#   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
import six
from op_test import OpTest, skip_check_grad_ci


class PReluTest(OpTest):
    def setUp(self):
        self.init_input_shape()
        self.init_attr()
        self.op_type = "prelu"

        x_np = np.random.uniform(-1, 1, self.x_shape)
        # Since zero point in prelu is not differentiable, avoid randomize
        # zero.
        x_np[np.abs(x_np) < 0.005] = 0.02

        if self.attrs == {'mode': "all"}:
            alpha_np = np.random.uniform(-1, -0.5, (1))
        elif self.attrs == {'mode': "channel"}:
            alpha_np = np.random.uniform(-1, -0.5, (1, x_np.shape[1], 1, 1))
        else:
            alpha_np = np.random.uniform(-1, -0.5, \
                (1, x_np.shape[1], x_np.shape[2], x_np.shape[3]))
        self.inputs = {'X': x_np, 'Alpha': alpha_np}

        out_np = np.maximum(self.inputs['X'], 0.)
        out_np = out_np + np.minimum(self.inputs['X'],
                                     0.) * self.inputs['Alpha']
        assert out_np is not self.inputs['X']
        self.outputs = {'Out': out_np}

    def init_input_shape(self):
        self.x_shape = (2, 100, 3, 4)

    def init_attr(self):
        self.attrs = {'mode': "channel"}

    def test_check_output(self):
        self.check_output()

    def test_check_grad_1_ignore_x(self):
        self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))

    def test_check_grad_2(self):
        self.check_grad(['X', 'Alpha'], 'Out')

    def test_check_grad_3_ignore_alpha(self):
        self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))


# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
if six.PY2:

    @skip_check_grad_ci(
        reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
    )
    class TestModeAll(PReluTest):
        def init_input_shape(self):
            self.x_shape = (2, 3, 4, 5)

        def init_attr(self):
            self.attrs = {'mode': "all"}

    class TestModeElt(PReluTest):
        def init_input_shape(self):
            self.x_shape = (3, 2, 5, 10)

        def init_attr(self):
            self.attrs = {'mode': "element"}


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

#   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
import six
from op_test import OpTest, skip_check_grad_ci


class PReluTest(OpTest):
    def setUp(self):
        self.init_input_shape()
        self.init_attr()
        self.op_type = "prelu"

        x_np = np.random.uniform(-1, 1, self.x_shape)
        # Since zero point in prelu is not differentiable, avoid randomize
        # zero.
        x_np[np.abs(x_np) < 0.005] = 0.02

        if self.attrs == {'mode': "all"}:
            alpha_np = np.random.uniform(-1, -0.5, (1))
        elif self.attrs == {'mode': "channel"}:
            alpha_np = np.random.uniform(-1, -0.5, (1, x_np.shape[1], 1, 1))
        else:
            alpha_np = np.random.uniform(-1, -0.5, \
                (1, x_np.shape[1], x_np.shape[2], x_np.shape[3]))
        self.inputs = {'X': x_np, 'Alpha': alpha_np}

        out_np = np.maximum(self.inputs['X'], 0.)
        out_np = out_np + np.minimum(self.inputs['X'],
                                     0.) * self.inputs['Alpha']
        assert out_np is not self.inputs['X']
        self.outputs = {'Out': out_np}

    def init_input_shape(self):
        self.x_shape = (2, 100, 3, 4)

    def init_attr(self):
        self.attrs = {'mode': "channel"}

    def test_check_output(self):
        self.check_output()

    def test_check_grad_1_ignore_x(self):
        self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))

    def test_check_grad_2(self):
        self.check_grad(['X', 'Alpha'], 'Out')

    def test_check_grad_3_ignore_alpha(self):
        self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))


# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
if six.PY2:

    @skip_check_grad_ci(
        reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
    )
    class TestModeAll(PReluTest):
        def init_input_shape(self):
            self.x_shape = (2, 3, 4, 5)

        def init_attr(self):
            self.attrs = {'mode': "all"}

    class TestModeElt(PReluTest):
        def init_input_shape(self):
            self.x_shape = (3, 2, 5, 10)

        def init_attr(self):
            self.attrs = {'mode': "element"}


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

#   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. # Since zero point in prelu is not differentiable, avoid randomize # zero. # TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues