main.py

import argparse, os, sys, datetime, glob
import numpy as np
import time
import torch
import torchvision
import pytorch_lightning as pl
import json
import pickle
import matplotlib.pyplot as plt

from packaging import version
from omegaconf import OmegaConf
from torch.utils.data import DataLoader, Dataset
from functools import partial
from PIL import Image

import torch.distributed as dist
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities import rank_zero_info
from pytorch_lightning.plugins import DDPPlugin

sys.path.append("./stable_diffusion")

from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config


def get_parser(**parser_kwargs):
    def str2bool(v):
        if isinstance(v, bool):
            return v
        if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
        elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
        else:
            raise argparse.ArgumentTypeError("Boolean value expected.")

    parser = argparse.ArgumentParser(**parser_kwargs)
    parser.add_argument(
        "-n",
        "--name",
        type=str,
        const=True,
        default="",
        nargs="?",
        help="postfix for logdir",
    )
    parser.add_argument(
        "-r",
        "--resume",
        type=str,
        const=True,
        default="",
        nargs="?",
        help="resume from logdir or checkpoint in logdir",
    )
    parser.add_argument(
        "-b",
        "--base",
        nargs="*",
        metavar="base_config.yaml",
        help="paths to base configs. Loaded from left-to-right. "
             "Parameters can be overwritten or added with command-line options of the form `--key value`.",
        default=list(),
    )
    parser.add_argument(
        "-t",
        "--train",
        type=str2bool,
        const=True,
        default=False,
        nargs="?",
        help="train",
    )
    parser.add_argument(
        "--no-test",
        type=str2bool,
        const=True,
        default=False,
        nargs="?",
        help="disable test",
    )
    parser.add_argument(
        "-p",
        "--project",
        help="name of new or path to existing project"
    )
    parser.add_argument(
        "-d",
        "--debug",
        type=str2bool,
        nargs="?",
        const=True,
        default=False,
        help="enable post-mortem debugging",
    )
    parser.add_argument(
        "-s",
        "--seed",
        type=int,
        default=23,
        help="seed for seed_everything",
    )
    parser.add_argument(
        "-f",
        "--postfix",
        type=str,
        default="",
        help="post-postfix for default name",
    )
    parser.add_argument(
        "-l",
        "--logdir",
        type=str,
        default="logs",
        help="directory for logging dat shit",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="scale base-lr by ngpu * batch_size * n_accumulate",
    )
    return parser


def nondefault_trainer_args(opt):
    parser = argparse.ArgumentParser()
    parser = Trainer.add_argparse_args(parser)
    args = parser.parse_args([])
    return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))


class WrappedDataset(Dataset):
    """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""

    def __init__(self, dataset):
        self.data = dataset

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]


def worker_init_fn(_):
    worker_info = torch.utils.data.get_worker_info()

    dataset = worker_info.dataset
    worker_id = worker_info.id

    if isinstance(dataset, Txt2ImgIterableBaseDataset):
        split_size = dataset.num_records // worker_info.num_workers
        # reset num_records to the true number to retain reliable length information
        dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
        current_id = np.random.choice(len(np.random.get_state()[1]), 1)
        return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
    else:
        return np.random.seed(np.random.get_state()[1][0] + worker_id)


class DataModuleFromConfig(pl.LightningDataModule):
    def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
                 wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
                 shuffle_val_dataloader=False):
        super().__init__()
        self.batch_size = batch_size
        self.dataset_configs = dict()
        self.num_workers = num_workers if num_workers is not None else batch_size * 2
        self.use_worker_init_fn = use_worker_init_fn
        if train is not None:
            self.dataset_configs["train"] = train
            self.train_dataloader = self._train_dataloader
        if validation is not None:
            self.dataset_configs["validation"] = validation
            self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
        if test is not None:
            self.dataset_configs["test"] = test
            self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
        if predict is not None:
            self.dataset_configs["predict"] = predict
            self.predict_dataloader = self._predict_dataloader
        self.wrap = wrap

    def prepare_data(self):
        for data_cfg in self.dataset_configs.values():
            instantiate_from_config(data_cfg)

