#   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 unittest
import numpy as np

import paddle.fluid.core as core
import paddle.fluid as fluid
from test_conv3d_transpose_op import TestConv3DTransposeOp


class TestWithSymmetricPad_NHWC(TestConv3DTransposeOp):

    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithAsymmetricPad_NHWC(TestConv3DTransposeOp):

    def init_test_case(self):
        self.pad = [1, 0, 1, 0, 1, 2]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithGroups_NHWC(TestConv3DTransposeOp):

    def init_test_case(self):
        self.check_no_filter = True
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 2
        self.input_size = [2, 5, 5, 5, 4]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 3, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithStride_NHWC(TestConv3DTransposeOp):

    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [2, 2, 2]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithDilation_NHWC(TestConv3DTransposeOp):

    def init_test_case(self):
        self.check_no_input = True
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [2, 2, 2]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestConv3DTransposeAPI(unittest.TestCase):

    def test_case1(self):
        data1 = fluid.layers.data(name='data1',
                                  shape=[3, 5, 5, 5],
                                  dtype='float32')
        data2 = fluid.layers.data(name='data2',
                                  shape=[5, 5, 5, 3],
                                  dtype='float32')

        out1 = fluid.layers.conv3d_transpose(input=data1,
                                             groups=1,
                                             num_filters=6,
                                             filter_size=3,
                                             data_format='NCDHW')
        out2 = fluid.layers.conv3d_transpose(input=data2,
                                             groups=1,
                                             num_filters=6,
                                             filter_size=3,
                                             data_format='NDHWC')
        out3 = fluid.layers.conv3d_transpose(input=data1,
                                             groups=1,
                                             num_filters=6,
                                             filter_size=3,
                                             padding=[[0, 0], [0, 0], [1, 1],
                                                      [0, 0], [1, 1]],
                                             data_format='NCDHW')
        out4 = fluid.layers.conv3d_transpose(input=data2,
                                             groups=3,
                                             num_filters=6,
                                             filter_size=3,
                                             padding=[[0, 0], [0, 0], [1, 1],
                                                      [1, 2], [0, 0]],
                                             data_format='NDHWC')
        out5 = fluid.layers.conv3d_transpose(input=data2,
                                             groups=1,
                                             num_filters=6,
                                             filter_size=3,
                                             padding='SAME',
                                             data_format='NCDHW')
        out6 = fluid.layers.conv3d_transpose(input=data2,
                                             groups=1,
                                             num_filters=6,
                                             filter_size=3,
                                             padding='VALID',
                                             data_format='NDHWC')
        out7 = fluid.layers.conv3d_transpose(input=data2,
                                             groups=1,
                                             num_filters=6,
                                             output_size=[7, 7, 7],
                                             padding=[0, 0, 0],
                                             data_format='NDHWC')

        data1_np = np.random.random((2, 3, 5, 5, 5)).astype("float32")
        data2_np = np.random.random((2, 5, 5, 5, 3)).astype("float32")

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        results = exe.run(fluid.default_main_program(),
                          feed={
                              "data1": data1_np,
                              "data2": data2_np
                          },
                          fetch_list=[out1, out2, out3, out4, out5, out6, out7],
                          return_numpy=True)
        self.assertIsNotNone(results[0])
        self.assertIsNotNone(results[1])
        self.assertIsNotNone(results[2])
        self.assertIsNotNone(results[3])
        self.assertIsNotNone(results[4])
        self.assertIsNotNone(results[5])
        self.assertIsNotNone(results[6])


class TestConv3DTransposeOpException(unittest.TestCase):

    def test_exception(self):
        data = fluid.layers.data(name='data',
                                 shape=[3, 5, 5, 5],
                                 dtype="float32")

        def attr_data_format():
            out = fluid.layers.conv2d_transpose(input=data,
                                                groups=1,
                                                num_filters=6,
                                                filter_size=3,
                                                data_format="NCDW")

        self.assertRaises(ValueError, attr_data_format)

        def attr_padding_str():
            out = fluid.layers.conv2d_transpose(input=data,
                                                groups=1,
                                                num_filters=6,
                                                filter_size=3,
                                                padding='Vald')

        self.assertRaises(ValueError, attr_padding_str)

        def attr_padding_list():
            out = fluid.layers.conv2d_transpose(input=data,
                                                groups=1,
                                                num_filters=6,
                                                filter_size=3,
                                                padding=[[1, 1], [1, 1], [0, 0],
                                                         [0, 0], [1, 1]])

        self.assertRaises(ValueError, attr_padding_list)

        def attr_padding_with_data_format():
            out = fluid.layers.conv2d_transpose(input=data,
                                                groups=1,
                                                num_filters=6,
                                                filter_size=3,
                                                padding=[[1, 1], [0, 0], [0, 0],
                                                         [1, 0], [1, 1]],
                                                data_format='NDHWC')

        self.assertRaises(ValueError, attr_padding_with_data_format)


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