329 lines
10 KiB
C++
329 lines
10 KiB
C++
|
// Tencent is pleased to support the open source community by making ncnn available.
|
||
|
|
//
|
||
|
|
// Copyright (C) 2020 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 "platform.h"
|
||
|
|
#include "net.h"
|
||
|
|
#include "testutil.h"
|
||
|
|
|
||
|
|
#include <stdio.h>
|
||
|
|
|
||
|
|
#ifdef __EMSCRIPTEN__
|
||
|
|
#include <emscripten.h>
|
||
|
|
#endif
|
||
|
|
|
||
|
|
static ncnn::Mat generate_ncnn_logo(int pixel_type_to, int w, int h)
|
||
|
|
{
|
||
|
|
// clang-format off
|
||
|
|
// *INDENT-OFF*
|
||
|
|
static const unsigned char ncnn_logo_data[16][16] =
|
||
|
|
{
|
||
|
|
{245, 245, 33, 245, 245, 245, 245, 245, 245, 245, 245, 245, 245, 33, 245, 245},
|
||
|
|
{245, 33, 33, 33, 245, 245, 245, 245, 245, 245, 245, 245, 33, 33, 33, 245},
|
||
|
|
{245, 33, 158, 158, 33, 245, 245, 245, 245, 245, 245, 33, 158, 158, 33, 245},
|
||
|
|
{ 33, 117, 158, 224, 158, 33, 245, 245, 245, 245, 33, 158, 224, 158, 117, 33},
|
||
|
|
{ 33, 117, 224, 224, 224, 66, 33, 33, 33, 33, 66, 224, 224, 224, 117, 33},
|
||
|
|
{ 33, 189, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 189, 33},
|
||
|
|
{ 33, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 224, 33},
|
||
|
|
{ 33, 224, 224, 97, 97, 97, 97, 224, 224, 97, 97, 97, 97, 224, 224, 33},
|
||
|
|
{ 33, 224, 224, 97, 33, 0, 189, 224, 224, 97, 0, 33, 97, 224, 224, 33},
|
||
|
|
{ 33, 224, 224, 97, 33, 0, 189, 224, 224, 97, 0, 33, 97, 224, 224, 33},
|
||
|
|
{ 33, 224, 224, 97, 97, 97, 97, 224, 224, 97, 189, 189, 97, 224, 224, 33},
|
||
|
|
{ 33, 66, 66, 66, 224, 224, 224, 224, 224, 224, 224, 224, 66, 66, 66, 33},
|
||
|
|
{ 66, 158, 158, 66, 66, 224, 224, 224, 224, 224, 224, 66, 158, 158, 66, 66},
|
||
|
|
{ 66, 158, 158, 208, 66, 224, 224, 224, 224, 224, 224, 66, 158, 158, 208, 66},
|
||
|
|
{ 66, 224, 202, 158, 66, 224, 224, 224, 224, 224, 224, 66, 224, 202, 158, 66},
|
||
|
|
{ 66, 158, 224, 158, 66, 224, 224, 224, 224, 224, 224, 66, 158, 224, 158, 66}
|
||
|
|
};
|
||
|
|
// *INDENT-ON*
|
||
|
|
// clang-format on
|
||
|
|
|
||
|
|
const unsigned char* p_ncnn_logo_data = (const unsigned char*)ncnn_logo_data;
|
||
|
|
ncnn::Mat logo = ncnn::Mat::from_pixels(p_ncnn_logo_data, ncnn::Mat::PIXEL_GRAY | (pixel_type_to << ncnn::Mat::PIXEL_CONVERT_SHIFT), 16, 16);
|
||
|
|
|
||
|
|
ncnn::Mat m;
|
||
|
|
ncnn::Option opt;
|
||
|
|
opt.num_threads = 1;
|
||
|
|
ncnn::resize_nearest(logo, m, w, h, opt);
|
||
|
|
return m;
|
||
|
|
}
|
||
|
|
|
||
|
|
struct compare_score_index
|
||
|
|
{
|
||
|
|
inline bool operator()(const std::pair<float, int>& a, const std::pair<float, int>& b)
|
||
|
|
{
|
||
|
|
return a.first > b.first;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
static int check_top2(const std::vector<float>& cls_scores, float epsilon = 0.001)
|
||
|
|
{
|
||
|
|
// partial sort topk with index
