mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-05 12:00:15 +00:00
534 lines
18 KiB
Python
534 lines
18 KiB
Python
import datetime
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import logging
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import os
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import signal
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import time
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from functools import partial
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torchvision
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from omegaconf import OmegaConf
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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.callbacks import Callback, LearningRateMonitor
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try:
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from pytorch_lightning.strategies import DDPStrategy
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except ImportError:
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# let's not break all of imaginairy just because a training import doesn't exist in an older version of PL
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# Use >= 1.6.0 to make this work
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DDPStrategy = None
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.utilities import rank_zero_info
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from torch.utils.data import DataLoader, Dataset
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from imaginairy import config
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from imaginairy.model_manager import get_diffusion_model
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from imaginairy.training_tools.single_concept import SingleConceptDataset
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from imaginairy.utils import get_device, instantiate_from_config
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mod_logger = logging.getLogger(__name__)
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referenced_by_string = [LearningRateMonitor]
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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, SingleConceptDataset):
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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[
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# worker_id * split_size : (worker_id + 1) * split_size
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# ]
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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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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__(
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self,
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batch_size,
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train=None,
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validation=None,
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test=None,
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predict=None,
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wrap=False,
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num_workers=None,
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shuffle_test_loader=False,
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use_worker_init_fn=False,
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shuffle_val_dataloader=False,
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num_val_workers=0,
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):
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super().__init__()
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self.batch_size = batch_size
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self.dataset_configs = {}
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self.num_workers = num_workers if num_workers is not None else batch_size * 2
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if num_val_workers is None:
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self.num_val_workers = self.num_workers
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else:
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self.num_val_workers = num_val_workers
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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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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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self.dataset_configs["validation"] = validation
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self.val_dataloader = partial(
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self._val_dataloader, shuffle=shuffle_val_dataloader
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)
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if test is not None:
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self.dataset_configs["test"] = test
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self.test_dataloader = partial(
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self._test_dataloader, shuffle=shuffle_test_loader
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)
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if predict is not None:
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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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self.datasets = None
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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 = {
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k: instantiate_from_config(c) for k, c in self.dataset_configs.items()
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}
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if self.wrap:
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self.datasets = {k: WrappedDataset(v) for k, v in self.datasets.items()}
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def _train_dataloader(self):
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is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
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if is_iterable_dataset or self.use_worker_init_fn:
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pass
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else:
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pass
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return DataLoader(
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self.datasets["train"],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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shuffle=True,
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worker_init_fn=worker_init_fn,
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)
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def _val_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets["validation"], SingleConceptDataset)
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or self.use_worker_init_fn
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):
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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(
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self.datasets["validation"],
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batch_size=self.batch_size,
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num_workers=self.num_val_workers,
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worker_init_fn=init_fn,
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shuffle=shuffle,
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)
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def _test_dataloader(self, shuffle=False):
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is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
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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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is_iterable_dataset = False
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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(
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self.datasets["test"],
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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=shuffle,
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)
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def _predict_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets["predict"], SingleConceptDataset)
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or self.use_worker_init_fn
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):
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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(
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self.datasets["predict"],
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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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)
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class SetupCallback(Callback):
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def __init__(
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self,
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resume,
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now,
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logdir,
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ckptdir,
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cfgdir,
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):
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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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def on_keyboard_interrupt(self, trainer, pl_module):
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if trainer.global_rank == 0:
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mod_logger.info("Stopping execution and saving final checkpoint.")
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ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
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trainer.save_checkpoint(ckpt_path)
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def on_fit_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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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__(
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self,
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batch_frequency,
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max_images,
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clamp=True,
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increase_log_steps=True,
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rescale=True,
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disabled=False,
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log_on_batch_idx=False,
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log_first_step=False,
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log_images_kwargs=None,
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log_all_val=False,
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concept_label=None,
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):
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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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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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self.log_all_val = log_all_val
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self.concept_label = concept_label
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@rank_zero_only
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def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
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root = os.path.join(save_dir, "logs", "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 = (
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f"{k}_gs-{global_step:06}_e-{current_epoch:06}_b-{batch_idx:06}.png"
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)
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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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# always generate the concept label
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batch["txt"][0] = self.concept_label
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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.log_all_val and split == "val":
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should_log = True
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else:
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should_log = self.check_frequency(check_idx)
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if (
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should_log
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and (batch_idx % self.batch_freq == 0)
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and hasattr(pl_module, "log_images")
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and callable(pl_module.log_images)
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and self.max_images > 0
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):
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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(
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batch, split=split, **self.log_images_kwargs
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)
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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], -1.0, 1.0)
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self.log_local(
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pl_module.logger.save_dir,
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split,
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images,
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pl_module.global_step,
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pl_module.current_epoch,
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batch_idx,
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)
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logger_log_images = self.logger_log_images.get(
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logger, lambda *args, **kwargs: None
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)
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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 and (
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check_idx > 0 or self.log_first_step
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):
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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):
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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(
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self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
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):
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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 (
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pl_module.calibrate_grad_norm and batch_idx % 25 == 0
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) 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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# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
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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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if "cuda" in get_device():
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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() # noqa
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def on_train_epoch_end(self, trainer, pl_module): # noqa
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if "cuda" in get_device():
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torch.cuda.synchronize(trainer.strategy.root_device.index)
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max_memory = (
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torch.cuda.max_memory_allocated(trainer.strategy.root_device.index)
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/ 2**20
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)
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epoch_time = time.time() - self.start_time
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try:
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max_memory = trainer.training_type_plugin.reduce(max_memory)
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epoch_time = trainer.training_type_plugin.reduce(epoch_time)
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rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
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rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
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except AttributeError:
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pass
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def train_diffusion_model(
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concept_label,
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concept_images_dir,
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class_label,
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class_images_dir,
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weights_location=config.DEFAULT_MODEL,
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logdir="logs",
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learning_rate=1e-6,
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accumulate_grad_batches=32,
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resume=None,
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):
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"""
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Train a diffusion model on a single concept.
