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darknet/src/maxpool_layer.c

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#include "maxpool_layer.h"
#include "convolutional_layer.h"
#include "dark_cuda.h"
#include "utils.h"
#include "gemm.h"
#include <stdio.h>
image get_maxpool_image(maxpool_layer l)
{
int h = l.out_h;
int w = l.out_w;
int c = l.c;
return float_to_image(w,h,c,l.output);
}
image get_maxpool_delta(maxpool_layer l)
{
int h = l.out_h;
int w = l.out_w;
int c = l.c;
return float_to_image(w,h,c,l.delta);
}
void create_maxpool_cudnn_tensors(layer *l)
{
#ifdef CUDNN
CHECK_CUDNN(cudnnCreatePoolingDescriptor(&l->poolingDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc));
CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc));
#endif // CUDNN
}
void cudnn_maxpool_setup(layer *l)
{
#ifdef CUDNN
CHECK_CUDNN(cudnnSetPooling2dDescriptor(
l->poolingDesc,
CUDNN_POOLING_MAX,
CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN
l->size,
l->size,
l->pad/2, //0, //l.pad,
l->pad/2, //0, //l.pad,
l->stride_x,
l->stride_y));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w));
#endif // CUDNN
}
void cudnn_local_avgpool_setup(layer *l)
{
#ifdef CUDNN
CHECK_CUDNN(cudnnSetPooling2dDescriptor(
l->poolingDesc,
CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING,
CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN
l->size,
l->size,
l->pad / 2, //0, //l.pad,
l->pad / 2, //0, //l.pad,
l->stride_x,
l->stride_y));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w));
CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w));
#endif // CUDNN
}
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, int avgpool, int train)
{
maxpool_layer l = { (LAYER_TYPE)0 };
l.avgpool = avgpool;
if (avgpool) l.type = LOCAL_AVGPOOL;
else l.type = MAXPOOL;
l.train = train;
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;
l.c = c;
l.pad = padding;
l.maxpool_depth = maxpool_depth;
l.out_channels = out_channels;
if (maxpool_depth) {
l.out_c = out_channels;
l.out_w = l.w;
l.out_h = l.h;
}
else {
l.out_w = (w + padding - size) / stride_x + 1;
l.out_h = (h + padding - size) / stride_y + 1;
l.out_c = c;
}
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = h*w*c;
l.size = size;
l.stride = stride_x;
l.stride_x = stride_x;
l.stride_y = stride_y;
int output_size = l.out_h * l.out_w * l.out_c * batch;
if (train) {
if (!avgpool) l.indexes = (int*)xcalloc(output_size, sizeof(int));
l.delta = (float*)xcalloc(output_size, sizeof(float));
}
l.output = (float*)xcalloc(output_size, sizeof(float));
if (avgpool) {
l.forward = forward_local_avgpool_layer;
l.backward = backward_local_avgpool_layer;
}
else {
l.forward = forward_maxpool_layer;
l.backward = backward_maxpool_layer;
}
#ifdef GPU
if (avgpool) {
l.forward_gpu = forward_local_avgpool_layer_gpu;
l.backward_gpu = backward_local_avgpool_layer_gpu;
}
else {
l.forward_gpu = forward_maxpool_layer_gpu;
l.backward_gpu = backward_maxpool_layer_gpu;
}
if (train) {
if (!avgpool) l.indexes_gpu = cuda_make_int_array(output_size);
l.delta_gpu = cuda_make_array(l.delta, output_size);
}
l.output_gpu = cuda_make_array(l.output, output_size);
create_maxpool_cudnn_tensors(&l);
if (avgpool) cudnn_local_avgpool_setup(&l);
else cudnn_maxpool_setup(&l);
#endif // GPU
l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.;
if (avgpool) {
if (stride_x == stride_y)
fprintf(stderr, "avg %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
else
fprintf(stderr, "avg %2dx%2d/%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);
}
else {
if (maxpool_depth)
fprintf(stderr, "max-depth %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
else if (stride_x == stride_y)
fprintf(stderr, "max %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops);
