#   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.

from __future__ import print_function
import unittest
import numpy as np
import copy
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard


def softmax(x):
    # clip to shiftx, otherwise, when calc loss with
    # log(exp(shiftx)), may get log(0)=INF
    shiftx = (x - np.max(x)).clip(-64.)
    exps = np.exp(shiftx)
    return exps / np.sum(exps)


def iou_matrix(a, b, norm=True):
    tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
    br_i = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])

    pad = not norm and 1 or 0

    area_i = np.prod(br_i - tl_i + pad, axis=2) * (tl_i < br_i).all(axis=2)
    area_a = np.prod(a[:, 2:] - a[:, :2] + pad, axis=1)
    area_b = np.prod(b[:, 2:] - b[:, :2] + pad, axis=1)
    area_o = (area_a[:, np.newaxis] + area_b - area_i)
    return area_i / (area_o + 1e-10)


def matrix_nms(boxes,
               scores,
               score_threshold,
               post_threshold=0.,
               nms_top_k=400,
               normalized=True,
               use_gaussian=False,
               gaussian_sigma=2.):
    all_scores = copy.deepcopy(scores)
    all_scores = all_scores.flatten()
    selected_indices = np.where(all_scores > score_threshold)[0]
    all_scores = all_scores[selected_indices]

    sorted_indices = np.argsort(-all_scores, axis=0, kind='mergesort')
    sorted_scores = all_scores[sorted_indices]
    sorted_indices = selected_indices[sorted_indices]
    if nms_top_k > -1 and nms_top_k < sorted_indices.shape[0]:
        sorted_indices = sorted_indices[:nms_top_k]
        sorted_scores = sorted_scores[:nms_top_k]

    selected_boxes = boxes[sorted_indices, :]
    ious = iou_matrix(selected_boxes, selected_boxes)
    ious = np.triu(ious, k=1)
    iou_cmax = ious.max(0)
    N = iou_cmax.shape[0]
    iou_cmax = np.repeat(iou_cmax[:, np.newaxis], N, axis=1)

    if use_gaussian:
        decay = np.exp((iou_cmax**2 - ious**2) * gaussian_sigma)
    else:
        decay = (1 - ious) / (1 - iou_cmax)
    decay = decay.min(0)
    decayed_scores = sorted_scores * decay

    if post_threshold > 0.:
        inds = np.where(decayed_scores > post_threshold)[0]
        selected_boxes = selected_boxes[inds, :]
        decayed_scores = decayed_scores[inds]
        sorted_indices = sorted_indices[inds]

    return decayed_scores, selected_boxes, sorted_indices


def multiclass_nms(boxes, scores, background, score_threshold, post_threshold,
                   nms_top_k, keep_top_k, normalized, use_gaussian,
                   gaussian_sigma):
    all_boxes = []
    all_cls = []
    all_scores = []
    all_indices = []
    for c in range(scores.shape[0]):
        if c == background:
            continue
        decayed_scores, selected_boxes, indices = matrix_nms(
            boxes, scores[c], score_threshold, post_threshold, nms_top_k,
            normalized, use_gaussian, gaussian_sigma)
        all_cls.append(np.full(len(decayed_scores), c, decayed_scores.dtype))
        all_boxes.append(selected_boxes)
        all_scores.append(decayed_scores)
        all_indices.append(indices)

    all_cls = np.concatenate(all_cls)
    all_boxes = np.concatenate(all_boxes)
    all_scores = np.concatenate(all_scores)
    all_indices = np.concatenate(all_indices)
    all_pred = np.concatenate(
        (all_cls[:, np.newaxis], all_scores[:, np.newaxis], all_boxes), axis=1)

    num_det = len(all_pred)
    if num_det == 0:
        return all_pred, np.array([], dtype=np.float32)

    inds = np.argsort(-all_scores, axis=0, kind='mergesort')
    all_pred = all_pred[inds, :]
    all_indices = all_indices[inds]

    if keep_top_k > -1 and num_det > keep_top_k:
        num_det = keep_top_k
        all_pred = all_pred[:keep_top_k, :]
        all_indices = all_indices[:keep_top_k]

    return all_pred, all_indices


def batched_multiclass_nms(boxes,
                           scores,
                           background,
                           score_threshold,
                           post_threshold,
                           nms_top_k,
                           keep_top_k,
                           normalized=True,
                           use_gaussian=False,
                           gaussian_sigma=2.):
    batch_size = scores.shape[0]
    det_outs = []
    index_outs = []
    lod = []
    for n in range(batch_size):
        nmsed_outs, indices = multiclass_nms(
            boxes[n], scores[n], background, score_threshold, post_threshold,
            nms_top_k, keep_top_k, normalized, use_gaussian, gaussian_sigma)
        nmsed_num = len(nmsed_outs)
        lod.append(nmsed_num)
        if nmsed_num == 0:
            continue
        indices += n * scores.shape[2]
        det_outs.append(nmsed_outs)
        index_outs.append(indices)
    if det_outs:
        det_outs = np.concatenate(det_outs)
        index_outs = np.concatenate(index_outs)
    return det_outs, index_outs, lod


