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Dec 19, 2022
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update train_unconditional_ort.py #1775

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Dec 19, 2022
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Dec 19, 2022
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71 changes: 65 additions & 6 deletions examples/unconditional_image_generation/train_unconditional_ort.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,16 @@ def parse_args():
parser.add_argument(
"--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
)
parser.add_argument(
"--logger",
type=str,
default="tensorboard",
choices=["tensorboard", "wandb"],
help=(
"Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
" for experiment tracking and logging of model metrics and model checkpoints"
),
)
parser.add_argument(
"--logging_dir",
type=str,
Expand All @@ -195,7 +205,6 @@ def parse_args():
"and an Nvidia Ampere GPU."
),
)

parser.add_argument(
"--prediction_type",
type=str,
Expand All @@ -206,6 +215,24 @@ def parse_args():

parser.add_argument("--ddpm_num_steps", type=int, default=1000)
parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)

args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
Expand Down Expand Up @@ -233,7 +260,7 @@ def main(args):
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
log_with=args.logger,
logging_dir=logging_dir,
)

Expand Down Expand Up @@ -321,6 +348,7 @@ def transforms(examples):
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
accelerator.register_for_checkpointing(lr_scheduler)

num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

Expand Down Expand Up @@ -353,11 +381,34 @@ def transforms(examples):
accelerator.init_trackers(run)

global_step = 0
for epoch in range(args.num_epochs):
first_epoch = 0
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1]
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = resume_global_step // num_update_steps_per_epoch
resume_step = resume_global_step % num_update_steps_per_epoch

for epoch in range(first_epoch, args.num_epochs):
model.train()
progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue

clean_images = batch["input"]
# Sample noise that we'll add to the images
noise = torch.randn(clean_images.shape).to(clean_images.device)
Expand Down Expand Up @@ -404,6 +455,12 @@ def transforms(examples):
progress_bar.update(1)
global_step += 1

if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")

logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
if args.use_ema:
logs["ema_decay"] = ema_model.decay
Expand Down Expand Up @@ -431,9 +488,11 @@ def transforms(examples):

# denormalize the images and save to tensorboard
images_processed = (images * 255).round().astype("uint8")
accelerator.trackers[0].writer.add_images(
"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
)

if args.logger == "tensorboard":
accelerator.get_tracker("tensorboard").add_images(
"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
)

if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
# save the model
Expand Down