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yolo3 86 route error

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This commit is contained in:
Francesco Gatti
2018-12-23 16:20:17 +01:00
parent 3bd725801d
commit 2e8d0b1002
4 changed files with 29 additions and 24 deletions
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include/NetworkRT.h
+3 -1
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@@ -18,7 +18,9 @@ public:
nvinfer1::ICudaEngine *engineRT; nvinfer1::ICudaEngine *engineRT;
nvinfer1::IExecutionContext *contextRT; nvinfer1::IExecutionContext *contextRT;
void* buffersRT[2];
const static int MAX_BUFFERS_RT = 10;
void* buffersRT[MAX_BUFFERS_RT];
int buf_input_idx, buf_output_idx; int buf_input_idx, buf_output_idx;
dataDim_t input_dim, output_dim; dataDim_t input_dim, output_dim;
src/NetworkRT.cpp
+17 -9
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@@ -77,6 +77,9 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
Ilay->setName( (l->getLayerName() + std::to_string(i)).c_str() ); Ilay->setName( (l->getLayerName() + std::to_string(i)).c_str() );
input = Ilay->getOutput(0); input = Ilay->getOutput(0);
if(l->getLayerType() == LAYER_YOLO)
networkRT->markOutput(*input);
tensors[l] = input; tensors[l] = input;
} }
if(input == NULL) if(input == NULL)
@@ -99,9 +102,9 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
contextRT = engineRT->createExecutionContext(); contextRT = engineRT->createExecutionContext();
// input and output buffer pointers that we pass to the engine - the engine requires exactly IEngine::getNbBindings(), // input and output buffer pointers that we pass to the engine - the engine requires exactly IEngine::getNbBindings(),
// of these, but in this case we know that there is exactly one input and one output. std::cout<<"Input/outputs numbers: "<<engineRT->getNbBindings()<<"\n";
if(engineRT->getNbBindings() != 2) if(engineRT->getNbBindings() > MAX_BUFFERS_RT)
FatalError("Incorrect buffers number"); FatalError("over RT buffer array size");
// In order to bind the buffers, we need to know the names of the input and output tensors. // In order to bind the buffers, we need to know the names of the input and output tensors.
// note that indices are guaranteed to be less than IEngine::getNbBindings() // note that indices are guaranteed to be less than IEngine::getNbBindings()
@@ -122,10 +125,13 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
output_dim.c = oDim.d[0]; output_dim.c = oDim.d[0];
output_dim.h = oDim.d[1]; output_dim.h = oDim.d[1];
output_dim.w = oDim.d[2]; output_dim.w = oDim.d[2];
output_dim.print();
// create GPU buffers and a stream // create GPU buffers and a stream
checkCuda(cudaMalloc(&buffersRT[buf_input_idx], input_dim.tot()*sizeof(dnnType))); for(int i=0; i<engineRT->getNbBindings(); i++) {
checkCuda(cudaMalloc(&buffersRT[buf_output_idx], output_dim.tot()*sizeof(dnnType))); Dims dim = engineRT->getBindingDimensions(i);
checkCuda(cudaMalloc(&buffersRT[i], dim.d[0]*dim.d[1]*dim.d[2]*sizeof(dnnType)));
}
checkCuda(cudaMalloc(&output, output_dim.tot()*sizeof(dnnType))); checkCuda(cudaMalloc(&output, output_dim.tot()*sizeof(dnnType)));
checkCuda(cudaStreamCreate(&stream)); checkCuda(cudaStreamCreate(&stream));
} }
@@ -136,9 +142,9 @@ NetworkRT::~NetworkRT() {
dnnType* NetworkRT::infer(dataDim_t &dim, dnnType* data) { dnnType* NetworkRT::infer(dataDim_t &dim, dnnType* data) {
checkCuda(cudaMemcpyAsync(buffersRT[buf_input_idx], data, input_dim.tot()*sizeof(float), cudaMemcpyDeviceToDevice, stream)); checkCuda(cudaMemcpyAsync(buffersRT[buf_input_idx], data, input_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
contextRT->enqueue(1, buffersRT, stream, nullptr); contextRT->enqueue(1, buffersRT, stream, nullptr);
checkCuda(cudaMemcpyAsync(output, buffersRT[buf_output_idx], output_dim.tot()*sizeof(float), cudaMemcpyDeviceToDevice, stream)); checkCuda(cudaMemcpyAsync(output, buffersRT[buf_output_idx], output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
cudaStreamSynchronize(stream); cudaStreamSynchronize(stream);
dim = output_dim; dim = output_dim;
@@ -301,7 +307,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Softmax *l) {
ILayer* NetworkRT::convert_layer(ITensor *input, Route *l) { ILayer* NetworkRT::convert_layer(ITensor *input, Route *l) {
//std::cout<<"convert route\n"; //std::cout<<"convert route\n";
ITensor *tens[256]; ITensor **tens = new ITensor*[l->layers_n];
for(int i=0; i<l->layers_n; i++) for(int i=0; i<l->layers_n; i++)
