diff --git a/benchmark/mmmu/bench_hf.py b/benchmark/mmmu/bench_hf.py index 385171e95..0295bc5dc 100644 --- a/benchmark/mmmu/bench_hf.py +++ b/benchmark/mmmu/bench_hf.py @@ -17,6 +17,13 @@ from transformers import AutoModel, AutoProcessor, GenerationConfig @torch.no_grad() def eval_mmmu(args): eval_args = EvalArgs.from_cli_args(args) + + sampling_params = get_sampling_params(eval_args) + generation_config = GenerationConfig( + max_new_tokens=sampling_params["max_new_tokens"], + do_sample=False, + ) + try: from transformers import AutoModelForImageTextToText @@ -27,12 +34,28 @@ def eval_mmmu(args): ) except Exception as first_exception: try: - model = AutoModel.from_pretrained( - args.model_path, - torch_dtype="auto", - trust_remote_code=True, - init_tts=False, - ) + # check if the model is belongs to internvl + if "InternVL" in args.model_path: + from internvl_utils import load_image + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(args.model_path) + model = AutoModel.from_pretrained( + args.model_path, + torch_dtype="auto", + trust_remote_code=True, + ) + generation_config_internvl = dict( + max_new_tokens=sampling_params["max_new_tokens"], do_sample=False + ) + + else: + model = AutoModel.from_pretrained( + args.model_path, + torch_dtype="auto", + trust_remote_code=True, + init_tts=False, + ) except Exception as second_exception: raise RuntimeError( f"Failed to load model: First attempt failed with {first_exception}, " @@ -48,12 +71,6 @@ def eval_mmmu(args): samples = prepare_samples(eval_args) out_samples = dict() - sampling_params = get_sampling_params(eval_args) - generation_config = GenerationConfig( - max_new_tokens=sampling_params["max_new_tokens"], - do_sample=False, - ) - answer_dict = {} for sample in tqdm(samples): prompt = sample["final_input_prompt"] @@ -61,6 +78,22 @@ def eval_mmmu(args): prefix = prompt.split("<")[0] suffix = prompt.split(">")[1] assert image is not None + + if "InternVL" in args.model_path: + pixel_values = load_image(sample["image_path"]).to(torch.bfloat16).cuda() + contents = "" + if prefix: + contents += prefix + contents += "\n" + if suffix: + contents += suffix + response = model.chat( + tokenizer, pixel_values, contents, generation_config_internvl + ) + print(f"response: {response}") + process_result(response, sample, answer_dict, out_samples) + continue + contents = [] if prefix: contents += [{"type": "text", "text": prefix}] diff --git a/benchmark/mmmu/internvl_utils.py b/benchmark/mmmu/internvl_utils.py new file mode 100644 index 000000000..44c62c99a --- /dev/null +++ b/benchmark/mmmu/internvl_utils.py @@ -0,0 +1,94 @@ +# copy from https://huggingface.co/OpenGVLab/InternVL3-1B +import torch +import torchvision.transforms as T +from PIL import Image +from torchvision.transforms.functional import InterpolationMode + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + + +def build_transform(input_size): + MEAN, STD = IMAGENET_MEAN, IMAGENET_STD + transform = T.Compose( + [ + T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), + T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=MEAN, std=STD), + ] + ) + return transform + + +def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float("inf") + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + return best_ratio + + +def dynamic_preprocess( + image, min_num=1, max_num=12, image_size=448, use_thumbnail=False +): + orig_width, orig_height = image.size + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (i, j) + for n in range(min_num, max_num + 1) + for i in range(1, n + 1) + for j in range(1, n + 1) + if i * j <= max_num and i * j >= min_num + ) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size + ) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + blocks = target_aspect_ratio[0] * target_aspect_ratio[1] + + # resize the image + resized_img = image.resize((target_width, target_height)) + processed_images = [] + for i in range(blocks): + box = ( + (i % (target_width // image_size)) * image_size, + (i // (target_width // image_size)) * image_size, + ((i % (target_width // image_size)) + 1) * image_size, + ((i // (target_width // image_size)) + 1) * image_size, + ) + # split the image + split_img = resized_img.crop(box) + processed_images.append(split_img) + assert len(processed_images) == blocks + if use_thumbnail and len(processed_images) != 1: + thumbnail_img = image.resize((image_size, image_size)) + processed_images.append(thumbnail_img) + return processed_images + + +def load_image(image_file, input_size=448, max_num=12): + image = Image.open(image_file).convert("RGB") + transform = build_transform(input_size=input_size) + images = dynamic_preprocess( + image, image_size=input_size, use_thumbnail=True, max_num=max_num + ) + pixel_values = [transform(image) for image in images] + pixel_values = torch.stack(pixel_values) + return pixel_values