# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import paddle import unittest from op_test import OpTest def overlap_add(x, hop_length, axis=-1): assert axis in [0, -1], 'axis should be 0/-1.' assert len(x.shape) >= 2, 'Input dims shoulb be >= 2.' squeeze_output = False if len(x.shape) == 2: squeeze_output = True dim = 0 if axis == -1 else -1 x = np.expand_dims(x, dim) # batch n_frames = x.shape[axis] frame_length = x.shape[1] if axis == 0 else x.shape[-2] # Assure no gaps between frames. assert ( 0 < hop_length <= frame_length ), f'hop_length should be in (0, frame_length({frame_length})], but got {hop_length}.' seq_length = (n_frames - 1) * hop_length + frame_length reshape_output = False if len(x.shape) > 3: reshape_output = True if axis == 0: target_shape = [seq_length] + list(x.shape[2:]) x = x.reshape(n_frames, frame_length, np.product(x.shape[2:])) else: target_shape = list(x.shape[:-2]) + [seq_length] x = x.reshape(np.product(x.shape[:-2]), frame_length, n_frames) if axis == 0: x = x.transpose((2, 1, 0)) y = np.zeros(shape=[np.product(x.shape[:-2]), seq_length], dtype=x.dtype) for i in range(x.shape[0]): for frame in range(x.shape[-1]): sample = frame * hop_length y[i, sample : sample + frame_length] += x[i, :, frame] if axis == 0: y = y.transpose((1, 0)) if reshape_output: y = y.reshape(target_shape) if squeeze_output: y = y.squeeze(-1) if axis == 0 else y.squeeze(0) return y class TestOverlapAddOp(OpTest): def setUp(self): self.op_type = "overlap_add" self.python_api = paddle.signal.overlap_add self.shape, self.type, self.attrs = self.initTestCase() self.inputs = { 'X': np.random.random(size=self.shape).astype(self.type), } self.outputs = {'Out': overlap_add(x=self.inputs['X'], **self.attrs)} def initTestCase(self): input_shape = (50, 3) input_type = 'float64' attrs = { 'hop_length': 4, 'axis': -1, } return input_shape, input_type, attrs def test_check_output(self): paddle.enable_static() self.check_output(check_eager=True) paddle.disable_static() def test_check_grad_normal(self): paddle.enable_static() self.check_grad(['X'], 'Out', check_eager=True) paddle.disable_static() class TestCase1(TestOverlapAddOp): def initTestCase(self): input_shape = (3, 50) input_type = 'float64' attrs = { 'hop_length': 4, 'axis': 0, } return input_shape, input_type, attrs class TestCase2(TestOverlapAddOp): def initTestCase(self): input_shape = (2, 40, 5) input_type = 'float64' attrs = { 'hop_length': 10, 'axis': -1, } return input_shape, input_type, attrs class TestCase3(TestOverlapAddOp): def initTestCase(self): input_shape = (5, 40, 2) input_type = 'float64' attrs = { 'hop_length': 10, 'axis': 0, } return input_shape, input_type, attrs class TestCase4(TestOverlapAddOp): def initTestCase(self): input_shape = (3, 5, 12, 8) input_type = 'float64' attrs = { 'hop_length': 5, 'axis': -1, } return input_shape, input_type, attrs class TestCase5(TestOverlapAddOp): def initTestCase(self): input_shape = (8, 12, 5, 3) input_type = 'float64' attrs = { 'hop_length': 5, 'axis': 0, } return input_shape, input_type, attrs if __name__ == '__main__': unittest.main()