|
||
|
|
int size = cls_scores.size();
|
||
|
|
std::vector<std::pair<float, int> > vec;
|
||
|
|
vec.resize(size);
|
||
|
|
for (int i = 0; i < size; i++)
|
||
|
|
{
|
||
|
|
vec[i] = std::make_pair(cls_scores[i], i);
|
||
|
|
}
|
||
|
|
|
||
|
|
std::partial_sort(vec.begin(), vec.begin() + 2, vec.end(), compare_score_index());
|
||
|
|
|
||
|
|
int expect_indexes[2] = {532, 920};
|
||
|
|
float expect_scores[2] = {0.189459f, 0.082801f};
|
||
|
|
|
||
|
|
for (int i = 0; i < 2; i++)
|
||
|
|
{
|
||
|
|
int index = vec[i].second;
|
||
|
|
float score = vec[i].first;
|
||
|
|
|
||
|
|
if (index != expect_indexes[i])
|
||
|
|
{
|
||
|
|
fprintf(stderr, "top %d index not match expect %d but got %d\n", i, expect_indexes[i], index);
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (!NearlyEqual(score, expect_scores[i], epsilon))
|
||
|
|
{
|
||
|
|
fprintf(stderr, "top %d score not match expect %f but got %f\n", i, expect_scores[i], score);
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
static void fread_or_error(void* buffer, size_t size, size_t count, FILE* fp, const char* s)
|
||
|
|
{
|
||
|
|
if (count != fread(buffer, size, count, fp))
|
||
|
|
{
|
||
|
|
fprintf(stderr, "Couldn't read from file: %s\n", s);
|
||
|
|
fclose(fp);
|
||
|
|
exit(EXIT_FAILURE);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
static std::string read_file_string(const char* filepath)
|
||
|
|
{
|
||
|
|
FILE* fp = fopen(filepath, "rb");
|
||
|
|
if (!fp)
|
||
|
|
{
|
||
|
|
fprintf(stderr, "fopen %s failed\n", filepath);
|
||
|
|
return std::string();
|
||
|
|
}
|
||
|
|
|
||
|
|
fseek(fp, 0, SEEK_END);
|
||
|
|
int len = ftell(fp);
|
||
|
|
rewind(fp);
|
||
|
|
|
||
|
|
std::string s;
|
||
|
|
s.resize(len + 1); // +1 for '\0'
|
||
|
|
|
||
|
|
fread_or_error((char*)s.c_str(), 1, len, fp, filepath);
|
||
|
|
fclose(fp);
|
||
|
|
|
||
|
|
s[len] = '\0';
|
||
|
|
|
||
|
|
return s;
|
||
|
|
}
|
||
|
|
|
||
|
|
static ncnn::Mat read_file_content(const char* filepath)
|
||
|
|
{
|
||
|
|
FILE* fp = fopen(filepath, "rb");
|
||
|
|
if (!fp)
|
||
|
|
{
|
||
|
|
fprintf(stderr, "fopen %s failed\n", filepath);
|
||
|
|
return ncnn::Mat();
|
||
|
|
}
|
||
|
|
|
||
|
|
fseek(fp, 0, SEEK_END);
|
||
|
|
int len = ftell(fp);
|
||
|
|
rewind(fp);
|
||
|
|
|
||
|
|
ncnn::Mat m(len, (size_t)1u, 1);
|
||
|
|
|
||
|
|
fread_or_error(m, 1, len, fp, filepath);
|
||
|
|
fclose(fp);
|
||
|
|
|
||
|
|
return m;
|
||
|
|
}
|
||
|
|
|
||
|
|
static int test_squeezenet(const ncnn::Option& opt, int load_model_type, float epsilon = 0.001)
|
||
|
|
{
|
||
|
|
ncnn::Net squeezenet;
|
||
|
|
|
||
|
|
squeezenet.opt = opt;
|
||
|
|
|
||
|
|
#ifdef __EMSCRIPTEN__
|
||
|
|
#define MODEL_DIR "/working"
|
||
|
|
#else
|
||
|
|
#define MODEL_DIR "../../examples"
|
||
|
|
#endif
|
||
|
|
|
||
|
|
std::string param_str;
|
||
|
|
ncnn::Mat param_data;
