stable-diffusion/main.py at main · lambda0424/stable-diffusion · GitHub

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import argparse, os, sys, datetime, glob, importlib, csv

import numpy as np

import time

import torch

import torchvision

import pytorch_lightning as pl

from packaging import version

from omegaconf import OmegaConf

from torch.utils.data import random_split, DataLoader, Dataset, Subset

from functools import partial

from PIL import Image

from pytorch_lightning import seed_everything

from pytorch_lightning.trainer import Trainer

from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor

from pytorch_lightning.utilities.distributed import rank_zero_only

from pytorch_lightning.utilities import rank_zero_info

from ldm.data.base import Txt2ImgIterableBaseDataset

from ldm.util import instantiate_from_config

def get_parser(**parser_kwargs):

def str2bool(v):

if isinstance(v, bool):

return v

if v.lower() in ("yes", "true", "t", "y", "1"):

return True

elif v.lower() in ("no", "false", "f", "n", "0"):

return False

else:

raise argparse.ArgumentTypeError("Boolean value expected.")

parser = argparse.ArgumentParser(**parser_kwargs)

parser.add_argument(

"-n",

"--name",

type=str,

const=True,

default="",

nargs="?",

help="postfix for logdir",

)

parser.add_argument(

"-r",

"--resume",

type=str,

const=True,

default="",

nargs="?",

help="resume from logdir or checkpoint in logdir",

)

parser.add_argument(

"-b",

"--base",

nargs="*",

metavar="base_config.yaml",

help="paths to base configs. Loaded from left-to-right. "

"Parameters can be overwritten or added with command-line options of the form `--key value`.",

default=list(),

)

parser.add_argument(

"-t",

"--train",

type=str2bool,

const=True,

default=False,

nargs="?",

help="train",

)

parser.add_argument(

"--no-test",

type=str2bool,

const=True,

default=False,

nargs="?",

help="disable test",

)

parser.add_argument(

"-p",

"--project",

help="name of new or path to existing project"

)

parser.add_argument(

"-d",

"--debug",

type=str2bool,

nargs="?",

const=True,

default=False,

help="enable post-mortem debugging",

)

parser.add_argument(

"-s",

"--seed",

type=int,

default=23,

help="seed for seed_everything",

)

parser.add_argument(

"-f",

"--postfix",

type=str,

default="",

help="post-postfix for default name",

)

parser.add_argument(

"-l",

"--logdir",

type=str,

default="logs",

help="directory for logging dat shit",

)

parser.add_argument(

"--scale_lr",

type=str2bool,

nargs="?",

const=True,

default=True,

help="scale base-lr by ngpu * batch_size * n_accumulate",

)

return parser

def nondefault_trainer_args(opt):

parser = argparse.ArgumentParser()

parser = Trainer.add_argparse_args(parser)

args = parser.parse_args([])

return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))

class WrappedDataset(Dataset):

"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""

def __init__(self, dataset):

self.data = dataset

def __len__(self):

return len(self.data)

def __getitem__(self, idx):

return self.data[idx]

def worker_init_fn(_):

worker_info = torch.utils.data.get_worker_info()

dataset = worker_info.dataset

worker_id = worker_info.id

if isinstance(dataset, Txt2ImgIterableBaseDataset):

split_size = dataset.num_records // worker_info.num_workers

# reset num_records to the true number to retain reliable length information

dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]

current_id = np.random.choice(len(np.random.get_state()[1]), 1)

return np.random.seed(np.random.get_state()[1][current_id] + worker_id)

else:

return np.random.seed(np.random.get_state()[1][0] + worker_id)

class DataModuleFromConfig(pl.LightningDataModule):

def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,

wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,

shuffle_val_dataloader=False):

super().__init__()

self.batch_size = batch_size

self.dataset_configs = dict()

self.num_workers = num_workers if num_workers is not None else batch_size * 2

self.use_worker_init_fn = use_worker_init_fn

if train is not None:

self.dataset_configs["train"] = train

self.train_dataloader = self._train_dataloader

if validation is not None:

self.dataset_configs["validation"] = validation

self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)

if test is not None:

self.dataset_configs["test"] = test

self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)

if predict is not None:

self.dataset_configs["predict"] = predict

self.predict_dataloader = self._predict_dataloader

self.wrap = wrap

def prepare_data(self):

