| import json |
| import subprocess |
|
|
| from llava.train.llava_trainer import LLaVATrainer |
|
|
|
|
| class LLaVAEvalTrainer(LLaVATrainer): |
| def evaluate(self, evaluate_args): |
| cmd = f"accelerate launch --num_processes {evaluate_args.eval_num_processes} -m lmms_eval \ |
| --model {evaluate_args.model} \ |
| --model_args {evaluate_args.model_args} \ |
| --tasks {evaluate_args.task_names} \ |
| --batch_size {evaluate_args.batch_size} \ |
| --log_samples_suffix {evaluate_args.log_samples_suffix} \ |
| --output_path {evaluate_args.output_path}" |
| if evaluate_args.limit: |
| cmd += f" --limit {evaluate_args.limit}" |
| if evaluate_args.num_fewshot: |
| cmd += f" --num_fewshot {evaluate_args.num_fewshot}" |
| if evaluate_args.gen_kwargs != "": |
| cmd += f" --gen_kwargs {evaluate_args.gen_kwargs}" |
| if evaluate_args.log_samples: |
| cmd += f" --log_samples" |
| else: |
| assert False, "Please log samples so that the result can be parsed" |
| results = subprocess.run([cmd], shell=True, capture_output=True, text=True) |
| try: |
| result_file_index_start = results.stdout.index("Saved samples to ") |
| result_file_index_end = results.stdout.index(f".json") |
| result_file_index_start += len("Saved samples to ") |
| file = results.stdout[result_file_index_start:result_file_index_end] |
| except: |
| result_file_index_start = results.stderr.index("Saved samples to ") |
| result_file_index_end = results.stderr.index(f".json") |
| result_file_index_start += len("Saved samples to ") |
| file = results.stderr[result_file_index_start:result_file_index_end] |
| file = file.split("/")[:-1] |
| file = "/".join(file) + "/results.json" |
| with open(file, "r") as f: |
| lmms_eval_results = json.load(f) |
| result_dict = {} |
| tasks_list = evaluate_args.task_names.split(",") |
| for task in tasks_list: |
| task_results = lmms_eval_results["results"][task] |
| for k, v in task_results.items(): |
| if k != "alias" and "stderr" not in k: |
| metric = k.split(",")[0] |
| result_dict[f"{task}_{metric}"] = v |
| return result_dict |
|
|
| """def evaluate(self, evaluate_args): |
| initialize_tasks() |
| tasks_list = evaluate_args.task_names.split(",") |
| result_dict = {} |
| results = evaluator.simple_evaluate( |
| model=evaluate_args.model, |
| model_args=evaluate_args.model_args, |
| tasks=tasks_list, |
| num_fewshot=evaluate_args.num_fewshot, |
| batch_size=evaluate_args.batch_size, |
| device=evaluate_args.device, |
| limit=evaluate_args.limit, |
| check_integrity=evaluate_args.check_integrity, |
| show_task_to_terminal=evaluate_args.show_task_to_terminal, |
| log_samples=evaluate_args.log_samples, |
| gen_kwargs=evaluate_args.gen_kwargs, |
| cli_args=evaluate_args, |
| ) |
| for task in tasks_list: |
| task_results = results["results"][task] |
| for k,v in task_results.items(): |
| if k != "alias" and "stderr" not in k: |
| metric = k.split(",")[0] |
| result_dict[f"{task}_{metric}"] = v |
| |
| return result_dict""" |
|
|
