118 lines
4.2 KiB
C++
118 lines
4.2 KiB
C++
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// 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/deconvolution.h"
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#include "testutil.h"
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static int test_deconvolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int output_pad_right, int output_pad_bottom, int output_w, int output_h)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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if (output_w > 0 && output_h > 0 && pad != -233 && pad != -234)
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{
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pad = -233;
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}
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ncnn::ParamDict pd;
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pd.set(0, outch); // num_output
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pd.set(1, kernel); // kernel_w
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pd.set(2, dilation); // dilation_w
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pd.set(3, stride); // stride_w
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pd.set(4, pad); // pad_w
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pd.set(5, bias); // bias_term
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pd.set(6, outch * c * kernel * kernel);
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int activation_type = RAND() % 5; // 0 1 2 3 4
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ncnn::Mat activation_params(2);
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activation_params[0] = 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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pd.set(18, output_pad_right);
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pd.set(19, output_pad_bottom);
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pd.set(20, output_w);
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pd.set(21, output_h);
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std::vector<ncnn::Mat> weights(2);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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weights[1] = RandomMat(outch);
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int ret = test_layer<ncnn::Deconvolution>("Deconvolution", pd, weights, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_deconvolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f] output_pad_right=%d output_pad_bottom=%d output_w=%d output_h=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1], output_pad_right, output_pad_bottom, output_w, output_h);
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}
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return ret;
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}
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static int test_deconvolution_0()
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{
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static const int kdsp[16][4] = {
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{1, 1, 1, 0},
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{1, 1, 2, 0},
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{2, 1, 1, 1},
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{2, 1, 2, -233},
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{3, 1, 1, 1},
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{3, 1, 2, 1},
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{3, 2, 1, 1},
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{4, 1, 1, -233},
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{4, 1, 2, -234},
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{4, 2, 1, -234},
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{5, 1, 1, 2},
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{5, 1, 2, 2},
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{5, 2, 2, 2},
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{7, 1, 1, 3},
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{7, 1, 2, 3},
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{7, 2, 1, -233},
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};
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for (int i = 0; i < 16; i++)
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{
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const int k = kdsp[i][0];
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const int d = kdsp[i][1];
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const int s = kdsp[i][2];
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const int p = kdsp[i][3];
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int ret = 0
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|| test_deconvolution(9, 7, 1, 1, k, d, s, p, 1, 0, 0, 0, 0)
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|| test_deconvolution(9, 7, 4, 13, k, d, s, p, 0, 1, 1, 7, 5)
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|| test_deconvolution(9, 7, 13, 4, k, d, s, p, 1, 1, 0, 0, 0)
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|| test_deconvolution(9, 7, 4, 8, k, d, s, p, 0, 0, 1, 0, 0)
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|| test_deconvolution(9, 7, 8, 4, k, d, s, p, 1, 0, 0, 7, 5)
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|| test_deconvolution(9, 7, 8, 13, k, d, s, p, 0, 2, 2, 0, 0)
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|| test_deconvolution(9, 7, 13, 8, k, d, s, p, 1, 2, 0, 0, 0)
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|| test_deconvolution(9, 7, 16, 16, k, d, s, p, 0, 0, 2, 7, 5);
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if (ret != 0)
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return -1;
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}
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return 0
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|| test_deconvolution(7, 5, 24, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 24, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 28, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 28, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 26, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 26, 4, 2, 2, 2, 1, 0, 0, 0, 0);
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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_deconvolution_0();
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}
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