experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Improve testing of the stack operation.

Browse Source 
-  add axis to V1 simple test.
-  use proper random numbers for Boolean
-  test more than 1D tensors against numpy

PiperOrigin-RevId: 308368279
Change-Id: I4cec2a05e970dda15aead4ae0d00d46a981e523e
This commit is contained in:
Andrew Selle 2020-04-24 19:23:33 -07:00 committed by TensorFlower Gardener
parent 5712d2cac6
commit 4440c68d02
1 changed files with 117 additions and 95 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

86
tensorflow/python/kernel_tests/stack_op_test.py
View File

@ -42,18 +42,26 @@ def np_split_squeeze(array, axis):
class StackOpTest(test.TestCase):
def randn(self, shape, dtype):
data = np.random.randn(*shape)
if dtype == np.bool:
return data < 0 # Naive casting yields True with P(1)!
else:
return data.astype(dtype)
@test_util.run_deprecated_v1
def testSimple(self):
np.random.seed(7)
with self.session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for shape in (2,), (3,), (2, 3), (3, 2), (8, 2, 10):
rank = len(shape)
for axis in range(-rank, rank):
for dtype in [np.bool, np.float32, np.int32, np.int64]:
data = np.random.randn(*shape).astype(dtype)
# Convert [data[0], data[1], ...] separately to tensorflow
# TODO(irving): Remove list() once we handle maps correctly
xs = list(map(constant_op.constant, data))
data = self.randn(shape, dtype)
xs = np_split_squeeze(data, axis)
# Stack back into a single tensorflow tensor
c = array_ops.stack(xs)
with self.subTest(shape=shape, axis=axis, dtype=dtype):
c = array_ops.stack(xs, axis=axis)
self.assertAllEqual(c.eval(), data)
@test_util.run_deprecated_v1
@ -61,7 +69,8 @@ class StackOpTest(test.TestCase):
np.random.seed(7)
with self.session(use_gpu=False):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2), (100, 24, 24, 3):
data = np.random.randn(*shape).astype(np.float32)
with self.subTest(shape=shape):
data = self.randn(shape, np.float32)
xs = list(map(constant_op.constant, data))
c = array_ops.parallel_stack(xs)
self.assertAllEqual(c.eval(), data)
@ -71,7 +80,8 @@ class StackOpTest(test.TestCase):
np.random.seed(7)
with self.session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2), (100, 24, 24, 3):
data = np.random.randn(*shape).astype(np.float32)
with self.subTest(shape=shape):
data = self.randn(shape, np.float32)
xs = list(map(constant_op.constant, data))
c = array_ops.parallel_stack(xs)
self.assertAllEqual(c.eval(), data)
@ -80,9 +90,16 @@ class StackOpTest(test.TestCase):
def testConst(self):
np.random.seed(7)
with self.session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
# Verify that shape induction works with shapes produced via const stack
a = constant_op.constant([1, 2, 3, 4, 5, 6])
b = array_ops.reshape(a, array_ops.stack([2, 3]))
self.assertAllEqual(b.get_shape(), [2, 3])
# Check on a variety of shapes and types
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2), (8, 2, 10):
for dtype in [np.bool, np.float32, np.int16, np.int32, np.int64]:
data = np.random.randn(*shape).astype(dtype)
with self.subTest(shape=shape, dtype=dtype):
data = self.randn(shape, dtype)
# Stack back into a single tensorflow tensor directly using np array
c = array_ops.stack(data)
# This is implemented via a Const:
@ -96,23 +113,19 @@ class StackOpTest(test.TestCase):
self.assertEqual(cl.op.type, "Const")
self.assertAllEqual(cl.eval(), data)
# Verify that shape induction works with shapes produced via const stack
a = constant_op.constant([1, 2, 3, 4, 5, 6])
b = array_ops.reshape(a, array_ops.stack([2, 3]))
self.assertAllEqual(b.get_shape(), [2, 3])
@test_util.run_deprecated_v1
def testConstParallelCPU(self):
np.random.seed(7)
with self.session(use_gpu=False):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape).astype(np.float32)
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2), (8, 2, 10):
with self.subTest(shape=shape):
data = self.randn(shape, np.float32)
if len(shape) == 1:
data_list = list(data)
cl = array_ops.parallel_stack(data_list)
self.assertAllEqual(cl.eval(), data)
