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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) |