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csresnext50-panet-spp works with TensorRT

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Signed-off-by: Francesco Gatti <gattifrancesco@hotmail.it>
This commit is contained in:
Francesco Gatti
2020-03-12 18:24:41 +01:00
parent 5f4508a5a6
commit 4845edbc88
8 changed files with 1225 additions and 102 deletions
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include/tkDNN/Layer.h
+2 -2
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@@ -321,7 +321,7 @@ public:
int winH, winW;
int strideH, strideW;
int paddingH, paddingW;
bool test;
bool maxpoolfixedsize;
tkdnnPoolingMode_t pool_mode;
Pooling(Network *net, int winH, int winW,
@@ -474,7 +474,7 @@ public:
dnnType *predictions;
static const int MAX_DETECTIONS = 4096;
static const int MAX_DETECTIONS = 1024;
static Yolo::detection *allocateDetections(int nboxes, int classes);
static void mergeDetections(Yolo::detection *dets, int ndets, int classes);
};
include/tkDNN/NetworkRT.h
+1
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@@ -36,6 +36,7 @@ using namespace nvinfer1;
#include "pluginsRT/DeformableConvRT.h"
#include "pluginsRT/FlattenConcatRT.h"
#include "pluginsRT/ReshapeRT.h"
#include "pluginsRT/MaxPoolingFixedSizeRT.h"
class PluginFactory : IPluginFactory
{
include/tkDNN/pluginsRT/MaxPoolingFixedSizeRT.h
+74
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@@ -0,0 +1,74 @@
#include<cassert>
#include "../kernels.h"
class MaxPoolFixedSizeRT : public IPlugin {
public:
MaxPoolFixedSizeRT(int c, int h, int w, int n, int strideH, int strideW, int winSize, int padding) {
this->c = c;
this->h = h;
this->w = w;
this->n = n;
this->stride_H = strideH;
this->stride_W = strideW;
this->winSize = winSize;
this->padding = padding;
}
~MaxPoolFixedSizeRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return DimsCHW{this->c, this->h, this->w};
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
}
int initialize() override {
return 0;
}
virtual void terminate() override {
}
virtual size_t getWorkspaceSize(int maxBatchSize) const override {
return 0;
}
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
std::cout<<this->n<<" "<<this->c<<" "<<this->h<<" "<<this->w<<" "<<this->stride_H<<" "<<this->stride_W<<" "<<this->winSize<<" "<<this->padding<<std::endl;
dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
MaxPoolingForward(srcData, dstData, this->n, this->c, this->h, this->w, this->stride_H, this->stride_W, this->winSize, this->padding);
return 0;
}
virtual size_t getSerializationSize() override {
return 8*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, this->c);
tk::dnn::writeBUF(buf, this->h);
tk::dnn::writeBUF(buf, this->w);
tk::dnn::writeBUF(buf, this->n);
tk::dnn::writeBUF(buf, this->stride_H);
tk::dnn::writeBUF(buf, this->stride_W);
tk::dnn::writeBUF(buf, this->winSize);
tk::dnn::writeBUF(buf, this->padding);
}
int n, c, h, w;
int stride_H, stride_W;
int winSize;
int padding;
};
include/tkDNN/pluginsRT/ShortcutRT.h
+11 -3
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@@ -4,7 +4,10 @@
class ShortcutRT : public IPlugin {
public:
ShortcutRT() {
ShortcutRT(tk::dnn::dataDim_t bdim) {
this->bc = bdim.c;
this->bh = bdim.h;
this->bw = bdim.w;
}
~ShortcutRT(){
@@ -44,22 +47,27 @@ public:
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
checkCuda( cudaMemcpyAsync(dstData, srcData, batchSize*c*h*w*sizeof(dnnType), cudaMemcpyDeviceToDevice, stream));
shortcutForward(srcDataBack, dstData, batchSize, c, h, w, 1, batchSize, c, h, w, 1, stream);
shortcutForward(srcDataBack, dstData, batchSize, c, h, w, 1, batchSize, bc, bh, bw, 1, stream);
return 0;
}
virtual size_t getSerializationSize() override {
