mmr/tkDNN
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Shelfnet works on cuDNN. To test everything else

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-22 18:11:19 +02:00
parent 9f1e30eaa9
commit 6fd261f628
11 changed files with 290 additions and 86 deletions
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src/LayerWgs.cpp
+2 -1
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@@ -26,10 +26,11 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
}
readBinaryFile(weights_path.c_str(), outputs, &bias_h, &bias_d, seek);
seek += outputs;
this->batchnorm = batchnorm;
if(batchnorm) {
seek += outputs;
readBinaryFile(weights_path.c_str(), outputs, &scales_h, &scales_d, seek);
seek += outputs;
readBinaryFile(weights_path.c_str(), outputs, &mean_h, &mean_d, seek);
src/NetworkRT.cpp
+2 -2
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@@ -512,7 +512,7 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Shortcut *l) {
else
{
// plugin version
IPlugin *plugin = new ShortcutRT(l->backLayer->output_dim);
IPlugin *plugin = new ShortcutRT(l->backLayer->output_dim, l->mul);
ITensor **inputs = new ITensor*[2];
inputs[0] = input;
inputs[1] = back_tens;
@@ -682,7 +682,7 @@ IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialDa
bdim.w = readBUF<int>(buf);
bdim.l = 1;
ShortcutRT *r = new ShortcutRT(bdim);
ShortcutRT *r = new ShortcutRT(bdim, readBUF<bool>(buf));
r->c = readBUF<int>(buf);
r->h = readBUF<int>(buf);
r->w = readBUF<int>(buf);
src/Reshape.cpp
+5
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@@ -15,6 +15,11 @@ Reshape::Reshape(Network *net, dataDim_t new_dim) : Layer(net) {
output_dim.w = new_dim.w;
output_dim.l = new_dim.l;
output_dim = new_dim;
if(input_dim.tot() != output_dim.tot())
FatalError("Reshape dimension mismatch");
}
Reshape::~Reshape() {
src/Resize.cpp
+38
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@@ -0,0 +1,38 @@
#include <iostream>
#include "Layer.h"
#include "kernels.h"
namespace tk { namespace dnn {
Resize::Resize(Network *net, int scale_c, int scale_h, int scale_w, bool fixed) : Layer(net) {
if(fixed){
output_dim.c = scale_c;
output_dim.h = scale_h;
output_dim.w = scale_w;
}
else{
output_dim.c *= scale_c;
output_dim.h *= scale_h;
output_dim.w *= scale_w;
}
checkCuda( cudaMalloc(&dstData, output_dim.tot()*sizeof(dnnType)) );
}
Resize::~Resize() {
checkCuda( cudaFree(dstData) );
}
dnnType* Resize::infer(dataDim_t &dim, dnnType* srcData) {
resizeForward(srcData, dstData, dim.n, dim.c, dim.h, dim.w,
output_dim.c, output_dim.h, output_dim.w);
dim = output_dim;
return dstData;
}
}}
src/Shortcut.cpp
+9 -6
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@@ -5,15 +5,18 @@
namespace tk { namespace dnn {
Shortcut::Shortcut(Network *net, Layer *backLayer) : Layer(net) {
Shortcut::Shortcut(Network *net, Layer *backLayer, bool mul) : Layer(net) {
this->backLayer = backLayer;
this->mul = mul;
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");
//FIXME
// 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() {
@@ -26,7 +29,7 @@ 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);
shortcutForward(this->backLayer->dstData, dstData, dim.n, dim.c, dim.h, dim.w, 1, bdim.n, bdim.c, bdim.h, bdim.w, 1, mul);
//update data dimensions
dim = output_dim;
src/kernels/resize.cu
+14 -27
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@@ -1,46 +1,33 @@
#include "kernels.h"
#include <stdio.h>
#define MIN(a,b) (((a)<(b))?(a):(b))
#define MAX(a,b) (((a)>(b))?(a):(b))
__global__ void resize_kernel( int i_N,float *x, int i_w, int i_h, int i_c,
__global__ void resize_kernel( int size,float *x, int i_w, int i_h, int i_c,
int o_w, int o_h, int o_c, int batch, float *out)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i >= i_N) return;
