main.py

# Copyright AstraZeneca UK Ltd. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse, os, sys, datetime, glob, importlib, csv
import numpy as np
import time
import torch

import torchvision
import pytorch_lightning as pl

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

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

from src.p2p.p2p_ldm_utils import LocalMask, AttentionMask
from src.p2p.ptp_utils import register_attention_control_t2i

def load_model_from_config(config, ckpt, verbose=False):
    """
    Load a model from a given configuration and checkpoint.

    Args:
        config (OmegaConf): Configuration object for the model.
        ckpt (str): Path to the checkpoint file.
        verbose (bool): If True, prints missing and unexpected keys in the state dictionary. Default is False.

    Returns:
        nn.Module: The loaded model.
    """
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    sd = pl_sd["state_dict"]
    config.model.params.ckpt_path = ckpt
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    return model

def get_parser(**parser_kwargs):
    """
    Get an argument parser with specified options.

    Returns:
        argparse.ArgumentParser: Configured argument parser.
    """
    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",
        type=str2bool,
        nargs="?",
        const=True,
        default=True,
        help="scale base-lr by ngpu * batch_size * n_accumulate",
    )

    parser.add_argument(
        "--datadir_in_name", 
        type=str2bool, 
        nargs="?", 
        const=True, 
        default=True, 
        help="Prepend the final directory in the data_root to the output directory name")

    parser.add_argument("--actual_resume", 
        type=str,
        required=True,
        help="Path to model to actually resume from")

    parser.add_argument("--data_root", 
        type=str, 
        required=True, 
        help="Path to directory with training images")

    parser.add_argument("--embedding_manager_ckpt", 
        type=str, 
        default="", 
        help="Initialize embedding manager from a checkpoint")

    parser.add_argument("--placeholder_string", 
        type=str, 
        help="MCPL: Placeholder string which will be used to denote the string holding multiple concepts. Overwrites the config options.")

    parser.add_argument("--presudo_words", 
        type=str, 
        help="MCPL: A list of presudo words corresponding to multiple concepts.")
    
    parser.add_argument("--presudo_words_infonce", 
        type=str, 
        default="", 
        help="PromptCL: A list of presudo words (semantic mutual exclusive) to calculate additional CL (infoNCE) loss")

    parser.add_argument("--adj_aug_infonce", 
        type=str, 
        default="", 
        help="Bind adjective: A list of adj. words to be treated as additional agumented positive of presudo_words_infonce in CL loss")

    parser.add_argument("--infonce_temperature",
        type=float,
        default=0.07,
        help="PromptCL: infonce_temperature",
    )

    parser.add_argument("--infonce_scale",
        type=float,
        default=1.0,
        help="PromptCL: infonce_scale",
    )
    
    parser.add_argument("--presudo_words_softmax", 
        type=str, 
        default="", 
        help="PromptCL: A list of presudo words to calculate additional softmax with, default means no additional softmax")

    parser.add_argument("--attn_words", 
        type=str, 
        help="Attention Mask: A list of keywords for attention masking.")

    parser.add_argument("--attn_mask_type", 
        type=str, 
        default="hard", 
        help="Attention Mask: Type of attention mask, choose from 'hard: apply threhold', 'soft: no threshold' or 'skip: no mask (cause we want to keep controller for CL)'")

    parser.add_argument("--batch_size",
        type=int,
        default=4,
        help="batch_size",
    )
    
    parser.add_argument("--init_word", 
        type=str, 
        help="Word to use as source for initial token embedding")

    return parser


def nondefault_trainer_args(opt):
    """
    Get non-default trainer arguments.

    Args:
        opt (argparse.Namespace): Parsed command-line arguments.

    Returns:
        list: List of non-default trainer arguments.
    """
    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.

    Args:
        dataset (object): The dataset object to wrap.
    """

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

    def __len__(self):
        """
        Get the length of the dataset.

        Returns:
            int: The length of the dataset.
        """
        return len(self.data)

    def __getitem__(self, idx):
        """
        Get an item from the dataset by index.

        Args:
            idx (int): The index of the item.

        Returns:
            object: The item at the specified index.
        """
        return self.data[idx]


def worker_init_fn(_):
    """
    Worker initialization function for setting random seed and handling dataset splits.

    Args:
        _ (torch.utils.data.get_worker_info): Worker information object.
    """
    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):
    """
    LightningDataModule for loading data from a configuration.

