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Initial fork and modified to use cmake

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This commit is contained in:
oscarkramer 2019-03-19 00:41:22 -04:00
parent 3a2a020c8b
commit 623a707be9
29 changed files with 1221 additions and 434 deletions
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2
.gitignore vendored
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@ -16,6 +16,8 @@ convnet/
decaf/
submission/
cfg/
weights/
build/
darknet
.fuse*

29
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55
CMakeLists.txt Normal file
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@ -0,0 +1,55 @@
cmake_minimum_required(VERSION 3.5)
project(darknet C)
set(GPU 1)
set(CUDNN 0)
set(OPENCV 0)
set(OPENMP 0)
set(DEBUG 1)
set(CMAKE_CXX_STANDARD 14)
set(CMAKE_BINARY_DIR ${CMAKE_CURRENT_SOURCE_DIR}/build)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
set(DEBUG 1)
include_directories(src include)
set (OPTS -Ofast)
if ( DEBUG )
set(OPTS ${OPTS} -O0 -g )
endif()
set ( NVCC nvcc )
set ( AR ar )
set ( ARFLAGS rcs )
set ( LDFLAGS -lm -pthread )
set ( CFLAGS ${OPTS} -Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC)
if ( OPENMP )
set(CFLAGS ${CFLAGS} -fopenmp )
endif()
if ( OPENCV )
set(COMMON ${COMMON} -DOPENCV )
set(CFLAGS ${CFLAGS} -DOPENCV )
set(LDFLAGS ${LDFLAGS} -L${env.OPENCV_HOME}/lib -lopencv_core -lstdc++)
endif()
if ( GPU )
set(COMMON ${COMMON} -DGPU -I/usr/local/cuda/include/ )
set(CFLAGS ${CFLAGS} -DGPU )
set(LDFLAGS ${LDFLAGS} -lstdc++ -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand)
endif()
if ( CUDNN )
set(COMMON ${COMMON} -DCUDNN )
set(CFLAGS ${CFLAGS} -DCUDNN )
set(LDFLAGS ${LDFLAGS} -lcudnn)
endif()
add_subdirectory(src)
add_subdirectory(examples)

105
Makefile
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@ -1,105 +0,0 @@
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0
ARCH= -gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=[sm_50,compute_50] \
-gencode arch=compute_52,code=[sm_52,compute_52]
# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
# This is what I use, uncomment if you know your arch and want to specify
# ARCH= -gencode arch=compute_52,code=compute_52
VPATH=./src/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/
CC=gcc
CPP=g++
NVCC=nvcc
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
ifeq ($(OPENMP), 1)
CFLAGS+= -fopenmp
endif
ifeq ($(DEBUG), 1)
OPTS=-O0 -g
endif
CFLAGS+=$(OPTS)
ifeq ($(OPENCV), 1)
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv` -lstdc++
COMMON+= `pkg-config --cflags opencv`
endif
ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
ifeq ($(CUDNN), 1)
COMMON+= -DCUDNN
CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
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 detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++
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 avgpool_layer_kernels.o
endif
EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h
all: obj backup results $(SLIB) $(ALIB) $(EXEC)
#all: obj results $(SLIB) $(ALIB) $(EXEC)
$(EXEC): $(EXECOBJ) $(ALIB)
$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)
$(ALIB): $(OBJS)
$(AR) $(ARFLAGS) $@ $^
$(SLIB): $(OBJS)
$(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)
$(OBJDIR)%.o: %.cpp $(DEPS)
$(CPP) $(COMMON) $(CFLAGS) -c $< -o $@
$(OBJDIR)%.o: %.c $(DEPS)
$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
$(OBJDIR)%.o: %.cu $(DEPS)
$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
obj:
mkdir -p obj
backup:
mkdir -p backup
results:
mkdir -p results
.PHONY: clean
clean:
rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*

