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Update gather_op_test and unique_op_test to use subTest for easier de…
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…bugging.

PiperOrigin-RevId: 311595699
Change-Id: I1a8cf8b5b314aada4aeeece2603e975bc8a4ff42
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@aselle @tensorflower-gardener
aselle authored and tensorflower-gardener committed May 14, 2020
1 parent 6676984 commit 6db3caf
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Showing 2 changed files with 145 additions and 133 deletions.
219 changes: 114 additions & 105 deletions tensorflow/python/kernel_tests/gather_op_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,46 +62,49 @@ def testScalar1D(self):
data = np.array([0, 1, 2, 3, 7, 5])
for dtype in _TEST_TYPES:
for indices in 4, [1, 2, 2, 4, 5]:
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices_tf = constant_op.constant(indices)
gather_t = array_ops.gather(params, indices_tf)
gather_val = self.evaluate(gather_t)
np_val = params_np[indices]
self.assertAllEqual(np_val, gather_val)
self.assertEqual(np_val.shape, gather_t.get_shape())
with self.subTest(dtype=dtype, indices=indices):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices_tf = constant_op.constant(indices)
gather_t = array_ops.gather(params, indices_tf)
gather_val = self.evaluate(gather_t)
np_val = params_np[indices]
self.assertAllEqual(np_val, gather_val)
self.assertEqual(np_val.shape, gather_t.get_shape())

def testScalar2D(self):
with self.session(use_gpu=True):
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11], [12, 13, 14]])
for dtype in _TEST_TYPES:
for axis in range(data.ndim):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices = constant_op.constant(2)
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = self.evaluate(gather_t)
self.assertAllEqual(np.take(params_np, 2, axis=axis), gather_val)
expected_shape = data.shape[:axis] + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())
with self.subTest(dtype=dtype, axis=axis):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
indices = constant_op.constant(2)
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = self.evaluate(gather_t)
self.assertAllEqual(np.take(params_np, 2, axis=axis), gather_val)
expected_shape = data.shape[:axis] + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())

def testSimpleTwoD32(self):
with self.session(use_gpu=True):
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11], [12, 13, 14]])
for dtype in _TEST_TYPES:
for axis in range(data.ndim):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
# The indices must be in bounds for any axis.
indices = constant_op.constant([0, 1, 0, 2])
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = self.evaluate(gather_t)
self.assertAllEqual(np.take(params_np, [0, 1, 0, 2], axis=axis),
gather_val)
expected_shape = data.shape[:axis] + (4,) + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())
with self.subTest(dtype=dtype, axis=axis):
params_np = self._buildParams(data, dtype)
params = constant_op.constant(params_np)
# The indices must be in bounds for any axis.
indices = constant_op.constant([0, 1, 0, 2])
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = self.evaluate(gather_t)
self.assertAllEqual(np.take(params_np, [0, 1, 0, 2], axis=axis),
gather_val)
expected_shape = data.shape[:axis] + (4,) + data.shape[axis + 1:]
self.assertEqual(expected_shape, gather_t.get_shape())

@test_util.run_deprecated_v1
def testHigherRank(self):
Expand All @@ -112,58 +115,60 @@ def testHigherRank(self):
for axis in range(len(shape)):
params = self._buildParams(np.random.randn(*shape), dtype)
indices = np.random.randint(shape[axis], size=indices_shape)
with self.cached_session(use_gpu=True) as sess:
tf_params = constant_op.constant(params)
tf_indices = constant_op.constant(indices)
# Check that both positive and negative indices for axis work.
tf_axis = constant_op.constant(axis)
tf_negative_axis = constant_op.constant(-len(shape) + axis)
gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
gather_negative_axis = array_ops.gather(
tf_params, tf_indices, axis=tf_negative_axis)
gather_value, gather_negative_axis_value = sess.run(
[gather, gather_negative_axis])
gather_np = np.take(params, indices, axis)
self.assertAllEqual(gather_np, gather_value)
self.assertAllEqual(gather_np, gather_negative_axis_value)
expected_shape = (params.shape[:axis] + indices.shape +
params.shape[axis + 1:])
self.assertEqual(expected_shape, gather.shape)
self.assertEqual(expected_shape, gather_negative_axis.shape)

