| import os | |
| import json | |
| import base64 | |
| import natsort | |
| import numpy as np | |
| from PIL import Image | |
| from tqdm import tqdm | |
| import torch | |
| from torch.utils.data import Dataset, DataLoader | |
| from config import config | |
| from src.refine import refine_answer | |
| from src.run_gpt import run_gpt | |
| class CustomDatasetGPT(Dataset): | |
| def __init__(self, questions, num_input_imgs, num_select): | |
| self.questions = questions | |
| self.num_input_imgs = num_input_imgs | |
| self.num_select = num_select | |
| def __getitem__(self, index): | |
| line = self.questions[index] | |
| num_select = self.num_select | |
| num_input_imgs = self.num_input_imgs | |
| giter = 0 | |
| imgs_group = 8 | |
| num_groups = num_input_imgs//imgs_group | |
| kf_paths = line["kf_paths"] | |
| keywords = line["keywords"] | |
| simvalue = line["simvalue"] | |
| concatimg_dir = line['concatimg_dir'] | |
| concatimg_paths = natsort.natsorted([f"{concatimg_dir}/{im}" for im in os.listdir(concatimg_dir) if "ipynb" not in im]) | |
| concatimages = [] | |
| concatimages_base64 = [] | |
| qs_org = [] | |
| kw_perconcat = [] | |
| kf_paths_perconcat = [] | |
| simvalue_perconcat = [] | |
| segment_timeline = [] | |
| for concatidx, img_path in enumerate(concatimg_paths): | |
| concatimages.append(Image.open(img_path).convert('RGB')) | |
| concatimages_base64.append(img_path) | |
| kw_sidx = imgs_group*(concatidx) | |
| kw_eidx = imgs_group*(concatidx+1) | |
| concat_kw = keywords[kw_sidx:kw_eidx] | |
| qs_org_ = create_question(concat_kw, num_select) | |
| kw_perconcat.append(concat_kw) | |
| qs_org.append(qs_org_) | |
| kf_paths_perconcat.append(kf_paths[kw_sidx:kw_eidx]) | |
| simvalue_perconcat.append(simvalue[kw_sidx:kw_eidx]) | |
| segment_timeline.append(line["segment_timeline"]) | |
| concatimg_paths = concatimg_paths[-num_groups:] | |
| concatimages_base64 = concatimages_base64[-num_groups:] | |
| qs_org = qs_org[-num_groups:] | |
| kw_perconcat = kw_perconcat[-num_groups:] | |
| kf_paths_perconcat = kf_paths_perconcat[-num_groups:] | |
| simvalue_perconcat = simvalue_perconcat[-num_groups:] | |
| segment_timeline = segment_timeline[-num_groups:] | |
| return concatimages_base64, concatimages[0].size, kw_perconcat, kf_paths_perconcat, qs_org, segment_timeline, concatimg_paths, simvalue_perconcat | |
| def __len__(self): | |
| return len(self.questions) | |
| def create_question(concat_kw, num_select): | |
| imgkw_sen = "" | |
| for i, e in enumerate(concat_kw): | |
| if i < len(concat_kw) - 1: imgkw_sen = imgkw_sen + f"Image_{i}: '{e}', " | |
| else: imgkw_sen = imgkw_sen + f"Image_{i}: '{e}'." | |
| if num_select == 3: | |
| prompt = f"Eight images, having egocentric perspectives, are juxtaposed, separated by a red vertical line and red horizontal line. In the first row, the images from left to right are named as image_0, image_1, image_2, image_3. In the second row, the images from left to right are named as image_4, image_5, image_6, image_7. Here are images and their associated guess words: {imgkw_sen}. Think step-by-step and list only the names of the {num_select} images most closely related to the guessed words. Do not select blurry images in your answer. If none of the images correspond to the provided guess words, choose any two images at random. Your answer should follow the JSON format shown below and should only include the JSON result. Do not output any warnings or notes under any circumstances. Instead, adhere strictly to the provided JSON format example.\n" | |
| prompt += "{\"image name\": write reason for your selection in 10 words}." | |
| prompt += " This is one example output format. {\n \"image_0\": \"Person washing a plate; linked to dish cleaning.\",\n \"image_2\": \"Person washing a bowl; linked to dish cleaning.\",\n \"image_6\": \"Person running water on a sponge; related to dish cleaning.\"\n}" | |
| elif num_select == 4: | |
