renaming

tests/test_model/test_kernel_neural_operator.py

@@ -0,0 +1,40 @@
+import torch
+from pina.model import KernelNeuralOperator, FeedForward
+
+input_dim = 2
+output_dim = 4
+embedding_dim = 24
+batch_size = 10
+numb = 256
+data = torch.rand(size=(batch_size, numb, input_dim), requires_grad=True)
+output_shape = torch.Size([batch_size, numb, output_dim])
+
+
+lifting_operator = FeedForward(input_dimensions=input_dim, output_dimensions=embedding_dim)
+projection_operator = FeedForward(input_dimensions=embedding_dim, output_dimensions=output_dim)
+integral_kernels = torch.nn.Sequential(FeedForward(input_dimensions=embedding_dim, 
+                                                   output_dimensions=embedding_dim),
+                                       FeedForward(input_dimensions=embedding_dim, 
+                                                   output_dimensions=embedding_dim),)
+
+def test_constructor():
+    KernelNeuralOperator(lifting_operator=lifting_operator,
+                         integral_kernels=integral_kernels,
+                         projection_operator=projection_operator)
+    
+def test_forward():
+    operator = KernelNeuralOperator(lifting_operator=lifting_operator,
+                         integral_kernels=integral_kernels,
+                         projection_operator=projection_operator)
+    out = operator(data)
+    assert out.shape == output_shape
+
+def test_backward():
+    operator = KernelNeuralOperator(lifting_operator=lifting_operator,
+                         integral_kernels=integral_kernels,
+                         projection_operator=projection_operator)
+    out = operator(data)
+    loss = torch.nn.functional.mse_loss(out, torch.zeros_like(out))
+    loss.backward()
+    grad = data.grad
+    assert grad.shape == data.shape