Davide Sapienza
7838cb4922
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com> Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
1087 lines
45 KiB
C++
1087 lines
45 KiB
C++
#include <iostream>
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#include "kernels.h"
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#include "Yolo3Detection.h"
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#include "tkdnn.h"
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#include <vector>
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#include <numeric> // std::iota
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#include <algorithm> // std::sort
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// #include "utils.h"
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const char *input_bin = "../tests/resnet101_cnet/debug/input.bin";
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const char *conv1_bin = "../tests/resnet101_cnet/layers/conv1.bin";
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//layer1
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const char *layer1_bin[]={
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"../tests/resnet101_cnet/layers/layer1-0-conv1.bin",
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"../tests/resnet101_cnet/layers/layer1-0-conv2.bin",
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"../tests/resnet101_cnet/layers/layer1-0-conv3.bin",
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"../tests/resnet101_cnet/layers/layer1-0-downsample-0.bin",
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"../tests/resnet101_cnet/layers/layer1-1-conv1.bin",
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"../tests/resnet101_cnet/layers/layer1-1-conv2.bin",
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"../tests/resnet101_cnet/layers/layer1-1-conv3.bin",
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"../tests/resnet101_cnet/layers/layer1-2-conv1.bin",
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"../tests/resnet101_cnet/layers/layer1-2-conv2.bin",
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"../tests/resnet101_cnet/layers/layer1-2-conv3.bin"};
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//layer2
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const char *layer2_bin[]={
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"../tests/resnet101_cnet/layers/layer2-0-conv1.bin",
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"../tests/resnet101_cnet/layers/layer2-0-conv2.bin",
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"../tests/resnet101_cnet/layers/layer2-0-conv3.bin",
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"../tests/resnet101_cnet/layers/layer2-0-downsample-0.bin",
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"../tests/resnet101_cnet/layers/layer2-1-conv1.bin",
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"../tests/resnet101_cnet/layers/layer2-1-conv2.bin",
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"../tests/resnet101_cnet/layers/layer2-1-conv3.bin",
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"../tests/resnet101_cnet/layers/layer2-2-conv1.bin",
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"../tests/resnet101_cnet/layers/layer2-2-conv2.bin",
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"../tests/resnet101_cnet/layers/layer2-2-conv3.bin",
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"../tests/resnet101_cnet/layers/layer2-3-conv1.bin",
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"../tests/resnet101_cnet/layers/layer2-3-conv2.bin",
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"../tests/resnet101_cnet/layers/layer2-3-conv3.bin"
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};
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//layer3
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const char *layer3_bin[]={
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"../tests/resnet101_cnet/layers/layer3-0-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-0-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-0-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-0-downsample-0.bin",
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"../tests/resnet101_cnet/layers/layer3-1-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-1-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-1-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-2-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-2-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-2-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-3-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-3-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-3-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-4-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-4-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-4-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-5-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-5-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-5-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-6-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-6-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-6-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-7-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-7-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-7-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-8-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-8-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-8-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-9-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-9-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-9-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-10-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-10-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-10-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-11-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-11-