Support MMMU benchmark for InternVL

benchmark/mmmu/bench_hf.py

@@ -17,6 +17,13 @@
 @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}, "
@@ -48,19 +71,29 @@ 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"]
         image = sample["image"]
         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 += "<image>\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}]

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