mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-17 04:41:47 +08:00
nihui
41f031e045
* adjust tests * opt shader coverage op * update gpu_coverage.cpp atomicor coalesce
602 lines
20 KiB
C++
602 lines
20 KiB
C++
// Copyright 2019 Tencent
|
|
// SPDX-License-Identifier: BSD-3-Clause
|
|
|
|
#include "testutil.h"
|
|
|
|
#include "layer_type.h"
|
|
|
|
#include <limits.h>
|
|
|
|
static int test_convolutiondepthwise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group, int activation_type)
|
|
{
|
|
ncnn::Mat a = RandomMat(w, h, c);
|
|
|
|
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 / group * c / group * kernel * kernel * group);
|
|
pd.set(7, group);
|
|
|
|
ncnn::Mat activation_params(2);
|
|
activation_params[0] = (activation_type == 6) ? 0.2f : -0.1f; // alpha
|
|
activation_params[1] = 0.3f; // beta
|
|
pd.set(9, activation_type);
|
|
pd.set(10, activation_params);
|
|
|
|
std::vector<ncnn::Mat> weights(2);
|
|
weights[0] = RandomMat(outch / group * c / group * kernel * kernel * group);
|
|
weights[1] = RandomMat(outch);
|
|
|
|
int ret = test_layer("ConvolutionDepthWise", pd, weights, a, 0.001);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_0()
|
|
{
|
|
// specialized kernels retain each depthwise and grouped packing transition
|
|
static const int specialized_geometry[][6] = {
|
|
{3, 1, 1, 1, 18, 17},
|
|
{3, 1, 2, 1, 25, 33},
|
|
{5, 1, 1, -234, 18, 17},
|
|
{5, 1, 2, 2, 25, 33},
|
|
};
|
|
static const int specialized_channels[][3] = {
|
|
{7, 7, 7},
|
|
{8, 8, 8},
|
|
{12, 12, 4},
|
|
{8, 8, 2},
|
|
{2, 2, 1},
|
|
{4, 2, 2},
|
|
{16, 8, 2},
|
|
{4, 4, 4},
|
|
{16, 16, 16},
|
|
};
|
|
for (int i = 0; i < 4; i++)
|
|
{
|
|
for (int j = 0; j < 9; j++)
|
|
{
|
|
const int* g = specialized_geometry[i];
|
|
const int* ch = specialized_channels[j];
|
|
if (test_convolutiondepthwise(g[4], g[5], ch[0], ch[1], g[0], g[1], g[2], g[3], 0, ch[2], 0) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
// generic geometry is orthogonal to scalar, pack4/8/16 and grouped implementations
|
|
static const int generic_geometry[][6] = {
|
|
{1, 1, 1, 0, 15, 7},
|
|
{1, 1, 2, 0, 18, 17},
|
|
{2, 1, 1, 1, 25, 33},
|
|
{2, 1, 2, -233, 15, 7},
|
|
{3, 2, 1, 1, 15, 7},
|
|
{4, 1, 1, 2, 18, 17},
|
|
{4, 1, 2, -233, 25, 33},
|
|
{4, 2, 1, -234, 15, 7},
|
|
{5, 2, 2, 2, 15, 7},
|
|
{7, 1, 1, 3, 18, 17},
|
|
{7, 1, 2, 3, 25, 33},
|
|
{7, 2, 1, -233, 15, 7},
|
|
};
|
|
static const int generic_channels[][3] = {
|
|
{7, 7, 7},
|
|
{4, 4, 4},
|
|
{8, 8, 8},
|
|
{16, 16, 16},
|
|
{12, 12, 4},
|
|
};
|
|
for (int i = 0; i < 12; i++)
|
|
{
|
|
const int* g = generic_geometry[i];
|
|
// scalar, pack16 and grouped convolution retain all geometries with intermediate pack anchors
|
|
for (int j = 0; j < 5; j++)
|
|
{
|
|
if (j != 0 && j != 3 && j != 4 && i != 2 && i != 4 && i != 7 && i != 8 && i != 11)
|
|
continue;
|
|
const int* ch = generic_channels[j];
|
|
if (test_convolutiondepthwise(g[4], g[5], ch[0], ch[1], g[0], g[1], g[2], g[3], 0, ch[2], 0) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
// specialized kernels, generic dilation and group packing conversions
|
|
// input channels, output channels, groups and bias
|
|
static const int packing[][4] = {
|
|
{1, 1, 1, 1},
|
|
{2, 2, 1, 0},
|
|
{2, 2, 2, 1},
|
|
{3, 3, 3, 0},
|
|
{4, 2, 2, 1},
|
|
{4, 4, 4, 0},
|
|
{7, 7, 7, 1},
|
|
{8, 8, 2, 0},
|
|
{8, 8, 8, 1},
|
|
{12, 12, 4, 0},
|
|
{15, 15, 15, 1},
|
|
{16, 8, 2, 0},
|
|
{16, 16, 16, 1},
|
|
};
|
|
static const int kernels[][4] = {
|
|
{3, 1, 1, 1},
|
|
{3, 1, 2, 1},
|
|
{5, 1, 1, 2},
|
|
{5, 1, 2, 2},
|
|
{7, 2, 1, -233},
|
|
};
|
|
for (int i = 0; i < 13; i++)
|
|
{
|
