mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
534 lines
18 KiB
Python
534 lines
18 KiB
Python
import datetime
|
|
import logging
|
|
import os
|
|
import signal
|
|
import time
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
import pytorch_lightning as pl
|
|
import torch
|
|
import torchvision
|
|
from omegaconf import OmegaConf
|
|
from PIL import Image
|
|
from pytorch_lightning import seed_everything
|
|
from pytorch_lightning.callbacks import Callback, LearningRateMonitor
|
|
|
|
try:
|
|
from pytorch_lightning.strategies import DDPStrategy
|
|
except ImportError:
|
|
# let's not break all of imaginairy just because a training import doesn't exist in an older version of PL
|
|
# Use >= 1.6.0 to make this work
|
|
DDPStrategy = None
|
|
from pytorch_lightning.trainer import Trainer
|
|
from pytorch_lightning.utilities import rank_zero_info
|
|
from pytorch_lightning.utilities.distributed import rank_zero_only
|
|
from torch.utils.data import DataLoader, Dataset
|
|
|
|
from imaginairy import config
|
|
from imaginairy.model_manager import get_diffusion_model
|
|
from imaginairy.training_tools.single_concept import SingleConceptDataset
|
|
from imaginairy.utils import get_device, instantiate_from_config
|
|
|
|
mod_logger = logging.getLogger(__name__)
|
|
|
|
referenced_by_string = [LearningRateMonitor]
|
|
|
|
|
|
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, SingleConceptDataset):
|
|
# 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)
|
|
|
|
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,
|
|
num_val_workers=0,
|
|
):
|
|
super().__init__()
|
|
self.batch_size = batch_size
|
|
self.dataset_configs = {}
|
|
self.num_workers = num_workers if num_workers is not None else batch_size * 2
|
|
if num_val_workers is None:
|
|
self.num_val_workers = self.num_workers
|
|
else:
|
|
self.num_val_workers = num_val_workers
|
|
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
|
|
self.datasets = None
|
|
|
|
def prepare_data(self):
|
|
for data_cfg in self.dataset_configs.values():
|
|
instantiate_from_config(data_cfg)
|
|
|
|
def setup(self, stage=None):
|
|
self.datasets = {
|
|
k: instantiate_from_config(c) for k, c in self.dataset_configs.items()
|
|
}
|
|
if self.wrap:
|
|
self.datasets = {k: WrappedDataset(v) for k, v in self.datasets.items()}
|
|
|
|
def _train_dataloader(self):
|
|
is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
|
|
if is_iterable_dataset or self.use_worker_init_fn:
|
|
pass
|
|
else:
|
|
pass
|
|
return DataLoader(
|
|
self.datasets["train"],
|
|
batch_size=self.batch_size,
|
|
num_workers=self.num_workers,
|
|
shuffle=True,
|
|
worker_init_fn=worker_init_fn,
|
|
)
|
|
|
|
def _val_dataloader(self, shuffle=False):
|
|
if (
|
|
isinstance(self.datasets["validation"], SingleConceptDataset)
|
|
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_val_workers,
|
|
worker_init_fn=init_fn,
|
|
shuffle=shuffle,
|
|
)
|
|
|
|
def _test_dataloader(self, shuffle=False):
|
|
is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
|
|
if is_iterable_dataset or self.use_worker_init_fn:
|
|
init_fn = worker_init_fn
|
|
else:
|
|
init_fn = None
|
|
is_iterable_dataset = False
|
|
|
|
# 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"], SingleConceptDataset)
|
|
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,
|
|
):
|
|
super().__init__()
|
|
self.resume = resume
|
|
self.now = now
|
|
self.logdir = logdir
|
|
self.ckptdir = ckptdir
|
|
self.cfgdir = cfgdir
|
|
|
|
def on_keyboard_interrupt(self, trainer, pl_module):
|
|
if trainer.global_rank == 0:
|
|
mod_logger.info("Stopping execution and saving final checkpoint.")
|
|
ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
|
|
trainer.save_checkpoint(ckpt_path)
|
|
|
|
def on_fit_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)
|
|
|
|
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,
|
|
log_all_val=False,
|
|
concept_label=None,
|
|
):
|
|
super().__init__()
|
|
self.rescale = rescale
|
|
self.batch_freq = batch_frequency
|
|
self.max_images = max_images
|
|
self.logger_log_images = {}
|
|
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
|
|
self.log_all_val = log_all_val
|
|
self.concept_label = concept_label
|
|
|
|
@rank_zero_only
|
|
def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
|
|
root = os.path.join(save_dir, "logs", "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 = (
|
|
f"{k}_gs-{global_step:06}_e-{current_epoch:06}_b-{batch_idx:06}.png"
|
|
)
|
|
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"):
|
|
# always generate the concept label
|
|
batch["txt"][0] = self.concept_label
|
|
|
|
check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
|
|
if self.log_all_val and split == "val":
|
|
should_log = True
|
|
else:
|
|
should_log = self.check_frequency(check_idx)
|
|
if (
|
|
should_log
|
|
and (batch_idx % self.batch_freq == 0)
|
|
and 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], -1.0, 1.0)
|
|
|
|
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 and (
|
|
check_idx > 0 or self.log_first_step
|
|
):
|
|
return True
|
|
return False
|
|
|
|
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_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
|
|
if "cuda" in get_device():
|
|
torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
|
|
torch.cuda.synchronize(trainer.strategy.root_device.index)
|
|
self.start_time = time.time() # noqa
|
|
|
|
def on_train_epoch_end(self, trainer, pl_module): # noqa
|
|
if "cuda" in get_device():
|
|
torch.cuda.synchronize(trainer.strategy.root_device.index)
|
|
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.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
|
|
|
|
|
|
def train_diffusion_model(
|
|
concept_label,
|
|
concept_images_dir,
|
|
class_label,
|
|
class_images_dir,
|
|
weights_location=config.DEFAULT_MODEL,
|
|
logdir="logs",
|
|
learning_rate=1e-6,
|
|
accumulate_grad_batches=32,
|
|
resume=None,
|
|
):
|
|
"""
|
|
Train a diffusion model on a single concept.
