mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

string input and flir test

Browse Source
This commit is contained in:
mbosi
2019-09-14 19:03:13 +02:00
parent 041968f38a
commit 8c629ebe7b
14 changed files with 1198 additions and 608 deletions
Download Patch File Download Diff File Expand all files Collapse all files
include/Layer.h
+5 -5
View File
@@ -75,7 +75,7 @@ class LayerWgs : public Layer {
public:
LayerWgs(Network *net, int inputs, int outputs, int kh, int kw, int kt,
const char* fname_weights, bool batchnorm = false);
std::string fname_weights, bool batchnorm = false);
virtual ~LayerWgs();
int inputs, outputs;
@@ -108,7 +108,7 @@ public:
class Dense : public LayerWgs {
public:
Dense(Network *net, int out_ch, const char* fname_weights);
Dense(Network *net, int out_ch, std::string fname_weights);
virtual ~Dense();
virtual layerType_t getLayerType() { return LAYER_DENSE; };
@@ -151,7 +151,7 @@ class Conv2d : public LayerWgs {
public:
Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
int strideH, int strideW, int paddingH, int paddingW,
const char* fname_weights, bool batchnorm = false);
std::string fname_weights, bool batchnorm = false);
virtual ~Conv2d();
virtual layerType_t getLayerType() { return LAYER_CONV2D; };
@@ -352,7 +352,7 @@ public:
int sort_class;
};
Yolo(Network *net, int classes, int num, const char* fname_weights);
Yolo(Network *net, int classes, int num, std::string fname_weights);
virtual ~Yolo();
virtual layerType_t getLayerType() { return LAYER_YOLO; };
@@ -389,7 +389,7 @@ class RegionInterpret {
public:
RegionInterpret(dataDim_t input_dim, dataDim_t output_dim,
int classes, int coords, int num, float thresh, const char* fname_weights);
int classes, int coords, int num, float thresh, std::string fname_weights);
~RegionInterpret();
dataDim_t input_dim, output_dim;
include/models/Yolo3.h
+289
View File
@@ -0,0 +1,289 @@
int preYoloFilters = (classes+5)*3;
std::string input_bin = bin_path + "/layers/input.bin";
std::vector<std::string> output_bins = {
bin_path + "/debug/layer82_out.bin",
bin_path + "/debug/layer94_out.bin",
bin_path + "/debug/layer106_out.bin"
};
std::string c0_bin = bin_path + "/layers/c0.bin";
std::string c1_bin = bin_path + "/layers/c1.bin";
std::string c2_bin = bin_path + "/layers/c2.bin";
std::string c3_bin = bin_path + "/layers/c3.bin";
std::string c5_bin = bin_path + "/layers/c5.bin";
std::string c6_bin = bin_path + "/layers/c6.bin";
std::string c7_bin = bin_path + "/layers/c7.bin";
std::string c9_bin = bin_path + "/layers/c9.bin";
std::string c10_bin = bin_path + "/layers/c10.bin";
std::string c12_bin = bin_path + "/layers/c12.bin";
std::string c13_bin = bin_path + "/layers/c13.bin";
std::string c14_bin = bin_path + "/layers/c14.bin";
std::string c16_bin = bin_path + "/layers/c16.bin";
std::string c17_bin = bin_path + "/layers/c17.bin";
std::string c19_bin = bin_path + "/layers/c19.bin";
std::string c20_bin = bin_path + "/layers/c20.bin";
std::string c22_bin = bin_path + "/layers/c22.bin";
std::string c23_bin = bin_path + "/layers/c23.bin";
std::string c25_bin = bin_path + "/layers/c25.bin";
std::string c26_bin = bin_path + "/layers/c26.bin";
std::string c28_bin = bin_path + "/layers/c28.bin";
std::string c29_bin = bin_path + "/layers/c29.bin";
std::string c31_bin = bin_path + "/layers/c31.bin";
std::string c32_bin = bin_path + "/layers/c32.bin";
std::string c34_bin = bin_path + "/layers/c34.bin";
std::string c35_bin = bin_path + "/layers/c35.bin";
std::string c37_bin = bin_path + "/layers/c37.bin";
std::string c38_bin = bin_path + "/layers/c38.bin";
std::string c39_bin = bin_path + "/layers/c39.bin";
std::string c41_bin = bin_path + "/layers/c41.bin";
std::string c42_bin = bin_path + "/layers/c42.bin";
std::string c44_bin = bin_path + "/layers/c44.bin";
std::string c45_bin = bin_path + "/layers/c45.bin";
std::string c47_bin = bin_path + "/layers/c47.bin";
std::string c48_bin = bin_path + "/layers/c48.bin";
std::string c50_bin = bin_path + "/layers/c50.bin";
std::string c51_bin = bin_path + "/layers/c51.bin";
std::string c53_bin = bin_path + "/layers/c53.bin";
std::string c54_bin = bin_path + "/layers/c54.bin";
std::string c56_bin = bin_path + "/layers/c56.bin";
std::string c57_bin = bin_path + "/layers/c57.bin";
std::string c59_bin = bin_path + "/layers/c59.bin";
std::string c60_bin = bin_path + "/layers/c60.bin";
std::string c62_bin = bin_path + "/layers/c62.bin";
std::string c63_bin = bin_path + "/layers/c63.bin";
std::string c64_bin = bin_path + "/layers/c64.bin";
std::string c66_bin = bin_path + "/layers/c66.bin";
std::string c67_bin = bin_path + "/layers/c67.bin";
std::string c69_bin = bin_path + "/layers/c69.bin";
std::string c70_bin = bin_path + "/layers/c70.bin";
std::string c72_bin = bin_path + "/layers/c72.bin";
std::string c73_bin = bin_path + "/layers/c73.bin";
std::string c75_bin = bin_path + "/layers/c75.bin";
std::string c76_bin = bin_path + "/layers/c76.bin";
std::string c77_bin = bin_path + "/layers/c77.bin";
std::string c78_bin = bin_path + "/layers/c78.bin";
std::string c79_bin = bin_path + "/layers/c79.bin";
std::string c80_bin = bin_path + "/layers/c80.bin";
std::string c81_bin = bin_path + "/layers/c81.bin";
std::string g82_bin = bin_path + "/layers/g82.bin";
std::string c84_bin = bin_path + "/layers/c84.bin";
std::string c87_bin = bin_path + "/layers/c87.bin";
std::string c88_bin = bin_path + "/layers/c88.bin";
std::string c89_bin = bin_path + "/layers/c89.bin";
std::string c90_bin = bin_path + "/layers/c90.bin";
std::string c91_bin = bin_path + "/layers/c91.bin";
std::string c92_bin = bin_path + "/layers/c92.bin";
std::string c93_bin = bin_path + "/layers/c93.bin";
std::string g94_bin = bin_path + "/layers/g94.bin";
std::string c96_bin = bin_path + "/layers/c96.bin";
std::string c99_bin = bin_path + "/layers/c99.bin";
std::string c100_bin = bin_path + "/layers/c100.bin";
std::string c101_bin = bin_path + "/layers/c101.bin";
std::string c102_bin = bin_path + "/layers/c102.bin";
std::string c103_bin = bin_path + "/layers/c103.bin";
std::string c104_bin = bin_path + "/layers/c104.bin";
std::string c105_bin = bin_path + "/layers/c105.bin";
std::string g106_bin = bin_path + "/layers/g106.bin";
tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s4 (&net, &a1);
tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s8 (&net, &a5);
tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s11 (&net, &s8);
tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s15 (&net, &a12);
tk::dnn::Conv2d c16 (&net, 128, 1, 1, 1, 1, 0, 0, c16_bin, true);
