Adding ConvRBM tests.

tests/learnergy/models/binary/test_conv_rbm.py

@@ -0,0 +1,334 @@
+import pytest
+import torch
+import torchvision
+
+from learnergy.models.binary import conv_rbm
+
+
+def test_conv_rbm_visible_shape():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.visible_shape == (28, 28)
+
+
+def test_conv_rbm_visible_shape_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.visible_shape = 'a'
+    except:
+        new_conv_rbm.visible_shape = (28, 28)
+
+    assert new_conv_rbm.visible_shape == (28, 28)
+
+
+def test_conv_rbm_filter_shape():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.filter_shape == (7, 7)
+
+
+def test_conv_rbm_filter_shape_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.filter_shape = 'a'
+    except:
+        new_conv_rbm.filter_shape = (7, 7)
+
+    assert new_conv_rbm.filter_shape == (7, 7)
+
+
+def test_conv_rbm_hidden_shape():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.hidden_shape == (22, 22)
+
+
+def test_conv_rbm_hidden_shape_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.hidden_shape = 'a'
+    except:
+        new_conv_rbm.hidden_shape = (22, 22)
+
+    assert new_conv_rbm.hidden_shape == (22, 22)
+
+
+def test_conv_rbm_n_filters():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.n_filters == 5
+
+
+def test_conv_rbm_n_filters_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.n_filters = 'a'
+    except:
+        new_conv_rbm.n_filters = 1
+
+    assert new_conv_rbm.n_filters == 1
+
+    try:
+        new_conv_rbm.n_filters = 0
+    except:
+        new_conv_rbm.n_filters = 1
+
+    assert new_conv_rbm.n_filters == 1
+
+
+def test_conv_rbm_n_channels():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.n_channels == 1
+
+
+def test_conv_rbm_n_channels_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.n_channels = 'a'
+    except:
+        new_conv_rbm.n_channels = 1
+
+    assert new_conv_rbm.n_channels == 1
+
+    try:
+        new_conv_rbm.n_channels = 0
+    except:
+        new_conv_rbm.n_channels = 1
+
+    assert new_conv_rbm.n_channels == 1
+
+
+def test_conv_rbm_steps():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.steps == 1
+
+
+def test_conv_rbm_steps_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.steps = 'a'
+    except:
+        new_conv_rbm.steps = 1
+
+    assert new_conv_rbm.steps == 1
+
+    try:
+        new_conv_rbm.steps = 0
+    except:
+        new_conv_rbm.steps = 1
+
+    assert new_conv_rbm.steps == 1
+
+
+def test_conv_rbm_lr():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.lr == 0.1
+
+
+def test_conv_rbm_lr_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.lr = 'a'
+    except:
+        new_conv_rbm.lr = 0.1
+
+    assert new_conv_rbm.lr == 0.1
+
+    try:
+        new_conv_rbm.lr = -1
+    except:
+        new_conv_rbm.lr = 0.1
+
+    assert new_conv_rbm.lr == 0.1
+
+
+def test_conv_rbm_momentum():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.momentum == 0
+
+
+def test_conv_rbm_momentum_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.momentum = 'a'
+    except:
+        new_conv_rbm.momentum = 0.1
+
+    assert new_conv_rbm.momentum == 0.1
+
+    try:
+        new_conv_rbm.momentum = -1
+    except:
+        new_conv_rbm.momentum = 0.1
+
+    assert new_conv_rbm.momentum == 0.1
+
+
+def test_conv_rbm_decay():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.decay == 0
+
+
+def test_conv_rbm_decay_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.decay = 'a'
+    except:
+        new_conv_rbm.decay = 0.1
+
+    assert new_conv_rbm.decay == 0.1
+
+    try:
+        new_conv_rbm.decay = -1