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-11-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-12-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-12-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-12-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-13-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-13-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-13-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-14-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-14-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-14-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-15-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-15-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-15-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-16-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-16-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-16-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-17-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-17-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-17-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-18-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-18-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-18-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-19-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-19-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-19-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-20-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-20-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-20-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-21-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-21-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-21-conv3.bin",
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"../tests/resnet101_cnet/layers/layer3-22-conv1.bin",
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"../tests/resnet101_cnet/layers/layer3-22-conv2.bin",
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"../tests/resnet101_cnet/layers/layer3-22-conv3.bin"};
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//layer4
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const char *layer4_bin[]={
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"../tests/resnet101_cnet/layers/layer4-0-conv1.bin",
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"../tests/resnet101_cnet/layers/layer4-0-conv2.bin",
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"../tests/resnet101_cnet/layers/layer4-0-conv3.bin",
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"../tests/resnet101_cnet/layers/layer4-0-downsample-0.bin",
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"../tests/resnet101_cnet/layers/layer4-1-conv1.bin",
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"../tests/resnet101_cnet/layers/layer4-1-conv2.bin",
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"../tests/resnet101_cnet/layers/layer4-1-conv3.bin",
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"../tests/resnet101_cnet/layers/layer4-2-conv1.bin",
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"../tests/resnet101_cnet/layers/layer4-2-conv2.bin",
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"../tests/resnet101_cnet/layers/layer4-2-conv3.bin"};
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const char *d_conv1_bin = "../tests/resnet101_cnet/layers/deconv_layers-0-conv_offset_mask.bin";
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const char *deform1_bin = "../tests/resnet101_cnet/layers/deconv_layers-0.bin";
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const char *deconv1_bin = "../tests/resnet101_cnet/layers/deconv_layers-3.bin";
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const char *d_conv2_bin = "../tests/resnet101_cnet/layers/deconv_layers-6-conv_offset_mask.bin";
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const char *deform2_bin = "../tests/resnet101_cnet/layers/deconv_layers-6.bin";
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const char *deconv2_bin = "../tests/resnet101_cnet/layers/deconv_layers-9.bin";
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const char *d_conv3_bin = "../tests/resnet101_cnet/layers/deconv_layers-12-conv_offset_mask.bin";
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const char *deform3_bin = "../tests/resnet101_cnet/layers/deconv_layers-12.bin";
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const char *deconv3_bin = "../tests/resnet101_cnet/layers/deconv_layers-15.bin";
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const char *hm_conv1_bin = "../tests/resnet101_cnet/layers/hm-0.bin";
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const char *hm_conv2_bin = "../tests/resnet101_cnet/layers/hm-2.bin";
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const char *wh_conv1_bin = "../tests/resnet101_cnet/layers/wh-0.bin";
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const char *wh_conv2_bin = "../tests/resnet101_cnet/layers/wh-2.bin";
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const char *reg_conv1_bin = "../tests/resnet101_cnet/layers/reg-0.bin";
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const char *reg_conv2_bin = "../tests/resnet101_cnet/layers/reg-2.bin";
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//final
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const char *fc_bin = "../tests/resnet101_cnet/layers/fc.bin";
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const char *output_bin[]={
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"../tests/resnet101_cnet/debug/hm.bin",
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"../tests/resnet101_cnet/debug/wh.bin",
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"../tests/resnet101_cnet/debug/reg.bin"};
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std::vector<size_t> sort_indexes(const std::vector<float> &v) {
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// initialize original index locations
