Merge pull request #39 from lijianshe02/master

refine loss related code and align to the newest develop api

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_names (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_names = []
+        self.losses = OrderedDict()
         self.model_names = []
         self.visual_names = []
         self.optimizers = []
@@ -115,13 +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"""
-        errors_ret = OrderedDict()
-        for name in self.loss_names:
-            if isinstance(name, str):
-                errors_ret[name] = float(
-                    getattr(self, 'loss_' + name)
-                )  # float(...) works for both scalar tensor and float number
-        return errors_ret
+        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

@@ -31,10 +31,6 @@ class CycleGANModel(BaseModel):
             opt (config)-- stores all the experiment flags; needs to be a subclass of Dict
         """
         BaseModel.__init__(self, opt)
-        # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
-        self.loss_names = [
-            'D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B'
-        ]
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
         visual_names_A = ['real_A', 'fake_B', 'rec_A']
         visual_names_B = ['real_B', 'fake_A', 'rec_B']
@@ -165,11 +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.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.losses['D_B_loss'] = self.loss_D_B
 
     def backward_G(self):
         """Calculate the loss for generators G_A and G_B"""
@@ -200,6 +198,13 @@ class CycleGANModel(BaseModel):
         # Backward cycle loss || G_A(G_B(B)) - B||
         self.loss_cycle_B = self.criterionCycle(self.rec_B,
                                                 self.real_B) * lambda_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/losses.py

@@ -47,16 +47,16 @@ class GANLoss(nn.Layer):
         """
         if target_is_real:
             if not hasattr(self, 'target_real_tensor'):
-                self.target_real_tensor = paddle.fill_constant(
+                self.target_real_tensor = paddle.full(
                     shape=paddle.shape(prediction),
-                    value=self.target_real_label,
+                    fill_value=self.target_real_label,
                     dtype='float32')
             target_tensor = self.target_real_tensor
         else:
             if not hasattr(self, 'target_fake_tensor'):
-                self.target_fake_tensor = paddle.fill_constant(
+                self.target_fake_tensor = paddle.full(
                     shape=paddle.shape(prediction),
-                    value=self.target_fake_label,
+                    fill_value=self.target_fake_label,
                     dtype='float32')
             target_tensor = self.target_fake_tensor
 

ppgan/models/makeup_model.py

@@ -50,19 +50,6 @@ class MakeupModel(BaseModel):
         """
         BaseModel.__init__(self, opt)
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
-        self.loss_names = [
-            'D_A',
-            'G_A',
-            'rec',
-            'idt',
-            'D_B',
-            'G_B',
-            'G_A_his',
-            'G_B_his',
-            'G_bg_consis',
-            'A_vgg',
-            'B_vgg',
-        ]
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
         visual_names_A = ['real_A', 'fake_A', 'rec_A']
         visual_names_B = ['real_B', 'fake_B', 'rec_B']
@@ -209,11 +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.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.losses['D_B_loss'] = self.loss_D_B
 
     def backward_G(self):
         """Calculate the loss for generators G_A and G_B"""
@@ -258,6 +247,9 @@ class MakeupModel(BaseModel):
         self.loss_cycle_B = self.criterionCycle(self.rec_B,
                                                 self.real_B) * lambda_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)
 
@@ -344,10 +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 = self.criterionL1(tmp_1, tmp_2) * 2048 * 255
-        #tmp_3 = self.hm_gt_B*self.hm_mask_weight_B
-        #tmp_4 = self.fake_B*self.hm_mask_weight_B
-        #self.loss_G_B_his = self.criterionL1(tmp_3, tmp_4) * 2048 * 255
+        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)
@@ -366,6 +356,11 @@ 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.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(
             (self.mask_A == 0), dtype='float32') + paddle.cast(

ppgan/models/pix2pix_model.py

@@ -31,7 +31,6 @@ class Pix2PixModel(BaseModel):
         """
         BaseModel.__init__(self, opt)
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
-        self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
         self.visual_names = ['real_A', 'fake_B', 'real_B']
         # specify the models you want to save to the disk.
@@ -114,6 +113,9 @@ class Pix2PixModel(BaseModel):
         else:
             self.loss_D.backward()
 
+        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"""
         # First, G(A) should fake the discriminator
@@ -134,6 +136,9 @@ class Pix2PixModel(BaseModel):
         else:
             self.loss_G.backward()
 
+        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)
         self.forward()

ppgan/models/vgg.py

@@ -45,7 +45,7 @@ def vgg16(pretrained=False):
     if pretrained:
         weight_path = get_weights_path_from_url(model_urls['vgg16'][0],
                                                 model_urls['vgg16'][1])
-        param, _ = paddle.load(weight_path)
+        param = paddle.load(weight_path)
         model.load_dict(param)
 
     return model

ppgan/modules/init.py

@@ -80,7 +80,7 @@ def calculate_gain(nonlinearity, param=None):
 
 @paddle.no_grad()
 def constant_(x, value):
-    temp_value = paddle.fill_constant(x.shape, x.dtype, value)
+    temp_value = paddle.full(x.shape, value, x.dtype)
     x.set_value(temp_value)
     return x