|
for (int j = 0; j < 5; j++)
|
|
{
|
|
// scalar, pack8, pack16 and every grouped route retain activated kernels
|
|
if (i != 0 && i != 1 && i != 4 && i != 7 && i != 8 && i != 9 && i != 11 && i != 12 && j != i % 5)
|
|
continue;
|
|
const int* ch = packing[i];
|
|
const int* k = kernels[j];
|
|
if (test_convolutiondepthwise(25, 33, ch[0], ch[1], k[0], k[1], k[2], k[3], ch[3], ch[2], 1) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
// grouped 1x1 output tiles with four, two and one element remainders
|
|
static const int tile_shapes[][2] = {{7, 2}, {7, 1}, {15, 7}};
|
|
for (int i = 0; i < 3; i++)
|
|
{
|
|
for (int bias = 0; bias < 2; bias++)
|
|
{
|
|
if (test_convolutiondepthwise(tile_shapes[i][0], tile_shapes[i][1], 16, 8, 1, 1, 1, 0, bias, 2, 0) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
// vector width remainders and both bias paths in specialized kernels
|
|
static const int tail_shapes[][4] = {{15, 7, 0, 0}, {18, 17, 1, 1}};
|
|
static const int tail_channels[] = {4, 8, 16};
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
for (int j = 0; j < 3; j++)
|
|
{
|
|
for (int k = 0; k < 4; k++)
|
|
{
|
|
const int c = tail_channels[j];
|
|
const int* shape = tail_shapes[i];
|
|
const int* geom = kernels[k];
|
|
if (test_convolutiondepthwise(shape[0], shape[1], c, c, geom[0], geom[1], geom[2], geom[3], shape[2], c, shape[3]) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
}
|
|
|
|
// explicit activations and bias for scalar, packed and grouped paths
|
|
static const int activation_channels[][3] = {
|
|
{3, 3, 3},
|
|
{4, 4, 4},
|
|
{8, 8, 8},
|
|
{16, 16, 16},
|
|
{4, 2, 2},
|
|
{12, 12, 4},
|
|
{8, 8, 2},
|
|
{16, 8, 2},
|
|
};
|
|
for (int i = 0; i < 8; i++)
|
|
{
|
|
for (int bias = 0; bias < 2; bias++)
|
|
{
|
|
for (int act = 0; act < 7; act++)
|
|
{
|
|
const int* ch = activation_channels[i];
|
|
if (test_convolutiondepthwise(9, 7, ch[0], ch[1], 2, 1, 1, 1, bias, ch[2], act) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
}
|
|
|
|
// kernel larger than the input and partial workgroups on large inputs
|
|
return 0
|
|
|| test_convolutiondepthwise(18, 17, 2, 2, 2, 1, 2, -233, 0, 1, 0)
|
|
|| test_convolutiondepthwise(15, 7, 1, 1, 5, 2, 2, 2, 1, 1, 0)
|
|
|| test_convolutiondepthwise(15, 7, 3, 3, 5, 2, 2, 2, 0, 3, 1)
|
|
|| test_convolutiondepthwise(65, 33, 7, 7, 3, 1, 1, 1, 1, 7, 2)
|
|
|| test_convolutiondepthwise(65, 33, 16, 16, 3, 1, 1, 1, 0, 16, 3);
|
|
}
|
|
|
|
static int test_convolutiondepthwise_activation_boundaries()
|
|
{
|
|
// identity kernels exercise all hard-swish regions without random saturation
|
|
static const int channels[][2] = {
|
|
{3, 3},
|
|
{4, 4},
|
|
{8, 8},
|
|
{16, 16},
|
|
{2, 1},
|
|
{12, 4},
|
|
{8, 2},
|
|
{16, 2},
|
|
};
|
|
const float input[] = {-4.f, -1.f, 0.f, 1.f, 4.f, 0.f};
|
|
const float output[] = {0.f, -0.1f, 0.f, 0.5f, 4.f};
|
|
for (int i = 0; i < 8; i++)
|
|
{
|
|
const int c = channels[i][0];
|
|
const int group = channels[i][1];
|
|
ncnn::Mat a(6, 2, c);
|
|
a.fill(0.f);
|
|
ncnn::Mat reference(5, 1, c);
|
|
for (int q = 0; q < c; q++)
|
|
{
|
|
float* aptr = a.channel(q);
|
|
float* rptr = reference.channel(q);
|
|
for (int x = 0; x < 6; x++)
|
|
aptr[x] = input[x];
|
|
for (int x = 0; x < 5; x++)
|
|
rptr[x] = output[x];
|
|
}
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, c);
|
|
pd.set(1, 2);
|
|
pd.set(2, 1);
|
|
pd.set(3, 1);
|
|
pd.set(4, 0);
|
|
pd.set(5, 0);
|
|
pd.set(6, c * c / group * 4);
|
|
pd.set(7, group);
|
|
pd.set(9, 6);
|
|
ncnn::Mat params(2);
|
|
params[0] = 0.2f;
|
|
params[1] = 0.3f;
|
|
pd.set(10, params);
|
|
std::vector<ncnn::Mat> weights(1);
|
|
