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import numpy as np
import random
from xtuner.utils import DEFAULT_IMAGE_TOKEN
GCG_QUESTIONS = [
DEFAULT_IMAGE_TOKEN + 'Could you please give me a brief description of the image? Please respond with interleaved segmentation masks for the corresponding parts of the answer.',
DEFAULT_IMAGE_TOKEN + 'Can you provide a brief description of the this image? Please output with interleaved segmentation masks for the corresponding phrases.',
DEFAULT_IMAGE_TOKEN + 'Please briefly describe the contents of the image. Please respond with interleaved segmentation masks for the corresponding parts of the answer.',
DEFAULT_IMAGE_TOKEN + 'Could you give a brief explanation of what can be found within this picture? Please output with interleaved segmentation masks for the corresponding phrases.',
DEFAULT_IMAGE_TOKEN + 'Could you give me an brief explanation of this picture? Please respond with interleaved segmentation masks for the corresponding phrases.',
DEFAULT_IMAGE_TOKEN + 'Could you provide me with a briefly analysis of this photo? Please output with interleaved segmentation masks for the corresponding parts of the answer.',
]
def refcocog_parse_annotations(example):
# example {'id': str, 'refs': [{"setence", 'bbox', 'segmentation'},], 'img_file_name': str, 'caption': str}
annotations = {'labels': [], 'caption': [], 'masks': [], 'tokens_positive': [],
'file_name': example['img_file_name'], 'image': example['img_file_name']}
orig_caption = example['caption'].strip('"').strip()
annotations['caption'] = orig_caption.lower()
for detail in example['refs']:
phrase = detail['sentence']
if phrase.lower() in annotations['caption']:
annotations['labels'].append(phrase)
index = annotations['caption'].find(phrase)
end_index = index + len(phrase) if index != -1 else -1
annotations['tokens_positive'].append([index, end_index])
# still polygon or rle
annotations['masks'].append(detail["segmentation"])
# Sort tokens_positive and corresponding lists
tokens_positive = annotations['tokens_positive']
sorted_indices = sorted(range(len(tokens_positive)), key=lambda i: tokens_positive[i][0])
annotations['tokens_positive'] = [tokens_positive[i] for i in sorted_indices]
annotations['masks'] = [annotations['masks'][i] for i in sorted_indices]
annotations['labels'] = [annotations['labels'][i] for i in sorted_indices]
# Trimming overlapping intervals
for i in range(len(tokens_positive)):
for j in range(i + 1, len(tokens_positive)):
# If there is overlap
if tokens_positive[i][1] >= tokens_positive[j][0]:
# Modify the end index of phrase i to be one less than the start index of phrase j
tokens_positive[i][1] = tokens_positive[j][0] - 1
# Modify the phrases to reflect the change in indices
annotations['labels'][i] = orig_caption[tokens_positive[i][0]:tokens_positive[i][1] + 1]
break # Exit inner loop since i was modified
return annotations
def refcocog_conversation(caption, tokens_positive):
# insert <p> </p> and [seg] to caption and select a question
question = random.choice(GCG_QUESTIONS).strip()
# Prepare caption with tags
def tag_caption(caption, tokens):
for start, end in sorted(tokens, key=lambda x: x[0], reverse=True):
caption = f"{caption[:start]}<p> {caption[start:end]} </p> [SEG]{caption[end:]}"
return caption
detailed_answer = tag_caption(caption, tokens_positive)
conversations = [{'from': 'human', 'value': question}, {'from': 'gpt', 'value': detailed_answer}]
return conversations
def refcocog_preprocess(example):
data_labels = example['labels']
masks = example['masks']
caption = example['caption']
tokens_positive = example['tokens_positive']
# Function to sort elements based on the start index of each phrase
def sort_by_start_index(items, order):
return [items[i] for i in order]
# Sort phrases based on their appearance in the sentence
phrase_order = sorted(range(len(tokens_positive)), key=lambda x: tokens_positive[x][0])
masks = sort_by_start_index(masks, phrase_order)
data_labels = sort_by_start_index(data_labels, phrase_order)
tokens_positive = sort_by_start_index(tokens_positive, phrase_order)
conversations = refcocog_conversation(caption, tokens_positive)
example['conversations'] = conversations
example['labels'] = data_labels
example['masks'] = masks
example['tokens_positive'] = tokens_positive
return example
def glamm_refcocog_map_fn(example):
# example {'id': str, 'refs': [{"setence", 'bbox', 'segmentation'},], 'img_file_name': str, 'caption': str}
example = refcocog_parse_annotations(example)
# example 'labels': [], 'caption': str, 'masks': [], 'tokens_positive': [], 'file_name': image_file
example = refcocog_preprocess(example)
# 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 grandf_parse_annotations(example):
image_path = example['file_name']
