|
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) |
|
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): |
|
|
|
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}") |
|
|
|
|
|
elif image_file.lower().endswith('.dcm'): |
|
try: |
|
dicom = pydicom.dcmread(image_file) |
|
if 'PixelData' in dicom: |
|
data = apply_voi_lut(dicom.pixel_array, dicom) |
|
|
|
|
|
if dicom.PhotometricInterpretation == "MONOCHROME1": |
|
data = np.max(data) - data |
|
|
|
|
|
data = data - np.min(data) |
|
data = data / np.max(data) |
|
data = (data * 255).astype(np.uint8) |
|
|
|
|
|
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}") |
|
|
|
|
|
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'): |
|
|
|
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") |
|
|
|
|
|
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.") |
|
|
|
|
|
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) |
|
|
|
|
|
cur_images = [processed_images[0]] |
|
prior_images = [processed_images[1]] |
|
|
|
|
|
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 |
|
""" |
|
|
|
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) |