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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 tests.common.tensorio import compare_tensor
- from akg.utils import kernel_exec as utils
- from tests.common.test_op import square_difference
- from tests.common.gen_random import random_gaussian
-
-
- def square_difference_run(shape1, shape2, 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(square_difference.square_difference, input_shapes=[shape1, shape2],
- input_types=[dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- expect, input1, input2, output = gen_data(dtype, shape1, shape2)
- return mod, expect, (input1, input2, output)
- else:
- return mod
- else:
- mod = utils.op_build_test(square_difference.square_difference, input_shapes=[shape1, shape2],
- input_types=[dtype, dtype], kernel_name=kernel_name, attrs=attrs)
- expect, input1, input2, output = gen_data(dtype, shape1, shape2)
- source_code = mod.imported_modules[0].get_source()
- utils.create_code(kernel_name, cce_path, source_code)
- output = utils.mod_launch(mod, (input1, input2, output), expect=expect)
- return (input1, input2), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
-
-
- def gen_data(dtype, shape1, shape2):
- support_list = {"float16": np.float16, "float32": np.float32}
- input1 = random_gaussian(shape1, miu=1, sigma=0.1).astype(support_list[dtype])
- input2 = random_gaussian(shape2, miu=1, sigma=0.1).astype(support_list[dtype])
- expect = np.square(np.subtract(input1, input2))
- out_shape = expect.shape
- output = np.full(out_shape, np.nan, dtype)
- return expect, input1, input2, output
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