RecPilot/load_best.py at main · TayTroye/RecPilot · GitHub

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import argparse
import os
from logging import getLogger

import torch
import math
import numpy as np
import yaml
import importlib
from accelerate import Accelerator

from collator import Collator
from trainer import Trainer
from utils import *
from data_utils import *
import warnings
warnings.filterwarnings("ignore")

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='Musical_Instruments', help='Dataset name')
    parser.add_argument('--config_file', type=str, default='./config/ftconfig.yaml', help='Config file')

    # === 新增这一行 ===
    parser.add_argument('--load_ckpt', type=str, default=None, help='Path to checkpoint for evaluation only (skips training)')

    return parser.parse_known_args()


def main(config):

    init_seed(config['rand_seed'], config['reproducibility'])
    init_logger(config)

    logger = getLogger()
    accelerator = config['accelerator']
    log(f'Device: {config["device"]}', accelerator, logger)
    log(f'Config: {str(config)}', accelerator, logger)

    # Tokenizer and Dataset
    tokenizer = get_tokenizer(config)
    # print(tokenizer)
    # exit()
    train_dataset, valid_dataset, test_dataset = get_datasets(config)
    train_collate_fn = Collator(config, tokenizer,split="train")
    test_collate_fn = Collator(config, tokenizer,split="valid")

    model_name = config['model_name']
    model_class = getattr(importlib.import_module(f'models.{model_name}.model'), model_name)

    with accelerator.main_process_first():
        model = model_class(config, train_dataset, tokenizer)
    log(model, accelerator, logger)
    log(model.n_parameters, accelerator, logger)

    train_data = get_dataloader(config, train_dataset, train_collate_fn, 'train')
    valid_data = get_dataloader(config, valid_dataset, test_collate_fn,'valid')
    test_data = get_dataloader(config, test_dataset, test_collate_fn, 'test')

    if config['val_delay'] >= config['epochs']:
        config['val_delay'] = config['epochs'] - 1

    trainer = Trainer(config, model, tokenizer, train_data)
    config['load_ckpt'] = args.load_ckpt
    if config['load_ckpt'] is not None:

        log(f"Skipping training. Loading checkpoint from: {config['load_ckpt']}", accelerator, logger)
        ckpt_path = config['load_ckpt']
    else:
        trainer.fit(train_data, valid_data, config['epochs'])
        ckpt_path = trainer.saved_model_ckpt

    accelerator.wait_for_everyone()
    model = accelerator.unwrap_model(trainer.model)
    if config["test_num_beams"] is not None:
        model.config['num_beams'] = config["test_num_beams"]

    model_states = torch.load(ckpt_path, map_location=accelerator.device)['model']
    model.load_state_dict(model_states)

    if accelerator.is_main_process:
        log(f'Loaded best model checkpoint from {ckpt_path}', accelerator, logger)

    trainer.model, test_data = accelerator.prepare(
        model, test_data
    )
    test_results, _ = trainer.evaluate(test_data, split='test', store=True)

    if accelerator.is_main_process:
        for key in test_results:
            accelerator.log({f'Test_Metric/{key}': test_results[key]})


    log(f'Test Results: {test_results}', accelerator, logger)
    trainer.end()


if __name__ == '__main__':
    args, unparsed_args = parse_args()
    command_line_configs = parse_command_line_args(unparsed_args)

    # Config
    config = {}
    config.update(yaml.safe_load(open(args.config_file, 'r')))
    config.update(command_line_configs)

    config['run_local_time'] = get_local_time()

    ckpt_name = get_file_name(config)
    config['ckpt_name'] = ckpt_name
    config['dataset'] = args.dataset
    config['data_dir'] = os.path.join(config['data_dir'], config['dataset'])
    config['ckpt_dir'] = os.path.join(config['ckpt_dir'], config['dataset'], ckpt_name)

    config = convert_config_dict(config)

    config['device'], config['use_ddp'] = init_device()
    config['accelerator'] = Accelerator()
    torch.distributed.barrier(device_ids=[int(os.environ['LOCAL_RANK'])])

    main(config)