Python tensorflow.ifft() Examples

The following are 8 code examples of tensorflow.ifft(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source File: layers.py    From neuron with GNU General Public License v3.0 6 votes vote down vote up 
def call(self, inputx):
        
        if not inputx.dtype in [tf.complex64, tf.complex128]:
            print('Warning: inputx is not complex. Converting.', file=sys.stderr)
        
            # if inputx is float, this will assume 0 imag channel
            inputx = tf.cast(inputx, tf.complex64)
        
        # get the right fft
        if self.ndims == 1:
            ifft = tf.ifft
        elif self.ndims == 2:
            ifft = tf.ifft2d
        else:
            ifft = tf.ifft3d

        perm_dims = [0, self.ndims + 1] + list(range(1, self.ndims + 1))
        invert_perm_ndims = [0] + list(range(2, self.ndims + 2)) + [1]
        
        perm_inputx = K.permute_dimensions(inputx, perm_dims)  # [batch_size, nb_features, *vol_size]
        ifft_inputx = ifft(perm_inputx)
        return K.permute_dimensions(ifft_inputx, invert_perm_ndims)
Example #2
Source File: compact_bilinear_pooling.py    From RGB-N with MIT License 5 votes vote down vote up 
def _ifft(bottom, sequential, compute_size):
    if sequential:
        return sequential_batch_ifft(bottom, compute_size)
    else:
        return tf.ifft(bottom)
Example #3
Source File: fft_ops_test.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _tfIFFTForRank(self, rank):
    if rank == 1:
      return tf.ifft
    elif rank == 2:
      return tf.ifft2d
    elif rank == 3:
      return tf.ifft3d
    else:
      raise ValueError("invalid rank")
Example #4
Source File: tfmri.py    From dl-cs with MIT License 5 votes vote down vote up 
def fftc(im,
         data_format='channels_last',
         orthonorm=True,
         transpose=False,
         name='fftc'):
    """Centered FFT on last non-channel dimension."""
    with tf.name_scope(name):
        im_out = im
        if data_format == 'channels_last':
            permute_orig = np.arange(len(im.shape))
            permute = permute_orig.copy()
            permute[-2] = permute_orig[-1]
            permute[-1] = permute_orig[-2]
            im_out = tf.transpose(im_out, permute)

        if orthonorm:
            fftscale = tf.sqrt(tf.cast(im_out.shape[-1], tf.float32))
        else:
            fftscale = 1.0
        fftscale = tf.cast(fftscale, dtype=tf.complex64)

        im_out = fftshift(im_out, axis=-1)
        if transpose:
            im_out = tf.ifft(im_out) * fftscale
        else:
            im_out = tf.fft(im_out) / fftscale
        im_out = fftshift(im_out, axis=-1)

        if data_format == 'channels_last':
            im_out = tf.transpose(im_out, permute)

    return im_out
Example #5
Source File: HolE.py    From KagNet with MIT License 5 votes vote down vote up 
def _cconv(self, a, b):
		return tf.ifft(tf.fft(a) * tf.fft(b)).real
Example #6
Source File: HolE.py    From KagNet with MIT License 5 votes vote down vote up 
def _ccorr(self, a, b):
		a = tf.cast(a, tf.complex64)
		b = tf.cast(b, tf.complex64)
		return tf.real(tf.ifft(tf.conj(tf.fft(a)) * tf.fft(b)))
Example #7
Source File: HolE.py    From CPL with MIT License 5 votes vote down vote up 
def _cconv(self, a, b):
		return tf.ifft(tf.fft(a) * tf.fft(b)).real
Example #8
Source File: HolE.py    From CPL with MIT License 5 votes vote down vote up 
def _ccorr(self, a, b):
		a = tf.cast(a, tf.complex64)
		b = tf.cast(b, tf.complex64)
		return tf.real(tf.ifft(tf.conj(tf.fft(a)) * tf.fft(b)))