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Running
on
Zero
Running
on
Zero
| 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, get_model_name_from_path, KeywordsStoppingCriteria | |
| 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] | |
| 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) | |
| questions = json.load(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") | |
| for i, line in enumerate(tqdm(questions)): | |
| idx = line["id"] | |
| question = line['conversations'][0] | |
| qs = question['value'].replace('<image>', '').strip() | |
| cur_prompt = qs | |
| if 'image' in line: | |
| image_file = line["image"] | |
| image = Image.open(os.path.join(args.image_folder, image_file)) | |
| image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] | |
| images = image_tensor.unsqueeze(0).half().cuda() | |
| if getattr(model.config, 'mm_use_im_start_end', False): | |
| qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs | |
| else: | |
| qs = DEFAULT_IMAGE_TOKEN + '\n' + qs | |
| cur_prompt = '<image>' + '\n' + cur_prompt | |
| else: | |
| images = None | |
| if args.single_pred_prompt: | |
| qs = qs + '\n' + "Answer with the option's letter from the given choices directly." | |
| cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly." | |
| 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() | |
| input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() | |
| stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 | |
| keywords = [stop_str] | |
| stopping_criteria = [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if conv.version == "v0" else None | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=images, | |
| do_sample=True if args.temperature > 0 else False, | |
| temperature=args.temperature, | |
| max_new_tokens=1024, | |
| use_cache=True, | |
| stopping_criteria=stopping_criteria, | |
| ) | |
| 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)] | |
| outputs = outputs.strip() | |
| # prompt for answer | |
| if args.answer_prompter: | |
| outputs_reasoning = outputs | |
| input_ids = tokenizer_image_token(prompt + outputs_reasoning + ' ###\nANSWER:', tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=images, | |
| do_sample=True if args.temperature > 0 else False, | |
| temperature=args.temperature, | |
| max_new_tokens=64, | |
| use_cache=True, | |
| stopping_criteria=[stopping_criteria]) | |
| 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)] | |
| outputs = outputs.strip() | |
| outputs = outputs_reasoning + '\n The answer is ' + outputs | |
| 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.json") | |
| parser.add_argument("--answers-file", type=str, default="answer.jsonl") | |
| parser.add_argument("--conv-mode", type=str, default="llava_v0") | |
| 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("--answer-prompter", action="store_true") | |
| parser.add_argument("--single-pred-prompt", action="store_true") | |
| args = parser.parse_args() | |
| eval_model(args) | |