Merge branch 'unit-tests-for-models' into 'NIFTy_5'

Unit tests for models

See merge request ift/nifty-dev!65

nifty5/extra/energy_and_model_tests.py

@@ -33,7 +33,10 @@ def _get_acceptable_model(M):
         raise ValueError('Initial Model value must be finite')
     dir = from_random("normal", M.position.domain)
     dirder = M.jacobian(dir)
-    dir = dir * val * (1e-5/dirder.norm())
+    if dirder.norm() == 0:
+        dir = dir * val * 1e-5
+    else:
+        dir = dir * val * (1e-5/dirder.norm())
     # Find a step length that leads to a "reasonable" Model
     for i in range(50):
         try:
@@ -88,11 +91,11 @@ def check_value_gradient_consistency(E, tol=1e-8, ntries=100):
             numgrad = (E2.value-val)/dirnorm
             if isinstance(E, Model):
                 xtol = tol * dirder.norm() / np.sqrt(dirder.size)
-                if (abs(numgrad-dirder) < xtol).all():
+                if (abs(numgrad-dirder) <= xtol).all():
                     break
             else:
                 xtol = tol*Emid.gradient_norm
-                if abs(numgrad-dirder) < xtol:
+                if abs(numgrad-dirder) <= xtol:
                     break
             dir = dir*0.5
             dirnorm *= 0.5

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)