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accumulate_grad_batches used to simulate a bigger batch size - https://arxiv.org/pdf/1711.00489.pdf
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"""
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if DDPStrategy is None:
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raise ImportError("Please install pytorch-lightning>=1.6.0 to train a model")
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batch_size = 1
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seed = 23
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num_workers = 1
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num_val_workers = 0
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now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
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logdir = os.path.join(logdir, now)
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ckpt_output_dir = os.path.join(logdir, "checkpoints")
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cfg_output_dir = os.path.join(logdir, "configs")
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seed_everything(seed)
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model = get_diffusion_model( # noqa
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weights_location=weights_location, half_mode=False, for_training=True
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)._model
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model.learning_rate = learning_rate * accumulate_grad_batches * batch_size
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# add callback which sets up log directory
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default_callbacks_cfg = {
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"setup_callback": {
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"target": "imaginairy.train.SetupCallback",
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"params": {
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"resume": False,
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"now": now,
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"logdir": logdir,
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"ckptdir": ckpt_output_dir,
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"cfgdir": cfg_output_dir,
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},
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},
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"image_logger": {
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"target": "imaginairy.train.ImageLogger",
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"params": {
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"batch_frequency": 10,
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"max_images": 1,
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"clamp": True,
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"increase_log_steps": False,
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"log_first_step": True,
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"log_all_val": True,
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"concept_label": concept_label,
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"log_images_kwargs": {
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"use_ema_scope": True,
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"inpaint": False,
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"plot_progressive_rows": False,
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"plot_diffusion_rows": False,
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"N": 1,
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"unconditional_guidance_scale:": 7.5,
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"unconditional_guidance_label": [""],
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"ddim_steps": 20,
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},
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},
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},
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"learning_rate_logger": {
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"target": "imaginairy.train.LearningRateMonitor",
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"params": {
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"logging_interval": "step",
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# "log_momentum": True
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},
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},
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"cuda_callback": {"target": "imaginairy.train.CUDACallback"},
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}
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default_modelckpt_cfg = {
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"target": "pytorch_lightning.callbacks.ModelCheckpoint",
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"params": {
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"dirpath": ckpt_output_dir,
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"filename": "{epoch:06}",
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"verbose": True,
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"save_last": True,
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"every_n_train_steps": 50,
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"save_top_k": -1,
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"monitor": None,
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},
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}
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modelckpt_cfg = OmegaConf.create(default_modelckpt_cfg)
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default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
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callbacks_cfg = OmegaConf.create(default_callbacks_cfg)
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dataset_config = {
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"concept_label": concept_label,
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"concept_images_dir": concept_images_dir,
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"class_label": class_label,
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"class_images_dir": class_images_dir,
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"image_transforms": [
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{
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"target": "torchvision.transforms.Resize",
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"params": {"size": 512, "interpolation": 3},
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},
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{"target": "torchvision.transforms.RandomCrop", "params": {"size": 512}},
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],
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}
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data_module_config = {
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"batch_size": batch_size,
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"num_workers": num_workers,
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"num_val_workers": num_val_workers,
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"train": {
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"target": "imaginairy.training_tools.single_concept.SingleConceptDataset",
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"params": dataset_config,
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},
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}
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trainer = Trainer(
|
|
benchmark=True,
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|
num_sanity_val_steps=0,
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|
accumulate_grad_batches=accumulate_grad_batches,
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|
strategy=DDPStrategy(),
|
|
callbacks=[
|
|
instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg # noqa
|
|
],
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|
gpus=1,
|
|
default_root_dir=".",
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|
)
|
|
trainer.logdir = logdir
|
|
|
|
data = DataModuleFromConfig(**data_module_config)
|
|
# 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()
|
|
|
|
def melk(*args, **kwargs):
|
|
if trainer.global_rank == 0:
|
|
mod_logger.info("Summoning checkpoint.")
|
|
ckpt_path = os.path.join(ckpt_output_dir, "last.ckpt")
|
|
trainer.save_checkpoint(ckpt_path)
|
|
|
|
signal.signal(signal.SIGUSR1, melk)
|
|
try:
|
|
try:
|
|
trainer.fit(model, data)
|
|
except Exception:
|
|
melk()
|
|
raise
|
|
finally:
|
|
mod_logger.info(trainer.profiler.summary())
|