From 8c5364f58569eaeb5582a4915b36b24fc5570c76 Mon Sep 17 00:00:00 2001 From: Joseph Redmon Date: Mon, 9 Nov 2015 11:31:39 -0800 Subject: [PATCH] New YOLO --- Makefile | 5 +- cfg/yolo.cfg | 6 +- data/scream.jpg | Bin 336773 -> 174515 bytes src/coco.c | 4 +- src/darknet.c | 3 - src/detection_layer.c | 296 +++++------ src/detection_layer.h | 8 +- src/layer.h | 4 - src/network.c | 11 - src/network.h | 1 - src/network_kernels.cu | 7 +- src/old.c | 607 ----------------------- src/parser.c | 28 +- src/region_layer.c | 259 ---------- src/region_layer.h | 18 - src/swag.c | 460 ----------------- src/yolo.c | 352 ++++++++----- src/{swag_kernels.cu => yolo_kernels.cu} | 15 +- 18 files changed, 412 insertions(+), 1672 deletions(-) delete mode 100644 src/old.c delete mode 100644 src/region_layer.c delete mode 100644 src/region_layer.h delete mode 100644 src/swag.c rename src/{swag_kernels.cu => yolo_kernels.cu} (77%) diff --git a/Makefile b/Makefile index 2180094f..412e8264 100644 --- a/Makefile +++ b/Makefile @@ -3,7 +3,6 @@ OPENCV=1 DEBUG=0 ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20 -ARCH= -arch=sm_52 --use_fast_math VPATH=./src/ EXEC=darknet @@ -35,9 +34,9 @@ CFLAGS+= -DGPU LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand endif -OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o swag.o classifier.o +OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o layer.o compare.o classifier.o ifeq ($(GPU), 1) -OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o swag_kernels.o +OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o yolo_kernels.o endif OBJS = $(addprefix $(OBJDIR), $(OBJ)) diff --git a/cfg/yolo.cfg b/cfg/yolo.cfg index ee167269..a652f466 100644 --- a/cfg/yolo.cfg +++ b/cfg/yolo.cfg @@ -9,8 +9,8 @@ decay=0.0005 learning_rate=0.001 policy=steps 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z{sDit)_f(Y>YE~t>fY|pSJP6?9T?uRn%X;7-d9n#xq&6V7huMP<;pW`WnbGLg@3a5 z!rf2cW&VSxL#|yzquT2CmiOPRxn*jWoK_~HMOiDMHEYE5!kqC7?NA@ zelg-|nA~m>ROw2MHyxbg&91#6Exm1i_G*7W)4m4e*c=bn<%yko*ql_kXHp4rHPP}p z#VytBy!C$<;GOP;Z(}XnZkAFeno&0Y05q5LH+h?*`{27a7YqU&xanU?__M&iAJR2# zXHK++gBccw=BGu!}_iMuGXUB?&em~R@TA^W%80FibZ6B z7C1nakr^EZYR^lzHMP4-&Aa=b|JmpJ B5Pkpv diff --git a/src/coco.c b/src/coco.c index e30eeb7e..aadf09d4 100644 --- a/src/coco.c +++ b/src/coco.c @@ -1,7 +1,7 @@ #include #include "network.h" -#include "region_layer.h" +#include "detection_layer.h" #include "cost_layer.h" #include "utils.h" #include "parser.h" @@ -366,7 +366,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) if(weightfile){ load_weights(&net, weightfile); } - region_layer l = net.layers[net.n-1]; + detection_layer l = net.layers[net.n-1]; set_batch_network(&net, 1); srand(2222222); clock_t time; diff --git a/src/darknet.c b/src/darknet.c index 78146119..c2a65965 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -13,7 +13,6 @@ extern void run_imagenet(int argc, char **argv); extern void run_yolo(int argc, char **argv); -extern void run_swag(int argc, char **argv); extern void run_coco(int argc, char **argv); extern void run_writing(int argc, char **argv); extern void run_captcha(int argc, char **argv); @@ -221,8 +220,6 @@ int main(int argc, char **argv) average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ run_yolo(argc, argv); - } else if (0 == strcmp(argv[1], "swag")){ - run_swag(argc, argv); } else if (0 == strcmp(argv[1], "coco")){ run_coco(argc, argv); } else if (0 == strcmp(argv[1], "classifier")){ diff --git a/src/detection_layer.c b/src/detection_layer.c index daeee042..33f4f0b3 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -6,42 +6,32 @@ #include "cuda.h" #include "utils.h" #include +#include #include #include -int get_detection_layer_locations(detection_layer l) -{ - return l.inputs / (l.classes+l.coords+l.joint+(l.background || l.objectness)); -} - -int get_detection_layer_output_size(detection_layer l) -{ - return get_detection_layer_locations(l)*((l.background || l.objectness) + l.classes + l.coords); -} - -detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int joint, int rescore, int background, int objectness) +detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore) { detection_layer l = {0}; l.type = DETECTION; - + + l.n = n; l.batch = batch; l.inputs = inputs; l.classes = classes; l.coords = coords; l.rescore = rescore; - l.objectness = objectness; - l.background = background; - l.joint = joint; + l.side = side; + assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); l.cost = calloc(1, sizeof(float)); - l.does_cost=1; - int outputs = get_detection_layer_output_size(l); - l.outputs = outputs; - l.output = calloc(batch*outputs, sizeof(float)); - l.delta = calloc(batch*outputs, sizeof(float)); - #ifdef GPU - l.output_gpu = cuda_make_array(l.output, batch*outputs); - l.delta_gpu = cuda_make_array(l.delta, batch*outputs); - #endif + l.outputs = l.inputs; + l.truths = l.side*l.side*(1+l.coords+l.classes); + l.output = calloc(batch*l.outputs, sizeof(float)); + l.delta = calloc(batch*l.outputs, sizeof(float)); +#ifdef GPU + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif fprintf(stderr, "Detection Layer\n"); srand(0); @@ -51,124 +41,164 @@ detection_layer make_detection_layer(int batch, int inputs, int classes, int coo void forward_detection_layer(const detection_layer l, network_state state) { - int in_i = 0; - int out_i = 0; - int locations = get_detection_layer_locations(l); + int locations = l.side*l.side; int i,j; - for(i = 0; i < l.batch*locations; ++i){ - int mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]); - float scale = 1; - if(l.joint) scale = state.input[in_i++]; - else if(l.objectness){ - l.output[out_i++] = 1-state.input[in_i++]; - scale = mask; - } - else if(l.background) l.output[out_i++] = scale*state.input[in_i++]; - - for(j = 0; j < l.classes; ++j){ - l.output[out_i++] = scale*state.input[in_i++]; - } - if(l.objectness){ - - }else if(l.background){ - softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background); - activate_array(state.input+in_i, l.coords, LOGISTIC); - } - for(j = 0; j < l.coords; ++j){ - l.output[out_i++] = mask*state.input[in_i++]; + memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + int b; + if (l.softmax){ + for(b = 0; b < l.batch; ++b){ + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + int offset = i*l.classes; + softmax_array(l.output + index + offset, l.classes, + l.output + index + offset); + } + int offset = locations*l.classes; + activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC); } } - float avg_iou = 0; - int count = 0; - if(l.does_cost && state.train){ + if(state.train){ + float avg_iou = 0; + float avg_cat = 0; + float avg_allcat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; *(l.cost) = 0; - int size = get_detection_layer_output_size(l) * l.batch; + int size = l.inputs * l.batch; memset(l.delta, 0, size * sizeof(float)); - for (i = 0; i < l.batch*locations; ++i) { - int classes = (l.objectness || l.background)+l.classes; - int offset = i*(classes+l.coords); - for (j = offset; j < offset+classes; ++j) { - *(l.cost) += pow(state.truth[j] - l.output[j], 2); - l.delta[j] = state.truth[j] - l.output[j]; - if(l.background && j == offset) l.delta[j] *= .1; - } - - box truth; - truth.x = state.truth[j+0]/7; - truth.y = state.truth[j+1]/7; - truth.w = pow(state.truth[j+2], 2); - truth.h = pow(state.truth[j+3], 2); - - box out; - out.x = l.output[j+0]/7; - out.y = l.output[j+1]/7; - out.w = pow(l.output[j+2], 2); - out.h = pow(l.output[j+3], 2); - - if(!(truth.w*truth.h)) continue; - float iou = box_iou(out, truth); - avg_iou += iou; - ++count; - - *(l.cost) += pow((1-iou), 2); - l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]); - l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]); - l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]); - l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]); - if(l.rescore){ - if(l.objectness){ - state.truth[offset] = iou; - l.delta[offset] = state.truth[offset] - l.output[offset]; + for (b = 0; b < l.batch; ++b){ + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + int truth_index = (b*locations + i)*(1+l.coords+l.classes); + int is_obj = state.truth[truth_index]; + for (j = 0; j < l.n; ++j) { + int p_index = index + locations*l.classes + i*l.n + j; + l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); + *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2); + avg_anyobj += l.output[p_index]; } - else{ - for (j = offset; j < offset+classes; ++j) { - if(state.truth[j]) state.truth[j] = iou; - l.delta[j] = state.truth[j] - l.output[j]; + + int best_index = -1; + float best_iou = 0; + float best_rmse = 20; + + if (!is_obj){ + continue; + } + + int class_index = index + i*l.classes; + for(j = 0; j < l.classes; ++j) { + l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]); + *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); + if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; + avg_allcat += l.output[class_index+j]; + } + + box truth = float_to_box(state.truth + truth_index + 1 + l.classes); + truth.x /= l.side; + truth.y /= l.side; + + for(j = 0; j < l.n; ++j){ + int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; + box out = float_to_box(l.output + box_index); + out.x /= l.side; + out.y /= l.side; + + if (l.sqrt){ + out.w = out.w*out.w; + out.h = out.h*out.h; + } + + float iou = box_iou(out, truth); + //iou = 0; + float rmse = box_rmse(out, truth); + if(best_iou > 0 || iou > 0){ + if(iou > best_iou){ + best_iou = iou; + best_index = j; + } + }else{ + if(rmse < best_rmse){ + best_rmse = rmse; + best_index = j; + } } } + + if(l.forced){ + if(truth.w*truth.h < .1){ + best_index = 1; + }else{ + best_index = 0; + } + } + + int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; + int tbox_index = truth_index + 1 + l.classes; + + box out = float_to_box(l.output + box_index); + out.x /= l.side; + out.y /= l.side; + if (l.sqrt) { + out.w = out.w*out.w; + out.h = out.h*out.h; + } + float iou = box_iou(out, truth); + + //printf("%d", best_index); + int p_index = index + locations*l.classes + i*l.n + best_index; + *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2); + *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2); + avg_obj += l.output[p_index]; + l.delta[p_index] = l.object_scale * (1.