Check for statically known rank

Parametrize tests

Author: nicolaspi

tensorflow_probability/python/stats/sample_stats.py

@@ -915,6 +915,12 @@ def _prepare_window_args(x, low_indices=None, high_indices=None, axis=0):
     low_indices = high_indices // 2
   else:
     low_indices = tf.convert_to_tensor(low_indices)
+
+  indices_rank = tf.get_static_value(ps.rank(low_indices))
+  x_rank = tf.get_static_value(ps.rank(x))
+  if indices_rank is None or x_rank is None:
+    raise ValueError("`indices` and `x` ranks must be statically known.")
+
   # Broadcast indices together.
   high_indices = high_indices + tf.zeros_like(low_indices)
   low_indices = low_indices + tf.zeros_like(high_indices)

tensorflow_probability/python/stats/sample_stats_test.py

@@ -15,10 +15,14 @@
 """Tests for Sample Stats Ops."""
 
 # Dependency imports
-import functools
+import itertools
+
 import numpy as np
 import tensorflow.compat.v1 as tf1
 import tensorflow.compat.v2 as tf
+from absl.testing import parameterized
+from tensorflow.python.framework.errors_impl import InvalidArgumentError
+
 from tensorflow_probability.python.internal import test_util
 from tensorflow_probability.python.stats import sample_stats
 
@@ -721,7 +725,8 @@ def apply_func(vector, l, h):
     out = np.transpose(t_out, axes=dims)
     return out
 
-  def check_gaussian_windowed(self, shape, indice_shape, axis,
+
+  def check_gaussian_windowed_func(self, shape, indice_shape, axis,
                               window_func, np_func):
     stat_shape = np.array(shape).astype(np.int32)
     stat_shape[axis] = 1
@@ -753,51 +758,56 @@ def check_gaussian_windowed(self, shape, indice_shape, axis,
   def _make_dynamic_shape(self, x):
     return tf1.placeholder_with_default(x, shape=(None,)*len(x.shape))
 
-  def check_windowed(self, func, numpy_func):
-    check_fn = functools.partial(self.check_gaussian_windowed,
-                                 window_func=func, np_func=numpy_func)
-    check_fn((64, 4, 8), (128, 1, 1), axis=0)
-    check_fn((64, 4, 8), (32, 1, 1), axis=0)
-    check_fn((64, 4, 8), (32, 4, 1), axis=0)
-    check_fn((64, 4, 8), (32, 4, 8), axis=0)
-    check_fn((64, 4, 8), (64, 4, 8), axis=0)
-    check_fn((64, 4, 8), (128, 1), axis=0)
-    check_fn((64, 4, 8), (32,), axis=0)
-    check_fn((64, 4, 8), (32, 4), axis=0)
-
-    check_fn((64, 4, 8), (64, 64, 1), axis=1)
-    check_fn((64, 4, 8), (1, 64, 1), axis=1)
-    check_fn((64, 4, 8), (64, 2, 8), axis=1)
-    check_fn((64, 4, 8), (64, 4, 8), axis=1)
-    check_fn((64, 4, 8), (16,), axis=1)
-    check_fn((64, 4, 8), (1, 64), axis=1)
-
-    check_fn((64, 4, 8), (64, 4, 64), axis=2)
-    check_fn((64, 4, 8), (1, 1, 64), axis=2)
-    check_fn((64, 4, 8), (64, 4, 4), axis=2)
-    check_fn((64, 4, 8), (1, 1, 4), axis=2)
-    check_fn((64, 4, 8), (64, 4, 8), axis=2)
-    check_fn((64, 4, 8), (16,), axis=2)
-    check_fn((64, 4, 8), (1, 4), axis=2)
-    check_fn((64, 4, 8), (64, 4), axis=2)
-
-    with self.assertRaises(Exception):
-      # Non broadcastable shapes
-      check_fn((64, 4, 8), (4, 1, 4), axis=2)
-
-    with self.assertRaises(Exception):
-      # Non broadcastable shapes
-      check_fn((64, 4, 8), (2, 4), axis=2)
-
-  def test_windowed_mean(self):
-    self.check_windowed(func=sample_stats.windowed_mean, numpy_func=np.mean)
-
-  def test_windowed_mean_graph(self):
-    func = tf.function(sample_stats.windowed_mean)
-    self.check_windowed(func=func, numpy_func=np.mean)
-
-  def test_windowed_variance(self):
-    self.check_windowed(func=sample_stats.windowed_variance, numpy_func=np.var)
+  @parameterized.named_parameters(*[(
+    f"{np_func.__name__} shape={a} indices_shape={b} axis={axis}", a, b, axis,
+    tf_func, np_func) for a, (b, axis), (tf_func, np_func) in
+    itertools.product([(64, 4, 8), ],
+                      [((128, 1, 1), 0),
+                       ((32, 1, 1), 0),
+                       ((32, 4, 1), 0),
+                       ((32, 4, 8), 0),
+                       ((64, 4, 8), 0),
+                       ((128, 1), 0),
+                       ((32,), 0),
+                       ((32, 4), 0),
+
+                       ((64, 64, 1), 1),
+                       ((1, 64, 1), 1),
+                       ((64, 2, 8), 1),
+                       ((64, 4, 8), 1),
+                       ((16,), 1),
+                       ((1, 64), 1),
+
+                       ((64, 4, 64), 2),
+                       ((1, 1, 64), 2),
+                       ((64, 4, 4), 2),
+                       ((1, 1, 4), 2),
+                       ((64, 4, 8), 2),
+                       ((16,), 2),
+                       ((1, 4), 2),
+                       ((64, 4), 2)],
+                      [
+                        (sample_stats.windowed_mean, np.mean),
+                        (sample_stats.windowed_variance, np.var)
+                      ])])
+  def test_windowed(self, shape, indice_shape, axis, window_func, np_func):
+    self.check_gaussian_windowed_func(shape, indice_shape, axis, window_func,
+                                      np_func)
+
+
+  @parameterized.named_parameters(*[(
+    f"{np_func.__name__} shape={a} indices_shape={b} axis={axis}", a, b, axis,
+    tf_func, np_func) for a, (b, axis), (tf_func, np_func) in
+    itertools.product([(64, 4, 8), ],
+      [((4, 1, 4), 2), ((2, 4), 2)],
+      [(sample_stats.windowed_mean, np.mean),
+       (sample_stats.windowed_variance, np.var)])])
+  def test_non_broadcastable_shapes(self, shape, indice_shape, axis,
+                                    window_func, np_func):
+    with self.assertRaisesRegexp((IndexError, ValueError, InvalidArgumentError),
+                                 '^shape mismatch|Incompatible shapes'):
+      self.check_gaussian_windowed_func(shape, indice_shape, axis, window_func,
+                                        np_func)
 
 
 @test_util.test_all_tf_execution_regimes