text: # Copyright 2019 Huawei Technologies Co., Ltd
  #
  # 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 numpy as np
  from tests.common.tensorio import compare_tensor
  from akg.utils import kernel_exec as utils
  from tests.common.test_op import bounding_box_encode
  import copy
  from tests.common.gen_random import random_gaussian
  
  COORDINATES_LEN = 4
  COORDINATES_PAD_LEN = 8
  
  
  # def encode_data(anchor_box, groundtruth_box, anchor_samples, dtype, scale_factors, epsilon):
  #     def _process_data_for_cordinates(data):
  #         '''
  #
  #         :param data: box (ymin, xmin, ymax, xmax)
  #         :return:
  #         '''
  #         data = np.abs(data)
  #         y_0, x_0, y_1, x_1 = [np.squeeze(i) for i in np.split(data, 4, -1)]
  #         y_max = np.maximum(y_0, y_1)
  #         y_min = np.minimum(y_0, y_1)
  #         x_max = np.maximum(x_0, x_1)
  #         x_min = np.minimum(x_0, x_1)
  #         return np.stack([y_min, x_min, y_max, x_max], -1)
  #
  #     def _get_center_coordinates_and_sizes(box_corners):
  #         """Computes the center coordinates, height and width of the boxes.
  #
  #         Args:
  #           scope: name scope of the function.
  #
  #         Returns:
  #           a list of 4 1-D tensors [ycenter, xcenter, height, width].
  #         """
  #         ymin, xmin, ymax, xmax = box_corners
  #         width = xmax - xmin
  #         height = ymax - ymin
  #         ycenter = ymin + height / 2.
  #         xcenter = xmin + width / 2.
  #         return ycenter, xcenter, height, width
  #
  #     def _encode(anchors, boxes, scale_factors, epsilon):
  #         ycenter_a, xcenter_a, ha, wa = _get_center_coordinates_and_sizes(anchors)
  #         ycenter, xcenter, h, w = _get_center_coordinates_and_sizes(boxes)
  #
  #         ha += epsilon
  #         wa += epsilon
  #         h += epsilon
  #         w += epsilon
  #
  #         tx = (xcenter - xcenter_a) / wa
  #         ty = (ycenter - ycenter_a) / ha
  #         tw = np.log(w / wa)
  #         th = np.log(h / ha)
  #         # Scales location targets as used in paper for joint training.
  #         if scale_factors:
  #             ty *= scale_factors[0]
  #             tx *= scale_factors[1]
  #             th *= scale_factors[2]
  #             tw *= scale_factors[3]
  #         return ty, tx, th, tw
  #
  #     anchor_box_data = random_gaussian(anchor_box, miu=1, sigma=0.1).astype(dtype)
  #     anchor_box_data = _process_data_for_cordinates(anchor_box_data)
  #     groundtruth_box_data = random_gaussian(groundtruth_box, miu=1, sigma=0.2).astype(dtype)
  #     groundtruth_box_data = _process_data_for_cordinates(groundtruth_box_data)
  #     anchor_samples_data = random_gaussian(anchor_samples, miu=1, sigma=0.3).astype("int32")
  #
  #     num_groundtruths = groundtruth_box_data.shape[1]
  #     batchsize = anchor_samples_data.shape[0]
  #     num_anchors = anchor_box_data.shape[0]
  #     output_shape = (batchsize, num_anchors, 4)
  #     expect = np.full(output_shape, 0, dtype)
  #
  #     for i in range(batchsize):
  #         for j in range(num_anchors):
  #             sample_value = anchor_samples_data[i,j]
  #             if sample_value < num_groundtruths:
  #                 expect[i,j] = _encode(anchor_box_data[j], groundtruth_box_data[i, sample_value], scale_factors, epsilon)
  #     return anchor_box_data, groundtruth_box_data, anchor_samples_data, expect
  
  def encode_data_vector(anchor_box, groundtruth_box, anchor_samples, dtype, scale_factors, epsilon):
      def _process_data_for_cordinates_vector(data):
          '''
  
          :param data: box (ymin, xmin, ymax, xmax)
          :return:
          '''
          data = np.abs(data)
          y_0, x_0, y_1, x_1, _, _, _, _ = [np.squeeze(i) for i in np.split(data, COORDINATES_PAD_LEN, -1)]
          y_max = np.maximum(y_0, y_1)
          y_min = np.minimum(y_0, y_1)
          x_max = np.maximum(x_0, x_1)
          x_min = np.minimum(x_0, x_1)
          data_cordinates = np.stack([y_min, x_min, y_max, x_max], -1)
          data[..., :COORDINATES_LEN] = data_cordinates
          return data
  
      def _encode_vector(ycenter_a, xcenter_a, ha, wa, ycenter, xcenter, h, w, scale_factors, epsilon):
          ha = np.add(ha, epsilon)
          wa = np.add(wa, epsilon)
          h = np.add(h, epsilon)
          w = np.add(w, epsilon)
  
          tx = np.divide(np.subtract(xcenter, xcenter_a), wa)
          ty = np.divide(np.subtract(ycenter, ycenter_a), ha)
          tw = np.log(np.divide(w, wa))
          th = np.log(np.divide(h, ha))
          # Scales location targets as used in paper for joint training.
          if scale_factors:
              ty = np.multiply(ty, scale_factors[0])
              tx = np.multiply(tx, scale_factors[1])
              th = np.multiply(th, scale_factors[2])
              tw = np.multiply(tw, scale_factors[3])
          return ty, tx, th, tw
  
      def _get_center_coordinates_and_sizes_vector(box_data):
          """Computes the center coordinates, height and width of the boxes.
  