-l.output[p_index]); + + if(l.rescore){ + l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); + } + + l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]); + l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]); + l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]); + if(l.sqrt){ + l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]); + } + + *(l.cost) += pow(1-iou, 2); + avg_iou += iou; + ++count; + } + if(l.softmax){ + gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords), + LOGISTIC, l.delta + index + locations*l.classes); } } - printf("Avg IOU: %f\n", avg_iou/count); + printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); } } void backward_detection_layer(const detection_layer l, network_state state) { - int locations = get_detection_layer_locations(l); - int i,j; - int in_i = 0; - int out_i = 0; - for(i = 0; i < l.batch*locations; ++i){ - float scale = 1; - float latent_delta = 0; - if(l.joint) scale = state.input[in_i++]; - else if (l.objectness) state.delta[in_i++] += -l.delta[out_i++]; - else if (l.background) state.delta[in_i++] += scale*l.delta[out_i++]; - for(j = 0; j < l.classes; ++j){ - latent_delta += state.input[in_i]*l.delta[out_i]; - state.delta[in_i++] += scale*l.delta[out_i++]; - } - - if (l.objectness) { - - }else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i); - for (j = 0; j < l.coords; ++j){ - state.delta[in_i++] += l.delta[out_i++]; - } - if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] += latent_delta; - } + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); } #ifdef GPU void forward_detection_layer_gpu(const detection_layer l, network_state state) { - int outputs = get_detection_layer_output_size(l); + if(!state.train){ + copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); + return; + } + float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); float *truth_cpu = 0; if(state.truth){ - truth_cpu = calloc(l.batch*outputs, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, l.batch*outputs); + int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); + truth_cpu = calloc(num_truth, sizeof(float)); + cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); network_state cpu_state; @@ -176,38 +206,16 @@ void forward_detection_layer_gpu(const detection_layer l, network_state state) cpu_state.truth = truth_cpu; cpu_state.input = in_cpu; forward_detection_layer(l, cpu_state); - cuda_push_array(l.output_gpu, l.output, l.batch*outputs); - cuda_push_array(l.delta_gpu, l.delta, l.batch*outputs); + cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); free(cpu_state.input); if(cpu_state.truth) free(cpu_state.truth); } void backward_detection_layer_gpu(detection_layer l, network_state state) { - int outputs = get_detection_layer_output_size(l); - - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *delta_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - truth_cpu = calloc(l.batch*outputs, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, l.batch*outputs); - } - network_state cpu_state; - cpu_state.train = state.train; - cpu_state.input = in_cpu; - cpu_state.truth = truth_cpu; - cpu_state.delta = delta_cpu; - - cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); - cuda_pull_array(state.delta, delta_cpu, l.batch*l.inputs); - cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs); - backward_detection_layer(l, cpu_state); - cuda_push_array(state.delta, delta_cpu, l.batch*l.inputs); - - if (truth_cpu) free(truth_cpu); - free(in_cpu); - free(delta_cpu); + axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); + //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1); } #endif diff --git a/src/detection_layer.h b/src/detection_layer.h index 38d96eec..5e34ac7a 100644 --- a/src/detection_layer.h +++ b/src/detection_layer.h @@ -1,16 +1,14 @@ -#ifndef DETECTION_LAYER_H -#define DETECTION_LAYER_H +#ifndef REGION_LAYER_H +#define REGION_LAYER_H #include "params.h" #include "layer.h" typedef layer detection_layer; -detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int joint, int rescore, int background, int objectness); +detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); void forward_detection_layer(const detection_layer l, network_state state); void backward_detection_layer(const detection_layer l, network_state state); -int get_detection_layer_output_size(detection_layer l); -int get_detection_layer_locations(detection_layer l); #ifdef GPU void forward_detection_layer_gpu(const detection_layer l, network_state state); diff --git a/src/layer.h b/src/layer.h index 2b136a05..0137c8ed 100644 --- a/src/layer.h +++ b/src/layer.h @@ -15,7 +15,6 @@ typedef enum { ROUTE, COST, NORMALIZATION, - REGION, AVGPOOL } LAYER_TYPE; @@ -30,9 +29,6 @@ typedef struct { int batch_normalize; int batch; int forced; - int object_logistic; - int class_logistic; - int coord_logistic; int inputs; int outputs; int truths; diff --git a/src/network.c b/src/network.c index 063a1bb8..9bcb2644 100644 --- a/src/network.c +++ b/src/network.c @@ -11,7 +11,6 @@ #include "convolutional_layer.h" #include "deconvolutional_layer.h" #include "detection_layer.h" -#include "region_layer.h" #include "normalization_layer.h" #include "maxpool_layer.h" #include "avgpool_layer.h" @@ -72,8 +71,6 @@ char *get_layer_string(LAYER_TYPE a) return "softmax"; case DETECTION: return "detection"; - case REGION: - return "region"; case DROPOUT: return "dropout"; case CROP: @@ -119,8 +116,6 @@ void forward_network(network net, network_state state) forward_normalization_layer(l, state); } else if(l.type == DETECTION){ forward_detection_layer(l, state); - } else if(l.type == REGION){ - forward_region_layer(l, state); } else if(l.type == CONNECTED){ forward_connected_layer(l, state); } else if(l.type == CROP){ @@ -180,10 +175,6 @@ float get_network_cost(network net) sum += net.layers[i].cost[0]; ++count; } - if(net.layers[i].type == REGION){ - sum += net.layers[i].cost[0]; - ++count; - } } return sum/count; } @@ -224,8 +215,6 @@ void backward_network(network net, network_state state) backward_dropout_layer(l, state); } else if(l.type == DETECTION){ backward_detection_layer(l, state); - } else if(l.type == REGION){ - backward_region_layer(l, state); } else if(l.type == SOFTMAX){ if(i != 0) backward_softmax_layer(l, state); } else if(l.type == CONNECTED){ diff --git a/src/network.h b/src/network.h index 1caf838d..0ad16ffe 100644 --- a/src/network.h +++ b/src/network.h @@ -89,7 +89,6 @@ int resize_network(network *net, int w, int h); void set_batch_network(network *net, int b); int get_network_input_size(network net); float get_network_cost(network net); -detection_layer get_network_detection_layer(network net); int get_network_nuisance(network net); int get_network_background(network net); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index d2c8bf9e..85613723 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -13,7 +13,6 @@ extern "C" { #include "crop_layer.h" #include "connected_layer.h" #include "detection_layer.h" -#include "region_layer.h" #include "convolutional_layer.h" #include "deconvolutional_layer.h" #include "maxpool_layer.h" @@ -44,8 +43,6 @@ void forward_network_gpu(network net, network_state state) forward_deconvolutional_layer_gpu(l, state); } else if(l.type == DETECTION){ forward_detection_layer_gpu(l, state); - } else if(l.type == REGION){ - forward_region_layer_gpu(l, state); } else if(l.type == CONNECTED){ forward_connected_layer_gpu(l, state); } else if(l.type == CROP){ @@ -96,8 +93,6 @@ void backward_network_gpu(network net, network_state state) backward_dropout_layer_gpu(l, state); } else if(l.type == DETECTION){ backward_detection_layer_gpu(l, state); - } else if(l.type == REGION){ - backward_region_layer_gpu(l, state); } else if(l.type == NORMALIZATION){ backward_normalization_layer_gpu(l, state); } else if(l.type == SOFTMAX){ @@ -134,7 +129,7 @@ float train_network_datum_gpu(network net, float *x, float *y) network_state state; int x_size = get_network_input_size(net)*net.batch; int y_size = get_network_output_size(net)*net.batch; - if(net.layers[net.n-1].type == REGION) y_size = net.layers[net.n-1].truths*net.batch; + if(net.layers[net.n-1].type == DETECTION) y_size = net.layers[net.n-1].truths*net.batch; if(!