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std::vector<size_t> idx(v.size());
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iota(idx.begin(), idx.end(), 0);
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// sort indexes based on comparing values in v
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sort(idx.begin(), idx.end(),
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[&v](size_t i1, size_t i2) {return v[i1] > v[i2];});
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return idx;
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}
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float _colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
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float get_color(int c, int x, int max)
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{
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float ratio = ((float)x/max)*5;
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int i = floor(ratio);
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int j = ceil(ratio);
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ratio -= i;
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float r = (1-ratio) * _colors[i % 6][c % 3] + ratio*_colors[j % 6][c % 3];
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//printf("%f\n", r);
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return r;
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}
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int computeDetections(dnnType *hm_d, dnnType *wh_d, dnnType *reg_d, int hm_dim, int wh_dim, int reg_dim, bool cat_spec_wh, int k){
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// _nms
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int kernel = 3;
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int pad = (kernel - 1)/2;
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std::cout<<"computeDetections\n";
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// dnnType *hmax;
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// tk::dnn::Pooling maxpool(&hmax, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX)
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// = (dnnType *)
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// net.functional.max_pool2d(
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// heat, (kernel, kernel), stride=1, padding=pad)
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// keep = (hmax == heat).float()
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// return heat * keep
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}
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int process(dnnType *hm_d, dnnType *wh_d, dnnType *reg_d, int hm_dim, int wh_dim, int reg_dim){
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std::cout<<"process\n";
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// computeDetections(hm_d, wh_d, reg_d, hm_dim, wh_dim, reg_dim, false, 100);
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}
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int main()
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{
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// Network layout
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tk::dnn::dataDim_t dim(1, 3, 224, 224, 1);
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tk::dnn::Network net(dim);
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tk::dnn::Conv2d conv1(&net, 64, 7, 7, 2, 2, 3, 3, conv1_bin, true);
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tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Pooling maxpool4(&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
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//layer 1
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int id_layer1_bin = 0;
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tk::dnn::Layer *last = &maxpool4;
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for(int i=0; i<3;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 64, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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if(i==0) {
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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} else {
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 2
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int id_layer2_bin = 0;
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for(int i=0; i<4;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 128, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
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if(i==0)
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 2, 2, 1, 1, layer2_bin[id_layer2_bin++], true);
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else
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 1, 1, 1, 1, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
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if(i==0)
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{
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 512, 1, 1, 2, 2, 0, 0, layer2_bin[id_layer2_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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}
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else
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{
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 3
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int id_layer3_bin = 0;
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for(int i=0; i<23;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
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if(i==0)
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 2, 2, 1, 1, layer3_bin[id_layer3_bin++], true);
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else
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layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
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if(i==0)
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{
