EVOLUTION-MANAGER

Edit File: fft_ops.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Fast-Fourier Transform ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import tensor_util as _tensor_util from tensorflow.python.ops import array_ops as _array_ops from tensorflow.python.ops import gen_spectral_ops from tensorflow.python.ops import manip_ops from tensorflow.python.ops import math_ops as _math_ops from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export def _infer_fft_length_for_rfft(input_tensor, fft_rank): """Infers the `fft_length` argument for a `rank` RFFT from `input_tensor`.""" # A TensorShape for the inner fft_rank dimensions. fft_shape = input_tensor.get_shape()[-fft_rank:] # If any dim is unknown, fall back to tensor-based math. if not fft_shape.is_fully_defined(): return _array_ops.shape(input_tensor)[-fft_rank:] # Otherwise, return a constant. return _ops.convert_to_tensor(fft_shape.as_list(), _dtypes.int32) def _infer_fft_length_for_irfft(input_tensor, fft_rank): """Infers the `fft_length` argument for a `rank` IRFFT from `input_tensor`.""" # A TensorShape for the inner fft_rank dimensions. fft_shape = input_tensor.get_shape()[-fft_rank:] # If any dim is unknown, fall back to tensor-based math. if not fft_shape.is_fully_defined(): fft_length = _array_ops.unstack(_array_ops.shape(input_tensor)[-fft_rank:]) fft_length[-1] = _math_ops.maximum(0, 2 * (fft_length[-1] - 1)) return _array_ops.stack(fft_length) # Otherwise, return a constant. fft_length = fft_shape.as_list() if fft_length: fft_length[-1] = max(0, 2 * (fft_length[-1] - 1)) return _ops.convert_to_tensor(fft_length, _dtypes.int32) def _maybe_pad_for_rfft(input_tensor, fft_rank, fft_length, is_reverse=False): """Pads `input_tensor` to `fft_length` on its inner-most `fft_rank` dims.""" fft_shape = _tensor_util.constant_value_as_shape(fft_length) # Edge case: skip padding empty tensors. if (input_tensor.shape.ndims is not None and any(dim.value == 0 for dim in input_tensor.shape.dims)): return input_tensor # If we know the shapes ahead of time, we can either skip or pre-compute the # appropriate paddings. Otherwise, fall back to computing paddings in # TensorFlow. if fft_shape.is_fully_defined() and input_tensor.shape.ndims is not None: # Slice the last FFT-rank dimensions from input_tensor's shape. input_fft_shape = input_tensor.shape[-fft_shape.ndims:] if input_fft_shape.is_fully_defined(): # In reverse, we only pad the inner-most dimension to fft_length / 2 + 1. if is_reverse: fft_shape = fft_shape[:-1].concatenate( fft_shape.dims[-1].value // 2 + 1) paddings = [[0, max(fft_dim.value - input_dim.value, 0)] for fft_dim, input_dim in zip( fft_shape.dims, input_fft_shape.dims)] if any(pad > 0 for _, pad in paddings): outer_paddings = [[0, 0]] * max((input_tensor.shape.ndims - fft_shape.ndims), 0) return _array_ops.pad(input_tensor, outer_paddings + paddings) return input_tensor # If we can't determine the paddings ahead of time, then we have to pad. If # the paddings end up as zero, tf.pad has a special-case that does no work. input_rank = _array_ops.rank(input_tensor) input_fft_shape = _array_ops.shape(input_tensor)[-fft_rank:] outer_dims = _math_ops.maximum(0, input_rank - fft_rank) outer_paddings = _array_ops.zeros([outer_dims], fft_length.dtype) # In reverse, we only pad the inner-most dimension to fft_length / 2 + 1. if is_reverse: fft_length = _array_ops.concat([fft_length[:-1], fft_length[-1:] // 2 + 1], 0) fft_paddings = _math_ops.maximum(0, fft_length - input_fft_shape) paddings = _array_ops.concat([outer_paddings, fft_paddings], 0) paddings = _array_ops.stack([_array_ops.zeros_like(paddings), paddings], axis=1) return _array_ops.pad(input_tensor, paddings) def _rfft_wrapper(fft_fn, fft_rank, default_name): """Wrapper around gen_spectral_ops.rfft* that infers fft_length argument.""" def _rfft(input_tensor, fft_length=None, name=None): """Wrapper around gen_spectral_ops.rfft* that infers fft_length argument.""" with _ops.name_scope(name, default_name, [input_tensor, fft_length]) as name: input_tensor = _ops.convert_to_tensor(input_tensor, preferred_dtype=_dtypes.float32) if input_tensor.dtype not in (_dtypes.float32, _dtypes.float64): raise ValueError( "RFFT requires tf.float32 or tf.float64 inputs, got: %s" % input_tensor) real_dtype = input_tensor.dtype if real_dtype == _dtypes.float32: complex_dtype = _dtypes.complex64 else: assert real_dtype == _dtypes.float64 complex_dtype = _dtypes.complex128 input_tensor.shape.with_rank_at_least(fft_rank) if fft_length is None: fft_length = _infer_fft_length_for_rfft(input_tensor, fft_rank) else: fft_length = _ops.convert_to_tensor(fft_length, _dtypes.int32) input_tensor = _maybe_pad_for_rfft(input_tensor, fft_rank, fft_length) fft_length_static = _tensor_util.constant_value(fft_length) if fft_length_static is not None: fft_length = fft_length_static return fft_fn(input_tensor, fft_length, Tcomplex=complex_dtype, name=name) _rfft.__doc__ = fft_fn.__doc__ return _rfft def _irfft_wrapper(ifft_fn, fft_rank, default_name): """Wrapper around gen_spectral_ops.irfft* that infers fft_length argument.""" def _irfft(input_tensor, fft_length=None, name=None): """Wrapper irfft* that infers fft_length argument.""" with _ops.name_scope(name, default_name, [input_tensor, fft_length]) as name: input_tensor = _ops.convert_to_tensor(input_tensor, preferred_dtype=_dtypes.complex64) input_tensor.shape.with_rank_at_least(fft_rank) if input_tensor.dtype not in (_dtypes.complex64, _dtypes.complex128): raise ValueError( "IRFFT requires tf.complex64 or tf.complex128 inputs, got: %s" % input_tensor) complex_dtype = input_tensor.dtype real_dtype = complex_dtype.real_dtype if fft_length is None: fft_length = _infer_fft_length_for_irfft(input_tensor, fft_rank) else: fft_length = _ops.convert_to_tensor(fft_length, _dtypes.int32) input_tensor = _maybe_pad_for_rfft(input_tensor, fft_rank, fft_length, is_reverse=True) fft_length_static = _tensor_util.constant_value(fft_length) if fft_length_static is not None: fft_length = fft_length_static return ifft_fn(input_tensor, fft_length, Treal=real_dtype, name=name) _irfft.__doc__ = 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)) def _fft_size_for_grad(grad, rank): return _math_ops.reduce_prod(_array_ops.shape(grad)[-rank:]) @_ops.RegisterGradient("FFT") def _fft_grad(_, grad): size = _math_ops.cast(_fft_size_for_grad(grad, 1), grad.dtype) return ifft(grad) * size @_ops.RegisterGradient("IFFT") def _ifft_grad(_, grad): rsize = _math_ops.cast( 1. / _math_ops.cast(_fft_size_for_grad(grad, 1), grad.dtype.real_dtype), grad.dtype) return fft(grad) * rsize @_ops.RegisterGradient("FFT2D") def _fft2d_grad(_, grad): size = _math_ops.cast(_fft_size_for_grad(grad, 2), grad.dtype) return ifft2d(grad) * size @_ops.RegisterGradient("IFFT2D") def _ifft2d_grad(_, grad): rsize = _math_ops.cast( 1. / _math_ops.cast(_fft_size_for_grad(grad, 2), grad.dtype.real_dtype), grad.dtype) return fft2d(grad) * rsize @_ops.RegisterGradient("FFT3D") def _fft3d_grad(_, grad): size = _math_ops.cast(_fft_size_for_grad(grad, 3), grad.dtype) return ifft3d(grad) * size @_ops.RegisterGradient("IFFT3D") def _ifft3d_grad(_, grad): rsize = _math_ops.cast( 