| | |
| | | #include "cuda.h" |
| | | #include <stdio.h> |
| | | |
| | | image get_crop_image(crop_layer layer) |
| | | image get_crop_image(crop_layer l) |
| | | { |
| | | int h = layer.crop_height; |
| | | int w = layer.crop_width; |
| | | int c = layer.c; |
| | | return float_to_image(h,w,c,layer.output); |
| | | int h = l.out_h; |
| | | int w = l.out_w; |
| | | int c = l.out_c; |
| | | return float_to_image(w,h,c,l.output); |
| | | } |
| | | |
| | | crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip) |
| | | 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) |
| | | { |
| | | fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c); |
| | | crop_layer *layer = calloc(1, sizeof(crop_layer)); |
| | | layer->batch = batch; |
| | | layer->h = h; |
| | | layer->w = w; |
| | | layer->c = c; |
| | | layer->flip = flip; |
| | | layer->crop_width = crop_width; |
| | | layer->crop_height = crop_height; |
| | | layer->output = calloc(crop_width*crop_height * c*batch, sizeof(float)); |
| | | crop_layer l = {0}; |
| | | l.type = CROP; |
| | | l.batch = batch; |
| | | l.h = h; |
| | | l.w = w; |
| | | l.c = c; |
| | | l.flip = flip; |
| | | l.angle = angle; |
| | | l.saturation = saturation; |
| | | l.exposure = exposure; |
| | | l.crop_width = crop_width; |
| | | l.crop_height = crop_height; |
| | | l.out_w = crop_width; |
| | | l.out_h = crop_height; |
| | | l.out_c = c; |
| | | l.inputs = l.w * l.h * l.c; |
| | | l.outputs = l.out_w * l.out_h * l.out_c; |
| | | l.output = calloc(crop_width*crop_height * c*batch, sizeof(float)); |
| | | #ifdef GPU |
| | | layer->output_gpu = cuda_make_array(layer->output, crop_width*crop_height*c*batch); |
| | | l.output_gpu = cuda_make_array(l.output, crop_width*crop_height*c*batch); |
| | | l.rand_gpu = cuda_make_array(0, l.batch*8); |
| | | #endif |
| | | return layer; |
| | | return l; |
| | | } |
| | | |
| | | void forward_crop_layer(const crop_layer layer, int train, float *input) |
| | | void forward_crop_layer(const crop_layer l, network_state state) |
| | | { |
| | | int i,j,c,b,row,col; |
| | | int index; |
| | | int count = 0; |
| | | int flip = (layer.flip && rand()%2); |
| | | int dh = rand()%(layer.h - layer.crop_height + 1); |
| | | int dw = rand()%(layer.w - layer.crop_width + 1); |
| | | if(!train){ |
| | | flip = 0; |
| | | dh = (layer.h - layer.crop_height)/2; |
| | | dw = (layer.w - layer.crop_width)/2; |
| | | int flip = (l.flip && rand()%2); |
| | | int dh = rand()%(l.h - l.crop_height + 1); |
| | | int dw = rand()%(l.w - l.crop_width + 1); |
| | | float scale = 2; |
| | | float trans = -1; |
| | | if(l.noadjust){ |
| | | scale = 1; |
| | | trans = 0; |
| | | } |
| | | for(b = 0; b < layer.batch; ++b){ |
| | | for(c = 0; c < layer.c; ++c){ |
| | | for(i = 0; i < layer.crop_height; ++i){ |
| | | for(j = 0; j < layer.crop_width; ++j){ |
| | | if(!state.train){ |
| | | flip = 0; |
| | | dh = (l.h - l.crop_height)/2; |
| | | dw = (l.w - l.crop_width)/2; |
| | | } |
| | | for(b = 0; b < l.batch; ++b){ |
| | | for(c = 0; c < l.c; ++c){ |
| | | for(i = 0; i < l.crop_height; ++i){ |
| | | for(j = 0; j < l.crop_width; ++j){ |
| | | if(flip){ |
| | | col = layer.w - dw - j - 1; |
| | | col = l.w - dw - j - 1; |
| | | }else{ |
| | | col = j + dw; |
| | | } |
| | | row = i + dh; |
| | | index = col+layer.w*(row+layer.h*(c + layer.c*b)); |
| | | layer.output[count++] = input[index]; |
| | | index = col+l.w*(row+l.h*(c + l.c*b)); |
| | | l.output[count++] = state.input[index]*scale + trans; |
| | | } |
| | | } |
| | | } |