diff options
| -rwxr-xr-x | models/pix2pixHD_model.py | 16 | ||||
| -rwxr-xr-x | train.py | 2 |
2 files changed, 13 insertions, 5 deletions
diff --git a/models/pix2pixHD_model.py b/models/pix2pixHD_model.py index 834fc18..b77868a 100755 --- a/models/pix2pixHD_model.py +++ b/models/pix2pixHD_model.py @@ -11,7 +11,13 @@ from . import networks class Pix2PixHDModel(BaseModel): def name(self): return 'Pix2PixHDModel' - + + def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss): + flags = (True, use_gan_feat_loss, use_vgg_loss, True, True) + def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake): + return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f] + return loss_filter + def initialize(self, opt): BaseModel.initialize(self, opt) if opt.resize_or_crop != 'none': # when training at full res this causes OOM @@ -20,7 +26,6 @@ class Pix2PixHDModel(BaseModel): self.use_features = opt.instance_feat or opt.label_feat self.gen_features = self.use_features and not self.opt.load_features input_nc = opt.label_nc if opt.label_nc != 0 else 3 - ##### define networks # Generator network netG_input_nc = input_nc @@ -65,13 +70,16 @@ class Pix2PixHDModel(BaseModel): self.old_lr = opt.lr # define loss functions + self.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss) + self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor) self.criterionFeat = torch.nn.L1Loss() if not opt.no_vgg_loss: self.criterionVGG = networks.VGGLoss(self.gpu_ids) + # Names so we can breakout loss - self.loss_names = ['G_GAN', 'G_GAN_Feat', 'G_VGG', 'D_real', 'D_fake'] + self.loss_names = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake') # initialize optimizers # optimizer G @@ -172,7 +180,7 @@ class Pix2PixHDModel(BaseModel): loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat # Only return the fake_B image if necessary to save BW - return [ [ loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ], None if not infer else fake_image ] + return [ self.loss_filter( loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ), None if not infer else fake_image ] def inference(self, label, inst): # Encode Inputs @@ -61,7 +61,7 @@ for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): # calculate final loss scalar loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 - loss_G = loss_dict['G_GAN'] + loss_dict['G_GAN_Feat'] + loss_dict['G_VGG'] + loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) ############### Backward Pass #################### # update generator weights |