|
||
|
|
ncnn::Mat model_data;
|
||
|
|
if (load_model_type == 0)
|
||
|
|
{
|
||
|
|
// load from plain model file
|
||
|
|
squeezenet.load_param(MODEL_DIR "/squeezenet_v1.1.param");
|
||
|
|
squeezenet.load_model(MODEL_DIR "/squeezenet_v1.1.bin");
|
||
|
|
}
|
||
|
|
if (load_model_type == 1)
|
||
|
|
{
|
||
|
|
// load from plain model memory
|
||
|
|
param_str = read_file_string(MODEL_DIR "/squeezenet_v1.1.param");
|
||
|
|
model_data = read_file_content(MODEL_DIR "/squeezenet_v1.1.bin");
|
||
|
|
squeezenet.load_param_mem((const char*)param_str.c_str());
|
||
|
|
squeezenet.load_model((const unsigned char*)model_data);
|
||
|
|
}
|
||
|
|
if (load_model_type == 2)
|
||
|
|
{
|
||
|
|
// load from binary model file
|
||
|
|
squeezenet.load_param_bin(MODEL_DIR "/squeezenet_v1.1.param.bin");
|
||
|
|
squeezenet.load_model(MODEL_DIR "/squeezenet_v1.1.bin");
|
||
|
|
}
|
||
|
|
if (load_model_type == 3)
|
||
|
|
{
|
||
|
|
// load from binary model memory
|
||
|
|
param_data = read_file_content(MODEL_DIR "/squeezenet_v1.1.param.bin");
|
||
|
|
model_data = read_file_content(MODEL_DIR "/squeezenet_v1.1.bin");
|
||
|
|
squeezenet.load_param((const unsigned char*)param_data);
|
||
|
|
squeezenet.load_model((const unsigned char*)model_data);
|
||
|
|
}
|
||
|
|
|
||
|
|
ncnn::Mat in = generate_ncnn_logo(ncnn::Mat::PIXEL_BGR, 227, 227);
|
||
|
|
|
||
|
|
const float mean_vals[3] = {104.f, 117.f, 123.f};
|
||
|
|
in.substract_mean_normalize(mean_vals, 0);
|
||
|
|
|
||
|
|
ncnn::Extractor ex = squeezenet.create_extractor();
|
||
|
|
|
||
|
|
ncnn::Mat out;
|
||
|
|
if (load_model_type == 0 || load_model_type == 1)
|
||
|
|
{
|
||
|
|
ex.input("data", in);
|
||
|
|
ex.extract("prob", out);
|
||
|
|
}
|
||
|
|
if (load_model_type == 2 || load_model_type == 3)
|
||
|
|
{
|
||
|
|
ex.input(0, in);
|
||
|
|
ex.extract(82, out);
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<float> cls_scores;
|
||
|
|
cls_scores.resize(out.w);
|
||
|
|
for (int j = 0; j < out.w; j++)
|
||
|
|
{
|
||
|
|
cls_scores[j] = out[j];
|
||
|
|
}
|
||
|
|
|
||
|
|
return check_top2(cls_scores, epsilon);
|
||
|
|
}
|
||
|
|
|
||
|
|
int main()
|
||
|
|
{
|
||
|
|
SRAND(7767517);
|
||
|
|
|
||
|
|
#ifdef __EMSCRIPTEN__
|
||
|
|
EM_ASM(
|
||
|
|
FS.mkdir('/working');
|
||
|
|
FS.mount(NODEFS, {root: '../../examples'}, '/working'););
|
||
|
|
#endif // __EMSCRIPTEN__
|
||
|
|
|
||
|
|
ncnn::UnlockedPoolAllocator g_blob_pool_allocator;
|
||
|
|
ncnn::PoolAllocator g_workspace_pool_allocator;
|
||
|
|
|
||
|
|
ncnn::Option opts[4];
|
||
|
|
|
||
|
|
opts[0].use_packing_layout = false;
|
||
|
|
opts[0].use_fp16_packed = false;
|
||
|
|
opts[0].use_fp16_storage = false;
|
||
|
|
opts[0].use_fp16_arithmetic = false;
|
||
|
|
opts[0].use_shader_pack8 = false;
|
||
|
|
opts[0].use_image_storage = false;
|
||
|
|
|
||
|
|
opts[1].use_packing_layout = true;
