977e38de2e77d46b36ace96b4c76910174f77ad7,models/face_shift_net/face_shiftnet_model.py,FaceShiftNetModel,set_input,#FaceShiftNetModel#Any#,127

Before Change

        real_A_flip = input["A_F"].to(self.device)
        // directly load mask offline
        self.mask_global = input["M"].to(self.device).byte()
        self.mask_global = self.mask_global.narrow(1,0,1)

        // create mask online
        if not self.opt.offline_loading_mask:
            if self.opt.mask_type == "center":
                self.mask_global.zero_()
                self.mask_global[:, :, int(self.opt.fineSize/4) + self.opt.overlap : int(self.opt.fineSize/2) + int(self.opt.fineSize/4) - self.opt.overlap,\
                                    int(self.opt.fineSize/4) + self.opt.overlap: int(self.opt.fineSize/2) + int(self.opt.fineSize/4) - self.opt.overlap] = 1
                self.rand_t, self.rand_l = int(self.opt.fineSize/4) + self.opt.overlap, int(self.opt.fineSize/4) + self.opt.overlap
            elif self.opt.mask_type == "random":
                self.mask_global = self.create_random_mask().type_as(self.mask_global).view(1, *self.mask_global.size()[-3:])
                // As generating random masks online are computation-heavy
                // So just generate one ranodm mask for a batch images.
                self.mask_global = self.mask_global.expand(self.opt.batchSize, *self.mask_global.size()[-3:])
            else:
                raise ValueError("Mask_type [%s] not recognized." % self.opt.mask_type)
        // For loading mask offline, we also need to change "opt.mask_type" and "opt.mask_sub_type"
        // to avoid forgetting such settings.
        else:
            self.opt.mask_type = "random"
            self.opt.mask_sub_type = "island"

        self.set_latent_mask(self.mask_global)

        real_A.narrow(1,0,1).masked_fill_(self.mask_global, 0.)/ǘ*123.0/255.0 - 1.0
        real_A.narrow(1,1,1).masked_fill_(self.mask_global, 0.)/ǘ*104.0/255.0 - 1.0
        real_A.narrow(1,2,1).masked_fill_(self.mask_global, 0.)/ǘ*117.0/255.0 - 1.0

        self.mask_global_flip = torch.flip(self.mask_global.float(), [3]).byte()
        real_A_flip.narrow(1,0,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*123.0/255.0 - 1.0
        real_A_flip.narrow(1,1,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*104.0/255.0 - 1.0
        real_A_flip.narrow(1,2,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*117.0/255.0 - 1.0


        if self.opt.add_mask2input:
            // make it 4 dimensions.
            // Mention: the extra dim, the masked part is filled with 0, non-mask part is filled with 1.
            real_A = torch.cat((real_A, (1 - self.mask_global).expand(real_A.size(0), 1, real_A.size(2), real_A.size(3)).type_as(real_A)), dim=1)
            real_A_flip = torch.cat((real_A_flip, (1 - self.mask_global_flip).expand(real_A_flip.size(0), 1, real_A.size(2), real_A.size(3)).type_as(real_A)), dim=1)

        self.real_A = real_A
        self.real_B = real_B
        self.real_A_flip = real_A_flip


    def set_latent_mask(self, mask_global):

After Change

        real_A_flip = input["A_F"].to(self.device)
        // directly load mask offline
        self.mask_global = input["M"].to(self.device).byte()
        self.mask_global = self.mask_global.narrow(1,0,1).bool()

        // create mask online
        if not self.opt.offline_loading_mask:
            if self.opt.mask_type == "center":
                self.mask_global.zero_()
                self.mask_global[:, :, int(self.opt.fineSize/4) + self.opt.overlap : int(self.opt.fineSize/2) + int(self.opt.fineSize/4) - self.opt.overlap,\
                                    int(self.opt.fineSize/4) + self.opt.overlap: int(self.opt.fineSize/2) + int(self.opt.fineSize/4) - self.opt.overlap] = 1
                self.rand_t, self.rand_l = int(self.opt.fineSize/4) + self.opt.overlap, int(self.opt.fineSize/4) + self.opt.overlap
            elif self.opt.mask_type == "random":
                self.mask_global = self.create_random_mask().type_as(self.mask_global).view(1, *self.mask_global.size()[-3:])
                // As generating random masks online are computation-heavy
                // So just generate one ranodm mask for a batch images.
                self.mask_global = self.mask_global.expand(self.opt.batchSize, *self.mask_global.size()[-3:])
            else:
                raise ValueError("Mask_type [%s] not recognized." % self.opt.mask_type)
        // For loading mask offline, we also need to change "opt.mask_type" and "opt.mask_sub_type"
        // to avoid forgetting such settings.
        else:
            self.opt.mask_type = "random"
            self.opt.mask_sub_type = "island"

        self.set_latent_mask(self.mask_global)

        real_A.narrow(1,0,1).masked_fill_(self.mask_global, 0.)/ǘ*123.0/255.0 - 1.0
        real_A.narrow(1,1,1).masked_fill_(self.mask_global, 0.)/ǘ*104.0/255.0 - 1.0
        real_A.narrow(1,2,1).masked_fill_(self.mask_global, 0.)/ǘ*117.0/255.0 - 1.0

        self.mask_global_flip = torch.flip(self.mask_global.float(), [3]).bool()
        real_A_flip.narrow(1,0,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*123.0/255.0 - 1.0
        real_A_flip.narrow(1,1,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*104.0/255.0 - 1.0
        real_A_flip.narrow(1,2,1).masked_fill_(self.mask_global_flip, 0.)/ǘ*117.0/255.0 - 1.0


        if self.opt.add_mask2input:
            // make it 4 dimensions.
            // Mention: the extra dim, the masked part is filled with 0, non-mask part is filled with 1.
            real_A = torch.cat((real_A, (~self.mask_global).expand(real_A.size(0), 1, real_A.size(2), real_A.size(3)).type_as(real_A)), dim=1)
            real_A_flip = torch.cat((real_A_flip, (~self.mask_global_flip).expand(real_A_flip.size(0), 1, real_A.size(2), real_A.size(3)).type_as(real_A)), dim=1)

        self.real_A = real_A
        self.real_B = real_B
        self.real_A_flip = real_A_flip


    def set_latent_mask(self, mask_global):

Instances