def _compare(self, signals, norm, dct_type, atol=5e-4, rtol=5e-4):
"""Compares (I)DCT to SciPy (if available) and a NumPy implementation."""
np_dct = NP_DCT[dct_type](signals, norm)
tf_dct = dct_ops.dct(signals, type=dct_type, norm=norm).eval()
self.assertAllClose(np_dct, tf_dct, atol=atol, rtol=rtol)
np_idct = NP_IDCT[dct_type](signals, norm)
tf_idct = dct_ops.idct(signals, type=dct_type, norm=norm).eval()
self.assertAllClose(np_idct, tf_idct, atol=atol, rtol=rtol)
if fftpack:
scipy_dct = fftpack.dct(signals, type=dct_type, norm=norm)
self.assertAllClose(scipy_dct, tf_dct, atol=atol, rtol=rtol)
scipy_idct = fftpack.idct(signals, type=dct_type, norm=norm)
self.assertAllClose(scipy_idct, tf_idct, atol=atol, rtol=rtol)
# Verify inverse(forward(s)) == s, up to a normalization factor.
tf_idct_dct = dct_ops.idct(
tf_dct, type=dct_type, norm=norm).eval()
tf_dct_idct = dct_ops.dct(
tf_idct, type=dct_type, norm=norm).eval()
if norm is None:
if dct_type == 1:
tf_idct_dct *= 0.5 / (signals.shape[-1] - 1)
tf_dct_idct *= 0.5 / (signals.shape[-1] - 1)
else:
tf_idct_dct *= 0.5 / signals.shape[-1]
tf_dct_idct *= 0.5 / signals.shape[-1]
self.assertAllClose(signals, tf_idct_dct, atol=atol, rtol=rtol)
self.assertAllClose(signals, tf_dct_idct, atol=atol, rtol=rtol)
def _compare(self, signals, n, norm, dct_type, atol, rtol):
"""Compares (I)DCT to SciPy (if available) and a NumPy implementation."""
np_dct = NP_DCT[dct_type](signals, n=n, norm=norm)
tf_dct = dct_ops.dct(signals, n=n, type=dct_type, norm=norm)
self.assertEqual(tf_dct.dtype.as_numpy_dtype, signals.dtype)
self.assertAllClose(np_dct, tf_dct, atol=atol, rtol=rtol)
np_idct = NP_IDCT[dct_type](signals, n=None, norm=norm)
tf_idct = dct_ops.idct(signals, type=dct_type, norm=norm)
self.assertEqual(tf_idct.dtype.as_numpy_dtype, signals.dtype)
self.assertAllClose(np_idct, tf_idct, atol=atol, rtol=rtol)
if fftpack and dct_type != 4:
scipy_dct = fftpack.dct(signals, n=n, type=dct_type, norm=norm)
self.assertAllClose(scipy_dct, tf_dct, atol=atol, rtol=rtol)
scipy_idct = fftpack.idct(signals, type=dct_type, norm=norm)
self.assertAllClose(scipy_idct, tf_idct, atol=atol, rtol=rtol)
# Verify inverse(forward(s)) == s, up to a normalization factor.
# Since `n` is not implemented for IDCT operation, re-calculating tf_dct
# without n.
tf_dct = dct_ops.dct(signals, type=dct_type, norm=norm)
tf_idct_dct = dct_ops.idct(tf_dct, type=dct_type, norm=norm)
tf_dct_idct = dct_ops.dct(tf_idct, type=dct_type, norm=norm)
if norm is None:
if dct_type == 1:
tf_idct_dct *= 0.5 / (signals.shape[-1] - 1)
tf_dct_idct *= 0.5 / (signals.shape[-1] - 1)
else:
tf_idct_dct *= 0.5 / signals.shape[-1]
tf_dct_idct *= 0.5 / signals.shape[-1]
self.assertAllClose(signals, tf_idct_dct, atol=atol, rtol=rtol)
self.assertAllClose(signals, tf_dct_idct, atol=atol, rtol=rtol)
def _compare(self, signals, norm, dct_type, atol=5e-4, rtol=5e-4):
"""Compares (I)DCT to SciPy (if available) and a NumPy implementation."""
