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| import argparse | |
| import torch | |
| 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 process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria | |
| import requests | |
| import pydicom | |
| from PIL import Image | |
| from io import BytesIO | |
| from pydicom.pixel_data_handlers.util import apply_voi_lut | |
| from transformers import TextStreamer | |
| 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 main(args): | |
| """ | |
| Main function to load a pretrained model, process images, and interact with the user through a conversation loop. | |
| Args: | |
| args (Namespace): A namespace object containing the following attributes: | |
| model_path (str): Path to the pretrained model. | |
| model_base (str): Base model name. | |
| load_8bit (bool): Flag to load the model in 8-bit precision. | |
| load_4bit (bool): Flag to load the model in 4-bit precision. | |
| device (str): Device to load the model on (e.g., 'cuda', 'cpu'). | |
| conv_mode (str, optional): Conversation mode to use. If None, it will be inferred from the model name. | |
| image_file (list): List of paths to image files to be processed. | |
| temperature (float): Sampling temperature for text generation. | |
| max_new_tokens (int): Maximum number of new tokens to generate. | |
| debug (bool): Flag to enable debug mode for additional output. | |
| Raises: | |
| EOFError: If an EOFError is encountered during user input, the loop will exit. | |
| """ | |
| # Model | |
| disable_torch_init() | |
| model_name = get_model_name_from_path(args.model_path) | |
| tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device) | |
| if 'libra' in model_name.lower(): | |
| conv_mode = "libra_v1" | |
| if args.conv_mode is not None and conv_mode != args.conv_mode: | |
| print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) | |
| else: | |
| args.conv_mode = conv_mode | |
| conv = conv_templates[args.conv_mode].copy() | |
| roles = conv.roles | |
| image=[] | |
| for path in args.image_file: | |
| img = load_images(path) | |
| image.append(img) | |
| # set dummy prior image | |
| if len(image) == 1: | |
| print("Contains only current image. Adding a dummy prior image.") | |
| image.append(image[0]) | |
| processed_images = [] | |
| for img_data in image: | |
| image_temp = process_images([img_data], image_processor, model.config)[0] | |
| image_temp = image_temp.to(device='cuda',non_blocking=True) | |
| processed_images.append(image_temp) | |
| cur_images = [processed_images[0]] | |
| prior_images = [processed_images[1]] | |
| image_tensor = torch.stack([torch.stack(cur_images), torch.stack(prior_images)]) | |
| image_tensor = image_tensor.to(model.device, dtype=torch.float16) | |
| while True: | |
| try: | |
| inp = input(f"{roles[0]}: ") | |
| except EOFError: | |
| inp = "" | |
| if not inp: | |
| print("exit...") | |
| break | |
| print(f"{roles[1]}: ", end="") | |
| if image is not None: | |
| # first message | |
| if model.config.mm_use_im_start_end: | |
| inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp | |
| else: | |
| inp = DEFAULT_IMAGE_TOKEN + '\n' + inp | |
| image = None | |
| conv.append_message(conv.roles[0], inp) | |
| 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).to(model.device) | |
| stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 | |
| keywords = [stop_str] | |
| stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) | |
| streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) | |
| attention_mask = torch.ones(input_ids.shape, dtype=torch.long) | |
| pad_token_id = tokenizer.pad_token_id | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=image_tensor, | |
| do_sample=True if args.temperature > 0 else False, | |
| temperature=args.temperature, | |
| max_new_tokens=args.max_new_tokens, | |
| streamer=streamer, | |
| use_cache=True, | |
| attention_mask=attention_mask, | |
| pad_token_id=pad_token_id, | |
| stopping_criteria=[stopping_criteria]) | |
| outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:],skip_special_tokens=True).strip() | |
| conv.messages[-1][-1] = outputs | |
| if args.debug: | |
| print("\n", {"prompt": prompt, "outputs": outputs}, "\n") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model-path", type=str, default="X-iZhang/libra-v1.0-7b") | |
| parser.add_argument("--model-base", type=str, default=None) | |
| parser.add_argument("--image-file", type=str, nargs="+", required=True, help="List of image files to process.") | |
| parser.add_argument("--device", type=str, default="cuda") | |
| parser.add_argument("--conv-mode", type=str, default="libra_v1") | |
| parser.add_argument("--temperature", type=float, default=0.5) | |
| parser.add_argument("--max-new-tokens", type=int, default=512) | |
| parser.add_argument("--load-8bit", action="store_true") | |
| parser.add_argument("--load-4bit", action="store_true") | |
| parser.add_argument("--debug", action="store_true") | |
| args = parser.parse_args() | |
| main(args) |