Update README.md

README.md

@@ -50,7 +50,7 @@ Specifically, each data sample originally consists of a simple question and a co
 An example with the original VQA and our proposed coarse-to-fine CoT process is shown in the following figure. Meanwhile, we adopt the form of text + numbers for the labels to enhance semantic understanding.
 ![A data example with the original VQA compared with our coarse-to-fine CoT VQA.](./example.png)
 
-### STORM Prompts
+### ORD Prompts
 *Generating the dataset for IQA*
 ```
 <image> You are now an advanced Image Quality Evaluator, and your task is to assess the quality of the provided image. Please evaluate the image’s quality based on a 5-rate scale: rate0(Bad), rate1(Poor), rate2(Fair), rate3(Good), rate4(Excellent). Please provide the coarse category that can help you answer the question better. Please first coarsely categorise the image: rate0-1(Below Fair), rate2(Fair), rate3-4(Above Fair). Based on the coarse classification, proceed to make a final rate prediction. The specific steps are as follows:
@@ -91,7 +91,86 @@ Answer: [Coarse answer], [Final answer]
 Answer: [Coarse answer], [Final answer]
 ```
 
-
 ## Evaluation
+Below, we provide simple examples to demonstrate how to quickly load the Qwen2.5-VL model using 🤗 Transformers, along with testing it on our benchmark datasets:
+```python
+import json
+from tqdm import tqdm 
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2.5-VL-3B-Instruct", 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(
+#     "Qwen/Qwen2.5-VL-3B-Instruct",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
+
+def write_jsonl(data, filename):
+    with open(filename, 'a', encoding='utf-8') as f:
+        json_str = json.dumps(data, ensure_ascii=False)  
+        f.write(json_str + '\n')
 
+file_path = 'ORD/IO_qwen_test_vqa_oc_80k.jsonl'
+output_json = "answer.jsonl"
+
+
+with open(file_path, 'r') as file:
+    for line in tqdm(list(file), desc="Testing"):
+        raw = {}
+        data = json.loads(line.strip())
+        
+        query = data.get('query')
+        response = data.get('response')
+        image_path = data.get('image_path')
+        
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                    "type": "image",
+                    "image": image_path,
+                    },
+                    {
+                    "type": "text", 
+                    "text": query},
+                ],
+            }
+        ]
+
+        text = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to("cuda")
+
+        generated_ids = model.generate(**inputs, max_new_tokens=512)
+        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
+        )
+        raw['label'] = response
+        raw['answer'] = output_text
+        # json_data.append(raw)
+        write_jsonl(raw, output_json)
+```
 ## Examples