tk::dnn::Activation a16 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c17 (&net, 256, 3, 3, 1, 1, 1, 1, c17_bin, true);
tk::dnn::Activation a17 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s18 (&net, &s15);
tk::dnn::Conv2d c19 (&net, 128, 1, 1, 1, 1, 0, 0, c19_bin, true);
tk::dnn::Activation a19 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c20 (&net, 256, 3, 3, 1, 1, 1, 1, c20_bin, true);
tk::dnn::Activation a20 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s21 (&net, &s18);
tk::dnn::Conv2d c22 (&net, 128, 1, 1, 1, 1, 0, 0, c22_bin, true);
tk::dnn::Activation a22 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c23 (&net, 256, 3, 3, 1, 1, 1, 1, c23_bin, true);
tk::dnn::Activation a23 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s24 (&net, &s21);
tk::dnn::Conv2d c25 (&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
tk::dnn::Activation a25 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c26 (&net, 256, 3, 3, 1, 1, 1, 1, c26_bin, true);
tk::dnn::Activation a26 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s27 (&net, &s24);
tk::dnn::Conv2d c28 (&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
tk::dnn::Activation a28 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c29 (&net, 256, 3, 3, 1, 1, 1, 1, c29_bin, true);
tk::dnn::Activation a29 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s30 (&net, &s27);
tk::dnn::Conv2d c31 (&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
tk::dnn::Activation a31 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c32 (&net, 256, 3, 3, 1, 1, 1, 1, c32_bin, true);
tk::dnn::Activation a32 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s33 (&net, &s30);
tk::dnn::Conv2d c34 (&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
tk::dnn::Activation a34 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c35 (&net, 256, 3, 3, 1, 1, 1, 1, c35_bin, true);
tk::dnn::Activation a35 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s36 (&net, &s33);
tk::dnn::Conv2d c37 (&net, 512, 3, 3, 2, 2, 1, 1, c37_bin, true);
tk::dnn::Activation a37 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c38 (&net, 256, 1, 1, 1, 1, 0, 0, c38_bin, true);
tk::dnn::Activation a38 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c39 (&net, 512, 3, 3, 1, 1, 1, 1, c39_bin, true);
tk::dnn::Activation a39 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s40 (&net, &a37);
tk::dnn::Conv2d c41 (&net, 256, 1, 1, 1, 1, 0, 0, c41_bin, true);
tk::dnn::Activation a41 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c42 (&net, 512, 3, 3, 1, 1, 1, 1, c42_bin, true);
tk::dnn::Activation a42 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s43 (&net, &s40);
tk::dnn::Conv2d c44 (&net, 256, 1, 1, 1, 1, 0, 0, c44_bin, true);
tk::dnn::Activation a44 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c45 (&net, 512, 3, 3, 1, 1, 1, 1, c45_bin, true);
tk::dnn::Activation a45 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s46 (&net, &s43);
tk::dnn::Conv2d c47 (&net, 256, 1, 1, 1, 1, 0, 0, c47_bin, true);
tk::dnn::Activation a47 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c48 (&net, 512, 3, 3, 1, 1, 1, 1, c48_bin, true);
tk::dnn::Activation a48 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s49 (&net, &s46);
tk::dnn::Conv2d c50 (&net, 256, 1, 1, 1, 1, 0, 0, c50_bin, true);
tk::dnn::Activation a50 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c51 (&net, 512, 3, 3, 1, 1, 1, 1, c51_bin, true);
tk::dnn::Activation a51 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s52 (&net, &s49);
tk::dnn::Conv2d c53 (&net, 256, 1, 1, 1, 1, 0, 0, c53_bin, true);
tk::dnn::Activation a53 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c54 (&net, 512, 3, 3, 1, 1, 1, 1, c54_bin, true);
tk::dnn::Activation a54 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s55 (&net, &s52);
tk::dnn::Conv2d c56 (&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
tk::dnn::Activation a56 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c57 (&net, 512, 3, 3, 1, 1, 1, 1, c57_bin, true);
tk::dnn::Activation a57 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s58 (&net, &s55);
tk::dnn::Conv2d c59 (&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
tk::dnn::Activation a59 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c60 (&net, 512, 3, 3, 1, 1, 1, 1, c60_bin, true);
tk::dnn::Activation a60 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s61 (&net, &s58);
tk::dnn::Conv2d c62 (&net,1024, 3, 3, 2, 2, 1, 1, c62_bin, true);
tk::dnn::Activation a62 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c63 (&net, 512, 1, 1, 1, 1, 0, 0, c63_bin, true);
tk::dnn::Activation a63 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c64 (&net,1024, 3, 3, 1, 1, 1, 1, c64_bin, true);
tk::dnn::Activation a64 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s65 (&net, &a62);
tk::dnn::Conv2d c66 (&net, 512, 1, 1, 1, 1, 0, 0, c66_bin, true);
tk::dnn::Activation a66 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c67 (&net,1024, 3, 3, 1, 1, 1, 1, c67_bin, true);
tk::dnn::Activation a67 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s68 (&net, &s65);
tk::dnn::Conv2d c69 (&net, 512, 1, 1, 1, 1, 0, 0, c69_bin, true);
tk::dnn::Activation a69 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c70 (&net,1024, 3, 3, 1, 1, 1, 1, c70_bin, true);
tk::dnn::Activation a70 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s71 (&net, &s68);
tk::dnn::Conv2d c72 (&net, 512, 1, 1, 1, 1, 0, 0, c72_bin, true);
tk::dnn::Activation a72 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c73 (&net,1024, 3, 3, 1, 1, 1, 1, c73_bin, true);
tk::dnn::Activation a73 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s74 (&net, &s71);
tk::dnn::Conv2d c75 (&net, 512, 1, 1, 1, 1, 0, 0, c75_bin, true);
tk::dnn::Activation a75 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c76 (&net,1024, 3, 3, 1, 1, 1, 1, c76_bin, true);
tk::dnn::Activation a76 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c77 (&net, 512, 1, 1, 1, 1, 0, 0, c77_bin, true);
tk::dnn::Activation a77 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c78 (&net,1024, 3, 3, 1, 1, 1, 1, c78_bin, true);
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c81 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c81_bin, false);
tk::dnn::Yolo yolo0 (&net, classes, 3, g82_bin);
tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1);
tk::dnn::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u85 (&net, 2);
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
tk::dnn::Route m86 (&net, m86_layers, 2);