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(id >= size) return;
int out_index = i;
int out_w = i%o_w;
i = i/o_w;
int out_h = i%o_h;
i = i/o_h;
int out_c = i%o_c;
i = i/o_c;
int i = id % o_w;
id /= o_w;
int j = id % o_h;
id /= o_h;
int k = id % o_c;
id /= o_c;
int b = id % batch;
//copying last column/last row as padding
int in_index = ((i*i_c + MIN(out_c,i_c-1))*i_h + MIN(out_h,i_h-1))*i_w + MIN(out_w, i_w-1);
out[out_index] = x[in_index];
int out_index = i + o_w*(j + o_h*(k + o_c*b));
int add_index = i/(o_w/i_w) + i_w*(j/(o_h/i_h) + i_h*(k + i_c*b));
out[out_index] = x[add_index];
}
void resizeForward( dnnType* srcData, dnnType* dstData, int n, int i_c, int i_h, int i_w,
int o_c, int o_h, int o_w, cudaStream_t stream )
{
int i_size = n*i_c*i_h*i_w;
int o_size = n*o_c*o_h*o_w;
int blocks = (o_size+255)/256;
int threads = 256;
if(i_c == o_c && i_h == o_h && i_w == o_w )
{
checkCuda(cudaMemcpy(dstData, srcData, i_size*sizeof(dnnType), cudaMemcpyDeviceToDevice));
}
else
{
checkCuda(cudaMemset(dstData, 0, o_size*sizeof(dnnType)));
resize_kernel<<<blocks, threads, 0, stream>>>(o_size, srcData, i_w, i_h, i_c, o_w, o_h, o_c, n, dstData);
// printDeviceVector(i_size, srcData);
// printDeviceVector(o_size, dstData);
}
resize_kernel<<<blocks, threads, 0, stream>>>(o_size, srcData, i_w, i_h, i_c, o_w, o_h, o_c, n, dstData);
}
src/kernels/shortcut.cu
+48 -15
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@@ -21,27 +21,60 @@ __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stri
//out[out_index] += add[add_index];
}
__global__ void shortcut_mul_kernel(int size, int minw, int minh, int minc, int sample, int batch,
int w1, int h1, int c1, dnnType *mul,
int w2, int h2, int c2, float s1, float s2, dnnType *out)
{
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if (id >= size) return;
int i = id % minw;
id /= minw;
int j = id % minh;
id /= minh;
int k = id % minc;
id /= minc;
int b = id % batch;
int out_index = i*sample + w1*(j*sample + h1*(k + c1*b));
out[out_index] = out[out_index] * mul[k + c2*b];
}
void shortcutForward(dnnType* srcData, dnnType* dstData, int n1, int c1, int h1, int w1, int s1,
int n2, int c2, int h2, int w2, int s2,
cudaStream_t stream)
bool mul, cudaStream_t stream)
{
assert(n1 == n2);
int batch = n1;
int minw = (w1 < w2) ? w1 : w2;
int minh = (h1 < h2) ? h1 : h2;
int minc = (c1 < c2) ? c1 : c2;
if(!mul){
int minw = (w1 < w2) ? w1 : w2;
int minh = (h1 < h2) ? h1 : h2;
int minc = (c1 < c2) ? c1 : c2;
int stride = w1/w2;
int sample = w2/w1;
assert(stride == h1/h2);
assert(sample == h2/h1);
if(stride < 1) stride = 1;
if(sample < 1) sample = 1;
int stride = w1/w2;
int sample = w2/w1;
assert(stride == h1/h2);
assert(sample == h2/h1);
if(stride < 1) stride = 1;
if(sample < 1) sample = 1;
int size = batch * minw * minh * minc;
int blocks = (size+255)/256;
int threads = 256;
shortcut_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, stride, sample, batch,
w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);
}
else{
int minw = w1;
int minh = h1;
int minc = c1;
int sample = 1;
int size = batch * minw * minh * minc;
int blocks = (size+255)/256;
int threads = 256;
shortcut_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, stride, sample, batch,
w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);
int size = batch * minw * minh * minc;
int blocks = (size+255)/256;
int threads = 256;
shortcut_mul_kernel<<<blocks, threads, 0, stream>>>(size, minw, minh, minc, sample, batch,
w1, h1, c1, srcData, w2, h2, c2, s1, s2, dstData);
}
}