# Copyright (c) 2020 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 import numpy as np from op_test import OpTest import paddle.fluid as fluid from paddle.fluid import Program, program_guard class TestIndexSelectOp(OpTest): def setUp(self): self.python_api = paddle.index_select self.op_type = "index_select" self.init_dtype_type() index_np = np.random.randint( low=0, high=self.x_shape[self.dim], size=self.index_size ) x_np = np.random.random(self.x_shape).astype(self.x_type) self.inputs = {'X': x_np, 'Index': index_np} self.attrs = {'dim': self.dim} outer_loop = np.prod(self.x_shape[: self.dim]) x_reshape = [outer_loop] + list(self.x_shape[self.dim :]) x_np_reshape = np.reshape(x_np, tuple(x_reshape)) out_list = [] for i in range(outer_loop): for j in range(self.index_size): out_list.append(x_np_reshape[i, index_np[j]]) self.out_shape = list(self.x_shape) self.out_shape[self.dim] = self.index_size self.out_shape = tuple(self.out_shape) out = np.reshape(out_list, self.out_shape) self.outputs = {'Out': out} def init_dtype_type(self): self.dim = 1 self.x_type = np.float64 self.index_type = np.int64 self.x_shape = (100, 4, 5) self.index_size = 100 def test_check_output(self): self.check_output(check_eager=True) def test_check_grad_normal(self): self.check_grad(['X'], 'Out', check_eager=True) class TestIndexSelectOpCase2(TestIndexSelectOp): def init_dtype_type(self): self.x_type = np.float32 self.index_type = np.int32 self.dim = -2 self.x_shape = (10, 10, 4, 10) self.index_size = 10 class TestIndexSelectOpCaseSingleThread(TestIndexSelectOp): def init_dtype_type(self): if fluid.is_compiled_with_cuda(): fluid.set_flags({'FLAGS_cudnn_deterministic': True}) self.x_type = np.float32 self.index_type = np.int32 self.dim = -2 self.x_shape = (10, 10, 4, 10) self.index_size = 10 class TestIndexSelectAPI(unittest.TestCase): def input_data(self): self.data_x = np.array( [ [1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0], ] ) self.data_index = np.array([0, 1, 1]).astype('int32') def test_index_select_api(self): self.input_data() # case 1: with program_guard(Program(), Program()): x = fluid.layers.data(name='x', shape=[-1, 4]) index = fluid.layers.data( name='index', shape=[3], dtype='int32', append_batch_size=False ) z = paddle.index_select(x, index, axis=1) exe = fluid.Executor(fluid.CPUPlace()) (res,) = exe.run( feed={'x': self.data_x, 'index': self.data_index}, fetch_list=[z.name], return_numpy=False, ) expect_out = np.array( [[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]] ) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) # case 2: with program_guard(Program(), Program()): x = fluid.layers.data(name='x', shape=[-1, 4]) index = fluid.layers.data( name='index', shape=[3], dtype='int32', append_batch_size=False ) z = paddle.index_select(x, index) exe = fluid.Executor(fluid.CPUPlace()) (res,) = exe.run( feed={'x': self.data_x, 'index': self.data_index}, fetch_list=[z.name], return_numpy=False, ) expect_out = np.array( [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]] ) np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) def test_dygraph_api(self): self.input_data() # case 1: with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(self.data_x) index = fluid.dygraph.to_variable(self.data_index) z = paddle.index_select(x, index) np_z = z.numpy() expect_out = np.array( [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]] ) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) # case 2: with fluid.dygraph.guard(): x = fluid.dygraph.to_variable(self.data_x) index = fluid.dygraph.to_variable(self.data_index) z = paddle.index_select(x, index, axis=1) np_z = z.numpy() expect_out = np.array( [[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]] ) np.testing.assert_allclose(expect_out, np_z, rtol=1e-05) if __name__ == '__main__': unittest.main()