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import json |
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import logging |
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import os |
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import re |
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from tqdm import tqdm |
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logger = logging.getLogger(__name__) |
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handler = logging.StreamHandler() |
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logger.addHandler(handler) |
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logger.setLevel(logging.INFO) |
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COUNTING_BASE_PROMPT = 'You should output a json string with format {"answer": a int number}.Your output should be directly parsed by json.loads function. eg.```json{"answer": 1}```.\nNow the question is:' |
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RELATION_BASE_PROMPT = 'You should output a json string with format {"answer": "str"}, where str must be one of ["up", "under", "back", "front", "left", "right"]. Your output should be directly parsed by json.loads function. eg.```json{"answer": "left"}```.\nNow the question is:' |
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VALID_RELATIONS = ["up", "under", "back", "front", "left", "right"] |
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SPLIT_SYMBOL = "="*50 |
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def parse_model_answer(model_output, task_type="counting"): |
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""" |
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Parse the output JSON format answer of the model |
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:param model_output: |
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:param task_type: |
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:return: |
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""" |
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pattern = r'```json\s*(\{.*?\})\s*```' |
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match = re.search(pattern, model_output, re.DOTALL) |
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parsed_answer = None |
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if match: |
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try: |
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json_str = match.group(1) |
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result = json.loads(json_str) |
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answer = result.get('answer') |
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if task_type == "counting": |
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if isinstance(answer, str) and answer.isdigit(): |
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parsed_answer = int(answer) |
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elif isinstance(answer, int): |
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parsed_answer = answer |
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else: |
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parsed_answer = None |
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else: |
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parsed_answer = answer if answer in VALID_RELATIONS else None |
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except Exception as e: |
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logger.error(f"{model_output};\n{str(e)}") |
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return parsed_answer |
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def eval_loop(model, dataset, process_fn, task_type="counting", **kwargs): |
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""" |
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:param dataset: |
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:param model: |
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:param process_fn: |
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:param task_type: |
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:param kwargs: |
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:return: |
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""" |
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eval_result = [] |
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for item in tqdm(dataset): |
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result = process_fn(model, item, task_type=task_type, **kwargs) |
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resp = { |
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"id": item["id"], |
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"question": item["question"], |
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"image_path": item['image_path'], |
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"model_answer": result['model_answer'], |
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"parsed_answer": result['parsed_answer'], |
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"ground_truth": item['answer'] |
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} |
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eval_item_info = f"""\n{json.dumps(resp, indent=4)}\n""" |
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logger.info(eval_item_info) |
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eval_result.append(resp) |
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return eval_result |
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def eval_pipeline(model_name, current_dir, params): |
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results = {} |
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parent_dir = os.path.split(current_dir)[0] |
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print("process counting dataset...") |
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counting_data_path = os.path.join(parent_dir, 'Counting.json') |
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relations_data_path = os.path.join(parent_dir, 'Relation.json') |
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combination_data_path = os.path.join(parent_dir, 'Combination.json') |
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counting_data = json.load(open(counting_data_path, 'r', encoding='utf-8')) |
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counting_results = eval_loop(dataset=counting_data, **params) |
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results["counting_results"] = counting_results |
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print("process relations dataset...") |
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relations_data = json.load(open(relations_data_path, 'r', encoding='utf-8')) |
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relations_results = eval_loop(dataset=relations_data, task_type="relation", **params) |
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results["relations_results"] = relations_results |
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print("process combination dataset...") |
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combination_data = json.load(open(combination_data_path, 'r', encoding='utf-8')) |
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combination_results = eval_loop(dataset=combination_data, **params) |
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results["combination_results"] = combination_results |
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result_parent_path = os.path.join(current_dir, './result/') |
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if not os.path.exists(result_parent_path): |
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os.makedirs(result_parent_path) |
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result_path = os.path.join(result_parent_path, f'{model_name}_results.json') |
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with open(result_path, 'w', encoding='utf-8') as f: |
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json.dump(results, f, ensure_ascii=False, indent=2) |
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print(f"The process has finished. The evaluation results are saved to ./result/{model_name}_results.json") |
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print(f"The number of counting samples processed successfully: {len(results['counting_results'])}") |
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print(f"The number of relationship samples processed successfully: {len(results['relations_results'])}") |
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print(f"The number of combination samples processed successfully: {len(results['combination_results'])}") |