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tk::dnn::Layer *route_1_0_layers[1] = { last };
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tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
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tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 2, 2, 0, 0, layer3_bin[id_layer3_bin++], true);
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
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}
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else
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{
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tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
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}
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tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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last = layer1_0_relu;
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}
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// layer 4
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int id_layer4_bin = 0;
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for(int i=0; i<3;i++)
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{
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tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
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tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
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tk::dnn::Conv2d *layer1_0_conv2;
|
|
if(i==0)
|
|
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 2, 2, 1, 1, layer4_bin[id_layer4_bin++], true);
|
|
else
|
|
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 1, 1, 1, 1, layer4_bin[id_layer4_bin++], true);
|
|
|
|
tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
|
|
if(i==0)
|
|
{
|
|
tk::dnn::Layer *route_1_0_layers[1] = { last };
|
|
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
|
tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 2, 2, 0, 0, layer4_bin[id_layer4_bin++], true);
|
|
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
|
|
}
|
|
else
|
|
{
|
|
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
|
|
}
|
|
tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
last = layer1_0_relu;
|
|
}
|
|
|
|
tk::dnn::DeformConv2d *layer0_deform1 = new tk::dnn::DeformConv2d(&net, 256, 1, 3, 3, 1, 1, 1, 1, deform1_bin, d_conv1_bin, true);
|
|
tk::dnn::Activation *layer0_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::DeConv2d *layer0_deconv1 = new tk::dnn::DeConv2d(&net, 256, 4, 4, 2, 2, 1, 1, deconv1_bin, true);
|
|
tk::dnn::Activation *layer0_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
|
|
tk::dnn::DeformConv2d *layer1_deform1 = new tk::dnn::DeformConv2d(&net, 128, 1, 3, 3, 1, 1, 1, 1, deform2_bin, d_conv2_bin, true);
|
|
tk::dnn::Activation *layer1_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::DeConv2d *layer1_deconv1 = new tk::dnn::DeConv2d(&net, 128, 4, 4, 2, 2, 1, 1, deconv2_bin, true);
|
|
tk::dnn::Activation *layer1_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
|
|
tk::dnn::DeformConv2d *layer2_deform1 = new tk::dnn::DeformConv2d(&net, 64, 1, 3, 3, 1, 1, 1, 1, deform3_bin, d_conv3_bin, true);
|
|
tk::dnn::Activation *layer2_deform1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::DeConv2d *layer2_deconv1 = new tk::dnn::DeConv2d(&net, 64, 4, 4, 2, 2, 1, 1, deconv3_bin, true);
|
|
tk::dnn::Activation *layer2_deconv1_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
|
|
tk::dnn::Layer *route_1_0_layers[1] = { layer2_deconv1_relu };
|
|
tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
|
|
tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 80, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false, false, true);
|
|
int kernel = 3;
|
|
int pad = (kernel - 1)/2;
|
|
tk::dnn::Activation *hm_sig = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
|
|
tk::dnn::Pooling *hmax = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX, true);
|
|
|
|
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
|
tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
|
|
tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false, false, true);
|
|
|
|
tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
|
tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
|
|
tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
|
tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false, false, true);
|
|
|
|
// Load input
|
|
dnnType *data;
|
|
dnnType *input_h;
|
|
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
|
|
// printDeviceVector(64, data, true);
|
|
|
|
//print network model
|
|
net.print();
|
|
|
|
//convert network to tensorRT
|
|
tk::dnn::NetworkRT netRT(&net, "resnet101_cnet.rt");
|
|
|
|
|
|
tk::dnn::dataDim_t dim1 = dim; //input dim
|
|
printCenteredTitle(" CUDNN inference ", '=', 30);
|
|
{
|
|
dim1.print();
|
|
TIMER_START
|
|
net.infer(dim1, data);
|
|
TIMER_STOP
|
|
dim1.print();
|
|
}
|
|
|
|
// printDeviceVector(64, cudnn_out, true);
|
|
|
|
tk::dnn::dataDim_t dim2 = dim;
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
|
{
|
|
dim2.print();
|
|
TIMER_START
|
|
netRT.infer(dim2, data);
|
|
TIMER_STOP
|
|
dim2.print();
|
|
}
|
|
|
|
tk::dnn::Layer *outs[3] = { hm, wh, reg };
|
|
int out_count = 1;
|
|
for(int i=0; i<3; i++) {
|
|
printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
|
|
|
|
outs[i]->output_dim.print();
|
|
|
|
dnnType *out, *out_h;
|
|
int odim = outs[i]->output_dim.tot();
|
|
readBinaryFile(output_bin[i], odim, &out_h, &out);
|
|
// std::cout<<"OUTPUT BIN:\n";
|
|
// printDeviceVector(odim, cudnn_out, true);
|
|
// std::cout<<"FILE BIN:\n";
|
|
// printDeviceVector(odim, out, true);
|
|
|
|
dnnType *cudnn_out, *rt_out;
|
|
cudnn_out = outs[i]->dstData;
|
|
rt_out = (dnnType *)netRT.buffersRT[i+out_count];
|
|
// there is the maxpool. It isn't an output but it is necessary for the process section
|
|
if(i==0)
|
|
out_count ++;
|
|
|
|
