from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import map_fn
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import special_math_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
# Linear algebra ops.
band_part = array_ops.matrix_band_part
cholesky = linalg_ops.cholesky
cholesky_solve = linalg_ops.cholesky_solve
det = linalg_ops.matrix_determinant
slogdet = gen_linalg_ops.log_matrix_determinant
tf_export('linalg.slogdet')(dispatch.add_dispatch_support(slogdet))
diag = array_ops.matrix_diag
diag_part = array_ops.matrix_diag_part
eigh = linalg_ops.self_adjoint_eig
eigvalsh = linalg_ops.self_adjoint_eigvals
einsum = special_math_ops.einsum
eye = linalg_ops.eye
inv = linalg_ops.matrix_inverse
logm = gen_linalg_ops.matrix_logarithm
lu = gen_linalg_ops.lu
tf_export('linalg.logm')(dispatch.add_dispatch_support(logm))
lstsq = linalg_ops.matrix_solve_ls
norm = linalg_ops.norm
qr = linalg_ops.qr
set_diag = array_ops.matrix_set_diag
solve = linalg_ops.matrix_solve
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import special_math_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
# Linear algebra ops.
band_part = array_ops.matrix_band_part
cholesky = dispatch.add_dispatch_support(linalg_ops.cholesky)
cholesky_solve = linalg_ops.cholesky_solve
det = dispatch.add_dispatch_support(linalg_ops.matrix_determinant)
slogdet = gen_linalg_ops.log_matrix_determinant
tf_export('linalg.slogdet')(slogdet)
diag = array_ops.matrix_diag
diag_part = dispatch.add_dispatch_support(array_ops.matrix_diag_part)
eigh = linalg_ops.self_adjoint_eig
eigvalsh = linalg_ops.self_adjoint_eigvals
einsum = special_math_ops.einsum
eye = linalg_ops.eye
inv = dispatch.add_dispatch_support(linalg_ops.matrix_inverse)
logm = gen_linalg_ops.matrix_logarithm
lu = gen_linalg_ops.lu
tf_export('linalg.logm')(logm)
lstsq = linalg_ops.matrix_solve_ls
_irfft.__doc__ = re.sub(" Treal.*?\n", "", ifft_fn.__doc__)
return _irfft
# FFT/IFFT 1/2/3D are exported via
# third_party/tensorflow/core/api_def/python_api/
fft = gen_spectral_ops.fft
ifft = gen_spectral_ops.ifft
fft2d = gen_spectral_ops.fft2d
ifft2d = gen_spectral_ops.ifft2d
fft3d = gen_spectral_ops.fft3d
ifft3d = gen_spectral_ops.ifft3d
rfft = _rfft_wrapper(gen_spectral_ops.rfft, 1, "rfft")
tf_export("signal.rfft",
v1=["signal.rfft",
"spectral.rfft"])(dispatch.add_dispatch_support(rfft))
irfft = _irfft_wrapper(gen_spectral_ops.irfft, 1, "irfft")
tf_export("signal.irfft",
v1=["signal.irfft",
"spectral.irfft"])(dispatch.add_dispatch_support(irfft))
rfft2d = _rfft_wrapper(gen_spectral_ops.rfft2d, 2, "rfft2d")
tf_export("signal.rfft2d",
v1=["signal.rfft2d",
"spectral.rfft2d"])(dispatch.add_dispatch_support(rfft2d))
irfft2d = _irfft_wrapper(gen_spectral_ops.irfft2d, 2, "irfft2d")
tf_export("signal.irfft2d",
v1=["signal.irfft2d",
"spectral.irfft2d"])(dispatch.add_dispatch_support(irfft2d))
rfft3d = _rfft_wrapper(gen_spectral_ops.rfft3d, 3, "rfft3d")
tf_export("signal.rfft3d",
v1=["signal.rfft3d",
def testBadIterableParametersError(self):
fn = lambda x: [t + 1 for t in x]
with self.assertRaisesRegex(
TypeError,
"iterable_parameters should be a list or tuple of string"):
dispatch.add_dispatch_support(iterable_parameters="x")(fn)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# pylint: disable=wildcard-import,unused-import
"""Protocol Buffer encoding and decoding from tensors."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops.gen_decode_proto_ops import decode_proto_v2 as decode_proto
from tensorflow.python.ops.gen_encode_proto_ops import encode_proto
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
tf_export("io.decode_proto")(dispatch.add_dispatch_support(decode_proto))
tf_export("io.encode_proto")(dispatch.add_dispatch_support(encode_proto))
ops.NotDifferentiable("DecodeProtoV2")
ops.NotDifferentiable("EncodeProto")
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_linalg_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import special_math_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
