Delete create_align_dataset.py

create_align_dataset.py

@@ -1,134 +0,0 @@
-import argparse
-import os
-import json
-from tqdm import tqdm
-import random
-import numpy as np
-from PIL import Image
-import webdataset as wds
-import torch
-from torchvision.datasets import ImageFolder
-import torchvision.transforms as transforms
-
-import openai
-from tenacity import (
-    retry,
-    stop_after_attempt,
-    wait_random_exponential,
-)  # for exponential backoff
-
-from minigpt4.common.config import Config
-from minigpt4.common.registry import registry
-from minigpt4.conversation.conversation import Chat
-
-openai.api_key = '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'
-
-
-def prepare_chatgpt_message(task_prompt, paragraph):
-    messages = [{"role": "system", "content": task_prompt},
-                {"role": "user", "content": paragraph}]
-    return messages
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def call_chatgpt(chatgpt_messages, max_tokens=200, model="gpt-3.5-turbo"):
-    response = openai.ChatCompletion.create(model=model, messages=chatgpt_messages, temperature=0.7, max_tokens=max_tokens)
-    reply = response['choices'][0]['message']['content']
-    total_tokens = response['usage']['total_tokens']
-    return reply, total_tokens
-
-
-def main(args):
-
-    print('Initializing Chat')
-    cfg = Config(args)
-
-    model_config = cfg.model_cfg
-    model_cls = registry.get_model_class(model_config.arch)
-    model = model_cls.from_config(model_config).to('cuda:{}'.format(args.device))
-
-    ckpt_path = '/ibex/project/c2133/vicuna_ckpt_test/Vicuna_pretrain_stage2_cc/20230405233_3GPU40kSTEP_MAIN/checkpoint_3.pth'
-    ckpt = torch.load(ckpt_path)
-    msg = model.load_state_dict(ckpt['model'], strict=False)
-
-
-    vis_processor_cfg = cfg.datasets_cfg.cc_combine.vis_processor.train
-    vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-
-    text_processor_cfg = cfg.datasets_cfg.laion.text_processor.train
-    text_processor = registry.get_processor_class(text_processor_cfg.name).from_config(text_processor_cfg)
-
-    chat = Chat(model, vis_processor, args.device)
-    print('Initialization Finished')
-
-
-
-    texts = {}
-    negative_list = []
-
-    for i in tqdm(range(args.begin_id, args.end_id)):
-        image = Image.open(os.path.join(args.save_dir, 'image/{}.jpg'.format(i))).convert('RGB')
-
-        fix_prompt = \
-            "Fix the error in the given paragraph. " \
-            "Remove any repeating sentences, meanless characters, not English sentences, and so on." \
-            "Remove unnecessary repetition." \
-            "Rewrite any incomplete sentences." \
-            "Return directly the results WITHOUT explanation." \
-            "Return directly the input paragraph if it is already correct WITHOUT explanation."
-
-        answers = []
-        answer_tokens = 0
-        chat.reset()
-        chat.upload_img(image)
-        chat.ask("Describe this image in detail. Give as many details as possible. Say everything you see.")
-        answer, tokens = chat.answer()
-        answers.append(answer)
-        answer_tokens += tokens
-        if len(answer_tokens) < 80:
-            chat.ask("Continue")
-            answer, answer_token = chat.answer()
-            answers.append(answer)
-            answer_tokens += tokens
-        answer = ' '.join(answers)
-
-        chatgpt_message = prepare_chatgpt_message(fix_prompt, answer)
-        improved_answer, num_token = call_chatgpt(chatgpt_message)
-
-        if 'already correct' in improved_answer:
-            if 'repetition' in improved_answer:
-                continue
-            improved_answer = answer
-        if 'incomplete' in improved_answer or len(improved_answer) < 50:
-            negative_list.append(improved_answer)
-        else:
-            texts[i] = improved_answer
-
-    with open(os.path.join(args.save_dir, "cap_{}_{}.json".format(args.begin_id, args.end_id)), "w") as outfile:
-        # write the dictionary to the file in JSON format
-        json.dump(texts, outfile)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Create Alignment")
-
-    parser.add_argument("--cfg-path", default='train_config/minigpt4_stage2_align.yaml')
-    parser.add_argument("--save-dir", default="/ibex/project/c2133/blip_dataset/image_alignment")
-    parser.add_argument("--begin-id", type=int)
-    parser.add_argument("--end-id", type=int)
-    parser.add_argument("--device", type=int)
-    parser.add_argument(
-        "--options",
-        nargs="+",
-        help="override some settings in the used config, the key-value pair "
-             "in xxx=yyy format will be merged into config file (deprecate), "
-             "change to --cfg-options instead.",
-    )
-
-    args = parser.parse_args()
-
-    print("begin_id: ", args.begin_id)
-    print("end_id: ",  args.end_id)
-    print("device:", args.device)
-
-    main(args)