Created by: Joejiong

PR types

Function optimization

PR changes

APIs

Describe

Logic运算包括以下算子：logical_and、logical_or、logical_xor

• 不支持broadcast
• 按目前paddle不支持braodcast进行验证，当两个输入tensor维度不相等时应该报错。

测试代码：

from __future__ import print_function

import op_test
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.static import Program, program_guard


def create_test_class(op_type, callback, binary_op=True):
    class Cls(op_test.OpTest):
        def setUp(self):
            a = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
            if binary_op:
                b = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
                c = callback(a, b)
            else:
                c = callback(a)
            self.outputs = {'Out': c}
            self.op_type = op_type
            if binary_op:
                self.inputs = {'X': a, 'Y': b}
            else:
                self.inputs = {'X': a}

        def test_output(self):
            self.check_output()

        def test_error(self):
            with program_guard(Program(), Program()):
                
                # test 1 type error, x, y must be bool type
                x = fluid.layers.data(name='x', shape=[2], dtype='bool')
                y = fluid.layers.data(name='y', shape=[2], dtype='bool')
                a = fluid.layers.data(name='a', shape=[2], dtype='int32')
                op = eval("fluid.layers.%s" % self.op_type)
                if self.op_type != "logical_not":
                    self.assertRaises(TypeError, op, x=x, y=y, out=1)
                    self.assertRaises(TypeError, op, x=x, y=a)
                    self.assertRaises(TypeError, op, x=a, y=y)
                else:
                    self.assertRaises(TypeError, op, x=x, out=1)
                    self.assertRaises(TypeError, op, x=a)
                
                # test 2 type error, x, y must be bool type
                x_data = fluid.layers.data(name='x_data', shape=[2], dtype='bool')
                y_data = fluid.layers.data(name='y_data', shape=[2,2], dtype='bool')
                
                if self.op_type != "logical_not":
                    self.assertRaises(TypeError, op, x=x_data, y=y_data, out=1)
                    self.assertRaises(TypeError, op, x=y_data, y=x_data)


    globals()[op_type] = Cls


create_test_class('logical_and', lambda _a, _b: np.logical_and(_a, _b))
create_test_class('logical_or', lambda _a, _b: np.logical_or(_a, _b))
create_test_class('logical_not', lambda _a: np.logical_not(_a), False)
create_test_class('logical_xor', lambda _a, _b: np.logical_xor(_a, _b))

if __name__ == '__main__':
    unittest.main()