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