add models and layers backward test

tests/test_model/test_mionet.py

@@ -36,6 +36,22 @@ def test_forward_extract_str():
     model(input_)
 
 
+def test_backward_extract_str():
+    data = torch.rand((20, 3))
+    data.requires_grad = True
+    input_vars = ['a', 'b', 'c']
+    input_ = LabelTensor(data, input_vars)
+    branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
+    networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']}
+    model = MIONet(networks=networks, reduction='+', aggregator='*')
+    model(input_)
+    l = torch.mean(model(input_))
+    l.backward()
+    assert data._grad.shape == torch.Size([20,3])
+
+
 def test_forward_extract_int():
     branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
     branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
@@ -45,6 +61,20 @@ def test_forward_extract_int():
     model(data)
 
 
+def test_backward_extract_int():
+    data = torch.rand((20, 3))
+    data.requires_grad = True
+    branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
+    networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]}
+    model = MIONet(networks=networks, reduction='+', aggregator='*')
+    model(data)
+    l = torch.mean(model(data))
+    l.backward()
+    assert data._grad.shape == torch.Size([20,3])
+
+
 def test_forward_extract_str_wrong():
     branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
     branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
@@ -53,3 +83,18 @@ def test_forward_extract_str_wrong():
     model = MIONet(networks=networks, reduction='+', aggregator='*')
     with pytest.raises(RuntimeError):
         model(data)
+
+
+def test_backward_extract_str_wrong():
+    data = torch.rand((20, 3))
+    data.requires_grad = True
+    branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
+    networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']}
+    model = MIONet(networks=networks, reduction='+', aggregator='*')
+    with pytest.raises(RuntimeError):
+        model(data)
+        l = torch.mean(model(data))
+        l.backward()
+        assert data._grad.shape == torch.Size([20,3])