DeOldify/fasterai/generators.py

from fastai.core import *
from fastai.conv_learner import model_meta, cut_model
from fasterai.modules import ConvBlock, UnetBlock, UpSampleBlock, SaveFeatures
from abc import ABC, abstractmethod

class GeneratorModule(ABC, nn.Module):
    def __init__(self):
        super().__init__()
    
    def set_trainable(self, trainable:bool):
        set_trainable(self, trainable)

    @abstractmethod
    def get_layer_groups(self, precompute:bool=False)->[]:
        pass

    def freeze_to(self, n:int):
        c=self.get_layer_groups()
        for l in c:     set_trainable(l, False)
        for l in c[n:]: set_trainable(l, True)

    def get_device(self):
        return next(self.parameters()).device

 
class Unet34(GeneratorModule): 
    @staticmethod
    def get_pretrained_resnet_base(layers_cut:int=0):
        f = resnet34
        cut,lr_cut = model_meta[f]
        cut-=layers_cut
        layers = cut_model(f(True), cut)
        return nn.Sequential(*layers), lr_cut

    def __init__(self, nf_factor:int=1, scale:int=1):
        super().__init__()
        assert (math.log(scale,2)).is_integer()
        leakyReLu=False
        self_attention=True
        bn=True
        sn=True
        self.rn, self.lr_cut = Unet34.get_pretrained_resnet_base()
        self.relu = nn.ReLU()
        self.up1 = UnetBlock(512,256,512*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up2 = UnetBlock(512*nf_factor,128,512*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up3 = UnetBlock(512*nf_factor,64,512*nf_factor, sn=sn, self_attention=self_attention, leakyReLu=leakyReLu, bn=bn)
        self.up4 = UnetBlock(512*nf_factor,64,256*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up5 = UpSampleBlock(256*nf_factor, 32*nf_factor, 2*scale, sn=sn, leakyReLu=leakyReLu, bn=bn) 
        self.out= nn.Sequential(ConvBlock(32*nf_factor, 3, ks=3, actn=False, bn=False, sn=sn), nn.Tanh())

    #Gets around irritating inconsistent halving coming from resnet
    def _pad(self, x:torch.Tensor, target:torch.Tensor)-> torch.Tensor:
        h = x.shape[2] 
        w = x.shape[3]

        target_h = target.shape[2]*2
        target_w = target.shape[3]*2

        if h<target_h or w<target_w:
            padh = target_h-h if target_h > h else 0
            padw = target_w-w if target_w > w else 0
            return F.pad(x, (0,padw,0,padh), "constant",0)

        return x
           
    def forward(self, x_in:torch.Tensor):
        x = self.rn[0](x_in)
        x = self.rn[1](x)
        x = self.rn[2](x)
        enc0 = x
        x = self.rn[3](x)
        x = self.rn[4](x)
        enc1 = x
        x = self.rn[5](x)
        enc2 = x
        x = self.rn[6](x)
        enc3 = x
        x = self.rn[7](x)

        x = self.relu(x)
        x = self.up1(x, self._pad(enc3, x))
        x = self.up2(x, self._pad(enc2, x))
        x = self.up3(x, self._pad(enc1, x))
        x = self.up4(x, self._pad(enc0, x))
        x = self.up5(x)
        x = self.out(x)
        return x
    
    def get_layer_groups(self, precompute:bool=False)->[]:
        lgs = list(split_by_idxs(children(self.rn), [self.lr_cut]))
        return lgs + [children(self)[1:]]
    
    def close(self):
        for sf in self.sfs: 
            sf.remove()


class Unet34_V2(GeneratorModule): 
    @staticmethod
    def get_pretrained_resnet_base(layers_cut:int=0):
        f = resnet34
        cut,lr_cut = model_meta[f]
        cut-=layers_cut
        layers = cut_model(f(True), cut)
        return nn.Sequential(*layers), lr_cut

    def __init__(self, nf_factor:int=1, scale:int=1):
        super().__init__()
        assert (math.log(scale,2)).is_integer()
        leakyReLu=False
        self_attention=True
        bn=True
        sn=True
        self.rn, self.lr_cut = Unet34.get_pretrained_resnet_base()
        self.relu = nn.ReLU()
        self.up1 = UnetBlock(512,256,512*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up2 = UnetBlock(512*nf_factor,128,512*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up3 = UnetBlock(512*nf_factor,64,256*nf_factor, sn=sn, self_attention=self_attention, leakyReLu=leakyReLu, bn=bn)
        self.up4 = UnetBlock(256*nf_factor,64,128*nf_factor, sn=sn, leakyReLu=leakyReLu, bn=bn)
        self.up5 = UpSampleBlock(128*nf_factor, 32*nf_factor, 2*scale, sn=sn, leakyReLu=leakyReLu, bn=bn) 
        self.out= nn.Sequential(ConvBlock(32*nf_factor, 3, ks=3, actn=False, bn=False, sn=sn), nn.Tanh())

    #Gets around irritating inconsistent halving coming from resnet
    def _pad(self, x:torch.Tensor, target:torch.Tensor)-> torch.Tensor:
        h = x.shape[2] 
        w = x.shape[3]
        target_h = target.shape[2]*2
        target_w = target.shape[3]*2

        if h<target_h or w<target_w:
            padh = target_h-h if target_h > h else 0
            padw = target_w-w if target_w > w else 0
            return F.pad(x, (0,padw,0,padh), "constant",0)

        return x
           
    def forward(self, x_in:torch.Tensor):
        x = self.rn[0](x_in)
        x = self.rn[1](x)
        x = self.rn[2](x)
        enc0 = x
        x = self.rn[3](x)
        x = self.rn[4](x)
        enc1 = x
        x = self.rn[5](x)
        enc2 = x
        x = self.rn[6](x)
        enc3 = x
        x = self.rn[7](x)

        x = self.relu(x)
        x = self.up1(x, self._pad(enc3, x))
        x = self.up2(x, self._pad(enc2, x))
        x = self.up3(x, self._pad(enc1, x))
        x = self.up4(x, self._pad(enc0, x))
        x = self.up5(x)
        x = self.out(x)
        return x
    
    def get_layer_groups(self, precompute:bool=False)->[]:
        lgs = list(split_by_idxs(children(self.rn), [self.lr_cut]))
        return lgs + [children(self)[1:]]
    
    def close(self):
        for sf in self.sfs: 
            sf.remove()