data = np.random.randn(*shape).astype(np.float32)
data = self.randn(shape, np.float32)
c = array_ops.parallel_stack(data)
self.assertAllEqual(c.eval(), data)
@ -121,22 +134,24 @@ class StackOpTest(test.TestCase):
np.random.seed(7)
with self.session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape).astype(np.float32)
with self.subTest(shape=shape):
data = self.randn(shape, np.float32)
if len(shape) == 1:
data_list = list(data)
cl = array_ops.parallel_stack(data_list)
self.assertAllEqual(cl.eval(), data)
data = np.random.randn(*shape).astype(np.float32)
data = self.randn(shape, np.float32)
c = array_ops.parallel_stack(data)
self.assertAllEqual(c.eval(), data)
@test_util.run_deprecated_v1
def testGradientsAxis0(self):
np.random.seed(7)
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for shape in (2,), (3,), (2, 3), (3, 2), (8, 2, 10):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
with self.subTest(shape=shape):
with self.cached_session(use_gpu=True):
# TODO(irving): Remove list() once we handle maps correctly
xs = list(map(constant_op.constant, data))
@ -147,16 +162,18 @@ class StackOpTest(test.TestCase):
@test_util.run_deprecated_v1
def testGradientsAxis1(self):
np.random.seed(7)
for shape in (2, 3), (3, 2), (4, 3, 2):
for shape in (2, 3), (3, 2), (8, 2, 10):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
out_shape = list(shape[1:])
out_shape.insert(1, shape[0])
with self.subTest(shape=shape):
with self.cached_session(use_gpu=True):
# TODO(irving): Remove list() once we handle maps correctly
xs = list(map(constant_op.constant, data))
c = array_ops.stack(xs, axis=1)
err = gradient_checker.compute_gradient_error(xs, shapes, c, out_shape)
err = gradient_checker.compute_gradient_error(xs, shapes, c,
out_shape)
self.assertLess(err, 1e-6)
@test_util.run_deprecated_v1
@ -164,6 +181,7 @@ class StackOpTest(test.TestCase):
# Verify that stack doesn't crash for zero size inputs
with self.session(use_gpu=False):
for shape in (0,), (3, 0), (0, 3):
with self.subTest(shape=shape):
x = np.zeros((2,) + shape).astype(np.int32)
p = array_ops.stack(list(x)).eval()
self.assertAllEqual(p, x)
@ -176,6 +194,7 @@ class StackOpTest(test.TestCase):
# Verify that stack doesn't crash for zero size inputs
with self.session(use_gpu=True):
for shape in (0,), (3, 0), (0, 3):
with self.subTest(shape=shape):
x = np.zeros((2,) + shape).astype(np.int32)
p = array_ops.stack(list(x)).eval()
self.assertAllEqual(p, x)
@ -207,19 +226,21 @@ class StackOpTest(test.TestCase):
def testAgainstNumpy(self):
# For 1 to 5 dimensions.
for i in range(1, 6):
expected = np.random.random(np.random.permutation(i) + 1)
for shape in (3,), (2, 2, 3), (4, 1, 2, 2), (8, 2, 10):
rank = len(shape)
expected = self.randn(shape, np.float32)
for dtype in [np.bool, np.float32, np.int32, np.int64]:
# For all the possible axis to split it, including negative indices.
for j in range(-i, i):
test_arrays = np_split_squeeze(expected, j)
for axis in range(-rank, rank):
test_arrays = np_split_squeeze(expected, axis)
with self.cached_session(use_gpu=True):
actual_pack = array_ops.stack(test_arrays, axis=j)
with self.subTest(shape=shape, dtype=dtype, axis=axis):
actual_pack = array_ops.stack(test_arrays, axis=axis)
self.assertEqual(expected.shape, actual_pack.get_shape())
actual_pack = self.evaluate(actual_pack)
actual_stack = array_ops.stack(test_arrays, axis=j)
actual_stack = array_ops.stack(test_arrays, axis=axis)
self.assertEqual(expected.shape, actual_stack.get_shape())
actual_stack = self.evaluate(actual_stack)
@ -238,9 +259,10 @@ class StackOpTest(test.TestCase):
def testComplex(self):
np.random.seed(7)
with self.session(use_gpu=True):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
for shape in (2,), (3,), (2, 3), (3, 2), (8, 2, 10):
for dtype in [np.complex64, np.complex128]:
data = np.random.randn(*shape).astype(dtype)
with self.subTest(shape=shape, dtype=dtype):
data = self.randn(shape, dtype)
xs = list(map(constant_op.constant, data))
c = array_ops.stack(xs)
self.assertAllEqual(self.evaluate(c), data)