          Returns:
            a list of 4 1-D tensors [ycenter, xcenter, height, width].
          """
          ymin, xmin, ymax, xmax = [np.squeeze(i) for i in np.split(box_data, 4, 0)]
          width = np.subtract(xmax, xmin)
          height = np.subtract(ymax, ymin)
          ycenter = np.add(ymin, np.multiply(height, 0.5))
          xcenter = np.add(xmin, np.multiply(width, 0.5))
          return ycenter, xcenter, height, width
  
      anchor_box_data = random_gaussian(anchor_box, miu=1, sigma=0.1).astype(dtype)
      anchor_box_data = _process_data_for_cordinates_vector(anchor_box_data)
      groundtruth_box_data = random_gaussian(groundtruth_box, miu=1, sigma=0.2).astype(dtype)
      groundtruth_box_data = _process_data_for_cordinates_vector(groundtruth_box_data)
  
      num_groundtruths = groundtruth_box_data.shape[1]
      num_anchors = anchor_box_data.shape[0]
      np.random.seed(1)
      anchor_samples_data = np.random.randint(0, num_anchors, anchor_samples, dtype="int32")
      limmit_data = np.full(anchor_samples, num_groundtruths, dtype="int32")
      limmit_index = np.where(anchor_samples_data > num_groundtruths)
      anchor_samples_data[limmit_index] = limmit_data[limmit_index]
  
      batchsize = anchor_samples_data.shape[0]
  
      anchor_box_data_broadcast = np.broadcast_to(anchor_box_data, (batchsize,) + tuple(anchor_box))
      anchor_box_data_broadcast = np.transpose(anchor_box_data_broadcast, axes=[2, 0, 1])
      anchor_samples_box_data = np.full((COORDINATES_LEN, batchsize, num_anchors), 0, dtype)
      for i in range(batchsize):
          for j in range(num_anchors):
              for m in range(COORDINATES_LEN):
                  for k in range(num_groundtruths):
                      if anchor_samples_data[i, j] == k:
                          anchor_samples_box_data[m, i, j] = groundtruth_box_data[i, k, m]
  
      ycenter_a, xcenter_a, height_a, width_a = _get_center_coordinates_and_sizes_vector(
          anchor_box_data_broadcast[:COORDINATES_LEN, ...])
      ycenter, xcenter, height, width = _get_center_coordinates_and_sizes_vector(
          anchor_samples_box_data[:COORDINATES_LEN, ...])
  
      ty, tx, th, tw = _encode_vector(ycenter_a, xcenter_a, height_a, width_a, ycenter, xcenter, height, width,
                                      scale_factors, epsilon)
      expect = np.stack([ty, tx, th, tw], axis=-1)
      return anchor_box_data, groundtruth_box_data, anchor_samples_data, expect
  
  
  def bounding_box_encode_run(anchor_box_shape, groundtruth_box_shape, anchor_samples_shape, dtype, scale_factors,
                              epsilon, kernel_name, attrs={}):
      # check_shape:
      # bachsize
      assert (groundtruth_box_shape[0] == anchor_samples_shape[0])
      # num_archors
      assert (anchor_box_shape[0] == anchor_samples_shape[1])
      assert (not scale_factors or len(scale_factors) == COORDINATES_LEN) and (
          anchor_box_shape[-1] == COORDINATES_PAD_LEN) and (
          groundtruth_box_shape[-1] == COORDINATES_PAD_LEN)
  
      op_attrs = [scale_factors, epsilon]
  
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(bounding_box_encode.bouding_box_encode,
                                    [anchor_box_shape, groundtruth_box_shape, anchor_samples_shape],
                                    [dtype, dtype, "int32"],
                                    op_attrs, kernel_name=kernel_name, attrs=attrs, dump_code=True, tuning=t)
          if t:
              anchor_box_data, anchor_samples_data, expect, groundtruth_box_data, output_data = gen_data(anchor_box_shape,
                                                                                                         anchor_samples_shape,
                                                                                                         dtype, epsilon,
                                                                                                         groundtruth_box_shape,
                                                                                                         scale_factors)
              return mod, expect, (anchor_box_data, groundtruth_box_data, anchor_samples_data, output_data)
          else:
              return mod
      else:
          mod = utils.op_build_test(bounding_box_encode.bouding_box_encode,
                                    [anchor_box_shape, groundtruth_box_shape, anchor_samples_shape],
                                    [dtype, dtype, "int32"],
                                    op_attrs, kernel_name=kernel_name, attrs=attrs, dump_code=True)
          anchor_box_data, anchor_samples_data, expect, groundtruth_box_data, output_data = gen_data(anchor_box_shape,
                                                                                                     anchor_samples_shape,
                                                                                                     dtype, epsilon,
                                                                                                     groundtruth_box_shape,
                                                                                                     scale_factors)
          output = utils.mod_launch(mod, (anchor_box_data, groundtruth_box_data, anchor_samples_data, output_data),
                                    expect=expect)
  
          # compare result
          compare_result = compare_tensor(output, expect, rtol=5e-3, equal_nan=True)
          return (anchor_box_data, groundtruth_box_data, anchor_samples_data), output, expect, compare_result
  
  
  def gen_data(anchor_box_shape, anchor_samples_shape, dtype, epsilon, groundtruth_box_shape, scale_factors):
      # Generate data
      anchor_box_data, groundtruth_box_data, anchor_samples_data, expect = encode_data_vector(anchor_box_shape,
                                                                                              groundtruth_box_shape,
                                                                                              anchor_samples_shape, dtype,
                                                                                              scale_factors, epsilon)
      # mod launch
      out_shape = expect.shape
      output_data = np.full(out_shape, np.nan, dtype)
      return anchor_box_data, anchor_samples_data, expect, groundtruth_box_data, output_data
  