guided_diffusion/script_util.py

import argparse
import inspect

from . import gaussian_diffusion as gd
from .respace import SpacedDiffusion, space_timesteps
from .unet import SuperResModel, UNetModel, EncoderUNetModel
from .wunet import WavUNetModel

NUM_CLASSES = 2


def diffusion_defaults():
    """
    Defaults for image and classifier training.
    """
    return dict(
        learn_sigma=False,
        diffusion_steps=1000,
        noise_schedule="linear",
        timestep_respacing="",
        use_kl=False,
        predict_xstart=False,
        rescale_timesteps=False,
        rescale_learned_sigmas=False,
        dataset='brats',
        dims=2,
        num_groups=32,
        in_channels=1,
    )


def classifier_defaults():
    """
    Defaults for classifier models.
    """
    return dict(
        image_size=64,
        classifier_use_fp16=False,
        classifier_width=128,
        classifier_depth=2,
        classifier_attention_resolutions="32,16,8",  # 16
        classifier_num_head_channels=64,
        classifier_use_scale_shift_norm=True,  # False
        classifier_resblock_updown=True,  # False
        classifier_pool="spatial",
        classifier_channel_mult="1,1,2,2,4,4",
        dataset='brats'
    )


def model_and_diffusion_defaults():
    """
    Defaults for image training.
    """
    res = dict(
        image_size=64,
        num_channels=128,
        num_res_blocks=2,
        num_heads=4,
        num_heads_upsample=-1,
        num_head_channels=-1,
        attention_resolutions="16,8",
        channel_mult="",
        dropout=0.0,
        class_cond=False,
        use_checkpoint=False,
        use_scale_shift_norm=True,
        resblock_updown=True,
        use_fp16=False,
        use_new_attention_order=False,
        dims=2,
        num_groups=32,
        in_channels=1,
        out_channels=0,  # automatically determine if 0
        bottleneck_attention=True,
        resample_2d=True,
        additive_skips=False,
        mode='default',
        use_freq=False,
        predict_xstart=False,
    )
    res.update(diffusion_defaults())
    return res


def classifier_and_diffusion_defaults():
    res = classifier_defaults()
    res.update(diffusion_defaults())
    return res


def create_model_and_diffusion(
    image_size,
    class_cond,
    learn_sigma,
    num_channels,
    num_res_blocks,
    channel_mult,
    num_heads,
    num_head_channels,
    num_heads_upsample,
    attention_resolutions,
    dropout,
    diffusion_steps,
    noise_schedule,
    timestep_respacing,
    use_kl,
    predict_xstart,
    rescale_timesteps,
    rescale_learned_sigmas,
    use_checkpoint,
    use_scale_shift_norm,
    resblock_updown,
    use_fp16,
    use_new_attention_order,
    dims,
    num_groups,
    in_channels,
    out_channels,
    bottleneck_attention,
    resample_2d,
    additive_skips,
    mode,
    use_freq,
    dataset,
):
    model = create_model(
        image_size,
        num_channels,
        num_res_blocks,
        channel_mult=channel_mult,
        learn_sigma=learn_sigma,
        class_cond=class_cond,
        use_checkpoint=use_checkpoint,
        attention_resolutions=attention_resolutions,
        num_heads=num_heads,
        num_head_channels=num_head_channels,
        num_heads_upsample=num_heads_upsample,
        use_scale_shift_norm=use_scale_shift_norm,
        dropout=dropout,
        resblock_updown=resblock_updown,
        use_fp16=use_fp16,
        use_new_attention_order=use_new_attention_order,
        dims=dims,
        num_groups=num_groups,
        in_channels=in_channels,
        out_channels=out_channels,
        bottleneck_attention=bottleneck_attention,
        resample_2d=resample_2d,
        additive_skips=additive_skips,
        use_freq=use_freq,
    )
    diffusion = create_gaussian_diffusion(
        steps=diffusion_steps,
        learn_sigma=learn_sigma,
        noise_schedule=noise_schedule,
        use_kl=use_kl,
        predict_xstart=predict_xstart,
        rescale_timesteps=rescale_timesteps,
        rescale_learned_sigmas=rescale_learned_sigmas,
        timestep_respacing=timestep_respacing,
        mode=mode,
    )
    return model, diffusion


