#   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 sys

sys.path.append("..")
import unittest
import math
import numpy as np
import paddle.fluid.core as core
from op_test_xpu import XPUOpTest
import paddle
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper

paddle.enable_static()


class XPUTestROIAlignOp(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = 'roi_align'
        self.use_dynamic_create_class = False

    class TestROIAlignOp(XPUOpTest):

        def set_data(self):
            self.init_test_case()
            self.make_rois()
            self.calc_roi_align()

            self.inputs = {
                'X': self.x,
                'ROIs': (self.rois[:, 1:5], self.rois_lod),
            }
            self.attrs = {
                'spatial_scale': self.spatial_scale,
                'pooled_height': self.pooled_height,
                'pooled_width': self.pooled_width,
                'sampling_ratio': self.sampling_ratio,
                'aligned': self.continuous_coordinate
            }

            self.outputs = {'Out': self.out_data}

        def init_test_case(self):
            self.batch_size = 3
            self.channels = 3
            self.height = 8
            self.width = 6

            self.xpu_version = core.get_xpu_device_version(0)

            # n, c, h, w
            self.x_dim = (self.batch_size, self.channels, self.height,
                          self.width)

            self.spatial_scale = 1.0 / 2.0
            self.pooled_height = 2
            self.pooled_width = 2
            self.sampling_ratio = -1
            if self.xpu_version == core.XPUVersion.XPU1:
                self.continuous_coordinate = False
            else:
                self.continuous_coordinate = bool(np.random.randint(2))
            self.x = np.random.random(self.x_dim).astype(self.dtype)

        def pre_calc(self, x_i, roi_xmin, roi_ymin, roi_bin_grid_h,
                     roi_bin_grid_w, bin_size_h, bin_size_w):
            count = roi_bin_grid_h * roi_bin_grid_w
            bilinear_pos = np.zeros([
                self.channels, self.pooled_height, self.pooled_width, count, 4
            ], np.float32)
            bilinear_w = np.zeros(
                [self.pooled_height, self.pooled_width, count, 4], np.float32)
            for ph in range(self.pooled_width):
                for pw in range(self.pooled_height):
                    c = 0
                    for iy in range(roi_bin_grid_h):
                        y = roi_ymin + ph * bin_size_h + (iy + 0.5) * \
                            bin_size_h / roi_bin_grid_h
                        for ix in range(roi_bin_grid_w):
                            x = roi_xmin + pw * bin_size_w + (ix + 0.5) * \
                                bin_size_w / roi_bin_grid_w
                            if y < -1.0 or y > self.height or \
                                x < -1.0 or x > self.width:
                                continue
                            if y <= 0:
                                y = 0
                            if x <= 0:
                                x = 0
                            y_low = int(y)
                            x_low = int(x)
                            if y_low >= self.height - 1:
                                y = y_high = y_low = self.height - 1
                            else:
                                y_high = y_low + 1
                            if x_low >= self.width - 1:
                                x = x_high = x_low = self.width - 1
                            else:
                                x_high = x_low + 1
                            ly = y - y_low
                            lx = x - x_low
                            hy = 1 - ly
                            hx = 1 - lx
                            for ch in range(self.channels):
                                bilinear_pos[ch, ph, pw, c, 0] = x_i[ch, y_low,
                                                                     x_low]
                                bilinear_pos[ch, ph, pw, c, 1] = x_i[ch, y_low,
                                                                     x_high]
                                bilinear_pos[ch, ph, pw, c, 2] = x_i[ch, y_high,
                                                                     x_low]
                                bilinear_pos[ch, ph, pw, c, 3] = x_i[ch, y_high,
                                                                     x_high]
                            bilinear_w[ph, pw, c, 0] = hy * hx
                            bilinear_w[ph, pw, c, 1] = hy * lx
                            bilinear_w[ph, pw, c, 2] = ly * hx
                            bilinear_w[ph, pw, c, 3] = ly * lx
                            c = c + 1
            return bilinear_pos, bilinear_w

