def testShapeInference(self):
dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
dtypes.uint8, dtypes.uint16]
with self.session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = constant_op.constant([[0], [3], [5]], dtype=dtype)
rhs = constant_op.constant([[1, 2, 4]], dtype=dtype)
and_tensor = bitwise_ops.bitwise_and(lhs, rhs)
or_tensor = bitwise_ops.bitwise_or(lhs, rhs)
xor_tensor = bitwise_ops.bitwise_xor(lhs, rhs)
ls_tensor = bitwise_ops.left_shift(lhs, rhs)
rs_tensor = bitwise_ops.right_shift(lhs, rhs)
and_result, or_result, xor_result, ls_result, rs_result = sess.run(
[and_tensor, or_tensor, xor_tensor, ls_tensor, rs_tensor])
# Compare shape inference with result
self.assertAllEqual(and_tensor.get_shape().as_list(), and_result.shape)
self.assertAllEqual(and_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(or_tensor.get_shape().as_list(), or_result.shape)
self.assertAllEqual(or_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(xor_tensor.get_shape().as_list(), xor_result.shape)
self.assertAllEqual(xor_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(ls_tensor.get_shape().as_list(), ls_result.shape)
self.assertAllEqual(ls_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(rs_tensor.get_shape().as_list(), rs_result.shape)
self.assertAllEqual(rs_tensor.get_shape().as_list(), [3, 3])
def testShapeInference(self):
dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
dtypes.uint8, dtypes.uint16]
with self.test_session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = constant_op.constant([[0], [3], [5]], dtype=dtype)
rhs = constant_op.constant([[1, 2, 4]], dtype=dtype)
and_tensor = bitwise_ops.bitwise_and(lhs, rhs)
or_tensor = bitwise_ops.bitwise_or(lhs, rhs)
xor_tensor = bitwise_ops.bitwise_xor(lhs, rhs)
ls_tensor = bitwise_ops.left_shift(lhs, rhs)
rs_tensor = bitwise_ops.right_shift(lhs, rhs)
and_result, or_result, xor_result, ls_result, rs_result = sess.run(
[and_tensor, or_tensor, xor_tensor, ls_tensor, rs_tensor])
# Compare shape inference with result
self.assertAllEqual(and_tensor.get_shape().as_list(), and_result.shape)
self.assertAllEqual(and_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(or_tensor.get_shape().as_list(), or_result.shape)
self.assertAllEqual(or_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(xor_tensor.get_shape().as_list(), xor_result.shape)
self.assertAllEqual(xor_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(ls_tensor.get_shape().as_list(), ls_result.shape)
self.assertAllEqual(ls_tensor.get_shape().as_list(), [3, 3])
self.assertAllEqual(rs_tensor.get_shape().as_list(), rs_result.shape)
self.assertAllEqual(rs_tensor.get_shape().as_list(), [3, 3])
def testShiftsWithNegativeLHS(self):
dtype_list = [np.int8, np.int16, np.int32, np.int64]
with self.test_session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = np.array([-1, -5, -3, -14], dtype=dtype)
rhs = np.array([5, 0, 7, 11], dtype=dtype)
left_shift_result, right_shift_result = sess.run(
[bitwise_ops.left_shift(lhs, rhs),
bitwise_ops.right_shift(lhs, rhs)])
self.assertAllEqual(left_shift_result, np.left_shift(lhs, rhs))
self.assertAllEqual(right_shift_result, np.right_shift(lhs, rhs))
def testShiftsWithNegativeLHS(self):
dtype_list = [np.int8, np.int16, np.int32, np.int64]
with self.session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = np.array([-1, -5, -3, -14], dtype=dtype)
rhs = np.array([5, 0, 7, 11], dtype=dtype)
left_shift_result, right_shift_result = sess.run(
[bitwise_ops.left_shift(lhs, rhs),
bitwise_ops.right_shift(lhs, rhs)])
self.assertAllEqual(left_shift_result, np.left_shift(lhs, rhs))
self.assertAllEqual(right_shift_result, np.right_shift(lhs, rhs))
def _shift_right_arithmetic_helper(x, y, name=None):
"""Performs an integer right arithmetic shift irrespective of input type."""
