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yolo3 layer 15

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This commit is contained in:
Francesco Gatti
2018-12-20 11:36:10 +01:00
parent e91db28756
commit 7a46601306
3 changed files with 42 additions and 12 deletions
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include/Layer.h
+1 -1
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@@ -293,7 +293,7 @@ public:
class Shortcut : public Layer {
public:
Shortcut(Network *net, Layer *backLayer, int layers_n);
Shortcut(Network *net, Layer *backLayer);
virtual ~Shortcut();
virtual layerType_t getLayerType() { return LAYER_SHORTCUT; };
src/Shortcut.cpp
+4 -2
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@@ -5,7 +5,7 @@
namespace tk { namespace dnn {
Shortcut::Shortcut(Network *net, Layer *backLayer, int layers_n) : Layer(net) {
Shortcut::Shortcut(Network *net, Layer *backLayer) : Layer(net) {
this->backLayer = backLayer;
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
@@ -18,8 +18,10 @@ Shortcut::~Shortcut() {
dnnType* Shortcut::infer(dataDim_t &dim, dnnType* srcData) {
dataDim_t bdim = this->backLayer->output_dim;
checkCuda(cudaMemcpy(dstData, srcData, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
shortcutForward(this->backLayer->dstData, dstData, dim.n, dim.c, dim.h, dim.w, 1, bdim.n, bdim.c, bdim.h, bdim.w, 1);
//update data dimensions
dim = output_dim;
tests/yolo3_berkeley/yolo3_berkeley.cpp
+37 -9
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@@ -6,7 +6,15 @@ 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 *output_bin = "../tests/yolo3_berkeley/debug/layer3_out.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 *output_bin = "../tests/yolo3_berkeley/debug/layer15_out.bin";
int main() {
@@ -14,14 +22,34 @@ int main() {
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::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);
// Load input
dnnType *data;