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EveryDream2trainer
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Forgot to add train.py earlier 🤦; move write_batch_schedule to train.py

This commit is contained in:
Joel Holdbrooks 2023-01-29 18:11:34 -08:00
parent 12a0cb6286
commit 56f130c027
2 changed files with 66 additions and 30 deletions
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18
data/every_dream.py
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@ -38,9 +38,7 @@ class EveryDreamBatch(Dataset):
crop_jitter=20,
seed=555,
tokenizer=None,
log_folder=None,
retain_contrast=False,
write_schedule=False,
shuffle_tags=False,
rated_dataset=False,
rated_dataset_dropout_target=0.5
@ -52,10 +50,8 @@ class EveryDreamBatch(Dataset):
self.crop_jitter = crop_jitter
self.unloaded_to_idx = 0
self.tokenizer = tokenizer
self.log_folder = log_folder
self.max_token_length = self.tokenizer.model_max_length
self.retain_contrast = retain_contrast
self.write_schedule = write_schedule
self.shuffle_tags = shuffle_tags
self.seed = seed
self.rated_dataset = rated_dataset
@ -64,18 +60,7 @@ class EveryDreamBatch(Dataset):
self.image_train_items = self.data_loader.get_shuffled_image_buckets(1.0)
num_images = len(self.image_train_items)
logging.info(f" ** Trainer Set: {num_images / self.batch_size:.0f}, num_images: {num_images}, batch_size: {self.batch_size}")
if self.write_schedule:
self.__write_batch_schedule(0)
def __write_batch_schedule(self, epoch_n):
with open(f"{self.log_folder}/ep{epoch_n}_batch_schedule.txt", "w", encoding='utf-8') as f:
for i in range(len(self.image_train_items)):
try:
f.write(f"step:{int(i / self.batch_size):05}, wh:{self.image_train_items[i].target_wh}, r:{self.image_train_items[i].runt_size}, path:{self.image_train_items[i].pathname}\n")
except Exception as e:
logging.error(f" * Error writing to batch schedule for file path: {self.image_train_items[i].pathname}")
def shuffle(self, epoch_n: int, max_epochs: int):
self.seed += 1
@ -87,9 +72,6 @@ class EveryDreamBatch(Dataset):
self.image_train_items = self.data_loader.get_shuffled_image_buckets(dropout_fraction)
if self.write_schedule:
self.__write_batch_schedule(epoch_n + 1)
def __len__(self):
return len(self.image_train_items)

