def test_stft_istft_identity(ctx, window_size, stride, fft_size, window_type,
center, pad_mode):
backend = ctx.backend[0].split(":")[0]
if backend == 'cuda':
pytest.skip(
'CUDA Convolution N-D is only supported in CUDNN extension')
x_shape = create_stft_input_shape(window_size)
x = np.random.randn(*x_shape)
# Skip for NOLA condition violation
length = x_shape[1]
if is_nola_violation(window_type, window_size, stride, fft_size, length,
center):
pytest.skip('NOLA condition violation.')
return
x = nn.Variable.from_numpy_array(x)
with nn.context_scope(ctx):
yr, yi = F.stft(x, window_size, stride, fft_size, window_type, center,
pad_mode)
z = F.istft(yr,
yi,
window_size,
stride,
fft_size,
window_type,
center,
pad_mode="constant")
z.forward()
assert (np.allclose(x.d, z.d, atol=1e-5, rtol=1e-5))
def test_istft_double_backward(ctx, seed, window_size, stride, fft_size, window_type, center, pad_mode, as_stft_backward):
backend = ctx.backend[0].split(":")[0]
if backend == 'cuda':
pytest.skip('CUDA Convolution N-D is only supported in CUDNN extension')
if not as_stft_backward:
if pad_mode != "constant":
pytest.skip(
'`pad_mode != "constant"` is only for `as_stft_backward == True`')
from nbla_test_utils import backward_function_tester
rng = np.random.RandomState(seed)
# Generate istft inputs by calling stft
x_shape = create_stft_input_shape(window_size)
stft_input = rng.randn(*x_shape).astype(np.float32)
y_r, y_i = ref_stft(stft_input, window_size, stride,
fft_size, window_type, center, pad_mode, False)
istft_inputs = [y_r, y_i]
if not as_stft_backward:
# Skip for NOLA condition violation
length = x_shape[1]
if is_nola_violation(window_type, window_size, stride, fft_size, length, center):
pytest.skip('NOLA condition violation.')
rng = np.random.RandomState(seed)
func_args = [window_size, stride, fft_size,
window_type, center, pad_mode, as_stft_backward]
backward_function_tester(rng, F.istft,
inputs=istft_inputs,
func_args=func_args,
ctx=ctx,
atol_accum=6e-2)
def test_istft_forward_backward(ctx, seed, window_size, stride, fft_size, window_type, center, pad_mode, as_stft_backward):
backend = ctx.backend[0].split(":")[0]
if backend == 'cuda':
pytest.skip('CUDA Convolution N-D is only supported in CUDNN extension')
if not as_stft_backward:
if pad_mode != "constant":
pytest.skip(
'`pad_mode != "constant"` is only for `as_stft_backward == True`')
func_name = "ISTFTCuda" if backend == 'cudnn' else "ISTFT"
from nbla_test_utils import function_tester
rng = np.random.RandomState(seed)
# Generate istft inputs by calling stft
x_shape = create_stft_input_shape(window_size)
stft_input = rng.randn(*x_shape).astype(np.float32)
y_r, y_i = ref_stft(stft_input, window_size, stride,
fft_size, window_type, center, pad_mode, False)
istft_inputs = [y_r, y_i]
# Check violation of NOLA condition
if not as_stft_backward:
length = x_shape[1]
if is_nola_violation(window_type, window_size, stride, fft_size, length, center):
check_nola_violation(
y_r, y_i, window_size, stride, fft_size, window_type, center, pad_mode, as_stft_backward)
return
function_tester(rng, F.istft, ref_istft, istft_inputs, func_args=[
window_size, stride, fft_size, window_type, center, pad_mode, as_stft_backward], ctx=ctx, func_name=func_name, atol_f=1e-5, atol_b=3e-2, dstep=1e-2)
def ref_istft_torch(y_r, y_i, window_size, stride, fft_size, window_type, center):
y_r = np.reshape(y_r, y_r.shape + (1,))
y_i = np.reshape(y_i, y_i.shape + (1,))
y = np.concatenate((y_r, y_i), axis=3)
y = torch.tensor(y)
y = torch.view_as_complex(y)
window = torch.tensor(create_window_func(window_type, window_size))
x_shape = create_stft_input_shape(window_size)
length = x_shape[1]
x = torch.istft(y, n_fft=fft_size, hop_length=stride,
win_length=window_size, window=window, center=center, length=length)
return x
def ref_istft(y_r, y_i, window_size, stride, fft_size, window_type, center,
pad_mode, as_stft_backward):
if not as_stft_backward:
# Use librosa.istft as the forward reference.
# Convert to librosa.istft input format.
y = y_r + 1j * y_i
# Get original signal length.
x_shape = create_stft_input_shape(window_size)
length = x_shape[1]
# librosa.istft does not support batched input.
b = y.shape[0]
xs = []
for i in range(b):
x = librosa.istft(y[i],
hop_length=stride,
win_length=window_size,
window=window_type,
center=center,
length=length)
xs.append(x)
return np.array(xs)
else:
# Use F.stft backward as the reference
y_r = nn.Variable.from_numpy_array(y_r)
y_i = nn.Variable.from_numpy_array(y_i)
# Just create stft inputs
x = F.istft(y_r, y_i, window_size, stride, fft_size, window_type,
center, pad_mode, True)
# Execute istft backward
x.need_grad = True
x.grad.zero()
z_r, z_i = F.stft(x, window_size, stride, fft_size, window_type,
center, pad_mode)
z_r.g = y_r.d
z_i.g = y_i.d
z = F.sink(z_r, z_i, one_input_grad=False)
z.forward()
z.backward()
return x.g