[tests] removed MLP tests, moved Autoencoder architecture test

bob/learn/pytorch/test/test.py

@@ -43,8 +43,6 @@ def test_architectures():
   assert emdedding.shape == torch.Size([1, 256])
 
   # LightCNN29
-  a = numpy.random.rand(1, 1, 128, 128).astype("float32")
-  t = torch.from_numpy(a)
   from ..architectures import LightCNN29
   net = LightCNN29()
   output, emdedding = net.forward(t)
@@ -52,8 +50,6 @@ def test_architectures():
   assert emdedding.shape == torch.Size([1, 256])
 
   # LightCNN29v2
-  a = numpy.random.rand(1, 1, 128, 128).astype("float32")
-  t = torch.from_numpy(a)
   from ..architectures import LightCNN29v2
   net = LightCNN29v2()
   output, emdedding = net.forward(t)
@@ -69,8 +65,6 @@ def test_architectures():
   assert output.shape == torch.Size([1, 1])
 
   # MCCNNv2
-  a = numpy.random.rand(1, 4, 128, 128).astype("float32")
-  t = torch.from_numpy(a)
   from ..architectures import MCCNNv2
   net = MCCNNv2(num_channels=4)
   output = net.forward(t)
@@ -117,7 +111,6 @@ def test_architectures():
   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
@@ -127,7 +120,6 @@ def test_architectures():
   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
@@ -136,6 +128,16 @@ def test_architectures():
   discriminator = ConditionalGAN_generator(100, 13)
   output = discriminator.forward(t, oht)
   assert output.shape == torch.Size([1, 3, 64, 64])
+  
+  # Convolutional Autoencoder
+  from bob.learn.pytorch.architectures import ConvAutoencoder
+  batch = torch.randn(1, 3, 64, 64)
+  model = ConvAutoencoder()
+  output = model(batch)
+  assert batch.shape == output.shape
+  model_embeddings = ConvAutoencoder(return_latent_embedding = True)
+  embedding = model_embeddings(batch)
+  assert list(embedding.shape) == [1, 16, 5, 5]
 
 
 def test_transforms():
@@ -186,15 +188,11 @@ def test_map_labels():
   assert '0' in new_labels, "new_labels = {}".format(new_labels)
   assert '1' in new_labels, "new_labels = {}".format(new_labels)
   assert '2' in new_labels, "new_labels = {}".format(new_labels)
-  #new_labels = sorted(new_labels)
-  #assert new_labels == ['0', '1', '2']
 
   new_labels = map_labels(labels, start_index = 5)
-  #new_labels = sorted(new_labels)
   assert '5' in new_labels, "new_labels = {}".format(new_labels)
   assert '6' in new_labels, "new_labels = {}".format(new_labels)
   assert '7' in new_labels, "new_labels = {}".format(new_labels)
-  #assert new_labels == ['5', '6', '7']
 
 
 from torch.utils.data import Dataset
@@ -390,22 +388,6 @@ def test_ConditionalGANTrainer():
   os.remove('netG_epoch_0.pth')
 
 
-def test_conv_autoencoder():
-    """
-    Test the ConvAutoencoder class.
-    """
-    from bob.learn.pytorch.architectures import ConvAutoencoder
-
-    batch = torch.randn(1, 3, 64, 64)
-    model = ConvAutoencoder()
-    output = model(batch)
-    assert batch.shape == output.shape
-
-    model_embeddings = ConvAutoencoder(return_latent_embedding = True)
-    embedding = model_embeddings(batch)
-    assert list(embedding.shape) == [1, 16, 5, 5]
-
-
 def test_extractors():
 
   # lightCNN9