std::cout << "CUDNN vs correct";
|
|
checkResult(odim, cudnn_out, out);
|
|
|
|
std::cout << "TRT vs correct";
|
|
checkResult(odim, rt_out, out);
|
|
std::cout << "CUDNN vs TRT ";
|
|
checkResult(odim, cudnn_out, rt_out);
|
|
}
|
|
|
|
TIMER_START
|
|
|
|
// -------- transofrm compose
|
|
cv::Mat imageOrig = cv::imread("/media/davide/DATA/shared_home/Projects/Professionale/repos/photo_2020-01-14_09-56-07.jpg");
|
|
cv::Mat imageF;
|
|
imageOrig.convertTo(imageF, CV_32FC3, 1/255.0);
|
|
cv::Mat image;
|
|
cv::Size sz = imageF.size();
|
|
std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
|
|
resize(imageF, image, cv::Size(256, 256));
|
|
const int cropSize = 224;
|
|
const int offsetW = (image.cols - cropSize) / 2;
|
|
const int offsetH = (image.rows - cropSize) / 2;
|
|
const cv::Rect roi(offsetW, offsetH, cropSize, cropSize);
|
|
image = image(roi).clone();
|
|
std::cout << "Cropped image dimension: " << image.cols << " X " << image.rows << std::endl;
|
|
|
|
cv::Scalar mean_;
|
|
mean_ << 0.485, 0.456, 0.406;
|
|
cv::Scalar stddev_;
|
|
stddev_ << 0.229, 0.224, 0.225;
|
|
cv::Size s_im = imageF.size();
|
|
std::cout<<"size: "<<s_im.height<<" "<<s_im.width<<" - "<<std::endl;
|
|
std::cout<<"mean: "<<mean_<<", std: "<<stddev_<<std::endl;
|
|
cv::add(image, -mean_, image);
|
|
cv::divide(image, stddev_, image);
|
|
cv::Mat bgr[3];
|
|
dnnType *input, *input_d;
|
|
dim2 = dim;
|
|
checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*dim2.tot()));
|
|
checkCuda(cudaMallocHost(&input, sizeof(dnnType)*dim2.tot()));
|
|
image.convertTo(image, CV_32FC3, 1/255.0);
|
|
|
|
//split channels
|
|
cv::split(image,bgr);//split source
|
|
|
|
//write channels
|
|
for(int i=0; i<dim2.c; i++) {
|
|
int idx = i*image.rows*image.cols;
|
|
int ch = dim2.c-1 -i;
|
|
memcpy((void*)&input[idx], (void*)bgr[ch].data, image.rows*image.cols*sizeof(dnnType));
|
|
}
|
|
|
|
checkCuda(cudaMemcpyAsync(input_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
|
|
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30); {
|
|
dim2.print();
|
|
TIMER_START
|
|
netRT.infer(dim2, input_d);
|
|
TIMER_STOP
|
|
dim2.print();
|
|
}
|
|
checkResult(dim2.tot(), input_h, input);
|
|
checkCuda(cudaFree(input_d));
|
|
checkCuda(cudaFreeHost(input));
|
|
// -----------------------------------pre-process ------------------------------------------
|
|
// it will resize the images to `512 x 512` in GETTING_STARTED.md
|
|
|
|
float scale = 1.0;
|
|
float new_height = sz.height * scale;
|
|
float new_width = sz.width * scale;
|
|
float inp_height = 224;//512;
|
|
float inp_width = 224;//512;
|
|
float c[] = {new_width / 2.0, new_height /2.0};
|
|
float s[2];
|
|
if(sz.width > sz.height){
|
|
s[0] = sz.width * 1.0;
|
|
s[1] = sz.width * 1.0;
|
|
}
|
|
else{
|
|
s[0] = sz.height * 1.0;
|
|
s[1] = sz.height * 1.0;
|
|
}
|
|
std::cout<<" "<<new_height<<" "<<new_width<<" "<<s[0]<<"-"<<s[1]<<" "<<c[0]<<"-"<<c[1]<<std::endl;
|
|
// ----------- get_affine_transform
|
|
// rot_rad = pi * 0 / 100 --> 0
|
|
cv::Mat src(cv::Size(2,3), CV_32F);
|
|
cv::Mat dst(cv::Size(2,3), CV_32F);
|
|
src.at<float>(0,0)=c[0];
|
|
src.at<float>(0,1)=c[1];
|
|
src.at<float>(1,0)=c[0];
|
|
src.at<float>(1,1)=c[1] + s[0] * -0.5;
|
|
dst.at<float>(0,0)=inp_width * 0.5;
|
|
dst.at<float>(0,1)=inp_height * 0.5;
|
|
dst.at<float>(1,0)=inp_width * 0.5;
|
|
dst.at<float>(1,1)=inp_height * 0.5 + inp_width * -0.5;
|
|
|
|
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
|
|
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
|
|
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
|
|
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
|
|
std::cout<<"src: "<<src<<std::endl;
|
|
std::cout<<"dst: "<<dst<<std::endl;
|
|
|
|
cv::Mat trans = cv::getAffineTransform( src, dst );
|
|
|
|
resize(imageOrig, image, cv::Size(new_width, new_height));
|
|
s_im = image.size();
|
|
std::cout<<"size: "<<s_im.height<<" "<<s_im.width<<" - "<<std::endl;
|
|
|
|
|
|
// image.convertTo(image, CV_32FC3, 1/255.0);
|
|
cv::warpAffine(image, image, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
|
|
// cv::Scalar mean, stddev;
|
|
s_im = image.size();
|
|
std::cout<<"size: "<<s_im.height<<" "<<s_im.width<<" - "<<std::endl;
|
|
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<(float)image.at<cv::Vec3b>(0,i)[0]<<" - "<<(float)image.at<cv::Vec3b>(0,i)[1]<<" - "<<(float)image.at<cv::Vec3b>(0,i)[2]<<" - "<<std::endl;
|
|
// if(i==10)
|
|
// break;
|
|
// }
|
|
// return 0;
|
|
/////////////////////////////// ok fin qui
|
|
|
|
|
|
// cv::meanStdDev(image, mean, stddev );
|
|
// cv::Scalar mean(0.408, 0.447, 0.47);
|
|
cv::Vec<float, 3> mean;
|
|
mean << 0.408, 0.447, 0.47;
|
|
// s_im = mean.size();
|
|
// std::cout<<"size: "<<s_im.height<<" "<<s_im.width<<" - "<<std::endl;
|
|
|
|
cv::Vec<float, 3> stddev;
|
|
stddev << 0.289, 0.274, 0.278;
|
|
|
|
cv::Size s_imag = image.size();
|
|
std::cout<<"size: "<<s_imag.height<<" "<<s_imag.width<<" - "<<std::endl;
|
|
image.convertTo(image, CV_32FC3, 1/255.0);
|
|
|
|
std::cout<<"mean: "<<mean<<", std: "<<stddev<<std::endl;
|
|
// cv::add(image, -mean, image);
|
|
// cv::divide(image, stddev, image);
|
|
cv::MatIterator_<cv::Vec<float, 3>> it;
|
|
for(it = image.begin<cv::Vec<float, 3>>(); it != image.end<cv::Vec<float, 3>>(); ++it)
|
|
{
|
|
(*it)[0] = (float)(*it)[0] - mean[0];
|
|
(*it)[1] = (float)(*it)[1] - mean[1];
|
|
(*it)[2] = (float)(*it)[2] - mean[2];
|
|
(*it)[0] = (float)(*it)[0] / stddev[0];
|
|
(*it)[1] = (float)(*it)[1] / stddev[1];
|
|
(*it)[2] = (float)(*it)[2] / stddev[2];
|
|
}
|
|
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<(float)image.at<cv::Vec<float, 3>>(0,i)[0]<<" - "<<(float)image.at<cv::Vec<float, 3>>(0,i)[1]<<" - "<<(float)image.at<cv::Vec<float, 3>>(0,i)[2]<<" - "<<std::endl;
|
|
// if(i==10)
|
|
// break;
|
|
// }
|
|
// return 0;
|
|
/////////////////////// ok fin qui
|
|
|
|
|
|
std::cout<<"size: "<<s_imag.height<<" "<<s_imag.width<<" - "<<std::endl;
|
|
|
|
cv::Mat bgr2[3];
|
|
dnnType *input2, *input_d2;
|
|
dim2 = dim;
|
|
