Rename files

tests/test_blocks/test_spectral_convolution.py

@@ -0,0 +1,84 @@
+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])