def create_model(
    image_size,
    num_channels,
    num_res_blocks,
    channel_mult="",
    learn_sigma=False,
    class_cond=False,
    use_checkpoint=False,
    attention_resolutions="16",
    num_heads=1,
    num_head_channels=-1,
    num_heads_upsample=-1,
    use_scale_shift_norm=False,
    dropout=0,
    resblock_updown=True,
    use_fp16=False,
    use_new_attention_order=False,
    num_groups=32,
    dims=2,
    in_channels=1,
    out_channels=0,  # automatically determine if 0
    bottleneck_attention=True,
    resample_2d=True,
    additive_skips=False,
    use_freq=False,
):
    if not channel_mult:
        if image_size == 512:
            channel_mult = (1, 1, 2, 2, 4, 4)
        elif image_size == 256:
            channel_mult = (1, 2, 2, 4, 4, 4)
        elif image_size == 128:
            channel_mult = (1, 2, 2, 4, 4)
        elif image_size == 64:
            channel_mult = (1, 2, 3, 4)
        else:
            raise ValueError(f"[MODEL] Unsupported image size: {image_size}")
    else:
        if isinstance(channel_mult, str):
            from ast import literal_eval
            channel_mult = literal_eval(channel_mult)
        elif isinstance(channel_mult, tuple):  # do nothing
            pass
        else:
            raise ValueError(f"[MODEL] Value for {channel_mult=} not supported")

    attention_ds = []
    if attention_resolutions:
        for res in attention_resolutions.split(","):
            attention_ds.append(image_size // int(res))
    if out_channels == 0:
        out_channels = (2*in_channels if learn_sigma else in_channels)

    if not use_freq:
        return UNetModel(
            image_size=image_size,
            in_channels=in_channels,
            model_channels=num_channels,
            out_channels=out_channels * (1 if not learn_sigma else 2),
            num_res_blocks=num_res_blocks,
            attention_resolutions=tuple(attention_ds),
            dropout=dropout,
            channel_mult=channel_mult,
            num_classes=(NUM_CLASSES if class_cond else None),
            use_checkpoint=use_checkpoint,
            use_fp16=use_fp16,
            num_heads=num_heads,
            num_head_channels=num_head_channels,
            num_heads_upsample=num_heads_upsample,
            use_scale_shift_norm=use_scale_shift_norm,
            resblock_updown=resblock_updown,
            use_new_attention_order=use_new_attention_order,
            dims=dims,
            num_groups=num_groups,
            bottleneck_attention=bottleneck_attention,
            additive_skips=additive_skips,
            resample_2d=resample_2d,
        )
    else:
        return WavUNetModel(
            image_size=image_size,
            in_channels=in_channels,
            model_channels=num_channels,
            out_channels=out_channels * (1 if not learn_sigma else 2),
            num_res_blocks=num_res_blocks,
            attention_resolutions=tuple(attention_ds),
            dropout=dropout,
            channel_mult=channel_mult,
            num_classes=(NUM_CLASSES if class_cond else None),
            use_checkpoint=use_checkpoint,
            use_fp16=use_fp16,
            num_heads=num_heads,
            num_head_channels=num_head_channels,
            num_heads_upsample=num_heads_upsample,
            use_scale_shift_norm=use_scale_shift_norm,
            resblock_updown=resblock_updown,
            use_new_attention_order=use_new_attention_order,
            dims=dims,
            num_groups=num_groups,
            bottleneck_attention=bottleneck_attention,
            additive_skips=additive_skips,
            use_freq=use_freq,
        )