checkCuda(cudaMalloc(&input_d2, sizeof(dnnType)*dim2.tot()));
|
|
checkCuda(cudaMallocHost(&input2, sizeof(dnnType)*dim2.tot()));
|
|
// image.convertTo(image, CV_32FC3, 1/255.0);
|
|
|
|
//split channels
|
|
cv::split(image,bgr2);//split source
|
|
|
|
// std::cout<<"ch:\n";
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<(float)bgr2[0].at<cv::Vec<float, 3>>(0,i)[0]<<" - "<<(float)bgr2[0].at<cv::Vec<float, 3>>(0,i)[1]<<" - "<<(float)bgr2[0].at<cv::Vec<float, 3>>(0,i)[2]<<std::endl;
|
|
// if(i==10)
|
|
// break;
|
|
// }
|
|
// std::cout<<"ch:\n";
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<(float)bgr2[1].at<float>(1,i)<<std::endl;
|
|
// if(i==10)
|
|
// break;
|
|
// }
|
|
// std::cout<<"ch:\n";
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<(float)bgr2[2].at<float>(2,i)<<std::endl;
|
|
// if(i==10)
|
|
// break;
|
|
// }
|
|
// return 0;
|
|
///////////////////// ok fin qui
|
|
std::cout<<"\n\n\ncome: \n"<<image.rows<<" - "<<image.cols<<std::endl;
|
|
std::cout<<"reprint shape dim2\n";
|
|
dim2.print();
|
|
std::cout<<std::endl;
|
|
//write channels
|
|
for(int i=0; i<dim2.c; i++) {
|
|
int idx = i*image.rows*image.cols;
|
|
int ch = dim2.c-3 +i;
|
|
std::cout<<"i: "<<i<<", idx: "<<idx<<", ch: "<<ch<<std::endl;
|
|
memcpy((void*)&input2[idx], (void*)bgr2[ch].data, image.rows*image.cols*sizeof(dnnType));
|
|
}
|
|
// int k100 = 0;
|
|
// for(int i=1; i<=image.rows*image.cols*dim2.c; i++ ){
|
|
// std::cout<<input2[i]<<" ";
|
|
// if (i % (image.rows*image.cols) == 0){
|
|
// std::cout<<"\n\n";
|
|
// k100 ++;
|
|
// }
|
|
|
|
// }
|
|
// std::cout<<std::endl;
|
|
// std::cout<<"ci sono "<<k100<<" r\n";
|
|
// return 0;
|
|
///////////////////// pseudo ok
|
|
|
|
checkCuda(cudaMemcpyAsync(input_d2, input2, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
|
|
|
|
printCenteredTitle(" TENSORRT inference ", '=', 30); {
|
|
dim2.print();
|
|
TIMER_START
|
|
netRT.infer(dim2, input_d2);
|
|
TIMER_STOP
|
|
dim2.print();
|
|
}
|
|
checkResult(dim2.tot(), input_h, input2);
|
|
for(int i=0; i<dim.tot(); i++ ){
|
|
std::cout<<input_h[i]<<" "<<input2[i]<<std::endl;
|
|
if(i==10)
|
|
break;
|
|
}
|
|
// for(int i=0; i<dim.tot(); i++ ){
|
|
// std::cout<<input2[i]<<" ";
|
|
// }
|
|
// std::cout<<std::endl;
|
|
// return 0;
|
|
checkCuda(cudaFree(input_d2));
|
|
checkCuda(cudaFreeHost(input2));
|
|
// ------------------------------------ process --------------------------------------------
|
|
dnnType *hm_h;
|
|
checkCuda( cudaMallocHost(&hm_h, hm->output_dim.tot()*sizeof(dnnType)) );
|
|
|
|
dnnType *rt_out[4];
|
|
rt_out[0] = (dnnType *)netRT.buffersRT[1];
|
|
rt_out[1] = (dnnType *)netRT.buffersRT[2];
|
|
rt_out[2] = (dnnType *)netRT.buffersRT[3];
|
|
rt_out[3] = (dnnType *)netRT.buffersRT[4];
|
|
|
|
// checkCuda( cudaMemcpy(hm_h, rt_out[0], hm->output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost) );
|
|
std::cout<<"hm\n";
|
|
hm->output_dim.print();
|
|
// for(int i=0; i<hm->output_dim.tot(); i++ ){
|
|
// std::cout<<hm_h[i]<<" ";
|
|
// if(i==100)
|
|
// break;
|
|
// }
|
|
// std::cout<<"\n";
|
|
|
|
|
|
activationSIGMOIDForward(rt_out[0], rt_out[0], hm->output_dim.tot());
|
|
checkCuda( cudaDeviceSynchronize() );
|
|
|
|
|
|
checkCuda( cudaMemcpy(hm_h, rt_out[0], hm->output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost) );
|
|
std::cout<<"hm\n";
|
|
hm->output_dim.print();
|
|
// for(int i=0; i<hm->output_dim.tot(); i++ ){
|
|
// std::cout<<hm_h[i]<<" ";
|
|
// if(i==100)
|
|
// break;
|
|
// }
|
|
// std::cout<<"\n";
|
|
// return 0;
|
|
//////////////////////////// ok
|
|
// ----------- ctdet_decode
|
|
// perform nms on heatmaps
|
|
// tk::dnn::Layer *route_hm_layers[1] = { hm };
|
|
// tk::dnn::Route *route_hm = new tk::dnn::Route(&net, route_hm_layers, 1);
|
|
|
|
// ----------- nms
|
|
// int kernel = 3;
|
|
// int pad = (kernel - 1)/2;
|
|
// tk::dnn::Pooling *hmax = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX);
|
|
|
|
// hmax_d = hmax->infer(hmax->input_dim.tot(), rt_out[0]);
|
|
// keep = (hmax == heat).float()
|
|
// return heat * keep
|
|
|
|
dnnType *hmax_h;
|
|
checkCuda( cudaMallocHost(&hmax_h, hmax->output_dim.tot()*sizeof(dnnType)) );
|
|
checkCuda( cudaMemcpy(hmax_h, rt_out[1], hmax->output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost) );
|
|
|
|
std::cout<<"hmax\n";
|
|
hmax->output_dim.print();
|
|
// for(int i=0; i<hmax->output_dim.tot(); i++ ){
|
|
// std::cout<<hmax_h[i]<<" ";
|
|
// if(i==100)
|
|
// break;
|
|
// }
|
|
// std::cout<<"\n";
|
|
// return 0;
|
|
// hm = hm * ( hmax == hm );
|
|
std::cout<<"hm:\n";
|
|
hm->output_dim.print();
|
|
std::cout<<"hmax:\n";
|
|
hmax->output_dim.print();
|
|
// return 0;
|
|
float toll = 0.000001;
|
|
for(int i=0; i < hm->output_dim.tot(); i++){
|
|
if(hm_h[i]-hmax_h[i] > toll || hm_h[i]-hmax_h[i] < -toll){
|
|
hm_h[i] = 0.0f;
|
|
}
|
|
}
|
|
// std::cout<<"\n";
|
|
// for(int i=0; i<hm->output_dim.tot(); i++ ){
|
|
// std::cout<<hm_h[i]<<" ";
|
|
// if(i==100)
|
|
// break;
|
|
// }
|
|
// std::cout<<"\n";
|
|
// return 0;
|
|
// checkCuda( cudaMemcpy(hm->dstData, hm_h, hm->output_dim.tot()*sizeof(dnnType), cudaMemcpyHostToDevice) );
|
|
checkCuda( cudaFreeHost(hmax_h) );
|
|
// ----------- nms end
|
|
// ----------- topk
|
|
int K = 100;
|
|
int width = 56; // TODO
|
|
float *topk_scores;
|
|
int *topk_inds_;
|
|
float *topk_ys_;
|
|
float *topk_xs_;
|
|
std::cout<<"mah: "<<hm->output_dim.c * K<<std::endl;
|
|
checkCuda( cudaMallocHost(&topk_scores, hm->output_dim.c * K *sizeof(float)) );
|
|
checkCuda( cudaMallocHost(&topk_inds_, hm->output_dim.c * K *sizeof(int)) );
|
|
checkCuda( cudaMallocHost(&topk_ys_, hm->output_dim.c * K *sizeof(float)) );
|
|
checkCuda( cudaMallocHost(&topk_xs_, hm->output_dim.c * K *sizeof(float)) );
|
|
std::cout<<"1\n";
|
|
dnnType *hm_aus;
|
|
checkCuda( cudaMallocHost(&hm_aus, hm->output_dim.h * hm->output_dim.w *sizeof(dnnType)) );
|
|
std::cout<<"2\n";
|
|
int count;
|
|
std::vector<float> v = {2.0, 3.0, 9.0};
|
|
for (auto i: sort_indexes(v)) {
|
|
std::cout << i<< "--" <<v[i] << std::endl;
|
|
}
