# 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 numpy as np import unittest import paddle import paddle.fluid as fluid from op_test import OpTest class TestStackOpBase(OpTest): def initDefaultParameters(self): self.num_inputs = 4 self.input_dim = (5, 6, 7) self.axis = 0 self.dtype = 'float64' def initParameters(self): pass def get_x_names(self): x_names = [] for i in range(self.num_inputs): x_names.append('x{}'.format(i)) return x_names def setUp(self): self.initDefaultParameters() self.initParameters() self.op_type = 'stack' self.x = [] for i in range(self.num_inputs): self.x.append( np.random.random(size=self.input_dim).astype(self.dtype)) tmp = [] x_names = self.get_x_names() for i in range(self.num_inputs): tmp.append((x_names[i], self.x[i])) self.inputs = {'X': tmp} self.outputs = {'Y': np.stack(self.x, axis=self.axis)} self.attrs = {'axis': self.axis} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(self.get_x_names(), 'Y') class TestStackOp1(TestStackOpBase): def initParameters(self): self.num_inputs = 16 class TestStackOp2(TestStackOpBase): def initParameters(self): self.num_inputs = 20 class TestStackOp3(TestStackOpBase): def initParameters(self): self.axis = -1 class TestStackOp4(TestStackOpBase): def initParameters(self): self.axis = -4 class TestStackOp5(TestStackOpBase): def initParameters(self): self.axis = 1 class TestStackOp6(TestStackOpBase): def initParameters(self): self.axis = 3 class TestStackAPIWithLoDTensorArray(unittest.TestCase): """ Test stack api when the input(x) is a LoDTensorArray. """ def setUp(self): self.axis = 1 self.iter_num = 3 self.input_shape = [2, 3] self.x = np.random.random(self.input_shape).astype("float32") self.place = fluid.CUDAPlace(0) \ if fluid.is_compiled_with_cuda() else fluid.CPUPlace() self.set_program() def set_program(self): self.program = fluid.Program() with fluid.program_guard(self.program): input = fluid.layers.assign(self.x) tensor_array = fluid.layers.create_array(dtype='float32') zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64") for i in range(self.iter_num): fluid.layers.array_write(input, zero + i, tensor_array) self.out_var = fluid.layers.stack(tensor_array, axis=self.axis) def test_case(self): self.assertTrue(self.out_var.shape[self.axis] == -1) exe = fluid.Executor(self.place) res = exe.run(self.program, fetch_list=self.out_var) self.assertTrue( np.array_equal( res[0], np.stack( [self.x] * self.iter_num, axis=self.axis))) class TestTensorStackAPIWithLoDTensorArray(unittest.TestCase): """ Test stack api when the input(x) is a LoDTensorArray. """ def setUp(self): self.axis = 1 self.iter_num = 3 self.input_shape = [2, 3] self.x = np.random.random(self.input_shape).astype("float32") self.place = fluid.CUDAPlace(0) \ if fluid.is_compiled_with_cuda() else fluid.CPUPlace() self.set_program() def set_program(self): self.program = fluid.Program() with fluid.program_guard(self.program): input = fluid.layers.assign(self.x) tensor_array = fluid.layers.create_array(dtype='float32') zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64") for i in range(self.iter_num): fluid.layers.array_write(input, zero + i, tensor_array) self.out_var = paddle.stack(tensor_array, axis=self.axis) def test_case(self): self.assertTrue(self.out_var.shape[self.axis] == -1) exe = fluid.Executor(self.place) res = exe.run(self.program, fetch_list=self.out_var) self.assertTrue( np.array_equal( res[0], np.stack( [self.x] * self.iter_num, axis=self.axis))) class API_test(unittest.TestCase): def test_out(self): with fluid.program_guard(fluid.Program(), fluid.Program()): data1 = fluid.layers.data('data1', shape=[1, 2], dtype='float64') data2 = fluid.layers.data('data2', shape=[1, 2], dtype='float64') data3 = fluid.layers.data('data3', shape=[1, 2], dtype='float64') result_stack = paddle.stack([data1, data2, data3], axis=0) place = fluid.CPUPlace() exe = fluid.Executor(place) input1 = np.random.random([1, 2]).astype('float64') input2 = np.random.random([1, 2]).astype('float64') input3 = np.random.random([1, 2]).astype('float64') result, = exe.run( feed={"data1": input1, "data2": input2, "data3": input3}, fetch_list=[result_stack]) expected_result = np.stack([input1, input2, input3], axis=0) self.assertTrue(np.allclose(expected_result, result)) def test_single_tensor_error(self): with fluid.program_guard(fluid.Program(), fluid.Program()): x = paddle.rand([2, 3]) self.assertRaises(TypeError, paddle.stack, x) class API_DygraphTest(unittest.TestCase): def test_out(self): data1 = np.array([[1.0, 2.0]]) data2 = np.array([[3.0, 4.0]]) data3 = np.array([[5.0, 6.0]]) with fluid.dygraph.guard(): x1 = fluid.dygraph.to_variable(data1) x2 = fluid.dygraph.to_variable(data2) x3 = fluid.dygraph.to_variable(data3) result = paddle.stack([x1, x2, x3]) result_np = result.numpy() expected_result = np.stack([data1, data2, data3]) self.assertTrue(np.allclose(expected_result, result_np)) with fluid.dygraph.guard(): y1 = fluid.dygraph.to_variable(data1) result = paddle.stack([y1], axis=0) result_np_2 = result.numpy() expected_result_2 = np.stack([data1], axis=0) self.assertTrue(np.allclose(expected_result_2, result_np_2)) def test_single_tensor_error(self): with fluid.dygraph.guard(): x = paddle.to_tensor([1, 2, 3]) self.assertRaises(Exception, paddle.stack, x) if __name__ == '__main__': unittest.main()