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.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, expand2square, KeywordsStoppingCriteria from llava.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IMAGE_TOKEN_INDEX from torch.utils.data import Dataset, DataLoader from typing import Dict, Optional, Sequence, List import transformers import re from PIL import Image import math from llava.slice_process import slice_image_minicpm, split_image, resize_image_keep_ratio 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 preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict: roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"} im_start, im_end = tokenizer.additional_special_tokens_ids nl_tokens = tokenizer("\n").input_ids _system = tokenizer("system").input_ids + nl_tokens _user = tokenizer("user").input_ids + nl_tokens _assistant = tokenizer("assistant").input_ids + nl_tokens # Apply prompt templates input_ids, targets = [], [] source = sources if roles[source[0]["from"]] != roles["human"]: source = source[1:] input_id, target = [], [] system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens input_id += system target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens assert len(input_id) == len(target) for j, sentence in enumerate(source): role = roles[sentence["from"]] if has_image and sentence["value"] is not None and "" in sentence["value"]: num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"])) texts = sentence["value"].split('') _input_id = tokenizer(role).input_ids + nl_tokens for i,text in enumerate(texts): _input_id += tokenizer(text).input_ids if iuser": _target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens elif role == "<|im_start|>assistant": _target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens else: raise NotImplementedError target += _target input_ids.append(input_id) targets.append(target) input_ids = torch.tensor(input_ids, dtype=torch.long) targets = torch.tensor(targets, dtype=torch.long) return input_ids # 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"] processor = self.image_processor 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] # 2x2切片 # image = expand2square(image, tuple(int(x*255) for x in processor.image_mean)) # sub_images = split_image(image, scale=672, grid=(2, 2)) # sub_images.append(image) # image = sub_images # image = processor.preprocess(image, return_tensors='pt')['pixel_values'] # bs, 3, h, w # image_tensor = image.flatten(0, 1) # adapt # image, _, _, _ = slice_image_minicpm( # image, max_slice_nums=7, scale_resolution=336, patch_size=14, never_split=False) # image = processor.preprocess(image, do_resize=False, do_center_crop=False, # do_rescale=True, do_normalize=True, return_tensors='pt')['pixel_values'][0] # image_tensor = image image = resize_image_keep_ratio(image, max_size=1024) source_image, patches, best_grid, ind_tokens = slice_image_minicpm( image, max_slice_nums=7, scale_resolution=336, patch_size=14, never_split=False) if best_grid is None: #说明没有切片 source_tensors = processor.preprocess(source_image, do_resize=False, do_center_crop=False, do_rescale=True, do_normalize=True, return_tensors='pt')['pixel_values'] # 1, 3, abs_h, abs_w crop_size = processor.crop_size patch_tensors = torch.zeros(1, 3, crop_size['height'], crop_size['width']) else: source_tensors = processor.preprocess(source_image, do_resize=False, do_center_crop=False, do_rescale=True, do_normalize=True, return_tensors='pt')['pixel_values'] # 1, 3, abs_h, abs_w patch_tensors = processor.preprocess(patches, do_resize=False, do_center_crop=False, do_rescale=True, do_normalize=True, return_tensors='pt')['pixel_values'] # num_slice, 3, s_h, s_w image_tensor = source_tensors[0] # 3, h, w patch_images = patch_tensors # bs, 3, h, w input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') return input_ids, image_tensor, image.size, patch_images, ind_tokens def __len__(self): return len(self.questions) def collate_fn(batch): input_ids, image_tensors, image_sizes, patch_images, ind_tokens = 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, patch_images, ind_tokens # 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, _args=args) 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, patch_images, ind_tokens), 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) image_tensor = [image_tensor[0].to(dtype=torch.float16, device='cuda', non_blocking=True)] patch_images = [item.to(dtype=torch.float16, device='cuda', non_blocking=True) for item in patch_images] args.conv_mode = "qwen_1_5" conv = conv_templates[args.conv_mode].copy() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor, image_sizes=image_sizes, patch_images=patch_images, ind_tokens=ind_tokens, 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] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.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("--fted_encoder", type=bool, default=True) args = parser.parse_args() eval_model(args)