225 lines
8.6 KiB
Python
225 lines
8.6 KiB
Python
import argparse
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import os
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import torch
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import torch.nn.functional as F
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from datasets import load_dataset
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from diffusers import DDPMPipeline, DDPMScheduler, UNetUnconditionalModel
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from diffusers.hub_utils import init_git_repo, push_to_hub
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import EMAModel
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from torchvision.transforms import (
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CenterCrop,
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Compose,
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InterpolationMode,
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Normalize,
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RandomHorizontalFlip,
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Resize,
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ToTensor,
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)
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from tqdm.auto import tqdm
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logger = get_logger(__name__)
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def main(args):
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logging_dir = os.path.join(args.output_dir, args.logging_dir)
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accelerator = Accelerator(
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mixed_precision=args.mixed_precision,
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log_with="tensorboard",
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logging_dir=logging_dir,
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)
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model = UNetUnconditionalModel(
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image_size=args.resolution,
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in_channels=3,
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out_channels=3,
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num_res_blocks=2,
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block_channels=(128, 128, 256, 256, 512, 512),
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down_blocks=(
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"UNetResDownBlock2D",
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"UNetResDownBlock2D",
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"UNetResDownBlock2D",
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"UNetResDownBlock2D",
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"UNetResAttnDownBlock2D",
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"UNetResDownBlock2D",
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),
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up_blocks=(
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"UNetResUpBlock2D",
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"UNetResAttnUpBlock2D",
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"UNetResUpBlock2D",
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"UNetResUpBlock2D",
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"UNetResUpBlock2D",
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"UNetResUpBlock2D",
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),
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)
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noise_scheduler = DDPMScheduler(num_train_timesteps=1000, tensor_format="pt")
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optimizer = torch.optim.AdamW(
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model.parameters(),
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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augmentations = Compose(
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[
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Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
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CenterCrop(args.resolution),
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RandomHorizontalFlip(),
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ToTensor(),
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Normalize([0.5], [0.5]),
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]
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)
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dataset = load_dataset(args.dataset, split="train")
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def transforms(examples):
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images = [augmentations(image.convert("RGB")) for image in examples["image"]]
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return {"input": images}
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dataset.set_transform(transforms)
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train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
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lr_scheduler = get_scheduler(
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args.lr_scheduler,
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optimizer=optimizer,
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num_warmup_steps=args.lr_warmup_steps,
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num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
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)
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model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, lr_scheduler
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)
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ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
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if args.push_to_hub:
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repo = init_git_repo(args, at_init=True)
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if accelerator.is_main_process:
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run = os.path.split(__file__)[-1].split(".")[0]
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accelerator.init_trackers(run)
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global_step = 0
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for epoch in range(args.num_epochs):
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model.train()
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progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
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progress_bar.set_description(f"Epoch {epoch}")
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for step, batch in enumerate(train_dataloader):
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clean_images = batch["input"]
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# Sample noise that we'll add to the images
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noise = torch.randn(clean_images.shape).to(clean_images.device)
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bsz = clean_images.shape[0]
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# Sample a random timestep for each image
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timesteps = torch.randint(
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0, noise_scheduler.num_train_timesteps, (bsz,), device=clean_images.device
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).long()
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# Add noise to the clean images according to the noise magnitude at each timestep
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# (this is the forward diffusion process)
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noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
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with accelerator.accumulate(model):
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# Predict the noise residual
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noise_pred = model(noisy_images, timesteps)["sample"]
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loss = F.mse_loss(noise_pred, noise)
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accelerator.backward(loss)
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accelerator.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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lr_scheduler.step()
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if args.use_ema:
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ema_model.step(model)
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optimizer.zero_grad()
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progress_bar.update(1)
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logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
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if args.use_ema:
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logs["ema_decay"] = ema_model.decay
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progress_bar.set_postfix(**logs)
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accelerator.log(logs, step=global_step)
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global_step += 1
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progress_bar.close()
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accelerator.wait_for_everyone()
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# Generate a sample image for visual inspection
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if accelerator.is_main_process:
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with torch.no_grad():
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pipeline = DDPMPipeline(
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unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
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scheduler=noise_scheduler,
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)
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generator = torch.manual_seed(0)
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# run pipeline in inference (sample random noise and denoise)
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images = pipeline(generator=generator, batch_size=args.eval_batch_size)
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# denormalize the images and save to tensorboard
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images_processed = (images.cpu() + 1.0) * 127.5
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images_processed = images_processed.clamp(0, 255).type(torch.uint8).numpy()
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accelerator.trackers[0].writer.add_images("test_samples", images_processed, epoch)
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if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
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# save the model
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if args.push_to_hub:
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push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
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else:
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pipeline.save_pretrained(args.output_dir)
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accelerator.wait_for_everyone()
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accelerator.end_training()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument("--local_rank", type=int, default=-1)
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parser.add_argument("--dataset", type=str, default="huggan/flowers-102-categories")
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parser.add_argument("--output_dir", type=str, default="ddpm-flowers-64")
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parser.add_argument("--overwrite_output_dir", action="store_true")
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parser.add_argument("--resolution", type=int, default=64)
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parser.add_argument("--train_batch_size", type=int, default=16)
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parser.add_argument("--eval_batch_size", type=int, default=16)
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parser.add_argument("--num_epochs", type=int, default=100)
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parser.add_argument("--save_model_epochs", type=int, default=5)
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parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
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parser.add_argument("--learning_rate", type=float, default=1e-4)
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parser.add_argument("--lr_scheduler", type=str, default="cosine")
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parser.add_argument("--lr_warmup_steps", type=int, default=500)
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parser.add_argument("--adam_beta1", type=float, default=0.95)
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parser.add_argument("--adam_beta2", type=float, default=0.999)
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parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
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parser.add_argument("--adam_epsilon", type=float, default=1e-3)
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parser.add_argument("--use_ema", action="store_true", default=True)
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parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
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parser.add_argument("--ema_power", type=float, default=3 / 4)
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parser.add_argument("--ema_max_decay", type=float, default=0.9999)
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parser.add_argument("--push_to_hub", action="store_true")
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parser.add_argument("--hub_token", type=str, default=None)
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parser.add_argument("--hub_model_id", type=str, default=None)
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parser.add_argument("--hub_private_repo", action="store_true")
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parser.add_argument("--logging_dir", type=str, default="logs")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default="no",
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU."
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),
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)
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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main(args)
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