|
|
for(int i=0; i<hm->output_dim.c; i++){
|
|
count = 0;
|
|
// get the hm->output_dim.h * hm->output_dim.w elements for each channel and sort it. Then find the first 100 elements
|
|
checkCuda( cudaMemcpy(hm_aus, hm_h + i * hm->output_dim.h * hm->output_dim.w,
|
|
hm->output_dim.h * hm->output_dim.w * sizeof(dnnType), cudaMemcpyHostToHost) );
|
|
// std::cout<<"top scores: "<<hm->output_dim.h * hm->output_dim.w<<"\n";
|
|
// for(int k=0; k<hm->output_dim.h * hm->output_dim.w; k++)
|
|
// std::cout<<hm_aus[k]<<" ";
|
|
// std::cout<<std::endl;
|
|
// std::vector<float> my_vector {arr, arr + arr_length}
|
|
std::vector<float> my_vector{hm_aus, hm_aus + hm->output_dim.h * hm->output_dim.w};
|
|
for (auto j: sort_indexes(my_vector)) {
|
|
// std::cout <<"j: "<<j<<" -> "<< hm_aus[j] << std::endl;
|
|
topk_scores[i*K + count] = hm_aus[j];
|
|
topk_inds_[i*K +count] = j;
|
|
topk_ys_[i*K +count] = (int)(j / width);
|
|
topk_xs_[i*K +count] = (int)(j % width);
|
|
if(++count == K)
|
|
break;
|
|
}
|
|
}
|
|
std::cout<<"topk_xs_[0]: "<<topk_xs_[0]<<std::endl;
|
|
for(int i = 0; i< hm->output_dim.c * K; i++)
|
|
std::cout<<topk_xs_[i]<<" ";
|
|
std::cout<<"\n3\n";
|
|
// final
|
|
float *scores;
|
|
int *clses;
|
|
int *topk_inds;
|
|
float *topk_ys;
|
|
float *topk_xs;
|
|
checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
|
|
checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
|
|
checkCuda( cudaMallocHost(&topk_inds, K *sizeof(int)) );
|
|
checkCuda( cudaMallocHost(&topk_ys, K *sizeof(float)) );
|
|
checkCuda( cudaMallocHost(&topk_xs, K *sizeof(float)) );
|
|
std::cout<<"4\n";
|
|
count = 0;
|
|
std::vector<float> my_vector{topk_scores, topk_scores + hm->output_dim.c * K };
|
|
for (auto j: sort_indexes(my_vector)) {
|
|
// std::cout <<"j: "<<j<<" -> "<< hm_aus[j] << std::endl;
|
|
scores[count] = topk_scores[j];
|
|
clses[count] = (int)(j / K);
|
|
topk_inds[count] = topk_inds_[j];
|
|
topk_ys[count] = topk_ys_[j];
|
|
topk_xs[count] = topk_xs_[j];
|
|
if(++count == K)
|
|
break;
|
|
}
|
|
checkCuda( cudaFreeHost(topk_scores) );
|
|
checkCuda( cudaFreeHost(topk_inds_) );
|
|
checkCuda( cudaFreeHost(topk_ys_) );
|
|
checkCuda( cudaFreeHost(topk_xs_) );
|
|
std::cout<<"5\n";
|
|
// ----------- topk end
|
|
std::cout<<"topk_xs[0]: "<<topk_xs[0]<<std::endl;
|
|
for(int i = 0; i< K; i++)
|
|
std::cout<<topk_xs[i]<<" ";
|
|
std::cout<<std::endl;
|
|
/////////////////////////////// fin qui ok
|
|
dnnType *reg_aus;
|
|
checkCuda( cudaMallocHost(®_aus, reg->output_dim.tot()*sizeof(dnnType)) );
|
|
checkCuda( cudaMemcpy(reg_aus, rt_out[3], reg->output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost) );
|
|
std::cout<<"reg:\n";
|
|
reg->output_dim.print();
|
|
// for(int i=0; i<reg->output_dim.tot(); i++ ){
|
|
// std::cout<<reg_aus[i]<<" ";
|
|
// if(i==100)
|
|
// break;
|
|
// }
|
|
// std::cout<<"\n";
|
|
// return 0;
|
|
/////////////// ok
|
|
// for(int i=0; i<K; i++ ){
|
|
// std::cout<<reg_aus[topk_inds[i]]<<" "<<reg_aus[topk_inds[i]+56*56]<<std::endl;
|
|
// }
|
|
// std::cout<<"\n";
|
|
// return 0;
|
|
///////////////////// ok fin qui
|
|
|
|
|
|
for(int i = 0; i < K; i++){
|
|
topk_xs[i] = topk_xs[i] + reg_aus[topk_inds[i]];
|
|
topk_ys[i] = topk_ys[i] + reg_aus[topk_inds[i]+reg->output_dim.h*reg->output_dim.w];
|
|
}
|
|
std::cout<<"topk_xs[0]: "<<topk_xs[0]<<std::endl;
|
|
checkCuda( cudaFreeHost(reg_aus) );
|
|
std::cout<<"6\n";
|
|
dnnType *wh_aus;
|
|
float *bboxes;
|
|
|
|
checkCuda( cudaMallocHost(&wh_aus, wh->output_dim.tot()*sizeof(dnnType)) );
|
|
checkCuda( cudaMallocHost(&bboxes, 4 * K *sizeof(dnnType)) );
|
|
checkCuda( cudaMemcpy(wh_aus, rt_out[2], wh->output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost) );
|
|
std::cout<<"7\n";
|
|
for(int i = 0; i< K; i++)
|
|
std::cout<<topk_xs[i]<<" ";
|
|
std::cout<<std::endl;
|
|
|
|
std::cout<<topk_xs[0]<<std::endl;
|
|
std::cout<<topk_inds[0]<<std::endl;
|
|
std::cout<<wh_aus[topk_inds[0]*2]<<std::endl;
|
|
for(int i = 0; i < K; i++){
|
|
bboxes[i * 4] = topk_xs[i] - wh_aus[topk_inds[i]] / 2;
|
|
bboxes[i * 4 + 1] = topk_ys[i] - wh_aus[topk_inds[i]+reg->output_dim.h*reg->output_dim.w] / 2;
|
|
bboxes[i * 4 + 2] = topk_xs[i] + wh_aus[topk_inds[i]] / 2;
|
|
bboxes[i * 4 + 3] = topk_ys[i] + wh_aus[topk_inds[i]+reg->output_dim.h*reg->output_dim.w] / 2;
|
|
}
|
|
////////////////// fin qui ok
|
|
|
|
checkCuda( cudaFreeHost(wh_aus) );
|
|
checkCuda( cudaFreeHost(topk_inds) );
|
|
checkCuda( cudaFreeHost(topk_ys) );
|
|
checkCuda( cudaFreeHost(topk_xs) );
|
|
|
|
std::cout<<"8\n";
|
|
float *detections;
|
|
std::cout<<"bboxes:\n";
|
|
for(int i = 0; i < K+1; i++){
|
|
std::cout<<bboxes[i]<<" ";
|
|
}
|
|
std::cout<<std::endl;
|
|
checkCuda( cudaMallocHost(&detections, 6 * K *sizeof(dnnType)) );
|
|
checkCuda( cudaMemcpy(detections, bboxes, 4 * K *sizeof(dnnType), cudaMemcpyHostToHost) );
|
|
checkCuda( cudaMemcpy(detections + 4 * K *sizeof(dnnType), scores, K *sizeof(dnnType), cudaMemcpyHostToHost) );
|
|
checkCuda( cudaMemcpy(detections + 5 * K *sizeof(dnnType), clses, K *sizeof(dnnType), cudaMemcpyHostToHost) );
|
|
checkCuda( cudaFreeHost(bboxes) );
|
|
// checkCuda( cudaFreeHost(scores) );
|
|
// checkCuda( cudaFreeHost(clses) );
|
|
// servono [bboxes, scores, clses]
|
|
checkCuda( cudaDeviceSynchronize() );
|
|
// ---------------------------------- post-process -----------------------------------------
|
|
|
|
// --------- ctdet_post_process
|
|
//for 1
|
|
// float *dets;
|
|
// checkCuda( cudaMallocHost(&dets, 2 * K *sizeof(float)) );
|
|
// checkCuda( cudaMemcpy(dets, detections, 2 * K *sizeof(float), cudaMemcpyHostToHost) );
|
|
// --------- transform_preds
|
|
float *target_coords;
|
|
checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
|
|
src.at<float>(0,0)=c[0];
|
|
src.at<float>(0,1)=c[1];
|
|
src.at<float>(1,0)=c[0];
|
|
src.at<float>(1,1)=c[1] + s[0] * -0.5;
|
|
dst.at<float>(0,0)=width * 0.5;
|
|
dst.at<float>(0,1)=width * 0.5;
|
|
dst.at<float>(1,0)=width * 0.5;
|
|
dst.at<float>(1,1)=width * 0.5 + width * -0.5;
|
|
|
|