class TestMatrixNMSOp(OpTest):
    def set_argument(self):
        self.post_threshold = 0.
        self.use_gaussian = False

    def setUp(self):
        self.set_argument()
        N = 7
        M = 1200
        C = 21
        BOX_SIZE = 4
        background = 0
        nms_top_k = 400
        keep_top_k = 200
        score_threshold = 0.01
        post_threshold = self.post_threshold
        use_gaussian = False
        if hasattr(self, 'use_gaussian'):
            use_gaussian = self.use_gaussian
        gaussian_sigma = 2.

        scores = np.random.random((N * M, C)).astype('float32')

        scores = np.apply_along_axis(softmax, 1, scores)
        scores = np.reshape(scores, (N, M, C))
        scores = np.transpose(scores, (0, 2, 1))

        boxes = np.random.random((N, M, BOX_SIZE)).astype('float32')
        boxes[:, :, 0:2] = boxes[:, :, 0:2] * 0.5
        boxes[:, :, 2:4] = boxes[:, :, 2:4] * 0.5 + 0.5

        det_outs, index_outs, lod = batched_multiclass_nms(
            boxes, scores, background, score_threshold, post_threshold,
            nms_top_k, keep_top_k, True, use_gaussian, gaussian_sigma)

        empty = len(det_outs) == 0
        det_outs = np.array([], dtype=np.float32) if empty else det_outs
        index_outs = np.array([], dtype=np.float32) if empty else index_outs
        nmsed_outs = det_outs.astype('float32')

        self.op_type = 'matrix_nms'
        self.inputs = {'BBoxes': boxes, 'Scores': scores}
        self.outputs = {
            'Out': (nmsed_outs, [lod]),
            'Index': (index_outs[:, None], [lod])
        }
        self.attrs = {
            'background_label': 0,
            'nms_top_k': nms_top_k,
            'keep_top_k': keep_top_k,
            'score_threshold': score_threshold,
            'post_threshold': post_threshold,
            'use_gaussian': use_gaussian,
            'gaussian_sigma': gaussian_sigma,
            'normalized': True,
        }

    def test_check_output(self):
        self.check_output()


class TestMatrixNMSOpNoOutput(TestMatrixNMSOp):
    def set_argument(self):
        self.post_threshold = 2.0


class TestMatrixNMSOpGaussian(TestMatrixNMSOp):
    def set_argument(self):
        self.post_threshold = 0.
        self.use_gaussian = True


class TestMatrixNMSError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            M = 1200
            N = 7
            C = 21
            BOX_SIZE = 4
            nms_top_k = 400
            keep_top_k = 200
            score_threshold = 0.01
            post_threshold = 0.

            boxes_np = np.random.random((M, C, BOX_SIZE)).astype('float32')
            scores = np.random.random((N * M, C)).astype('float32')
            scores = np.apply_along_axis(softmax, 1, scores)
            scores = np.reshape(scores, (N, M, C))
            scores_np = np.transpose(scores, (0, 2, 1))

            boxes_data = fluid.data(
                name='bboxes', shape=[M, C, BOX_SIZE], dtype='float32')
            scores_data = fluid.data(
                name='scores', shape=[N, C, M], dtype='float32')

            def test_bboxes_Variable():
                # the bboxes type must be Variable
                fluid.layers.matrix_nms(
                    bboxes=boxes_np,
                    scores=scores_data,
                    nms_top_k=nms_top_k,
                    keep_top_k=keep_top_k,
                    score_threshold=score_threshold,
                    post_threshold=post_threshold)

            def test_scores_Variable():
                # the scores type must be Variable
                fluid.layers.matrix_nms(
                    bboxes=boxes_data,
                    scores=scores_np,
                    nms_top_k=nms_top_k,
                    keep_top_k=keep_top_k,
                    score_threshold=score_threshold,
                    post_threshold=post_threshold)

            def test_empty():
                # when all score are lower than threshold
                try:
                    fluid.layers.matrix_nms(
                        bboxes=boxes_data,
                        scores=scores_data,
                        nms_top_k=nms_top_k,
                        keep_top_k=keep_top_k,
                        score_threshold=10.,
                        post_threshold=post_threshold)
                except Exception as e:
                    self.fail(e)

            def test_coverage():
                # cover correct workflow
                try:
                    fluid.layers.matrix_nms(
                        bboxes=boxes_data,
                        scores=scores_data,
                        nms_top_k=nms_top_k,
                        keep_top_k=keep_top_k,
                        score_threshold=score_threshold,
                        post_threshold=post_threshold)
                except Exception as e:
                    self.fail(e)

            self.assertRaises(TypeError, test_bboxes_Variable)
            self.assertRaises(TypeError, test_scores_Variable)
            test_coverage()


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