    def setup(self, stage=None):
        self.datasets = dict(
            (k, instantiate_from_config(self.dataset_configs[k]))
            for k in self.dataset_configs)
        if self.wrap:
            for k in self.datasets:
                self.datasets[k] = WrappedDataset(self.datasets[k])

    def _train_dataloader(self):
        is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
        if is_iterable_dataset or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None
        return DataLoader(self.datasets["train"], batch_size=self.batch_size,
                          num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
                          worker_init_fn=init_fn, persistent_workers=True)

    def _val_dataloader(self, shuffle=False):
        if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None
        return DataLoader(self.datasets["validation"],
                          batch_size=self.batch_size,
                          num_workers=self.num_workers,
                          worker_init_fn=init_fn,
                          shuffle=shuffle, persistent_workers=True)

    def _test_dataloader(self, shuffle=False):
        is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
        if is_iterable_dataset or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None

        # do not shuffle dataloader for iterable dataset
        shuffle = shuffle and (not is_iterable_dataset)

        return DataLoader(self.datasets["test"], batch_size=self.batch_size,
                          num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle, persistent_workers=True)

    def _predict_dataloader(self, shuffle=False):
        if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
            init_fn = worker_init_fn
        else:
            init_fn = None
        return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
                          num_workers=self.num_workers, worker_init_fn=init_fn, persistent_workers=True)


class SetupCallback(Callback):
    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
        super().__init__()
        self.resume = resume
        self.now = now
        self.logdir = logdir
        self.ckptdir = ckptdir
        self.cfgdir = cfgdir
        self.config = config
        self.lightning_config = lightning_config

    def on_keyboard_interrupt(self, trainer, pl_module):
        if trainer.global_rank == 0:
            print("Summoning checkpoint.")
            ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
            trainer.save_checkpoint(ckpt_path)

    def on_pretrain_routine_start(self, trainer, pl_module):
        if trainer.global_rank == 0:
            # Create logdirs and save configs
            # os.makedirs(self.logdir, exist_ok=True)
            # os.makedirs(self.ckptdir, exist_ok=True)
            # os.makedirs(self.cfgdir, exist_ok=True)

            if "callbacks" in self.lightning_config:
                if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
                    os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
            print("Project config")
            print(OmegaConf.to_yaml(self.config))
            OmegaConf.save(self.config,
                           os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))

            print("Lightning config")
            print(OmegaConf.to_yaml(self.lightning_config))
            OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
                           os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))

def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()

def all_gather(data):
    """
    Run all_gather on arbitrary picklable data (not necessarily tensors)
    Args:
        data: any picklable object
    Returns:
        list[data]: list of data gathered from each rank
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    origin_size = None
    if not isinstance(data, torch.Tensor):
        buffer = pickle.dumps(data)
        storage = torch.ByteStorage.from_buffer(buffer)
        tensor = torch.ByteTensor(storage).to("cuda")
    else:
        origin_size = data.size()
        tensor = data.reshape(-1)

    tensor_type = tensor.dtype

    # obtain Tensor size of each rank
    local_size = torch.LongTensor([tensor.numel()]).to("cuda")
    size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    # receiving Tensor from all ranks
    # we pad the tensor because torch all_gather does not support
    # gathering tensors of different shapes
    tensor_list = []
    for _ in size_list:
        tensor_list.append(torch.FloatTensor(size=(max_size,)).cuda().to(tensor_type))
    if local_size != max_size:
        padding = torch.FloatTensor(size=(max_size - local_size,)).cuda().to(tensor_type)
        tensor = torch.cat((tensor, padding), dim=0)
    dist.all_gather(tensor_list, tensor)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        if origin_size is None:
            buffer = tensor.cpu().numpy().tobytes()[:size]
            data_list.append(pickle.loads(buffer))
        else:
            buffer = tensor[:size]
            data_list.append(buffer)

    if origin_size is not None:
        new_shape = [-1] + list(origin_size[1:])
        resized_list = []
        for data in data_list:
            # suppose the difference of tensor size exist in first dimension
            data = data.reshape(new_shape)
            resized_list.append(data)