for data_cfg in self.dataset_configs.values():

instantiate_from_config(data_cfg)

def setup(self, stage=None):

self.datasets = dict(

(k, instantiate_from_config(self.dataset_configs[k]))

for k in self.dataset_configs)

if self.wrap:

for k in self.datasets:

self.datasets[k] = WrappedDataset(self.datasets[k])

def _train_dataloader(self):

is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)

if is_iterable_dataset or self.use_worker_init_fn:

init_fn = worker_init_fn

else:

init_fn = None

return DataLoader(self.datasets["train"], batch_size=self.batch_size,

num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,

worker_init_fn=init_fn)

def _val_dataloader(self, shuffle=False):

if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:

init_fn = worker_init_fn

else:

init_fn = None

return DataLoader(self.datasets["validation"],

batch_size=self.batch_size,

num_workers=self.num_workers,

worker_init_fn=init_fn,

shuffle=shuffle)

def _test_dataloader(self, shuffle=False):

is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)

if is_iterable_dataset or self.use_worker_init_fn:

init_fn = worker_init_fn

else:

init_fn = None

# do not shuffle dataloader for iterable dataset

shuffle = shuffle and (not is_iterable_dataset)

return DataLoader(self.datasets["test"], batch_size=self.batch_size,

num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)

def _predict_dataloader(self, shuffle=False):

if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:

init_fn = worker_init_fn

else:

init_fn = None

return DataLoader(self.datasets["predict"], batch_size=self.batch_size,

num_workers=self.num_workers, worker_init_fn=init_fn)

class SetupCallback(Callback):

def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):

super().__init__()

self.resume = resume

self.now = now

self.logdir = logdir

self.ckptdir = ckptdir

self.cfgdir = cfgdir

self.config = config

self.lightning_config = lightning_config

def on_keyboard_interrupt(self, trainer, pl_module):

if trainer.global_rank == 0:

print("Summoning checkpoint.")

ckpt_path = os.path.join(self.ckptdir, "last.ckpt")

trainer.save_checkpoint(ckpt_path)

def on_pretrain_routine_start(self, trainer, pl_module):

if trainer.global_rank == 0:

# Create logdirs and save configs

os.makedirs(self.logdir, exist_ok=True)

os.makedirs(self.ckptdir, exist_ok=True)

os.makedirs(self.cfgdir, exist_ok=True)

if "callbacks" in self.lightning_config:

if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:

os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)

print("Project config")

print(OmegaConf.to_yaml(self.config))

OmegaConf.save(self.config,

os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))

print("Lightning config")

print(OmegaConf.to_yaml(self.lightning_config))

OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),

os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))

else:

# ModelCheckpoint callback created log directory --- remove it

if not self.resume and os.path.exists(self.logdir):

dst, name = os.path.split(self.logdir)

dst = os.path.join(dst, "child_runs", name)

os.makedirs(os.path.split(dst)[0], exist_ok=True)

try:

os.rename(self.logdir, dst)

except FileNotFoundError:

pass

class ImageLogger(Callback):

def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,

rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,

log_images_kwargs=None):

super().__init__()

self.rescale = rescale

self.batch_freq = batch_frequency

self.max_images = max_images

self.logger_log_images = {

pl.loggers.TestTubeLogger: self._testtube,

}

self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]

if not increase_log_steps:

self.log_steps = [self.batch_freq]

self.clamp = clamp

self.disabled = disabled

self.log_on_batch_idx = log_on_batch_idx

self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}

self.log_first_step = log_first_step

@rank_zero_only

def _testtube(self, pl_module, images, batch_idx, split):

for k in images:

grid = torchvision.utils.make_grid(images[k])

grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w

tag = f"{split}/{k}"

pl_module.logger.experiment.add_image(

tag, grid,

global_step=pl_module.global_step)

@rank_zero_only

def log_local(self, save_dir, split, images,

global_step, current_epoch, batch_idx):

root = os.path.join(save_dir, "images", split)

for k in images:

grid = torchvision.utils.make_grid(images[k], nrow=4)

if self.rescale:

grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w

grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)

grid = grid.numpy()

grid = (grid * 255).astype(np.uint8)

filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(

global_step,

current_epoch,

batch_idx)

path = os.path.join(root, filename)

os.makedirs(os.path.split(path)[0], exist_ok=True)

