import argparse import torch import os import json from tqdm import tqdm import shortuuid import numpy as np import re from libra.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from libra.conversation import conv_templates, SeparatorStyle from libra.model.builder import load_pretrained_model from libra.utils import disable_torch_init from libra.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path, KeywordsStoppingCriteria import math import pydicom from PIL import Image from io import BytesIO from pydicom.pixel_data_handlers.util import apply_voi_lut 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 load_images(image_file): """ Load an image from a local file, a URL, or a DICOM file. Args: image_file (str): The path or URL of the image file to load. Returns: PIL.Image.Image: The loaded image in RGB format. Raises: ValueError: If the DICOM file does not contain image data. TypeError: If the input is neither a valid file path nor a URL. """ if isinstance(image_file, str): # Case 1: Load from URL if image_file.startswith(('http://', 'https://')): try: response = requests.get(image_file) response.raise_for_status() image = Image.open(BytesIO(response.content)).convert('RGB') except Exception as e: raise ValueError(f"Error loading image from URL: {image_file}\n{e}") # Case 2: Load from DICOM file elif image_file.lower().endswith('.dcm'): try: dicom = pydicom.dcmread(image_file) if 'PixelData' in dicom: data = apply_voi_lut(dicom.pixel_array, dicom) # Handle MONOCHROME1 images if dicom.PhotometricInterpretation == "MONOCHROME1": data = np.max(data) - data # Normalize the image data data = data - np.min(data) data = data / np.max(data) data = (data * 255).astype(np.uint8) # Convert to 3-channel RGB if necessary if data.ndim == 2: data = np.stack([data] * 3, axis=-1) image = Image.fromarray(data).convert('RGB') else: raise ValueError("DICOM file does not contain image data") except Exception as e: raise ValueError(f"Error loading DICOM file: {image_file}\n{e}") # Case 3: Load standard image files (e.g., PNG, JPG) else: try: image = Image.open(image_file).convert('RGB') except Exception as e: raise ValueError(f"Error loading standard image file: {image_file}\n{e}") else: raise TypeError("image_file must be a string representing a file path or URL") return image def get_image_tensors(image_file, image_folder, image_processor, model, device='cuda'): # Load and preprocess the images if isinstance(image_file, str): image = [] image_path = os.path.join(image_folder, image_file) img = load_images(image_path) image.append(img) elif isinstance(image_file, (list, tuple)): image = [] image_paths = [os.path.join(image_folder, file_name) for file_name in image_file] for path in image_paths: img = load_images(path) image.append(img) else: raise TypeError("image_file must be a string or a str/list/tuple of strings") # Ensure two images are present if len(image) != 2: image.append(image[0]) if model.config.mm_projector_type == "TAC": print("Contains only current image. Adding a dummy prior image for TAC.") # Process each image processed_images = [] for img_data in image: image_temp = process_images([img_data], image_processor, model.config)[0] image_temp = image_temp.to(device=device, non_blocking=True) processed_images.append(image_temp) # Separate current and prior images cur_images = [processed_images[0]] prior_images = [processed_images[1]] # Stack and return as batched tensor batch_images = torch.stack([torch.stack(cur_images), torch.stack(prior_images)]) return batch_images def eval_model(args): """ Evaluate a pre-trained model on a set of questions and images. Args: args (Namespace): A namespace object containing the following attributes: - model_path (str): Path to the pre-trained model. - model_base (str): Base model name. - question_file (str): Path to the JSON file containing questions. - num_chunks (int): Number of chunks to split the questions into. - chunk_idx (int): Index of the chunk to process. - answers_file (str): Path to the file where answers will be saved. - image_folder (str): Folder containing the images. - conv_mode (str): Conversation mode to use. - temperature (float): Sampling temperature for generation. - top_p (float): Top-p sampling parameter. - num_beams (int): Number of beams for beam search. - max_new_tokens (int): Maximum number of new tokens to generate. - length_penalty (float): Length penalty for beam search. - num_return_sequences (int): Number of sequences to return. Raises: TypeError: If `image_file` is neither a string nor a list/tuple of strings. Returns: None """ # 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.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") for line in tqdm(questions): idx = line["question_id"] image_file = line["image"] qs = line["text"] cur_prompt = qs if 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() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() attention_mask = torch.ones(input_ids.shape, dtype=torch.long) pad_token_id = tokenizer.pad_token_id image_tensors = get_image_tensors(image_file, args.image_folder, image_processor, model) 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(): torch.cuda.empty_cache() if args.num_beams > 1: output_ids = model.generate( input_ids=input_ids, images=image_tensors, do_sample=False, num_beams=args.num_beams, no_repeat_ngram_size=3, max_new_tokens=args.max_new_tokens, stopping_criteria=[stopping_criteria], use_cache=True, length_penalty=args.length_penalty, output_scores=True, num_return_sequences = args.num_return_sequences, attention_mask=attention_mask, pad_token_id=pad_token_id) else: output_ids = model.generate( input_ids, images=image_tensors, do_sample= True, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, no_repeat_ngram_size=3, max_new_tokens=args.max_new_tokens, stopping_criteria=[stopping_criteria], use_cache=True, attention_mask=attention_mask, pad_token_id=pad_token_id) torch.cuda.empty_cache() 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() 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="libra") 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="question.jsonl") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="libra_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("--num_return_sequences", type=int, default=None) parser.add_argument("--length_penalty", type=float, default=1.0) parser.add_argument("--max_new_tokens", type=int, default=128) args = parser.parse_args() eval_model(args)