test_elementwise_pow_op.py

#  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, skip_check_grad_ci
import paddle.fluid as fluid


class TestElementwisePowOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(1, 2, [20, 5]).astype("float64"),
            'Y': np.random.uniform(1, 2, [20, 5]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['X', 'Y'], 'Out')


class TestElementwisePowOp_big_shape_1(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(1, 2, [10, 10]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [10, 10]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


class TestElementwisePowOp_big_shape_2(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(1, 2, [10, 10]).astype("float64"),
            'Y': np.random.uniform(0.2, 2, [10, 10]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


@skip_check_grad_ci(
    reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwisePowOp_scalar(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [3, 3, 4]).astype(np.float64),
            'Y': np.random.uniform(0.1, 1, [1]).astype(np.float64)
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


class TestElementwisePowOp_tensor(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [100]).astype("float64"),
            'Y': np.random.uniform(1, 3, [100]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


class TestElementwisePowOp_broadcast_0(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [2, 1, 100]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


class TestElementwisePowOp_broadcast_1(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [2, 100, 1]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
        }
        self.attrs = {'axis': 1}
        self.outputs = {
            'Out': np.power(self.inputs['X'], self.inputs['Y'].reshape(100, 1))
        }


class TestElementwisePowOp_broadcast_2(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [100, 3, 1]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
        }
        self.attrs = {'axis': 0}
        self.outputs = {
            'Out':
            np.power(self.inputs['X'], self.inputs['Y'].reshape(100, 1, 1))
        }


class TestElementwisePowOp_broadcast_3(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [2, 20, 5, 1]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [20, 5]).astype("float64")
        }
        self.attrs = {'axis': 1}
        self.outputs = {
            'Out': np.power(self.inputs['X'], self.inputs['Y'].reshape(1, 20, 5,
                                                                       1))
        }


class TestElementwisePowOp_broadcast_4(TestElementwisePowOp):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {
            'X': np.random.uniform(0.1, 1, [2, 10, 3, 5]).astype("float64"),
            'Y': np.random.uniform(0.1, 1, [2, 10, 1, 5]).astype("float64")
        }
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}


class TestElementwisePowOpInt(OpTest):
    def setUp(self):
        self.op_type = "elementwise_pow"
        self.inputs = {'X': np.asarray([1, 3, 6]), 'Y': np.asarray([1, 1, 1])}
        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}

    def test_check_output(self):
        self.check_output()


class TestElementwisePowGradOpInt(unittest.TestCase):
    def setUp(self):
        self.x = np.asarray([1, 3, 6])
        self.y = np.asarray([1, 1, 1])
        self.res = self.x**self.y
        # dout = 1
        self.grad_res = np.asarray([1, 1, 1])
        # dx = dout * y * pow(x, y-1)
        self.grad_x = self.grad_res * self.y * (self.x
                                                **(self.y - 1)).astype("int")
        # dy = dout * log(x) * pow(x, y)
        self.grad_y = (self.grad_res * np.log(self.x) *
                       (self.x**self.y)).astype("int")
        print(self.grad_res, self.grad_x, self.grad_y)

    def test_grad(self):
        places = [fluid.CPUPlace()]
        if fluid.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for place in places:
            with fluid.dygraph.guard(place):
                x = fluid.dygraph.to_variable(self.x, zero_copy=False)
                y = fluid.dygraph.to_variable(self.y, zero_copy=False)
                print(x, y)
                x.stop_gradient = False
                y.stop_gradient = False
                res = x**y
                res.backward()
                self.assertTrue(np.array_equal(res.gradient(), self.grad_res))
                self.assertTrue(np.array_equal(x.gradient(), self.grad_x))
                self.assertTrue(np.array_equal(y.gradient(), self.grad_y))


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