Update README.md

README.md

@@ -1,247 +1,237 @@
----
-license: mit
-pipeline_tag: video-text-to-text
-extra_gated_prompt: >-
-  You agree to not use the model to conduct experiments that cause harm to human
-  subjects.
-extra_gated_fields:
-  Name: text
-  Company/Organization: text
-  Country: text
-  E-Mail: text
----
-
-# InternVideo2-Chat-8B-HD
-
-[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2)   [\[📜 Tech Report\]](https://arxiv.org/abs/2403.15377) 
-<!-- [\[🗨️ Chat Demo\]](https://vchat.opengvlab.com/)   -->
-
-To further enrich the semantics embedded in **InternVideo2** and improve its user-friendly in human communications, we tune InternVideo2 by incorporating it into a VideoLLM with a LLM and a video BLIP. We employ the progressive learning scheme in [VideoChat](https://arxiv.org/abs/2311.17005) by using InternVideo2 as the video encoder and train a video blip for
-communicating with open-sourced LLM. In training, the video encoder will be updated. Detailed training recipts are in [VideoChat](https://arxiv.org/abs/2311.17005).This model has HD training. 
-
-The BaseLLM of this model is Mistral-7B.**Before using it, please ensure that you have obtained the access permission of Mistral-7B**, if not yet obtained, please go to[Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) to obtain the access permission and add your `HF_token` to the environment variable. 
-
-## 🚀 How to use the model
-
-1. Apply for the permission of this project and the base LLM permission 
-
-2. Fill the HF user access token into the environment variable
-
-```shell
-export HF_TOKEN=hf_....
-```
-If you don't know how to obtain the token starting with "hf_", please refer to: [How to Get HF User access Token](https://huggingface.co/docs/hub/security-tokens#user-access-tokens)
-
-3. make sure to have `transformers >= 4.38.0, peft==0.5.0`
-
-Install the requisite Python packages from [pip_requirements](https://huggingface.co/OpenGVLab/InternVideo2_chat_8B_HD/blob/main/requirements.txt) 
-   
-4. Inference with Video input
-
-```Python
-import os
-import torch
-
-from transformers import AutoTokenizer, AutoModel
-
-tokenizer =  AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
-    trust_remote_code=True,
-    use_fast=False,)
-if torch.cuda.is_available():
-  model = AutoModel.from_pretrained(
-      'OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
-      torch_dtype=torch.bfloat16,
-      trust_remote_code=True).cuda()
-else:
-  model = AutoModel.from_pretrained(
-      'OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
-      torch_dtype=torch.bfloat16,
-      trust_remote_code=True)
-
-
-from decord import VideoReader, cpu
-from PIL import Image
-import numpy as np
-import numpy as np
-import decord
-from decord import VideoReader, cpu
-import torch.nn.functional as F
-import torchvision.transforms as T
-from torchvision.transforms import PILToTensor
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-decord.bridge.set_bridge("torch")
-
-def get_index(num_frames, num_segments):
-    seg_size = float(num_frames - 1) / num_segments
-    start = int(seg_size / 2)
-    offsets = np.array([
-        start + int(np.round(seg_size * idx)) for idx in range(num_segments)
-    ])
-    return offsets
-
-
-def load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=4, padding=False):
-    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
-    num_frames = len(vr)
-    frame_indices = get_index(num_frames, num_segments)
-
-    mean = (0.485, 0.456, 0.406)
-    std = (0.229, 0.224, 0.225)
-
-    transform = transforms.Compose([
-        transforms.Lambda(lambda x: x.float().div(255.0)),
-        transforms.Normalize(mean, std)
-    ])
-
-    frames = vr.get_batch(frame_indices)
-    frames = frames.permute(0, 3, 1, 2)
-
-    if padding:
-        frames = HD_transform_padding(frames.float(), image_size=resolution, hd_num=hd_num)
-    else:
-        frames = HD_transform_no_padding(frames.float(), image_size=resolution, hd_num=hd_num)
-
-    frames = transform(frames)
-    # print(frames.shape)
-    T_, C, H, W = frames.shape
-
-    sub_img = frames.reshape(
-        1, T_, 3, H//resolution, resolution, W//resolution, resolution
-    ).permute(0, 3, 5, 1, 2, 4, 6).reshape(-1, T_, 3, resolution, resolution).contiguous()
-
-    glb_img = F.interpolate(
-        frames.float(), size=(resolution, resolution), mode='bicubic', align_corners=False
-    ).to(sub_img.dtype).unsqueeze(0)
-
-    frames = torch.cat([sub_img, glb_img]).unsqueeze(0)
-
-    if return_msg:
-        fps = float(vr.get_avg_fps())
-        sec = ", ".join([str(round(f / fps, 1)) for f in frame_indices])
-        # " " should be added in the start and end
-        msg = f"The video contains {len(frame_indices)} frames sampled at {sec} seconds."
