# ------------------------------------------------------------------------ # Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ------------------------------------------------------------------------ # Modified from LLaVA (https://github.com/haotian-liu/LLaVA) and S^2(https://github.com/bfshi/scaling_on_scales) # Copyright 2024 Jiachen Li # ------------------------------------------------------------------------ import argparse import torch import os import json from tqdm import tqdm import shortuuid from cumo.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from cumo.conversation import conv_templates, SeparatorStyle from cumo.model.builder import load_pretrained_model from cumo.utils import disable_torch_init from cumo.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 import pdb def split_list(lst, n): """Split a list into n (roughly) equal-sized chunks""" chunk_size = math.ceil(len(lst) / n) # integer division 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] # Custom dataset class class CustomDataset(Dataset): def __init__(self, questions, image_folder, tokenizer, image_processor, model_config): self.questions = questions self.image_folder = image_folder self.tokenizer = tokenizer self.image_processor = image_processor self.model_config = model_config 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 = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() 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 # DataLoader def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, 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) 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): # Model 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) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, use_flash_attn=args.use_flash_attn) model.config.training = False 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") 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) 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, pad_token_id=tokenizer.eos_token_id, 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("--load-8bit", action="store_true") parser.add_argument("--load-4bit", action="store_true") parser.add_argument("--use-flash-attn", action="store_true") parser.add_argument("--max_new_tokens", type=int, default=128) args = parser.parse_args() eval_model(args)