*net.input_gpu){ *net.input_gpu = cuda_make_array(x, x_size); *net.truth_gpu = cuda_make_array(y, y_size); diff --git a/src/old.c b/src/old.c deleted file mode 100644 index 52b87fbf..00000000 --- a/src/old.c +++ /dev/null @@ -1,607 +0,0 @@ -void save_network(network net, char *filename) -{ - FILE *fp = fopen(filename, "w"); - if(!fp) file_error(filename); - int i; - for(i = 0; i < net.n; ++i) - { - if(net.types[i] == CONVOLUTIONAL) - print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i); - else if(net.types[i] == DECONVOLUTIONAL) - print_deconvolutional_cfg(fp, (deconvolutional_layer *)net.layers[i], net, i); - else if(net.types[i] == CONNECTED) - print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i); - else if(net.types[i] == CROP) - print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i); - else if(net.types[i] == MAXPOOL) - print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i); - else if(net.types[i] == DROPOUT) - print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i); - else if(net.types[i] == SOFTMAX) - print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i); - else if(net.types[i] == DETECTION) - print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i); - else if(net.types[i] == COST) - print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i); - } - fclose(fp); -} - -void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count) -{ -#ifdef GPU - if(gpu_index >= 0) pull_convolutional_layer(*l); -#endif - int i; - fprintf(fp, "[convolutional]\n"); - fprintf(fp, "filters=%d\n" - "size=%d\n" - "stride=%d\n" - "pad=%d\n" - "activation=%s\n", - l->n, l->size, l->stride, l->pad, - get_activation_string(l->activation)); - fprintf(fp, "biases="); - for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]); - fprintf(fp, "\n"); - fprintf(fp, "weights="); - for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]); - fprintf(fp, "\n\n"); -} - -void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count) -{ -#ifdef GPU - if(gpu_index >= 0) pull_deconvolutional_layer(*l); -#endif - int i; - fprintf(fp, "[deconvolutional]\n"); - fprintf(fp, "filters=%d\n" - "size=%d\n" - "stride=%d\n" - "activation=%s\n", - l->n, l->size, l->stride, - get_activation_string(l->activation)); - fprintf(fp, "biases="); - for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]); - fprintf(fp, "\n"); - fprintf(fp, "weights="); - for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]); - fprintf(fp, "\n\n"); -} - -void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count) -{ - fprintf(fp, "[dropout]\n"); - fprintf(fp, "probability=%g\n\n", l->probability); -} - -void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count) -{ -#ifdef GPU - if(gpu_index >= 0) pull_connected_layer(*l); -#endif - int i; - fprintf(fp, "[connected]\n"); - fprintf(fp, "output=%d\n" - "activation=%s\n", - l->outputs, - get_activation_string(l->activation)); - fprintf(fp, "biases="); - for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]); - fprintf(fp, "\n"); - fprintf(fp, "weights="); - for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]); - fprintf(fp, "\n\n"); -} - -void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count) -{ - fprintf(fp, "[crop]\n"); - fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip); -} - -void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count) -{ - fprintf(fp, "[maxpool]\n"); - fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride); -} - -void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count) -{ - fprintf(fp, "[softmax]\n"); - fprintf(fp, "\n"); -} - -void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count) -{ - fprintf(fp, "[detection]\n"); - fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance); - fprintf(fp, "\n"); -} - -void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count) -{ - fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type)); - fprintf(fp, "\n"); -} - - -#ifndef NORMALIZATION_LAYER_H -#define NORMALIZATION_LAYER_H - -#include "image.h" -#include "params.h" - -typedef struct { - int batch; - int h,w,c; - int size; - float alpha; - float beta; - float kappa; - float *delta; - float *output; - float *sums; -} normalization_layer; - -image get_normalization_image(normalization_layer layer); -normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa); -void resize_normalization_layer(normalization_layer *layer, int h, int w); -void forward_normalization_layer(const normalization_layer layer, network_state state); -void backward_normalization_layer(const normalization_layer layer, network_state state); -void visualize_normalization_layer(normalization_layer layer, char *window); - -#endif -#include "normalization_layer.h" -#include - -image get_normalization_image(normalization_layer layer) -{ - int h = layer.h; - int w = layer.w; - int c = layer.c; - return float_to_image(w,h,c,layer.output); -} - -image get_normalization_delta(normalization_layer layer) -{ - int h = layer.h; - int w = layer.w; - int c = layer.c; - return float_to_image(w,h,c,layer.delta); -} - -normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa) -{ - fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", h,w,c,size); - normalization_layer *layer = calloc(1, sizeof(normalization_layer)); - layer->batch = batch; - layer->h = h; - layer->w = w; - layer->c = c; - layer->kappa = kappa; - layer->size = size; - layer->alpha = alpha; - layer->beta = beta; - layer->output = calloc(h * w * c * batch, sizeof(float)); - layer->delta = calloc(h * w * c * batch, sizeof(float)); - layer->sums = calloc(h*w, sizeof(float)); - return layer; -} - -void resize_normalization_layer(normalization_layer *layer, int h, int w) -{ - layer->h = h; - layer->w = w; - layer->output = realloc(layer->output, h * w * layer->c * layer->batch * sizeof(float)); - layer->delta = realloc(layer->delta, h * w * layer->c * layer->batch * sizeof(float)); - layer->sums = realloc(layer->sums, h*w * sizeof(float)); -} - -void add_square_array(float *src, float *dest, int n) -{ - int i; - for(i = 0; i < n; ++i){ - dest[i] += src[i]*src[i]; - } -} -void sub_square_array(float *src, float *dest, int n) -{ - int i; - for(i = 0; i < n; ++i){ - dest[i] -= src[i]*src[i]; - } -} - -void forward_normalization_layer(const normalization_layer layer, network_state state) -{ - int i,j,k; - memset(layer.sums, 0, layer.h*layer.w*sizeof(float)); - int imsize = layer.h*layer.w; - for(j = 0; j < layer.size/2; ++j){ - if(j < layer.c) add_square_array(state.input+j*imsize, layer.sums, imsize); - } - for(k = 0; k < layer.c; ++k){ - int next = k+layer.size/2; - int prev = k-layer.size/2-1; - if(next < layer.c) add_square_array(state.input+next*imsize, layer.sums, imsize); - if(prev > 0) sub_square_array(state.input+prev*imsize, layer.sums, imsize); - for(i = 0; i < imsize; ++i){ - layer.output[k*imsize + i] = state.input[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta); - } - } -} - -void backward_normalization_layer(const normalization_layer layer, network_state state) -{ - // TODO! - // OR NOT TODO!! -} - -void visualize_normalization_layer(normalization_layer layer, char *window) -{ - image delta = get_normalization_image(layer); - image dc = collapse_image_layers(delta, 1); - char buff[256]; - sprintf(buff, "%s: Output", window); - show_image(dc, buff); - save_image(dc, buff); - free_image(dc); -} - -void test_load() -{ - image dog = load_image("dog.jpg", 300, 400); - show_image(dog, "Test Load"); - show_image_layers(dog, "Test Load"); -} - -void test_parser() -{ - network net = parse_network_cfg("cfg/trained_imagenet.cfg"); - save_network(net, "cfg/trained_imagenet_smaller.cfg"); -} - -void test_init(char *cfgfile) -{ - gpu_index = -1; - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); - srand(2222222); - int i = 0; - char *filename = "data/test.jpg"; - - image im = load_image_color(filename, 256, 256); - //z_normalize_image(im); - translate_image(im, -128); - scale_image(im, 1/128.); - float *X = im.data; - forward_network(net, X, 0, 1); - for(i = 0; i < net.n; ++i){ - if(net.types[i] == CONVOLUTIONAL){ - convolutional_layer layer = *(convolutional_layer *)net.layers[i]; - image output = get_convolutional_image(layer); - int size = output.h*output.w*output.c; - float v = variance_array(layer.output, size); - float m = mean_array(layer.output, size); - printf("%d: Convolutional, mean: %f, variance %f\n", i, m, v); - } - else if(net.types[i] == CONNECTED){ - connected_layer layer = *(connected_layer *)net.layers[i]; - int size = layer.outputs; - float v = variance_array(layer.output, size); - float m = mean_array(layer.output, size); - printf("%d: Connected, mean: %f, variance %f\n", i, m, v); - } - } - free_image(im); -} -void test_dog(char *cfgfile) -{ - image im = load_image_color("data/dog.jpg", 256, 256); - translate_image(im, -128); - print_image(im); - float *X = im.data; - network net = parse_network_cfg(cfgfile); - set_batch_network(&net, 1); - network_predict(net, X); - image crop = get_network_image_layer(net, 0); - show_image(crop, "cropped"); - print_image(crop); - show_image(im, "orig"); - float * inter = get_network_output(net); - pm(1000, 1, inter); - cvWaitKey(0); -} - -void test_voc_segment(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - while(1){ - char