1. / _math_ops.cast(_fft_size_for_grad(grad, 3), grad.dtype.real_dtype), grad.dtype) return fft3d(grad) * rsize def _rfft_grad_helper(rank, irfft_fn): """Returns a gradient function for an RFFT of the provided rank.""" # Can't happen because we don't register a gradient for RFFT3D. assert rank in (1, 2), "Gradient for RFFT3D is not implemented." def _grad(op, grad): """A gradient function for RFFT with the provided `rank` and `irfft_fn`.""" fft_length = op.inputs[1] complex_dtype = grad.dtype real_dtype = complex_dtype.real_dtype input_shape = _array_ops.shape(op.inputs[0]) is_even = _math_ops.cast(1 - (fft_length[-1] % 2), complex_dtype) def _tile_for_broadcasting(matrix, t): expanded = _array_ops.reshape( matrix, _array_ops.concat([ _array_ops.ones([_array_ops.rank(t) - 2], _dtypes.int32), _array_ops.shape(matrix) ], 0)) return _array_ops.tile( expanded, _array_ops.concat([_array_ops.shape(t)[:-2], [1, 1]], 0)) def _mask_matrix(length): """Computes t_n = exp(sqrt(-1) * pi * n^2 / line_len).""" # TODO(rjryan): Speed up computation of twiddle factors using the # following recurrence relation and cache them across invocations of RFFT. # # t_n = exp(sqrt(-1) * pi * n^2 / line_len) # for n = 0, 1,..., line_len-1. # For n > 2, use t_n = t_{n-1}^2 / t_{n-2} * t_1^2 a = _array_ops.tile( _array_ops.expand_dims(_math_ops.range(length), 0), (length, 1)) b = _array_ops.transpose(a, [1, 0]) return _math_ops.exp( -2j * np.pi * _math_ops.cast(a * b, complex_dtype) / _math_ops.cast(length, complex_dtype)) def _ymask(length): """A sequence of [1+0j, -1+0j, 1+0j, -1+0j, ...] with length `length`.""" return _math_ops.cast(1 - 2 * (_math_ops.range(length) % 2), complex_dtype) y0 = grad[..., 0:1] if rank == 1: ym = grad[..., -1:] extra_terms = y0 + is_even * ym * _ymask(input_shape[-1]) elif rank == 2: # Create a mask matrix for y0 and ym. base_mask = _mask_matrix(input_shape[-2]) # Tile base_mask to match y0 in shape so that we can batch-matmul the # inner 2 dimensions. tiled_mask = _tile_for_broadcasting(base_mask, y0) y0_term = _math_ops.matmul(tiled_mask, _math_ops.conj(y0)) extra_terms = y0_term ym = grad[..., -1:] ym_term = _math_ops.matmul(tiled_mask, _math_ops.conj(ym)) inner_dim = input_shape[-1] ym_term = _array_ops.tile( ym_term, _array_ops.concat([ _array_ops.ones([_array_ops.rank(grad) - 1], _dtypes.int32), [inner_dim] ], 0)) * _ymask(inner_dim) extra_terms += is_even * ym_term # The gradient of RFFT is the IRFFT of the incoming gradient times a scaling # factor, plus some additional terms to make up for the components dropped # due to Hermitian symmetry. input_size = _math_ops.cast( _fft_size_for_grad(op.inputs[0], rank), real_dtype) the_irfft = irfft_fn(grad, fft_length) return 0.5 * (the_irfft * input_size + _math_ops.real(extra_terms)), None return _grad def _irfft_grad_helper(rank, rfft_fn): """Returns a gradient function for an IRFFT of the provided rank.""" # Can't happen because we don't register a gradient for IRFFT3D. assert rank in (1, 2), "Gradient for IRFFT3D is not implemented." def _grad(op, grad): """A gradient function for IRFFT with the provided `rank` and `rfft_fn`.""" # Generate a simple mask like [1.0, 2.0, ..., 2.0, 1.0] for even-length FFTs # and [1.0, 2.0, ..., 2.0] for odd-length FFTs. To reduce extra ops in the # graph we special-case the situation where the FFT length and last # dimension of the input are known at graph construction time. fft_length = op.inputs[1] fft_length_static = _tensor_util.constant_value(fft_length) if fft_length_static is not None: fft_length = fft_length_static real_dtype = grad.dtype if real_dtype == _dtypes.float32: complex_dtype = _dtypes.complex64 elif real_dtype == _dtypes.float64: complex_dtype = _dtypes.complex128 is_odd = _math_ops.mod(fft_length[-1], 2) input_last_dimension = _array_ops.shape(op.inputs[0])[-1] mask = _array_ops.concat( [[1.0], 2.0 * _array_ops.ones( [input_last_dimension - 2 + is_odd], real_dtype), _array_ops.ones([1 - is_odd], real_dtype)], 0) rsize = _math_ops.reciprocal(_math_ops.cast( _fft_size_for_grad(grad, rank), real_dtype)) # The gradient of IRFFT is the RFFT of the incoming gradient times a scaling # factor and a mask. The mask scales the gradient for the Hermitian # symmetric components of the RFFT by a factor of two, since these # components are de-duplicated in the RFFT. the_rfft = rfft_fn(grad, fft_length) return the_rfft * _math_ops.cast(rsize * mask, complex_dtype), None return _grad @tf_export("signal.fftshift") @dispatch.add_dispatch_support def fftshift(x, axes=None, name=None): """Shift the zero-frequency component to the center of the spectrum. This function swaps half-spaces for all axes listed (defaults to all). Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even. @compatibility(numpy) Equivalent to numpy.fft.fftshift. https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.fftshift.html @end_compatibility For example: ```python x = tf.signal.fftshift([ 0., 1., 2., 3., 4., -5., -4., -3., -2., -1.]) x.numpy() # array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.]) ``` Args: x: `Tensor`, input tensor. axes: `int` or shape `tuple`, optional Axes over which to shift. Default is None, which shifts all axes. name: An optional name for the operation. Returns: A `Tensor`, The shifted tensor. """ with _ops.name_scope(name, "fftshift") as name: x = _ops.convert_to_tensor(x) if axes is None: axes = tuple(range(x.shape.ndims)) shift = _array_ops.shape(x) // 2 elif isinstance(axes, int): shift = _array_ops.shape(x)[axes] // 2 else: rank = _array_ops.rank(x) # allows negative axis axes = _array_ops.where(_math_ops.less(axes, 0), axes + rank, axes) shift = _array_ops.gather(_array_ops.shape(x), axes) // 2 return manip_ops.roll(x, shift, axes, name) @tf_export("signal.ifftshift") @dispatch.add_dispatch_support def ifftshift(x, axes=None, name=None): """The inverse of fftshift. Although identical for even-length x, the functions differ by one sample for odd-length x. @compatibility(numpy) Equivalent to numpy.fft.ifftshift. https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.ifftshift.html @end_compatibility For example: ```python x = tf.signal.ifftshift([[ 0., 1., 2.],[ 3., 4., -4.],[-3., -2., -1.]]) x.numpy() # array([[ 4., -4., 3.],[-2., -1., -3.],[ 1., 2., 0.]]) ``` Args: x: `Tensor`, input tensor. axes: `int` or shape `tuple` Axes over which to calculate. Defaults to None, which shifts all axes. name: An optional name for the operation. Returns: A `Tensor`, The shifted tensor. """ with _ops.name_scope(name, "ifftshift") as name: x = _ops.convert_to_tensor(x) if axes is None: axes = tuple(range(x.shape.ndims)) shift = -(_array_ops.shape(x) // 2) elif isinstance(axes, int): shift = -(_array_ops.shape(x)[axes] // 2) else: rank = _array_ops.rank(x) # allows negative axis axes = _array_ops.where(_math_ops.less(axes, 0), axes + rank, axes) shift = -(_array_ops.gather(_array_ops.shape(x), axes) // 2) return manip_ops.roll(x, shift, axes, name) _ops.RegisterGradient("RFFT")(_rfft_grad_helper(1, irfft)) _ops.RegisterGradient("IRFFT")(_irfft_grad_helper(1, rfft)) _ops.RegisterGradient("RFFT2D")(_rfft_grad_helper(2, irfft2d)) _ops.RegisterGradient("IRFFT2D")(_irfft_grad_helper(2, rfft2d))