# 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 numpy as np from op_test import OpTest class TestSeqAvgPool(OpTest): def convert_to_offset(self, lod): offset = [[0] for i in lod] for i, level in enumerate(lod): for seq_len in level: offset[i].append(offset[i][-1] + seq_len) return offset def set_data(self): self.op_type = 'sequence_pool' # one level, batch size is 4 x = np.random.uniform(0.1, 1, [11, 23]).astype('float32') lod = [[4, 1, 3, 3]] self.inputs = {'X': (x, lod)} offset = self.convert_to_offset(lod) out = np.zeros((4, 23)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): self.attrs = {'pooltype': "AVERAGE"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] out[i] = sub_x.mean(axis=0) def setUp(self): x, offset, out = self.set_data() self.compute(x, offset, out) def test_check_output(self): self.check_output() def test_check_grad(self): # Remove MaxIndex after check_grad is refined. self.outputs['MaxIndex'] = \ np.zeros(self.outputs['Out'].shape).astype('int32') self.check_grad(["X"], "Out") class TestSeqSumPool(TestSeqAvgPool): def compute(self, x, offset, out): self.attrs = {'pooltype': "SUM"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] out[i] = sub_x.sum(axis=0) class TestSeqMaxPool(TestSeqAvgPool): def set_data(self): self.op_type = 'sequence_pool' x = np.random.uniform(0.1, 1, [13, 23]).astype('float32') lod = [[4, 1, 3, 5]] offset = self.convert_to_offset(lod) for i in range(len(offset[0]) - 1): l = offset[0][i + 1] - offset[0][i] x[offset[0][i] + np.random.randint(l), :] += 2.0 self.inputs = {'X': (x, lod)} out = np.zeros((4, 23)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): self.attrs = {'pooltype': "MAX"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] out[i] = np.amax(sub_x, axis=0) class TestSeqSqrtPool(TestSeqAvgPool): def compute(self, x, offset, out): self.attrs = {'pooltype': "SQRT"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] seq_len = offset[0][i + 1] - offset[0][i] out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len) class TestSeqLastPool(TestSeqAvgPool): def compute(self, x, offset, out): self.attrs = {'pooltype': "LAST"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] out[i] = sub_x[-1, :] class TestSeqFirstPool(TestSeqAvgPool): def compute(self, x, offset, out): self.attrs = {'pooltype': "FIRST"} for i in range(len(offset[0]) - 1): sub_x = x[offset[0][i]:offset[0][i + 1], :] out[i] = sub_x[0, :] class TestSeqAvgPool2D(TestSeqAvgPool): def set_data(self): self.op_type = 'sequence_pool' # one level, batch size is 4 x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32') lod = [[4, 1, 3, 5]] self.inputs = {'X': (x, lod)} offset = self.convert_to_offset(lod) out = np.zeros((4, 3, 17)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): self.attrs = {'pooltype': "AVERAGE"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 17)) out[i] = np.reshape(sub_x.mean(axis=0), (3, 17)) class TestSeqSumPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): self.attrs = {'pooltype': "SUM"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 17)) out[i] = np.reshape(sub_x.sum(axis=0), (3, 17)) class TestSeqSqrtPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): self.attrs = {'pooltype': "SQRT"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 17)) seq_len = offset[0][i + 1] - offset[0][i] out[i] = np.reshape(sub_x.sum(axis=0) / np.sqrt(seq_len), (3, 17)) def test_check_grad(self): # Remove MaxIndex after check_grad is refined. self.outputs['MaxIndex'] = \ np.zeros(self.outputs['Out'].shape).astype('int32') self.check_grad(["X"], "Out", max_relative_error=0.06) class TestSeqMaxPool2D(TestSeqAvgPool2D): def set_data(self): self.op_type = 'sequence_pool' x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32') lod = [[4, 1, 3, 5]] self.inputs = {'X': (x, lod)} offset = self.convert_to_offset(lod) for i in range(len(offset[0]) - 1): l = offset[0][i + 1] - offset[0][i] x[offset[0][i] + np.random.randint(l), :] += 1.0 out = np.zeros((4, 3, 11)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): self.attrs = {'pooltype': "MAX"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 11)) out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) class TestSeqLastPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): self.attrs = {'pooltype': "LAST"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 17)) out[i] = np.reshape(sub_x[-1, :], (3, 17)) class TestSeqFirstPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): self.attrs = {'pooltype': "FIRST"} for i in range(len(offset[0]) - 1): sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 17)) out[i] = np.reshape(sub_x[0, :], (3, 17)) if __name__ == '__main__': unittest.main()