# 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. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import paddle.fluid as fluid from paddle.fluid import compiler, Program, program_guard import paddle # Situation 1: shape is a list(without tensor) class TestExpandV2OpRank1(OpTest): def setUp(self): self.op_type = "expand_v2" self.init_data() self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")} self.attrs = {'shape': self.shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.shape = [100] self.expand_times = [1] def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out') class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1): def init_data(self): self.ori_shape = [120] self.shape = [2, 120] self.expand_times = [2, 1] class TestExpandV2OpRank2(TestExpandV2OpRank1): def init_data(self): self.ori_shape = [1, 140] self.shape = [12, 140] self.expand_times = [12, 1] class TestExpandV2OpRank3_Corner(TestExpandV2OpRank1): def init_data(self): self.ori_shape = (2, 10, 5) self.shape = (2, 10, 5) self.expand_times = (1, 1, 1) class TestExpandV2OpRank4(TestExpandV2OpRank1): def init_data(self): self.ori_shape = (2, 4, 5, 7) self.shape = (-1, -1, -1, -1) self.expand_times = (1, 1, 1, 1) # Situation 2: shape is a list(with tensor) class TestExpandV2OpRank1_tensor_attr(OpTest): def setUp(self): self.op_type = "expand_v2" self.init_data() expand_shapes_tensor = [] for index, ele in enumerate(self.expand_shape): expand_shapes_tensor.append(("x" + str(index), np.ones( (1)).astype('int32') * ele)) self.inputs = { 'X': np.random.random(self.ori_shape).astype("float64"), 'expand_shapes_tensor': expand_shapes_tensor, } self.attrs = {"shape": self.infer_expand_shape} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [1] self.expand_shape = [100] self.infer_expand_shape = [-1] def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out') class TestExpandV2OpRank2_Corner_tensor_attr(TestExpandV2OpRank1_tensor_attr): def init_data(self): self.ori_shape = [12, 14] self.expand_times = [1, 1] self.expand_shape = [12, 14] self.infer_expand_shape = [12, -1] # Situation 3: shape is a tensor class TestExpandV2OpRank1_tensor(OpTest): def setUp(self): self.op_type = "expand_v2" self.init_data() self.inputs = { 'X': np.random.random(self.ori_shape).astype("float64"), 'Shape': np.array(self.expand_shape).astype("int32"), } self.attrs = {} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} def init_data(self): self.ori_shape = [100] self.expand_times = [2, 1] self.expand_shape = [2, 100] def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out') # Situation 4: input x is Integer class TestExpandV2OpInteger(OpTest): def setUp(self): self.op_type = "expand_v2" self.inputs = { 'X': np.random.randint( 10, size=(2, 4, 5)).astype("int32") } self.attrs = {'shape': [2, 4, 5]} output = np.tile(self.inputs['X'], (1, 1, 1)) self.outputs = {'Out': output} def test_check_output(self): self.check_output() # Situation 5: input x is Bool class TestExpandV2OpBoolean(OpTest): def setUp(self): self.op_type = "expand_v2" self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")} self.attrs = {'shape': [2, 4, 5]} output = np.tile(self.inputs['X'], (1, 1, 1)) self.outputs = {'Out': output} def test_check_output(self): self.check_output() # Situation 56: input x is Integer class TestExpandV2OpInt64_t(OpTest): def setUp(self): self.op_type = "expand_v2" self.inputs = { 'X': np.random.randint( 10, size=(2, 4, 5)).astype("int64") } self.attrs = {'shape': [2, 4, 5]} output = np.tile(self.inputs['X'], (1, 1, 1)) self.outputs = {'Out': output} def test_check_output(self): self.check_output() class TestExpandV2Error(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): x1 = fluid.create_lod_tensor( np.array([[-1]]), [[1]], fluid.CPUPlace()) shape = [2, 2] self.assertRaises(TypeError, paddle.tensor.expand, x1, shape) x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8") self.assertRaises(TypeError, paddle.tensor.expand, x2, shape) x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool") x3.stop_gradient = False self.assertRaises(ValueError, paddle.tensor.expand, x3, shape) # Test python API class TestExpandV2API(unittest.TestCase): def test_api(self): input = np.random.random([12, 14]).astype("float32") x = fluid.layers.data( name='x', shape=[12, 14], append_batch_size=False, dtype="float32") positive_2 = fluid.layers.fill_constant([1], "int32", 12) expand_shape = fluid.layers.data( name="expand_shape", shape=[2], append_batch_size=False, dtype="int32") out_1 = paddle.expand(x, shape=[12, 14]) out_2 = paddle.expand(x, shape=[positive_2, 14]) out_3 = paddle.expand(x, shape=expand_shape) g0 = fluid.backward.calc_gradient(out_2, x) exe = fluid.Executor(place=fluid.CPUPlace()) res_1, res_2, res_3 = exe.run(fluid.default_main_program(), feed={ "x": input, "expand_shape": np.array([12, 14]).astype("int32") }, fetch_list=[out_1, out_2, out_3]) assert np.array_equal(res_1, np.tile(input, (1, 1))) assert np.array_equal(res_2, np.tile(input, (1, 1))) assert np.array_equal(res_3, np.tile(input, (1, 1))) if __name__ == "__main__": unittest.main()