        def calc_roi_align(self):
            self.out_data = np.zeros(
                (self.rois_num, self.channels, self.pooled_height,
                 self.pooled_width)).astype(self.dtype)

            for i in range(self.rois_num):
                roi = self.rois[i]
                roi_batch_id = int(roi[0])
                x_i = self.x[roi_batch_id]
                roi_offset = 0.5 if self.continuous_coordinate else 0
                roi_xmin = roi[1] * self.spatial_scale - roi_offset
                roi_ymin = roi[2] * self.spatial_scale - roi_offset
                roi_xmax = roi[3] * self.spatial_scale - roi_offset
                roi_ymax = roi[4] * self.spatial_scale - roi_offset
                roi_width = roi_xmax - roi_xmin
                roi_height = roi_ymax - roi_ymin
                if not self.continuous_coordinate:
                    roi_width = max(roi_width, 1)
                    roi_height = max(roi_height, 1)
                bin_size_h = float(roi_height) / float(self.pooled_height)
                bin_size_w = float(roi_width) / float(self.pooled_width)
                roi_bin_grid_h = self.sampling_ratio if self.sampling_ratio > 0 else \
                                    math.ceil(roi_height / self.pooled_height)
                roi_bin_grid_w = self.sampling_ratio if self.sampling_ratio > 0 else \
                                    math.ceil(roi_width / self.pooled_width)
                count = int(roi_bin_grid_h * roi_bin_grid_w)
                pre_size = count * self.pooled_width * self.pooled_height
                bilinear_pos, bilinear_w = self.pre_calc(
                    x_i, roi_xmin, roi_ymin, int(roi_bin_grid_h),
                    int(roi_bin_grid_w), bin_size_h, bin_size_w)
                for ch in range(self.channels):
                    align_per_bin = (bilinear_pos[ch] * bilinear_w).sum(axis=-1)
                    output_val = align_per_bin.mean(axis=-1)
                    self.out_data[i, ch, :, :] = output_val

        def make_rois(self):
            rois = []
            self.rois_lod = [[]]
            for bno in range(self.batch_size):
                self.rois_lod[0].append(bno + 1)
                for i in range(bno + 1):
                    x1 = np.random.random_integers(
                        0, self.width // self.spatial_scale - self.pooled_width)
                    y1 = np.random.random_integers(
                        0,
                        self.height // self.spatial_scale - self.pooled_height)

                    x2 = np.random.random_integers(
                        x1 + self.pooled_width,
                        self.width // self.spatial_scale)
                    y2 = np.random.random_integers(
                        y1 + self.pooled_height,
                        self.height // self.spatial_scale)

                    roi = [bno, x1, y1, x2, y2]
                    rois.append(roi)
            self.rois_num = len(rois)
            self.rois = np.array(rois).astype(self.dtype)

        def setUp(self):
            self.set_xpu()
            self.op_type = "roi_align"
            self.place = paddle.XPUPlace(0)
            self.dtype = self.in_type
            self.set_data()

        def set_xpu(self):
            self.__class__.use_xpu = True

        def test_check_output(self):
            self.check_output_with_place(self.place)

        def test_check_grad(self):
            self.check_grad_with_place(self.place, {'X'}, 'Out')

    class TestROIAlignInLodOp(TestROIAlignOp):

        def set_data(self):
            self.init_test_case()
            self.make_rois()
            self.calc_roi_align()

            seq_len = self.rois_lod[0]

            self.inputs = {
                'X': self.x,
                'ROIs': (self.rois[:, 1:5], self.rois_lod),
                'RoisNum': np.asarray(seq_len).astype('int32')
            }

            self.attrs = {
                'spatial_scale': self.spatial_scale,
                'pooled_height': self.pooled_height,
                'pooled_width': self.pooled_width,
                'sampling_ratio': self.sampling_ratio,
                'aligned': self.continuous_coordinate
            }

            self.outputs = {'Out': self.out_data}


support_types = get_xpu_op_support_types('roi_align')
for stype in support_types:
    create_test_class(globals(), XPUTestROIAlignOp, stype)

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