src/deoldify/generators.py

from fastai.vision import *
from fastai.vision.learner import cnn_config
from .unet import DynamicUnetWide, DynamicUnetDeep
from .loss import FeatureLoss
from .dataset import *

# Weights are implicitly read from ./models/ folder
def gen_inference_wide(
    root_folder: Path, weights_name: str, nf_factor: int = 2, arch=models.resnet101) -> Learner:
    data = get_dummy_databunch()
    learn = gen_learner_wide(
        data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch
    )
    learn.path = root_folder
    learn.load(weights_name)
    learn.model.eval()
    return learn


def gen_learner_wide(
    data: ImageDataBunch, gen_loss, arch=models.resnet101, nf_factor: int = 2
) -> Learner:
    return unet_learner_wide(
        data,
        arch=arch,
        wd=1e-3,
        blur=True,
        norm_type=NormType.Spectral,
        self_attention=True,
        y_range=(-3.0, 3.0),
        loss_func=gen_loss,
        nf_factor=nf_factor,
    )


# The code below is meant to be merged into fastaiv1 ideally
def unet_learner_wide(
    data: DataBunch,
    arch: Callable,
    pretrained: bool = True,
    blur_final: bool = True,
    norm_type: Optional[NormType] = NormType,
    split_on: Optional[SplitFuncOrIdxList] = None,
    blur: bool = False,
    self_attention: bool = False,
    y_range: Optional[Tuple[float, float]] = None,
    last_cross: bool = True,
    bottle: bool = False,
    nf_factor: int = 1,
    **kwargs: Any
) -> Learner:
    "Build Unet learner from `data` and `arch`."
    meta = cnn_config(arch)
    body = create_body(arch, pretrained)
    model = to_device(
        DynamicUnetWide(
            body,
            n_classes=data.c,
            blur=blur,
            blur_final=blur_final,
            self_attention=self_attention,
            y_range=y_range,
            norm_type=norm_type,
            last_cross=last_cross,
            bottle=bottle,
            nf_factor=nf_factor,
        ),
        data.device,
    )
    learn = Learner(data, model, **kwargs)
    learn.split(ifnone(split_on, meta['split']))
    if pretrained:
        learn.freeze()
    apply_init(model[2], nn.init.kaiming_normal_)
    return learn


# ----------------------------------------------------------------------

# Weights are implicitly read from ./models/ folder
def gen_inference_deep(
    root_folder: Path, weights_name: str, arch=models.resnet34, nf_factor: float = 1.5) -> Learner:
    data = get_dummy_databunch()
    learn = gen_learner_deep(
        data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor
    )
    learn.path = root_folder
    learn.load(weights_name)
    learn.model.eval()
    return learn


def gen_learner_deep(
    data: ImageDataBunch, gen_loss, arch=models.resnet34, nf_factor: float = 1.5
) -> Learner:
    return unet_learner_deep(
        data,
        arch,
        wd=1e-3,
        blur=True,
        norm_type=NormType.Spectral,
        self_attention=True,
        y_range=(-3.0, 3.0),
        loss_func=gen_loss,
        nf_factor=nf_factor,
    )


# The code below is meant to be merged into fastaiv1 ideally
def unet_learner_deep(
    data: DataBunch,
    arch: Callable,
    pretrained: bool = True,
    blur_final: bool = True,
    norm_type: Optional[NormType] = NormType,
    split_on: Optional[SplitFuncOrIdxList] = None,
    blur: bool = False,
    self_attention: bool = False,
    y_range: Optional[Tuple[float, float]] = None,
    last_cross: bool = True,
    bottle: bool = False,
    nf_factor: float = 1.5,
    **kwargs: Any
) -> Learner:
    "Build Unet learner from `data` and `arch`."
    meta = cnn_config(arch)
    body = create_body(arch, pretrained)
    model = to_device(
        DynamicUnetDeep(
            body,
            n_classes=data.c,
            blur=blur,
            blur_final=blur_final,
            self_attention=self_attention,
            y_range=y_range,
            norm_type=norm_type,
            last_cross=last_cross,
            bottle=bottle,
            nf_factor=nf_factor,
        ),
        data.device,
    )
    learn = Learner(data, model, **kwargs)
    learn.split(ifnone(split_on, meta['split']))
    if pretrained:
        learn.freeze()
    apply_init(model[2], nn.init.kaiming_normal_)
    return learn


# -----------------------------