Upload infer_qwen2_vl.py with huggingface_hub

infer_qwen2_vl.py

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+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import json
+
+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)
+model_path = '/inspire/hdd/ws-ba572160-47f8-4ca1-984e-d6bcdeb95dbb/a100-maybe/albus/ICCV_2025/qvq/models/QVQ-72B-Preview'
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto"
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained(model_path)
+
+# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
+# min_pixels = 256*28*28
+# max_pixels = 1280*28*28
+#processor = AutoProcessor.from_pretrained("Qwen/QVQ-72B-Preview", min_pixels=min_pixels, max_pixels=max_pixels)
+
+import glob
+from PIL import Image
+import argparse
+import os
+
+# parser = argparse.ArgumentParser(description="Process a dataset with specific index range.")
+# parser.add_argument("--batch_size", type=int, default = 1,help="batch size")
+# #parser.add_argument("--index", type=int, default = 0,help="index")
+# args = parser.parse_args()
+
+
+folder = "/inspire/hdd/ws-ba572160-47f8-4ca1-984e-d6bcdeb95dbb/a100-maybe/albus/ICCV_2025/qvq/dataset"
+
+file_names = os.listdir(folder)
+
+num_image = len(file_names)
+
+begin, end, batch_size= 0, num_image, 6
+print(f"beigin : {begin}, end : {end}, batch_size : {batch_size}")
+messages = [
+    {
+        "role": "system",
+        "content": [
+            {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}
+        ],
+    },
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/QVQ/demo.png",
+            },
+            {"type": "text", "text": "Please describe in detail the content of the picture."},
+        ],
+    }
+]
+
+from tqdm import tqdm
+# Preparation for inference
+ans = []
+counter = 0
+for batch_idx in tqdm(range(begin, end, batch_size)):
+    up = min(batch_idx + batch_size, end)
+    batch = file_names[batch_idx: up]
+    print(f"data index range : {batch_idx} ~ {up}")
+    image_inputs_batch, video_inputs_batch,text_batch = [], [], []
+    for idx,i in enumerate(batch):
+        #img = batch[i]
+        #print('gain image successfully !')
+        img_path = '/inspire/hdd/ws-ba572160-47f8-4ca1-984e-d6bcdeb95dbb/a100-maybe/albus/ICCV_2025/qvq/dataset/' + i
+        #print(img_path)
+        messages[1]["content"][0]["image"] = '/inspire/hdd/ws-ba572160-47f8-4ca1-984e-d6bcdeb95dbb/a100-maybe/albus/ICCV_2025/qvq/dataset/' + i
+        text = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        text_batch.append(text)
+        image_inputs, video_inputs = process_vision_info(messages)
+        print(video_inputs)
+        image_inputs_batch.append(image_inputs)
+        video_inputs_batch.append(video_inputs)
+    inputs = processor(
+        text=text_batch, # [text]
+        images=image_inputs_batch,
+        videos=None,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to("cuda")
+
+    # Inference: Generation of the output
+    
+    #print(inputs)
+
+    # for x in range(len(inputs)):
+    #     print(f"Generating {x}th image")
+    #     generated_ids = model.generate(**x, max_new_tokens=8192)
+    #     generated_ids_trimmed = [
+    #         out_ids[len(in_ids) :] for in_ids, out_ids in zip(x.input_ids, generated_ids)
+    #     ]
+    #     output_text = processor.batch_decode(
+    #         generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True
+    #     )
+    #     ans.append(output_text)
+
+    generated_ids = model.generate(**inputs, max_new_tokens=8192)
+    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
+    )
+    ans.append(output_text)
+    save_path = "output_final.json"
+    counter = counter + 1
+    if counter % 10 == 0 or up + 10 >= end:
+        print(f"Saving data at iteration {idx + 1}")
+        write_json(save_path, ans)
+
+