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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 akg.utils import kernel_exec as utils
- from tests.common.gen_random import random_gaussian
- from tests.common.tensorio import compare_tensor
- from tests.common.test_op.matrix_diag import matrix_diag
-
-
- def matrix_diag_run(shape, out_shape, dtype, attrs=None):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- mod = utils.op_build_test(matrix_diag, [shape], [dtype], op_attrs=[out_shape],
- kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- expect, input, output = gen_data(dtype, out_shape, shape)
- return mod, expect, (input, output)
- else:
- return mod
- else:
- mod = utils.op_build_test(matrix_diag, [shape], [dtype], op_attrs=[out_shape],
- kernel_name="matrix_diag", attrs=attrs)
- expect, input, output = gen_data(dtype, out_shape, shape)
- output = utils.mod_launch(mod, (input, output), expect=expect)
- result = compare_tensor(output, expect)
- return input, output, expect, result
-
-
- def gen_data(dtype, out_shape, shape):
- input = random_gaussian(size=shape, miu=0, sigma=10).astype(dtype)
- expect = get_matrix_diag(input, out_shape)
- output = np.full(out_shape, np.nan, dtype)
- return expect, input, output
-
-
- def get_matrix_diag(data, out_shape):
- res = np.zeros(out_shape, dtype=data.dtype)
- n = np.prod(res.shape[:-2]) if len(out_shape) > 2 else 1
- res = res.reshape(n, res.shape[-2], res.shape[-1])
- data = data.reshape(n, data.shape[-1])
- for i in range(res.shape[0]):
- for j in range(min(res.shape[1], res.shape[2], data.shape[1])):
- res[i][j][j] = data[i][j]
- return res.reshape(out_shape)
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