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true);
tk::dnn::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
tk::dnn::Activation a88 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c89 (&net, 256, 1, 1, 1, 1, 0, 0, c89_bin, true);
tk::dnn::Activation a89 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c90 (&net, 512, 3, 3, 1, 1, 1, 1, c90_bin, true);
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c93 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c93_bin, false);
tk::dnn::Yolo yolo1 (&net, classes, 3, g94_bin);
tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1);
tk::dnn::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u97 (&net, 2);
tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
tk::dnn::Route m98 (&net, m98_layers, 2);
tk::dnn::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
tk::dnn::Activation a100 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c101 (&net, 128, 1, 1, 1, 1, 0, 0, c101_bin, true);
tk::dnn::Activation a101 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c102 (&net, 256, 3, 3, 1, 1, 1, 1, c102_bin, true);
tk::dnn::Activation a102 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c103 (&net, 128, 1, 1, 1, 1, 0, 0, c103_bin, true);
tk::dnn::Activation a103 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c105 (&net, preYoloFilters, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo yolo2 (&net, classes, 3, g106_bin);
yolo[0] = &yolo0;
yolo[1] = &yolo1;
yolo[2] = &yolo2;
include/utils.h
+1 -1
View File
@@ -90,7 +90,7 @@
void printCenteredTitle(const char *title, char fill, int dim);
bool fileExist(const char *fname);
void readBinaryFile(const char* fname, int size, dnnType** data_h, dnnType** data_d, int seek = 0);
void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek = 0);
int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device = true);
void printDeviceVector(int size, dnnType* vec_d, bool device = true);
void resize(int size, dnnType **data);
src/Conv2d.cpp
+1 -1
View File
@@ -6,7 +6,7 @@ namespace tk { namespace dnn {
Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW,
int strideH, int strideW, int paddingH, int paddingW,
const char* fname_weights, bool batchnorm) :
std::string fname_weights, bool batchnorm) :
LayerWgs(net, net->getOutputDim().c, out_ch, kernelH, kernelW, 1,
fname_weights, batchnorm) {
src/Dense.cpp
+1 -1
View File
@@ -4,7 +4,7 @@
namespace tk { namespace dnn {
Dense::Dense(Network *net, int out_ch, const char* fname_weights) :
Dense::Dense(Network *net, int out_ch, std::string fname_weights) :
LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights) {
output_dim.n = 1;
src/LayerWgs.cpp
+1 -1
View File
@@ -8,7 +8,7 @@ namespace tk { namespace dnn {
LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
int kh, int kw, int kl,
const char* fname_weights, bool batchnorm) : Layer(net) {
std::string fname_weights, bool batchnorm) : Layer(net) {
this->inputs = inputs;
this->outputs = outputs;
src/Region.cpp
+1 -1
View File
@@ -66,7 +66,7 @@ dnnType* Region::infer(dataDim_t &dim, dnnType* srcData) {
/* Intepret class */
RegionInterpret::RegionInterpret(dataDim_t input_dim, dataDim_t output_dim,
int classes, int coords, int num, float thresh, const char* fname_weights) {
int classes, int coords, int num, float thresh, std::string fname_weights) {
this->input_dim = input_dim;
this->output_dim = output_dim;
src/Yolo.cpp
+2 -2
View File
@@ -11,14 +11,14 @@
namespace tk { namespace dnn {
Yolo::Yolo(Network *net, int classes, int num, const char* fname_weights) :
Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights) :
Layer(net) {
this->classes = classes;
this->num = num;
// load anchors
if(fname_weights != nullptr) {
if(fname_weights != "") {
int seek = 0;
readBinaryFile(fname_weights, num, &mask_h, &mask_d, seek);
seek += num;
src/Yolo3Detection.cpp
+1 -1
View File
@@ -32,7 +32,7 @@ bool Yolo3Detection::init(std::string tensor_path) {
num = yRT->num;
// make a yolo layer for interpret predictions
yolo[i] = new tk::dnn::Yolo(nullptr, classes, num, nullptr); // yolo without input and bias
yolo[i] = new tk::dnn::Yolo(nullptr, classes, num, ""); // yolo without input and bias
yolo[i]->mask_h = new dnnType[num];
yolo[i]->bias_h = new dnnType[num*3*2];
memcpy(yolo[i]->mask_h, yRT->mask, sizeof(dnnType)*num);
src/utils.cpp
+1 -1
View File
@@ -21,7 +21,7 @@ bool fileExist(const char *fname) {
}
void readBinaryFile(const char* fname, int size, dnnType** data_h, dnnType** data_d, int seek)
void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** data_d, int seek)
{
std::ifstream dataFile (fname, std::ios::in | std::ios::binary);
std::stringstream error_s;
tests/yolo3_berkeley/yolo3_berkeley.cpp
+11 -297
View File
@@ -1,295 +1,18 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
const char *input_bin = "../tests/yolo3_berkeley/layers/input.bin";
const char *c0_bin = "../tests/yolo3_berkeley/layers/c0.bin";
const char *c1_bin = "../tests/yolo3_berkeley/layers/c1.bin";
const char *c2_bin = "../tests/yolo3_berkeley/layers/c2.bin";
const char *c3_bin = "../tests/yolo3_berkeley/layers/c3.bin";
const char *c5_bin = "../tests/yolo3_berkeley/layers/c5.bin";
const char *c6_bin = "../tests/yolo3_berkeley/layers/c6.bin";
const char *c7_bin = "../tests/yolo3_berkeley/layers/c7.bin";
const char *c9_bin = "../tests/yolo3_berkeley/layers/c9.bin";
const char *c10_bin = "../tests/yolo3_berkeley/layers/c10.bin";
const char *c12_bin = "../tests/yolo3_berkeley/layers/c12.bin";
const char *c13_bin = "../tests/yolo3_berkeley/layers/c13.bin";
const char *c14_bin = "../tests/yolo3_berkeley/layers/c14.bin";
const char *c16_bin = "../tests/yolo3_berkeley/layers/c16.bin";
const char *c17_bin = "../tests/yolo3_berkeley/layers/c17.bin";
const char *c19_bin = "../tests/yolo3_berkeley/layers/c19.bin";
const char *c20_bin = "../tests/yolo3_berkeley/layers/c20.bin";
const char *c22_bin = "../tests/yolo3_berkeley/layers/c22.bin";
const char *c23_bin = "../tests/yolo3_berkeley/layers/c23.bin";
const char *c25_bin = "../tests/yolo3_berkeley/layers/c25.bin";
const char *c26_bin = "../tests/yolo3_berkeley/layers/c26.bin";
const char *c28_bin = "../tests/yolo3_berkeley/layers/c28.bin";
const char *c29_bin = "../tests/yolo3_berkeley/layers/c29.bin";
const char *c31_bin = "../tests/yolo3_berkeley/layers/c31.bin";
const char *c32_bin = "../tests/yolo3_berkeley/layers/c32.bin";
const char *c34_bin = "../tests/yolo3_berkeley/layers/c34.bin";
const char *c35_bin = "../tests/yolo3_berkeley/layers/c35.bin";
const char *c37_bin = "../tests/yolo3_berkeley/layers/c37.bin";
