code: # 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.
  
  from tensorio import compare_tensor
  import numpy as np
  from akg.utils import kernel_exec as utils
  from akg.ops.nn import avgpool
  from akg.utils.dsl_create import cal_pad_shapes_by_strategy
  from gen_random import random_gaussian
  
  def benchmark(input, kernel, stride, pad):
      sh, sw = stride
      N, C1, H, W, C0 = input.shape
      KH, KW = kernel
  
      [ph_h, ph_t, pw_h, pw_t], [out_size_h, out_size_w] = cal_pad_shapes_by_strategy(input.shape, kernel, stride, pad)
      out_shape = (N, C1, out_size_h, out_size_w, C0)
  
      out = np.zeros(out_shape)
  
      inputpad = np.zeros((N, C1, H + ph_h + ph_t, W + pw_h + pw_t, C0))
      inputpad[:, :, ph_h:ph_h + H, pw_h:pw_h + W, :] = input
  
      for i in range(out_size_h):
          for j in range(out_size_w):
              out[:, :, i, j, :] = np.mean(inputpad[:, :, i * sh:i * sh + KH, j * sw:j * sw + KW, :], axis=(2, 3))
      return out
  
  
  def avgpool_run(shape, kernel, stride, strategy, dtype, attrs):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(avgpool.avgpool, [shape], [dtype], op_attrs=[kernel, stride, strategy],
                                    kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, input, output = gen_data(dtype, kernel, shape, strategy, stride)
              return mod, expect, (input, output)
          else:
              return mod
      else:
          mod = utils.op_build_test(avgpool.avgpool, [shape], [dtype], op_attrs=[kernel, stride, strategy],
                                    kernel_name='avgpool', attrs=attrs)
          expect, input, output = gen_data(dtype, kernel, shape, strategy, stride)
          output = utils.mod_launch(mod, [input, output], expect=expect)
          return input, output, expect, compare_tensor(output, expect, rtol=5e-03, atol=5e-03, equal_nan=True)
  
  
  def gen_data(dtype, kernel, shape, strategy, stride):
      support_list = {"float16": np.float16, "float32": np.float32}
      input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
      expect = benchmark(input, kernel, stride, strategy)
      out_shape = expect.shape
      output = np.full(out_shape, 0, dtype)
      return expect, input, output
  
apis: numpy.mean numpy.full numpy.zeros