fix paddle.audio.function.get_window security error (#47453)

python/paddle/audio/functional/window.py

@@ -19,129 +19,155 @@ import paddle
 from paddle import Tensor
 
 
+class WindowFunctionRegister(object):
+    def __init__(self):
+        self._functions_dict = dict()
+
+    def register(self, func=None):
+        def add_subfunction(func):
+            name = func.__name__
+            self._functions_dict[name] = func
+            return func
+
+        return add_subfunction
+
+    def get(self, name):
+        return self._functions_dict[name]
+
+
+window_function_register = WindowFunctionRegister()
+
+
+@window_function_register.register()
 def _cat(x: List[Tensor], data_type: str) -> Tensor:
     l = [paddle.to_tensor(_, data_type) for _ in x]
     return paddle.concat(l)
 
 
+@window_function_register.register()
 def _acosh(x: Union[Tensor, float]) -> Tensor:
     if isinstance(x, float):
         return math.log(x + math.sqrt(x**2 - 1))
     return paddle.log(x + paddle.sqrt(paddle.square(x) - 1))
 
 
+@window_function_register.register()
 def _extend(M: int, sym: bool) -> bool:
-    """Extend window by 1 sample if needed for DFT-even symmetry. """
+    """Extend window by 1 sample if needed for DFT-even symmetry."""
     if not sym:
         return M + 1, True
     else:
         return M, False
 
 
+@window_function_register.register()
 def _len_guards(M: int) -> bool:
-    """Handle small or incorrect window lengths. """
+    """Handle small or incorrect window lengths."""
     if int(M) != M or M < 0:
         raise ValueError('Window length M must be a non-negative integer')
 
     return M <= 1
 
 
+@window_function_register.register()
 def _truncate(w: Tensor, needed: bool) -> Tensor:
-    """Truncate window by 1 sample if needed for DFT-even symmetry. """
+    """Truncate window by 1 sample if needed for DFT-even symmetry."""
     if needed:
         return w[:-1]
     else:
         return w
 
 
-def _general_gaussian(M: int,
-                      p,
-                      sig,
-                      sym: bool = True,
-                      dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _general_gaussian(
+    M: int, p, sig, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a window with a generalized Gaussian shape.
     This function is consistent with scipy.signal.windows.general_gaussian().
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
 
     n = paddle.arange(0, M, dtype=dtype) - (M - 1.0) / 2.0
-    w = paddle.exp(-0.5 * paddle.abs(n / sig)**(2 * p))
+    w = paddle.exp(-0.5 * paddle.abs(n / sig) ** (2 * p))
 
     return _truncate(w, needs_trunc)
 
 
-def _general_cosine(M: int,
-                    a: float,
-                    sym: bool = True,
-                    dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _general_cosine(
+    M: int, a: float, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a generic weighted sum of cosine terms window.
     This function is consistent with scipy.signal.windows.general_cosine().
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
     fac = paddle.linspace(-math.pi, math.pi, M, dtype=dtype)
-    w = paddle.zeros((M, ), dtype=dtype)
+    w = paddle.zeros((M,), dtype=dtype)
     for k in range(len(a)):
         w += a[k] * paddle.cos(k * fac)
     return _truncate(w, needs_trunc)
 
 
-def _general_hamming(M: int,
-                     alpha: float,
-                     sym: bool = True,
-                     dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _general_hamming(
+    M: int, alpha: float, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a generalized Hamming window.
     This function is consistent with scipy.signal.windows.general_hamming()
     """
-    return _general_cosine(M, [alpha, 1. - alpha], sym, dtype=dtype)
+    return _general_cosine(M, [alpha, 1.0 - alpha], sym, dtype=dtype)
 
 
-def _taylor(M: int,
-            nbar=4,
-            sll=30,
-            norm=True,
-            sym: bool = True,
-            dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _taylor(
+    M: int, nbar=4, sll=30, norm=True, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a Taylor window.
     The Taylor window taper function approximates the Dolph-Chebyshev window's
     constant sidelobe level for a parameterized number of near-in sidelobes.
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
     # Original text uses a negative sidelobe level parameter and then negates
     # it in the calculation of B. To keep consistent with other methods we
     # assume the sidelobe level parameter to be positive.
-    B = 10**(sll / 20)
+    B = 10 ** (sll / 20)
     A = _acosh(B) / math.pi
-    s2 = nbar**2 / (A**2 + (nbar - 0.5)**2)
+    s2 = nbar**2 / (A**2 + (nbar - 0.5) ** 2)
     ma = paddle.arange(1, nbar, dtype=dtype)
 
