import argparse import torch import os import json from tqdm import tqdm import shortuuid import random 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.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 torchvision import transforms from open_flamingo.eval.models.of_eval_model_adv import EvalModelAdv from open_flamingo.eval.vqa_metric import ( compute_vqa_accuracy, postprocess_vqa_generation, ) from PIL import Image import math import warnings warnings.filterwarnings("ignore") 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] def get_of_args(pretrained_rob_path=None): model_args = {} model_args['vision_encoder_pretrained'] = pretrained_rob_path model_args['vision_encoder_path'] = 'ViT-L-14' model_args['lm_path'] = 'anas-awadalla/mpt-7b' model_args['lm_tokenizer_path'] = 'anas-awadalla/mpt-7b' model_args['checkpoint_path'] = '/data/naman_deep_singh/project_multimodal/OpenFlamingo-9B-vitl-mpt7b.pt' # model_args['device'] = 'cuda' model_args['cross_attn_every_n_layers'] = 4 model_args['precision'] = 'float32' return model_args # Custom dataset class class CustomDataset(Dataset): def __init__(self, questions, image_folder, tokenizer, image_processor, model_config, model='LLAVA'): self.questions = questions self.image_folder = image_folder self.tokenizer = tokenizer self.image_processor = image_processor self.model_config = model_config self.model = model def __getitem__(self, index): line = self.questions[index] image_file = line["image"] qs = line["text"] if self.model == 'LLAVA': 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 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() if self.model == 'LLAVA': 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] else: image = Image.open(os.path.join(self.image_folder, image_file)) # image.load() transform = transforms.Compose([ transforms.ToTensor() ]) image_tensor = transform(image) #.squeeze(0) #.load() input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') return input_ids, image_tensor def __len__(self): return len(self.questions) # DataLoader def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4, model='LLAVA'): assert batch_size == 1, "batch_size must be 1" dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config, model) data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False) 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) if args.pretrained_rob_path == 'None': args.pretrained_rob_path = None print(f"Model at: {args.pretrained_rob_path}") print(f"Need to load llava") if args.eval_model == 'LLAVA': model, image_processor, tokenizer, context_len = load_pretrained_model(model_path, args.model_base, model_name, pretrained_rob_path=args.pretrained_rob_path) else: _, image_processor, tokenizer, context_len = load_pretrained_model(model_path, args.model_base, model_name, pretrained_rob_path=args.pretrained_rob_path) model_args = get_of_args(args.pretrained_rob_path) eval_model = EvalModelAdv(model_args, adversarial=False) os.environ["CUDA_VISIBLE_DEVICES"] = str(0) device_id = 0 eval_model.set_device(device_id) # model.config = None 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 if args.eval_model == 'LLAVA' else None, model.config if args.eval_model == 'LLAVA' else None, model=args.eval_model) for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)): idx = line["question_id"] cur_prompt = line["text"] if args.eval_model == 'LLAVA': stop_str = conv_templates[args.conv_mode].sep if conv_templates[args.conv_mode].sep_style != SeparatorStyle.TWO else conv_templates[args.conv_mode].sep2 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), 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=128, use_cache=True) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] predictions = outputs.strip() else: transs = transforms.ToPILImage() ims = [] ims.append(transs(image_tensor.squeeze())) image_tensor = [] image_tensor.append(ims) batch_images = eval_model._prepare_images(image_tensor) batch_text = [] yes_no = random.choice(['yes', 'no']) add_str_1 = 'Is there some object in the image?' add_str_2 = 'Is the image taken during day time?' context_text = f"Question:{add_str_1} answer:{yes_no}<|endofchunk|>" context_text += f"Question:{add_str_2} answer:{yes_no}<|endofchunk|>" context_text += f"Question:{cur_prompt} answer:" # Keep the text but remove the image tags for the zero-shot case # if num_shots == 0: # context_text = context_text.replace("", "") batch_text.append( context_text + eval_model.get_vqa_prompt(question=cur_prompt) ) # print(cur_prompt) # batch_text.append(cur_prompt) outputs = eval_model.get_outputs( batch_images=batch_images, batch_text=batch_text, min_generation_length=0, max_generation_length=1, num_beams=3, length_penalty=-2.0, ) dataset_name = 'coco' process_function = ( postprocess_ok_vqa_generation if dataset_name == "ok_vqa" else postprocess_vqa_generation ) new_predictions = map(process_function, outputs) #.strip() predictions = [] for new_prediction, sample_id in zip(new_predictions, cur_prompt): predictions.append(new_prediction) # outputs = outputs.strip() predictions = predictions[0].strip() # print(predictions) ans_id = shortuuid.uuid() ans_file.write(json.dumps({"question_id": idx, "prompt": cur_prompt, "text": predictions, "answer_id": ans_id, "model_id": model_name if args.eval_model == 'LLAVA' else args.eval_model, "metadata": {}}) + "\n") # ans_file.flush() ans_file.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="liuhaotian/llava-v1.5-7b") parser.add_argument("--pretrained_rob_path", type=str, default='openai', help='Pass None, openai or path-to-rob-ckpt') # "/data/naman_deep_singh/project_multimodal/clip-finetune/sbatch/ViT-L-14_openai_imagenet_txtSup_False_vit-l-unsup-clean-0p1-eps4-3adv-lr1e-4-wd-1e-3_f8o0v/checkpoints/final.pt") # /mnt/nsingh/project_multimodal/models/ViT-L-14_openai_imagenet_txtSup_False_vit-l-unsup-clean-0p1-eps4-3adv-lr1e-4-wd-1e-3_f8o0v/checkpoints/final.pt parser.add_argument("--eval-model", type=str, default='LLAVA') 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) args = parser.parse_args() eval_model(args)