const char *c38_bin = "../tests/yolo3_berkeley/layers/c38.bin";
const char *c39_bin = "../tests/yolo3_berkeley/layers/c39.bin";
const char *c41_bin = "../tests/yolo3_berkeley/layers/c41.bin";
const char *c42_bin = "../tests/yolo3_berkeley/layers/c42.bin";
const char *c44_bin = "../tests/yolo3_berkeley/layers/c44.bin";
const char *c45_bin = "../tests/yolo3_berkeley/layers/c45.bin";
const char *c47_bin = "../tests/yolo3_berkeley/layers/c47.bin";
const char *c48_bin = "../tests/yolo3_berkeley/layers/c48.bin";
const char *c50_bin = "../tests/yolo3_berkeley/layers/c50.bin";
const char *c51_bin = "../tests/yolo3_berkeley/layers/c51.bin";
const char *c53_bin = "../tests/yolo3_berkeley/layers/c53.bin";
const char *c54_bin = "../tests/yolo3_berkeley/layers/c54.bin";
const char *c56_bin = "../tests/yolo3_berkeley/layers/c56.bin";
const char *c57_bin = "../tests/yolo3_berkeley/layers/c57.bin";
const char *c59_bin = "../tests/yolo3_berkeley/layers/c59.bin";
const char *c60_bin = "../tests/yolo3_berkeley/layers/c60.bin";
const char *c62_bin = "../tests/yolo3_berkeley/layers/c62.bin";
const char *c63_bin = "../tests/yolo3_berkeley/layers/c63.bin";
const char *c64_bin = "../tests/yolo3_berkeley/layers/c64.bin";
const char *c66_bin = "../tests/yolo3_berkeley/layers/c66.bin";
const char *c67_bin = "../tests/yolo3_berkeley/layers/c67.bin";
const char *c69_bin = "../tests/yolo3_berkeley/layers/c69.bin";
const char *c70_bin = "../tests/yolo3_berkeley/layers/c70.bin";
const char *c72_bin = "../tests/yolo3_berkeley/layers/c72.bin";
const char *c73_bin = "../tests/yolo3_berkeley/layers/c73.bin";
const char *c75_bin = "../tests/yolo3_berkeley/layers/c75.bin";
const char *c76_bin = "../tests/yolo3_berkeley/layers/c76.bin";
const char *c77_bin = "../tests/yolo3_berkeley/layers/c77.bin";
const char *c78_bin = "../tests/yolo3_berkeley/layers/c78.bin";
const char *c79_bin = "../tests/yolo3_berkeley/layers/c79.bin";
const char *c80_bin = "../tests/yolo3_berkeley/layers/c80.bin";
const char *c81_bin = "../tests/yolo3_berkeley/layers/c81.bin";
const char *g82_bin = "../tests/yolo3_berkeley/layers/g82.bin";
const char *c84_bin = "../tests/yolo3_berkeley/layers/c84.bin";
const char *c87_bin = "../tests/yolo3_berkeley/layers/c87.bin";
const char *c88_bin = "../tests/yolo3_berkeley/layers/c88.bin";
const char *c89_bin = "../tests/yolo3_berkeley/layers/c89.bin";
const char *c90_bin = "../tests/yolo3_berkeley/layers/c90.bin";
const char *c91_bin = "../tests/yolo3_berkeley/layers/c91.bin";
const char *c92_bin = "../tests/yolo3_berkeley/layers/c92.bin";
const char *c93_bin = "../tests/yolo3_berkeley/layers/c93.bin";
const char *g94_bin = "../tests/yolo3_berkeley/layers/g94.bin";
const char *c96_bin = "../tests/yolo3_berkeley/layers/c96.bin";
const char *c99_bin = "../tests/yolo3_berkeley/layers/c99.bin";
const char *c100_bin = "../tests/yolo3_berkeley/layers/c100.bin";
const char *c101_bin = "../tests/yolo3_berkeley/layers/c101.bin";
const char *c102_bin = "../tests/yolo3_berkeley/layers/c102.bin";
const char *c103_bin = "../tests/yolo3_berkeley/layers/c103.bin";
const char *c104_bin = "../tests/yolo3_berkeley/layers/c104.bin";
const char *c105_bin = "../tests/yolo3_berkeley/layers/c105.bin";
const char *g106_bin = "../tests/yolo3_berkeley/layers/g106.bin";
const char *output_bins[3] = {
"../tests/yolo3_berkeley/debug/layer82_out.bin",
"../tests/yolo3_berkeley/debug/layer94_out.bin",
"../tests/yolo3_berkeley/debug/layer106_out.bin"
};
int main() {
// Network layout
tk::dnn::dataDim_t dim(1, 3, 320, 544, 1);
tk::dnn::Network net(dim);
tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s4 (&net, &a1);
tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s8 (&net, &a5);
tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s11 (&net, &s8);
tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s15 (&net, &a12);
tk::dnn::Conv2d c16 (&net, 128, 1, 1, 1, 1, 0, 0, c16_bin, true);
tk::dnn::Activation a16 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c17 (&net, 256, 3, 3, 1, 1, 1, 1, c17_bin, true);
tk::dnn::Activation a17 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s18 (&net, &s15);
tk::dnn::Conv2d c19 (&net, 128, 1, 1, 1, 1, 0, 0, c19_bin, true);
tk::dnn::Activation a19 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c20 (&net, 256, 3, 3, 1, 1, 1, 1, c20_bin, true);
tk::dnn::Activation a20 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s21 (&net, &s18);
tk::dnn::Conv2d c22 (&net, 128, 1, 1, 1, 1, 0, 0, c22_bin, true);
tk::dnn::Activation a22 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c23 (&net, 256, 3, 3, 1, 1, 1, 1, c23_bin, true);
tk::dnn::Activation a23 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s24 (&net, &s21);
tk::dnn::Conv2d c25 (&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
tk::dnn::Activation a25 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c26 (&net, 256, 3, 3, 1, 1, 1, 1, c26_bin, true);
tk::dnn::Activation a26 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s27 (&net, &s24);
tk::dnn::Conv2d c28 (&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
tk::dnn::Activation a28 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c29 (&net, 256, 3, 3, 1, 1, 1, 1, c29_bin, true);
tk::dnn::Activation a29 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s30 (&net, &s27);
tk::dnn::Conv2d c31 (&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
tk::dnn::Activation a31 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c32 (&net, 256, 3, 3, 1, 1, 1, 1, c32_bin, true);
tk::dnn::Activation a32 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s33 (&net, &s30);
tk::dnn::Conv2d c34 (&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
tk::dnn::Activation a34 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c35 (&net, 256, 3, 3, 1, 1, 1, 1, c35_bin, true);
tk::dnn::Activation a35 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s36 (&net, &s33);
tk::dnn::Conv2d c37 (&net, 512, 3, 3, 2, 2, 1, 1, c37_bin, true);
tk::dnn::Activation a37 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c38 (&net, 256, 1, 1, 1, 1, 0, 0, c38_bin, true);
tk::dnn::Activation a38 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c39 (&net, 512, 3, 3, 1, 1, 1, 1, c39_bin, true);
tk::dnn::Activation a39 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s40 (&net, &a37);
tk::dnn::Conv2d c41 (&net, 256, 1, 1, 1, 1, 0, 0, c41_bin, true);
tk::dnn::Activation a41 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c42 (&net, 512, 3, 3, 1, 1, 1, 1, c42_bin, true);
tk::dnn::Activation a42 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s43 (&net, &s40);
