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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.
-
- """clip_run"""
- import numpy as np
- from akg.utils import kernel_exec as utils
- from tests.common.test_op import clip
- from tests.common.tensorio import compare_tensor
- from tests.common.gen_random import random_gaussian
-
- def clip_run(shape, min_val, max_val, dtype, attrs):
- """clip_run"""
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(clip.clip, [shape], [dtype], kernel_name=kernel_name,
- op_attrs=[min_val, max_val], attrs=attrs, tuning=t)
- if t:
- exp_output, inputs, output = gen_data(dtype, max_val, min_val, shape)
- return mod, exp_output, (inputs, output)
- return mod
- else:
- # op_attrs=[shape, dtype]
- mod = utils.op_build_test(clip.clip, [shape], [dtype], kernel_name='clip',
- op_attrs=[min_val, max_val], attrs=attrs)
- exp_output, inputs, output = gen_data(dtype, max_val, min_val, shape)
- # result_tvm
- acu_output = utils.mod_launch(mod, (inputs, output), expect=exp_output)
- # compare result
- compare_result = compare_tensor(acu_output, exp_output, rtol=5e-03, equal_nan=True)
-
- return inputs, acu_output, exp_output, compare_result
-
-
- def gen_data(dtype, max_val, min_val, shape):
- # Result_Numpy
- inputs = random_gaussian(shape, miu=1, sigma=10.0).astype(dtype)
- exp_output = np.clip(inputs, min_val, max_val)
- # inputs and output to hold the data
- output = np.full(shape, np.nan, dtype)
- return exp_output, inputs, output
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