int resize_network(network *net, int w, int h)
{
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
int inputs = 0;
//fprintf(stderr, "Resizing to %d x %d...", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
break;
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
error("Cannot resize this type of layer");
}
inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
}
//fprintf(stderr, " Done!\n");
return 0;
}
int resize_network(network *net, int w, int h)
{
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
int inputs = 0;
size_t workspace_size = 0;
//fprintf(stderr, "Resizing to %d x %d...", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}else if(l.type == CROP){
resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
error("Cannot resize this type of layer");
}
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
if(l.type == AVGPOOL) break;
}
#ifdef GPU
cuda_free(net->workspace);
net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
#else
free(net->workspace);
net->workspace = calloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
}
int resize_network(network *net, int w, int h)
{
#ifdef GPU
cuda_set_device(net->gpu_index);
cuda_free(net->workspace);
#endif
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
int inputs = 0;
size_t workspace_size = 0;
//fprintf(stderr, "Resizing to %d x %d...\n", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}else if(l.type == CROP){
resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == REGION){
resize_region_layer(&l, w, h);
}else if(l.type == ROUTE){
resize_route_layer(&l, net);
}else if(l.type == REORG){
resize_reorg_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
error("Cannot resize this type of layer");
}
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
if(l.type == AVGPOOL) break;
}
layer out = get_network_output_layer(*net);
net->inputs = net->layers[0].inputs;
net->outputs = out.outputs;
net->truths = out.outputs;
if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths;
net->output = out.output;
free(net->input);
free(net->truth);
net->input = calloc(net->inputs*net->batch, sizeof(float));
net->truth = calloc(net->truths*net->batch, sizeof(float));
#ifdef GPU
if(gpu_index >= 0){
cuda_free(net->input_gpu);
cuda_free(net->truth_gpu);
net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch);
net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch);
net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
}else {
free(net->workspace);
net->workspace = calloc(1, workspace_size);
}
#else
free(net->workspace);
net->workspace = calloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
}
int resize_network(network *net, int w, int h)
{
#ifdef GPU
cuda_set_device(net->gpu_index);
if(gpu_index >= 0){
cuda_free(net->workspace);
if (net->input_gpu) {
cuda_free(*net->input_gpu);
*net->input_gpu = 0;
cuda_free(*net->truth_gpu);
*net->truth_gpu = 0;
}
}
#endif
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
int inputs = 0;
size_t workspace_size = 0;
//fprintf(stderr, "Resizing to %d x %d...\n", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
//printf(" %d: layer = %d,", i, l.type);
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}else if(l.type == CROP){
resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == REGION){
resize_region_layer(&l, w, h);
}else if (l.type == YOLO) {
resize_yolo_layer(&l, w, h);
}else if(l.type == ROUTE){
resize_route_layer(&l, net);
}else if (l.type == SHORTCUT) {
resize_shortcut_layer(&l, w, h);
}else if (l.type == UPSAMPLE) {
resize_upsample_layer(&l, w, h);
}else if(l.type == REORG){
resize_reorg_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
fprintf(stderr, "Resizing type %d \n", (int)l.type);
error("Cannot resize this type of layer");
}
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
if(l.type == AVGPOOL) break;
}
#ifdef GPU
if(gpu_index >= 0){
printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1);
net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
printf(" CUDA allocate done! \n");
}else {
free(net->workspace);
net->workspace = calloc(1, workspace_size);
}
#else
free(net->workspace);
net->workspace = calloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
}