# Test gradients
gather_grad = np.random.randn(
*gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
if dtype.is_complex:
gather_grad -= 1j * gather_grad
params_grad, indices_grad, axis_grad = gradients_impl.gradients(
gather, [tf_params, tf_indices, tf_axis], gather_grad)
self.assertEqual(indices_grad, None)
self.assertEqual(axis_grad, None)
if dtype.is_integer:
self.assertEqual(params_grad, None)
continue
# For axis 0, we are able to create an efficient IndexedSlices for
# the gradient.
if axis == 0:
self.assertEqual(type(params_grad), ops.IndexedSlices)
params_grad = ops.convert_to_tensor(params_grad)
correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
outer_dims = axis
inner_dims = len(shape) - axis - 1
gather_grad = gather_grad.reshape(
shape[:axis] + (indices.size,) + shape[axis + 1:])
for source_index, dest_index in enumerate(indices.flat):
dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
(slice(None),) * inner_dims)
source_slice = ((slice(None),) * outer_dims + (source_index,) +
with self.subTest(indices_shape=indices_shape, dtype=dtype, axis=axis,
indices=indices):
with self.cached_session(use_gpu=True) as sess:
tf_params = constant_op.constant(params)
tf_indices = constant_op.constant(indices)
# Check that both positive and negative indices for axis work.
tf_axis = constant_op.constant(axis)
tf_negative_axis = constant_op.constant(-len(shape) + axis)
gather = array_ops.gather(tf_params, tf_indices, axis=tf_axis)
gather_negative_axis = array_ops.gather(
tf_params, tf_indices, axis=tf_negative_axis)
gather_value, gather_negative_axis_value = sess.run(
[gather, gather_negative_axis])
gather_np = np.take(params, indices, axis)
self.assertAllEqual(gather_np, gather_value)
self.assertAllEqual(gather_np, gather_negative_axis_value)
expected_shape = (params.shape[:axis] + indices.shape +
params.shape[axis + 1:])
self.assertEqual(expected_shape, gather.shape)
self.assertEqual(expected_shape, gather_negative_axis.shape)

# Test gradients
gather_grad = np.random.randn(
*gather.get_shape().as_list()).astype(dtype.as_numpy_dtype)
if dtype.is_complex:
gather_grad -= 1j * gather_grad
params_grad, indices_grad, axis_grad = gradients_impl.gradients(
gather, [tf_params, tf_indices, tf_axis], gather_grad)
self.assertEqual(indices_grad, None)
self.assertEqual(axis_grad, None)
if dtype.is_integer:
self.assertEqual(params_grad, None)
continue
# For axis 0, we are able to create an efficient IndexedSlices for
# the gradient.
if axis == 0:
self.assertEqual(type(params_grad), ops.IndexedSlices)
params_grad = ops.convert_to_tensor(params_grad)
correct_params_grad = np.zeros(shape).astype(dtype.as_numpy_dtype)
outer_dims = axis
inner_dims = len(shape) - axis - 1
gather_grad = gather_grad.reshape(
shape[:axis] + (indices.size,) + shape[axis + 1:])
for source_index, dest_index in enumerate(indices.flat):
dest_slice = ((slice(None),) * outer_dims + (dest_index,) +
(slice(None),) * inner_dims)
correct_params_grad[dest_slice] += gather_grad[source_slice]
self.assertAllClose(
correct_params_grad,
self.evaluate(params_grad),
atol=2e-6,
rtol=2e-6)
source_slice = ((slice(None),) * outer_dims + (source_index,) +
(slice(None),) * inner_dims)
correct_params_grad[dest_slice] += gather_grad[source_slice]
self.assertAllClose(
correct_params_grad,
self.evaluate(params_grad),
atol=2e-6,
rtol=2e-6)

