上色算法建模训练

上色建模算法

通过算法的编写，进行系统建模模。建模完后系统会自动的筛选训练选择出图片每一部分的最适合的颜色。能够在黑白图片下进行色彩的还原，同时也能就行对有加工图片，有滤镜和色彩调整过的图片，进行图像的高还原率的还原到最真实的图像。

成品展示

代码专区

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

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=FeatureLoss(), 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.,3.), loss_func=gen_loss, nf_factor=nf_factor)

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=FeatureLoss(), 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.,3.), loss_func=gen_loss, nf_factor=nf_factor)

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

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