786 lines
29 KiB
Python
786 lines
29 KiB
Python
import argparse, os, sys, datetime, glob, importlib, csv
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import numpy as np
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import time
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import torch
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import torchvision
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import pytorch_lightning as pl
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from packaging import version
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from omegaconf import OmegaConf
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from torch.utils.data import random_split, DataLoader, Dataset, Subset
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from functools import partial
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from PIL import Image
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from pytorch_lightning import seed_everything
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from pytorch_lightning.utilities import rank_zero_info
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from ldm.data.base import Txt2ImgIterableBaseDataset
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from ldm.util import instantiate_from_config
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## Un-comment this for windows
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## os.environ["PL_TORCH_DISTRIBUTED_BACKEND"] = "gloo"
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"ckpt: {ckpt} has {pl_sd['global_step']} steps")
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## sd = pl_sd["state_dict"]
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if "state_dict" in pl_sd:
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print("load_state_dict from state_dict")
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sd = pl_sd["state_dict"]
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else:
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print("load_state_dict from directly")
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sd = pl_sd
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config.model.params.ckpt_path = ckpt
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.cuda()
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return model
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def get_parser(**parser_kwargs):
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def str2bool(v):
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if isinstance(v, bool):
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return v
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if v.lower() in ("yes", "true", "t", "y", "1"):
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return True
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elif v.lower() in ("no", "false", "f", "n", "0"):
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return False
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else:
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raise argparse.ArgumentTypeError("Boolean value expected.")
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parser = argparse.ArgumentParser(**parser_kwargs)
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parser.add_argument(
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"-n",
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"--name",
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type=str,
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const=True,
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default="",
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nargs="?",
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help="postfix for logdir",
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)
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parser.add_argument(
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"-r",
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"--resume",
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type=str,
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const=True,
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default="",
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nargs="?",
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help="resume from logdir or checkpoint in logdir",
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)
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parser.add_argument(
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"-b",
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"--base",
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nargs="*",
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metavar="base_config.yaml",
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help="paths to base configs. Loaded from left-to-right. "
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"Parameters can be overwritten or added with command-line options of the form `--key value`.",
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default=list(),
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)
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parser.add_argument(
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"-t",
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"--train",
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type=str2bool,
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const=True,
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default=False,
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nargs="?",
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help="train",
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)
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parser.add_argument(
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"--no-test",
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type=str2bool,
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const=True,
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default=False,
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nargs="?",
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help="disable test",
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)
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parser.add_argument(
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"-p",
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"--project",
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help="name of new or path to existing project"
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)
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parser.add_argument(
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"-d",
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"--debug",
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type=str2bool,
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nargs="?",
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const=True,
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default=False,
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help="enable post-mortem debugging",
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)
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parser.add_argument(
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"-s",
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"--seed",
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type=int,
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default=23,
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help="seed for seed_everything",
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)
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parser.add_argument(
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"-f",
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"--postfix",
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type=str,
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default="",
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help="post-postfix for default name",
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)
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parser.add_argument(
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"-l",
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"--logdir",
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type=str,
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default="logs",
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help="directory for logging",
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)
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parser.add_argument(
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"--scale_lr",
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type=str2bool,
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nargs="?",
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const=False,
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default=False,
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help="scale base-lr by ngpu * batch_size * n_accumulate",
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)
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parser.add_argument(
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"--datadir_in_name",
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type=str2bool,
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nargs="?",
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const=True,
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default=True,
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help="Prepend the final directory in the data_root to the output directory name")
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parser.add_argument("--actual_resume",
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type=str,
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required=True,
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help="Path to model to actually resume from")
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parser.add_argument("--data_root",
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type=str,
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required=True,
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help="Path to directory with training images")
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parser.add_argument("--embedding_manager_ckpt",
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type=str,
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default="",
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help="Initialize embedding manager from a checkpoint")
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parser.add_argument("--init_word",
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type=str,
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help="Word to use as source for initial token embedding")
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return parser
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def nondefault_trainer_args(opt):
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parser = argparse.ArgumentParser()
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args([])
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return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
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class WrappedDataset(Dataset):
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"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
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def __init__(self, dataset):
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self.data = dataset
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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return self.data[idx]
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def worker_init_fn(_):
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worker_info = torch.utils.data.get_worker_info()
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dataset = worker_info.dataset
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worker_id = worker_info.id
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if isinstance(dataset, Txt2ImgIterableBaseDataset):
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split_size = dataset.num_records // worker_info.num_workers
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# reset num_records to the true number to retain reliable length information
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dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
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current_id = np.random.choice(len(np.random.get_state()[1]), 1)
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return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
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else:
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return np.random.seed(np.random.get_state()[1][0] + worker_id)
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class DataModuleFromConfig(pl.LightningDataModule):
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def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
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wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
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shuffle_val_dataloader=False):
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super().__init__()
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self.batch_size = batch_size
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self.dataset_configs = dict()
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self.num_workers = num_workers if num_workers is not None else batch_size * 2
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self.use_worker_init_fn = use_worker_init_fn
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if train is not None:
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train.params.batch_size = self.batch_size
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train.params.set = 'train'
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self.dataset_configs["train"] = train