-    Fm = paddle.empty((nbar - 1, ), dtype=dtype)
+    Fm = paddle.empty((nbar - 1,), dtype=dtype)
     signs = paddle.empty_like(ma)
     signs[::2] = 1
     signs[1::2] = -1
     m2 = ma * ma
     for mi in range(len(ma)):
-        numer = signs[mi] * paddle.prod(1 - m2[mi] / s2 / (A**2 +
-                                                           (ma - 0.5)**2))
+        numer = signs[mi] * paddle.prod(
+            1 - m2[mi] / s2 / (A**2 + (ma - 0.5) ** 2)
+        )
         if mi == 0:
-            denom = 2 * paddle.prod(1 - m2[mi] / m2[mi + 1:])
+            denom = 2 * paddle.prod(1 - m2[mi] / m2[mi + 1 :])
         elif mi == len(ma) - 1:
             denom = 2 * paddle.prod(1 - m2[mi] / m2[:mi])
         else:
-            denom = 2 * paddle.prod(1 - m2[mi] / m2[:mi]) * paddle.prod(
-                1 - m2[mi] / m2[mi + 1:])
+            denom = (
+                2
+                * paddle.prod(1 - m2[mi] / m2[:mi])
+                * paddle.prod(1 - m2[mi] / m2[mi + 1 :])
+            )
 
         Fm[mi] = numer / denom
 
     def W(n):
         return 1 + 2 * paddle.matmul(
             Fm.unsqueeze(0),
-            paddle.cos(2 * math.pi * ma.unsqueeze(1) * (n - M / 2. + 0.5) / M))
+            paddle.cos(2 * math.pi * ma.unsqueeze(1) * (n - M / 2.0 + 0.5) / M),
+        )
 
     w = W(paddle.arange(0, M, dtype=dtype))
 
@@ -153,6 +179,7 @@ def _taylor(M: int,
     return _truncate(w, needs_trunc)
 
 
+@window_function_register.register()
 def _hamming(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     """Compute a Hamming window.
     The Hamming window is a taper formed by using a raised cosine with
@@ -161,6 +188,7 @@ def _hamming(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     return _general_hamming(M, 0.54, sym, dtype=dtype)
 
 
+@window_function_register.register()
 def _hann(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     """Compute a Hann window.
     The Hann window is a taper formed by using a raised cosine or sine-squared
@@ -169,18 +197,18 @@ def _hann(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     return _general_hamming(M, 0.5, sym, dtype=dtype)
 
 
-def _tukey(M: int,
-           alpha=0.5,
-           sym: bool = True,
-           dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _tukey(
+    M: int, alpha=0.5, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a Tukey window.
     The Tukey window is also known as a tapered cosine window.
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
 
     if alpha <= 0:
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     elif alpha >= 1.0:
         return hann(M, sym=sym)
 
@@ -188,57 +216,58 @@ def _tukey(M: int,
 
     n = paddle.arange(0, M, dtype=dtype)
     width = int(alpha * (M - 1) / 2.0)
-    n1 = n[0:width + 1]
-    n2 = n[width + 1:M - width - 1]
-    n3 = n[M - width - 1:]
+    n1 = n[0 : width + 1]
+    n2 = n[width + 1 : M - width - 1]
+    n3 = n[M - width - 1 :]
 
     w1 = 0.5 * (1 + paddle.cos(math.pi * (-1 + 2.0 * n1 / alpha / (M - 1))))
     w2 = paddle.ones(n2.shape, dtype=dtype)
-    w3 = 0.5 * (1 + paddle.cos(math.pi * (-2.0 / alpha + 1 + 2.0 * n3 / alpha /
-                                          (M - 1))))
+    w3 = 0.5 * (
+        1
+        + paddle.cos(math.pi * (-2.0 / alpha + 1 + 2.0 * n3 / alpha / (M - 1)))
+    )
     w = paddle.concat([w1, w2, w3])
 
     return _truncate(w, needs_trunc)
 
 
-def _kaiser(M: int,
-            beta: float,
-            sym: bool = True,
-            dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _kaiser(
+    M: int, beta: float, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a Kaiser window.
     The Kaiser window is a taper formed by using a Bessel function.
     """
     raise NotImplementedError()
 
 
-def _gaussian(M: int,
-              std: float,
-              sym: bool = True,
-              dtype: str = 'float64') -> Tensor:
+@window_function_register.register()
+def _gaussian(
+    M: int, std: float, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
     """Compute a Gaussian window.
     The Gaussian widows has a Gaussian shape defined by the standard deviation(std).
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
 
     n = paddle.arange(0, M, dtype=dtype) - (M - 1.0) / 2.0
     sig2 = 2 * std * std
-    w = paddle.exp(-n**2 / sig2)
+    w = paddle.exp(-(n**2) / sig2)
 
     return _truncate(w, needs_trunc)
 