        return resized_list
    else:
        return data_list

class ImageLogger(Callback):
    def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps=True,
                 rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
                 log_images_kwargs=None):
        super().__init__()
        self.rescale = rescale
        self.batch_freq = batch_frequency
        self.max_images = max_images
        self.logger_log_images = {
            pl.loggers.TestTubeLogger: self._testtube,
        }
        self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
        if not increase_log_steps:
            self.log_steps = [self.batch_freq]
        self.clamp = clamp
        self.disabled = disabled
        self.log_on_batch_idx = log_on_batch_idx
        self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
        self.log_first_step = log_first_step
        
    def on_validation_epoch_end(self, trainer, pl_module):
        if not self.disabled:
            try:
                # 检查是否有 clip_scores 和 losses 属性
                if hasattr(pl_module, 'clip_scores') and hasattr(pl_module, 'losses'):
                    if len(pl_module.clip_scores) > 0 and len(pl_module.losses) > 0:
                        # 绘制 trade-off 曲线
                        plt.figure(figsize=(12,5))
                        
                        # 第一个子图: CLIP-Loss trade-off
                        plt.subplot(1,2,1)
                        plt.scatter(pl_module.clip_scores, pl_module.losses, alpha=0.5)
                        plt.xlabel('CLIP Score')
                        plt.ylabel('Loss')
                        plt.title('CLIP-Loss Trade-off')
                        
                        # 添加趋势线
                        z = np.polyfit(pl_module.clip_scores, pl_module.losses, 1)
                        p = np.poly1d(z)
                        plt.plot(pl_module.clip_scores, p(pl_module.clip_scores), "r--", alpha=0.8)
                        
                        # 第二个子图: Loss随epoch变化
                        plt.subplot(1,2,2)
                        epochs = range(len(pl_module.losses))
                        plt.plot(epochs, pl_module.losses, 'b-', label='Training Loss')
                        plt.xlabel('Steps')
                        plt.ylabel('Loss')
                        plt.title('Training Loss Curve')
                        plt.legend()
                        
                        # 保存图像
                        save_path = f"{trainer.logger.log_dir}/training_curves_epoch_{trainer.current_epoch}.png"
                        plt.savefig(save_path)
                        plt.close()
                        
                        # 同时将数据保存为CSV,方便后续分析
                        import pandas as pd
                        df = pd.DataFrame({
                            'clip_score': pl_module.clip_scores,
                            'loss': pl_module.losses
                        })
                        df.to_csv(f"{trainer.logger.log_dir}/metrics_epoch_{trainer.current_epoch}.csv")
                        
            except Exception as e:
                print(f"Warning: Failed to plot curves: {str(e)}")
                pass

    @rank_zero_only
    def _testtube(self, pl_module, images, batch_idx, split):
        for k in images:
            grid = torchvision.utils.make_grid(images[k])
            grid = (grid + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w

            tag = f"{split}/{k}"
            pl_module.logger.experiment.add_image(
                tag, grid,
                global_step=pl_module.global_step)

    @rank_zero_only
    def log_local(self, save_dir, split, images, prompts,
                  global_step, current_epoch, batch_idx):
        root = os.path.join(save_dir, "images", split)
        names = {"reals": "before", "inputs": "after", "reconstruction": "before-vq", "samples": "after-gen"}
        # print(root)
        for k in images:
            grid = torchvision.utils.make_grid(images[k], nrow=8)
            if self.rescale:
                grid = (grid + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w
            grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
            grid = grid.numpy()
            grid = (grid * 255).astype(np.uint8)
            filename = "gs-{:06}_e-{:06}_b-{:06}_{}.png".format(
                global_step,
                current_epoch,
                batch_idx,
                names[k])
            path = os.path.join(root, filename)
            os.makedirs(os.path.split(path)[0], exist_ok=True)
            # print(path)
            Image.fromarray(grid).save(path)

        filename = "gs-{:06}_e-{:06}_b-{:06}_prompt.json".format(
            global_step,
            current_epoch,
            batch_idx)
        path = os.path.join(root, filename)
        with open(path, "w") as f:
            for p in prompts:
                f.write(f"{json.dumps(p)}\n")

    def log_img(self, pl_module, batch, batch_idx, split="train"):
        check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
        if (self.check_frequency(check_idx) and  # batch_idx % self.batch_freq == 0
                hasattr(pl_module, "log_images") and
                callable(pl_module.log_images) and
                self.max_images > 0) or (split == "val" and batch_idx == 0):
            logger = type(pl_module.logger)

