| """
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| Train SAR-DDPM model.
|
| """
|
|
|
| import argparse
|
|
|
| import torch.nn.functional as F
|
|
|
| from guided_diffusion import dist_util, logger
|
| from guided_diffusion.image_datasets import load_data
|
| from guided_diffusion.resample import create_named_schedule_sampler
|
| from guided_diffusion.script_util import (
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| sr_model_and_diffusion_defaults,
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| sr_create_model_and_diffusion,
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| args_to_dict,
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| add_dict_to_argparser,
|
| )
|
| from guided_diffusion.train_util import TrainLoop
|
| from torch.utils.data import DataLoader
|
|
|
| from valdata import ValData, ValDataNew
|
|
|
| train_dir = 'path_to_training_data/'
|
|
|
| val_dir = 'path_to_validation_data/'
|
|
|
| pretrained_weight_path = "./weights/64_256_upsampler.pt"
|
|
|
|
|
| def main():
|
| args = create_argparser().parse_args()
|
|
|
| dist_util.setup_dist()
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| logger.configure()
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|
|
| logger.log("creating model...")
|
| model, diffusion = sr_create_model_and_diffusion(
|
| **args_to_dict(args, sr_model_and_diffusion_defaults().keys())
|
| )
|
| model.to(dist_util.dev())
|
| schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
|
|
|
| logger.log("creating data loader...")
|
|
|
|
|
|
|
| val_data = DataLoader(ValDataNew(dataset_path=val_dir), batch_size=1, shuffle=False, num_workers=1)
|
|
|
|
|
| print(args)
|
| data = load_sar_data(
|
| args.data_dir,
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| train_dir,
|
| args.batch_size,
|
| large_size=256,
|
| small_size=256,
|
| class_cond=False,
|
| )
|
|
|
| logger.log("training...")
|
| TrainLoop(
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| model=model,
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| diffusion=diffusion,
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| data=data,
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| val_dat=val_data,
|
| batch_size=args.batch_size,
|
| microbatch=args.microbatch,
|
| lr=args.lr,
|
| ema_rate=args.ema_rate,
|
| log_interval=args.log_interval,
|
| save_interval=args.save_interval,
|
| resume_checkpoint=args.resume_checkpoint,
|
| args = args,
|
| use_fp16=args.use_fp16,
|
| fp16_scale_growth=args.fp16_scale_growth,
|
| schedule_sampler=schedule_sampler,
|
| weight_decay=args.weight_decay,
|
| lr_anneal_steps=args.lr_anneal_steps,
|
| ).run_loop()
|
|
|
|
|
| def load_sar_data(data_dir,gt_dirs, batch_size, large_size, small_size, class_cond=False):
|
| data = load_data(
|
| data_dir=data_dir,
|
| gt_dir=gt_dirs,
|
| batch_size=batch_size,
|
| image_size=large_size,
|
| class_cond=False,
|
| )
|
| for large_batch, model_kwargs in data:
|
| yield large_batch, model_kwargs
|
|
|
|
|
| def create_argparser():
|
| defaults = dict(
|
| data_dir = train_dir,
|
| schedule_sampler="uniform",
|
| lr=1e-4,
|
|
|
| weight_decay=0.0,
|
| lr_anneal_steps=0,
|
| batch_size=2,
|
| microbatch=1,
|
| ema_rate="0.9999",
|
| log_interval=1000,
|
| save_interval=10,
|
| resume_checkpoint=pretrained_weight_path,
|
| use_fp16=False,
|
| fp16_scale_growth=1e-3,
|
| )
|
| defaults.update(sr_model_and_diffusion_defaults())
|
| parser = argparse.ArgumentParser()
|
| add_dict_to_argparser(parser, defaults)
|
| return parser
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