-        return frames, msg
-    else:
-        return frames
-
-def HD_transform_padding(frames, image_size=224, hd_num=6):
-    def _padding_224(frames):
-        _, _, H, W = frames.shape
-        tar = int(np.ceil(H / 224) * 224)
-        top_padding = (tar - H) // 2
-        bottom_padding = tar - H - top_padding
-        left_padding = 0
-        right_padding = 0
-
-        padded_frames = F.pad(
-            frames,
-            pad=[left_padding, right_padding, top_padding, bottom_padding],
-            mode='constant', value=255
-        )
-        return padded_frames
-
-    _, _, H, W = frames.shape
-    trans = False
-    if W < H:
-        frames = frames.flip(-2, -1)
-        trans = True
-        width, height = H, W
-    else:
-        width, height = W, H
-
-    ratio = width / height
-    scale = 1
-    while scale * np.ceil(scale / ratio) <= hd_num:
-        scale += 1
-    scale -= 1
-    new_w = int(scale * image_size)
-    new_h = int(new_w / ratio)
-
-    resized_frames = F.interpolate(
-        frames, size=(new_h, new_w),
-        mode='bicubic',
-        align_corners=False
-    )
-    padded_frames = _padding_224(resized_frames)
-
-    if trans:
-        padded_frames = padded_frames.flip(-2, -1)
-
-    return padded_frames
-
-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 HD_transform_no_padding(frames, image_size=224, hd_num=6, fix_ratio=(2,1)):
-    min_num = 1
-    max_num = hd_num
-    _, _, orig_height, orig_width = frames.shape
-    aspect_ratio = orig_width / orig_height
-
-    # calculate the existing video 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
-    if fix_ratio:
-        target_aspect_ratio = fix_ratio
-    else:
-        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 frames
-    resized_frame = F.interpolate(
-        frames, size=(target_height, target_width),
-        mode='bicubic', align_corners=False
-    )
-    return resized_frame
-
-video_path = "yoga.mp4"
-# sample uniformly 8 frames from the video
-video_tensor = load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=6)
-video_tensor = video_tensor.to(model.device)
-
-chat_history = []
-response, chat_history = model.chat(tokenizer, '', 'Describe the video step by step',instruction= "Carefully watch the video and pay attention to the cause and sequence of events, the detail and movement of objects, and the action and pose of persons. Based on your observations, select the best option that accurately addresses the question.\n", media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False,'max_new_tokens':512,})
-print(response) 
-```
-
-## ✏️ Citation
-If this work is helpful for your research, please consider citing InternVideo and VideoChat.
-
-```
-@article{wang2024internvideo2,
-  title={Internvideo2: Scaling video foundation models for multimodal video understanding},
-  author={Wang, Yi and Li, Kunchang and Li, Xinhao and Yu, Jiashuo and He, Yinan and Wang, Chenting and Chen, Guo and Pei, Baoqi and Zheng, Rongkun and Xu, Jilan and Wang, Zun and others},
-  journal={arXiv preprint arXiv:2403.15377},
-  year={2024}
-}
-
-@article{li2023videochat,
-  title={Videochat: Chat-centric video understanding},
-  author={Li, KunChang and He, Yinan and Wang, Yi and Li, Yizhuo and Wang, Wenhai and Luo, Ping and Wang, Yali and Wang, Limin and Qiao, Yu},
-  journal={arXiv preprint arXiv:2305.06355},
-  year={2023}
-}
-```
+---
+license: mit
+pipeline_tag: video-text-to-text
+extra_gated_prompt: >-
+  You agree to not use the model to conduct experiments that cause harm to human
+  subjects.
