predict single image

1 files changed, 10 insertions, 9 deletions

diff --git a/models/pix2pix_model.py b/models/pix2pix_model.py
index 4581d33..e44529b 100644
--- a/models/pix2pix_model.py
+++ b/models/pix2pix_model.py
@@ -8,6 +8,7 @@ from util.image_pool import ImagePool
 from .base_model import BaseModel
 from . import networks
 
+
 class Pix2PixModel(BaseModel):
     def name(self):
         return 'Pix2PixModel'
@@ -23,12 +24,12 @@ class Pix2PixModel(BaseModel):
 
         # load/define networks
         self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf,
-                                    opt.which_model_netG, opt.norm, opt.use_dropout, self.gpu_ids)
+                                      opt.which_model_netG, opt.norm, opt.use_dropout, self.gpu_ids)
         if self.isTrain:
             use_sigmoid = opt.no_lsgan
             self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf,
-                                         opt.which_model_netD,
-                                         opt.n_layers_D, opt.norm, use_sigmoid, self.gpu_ids)
+                                          opt.which_model_netD,
+                                          opt.n_layers_D, opt.norm, use_sigmoid, self.gpu_ids)
         if not self.isTrain or opt.continue_train:
             self.load_network(self.netG, 'G', opt.which_epoch)
             if self.isTrain:
@@ -71,7 +72,7 @@ class Pix2PixModel(BaseModel):
         self.fake_B = self.netG.forward(self.real_A)
         self.real_B = Variable(self.input_B, volatile=True)
 
-    #get image paths
+    # get image paths
     def get_image_paths(self):
         return self.image_paths
 
@@ -83,7 +84,7 @@ class Pix2PixModel(BaseModel):
         self.loss_D_fake = self.criterionGAN(self.pred_fake, False)
 
         # Real
-        real_AB = torch.cat((self.real_A, self.real_B), 1)#.detach()
+        real_AB = torch.cat((self.real_A, self.real_B), 1)
         self.pred_real = self.netD.forward(real_AB)
         self.loss_D_real = self.criterionGAN(self.pred_real, True)
 
@@ -118,10 +119,10 @@ class Pix2PixModel(BaseModel):
 
     def get_current_errors(self):
         return OrderedDict([('G_GAN', self.loss_G_GAN.data[0]),
-                ('G_L1', self.loss_G_L1.data[0]),
-                ('D_real', self.loss_D_real.data[0]),
-                ('D_fake', self.loss_D_fake.data[0])
-        ])
+                            ('G_L1', self.loss_G_L1.data[0]),
+                            ('D_real', self.loss_D_real.data[0]),
+                            ('D_fake', self.loss_D_fake.data[0])
+                            ])
 
     def get_current_visuals(self):
         real_A = util.tensor2im(self.real_A.data)