@test_util.run_deprecated_v1
def testString(self):
Expand All @@ -177,12 +182,14 @@ def testString(self):
@test_util.run_deprecated_v1
def testUInt32AndUInt64(self):
for unsigned_type in (dtypes.uint32, dtypes.uint64):
params = self._buildParams(
np.array([[1, 2, 3], [7, 8, 9]]), unsigned_type)
with self.cached_session():
self.assertAllEqual([7, 8, 9],
array_ops.gather(params, 1, axis=0).eval())
self.assertAllEqual([1, 7], array_ops.gather(params, 0, axis=1).eval())
with self.subTest(unsigned_type=unsigned_type):
params = self._buildParams(
np.array([[1, 2, 3], [7, 8, 9]]), unsigned_type)
with self.cached_session():
self.assertAllEqual([7, 8, 9],
array_ops.gather(params, 1, axis=0).eval())
self.assertAllEqual([1, 7],
array_ops.gather(params, 0, axis=1).eval())

@test_util.run_deprecated_v1
def testUnknownIndices(self):
Expand Down Expand Up @@ -237,35 +244,37 @@ def testBadAxis(self):
indices = 0
for bad_axis in (1, 2, -2):
# Shape inference can validate axis for known params rank.
with self.assertRaisesWithPredicateMatch(
ValueError, "Shape must be at least rank . but is rank 1"):
array_ops.gather(params, indices, axis=bad_axis)
# If params rank is unknown, an op error occurs.
with self.assertRaisesOpError(
r"Expected axis in the range \[-1, 1\), but got %s" % bad_axis):
array_ops.gather(params_ph, indices, axis=bad_axis).eval(
feed_dict={params_ph: params})
with self.subTest(bad_axis=bad_axis):
with self.assertRaisesWithPredicateMatch(
ValueError, "Shape must be at least rank . but is rank 1"):
array_ops.gather(params, indices, axis=bad_axis)
# If params rank is unknown, an op error occurs.
with self.assertRaisesOpError(
r"Expected axis in the range \[-1, 1\), but got %s" % bad_axis):
array_ops.gather(params_ph, indices, axis=bad_axis).eval(
feed_dict={params_ph: params})

@test_util.run_deprecated_v1
def testEmptySlices(self):
with self.session(use_gpu=True):
for dtype in _TEST_TYPES:
for itype in np.int32, np.int64:
# Leading axis gather.
params = np.zeros((7, 0, 0), dtype=dtype.as_numpy_dtype)
indices = np.array([3, 4], dtype=itype)
gather = array_ops.gather(params, indices, axis=0)
self.assertAllEqual(gather.eval(), np.zeros((2, 0, 0)))

# Middle axis gather.
params = np.zeros((0, 7, 0), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=1)
self.assertAllEqual(gather.eval(), np.zeros((0, 2, 0)))

# Trailing axis gather.
params = np.zeros((0, 0, 7), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=2)
self.assertAllEqual(gather.eval(), np.zeros((0, 0, 2)))
with self.subTest(dtype=dtype, itype=itype):
params = np.zeros((7, 0, 0), dtype=dtype.as_numpy_dtype)
indices = np.array([3, 4], dtype=itype)
gather = array_ops.gather(params, indices, axis=0)
self.assertAllEqual(gather.eval(), np.zeros((2, 0, 0)))

# Middle axis gather.
params = np.zeros((0, 7, 0), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=1)
self.assertAllEqual(gather.eval(), np.zeros((0, 2, 0)))

# Trailing axis gather.
params = np.zeros((0, 0, 7), dtype=dtype.as_numpy_dtype)
gather = array_ops.gather(params, indices, axis=2)
self.assertAllEqual(gather.eval(), np.zeros((0, 0, 2)))