annotations = {
'labels': [], 'caption': [], 'masks': [],
'tokens_positive': [], 'file_name': image_path,
'image': image_path}
annotations['caption'] = example['caption'].strip('"').strip()
for word, grounding in example["groundings"].items():
if grounding is None:
continue
annotations['labels'].append(word)
annotations['tokens_positive'].append(grounding["token_positives"])
annotations['masks'].append(grounding["rle_masks"])
return annotations
def grandf_conversation(caption, tokens_positive):
question = random.choice(GCG_QUESTIONS).strip()
# Prepare caption with tags
def tag_caption(caption, tokens):
for start, end in sorted(tokens, key=lambda x: x[0], reverse=True):
caption = f"{caption[:start]}<p> {caption[start:end]} </p> [SEG]{caption[end:]}"
return caption
detailed_answer = tag_caption(caption, tokens_positive)
conversations = [{'from': 'human', 'value': question}, {'from': 'gpt', 'value': detailed_answer}]
return conversations
def grandf_preprocess(example):
data_labels = example['labels']
masks = example['masks']
caption = example['caption']
tokens_positive = example['tokens_positive']
# Function to sort elements based on the start index of each phrase
def sort_by_start_index(items, order):
return [items[i] for i in order]
# Sort phrases based on their appearance in the sentence
phrase_order = sorted(range(len(tokens_positive)), key=lambda x: tokens_positive[x][0])
masks = sort_by_start_index(masks, phrase_order)
data_labels = sort_by_start_index(data_labels, phrase_order)
tokens_positive = sort_by_start_index(tokens_positive, phrase_order)
conversations = grandf_conversation(caption, tokens_positive)
example['conversations'] = conversations
example['labels'] = data_labels
example['masks'] = masks
example['tokens_positive'] = tokens_positive
return example
def glamm_granf_map_fn(example):
# example {'file_name': str, "height": int, "width": int, "image_id": str, caption: "str",
# "groundings": {ground_words: {'token_positives', 'rle_masks', }}}
example = grandf_parse_annotations(example)
# example 'labels': [], 'caption': str, 'masks': [], 'tokens_positive': [], 'file_name': image_file
example = grandf_preprocess(example)
# 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
glamm_openpsg_map_fn = glamm_granf_map_fn
def flickr_parse_annotations(example):
annotations = {'bboxes': [], 'labels': [], 'bboxes_ignore': [], 'caption': example['caption'], 'masks': [],
'tokens_positive': [], 'image': example['file_name']}
ann_info = example["ann_info"]
for ann in ann_info:
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
inter_w = max(0, min(x1 + w, example['width']) - max(x1, 0))
inter_h = max(0, min(y1 + h, example['height']) - max(y1, 0))
if inter_w * inter_h == 0 or ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w, y1 + h]
annotations['bboxes'].append(bbox)
tokens_positive = ann['tokens_positive']
gt_label = [example['caption'][span[0]:span[1]] for span in tokens_positive]
annotations['labels'].append(gt_label[0])
annotations['tokens_positive'].append(tokens_positive[0])
rle = ann['sam_mask']
annotations['masks'].append(rle)
# Convert bounding boxes to numpy arrays
annotations['bboxes'] = np.array(annotations['bboxes'], dtype=np.float32) if annotations[
'bboxes'] else np.zeros((0, 4), dtype=np.float32)
annotations['bboxes_ignore'] = np.array(annotations['bboxes_ignore'], dtype=np.float32) if annotations[
'bboxes_ignore'] else np.zeros((0, 4), dtype=np.float32)
return annotations
def flickr_preprocess(example):
data_labels = example['labels']
masks = example['masks']
caption = example['caption']
tokens_positive = example['tokens_positive']
# Function to sort elements based on the start index of each phrase
def sort_by_start_index(items, order):
return [items[i] for i in order]
# Sort phrases based on their appearance in the sentence
phrase_order = sorted(range(len(tokens_positive)), key=lambda x: tokens_positive[x][0])
masks = sort_by_start_index(masks, phrase_order)
data_labels = sort_by_start_index(data_labels, phrase_order)
tokens_positive = sort_by_start_index(tokens_positive, phrase_order)
conversations = grandf_conversation(caption, tokens_positive)
example['conversations'] = conversations
example['labels'] = data_labels
example['masks'] = masks
example['tokens_positive'] = tokens_positive
return example
def glamm_flickr_map_fn(example):
# example {'file_name': str, "height": int, "width": int, "image_id": str, caption: "str",
# "groundings": {ground_words: {'token_positives', 'rle_masks', }}}
example = flickr_parse_annotations(example)
example = flickr_preprocess(example)
# 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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