tk::dnn::Conv2d c44 (&net, 256, 1, 1, 1, 1, 0, 0, c44_bin, true);
tk::dnn::Activation a44 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c45 (&net, 512, 3, 3, 1, 1, 1, 1, c45_bin, true);
tk::dnn::Activation a45 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s46 (&net, &s43);
tk::dnn::Conv2d c47 (&net, 256, 1, 1, 1, 1, 0, 0, c47_bin, true);
tk::dnn::Activation a47 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c48 (&net, 512, 3, 3, 1, 1, 1, 1, c48_bin, true);
tk::dnn::Activation a48 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s49 (&net, &s46);
tk::dnn::Conv2d c50 (&net, 256, 1, 1, 1, 1, 0, 0, c50_bin, true);
tk::dnn::Activation a50 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c51 (&net, 512, 3, 3, 1, 1, 1, 1, c51_bin, true);
tk::dnn::Activation a51 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s52 (&net, &s49);
tk::dnn::Conv2d c53 (&net, 256, 1, 1, 1, 1, 0, 0, c53_bin, true);
tk::dnn::Activation a53 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c54 (&net, 512, 3, 3, 1, 1, 1, 1, c54_bin, true);
tk::dnn::Activation a54 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s55 (&net, &s52);
tk::dnn::Conv2d c56 (&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
tk::dnn::Activation a56 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c57 (&net, 512, 3, 3, 1, 1, 1, 1, c57_bin, true);
tk::dnn::Activation a57 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s58 (&net, &s55);
tk::dnn::Conv2d c59 (&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
tk::dnn::Activation a59 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c60 (&net, 512, 3, 3, 1, 1, 1, 1, c60_bin, true);
tk::dnn::Activation a60 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s61 (&net, &s58);
tk::dnn::Conv2d c62 (&net,1024, 3, 3, 2, 2, 1, 1, c62_bin, true);
tk::dnn::Activation a62 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c63 (&net, 512, 1, 1, 1, 1, 0, 0, c63_bin, true);
tk::dnn::Activation a63 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c64 (&net,1024, 3, 3, 1, 1, 1, 1, c64_bin, true);
tk::dnn::Activation a64 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s65 (&net, &a62);
tk::dnn::Conv2d c66 (&net, 512, 1, 1, 1, 1, 0, 0, c66_bin, true);
tk::dnn::Activation a66 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c67 (&net,1024, 3, 3, 1, 1, 1, 1, c67_bin, true);
tk::dnn::Activation a67 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s68 (&net, &s65);
tk::dnn::Conv2d c69 (&net, 512, 1, 1, 1, 1, 0, 0, c69_bin, true);
tk::dnn::Activation a69 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c70 (&net,1024, 3, 3, 1, 1, 1, 1, c70_bin, true);
tk::dnn::Activation a70 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s71 (&net, &s68);
tk::dnn::Conv2d c72 (&net, 512, 1, 1, 1, 1, 0, 0, c72_bin, true);
tk::dnn::Activation a72 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c73 (&net,1024, 3, 3, 1, 1, 1, 1, c73_bin, true);
tk::dnn::Activation a73 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s74 (&net, &s71);
tk::dnn::Conv2d c75 (&net, 512, 1, 1, 1, 1, 0, 0, c75_bin, true);
tk::dnn::Activation a75 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c76 (&net,1024, 3, 3, 1, 1, 1, 1, c76_bin, true);
tk::dnn::Activation a76 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c77 (&net, 512, 1, 1, 1, 1, 0, 0, c77_bin, true);
tk::dnn::Activation a77 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c78 (&net,1024, 3, 3, 1, 1, 1, 1, c78_bin, true);
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c81 (&net, 45, 1, 1, 1, 1, 0, 0, c81_bin, false);
tk::dnn::Yolo yolo0 (&net, 10, 3, g82_bin);
tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1);
tk::dnn::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u85 (&net, 2);
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
tk::dnn::Route m86 (&net, m86_layers, 2);
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true);
tk::dnn::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
tk::dnn::Activation a88 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c89 (&net, 256, 1, 1, 1, 1, 0, 0, c89_bin, true);
tk::dnn::Activation a89 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c90 (&net, 512, 3, 3, 1, 1, 1, 1, c90_bin, true);
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c93 (&net, 45, 1, 1, 1, 1, 0, 0, c93_bin, false);
tk::dnn::Yolo yolo1 (&net, 10, 3, g94_bin);
tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1);
tk::dnn::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u97 (&net, 2);
tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
tk::dnn::Route m98 (&net, m98_layers, 2);
tk::dnn::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
tk::dnn::Activation a100 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c101 (&net, 128, 1, 1, 1, 1, 0, 0, c101_bin, true);
tk::dnn::Activation a101 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c102 (&net, 256, 3, 3, 1, 1, 1, 1, c102_bin, true);
tk::dnn::Activation a102 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c103 (&net, 128, 1, 1, 1, 1, 0, 0, c103_bin, true);
tk::dnn::Activation a103 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c105 (&net, 45, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo yolo2 (&net, 10, 3, g106_bin);
// create yolo3 model
std::string bin_path = "../tests/yolo3_berkeley";
int classes = 10;
tk::dnn::Yolo *yolo [3];
#include "models/Yolo3.h"
// Load input
dnnType *data;
@@ -304,9 +27,7 @@ int main() {
// the network have 3 outputs
tk::dnn::dataDim_t out_dim[3];
out_dim[0] = yolo0.output_dim;
out_dim[1] = yolo1.output_dim;
out_dim[2] = yolo2.output_dim;
for(int i=0; i<3; i++) out_dim[i] = yolo[i]->output_dim;
dnnType *cudnn_out[3], *rt_out[3];
tk::dnn::dataDim_t dim1 = dim; //input dim
@@ -317,18 +38,13 @@ int main() {
TIMER_STOP
dim1.print();
}
cudnn_out[0] = yolo0.dstData;
cudnn_out[1] = yolo1.dstData;
cudnn_out[2] = yolo2.dstData;
for(int i=0; i<3; i++) cudnn_out[i] = yolo[i]->dstData;
printCenteredTitle(" compute detections ", '=', 30);
TIMER_START
int ndets = 0;
int classes = yolo0.classes;
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
yolo0.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
yolo1.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
yolo2.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
for(int i=0; i<3; i++) yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
for(int j=0; j<ndets; j++) {
@@ -356,9 +72,7 @@ int main() {
TIMER_STOP
dim2.print();
}
rt_out[0] = (dnnType*)netRT.buffersRT[1];
rt_out[1] = (dnnType*)netRT.buffersRT[2];
