remove back compatibility files for version 0.2

tests/test_block/test_low_rank_block.py

@@ -0,0 +1,70 @@
+import torch
+import pytest
+
+from pina.model.block import LowRankBlock
+from pina import LabelTensor
+
+
+input_dimensions = 2
+embedding_dimenion = 1
+rank = 4
+inner_size = 20
+n_layers = 2
+func = torch.nn.Tanh
+bias = True
+
+
+def test_constructor():
+    LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+
+
+def test_constructor_wrong():
+    with pytest.raises(ValueError):
+        LowRankBlock(
+            input_dimensions=input_dimensions,
+            embedding_dimenion=embedding_dimenion,
+            rank=0.5,
+            inner_size=inner_size,
+            n_layers=n_layers,
+            func=func,
+            bias=bias,
+        )
+
+
+def test_forward():
+    block = LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+    data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"])
+    block(data.extract("u"), data.extract(["x", "y"]))
+
+
+def test_backward():
+    block = LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+    data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"])
+    data.requires_grad_(True)
+    out = block(data.extract("u"), data.extract(["x", "y"]))
+    loss = out.mean()
+    loss.backward()