Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

tests/test_blocks/test_residual.py

@@ -0,0 +1,102 @@
+from pina.model.block import ResidualBlock, EnhancedLinear
+import torch
+import torch.nn as nn
+
+
+def test_constructor_residual_block():
+
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
+
+    res_block = ResidualBlock(input_dim=10,
+                              output_dim=3,
+                              hidden_dim=4,
+                              spectral_norm=True)
+
+
+def test_forward_residual_block():
+
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
+
+    x = torch.rand(size=(80, 10))
+    y = res_block(x)
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]
+
+def test_backward_residual_block():
+    
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = res_block(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80,10])
+
+def test_constructor_no_activation_no_dropout():
+    linear_layer = nn.Linear(10, 20)
+    enhanced_linear = EnhancedLinear(linear_layer)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
+
+def test_constructor_with_activation_no_dropout():
+    linear_layer = nn.Linear(10, 20)
+    activation = nn.ReLU()
+    enhanced_linear = EnhancedLinear(linear_layer, activation)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
+
+def test_constructor_no_activation_with_dropout():
+    linear_layer = nn.Linear(10, 20)
+    dropout_prob = 0.5
+    enhanced_linear = EnhancedLinear(linear_layer, dropout=dropout_prob)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
+
+def test_constructor_with_activation_with_dropout():
+    linear_layer = nn.Linear(10, 20)
+    activation = nn.ReLU()
+    dropout_prob = 0.5
+    enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
+
+def test_forward_enhanced_linear_no_dropout():
+
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
+
+    x = torch.rand(size=(80, 10))
+    y = enhanced_linear(x)
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]
+
+def test_backward_enhanced_linear_no_dropout():
+    
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = enhanced_linear(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80, 10])
+
+def test_forward_enhanced_linear_dropout():
+
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
+
+    x = torch.rand(size=(80, 10))
+    y = enhanced_linear(x)
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]
+
+def test_backward_enhanced_linear_dropout():
+    
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = enhanced_linear(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80, 10])