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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 akg.utils import kernel_exec as utils
- from tests.common.test_op import discontinous_mov
- from tests.common.tensorio import compare_tensor
- from tests.common.gen_random import random_gaussian
-
- def discontinous_mov_run(shapes, dtype, attrs):
- # Result_Numpy
- shape1 = shapes[0]
- shape2 = shapes[1]
- op_attrs = [shape2]
-
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(discontinous_mov.discontinous_mov, [shape1], [dtype], op_attrs,
- kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- args, exp_output, input = gen_data(dtype, shape1, shape2)
- return mod, exp_output, args
- else:
- return mod
- else:
- mod = utils.op_build_test(discontinous_mov.discontinous_mov, [shape1], [dtype], op_attrs,
- kernel_name='discontinous_mov', attrs=attrs)
- args, exp_output, input = gen_data(dtype, shape1, shape2)
- acu_output = utils.mod_launch(mod, args, expect=exp_output)
-
- # compare result
- TestCase_Result = compare_tensor(acu_output, exp_output, rtol=5e-03, equal_nan=True)
- return input, acu_output, exp_output, TestCase_Result
-
-
- def gen_data(dtype, shape1, shape2):
- support_list = {"float16": np.float16, "float32": np.float32}
- input = random_gaussian(shape1, miu=1, sigma=0.1).astype(support_list[dtype])
- exp_output = input[0:len(input) + 1:2]
- exp_output = np.concatenate((exp_output, exp_output))
- exp_output = exp_output.reshape(shape2)
- # inputs and output to hold the data
- output = np.full(shape2, np.nan, dtype)
- args = [input, output]
- return args, exp_output, input
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