6
examples/CMakeLists.txt Normal file
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@ -0,0 +1,6 @@
message("In ${CMAKE_CURRENT_SOURCE_DIR}")
FILE(GLOB EXAMPLE_SOURCES "*.c" PARENT_SCOPE)
add_executable(darknet ${EXAMPLE_SOURCES} )
SET_TARGET_PROPERTIES(darknet PROPERTIES LINKER_LANGUAGE C)
TARGET_LINK_LIBRARIES(darknet DarkNet pthread m)

4
examples/attention.c
View File

@ -287,7 +287,7 @@ void validate_attention_single(char *datacfg, char *filename, char *weightfile)
printf("\n");
copy_cpu(classes, pred, 1, avgs, 1);
top_k(pred + classes, divs*divs, divs*divs, inds);
show_image(crop, "crop");
show_image(crop, "crop", 0);
for(j = 0; j < extra; ++j){
int index = inds[j];
int row = index / divs;
@ -298,7 +298,7 @@ void validate_attention_single(char *datacfg, char *filename, char *weightfile)
image tile = crop_image(crop, x, y, net->w, net->h);
float *pred = network_predict(net, tile.data);
axpy_cpu(classes, 1., pred, 1, avgs, 1);
show_image(tile, "tile");
show_image(tile, "tile", 0);
//cvWaitKey(10);
}
if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);

0
examples/dice.c → examples/dice.c.HIDE
View File

18
examples/swag.c
View File

@ -9,16 +9,16 @@ void train_swag(char *cfgfile, char *weightfile)
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
network* net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, 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;
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];
layer l = net->layers[net->n - 1];
int side = l.side;
int classes = l.classes;
@ -29,8 +29,8 @@ void train_swag(char *cfgfile, char *weightfile)
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
@ -43,7 +43,7 @@ void train_swag(char *cfgfile, char *weightfile)
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){
while(get_current_batch(net) < net->max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);

24
examples/voxel.c
View File

@ -44,13 +44,13 @@ void train_voxel(char *cfgfile, char *weightfile)
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
network* net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, 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;
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;
@ -59,8 +59,8 @@ void train_voxel(char *cfgfile, char *weightfile)
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.scale = 4;
args.paths = paths;
args.n = imgs;
@ -71,7 +71,7 @@ void train_voxel(char *cfgfile, char *weightfile)
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){
while(get_current_batch(net) < net->max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -105,11 +105,11 @@ void train_voxel(char *cfgfile, char *weightfile)
void test_voxel(char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
network* net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
load_weights(net, weightfile);
}
set_batch_network(&net, 1);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
@ -126,7 +126,7 @@ void test_voxel(char *cfgfile, char *weightfile, char *filename)
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
resize_network(&net, im.w, im.h);
resize_network(net, im.w, im.h);
printf("%d %d\n", im.w, im.h);
float *X = im.data;

34
examples/writing.c
View File

@ -7,12 +7,12 @@ void train_writing(char *cfgfile, char *weightfile)
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
network* net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, 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;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
int imgs = net->batch*net->subdivisions;
list *plist = get_paths("figures.list");
char **paths = (char **)list_to_array(plist);
clock_t time;
@ -23,8 +23,8 @@ void train_writing(char *cfgfile, char *weightfile)
data train, buffer;
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.out_w = out.w;
args.out_h = out.h;
args.paths = paths;
@ -34,8 +34,8 @@ void train_writing(char *cfgfile, char *weightfile)
args.type = WRITING_DATA;
pthread_t load_thread = load_data_in_thread(args);
int epoch = (*net.seen)/N;
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
int epoch = *(net->seen)/N;
while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
time=clock();
pthread_join(load_thread, 0);
train = buffer;
@ -63,15 +63,15 @@ void train_writing(char *cfgfile, char *weightfile)
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*(net->seen))/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
free_data(train);
if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s_batch_%ld.weights", backup_directory, base, get_current_batch(net));
save_weights(net, buff);
}
if(*net.seen/N > epoch){
epoch = *net.seen/N;
if(*net->seen/N > epoch){
epoch = *net->seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
@ -81,11 +81,11 @@ void train_writing(char *cfgfile, char *weightfile)
void test_writing(char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
network* net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
load_weights(net, weightfile);
}
set_batch_network(&net, 1);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
@ -102,7 +102,7 @@ void test_writing(char *cfgfile, char *weightfile, char *filename)
}
image im = load_image_color(input, 0, 0);
resize_network(&net, im.w, im.h);
resize_network(net, im.w, im.h);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
@ -114,8 +114,8 @@ void test_writing(char *cfgfile, char *weightfile, char *filename)
image thresh = threshold_image(upsampled, .5);
pred = thresh;
show_image(pred, "prediction");
show_image(im, "orig");
show_image(pred, "prediction", 0);
show_image(im, "orig", 0);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();