weights[0].create(c * c / group * 4);
|
|
weights[0].fill(0.f);
|
|
for (int q = 0; q < c; q++)
|
|
weights[0][q * c / group * 4] = 1.f;
|
|
|
|
ncnn::Mat b;
|
|
int ret = test_layer_naive(ncnn::LayerType::ConvolutionDepthWise, pd, weights, a, b, 0);
|
|
if (ret == 0)
|
|
ret = CompareMat(reference, b, 0.000001f);
|
|
if (ret == 0)
|
|
ret = test_layer("ConvolutionDepthWise", pd, weights, a);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise_activation_boundaries failed c=%d group=%d\n", c, group);
|
|
return ret;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_packed_activation_tails()
|
|
{
|
|
// odd output rows and columns cover both packed vector blocks and tails
|
|
for (int bias = 0; bias < 2; bias++)
|
|
{
|
|
for (int act = 1; act <= 6; act++)
|
|
{
|
|
if (test_convolutiondepthwise(8, 6, 8, 8, 2, 1, 1, 1, bias, 8, act) != 0)
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_activation_params_text()
|
|
{
|
|
#if NCNN_STRING
|
|
const char* params[] = {"0=1 1=1 6=1 9=3 -23310=2,-1,2", "0=1 1=1 6=1 9=3 -23310=2,-1.0,2.0"};
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
TestParamDict pd;
|
|
if (pd.load_param(params[i]) != 0 || pd.type(10) != 5 + i)
|
|
return -1;
|
|
|
|
std::vector<ncnn::Mat> weights(1);
|
|
weights[0].create(1);
|
|
weights[0][0] = 1.f;
|
|
|
|
ncnn::Mat a(2, 1, 1);
|
|
a[0] = -3.f;
|
|
a[1] = 3.f;
|
|
ncnn::Mat reference(2, 1, 1);
|
|
reference[0] = -1.f;
|
|
reference[1] = 2.f;
|
|
ncnn::Mat b;
|
|
int ret = test_layer_naive(ncnn::LayerType::ConvolutionDepthWise, pd, weights, a, b, 0);
|
|
if (ret == 0)
|
|
ret = CompareMat(reference, b, 0.f);
|
|
if (ret != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise_activation_params_text failed params=%s ret=%d\n", params[i], ret);
|
|
return ret;
|
|
}
|
|
|
|
const ncnn::Mat original = pd.get(10, ncnn::Mat());
|
|
const int* p = original;
|
|
if (i == 0 ? (p[0] != -1 || p[1] != 2) : (original[0] != -1.f || original[1] != 2.f))
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise_activation_params_text modified params=%s\n", params[i]);
|
|
return -1;
|
|
}
|
|
}
|
|
#endif
|
|
return 0;
|
|
}
|
|
|
|
#if NCNN_VALIDATION
|
|
static int test_convolutiondepthwise_load_param_activation(const ncnn::ParamDict& base, int activation_type, const ncnn::Mat& activation_params, int expected_ret)
|
|
{
|
|
ncnn::ParamDict pd = base;
|
|
pd.set(9, activation_type);
|
|
pd.set(10, activation_params);
|
|
|
|
return test_layer_param(ncnn::LayerType::ConvolutionDepthWise, pd, expected_ret);
|
|
}
|
|
|
|
static int test_convolutiondepthwise_load_param()
|
|
{
|
|
ncnn::ParamDict base;
|
|
base.set(0, 8);
|
|
base.set(1, 3);
|
|
base.set(6, 216);
|
|
base.set(7, 2);
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 0) != 0)
|
|
return -1;
|
|
|
|
ncnn::Mat params(2);
|
|
params[0] = 0.1f;
|
|
params[1] = 0.5f;
|
|
|
|
int ret = 0
|
|
|| test_convolutiondepthwise_load_param_activation(base, 0, ncnn::Mat(), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 0, ncnn::Mat(0), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 0, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 0, params.range(0, 1), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 1, ncnn::Mat(), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 1, ncnn::Mat(0), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 1, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 1, params.range(0, 1), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 2, ncnn::Mat(), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 2, ncnn::Mat(0), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 2, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 