| prompt = f"Eight images, having egocentric perspectives, are juxtaposed, separated by a red vertical line and red horizontal line. In the first row, the images from left to right are named as image_0, image_1, image_2, image_3. In the second row, the images from left to right are named as image_4, image_5, image_6, image_7. Here are images and their associated guess words: {imgkw_sen}. Think step-by-step and list only the names of the {num_select} images most closely related to the guessed words. Do not select blurry images in your answer. If none of the images correspond to the provided guess words, choose any two images at random. Your answer should follow the JSON format shown below and should only include the JSON result. Do not output any warnings or notes under any circumstances. Instead, adhere strictly to the provided JSON format example.\n" | |
| prompt += "{\"image name\": write reason for your selection in 10 words}." | |
| prompt += " This is one example output format. {\n \"image_0\": \"Person washing a plate; linked to dish cleaning.\",\n \"image_2\": \"Person washing a bowl; linked to dish cleaning.\",\n \"image_6\": \"Person running water on a sponge; related to dish cleaning.\",\n \"image_7\": \"Person moves mouse; linked to working.\"\n}" | |
| else: assert False, f"num_select:{num_select} is not defined yet" | |
| return prompt | |
| def encode_image(image_path): | |
| with open(image_path, "rb") as image_file: | |
| return base64.b64encode(image_file.read()).decode('utf-8') | |
| def create_data_loader_gpt(questions, num_input_imgs, num_select, batch_size=1, num_workers=4): | |
| assert batch_size == 1, "batch_size must be 1" | |
| dataset = CustomDatasetGPT(questions, num_input_imgs, num_select) | |
| data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False) | |
| return data_loader, dataset | |
| def eval_model(): | |
| question_path, vlm, num_input_imgs, num_select, temp = config.kf_question_path, config.kf_vlm, config.kf_num_input_imgs, config.kf_num_select, config.kf_temp | |
| questions = [json.loads(q) for q in open(os.path.expanduser(question_path), "r")] | |
| num_questions = len(questions) | |
| giter = 0 | |
| answer_path = config.kf_answer_path | |
| os.makedirs(os.path.dirname(answer_path), exist_ok=True) | |
| print(f"question_path:{question_path}\nanswer_path:{answer_path}") | |
| ans_file = open(answer_path, "w") | |
| data_loader, dataset = create_data_loader_gpt(questions, num_input_imgs, num_select) | |
| outputs = "" | |
| for (image_paths, image_sizes, kw_perconcat, kf_paths_perconcat, cur_prompts, segment_timeline, concatimg_paths, simvalue_perconcat), line in tqdm(zip(data_loader, questions), total=len(questions)): | |
| idx, q_uid = line["q_uid"], line["q_uid"] | |
| CA = line["CA"] if "CA" in line else None | |
| option0 = line['option 0'] | |
| option1 = line['option 1'] | |
| option2 = line['option 2'] | |
| option3 = line['option 3'] | |
| option4 = line['option 4'] | |
| question = line['question'] | |
| pastobj = None | |
| past_VLM_path = None | |
| past_VLM_timeline = None | |
| kw_VLM = [] | |
| kf_paths_VLM = [] | |
| kf_timeline = [] | |
| kw_VLM_ordered = [] | |
| kf_paths_VLM_ordered = [] | |
| kf_timeline_ordered = [] | |
| prompts = [x[0] for x in cur_prompts] | |
| image_paths = [x[0] for x in image_paths] | |
| output_VLM = run_gpt( | |
| images=image_paths, | |
| texts=prompts, | |
| api_keys = list(config.dict_api.values()), | |
| max_tokens=2000, | |
| model=vlm, | |
| temperature=temp, | |
| num_threads=20, | |
| backoff_time=1*60, | |
| silent=False, | |
| dataset="egoschema", | |
| verbose=False, | |
| ) | |
| output_VLM = list(output_VLM) | |
| for j, _ in enumerate(cur_prompts): | |
| kf_paths_perconcat_ = kf_paths_perconcat[j] | |
| kf_timeline.append([f"{e[0].split('_')[-2]}.{e[0].split('_')[-1].split('.')[0]}" for e in kf_paths_perconcat_]) | |
| line_frame = line.copy() | |
| line_frame["output_VLM"] = output_VLM | |
| line_frame["concatimg_paths"] = concatimg_paths | |
| line_frame["kf_paths_VLM"] = kf_paths_perconcat | |
| line_frame["kf_timeline"] = kf_timeline | |
| line_frame["kw_perconcat_clip"] = kw_perconcat | |
| line_frame["iter"] = giter | |
| line_frame.pop("filepath") | |
| line_frame.pop("kf_paths") | |
| line_frame.pop("google_drive_id") | |
| try: ans_file.write(json.dumps(line_frame) + "\n") | |
| except: assert False, f"line_frame:{line_frame}" | |
| ans_file.close() | |
| print(f"question_path:{question_path}\nanswer_path:{answer_path}") | |
| print("job is done") | |
| if __name__ == "__main__": | |
| eval_model() | |
| refine_answer() | |