| | |
| | | #include "upsample_layer.h"
|
| | | #include "dark_cuda.h"
|
| | | #include "utils.h"
|
| | | #include "blas.h"
|
| | |
|
| | | #include <stdio.h>
|
| | |
|
| | | layer make_upsample_layer(int batch, int w, int h, int c, int stride)
|
| | | {
|
| | | layer l = { (LAYER_TYPE)0 };
|
| | | l.type = UPSAMPLE;
|
| | | l.batch = batch;
|
| | | l.w = w;
|
| | | l.h = h;
|
| | | l.c = c;
|
| | | l.out_w = w*stride;
|
| | | l.out_h = h*stride;
|
| | | l.out_c = c;
|
| | | if(stride < 0){
|
| | | stride = -stride;
|
| | | l.reverse=1;
|
| | | l.out_w = w/stride;
|
| | | l.out_h = h/stride;
|
| | | }
|
| | | l.stride = stride;
|
| | | l.outputs = l.out_w*l.out_h*l.out_c;
|
| | | l.inputs = l.w*l.h*l.c;
|
| | | l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float));
|
| | | l.output = (float*)xcalloc(l.outputs * batch, sizeof(float));
|
| | |
|
| | | l.forward = forward_upsample_layer;
|
| | | l.backward = backward_upsample_layer;
|
| | | #ifdef GPU
|
| | | l.forward_gpu = forward_upsample_layer_gpu;
|
| | | l.backward_gpu = backward_upsample_layer_gpu;
|
| | |
|
| | | l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch);
|
| | | l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
|
| | | #endif
|
| | | if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
|
| | | else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c);
|
| | | return l;
|
| | | }
|
| | |
|
| | | void resize_upsample_layer(layer *l, int w, int h)
|
| | | {
|
| | | l->w = w;
|
| | | l->h = h;
|
| | | l->out_w = w*l->stride;
|
| | | l->out_h = h*l->stride;
|
| | | if(l->reverse){
|
| | | l->out_w = w/l->stride;
|
| | | l->out_h = h/l->stride;
|
| | | }
|
| | | l->outputs = l->out_w*l->out_h*l->out_c;
|
| | | l->inputs = l->h*l->w*l->c;
|
| | | l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float));
|
| | | l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float));
|
| | |
|
| | | #ifdef GPU
|
| | | cuda_free(l->output_gpu);
|
| | | cuda_free(l->delta_gpu);
|
| | | l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
|
| | | l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
|
| | | #endif
|
| | |
|
| | | }
|
| | |
|
| | | void forward_upsample_layer(const layer l, network_state net)
|
| | | {
|
| | | fill_cpu(l.outputs*l.batch, 0, l.output, 1);
|
| | | if(l.reverse){
|
| | | upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input);
|
| | | }else{
|
| | | upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output);
|
| | | }
|
| | | }
|
| | |
|
| | | void backward_upsample_layer(const layer l, network_state state)
|
| | | {
|
| | | if(l.reverse){
|
| | | upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, state.delta);
|
| | | }else{
|
| | | upsample_cpu(state.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta);
|
| | | }
|
| | | }
|
| | |
|
| | | #ifdef GPU
|
| | | void forward_upsample_layer_gpu(const layer l, network_state state)
|
| | | {
|
| | | fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
|
| | | if(l.reverse){
|
| | | upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, state.input);
|
| | | }else{
|
| | | upsample_gpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu);
|
| | | }
|
| | | }
|
| | |
|
| | | void backward_upsample_layer_gpu(const layer l, network_state state)
|
| | | {
|
| | | if(l.reverse){
|
| | | upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, state.delta);
|
| | | }else{
|
| | | upsample_gpu(state.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu);
|
| | | }
|
| | | }
|
| | | #endif
|
| | | #include "upsample_layer.h" |
| | | #include "dark_cuda.h" |
| | | #include "utils.h" |
| | | #include "blas.h" |
| | | |
| | | #include <stdio.h> |
| | | |
| | | layer make_upsample_layer(int batch, int w, int h, int c, int stride) |
| | | { |
| | | layer l = { (LAYER_TYPE)0 }; |
| | | l.type = UPSAMPLE; |
| | | l.batch = batch; |
| | | l.w = w; |
| | | l.h = h; |
| | | l.c = c; |
| | | l.out_w = w*stride; |
| | | l.out_h = h*stride; |
| | | l.out_c = c; |
| | | if(stride < 0){ |
| | | stride = -stride; |
| | | l.reverse=1; |
| | | l.out_w = w/stride; |
| | | l.out_h = h/stride; |
| | | } |
| | | l.stride = stride; |
| | | l.outputs = l.out_w*l.out_h*l.out_c; |
| | | l.inputs = l.w*l.h*l.c; |
| | | l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); |
| | | l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); |
| | | |
| | | l.forward = forward_upsample_layer; |
| | | l.backward = backward_upsample_layer; |
| | | #ifdef GPU |
| | | l.forward_gpu = forward_upsample_layer_gpu; |
| | | l.backward_gpu = backward_upsample_layer_gpu; |
| | | |
| | | l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); |
| | | l.output_gpu = cuda_make_array(l.output, l.outputs*batch); |
| | | #endif |
| | | if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); |
| | | else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); |
| | | return l; |
| | | } |
| | | |
| | | void resize_upsample_layer(layer *l, int w, int h) |
| | | { |
| | | l->w = w; |
| | | l->h = h; |
| | | l->out_w = w*l->stride; |
| | | l->out_h = h*l->stride; |
| | | if(l->reverse){ |
| | | l->out_w = w/l->stride; |
| | | l->out_h = h/l->stride; |
| | | } |
| | | l->outputs = l->out_w*l->out_h*l->out_c; |
| | | l->inputs = l->h*l->w*l->c; |
| | | l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); |
| | | l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); |
| | | |
| | | #ifdef GPU |
| | | cuda_free(l->output_gpu); |
| | | cuda_free(l->delta_gpu); |
| | | l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); |
| | | l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); |
| | | #endif |
| | | |
| | | } |
| | | |
| | | void forward_upsample_layer(const layer l, network_state net) |
| | | { |
| | | fill_cpu(l.outputs*l.batch, 0, l.output, 1); |
| | | if(l.reverse){ |
| | | upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input); |
| | | }else{ |
| | | upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output); |
| | | } |
| | | } |
| | | |
| | | void backward_upsample_layer(const layer l, network_state state) |
| | | { |
| | | if(l.reverse){ |
| | | upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, state.delta); |
| | | }else{ |
| | | upsample_cpu(state.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta); |
| | | } |
| | | } |
| | | |
| | | #ifdef GPU |
| | | void forward_upsample_layer_gpu(const layer l, network_state state) |
| | | { |
| | | fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); |
| | | if(l.reverse){ |
| | | upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, state.input); |
| | | }else{ |
| | | upsample_gpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu); |
| | | } |
| | | } |
| | | |
| | | void backward_upsample_layer_gpu(const layer l, network_state state) |
| | | { |
| | | if(l.reverse){ |
| | | upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, state.delta); |
| | | }else{ |
| | | upsample_gpu(state.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu); |
| | | } |
| | | } |
| | | #endif |