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| from __future__ import annotations | |
| import json | |
| import time | |
| from argparse import ArgumentParser | |
| from pathlib import Path | |
| from typing import Optional | |
| import datasets | |
| import numpy as np | |
| import openai | |
| from tqdm.auto import tqdm | |
| DELIMITER_0 = "\n##\n" | |
| DELIMITER_1 = "\n%%\n" | |
| STOP = "\nEND" | |
| def generate( | |
| openai_model: str, | |
| caption: str, | |
| num_retries: int = 3, | |
| max_tokens: int = 256, | |
| temperature: float = 0.7, | |
| top_p: float = 1.0, | |
| frequency_penalty: float = 0.1, | |
| presence_penalty: float = 0.0, | |
| sleep_on_error: float = 1.0, | |
| ) -> Optional[tuple[str, str]]: | |
| for _ in range(1 + num_retries): | |
| try: | |
| response = openai.Completion.create( | |
| model=openai_model, | |
| prompt=caption + DELIMITER_0, | |
| temperature=temperature, | |
| max_tokens=max_tokens, | |
| top_p=top_p, | |
| frequency_penalty=frequency_penalty, | |
| presence_penalty=presence_penalty, | |
| stop=[STOP], | |
| ) | |
| except Exception as e: | |
| print(e) | |
| time.sleep(sleep_on_error) | |
| continue | |
| output = response["choices"][0]["text"].split(DELIMITER_1) | |
| if len(output) == 2: | |
| instruction, edited_caption = output | |
| results = openai.Moderation.create([instruction, edited_caption])["results"] | |
| if results[0]["flagged"] or results[1]["flagged"]: | |
| continue | |
| if caption.strip().strip(".!?").lower() != edited_caption.strip().strip(".!?").lower(): | |
| return instruction, edited_caption | |
| def main(openai_model: str, num_samples: int, num_partitions: int, partition: int, seed: int): | |
| dataset = datasets.load_dataset("ChristophSchuhmann/improved_aesthetics_6.5plus", split="train") | |
| # Other datasets we considered that may be worth trying: | |
| # dataset = datasets.load_dataset("ChristophSchuhmann/MS_COCO_2017_URL_TEXT", split="train") | |
| # dataset = datasets.load_dataset("laion/laion-coco", split="train") | |
| np.random.seed(seed) | |
| permutation = np.array_split(np.random.permutation(len(dataset)), num_partitions)[partition] | |
| dataset = dataset[permutation] | |
| captions = dataset["TEXT"] | |
| urls = dataset["URL"] | |
| output_path = f"data/dataset=laion-aesthetics-6.5_model={openai_model}_samples={num_samples}_partition={partition}.jsonl" # fmt: skip | |
| print(f"Prompt file path: {output_path}") | |
| count = 0 | |
| caption_set = set() | |
| url_set = set() | |
| if Path(output_path).exists(): | |
| with open(output_path, "r") as f: | |
| for line in tqdm(f, desc="Resuming from existing prompts"): | |
| prompt = json.loads(line) | |
| if prompt["caption"] not in caption_set and prompt["url"] not in url_set: | |
| caption_set.add(prompt["caption"]) | |
| url_set.add(prompt["url"]) | |
| count += 1 | |
| with open(output_path, "a") as fp: | |
| with tqdm(total=num_samples - count, desc="Generating instructions and edited captions") as progress_bar: | |
| for caption, url in zip(captions, urls): | |
| if caption in caption_set or url in url_set: | |
| continue | |
| if openai.Moderation.create(caption)["results"][0]["flagged"]: | |
| continue | |
| edit_output = generate(openai_model, caption) | |
| if edit_output is not None: | |
| edit, output = edit_output | |
| fp.write(f"{json.dumps(dict(caption=caption, edit=edit, output=output, url=url))}\n") | |
| count += 1 | |
| progress_bar.update() | |
| caption_set.add(caption) | |
| url_set.add(url) | |
| if count == num_samples: | |
| break | |
| if __name__ == "__main__": | |
| parser = ArgumentParser() | |
| parser.add_argument("--openai-api-key", required=True, type=str) | |
| parser.add_argument("--openai-model", required=True, type=str) | |
| parser.add_argument("--num-samples", default=10000, type=int) | |
| parser.add_argument("--num-partitions", default=1, type=int) | |
| parser.add_argument("--partition", default=0, type=int) | |
| parser.add_argument("--seed", default=0, type=int) | |
| args = parser.parse_args() | |
| openai.api_key = args.openai_api_key | |
| main(args.openai_model, args.num_samples, args.num_partitions, args.partition, args.seed) | |