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tkDNN/tests/resnet101/resnet101.cpp
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Micaela Verucchi 2fbac7705d Move download of weights inside build folder 
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
2020-04-17 22:36:25 +02:00

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#include <iostream>
#include "tkdnn.h"
const char *input_bin = "resnet101/debug/input.bin";
const char *conv1_bin = "resnet101/layers/conv1.bin";
//layer1
const char *layer1_bin[]={
"resnet101/layers/layer1-0-conv1.bin",
"resnet101/layers/layer1-0-conv2.bin",
"resnet101/layers/layer1-0-conv3.bin",
"resnet101/layers/layer1-0-downsample-0.bin",
"resnet101/layers/layer1-1-conv1.bin",
"resnet101/layers/layer1-1-conv2.bin",
"resnet101/layers/layer1-1-conv3.bin",
"resnet101/layers/layer1-2-conv1.bin",
"resnet101/layers/layer1-2-conv2.bin",
"resnet101/layers/layer1-2-conv3.bin"};
//layer2
const char *layer2_bin[]={
"resnet101/layers/layer2-0-conv1.bin",
"resnet101/layers/layer2-0-conv2.bin",
"resnet101/layers/layer2-0-conv3.bin",
"resnet101/layers/layer2-0-downsample-0.bin",
"resnet101/layers/layer2-1-conv1.bin",
"resnet101/layers/layer2-1-conv2.bin",
"resnet101/layers/layer2-1-conv3.bin",
"resnet101/layers/layer2-2-conv1.bin",
"resnet101/layers/layer2-2-conv2.bin",
"resnet101/layers/layer2-2-conv3.bin",
"resnet101/layers/layer2-3-conv1.bin",
"resnet101/layers/layer2-3-conv2.bin",
"resnet101/layers/layer2-3-conv3.bin"
};
//layer3
const char *layer3_bin[]={
"resnet101/layers/layer3-0-conv1.bin",
"resnet101/layers/layer3-0-conv2.bin",
"resnet101/layers/layer3-0-conv3.bin",
"resnet101/layers/layer3-0-downsample-0.bin",
"resnet101/layers/layer3-1-conv1.bin",
"resnet101/layers/layer3-1-conv2.bin",
"resnet101/layers/layer3-1-conv3.bin",
"resnet101/layers/layer3-2-conv1.bin",
"resnet101/layers/layer3-2-conv2.bin",
"resnet101/layers/layer3-2-conv3.bin",
"resnet101/layers/layer3-3-conv1.bin",
"resnet101/layers/layer3-3-conv2.bin",
"resnet101/layers/layer3-3-conv3.bin",
"resnet101/layers/layer3-4-conv1.bin",
"resnet101/layers/layer3-4-conv2.bin",
"resnet101/layers/layer3-4-conv3.bin",
"resnet101/layers/layer3-5-conv1.bin",
"resnet101/layers/layer3-5-conv2.bin",
"resnet101/layers/layer3-5-conv3.bin",
"resnet101/layers/layer3-6-conv1.bin",
"resnet101/layers/layer3-6-conv2.bin",
"resnet101/layers/layer3-6-conv3.bin",
"resnet101/layers/layer3-7-conv1.bin",
"resnet101/layers/layer3-7-conv2.bin",
"resnet101/layers/layer3-7-conv3.bin",
"resnet101/layers/layer3-8-conv1.bin",
"resnet101/layers/layer3-8-conv2.bin",
"resnet101/layers/layer3-8-conv3.bin",
"resnet101/layers/layer3-9-conv1.bin",
"resnet101/layers/layer3-9-conv2.bin",
"resnet101/layers/layer3-9-conv3.bin",
"resnet101/layers/layer3-10-conv1.bin",
"resnet101/layers/layer3-10-conv2.bin",
"resnet101/layers/layer3-10-conv3.bin",
"resnet101/layers/layer3-11-conv1.bin",
"resnet101/layers/layer3-11-conv2.bin",
"resnet101/layers/layer3-11-conv3.bin",
"resnet101/layers/layer3-12-conv1.bin",
"resnet101/layers/layer3-12-conv2.bin",
"resnet101/layers/layer3-12-conv3.bin",
"resnet101/layers/layer3-13-conv1.bin",
"resnet101/layers/layer3-13-conv2.bin",
"resnet101/layers/layer3-13-conv3.bin",
"resnet101/layers/layer3-14-conv1.bin",
"resnet101/layers/layer3-14-conv2.bin",
"resnet101/layers/layer3-14-conv3.bin",
"resnet101/layers/layer3-15-conv1.bin",
"resnet101/layers/layer3-15-conv2.bin",
"resnet101/layers/layer3-15-conv3.bin",
"resnet101/layers/layer3-16-conv1.bin",
"resnet101/layers/layer3-16-conv2.bin",
"resnet101/layers/layer3-16-conv3.bin",
"resnet101/layers/layer3-17-conv1.bin",
"resnet101/layers/layer3-17-conv2.bin",
"resnet101/layers/layer3-17-conv3.bin",
"resnet101/layers/layer3-18-conv1.bin",
"resnet101/layers/layer3-18-conv2.bin",
"resnet101/layers/layer3-18-conv3.bin",
"resnet101/layers/layer3-19-conv1.bin",
"resnet101/layers/layer3-19-conv2.bin",
"resnet101/layers/layer3-19-conv3.bin",
"resnet101/layers/layer3-20-conv1.bin",
"resnet101/layers/layer3-20-conv2.bin",
"resnet101/layers/layer3-20-conv3.bin",
"resnet101/layers/layer3-21-conv1.bin",
"resnet101/layers/layer3-21-conv2.bin",
"resnet101/layers/layer3-21-conv3.bin",
"resnet101/layers/layer3-22-conv1.bin",
"resnet101/layers/layer3-22-conv2.bin",
"resnet101/layers/layer3-22-conv3.bin"};
//layer4
const char *layer4_bin[]={
"resnet101/layers/layer4-0-conv1.bin",
"resnet101/layers/layer4-0-conv2.bin",
"resnet101/layers/layer4-0-conv3.bin",
"resnet101/layers/layer4-0-downsample-0.bin",
"resnet101/layers/layer4-1-conv1.bin",
"resnet101/layers/layer4-1-conv2.bin",
"resnet101/layers/layer4-1-conv3.bin",
"resnet101/layers/layer4-2-conv1.bin",
"resnet101/layers/layer4-2-conv2.bin",
"resnet101/layers/layer4-2-conv3.bin"};
//final
const char *fc_bin = "resnet101/layers/fc.bin";
const char *output_bin = "resnet101/debug/fc.bin";
int main()
{
// Network layout
tk::dnn::dataDim_t dim(1, 3, 224, 224, 1);
tk::dnn::Network net(dim);
tk::dnn::Conv2d conv1(&net, 64, 7, 7, 2, 2, 3, 3, conv1_bin, true);
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Pooling maxpool4(&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
