mirror of https://github.com/pjreddie/darknet.git
104 lines
2.7 KiB
C
104 lines
2.7 KiB
C
#include "crop_layer.h"
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#include "cuda.h"
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#include <stdio.h>
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image get_crop_image(crop_layer l)
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{
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int h = l.out_h;
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int w = l.out_w;
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int c = l.out_c;
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return float_to_image(w,h,c,l.output);
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}
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void backward_crop_layer(const crop_layer l, network net){}
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void backward_crop_layer_gpu(const crop_layer l, network net){}
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crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure)
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{
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fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
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crop_layer l = {0};
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l.type = CROP;
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l.batch = batch;
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l.h = h;
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l.w = w;
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l.c = c;
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l.scale = (float)crop_height / h;
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l.flip = flip;
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l.angle = angle;
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l.saturation = saturation;
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l.exposure = exposure;
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l.out_w = crop_width;
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l.out_h = crop_height;
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l.out_c = c;
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l.inputs = l.w * l.h * l.c;
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l.outputs = l.out_w * l.out_h * l.out_c;
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l.output = calloc(l.outputs*batch, sizeof(float));
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l.forward = forward_crop_layer;
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l.backward = backward_crop_layer;
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#ifdef GPU
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l.forward_gpu = forward_crop_layer_gpu;
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l.backward_gpu = backward_crop_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
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l.rand_gpu = cuda_make_array(0, l.batch*8);
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#endif
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return l;
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}
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void resize_crop_layer(layer *l, int w, int h)
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{
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l->w = w;
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l->h = h;
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l->out_w = l->scale*w;
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l->out_h = l->scale*h;
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l->inputs = l->w * l->h * l->c;
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l->outputs = l->out_h * l->out_w * l->out_c;
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l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
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#ifdef GPU
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cuda_free(l->output_gpu);
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l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
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#endif
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}
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void forward_crop_layer(const crop_layer l, network net)
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{
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int i,j,c,b,row,col;
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int index;
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int count = 0;
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int flip = (l.flip && rand()%2);
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int dh = rand()%(l.h - l.out_h + 1);
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int dw = rand()%(l.w - l.out_w + 1);
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float scale = 2;
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float trans = -1;
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if(l.noadjust){
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scale = 1;
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trans = 0;
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}
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if(!net.train){
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flip = 0;
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dh = (l.h - l.out_h)/2;
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dw = (l.w - l.out_w)/2;
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}
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for(b = 0; b < l.batch; ++b){
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for(c = 0; c < l.c; ++c){
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for(i = 0; i < l.out_h; ++i){
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for(j = 0; j < l.out_w; ++j){
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if(flip){
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col = l.w - dw - j - 1;
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}else{
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col = j + dw;
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}
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row = i + dh;
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index = col+l.w*(row+l.h*(c + l.c*b));
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l.output[count++] = net.input[index]*scale + trans;
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}
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}
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}
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}
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}
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