test

README.md

@@ -1,51 +1,138 @@
----
-frameworks:
-- Pytorch
-license: other
-tasks:
-- image-text-to-text
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
----
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
-
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
+# CogVLM2-Llama3-Caption
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/THUDM/CogVLM2/cf9cb3c60a871e0c8e5bde7feaf642e3021153e6/resources/logo.svg> 
+</div>
+
+通常情况下，大部分视频数据并没有附带相应的描述性文本，因此有必要将视频数据转换成文本描述，以提供文本到视频模型所需的必要训练数据。
+
+## 使用方式
 ```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('ZhipuAI/cogvlm2-llama3-caption')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/ZhipuAI/cogvlm2-llama3-caption.git
+import io
+import numpy as np
+import torch
+from decord import cpu, VideoReader, bridge
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import argparse
+
+MODEL_PATH = "THUDM/cogvlm2-llama3-caption"
+
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[
+    0] >= 8 else torch.float16
+
+parser = argparse.ArgumentParser(description="CogVLM2-Video CLI Demo")
+parser.add_argument('--quant', type=int, choices=[4, 8], help='Enable 4-bit or 8-bit precision loading', default=0)
+args = parser.parse_args([])
+
+
+def load_video(video_data, strategy='chat'):
+    bridge.set_bridge('torch')
+    mp4_stream = video_data
+    num_frames = 24
+    decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
+
+    frame_id_list = None
+    total_frames = len(decord_vr)
+    if strategy == 'base':
+        clip_end_sec = 60
+        clip_start_sec = 0
+        start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
+        end_frame = min(total_frames,
+                        int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
+        frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
+    elif strategy == 'chat':
+        timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
+        timestamps = [i[0] for i in timestamps]
+        max_second = round(max(timestamps)) + 1
+        frame_id_list = []
+        for second in range(max_second):
+            closest_num = min(timestamps, key=lambda x: abs(x - second))
+            index = timestamps.index(closest_num)
+            frame_id_list.append(index)
+            if len(frame_id_list) >= num_frames:
+                break
+
+    video_data = decord_vr.get_batch(frame_id_list)
+    video_data = video_data.permute(3, 0, 1, 2)
+    return video_data
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_PATH,
+    trust_remote_code=True,
+    # padding_side="left"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=TORCH_TYPE,
+    trust_remote_code=True
+).eval().to(DEVICE)
+
+
+def predict(prompt, video_data, temperature):
+    strategy = 'chat'
+
+    video = load_video(video_data, strategy=strategy)
+
+    history = []
+    query = prompt
+    inputs = model.build_conversation_input_ids(
+        tokenizer=tokenizer,
+        query=query,
+        images=[video],
+        history=history,
+        template_version=strategy
+    )
+    inputs = {
+        'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
+        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
+        'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
+        'images': [[inputs['images'][0].to('cuda').to(TORCH_TYPE)]],
+    }
+    gen_kwargs = {
+        "max_new_tokens": 2048,
+        "pad_token_id": 128002,
+        "top_k": 1,
+        "do_sample": False,
+        "top_p": 0.1,
+        "temperature": temperature,
+    }
+    with torch.no_grad():
+        outputs = model.generate(**inputs, **gen_kwargs)
+        outputs = outputs[:, inputs['input_ids'].shape[1]:]
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+
+
+def test():
+    prompt = "Please describe this video in detail."
+    temperature = 0.1
+    video_data = open('test.mp4', 'rb').read()
+    response = predict(prompt, video_data, temperature)
+    print(response)
+
+
+if __name__ == '__main__':
+    test()
+
 ```
 
-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+## 模型协议
+
+此模型根据 CogVLM2 [LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LICENSE&status=0) 发布。对于使用 Meta Llama 3 构建的模型，还请遵守
+[LLAMA3_LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LLAMA3_LICENSE&status=0)。
+
+## 引用
+
+🌟 If you find our work helpful, please leave us a star and cite our paper.
+
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}
+```

