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

upsample ok, route have problems

Browse Source
This commit is contained in:
Francesco Gatti
2018-12-22 23:56:01 +01:00
parent 34be4cd00f
commit 3bd725801d
6 changed files with 112 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files
include/NetworkRT.h
+1
View File
@@ -45,6 +45,7 @@ public:
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Region *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Yolo *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Upsample *l);
bool serialize(const char *filename);
bool deserialize(const char *filename);
src/NetworkRT.cpp
+20
View File
@@ -16,6 +16,7 @@ using namespace nvinfer1;
#include "pluginsRT/RegionRT.cpp"
#include "pluginsRT/ShortcutRT.cpp"
#include "pluginsRT/YoloRT.cpp"
#include "pluginsRT/UpsampleRT.cpp"
#include "pluginsRT/Int8Calibrator.cpp"
// Logger for info/warning/errors
@@ -173,6 +174,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
return convert_layer(input, (Shortcut*) l);
if(type == LAYER_YOLO)
return convert_layer(input, (Yolo*) l);
if(type == LAYER_UPSAMPLE)
return convert_layer(input, (Upsample*) l);
FatalError("Layer not implemented in tensorRT");
return NULL;
@@ -350,6 +353,15 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Yolo *l) {
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, Upsample *l) {
//std::cout<<"convert Upsample\n";
//std::cout<<"New plugin UPSAMPLE\n";
IPlugin *plugin = new UpsampleRT(l->stride);
IPluginLayer *lRT = networkRT->addPlugin(&input, 1, *plugin);
checkNULL(lRT);
return lRT;
}
bool NetworkRT::serialize(const char *filename) {
@@ -418,6 +430,14 @@ public:
return r;
}
if(name.find("Upsample") == 0) {
UpsampleRT *r = new UpsampleRT(readBUF<int>(buf)); //stride
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
return r;
}
FatalError("Cant deserialize Plugin");
return NULL;
}
src/pluginsRT/RegionRT.cpp
+1 -1
View File
@@ -69,7 +69,7 @@ public:
virtual size_t getSerializationSize() override {
return 6*sizeof(int) + 1*sizeof(float);
return 6*sizeof(int);
}
virtual void serialize(void* buffer) override {
src/pluginsRT/UpsampleRT.cpp
+65
View File
@@ -0,0 +1,65 @@
#include<cassert>
#include "kernels.h"
class UpsampleRT : public IPlugin {
public:
UpsampleRT(int stride) {
this->stride = stride;
}
~UpsampleRT(){
}
int getNbOutputs() const override {
return 1;
}
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
return DimsCHW(inputs[0].d[0], inputs[0].d[1]*stride, inputs[0].d[2]*stride);
}
void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {
c = inputDims[0].d[0];
h = inputDims[0].d[1];
w = inputDims[0].d[2];
}
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 {
dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
fill(dstData, batchSize*c*h*w, 0.0);
upsampleForward(srcData, dstData, batchSize, c, h, w, stride, 1, 1);
return 0;
}
virtual size_t getSerializationSize() override {
return 4*sizeof(int);
}
virtual void serialize(void* buffer) override {
char *buf = reinterpret_cast<char*>(buffer);
tk::dnn::writeBUF(buf, stride);
tk::dnn::writeBUF(buf, c);
tk::dnn::writeBUF(buf, h);
tk::dnn::writeBUF(buf, w);
}
int c, h, w, stride;
};
src/pluginsRT/YoloRT.cpp
+2 -1
View File
@@ -57,12 +57,13 @@ public:
}
}
std::cout<<"YOLO END\n";
return 0;
}
virtual size_t getSerializationSize() override {
return 5*sizeof(int) + 1*sizeof(float);
return 5*sizeof(int);
}
virtual void serialize(void* buffer) override {
tests/yolo3_berkeley/yolo3_berkeley.cpp
+23 -14
View File
@@ -77,7 +77,7 @@ 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 *output_bin = "../tests/yolo3_berkeley/debug/layer82_out.bin";
const char *output_bin = "../tests/yolo3_berkeley/debug/layer93_out.bin";
int main() {
@@ -231,19 +231,21 @@ int main() {
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 g82 (&net, 10, 3);
/*
tk::dnn::Yolo y82 (&net, 10, 3);
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::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);
@@ -254,19 +256,21 @@ int main() {
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 g94 (&net, 10, 3);
tk::dnn::Yolo y94 (&net, 10, 3);
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::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);
@@ -282,8 +286,13 @@ int main() {
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 g106 (&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
dnnType *data;
dnnType *input_h;
@@ -318,9 +327,9 @@ int main() {
printCenteredTitle(" CHECK RESULTS ", '=', 30);
dnnType *out, *out_h;
int out_dim = net.getOutputDim().tot();
readBinaryFile(output_bin, out_dim, &out_h, &out);
std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
std::cout<<"TRT vs correct"; checkResult(out_dim, out_data2, out);
//readBinaryFile(output_bin, out_dim, &out_h, &out);
//std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
//std::cout<<"TRT vs correct"; checkResult(out_dim, out_data2, out);
std::cout<<"CUDNN vs TRT "; checkResult(out_dim, out_data, out_data2);
return 0;
}