assert y.dtype == x.dtype
dtype = x.dtype
unsigned = dtype in _UNSIGNED_TO_SIGNED_TABLE
if unsigned:
signed_dtype = _UNSIGNED_TO_SIGNED_TABLE[dtype]
x = math_ops.cast(x, signed_dtype)
y = math_ops.cast(y, signed_dtype)
output = bitwise_ops.right_shift(x, y, name=name)
if unsigned:
output = math_ops.cast(output, dtype)
return output
def _shift_right_logical_helper(x, y, name=None):
"""Performs an integer right logical shift irrespective of input type."""
assert y.dtype == x.dtype
dtype = x.dtype
signed = dtype in _SIGNED_TO_UNSIGNED_TABLE
if signed:
unsigned_dtype = _SIGNED_TO_UNSIGNED_TABLE[dtype]
x = math_ops.cast(x, unsigned_dtype)
y = math_ops.cast(y, unsigned_dtype)
output = bitwise_ops.right_shift(x, y, name=name)
if signed:
output = math_ops.cast(output, dtype)
return output
def _shift_right_arithmetic_helper(x, y, name=None):
"""Performs an integer right arithmetic shift irrespective of input type."""
assert y.dtype == x.dtype
dtype = x.dtype
unsigned = dtype in _UNSIGNED_TO_SIGNED_TABLE
if unsigned:
signed_dtype = _UNSIGNED_TO_SIGNED_TABLE[dtype]
x = math_ops.cast(x, signed_dtype)
y = math_ops.cast(y, signed_dtype)
output = bitwise_ops.right_shift(x, y, name=name)
if unsigned:
output = math_ops.cast(output, dtype)
return output
def _shift_right_logical_helper(x, y, name=None):
"""Performs an integer right logical shift irrespective of input type."""
assert y.dtype == x.dtype
dtype = x.dtype
signed = dtype in _SIGNED_TO_UNSIGNED_TABLE
if signed:
unsigned_dtype = _SIGNED_TO_UNSIGNED_TABLE[dtype]
x = math_ops.cast(x, unsigned_dtype)
y = math_ops.cast(y, unsigned_dtype)
output = bitwise_ops.right_shift(x, y, name=name)
if signed:
output = math_ops.cast(output, dtype)
return output
def testImplementationDefinedShiftsDoNotCrash(self):
dtype_list = [np.int8, np.int16, np.int32, np.int64]
with self.test_session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = np.array([-1, -5, -3, -14], dtype=dtype)
rhs = np.array([-2, 64, 101, 32], dtype=dtype)
# We intentionally do not test for specific values here since the exact
# outputs are implementation-defined. However, we should not crash or
# trigger an undefined-behavior error from tools such as
# AddressSanitizer.
sess.run([bitwise_ops.left_shift(lhs, rhs),
bitwise_ops.right_shift(lhs, rhs)])
def testImplementationDefinedShiftsDoNotCrash(self):
dtype_list = [np.int8, np.int16, np.int32, np.int64]
with self.session(use_gpu=True) as sess:
for dtype in dtype_list:
lhs = np.array([-1, -5, -3, -14], dtype=dtype)
rhs = np.array([-2, 64, 101, 32], dtype=dtype)
# We intentionally do not test for specific values here since the exact
# outputs are implementation-defined. However, we should not crash or
# trigger an undefined-behavior error from tools such as
# AddressSanitizer.
sess.run([bitwise_ops.left_shift(lhs, rhs),
bitwise_ops.right_shift(lhs, rhs)])
def testShiftsWithPositiveLHS(self):
dtype_list = [np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64]
with self.session() as sess:
for dtype in dtype_list:
lhs = np.array([0, 5, 3, 14], dtype=dtype)
rhs = np.array([5, 0, 7, 3], dtype=dtype)
left_shift_result, right_shift_result = sess.run(
[bitwise_ops.left_shift(lhs, rhs),
bitwise_ops.right_shift(lhs, rhs)])
self.assertAllEqual(left_shift_result, np.left_shift(lhs, rhs))
self.assertAllEqual(right_shift_result, np.right_shift(lhs, rhs))
