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Use eager-friendly evaluation for bincount_op_test.

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PiperOrigin-RevId: 236690668
This commit is contained in:
Martin Wicke 2019-03-04 11:23:57 -08:00 committed by TensorFlower Gardener
parent 6163623e0e
commit 70438aaa2b
1 changed files with 36 additions and 32 deletions
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68
tensorflow/python/kernel_tests/bincount_op_test.py
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@ -30,44 +30,48 @@ from tensorflow.python.platform import googletest
class BincountTest(test_util.TensorFlowTestCase): class BincountTest(test_util.TensorFlowTestCase):
@test_util.run_deprecated_v1
def test_empty(self): def test_empty(self):
with self.session(use_gpu=True): with self.session(use_gpu=True):
self.assertAllEqual( self.assertAllEqual(self.evaluate(math_ops.bincount([], minlength=5)),
math_ops.bincount([], minlength=5).eval(), [0, 0, 0, 0, 0]) [0, 0, 0, 0, 0])
self.assertAllEqual(math_ops.bincount([], minlength=1).eval(), [0]) self.assertAllEqual(self.evaluate(math_ops.bincount([], minlength=1)),
self.assertAllEqual(math_ops.bincount([], minlength=0).eval(), []) [0])
self.assertEqual( self.assertAllEqual(self.evaluate(math_ops.bincount([], minlength=0)),
math_ops.bincount([], minlength=0, dtype=np.float32).eval().dtype, [])
np.float32) self.assertEqual(self.evaluate(math_ops.bincount([], minlength=0,
self.assertEqual( dtype=np.float32)).dtype,
math_ops.bincount([], minlength=3, dtype=np.float64).eval().dtype, np.float32)
np.float64) self.assertEqual(self.evaluate(math_ops.bincount([], minlength=3,
dtype=np.float64)).dtype,
np.float64)
@test_util.run_deprecated_v1
def test_values(self): def test_values(self):
with self.session(use_gpu=True): with self.session(use_gpu=True):
self.assertAllEqual( self.assertAllEqual(self.evaluate(math_ops.bincount([1, 1, 1, 2, 2, 3])),
math_ops.bincount([1, 1, 1, 2, 2, 3]).eval(), [0, 3, 2, 1]) [0, 3, 2, 1])
arr = [1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5] arr = [1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5]
self.assertAllEqual(math_ops.bincount(arr).eval(), [0, 5, 4, 3, 2, 1]) self.assertAllEqual(self.evaluate(math_ops.bincount(arr)),
[0, 5, 4, 3, 2, 1])
arr += [0, 0, 0, 0, 0, 0] arr += [0, 0, 0, 0, 0, 0]
self.assertAllEqual(math_ops.bincount(arr).eval(), [6, 5, 4, 3, 2, 1]) self.assertAllEqual(self.evaluate(math_ops.bincount(arr)),
[6, 5, 4, 3, 2, 1])
self.assertAllEqual(math_ops.bincount([]).eval(), []) self.assertAllEqual(self.evaluate(math_ops.bincount([])), [])
self.assertAllEqual(math_ops.bincount([0, 0, 0]).eval(), [3]) self.assertAllEqual(self.evaluate(math_ops.bincount([0, 0, 0])), [3])
self.assertAllEqual(math_ops.bincount([5]).eval(), [0, 0, 0, 0, 0, 1]) self.assertAllEqual(self.evaluate(math_ops.bincount([5])),
self.assertAllEqual( [0, 0, 0, 0, 0, 1])
math_ops.bincount(np.arange(10000)).eval(), np.ones(10000)) self.assertAllEqual(self.evaluate(math_ops.bincount(np.arange(10000))),
np.ones(10000))
@test_util.run_deprecated_v1
def test_maxlength(self): def test_maxlength(self):
with self.session(use_gpu=True): with self.session(use_gpu=True):
self.assertAllEqual(math_ops.bincount([5], maxlength=3).eval(), [0, 0, 0]) self.assertAllEqual(self.evaluate(math_ops.bincount([5], maxlength=3)),
self.assertAllEqual(math_ops.bincount([1], maxlength=3).eval(), [0, 1]) [0, 0, 0])
self.assertAllEqual(math_ops.bincount([], maxlength=3).eval(), []) self.assertAllEqual(self.evaluate(math_ops.bincount([1], maxlength=3)),
[0, 1])
self.assertAllEqual(self.evaluate(math_ops.bincount([], maxlength=3)),
[])
@test_util.run_deprecated_v1
def test_random_with_weights(self): def test_random_with_weights(self):
num_samples = 10000 num_samples = 10000
with self.session(use_gpu=True): with self.session(use_gpu=True):
@ -79,9 +83,9 @@ class BincountTest(test_util.TensorFlowTestCase):
else: else:
weights = np.random.random(num_samples) weights = np.random.random(num_samples)
self.assertAllClose( self.assertAllClose(
math_ops.bincount(arr, weights).eval(), np.bincount(arr, weights)) self.evaluate(math_ops.bincount(arr, weights)),
np.bincount(arr, weights))
@test_util.run_deprecated_v1
def test_random_without_weights(self): def test_random_without_weights(self):
num_samples = 10000 num_samples = 10000
with self.session(use_gpu=True): with self.session(use_gpu=True):
@ -90,20 +94,20 @@ class BincountTest(test_util.TensorFlowTestCase):
arr = np.random.randint(0, 1000, num_samples) arr = np.random.randint(0, 1000, num_samples)
weights = np.ones(num_samples).astype(dtype) weights = np.ones(num_samples).astype(dtype)
self.assertAllClose( self.assertAllClose(
math_ops.bincount(arr, None).eval(), np.bincount(arr, weights)) self.evaluate(math_ops.bincount(arr, None)),
np.bincount(arr, weights))
@test_util.run_deprecated_v1
def test_zero_weights(self): def test_zero_weights(self):
with self.session(use_gpu=True): with self.session(use_gpu=True):
self.assertAllEqual( self.assertAllEqual(
math_ops.bincount(np.arange(1000), np.zeros(1000)).eval(), self.evaluate(math_ops.bincount(np.arange(1000), np.zeros(1000))),
np.zeros(1000)) np.zeros(1000))
def test_negative(self): def test_negative(self):
# unsorted_segment_sum will only report InvalidArgumentError on CPU # unsorted_segment_sum will only report InvalidArgumentError on CPU
with self.cached_session(): with self.cached_session():
with self.assertRaises(errors.InvalidArgumentError): with self.assertRaises(errors.InvalidArgumentError):
math_ops.bincount([1, 2, 3, -1, 6, 8]).eval() self.evaluate(math_ops.bincount([1, 2, 3, -1, 6, 8]))
@test_util.run_deprecated_v1 @test_util.run_deprecated_v1
def test_shape_function(self): def test_shape_function(self):