def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
# Determine FFT dimensions and functions
pad_dims = fft_utils_tf.get_fft_pad_dims(dt, kt)
optim_dims = fft_utils_tf.optimize_dims(pad_dims, mode)
fft_fwd, fft_rev = fft_utils_tf.get_fft_tf_fns(dt.shape.ndims)
# Run convolution of data 'd' with kernel 'k'
dk_fft = fft_fwd(kt, fft_length=optim_dims)
dconv = fft_utils_tf.convolve(dt, dk_fft, optim_dims, fft_fwd, fft_rev)
# Extract patch from result matching dimensions of original data array
return fft_utils_tf.extract(dconv, tf.shape(dt), pad_dims)
def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
pad_dims = fft_utils_tf.get_fft_pad_dims(dt, kt)
return fft_utils_tf.optimize_dims(pad_dims, mode)
def tf_fn():
dt = tf.constant(d, dtype=tf.float32)
kt = tf.constant(k, dtype=tf.float32)
return fft_utils_tf.get_fft_pad_dims(dt, kt)