Diffusion-QSM/QSM_Diffusion_train.py at main · AMRI-Lab/Diffusion-QSM · GitHub

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import argparse
import os
import yaml

import argparse

from guided_diffusion import dist_util, logger
from guided_diffusion.image_datasets import load_data,load_chisep_data
from guided_diffusion.resample import create_named_schedule_sampler
from guided_diffusion.script_util import (
    model_and_diffusion_defaults,
    create_model_and_diffusion,
    args_to_dict,
    add_dict_to_argparser,
)
from guided_diffusion.train_util import TrainLoop

def main():
    args = create_argparser().parse_args()

    dist_util.setup_dist()
    logger.configure()

    logger.log("creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(
        **args_to_dict(args, model_and_diffusion_defaults().keys())
    )
    model.to(dist_util.dev_sample(args.gpu))
    schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)

    logger.log("creating data loader...")

    data = load_chisep_data(args.data_dir,
                            args.name_list,
                            deterministic=False,
                            isnormFlag=args.isnormFlag)

    logger.log("training...")

    args1=dict(folder=args.save_dir+'/writer')
    TrainLoop(
        model=model,
        diffusion=diffusion,
        data=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,
        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,
        save_dir=args.save_dir
    ).run_loop(args1)


def create_argparser():
    defaults = dict(
        data_dir="/home/zhangm/upload/data/", #where the training patch is saved
        name_list=list(range(1,300000)),
        save_dir="/home/zhangm/tap_new/model/new_0p1_both_variance_noise2/", #saved path
        schedule_sampler="uniform",
        lr=1e-4,
        weight_decay=0.0,
        lr_anneal_steps=0,
        batch_size=4,
        microbatch=-1,  # -1 disables microbatches
        ema_rate="0.9999",  # comma-separated list of EMA values
        log_interval=10,
        save_interval=10000,
        resume_checkpoint="",
        use_fp16=False,
        fp16_scale_growth=1e-3,
        gpu=1,
        isnormFlag=False
    )
    defaults.update(model_and_diffusion_defaults())
    defaults['predict_xstart']=False
    defaults['learn_sigma']=True
    print(defaults)

    parser = argparse.ArgumentParser()
    add_dict_to_argparser(parser, defaults)
    return parser


if __name__ == "__main__":
    main()