ST_PLACEHOLDER, "").replace(PHRASE_ED_PLACEHOLDER, BOXES_PLACEHOLDER)
answer_boxes_seq = question['cot']['seq']
elif self.atype == 'c':
boxes = []
answer = question['cot']['value'].replace(PHRASE_ST_PLACEHOLDER, "").replace(PHRASE_ED_PLACEHOLDER, "")
answer_boxes_seq = []
elif self.atype == 'bs':
boxes, bss, answer_boxes_seq = get_bss_example(question, scene)
answer = f"{bss}. The answer is {question['answer']}."
elif self.atype == 's':
boxes = []
ss = REFID_PAT.sub('', question['semanticStr'])
answer = f"{ss}. The answer is {question['answer']}."
answer_boxes_seq = []
elif self.atype == 'bl':
boxes, answer, answer_boxes_seq = get_bl_example(question, scene)
elif self.atype == 'l':
boxes = []
_, answer, _ = get_bl_example(question, scene)
answer = answer.replace(BOXES_PLACEHOLDER, "")
answer_boxes_seq = []
elif self.atype == 'a':
boxes = []
answer = f"The answer is {question['answer']}."
answer_boxes_seq = []
else:
assert False
# question
if self.qtype == 'q':
boxes, query, query_boxes_seq = prepare_query_dummy(boxes, question, scene)
elif self.qtype == 'qb':
boxes, query, query_boxes_seq = prepare_query_box(boxes, question, scene)
elif self.qtype == 'qbp':
if self.rng.uniform() > self.question_box_prob:
boxes, query, query_boxes_seq = prepare_query_dummy(boxes, question, scene)
else:
boxes, query, query_boxes_seq = prepare_query_box(boxes, question, scene)
else:
assert False
final_query = self.get_template().replace(QUESTION_PLACEHOLDER, query)
ret = {
'image': image,
'target': {'boxes': boxes},
'conversations': [
{
'from': 'human',
'value': final_query,
'boxes_seq': query_boxes_seq,
},
{
'from': 'gpt',
'value': answer,
'boxes_seq': answer_boxes_seq,
}
]
}
return ret
def prepare_query_dummy(boxes_list, q, scene):
return boxes_list, q['question'], []
def prepare_query_box(boxes_list, q, scene):
def get_boxes_idx(box):
if box in boxes_list:
return boxes_list.index(box)
else:
boxes_list.append(box)
return len(boxes_list) - 1
def add_boxes_by_rids(rids):
def get_box_xyxy(obj):
x, y, w, h = obj['x'], obj['y'], obj['w'], obj['h']
return x, y, x + w, y + h
boxes_idx = []
for rid in rids:
ref = scene['objects'][rid]
ref_box = list(get_box_xyxy(ref))
boxes_idx.append(get_boxes_idx(ref_box))
return boxes_idx
sent = list(q['question'].split())
query_boxes_seq = []
for span, rids_str in q['annotations']['question'].items():
span = tuple(map(int, span.split(':')))
if len(span) == 1:
span = [span[0], span[0] + 1]
sent[span[1] - 1] = f"{sent[span[1] - 1]}{BOXES_PLACEHOLDER}"
boxes_idx = add_boxes_by_rids(rids_str.split(','))
query_boxes_seq.append(boxes_idx)
sent_converted = " ".join(sent).strip()
return boxes_list, sent_converted, query_boxes_seq
def add_boxes_by_rids(boxes_list, rids, scene):
def get_boxes_idx(boxes_list, box):
if box in boxes_list:
return boxes_list.index(box)
else:
boxes_list.append(box)
return len(boxes_list) - 1
def get_box_xyxy(obj):
x, y, w, h = obj['x'], obj['y'], obj['w'], obj['h']
return x, y, x + w, y + h
boxes_idx = []
for rid in rids:
ref = scene['objects'][rid]
ref_box = list(get_box_xyxy(ref))
boxes_idx.append(get_boxes_idx(boxes_list, ref_box))
return boxes_idx
def get_bss_example(question, scene):
def format_refids(item):
item = item.strip()[1:-1]
return item.split(',')
s = question['semanticStr']
print(REFID_PAT.findall(s))
formats = []
boxes = []
seqs = []
for item in REFID_PAT.findall(s):
if '-' in item:
formats.append('')
else:
formats.append('<boxes>')
refids = format_refids(item)
idx = add_boxes_by_rids(boxes, refids, scene)
seqs.append(idx)
answer = REFID_PAT.sub('{}', s).format(*formats)
print(answer)
print(boxes)
print(seqs)
return boxes, answer, seqs
def get_bl_example(ann, scene):
boxes = []
boxes_seq = []
origin_sent = ann['fullAnswer']
origin_sent = re.sub('(?:^Yes,)|(?:^No,)', '', origin_sent).strip()
sent = list(origin_sent.split())
for span, rids_str in ann['annotations']['fullAnswer'].items():
span = tuple(map(int, span.split(':')))
if len(span) == 1:
span = [span[0], span[0] + 1]
sent[span[1] - 1] = f"{sent[span[1] - 1]}{BOXES_PLACEHOLDER}"
rids = rids_str.split(',')
boxes_idx = add_boxes_by_rids(boxes, rids, scene)
boxes_seq.append(boxes_idx)
answer = "".join(sent)
answer += f"The answer is {ann['answer']}."
return boxes, answer, boxes_seq
@METRICS.register_module()
class GQAComputeMetrics(BaseComputeMetrics):
def extract_ans(self, string: str):
try:
found = ANS_EXTRACT_PAT.findall(string.strip())
if len(found) != 1:
return None
return found[0].strip().rstrip('.').strip()
except (IndexError, AttributeError):
return None
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