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| import argparse | |
| import torch | |
| import os | |
| import json | |
| from tqdm import tqdm | |
| import shortuuid | |
| from minigemini.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
| from minigemini.conversation import conv_templates, SeparatorStyle | |
| from minigemini.model.builder import load_pretrained_model | |
| from minigemini.utils import disable_torch_init | |
| from minigemini.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) # 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') | |
| if hasattr(self.model_config, 'image_size_aux'): | |
| if not hasattr(self.image_processor, 'image_size_raw'): | |
| self.image_processor.image_size_raw = self.image_processor.crop_size.copy() | |
| self.image_processor.crop_size['height'] = self.model_config.image_size_aux | |
| self.image_processor.crop_size['width'] = self.model_config.image_size_aux | |
| self.image_processor.size['shortest_edge'] = self.model_config.image_size_aux | |
| 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') | |
| image_grid = getattr(self.model_config, 'image_grid', 1) | |
| if hasattr(self.model_config, 'image_size_aux'): | |
| raw_shape = [self.image_processor.image_size_raw['height'] * image_grid, | |
| self.image_processor.image_size_raw['width'] * image_grid] | |
| image_tensor_aux = image_tensor | |
| image_tensor = torch.nn.functional.interpolate(image_tensor[None], | |
| size=raw_shape, | |
| mode='bilinear', | |
| align_corners=False)[0] | |
| else: | |
| image_tensor_aux = [] | |
| if image_grid >= 2: | |
| raw_image = image_tensor.reshape(3, | |
| image_grid, | |
| self.image_processor.image_size_raw['height'], | |
| image_grid, | |
| self.image_processor.image_size_raw['width']) | |
| raw_image = raw_image.permute(1, 3, 0, 2, 4) | |
| raw_image = raw_image.reshape(-1, 3, | |
| self.image_processor.image_size_raw['height'], | |
| self.image_processor.image_size_raw['width']) | |
| if getattr(self.model_config, 'image_global', False): | |
| global_image = image_tensor | |
| if len(global_image.shape) == 3: | |
| global_image = global_image[None] | |
| global_image = torch.nn.functional.interpolate(global_image, | |
| size=[self.image_processor.image_size_raw['height'], | |
| self.image_processor.image_size_raw['width']], | |
| mode='bilinear', | |
| align_corners=False) | |
| # [image_crops, image_global] | |
| raw_image = torch.cat([raw_image, global_image], dim=0) | |
| image_tensor = raw_image.contiguous() | |
| return input_ids, image_tensor, image_tensor_aux | |
| 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): | |
| 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) | |
| 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, load_8bit=args.load_8bit) | |
| 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 args.conv_mode 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_tensor_aux), line in tqdm(zip(data_loader, questions), total=len(questions)): | |
| idx = line["question_id"] | |
| cur_prompt = line["text"] | |
| input_ids = input_ids.to(device=model.device, non_blocking=True) | |
| if hasattr(model, "update_prompt"): | |
| model.update_prompt([[cur_prompt]]) | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=image_tensor.to(dtype=model.dtype, device=model.device, non_blocking=True), | |
| images_aux=image_tensor_aux.to(dtype=model.dtype, device=model.device, non_blocking=True) if len(image_tensor_aux)>0 else None, | |
| 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, | |
| bos_token_id=tokenizer.bos_token_id, # Begin of sequence token | |
| eos_token_id=tokenizer.eos_token_id, # End of sequence token | |
| pad_token_id=tokenizer.pad_token_id, # Pad token | |
| 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', type=bool, default=False) | |
| parser.add_argument("--max_new_tokens", type=int, default=128) | |
| args = parser.parse_args() | |
| eval_model(args) | |