filename[256]; - fgets(filename, 256, stdin); - strtok(filename, "\n"); - image im = load_image_color(filename, 500, 500); - //resize_network(net, im.h, im.w, im.c); - translate_image(im, -128); - scale_image(im, 1/128.); - //float *predictions = network_predict(net, im.data); - network_predict(net, im.data); - free_image(im); - image output = get_network_image_layer(net, net.n-2); - show_image(output, "Segment Output"); - cvWaitKey(0); - } -} -void test_visualize(char *filename) -{ - network net = parse_network_cfg(filename); - visualize_network(net); - cvWaitKey(0); -} - -void test_cifar10(char *cfgfile) -{ - network net = parse_network_cfg(cfgfile); - data test = load_cifar10_data("data/cifar10/test_batch.bin"); - clock_t start = clock(), end; - float test_acc = network_accuracy_multi(net, test, 10); - end = clock(); - printf("%f in %f Sec\n", test_acc, sec(end-start)); - //visualize_network(net); - //cvWaitKey(0); -} - -void train_cifar10(char *cfgfile) -{ - srand(555555); - srand(time(0)); - network net = parse_network_cfg(cfgfile); - data test = load_cifar10_data("data/cifar10/test_batch.bin"); - int count = 0; - int iters = 50000/net.batch; - data train = load_all_cifar10(); - while(++count <= 10000){ - clock_t time = clock(); - float loss = train_network_sgd(net, train, iters); - - if(count%10 == 0){ - float test_acc = network_accuracy(net, test); - printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time)); - char buff[256]; - sprintf(buff, "/home/pjreddie/imagenet_backup/cifar10_%d.cfg", count); - save_network(net, buff); - }else{ - printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time)); - } - - } - free_data(train); -} - -void compare_nist(char *p1,char *p2) -{ - srand(222222); - network n1 = parse_network_cfg(p1); - network n2 = parse_network_cfg(p2); - data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); - normalize_data_rows(test); - compare_networks(n1, n2, test); -} - -void test_nist(char *path) -{ - srand(222222); - network net = parse_network_cfg(path); - data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); - normalize_data_rows(test); - clock_t start = clock(), end; - float test_acc = network_accuracy(net, test); - end = clock(); - printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC); -} - -void train_nist(char *cfgfile) -{ - srand(222222); - // srand(time(0)); - data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10); - data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); - network net = parse_network_cfg(cfgfile); - int count = 0; - int iters = 6000/net.batch + 1; - while(++count <= 100){ - clock_t start = clock(), end; - normalize_data_rows(train); - normalize_data_rows(test); - float loss = train_network_sgd(net, train, iters); - float test_acc = 0; - if(count%1 == 0) test_acc = network_accuracy(net, test); - end = clock(); - printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC); - } - free_data(train); - free_data(test); - char buff[256]; - sprintf(buff, "%s.trained", cfgfile); - save_network(net, buff); -} - -/* - void train_nist_distributed(char *address) - { - srand(time(0)); - network net = parse_network_cfg("cfg/nist.client"); - data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10); -//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); -normalize_data_rows(train); -//normalize_data_rows(test); -int count = 0; -int iters = 50000/net.batch; -iters = 1000/net.batch + 1; -while(++count <= 2000){ -clock_t start = clock(), end; -float loss = train_network_sgd(net, train, iters); -client_update(net, address); -end = clock(); -//float test_acc = network_accuracy_gpu(net, test); -//float test_acc = 0; -printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC); -} -} - */ - -void test_ensemble() -{ - int i; - srand(888888); - data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10); - normalize_data_rows(d); - data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10); - normalize_data_rows(test); - data train = d; - // data *split = split_data(d, 1, 10); - // data train = split[0]; - // data test = split[1]; - matrix prediction = make_matrix(test.y.rows, test.y.cols); - int n = 30; - for(i = 0; i < n; ++i){ - int count = 0; - float lr = .0005; - float momentum = .9; - float decay = .01; - network net = parse_network_cfg("nist.cfg"); - while(++count <= 15){ - float acc = train_network_sgd(net, train, train.X.rows); - printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay ); - lr /= 2; - } - matrix partial = network_predict_data(net, test); - float acc = matrix_topk_accuracy(test.y, partial,1); - printf("Model Accuracy: %lf\n", acc); - matrix_add_matrix(partial, prediction); - acc = matrix_topk_accuracy(test.y, prediction,1); - printf("Current Ensemble Accuracy: %lf\n", acc); - free_matrix(partial); - } - float acc = matrix_topk_accuracy(test.y, prediction,1); - printf("Full Ensemble Accuracy: %lf\n", acc); -} - -void visualize_cat() -{ - network net = parse_network_cfg("cfg/voc_imagenet.cfg"); - image im = load_image_color("data/cat.png", 0, 0); - printf("Processing %dx%d image\n", im.h, im.w); - resize_network(net, im.h, im.w, im.c); - forward_network(net, im.data, 0, 0); - - visualize_network(net); - cvWaitKey(0); -} - -void test_correct_nist() -{ - network net = parse_network_cfg("cfg/nist_conv.cfg"); - srand(222222); - net = parse_network_cfg("cfg/nist_conv.cfg"); - data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10); - data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10); - normalize_data_rows(train); - normalize_data_rows(test); - int count = 0; - int iters = 1000/net.batch; - - while(++count <= 5){ - clock_t start = clock(), end; - float loss = train_network_sgd(net, train, iters); - end = clock(); - float test_acc = network_accuracy(net, test); - printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay); - } - save_network(net, "cfg/nist_gpu.cfg"); - - gpu_index = -1; - count = 0; - srand(222222); - net = parse_network_cfg("cfg/nist_conv.cfg"); - while(++count <= 5){ - clock_t start = clock(), end; - float loss = train_network_sgd(net, train, iters); - end = clock(); - float test_acc = network_accuracy(net, test); - printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay); - } - save_network(net, "cfg/nist_cpu.cfg"); -} - -void test_correct_alexnet() -{ - char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list"); - list *plist = get_paths("/data/imagenet/cls.train.list"); - char **paths = (char **)list_to_array(plist); - printf("%d\n", plist->size); - clock_t time; - int count = 0; - network net; - - srand(222222); - net = parse_network_cfg("cfg/net.cfg"); - int imgs = net.batch; - - count = 0; - while(++count <= 5){ - time=clock(); - data train = load_data(paths, imgs, plist->size, labels, 1000, 256, 256); - normalize_data_rows(train); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch); - free_data(train); - } - - gpu_index = -1; - count = 0; - srand(222222); - net = parse_network_cfg("cfg/net.cfg"); - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - while(++count <= 5){ - time=clock(); - data train = load_data(paths, imgs, plist->size, labels, 1000, 256,256); - normalize_data_rows(train); - printf("Loaded: %lf seconds\n", sec(clock()-time)); - time=clock(); - float loss = train_network(net, train); - printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch); - free_data(train); - } -} - -/* - void run_server() - { - srand(time(0)); - network net = parse_network_cfg("cfg/net.cfg"); - set_batch_network(&net, 1); - server_update(net); - } - - void test_client() - { - network net = parse_network_cfg("cfg/alexnet.client"); - clock_t time=clock(); - client_update(net, "localhost"); - printf("1\n"); - client_update(net, "localhost"); - printf("2\n"); - client_update(net, "localhost"); - printf("3\n"); - printf("Transfered: %lf seconds\n", sec(clock()-time)); - } - */ diff --git a/src/parser.c b/src/parser.c index 254da5c2..b095294c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -14,7 +14,6 @@ #include "softmax_layer.h" #include "dropout_layer.h" #include "detection_layer.h" -#include "region_layer.h" #include "avgpool_layer.h" #include "route_layer.h" #include "list.h" @@ -38,7 +37,6 @@ int is_normalization(section *s); int is_crop(section *s); int is_cost(section *s); int is_detection(section *s); -int is_region(section *s); int is_route(section *s); list *read_cfg(char *filename); @@ -164,39 +162,23 @@ softmax_layer parse_softmax(list *options, size_params params) } detection_layer parse_detection(list *options, size_params params) -{ - int coords = option_find_int(options, "coords", 1); - int classes = option_find_int(options, "classes", 1); - int rescore = option_find_int(options, "rescore", 0); - int joint = option_find_int(options, "joint", 0); - int objectness = option_find_int(options, "objectness", 0); - int background = option_find_int(options, "background", 0); - detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness); - return layer; -} - -region_layer parse_region(list *options, size_params params) { int coords = option_find_int(options, "coords", 1); int classes = option_find_int(options, "classes", 1); int rescore = option_find_int(options, "rescore", 0); int num = option_find_int(options, "num", 1); int side = option_find_int(options, "side", 