TianYuan
3 years ago
parent
ba978fca98
commit
5b9950cdd0
@ -1,146 +0,0 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections.abc as collections_abc
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import paddle
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_i0A = [
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-4.41534164647933937950E-18, 3.33079451882223809783E-17,
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-2.43127984654795469359E-16, 1.71539128555513303061E-15,
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-1.16853328779934516808E-14, 7.67618549860493561688E-14,
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-4.85644678311192946090E-13, 2.95505266312963983461E-12,
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-1.72682629144155570723E-11, 9.67580903537323691224E-11,
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-5.18979560163526290666E-10, 2.65982372468238665035E-9,
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-1.30002500998624804212E-8, 6.04699502254191894932E-8,
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-2.67079385394061173391E-7, 1.11738753912010371815E-6,
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-4.41673835845875056359E-6, 1.64484480707288970893E-5,
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-5.75419501008210370398E-5, 1.88502885095841655729E-4,
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-5.76375574538582365885E-4, 1.63947561694133579842E-3,
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-4.32430999505057594430E-3, 1.05464603945949983183E-2,
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-2.37374148058994688156E-2, 4.93052842396707084878E-2,
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-9.49010970480476444210E-2, 1.71620901522208775349E-1,
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-3.04682672343198398683E-1, 6.76795274409476084995E-1
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]
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_i0B = [
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-7.23318048787475395456E-18, -4.83050448594418207126E-18,
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4.46562142029675999901E-17, 3.46122286769746109310E-17,
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-2.82762398051658348494E-16, -3.42548561967721913462E-16,
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1.77256013305652638360E-15, 3.81168066935262242075E-15,
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-9.55484669882830764870E-15, -4.15056934728722208663E-14,
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1.54008621752140982691E-14, 3.85277838274214270114E-13,
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7.18012445138366623367E-13, -1.79417853150680611778E-12,
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-1.32158118404477131188E-11, -3.14991652796324136454E-11,
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1.18891471078464383424E-11, 4.94060238822496958910E-10,
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3.39623202570838634515E-9, 2.26666899049817806459E-8,
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2.04891858946906374183E-7, 2.89137052083475648297E-6,
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6.88975834691682398426E-5, 3.36911647825569408990E-3,
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8.04490411014108831608E-1
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]
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def piecewise(x, condlist, funclist, *args, **kw):
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n2 = len(funclist)
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# n = len(condlist)
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n = 1
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if n == n2 - 1: # compute the "otherwise" condition.
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condelse = ~paddle.any(condlist, axis=0, keepdim=True)
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condlist = paddle.concat([condlist, condelse], axis=0)
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n += 1
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elif n != n2:
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raise ValueError(
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"with {} condition(s), either {} or {} functions are expected"
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.format(n, n, n + 1))
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y = paddle.zeros(paddle.shape(x), x.dtype)
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for k in range(n):
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item = funclist[k]
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if not isinstance(item, collections_abc.Callable):
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y[condlist[k]] = item
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else:
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temp = condlist[k]
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if paddle.shape(x) == paddle.ones([1]):
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vals = x
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y = item(vals, *args, **kw)
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else:
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vals = x[temp]
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y[temp] = item(vals, *args, **kw)
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return y
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def _chbevl(x, vals):
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b0 = vals[0]
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b1 = 0.0
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for i in range(1, len(vals)):
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b2 = b1
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b1 = b0
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b0 = x * b1 - b2 + vals[i]
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return 0.5 * (b0 - b2)
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def _i0_1(x):
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out = paddle.exp(x) * _chbevl(x / 2.0 - 2, _i0A)
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return paddle.cast(out, dtype="float32")
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def _i0_2(x):
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out = paddle.exp(x) * _chbevl(32.0 / x - 2.0, _i0B) / paddle.sqrt(x)
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return paddle.cast(out, dtype="float32")
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def _i0_dispatcher(x):
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return (x, )
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def i0(x):
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x = paddle.abs(x)
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condlist = x <= paddle.full([1], 8.0)
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condlist = condlist.unsqueeze(0)
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return piecewise(x, condlist, [_i0_1, _i0_2])
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def _len_guards(M):
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"""Handle small or incorrect window lengths"""
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if int(M) != M or M < 0:
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raise ValueError('Window length M must be a non-negative integer')
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return M <= 1
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def _extend(M, sym):
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"""Extend window by 1 sample if needed for DFT-even symmetry"""
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if not sym:
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return M + 1, True
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else:
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return M, False
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def _truncate(w, needed):
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"""Truncate window by 1 sample if needed for DFT-even symmetry"""
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if needed:
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return w[:-1]
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else:
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return w
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def kaiser(M, beta, sym=True):
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if _len_guards(M):
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return paddle.ones(M)
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M, needs_trunc = _extend(M, sym)
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n = paddle.arange(0, M)
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alpha = (M - 1) / 2.0
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a = i0(beta * paddle.sqrt(1 - ((n - alpha) / alpha)**2.0))
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b = i0(paddle.full([1], beta))
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w = a / b
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return _truncate(w, needs_trunc)
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