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self.train_dataloader = self._train_dataloader
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if validation is not None:
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validation.params.batch_size = self.batch_size
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validation.params.set = 'val'
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print(f" ****** validation: {validation}")
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self.dataset_configs["validation"] = validation
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self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
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if test is not None:
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test.params.batch_size = self.batch_size
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test.params.set = 'test'
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self.dataset_configs["test"] = test
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self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
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if predict is not None:
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predict.params.batch_size = self.batch_size
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self.dataset_configs["predict"] = predict
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self.predict_dataloader = self._predict_dataloader
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self.wrap = wrap
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def prepare_data(self):
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for data_cfg in self.dataset_configs.values():
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instantiate_from_config(data_cfg)
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def setup(self, stage=None):
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self.datasets = dict(
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(k, instantiate_from_config(self.dataset_configs[k]))
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for k in self.dataset_configs)
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if self.wrap:
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for k in self.datasets:
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self.datasets[k] = WrappedDataset(self.datasets[k])
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def _train_dataloader(self):
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is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
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if is_iterable_dataset or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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dataset = self.datasets["train"]
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return DataLoader(dataset, batch_size=self.batch_size,
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num_workers=self.num_workers, shuffle=False,
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worker_init_fn=init_fn)
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def _val_dataloader(self, shuffle=False):
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if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(self.datasets["validation"],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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shuffle=False)
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def _test_dataloader(self, shuffle=False):
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is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
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if is_iterable_dataset or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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# do not shuffle dataloader for iterable dataset
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shuffle = shuffle and (not is_iterable_dataset)
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return DataLoader(self.datasets["test"], batch_size=self.batch_size,
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num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
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def _predict_dataloader(self, shuffle=False):
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if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
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num_workers=self.num_workers, worker_init_fn=init_fn)
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class SetupCallback(Callback):
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def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
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super().__init__()
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self.resume = resume
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self.now = now
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self.logdir = logdir
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self.ckptdir = ckptdir
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self.cfgdir = cfgdir
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self.config = config
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self.lightning_config = lightning_config
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def on_keyboard_interrupt(self, trainer, pl_module):
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if trainer.global_rank == 0:
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print("Keyboard interrupt. Summoning checkpoint.")
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print(f"Steps completed: {trainer.global_step} {trainer.current_epoch}")
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# "{epoch:02d}-{step:05d}"
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ckpt_path = os.path.join(self.ckptdir, f"interrupted_epoch={trainer.current_epoch:02d}-step={trainer.global_step:05d}.ckpt")
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trainer.save_checkpoint(ckpt_path)
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def on_pretrain_routine_start(self, trainer, pl_module):
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if trainer.global_rank == 0:
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# Create logdirs and save configs
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os.makedirs(self.logdir, exist_ok=True)
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os.makedirs(self.ckptdir, exist_ok=True)
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os.makedirs(self.cfgdir, exist_ok=True)
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if "callbacks" in self.lightning_config:
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if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
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os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
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print("Project config")
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print(OmegaConf.to_yaml(self.config))
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OmegaConf.save(self.config,
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os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
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print("Lightning config")
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print(OmegaConf.to_yaml(self.lightning_config))
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OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
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os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
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else:
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# ModelCheckpoint callback created log directory --- remove it
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if not self.resume and os.path.exists(self.logdir):
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dst, name = os.path.split(self.logdir)
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dst = os.path.join(dst, "child_runs", name)
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os.makedirs(os.path.split(dst)[0], exist_ok=True)
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try:
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os.rename(self.logdir, dst)
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except FileNotFoundError:
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pass
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class ImageLogger(Callback):
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def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
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rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
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log_images_kwargs=None):
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super().__init__()
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self.rescale = rescale
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self.batch_freq = batch_frequency
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self.max_images = max_images
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self.logger_log_images = {
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pl.loggers.TestTubeLogger: self._testtube,
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}
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self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
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if not increase_log_steps:
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self.log_steps = [self.batch_freq]
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self.clamp = clamp
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self.disabled = disabled
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self.log_on_batch_idx = log_on_batch_idx
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self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
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self.log_first_step = log_first_step
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@rank_zero_only
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def _testtube(self, pl_module, images, batch_idx, split):
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for k in images:
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grid = torchvision.utils.make_grid(images[k])
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grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
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tag = f"{split}/{k}"
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pl_module.logger.experiment.add_image(
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tag, grid,
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global_step=pl_module.global_step)
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@rank_zero_only
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def log_local(self, save_dir, split, images,
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global_step, current_epoch, batch_idx):
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root = os.path.join(save_dir, "images", split)
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for k in images:
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grid = torchvision.utils.make_grid(images[k], nrow=4)
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if self.rescale:
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grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
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grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
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grid = grid.numpy()
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grid = (grid * 255).astype(np.uint8)
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filename = "{}_gs-{:05}_ep-{:02}_batch-{:04}.jpg".format(
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k,
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global_step,
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current_epoch,
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batch_idx)
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path = os.path.join(root, filename)
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os.makedirs(os.path.split(path)[0], exist_ok=True)
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Image.fromarray(grid).save(path)
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def log_img(self, pl_module, batch, batch_idx, split="train"):
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check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
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if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
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hasattr(pl_module, "log_images") and
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callable(pl_module.log_images) and
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self.max_images > 0):
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logger = type(pl_module.logger)
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is_train = pl_module.training
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if is_train:
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pl_module.eval()
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with torch.no_grad():
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images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
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for k in images:
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N = min(images[k].shape[0], self.max_images)
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images[k] = images[k][:N]
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if isinstance(images[k], torch.Tensor):
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images[k] = images[k].detach().cpu()
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if self.clamp:
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images[k] = torch.clamp(images[k], -1., 1.)
