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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 cos_ad
- from tests.common.gen_random import random_gaussian
- from tests.common.base import get_rtol_atol
-
- def cos_ad_run(shape, dtype, attrs):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- expect, head_np, inputs = get_input_data(dtype, shape)
- mod = utils.op_build_test(cos_ad.cos_ad, [expect.shape, shape], [dtype, dtype], kernel_name=kernel_name,
- attrs=attrs, tuning=t)
- if t:
- # inputs and output to hold the data
- output = np.full(expect.shape, np.nan, dtype)
- return mod, expect, (head_np, inputs, output)
- else:
- return mod
- else:
- expect, head_np, inputs = get_input_data(dtype, shape)
- mod = utils.op_build_test(cos_ad.cos_ad, [expect.shape, shape], [dtype, dtype],
- kernel_name='cos_ad', attrs=attrs)
- # inputs and output to hold the data
- output = np.full(expect.shape, np.nan, dtype)
- output = utils.mod_launch(mod, (head_np, inputs, output), expect=expect)
- # compare result
- rtol, atol = get_rtol_atol("cos_ad", dtype)
- TestCase_Result = compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=False)
- return (head_np, inputs), output, expect, TestCase_Result
-
-
- def get_input_data(dtype, shape):
- # Generate data for testing the op
- inputs = random_gaussian(shape, miu=0, sigma=0.1).astype(dtype)
- head_np = random_gaussian(shape, miu=0, sigma=0.1).astype(dtype)
- expect = -np.sin(inputs) * head_np
- return expect, head_np, inputs
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