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- from fastai.vision import *
- from fastai.vision.learner import cnn_config
- from .unet import CustomDynamicUnet, CustomDynamicUnet2
- from .loss import FeatureLoss
- from .dataset import *
- #Weights are implicitly read from ./models/ folder
- def colorize_gen_inference(root_folder:Path, weights_name:str, nf_factor:float)->Learner:
- data = get_dummy_databunch()
- learn = colorize_gen_learner(data=data, gen_loss=F.l1_loss, nf_factor=nf_factor)
- learn.path = root_folder
- learn.load(weights_name)
- learn.model.eval()
- return learn
- def colorize_gen_learner(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34, nf_factor:float=1.0)->Learner:
- return custom_unet_learner(data, arch, wd=1e-3, blur=True, norm_type=NormType.Spectral,
- self_attention=True, y_range=(-3.,3.), loss_func=gen_loss, nf_factor=nf_factor)
- #The code below is meant to be merged into fastaiv1 ideally
- def custom_unet_learner(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=1.0, **kwargs:Any)->Learner:
- "Build Unet learner from `data` and `arch`."
- meta = cnn_config(arch)
- body = create_body(arch, pretrained)
- model = to_device(CustomDynamicUnet(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 colorize_gen_inference2(root_folder:Path, weights_name:str, nf_factor:int)->Learner:
- data = get_dummy_databunch()
- learn = colorize_gen_learner2(data=data, gen_loss=F.l1_loss, nf_factor=nf_factor)
- learn.path = root_folder
- learn.load(weights_name)
- learn.model.eval()
- return learn
- def colorize_gen_learner2(data:ImageDataBunch, gen_loss=FeatureLoss(), arch=models.resnet34, nf_factor:int=1)->Learner:
- return custom_unet_learner2(data, arch, wd=1e-3, blur=True, norm_type=NormType.Spectral,
- self_attention=True, y_range=(-3.,3.), loss_func=gen_loss, nf_factor=nf_factor)
- #The code below is meant to be merged into fastaiv1 ideally
- def custom_unet_learner2(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(CustomDynamicUnet2(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
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