2, params.range(0, 1), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 3, ncnn::Mat(), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 3, ncnn::Mat(0), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 3, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 3, params.range(0, 1), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 4, ncnn::Mat(), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 4, ncnn::Mat(0), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 4, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 4, params.range(0, 1), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 5, ncnn::Mat(), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 5, ncnn::Mat(0), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 5, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 5, params.range(0, 1), 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 6, ncnn::Mat(), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 6, ncnn::Mat(0), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 6, params, 0)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 6, params.range(0, 1), -1)
|
|
|| test_convolutiondepthwise_load_param_activation(base, 7, ncnn::Mat(), -1);
|
|
if (ret != 0)
|
|
return ret;
|
|
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 10, 1, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 10, 1.f, -1))
|
|
return -1;
|
|
|
|
ncnn::Mat missing_data(0);
|
|
missing_data.w = 1;
|
|
|
|
const ncnn::Mat bad[] = {ncnn::Mat(2, (size_t)1u), ncnn::Mat(2, (size_t)2u), ncnn::Mat(2, 2), ncnn::Mat(2, (size_t)16u, 4), missing_data};
|
|
for (int i = 0; i < 5; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 10, bad[i], -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
ret = 0
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 0, 0, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 1, 0, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 2, 0, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 3, 0, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 1, INT_MAX, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 6, 217, -1);
|
|
if (ret != 0)
|
|
return ret;
|
|
|
|
const int groups[] = {0, -1, -8, INT_MIN, 3};
|
|
for (int i = 0; i < 5; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 7, groups[i], -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
{
|
|
ncnn::ParamDict pd = base;
|
|
pd.set(0, INT_MIN);
|
|
pd.set(7, -1); // avoid evaluating INT_MIN % -1
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, pd, -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_load_param_int8()
|
|
{
|
|
ncnn::ParamDict base;
|
|
base.set(0, 1);
|
|
base.set(1, 1);
|
|
base.set(6, 1);
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 0) != 0)
|
|
return -1;
|
|
|
|
const int valid[] = {1, 2, 101, 102};
|
|
for (int i = 0; i < 4; i++)
|
|
{
|
|
#if NCNN_INT8
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 8, valid[i], 0) != 0)
|
|
#else
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 8, valid[i], -1) != 0)
|
|
#endif
|
|
return -1;
|
|
}
|
|
|
|
const int invalid[] = {-1, 3, 100, 103, INT_MIN, INT_MAX};
|
|
for (int i = 0; i < 6; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 8, invalid[i], -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int test_convolutiondepthwise_load_param_dynamic()
|
|
{
|
|
ncnn::ParamDict base;
|
|
base.set(0, 5);
|
|
base.set(1, 1);
|
|
base.set(6, 10);
|
|
base.set(7, 2);
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, -1) != 0)
|
|
return -1;
|
|
|
|
ncnn::ParamDict dynamic = base;
|
|
dynamic.set(19, 1);
|
|
|
|
const int num_outputs[] = {0, -1, 5, INT_MIN, INT_MAX};
|
|
