main.py

import os
import argparse, os, sys, datetime, glob, importlib
from omegaconf import OmegaConf
import torch
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor, EarlyStopping
from pytorch_lightning.utilities.distributed import rank_zero_only
from imitator.utils.custom_callbacks import Callback, Save_results

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


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",
    )

    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))


def instantiate_from_config(config):
    if not "target" in config:
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))


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]


class DataModuleFromConfig(pl.LightningDataModule):
    def __init__(self, batch_size, train=None, validation=None, test=None, test_unseen=None,
                 wrap=False, num_workers=None):
        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
        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 = self._val_dataloader
        if test is not None:
            self.dataset_configs["test"] = test
            self.test_dataloader = self._test_dataloader
        if test_unseen is not None:
            self.dataset_configs["test_unseen"] = test_unseen
            self._test_unseen_dataloader = self._test_unseen_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):
        return DataLoader(self.datasets["train"], batch_size=self.batch_size,
                          num_workers=self.num_workers, shuffle=True)

    def _val_dataloader(self):
        return DataLoader(self.datasets["validation"],
                          batch_size=self.batch_size,
                          num_workers=self.num_workers)

    def _test_dataloader(self):
        return DataLoader(self.datasets["test"], batch_size=self.batch_size,
                          num_workers=self.num_workers)
    
    def _test_unseen_dataloader(self):
        return DataLoader(self.datasets["test_unseen"], batch_size=self.batch_size,
                          num_workers=self.num_workers)
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_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)

            print("Project config")
            print(self.config.pretty())
            OmegaConf.save(self.config,
                           os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))

            print("Lightning config")
            print(self.lightning_config.pretty())
            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

if __name__ == "__main__":
    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
    sys.path.append(os.getcwd())

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

    opt, unknown = parser.parse_known_args()
    seed_everything(opt.seed)

    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"
        )
    first_run=True
    if opt.resume:
        print("\nResume training from the model", opt.resume, "\n")
        first_run=False
        if not os.path.exists(opt.resume):
            raise ValueError("Cannot find {}".format(opt.resume))
        if os.path.isfile(opt.resume):
            paths = opt.resume.split("/")
            idx = len(paths)-paths[::-1].index("logs")+1
            logdir = "/".join(paths[:idx])
            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[_tmp.index("logs")+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 = ""
        nowname = now+name+opt.postfix
        logdir = os.path.join(os.getenv("LOGHOME"), "logs", nowname)


    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["distributed_backend"] = "ddp"
        for k in nondefault_trainer_args(opt):
            trainer_config[k] = getattr(opt, k)
        if not "gpus" in trainer_config:
            del trainer_config["distributed_backend"]
            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

        # added by bala
        if first_run:
            model_config = config.get("model", OmegaConf.create())
            model_opt = argparse.Namespace(**model_config)
            experiment_name = "_ADAM_nep%s" % (trainer_opt.max_epochs)
            nowname = now+name+experiment_name+opt.postfix
            logdir = os.path.join(os.getenv("LOGHOME"), "logs", nowname)


        # code moved here by bala
        ckptdir = os.path.join(logdir, "checkpoints")
        cfgdir = os.path.join(logdir, "configs")


        # 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()

        # model
        model = instantiate_from_config(config.model)

        # trainer and callbacks
        trainer_kwargs = dict()

        # default logger configs
        # NOTE wandb < 0.10.0 interferes with shutdown
        # wandb >= 0.10.0 seems to fix it but still interferes with pudb
        # debugging (wrongly sized pudb ui)
        # thus prefer testtube for now
        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,
                }
            },
            "tensorboard": {
                "target": "pytorch_lightning.loggers.TensorBoardLogger",
                "params": {
                    "name": "tb",
                    "save_dir": logdir,
                }
            },
        }
        default_logger_cfg = default_logger_cfgs["tensorboard"]
        logger_cfg = lightning_config.logger or 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
            # commit modififed the code save the top 1, since every pixelcnn model is 4Gb
            save_top_k = trainer_config.get("save_top_k", 1)
            default_modelckpt_cfg["params"]["save_top_k"] = save_top_k

        modelckpt_cfg = lightning_config.modelcheckpoint or OmegaConf.create()
        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
        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,
                }
            },
            "learning_rate_logger": {
                "target": "main.LearningRateMonitor",
                "params": {
                    "logging_interval": "step",
                    #"log_momentum": True
                }
            },
        }

        early_stopping_params = trainer_config.get("early_stopping_metric")
        if early_stopping_params is not None:
            early_stopping_params["monitor"] = model.monitor
            early_stopping_cfg = {
                "target": "pytorch_lightning.callbacks.EarlyStopping",
                "params": early_stopping_params
            }
            default_callbacks_cfg["early_stopping"] = early_stopping_cfg
            print("early_stopping_params", early_stopping_params)

        callbacks_cfg = lightning_config.callbacks or OmegaConf.create()
        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]

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

        # trainer = Trainer.from_argparse_args(trainer_opt)
        # configure learning rate
        # bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
        bs, base_lr = config.data.params.batch_size, config.model.params.lr
        if not cpu:
            print(lightning_config.trainer.gpus)
            ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
        else:
            ngpu = 1
        accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches or 1
        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
        # model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
        model.learning_rate = 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))

        # 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)

        pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        print()
        print("Printed number of trainable params", pytorch_total_params)
        print()
        model.summarize()
        print()
        model.summarize(mode='full')
        print()

        # run
        if opt.train:
            try:  
                trainer.fit(model, data)
            except Exception:
                melk()
                raise "training failed"

    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)