Files
ncnn/tests/test_deconvolution_oom.cpp
2026-05-30 14:59:33 +08:00

64 lines
1.9 KiB
C++

// Copyright 2025 Tencent
// SPDX-License-Identifier: BSD-3-Clause
#include "testutil.h"
static int test_deconvolution_oom(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)
{
ncnn::Mat a = RandomMat(w, h, c);
if (output_w > 0 && output_h > 0 && pad != -233 && pad != -234)
{
pad = -233;
}
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() % 5; // 0 1 2 3 4
ncnn::Mat activation_params(2);
activation_params[0] = RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);
pd.set(18, output_pad_right);
pd.set(19, output_pad_bottom);
pd.set(20, output_w);
pd.set(21, output_h);
std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);
int ret = test_layer_oom("Deconvolution", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_deconvolution_oom 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);
return ret;
}
return ret;
}
static int test_deconvolution_0()
{
return 0
|| test_deconvolution_oom(9, 7, 8, 8, 3, 1, 1, 1, 1, 0, 0, 0, 0)
|| test_deconvolution_oom(9, 7, 16, 16, 3, 1, 1, 1, 1, 0, 0, 0, 0);
}
int main()
{
SRAND(7767517);
return test_deconvolution_0();
}