# Linear algebra ops.
band_part = array_ops.matrix_band_part
cholesky = dispatch.add_dispatch_support(linalg_ops.cholesky)
cholesky_solve = linalg_ops.cholesky_solve
det = dispatch.add_dispatch_support(linalg_ops.matrix_determinant)
slogdet = gen_linalg_ops.log_matrix_determinant
tf_export('linalg.slogdet')(slogdet)
diag = array_ops.matrix_diag
diag_part = dispatch.add_dispatch_support(array_ops.matrix_diag_part)
eigh = linalg_ops.self_adjoint_eig
eigvalsh = linalg_ops.self_adjoint_eigvals
einsum = special_math_ops.einsum
eye = linalg_ops.eye
inv = dispatch.add_dispatch_support(linalg_ops.matrix_inverse)
logm = gen_linalg_ops.matrix_logarithm
lu = gen_linalg_ops.lu
tf_export('linalg.logm')(logm)
lstsq = linalg_ops.matrix_solve_ls
return ifft_fn(input_tensor, fft_length, Treal=real_dtype, name=name)
_irfft.__doc__ = re.sub(" Treal.*?\n", "", ifft_fn.__doc__)
return _irfft
# FFT/IFFT 1/2/3D are exported via
# third_party/tensorflow/core/api_def/python_api/
fft = gen_spectral_ops.fft
ifft = gen_spectral_ops.ifft
fft2d = gen_spectral_ops.fft2d
ifft2d = gen_spectral_ops.ifft2d
fft3d = gen_spectral_ops.fft3d
ifft3d = gen_spectral_ops.ifft3d
rfft = _rfft_wrapper(gen_spectral_ops.rfft, 1, "rfft")
tf_export("signal.rfft", v1=["signal.rfft", "spectral.rfft"])(
dispatch.add_dispatch_support(rfft))
irfft = _irfft_wrapper(gen_spectral_ops.irfft, 1, "irfft")
tf_export("signal.irfft", v1=["signal.irfft", "spectral.irfft"])(
dispatch.add_dispatch_support(irfft))
rfft2d = _rfft_wrapper(gen_spectral_ops.rfft2d, 2, "rfft2d")
tf_export("signal.rfft2d", v1=["signal.rfft2d", "spectral.rfft2d"])(
dispatch.add_dispatch_support(rfft2d))
irfft2d = _irfft_wrapper(gen_spectral_ops.irfft2d, 2, "irfft2d")
tf_export("signal.irfft2d", v1=["signal.irfft2d", "spectral.irfft2d"])(
dispatch.add_dispatch_support(irfft2d))
rfft3d = _rfft_wrapper(gen_spectral_ops.rfft3d, 3, "rfft3d")
tf_export("signal.rfft3d", v1=["signal.rfft3d", "spectral.rfft3d"])(
dispatch.add_dispatch_support(rfft3d))
irfft3d = _irfft_wrapper(gen_spectral_ops.irfft3d, 3, "irfft3d")
tf_export("signal.irfft3d", v1=["signal.irfft3d", "spectral.irfft3d"])(
dispatch.add_dispatch_support(irfft3d))