rt_out[2] = (dnnType*)netRT.buffersRT[3];
for(int i=0; i<3; i++) rt_out[i] = (dnnType*)netRT.buffersRT[i+1];
for(int i=0; i<3; i++) {
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
tests/yolo3_coco4/yolo3_coco4.cpp
+11 -297
View File
@@ -1,295 +1,18 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
const char *input_bin = "../tests/yolo3_coco4/layers/input.bin";
const char *c0_bin = "../tests/yolo3_coco4/layers/c0.bin";
const char *c1_bin = "../tests/yolo3_coco4/layers/c1.bin";
const char *c2_bin = "../tests/yolo3_coco4/layers/c2.bin";
const char *c3_bin = "../tests/yolo3_coco4/layers/c3.bin";
const char *c5_bin = "../tests/yolo3_coco4/layers/c5.bin";
const char *c6_bin = "../tests/yolo3_coco4/layers/c6.bin";
const char *c7_bin = "../tests/yolo3_coco4/layers/c7.bin";
const char *c9_bin = "../tests/yolo3_coco4/layers/c9.bin";
const char *c10_bin = "../tests/yolo3_coco4/layers/c10.bin";
const char *c12_bin = "../tests/yolo3_coco4/layers/c12.bin";
const char *c13_bin = "../tests/yolo3_coco4/layers/c13.bin";
const char *c14_bin = "../tests/yolo3_coco4/layers/c14.bin";
const char *c16_bin = "../tests/yolo3_coco4/layers/c16.bin";
const char *c17_bin = "../tests/yolo3_coco4/layers/c17.bin";
const char *c19_bin = "../tests/yolo3_coco4/layers/c19.bin";
const char *c20_bin = "../tests/yolo3_coco4/layers/c20.bin";
const char *c22_bin = "../tests/yolo3_coco4/layers/c22.bin";
const char *c23_bin = "../tests/yolo3_coco4/layers/c23.bin";
const char *c25_bin = "../tests/yolo3_coco4/layers/c25.bin";
const char *c26_bin = "../tests/yolo3_coco4/layers/c26.bin";
const char *c28_bin = "../tests/yolo3_coco4/layers/c28.bin";
const char *c29_bin = "../tests/yolo3_coco4/layers/c29.bin";
const char *c31_bin = "../tests/yolo3_coco4/layers/c31.bin";
const char *c32_bin = "../tests/yolo3_coco4/layers/c32.bin";
const char *c34_bin = "../tests/yolo3_coco4/layers/c34.bin";
const char *c35_bin = "../tests/yolo3_coco4/layers/c35.bin";
const char *c37_bin = "../tests/yolo3_coco4/layers/c37.bin";
const char *c38_bin = "../tests/yolo3_coco4/layers/c38.bin";
const char *c39_bin = "../tests/yolo3_coco4/layers/c39.bin";
const char *c41_bin = "../tests/yolo3_coco4/layers/c41.bin";
const char *c42_bin = "../tests/yolo3_coco4/layers/c42.bin";
const char *c44_bin = "../tests/yolo3_coco4/layers/c44.bin";
const char *c45_bin = "../tests/yolo3_coco4/layers/c45.bin";
const char *c47_bin = "../tests/yolo3_coco4/layers/c47.bin";
const char *c48_bin = "../tests/yolo3_coco4/layers/c48.bin";
const char *c50_bin = "../tests/yolo3_coco4/layers/c50.bin";
const char *c51_bin = "../tests/yolo3_coco4/layers/c51.bin";
const char *c53_bin = "../tests/yolo3_coco4/layers/c53.bin";
const char *c54_bin = "../tests/yolo3_coco4/layers/c54.bin";
const char *c56_bin = "../tests/yolo3_coco4/layers/c56.bin";
const char *c57_bin = "../tests/yolo3_coco4/layers/c57.bin";
const char *c59_bin = "../tests/yolo3_coco4/layers/c59.bin";
const char *c60_bin = "../tests/yolo3_coco4/layers/c60.bin";
const char *c62_bin = "../tests/yolo3_coco4/layers/c62.bin";
const char *c63_bin = "../tests/yolo3_coco4/layers/c63.bin";
const char *c64_bin = "../tests/yolo3_coco4/layers/c64.bin";
const char *c66_bin = "../tests/yolo3_coco4/layers/c66.bin";
const char *c67_bin = "../tests/yolo3_coco4/layers/c67.bin";
const char *c69_bin = "../tests/yolo3_coco4/layers/c69.bin";
const char *c70_bin = "../tests/yolo3_coco4/layers/c70.bin";
const char *c72_bin = "../tests/yolo3_coco4/layers/c72.bin";
const char *c73_bin = "../tests/yolo3_coco4/layers/c73.bin";
const char *c75_bin = "../tests/yolo3_coco4/layers/c75.bin";
const char *c76_bin = "../tests/yolo3_coco4/layers/c76.bin";
const char *c77_bin = "../tests/yolo3_coco4/layers/c77.bin";
const char *c78_bin = "../tests/yolo3_coco4/layers/c78.bin";
const char *c79_bin = "../tests/yolo3_coco4/layers/c79.bin";
const char *c80_bin = "../tests/yolo3_coco4/layers/c80.bin";
const char *c81_bin = "../tests/yolo3_coco4/layers/c81.bin";
const char *g82_bin = "../tests/yolo3_coco4/layers/g82.bin";
const char *c84_bin = "../tests/yolo3_coco4/layers/c84.bin";
const char *c87_bin = "../tests/yolo3_coco4/layers/c87.bin";
const char *c88_bin = "../tests/yolo3_coco4/layers/c88.bin";
const char *c89_bin = "../tests/yolo3_coco4/layers/c89.bin";
const char *c90_bin = "../tests/yolo3_coco4/layers/c90.bin";
const char *c91_bin = "../tests/yolo3_coco4/layers/c91.bin";
const char *c92_bin = "../tests/yolo3_coco4/layers/c92.bin";
const char *c93_bin = "../tests/yolo3_coco4/layers/c93.bin";
const char *g94_bin = "../tests/yolo3_coco4/layers/g94.bin";
const char *c96_bin = "../tests/yolo3_coco4/layers/c96.bin";
const char *c99_bin = "../tests/yolo3_coco4/layers/c99.bin";
const char *c100_bin = "../tests/yolo3_coco4/layers/c100.bin";
const char *c101_bin = "../tests/yolo3_coco4/layers/c101.bin";
const char *c102_bin = "../tests/yolo3_coco4/layers/c102.bin";
const char *c103_bin = "../tests/yolo3_coco4/layers/c103.bin";
const char *c104_bin = "../tests/yolo3_coco4/layers/c104.bin";
const char *c105_bin = "../tests/yolo3_coco4/layers/c105.bin";
const char *g106_bin = "../tests/yolo3_coco4/layers/g106.bin";
const char *output_bins[3] = {
"../tests/yolo3_coco4/debug/layer82_out.bin",
"../tests/yolo3_coco4/debug/layer94_out.bin",
"../tests/yolo3_coco4/debug/layer106_out.bin"
};
int main() {
// Network layout
tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
tk::dnn::Network net(dim);
tk::dnn::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c1 (&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
tk::dnn::Activation a1 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c2 (&net, 32, 1, 1, 1, 1, 0, 0, c2_bin, true);
tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c3 (&net, 64, 3, 3, 1, 1, 1, 1, c3_bin, true);
tk::dnn::Activation a3 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s4 (&net, &a1);
tk::dnn::Conv2d c5 (&net, 128, 3, 3, 2, 2, 1, 1, c5_bin, true);
tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c6 (&net, 64, 1, 1, 1, 1, 0, 0, c6_bin, true);
tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c7 (&net, 128, 3, 3, 1, 1, 1, 1, c7_bin, true);
tk::dnn::Activation a7 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s8 (&net, &a5);
tk::dnn::Conv2d c9 (&net, 64, 1, 1, 1, 1, 0, 0, c9_bin, true);
tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c10 (&net, 128, 3, 3, 1, 1, 1, 1, c10_bin, true);
tk::dnn::Activation a10 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s11 (&net, &s8);
tk::dnn::Conv2d c12 (&net, 256, 3, 3, 2, 2, 1, 1, c12_bin, true);