            is_train = pl_module.training
            if is_train:
                pl_module.eval()

            with torch.no_grad():
                images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)

            prompts = batch["edit"]["c_crossattn"][:self.max_images]
            prompts = [p for ps in all_gather(prompts) for p in ps]

            for k in images:
                N = min(images[k].shape[0], self.max_images)
                images[k] = images[k][:N]
                images[k] = torch.cat(all_gather(images[k][:N]))
                if isinstance(images[k], torch.Tensor):
                    images[k] = images[k].detach().cpu()
                    if self.clamp:
                        images[k] = torch.clamp(images[k], -1., 1.)

            self.log_local(pl_module.logger.save_dir, split, images, prompts,
                           pl_module.global_step, pl_module.current_epoch, batch_idx)

            logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
            logger_log_images(pl_module, images, pl_module.global_step, split)

            if is_train:
                pl_module.train()

    def check_frequency(self, check_idx):
        if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
                check_idx > 0 or self.log_first_step):
            if len(self.log_steps) > 0:
                self.log_steps.pop(0)
            return True
        return False

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
        if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
            self.log_img(pl_module, batch, batch_idx, split="train")

    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
        if not self.disabled and pl_module.global_step > 0:
            self.log_img(pl_module, batch, batch_idx, split="val")
        if hasattr(pl_module, 'calibrate_grad_norm'):
            if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
                self.log_gradients(trainer, pl_module, batch_idx=batch_idx)


class CUDACallback(Callback):
    # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
    def on_train_epoch_start(self, trainer, pl_module):
        # Reset the memory use counter
        torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
        torch.cuda.synchronize(trainer.root_gpu)
        self.start_time = time.time()

    def on_train_epoch_end(self, trainer, pl_module, outputs):
        torch.cuda.synchronize(trainer.root_gpu)
        max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
        epoch_time = time.time() - self.start_time

        try:
            max_memory = trainer.training_type_plugin.reduce(max_memory)
            epoch_time = trainer.training_type_plugin.reduce(epoch_time)

            rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
            rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
        except AttributeError:
            pass


# 添加一个新的回调类来记录训练数据
class TrainingMonitor(Callback):
    def __init__(self, save_dir):
        super().__init__()
        self.save_dir = save_dir
        # 训练指标
        self.train_metrics = {
            'step': [],
            'epoch': [],
            'loss': [],
            'lr': []
        }
        # 验证指标
        self.val_metrics = {
            'step': [],
            'epoch': [],
            'loss': [],
            'clip_score': []
        }
        
    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, *args, **kwargs):
        # 记录验证指标
        metrics = trainer.callback_metrics
        self.val_metrics['step'].append(trainer.global_step)
        self.val_metrics['epoch'].append(trainer.current_epoch)
        
        loss = metrics.get('val/loss')
        if isinstance(loss, torch.Tensor):
            loss = loss.detach().cpu().item()
        self.val_metrics['loss'].append(loss if loss is not None else float('nan'))
        
        clip_score = metrics.get('val/clip_score')
        if isinstance(clip_score, torch.Tensor):
            clip_score = clip_score.detach().cpu().item()
        self.val_metrics['clip_score'].append(clip_score if clip_score is not None else float('nan'))
        
        # 每个验证步骤后保存验证数据
        self.save_metrics('val')
            
    def on_train_end(self, trainer, pl_module):
        # 训练结束时保存所有数据
        self.save_metrics('train')
        self.save_metrics('val')
        
    def save_metrics(self, mode='train'):
        import pandas as pd
        import os
        
        metrics = self.train_metrics if mode == 'train' else self.val_metrics
        
        # 确保所有列的长度相同
        max_len = max(len(v) for v in metrics.values())
        for k in metrics:
            while len(metrics[k]) < max_len:
                metrics[k].append(float('nan'))
                
        df = pd.DataFrame(metrics)
        save_path = os.path.join(self.save_dir, f'{mode}_metrics.csv')
        df.to_csv(save_path, index=False)


if __name__ == "__main__":
    # custom parser to specify config files, train, test and debug mode,
    # postfix, resume.
    # `--key value` arguments are interpreted as arguments to the trainer.
    # `nested.key=value` arguments are interpreted as config parameters.
    # configs are merged from left-to-right followed by command line parameters.

