[test] unit tests for Conditional GAN

bob/learn/pytorch/test/test.py

@@ -47,7 +47,28 @@ def test_architectures():
   generator = DCGAN_generator(1)
   output = generator.forward(t)
   assert output.shape == torch.Size([1, 3, 64, 64])
- 
+
+  # Conditional GAN
+  d = numpy.random.rand(1, 3, 64, 64).astype("float32")
+  t = torch.from_numpy(d)
+  cfm = numpy.zeros((1, 13, 64, 64), dtype="float32")
+  cfm[:, 0, :, :] = 1
+  cfmt = torch.from_numpy(cfm)
+  from ..architectures import ConditionalGAN_discriminator
+  discriminator = ConditionalGAN_discriminator(13)
+  output = discriminator.forward(t, cfmt)
+  assert output.shape == torch.Size([1])
+
+  g = numpy.random.rand(1, 100, 1, 1).astype("float32")
+  t = torch.from_numpy(g)
+  oh = numpy.zeros((1, 13, 1, 1), dtype="float32")
+  oh[0] = 1
+  oht = torch.from_numpy(oh)
+  from ..architectures import ConditionalGAN_generator
+  discriminator = ConditionalGAN_generator(100, 13)
+  output = discriminator.forward(t, oht)
+  assert output.shape == torch.Size([1, 3, 64, 64])
+
 
 def test_transforms():
 
@@ -149,3 +170,33 @@ def test_DCGANtrainer():
   os.remove('netD_epoch_0.pth')
   os.remove('netG_epoch_0.pth')
 
+class DummyDataSetConditionalGAN(Dataset):
+  def __init__(self):
+    pass
+  def __len__(self):
+    return 100
+  def __getitem__(self, idx):
+    data =  numpy.random.rand(3, 64, 64).astype("float32")
+    sample = {'image': torch.from_numpy(data), 'pose': numpy.random.randint(0, 13)}
+    return sample
+
+def test_ConditionalGANTrainer():
+
+  from ..architectures import ConditionalGAN_generator
+  from ..architectures import ConditionalGAN_discriminator
+  g = ConditionalGAN_generator(100, 13)
+  d = ConditionalGAN_discriminator(13)
+
+  dataloader = torch.utils.data.DataLoader(DummyDataSetConditionalGAN(), batch_size=32, shuffle=True)
+  
+  from ..trainers import ConditionalGANTrainer
+  trainer = ConditionalGANTrainer(g, d, [3, 64, 64], batch_size=32, noise_dim=100, conditional_dim=13)
+  trainer.train(dataloader, n_epochs=1, output_dir='.')
+  
+  import os
+  assert os.path.isfile('fake_samples_epoch_000.png')
+  assert os.path.isfile('netD_epoch_0.pth')
+  assert os.path.isfile('netG_epoch_0.pth')
+  os.remove('fake_samples_epoch_000.png')
+  os.remove('netD_epoch_0.pth')
+  os.remove('netG_epoch_0.pth')