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Added antialiasing=1 param for [maxpool]-layer on GPU and CPU

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AlexeyAB
2019-09-02 15:25:42 +03:00
parent 80ceee4fca
commit 9e26472b1a
5 changed files with 126 additions and 38 deletions
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115
src/maxpool_layer.c
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@ -1,4 +1,5 @@
#include "maxpool_layer.h"
#include "convolutional_layer.h"
#include "dark_cuda.h"
#include "gemm.h"
#include <stdio.h>
@ -45,10 +46,18 @@ void cudnn_maxpool_setup(layer *l)
}
maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels)
maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing)
{
maxpool_layer l = { (LAYER_TYPE)0 };
l.type = MAXPOOL;
const int blur_stride_x = stride_x;
const int blur_stride_y = stride_y;
l.antialiasing = antialiasing;
if (antialiasing) {
stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer
}
l.batch = batch;
l.h = h;
l.w = w;
@ -94,6 +103,46 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s
else
fprintf(stderr, "max %d x %d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
if (l.antialiasing) {
printf("AA: ");
l.input_layer = (layer*)calloc(1, sizeof(layer));
const int blur_size = 3;
*(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL);
const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size;
int i;
for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) {
/*
l.input_layer->weights[i + 0] = 0;
l.input_layer->weights[i + 1] = 0;
l.input_layer->weights[i + 2] = 0;
l.input_layer->weights[i + 3] = 0;
l.input_layer->weights[i + 4] = 1;
l.input_layer->weights[i + 5] = 0;
l.input_layer->weights[i + 6] = 0;
l.input_layer->weights[i + 7] = 0;
l.input_layer->weights[i + 8] = 0;
*/
l.input_layer->weights[i + 0] = 1 / 16.f;
l.input_layer->weights[i + 1] = 2 / 16.f;
l.input_layer->weights[i + 2] = 1 / 16.f;
l.input_layer->weights[i + 3] = 2 / 16.f;
l.input_layer->weights[i + 4] = 4 / 16.f;
l.input_layer->weights[i + 5] = 2 / 16.f;
l.input_layer->weights[i + 6] = 1 / 16.f;
l.input_layer->weights[i + 7] = 2 / 16.f;
l.input_layer->weights[i + 8] = 1 / 16.f;
}
for (i = 0; i < l.out_c; ++i) l.input_layer->biases[i] = 0;
#ifdef GPU
l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs);
push_convolutional_layer(*(l.input_layer));
#endif // GPU
}
return l;
}
@ -159,42 +208,54 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state)
if (!state.train && l.stride_x == l.stride_y) {
forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch);
return;
}
else {
int b,i,j,k,m,n;
int w_offset = -l.pad / 2;
int h_offset = -l.pad / 2;
int b, i, j, k, m, n;
int w_offset = -l.pad / 2;
int h_offset = -l.pad / 2;
int h = l.out_h;
int w = l.out_w;
int c = l.c;
int h = l.out_h;
int w = l.out_w;
int c = l.c;
for(b = 0; b < l.batch; ++b){
for(k = 0; k < c; ++k){
for(i = 0; i < h; ++i){
for(j = 0; j < w; ++j){
int out_index = j + w*(i + h*(k + c*b));
float max = -FLT_MAX;
int max_i = -1;
for(n = 0; n < l.size; ++n){
for(m = 0; m < l.size; ++m){
int cur_h = h_offset + i*l.stride_y + n;
int cur_w = w_offset + j*l.stride_x + m;
int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c));
int valid = (cur_h >= 0 && cur_h < l.h &&
cur_w >= 0 && cur_w < l.w);
float val = (valid != 0) ? state.input[index] : -FLT_MAX;
max_i = (val > max) ? index : max_i;
max = (val > max) ? val : max;
for (b = 0; b < l.batch; ++b) {
for (k = 0; k < c; ++k) {
for (i = 0; i < h; ++i) {
for (j = 0; j < w; ++j) {
int out_index = j + w*(i + h*(k + c*b));
float max = -FLT_MAX;
int max_i = -1;
for (n = 0; n < l.size; ++n) {
for (m = 0; m < l.size; ++m) {
int cur_h = h_offset + i*l.stride_y + n;
int cur_w = w_offset + j*l.stride_x + m;
int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c));
int valid = (cur_h >= 0 && cur_h < l.h &&
cur_w >= 0 && cur_w < l.w);
float val = (valid != 0) ? state.input[index] : -FLT_MAX;
max_i = (val > max) ? index : max_i;
max = (val > max) ? val : max;
}
}
l.output[out_index] = max;
l.indexes[out_index] = max_i;
}
l.output[out_index] = max;
l.indexes[out_index] = max_i;
}
}
}
}
if (l.antialiasing) {
network_state s = { 0 };
s.train = state.train;
s.workspace = state.workspace;
s.net = state.net;
s.input = l.output;
forward_convolutional_layer(*(l.input_layer), s);
//simple_copy_ongpu(l.outputs*l.batch, l.output, l.input_antialiasing);
memcpy(l.output, l.input_layer->output, l.input_layer->outputs * l.input_layer->batch * sizeof(float));
}
}
void backward_maxpool_layer(const maxpool_layer l, network_state state)