Test models: WIP consistency check

* fails for constant models

test/test_models/test_model_gradients.py

@@ -25,15 +25,70 @@ import numpy as np
 
 
 class Model_Tests(unittest.TestCase):
-    @expand(product([ift.GLSpace(15),
-                     ift.RGSpace(64, distances=.789),
-                     ift.RGSpace([32, 32], distances=.789)],
-                    [4, 78, 23]))
-    def testMul(self, space, seed):
-        np.random.seed(seed)
-        S = ift.ScalingOperator(1., space)
-        s1 = S.draw_sample()
-        s2 = S.draw_sample()
-        s1_var = ift.Variable(ift.MultiField.from_dict({'s1': s1}))['s1']
-        s2_var = ift.Variable(ift.MultiField.from_dict({'s2': s2}))['s2']
-        ift.extra.check_value_gradient_consistency(s1_var*s2_var)
+    def make_model(self, type, **kwargs):
+        if type == 'Constant':
+            np.random.seed(kwargs['seed'])
+            S = ift.ScalingOperator(1., kwargs['space'])
+            s = S.draw_sample()
+            return ift.Constant(
+                ift.MultiField.from_dict({kwargs['space_key']: s}),
+                ift.MultiField.from_dict({kwargs['space_key']: s}))
+        elif type == 'Variable':
+            np.random.seed(kwargs['seed'])
+            S = ift.ScalingOperator(1., kwargs['space'])
+            s = S.draw_sample()
+            return ift.Variable(
+                ift.MultiField.from_dict({kwargs['space_key']: s}))
+        elif type == 'LinearModel':
+            return ift.LinearModel(
+                inp=kwargs['model'], lin_op=kwargs['lin_op'])
+        else:
+            raise ValueError('unknown type passed')
+
+    def make_linear_operator(self, type, **kwargs):
+        if type == 'ScalingOperator':
+            lin_op = ift.ScalingOperator(1., kwargs['space'])
+        else:
+            raise ValueError('unknown type passed')
+        return lin_op
+
+    @expand(product(
+        ['Variable', 'Constant'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testBasics(self, type1, space, seed):
+        model1 = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        ift.extra.check_value_gradient_consistency(model1)
+
+    @expand(product(
+        ['Variable', 'Constant'],
+        ['Variable'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testMul(self, type1, type2, space, seed):
+        model1 = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        model2 = self.make_model(
+            type2, space_key='s2', space=space, seed=seed+1)['s2']
+        ift.extra.check_value_gradient_consistency(model1*model2)
+
+    @expand(product(
+        ['Variable', 'Constant'],
+        [ift.GLSpace(15),
+         ift.RGSpace(64, distances=.789),
+         ift.RGSpace([32, 32], distances=.789)],
+        [4, 78, 23]
+        ))
+    def testLinModel(self, type1, space, seed):
+        model1 = self.make_model(
+            type1, space_key='s1', space=space, seed=seed)['s1']
+        lin_op = self.make_linear_operator('ScalingOperator', space=space)
+        model2 = self.make_model('LinearModel', model=model1, lin_op=lin_op)
+        ift.extra.check_value_gradient_consistency(model1*model2)