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| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. | ||
| // | ||
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| // in compliance with the License. You may obtain a copy of the License at | ||
| // | ||
| // https://opensource.org/licenses/BSD-3-Clause | ||
| // | ||
| // 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. | ||
|
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| #include "testutil.h" | ||
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| static int test_requantize_pack1_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta) | ||
| { | ||
| ncnn::ParamDict pd; | ||
| pd.set(0, scale_in_data_size); | ||
| pd.set(1, scale_out_data_size); | ||
| pd.set(2, bias_data_size); | ||
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|
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| ncnn::Mat activation_params(2); | ||
| activation_params[0] = alpha; | ||
| activation_params[1] = beta; | ||
| pd.set(3, activation_type); | ||
| pd.set(4, activation_params); | ||
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| std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2); | ||
| weights[0] = RandomMat(scale_in_data_size); | ||
| weights[1] = RandomMat(scale_out_data_size); | ||
| if (bias_data_size) | ||
| weights[2] = RandomMat(bias_data_size); | ||
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| Randomize(weights[0], 0.0001, 0.001); | ||
| Randomize(weights[1], 10, 100); | ||
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| int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_DISABLE_AUTO_INPUT_PACKING; | ||
| int ret = test_layer_oom("Requantize", pd, weights, a, flag); | ||
| if (ret != 0) | ||
| { | ||
| fprintf(stderr, "test_requantize_pack1_oom failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]); | ||
| } | ||
|
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| return ret; | ||
| } | ||
|
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| static int test_requantize_pack1_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size) | ||
| { | ||
| return 0 | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f) | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f) | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f) | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1)) | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f) | ||
| || test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f); | ||
| } | ||
|
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| static int test_requantize_pack8_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta) | ||
| { | ||
| ncnn::ParamDict pd; | ||
| pd.set(0, scale_in_data_size); | ||
| pd.set(1, scale_out_data_size); | ||
| pd.set(2, bias_data_size); | ||
|
|
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| ncnn::Mat activation_params(2); | ||
| activation_params[0] = alpha; | ||
| activation_params[1] = beta; | ||
| pd.set(3, activation_type); | ||
| pd.set(4, activation_params); | ||
|
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| std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2); | ||
| weights[0] = RandomMat(scale_in_data_size); | ||
| weights[1] = RandomMat(scale_out_data_size); | ||
| if (bias_data_size) | ||
| weights[2] = RandomMat(bias_data_size); | ||
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| Randomize(weights[0], 0.0001, 0.001); | ||
| Randomize(weights[1], 10, 100); | ||
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| int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_ENABLE_FORCE_INPUT_PACK8; | ||
| int ret = test_layer_oom("Requantize", pd, weights, a, flag); | ||
| if (ret != 0) | ||
| { | ||
| fprintf(stderr, "test_requantize_pack8_oom failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]); | ||
| } | ||
|
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||
| return ret; | ||
| } | ||
|
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| static int test_requantize_pack8_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size) | ||
| { | ||
| return 0 | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f) | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f) | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f) | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1)) | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f) | ||
| || test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f); | ||
| } | ||
|
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| static int test_requantize_0() | ||
| { | ||
| return 0 | ||
| || test_requantize_pack1_oom(RandomIntMat(7, 9, 12), 12, 12, 12) | ||
| || test_requantize_pack1_oom(RandomIntMat(3, 5, 13), 13, 13, 13); | ||
| } | ||
|
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||
| static int test_requantize_1() | ||
| { | ||
| return 0 | ||
| || test_requantize_pack1_oom(RandomIntMat(17, 12), 12, 12, 12) | ||
| || test_requantize_pack1_oom(RandomIntMat(19, 15), 15, 15, 15); | ||
| } | ||
|
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||
| static int test_requantize_2() | ||
| { | ||
| return test_requantize_pack1_oom(RandomIntMat(124), 1, 1, 1); | ||
| } | ||
|
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| static int test_requantize_3() | ||
| { | ||
| return 0 | ||
| || test_requantize_pack8_oom(RandomIntMat(5, 7, 24), 24, 24, 24) | ||
| || test_requantize_pack8_oom(RandomIntMat(15, 24), 24, 24, 24) | ||
| || test_requantize_pack8_oom(RandomIntMat(128), 1, 1, 1); | ||
| } | ||
|
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| int main() | ||
| { | ||
| SRAND(7767517); | ||
|
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| return 0 | ||
| || test_requantize_0() | ||
| || test_requantize_1() | ||
| || test_requantize_2() | ||
| || test_requantize_3(); | ||
| } |