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self.log_local(pl_module.logger.save_dir, split, images,
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pl_module.global_step, pl_module.current_epoch, batch_idx)
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logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
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logger_log_images(pl_module, images, pl_module.global_step, split)
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if is_train:
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pl_module.train()
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def check_frequency(self, check_idx):
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if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
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check_idx > 0 or self.log_first_step):
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try:
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self.log_steps.pop(0)
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except IndexError as e:
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print(e)
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pass
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return True
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return False
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
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self.log_img(pl_module, batch, batch_idx, split="train")
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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pass
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#if not self.disabled and pl_module.global_step > 0:
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#self.log_img(pl_module, batch, batch_idx, split="val")
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#if hasattr(pl_module, 'calibrate_grad_norm'):
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#if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
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#self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
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class CUDACallback(Callback):
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def on_train_epoch_start(self, trainer, pl_module):
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# Reset the memory use counter
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torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
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torch.cuda.synchronize(trainer.strategy.root_device.index)
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self.start_time = time.time()
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def on_train_epoch_end(self, trainer, pl_module):
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torch.cuda.synchronize(trainer.strategy.root_device.index)
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max_memory = torch.cuda.max_memory_allocated(trainer.strategy.root_device.index) / 2 ** 20
|
|
epoch_time = time.time() - self.start_time
|
|
|
|
try:
|
|
max_memory = trainer.strategy.reduce(max_memory)
|
|
epoch_time = trainer.strategy.reduce(epoch_time)
|
|
|
|
epoch_time_msg =f"Average Epoch time: {epoch_time:.2f} seconds"
|
|
epoch_peak_mem_msg = f"Average Peak memory {max_memory:.2f}MiB"
|
|
rank_zero_info(epoch_time_msg)
|
|
rank_zero_info(epoch_peak_mem_msg)
|
|
except AttributeError:
|
|
pass
|
|
|
|
class ModeSwapCallback(Callback):
|
|
|
|
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):
|
|
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__":
|
|
|
|
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("/")
|
|
# idx = len(paths)-paths[::-1].index("logs")+1
|
|
# logdir = "/".join(paths[:idx])
|
|
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())
|
|
|
|
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
|
|
|
|
if opt.actual_resume:
|
|
model = load_model_from_config(config, opt.actual_resume)
|
|
else:
|
|
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,
|
|
"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:03}-{global_step:05}",
|
|
"verbose": 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"] = 3 #moved to yaml
|
|
|
|
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": 500,
|
|
"max_images": 8,
|
|
"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()
|
|
|
|
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, **trainer_kwargs)
|
|
trainer.logdir = logdir ###
|
|
|
|
# data
|
|
config.data.params.train.params.data_root = opt.data_root
|
|
config.data.params.validation.params.data_root = opt.data_root
|
|
config.data.params.test.params.data_root = opt.data_root
|
|
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:
|
|
last_ckpt_name = "last.ckpt"
|
|
print(f"Training halted. Summoning checkpoint as {last_ckpt_name}")
|
|
ckpt_path = os.path.join(ckptdir, last_ckpt_name)
|
|
trainer.save_checkpoint(ckpt_path)
|
|
|
|
|
|
def divein(*args, **kwargs):
|
|
if trainer.global_rank == 0:
|
|
import pudb;
|
|
pudb.set_trace()
|
|
|
|
|
|
import signal
|
|
|
|
|
|
# Changed to work with windows
|
|
signal.signal(signal.SIGTERM, melk)
|
|
#signal.signal(signal.SIGUSR1, melk)
|
|
signal.signal(signal.SIGTERM, divein)
|
|
#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("Training complete. max_steps or max_epochs reached, or we blew up.")
|
|
print(trainer.profiler.summary())
|