//layer 1
int id_layer1_bin = 0;
tk::dnn::Layer *last = &maxpool4;
for(int i=0; i<3;i++)
{
tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 64, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv2 = new tk::dnn::Conv2d(&net, 64, 3, 3, 1, 1, 1, 1, layer1_bin[id_layer1_bin++], true);
tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
if(i==0) {
tk::dnn::Layer *route_1_0_layers[1] = { last };
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer1_bin[id_layer1_bin++], true);
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
} else {
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
}
tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
last = layer1_0_relu;
}
// tk::dnn::Activation *last_activation = (tk::dnn::Activation *) net.layers[net.num_layers-1];
// layer 2
int id_layer2_bin = 0;
for(int i=0; i<4;i++)
{
tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 128, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv2;
if(i==0)
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 2, 2, 1, 1, layer2_bin[id_layer2_bin++], true);
else
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 128, 3, 3, 1, 1, 1, 1, layer2_bin[id_layer2_bin++], true);
tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer2_bin[id_layer2_bin++], true);
if(i==0)
{
tk::dnn::Layer *route_1_0_layers[1] = { last };
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 512, 1, 1, 2, 2, 0, 0, layer2_bin[id_layer2_bin++], true);
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
}
else
{
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
}
tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
last = layer1_0_relu;
}
// layer 3
int id_layer3_bin = 0;
for(int i=0; i<23;i++)
{
tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 256, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv2;
if(i==0)
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 2, 2, 1, 1, layer3_bin[id_layer3_bin++], true);
else
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, layer3_bin[id_layer3_bin++], true);
tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 1, 1, 0, 0, layer3_bin[id_layer3_bin++], true);
if(i==0)
{
tk::dnn::Layer *route_1_0_layers[1] = { last };
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 1024, 1, 1, 2, 2, 0, 0, layer3_bin[id_layer3_bin++], true);
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
}
else
{
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
}
tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
last = layer1_0_relu;
}
// layer 4
int id_layer4_bin = 0;
for(int i=0; i<3;i++)
{
tk::dnn::Conv2d *layer1_0_conv1 = new tk::dnn::Conv2d(&net, 512, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
tk::dnn::Activation *relu1_0_1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv2;
if(i==0)
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 2, 2, 1, 1, layer4_bin[id_layer4_bin++], true);
else
layer1_0_conv2 = new tk::dnn::Conv2d(&net, 512, 3, 3, 1, 1, 1, 1, layer4_bin[id_layer4_bin++], true);
tk::dnn::Activation *relu1_0_2 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d *layer1_0_conv3 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 1, 1, 0, 0, layer4_bin[id_layer4_bin++], true);
if(i==0)
{
tk::dnn::Layer *route_1_0_layers[1] = { last };
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
tk::dnn::Conv2d *layer1_0_downsample_0 = new tk::dnn::Conv2d(&net, 2048, 1, 1, 2, 2, 0, 0, layer4_bin[id_layer4_bin++], true);
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, layer1_0_conv3);
}
else
{
tk::dnn::Shortcut *s1_0 = new tk::dnn::Shortcut(&net, last);
}
tk::dnn::Activation *layer1_0_relu = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
last = layer1_0_relu;
}
//final
tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
tk::dnn::Dense fc(&net, 1000, fc_bin);
// Load input
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
//printDeviceVector(64, data, true);
//print network model
net.print();
//convert network to tensorRT
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("resnet101"));
tk::dnn::dataDim_t out_dim;
out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *cudnn_out, *rt_out;
tk::dnn::dataDim_t dim1 = dim; //input dim
printCenteredTitle(" CUDNN inference ", '=', 30);
{
dim1.print();
TIMER_START
net.infer(dim1, data);
TIMER_STOP
dim1.print();
}
cudnn_out = net.layers[net.num_layers-1]->dstData;
//printDeviceVector(64, cudnn_out, true);
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
{
dim2.print();
TIMER_START
netRT.infer(dim2, data);
TIMER_STOP
dim2.print();
}
rt_out = (dnnType *)netRT.buffersRT[1];
printCenteredTitle(std::string(" RESNET CHECK RESULTS ").c_str(), '=', 30);
dnnType *out, *out_h;
int odim = out_dim.tot();
readBinaryFile(output_bin, odim, &out_h, &out);
std::cout<<"CUDNN vs correct";
int ret_cudnn = checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
std::cout<<"TRT vs correct";
int ret_tensorrt = checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
std::cout<<"CUDNN vs TRT ";
int ret_cudnn_tensorrt = checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}
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