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
|
|
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
|
|
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
|
|
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
|
|
std::cout<<"src: "<<src<<std::endl;
|
|
std::cout<<"dst: "<<dst<<std::endl;
|
|
cv::Size s_aus;
|
|
cv::Mat trans2(cv::Size(3,2), CV_32F);
|
|
trans2 = cv::getAffineTransform( dst, src );
|
|
s_aus = trans2.size();
|
|
std::cout<<"trnas2: "<<trans2<<std::endl;
|
|
std::cout<<trans2.at<double>(0,0)<<" - "<<trans2.at<double>(0,1)<<" - "<<trans2.at<double>(0,2)<<"\n"<<trans2.at<double>(1,0)<<" - "<<trans2.at<double>(1,1)<<" - "<<trans2.at<double>(1,2)<<std::endl;
|
|
std::cout<<"size: "<<s_aus.height<<" "<<s_aus.width<<" - "<<std::endl;
|
|
|
|
cv::Mat new_pt1(cv::Size(1,2), CV_32F);
|
|
cv::Mat new_pt2(cv::Size(1,2), CV_32F);
|
|
for(int i = 0; i<K; i++){
|
|
// new_pt1.at<float>(0,0)=detections[i*4];
|
|
// new_pt1.at<float>(0,1)=detections[i*4+1];
|
|
// new_pt1.at<float>(0,2)=1.0;
|
|
// new_pt1 << detections[i], detections[i+K], 1.0;
|
|
// std::cout<<"----\ni: "<<i<<std::endl;//<<" newpt: "<<new_pt1<<std::endl;
|
|
// std::cout<<"origi: "<<detections[i*4]<<", "<<detections[i*4+1]<<", "<<1.0<<std::endl;
|
|
s_aus = new_pt1.size();
|
|
|
|
// std::cout<<"size: "<<s_aus.height<<" "<<s_aus.width<<" - "<<std::endl;
|
|
// new_pt2 = trans2.dot(new_pt1);
|
|
new_pt1.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*detections[i*4] +
|
|
static_cast<float>(trans2.at<double>(0,1))*detections[i*4+1] +
|
|
static_cast<float>(trans2.at<double>(0,2))*1.0;
|
|
new_pt1.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*detections[i*4] +
|
|
static_cast<float>(trans2.at<double>(1,1))*detections[i*4+1] +
|
|
static_cast<float>(trans2.at<double>(1,2))*1.0;
|
|
|
|
new_pt2.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*detections[i*4+2] +
|
|
static_cast<float>(trans2.at<double>(0,1))*detections[i*4+3] +
|
|
static_cast<float>(trans2.at<double>(0,2))*1.0;
|
|
new_pt2.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*detections[i*4+2] +
|
|
static_cast<float>(trans2.at<double>(1,1))*detections[i*4+3] +
|
|
static_cast<float>(trans2.at<double>(1,2))*1.0;
|
|
|
|
|
|
// std::cout<<"\n new: "<<new_pt1<<" - "<<new_pt2<<std::endl;
|
|
target_coords[i*4] = new_pt1.at<float>(0,0);
|
|
target_coords[i*4+1] = new_pt1.at<float>(0,1);
|
|
target_coords[i*4+2] = new_pt2.at<float>(0,0);
|
|
target_coords[i*4+3] = new_pt2.at<float>(0,1);
|
|
// std::cout<<new_pt1.at<float>(0,0)<<", "<<new_pt1.at<float>(0,1)<<", "<<new_pt2.at<float>(0,0)<<", "<<new_pt2.at<float>(0,1)<<std::endl;
|
|
|
|
}
|
|
// return 0;
|
|
// /////////////// ok fin qui
|
|
const char *coco_class_name_ [] = {"person", "bicycle", "car", "motorcycle", "airplane",
|
|
"bus", "train", "truck", "boat", "traffic light", "fire hydrant",
|
|
"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
|
|
"sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
|
|
"umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
|
|
"snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
|
|
"skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
|
|
"cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
|
|
"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
|
|
"chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
|
|
"laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
|
|
"oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
|
|
"scissors", "teddy bear", "hair drier", "toothbrush"};
|
|
|
|
std::vector<std::string> coco_class_name(coco_class_name_, std::end( coco_class_name_ ));
|
|
int num_classes = 80;
|
|
float vis_threshold = 0.3;
|
|
// int *classes;
|
|
std::vector<tk::dnn::box> detected;
|
|
// checkCuda( cudaMallocHost(&classes, K *sizeof(int)) );
|
|
// checkCuda( cudaMemcpy(classes, detections + 5 * K *sizeof(dnnType), K *sizeof(dnnType), cudaMemcpyHostToHost) );
|
|
for(int i = 0; i<num_classes; i++){
|
|
for(int j=0; j<K; j++)
|
|
if(clses[j] == i){
|
|
//TODO recupera detections[j +0 +1 +2 +3 +4(+5 è la classes, già presa)]
|
|
// queste compongono un ogg assegnato alla classe i (0, 79) --> i+1 (1:80);
|
|
|
|
if(scores[j] > vis_threshold){
|
|
std::cout<<"th: "<<scores[j]<<" - cl: "<<clses[j]<<" i: "<<i<<std::endl;
|
|
//add coco bbox
|
|
//det[0:4], i, det[4]
|
|
int x0 = target_coords[j*4];
|
|
int y0 = target_coords[j*4+1];
|
|
int x1 = target_coords[j*4+2];
|
|
int y1 = target_coords[j*4+3];
|
|
int obj_class = clses[j];
|
|
float prob = scores[j];
|
|
std::cout<<"("<<x0<<", "<<y0<<"),("<<x1<<", "<<y1<<")"<<std::endl;
|
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tk::dnn::box res;
|
|
res.cl = obj_class;
|
|
res.prob = prob;
|
|
res.x = x0;
|
|
res.y = y0;
|
|
res.w = x1 - x0;
|
|
res.h = y1 - y0;
|
|
detected.push_back(res);
|
|
}
|
|
}
|
|
}
|
|
tk::dnn::box b;
|
|
int x0, w, x1, y0, h, y1;
|
|
int objClass;
|
|
std::string det_class;
|
|
;
|
|
|
|
|
|
int baseline = 0;
|
|
float fontScale = 0.5;
|
|
int thickness = 2;
|
|
cv::Scalar colors[256];
|
|
for(int c=0; c<num_classes; c++) {
|
|
int offset = c*123457 % num_classes;
|
|
float r = get_color(2, offset, num_classes);
|
|
float g = get_color(1, offset, num_classes);
|
|
float b = get_color(0, offset, num_classes);
|
|
colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
|
|
}
|
|
int num_detected = detected.size();
|
|
for (int i = 0; i < num_detected; i++){
|
|
b = detected[i];
|
|
x0 = b.x;
|
|
w = b.w;
|
|
x1 = b.x + w;
|
|
y0 = b.y;
|
|
h = b.h;
|
|
y1 = b.y + h;
|
|
objClass = b.cl;
|
|
det_class = coco_class_name[objClass];
|
|
cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point(x1, y1), colors[objClass], 2);
|
|
// draw label
|
|
cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
|
|
cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);
|
|
cv::putText(imageOrig, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
|
|
|
|
}
|
|
cv::namedWindow("cnet", cv::WINDOW_NORMAL);
|
|
cv::imshow("cnet", imageOrig);
|
|
cv::waitKey(10000);
|
|
TIMER_STOP
|
|
return 0;
|
|
}
|