 
-def _exponential(M: int,
-                 center=None,
-                 tau=1.,
-                 sym: bool = True,
-                 dtype: str = 'float64') -> Tensor:
-    """Compute an exponential (or Poisson) window. """
+@window_function_register.register()
+def _exponential(
+    M: int, center=None, tau=1.0, sym: bool = True, dtype: str = 'float64'
+) -> Tensor:
+    """Compute an exponential (or Poisson) window."""
     if sym and center is not None:
         raise ValueError("If sym==True, center must be None.")
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
 
     if center is None:
@@ -250,11 +279,11 @@ def _exponential(M: int,
     return _truncate(w, needs_trunc)
 
 
+@window_function_register.register()
 def _triang(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
-    """Compute a triangular window.
-    """
+    """Compute a triangular window."""
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
 
     n = paddle.arange(1, (M + 1) // 2 + 1, dtype=dtype)
@@ -268,22 +297,25 @@ def _triang(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     return _truncate(w, needs_trunc)
 
 
+@window_function_register.register()
 def _bohman(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     """Compute a Bohman window.
     The Bohman window is the autocorrelation of a cosine window.
     """
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
 
     fac = paddle.abs(paddle.linspace(-1, 1, M, dtype=dtype)[1:-1])
     w = (1 - fac) * paddle.cos(math.pi * fac) + 1.0 / math.pi * paddle.sin(
-        math.pi * fac)
+        math.pi * fac
+    )
     w = _cat([0, w, 0], dtype)
 
     return _truncate(w, needs_trunc)
 
 
+@window_function_register.register()
 def _blackman(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     """Compute a Blackman window.
     The Blackman window is a taper formed by using the first three terms of
@@ -294,25 +326,27 @@ def _blackman(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
     return _general_cosine(M, [0.42, 0.50, 0.08], sym, dtype=dtype)
 
 
+@window_function_register.register()
 def _cosine(M: int, sym: bool = True, dtype: str = 'float64') -> Tensor:
-    """Compute a window with a simple cosine shape.
-    """
+    """Compute a window with a simple cosine shape."""
     if _len_guards(M):
-        return paddle.ones((M, ), dtype=dtype)
+        return paddle.ones((M,), dtype=dtype)
     M, needs_trunc = _extend(M, sym)
-    w = paddle.sin(math.pi / M * (paddle.arange(0, M, dtype=dtype) + .5))
+    w = paddle.sin(math.pi / M * (paddle.arange(0, M, dtype=dtype) + 0.5))
 
     return _truncate(w, needs_trunc)
 
 
-def get_window(window: Union[str, Tuple[str, float]],
-               win_length: int,
-               fftbins: bool = True,
-               dtype: str = 'float64') -> Tensor:
+def get_window(
+    window: Union[str, Tuple[str, float]],
+    win_length: int,
+    fftbins: bool = True,
+    dtype: str = 'float64',
+) -> Tensor:
     """Return a window of a given length and type.
 
     Args:
-        window (Union[str, Tuple[str, float]]): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'.
+        window (Union[str, Tuple[str, float]]): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'general_gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'.
         win_length (int): Number of samples.
         fftbins (bool, optional): If True, create a "periodic" window. Otherwise, create a "symmetric" window, for use in filter design. Defaults to True.
         dtype (str, optional): The data type of the return window. Defaults to 'float64'.
@@ -340,19 +374,22 @@ def get_window(window: Union[str, Tuple[str, float]],
             args = window[1:]
     elif isinstance(window, str):
         if window in ['gaussian', 'exponential']:
-            raise ValueError("The '" + window + "' window needs one or "
-                             "more parameters -- pass a tuple.")
+            raise ValueError(
+                "The '" + window + "' window needs one or "
+                "more parameters -- pass a tuple."
+            )
         else:
             winstr = window
     else:
-        raise ValueError("%s as window type is not supported." %
-                         str(type(window)))
+        raise ValueError(
+            "%s as window type is not supported." % str(type(window))
+        )
 
     try:
-        winfunc = eval('_' + winstr)
-    except NameError as e:
+        winfunc = window_function_register.get('_' + winstr)
+    except KeyError as e:
         raise ValueError("Unknown window type.") from e
 
-    params = (win_length, ) + args
+    params = (win_length,) + args
     kwargs = {'sym': sym}
     return winfunc(*params, dtype=dtype, **kwargs)