tens[i] = tensors[l->layers[i]]; tens[i] = tensors[l->layers[i]];
IConcatenationLayer *lRT = networkRT->addConcatenation(tens, l->layers_n); IConcatenationLayer *lRT = networkRT->addConcatenation(tens, l->layers_n);
@@ -337,7 +343,9 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
ITensor *back_tens = tensors[l->backLayer]; ITensor *back_tens = tensors[l->backLayer];
IPlugin *plugin = new ShortcutRT(); IPlugin *plugin = new ShortcutRT();
ITensor *inputs[2] = { input, back_tens }; ITensor **inputs = new ITensor*[2];
inputs[0] = input;
inputs[1] = back_tens;
IPluginLayer *lRT = networkRT->addPlugin(inputs, 2, *plugin); IPluginLayer *lRT = networkRT->addPlugin(inputs, 2, *plugin);
checkNULL(lRT); checkNULL(lRT);
return lRT; return lRT;
src/Shortcut.cpp
+5
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@@ -9,6 +9,11 @@ Shortcut::Shortcut(Network *net, Layer *backLayer) : Layer(net) {
this->backLayer = backLayer; this->backLayer = backLayer;
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) ); checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
if( backLayer->output_dim.c != input_dim.c ||
backLayer->output_dim.w != input_dim.w ||
backLayer->output_dim.h != input_dim.h )
FatalError("Shortcut dim missmatch");
} }
Shortcut::~Shortcut() { Shortcut::~Shortcut() {
tests/yolo3_berkeley/yolo3_berkeley.cpp
+4 -14
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@@ -231,7 +231,6 @@ int main() {
tk::dnn::Activation a78 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c79 (&net, 512, 1, 1, 1, 1, 0, 0, c79_bin, true);
tk::dnn::Activation a79 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c80 (&net,1024, 3, 3, 1, 1, 1, 1, c80_bin, true);
tk::dnn::Activation a80 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c81 (&net, 45, 1, 1, 1, 1, 0, 0, c81_bin, false);
@@ -239,13 +238,12 @@ int main() {
tk::dnn::Layer *m83_layers[1] = { &a79 }; tk::dnn::Layer *m83_layers[1] = { &a79 };
tk::dnn::Route m83 (&net, m83_layers, 1); 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::Conv2d c84 (&net, 256, 1, 1, 1, 1, 0, 0, c84_bin, true);
tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY); tk::dnn::Activation a84 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u85 (&net, 2); tk::dnn::Upsample u85 (&net, 2);
// tk::dnn::Layer *m86_layers[2] = { &u85, &s61 }; tk::dnn::Layer *m86_layers[2] = { &u85, &s61 };
// tk::dnn::Route m86 (&net, m86_layers, 2); tk::dnn::Route m86 (&net, m86_layers, 1); // ROUTE ERROR IN RT INFERENCE
tk::dnn::Conv2d c87 (&net, 256, 1, 1, 1, 1, 0, 0, c87_bin, true); 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::Activation a87 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true); tk::dnn::Conv2d c88 (&net, 512, 3, 3, 1, 1, 1, 1, c88_bin, true);
@@ -256,7 +254,6 @@ int main() {
tk::dnn::Activation a90 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c91 (&net, 256, 1, 1, 1, 1, 0, 0, c91_bin, true);
tk::dnn::Activation a91 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c92 (&net, 512, 3, 3, 1, 1, 1, 1, c92_bin, true);
tk::dnn::Activation a92 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c93 (&net, 45, 1, 1, 1, 1, 0, 0, c93_bin, false);
@@ -264,14 +261,12 @@ int main() {
tk::dnn::Layer *m95_layers[1] = { &a91 }; tk::dnn::Layer *m95_layers[1] = { &a91 };
tk::dnn::Route m95 (&net, m95_layers, 1); 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::Conv2d c96 (&net, 128, 1, 1, 1, 1, 0, 0, c96_bin, true);
tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY); tk::dnn::Activation a96 (&net, tk::dnn::ACTIVATION_LEAKY);
tk::dnn::Upsample u97 (&net, 2); tk::dnn::Upsample u97 (&net, 2);
// tk::dnn::Layer *m98_layers[2] = { &u97, &s36 };
// tk::dnn::Route m98 (&net, m98_layers, 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::Conv2d c99 (&net, 128, 1, 1, 1, 1, 0, 0, c99_bin, true);
tk::dnn::Activation a99 (&net, tk::dnn::ACTIVATION_LEAKY); 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::Conv2d c100 (&net, 256, 3, 3, 1, 1, 1, 1, c100_bin, true);
@@ -288,11 +283,6 @@ int main() {
tk::dnn::Conv2d c105 (&net, 45, 1, 1, 1, 1, 0, 0, c105_bin, false); tk::dnn::Conv2d c105 (&net, 45, 1, 1, 1, 1, 0, 0, c105_bin, false);
tk::dnn::Yolo y106 (&net, 10, 3); tk::dnn::Yolo y106 (&net, 10, 3);
// merge all yolos
// tk::dnn::Layer *m107_layers[2] = { &y82, &y94, &y106 };
// tk::dnn::Route m107 (&net, m107_layers, 3);
// Load input // Load input
dnnType *data; dnnType *data;
dnnType *input_h; dnnType *input_h;