tk::dnn::Activation a12 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c13 (&net, 128, 1, 1, 1, 1, 0, 0, c13_bin, true);
tk::dnn::Activation a13 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c14 (&net, 256, 3, 3, 1, 1, 1, 1, c14_bin, true);
tk::dnn::Activation a14 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s15 (&net, &a12);
tk::dnn::Conv2d c16 (&net, 128, 1, 1, 1, 1, 0, 0, c16_bin, true);
tk::dnn::Activation a16 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c17 (&net, 256, 3, 3, 1, 1, 1, 1, c17_bin, true);
tk::dnn::Activation a17 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s18 (&net, &s15);
tk::dnn::Conv2d c19 (&net, 128, 1, 1, 1, 1, 0, 0, c19_bin, true);
tk::dnn::Activation a19 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c20 (&net, 256, 3, 3, 1, 1, 1, 1, c20_bin, true);
tk::dnn::Activation a20 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s21 (&net, &s18);
tk::dnn::Conv2d c22 (&net, 128, 1, 1, 1, 1, 0, 0, c22_bin, true);
tk::dnn::Activation a22 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c23 (&net, 256, 3, 3, 1, 1, 1, 1, c23_bin, true);
tk::dnn::Activation a23 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s24 (&net, &s21);
tk::dnn::Conv2d c25 (&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
tk::dnn::Activation a25 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c26 (&net, 256, 3, 3, 1, 1, 1, 1, c26_bin, true);
tk::dnn::Activation a26 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s27 (&net, &s24);
tk::dnn::Conv2d c28 (&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
tk::dnn::Activation a28 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c29 (&net, 256, 3, 3, 1, 1, 1, 1, c29_bin, true);
tk::dnn::Activation a29 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s30 (&net, &s27);
tk::dnn::Conv2d c31 (&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
tk::dnn::Activation a31 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c32 (&net, 256, 3, 3, 1, 1, 1, 1, c32_bin, true);
tk::dnn::Activation a32 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s33 (&net, &s30);
tk::dnn::Conv2d c34 (&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
tk::dnn::Activation a34 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c35 (&net, 256, 3, 3, 1, 1, 1, 1, c35_bin, true);
tk::dnn::Activation a35 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s36 (&net, &s33);
tk::dnn::Conv2d c37 (&net, 512, 3, 3, 2, 2, 1, 1, c37_bin, true);
tk::dnn::Activation a37 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c38 (&net, 256, 1, 1, 1, 1, 0, 0, c38_bin, true);
tk::dnn::Activation a38 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c39 (&net, 512, 3, 3, 1, 1, 1, 1, c39_bin, true);
tk::dnn::Activation a39 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s40 (&net, &a37);
tk::dnn::Conv2d c41 (&net, 256, 1, 1, 1, 1, 0, 0, c41_bin, true);
tk::dnn::Activation a41 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c42 (&net, 512, 3, 3, 1, 1, 1, 1, c42_bin, true);
tk::dnn::Activation a42 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s43 (&net, &s40);
tk::dnn::Conv2d c44 (&net, 256, 1, 1, 1, 1, 0, 0, c44_bin, true);
tk::dnn::Activation a44 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c45 (&net, 512, 3, 3, 1, 1, 1, 1, c45_bin, true);
tk::dnn::Activation a45 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s46 (&net, &s43);
tk::dnn::Conv2d c47 (&net, 256, 1, 1, 1, 1, 0, 0, c47_bin, true);
tk::dnn::Activation a47 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c48 (&net, 512, 3, 3, 1, 1, 1, 1, c48_bin, true);
tk::dnn::Activation a48 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s49 (&net, &s46);
tk::dnn::Conv2d c50 (&net, 256, 1, 1, 1, 1, 0, 0, c50_bin, true);
tk::dnn::Activation a50 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c51 (&net, 512, 3, 3, 1, 1, 1, 1, c51_bin, true);
tk::dnn::Activation a51 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s52 (&net, &s49);
tk::dnn::Conv2d c53 (&net, 256, 1, 1, 1, 1, 0, 0, c53_bin, true);
tk::dnn::Activation a53 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c54 (&net, 512, 3, 3, 1, 1, 1, 1, c54_bin, true);
tk::dnn::Activation a54 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s55 (&net, &s52);
tk::dnn::Conv2d c56 (&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
tk::dnn::Activation a56 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c57 (&net, 512, 3, 3, 1, 1, 1, 1, c57_bin, true);
tk::dnn::Activation a57 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s58 (&net, &s55);
tk::dnn::Conv2d c59 (&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
tk::dnn::Activation a59 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c60 (&net, 512, 3, 3, 1, 1, 1, 1, c60_bin, true);
tk::dnn::Activation a60 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s61 (&net, &s58);
tk::dnn::Conv2d c62 (&net,1024, 3, 3, 2, 2, 1, 1, c62_bin, true);
tk::dnn::Activation a62 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c63 (&net, 512, 1, 1, 1, 1, 0, 0, c63_bin, true);
tk::dnn::Activation a63 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c64 (&net,1024, 3, 3, 1, 1, 1, 1, c64_bin, true);
tk::dnn::Activation a64 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s65 (&net, &a62);
tk::dnn::Conv2d c66 (&net, 512, 1, 1, 1, 1, 0, 0, c66_bin, true);
tk::dnn::Activation a66 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c67 (&net,1024, 3, 3, 1, 1, 1, 1, c67_bin, true);
tk::dnn::Activation a67 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s68 (&net, &s65);
tk::dnn::Conv2d c69 (&net, 512, 1, 1, 1, 1, 0, 0, c69_bin, true);
tk::dnn::Activation a69 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c70 (&net,1024, 3, 3, 1, 1, 1, 1, c70_bin, true);
tk::dnn::Activation a70 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s71 (&net, &s68);
tk::dnn::Conv2d c72 (&net, 512, 1, 1, 1, 1, 0, 0, c72_bin, true);
tk::dnn::Activation a72 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c73 (&net,1024, 3, 3, 1, 1, 1, 1, c73_bin, true);
tk::dnn::Activation a73 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Shortcut s74 (&net, &s71);
tk::dnn::Conv2d c75 (&net, 512, 1, 1, 1, 1, 0, 0, c75_bin, true);
tk::dnn::Activation a75 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c76 (&net,1024, 3, 3, 1, 1, 1, 1, c76_bin, true);
tk::dnn::Activation a76 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c77 (&net, 512, 1, 1, 1, 1, 0, 0, c77_bin, true);
tk::dnn::Activation a77 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c78 (&net,1024, 3, 3, 1, 1, 1, 1, c78_bin, true);