return 3*sizeof(int);
return 6*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, bc);
tk::dnn::writeBUF(buf, bh);
tk::dnn::writeBUF(buf, bw);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
}
int c, h, w;
int bc, bh, bw;
};
src/NetworkRT.cpp
+50 -19
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@@ -304,23 +304,36 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Pooling *l) {
if(l->pool_mode == tkdnnPoolingMode_t::POOLING_AVERAGE) ptype = PoolingType::kAVERAGE;
if(l->pool_mode == tkdnnPoolingMode_t::POOLING_AVERAGE_EXCLUDE_PADDING) ptype = PoolingType::kMAX_AVERAGE_BLEND;
if(l->paddingH == 0 && l->paddingW == 0 && l->input_dim.h == l->output_dim.h && l->input_dim.w == l->output_dim.w)
if(l->maxpoolfixedsize)
{
IPlugin *plugin = new ResizeLayerRT( l->output_dim.c,l->output_dim.h+1,l->output_dim.w+1 );
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
lRT->setName( "Resize" );
input = lRT->getOutput(0);
IPlugin *plugin = new MaxPoolFixedSizeRT(l->output_dim.c, l->output_dim.h, l->output_dim.w, l->output_dim.n, l->strideH, l->strideW, l->winH, l->winH-1);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
lRT->setName( "MaxPoolingFixedSize" );
return lRT;
}
else
{
if(l->paddingH == 0 && l->paddingW == 0 && l->input_dim.h == l->output_dim.h && l->input_dim.w == l->output_dim.w)
{
IPlugin *plugin = new ResizeLayerRT( l->output_dim.c,l->output_dim.h+1,l->output_dim.w+1 );
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
lRT->setName( "Resize" );
IPoolingLayer *lRT = networkRT->addPooling(*input, ptype, DimsHW{l->winH, l->winW});
checkNULL(lRT);
input = lRT->getOutput(0);
}
lRT->setPadding(DimsHW{l->paddingH, l->paddingW});
lRT->setStride(DimsHW{l->strideH, l->strideW});
return lRT;
IPoolingLayer *lRT = networkRT->addPooling(*input, ptype, DimsHW{l->winH, l->winW});
checkNULL(lRT);
lRT->setPadding(DimsHW{l->paddingH, l->paddingW});
lRT->setStride(DimsHW{l->strideH, l->strideW});
return lRT;
}
}
ILayer* NetworkRT::convert_layer(ITensor *input, Activation *l) {
@@ -437,18 +450,18 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
//std::cout<<"New plugin Shortcut\n";
ITensor *back_tens = tensors[l->backLayer];
/*
// plugin version
IPlugin *plugin = new ShortcutRT();
IPlugin *plugin = new ShortcutRT(l->backLayer->output_dim);
ITensor **inputs = new ITensor*[2];
inputs[0] = input;
inputs[1] = back_tens;
IPluginLayer *lRT = networkRT->addPlugin(inputs, 2, *plugin);
checkNULL(lRT);
*/
IElementWiseLayer *lRT = networkRT->addElementWise(*input, *back_tens, ElementWiseOperation::kSUM);
checkNULL(lRT);
// IElementWiseLayer *lRT = networkRT->addElementWise(*input, *back_tens, ElementWiseOperation::kSUM);
// checkNULL(lRT);
return lRT;
}
@@ -602,13 +615,31 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
}
if(name.find("Shortcut") == 0) {
ShortcutRT *r = new ShortcutRT();
tk::dnn::dataDim_t bdim;
bdim.c = readBUF<int>(buf);
bdim.h = readBUF<int>(buf);
bdim.w = readBUF<int>(buf);
bdim.l = 1;
ShortcutRT *r = new ShortcutRT(bdim);
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
return r;
}
if(name.find("Pooling") == 0) {
MaxPoolFixedSizeRT *r = new MaxPoolFixedSizeRT( readBUF<int>(buf), //c
readBUF<int>(buf), //h
readBUF<int>(buf), //w
readBUF<int>(buf), //n
readBUF<int>(buf), //strideH
readBUF<int>(buf), //strideW
readBUF<int>(buf), //winSize
readBUF<int>(buf)); //padding
return r;
}
if(name.find("Resize") == 0) {
ResizeLayerRT *r = new ResizeLayerRT(readBUF<int>(buf), //o_c
readBUF<int>(buf), //o_h
src/Pooling.cpp
+3 -3
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@@ -7,7 +7,7 @@ namespace tk { namespace dnn {
Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