def create_classifier_and_diffusion(
    image_size,
    classifier_use_fp16,
    classifier_width,
    classifier_depth,
    classifier_attention_resolutions,
    classifier_num_head_channels,
    classifier_use_scale_shift_norm,
    classifier_resblock_updown,
    classifier_pool,
    classifier_channel_mult,
    learn_sigma,
    diffusion_steps,
    noise_schedule,
    timestep_respacing,
    use_kl,
    predict_xstart,
    rescale_timesteps,
    rescale_learned_sigmas,
    dataset,
    dims,
    num_groups,
    in_channels,
):
    print('timestepresp2', timestep_respacing)
    classifier = create_classifier(
        image_size,
        classifier_use_fp16,
        classifier_width,
        classifier_depth,
        classifier_attention_resolutions,
        classifier_use_scale_shift_norm,
        classifier_resblock_updown,
        classifier_pool,
        dataset,
        dims=dims,
        num_groups=num_groups,
        in_channels=in_channels,
        num_head_channels=classifier_num_head_channels,
        classifier_channel_mult=classifier_channel_mult,
    )
    diffusion = create_gaussian_diffusion(
        steps=diffusion_steps,
        learn_sigma=learn_sigma,
        noise_schedule=noise_schedule,
        use_kl=use_kl,
        predict_xstart=predict_xstart,
        rescale_timesteps=rescale_timesteps,
        rescale_learned_sigmas=rescale_learned_sigmas,
        timestep_respacing=timestep_respacing,
    )
    return classifier, diffusion


def create_classifier(
    image_size,
    classifier_use_fp16,
    classifier_width,
    classifier_depth,
    classifier_attention_resolutions,
    classifier_use_scale_shift_norm,
    classifier_resblock_updown,
    classifier_pool,
    dataset,
    num_groups=32,
    dims=2,
    in_channels=1,
    num_head_channels=64,
    classifier_channel_mult="",
):
    channel_mult = classifier_channel_mult
    if not channel_mult:
        if image_size == 256:
            channel_mult = (1, 1, 2, 2, 4, 4)
        elif image_size == 128:
            channel_mult = (1, 1, 2, 3, 4)
        elif image_size == 64:
            channel_mult = (1, 2, 3, 4)
        else:
            raise ValueError(f"unsupported image size: {image_size}")
    else:
        if isinstance(channel_mult, str):
            #channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
            from ast import literal_eval
            channel_mult = literal_eval(channel_mult)
        elif isinstance(channel_mult, tuple):  # do nothing
            pass
        else:
            raise ValueError(f"value for {channel_mult=} not supported")

    attention_ds = []
    if classifier_attention_resolutions:
        for res in classifier_attention_resolutions.split(","):
            attention_ds.append(image_size // int(res))

    print('number_in_channels classifier', in_channels)
      

    return EncoderUNetModel(
        image_size=image_size,
        in_channels=in_channels,
        model_channels=classifier_width,
        out_channels=2,
        num_res_blocks=classifier_depth,
        attention_resolutions=tuple(attention_ds),
        channel_mult=channel_mult,
        use_fp16=classifier_use_fp16,
        num_head_channels=num_head_channels,
        use_scale_shift_norm=classifier_use_scale_shift_norm,
        resblock_updown=classifier_resblock_updown,
        pool=classifier_pool,
        num_groups=num_groups,
        dims=dims,
    )


def sr_model_and_diffusion_defaults():
    res = model_and_diffusion_defaults()
    res["large_size"] = 256
    res["small_size"] = 64
    arg_names = inspect.getfullargspec(sr_create_model_and_diffusion)[0]
    for k in res.copy().keys():
        if k not in arg_names:
            del res[k]
    return res


