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README.md

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 ---
 license: mit
+base_model:
+- OpenGVLab/InternViT-300M-448px
+- internlm/internlm2_5-7b-chat
+new_version: OpenGVLab/PVC-InternVL-8B
+language:
+- multilingual
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- internvl
+- video
+- token compression
 ---
+
+# PVC-InternVL2-8B
+
+[\[📂 GitHub\]](https://github.com/OpenGVLab/PVC)
+
+## Introduction
+
+We introduce the **Progressive Visual Token Compression (PVC)** in large vision-language models (VLMs), which unifies the visual inputs as videos and progressively compresses vision tokens across video frames. Our PVC achieves:
+
+* Preserve spatial details and temporal dynamics for both images and videos.
+* Effectively reduce the tokens used for each video frame and image tile.
+* SoTA performance on various video benchmarks, including long and fine-grained short video tasks.
+* No performance loss on image benchmarks, especially on detail-sensitive tasks.
+
+<div style="text-align: center;">
+    <img src="./assets/overview.png" width="70%"/>
+</div>
+
+## Results
+
+Our implementation is based on the [InternVL2](https://github.com/OpenGVLab/InternVL) model, referred to as **PVC<sub>InternVL2</sub>**
+
+### Video Understanding Benckmarks
+
+| Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVC<sub>InternVL2</sub>-8B |
+| :--------------: | :--: | :--: | :--: | :--: |
+| \# token/frame   | 196  | -    | 256  | 64   |
+|                  |      |      |      |      |
+| MVbench          | 56.7 | 67.0 | 66.4 | 73.8 |
+| VideoMME w/o-sub | 58.2 | 63.3 | 54.0 | 64.1 |
+| VideoMME w-sub   | 61.5 | 69.0 | 56.9 | 69.7 |
+| MLVU             | 64.7 | -    | 52.0 | 72.4 |
+| LongVideoBench   | 56.5 | -    | -    | 59.2 |
+| NextQA           | 79.4 | -    | -    | 82.0 |
+| Egoschema        | 60.1 | 66.7 | 55.0 | 59.6 |
+| PercepTest       | 57.1 | 62.3 | 52.0 | 68.4 |
+| AcNet-QA         | 56.6 | -    | -    | 57.1 |
+
+### Image Understanding Benckmarks
+
+| Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVC<sub>InternVL2</sub>-8B |
+| :--------------------: | :--: | :--: | :--: | :--: |
+| \# token/image tile    | 729  | -    | 256  | 64   |
+|                        |      |      |      |      |
+| AI2D<sub>test</sub>    | 81.4 | 83.0 | 83.8 | 83.8 |
+| ChartQA<sub>test</sub> | 80.0 | 83.0 | 83.3 | 84.1 |
+| DocVQA<sub>test</sub>  | 87.5 | 94.5 | 91.6 | 92.5 |
+| InfoVQA<sub>test</sub> | 68.8 | 76.5 | 74.8 | 75.0 |
+| SQA<sub>test</sub>     | 96.0 | -    | 97.1 | 97.7 |
+| TextVQA<sub>val</sub>  | -    | 84.3 | 77.4 | 80.0 |
+| MMB<sub>en-test</sub>  | -    | 83.0 | 81.7 | 83.9 |
+| MME<sub>sum</sub>      | 1998 | 2327 | 2210 | 2282 |
+| MMMU<sub>val</sub>     | 48.8 | 54.1 | 49.3 | 50.9 |
+| SEED<sub>I</sub>       | 75.4 | -    | 76.2 | 77.2 |
+| OCRBench               | -    | 866  | 794  | 807  |
+
+## Quick Start
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from decord import VideoReader, cpu
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+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
+
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+    if bound:
+        start, end = bound[0], bound[1]
+    else:
+        start, end = -100000, 100000
+    start_idx = max(first_idx, round(start * fps))
+    end_idx = min(round(end * fps), max_frame)
+    seg_size = float(end_idx - start_idx) / num_segments
+    frame_indices = np.array([
+        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+        for idx in range(num_segments)
+    ])
+    return frame_indices
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    max_frame = len(vr) - 1
+    fps = float(vr.get_avg_fps())
+
+    pixel_values_list, num_patches_list = [], []
+    transform = build_transform(input_size=input_size)
+    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+    for frame_index in frame_indices:
+        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
+        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        pixel_values = [transform(tile) for tile in img]
+        pixel_values = torch.stack(pixel_values)
+        num_patches_list.append(pixel_values.shape[0])
+        pixel_values_list.append(pixel_values)
+    pixel_values = torch.cat(pixel_values_list)
+    return pixel_values, num_patches_list
+
+
+path = 'OpenGVLab/PVC-InternVL2-8B'
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+# single-image conversation
+pixel_values = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+data_flag = torch.tensor([1], dtype=torch.long).cuda()
+
+question = '<image>\nWhat is in the image?'
+response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag)
+print(f'User: {question}\nAssistant: {response}')
+
+# multi-image conversation
+pixel_values1 = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./assets/example_image2.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+data_flag = torch.tensor([2], dtype=torch.long).cuda()
+num_patches_list = [pixel_values1.shape[0], pixel_values2.shape[0]]
+
+question = 'Image-1: <image>\nImage-2: <image>\nWhat are the similarities and differences between these two images.'
+response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
+print(f'User: {question}\nAssistant: {response}')
+
+# video conversation
+pixel_values, num_patches_list = load_video('./assets/example_video.mp4', num_segments=64, max_num=1)
+pixel_values = pixel_values.to(torch.bfloat16).cuda()
+video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+# Frame1: <image>\nFrame2: <image>\n...\nFrameN: <image>\n{question}
+data_flag = torch.tensor([3], dtype=torch.long).cuda()
+
+question = video_prefix + 'Describe this video in detail.'
+response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
+print(f'User: {question}\nAssistant: {response}')
+```
+
+## License
+
+This project is released under the MIT license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.

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