|
- # 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 greater_equal
- from tests.common.gen_random import random_gaussian
-
-
- def greater_equal_run(shapes, dtype, kernel_name, attrs, cce_path="./"):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(greater_equal.greater_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- benchMark, inputs, output = gen_data(shapes)
- return mod, benchMark, inputs + [output]
- else:
- return mod
- else:
- mod = utils.op_build_test(greater_equal.greater_equal, shapes, [dtype, dtype], kernel_name=kernel_name, attrs=attrs)
- benchMark, inputs, output = gen_data(shapes)
- output = utils.mod_launch(mod, inputs + [output], expect=benchMark)
-
- return inputs, output, benchMark, np.array_equal(output, benchMark)
-
-
- def gen_data(shapes):
- inputs = []
- for i in range(len(shapes)):
- shape = shapes[i]
- input = random_gaussian(shape, miu=1, sigma=0.1).astype(np.float16)
- inputs.append(input)
-
- if len(inputs) != 2:
- raise RuntimeError("inputs num should be 2")
- benchMark = np.greater_equal(inputs[0], inputs[1])
- output = np.full(benchMark.shape, 0, bool)
- return benchMark, inputs, output
|