|
||
|
|
opts[1].use_fp16_packed = true;
|
||
|
|
opts[1].use_fp16_storage = false;
|
||
|
|
opts[1].use_fp16_arithmetic = false;
|
||
|
|
opts[1].use_shader_pack8 = true;
|
||
|
|
opts[1].use_image_storage = false;
|
||
|
|
|
||
|
|
opts[2].use_packing_layout = true;
|
||
|
|
opts[2].use_fp16_packed = true;
|
||
|
|
opts[2].use_fp16_storage = true;
|
||
|
|
opts[2].use_fp16_arithmetic = false;
|
||
|
|
opts[2].use_bf16_storage = true;
|
||
|
|
opts[2].use_shader_pack8 = true;
|
||
|
|
opts[2].use_image_storage = true;
|
||
|
|
opts[2].blob_allocator = &g_blob_pool_allocator;
|
||
|
|
opts[2].workspace_allocator = &g_workspace_pool_allocator;
|
||
|
|
|
||
|
|
opts[3].use_packing_layout = true;
|
||
|
|
opts[3].use_fp16_packed = true;
|
||
|
|
opts[3].use_fp16_storage = true;
|
||
|
|
opts[3].use_fp16_arithmetic = false; // FIXME enable me
|
||
|
|
opts[3].use_bf16_storage = false;
|
||
|
|
opts[3].use_shader_pack8 = true;
|
||
|
|
opts[3].use_image_storage = true;
|
||
|
|
opts[3].blob_allocator = &g_blob_pool_allocator;
|
||
|
|
opts[3].workspace_allocator = &g_workspace_pool_allocator;
|
||
|
|
|
||
|
|
int load_model_types[4] = {0, 1, 2, 3};
|
||
|
|
|
||
|
|
for (int i = 0; i < 4; i++)
|
||
|
|
{
|
||
|
|
opts[i].num_threads = 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int i = 0; i < 4; i++)
|
||
|
|
{
|
||
|
|
const ncnn::Option& opt = opts[i];
|
||
|
|
|
||
|
|
float epsilon;
|
||
|
|
if (opt.use_bf16_storage || opt.use_fp16_packed || opt.use_fp16_storage)
|
||
|
|
{
|
||
|
|
epsilon = 0.1;
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
epsilon = 0.01;
|
||
|
|
}
|
||
|
|
|
||
|
|
int ret;
|
||
|
|
|
||
|
|
ncnn::Option opt_cpu = opt;
|
||
|
|
opt_cpu.use_vulkan_compute = false;
|
||
|
|
ret = test_squeezenet(opt_cpu, load_model_types[i], epsilon);
|
||
|
|
if (ret != 0)
|
||
|
|
{
|
||
|
|
fprintf(stderr, "test_squeezenet cpu failed use_packing_layout=%d use_fp16_packed=%d use_fp16_storage=%d use_shader_pack8=%d use_bf16_storage=%d use_image_storage=%d\n", opt.use_packing_layout, opt.use_fp16_packed, opt.use_fp16_storage, opt.use_shader_pack8, opt.use_bf16_storage, opt.use_image_storage);
|
||
|
|
return ret;
|
||
|
|
}
|
||
|
|
|
||
|
|
#if NCNN_VULKAN
|
||
|
|
ncnn::Option opt_gpu = opt;
|
||
|
|
opt_gpu.use_vulkan_compute = true;
|
||
|
|
ret = test_squeezenet(opt_gpu, load_model_types[i], epsilon);
|
||
|
|
if (ret != 0)
|
||
|
|
{
|
||
|
|
fprintf(stderr, "test_squeezenet gpu failed use_packing_layout=%d use_fp16_packed=%d use_fp16_storage=%d use_shader_pack8=%d use_bf16_storage=%d use_image_storage=%d\n", opt.use_packing_layout, opt.use_fp16_packed, opt.use_fp16_storage, opt.use_shader_pack8, opt.use_bf16_storage, opt.use_image_storage);
|
||
|
|
return ret;
|
||
|
|
}
|
||
|
|
#endif // NCNN_VULKAN
|
||
|
|
}
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|