+extra_gated_fields:
+  Name: text
+  Company/Organization: text
+  Country: text
+  E-Mail: text
+---
+
+# InternVideo2-Chat-8B-InternLM2.5
+
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2)   [\[📜 Tech Report\]](https://arxiv.org/abs/2403.15377) 
+
+To further enrich the semantics embedded in **InternVideo2** and improve its user-friendly in human communications, we tune InternVideo2 by incorporating it into a VideoLLM with a LLM and a video BLIP. We employ the progressive learning scheme in [VideoChat](https://arxiv.org/abs/2311.17005) by using InternVideo2 as the video encoder and train a video blip for
+communicating with open-sourced LLM. In training, the video encoder will be updated. Detailed training recipts are in [VideoChat](https://arxiv.org/abs/2311.17005). This model has HD training. 
+
+The BaseLLM of this model is [InternLM2.5-7B](https://huggingface.co/internlm/internlm2_5-7b-chat-1m) with 1M long context window. 
+
+## 🚀 How to use the model
+
+1. make sure to have `transformers >= 4.38.0, peft==0.5.0`
+
+Install the requisite Python packages from [pip_requirements](https://huggingface.co/OpenGVLab/InternVideo2_chat_8B_HD/blob/main/requirements.txt) 
+   
+2. Inference with Video input
+
+```Python
+import os
+import torch
+
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer =  AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
+    trust_remote_code=True,
+    use_fast=False,)
+if torch.cuda.is_available():
+  model = AutoModel.from_pretrained(
+      'OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
+      torch_dtype=torch.bfloat16,
+      trust_remote_code=True).cuda()
+else:
+  model = AutoModel.from_pretrained(
+      'OpenGVLab/InternVideo2_Chat_8B_InternLM2_5',
+      torch_dtype=torch.bfloat16,
+      trust_remote_code=True)
+
+
+from decord import VideoReader, cpu
+from PIL import Image
+import numpy as np
+import numpy as np
+import decord
+from decord import VideoReader, cpu
+import torch.nn.functional as F
+import torchvision.transforms as T
+from torchvision.transforms import PILToTensor
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+decord.bridge.set_bridge("torch")
+
+def get_index(num_frames, num_segments):
+    seg_size = float(num_frames - 1) / num_segments
+    start = int(seg_size / 2)
+    offsets = np.array([
+        start + int(np.round(seg_size * idx)) for idx in range(num_segments)
+    ])
+    return offsets
+
+
+def load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=4, padding=False):
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    num_frames = len(vr)
+    frame_indices = get_index(num_frames, num_segments)
+
+    mean = (0.485, 0.456, 0.406)
+    std = (0.229, 0.224, 0.225)
+
+    transform = transforms.Compose([
+        transforms.Lambda(lambda x: x.float().div(255.0)),
+        transforms.Normalize(mean, std)
+    ])
+
+    frames = vr.get_batch(frame_indices)
+    frames = frames.permute(0, 3, 1, 2)
+
+    if padding:
+        frames = HD_transform_padding(frames.float(), image_size=resolution, hd_num=hd_num)
+    else:
+        frames = HD_transform_no_padding(frames.float(), image_size=resolution, hd_num=hd_num)
+
+    frames = transform(frames)
+    # print(frames.shape)
+    T_, C, H, W = frames.shape
+
+    sub_img = frames.reshape(
+        1, T_, 3, H//resolution, resolution, W//resolution, resolution
+    ).permute(0, 3, 5, 1, 2, 4, 6).reshape(-1, T_, 3, resolution, resolution).contiguous()
+
+    glb_img = F.interpolate(
+        frames.float(), size=(resolution, resolution), mode='bicubic', align_corners=False
+    ).to(sub_img.dtype).unsqueeze(0)
+
+    frames = torch.cat([sub_img, glb_img]).unsqueeze(0)
+
+    if return_msg:
+        fps = float(vr.get_avg_fps())
+        sec = ", ".join([str(round(f / fps, 1)) for f in frame_indices])
+        # " " should be added in the start and end
+        msg = f"The video contains {len(frame_indices)} frames sampled at {sec} seconds."