    Args:
        batch_size (int): The batch size for data loading.
        train (dict, optional): Configuration for training dataset.
        validation (dict, optional): Configuration for validation dataset.
        test (dict, optional): Configuration for test dataset.
        predict (dict, optional): Configuration for predict dataset.
        wrap (bool, optional): Whether to wrap the dataset.
        num_workers (int, optional): Number of workers for data loading.
        shuffle_test_loader (bool, optional): Whether to shuffle the test dataloader.
        use_worker_init_fn (bool, optional): Whether to use the worker initialization function.
        shuffle_val_dataloader (bool, optional): Whether to shuffle the validation dataloader.
    """

    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):
        """
        Prepare data for training, validation, testing, and prediction.
        """
        for data_cfg in self.dataset_configs.values():
            instantiate_from_config(data_cfg)

    def setup(self, stage=None):
        """
        Set up datasets for training, validation, testing, and prediction.

        Args:
            stage (str, optional): Stage to set up (train, val, test, predict).
        """
        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):
        """
        Create the training dataloader.

        Returns:
            DataLoader: The training dataloader.
        """
        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)

    def _val_dataloader(self, shuffle=False):
        """
        Create the validation dataloader.

        Args:
            shuffle (bool, optional): Whether to shuffle the validation dataloader.

        Returns:
            DataLoader: The validation dataloader.
        """
        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)

    def _test_dataloader(self, shuffle=False):
        """
        Create the test dataloader.

        Args:
            shuffle (bool, optional): Whether to shuffle the test dataloader.

        Returns:
            DataLoader: The test dataloader.
        """
        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)

    def _predict_dataloader(self, shuffle=False):
        """
        Create the predict dataloader.

        Args:
            shuffle (bool, optional): Whether to shuffle the predict dataloader.

        Returns:
            DataLoader: The predict dataloader.
        """
        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)


class SetupCallback(Callback):
    """
    A callback for setting up directories and saving configurations before training starts.

    Args:
        resume (str): Path to resume from.
        now (str): Current timestamp.
        logdir (str): Directory for logging.
        ckptdir (str): Directory for checkpoints.
        cfgdir (str): Directory for configurations.
        config (OmegaConf): Project configuration.
        lightning_config (OmegaConf): Lightning configuration.
    """
    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):
        """
        Handle keyboard interrupt and save the checkpoint.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning 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):
        """
        Set up directories and save configurations before training starts.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning 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)))

        else:
            # ModelCheckpoint callback created log directory --- remove it
            if not self.resume and os.path.exists(self.logdir):
                dst, name = os.path.split(self.logdir)
                dst = os.path.join(dst, "child_runs", name)
                os.makedirs(os.path.split(dst)[0], exist_ok=True)
                try:
                    os.rename(self.logdir, dst)
                except FileNotFoundError:
                    pass


class ImageLogger(Callback):
    """
    A callback for logging images during training and validation.

    Args:
        batch_frequency (int): Frequency of logging images in terms of batches.
        max_images (int): Maximum number of images to log.
        clamp (bool, optional): Whether to clamp image values. Default is True.
        increase_log_steps (bool, optional): Whether to increase log steps exponentially. Default is True.
        rescale (bool, optional): Whether to rescale images to [0, 1]. Default is True.
        disabled (bool, optional): Whether to disable image logging. Default is False.
        log_on_batch_idx (bool, optional): Whether to log on batch index instead of global step. Default is False.
        log_first_step (bool, optional): Whether to log the first step. Default is False.
        log_images_kwargs (dict, optional): Additional keyword arguments for logging images.
    """
    def __init__(self, batch_frequency, max_images, 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

    @rank_zero_only
    def _testtube(self, pl_module, images, batch_idx, split):
        """
        Log images to TestTube logger.

        Args:
            pl_module (pl.LightningModule): The Lightning module.
            images (dict): Dictionary of images to log.
            batch_idx (int): Batch index.
            split (str): Data split ('train' or 'val').
        """
        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,
                  global_step, current_epoch, batch_idx):
        """
        Save images locally.

        Args:
            save_dir (str): Directory to save images.
            split (str): Data split ('train' or 'val').
            images (dict): Dictionary of images to save.
            global_step (int): Global step value.
            current_epoch (int): Current epoch number.
            batch_idx (int): Batch index.
        """
        root = os.path.join(save_dir, "images", split)
        for k in images:
            grid = torchvision.utils.make_grid(images[k], nrow=4)
            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}.jpg".format(
                k,
                global_step,
                current_epoch,
                batch_idx)
            path = os.path.join(root, filename)
            os.makedirs(os.path.split(path)[0], exist_ok=True)
            Image.fromarray(grid).save(path)

    def log_img(self, pl_module, batch, batch_idx, split="train"):
        """
        Log images using the specified logging mechanism.

