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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.
-
- from akg.utils import kernel_exec as utils
- from tests.common.tensorio import compare_tensor
- import numpy as np
- from tests.common.test_op import detection_five2four
- from tests.common.gen_random import random_gaussian
-
- def detection_five2four_np(arg_list):
- bs_i, box_num, wi = arg_list
- shape_i = [bs_i, wi * wi * box_num, 4]
- result = random_gaussian(shape_i, miu=1, sigma=0.1).astype("float16")
- input_data = result.reshape(bs_i * wi * wi, box_num * 4)
- if box_num % 4 != 0:
- pad_box = 4 - box_num % 4
- input_data = np.pad(input_data, ((0, 0), (0, pad_box * 4)), 'constant')
- box_num += pad_box
- input_data = input_data.reshape(bs_i, wi, wi, box_num // 4, 16).transpose([0, 3, 1, 2, 4])
- return input_data, result
-
-
- def detection_five2four_run(input_box, dtype, attrs):
- if 'tuning' in attrs.keys():
- t = attrs.get("tuning", False)
- kernel_name = attrs.get("kernel_name", False)
- inputs, expect = detection_five2four_np(input_box)
- mod = utils.op_build_test(detection_five2four.detection_five2four, [inputs.shape], [dtype],
- op_attrs=[input_box[1]], kernel_name=kernel_name, attrs=attrs, tuning=t)
- if t:
- out_shape = expect.shape
- output = np.full(out_shape, 0, dtype)
- return mod, expect, [inputs, output]
- else:
- return mod
- else:
- inputs, expect = detection_five2four_np(input_box)
- mod = utils.op_build_test(detection_five2four.detection_five2four, [inputs.shape], [dtype],
- op_attrs=[input_box[1]], kernel_name='detection_five2four', attrs=attrs)
- out_shape = expect.shape
- output = np.full(out_shape, 0, dtype)
- output = utils.mod_launch(mod, [inputs, output], expect=expect)
- return inputs, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
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