Fix predict test cases
JIRA: MADLIB-1438
Removed the mocking of keras sessions in predict since they would globally mock the set_session and clear_session functions which made our
fit/eval transition test cases fail.
This wasn't an issue before because fit/eval were also mocking these
functions but now we don't need to mock them anywhere.
Also fixed test_predict_error_should_clear_sd by making it fail after we
populate SD. Previously it was failing before SD was set. Also setting
normalizing_const to 0 doesn't make it fail so we set current_seg_id to
-1 to inject failure
Co-authored-by: Ekta Khanna <ekhanna@vmware.com>
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
index 7cdd83c..8d67c09 100644
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
+++ b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
@@ -776,9 +776,6 @@
self.independent_var = [[[240]]]
self.total_images_per_seg = [3,3,4]
- self.subject.K.set_session = Mock()
- self.subject.clear_keras_session = Mock()
-
def tearDown(self):
self.module_patcher.stop()
@@ -789,7 +786,6 @@
serialized_weights = np.array(model_weights, dtype=np.float32).tostring()
k = {'SD': {}}
- is_response = True
result = self.subject.internal_keras_predict(
self.independent_var, self.model.to_json(),
serialized_weights, 255, 0, self.all_seg_ids,
@@ -803,7 +799,6 @@
k = {'SD': { 'row_count': 1}}
k['SD']['segment_model_predict'] = self.model
- is_response = True
result = self.subject.internal_keras_predict(
self.independent_var, None, None, 255, 0,
self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
@@ -817,17 +812,6 @@
k = {'SD': { 'row_count': 2}}
k['SD']['segment_model_predict'] = self.model
- is_response = True
- result = self.subject.internal_keras_predict(
- self.independent_var, None, None, 255, 0,
- self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
- self.assertEqual(3, len(result))
- self.assertEqual(False, 'row_count' in k['SD'])
- self.assertEqual(False, 'segment_model_predict' in k['SD'])
-
- k = {'SD': { 'row_count': 2}}
- k['SD']['segment_model_predict'] = self.model
- is_response = False
result = self.subject.internal_keras_predict(
self.independent_var, None, None, 255, 0,
self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
@@ -838,20 +822,21 @@
self.assertEqual(False, 'row_count' in k['SD'])
self.assertEqual(False, 'segment_model_predict' in k['SD'])
-
def test_predict_error_should_clear_sd(self):
self.subject.is_platform_pg = Mock(return_value = False)
- self.model.add(Dense(3))
+ # self.model.add(Dense(3))
+ model_weights = [1, 2, 3, 4]
+ serialized_weights = np.array(model_weights, dtype=np.float32).tostring()
- # inject error by passing 0 as the normalizing const so that we get a
- # divide by zero error
- normalizing_const = 0
+ # inject error by passing current_seg_id as -1
+ current_seg_id = -1
k = {'SD':{}}
- is_response = True
- with self.assertRaises(plpy.PLPYException):
+ with self.assertRaises(plpy.PLPYException) as error:
self.subject.internal_keras_predict(
- self.independent_var, None, None, normalizing_const,
- 0, self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
+ self.independent_var, self.model.to_json(), serialized_weights,
+ 255, current_seg_id, self.all_seg_ids,
+ self.total_images_per_seg, False, 0, 4, **k)
+ self.assertEqual("ValueError('-1 is not in list',)", str(error.exception))
self.assertEqual(False, 'row_count' in k['SD'])
self.assertEqual(False, 'segment_model_predict' in k['SD'])
@@ -1447,6 +1432,7 @@
obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1,0,0,1])
self.assertIn('does not have gpu', str(error.exception).lower())
+
class MadlibSerializerTestCase(unittest.TestCase):
def setUp(self):
self.plpy_mock = Mock(spec='error')