    # model:
    #   base_learning_rate: float
    #   target: path to lightning module
    #   params:
    #       key: value
    # data:
    #   target: main.DataModuleFromConfig
    #   params:
    #      batch_size: int
    #      wrap: bool
    #      train:
    #          target: path to train dataset
    #          params:
    #              key: value
    #      validation:
    #          target: path to validation dataset
    #          params:
    #              key: value
    #      test:
    #          target: path to test dataset
    #          params:
    #              key: value
    # lightning: (optional, has sane defaults and can be specified on cmdline)
    #   trainer:
    #       additional arguments to trainer
    #   logger:
    #       logger to instantiate
    #   modelcheckpoint:
    #       modelcheckpoint to instantiate
    #   callbacks:
    #       callback1:
    #           target: importpath
    #           params:
    #               key: value

    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")

    # add cwd for convenience and to make classes in this file available when
    # running as `python main.py`
    # (in particular `main.DataModuleFromConfig`)
    sys.path.append(os.getcwd())

    parser = get_parser()
    parser = Trainer.add_argparse_args(parser)

    opt, unknown = parser.parse_known_args()

    assert opt.name
    cfg_fname = os.path.split(opt.base[0])[-1]
    cfg_name = os.path.splitext(cfg_fname)[0]
    nowname = f"{cfg_name}_{opt.name}"
    logdir = os.path.join(opt.logdir, nowname)
    ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
    resume = False

    if os.path.isfile(ckpt):
        opt.resume_from_checkpoint = ckpt
        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
        opt.base = base_configs + opt.base
        _tmp = logdir.split("/")
        nowname = _tmp[-1]
        resume = True

    ckptdir = os.path.join(logdir, "checkpoints")
    cfgdir = os.path.join(logdir, "configs")

    os.makedirs(logdir, exist_ok=True)
    os.makedirs(ckptdir, exist_ok=True)
    os.makedirs(cfgdir, exist_ok=True)

    try:
        # init and save configs
        configs = [OmegaConf.load(cfg) for cfg in opt.base]
        cli = OmegaConf.from_dotlist(unknown)
        config = OmegaConf.merge(*configs, cli)
        
        # 修改这里: 使用 get 而不是 pop,或者 pop 后重新赋值
        lightning_config = config.pop("lightning", OmegaConf.create())
        config.lightning = lightning_config  # 添加这行,保证 lightning 配置仍然可用
        
        # merge trainer cli with config
        trainer_config = lightning_config.get("trainer", OmegaConf.create())
        
        # default to ddp
        trainer_config["accelerator"] = "ddp"
        for k in nondefault_trainer_args(opt):
            trainer_config[k] = getattr(opt, k)
        if not "gpus" in trainer_config:
            del trainer_config["accelerator"]
            cpu = True
        else:
            gpuinfo = trainer_config["gpus"]
            print(f"Running on GPUs {gpuinfo}")
            cpu = False
        trainer_opt = argparse.Namespace(**trainer_config)
        lightning_config.trainer = trainer_config

        # model
        model = instantiate_from_config(config.model)

        # trainer and callbacks
        trainer_kwargs = dict()

        # default logger configs
        default_logger_cfgs = {
            "wandb": {
                "target": "pytorch_lightning.loggers.WandbLogger",
                "params": {
                    "name": nowname,
                    "save_dir": logdir,
                    "id": nowname,
                }
            },
            "testtube": {
                "target": "pytorch_lightning.loggers.TestTubeLogger",
                "params": {
                    "name": "testtube",
                    "save_dir": logdir,
                }
            },
        }
        default_logger_cfg = default_logger_cfgs["wandb"]
        if "logger" in lightning_config:
            logger_cfg = lightning_config.logger
        else:
            logger_cfg = OmegaConf.create()
        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)

