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| 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) |