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| 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 | |
| # ----------------------------- | |