+        return frames, msg
+    else:
+        return frames
+
+def HD_transform_padding(frames, image_size=224, hd_num=6):
+    def _padding_224(frames):
+        _, _, H, W = frames.shape
+        tar = int(np.ceil(H / 224) * 224)
+        top_padding = (tar - H) // 2
+        bottom_padding = tar - H - top_padding
+        left_padding = 0
+        right_padding = 0
+
+        padded_frames = F.pad(
+            frames,
+            pad=[left_padding, right_padding, top_padding, bottom_padding],
+            mode='constant', value=255
+        )
+        return padded_frames
+
+    _, _, H, W = frames.shape
+    trans = False
+    if W < H:
+        frames = frames.flip(-2, -1)
+        trans = True
+        width, height = H, W
+    else:
+        width, height = W, H
+
+    ratio = width / height
+    scale = 1
+    while scale * np.ceil(scale / ratio) <= hd_num:
+        scale += 1
+    scale -= 1
+    new_w = int(scale * image_size)
+    new_h = int(new_w / ratio)
+
+    resized_frames = F.interpolate(
+        frames, size=(new_h, new_w),
+        mode='bicubic',
+        align_corners=False
+    )
+    padded_frames = _padding_224(resized_frames)
+
+    if trans:
+        padded_frames = padded_frames.flip(-2, -1)
+
+    return padded_frames
+
+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 HD_transform_no_padding(frames, image_size=224, hd_num=6, fix_ratio=(2,1)):
+    min_num = 1
+    max_num = hd_num
+    _, _, orig_height, orig_width = frames.shape
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing video 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
+    if fix_ratio:
+        target_aspect_ratio = fix_ratio
+    else:
+        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 frames
+    resized_frame = F.interpolate(
+        frames, size=(target_height, target_width),
+        mode='bicubic', align_corners=False
+    )
+    return resized_frame
+
+video_path = "yoga.mp4"
+# sample uniformly 8 frames from the video
+video_tensor = load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=6)
+video_tensor = video_tensor.to(model.device)
+
+chat_history = []
+response, chat_history = model.chat(tokenizer, '', 'Describe the video step by step',instruction= "Carefully watch the video and pay attention to the cause and sequence of events, the detail and movement of objects, and the action and pose of persons. Based on your observations, select the best option that accurately addresses the question.\n", media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False,'max_new_tokens':512,})
+print(response) 
+```
+
+## ✏️ Citation
+If this work is helpful for your research, please consider citing InternVideo and VideoChat.
+
+```
+@article{wang2024internvideo2,
+  title={Internvideo2: Scaling video foundation models for multimodal video understanding},
+  author={Wang, Yi and Li, Kunchang and Li, Xinhao and Yu, Jiashuo and He, Yinan and Wang, Chenting and Chen, Guo and Pei, Baoqi and Zheng, Rongkun and Xu, Jilan and Wang, Zun and others},
+  journal={arXiv preprint arXiv:2403.15377},
+  year={2024}
+}
+
+@article{li2023videochat,
+  title={Videochat: Chat-centric video understanding},
+  author={Li, KunChang and He, Yinan and Wang, Yi and Li, Yizhuo and Wang, Wenhai and Luo, Ping and Wang, Yali and Wang, Limin and Qiao, Yu},
+  journal={arXiv preprint arXiv:2305.06355},
+  year={2023}
+}
+```