int paddingH, int paddingW,
tkdnnPoolingMode_t pool_mode, bool final, bool test) :
tkdnnPoolingMode_t pool_mode, bool final, bool maxpoolfixedsize) :
Layer(net, final) {
this->winH = winH;
@@ -17,7 +17,7 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
this->pool_mode = pool_mode;
this->paddingH = paddingH;
this->paddingW = paddingW;
this->test = test;
this->maxpoolfixedsize = maxpoolfixedsize;
checkCUDNN( cudnnCreatePoolingDescriptor(&poolingDesc) );
@@ -114,7 +114,7 @@ dnnType* Pooling::infer(dataDim_t &dim, dnnType* srcData) {
if(this->test)
if(this->maxpoolfixedsize)
{
MaxPoolingForward(poolSrc, poolDst, dim.n, dim.c, dim.h, dim.w, this->strideH, this->strideW, this->winH, this->winH-1);
}
tests/csresnext50-panet-spp/csresnext50-panet-spp.cfg
+1018
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File diff suppressed because it is too large Load Diff
tests/csresnext50-panet-spp/csresnext50-panet-spp.cpp
+66 -75
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@@ -339,7 +339,7 @@ int main()
tk::dnn::Conv2d c83(&net, 512, 1, 1, 1, 1, 0, 0, c83_bin, true);
tk::dnn::Activation a83(&net, tk::dnn::ACTIVATION_LEAKY);
// //SPP
//SPP
tk::dnn::Pooling p84(&net, 5, 5, 1, 1,0,0, tk::dnn::POOLING_MAX, false, true);
tk::dnn::Layer *r85_layers[1] = {&a83};
tk::dnn::Route r85(&net, r85_layers, 1);
@@ -351,7 +351,7 @@ int main()
tk::dnn::Pooling p88(&net, 13, 13, 1, 1, 12, 12, tk::dnn::POOLING_MAX, false, true);
tk::dnn::Layer *r89_layers[4] = {&p88, &p86, &p84, &a83};
tk::dnn::Route r89(&net, r89_layers, 4);
// //END SPP
//END SPP
tk::dnn::Conv2d c90(&net, 512, 1, 1, 1, 1, 0, 0, c90_bin, true);
tk::dnn::Activation a90(&net, tk::dnn::ACTIVATION_LEAKY);
@@ -454,17 +454,15 @@ int main()
tk::dnn::Conv2d c136(&net, 255, 1, 1, 1, 1, 0, 0, c136_bin, false);
tk::dnn::Yolo yolo137(&net, classes, 3, g137_bin);
yolo[0] = &yolo115;
yolo[1] = &yolo126;
yolo[2] = &yolo137;
// yolo[0] = &yolo115;
// yolo[1] = &yolo126;
// yolo[2] = &yolo137;
// // fill classes names
// for (int i = 0; i < 3; i++)
// {
// yolo[i]->classesNames = {"person", "bicycle", "car", "motorbike", "aeroplane", "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", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};
// }
// fill classes names
for (int i = 0; i < 3; i++)
{
yolo[i]->classesNames = {"person", "bicycle", "car", "motorbike", "aeroplane", "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", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};
}
// Load input
dnnType *data;
@@ -475,13 +473,13 @@ int main()
net.print();
// //convert network to tensorRT
// tk::dnn::NetworkRT netRT(&net, "csresnext50-panet-spp.rt");
tk::dnn::NetworkRT netRT(&net, "csresnext50-panet-spp.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 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);
@@ -492,69 +490,62 @@ int main()
TIMER_STOP
dim1.print();
}
dnnType *cudnn_out = net.layers[net.num_layers-1]->dstData;
tk::dnn::dataDim_t out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *out1, *out1_h;
int odim1 = out_dim.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
std::cout << "CUDNN vs correct" << std::endl;
// printDeviceVector(odim1, cudnn_out);
checkResult(odim1, cudnn_out, out1);
for (int i = 0; i < 3; i++)
cudnn_out[i] = yolo[i]->dstData;
// 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);
// 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.);
// 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
// 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();
}
// 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++)
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]);
// }
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;
}