        # modelcheckpoint配置
        default_modelckpt_cfg = {
            "target": "pytorch_lightning.callbacks.ModelCheckpoint",
            "params": {
                "dirpath": ckptdir,
                "filename": "best-{epoch:04}-{train/loss:.4f}",  # 使用loss而不是clip_score
                "monitor": "train/loss",                         # 监控loss
                "mode": "min",                                   # 最小化loss
                "save_top_k": 1,                                # 只保存最好的一个
                "verbose": True,
                "save_last": True,                              # 同时保存最新的模型为last.ckpt
                "auto_insert_metric_name": False
            }
        }

        if "modelcheckpoint" in lightning_config:
            modelckpt_cfg = lightning_config.modelcheckpoint
        else:
            modelckpt_cfg =  OmegaConf.create()
        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
        print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
        if version.parse(pl.__version__) < version.parse('1.4.0'):
            trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)

        # add callback which sets up log directory
        default_callbacks_cfg = {
            "setup_callback": {
                "target": "main.SetupCallback",
                "params": {
                    "resume": opt.resume,
                    "now": now,
                    "logdir": logdir,
                    "ckptdir": ckptdir,
                    "cfgdir": cfgdir,
                    "config": config,
                    "lightning_config": lightning_config,
                }
            },
            "training_monitor": {  # 添加新的回调
                "target": "main.TrainingMonitor",
                "params": {
                    "save_dir": logdir,  # 保存到日志目录
                }
            },
            "learning_rate_logger": {
                "target": "main.LearningRateMonitor",
                "params": {
                    "logging_interval": "step",
                }
            },
            "cuda_callback": {
                "target": "main.CUDACallback"
            },
        }
        if version.parse(pl.__version__) >= version.parse('1.4.0'):
            default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})

        if "callbacks" in lightning_config:
            callbacks_cfg = lightning_config.callbacks
        else:
            callbacks_cfg = OmegaConf.create()

        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
        if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
            callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
        elif 'ignore_keys_callback' in callbacks_cfg:
            del callbacks_cfg['ignore_keys_callback']

        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]

        trainer = Trainer.from_argparse_args(trainer_opt, plugins=DDPPlugin(find_unused_parameters=False), **trainer_kwargs)
        trainer.logdir = logdir  ###

        # data
        data = instantiate_from_config(config.data)
        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
        # calling these ourselves should not be necessary but it is.
        # lightning still takes care of proper multiprocessing though
        data.prepare_data()
        data.setup()
        print("#### Data #####")
        for k in data.datasets:
            print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")

        # configure learning rate
        bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
        if not cpu:
            ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
        else:
            ngpu = 1
        if 'accumulate_grad_batches' in lightning_config.trainer:
            accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
        else:
            accumulate_grad_batches = 1
        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
        if opt.scale_lr:
            model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
            print(
                "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
                    model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
        else:
            model.learning_rate = base_lr
            print("++++ NOT USING LR SCALING ++++")
            print(f"Setting learning rate to {model.learning_rate:.2e}")


        # allow checkpointing via USR1
        def melk(*args, **kwargs):
            # run all checkpoint hooks
            if trainer.global_rank == 0:
                print("Summoning checkpoint.")
                ckpt_path = os.path.join(ckptdir, "last.ckpt")
                trainer.save_checkpoint(ckpt_path)


        def divein(*args, **kwargs):
            if trainer.global_rank == 0:
                import pudb;
                pudb.set_trace()


        import signal

        signal.signal(signal.SIGUSR1, melk)
        signal.signal(signal.SIGUSR2, divein)

        # run
        if opt.train:
            try:
                trainer.fit(model, data)
            except Exception:
                melk()
                raise
        if not opt.no_test and not trainer.interrupted:
            trainer.test(model, data)
    except Exception:
        if opt.debug and trainer.global_rank == 0:
            try:
                import pudb as debugger
            except ImportError:
                import pdb as debugger
            debugger.post_mortem()
        raise
    finally:
        # move newly created debug project to debug_runs
        if opt.debug and not opt.resume and trainer.global_rank == 0:
            dst, name = os.path.split(logdir)
            dst = os.path.join(dst, "debug_runs", name)
            os.makedirs(os.path.split(dst)[0], exist_ok=True)
            os.rename(logdir, dst)
        if trainer.global_rank == 0:
            print(trainer.profiler.summary())