7); - region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore); + detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore); layer.softmax = option_find_int(options, "softmax", 0); layer.sqrt = option_find_int(options, "sqrt", 0); - layer.object_logistic = option_find_int(options, "object_logistic", 0); - layer.class_logistic = option_find_int(options, "class_logistic", 0); - layer.coord_logistic = option_find_int(options, "coord_logistic", 0); - layer.coord_scale = option_find_float(options, "coord_scale", 1); layer.forced = option_find_int(options, "forced", 0); layer.object_scale = option_find_float(options, "object_scale", 1); layer.noobject_scale = option_find_float(options, "noobject_scale", 1); layer.class_scale = option_find_float(options, "class_scale", 1); - layer.jitter = option_find_float(options, "jitter", .1); + layer.jitter = option_find_float(options, "jitter", .2); return layer; } @@ -430,8 +412,6 @@ network parse_network_cfg(char *filename) l = parse_cost(options, params); }else if(is_detection(s)){ l = parse_detection(options, params); - }else if(is_region(s)){ - l = parse_region(options, params); }else if(is_softmax(s)){ l = parse_softmax(options, params); }else if(is_normalization(s)){ @@ -485,10 +465,6 @@ int is_detection(section *s) { return (strcmp(s->type, "[detection]")==0); } -int is_region(section *s) -{ - return (strcmp(s->type, "[region]")==0); -} int is_deconvolutional(section *s) { return (strcmp(s->type, "[deconv]")==0 diff --git a/src/region_layer.c b/src/region_layer.c deleted file mode 100644 index 3fff22bf..00000000 --- a/src/region_layer.c +++ /dev/null @@ -1,259 +0,0 @@ -#include "region_layer.h" -#include "activations.h" -#include "softmax_layer.h" -#include "blas.h" -#include "box.h" -#include "cuda.h" -#include "utils.h" -#include -#include -#include -#include - -region_layer make_region_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore) -{ - region_layer l = {0}; - l.type = REGION; - - l.n = n; - l.batch = batch; - l.inputs = inputs; - l.classes = classes; - l.coords = coords; - l.rescore = rescore; - l.side = side; - assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); - l.cost = calloc(1, sizeof(float)); - l.outputs = l.inputs; - l.truths = l.side*l.side*(1+l.coords+l.classes); - l.output = calloc(batch*l.outputs, sizeof(float)); - l.delta = calloc(batch*l.outputs, sizeof(float)); -#ifdef GPU - l.output_gpu = cuda_make_array(l.output, batch*l.outputs); - l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); -#endif - - fprintf(stderr, "Region Layer\n"); - srand(0); - - return l; -} - -void forward_region_layer(const region_layer l, network_state state) -{ - int locations = l.side*l.side; - int i,j; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); - int b; - if (l.softmax){ - for(b = 0; b < l.batch; ++b){ - int index = b*l.inputs; - for (i = 0; i < locations; ++i) { - int offset = i*l.classes; - softmax_array(l.output + index + offset, l.classes, - l.output + index + offset); - } - int offset = locations*l.classes; - activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC); - } - } - if (l.object_logistic) { - for(b = 0; b < l.batch; ++b){ - int index = b*l.inputs; - int p_index = index + locations*l.classes; - activate_array(l.output + p_index, locations*l.n, LOGISTIC); - } - } - - if (l.coord_logistic) { - for(b = 0; b < l.batch; ++b){ - int index = b*l.inputs; - int coord_index = index + locations*(l.classes + l.n); - activate_array(l.output + coord_index, locations*l.n*l.coords, LOGISTIC); - } - } - - if (l.class_logistic) { - for(b = 0; b < l.batch; ++b){ - int class_index = b*l.inputs; - activate_array(l.output + class_index, locations*l.classes, LOGISTIC); - } - } - - if(state.train){ - float avg_iou = 0; - float avg_cat = 0; - float avg_allcat = 0; - float avg_obj = 0; - float avg_anyobj = 0; - int count = 0; - *(l.cost) = 0; - int size = l.inputs * l.batch; - memset(l.delta, 0, size * sizeof(float)); - for (b = 0; b < l.batch; ++b){ - int index = b*l.inputs; - for (i = 0; i < locations; ++i) { - int truth_index = (b*locations + i)*(1+l.coords+l.classes); - int is_obj = state.truth[truth_index]; - for (j = 0; j < l.n; ++j) { - int p_index = index + locations*l.classes + i*l.n + j; - l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); - *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2); - avg_anyobj += l.output[p_index]; - } - - int best_index = -1; - float best_iou = 0; - float best_rmse = 20; - - if (!is_obj){ - continue; - } - - int class_index = index + i*l.classes; - for(j = 0; j < l.classes; ++j) { - l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]); - *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); - if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; - avg_allcat += l.output[class_index+j]; - } - - box truth = float_to_box(state.truth + truth_index + 1 + l.classes); - truth.x /= l.side; - truth.y /= l.side; - - for(j = 0; j < l.n; ++j){ - int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; - box out = float_to_box(l.output + box_index); - out.x /= l.side; - out.y /= l.side; - - if (l.sqrt){ - out.w = out.w*out.w; - out.h = out.h*out.h; - } - - float iou = box_iou(out, truth); - //iou = 0; - float rmse = box_rmse(out, truth); - if(best_iou > 0 || iou > 0){ - if(iou > best_iou){ - best_iou = iou; - best_index = j; - } - }else{ - if(rmse < best_rmse){ - best_rmse = rmse; - best_index = j; - } - } - } - - if(l.forced){ - if(truth.w*truth.h < .1){ - best_index = 1; - }else{ - best_index = 0; - } - } - - int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; - int tbox_index = truth_index + 1 + l.classes; - - box out = float_to_box(l.output + box_index); - out.x /= l.side; - out.y /= l.side; - if (l.sqrt) { - out.w = out.w*out.w; - out.h = out.h*out.h; - } - float iou = box_iou(out, truth); - - //printf("%d", best_index); - int p_index = index + locations*l.classes + i*l.n + best_index; - *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2); - *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2); - avg_obj += l.output[p_index]; - l.delta[p_index] = l.object_scale * (1.-l.output[p_index]); - - if(l.rescore){ - l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); - } - - l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]); - l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]); - l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]); - if(l.sqrt){ - l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]); - l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]); - } - - *(l.cost) += pow(1-iou, 2); - avg_iou += iou; - ++count; - } - if(l.softmax){ - gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords), - LOGISTIC, l.delta + index + locations*l.classes); - } - if (l.object_logistic) { - int p_index = index + locations*l.classes; - gradient_array(l.output + p_index, locations*l.n, LOGISTIC, l.delta + p_index); - } - - if (l.class_logistic) { - int class_index = index; - gradient_array(l.output + class_index, locations*l.classes, LOGISTIC, l.delta + class_index); - } - - if (l.coord_logistic) { - int coord_index = index + locations*(l.classes + l.n); - gradient_array(l.output + coord_index, locations*l.n*l.coords, LOGISTIC, l.delta + coord_index); - } - //printf("\n"); - } - printf("Region Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); - } -} - -void backward_region_layer(const region_layer l, network_state state) -{ - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); -} - -#ifdef GPU - -void forward_region_layer_gpu(const region_layer l, network_state state) -{ - if(!state.train){ - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); - return; - } - - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - float *truth_cpu = 0; - if(state.truth){ - int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); - } - cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); - network_state cpu_state; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_region_layer(l, cpu_state); - cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); - cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); - free(cpu_state.input); - if(cpu_state.truth) free(cpu_state.truth); -} - -void backward_region_layer_gpu(region_layer l, network_state state) -{ - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); - //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1); -} -#endif - diff --git a/src/region_layer.h b/src/region_layer.h deleted file mode 100644 index 95f8e919..00000000 --- a/src/region_layer.h +++ /dev/null @@ -1,18 +0,0 @@ -#ifndef REGION_LAYER_H -#define REGION_LAYER_H - -#include "params.h" -#include "layer.h" - -typedef layer region_layer; - -region_layer make_region_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); -void forward_region_layer(const region_layer l, network_state state); -void backward_region_layer(const region_layer l, network_state state); - -#ifdef GPU -void forward_region_layer_gpu(const region_layer l, network_state state); -void backward_region_layer_gpu(region_layer l, network_state state); -#endif - -#endif diff --git a/src/swag.c b/src/swag.c deleted file mode 100644 index 4dc6bf93..00000000 --- a/src/swag.c +++ /dev/null @@ -1,460 +0,0 @@ -#include "network.h" -#include "region_layer.h" -#include "detection_layer.h" -#include "cost_layer.h" -#include "utils.h" -#include "parser.h" -#include "box.h" - -#ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#endif - -char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; - -void draw_swag(image im, int num, float thresh, box *boxes, float **probs, char *label) -{ - int classes = 20; - int i; - - for(i = 0; i < num; ++i){ - int class = max_index(probs[i], classes); - float prob = probs[i][class]; - if(prob > thresh){ - int width = pow(prob, 1./3.)