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- # 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 tests.common.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 tests.common.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
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