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import sys
import datasets
import torch
import re
import os
import subprocess
import numpy as np
from llava.datasets.builder import DATASETS
from typing import Dict, Optional, Sequence, List
from llava.datasets.data_cfgs import data_configs
from llava.datasets.base_dataset import FramesTaskDataset
from llava.datasets.data_cfgs import data_configs
from llava.utils import master_print
import pickle
from pathlib import Path
import random
from llava.datasets.prompts import tt_caption_prompt, internvid_prompt
from llava.constants import DEFAULT_VIDEO_TOKEN
from PIL import Image
import json
import numpy as np
class GPT4VInternalDataset(FramesTaskDataset):
def __init__(self, anno_path=None, data_args=None, fps=0.5, conv_type='single', task_types=None, name='gpt4v_internal'):
self.default_fps = 2.0
self.fps = fps
self.conv_type = conv_type
self.task_types = task_types
self.annotation = self.get_dataset(anno_path)
assert self.conv_type in ('single', 'multi'), "gpt4v_public conv type must in single/multi"
# assert hasattr(self.data_args, 'task_types') , "gpt4v_internal must have key 'task_types' in yaml config"
# master_print(f"Finished loading dataset {name} {len(self.annotation)} samples...")
super().__init__(anno_path=anno_path,
data_args=data_args,
fps=fps,
name=name)
def __len__(self):
return len(self.annotation)
def get_dataset(self, anno_path):
dataset = []
anno_path = Path(anno_path)
with anno_path.open('rb') as f:
data = json.load(f)
for info in data:
filtered_qa = []
for qa in info['qa_pairs']:
if len(qa['question']) == 0 or len(qa['answer']) == 0:
continue
filtered_qa.append(qa)
info['qa_pairs'] = filtered_qa
for task_type in self.task_types:
info_task = info.copy()
if len(info_task[task_type]) == 0:
continue
if task_type == 'qa_pairs' and self.conv_type == 'single':
for qa_pair in info_task[task_type]:
one_info = info_task.copy()
one_info[task_type] = [qa_pair]
one_info.update({
'task_type': task_type
})
dataset.append(one_info)
else:
info_task.update({
'task_type': task_type
})
dataset.append(info_task)
return dataset
@staticmethod
def _sample_frames(frames, num_segments):
indices = np.linspace(start=0, stop=len(frames) - 1, num=num_segments).astype(int)
frames = [frames[ind] for ind in indices]
return frames
def text_preprocess(self, item) -> List[Dict[str, str]]:
all_convs = []
# TODO: different prompt for summary and detail
if item['task_type'] == 'summary':
all_convs.append([
{
'from': 'human',
'value': random.choice(internvid_prompt)
},
{
'from': 'model',
'value': item['summary']
}
])
elif item['task_type'] == 'detail':
all_convs.append([
{
'from': 'human',
'value': random.choice(tt_caption_prompt)
},
{
'from': 'model',
'value': item['detail']
}
])
else:
for qa in item['qa_pairs']:
all_convs.append([
{
'from': 'human',
'value': qa['question']
},
{
'from': 'model',
'value': qa['answer']
}
])
conversations = []
random.shuffle(all_convs)
for idx, conv in enumerate(all_convs):
if idx == 0:
conv[0]['value'] = DEFAULT_VIDEO_TOKEN + conv[0]['value']
conversations.extend(conv)
return conversations
def vis_preprocess(self, vis_path):
image_files = [(os.path.splitext(img)[0], img) for img in os.listdir(vis_path) if not img.startswith('cuttime')]
image_files = [(int(x[0]), x[1]) for x in image_files]
image_files = sorted(image_files, key=lambda img: img[0])
intervals = np.linspace(start=0, stop=len(image_files)-1, num=10).astype(int)
image_files = [image_files[i] for i in intervals]
if self.num_segments > 0 and len(image_files) > self.num_segments:
image_files = self._sample_frames(image_files, self.num_segments)
images = []
for image_file in image_files:
try:
images.append(Image.open(os.path.join(vis_path, image_file[1])).convert('RGB'))
except Exception as e:
continue
formatted_images = []
for image in images:
im = self.preprocess_image(image)
if isinstance(im, list):
formatted_images.extend(im)
else:
formatted_images.append(im)
# images = [self.preprocess_image(image) for image in images]
return formatted_images
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
item = self.annotation[i]
ret = {
'images': self.vis_preprocess(item['vis_path']),
'conversations': self.text_preprocess(item)
}
if 'id' in item:
ret['id'] = item['id']
return ret
@DATASETS.register_obj
def gpt4v_internal(data_args):
data_cfg = data_configs['gpt4v_internal']
train_data_path = None
if 'train_data_path' in data_args.external_args:
train_data_path = data_args.external_args['train_data_path']
else:
train_data_path = data_cfg['train_data_path']
fps, conv_type, task_types = data_args.external_args['fps'], data_args.external_args['conv_type'], data_args.external_args['task_types']
return GPT4VInternalDataset(train_data_path, data_args, fps, conv_type, task_types)
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