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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.
-
- """minimum_run"""
- import numpy as np
- from akg.utils import kernel_exec as utils
- from tests.common.test_op import minimum
- from akg.utils import validation_check as vc_util
- from tests.common.tensorio import compare_tensor
- from tests.common.gen_random import random_gaussian
-
- def minimum_run(shape1, shape2, dtype, attrs):
- """minimum_run"""
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(minimum.minimum, [shape1, shape2], [dtype, dtype],
- kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- expect, lhd, output, rhd = gen_data(dtype, shape1, shape2)
- return mod, expect, (lhd, rhd, output)
- return mod
- else:
- mod = utils.op_build_test(minimum.minimum, [shape1, shape2], [dtype, dtype], kernel_name='minimum', attrs=attrs)
- expect, lhd, output, rhd = gen_data(dtype, shape1, shape2)
- # result_tvm
- output = utils.mod_launch(mod, (lhd, rhd, output), expect=expect)
- # compare result
- compare_result = compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
-
- return (lhd, rhd), output, expect, compare_result
-
-
- def gen_data(dtype, shape1, shape2):
- """gen_data"""
- # check shapes
- vc_util.check_shape(shape1)
- vc_util.check_shape(shape2)
- # check types
- support_list = {"float16": np.float16, "float32": np.float32, "int32": np.int32, "int8": np.int8, "uint8": np.uint8}
- if not dtype.lower() in support_list:
- raise RuntimeError("minimum_cce only support %s while dtype is %s" % (",".join(support_list.keys()), dtype))
-
- # Result_Numpy
- lhd = random_gaussian(shape1, miu=1, sigma=1).astype(support_list[dtype])
- rhd = random_gaussian(shape2, miu=1, sigma=1).astype(support_list[dtype])
- expect = np.minimum(lhd, rhd)
- # inputs and output to hold the data
- output = np.full(shape1, np.nan, dtype)
- return expect, lhd, output, rhd
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