refine code

applications/tools/first-order-demo.py

@@ -15,7 +15,7 @@
 import argparse
 
 import paddle
-from ppgan.first_order_predictor import FirstOrderPredictor
+from ppgan.apps.first_order_predictor import FirstOrderPredictor
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", default=None, help="path to config")

ppgan/models/base_model.py

@@ -26,7 +26,7 @@ class BaseModel(ABC):
         When creating your custom class, you need to implement your own initialization.
         In this function, you should first call <BaseModel.__init__(self, opt)>
         Then, you need to define four lists:
-            -- self.loss (str list):          specify the training losses that you want to plot and save.
+            -- self.losses (str list):          specify the training losses that you want to plot and save.
             -- self.model_names (str list):         define networks used in our training.
             -- self.visual_names (str list):        specify the images that you want to display and save.
             -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
@@ -37,7 +37,7 @@ class BaseModel(ABC):
             opt.output_dir,
             opt.model.name)  # save all the checkpoints to save_dir
 
-        self.loss = OrderedDict()
+        self.losses = OrderedDict()
         self.model_names = []
         self.visual_names = []
         self.optimizers = []
@@ -115,7 +115,7 @@ class BaseModel(ABC):
 
     def get_current_losses(self):
         """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
-        return self.loss
+        return self.losses
 
     def set_requires_grad(self, nets, requires_grad=False):
         """Set requies_grad=Fasle for all the networks to avoid unnecessary computations

ppgan/models/cycle_gan_model.py

@@ -161,13 +161,13 @@ class CycleGANModel(BaseModel):
         """Calculate GAN loss for discriminator D_A"""
         fake_B = self.fake_B_pool.query(self.fake_B)
         self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
-        self.loss['D_A_loss'] = self.loss_D_A
+        self.losses['D_A_loss'] = self.loss_D_A
 
     def backward_D_B(self):
         """Calculate GAN loss for discriminator D_B"""
         fake_A = self.fake_A_pool.query(self.fake_A)
         self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
-        self.loss['D_B_loss'] = self.loss_D_B
+        self.losses['D_B_loss'] = self.loss_D_B
 
     def backward_G(self):
         """Calculate the loss for generators G_A and G_B"""
@@ -199,12 +199,12 @@ class CycleGANModel(BaseModel):
         self.loss_cycle_B = self.criterionCycle(self.rec_B,
                                                 self.real_B) * lambda_B
 
-        self.loss['G_idt_A_loss'] = self.loss_idt_A
-        self.loss['G_idt_B_loss'] = self.loss_idt_B
-        self.loss['G_A_adv_loss'] = self.loss_G_A
-        self.loss['G_B_adv_loss'] = self.loss_G_B
-        self.loss['G_A_cycle_loss'] = self.loss_cycle_A
-        self.loss['G_B_cycle_loss'] = self.loss_cycle_B
+        self.losses['G_idt_A_loss'] = self.loss_idt_A
+        self.losses['G_idt_B_loss'] = self.loss_idt_B
+        self.losses['G_A_adv_loss'] = self.loss_G_A
+        self.losses['G_B_adv_loss'] = self.loss_G_B
+        self.losses['G_A_cycle_loss'] = self.loss_cycle_A
+        self.losses['G_B_cycle_loss'] = self.loss_cycle_B
         # combined loss and calculate gradients
         self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B
 

ppgan/models/makeup_model.py

@@ -196,13 +196,13 @@ class MakeupModel(BaseModel):
         """Calculate GAN loss for discriminator D_A"""
         fake_B = self.fake_B_pool.query(self.fake_B)
         self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
-        self.loss['D_A_loss'] = self.loss_D_A
+        self.losses['D_A_loss'] = self.loss_D_A
 
     def backward_D_B(self):
         """Calculate GAN loss for discriminator D_B"""
         fake_A = self.fake_A_pool.query(self.fake_A)
         self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
-        self.loss['D_B_loss'] = self.loss_D_B
+        self.losses['D_B_loss'] = self.loss_D_B
 
     def backward_G(self):
         """Calculate the loss for generators G_A and G_B"""
@@ -247,8 +247,8 @@ class MakeupModel(BaseModel):
         self.loss_cycle_B = self.criterionCycle(self.rec_B,
                                                 self.real_B) * lambda_B
 
-        self.loss['G_A_adv_loss'] = self.loss_G_A
-        self.loss['G_B_adv_loss'] = self.loss_G_B
+        self.losses['G_A_adv_loss'] = self.loss_G_A
+        self.losses['G_B_adv_loss'] = self.loss_G_B
 
         mask_A_lip = self.mask_A_aug[:, 0].unsqueeze(1)
         mask_B_lip = self.mask_B_aug[:, 0].unsqueeze(1)
@@ -336,8 +336,8 @@ class MakeupModel(BaseModel):
         self.loss_G_B_his = (g_B_eye_loss_his + g_B_lip_loss_his +
                              g_B_skin_loss_his * 0.1) * 0.01
 
-        self.loss['G_A_his_loss'] = self.loss_G_A_his
-        self.loss['G_B_his_loss'] = self.loss_G_A_his
+        self.losses['G_A_his_loss'] = self.loss_G_A_his
+        self.losses['G_B_his_loss'] = self.loss_G_A_his
 
         #vgg loss
         vgg_s = self.vgg(self.real_A)
@@ -356,10 +356,10 @@ class MakeupModel(BaseModel):
                          self.loss_A_vgg + self.loss_B_vgg) * 0.2
         self.loss_idt = (self.loss_idt_A + self.loss_idt_B) * 0.2
 
-        self.loss['G_A_vgg_loss'] = self.loss_A_vgg
-        self.loss['G_B_vgg_loss'] = self.loss_B_vgg
-        self.loss['G_rec_loss'] = self.loss_rec
-        self.loss['G_idt_loss'] = self.loss_idt
+        self.losses['G_A_vgg_loss'] = self.loss_A_vgg
+        self.losses['G_B_vgg_loss'] = self.loss_B_vgg
+        self.losses['G_rec_loss'] = self.loss_rec
+        self.losses['G_idt_loss'] = self.loss_idt
 
         # bg consistency loss
         mask_A_consis = paddle.cast(

ppgan/models/pix2pix_model.py

@@ -113,8 +113,8 @@ class Pix2PixModel(BaseModel):
         else:
             self.loss_D.backward()
 
-        self.loss['D_fake_loss'] = self.loss_D_fake
-        self.loss['D_real_loss'] = self.loss_D_real
+        self.losses['D_fake_loss'] = self.loss_D_fake
+        self.losses['D_real_loss'] = self.loss_D_real
 
     def backward_G(self):
         """Calculate GAN and L1 loss for the generator"""
@@ -136,8 +136,8 @@ class Pix2PixModel(BaseModel):
         else:
             self.loss_G.backward()
 
-        self.loss['G_adv_loss'] = self.loss_G_GAN
-        self.loss['G_L1_loss'] = self.loss_G_L1
+        self.losses['G_adv_loss'] = self.loss_G_GAN
+        self.losses['G_L1_loss'] = self.loss_G_L1
 
     def optimize_parameters(self):
         # compute fake images: G(A)