| | |
| | | return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords); |
| | | } |
| | | |
| | | detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background) |
| | | detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance) |
| | | { |
| | | detection_layer *layer = calloc(1, sizeof(detection_layer)); |
| | | |
| | |
| | | layer->classes = classes; |
| | | layer->coords = coords; |
| | | layer->rescore = rescore; |
| | | layer->nuisance = nuisance; |
| | | layer->background = background; |
| | | int outputs = get_detection_layer_output_size(*layer); |
| | | layer->output = calloc(batch*outputs, sizeof(float)); |
| | |
| | | int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]); |
| | | float scale = 1; |
| | | if(layer.rescore) scale = state.input[in_i++]; |
| | | if(layer.background) layer.output[out_i++] = scale*state.input[in_i++]; |
| | | else if(layer.nuisance){ |
| | | layer.output[out_i++] = 1-state.input[in_i++]; |
| | | scale = mask; |
| | | } |
| | | else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++]; |
| | | |
| | | for(j = 0; j < layer.classes; ++j){ |
| | | layer.output[out_i++] = scale*state.input[in_i++]; |
| | | } |
| | | if(layer.background){ |
| | | if(layer.nuisance){ |
| | | |
| | | }else if(layer.background){ |
| | | softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background); |
| | | activate_array(state.input+in_i, layer.coords, LOGISTIC); |
| | | } |
| | |
| | | layer.output[out_i++] = mask*state.input[in_i++]; |
| | | } |
| | | } |
| | | /* |
| | | int count = 0; |
| | | for(i = 0; i < layer.batch*locations; ++i){ |
| | | for(j = 0; j < layer.classes+layer.background; ++j){ |
| | | printf("%f, ", layer.output[count++]); |
| | | } |
| | | printf("\n"); |
| | | for(j = 0; j < layer.coords; ++j){ |
| | | printf("%f, ", layer.output[count++]); |
| | | } |
| | | printf("\n"); |
| | | } |
| | | */ |
| | | /* |
| | | if(layer.background || 1){ |
| | | for(i = 0; i < layer.batch*locations; ++i){ |
| | | int index = i*(layer.classes+layer.coords+layer.background); |
| | |
| | | } |
| | | } |
| | | } |
| | | */ |
| | | } |
| | | |
| | | void backward_detection_layer(const detection_layer layer, network_state state) |
| | |
| | | float scale = 1; |
| | | float latent_delta = 0; |
| | | if(layer.rescore) scale = state.input[in_i++]; |
| | | if(layer.background) state.delta[in_i++] = scale*layer.delta[out_i++]; |
| | | else if (layer.nuisance) state.delta[in_i++] = -layer.delta[out_i++]; |
| | | else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++]; |
| | | for(j = 0; j < layer.classes; ++j){ |
| | | latent_delta += state.input[in_i]*layer.delta[out_i]; |
| | | state.delta[in_i++] = scale*layer.delta[out_i++]; |
| | | } |
| | | |
| | | if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i); |
| | | if (layer.nuisance) { |
| | | |
| | | }else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i); |
| | | for(j = 0; j < layer.coords; ++j){ |
| | | state.delta[in_i++] = layer.delta[out_i++]; |
| | | } |