remove back compatibility files for version 0.2

tests/test_block/test_spectral_convolution.py

@@ -0,0 +1,106 @@
+from pina.model.block import (
+    SpectralConvBlock1D,
+    SpectralConvBlock2D,
+    SpectralConvBlock3D,
+)
+import torch
+
+input_numb_fields = 3
+output_numb_fields = 4
+batch = 5
+
+
+def test_constructor_1d():
+    SpectralConvBlock1D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=5,
+    )
+
+
+def test_forward_1d():
+    sconv = SpectralConvBlock1D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=4,
+    )
+    x = torch.rand(batch, input_numb_fields, 10)
+    sconv(x)
+
+
+def test_backward_1d():
+    sconv = SpectralConvBlock1D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=4,
+    )
+    x = torch.rand(batch, input_numb_fields, 10)
+    x.requires_grad = True
+    sconv(x)
+    l = torch.mean(sconv(x))
+    l.backward()
+    assert x._grad.shape == torch.Size([5, 3, 10])
+
+
+def test_constructor_2d():
+    SpectralConvBlock2D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4],
+    )
+
+
+def test_forward_2d():
+    sconv = SpectralConvBlock2D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4],
+    )
+    x = torch.rand(batch, input_numb_fields, 10, 10)
+    sconv(x)
+
+
+def test_backward_2d():
+    sconv = SpectralConvBlock2D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4],
+    )
+    x = torch.rand(batch, input_numb_fields, 10, 10)
+    x.requires_grad = True
+    sconv(x)
+    l = torch.mean(sconv(x))
+    l.backward()
+    assert x._grad.shape == torch.Size([5, 3, 10, 10])
+
+
+def test_constructor_3d():
+    SpectralConvBlock3D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4, 4],
+    )
+
+
+def test_forward_3d():
+    sconv = SpectralConvBlock3D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4, 4],
+    )
+    x = torch.rand(batch, input_numb_fields, 10, 10, 10)
+    sconv(x)
+
+
+def test_backward_3d():
+    sconv = SpectralConvBlock3D(
+        input_numb_fields=input_numb_fields,
+        output_numb_fields=output_numb_fields,
+        n_modes=[5, 4, 4],
+    )
+    x = torch.rand(batch, input_numb_fields, 10, 10, 10)
+    x.requires_grad = True
+    sconv(x)
+    l = torch.mean(sconv(x))
+    l.backward()
+    assert x._grad.shape == torch.Size([5, 3, 10, 10, 10])