def sr_create_model_and_diffusion(
    large_size,
    small_size,
    class_cond,
    learn_sigma,
    num_channels,
    num_res_blocks,
    num_heads,
    num_head_channels,
    num_heads_upsample,
    attention_resolutions,
    dropout,
    diffusion_steps,
    noise_schedule,
    timestep_respacing,
    use_kl,
    predict_xstart,
    rescale_timesteps,
    rescale_learned_sigmas,
    use_checkpoint,
    use_scale_shift_norm,
    resblock_updown,
    use_fp16,
):
    print('timestepresp3', timestep_respacing)
    model = sr_create_model(
        large_size,
        small_size,
        num_channels,
        num_res_blocks,
        learn_sigma=learn_sigma,
        class_cond=class_cond,
        use_checkpoint=use_checkpoint,
        attention_resolutions=attention_resolutions,
        num_heads=num_heads,
        num_head_channels=num_head_channels,
        num_heads_upsample=num_heads_upsample,
        use_scale_shift_norm=use_scale_shift_norm,
        dropout=dropout,
        resblock_updown=resblock_updown,
        use_fp16=use_fp16,
    )
    diffusion = create_gaussian_diffusion(
        steps=diffusion_steps,
        learn_sigma=learn_sigma,
        noise_schedule=noise_schedule,
        use_kl=use_kl,
        predict_xstart=predict_xstart,
        rescale_timesteps=rescale_timesteps,
        rescale_learned_sigmas=rescale_learned_sigmas,
        timestep_respacing=timestep_respacing,
    )
    return model, diffusion


def sr_create_model(
    large_size,
    small_size,
    num_channels,
    num_res_blocks,
    learn_sigma,
    class_cond,
    use_checkpoint,
    attention_resolutions,
    num_heads,
    num_head_channels,
    num_heads_upsample,
    use_scale_shift_norm,
    dropout,
    resblock_updown,
    use_fp16,
):
    _ = small_size  # hack to prevent unused variable

    if large_size == 512:
        channel_mult = (1, 1, 2, 2, 4, 4)
    elif large_size == 256:
        channel_mult = (1, 1, 2, 2, 4, 4)
    elif large_size == 64:
        channel_mult = (1, 2, 3, 4)
    else:
        raise ValueError(f"unsupported large size: {large_size}")

    attention_ds = []
    for res in attention_resolutions.split(","):
        attention_ds.append(large_size // int(res))

    return SuperResModel(
        image_size=large_size,
        in_channels=3,
        model_channels=num_channels,
        out_channels=(3 if not learn_sigma else 6),
        num_res_blocks=num_res_blocks,
        attention_resolutions=tuple(attention_ds),
        dropout=dropout,
        channel_mult=channel_mult,
        num_classes=(NUM_CLASSES if class_cond else None),
        use_checkpoint=use_checkpoint,
        num_heads=num_heads,
        num_head_channels=num_head_channels,
        num_heads_upsample=num_heads_upsample,
        use_scale_shift_norm=use_scale_shift_norm,
        resblock_updown=resblock_updown,
        use_fp16=use_fp16,
    )


def create_gaussian_diffusion(
    *,
    steps=1000,
    learn_sigma=False,
    sigma_small=False,
    noise_schedule="linear",
    use_kl=False,
    predict_xstart=False,
    rescale_timesteps=False,
    rescale_learned_sigmas=False,
    timestep_respacing="",
    mode='default',
):
    betas = gd.get_named_beta_schedule(noise_schedule, steps)
    if use_kl:
        loss_type = gd.LossType.RESCALED_KL
    elif rescale_learned_sigmas:
        loss_type = gd.LossType.RESCALED_MSE
    else:
        loss_type = gd.LossType.MSE

    if not timestep_respacing:
        timestep_respacing = [steps]

    return SpacedDiffusion(
        use_timesteps=space_timesteps(steps, timestep_respacing),
        betas=betas,
        model_mean_type=(gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X),
        model_var_type=(
            (
                gd.ModelVarType.FIXED_LARGE
                if not sigma_small
                else gd.ModelVarType.FIXED_SMALL
            )
            if not learn_sigma
            else gd.ModelVarType.LEARNED_RANGE
        ),
        loss_type=loss_type,
        rescale_timesteps=rescale_timesteps,
        mode=mode,
    )


def add_dict_to_argparser(parser, default_dict):
    for k, v in default_dict.items():
        v_type = type(v)
        if v is None:
            v_type = str
        elif isinstance(v, bool):
            v_type = str2bool
        parser.add_argument(f"--{k}", default=v, type=v_type)


def args_to_dict(args, keys):
    return {k: getattr(args, k) for k in keys}


def str2bool(v):
    """
    https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
    """
    if isinstance(v, bool):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("boolean value expected")