| from re import I |
| import sys, os |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" |
| sys.path.append(os.getcwd()) |
|
|
| from ddpm import Unet3D, GaussianDiffusion, Trainer |
| import hydra |
| from omegaconf import DictConfig, OmegaConf, open_dict |
| import torch |
| import os |
| from ddpm.unet import UNet |
| from dataset.dataloader import get_loader |
|
|
| @hydra.main(config_path='config', config_name='base_cfg', version_base=None) |
| def run(cfg: DictConfig): |
| torch.cuda.set_device(cfg.model.gpus) |
| with open_dict(cfg): |
| cfg.model.results_folder = os.path.join( |
| cfg.model.results_folder, cfg.dataset.name, cfg.model.results_folder_postfix) |
|
|
| if cfg.model.denoising_fn == 'Unet3D': |
| model = Unet3D( |
| dim=cfg.model.diffusion_img_size, |
| dim_mults=cfg.model.dim_mults, |
| channels=cfg.model.diffusion_num_channels, |
| out_dim=cfg.model.out_dim |
| ).cuda() |
| else: |
| raise ValueError(f"Model {cfg.model.denoising_fn} doesn't exist") |
|
|
| diffusion = GaussianDiffusion( |
| model, |
| vqgan_ckpt=cfg.model.vqgan_ckpt, |
| image_size=cfg.model.diffusion_img_size, |
| num_frames=cfg.model.diffusion_depth_size, |
| channels=cfg.model.diffusion_num_channels, |
| timesteps=cfg.model.timesteps, |
| loss_type=cfg.model.loss_type, |
| ).cuda() |
|
|
| train_dataloader, train_sampler, dataset_size = get_loader(cfg.dataset) |
| val_dataloader=None |
|
|
| trainer = Trainer( |
| diffusion, |
| cfg=cfg, |
| dataset=train_dataloader, |
| train_batch_size=cfg.model.batch_size, |
| save_and_sample_every=cfg.model.save_and_sample_every, |
| train_lr=cfg.model.train_lr, |
| train_num_steps=cfg.model.train_num_steps, |
| gradient_accumulate_every=cfg.model.gradient_accumulate_every, |
| ema_decay=cfg.model.ema_decay, |
| amp=cfg.model.amp, |
| num_sample_rows=cfg.model.num_sample_rows, |
| results_folder=cfg.model.results_folder, |
| num_workers=cfg.model.num_workers, |
| ) |
|
|
| if cfg.model.load_milestone: |
| trainer.load(cfg.model.load_milestone) |
|
|
| trainer.train() |
|
|
|
|
| if __name__ == '__main__': |
| run() |
