#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. from __future__ import print_function import unittest import numpy as np import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from op_test import OpTest from paddle.fluid import compiler, Program, program_guard from paddle.fluid.op import Operator from paddle.fluid.backward import append_backward class TestWhereOp(OpTest): def setUp(self): self.op_type = "where" self.init_config() self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y} self.outputs = {'Out': np.where(self.cond, self.x, self.y)} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X', 'Y'], 'Out') def init_config(self): self.x = np.random.uniform(-3, 5, (100)).astype("float64") self.y = np.random.uniform(-3, 5, (100)).astype("float64") self.cond = np.zeros((100)).astype("bool") class TestWhereOp2(TestWhereOp): def init_config(self): self.x = np.random.uniform(-5, 5, (60, 2)).astype("float64") self.y = np.random.uniform(-5, 5, (60, 2)).astype("float64") self.cond = np.ones((60, 2)).astype("bool") class TestWhereOp3(TestWhereOp): def init_config(self): self.x = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64") self.y = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64") self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool) class TestWhereAPI(unittest.TestCase): def setUp(self): self.init_data() def init_data(self): self.shape = [10, 15] self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool) self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32) self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32) self.out = np.where(self.cond, self.x, self.y) def ref_x_backward(self, dout): return np.where(self.cond == True, dout, 0) def ref_y_backward(self, dout): return np.where(self.cond == False, dout, 0) def test_api(self, use_cuda=False): for x_stop_gradient in [False, True]: for y_stop_gradient in [False, True]: with fluid.program_guard(Program(), Program()): cond = fluid.layers.data( name='cond', shape=self.shape, dtype='bool') x = fluid.layers.data( name='x', shape=self.shape, dtype='float32') y = fluid.layers.data( name='y', shape=self.shape, dtype='float32') x.stop_gradient = x_stop_gradient y.stop_gradient = y_stop_gradient result = paddle.where(cond, x, y) append_backward(layers.mean(result)) for use_cuda in [False, True]: if use_cuda and not fluid.core.is_compiled_with_cuda(): break place = fluid.CUDAPlace( 0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) fetch_list = [result, result.grad_name] if x_stop_gradient is False: fetch_list.append(x.grad_name) if y_stop_gradient is False: fetch_list.append(y.grad_name) out = exe.run( fluid.default_main_program(), feed={'cond': self.cond, 'x': self.x, 'y': self.y}, fetch_list=fetch_list) assert np.array_equal(out[0], self.out) if x_stop_gradient is False: assert np.array_equal(out[2], self.ref_x_backward(out[1])) if y.stop_gradient is False: assert np.array_equal( out[3], self.ref_y_backward(out[1])) elif y.stop_gradient is False: assert np.array_equal(out[2], self.ref_y_backward(out[1])) def test_api_broadcast(self, use_cuda=False): main_program = Program() with fluid.program_guard(main_program): x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32') y = fluid.layers.data(name='y', shape=[4, 2], dtype='float32') x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32") y_i = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]).astype("float32") result = paddle.where(x > 1, x=x, y=y) for use_cuda in [False, True]: if use_cuda and not fluid.core.is_compiled_with_cuda(): return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) out = exe.run(fluid.default_main_program(), feed={'x': x_i, 'y': y_i}, fetch_list=[result]) assert np.array_equal(out[0], np.where(x_i > 1, x_i, y_i)) class TestWhereDygraphAPI(unittest.TestCase): def test_api(self): with fluid.dygraph.guard(): x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64") y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64") cond_i = np.array([False, False, True, True]).astype("bool") x = fluid.dygraph.to_variable(x_i) y = fluid.dygraph.to_variable(y_i) cond = fluid.dygraph.to_variable(cond_i) out = paddle.where(cond, x, y) assert np.array_equal(out.numpy(), np.where(cond_i, x_i, y_i)) class TestWhereOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64") y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64") cond_i = np.array([False, False, True, True]).astype("bool") def test_Variable(): paddle.where(cond_i, x_i, y_i) self.assertRaises(TypeError, test_Variable) def test_type(): x = fluid.layers.data(name='x', shape=[4], dtype='bool') y = fluid.layers.data(name='y', shape=[4], dtype='float16') cond = fluid.layers.data(name='cond', shape=[4], dtype='int32') paddle.where(cond, x, y) self.assertRaises(TypeError, test_type) if __name__ == '__main__': unittest.main()