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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.
-
- 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 mean_square
- from akg.utils.dsl_create import get_reduce_out_shape
- from tests.common.gen_random import random_gaussian
-
- def mean_square_run(shape, reduce_axis, keepdims, dtype, attrs):
- op_attrs = [reduce_axis, keepdims]
-
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(mean_square.mean_square, [shape], [dtype], op_attrs, kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- expect, input1, output = gen_data(dtype, keepdims, reduce_axis, shape)
- return mod, expect, (input1, output)
- else:
- return mod
- else:
- mod = utils.op_build_test(mean_square.mean_square, [shape], [dtype], op_attrs, attrs=attrs)
- expect, input1, output = gen_data(dtype, keepdims, reduce_axis, shape)
- output = utils.mod_launch(mod, (input1, output), expect=expect)
- return input1, output, expect, compare_tensor(output, expect, rtol=5e-3, atol=5e-3, equal_nan=True)
-
-
- def gen_data(dtype, keepdims, reduce_axis, shape):
- input1 = random_gaussian(shape, miu=1, sigma=0.1)
- input1 = input1.astype(dtype)
- input1_square = np.square(input1)
- expect = np.mean(input1_square, axis=reduce_axis, keepdims=keepdims)
- out_shape = get_reduce_out_shape(shape, axis=reduce_axis, keepdims=keepdims)
- output = np.full(out_shape, np.nan, dtype)
- return expect, input1, output
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