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from xtuner.utils import DEFAULT_IMAGE_TOKEN
SEG_QUESTIONS = [
"Can you segment the {class_name} in this image?",
"Please segment {class_name} in this image.",
"What is {class_name} in this image? Please respond with segmentation mask.",
"What is {class_name} in this image? Please output segmentation mask.",
"Can you segment the {class_name} in this image",
"Please segment {class_name} in this image",
"What is {class_name} in this image? Please respond with segmentation mask",
"What is {class_name} in this image? Please output segmentation mask",
"Could you provide a segmentation mask for the {class_name} in this image?",
"Please identify and segment the {class_name} in this image.",
"Where is the {class_name} in this picture? Please respond with a segmentation mask.",
"Can you highlight the {class_name} in this image with a segmentation mask?",
"Could you provide a segmentation mask for the {class_name} in this image",
"Please identify and segment the {class_name} in this image",
"Where is the {class_name} in this picture? Please respond with a segmentation mask",
"Can you highlight the {class_name} in this image with a segmentation mask",
]
ANSWER_LIST = [
"It is [SEG].",
"Sure, [SEG].",
"Sure, it is [SEG].",
"Sure, the segmentation result is [SEG].",
"[SEG].",
]
ANSWER_LIST_GCG_FORMAT = [
"<p> {} </p> [SEG].",
]
def referring_seg_conversations(labels):
questions = []
answers = []
for i, label in enumerate(labels):
label = label.strip()
assert len(label.split("||")) == 1
question_template = random.choice(SEG_QUESTIONS)
questions.append(question_template.format(class_name=label.lower()))
answers.append(random.choice(ANSWER_LIST))
ret = []
for i, (question, answer) in enumerate(zip(questions, answers)):
if i == 0:
ret.append(
{'from': 'human', 'value': DEFAULT_IMAGE_TOKEN+question}
)
else:
ret.append(
{'from': 'human', 'value': question}
)
ret.append(
{'from': 'gpt', 'value': answer}
)
return ret
def referring_seg_map_fn(example):
# example {'sampled_sents'}
messages = referring_seg_conversations(example['sampled_sents'])
input = ''
conversation = []
while messages and messages[0]['from'] == 'gpt':
# Skip the first one if it is from gpt
messages = messages[1:]
for msg in messages:
if msg['from'] == 'human':
if DEFAULT_IMAGE_TOKEN in msg['value']:
msg['value'] = msg['value'].replace(DEFAULT_IMAGE_TOKEN,
'').strip()
msg['value'] = DEFAULT_IMAGE_TOKEN + '\n' + msg['value']
msg['value'] = msg['value'].strip()
input += msg['value']
elif msg['from'] == 'gpt':
conversation.append({'input': input, 'output': msg['value']})
input = ''
else:
raise NotImplementedError
example.update({'conversation': conversation})
return example
def referring_seg_gcg_format_conversations(labels):
questions = []
answers = []
for i, label in enumerate(labels):
label = label.strip()
assert len(label.split("||")) == 1
question_template = random.choice(SEG_QUESTIONS)
questions.append(question_template.format(class_name=label.lower()))
answers.append(random.choice(ANSWER_LIST_GCG_FORMAT).format(label.lower().capitalize()))
ret = []
for i, (question, answer) in enumerate(zip(questions, answers)):
if i == 0:
ret.append(
{'from': 'human', 'value': DEFAULT_IMAGE_TOKEN+question}
)
else:
ret.append(
{'from': 'human', 'value': question}
)
ret.append(
{'from': 'gpt', 'value': answer}
)
return ret
def referring_seg_gcg_format_map_fn(example):
# example {'sampled_sents'}
messages = referring_seg_gcg_format_conversations(example['sampled_sents'])
input = ''
conversation = []
while messages and messages[0]['from'] == 'gpt':
# Skip the first one if it is from gpt
messages = messages[1:]
for msg in messages:
if msg['from'] == 'human':
if DEFAULT_IMAGE_TOKEN in msg['value']:
msg['value'] = msg['value'].replace(DEFAULT_IMAGE_TOKEN,
'').strip()
msg['value'] = DEFAULT_IMAGE_TOKEN + '\n' + msg['value']
msg['value'] = msg['value'].strip()
input += msg['value']
elif msg['from'] == 'gpt':
conversation.append({'input': input, 'output': msg['value']})
input = ''
else:
raise NotImplementedError
example.update({'conversation': conversation})
return example |