remove back compatibility files for version 0.2

tests/test_block/test_pirate_network_block.py

@@ -0,0 +1,53 @@
+import torch
+import pytest
+from pina.model.block import PirateNetBlock
+
+data = torch.rand((20, 3))
+
+
+@pytest.mark.parametrize("inner_size", [10, 20])
+def test_constructor(inner_size):
+
+    PirateNetBlock(inner_size=inner_size, activation=torch.nn.Tanh)
+
+    # Should fail if inner_size is negative
+    with pytest.raises(AssertionError):
+        PirateNetBlock(inner_size=-1, activation=torch.nn.Tanh)
+
+
+@pytest.mark.parametrize("inner_size", [10, 20])
+def test_forward(inner_size):
+
+    model = PirateNetBlock(inner_size=inner_size, activation=torch.nn.Tanh)
+
+    # Create dummy embedding
+    dummy_embedding = torch.nn.Linear(data.shape[1], inner_size)
+    x = dummy_embedding(data)
+
+    # Create dummy U and V tensors
+    U = torch.rand((data.shape[0], inner_size))
+    V = torch.rand((data.shape[0], inner_size))
+
+    output_ = model(x, U, V)
+    assert output_.shape == (data.shape[0], inner_size)
+
+
+@pytest.mark.parametrize("inner_size", [10, 20])
+def test_backward(inner_size):
+
+    model = PirateNetBlock(inner_size=inner_size, activation=torch.nn.Tanh)
+    data.requires_grad_()
+
+    # Create dummy embedding
+    dummy_embedding = torch.nn.Linear(data.shape[1], inner_size)
+    x = dummy_embedding(data)
+
+    # Create dummy U and V tensors
+    U = torch.rand((data.shape[0], inner_size))
+    V = torch.rand((data.shape[0], inner_size))
+
+    output_ = model(x, U, V)
+
+    loss = torch.mean(output_)
+    loss.backward()
+    assert data.grad.shape == data.shape