Create llamav-o1-inference.py

llamav-o1-inference.py

@@ -0,0 +1,129 @@
+from PIL import Image
+import os
+import torch
+import json
+from tqdm import tqdm
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--num_beams", type=int, default=1)
+args = parser.parse_args()
+
+model_id = "/proj/berzelius-2023-191/CoT/llama-recipes/finetuned_model_llama_pixmogeo_mt/Llama-3.2-11B-Vision-Instruct_epoch_2"
+
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+).eval()
+processor = AutoProcessor.from_pretrained(model_id)
+num_beams = args.num_beams
+max_new_tokens = 1024
+summary_prompt = "\nSummarize how you will approach the problem and explain the steps you will take to reach the answer."
+caption_prompt = "Provide a detailed description of the image, particularly emphasizing the aspects related to the question."
+reasoning_prompt = "Provide a chain-of-thought, logical explanation of the problem. This should outline step-by-step reasoning."
+conclusion_prompt = "State the final answer in a clear and direct format. It must match the correct answer exactly."
+
+def generate_inner(question, image):
+    start_n = 1
+    kwargs = {
+        'max_new_tokens': max_new_tokens,
+        "top_p": 0.9,
+        "pad_token_id": 128004,
+        "bos_token_id": 128000,
+        "do_sample": False,
+        "eos_token_id": [
+            128001,
+            128008,
+            128009
+        ],
+        "temperature": 0.6,
+        "num_beams": num_beams,
+        "use_cache": True,
+
+    }
+    messages = [[
+
+        {
+            'role': 'user', 
+            'content': [
+                {'type': 'image'},
+                {'type': 'text', 'text': question+summary_prompt}
+            ],
+        }
+    
+    ]]
+
+    def infer(messages: dict, n) -> str:
+        input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = processor(image, input_text, return_tensors='pt').to(model.device)
+        output = model.generate(**inputs, **kwargs)
+        return [processor.decode(output[i][inputs['input_ids'].shape[1]:]).replace('<|eot_id|>', '').replace("<|end_of_text|>", "") for i in range(n)]
+
+    def tmp(inp, out):
+        return [
+            {
+                'role': 'assistant',
+                'content': [
+                    {'type': 'text', 'text': inp}
+                ]
+            },
+            {
+                'role': 'user',
+                'content': [
+                    {'type': 'text', 'text': out}
+                ]
+            }
+        ]
+    outs = infer(messages[0])
+    for i, out in enumerate(outs):
+        messages[i].extend(tmp(out, caption_prompt))
+    out = infer(messages)
+    messages.extend(tmp(out, reasoning_prompt))
+    reasoning = infer(messages)
+    messages.extend(tmp(reasoning, conclusion_prompt))
+    out = infer(messages)
+    print(f"Question: {question}\nAnswer: {out}")
+    return out, reasoning    
+
+
+def reasoning_steps_answer(img, question, choices):
+    
+    predicted_answer, reasoning = generate_inner(question, img)
+    return predicted_answer, reasoning
+
+print(f"Evaluating with {num_beams=}")
+print("="*50)
+
+all_data = []
+json_paths = "/proj/berzelius-2023-191/CoT/cot_eval/jsonv2"
+image_path = "/proj/berzelius-2023-191/CoT/cot_eval/images"
+for file in tqdm(os.listdir(json_paths)):
+    if not file.endswith(".json"): continue
+    with open(f"{json_paths}/{file}", "r") as json_file:
+        data = json.load(json_file)
+        try:
+            image = Image.open(f"{image_path}/{data['image']}")
+            question = data["question"]
+            final_answer = data["final_answer"]
+            idx = data["idx"]
+            reasoning_answer = data["answer"]
+            question += "\nPlease select the correct option by its letter." if "Choices" in question else ""
+            model_answer, reasoning = generate_inner(question, image)
+            
+            all_data.append({
+                "idx": idx,
+                "question": question,
+                "final_answer": final_answer,
+                "answer": reasoning_answer,
+                "llm_response": reasoning+"\n\n\n"+model_answer,
+            })
+        except Exception as e:
+            print("Skipping file", file, "for", e)
+            continue
+
+model_pref = model_id.replace("/", "_")
+with open(f"results_llavao1_pixmogeo_mt_beams{num_beams}_nosample.json", "w") as json_file:
+    json.dump(all_data, json_file, indent=4)