*10 + 1; - printf("%f %s\n", prob, voc_names[class]); - float red = get_color(0,class,classes); - float green = get_color(1,class,classes); - float blue = get_color(2,class,classes); - //red = green = blue = 0; - box b = boxes[i]; - - int left = (b.x-b.w/2.)*im.w; - int right = (b.x+b.w/2.)*im.w; - int top = (b.y-b.h/2.)*im.h; - int bot = (b.y+b.h/2.)*im.h; - draw_box_width(im, left, top, right, bot, width, red, green, blue); - } - } - show_image(im, label); -} - -void train_swag(char *cfgfile, char *weightfile) -{ - //char *train_images = "/home/pjreddie/data/voc/person_detection/2010_person.txt"; - //char *train_images = "/home/pjreddie/data/people-art/train.txt"; - //char *train_images = "/home/pjreddie/data/voc/test/2012_trainval.txt"; - char *train_images = "/home/pjreddie/data/voc/test/train.txt"; - //char *train_images = "/home/pjreddie/data/voc/test/train_all.txt"; - //char *train_images = "/home/pjreddie/data/voc/test/2007_trainval.txt"; - char *backup_directory = "/home/pjreddie/backup/"; - srand(time(0)); - data_seed = time(0); - char *base = basecfg(cfgfile); - printf("%s\n", base); - float avg_loss = -1; - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - int imgs = net.batch*net.subdivisions; - int i = *net.seen/imgs; - data train, buffer; - - - layer l = net.layers[net.n - 1]; - - int side = l.side; - int classes = l.classes; - float jitter = l.jitter; - - list *plist = get_paths(train_images); - //int N = plist->size; - char **paths = (char **)list_to_array(plist); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.paths = paths; - args.n = imgs; - args.m = plist->size; - args.classes = classes; - args.jitter = jitter; - args.num_boxes = side; - args.d = &buffer; - args.type = REGION_DATA; - - pthread_t load_thread = load_data_in_thread(args); - clock_t time; - //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - i += 1; - time=clock(); - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data_in_thread(args); - - printf("Loaded: %lf seconds\n", sec(clock()-time)); - - /* - image im = float_to_image(net.w, net.h, 3, train.X.vals[113]); - image copy = copy_image(im); - draw_swag(copy, train.y.vals[113], 7, "truth"); - cvWaitKey(0); - free_image(copy); - */ - - time=clock(); - float loss = train_network(net, train); - if (avg_loss < 0) avg_loss = loss; - avg_loss = avg_loss*.9 + loss*.1; - - printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); - if(i%1000==0){ - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } - free_data(train); - } - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - save_weights(net, buff); -} - -void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) -{ - int i,j,n; - //int per_cell = 5*num+classes; - for (i = 0; i < side*side; ++i){ - int row = i / side; - int col = i % side; - for(n = 0; n < num; ++n){ - int index = i*num + n; - int p_index = side*side*classes + i*num + n; - float scale = predictions[p_index]; - int box_index = side*side*(classes + num) + (i*num + n)*4; - boxes[index].x = (predictions[box_index + 0] + col) / side * w; - boxes[index].y = (predictions[box_index + 1] + row) / side * h; - boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w; - boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h; - for(j = 0; j < classes; ++j){ - int class_index = i*classes; - float prob = scale*predictions[class_index+j]; - probs[index][j] = (prob > thresh) ? prob : 0; - } - if(only_objectness){ - probs[index][0] = scale; - } - } - } -} - -void print_swag_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) -{ - int i, j; - for(i = 0; i < total; ++i){ - float xmin = boxes[i].x - boxes[i].w/2.; - float xmax = boxes[i].x + boxes[i].w/2.; - float ymin = boxes[i].y - boxes[i].h/2.; - float ymax = boxes[i].y + boxes[i].h/2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for(j = 0; j < classes; ++j){ - if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j], - xmin, ymin, xmax, ymax); - } - } -} - -void validate_swag(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/comp4_det_test_"; - //base = "/home/pjreddie/comp4_det_test_"; - //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt"); - //list *plist = get_paths("/home/pjreddie/data/cubist/test.txt"); - list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - int square = l.sqrt; - int side = l.side; - - int j; - FILE **fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); - fps[j] = fopen(buff, "w"); - } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - int t; - - float thresh = .001; - int nms = 1; - float iou_thresh = .5; - - int nthreads = 2; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = {0}; - args.w = net.w; - args.h = net.h; - args.type = IMAGE_DATA; - - for(t = 0; t < nthreads; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for(i = nthreads; i < m+nthreads; i += nthreads){ - fprintf(stderr, "%d\n", i); - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for(t = 0; t < nthreads && i+t < m; ++t){ - args.path = paths[i+t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for(t = 0; t < nthreads && i+t-nthreads < m; ++t){ - char *path = paths[i+t-nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - float *predictions = network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); - print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h); - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); -} - -void validate_swag_recall(char *cfgfile, char *weightfile) -{ - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - char *base = "results/comp4_det_test_"; - list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n-1]; - int classes = l.classes; - int square = l.sqrt; - int side = l.side; - - int j, k; - FILE **fps = calloc(classes, sizeof(FILE *)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); - fps[j] = fopen(buff, "w"); - } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); - - int m = plist->size; - int i=0; - - float thresh = .001; - int nms = 0; - float iou_thresh = .5; - float nms_thresh = .5; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for(i = 0; i < m; ++i){ - char *path = paths[i]; - image orig = load_image_color(path, 0, 0); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - float *predictions = network_predict(net, sized.data); - convert_swag_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); - - char *labelpath = find_replace(path, "images", "labels"); - labelpath = find_replace(labelpath, "JPEGImages", "labels"); - labelpath = find_replace(labelpath, ".jpg", ".txt"); - labelpath = find_replace(labelpath, ".JPEG", ".txt"); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for(k = 0; k < side*side*l.n; ++k){ - if(probs[k][0] > thresh){ - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; - float best_iou = 0; - for(k = 0; k < side*side*l.n; ++k){ - float iou = box_iou(boxes[k], t); - if(probs[k][0] > thresh && iou > best_iou){ - best_iou = iou; - } - } - avg_iou += best_iou; - if(best_iou > iou_thresh){ - ++correct; - } - } - - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); - free_image(orig); - free_image(sized); - } -} - -void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh) -{ - - network net = parse_network_cfg(cfgfile); - if(weightfile){ - load_weights(&net, weightfile); - } - region_layer l = net.layers[net.n-1]; - set_batch_network(&net, 1); - srand(2222222); - clock_t time; - char buff[256]; - char *input = buff; - int j; - float nms=.5; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); - while(1){ - if(filename){ - strncpy(input, filename, 256); - } else { - printf("Enter Image Path: "); - fflush(stdout); - input = fgets(input, 256, stdin); - if(!input) return; - strtok(input, "\n"); - } - image im = load_image_color(input,0,0); - image sized = resize_image(im, net.w, net.h); - float *X = sized.data; - time=clock(); - float *predictions = network_predict(net, X); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - convert_swag_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); - if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); - draw_swag(im, l.side*l.side*l.n, thresh, boxes, probs, "predictions"); - - show_image(sized, "resized"); - free_image(im); - free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif - if (filename) break; - } -} - - -/* -#ifdef OPENCV -image ipl_to_image(IplImage* src); -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" - -void demo_swag(char *cfgfile, char *weightfile, float thresh) -{ -network net = parse_network_cfg(cfgfile); -if(weightfile){ -load_weights(&net, weightfile); -} -region_layer layer = net.layers[net.n-1]; -CvCapture *capture = cvCaptureFromCAM(-1); -set_batch_network(&net, 1); -srand(2222222); -while(1){ -IplImage* frame = cvQueryFrame(capture); -image im = ipl_to_image(frame); -cvReleaseImage(&frame); -rgbgr_image(im); - -image