else
fprintf(stderr, "max %2dx%2d/%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));
int blur_size = 3;
int blur_pad = blur_size / 2;
if (l.antialiasing == 2) {
blur_size = 2;
blur_pad = 0;
}
*(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_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0, 0, train);
const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size;
int i;
if (blur_size == 2) {
for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) {
l.input_layer->weights[i + 0] = 1 / 4.f;
l.input_layer->weights[i + 1] = 1 / 4.f;
l.input_layer->weights[i + 2] = 1 / 4.f;
l.input_layer->weights[i + 3] = 1 / 4.f;
}
}
else {
for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) {
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
if (gpu_index >= 0) {
if (l.antialiasing) l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs);
push_convolutional_layer(*(l.input_layer));
}
#endif // GPU
}
return l;
}
void resize_maxpool_layer(maxpool_layer *l, int w, int h)
{
l->h = h;
l->w = w;
l->inputs = h*w*l->c;
l->out_w = (w + l->pad - l->size) / l->stride_x + 1;
l->out_h = (h + l->pad - l->size) / l->stride_y + 1;
l->outputs = l->out_w * l->out_h * l->out_c;
int output_size = l->outputs * l->batch;
if (l->train) {
if (!l->avgpool) l->indexes = (int*)xrealloc(l->indexes, output_size * sizeof(int));
l->delta = (float*)xrealloc(l->delta, output_size * sizeof(float));
}
l->output = (float*)xrealloc(l->output, output_size * sizeof(float));
#ifdef GPU
CHECK_CUDA(cudaFree(l->output_gpu));
l->output_gpu = cuda_make_array(l->output, output_size);
if (l->train) {
if (!l->avgpool) {
CHECK_CUDA(cudaFree((float *)l->indexes_gpu));
l->indexes_gpu = cuda_make_int_array(output_size);
}
CHECK_CUDA(cudaFree(l->delta_gpu));
l->delta_gpu = cuda_make_array(l->delta, output_size);
}
if(l->avgpool) cudnn_local_avgpool_setup(l);
else cudnn_maxpool_setup(l);
#endif
}
void forward_maxpool_layer(const maxpool_layer l, network_state state)
{
if (l.maxpool_depth)
{
int b, i, j, k, g;
for (b = 0; b < l.batch; ++b) {
#pragma omp parallel for
for (i = 0; i < l.h; ++i) {
for (j = 0; j < l.w; ++j) {
for (g = 0; g < l.out_c; ++g)
{
int out_index = j + l.w*(i + l.h*(g + l.out_c*b));
float max = -FLT_MAX;
int max_i = -1;
for (k = g; k < l.c; k += l.out_c)
{
int in_index = j + l.w*(i + l.h*(k + l.c*b));
float val = state.input[in_index];
max_i = (val > max) ? in_index : max_i;
max = (val > max) ? val : max;
}
l.output[out_index] = max;
if (l.indexes) l.indexes[out_index] = max_i;
}
}
}
}
return;
}
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);
}
else
{
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;
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;
if (l.indexes) 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)
{
int i;
int h = l.out_h;
int w = l.out_w;
int c = l.out_c;
#pragma omp parallel for
for(i = 0; i < h*w*c*l.batch; ++i){
int index = l.indexes[i];
state.delta[index] += l.delta[i];
}
}
void forward_local_avgpool_layer(const maxpool_layer l, network_state state)
{
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;
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 avg = 0;
int counter = 0;
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);
if (valid) {
counter++;
avg += state.input[index];
}
}
}
l.output[out_index] = avg / counter;
}
}
}
}
}
void backward_local_avgpool_layer(const maxpool_layer l, network_state state)
{
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;
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));
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);
if (valid) state.delta[index] += l.delta[out_index] / (l.size*l.size);
}
}
}
}
}
}
}
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