create app.py

app.py

@@ -0,0 +1,107 @@
+import io
+import argparse
+import numpy as np
+import torch
+from decord import cpu, VideoReader, bridge
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+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 to Text")
+parser.add_argument('--video', type=str, required=True, help="Path to the video file")
+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_path, strategy='chat'):
+    bridge.set_bridge('torch')
+    
+    with open(video_path, 'rb') as f:
+        video_stream = f.read()
+
+    num_frames = 24
+    decord_vr = VideoReader(io.BytesIO(video_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,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=TORCH_TYPE,
+    trust_remote_code=True
+).eval().to(DEVICE)
+
+
+def predict(video_path, temperature=0.1):
+    strategy = 'chat'
+    prompt = "Please describe this video in detail."
+
+    video_data = load_video(video_path, strategy=strategy)
+    
+    history = []
+    inputs = model.build_conversation_input_ids(
+        tokenizer=tokenizer,
+        query=prompt,
+        images=[video_data],
+        history=history,
+        template_version=strategy
+    )
+    inputs = {
+        'input_ids': inputs['input_ids'].unsqueeze(0).to(DEVICE),
+        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(DEVICE),
+        'attention_mask': inputs['attention_mask'].unsqueeze(0).to(DEVICE),
+        'images': [[inputs['images'][0].to(DEVICE).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
+
+
+if __name__ == '__main__':
+    video_file = args.video
+    response_text = predict(video_file)
+    print("\nGenerated Text Description:\n")
+    print(response_text)