# Copyright (c) 2019 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 import paddle from paddle.fluid.framework import Program, program_guard class TestGatherTreeOp(OpTest): def setUp(self): self.op_type = "gather_tree" self.python_api = paddle.nn.functional.gather_tree max_length, batch_size, beam_size = 5, 2, 2 ids = np.random.randint( 0, high=10, size=(max_length, batch_size, beam_size) ) parents = np.random.randint( 0, high=beam_size, size=(max_length, batch_size, beam_size) ) self.inputs = {"Ids": ids, "Parents": parents} self.outputs = {'Out': self.backtrace(ids, parents)} def test_check_output(self): self.check_output(check_eager=True) @staticmethod def backtrace(ids, parents): out = np.zeros_like(ids) (max_length, batch_size, beam_size) = ids.shape for batch in range(batch_size): for beam in range(beam_size): out[max_length - 1, batch, beam] = ids[ max_length - 1, batch, beam ] parent = parents[max_length - 1, batch, beam] for step in range(max_length - 2, -1, -1): out[step, batch, beam] = ids[step, batch, parent] parent = parents[step, batch, parent] return out class TestGatherTreeOpAPI(unittest.TestCase): def test_case(self): paddle.enable_static() ids = paddle.static.data(name='ids', shape=[5, 2, 2], dtype='int64') parents = paddle.static.data( name='parents', shape=[5, 2, 2], dtype='int64', ) final_sequences = paddle.nn.functional.gather_tree(ids, parents) paddle.disable_static() def test_case2(self): ids = paddle.to_tensor( [[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]] ) parents = paddle.to_tensor( [[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]] ) final_sequences = paddle.nn.functional.gather_tree(ids, parents) class TestGatherTreeOpError(unittest.TestCase): def test_errors(self): paddle.enable_static() with program_guard(Program(), Program()): ids = paddle.static.data(name='ids', shape=[5, 2, 2], dtype='int64') parents = paddle.static.data( name='parents', shape=[5, 2, 2], dtype='int64' ) def test_Variable_ids(): # the input type must be Variable np_ids = np.random.random((5, 2, 2), dtype='int64') paddle.nn.functional.gather_tree(np_ids, parents) self.assertRaises(TypeError, test_Variable_ids) def test_Variable_parents(): # the input type must be Variable np_parents = np.random.random((5, 2, 2), dtype='int64') paddle.nn.functional.gather_tree(ids, np_parents) self.assertRaises(TypeError, test_Variable_parents) def test_type_ids(): # dtype must be int32 or int64 bad_ids = paddle.static.data( name='bad_ids', shape=[5, 2, 2], dtype='float32' ) paddle.nn.functional.gather_tree(bad_ids, parents) self.assertRaises(TypeError, test_type_ids) def test_type_parents(): # dtype must be int32 or int64 bad_parents = paddle.static.data( name='bad_parents', shape=[5, 2, 2], dtype='float32' ) paddle.nn.functional.gather_tree(ids, bad_parents) self.assertRaises(TypeError, test_type_parents) def test_ids_ndim(): bad_ids = paddle.static.data( name='bad_test_ids', shape=[5, 2], dtype='int64' ) paddle.nn.functional.gather_tree(bad_ids, parents) self.assertRaises(ValueError, test_ids_ndim) def test_parents_ndim(): bad_parents = paddle.static.data( name='bad_test_parents', shape=[5, 2], dtype='int64' ) paddle.nn.functional.gather_tree(ids, bad_parents) self.assertRaises(ValueError, test_parents_ndim) paddle.disable_static() if __name__ == "__main__": paddle.enable_static() unittest.main()