        Args:
            pl_module (pl.LightningModule): The Lightning module.
            batch (dict): The batch of data.
            batch_idx (int): Batch index.
            split (str, optional): Data split ('train' or 'val'). Default is 'train'.
        """
        if pl_module.controller is not None:
            print('AttentionMask-log_img-re-register: update attn layer counts at log_img ...')
            register_attention_control_t2i(pl_module, pl_module.controller)
            pl_module.controller.reset()

        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):
            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)

            for k in images:
                N = min(images[k].shape[0], self.max_images)
                images[k] = 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,
                           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):
        """
        Check if the current step or batch index matches the logging frequency.

        Args:
            check_idx (int): Index to check.

        Returns:
            bool: True if logging is required, False otherwise.
        """
        if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
                check_idx > 0 or self.log_first_step):
            try:
                self.log_steps.pop(0)
            except IndexError as e:
                print(e)
                pass
            return True
        return False

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
        """
        Hook to log images at the end of a training batch.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning module.
            outputs (dict): Outputs from the training step.
            batch (dict): The batch of data.
            batch_idx (int): Batch index.
            dataloader_idx (int): Index of the dataloader.
        """
        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):
        """
        Hook to log images at the end of a validation batch.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning module.
            outputs (dict): Outputs from the validation step.
            batch (dict): The batch of data.
            batch_idx (int): Batch index.
            dataloader_idx (int): Index of the dataloader.
        """
        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):
    """
    Callback to log CUDA memory usage and training time for each epoch.
    """
    # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
    def on_train_epoch_start(self, trainer, pl_module):
        """
        Hook that runs at the start of each training epoch to reset CUDA memory stats.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning 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):
        """
        Hook that runs at the end of each training epoch to log CUDA memory usage and epoch time.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning module.
        """
        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

class ModeSwapCallback(Callback):
    """
    A callback to swap training modes at a specified step during training.

    Args:
        swap_step (int): The step at which to swap the training mode. Default is 2000.
    """

    def __init__(self, swap_step=2000):
        super().__init__()
        self.is_frozen = False
        self.swap_step = swap_step

    def on_train_epoch_start(self, trainer, pl_module):
        """
        Hook that runs at the start of each training epoch to check and swap the training mode.

        Args:
            trainer (pl.Trainer): The trainer instance.
            pl_module (pl.LightningModule): The Lightning module.
        """
        if trainer.global_step < self.swap_step and not self.is_frozen:
            self.is_frozen = True
            trainer.optimizers = [pl_module.configure_opt_embedding()]

        if trainer.global_step > self.swap_step and self.is_frozen:
            self.is_frozen = False
            trainer.optimizers = [pl_module.configure_opt_model()]

if __name__ == "__main__":
    """
    Main function to parse arguments, set up configurations, and run the training/testing process.
    """
    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`
    sys.path.append(os.getcwd())

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

    opt, unknown = parser.parse_known_args()
    if opt.name and opt.resume:
        raise ValueError(
            "-n/--name and -r/--resume cannot be specified both."
            "If you want to resume training in a new log folder, "
            "use -n/--name in combination with --resume_from_checkpoint"
        )
    if opt.resume:
        if not os.path.exists(opt.resume):
            raise ValueError("Cannot find {}".format(opt.resume))
        if os.path.isfile(opt.resume):
            paths = opt.resume.split("/")
            logdir = "/".join(paths[:-2])
            ckpt = opt.resume
        else:
            assert os.path.isdir(opt.resume), opt.resume
            logdir = opt.resume.rstrip("/")
            ckpt = os.path.join(logdir, "checkpoints", "last.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]
    else:
        if opt.name:
            name = "_" + opt.name
        elif opt.base:
            cfg_fname = os.path.split(opt.base[0])[-1]
            cfg_name = os.path.splitext(cfg_fname)[0]
            name = "_" + cfg_name
        else:
            name = ""

        if opt.datadir_in_name:
            now = os.path.basename(os.path.normpath(opt.data_root)) + now
            
        nowname = now + name + opt.postfix
        logdir = os.path.join(opt.logdir, nowname)

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

    try:
        # init and save configs
        configs = [OmegaConf.load(cfg) for cfg in opt.base]
        cli = OmegaConf.from_dotlist(unknown)
        config = OmegaConf.merge(*configs, cli)
        lightning_config = config.pop("lightning", OmegaConf.create())
        # 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

        # config.model.params.personalization_config.params.init_word = opt.init_word
        config.model.params.personalization_config.params.embedding_manager_ckpt = opt.embedding_manager_ckpt
        if opt.placeholder_string:
            config.model.params.personalization_config.params.placeholder_strings = opt.placeholder_string.split(' ')
            if opt.presudo_words is not None:
                config.model.params.personalization_config.params.presudo_words = opt.presudo_words.split(',')
            if 'RELATE' in opt.placeholder_string:
                config.data.params.train.params.placeholder_token = opt.placeholder_string

        if opt.init_word:
            config.model.params.personalization_config.params.initializer_words[0] = opt.init_word

        if opt.actual_resume:
            model = load_model_from_config(config, opt.actual_resume)
        else:
            model = instantiate_from_config(config.model)