|
|
|
|
accumulate_grad_batches used to simulate a bigger batch size - https://arxiv.org/pdf/1711.00489.pdf
|
|
"""
|
|
if DDPStrategy is None:
|
|
raise ImportError("Please install pytorch-lightning>=1.6.0 to train a model")
|
|
|
|
batch_size = 1
|
|
seed = 23
|
|
num_workers = 1
|
|
num_val_workers = 0
|
|
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
|
logdir = os.path.join(logdir, now)
|
|
|
|
ckpt_output_dir = os.path.join(logdir, "checkpoints")
|
|
cfg_output_dir = os.path.join(logdir, "configs")
|
|
seed_everything(seed)
|
|
model = get_diffusion_model( # noqa
|
|
weights_location=weights_location, half_mode=False, for_training=True
|
|
)._model
|
|
model.learning_rate = learning_rate * accumulate_grad_batches * batch_size
|
|
|
|
# add callback which sets up log directory
|
|
default_callbacks_cfg = {
|
|
"setup_callback": {
|
|
"target": "imaginairy.train.SetupCallback",
|
|
"params": {
|
|
"resume": False,
|
|
"now": now,
|
|
"logdir": logdir,
|
|
"ckptdir": ckpt_output_dir,
|
|
"cfgdir": cfg_output_dir,
|
|
},
|
|
},
|
|
"image_logger": {
|
|
"target": "imaginairy.train.ImageLogger",
|
|
"params": {
|
|
"batch_frequency": 10,
|
|
"max_images": 1,
|
|
"clamp": True,
|
|
"increase_log_steps": False,
|
|
"log_first_step": True,
|
|
"log_all_val": True,
|
|
"concept_label": concept_label,
|
|
"log_images_kwargs": {
|
|
"use_ema_scope": True,
|
|
"inpaint": False,
|
|
"plot_progressive_rows": False,
|
|
"plot_diffusion_rows": False,
|
|
"N": 1,
|
|
"unconditional_guidance_scale:": 7.5,
|
|
"unconditional_guidance_label": [""],
|
|
"ddim_steps": 20,
|
|
},
|
|
},
|
|
},
|
|
"learning_rate_logger": {
|
|
"target": "imaginairy.train.LearningRateMonitor",
|
|
"params": {
|
|
"logging_interval": "step",
|
|
# "log_momentum": True
|
|
},
|
|
},
|
|
"cuda_callback": {"target": "imaginairy.train.CUDACallback"},
|
|
}
|
|
|
|
default_modelckpt_cfg = {
|
|
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
|
|
"params": {
|
|
"dirpath": ckpt_output_dir,
|
|
"filename": "{epoch:06}",
|
|
"verbose": True,
|
|
"save_last": True,
|
|
"every_n_train_steps": 50,
|
|
"save_top_k": -1,
|
|
"monitor": None,
|
|
},
|
|
}
|
|
|
|
modelckpt_cfg = OmegaConf.create(default_modelckpt_cfg)
|
|
default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
|
|
|
|
callbacks_cfg = OmegaConf.create(default_callbacks_cfg)
|
|
|
|
dataset_config = {
|
|
"concept_label": concept_label,
|
|
"concept_images_dir": concept_images_dir,
|
|
"class_label": class_label,
|
|
"class_images_dir": class_images_dir,
|
|
"image_transforms": [
|
|
{
|
|
"target": "torchvision.transforms.Resize",
|
|
"params": {"size": 512, "interpolation": 3},
|
|
},
|
|
{"target": "torchvision.transforms.RandomCrop", "params": {"size": 512}},
|
|
],
|
|
}
|
|
|
|
data_module_config = {
|
|
"batch_size": batch_size,
|
|
"num_workers": num_workers,
|
|
"num_val_workers": num_val_workers,
|
|
"train": {
|
|
"target": "imaginairy.training_tools.single_concept.SingleConceptDataset",
|
|
"params": dataset_config,
|
|
},
|
|
}
|
|
trainer = Trainer(
|
|
benchmark=True,
|
|
num_sanity_val_steps=0,
|
|
accumulate_grad_batches=accumulate_grad_batches,
|
|
strategy=DDPStrategy(),
|
|
callbacks=[
|
|
instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg # noqa
|
|
],
|
|
gpus=1,
|
|
default_root_dir=".",
|
|
)
|
|
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())
|