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# Copyright (c) OpenMMLab. All rights reserved.
import random
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 semantic_seg_conversations(labels):
ret = []
for i, label in enumerate(labels):
label = label.strip()
assert len(label.split("||")) == 1
for question_template in SEG_QUESTIONS:
for answer_template in ANSWER_LIST:
item = {}
item['conversations'] = [{'from': 'human', 'value': DEFAULT_IMAGE_TOKEN+question_template.format(class_name=label.lower())},
{'from': 'gpt', 'value': answer_template}]
item['class_id'] = i
ret.append(item)
return ret
def semantic_seg_map_fn(example):
# example {'conversations', 'class_id'}
messages = example['conversations']
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 pascal_part_conversation(selected_labels):
conversations = []
for i, selected_label in enumerate(selected_labels):
question = random.choice(SEG_QUESTIONS).format(class_name=selected_label.lower()).strip()
answer = random.choice(ANSWER_LIST)
if i == 0:
question = DEFAULT_IMAGE_TOKEN + question
conversations.append({'from': 'human', 'value': question})
conversations.append({'from': 'gpt', 'value': answer})
return conversations
def pascal_part_preprocess(example):
selected_labels = example["selected_labels"]
conversations = pascal_part_conversation(selected_labels)
example['conversations'] = conversations
return example
def pascal_part_map_fn(example):
example = pascal_part_preprocess(example)
example['image'] = example["file_name"]
# do llava preprocess
messages = example['conversations']
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 semantic_seg_gcg_format_conversations(labels):
ret = []
for i, label in enumerate(labels):
label = label.strip()
assert len(label.split("||")) == 1
for question_template in SEG_QUESTIONS:
for answer_template in ANSWER_LIST_GCG_FORMAT:
item = {}
item['conversations'] = [{'from': 'human', 'value': DEFAULT_IMAGE_TOKEN+question_template.format(class_name=label.lower())},
{'from': 'gpt', 'value': answer_template.format(label.lower().capitalize())}]
item['class_id'] = i
ret.append(item)
return ret
def semantic_seg_gcg_format_map_fn(example):
# example {'conversations', 'class_id'}
messages = example['conversations']
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 pascal_part_gcg_format_conversation(selected_labels):
conversations = []
for i, selected_label in enumerate(selected_labels):
question = random.choice(SEG_QUESTIONS).format(class_name=selected_label.lower()).strip()
answer = random.choice(ANSWER_LIST).format(selected_label.lower().capitalize())
if i == 0:
question = DEFAULT_IMAGE_TOKEN + question
conversations.append({'from': 'human', 'value': question})
conversations.append({'from': 'gpt', 'value': answer})
return conversations
def pascal_part_gcg_format_preprocess(example):
selected_labels = example["selected_labels"]
conversations = pascal_part_gcg_format_conversation(selected_labels)
example['conversations'] = conversations
return example
def pascal_part_gcg_format_map_fn(example):
example = pascal_part_gcg_format_preprocess(example)
example['image'] = example["file_name"]
# do llava preprocess
messages = example['conversations']
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
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