[trainers] added some comments and debug code

bob/learn/pytorch/trainers/ConditionalGANTrainer.py

@@ -15,6 +15,7 @@ logger = bob.core.log.setup("bob.learn.pytorch")
 
 import time
 
+from matplotlib import pyplot
 
 class ConditionalGANTrainer(object):
   """
@@ -76,7 +77,9 @@ class ConditionalGANTrainer(object):
       input_generator_examples[k] = torch.cat((noise[k], one_hot[k]), 0) 
     self.fixed_noise = torch.FloatTensor(conditional_dim, noise_dim + conditional_dim, 1, 1)
     self.fixed_noise.resize_(self.conditional_dim, (self.noise_dim + self.conditional_dim), 1, 1).copy_(input_generator_examples)
-   
+    self.fixed_noise_v = Variable(self.fixed_noise)
+  
+
     # binary cross-entropy loss
     self.criterion = nn.BCELoss()
 
@@ -86,9 +89,6 @@ class ConditionalGANTrainer(object):
       self.netG.cuda()
       self.criterion.cuda()
       self_fixed_noise = self.fixed_noise.cuda()
-    
-    self.fixed_noise_v = Variable(self.fixed_noise)
-    if self.use_gpu:
       self.fixed_noise_v = self.fixed_noise_v.cuda() 
 
     bob.core.log.set_verbosity_level(logger, verbosity_level)
@@ -122,8 +122,6 @@ class ConditionalGANTrainer(object):
     optimizerD = optim.Adam(self.netD.parameters(), lr=learning_rate, betas=(beta1, 0.999))
     optimizerG = optim.Adam(self.netG.parameters(), lr=learning_rate, betas=(beta1, 0.999))
     
-
-
     for epoch in range(n_epochs):
       for i, data in enumerate(dataloader, 0):
      
@@ -174,10 +172,9 @@ class ConditionalGANTrainer(object):
         #pyplot.show()
 
         labelv = Variable(label)
-        output = self.netD(inputv)
-        errD_real = self.criterion(output, labelv)
+        output_real = self.netD(inputv)
+        errD_real = self.criterion(output_real, labelv)
         errD_real.backward()
-        D_x = output.data.mean()
 
         # === FAKE DATA ===
         
@@ -212,6 +209,11 @@ class ConditionalGANTrainer(object):
           fake_images = fake.data
           fake = fake.cuda()
 
+        #from matplotlib import pyplot
+        #first_fake = (fake_images[0].numpy() + 1)/2.
+        #pyplot.imshow(numpy.rollaxis(numpy.rollaxis(first_fake, 2),2))
+        #pyplot.show()
+
         input_discriminator_fake = torch.FloatTensor(batch_size, (self.image_size[0] + self.conditional_dim), self.image_size[1], self.image_size[2])
         for k in range(batch_size):
           input_discriminator_fake[k] = torch.cat((fake_images[k], conditional_maps[k]), 0) 
@@ -222,11 +224,10 @@ class ConditionalGANTrainer(object):
         # train with fake
         fake_input_v = Variable(input_discriminator_fake)
         labelv = Variable(label.fill_(fake_label))
-        output = self.netD(fake_input_v)
-        errD_fake = self.criterion(output, labelv)
+        output_fake = self.netD(fake_input_v)
+        errD_fake = self.criterion(output_fake, labelv)
         errD_fake.backward()
         errD = errD_real + errD_fake
-        #print errD_fake.grad_fn.next_functions[0][0]
         
         # perform optimization (i.e. update discriminator parameters)
         optimizerD.step()

bob/learn/pytorch/trainers/DCGANTrainer.py

@@ -124,7 +124,7 @@ class DCGANTrainer(object):
         noisev = Variable(self.noise)
         fake = self.netG(noisev)
         labelv = Variable(self.label.fill_(fake_label))
-        output = self.netD(fake.detach())
+        output = self.netD(fake.detach()) # detach() -> done for speed, not correctness (PyTorch github's issue says so ...)
         errD_fake = self.criterion(output, labelv)
         errD_fake.backward()
         D_G_z1 = output.data.mean()