+    except:
+        new_conv_rbm.decay = 0.1
+
+    assert new_conv_rbm.decay == 0.1
+
+
+def test_conv_rbm_W():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.W.size(0) == 5
+    assert new_conv_rbm.W.size(1) == 1
+    assert new_conv_rbm.W.size(2) == 7
+    assert new_conv_rbm.W.size(3) == 7
+
+
+def test_conv_rbm_W_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.W = 1
+    except:
+        new_conv_rbm.W = torch.nn.Parameter(torch.randn(5, 1, 7, 7) * 0.01)
+
+    assert new_conv_rbm.W.size(0) == 5
+    assert new_conv_rbm.W.size(1) == 1
+    assert new_conv_rbm.W.size(2) == 7
+    assert new_conv_rbm.W.size(3) == 7
+
+
+def test_conv_rbm_a():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.a.size(0) == 1
+
+
+def test_conv_rbm_a_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.a = 1
+    except:
+        new_conv_rbm.a = torch.nn.Parameter(torch.zeros(1))
+
+    assert new_conv_rbm.a.size(0) == 1
+
+
+def test_conv_rbm_b():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert new_conv_rbm.b.size(0) == 5
+
+
+def test_conv_rbm_b_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.b = 1
+    except:
+        new_conv_rbm.b = torch.nn.Parameter(torch.zeros(5))
+
+    assert new_conv_rbm.b.size(0) == 5
+
+
+def test_conv_rbm_optimizer():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    assert type(new_conv_rbm.optimizer).__name__ == 'SGD'
+
+
+def test_conv_rbm_optimizer_setter():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    try:
+        new_conv_rbm.optimizer = 'OPT'
+    except:
+        new_conv_rbm.optimizer = torch.optim.SGD(
+            new_conv_rbm.parameters(), lr=0.1)
+
+    assert type(new_conv_rbm.optimizer).__name__ == 'SGD'
+
+
+def test_conv_rbm_hidden_sampling():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    v = torch.ones(5, 1, 7, 7)
+
+    probs, states = new_conv_rbm.hidden_sampling(v)
+
+    assert probs.size(1) == 5
+    assert states.size(1) == 5
+
+
+def test_conv_rbm_visible_sampling():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    h = torch.ones(1, 5, 7, 7)
+
+    probs, states = new_conv_rbm.visible_sampling(h)
+
+    assert probs.size(1) == 1
+    assert states.size(1) == 1
+
+
+def test_conv_rbm_energy():
+    new_conv_rbm = conv_rbm.ConvRBM()
+
+    samples = torch.ones(5, 1, 7, 7)
+
+    energy = new_conv_rbm.energy(samples)
+
+    assert torch.mean(energy).detach().numpy() < 0
+
+
+def test_conv_rbm_fit():
+    train = torchvision.datasets.MNIST(
+        root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor())
+
+    new_conv_rbm = conv_rbm.ConvRBM(visible_shape=(28, 28), filter_shape=(1, 1), n_filters=1, n_channels=1,
+                                    steps=1, learning_rate=0.01, momentum=0, decay=0, use_gpu=True)
+
+    e = new_conv_rbm.fit(train, batch_size=128, epochs=1)
+
+    assert e >= 0
+
+
+def test_conv_rbm_reconstruct():
+    test = torchvision.datasets.MNIST(
+        root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())
+
+    new_conv_rbm = conv_rbm.ConvRBM(visible_shape=(28, 28), filter_shape=(1, 1), n_filters=1, n_channels=1,
+                                    steps=1, learning_rate=0.01, momentum=0, decay=0, use_gpu=True)
+
+    e, v = new_conv_rbm.reconstruct(test)
+
+    assert e >= 0
+    assert v.size(1) == 1
+    assert v.size(2) == 28
+    assert v.size(3) == 28

tests/learnergy/models/binary/test_rbm.py

@@ -212,7 +212,7 @@ def test_rbm_a_setter():
 def test_rbm_b():
     new_rbm = rbm.RBM()
 
-    assert new_rbm.a.size(0) == 128
+    assert new_rbm.b.size(0) == 128
 
 
 def test_rbm_b_setter():