mirror of https://github.com/AlexeyAB/darknet.git
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 lines
2.9 KiB
88 lines
2.9 KiB
#include "dropout_layer.h" |
|
#include "utils.h" |
|
#include "dark_cuda.h" |
|
#include <stdlib.h> |
|
#include <stdio.h> |
|
|
|
dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size_rel, int dropblock_size_abs, int w, int h, int c) |
|
{ |
|
dropout_layer l = { (LAYER_TYPE)0 }; |
|
l.type = DROPOUT; |
|
l.probability = probability; |
|
l.dropblock = dropblock; |
|
l.dropblock_size_rel = dropblock_size_rel; |
|
l.dropblock_size_abs = dropblock_size_abs; |
|
if (l.dropblock) { |
|
l.out_w = l.w = w; |
|
l.out_h = l.h = h; |
|
l.out_c = l.c = c; |
|
|
|
if (l.w <= 0 || l.h <= 0 || l.c <= 0) { |
|
printf(" Error: DropBlock - there must be positive values for: l.w=%d, l.h=%d, l.c=%d \n", l.w, l.h, l.c); |
|
exit(0); |
|
} |
|
} |
|
l.inputs = inputs; |
|
l.outputs = inputs; |
|
l.batch = batch; |
|
l.rand = (float*)xcalloc(inputs * batch, sizeof(float)); |
|
l.scale = 1./(1.0 - probability); |
|
l.forward = forward_dropout_layer; |
|
l.backward = backward_dropout_layer; |
|
#ifdef GPU |
|
l.forward_gpu = forward_dropout_layer_gpu; |
|
l.backward_gpu = backward_dropout_layer_gpu; |
|
l.rand_gpu = cuda_make_array(l.rand, inputs*batch); |
|
if (l.dropblock) { |
|
l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch); |
|
l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch); |
|
} |
|
#endif |
|
if (l.dropblock) { |
|
if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.3f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); |
|
else fprintf(stderr, "dropblock p = %.3f l.dropblock_size_rel = %.2f %4d -> %4d\n", probability, l.dropblock_size_rel, inputs, inputs); |
|
} |
|
else fprintf(stderr, "dropout p = %.3f %4d -> %4d\n", probability, inputs, inputs); |
|
return l; |
|
} |
|
|
|
void resize_dropout_layer(dropout_layer *l, int inputs) |
|
{ |
|
l->inputs = l->outputs = inputs; |
|
l->rand = (float*)xrealloc(l->rand, l->inputs * l->batch * sizeof(float)); |
|
#ifdef GPU |
|
cuda_free(l->rand_gpu); |
|
l->rand_gpu = cuda_make_array(l->rand, l->inputs*l->batch); |
|
|
|
if (l->dropblock) { |
|
cudaFreeHost(l->drop_blocks_scale); |
|
l->drop_blocks_scale = cuda_make_array_pinned(l->rand, l->batch); |
|
|
|
cuda_free(l->drop_blocks_scale_gpu); |
|
l->drop_blocks_scale_gpu = cuda_make_array(l->rand, l->batch); |
|
} |
|
#endif |
|
} |
|
|
|
void forward_dropout_layer(dropout_layer l, network_state state) |
|
{ |
|
int i; |
|
if (!state.train) return; |
|
for(i = 0; i < l.batch * l.inputs; ++i){ |
|
float r = rand_uniform(0, 1); |
|
l.rand[i] = r; |
|
if(r < l.probability) state.input[i] = 0; |
|
else state.input[i] *= l.scale; |
|
} |
|
} |
|
|
|
void backward_dropout_layer(dropout_layer l, network_state state) |
|
{ |
|
int i; |
|
if(!state.delta) return; |
|
for(i = 0; i < l.batch * l.inputs; ++i){ |
|
float r = l.rand[i]; |
|
if(r < l.probability) state.delta[i] = 0; |
|
else state.delta[i] *= l.scale; |
|
} |
|
}
|
|
|