        # AttentionMask
        if opt.attn_words is not None:
            config.model.params.personalization_config.params.attn_words = opt.attn_words.split(',')
            opt.attn_words = opt.attn_words.split(',')
        config.model.params.personalization_config.params.presudo_words_softmax = opt.presudo_words_softmax.split(',')
        config.model.params.personalization_config.params.attn_mask_type = opt.attn_mask_type
        
        # CL-InfoNCE
        config.model.params.personalization_config.params.presudo_words_infonce = opt.presudo_words_infonce.split(',')
        config.model.params.personalization_config.params.adj_aug_infonce = opt.adj_aug_infonce.split(',')
        config.model.params.personalization_config.params.infonce_temperature = opt.infonce_temperature
        config.model.params.personalization_config.params.infonce_scale = opt.infonce_scale
        config.model.params.personalization_config.params.n_gpu = gpuinfo.split(',')[0]
        
        if opt.attn_words is not None and len(opt.attn_words) > 0:
            tokenizer = model.cond_stage_model.tknz_fn.tokenizer
            # fake prompts and keywords to initialise controller for register purpose
            fake_prompts = config.data.params.batch_size*['a photo of ' + opt.placeholder_string]
            fake_keywords = [opt.attn_words for _ in range(config.data.params.batch_size)]
            presudo_words_softmax = config.model.params.personalization_config.params.presudo_words_softmax
            presudo_words_infonce = config.model.params.personalization_config.params.presudo_words_infonce
            adj_aug_infonce = config.model.params.personalization_config.params.adj_aug_infonce
            lb = LocalMask(tokenizer, fake_prompts, fake_keywords, opt.attn_mask_type, \
                presudo_words_softmax, presudo_words_infonce, adj_aug_infonce, gpuinfo.split(',')[0])
            controller = AttentionMask(local_blend=lb)
            model.controller = controller
            register_attention_control_t2i(model, controller)

        # trainer and callbacks
        trainer_kwargs = dict()

        # default logger configs
        default_logger_cfgs = {
            "wandb": {
                "target": "pytorch_lightning.loggers.WandbLogger",
                "params": {
                    "name": nowname,
                    "save_dir": logdir,
                    "offline": opt.debug,
                    "id": nowname,
                }
            },
            "testtube": {
                "target": "pytorch_lightning.loggers.TestTubeLogger",
                "params": {
                    "name": "testtube",
                    "save_dir": logdir,
                }
            },
        }
        default_logger_cfg = default_logger_cfgs["testtube"]
        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 - use TrainResult/EvalResult(checkpoint_on=metric) to
        # specify which metric is used to determine best models
        default_modelckpt_cfg = {
            "target": "pytorch_lightning.callbacks.ModelCheckpoint",
            "params": {
                "dirpath": ckptdir,
                "filename": "{epoch:06}",
                "verbose": True,
                "save_last": True,
            }
        }
        if hasattr(model, "monitor"):
            print(f"Monitoring {model.monitor} as checkpoint metric.")
            default_modelckpt_cfg["params"]["monitor"] = model.monitor
            default_modelckpt_cfg["params"]["save_top_k"] = 1

        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,
                }
            },
            "image_logger": {
                "target": "main.ImageLogger",
                "params": {
                    "batch_frequency": 750,
                    "max_images": 4,
                    "clamp": True
                }
            },
            "learning_rate_logger": {
                "target": "main.LearningRateMonitor",
                "params": {
                    "logging_interval": "step",
                    # "log_momentum": True
                }
            },
            "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()

        if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
            print(
                'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
            default_metrics_over_trainsteps_ckpt_dict = {
                'metrics_over_trainsteps_checkpoint':
                    {"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
                     'params': {
                         "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
                         "filename": "{epoch:06}-{step:09}",
                         "verbose": True,
                         'save_top_k': -1,
                         'every_n_train_steps': 10000,
                         'save_weights_only': True
                     }
                     }
            }
            default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)

        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_kwargs["max_steps"] = trainer_opt.max_steps

        trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
        trainer.logdir = logdir  ###

        # data
        config.data.params.batch_size = opt.batch_size
        config.data.params.train.params.data_root = opt.data_root
        config.data.params.validation.params.data_root = opt.data_root
        data = instantiate_from_config(config.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:
                with torch.autograd.set_detect_anomaly(True):
                    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())