mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-18 21:54:39 +08:00
nihui
109 lines
4.5 KiB
C++
109 lines
4.5 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "layer/convolution.h"
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#include "testutil.h"
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static int test_convolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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ncnn::ParamDict pd;
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pd.set(0, outch);
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pd.set(1, kernel);
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pd.set(2, dilation);
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pd.set(3, stride);
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pd.set(4, pad);
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pd.set(5, bias);
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pd.set(6, outch * c * kernel * kernel);
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int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
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activation_params[1] = RandomFloat(0, 1); // beta
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(bias ? 2 : 1);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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if (bias)
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weights[1] = RandomMat(outch);
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float epsilon = 0.001;
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// larget epsilon for winograd optimization
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if (kernel == 3 && dilation == 1 && stride == 1 && c >= 16 && outch >= 16)
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{
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Randomize(a, -1, 1);
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if (c >= 64 || outch >= 64)
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Randomize(weights[0], -0.3, 0.3);
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else
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Randomize(weights[0], -1, 1);
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epsilon = 0.002;
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}
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int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, epsilon);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_convolution_0()
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{
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return 0
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|| test_convolution(7, 5, 1, 4, 3, 1, 1, 1, 1)
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|| test_convolution(14, 5, 1, 4, 3, 1, 2, 1, 1)
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|| test_convolution(11, 5, 2, 12, 2, 2, 2, 1, 1)
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|| test_convolution(15, 11, 4, 4, 3, 1, 1, 1, 1)
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|| test_convolution(15, 11, 8, 8, 3, 1, 1, 1, 1)
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|| test_convolution(11, 11, 8, 16, 3, 1, 1, 1, 1)
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|| test_convolution(13, 16, 16, 24, 3, 1, 1, 1, 1)
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|| test_convolution(20, 19, 24, 24, 3, 1, 1, 1, 1)
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|| test_convolution(8, 8, 16, 24, 3, 1, 1, 1, 0)
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|| test_convolution(4, 8, 16, 24, 3, 1, 1, 1, 1)
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|| test_convolution(4, 20, 16, 24, 3, 1, 1, 1, 0)
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|| test_convolution(6, 7, 64, 64, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 24, 32, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 24, 32, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 24, 32, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 24, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 24, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 24, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 28, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 28, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 28, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 26, 32, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 26, 32, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 26, 32, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 26, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 26, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 26, 3, 1, 2, 0, 1)
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|| test_convolution(30, 30, 32, 26, 3, 1, 1, 1, 0)
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|| test_convolution(12, 18, 8, 16, 3, 1, 1, 1, 1)
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|| test_convolution(42, 18, 32, 160, 3, 1, 1, 1, 1)
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|| test_convolution(12, 18, 32, 160, 3, 1, 1, 1, 1)
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|| test_convolution(12, 18, 4, 12, 3, 1, 1, 1, 1)
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|| test_convolution(42, 18, 28, 140, 3, 1, 1, 1, 1)
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|| test_convolution(12, 18, 28, 140, 3, 1, 1, 1, 1);
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}
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int main()
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{
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SRAND(7767517);
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return test_convolution_0();
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}
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