for (int i = 0; i < 5; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, dynamic, 0, num_outputs[i], 0) != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, base, 0, 2, 0)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, dynamic, 7, 0, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, dynamic, 7, -1, -1)
|
|
|| test_layer_param(ncnn::LayerType::ConvolutionDepthWise, dynamic, 7, INT_MIN, -1);
|
|
}
|
|
|
|
static int test_convolutiondepthwise_load_param_text()
|
|
{
|
|
#if NCNN_STRING
|
|
const char* params[] = {"0=1 1=1 6=1 9=3 -23310=2,-1,2", "0=1 1=1 6=1 9=3 -23310=2,-1.0,2.0"};
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
TestParamDict pd;
|
|
if (pd.load_param(params[i]) != 0 || pd.type(10) != 5 + i)
|
|
return -1;
|
|
|
|
if (test_layer_param(ncnn::LayerType::ConvolutionDepthWise, pd, 0) != 0)
|
|
return -1;
|
|
}
|
|
#endif
|
|
return 0;
|
|
}
|
|
#endif // NCNN_VALIDATION
|
|
|
|
static int test_convolutiondepthwise_activation_extremes()
|
|
{
|
|
// identity inputs cover sigmoid exponent and mish saturation boundaries
|
|
static const float values[] = {-20.f, -2.f, 0.f, 2.f, 20.f};
|
|
for (int act = 4; act <= 5; act++)
|
|
{
|
|
ncnn::Mat a(5, 1, 1);
|
|
for (int i = 0; i < 5; i++)
|
|
a[i] = values[i];
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, 1);
|
|
pd.set(1, 1);
|
|
pd.set(5, 0);
|
|
pd.set(6, 1);
|
|
pd.set(7, 1);
|
|
pd.set(9, act);
|
|
std::vector<ncnn::Mat> weights(1);
|
|
weights[0].create(1);
|
|
weights[0][0] = 1.f;
|
|
if (test_layer("ConvolutionDepthWise", pd, weights, a, 0.001) != 0)
|
|
{
|
|
fprintf(stderr, "test_convolutiondepthwise_activation_extremes failed act=%d\n", act);
|
|
return -1;
|
|
}
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
static int test_rectangular_kernels()
|
|
{
|
|
// channel dimensions and the two independent strip-kernel orientations
|
|
static const int cases[][4] = {{8, 8, 1, 3}, {8, 8, 3, 1}};
|
|
static const float values[] = {-4.f, -2.f, -1.f, 0.f, 1.f, 2.f, 4.f};
|
|
for (int i = 0; i < 2; i++)
|
|
{
|
|
const int* c = cases[i];
|
|
ncnn::Mat a(7, 5, c[0]);
|
|
for (int q = 0; q < c[0]; q++)
|
|
{
|
|
float* ptr = a.channel(q);
|
|
for (int y = 0; y < 5; y++)
|
|
{
|
|
for (int x = 0; x < 7; x++)
|
|
ptr[y * 7 + x] = values[(x + y + q) % 7];
|
|
}
|
|
}
|
|
|
|
ncnn::ParamDict pd;
|
|
pd.set(0, c[1]);
|
|
pd.set(1, c[2]);
|
|
pd.set(11, c[3]);
|
|
pd.set(2, 1);
|
|
pd.set(3, 1);
|
|
pd.set(4, c[2] == 3 ? 1 : 0);
|
|
pd.set(15, c[2] == 3 ? 1 : 0);
|
|
pd.set(14, c[3] == 3 ? 1 : 0);
|
|
pd.set(16, c[3] == 3 ? 1 : 0);
|
|
pd.set(5, 0);
|
|
pd.set(7, c[0]);
|
|
const int weight_count = c[0] * c[2] * c[3];
|
|
pd.set(6, weight_count);
|
|
std::vector<ncnn::Mat> weights(1);
|
|
weights[0].create(weight_count);
|
|
for (int k = 0; k < weight_count; k++)
|
|
weights[0][k] = (float)(k % 3 - 1);
|
|
if (test_layer("ConvolutionDepthWise", pd, weights, a, 0.001) != 0)
|
|
{
|
|
fprintf(stderr, "test_rectangular_kernels failed c=%d outch=%d kernel=%dx%d\n", c[0], c[1], c[2], c[3]);
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return 0
|
|
|| test_convolutiondepthwise_0()
|
|
|| test_convolutiondepthwise_activation_boundaries()
|
|
|| test_convolutiondepthwise_packed_activation_tails()
|
|
|| test_convolutiondepthwise_activation_params_text()
|
|
#if NCNN_VALIDATION
|
|
|| test_convolutiondepthwise_load_param()
|
|
|| test_convolutiondepthwise_load_param_text()
|
|
|| test_convolutiondepthwise_load_param_dynamic()
|
|
|| test_convolutiondepthwise_load_param_int8()
|
|
#endif // NCNN_VALIDATION
|
|
|| test_rectangular_kernels()
|
|
|| test_convolutiondepthwise_activation_extremes();
|
|
}
|