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infer_3.py

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+import os
+
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import json
+from tqdm import tqdm
+import os
+
+def read_json(file_path): 
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    return data
+
+def write_json(file_path, data):
+    with open(file_path, 'w', encoding='utf-8') as file:
+        json.dump(data, file, ensure_ascii=False, indent=4)
+
+# default: Load the model on the available device(s)
+print(torch.cuda.device_count())
+model_path = "/home/zbz5349/WorkSpace/aigeeks/Qwen2.5-VL/LLaMA-Factory/output/Qwen2.5-VL-3B_all"
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     model_path, torch_dtype="auto", device_map="auto"
+# )
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+# default processor
+processor = AutoProcessor.from_pretrained(model_path)
+print(model.device)
+
+
+
+
+data = read_json('/home/zbz5349/WorkSpace/aigeeks/Qwen2.5-VL/LLaMA-Factory/data/Percption.json')
+save_data = []
+correct_num = 0
+begin = 0
+end = len(data)
+batch_size = 2
+for batch_idx in tqdm(range(begin, end, batch_size)):
+    batch = data[batch_idx:batch_idx + batch_size] 
+    
+    image_list = []
+    input_text_list = []
+    data_list = []
+    save_data = []
+    
+    # while True:
+    for idx, i in enumerate(batch):
+        save_ =  {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "max_pixels": 360 * 420,
+                "fps": 1.0,
+            },
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "Describe this video."},
+        ],
+        "answer":""
+    }
+        messages =  {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "max_pixels": 360 * 420,
+                "fps": 1.0,
+            },
+          {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+
+
+        video_path = i['videos']
+        image_path = i['images']
+        question = i['messages'][0]['content']
+        answer = i['messages'][1]['content']
+        messages['content'][0]['video'] = video_path
+        messages['content'][1]['image'] = image_path
+        messages['content'][2]['text'] = question
+
+        save_['content'][0]['video'] = video_path
+        save_['content'][1]['image'] = image_path
+        save_['content'][2]['text'] = question
+        data_list.append(messages)
+        save_data.append(save_)
+
+    text = processor.apply_chat_template(data_list, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs, video_kwargs = process_vision_info(data_list, return_video_kwargs=True)
+    fps = 1
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+        **video_kwargs,
+    )
+    inputs = inputs.to(model.device)
+
+    # Inference
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    save_["answer"] = output_text
+    if output_text == answer:
+        correct_num = correct_num + 1
+    save_data.append(save_)
+print("correct_num", correct_num)
+write_json(save_data, "infer_answer.json")
+
+