np_dct = NP_DCT[dct_type](signals, norm)
tf_dct = dct_ops.dct(signals, type=dct_type, norm=norm).eval()
self.assertAllClose(np_dct, tf_dct, atol=atol, rtol=rtol)
np_idct = NP_IDCT[dct_type](signals, norm)
tf_idct = dct_ops.idct(signals, type=dct_type, norm=norm).eval()
self.assertAllClose(np_idct, tf_idct, atol=atol, rtol=rtol)
if fftpack:
scipy_dct = fftpack.dct(signals, type=dct_type, norm=norm)
self.assertAllClose(scipy_dct, tf_dct, atol=atol, rtol=rtol)
scipy_idct = fftpack.idct(signals, type=dct_type, norm=norm)
self.assertAllClose(scipy_idct, tf_idct, atol=atol, rtol=rtol)
# Verify inverse(forward(s)) == s, up to a normalization factor.
tf_idct_dct = dct_ops.idct(tf_dct, type=dct_type, norm=norm).eval()
tf_dct_idct = dct_ops.dct(tf_idct, type=dct_type, norm=norm).eval()
if norm is None:
if dct_type == 1:
tf_idct_dct *= 0.5 / (signals.shape[-1] - 1)
tf_dct_idct *= 0.5 / (signals.shape[-1] - 1)
else:
tf_idct_dct *= 0.5 / signals.shape[-1]
tf_dct_idct *= 0.5 / signals.shape[-1]
self.assertAllClose(signals, tf_idct_dct, atol=atol, rtol=rtol)
self.assertAllClose(signals, tf_dct_idct, atol=atol, rtol=rtol)
def inverse_stdct(stdcts,
frame_length,
frame_step,
fft_length=None,
window_fn=window_ops.hann_window,
name=None):
"""
Inverse short-time discrete cosine transform.
Argument/s:
Returns:
"""
with ops.name_scope(name, 'inverse_stdct', [stdcts]):
stdcts = ops.convert_to_tensor(stdcts, name='stdcts')
stdcts.shape.with_rank_at_least(2)
frame_length = ops.convert_to_tensor(frame_length, name='frame_length')
frame_length.shape.assert_has_rank(0)
frame_step = ops.convert_to_tensor(frame_step, name='frame_step')
frame_step.shape.assert_has_rank(0)
if fft_length is None:
fft_length = _enclosing_power_of_two(frame_length)
else:
fft_length = ops.convert_to_tensor(fft_length, name='fft_length')
fft_length.shape.assert_has_rank(0)
frames = dct_ops.idct(stdcts, n=fft_length)
# frame_length may be larger or smaller than fft_length, so we pad or
# truncate frames to frame_length.
frame_length_static = tensor_util.constant_value(frame_length)
# If we don't know the shape of frames's inner dimension, pad and
# truncate to frame_length.
if (frame_length_static is None or frames.shape.ndims is None or
frames.shape.as_list()[-1] is None):
frames = frames[..., :frame_length]
frames_rank = array_ops.rank(frames)
frames_shape = array_ops.shape(frames)
paddings = array_ops.concat(
[array_ops.zeros([frames_rank - 1, 2],
dtype=frame_length.dtype),
[[0, math_ops.maximum(0, frame_length - frames_shape[-1])]]], 0)
frames = array_ops.pad(frames, paddings)
# We know frames's last dimension and frame_length statically. If they
# are different, then pad or truncate frames to frame_length.
elif frames.shape.as_list()[-1] > frame_length_static:
frames = frames[..., :frame_length_static]
elif frames.shape.as_list()[-1] < frame_length_static:
pad_amount = frame_length_static - frames.shape.as_list()[-1]
frames = array_ops.pad(frames,
[[0, 0]] * (frames.shape.ndims - 1) +
[[0, pad_amount]])
# The above code pads the inner dimension of frames to frame_length,
# but it does so in a way that may not be shape-inference friendly.
# Restore shape information if we are able to.
if frame_length_static is not None and frames.shape.ndims is not None:
frames.set_shape([None] * (frames.shape.ndims - 1) +
[frame_length_static])
# Optionally window and overlap-add the inner 2 dimensions of frames
# into a single [samples] dimension.
if window_fn is not None:
window = window_fn(frame_length, dtype=stdcts.dtype.real_dtype)
frames *= window
return reconstruction_ops.overlap_and_add(frames, frame_step)