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- # Copyright 2020 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.
-
- """run test function for cross"""
-
- import numpy as np
-
- from akg.utils import kernel_exec as utils
- from tests.common.tensorio import compare_tensor
- from tests.common.base import get_rtol_atol
- from tests.common.test_op import cross
- from tests.common.gen_random import random_gaussian
-
-
- def cross_run(shape1, dtype1, shape2, dtype2, attrs):
- """run function for cross"""
- mod = utils.op_build_test(cross.cross,
- [shape1, shape2], [dtype1, dtype2],
- kernel_name="cross",
- attrs=attrs)
- expect, inputs, out_buf = gen_data(dtype1, dtype2, shape1, shape2)
- output = utils.mod_launch(mod, (*inputs, out_buf), expect=expect)
- rtol, atol = get_rtol_atol("cross", dtype1)
- cmp_res = compare_tensor(output, expect, rtol=rtol, atol=atol)
- return inputs, output, expect, cmp_res
-
- def gen_data(dtype1, dtype2, shape1, shape2):
- """generate valid test data for cross"""
- input1 = random_gaussian(shape1).astype(dtype1)
- input2 = random_gaussian(shape2).astype(dtype2)
-
- if dtype1 in ("int8", "uint8", "int32"):
- # for overflow case, numpy will truncate the result, but davinci will
- # make it maximum or minimuam value.
- expect = np.cross(input1.astype("float32"), input2.astype("float32"),
- axisa=0, axisb=0, axisc=0)
- expect = np.maximum(expect, np.ones_like(expect) * np.iinfo(dtype1).min)
- expect = np.minimum(expect, np.ones_like(expect) * np.iinfo(dtype1).max)
- expect = expect.astype(dtype1)
- else:
- expect = np.cross(input1, input2, axisa=0, axisb=0, axisc=0)
-
- out_buf = np.full(expect.shape, np.nan, dtype1)
- return expect, (input1, input2), out_buf
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