| import argparse |
| import torch |
| import os |
| import json |
| from tqdm import tqdm |
| import shortuuid |
|
|
| from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN |
| from llava.conversation import conv_templates, SeparatorStyle |
| from llava import conversation as conversation_lib |
| from llava.model.builder import load_pretrained_model |
| from llava.utils import disable_torch_init |
| from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path |
| from torch.utils.data import Dataset, DataLoader |
|
|
| from PIL import Image |
| import math |
|
|
|
|
| def split_list(lst, n): |
| """Split a list into n (roughly) equal-sized chunks""" |
| chunk_size = math.ceil(len(lst) / n) |
| return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] |
|
|
|
|
| def get_chunk(lst, n, k): |
| chunks = split_list(lst, n) |
| return chunks[k] |
|
|
|
|
| |
| class CustomDataset(Dataset): |
| def __init__(self, questions, image_folder, tokenizer, image_processor, model_config, voco_num): |
| self.questions = questions |
| self.image_folder = image_folder |
| self.tokenizer = tokenizer |
| self.image_processor = image_processor |
| self.model_config = model_config |
| self.voco_num = voco_num |
| print("voco_num is ", voco_num) |
|
|
| def __getitem__(self, index): |
| line = self.questions[index] |
| image_file = line["image"] |
| qs = line["text"] |
| if self.model_config.mm_use_im_start_end: |
| qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs |
| else: |
| qs = DEFAULT_IMAGE_TOKEN + '\n' + qs |
|
|
| |
| conv = conversation_lib.voco_default_conversation.copy() |
| conv.append_message(conv.roles[0], qs) |
| conv.append_message(conv.roles[1], None) |
| prompt = conv.get_prompt() |
|
|
| |
| maybe_voco_str = "".join( |
| ["<voco>" for _ in range(self.voco_num)] |
| ) |
| prompt = f"<image>\n{maybe_voco_str}\n" + prompt.replace("\n", '').replace("<image>", '') |
|
|
| image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB') |
| image_tensor = process_images([image], self.image_processor, self.model_config)[0] |
|
|
| input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') |
|
|
| return input_ids, image_tensor, image.size |
|
|
| def __len__(self): |
| return len(self.questions) |
|
|
|
|
| def collate_fn(batch): |
| input_ids, image_tensors, image_sizes = zip(*batch) |
| input_ids = torch.stack(input_ids, dim=0) |
| image_tensors = torch.stack(image_tensors, dim=0) |
| return input_ids, image_tensors, image_sizes |
|
|
|
|
| |
| def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, voco_num, batch_size=1, num_workers=4): |
| assert batch_size == 1, "batch_size must be 1" |
| dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config, voco_num) |
| data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, collate_fn=collate_fn) |
| return data_loader |
|
|
|
|
| def eval_model(args): |
| |
| disable_torch_init() |
| model_path = os.path.expanduser(args.model_path) |
| model_name = get_model_name_from_path(model_path) |
| tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, llava_model="initial") |
|
|
| print("*************", len(tokenizer)) |
|
|
| questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] |
| questions = get_chunk(questions, args.num_chunks, args.chunk_idx) |
| answers_file = os.path.expanduser(args.answers_file) |
| os.makedirs(os.path.dirname(answers_file), exist_ok=True) |
| ans_file = open(answers_file, "w") |
| voco_num = args.voco_num |
|
|
| if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode: |
| args.conv_mode = args.conv_mode + '_mmtag' |
| print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.') |
|
|
| data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config, voco_num) |
|
|
| for (input_ids, image_tensor, image_sizes), line in tqdm(zip(data_loader, questions), total=len(questions)): |
| idx = line["question_id"] |
| cur_prompt = line["text"] |
|
|
| input_ids = input_ids.to(device='cuda', non_blocking=True) |
|
|
| with torch.inference_mode(): |
| output_ids = model.generate( |
| input_ids, |
| images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True), |
| image_sizes=image_sizes, |
| do_sample=True if args.temperature > 0 else False, |
| temperature=args.temperature, |
| top_p=args.top_p, |
| num_beams=args.num_beams, |
| max_new_tokens=args.max_new_tokens, |
| use_cache=True) |
|
|
| outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() |
|
|
| ans_id = shortuuid.uuid() |
| ans_file.write(json.dumps({"question_id": idx, |
| "prompt": cur_prompt, |
| "text": outputs, |
| "answer_id": ans_id, |
| "model_id": model_name, |
| "metadata": {}}) + "\n") |
| ans_file.flush() |
| ans_file.close() |
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--model-path", type=str, default="facebook/opt-350m") |
| parser.add_argument("--model-base", type=str, default=None) |
| parser.add_argument("--image-folder", type=str, default="") |
| parser.add_argument("--question-file", type=str, default="tables/question.jsonl") |
| parser.add_argument("--answers-file", type=str, default="answer.jsonl") |
| parser.add_argument("--conv-mode", type=str, default="llava_v1") |
| parser.add_argument("--num-chunks", type=int, default=1) |
| parser.add_argument("--chunk-idx", type=int, default=0) |
| parser.add_argument("--temperature", type=float, default=0.2) |
| parser.add_argument("--top_p", type=float, default=None) |
| parser.add_argument("--num_beams", type=int, default=1) |
| parser.add_argument("--max_new_tokens", type=int, default=128) |
| parser.add_argument("--voco_num", type=int, default=None) |
| args = parser.parse_args() |
|
|
| eval_model(args) |
|
|
