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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)
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