Image.fromarray(grid).save(path)

def log_img(self, pl_module, batch, batch_idx, split="train"):

check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step

if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0

hasattr(pl_module, "log_images") and

callable(pl_module.log_images) and

self.max_images > 0):

logger = type(pl_module.logger)

is_train = pl_module.training

if is_train:

pl_module.eval()

with torch.no_grad():

images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)

for k in images:

N = min(images[k].shape[0], self.max_images)

images[k] = images[k][:N]

if isinstance(images[k], torch.Tensor):

images[k] = images[k].detach().cpu()

if self.clamp:

images[k] = torch.clamp(images[k], -1., 1.)

self.log_local(pl_module.logger.save_dir, split, images,

pl_module.global_step, pl_module.current_epoch, batch_idx)

logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)

logger_log_images(pl_module, images, pl_module.global_step, split)

if is_train:

pl_module.train()

def check_frequency(self, check_idx):

if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (

check_idx > 0 or self.log_first_step):

try:

self.log_steps.pop(0)

except IndexError as e:

print(e)

pass

return True

return False

def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):

if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):

self.log_img(pl_module, batch, batch_idx, split="train")

def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):

if not self.disabled and pl_module.global_step > 0:

self.log_img(pl_module, batch, batch_idx, split="val")

if hasattr(pl_module, 'calibrate_grad_norm'):

if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:

self.log_gradients(trainer, pl_module, batch_idx=batch_idx)

class CUDACallback(Callback):

# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py

def on_train_epoch_start(self, trainer, pl_module):

# Reset the memory use counter

torch.cuda.reset_peak_memory_stats(trainer.root_gpu)

torch.cuda.synchronize(trainer.root_gpu)

self.start_time = time.time()

def on_train_epoch_end(self, trainer, pl_module, outputs):

torch.cuda.synchronize(trainer.root_gpu)

max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20

epoch_time = time.time() - self.start_time

try:

max_memory = trainer.training_type_plugin.reduce(max_memory)

epoch_time = trainer.training_type_plugin.reduce(epoch_time)

rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")

rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")

except AttributeError:

pass

if __name__ == "__main__":

# custom parser to specify config files, train, test and debug mode,

# postfix, resume.

# `--key value` arguments are interpreted as arguments to the trainer.

# `nested.key=value` arguments are interpreted as config parameters.

# configs are merged from left-to-right followed by command line parameters.

# model:

# base_learning_rate: float

# target: path to lightning module

# params:

# key: value

# data:

# target: main.DataModuleFromConfig

# params:

# batch_size: int

# wrap: bool

# train:

# target: path to train dataset

# params:

# key: value

# validation:

# target: path to validation dataset

# params:

# key: value

# test:

# target: path to test dataset

# params:

# key: value

# lightning: (optional, has sane defaults and can be specified on cmdline)

# trainer:

# additional arguments to trainer

# logger:

# logger to instantiate

# modelcheckpoint:

# modelcheckpoint to instantiate

# callbacks:

# callback1:

# target: importpath

# params:

# key: value

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

# add cwd for convenience and to make classes in this file available when

# running as `python main.py`

# (in particular `main.DataModuleFromConfig`)

sys.path.append(os.getcwd())

parser = get_parser()

parser = Trainer.add_argparse_args(parser)

opt, unknown = parser.parse_known_args()

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 = ""

nowname = now + name + opt.postfix

logdir = os.path.join(opt.logdir, nowname)

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

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

seed_everything(opt.seed)

try:

# init and save configs

configs = [OmegaConf.load(cfg) for cfg in opt.base]

cli = OmegaConf.from_dotlist(unknown)

config = OmegaConf.merge(*configs, cli)

lightning_config = config.pop("lightning", OmegaConf.create())

# merge trainer cli with config

trainer_config = lightning_config.get("trainer", OmegaConf.create())

# default to ddp

trainer_config["accelerator"] = "ddp"

for k in nondefault_trainer_args(opt):

trainer_config[k] = getattr(opt, k)

if not "gpus" in trainer_config:

del trainer_config["accelerator"]

cpu = True

else:

gpuinfo = trainer_config["gpus"]

print(f"Running on GPUs {gpuinfo}")

cpu = False

trainer_opt = argparse.Namespace(**trainer_config)

lightning_config.trainer = trainer_config

# model

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:06}",

"verbose": True,

"save_last": True,

}

if hasattr(model, "monitor"):

print(f"Monitoring {model.monitor} as checkpoint metric.")

