# Copyright (c) 2018 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 unittest import paddle.fluid as fluid import paddle.fluid.core as core from op_test import OpTest import numpy as np class TestSequenceReverseBase(OpTest): def initParameters(self): pass def setUp(self): self.size = (10, 3, 4) self.lod = [2, 3, 5] self.dtype = 'float32' self.initParameters() self.op_type = 'sequence_reverse' self.x = np.random.random(self.size).astype(self.dtype) self.y = self.get_output() self.inputs = {'X': (self.x, [self.lod, ]), } self.outputs = {'Y': (self.y, [self.lod, ]), } def get_output(self): tmp_x = np.reshape(self.x, newshape=[self.x.shape[0], -1]) tmp_y = np.ndarray(tmp_x.shape).astype(self.dtype) prev_idx = 0 for cur_len in self.lod: idx_range = range(prev_idx, prev_idx + cur_len) tmp_y[idx_range, :] = np.flip(tmp_x[idx_range, :], 0) prev_idx += cur_len return np.reshape(tmp_y, newshape=self.x.shape).astype(self.dtype) def test_output(self): self.check_output(0) def test_grad(self): self.check_grad(['X'], 'Y') class TestSequenceReserve1(TestSequenceReverseBase): def initParameters(self): self.size = (12, 10) self.lod = [4, 5, 3] class TestSequenceReverse2(TestSequenceReverseBase): def initParameters(self): self.size = (12, 10) self.lod = [12] if __name__ == '__main__': unittest.main()