@parameterized.parameters([
# batch_dims=0 (equivalent to tf.gather)
Expand Down
59 changes: 31 additions & 28 deletions tensorflow/python/kernel_tests/unique_op_test.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -61,17 +61,18 @@ def testString(self):

def testInt32Axis(self):
for dtype in [np.int32, np.int64]:
x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
y0, idx0 = gen_array_ops.unique_v2(x, axis=np.array([0], dtype))
self.assertEqual(y0.shape.rank, 2)
tf_y0, tf_idx0 = self.evaluate([y0, idx0])
y1, idx1 = gen_array_ops.unique_v2(x, axis=np.array([1], dtype))
self.assertEqual(y1.shape.rank, 2)
tf_y1, tf_idx1 = self.evaluate([y1, idx1])
self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]]))
self.assertAllEqual(tf_idx1, np.array([0, 1, 1]))
with self.subTest(dtype=dtype):
x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
y0, idx0 = gen_array_ops.unique_v2(x, axis=np.array([0], dtype))
self.assertEqual(y0.shape.rank, 2)
tf_y0, tf_idx0 = self.evaluate([y0, idx0])
y1, idx1 = gen_array_ops.unique_v2(x, axis=np.array([1], dtype))
self.assertEqual(y1.shape.rank, 2)
tf_y1, tf_idx1 = self.evaluate([y1, idx1])
self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]]))
self.assertAllEqual(tf_idx1, np.array([0, 1, 1]))

def testInt32V2(self):
# This test is only temporary, once V2 is used
Expand Down Expand Up @@ -144,26 +145,28 @@ def testString(self):
for i in range(len(x)):
self.assertEqual(x[i], tf_y[tf_idx[i]].decode('ascii'))
for value, count in zip(tf_y, tf_count):
v = [1 if x[i] == value.decode('ascii') else 0 for i in range(7000)]
self.assertEqual(count, sum(v))
with self.subTest(value=value, count=count):
v = [1 if x[i] == value.decode('ascii') else 0 for i in range(7000)]
self.assertEqual(count, sum(v))

def testInt32Axis(self):
for dtype in [np.int32, np.int64]:
x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
y0, idx0, count0 = gen_array_ops.unique_with_counts_v2(
x, axis=np.array([0], dtype))
self.assertEqual(y0.shape.rank, 2)
tf_y0, tf_idx0, tf_count0 = self.evaluate([y0, idx0, count0])
y1, idx1, count1 = gen_array_ops.unique_with_counts_v2(
x, axis=np.array([1], dtype))
self.assertEqual(y1.shape.rank, 2)
tf_y1, tf_idx1, tf_count1 = self.evaluate([y1, idx1, count1])
self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
self.assertAllEqual(tf_count0, np.array([2, 1]))
self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]]))
self.assertAllEqual(tf_idx1, np.array([0, 1, 1]))
self.assertAllEqual(tf_count1, np.array([1, 2]))
with self.subTest(dtype=dtype):
x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
y0, idx0, count0 = gen_array_ops.unique_with_counts_v2(
x, axis=np.array([0], dtype))
self.assertEqual(y0.shape.rank, 2)
tf_y0, tf_idx0, tf_count0 = self.evaluate([y0, idx0, count0])
y1, idx1, count1 = gen_array_ops.unique_with_counts_v2(
x, axis=np.array([1], dtype))
self.assertEqual(y1.shape.rank, 2)
tf_y1, tf_idx1, tf_count1 = self.evaluate([y1, idx1, count1])
self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
self.assertAllEqual(tf_count0, np.array([2, 1]))
self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]]))
self.assertAllEqual(tf_idx1, np.array([0, 1, 1]))
self.assertAllEqual(tf_count1, np.array([1, 2]))

def testInt32V2(self):
# This test is only temporary, once V2 is used
Expand Down

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