78
train.py
View File

@ -15,6 +15,7 @@ limitations under the License.
"""
import os
import pprint
import sys
import math
import signal
@ -48,11 +49,15 @@ from accelerate.utils import set_seed
import wandb
from torch.utils.tensorboard import SummaryWriter
from data.data_loader import DataLoaderMultiAspect
from data.every_dream import EveryDreamBatch
from data.image_train_item import ImageTrainItem
from utils.huggingface_downloader import try_download_model_from_hf
from utils.convert_diff_to_ckpt import convert as converter
from utils.gpu import GPU
import data.aspects as aspects
import data.resolver as resolver
_SIGTERM_EXIT_CODE = 130
_VERY_LARGE_NUMBER = 1e9
@ -265,6 +270,8 @@ def setup_args(args):
logging.info(logging.info(f"{Fore.CYAN} * Activating rated images learning with a target rate of {args.rated_dataset_target_dropout_percent}% {Style.RESET_ALL}"))
args.aspects = aspects.get_aspect_buckets(args.resolution)
return args
def update_grad_scaler(scaler: GradScaler, global_step, epoch, step):
@ -288,6 +295,35 @@ def update_grad_scaler(scaler: GradScaler, global_step, epoch, step):
scaler.set_growth_factor(factor)
scaler.set_backoff_factor(1/factor)
scaler.set_growth_interval(100)
def report_image_train_item_problems(log_folder, items: list[ImageTrainItem]) -> None:
for item in items:
if item.error is not None:
logging.error(f"{Fore.LIGHTRED_EX} *** Error opening {Fore.LIGHTYELLOW_EX}{item.pathname}{Fore.LIGHTRED_EX} to get metadata. File may be corrupt and will be skipped.{Style.RESET_ALL}")
logging.error(f" *** exception: {item.error}")
undersized_items = [item for item in items if item.is_undersized]
if len(undersized_items) > 0:
underized_log_path = os.path.join(log_folder, "undersized_images.txt")
logging.warning(f"{Fore.LIGHTRED_EX} ** Some images are smaller than the target size, consider using larger images{Style.RESET_ALL}")
logging.warning(f"{Fore.LIGHTRED_EX} ** Check {underized_log_path} for more information.{Style.RESET_ALL}")
with open(underized_log_path, "w") as undersized_images_file:
undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:")
for undersized_item in undersized_items:
message = f" *** {undersized_item.pathname} with size: {undersized_item.image_size} is smaller than target size: {undersized_item.target_wh}\n"
undersized_images_file.write(message)
def write_batch_schedule(log_folder, train_batch, epoch):
if args.write_schedule:
with open(f"{log_folder}/ep{epoch}_batch_schedule.txt", "w", encoding='utf-8') as f:
for i in range(len(train_batch.image_train_items)):
try:
item = train_batch.image_train_items[i]
f.write(f"step:{int(i / train_batch.batch_size):05}, wh:{item.target_wh}, r:{item.runt_size}, path:{item.pathname}\n")
except Exception as e:
logging.error(f" * Error writing to batch schedule for file path: {item.pathname}")
def main(args):
"""
@ -313,6 +349,8 @@ def main(args):
if not os.path.exists(log_folder):
os.makedirs(log_folder)
args.log_folder = log_folder
@torch.no_grad()
def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir, yaml_name, save_full_precision=False):
@ -547,23 +585,34 @@ def main(args):
)
log_optimizer(optimizer, betas, epsilon)
logging.info(f"* DLMA resolution {args.resolution}, buckets: {args.aspects}")
logging.info(" Preloading images...")
image_train_items = resolver.resolve(args.data_root, args)
report_image_train_item_problems(log_folder, image_train_items)
image_paths = set(map(lambda item: item.pathname, image_train_items))
# Remove erroneous items
image_train_items = [item for item in image_train_items if item.error is None]
print (f" * DLMA: {len(image_train_items)} images loaded from {len(image_paths)} files")
data_loader = DataLoaderMultiAspect(
image_train_items=image_train_items,
seed=seed,
batch_size=args.batch_size,
)
train_batch = EveryDreamBatch(
data_root=args.data_root,
flip_p=args.flip_p,
data_loader=data_loader,
debug_level=1,
batch_size=args.batch_size,
conditional_dropout=args.cond_dropout,
resolution=args.resolution,
tokenizer=tokenizer,
seed = seed,
log_folder=log_folder,
write_schedule=args.write_schedule,
shuffle_tags=args.shuffle_tags,
rated_dataset=args.rated_dataset,
rated_dataset_dropout_target=(1.0 - (args.rated_dataset_target_dropout_percent / 100.0))
)
torch.cuda.benchmark = False
epoch_len = math.ceil(len(train_batch) / args.batch_size)
@ -589,10 +638,11 @@ def main(args):
if args.wandb is not None and args.wandb:
wandb.init(project=args.project_name, sync_tensorboard=True, )
log_writer = SummaryWriter(log_dir=log_folder,
flush_secs=5,
comment="EveryDream2FineTunes",
)
log_writer = SummaryWriter(
log_dir=log_folder,
flush_secs=5,
comment="EveryDream2FineTunes",
)
def log_args(log_writer, args):
arglog = "args:\n"
@ -729,6 +779,8 @@ def main(args):
# # discard the grads, just want to pin memory
# optimizer.zero_grad(set_to_none=True)
write_batch_schedule(log_folder, train_batch, 0)
for epoch in range(args.max_epochs):
loss_epoch = []
epoch_start_time = time.time()
@ -879,6 +931,7 @@ def main(args):
epoch_pbar.update(1)
if epoch < args.max_epochs - 1:
train_batch.shuffle(epoch_n=epoch, max_epochs = args.max_epochs)
write_batch_schedule(log_folder, train_batch, epoch + 1)
loss_local = sum(loss_epoch) / len(loss_epoch)
log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_local, global_step=global_step)
@ -943,7 +996,6 @@ if __name__ == "__main__":
t_args = argparse.Namespace()
t_args.__dict__.update(json.load(f))
update_old_args(t_args) # update args to support older configs
print(f" args: \n{t_args.__dict__}")
args = argparser.parse_args(namespace=t_args)
else:
print("No config file specified, using command line args")
@ -992,4 +1044,6 @@ if __name__ == "__main__":
args, _ = argparser.parse_known_args()
print(f" Args:")
pprint.pprint(args.__dict__)
main(args)