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# TODO: extract images from refcoco series
import sys
sys.path.append(".")
import base64
import io
import json
import logging
import os
import os.path as osp
import datasets
import hydra
import numpy as np
import tqdm
from hydra.core.hydra_config import HydraConfig
from hydra.core.utils import configure_log
from omegaconf import DictConfig, OmegaConf
from PIL import Image
import pycocotools.mask
from utils.git_utils import TSVWriter
from src.arguments import Arguments, global_setup
import logging
from hydra.utils import instantiate
from transformers import set_seed, AutoTokenizer
from datasets import interleave_datasets, concatenate_datasets
import torch
from src.train import prepare_datasets
import sqlite3
import json
from torch.utils.data import IterableDataset, DataLoader
logger = logging.getLogger(__name__)
@hydra.main(version_base="1.3", config_path="../../src/conf", config_name="conf")
def main(args: Arguments):
logger.warning(f"Turn no_cuda = True.")
args.training.no_cuda = True
# NOTE: ddp is initialized in _setup_devices class in `transformers/training_args.py`
args, training_args, _ = global_setup(args)
# Set seed before initializing model.
set_seed(args.training.seed)
# Initialize our dataset and prepare it
train_dataset, eval_dataset = prepare_datasets(args)
if len(eval_dataset) > 1:
raise ValueError(f"Only support one eval dataset, but got {len(eval_dataset)}. args: {args.eval_data}")
# NOTE: According to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb,
# we use alternatively GPT2TokenizerFast.
tokenizer = AutoTokenizer.from_pretrained("gpt2")
# Connect to the SQLite database (this creates a new file called "coco_annotations.db" if it doesn't exist)
db_path = os.path.join(training_args.output_dir, "annotations.db")
if os.path.exists(db_path):
logger.info(f"Remove existing db file: {db_path}, and create a new one.")
os.remove(db_path)
conn = sqlite3.connect(db_path)
def _get_dataset_name_from_path(path):
return osp.splitext(osp.basename(path))[0]
process_dataset(
train_dataset,
training_args.max_train_samples,
"_".join([_get_dataset_name_from_path(i["path"]) for i in args.train_data]),
"train",
training_args,
tokenizer,
args,
conn,
)
for (eval_dataset_k, eval_dataset_v), eval_data_ in zip(eval_dataset.items(), args.eval_data):
process_dataset(
eval_dataset_v,
training_args.max_eval_samples,
_get_dataset_name_from_path(eval_data_["path"]),
f"eval-{eval_dataset_k}",
training_args,
tokenizer,
args,
conn,
)
# Commit the changes and close the connection
conn.commit()
conn.close()
SAMPLE_KEYS = ["image_id", "width", "height", "file_name", "coco_url", "task_type", "regions"]
REGION_KEYS = ["region_id", "image_id", "phrases", "x", "y", "width", "height"]
class PerRegionIterableDataset(IterableDataset):
def __init__(self, dataset, tokenizer, args, max_samples):
self.dataset = dataset
self.tokenizer = tokenizer
self.args = args
self.max_samples = max_samples
def get_len(self):
return len(self.dataset)
def generate(self):
for sample_idx, sample in enumerate(self.dataset):
if self.max_samples is not None and sample_idx >= self.max_samples:
break
for region in sample["regions"]:
phrases = region["phrases"]
tokenized_phrases = [self.tokenizer.tokenize(phrase) for phrase in phrases]
region["tokenized_phrases"] = tokenized_phrases
yield sample_idx, sample, region
def __iter__(self):
from itertools import islice
worker_info = torch.utils.data.get_worker_info()
worker_id = worker_info.id
num_workers = worker_info.num_workers
return_iter= islice(self.generate(), worker_id, None, num_workers)
return return_iter
def process_dataset(dataset, max_samples, dataset_name, split_name, training_args, tokenizer, args, conn, batch_size=100_000):
logger.info(f"Processing {dataset_name}.{split_name}: {dataset}...")
if dataset is None:
logger.warning(
f"[{training_args.process_index}/{training_args.world_size}]: {split_name} is None, skip processing"
)
return
per_region_dataset = PerRegionIterableDataset(dataset, tokenizer, args, max_samples)
pre_region_dataloader = DataLoader(per_region_dataset, batch_size=1, num_workers=args.training.dataloader_num_workers, collate_fn=lambda x: x[0])
cursor = conn.cursor()
# Create a table for storing the annotations
# cursor.execute(
# """
# CREATE TABLE IF NOT EXISTS annotations (
# id INTEGER PRIMARY KEY,
# image_id INTEGER,
# category_id INTEGER,
# segmentation TEXT,
# area REAL,
# bbox TEXT,
# iscrowd INTEGER
# )
# """
# )
def _clean_table_name(name):
return (
name.replace("-", "_")
.replace(".", "_")
.replace(" ", "_")
.replace(":", "_")
.replace("/", "_")
.lower()
.strip()
.replace("__", "_")
)
table_name = f"{dataset_name}_{split_name}"
table_name = _clean_table_name(table_name)
print(f"Creating {table_name} table...")
cursor.execute(
f"""
CREATE TABLE IF NOT EXISTS {table_name} (
region_id INTEGER PRIMARY KEY,
image_id INTEGER,
width INTEGER,
height INTEGER,
file_name TEXT,
coco_url TEXT,
task_type TEXT,
phrases TEXT,
tokenized_phrases TEXT,
x REAL,
y REAL,
region_width REAL,
region_height REAL
)
"""
)
region_cnt = 0
sample_cnt = 0
sent_cnt = 0
token_cnt = 0
word_cnt = 0
pbar = tqdm.tqdm(total=per_region_dataset.get_len())
prev_sample_idx = None
for sample_idx, sample, region in pre_region_dataloader:
phrases = region["phrases"]
tokenized_phrases = region["tokenized_phrases"]
dumped_phrases = json.dumps(phrases)
dumped_tokenized_phrases = json.dumps(tokenized_phrases)
sent_cnt += len(phrases)
region_cnt += 1
cursor.execute(
f"""
INSERT INTO {table_name} (region_id, image_id, width, height, file_name, coco_url, task_type, phrases, tokenized_phrases, x, y, region_width, region_height)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
region["region_id"],
sample["image_id"],
sample["width"],
sample["height"],
sample["file_name"],
sample["coco_url"],
sample["task_type"],
dumped_phrases,
dumped_tokenized_phrases,
region["x"],
region["y"],
region["width"],
region["height"],
),
)
sample_cnt += 1
if sample_cnt % batch_size == 0:
conn.commit()
if prev_sample_idx != sample_idx:
pbar.set_description(
f"[{training_args.process_index}/{training_args.world_size}]: Already processing {sample_cnt} samples, {region_cnt} regions, {sent_cnt} sentences, and {token_cnt} tokens."
)
pbar.update(1)
prev_sample_idx = sample_idx
conn.commit()
logger.info(
f"[{training_args.process_index}/{training_args.world_size}]: Total samples: {sample_cnt}, total regions: {region_cnt}, total sents: {sent_cnt}, total tokens: {token_cnt}"
)
if training_args.process_index == 0:
all_sample_cnt = sample_cnt
all_region_cnt = region_cnt
all_sent_cnt = sent_cnt
all_token_cnt = token_cnt
all_word_cnt = word_cnt
logger.info(
f"[FULL]: split name: {split_name}, total samples: {all_sample_cnt}, total regions: {all_region_cnt}, total sents: {all_sent_cnt}, total tokens: {all_token_cnt}, total words: {all_word_cnt}"
)
if __name__ == "__main__":
main()
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