sized = resize_image(im, net.w, net.h); -float *X = sized.data; -float *predictions = network_predict(net, X); -draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh); -free_image(im); -free_image(sized); -cvWaitKey(10); -} -} -#else -void demo_swag(char *cfgfile, char *weightfile, float thresh){} -#endif - */ - -void demo_swag(char *cfgfile, char *weightfile, float thresh); -#ifndef GPU -void demo_swag(char *cfgfile, char *weightfile, float thresh){} -#endif - -void run_swag(int argc, char **argv) -{ - float thresh = find_float_arg(argc, argv, "-thresh", .2); - if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); - return; - } - - char *cfg = argv[3]; - char *weights = (argc > 4) ? argv[4] : 0; - char *filename = (argc > 5) ? argv[5]: 0; - if(0==strcmp(argv[2], "test")) test_swag(cfg, weights, filename, thresh); - else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights); - else if(0==strcmp(argv[2], "recall")) validate_swag_recall(cfg, weights); - else if(0==strcmp(argv[2], "demo")) demo_swag(cfg, weights, thresh); -} diff --git a/src/yolo.c b/src/yolo.c index 4b241f32..77dae396 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -9,44 +9,36 @@ #include "opencv2/highgui/highgui_c.h" #endif -char *voc_class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; +char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; -void draw_yolo(image im, float *box, int side, int objectness, char *label, float thresh) +void draw_yolo(image im, int num, float thresh, box *boxes, float **probs, char *label) { int classes = 20; - int elems = 4+classes+objectness; - int j; - int r, c; + int i; - for(r = 0; r < side; ++r){ - for(c = 0; c < side; ++c){ - j = (r*side + c) * elems; - float scale = 1; - if(objectness) scale = 1 - box[j++]; - int class = max_index(box+j, classes); - if(scale * box[j+class] > thresh){ - int width = sqrt(scale*box[j+class])*5 + 1; - printf("%f %s\n", scale * box[j+class], voc_class_names[class]); - float red = get_color(0,class,classes); - float green = get_color(1,class,classes); - float blue = get_color(2,class,classes); + for(i = 0; i < num; ++i){ + int class = max_index(probs[i], classes); + float prob = probs[i][class]; + if(prob > thresh){ + int width = pow(prob, 1./2.)*10; + printf("%f %s\n", prob, voc_names[class]); + float red = get_color(0,class,classes); + float green = get_color(1,class,classes); + float blue = get_color(2,class,classes); + //red = green = blue = 0; + box b = boxes[i]; - j += classes; - float x = box[j+0]; - float y = box[j+1]; - x = (x+c)/side; - y = (y+r)/side; - float w = box[j+2]; //*maxwidth; - float h = box[j+3]; //*maxheight; - h = h*h; - w = w*w; + int left = (b.x-b.w/2.)*im.w; + int right = (b.x+b.w/2.)*im.w; + int top = (b.y-b.h/2.)*im.h; + int bot = (b.y+b.h/2.)*im.h; - int left = (x-w/2)*im.w; - int right = (x+w/2)*im.w; - int top = (y-h/2)*im.h; - int bot = (y+h/2)*im.h; - draw_box_width(im, left, top, right, bot, width, red, green, blue); - } + if(left < 0) left = 0; + if(right > im.w-1) right = im.w-1; + if(top < 0) top = 0; + if(bot > im.h-1) bot = im.h-1; + + draw_box_width(im, left, top, right, bot, width, red, green, blue); } } show_image(im, label); @@ -54,7 +46,13 @@ void draw_yolo(image im, float *box, int side, int objectness, char *label, floa void train_yolo(char *cfgfile, char *weightfile) { + //char *train_images = "/home/pjreddie/data/voc/person_detection/2010_person.txt"; + //char *train_images = "/home/pjreddie/data/people-art/train.txt"; + //char *train_images = "/home/pjreddie/data/voc/test/2012_trainval.txt"; + //char *train_images = "/home/pjreddie/data/voc/test/2010_trainval.txt"; char *train_images = "/home/pjreddie/data/voc/test/train.txt"; + //char *train_images = "/home/pjreddie/data/voc/test/train_all.txt"; + //char *train_images = "/home/pjreddie/data/voc/test/2007_trainval.txt"; char *backup_directory = "/home/pjreddie/backup/"; srand(time(0)); data_seed = time(0); @@ -65,27 +63,21 @@ void train_yolo(char *cfgfile, char *weightfile) if(weightfile){ load_weights(&net, weightfile); } - int imgs = 128; + printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + int imgs = net.batch*net.subdivisions; int i = *net.seen/imgs; - - char **paths; - list *plist = get_paths(train_images); - int N = plist->size; - paths = (char **)list_to_array(plist); - - if(i*imgs > N*80){ - net.layers[net.n-1].objectness = 0; - net.layers[net.n-1].joint = 1; - } - if(i*imgs > N*120){ - net.layers[net.n-1].rescore = 1; - } data train, buffer; - detection_layer layer = get_network_detection_layer(net); - int classes = layer.classes; - int background = layer.objectness; - int side = sqrt(get_detection_layer_locations(layer)); + + layer l = net.layers[net.n - 1]; + + int side = l.side; + int classes = l.classes; + float jitter = l.jitter; + + list *plist = get_paths(train_images); + //int N = plist->size; + char **paths = (char **)list_to_array(plist); load_args args = {0}; args.w = net.w; @@ -94,13 +86,14 @@ void train_yolo(char *cfgfile, char *weightfile) args.n = imgs; args.m = plist->size; args.classes = classes; + args.jitter = jitter; args.num_boxes = side; - args.background = background; args.d = &buffer; - args.type = DETECTION_DATA; + args.type = REGION_DATA; pthread_t load_thread = load_data_in_thread(args); clock_t time; + //while(i*imgs < N*120){ while(get_current_batch(net) < net.max_batches){ i += 1; time=clock(); @@ -109,36 +102,21 @@ void train_yolo(char *cfgfile, char *weightfile) load_thread = load_data_in_thread(args); printf("Loaded: %lf seconds\n", sec(clock()-time)); + + /* + image im = float_to_image(net.w, net.h, 3, train.X.vals[113]); + image copy = copy_image(im); + draw_yolo(copy, train.y.vals[113], 7, "truth"); + cvWaitKey(0); + free_image(copy); + */ + time=clock(); float loss = train_network(net, train); if (avg_loss < 0) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N); - - if((i-1)*imgs <= 80*N && i*imgs > N*80){ - fprintf(stderr, "Second stage done.\n"); - char buff[256]; - sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base); - save_weights(net, buff); - net.layers[net.n-1].joint = 1; - net.layers[net.n-1].objectness = 0; - background = 0; - - pthread_join(load_thread, 0); - free_data(buffer); - args.background = background; - load_thread = load_data_in_thread(args); - } - - if((i-1)*imgs <= 120*N && i*imgs > N*120){ - fprintf(stderr, "Third stage done.\n"); - char buff[256]; - sprintf(buff, "%s/%s_final.weights", backup_directory, base); - net.layers[net.n-1].rescore = 1; - save_weights(net, buff); - } - + printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); if(i%1000==0){ char buff[256]; sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); @@ -147,36 +125,42 @@ void train_yolo(char *cfgfile, char *weightfile) free_data(train); } char buff[256]; - sprintf(buff, "%s/%s_rescore.weights", backup_directory, base); + sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); } -void convert_yolo_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes) +void convert_yolo_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) { - int i,j; - int per_box = 4+classes+(background || objectness); - for (i = 0; i < num_boxes*num_boxes; ++i){ - float scale = 1; - if(objectness) scale = 1-predictions[i*per_box]; - int offset = i*per_box+(background||objectness); - for(j = 0; j < classes; ++j){ - float prob = scale*predictions[offset+j]; - probs[i][j] = (prob > thresh) ? prob : 0; + int i,j,n; + //int per_cell = 5*num+classes; + for (i = 0; i < side*side; ++i){ + int row = i / side; + int col = i % side; + for(n = 0; n < num; ++n){ + int index = i*num + n; + int p_index = side*side*classes + i*num + n; + float scale = predictions[p_index]; + int box_index = side*side*(classes + num) + (i*num + n)*4; + boxes[index].x = (predictions[box_index + 0] + col) / side * w; + boxes[index].y = (predictions[box_index + 1] + row) / side * h; + boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w; + boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h; + for(j = 0; j < classes; ++j){ + int class_index = i*classes; + float prob = scale*predictions[class_index+j]; + probs[index][j] = (prob > thresh) ? prob : 0; + } + if(only_objectness){ + probs[index][0] = scale; + } } - int row = i / num_boxes; - int col = i % num_boxes; - offset += classes; - boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w; - boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h; - boxes[i].w = pow(predictions[offset + 2], 2) * w; - boxes[i].h = pow(predictions[offset + 3], 2) * h; } } -void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h) +void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h) { int i, j; - for(i = 0; i < num_boxes*num_boxes; ++i){ + for(i = 0; i < total; ++i){ float xmin = boxes[i].x - boxes[i].w/2.; float xmax = boxes[i].x + boxes[i].w/2.; float ymin = boxes[i].y - boxes[i].h/2.; @@ -201,29 +185,33 @@ void validate_yolo(char *cfgfile, char *weightfile) load_weights(&net, weightfile); } set_batch_network(&net, 1); - detection_layer layer = get_network_detection_layer(net); fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); srand(time(0)); char *base = "results/comp4_det_test_"; + //base = "/home/pjreddie/comp4_det_test_"; + //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt"); + //list *plist = get_paths("/home/pjreddie/data/cubist/test.txt"); + list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); + //list *plist = get_paths("/home/pjreddie/data/voc/test_2012.txt"); char **paths = (char **)list_to_array(plist); - int classes = layer.classes; - int objectness = layer.objectness; - int background = layer.background; - int num_boxes = sqrt(get_detection_layer_locations(layer)); + layer l = net.layers[net.n-1]; + int classes = l.classes; + int square = l.sqrt; + int side = l.side; int j; FILE **fps = calloc(classes, sizeof(FILE *)); for(j = 0; j < classes; ++j){ char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_class_names[j]); + snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(num_boxes*num_boxes, sizeof(box)); - float **probs = calloc(num_boxes*num_boxes, sizeof(float *)); - for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *)); + box *boxes = calloc(side*side*l.n, sizeof(box)); + float **probs = calloc(side*side*l.n, sizeof(float *)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); int m = plist->size; int i=0; @@ -233,7 +221,7 @@ void validate_yolo(char *cfgfile, char *weightfile) int nms = 1; float iou_thresh = .5; - int nthreads = 8; + int nthreads = 2; image *val = calloc(nthreads, sizeof(image)); image *val_resized = calloc(nthreads, sizeof(image)); image *buf = calloc(nthreads, sizeof(image)); @@ -272,9 +260,9 @@ void validate_yolo(char *cfgfile, char *weightfile) float *predictions = network_predict(net, X); int w = val[t].w; int h = val[t].h; - convert_yolo_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes); - if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh); - print_yolo_detections(fps, id, boxes, probs, num_boxes, classes, w, h); + convert_yolo_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0); + if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); + print_yolo_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h); free(id); free_image(val[t]); free_image(val_resized[t]); @@ -283,6 +271,93 @@ void validate_yolo(char *cfgfile, char *weightfile) fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); } +void validate_yolo_recall(char *cfgfile, char *weightfile) +{ + network net = parse_network_cfg(cfgfile); + if(weightfile){ + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + srand(time(0)); + + char *base = "results/comp4_det_test_"; + list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); + char **paths = (char **)list_to_array(plist); + + layer l = net.layers[net.n-1]; + int classes = l.classes; + int square = l.sqrt; + int side = l.side; + + int j, k; + FILE **fps = calloc(classes, sizeof(FILE *)); + for(j = 0; j < classes; ++j){ + char buff[1024]; + snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); + fps[j] = fopen(buff, "w"); + } + box *boxes = calloc(side*side*l.n, sizeof(box)); + float **probs = calloc(side*side*l.n, sizeof(float *)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); + + int m = plist->size; + int i=0; + + float thresh = .001; + int nms = 0; + float iou_thresh = .5; + float nms_thresh = .5; + + int total = 0; + int correct = 0; + int proposals = 0; + float avg_iou = 0; + + for(i = 0; i < m; ++i){ + char *path = paths[i]; + image orig = load_image_color(path, 0, 0); + image sized = resize_image(orig, net.w, net.h); + char *id = basecfg(path); + float *predictions = network_predict(net, sized.data); + convert_yolo_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1); + if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); + + char *labelpath = find_replace(path, "images", "labels"); + labelpath = find_replace(labelpath, "JPEGImages", "labels"); + labelpath = find_replace(labelpath, ".jpg", ".txt"); + labelpath = find_replace(labelpath, ".JPEG", ".txt"); + + int num_labels = 0; + box_label *truth = read_boxes(labelpath, &num_labels); + for(k = 0; k < side*side*l.n; ++k){ + if(probs[k][0] > thresh){ + ++proposals; + } + } + for (j = 0; j < num_labels; ++j) { + ++total; + box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h}; + float best_iou = 0; + for(k = 0; k < side*side*l.n; ++k){ + float iou = box_iou(boxes[k], t); + if(probs[k][0] > thresh && iou > best_iou){ + best_iou = iou; + } + } + avg_iou += best_iou; + if(best_iou > iou_thresh){ + ++correct; + } + } + + fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); + free(id); + free_image(orig); + free_image(sized); + } +} + void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) { @@ -290,12 +365,18 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) if(weightfile){ load_weights(&net, weightfile); } - detection_layer layer = get_network_detection_layer(net); + detection_layer l = net.layers[net.n-1]; set_batch_network(&net, 1); srand(2222222); clock_t time; char buff[256]; char *input = buff; + int j; + float nms=.5; + printf("%d %d %d", l.side, l.n, l.classes); + box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); + float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); + for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); while(1){ if(filename){ strncpy(input, filename, 256); @@ -312,7 +393,11 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) time=clock(); float *predictions = network_predict(net, X); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - draw_yolo(im, predictions, 7, layer.objectness, "predictions", thresh); + convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); + if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); + draw_yolo(im, l.side*l.side*l.n, thresh, boxes, probs, "predictions"); + + show_image(sized, "resized"); free_image(im); free_image(sized); #ifdef OPENCV @@ -323,6 +408,47 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) } } +/* +#ifdef OPENCV +image ipl_to_image(IplImage* src); +#include "opencv2/highgui/highgui_c.h" +#include "opencv2/imgproc/imgproc_c.h" + +void demo_swag(char *cfgfile, char *weightfile, float thresh) +{ +network net = parse_network_cfg(cfgfile); +if(weightfile){ +load_weights(&net, weightfile); +} +detection_layer layer = net.layers[net.n-1]; +CvCapture *capture = cvCaptureFromCAM(-1); +set_batch_network(&net, 1); +srand(2222222); +while(1){ +IplImage* frame = cvQueryFrame(capture); +image im = ipl_to_image(frame); +cvReleaseImage(&frame); +rgbgr_image(im); + +image sized = resize_image(im, net.w, net.h); +float *X = sized.data; +float *predictions = network_predict(net, X); +draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh); +free_image(im); +free_image(sized); +cvWaitKey(10); +} +} +#else +void demo_swag(char *cfgfile, char *weightfile, float thresh){} +#endif + */ + +void demo_yolo(char *cfgfile, char *weightfile, float thresh); +#ifndef GPU +void demo_yolo(char *cfgfile, char *weightfile, float thresh){} +#endif + void run_yolo(int argc, char **argv) { float thresh = find_float_arg(argc, argv, "-thresh", .2); @@ -337,4 +463,6 @@ void run_yolo(int argc, char **argv) if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh); else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); + else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); + else if(0==strcmp(argv[2], "demo")) demo_yolo(cfg, weights, thresh); } diff --git a/src/swag_kernels.cu b/src/yolo_kernels.cu similarity index 77% rename from src/swag_kernels.cu rename to src/yolo_kernels.cu index 5cba15cc..f02b7a23 100644 --- a/src/swag_kernels.cu +++ b/src/yolo_kernels.cu @@ -1,6 +1,5 @@ extern "C" { #include "network.h" -#include "region_layer.h" #include "detection_layer.h" #include "cost_layer.h" #include "utils.h" @@ -13,16 +12,16 @@ extern "C" { #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" extern "C" image ipl_to_image(IplImage* src); -extern "C" void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness); -extern "C" void draw_swag(image im, int num, float thresh, box *boxes, float **probs, char *label); +extern "C" void convert_yolo_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness); +extern "C" void draw_yolo(image im, int num, float thresh, box *boxes, float **probs, char *label); -extern "C" void demo_swag(char *cfgfile, char *weightfile, float thresh) +extern "C" void demo_yolo(char *cfgfile, char *weightfile, float thresh) { network net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } - region_layer l = net.layers[net.n-1]; + detection_layer l = net.layers[net.n-1]; cv::VideoCapture cap(0); set_batch_network(&net, 1); @@ -43,12 +42,12 @@ extern "C" void demo_swag(char *cfgfile, char *weightfile, float thresh) image sized = resize_image(im, net.w, net.h); float *X = sized.data; float *predictions = network_predict(net, X); - convert_swag_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); + convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms); printf("\033[2J"); printf("\033[1;1H"); printf("\nObjects:\n\n"); - draw_swag(im, l.side*l.side*l.n, thresh, boxes, probs, "predictions"); + draw_yolo(im, l.side*l.side*l.n, thresh, boxes, probs, "predictions"); free_image(im); free_image(sized); @@ -56,6 +55,6 @@ extern "C" void demo_swag(char *cfgfile, char *weightfile, float thresh) } } #else -extern "C" void demo_swag(char *cfgfile, char *weightfile, float thresh){} +extern "C" void demo_yolo(char *cfgfile, char *weightfile, float thresh){} #endif