194 lines
7.7 KiB
Python
194 lines
7.7 KiB
Python
import argparse
|
|
import os
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
import PIL.Image
|
|
from accelerate import Accelerator
|
|
from datasets import load_dataset
|
|
from diffusers import DDPM, DDPMScheduler, UNetModel
|
|
from diffusers.hub_utils import init_git_repo, push_to_hub
|
|
from diffusers.modeling_utils import unwrap_model
|
|
from diffusers.utils import logging
|
|
from torchvision.transforms import (
|
|
CenterCrop,
|
|
Compose,
|
|
InterpolationMode,
|
|
Lambda,
|
|
RandomHorizontalFlip,
|
|
Resize,
|
|
ToTensor,
|
|
)
|
|
from tqdm.auto import tqdm
|
|
from diffusers.optimization import get_scheduler
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
def main(args):
|
|
accelerator = Accelerator(mixed_precision=args.mixed_precision)
|
|
|
|
model = UNetModel(
|
|
attn_resolutions=(16,),
|
|
ch=128,
|
|
ch_mult=(1, 2, 4, 8),
|
|
dropout=0.0,
|
|
num_res_blocks=2,
|
|
resamp_with_conv=True,
|
|
resolution=args.resolution,
|
|
)
|
|
noise_scheduler = DDPMScheduler(timesteps=1000)
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
|
|
|
|
augmentations = Compose(
|
|
[
|
|
Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
|
|
CenterCrop(args.resolution),
|
|
RandomHorizontalFlip(),
|
|
ToTensor(),
|
|
Lambda(lambda x: x * 2 - 1),
|
|
]
|
|
)
|
|
dataset = load_dataset(args.dataset, split="train")
|
|
|
|
def transforms(examples):
|
|
images = [augmentations(image.convert("RGB")) for image in examples["image"]]
|
|
return {"input": images}
|
|
|
|
dataset.set_transform(transforms)
|
|
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
|
|
|
|
lr_scheduler = get_scheduler(
|
|
"linear",
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.warmup_steps,
|
|
num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
|
|
)
|
|
|
|
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
model, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
if args.push_to_hub:
|
|
repo = init_git_repo(args, at_init=True)
|
|
|
|
# Train!
|
|
is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
|
|
world_size = torch.distributed.get_world_size() if is_distributed else 1
|
|
total_train_batch_size = args.batch_size * args.gradient_accumulation_steps * world_size
|
|
max_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_epochs
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataloader.dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {max_steps}")
|
|
|
|
for epoch in range(args.num_epochs):
|
|
model.train()
|
|
with tqdm(total=len(train_dataloader), unit="ba") as pbar:
|
|
pbar.set_description(f"Epoch {epoch}")
|
|
for step, batch in enumerate(train_dataloader):
|
|
clean_images = batch["input"]
|
|
noisy_images = torch.empty_like(clean_images)
|
|
noise_samples = torch.empty_like(clean_images)
|
|
bsz = clean_images.shape[0]
|
|
|
|
timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
|
|
for idx in range(bsz):
|
|
noise = torch.randn(clean_images.shape[1:]).to(clean_images.device)
|
|
noise_samples[idx] = noise
|
|
noisy_images[idx] = noise_scheduler.forward_step(clean_images[idx], noise, timesteps[idx])
|
|
|
|
if step % args.gradient_accumulation_steps != 0:
|
|
with accelerator.no_sync(model):
|
|
output = model(noisy_images, timesteps)
|
|
# predict the noise residual
|
|
loss = F.mse_loss(output, noise_samples)
|
|
loss = loss / args.gradient_accumulation_steps
|
|
accelerator.backward(loss)
|
|
else:
|
|
output = model(noisy_images, timesteps)
|
|
# predict the noise residual
|
|
loss = F.mse_loss(output, noise_samples)
|
|
loss = loss / args.gradient_accumulation_steps
|
|
accelerator.backward(loss)
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
pbar.update(1)
|
|
pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
|
|
|
|
optimizer.step()
|
|
if is_distributed:
|
|
torch.distributed.barrier()
|
|
|
|
# Generate a sample image for visual inspection
|
|
if args.local_rank in [-1, 0]:
|
|
model.eval()
|
|
with torch.no_grad():
|
|
pipeline = DDPM(unet=unwrap_model(model), noise_scheduler=noise_scheduler)
|
|
|
|
generator = torch.manual_seed(0)
|
|
# run pipeline in inference (sample random noise and denoise)
|
|
image = pipeline(generator=generator)
|
|
|
|
# process image to PIL
|
|
image_processed = image.cpu().permute(0, 2, 3, 1)
|
|
image_processed = (image_processed + 1.0) * 127.5
|
|
image_processed = image_processed.type(torch.uint8).numpy()
|
|
image_pil = PIL.Image.fromarray(image_processed[0])
|
|
|
|
# save image
|
|
test_dir = os.path.join(args.output_dir, "test_samples")
|
|
os.makedirs(test_dir, exist_ok=True)
|
|
image_pil.save(f"{test_dir}/{epoch}.png")
|
|
|
|
# save the model
|
|
if args.push_to_hub:
|
|
push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
|
|
else:
|
|
pipeline.save_pretrained(args.output_dir)
|
|
if is_distributed:
|
|
torch.distributed.barrier()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
|
parser.add_argument("--local_rank", type=int, default=-1)
|
|
parser.add_argument("--dataset", type=str, default="huggan/flowers-102-categories")
|
|
parser.add_argument("--output_dir", type=str, default="ddpm-model")
|
|
parser.add_argument("--overwrite_output_dir", action="store_true")
|
|
parser.add_argument("--resolution", type=int, default=64)
|
|
parser.add_argument("--batch_size", type=int, default=16)
|
|
parser.add_argument("--num_epochs", type=int, default=100)
|
|
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
|
|
parser.add_argument("--lr", type=float, default=1e-4)
|
|
parser.add_argument("--warmup_steps", type=int, default=500)
|
|
parser.add_argument("--push_to_hub", action="store_true")
|
|
parser.add_argument("--hub_token", type=str, default=None)
|
|
parser.add_argument("--hub_model_id", type=str, default=None)
|
|
parser.add_argument("--hub_private_repo", action="store_true")
|
|
parser.add_argument(
|
|
"--mixed_precision",
|
|
type=str,
|
|
default="no",
|
|
choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose"
|
|
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
|
|
"and an Nvidia Ampere GPU."
|
|
),
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
|
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
|
args.local_rank = env_local_rank
|
|
|
|
main(args)
|