default_modelckpt_cfg["params"]["monitor"] = model.monitor

default_modelckpt_cfg["params"]["save_top_k"] = 3

if "modelcheckpoint" in lightning_config:

modelckpt_cfg = lightning_config.modelcheckpoint

else:

modelckpt_cfg = OmegaConf.create()

modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)

print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")

if version.parse(pl.__version__) < version.parse('1.4.0'):

trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)

# add callback which sets up log directory

default_callbacks_cfg = {

"setup_callback": {

"target": "main.SetupCallback",

"params": {

"resume": opt.resume,

"now": now,

"logdir": logdir,

"ckptdir": ckptdir,

"cfgdir": cfgdir,

"config": config,

"lightning_config": lightning_config,

}

"image_logger": {

"target": "main.ImageLogger",

"params": {

"batch_frequency": 750,

"max_images": 4,

"clamp": True

}

"learning_rate_logger": {

"target": "main.LearningRateMonitor",

"params": {

"logging_interval": "step",

# "log_momentum": True

}

"cuda_callback": {

"target": "main.CUDACallback"

}

if version.parse(pl.__version__) >= version.parse('1.4.0'):

default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})

if "callbacks" in lightning_config:

callbacks_cfg = lightning_config.callbacks

else:

callbacks_cfg = OmegaConf.create()

if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:

print(

'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')

default_metrics_over_trainsteps_ckpt_dict = {

'metrics_over_trainsteps_checkpoint':

{"target": 'pytorch_lightning.callbacks.ModelCheckpoint',

'params': {

"dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),

"filename": "{epoch:06}-{step:09}",

"verbose": True,

'save_top_k': -1,

'every_n_train_steps': 10000,

'save_weights_only': True

}

default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)

callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)

if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):

callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint

elif 'ignore_keys_callback' in callbacks_cfg:

del callbacks_cfg['ignore_keys_callback']

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

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

trainer.logdir = logdir ###

# data

data = instantiate_from_config(config.data)

# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html

# calling these ourselves should not be necessary but it is.

# lightning still takes care of proper multiprocessing though

data.prepare_data()

data.setup()

print("#### Data #####")

for k in data.datasets:

print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")

# configure learning rate

bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate

if not cpu:

ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))

else:

ngpu = 1

if 'accumulate_grad_batches' in lightning_config.trainer:

accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches

else:

accumulate_grad_batches = 1

print(f"accumulate_grad_batches = {accumulate_grad_batches}")

lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches

if opt.scale_lr:

model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr

print(

"Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(

model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))

else:

model.learning_rate = base_lr

print("++++ NOT USING LR SCALING ++++")

print(f"Setting learning rate to {model.learning_rate:.2e}")

# allow checkpointing via USR1

def melk(*args, **kwargs):

# run all checkpoint hooks

if trainer.global_rank == 0:

print("Summoning checkpoint.")

ckpt_path = os.path.join(ckptdir, "last.ckpt")

trainer.save_checkpoint(ckpt_path)

def divein(*args, **kwargs):

if trainer.global_rank == 0:

import pudb;

pudb.set_trace()

import signal

signal.signal(signal.SIGUSR1, melk)

signal.signal(signal.SIGUSR2, divein)

# run

if opt.train:

try:

trainer.fit(model, data)

except Exception:

melk()

raise

if not opt.no_test and not trainer.interrupted:

trainer.test(model, data)

except Exception:

if opt.debug and trainer.global_rank == 0:

try:

import pudb as debugger

except ImportError:

import pdb as debugger

debugger.post_mortem()

raise

finally:

# move newly created debug project to debug_runs

if opt.debug and not opt.resume and trainer.global_rank == 0:

dst, name = os.path.split(logdir)

dst = os.path.join(dst, "debug_runs", name)

os.makedirs(os.path.split(dst)[0], exist_ok=True)

os.rename(logdir, dst)

if trainer.global_rank == 0:

print(trainer.profiler.summary())

stable-diffusion/main.py at main · lambda0424/stable-diffusion · GitHub

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