File size: 3,019 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 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.
#include "layer/deformableconv2d.h"
#include "testutil.h"
static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
const int kernel_extent_w = dilation * (kernel - 1) + 1;
const int kernel_extent_h = dilation * (kernel - 1) + 1;
const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
std::vector<ncnn::Mat> a(3);
a[0] = RandomMat(w, h, c);
a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
a[2] = RandomMat(out_w, out_h, kernel * kernel);
ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);
int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);
std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);
float epsilon = 0.001;
int ret = test_layer<ncnn::DeformableConv2D>("DeformableConv2D", pd, weights, a, 1, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_deformableconv2d 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]);
}
return ret;
}
static int test_deformableconv2d_0()
{
return 0
|| test_deformableconv2d(7, 5, 24, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 24, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 28, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 28, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 26, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 26, 4, 2, 2, 2, 1);
}
int main()
{
SRAND(7767517);
return test_deformableconv2d_0();
}
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