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import json
import logging
import os
import random
import re
import time
import traceback
import warnings
from io import BytesIO
import pandas as pd
import h5py
import numpy as np
import torch
from icecream import ic
from PIL import Image, ImageFile
from torch.utils.data import Dataset, Subset
from utils import get_args
ImageFile.LOAD_TRUNCATED_IMAGES = True
ImageFile.MAX_IMAGE_PIXELS = None
Image.MAX_IMAGE_PIXELS = None
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
warnings.filterwarnings("ignore")
logger = logging.getLogger(__name__)
def load_jsonl(filename):
with open(filename, "r", encoding="utf-8") as f:
return [json.loads(l.strip("\n")) for l in f.readlines()]
class MultiModalDataset(Dataset):
"""MultiModal dataset"""
def __init__(self, input_file, tokenizer, processor,
max_length=2048,
media_tokens=['<image>', '<|video|>'], loss_objective = 'sequential'):
args = get_args()
self.loss_objective = loss_objective
if 'sequential' in self.loss_objective:
self.dataset = pd.read_csv(input_file)
self.dataset = self.dataset.dropna()
else:
raise NotImplementedError('dataset loader not implemented for other loss objectives')
self.dataset = pd.read_csv(input_file)
self.tokenizer = tokenizer
self.max_length = max_length
self.processor = processor
self.media_tokens = {k: -int(i+1) for i, k in enumerate(media_tokens)}
self.media_lengths = {'<image>': 1+64,'<|video|>': 1+64}
print("num_media_token: ", self.media_lengths)
print(len(self.dataset))
self.bucket = {}
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
data = self.dataset.iloc[index]
videopath = data['videopath']
caption = data['caption']
video_input = self.processor(videos=[videopath], num_frames=32, return_tensors='pt') # video_pixel_values
text_input = self._extract_text_token_from_conversation(caption, self.max_length, index)
item = {'video': video_input, 'text': text_input, 'videopath': videopath, 'caption': caption}
return item
def _extract_text_token_from_conversation(self, data, max_length, index):
# output enc_chunk
enc_chunk = []
if self.tokenizer.bos_token_id > 0:
prompt_chunk = [self.tokenizer.bos_token_id]
else:
prompt_chunk = []
# conversation = data["completion"]
conversation = data
# For Text only data
if all([media_token not in conversation for media_token in self.media_tokens.keys()]):
pattern = '|'.join(map(re.escape, ['AI: ', '\nHuman: ']))
chunk_strs = re.split(f'({pattern})', conversation)
prompt_length = -1
stop_flag = False
for idx, chunk_str in enumerate(chunk_strs):
if idx == 0:
enc_chunk = prompt_chunk + \
self.tokenizer(chunk_str, add_special_tokens=False)[
'input_ids']
enc_length = len(enc_chunk)
label_chunk = [0] * enc_length
else:
if chunk_strs[idx-1] == 'AI: ':
curr_chunk = self.tokenizer(
chunk_str, add_special_tokens=False)['input_ids']
if enc_length + len(curr_chunk) >= max_length:
curr_chunk = curr_chunk[:max_length-enc_length]
stop_flag = True
curr_chunk += [self.tokenizer.eos_token_id]
enc_length += len(curr_chunk)
enc_chunk += curr_chunk
label_chunk += [1] * len(curr_chunk)
else:
curr_chunk = self.tokenizer(
chunk_str, add_special_tokens=False)['input_ids']
if enc_length + len(curr_chunk) >= max_length + 1:
curr_chunk = curr_chunk[:max_length+1-enc_length]
stop_flag = True
enc_length += len(curr_chunk)
enc_chunk += curr_chunk
label_chunk += [0] * len(curr_chunk)
if stop_flag:
break
# For Image-Text Data
else:
enc_length = 0
prompt_length = -2
pattern = '|'.join(
map(re.escape, list(self.media_tokens.keys()) + ['AI: ', '\nHuman: ']))
chunk_strs = re.split(f'({pattern})', conversation)
chunk_strs = [x for x in chunk_strs if len(x) > 0]
for idx, chunk_str in enumerate(chunk_strs):
if enc_length >= max_length + 1:
break
if idx == 0:
enc_chunk = prompt_chunk + \
self.tokenizer(chunk_str, add_special_tokens=False)[
'input_ids']
enc_length = len(enc_chunk)
label_chunk = [0] * enc_length
else:
if chunk_str in self.media_tokens:
# [CLS] + 256 + [EOS]
if enc_length + self.media_lengths[chunk_str] > max_length + 1:
break
else:
enc_chunk += [self.media_tokens[chunk_str]
] * self.media_lengths[chunk_str]
enc_length += self.media_lengths[chunk_str]
label_chunk += [0] * self.media_lengths[chunk_str]
else:
if chunk_strs[idx-1] == 'AI: ':
curr_chunk = self.tokenizer(
chunk_str, add_special_tokens=False)['input_ids']
if enc_length + len(curr_chunk) >= max_length:
curr_chunk = curr_chunk[:max_length-enc_length]
curr_chunk += [self.tokenizer.eos_token_id]
enc_length += len(curr_chunk)
enc_chunk += curr_chunk
label_chunk += [1] * len(curr_chunk)
else:
curr_chunk = self.tokenizer(
chunk_str, add_special_tokens=False)['input_ids']
if enc_length + len(curr_chunk) >= max_length + 1:
curr_chunk = curr_chunk[:max_length +
1-enc_length]
enc_length += len(curr_chunk)
enc_chunk += curr_chunk
label_chunk += [0] * len(curr_chunk)
if enc_length < max_length + 1:
padding_chunk = [self.tokenizer.pad_token_id] * \
(max_length + 1 - enc_length)
padding_length = len(padding_chunk)
label_chunk += [0] * (max_length + 1 - enc_length)
enc_chunk = enc_chunk + padding_chunk
else:
padding_length = 0
assert enc_length + padding_length == max_length + \
1, (index, prompt_length, enc_length,
padding_length, max_length + 1)
assert len(label_chunk) == max_length + \
1, (len(label_chunk), max_length + 1)
non_padding_mask = [1 if i < enc_length -
1 else 0 for i in range(max_length)]
enc_chunk = torch.tensor(enc_chunk).long()
non_padding_mask = torch.tensor(non_padding_mask).long()
prompt_mask = torch.tensor(label_chunk)[1:].long()
prompt_length = torch.tensor([prompt_length]).long()
# Create loss mask
if all([media_token not in conversation for media_token in self.media_tokens.keys()]):
non_media_mask = torch.ones_like(non_padding_mask).long()
else:
tmp_enc_chunk = enc_chunk.clone()
tmp_enc_chunk[tmp_enc_chunk >= 0] = 1
tmp_enc_chunk[tmp_enc_chunk < 0] = 0
non_media_mask = torch.tensor(tmp_enc_chunk).long()
non_media_mask = non_media_mask[1:].long()
return {'input_ids': enc_chunk, "prompt_length": prompt_length, 'seq_length': enc_length,
"non_padding_mask": non_padding_mask, 'non_media_mask': non_media_mask, 'prompt_mask': prompt_mask} |