README_zh.md

@@ -0,0 +1,143 @@
+# CogVLM2-Llama3-Caption
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/THUDM/CogVLM2/cf9cb3c60a871e0c8e5bde7feaf642e3021153e6/resources/logo.svg> 
+</div>
+
+# Introduction
+
+Typically, most video data does not come with corresponding descriptive text, so it is necessary to convert the video
+data into textual descriptions to provide the essential training data for text-to-video models.
+
+## Usage
+
+```python
+import io
+import numpy as np
+import torch
+from decord import cpu, VideoReader, bridge
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import argparse
+
+MODEL_PATH = "THUDM/cogvlm2-llama3-caption"
+
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[
+    0] >= 8 else torch.float16
+
+parser = argparse.ArgumentParser(description="CogVLM2-Video CLI Demo")
+parser.add_argument('--quant', type=int, choices=[4, 8], help='Enable 4-bit or 8-bit precision loading', default=0)
+args = parser.parse_args([])
+
+
+def load_video(video_data, strategy='chat'):
+    bridge.set_bridge('torch')
+    mp4_stream = video_data
+    num_frames = 24
+    decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
+
+    frame_id_list = None
+    total_frames = len(decord_vr)
+    if strategy == 'base':
+        clip_end_sec = 60
+        clip_start_sec = 0
+        start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
+        end_frame = min(total_frames,
+                        int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
+        frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
+    elif strategy == 'chat':
+        timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
+        timestamps = [i[0] for i in timestamps]
+        max_second = round(max(timestamps)) + 1
+        frame_id_list = []
+        for second in range(max_second):
+            closest_num = min(timestamps, key=lambda x: abs(x - second))
+            index = timestamps.index(closest_num)
+            frame_id_list.append(index)
+            if len(frame_id_list) >= num_frames:
+                break
+
+    video_data = decord_vr.get_batch(frame_id_list)
+    video_data = video_data.permute(3, 0, 1, 2)
+    return video_data
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_PATH,
+    trust_remote_code=True,
+    # padding_side="left"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=TORCH_TYPE,
+    trust_remote_code=True
+).eval().to(DEVICE)
+
+
+def predict(prompt, video_data, temperature):
+    strategy = 'chat'
+
+    video = load_video(video_data, strategy=strategy)
+
+    history = []
+    query = prompt
+    inputs = model.build_conversation_input_ids(
+        tokenizer=tokenizer,
+        query=query,
+        images=[video],
+        history=history,
+        template_version=strategy
+    )
+    inputs = {
+        'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
+        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
+        'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
+        'images': [[inputs['images'][0].to('cuda').to(TORCH_TYPE)]],
+    }
+    gen_kwargs = {
+        "max_new_tokens": 2048,
+        "pad_token_id": 128002,
+        "top_k": 1,
+        "do_sample": False,
+        "top_p": 0.1,
+        "temperature": temperature,
+    }
+    with torch.no_grad():
+        outputs = model.generate(**inputs, **gen_kwargs)
+        outputs = outputs[:, inputs['input_ids'].shape[1]:]
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+
+
+def test():
+    prompt = "Please describe this video in detail."
+    temperature = 0.1
+    video_data = open('test.mp4', 'rb').read()
+    response = predict(prompt, video_data, temperature)
+    print(response)
+
+
+if __name__ == '__main__':
+    test()
+
+```
+
+## License
+
+This model is released under the
+CogVLM2  [LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LICENSE&status=0).
+For models built with Meta Llama 3, please also adhere to
+the [LLAMA3_LICENSE](https://modelscope.cn/models/ZhipuAI/cogvlm2-video-llama3-base/file/view/master?fileName=LLAMA3_LICENSE&status=0).
+
+## Citation
+
+🌟 If you find our work helpful, please leave us a star and cite our paper.
+
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}