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c81 (&net, 27, 1, 1, 1, 1, 0, 0, c81_bin, false);
tk::dnn::Yolo yolo0 (&net, 4, 3, g82_bin);
tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1);
tk::dnn::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u85 (&net, 2);
tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
tk::dnn::Route m86 (&net, m86_layers, 2);
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true);
tk::dnn::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
tk::dnn::Activation a88 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c89 (&net, 256, 1, 1, 1, 1, 0, 0, c89_bin, true);
tk::dnn::Activation a89 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c90 (&net, 512, 3, 3, 1, 1, 1, 1, c90_bin, true);
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c93 (&net, 27, 1, 1, 1, 1, 0, 0, c93_bin, false);
tk::dnn::Yolo yolo1 (&net, 4, 3, g94_bin);
tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1);
tk::dnn::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u97 (&net, 2);
tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
tk::dnn::Route m98 (&net, m98_layers, 2);
tk::dnn::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
tk::dnn::Activation a100 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c101 (&net, 128, 1, 1, 1, 1, 0, 0, c101_bin, true);
tk::dnn::Activation a101 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c102 (&net, 256, 3, 3, 1, 1, 1, 1, c102_bin, true);
tk::dnn::Activation a102 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c103 (&net, 128, 1, 1, 1, 1, 0, 0, c103_bin, true);
tk::dnn::Activation a103 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c104 (&net, 256, 3, 3, 1, 1, 1, 1, c104_bin, true);
tk::dnn::Activation a104 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c105 (&net, 27, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo yolo2 (&net, 4, 3, g106_bin);
// create yolo3 model
std::string bin_path = "../tests/yolo3_coco4";
int classes = 4;
tk::dnn::Yolo *yolo [3];
#include "models/Yolo3.h"
// Load input
dnnType *data;
@@ -304,9 +27,7 @@ int main() {
// the network have 3 outputs
tk::dnn::dataDim_t out_dim[3];
out_dim[0] = yolo0.output_dim;
out_dim[1] = yolo1.output_dim;
out_dim[2] = yolo2.output_dim;
for(int i=0; i<3; i++) out_dim[i] = yolo[i]->output_dim;
dnnType *cudnn_out[3], *rt_out[3];
tk::dnn::dataDim_t dim1 = dim; //input dim
@@ -317,18 +38,13 @@ int main() {
TIMER_STOP
dim1.print();
}
cudnn_out[0] = yolo0.dstData;
cudnn_out[1] = yolo1.dstData;
cudnn_out[2] = yolo2.dstData;
for(int i=0; i<3; i++) cudnn_out[i] = yolo[i]->dstData;
printCenteredTitle(" compute detections ", '=', 30);
TIMER_START
int ndets = 0;
int classes = yolo0.classes;
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
yolo0.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
yolo1.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
yolo2.computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
for(int i=0; i<3; i++) yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
for(int j=0; j<ndets; j++) {
@@ -356,9 +72,7 @@ int main() {
TIMER_STOP
dim2.print();
}
rt_out[0] = (dnnType*)netRT.buffersRT[1];
rt_out[1] = (dnnType*)netRT.buffersRT[2];
rt_out[2] = (dnnType*)netRT.buffersRT[3];
for(int i=0; i<3; i++) rt_out[i] = (dnnType*)netRT.buffersRT[i+1];
for(int i=0; i<3; i++) {
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
tests/yolo3_flir/yolo3_flir.cfg
+785
View File
@@ -0,0 +1,785 @@
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=32
subdivisions=8
width=544
height=320
channels=1
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 10000
policy=steps
steps=8000,9000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[yolo]
mask = 6,7,8
anchors = 8.2087,8.5515, 18.4134,20.3391, 40.2194,29.2990, 31.6137,69.2240, 69.8497,48.3838, 108.8817,76.6316, 96.5753,145.5743, 165.9182,117.4493, 215.7497,198.4648
classes=5
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=0
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[yolo]
mask = 3,4,5
anchors = 8.2087,8.5515, 18.4134,20.3391, 40.2194,29.2990, 31.6137,69.2240, 69.8497,48.3838, 108.8817,76.6316, 96.5753,145.5743, 165.9182,117.4493, 215.7497,198.4648
classes=5
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=0
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear
[yolo]
mask = 0,1,2
anchors = 8.2087,8.5515, 18.4134,20.3391, 40.2194,29.2990, 31.6137,69.2240, 69.8497,48.3838, 108.8817,76.6316, 96.5753,145.5743, 165.9182,117.4493, 215.7497,198.4648
classes=5
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=0
tests/yolo3_flir/yolo3_flir.cpp
+88
View File
@@ -0,0 +1,88 @@
#include<iostream>
#include<vector>
#include "tkdnn.h"
int main() {
// Network layout
tk::dnn::dataDim_t dim(1, 1, 320, 544, 1);
tk::dnn::Network net(dim);
// create yolo3 model
std::string bin_path = "../tests/yolo3_flir";
int classes = 5;
tk::dnn::Yolo *yolo [3];
#include "models/Yolo3.h"
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//print network model
net.print();
//convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, "yolo3_flir.rt");
// the network have 3 outputs
tk::dnn::dataDim_t out_dim[3];
for(int i=0; i<3; i++) out_dim[i] = yolo[i]->output_dim;
dnnType *cudnn_out[3], *rt_out[3];
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30); {
dim1.print();
TIMER_START
net.infer(dim1, data);
TIMER_STOP
dim1.print();
}
for(int i=0; i<3; i++) cudnn_out[i] = yolo[i]->dstData;
printCenteredTitle(" compute detections ", '=', 30);
TIMER_START
int ndets = 0;
tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
for(int i=0; i<3; i++) yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
for(int j=0; j<ndets; j++) {
tk::dnn::Yolo::box b = dets[j].bbox;
int x0 = (b.x-b.w/2.);
int x1 = (b.x+b.w/2.);
int y0 = (b.y-b.h/2.);
int y1 = (b.y+b.h/2.);
int cl = 0;
for(int c = 0; c < classes; ++c){
float prob = dets[j].prob[c];
if(prob > 0)
cl = c;
}
std::cout<<cl<<": "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
}
TIMER_STOP
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30); {
dim2.print();
TIMER_START
netRT.infer(dim2, data);
TIMER_STOP
dim2.print();
}
for(int i=0; i<3; i++) rt_out[i] = (dnnType*)netRT.buffersRT[i+1];
for(int i=0; i<3; i++) {
printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
dnnType *out, *out_h;
int odim = out_dim[i].tot();
readBinaryFile(output_bins[i], odim, &out_h, &out);
std::cout<<"CUDNN vs correct"; checkResult(odim, cudnn_out[i], out);
std::cout<<"TRT vs correct"; checkResult(odim, rt_out[i], out);
std::cout<<"CUDNN vs TRT "; checkResult(odim, cudnn_out[i], rt_out[i]);
}
return 0;
}