# 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.

import paddle
import paddle.fluid as fluid
import numpy as np
import unittest
from op_test import OpTest
from test_sigmoid_focal_loss_op import sigmoid_focal_loss_forward


def call_sfl_functional(logit,
                        label,
                        normalizer,
                        alpha=0.25,
                        gamma=2.0,
                        reduction='sum'):
    res = paddle.nn.functional.sigmoid_focal_loss(
        logit, label, normalizer, alpha=alpha, gamma=gamma, reduction=reduction)
    return res


def test_static(place,
                logit_np,
                label_np,
                normalizer_np,
                alpha=0.25,
                gamma=2.0,
                reduction='sum'):
    paddle.enable_static()
    prog = paddle.static.Program()
    startup_prog = paddle.static.Program()
    with paddle.static.program_guard(prog, startup_prog):
        logit = paddle.fluid.data(
            name='logit', shape=logit_np.shape, dtype='float64')
        label = paddle.fluid.data(
            name='label', shape=label_np.shape, dtype='float64')
        feed_dict = {"logit": logit_np, "label": label_np}

        normalizer = None
        if normalizer_np is not None:
            normalizer = paddle.fluid.data(
                name='normalizer', shape=normalizer_np.shape, dtype='float64')
            feed_dict["normalizer"] = normalizer_np

        res = call_sfl_functional(logit, label, normalizer, alpha, gamma,
                                  reduction)
        exe = paddle.static.Executor(place)
        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
    return static_result


def test_dygraph(place,
                 logit_np,
                 label_np,
                 normalizer_np,
                 alpha=0.25,
                 gamma=2.0,
                 reduction='sum'):
    paddle.disable_static()
    logit = paddle.to_tensor(logit_np)
    label = paddle.to_tensor(label_np)
    normalizer = None
    if normalizer_np is not None:
        normalizer = paddle.to_tensor(normalizer_np)
    dy_res = call_sfl_functional(logit, label, normalizer, alpha, gamma,
                                 reduction)
    dy_result = dy_res.numpy()
    paddle.enable_static()
    return dy_result


def calc_sigmoid_focal_loss(logit_np,
                            label_np,
                            normalizer_np,
                            alpha=0.25,
                            gamma=2.0,
                            reduction='sum'):

    loss = np.maximum(
        logit_np,
        0) - logit_np * label_np + np.log(1 + np.exp(-np.abs(logit_np)))

    pred = 1 / (1 + np.exp(-logit_np))
    p_t = pred * label_np + (1 - pred) * (1 - label_np)

    if alpha is not None:
        alpha_t = alpha * label_np + (1 - alpha) * (1 - label_np)
        loss = alpha_t * loss

    if gamma is not None:
        loss = loss * ((1 - p_t)**gamma)

    if normalizer_np is not None:
        loss = loss / normalizer_np

    if reduction == 'mean':
        loss = np.mean(loss)
    elif reduction == 'sum':
        loss = np.sum(loss)

    return loss


class TestSigmoidFocalLoss(unittest.TestCase):
    def test_SigmoidFocalLoss(self):
        logit_np = np.random.uniform(
            0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
        label_np = np.random.randint(
            0, 2, size=(2, 3, 4, 10)).astype(np.float64)
        normalizer_nps = [
            np.asarray(
                [np.sum(label_np > 0)], dtype=label_np.dtype), None
        ]
        places = [fluid.CPUPlace()]
        if fluid.core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        reductions = ['sum', 'mean', 'none']
        alphas = [0.25, 0.5]
        gammas = [3, 0.]
        for place in places:
            for reduction in reductions:
                for alpha in alphas:
                    for gamma in gammas:
                        for normalizer_np in normalizer_nps:
                            static_result = test_static(place, logit_np,
                                                        label_np, normalizer_np,
                                                        alpha, gamma, reduction)
                            dy_result = test_dygraph(place, logit_np, label_np,
                                                     normalizer_np, alpha,
                                                     gamma, reduction)
                            expected = calc_sigmoid_focal_loss(
                                logit_np, label_np, normalizer_np, alpha, gamma,
                                reduction)
                            self.assertTrue(
                                np.allclose(static_result, expected))
                            self.assertTrue(
                                np.allclose(static_result, dy_result))
                            self.assertTrue(np.allclose(dy_result, expected))

    def test_SigmoidFocalLoss_error(self):
        paddle.disable_static()
        logit = paddle.to_tensor([[0.97], [0.91], [0.03]], dtype='float32')
        label = paddle.to_tensor([[1.0], [1.0], [0.0]], dtype='float32')
        self.assertRaises(
            ValueError,
            paddle.nn.functional.sigmoid_focal_loss,
            logit=logit,
            label=label,
            normalizer=None,
            reduction="unsupport reduction")
        paddle.enable_static()


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