15
src/CMakeLists.txt Normal file
View File

@ -0,0 +1,15 @@
message("In ${CMAKE_CURRENT_SOURCE_DIR}")
FILE(GLOB_RECURSE HEADER_FILES "*.h" PARENT_SCOPE)
FILE(GLOB_RECURSE SOURCE_FILES "*.c" PARENT_SCOPE)
set(HEADER_FILES ${HEADER_FILES} include/darknet.h PARENT_SCOPE)
set(CUDA_FILES)
if ( GPU )
message("Compiling for GPU...")
FILE(GLOB_RECURSE CUDA_FILES "*.cu" PARENT_SCOPE)
message("CUDA_FILES = ${CUDA_FILES}")
endif()
message("SOURCE_FILES = ${SOURCE_FILES}")
add_library(DarkNet ${HEADER_FILES} ${SOURCE_FILES} ${CUDA_FILES})
SET_TARGET_PROPERTIES(DarkNet PROPERTIES LINKER_LANGUAGE C)

0
src/compare.c → src/compare.c.HIDE
View File

579
src/convolutional_layer.c
View File

@ -14,12 +14,12 @@
void swap_binary(convolutional_layer *l)
{
float *swap = l->weights;
l->weights = l->binary_weights;
l->binary_weights = swap;
float *swap = l->weights;
l->weights = l->binary_weights;
l->binary_weights = swap;
#ifdef GPU
swap = l->weights_gpu;
swap = l->weights_gpu;
l->weights_gpu = l->binary_weights_gpu;
l->binary_weights_gpu = swap;
#endif
@ -27,65 +27,65 @@ void swap_binary(convolutional_layer *l)
void binarize_weights(float *weights, int n, int size, float *binary)
{
int i, f;
for(f = 0; f < n; ++f){
float mean = 0;
for(i = 0; i < size; ++i){
mean += fabs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
}
}
int i, f;
for(f = 0; f < n; ++f){
float mean = 0;
for(i = 0; i < size; ++i){
mean += fabs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
}
}
}
void binarize_cpu(float *input, int n, float *binary)
{
int i;
for(i = 0; i < n; ++i){
binary[i] = (input[i] > 0) ? 1 : -1;
}
int i;
for(i = 0; i < n; ++i){
binary[i] = (input[i] > 0) ? 1 : -1;
}
}
void binarize_input(float *input, int n, int size, float *binary)
{
int i, s;
for(s = 0; s < size; ++s){
float mean = 0;
for(i = 0; i < n; ++i){
mean += fabs(input[i*size + s]);
}
mean = mean / n;
for(i = 0; i < n; ++i){
binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
}
}
int i, s;
for(s = 0; s < size; ++s){
float mean = 0;
for(i = 0; i < n; ++i){
mean += fabs(input[i*size + s]);
}
mean = mean / n;
for(i = 0; i < n; ++i){
binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
}
}
}
int convolutional_out_height(convolutional_layer l)
{
return (l.h + 2*l.pad - l.size) / l.stride + 1;
return (l.h + 2*l.pad - l.size) / l.stride + 1;
}
int convolutional_out_width(convolutional_layer l)
{
return (l.w + 2*l.pad - l.size) / l.stride + 1;
return (l.w + 2*l.pad - l.size) / l.stride + 1;
}
image get_convolutional_image(convolutional_layer l)
{
return float_to_image(l.out_w,l.out_h,l.out_c,l.output);
return float_to_image(l.out_w,l.out_h,l.out_c,l.output);
}
image get_convolutional_delta(convolutional_layer l)
{
return float_to_image(l.out_w,l.out_h,l.out_c,l.delta);
return float_to_image(l.out_w,l.out_h,l.out_c,l.delta);
}
static size_t get_workspace_size(layer l){
#ifdef CUDNN
if(gpu_index >= 0){
if(gpu_index >= 0){
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
@ -115,7 +115,7 @@ static size_t get_workspace_size(layer l){
return most;
}
#endif
return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float);
return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float);
}
#ifdef GPU
@ -173,93 +173,96 @@ void cudnn_convolutional_setup(layer *l)
#endif
#endif
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam)
convolutional_layer
make_convolutional_layer(int batch, int h, int w, int c, int n, int groups,
int size, int stride, int padding, ACTIVATION activation,
int batch_normalize, int binary, int xnor, int adam)
{
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
l.groups = groups;
l.h = h;
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = padding;
l.batch_normalize = batch_normalize;
l.groups = groups;
l.h = h;
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = padding;
l.batch_normalize = batch_normalize;
l.weights = calloc(c/groups*n*size*size, sizeof(float));
l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));
l.weights = calloc(c/groups*n*size*size, sizeof(float));
l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
l.nweights = c/groups*n*size*size;
l.nbiases = n;
l.nweights = c/groups*n*size*size;
l.nbiases = n;
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c/l.groups));
//printf("convscale %f\n", scale);
//scale = .02;
//for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
int out_w = convolutional_out_width(l);
int out_h = convolutional_out_height(l);
l.out_h = out_h;
l.out_w = out_w;
l.out_c = n;
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c/l.groups));
//printf("convscale %f\n", scale);
//scale = .02;
//for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
int out_w = convolutional_out_width(l);
int out_h = convolutional_out_height(l);
l.out_h = out_h;
l.out_w = out_w;
l.out_c = n;
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
l.output = calloc(l.batch*l.outputs, sizeof(float));
l.delta = calloc(l.batch*l.outputs, sizeof(float));
l.output = calloc(l.batch*l.outputs, sizeof(float));
l.delta = calloc(l.batch*l.outputs, sizeof(float));
l.forward = forward_convolutional_layer;
l.backward = backward_convolutional_layer;
l.update = update_convolutional_layer;
if(binary){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.cweights = calloc(l.nweights, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
l.forward = forward_convolutional_layer;
l.backward = backward_convolutional_layer;
l.update = update_convolutional_layer;
if(binary){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.cweights = calloc(l.nweights, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
l.scales[i] = 1;
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
l.scales[i] = 1;
}
l.mean = calloc(n, sizeof(float));
l.variance = calloc(n, sizeof(float));
l.mean = calloc(n, sizeof(float));
l.variance = calloc(n, sizeof(float));
l.mean_delta = calloc(n, sizeof(float));
l.variance_delta = calloc(n, sizeof(float));
l.mean_delta = calloc(n, sizeof(float));
l.variance_delta = calloc(n, sizeof(float));
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
l.x = calloc(l.batch*l.outputs, sizeof(float));
l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
}
if(adam){
l.m = calloc(l.nweights, sizeof(float));
l.v = calloc(l.nweights, sizeof(float));
l.bias_m = calloc(n, sizeof(float));
l.scale_m = calloc(n, sizeof(float));
l.bias_v = calloc(n, sizeof(float));
l.scale_v = calloc(n, sizeof(float));
}
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
l.x = calloc(l.batch*l.outputs, sizeof(float));
l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
}
if(adam){
l.m = calloc(l.nweights, sizeof(float));
l.v = calloc(l.nweights, sizeof(float));
l.bias_m = calloc(n, sizeof(float));
l.scale_m = calloc(n, sizeof(float));
l.bias_v = calloc(n, sizeof(float));
l.scale_v = calloc(n, sizeof(float));
}
#ifdef GPU
l.forward_gpu = forward_convolutional_layer_gpu;
l.forward_gpu = forward_convolutional_layer_gpu;
l.backward_gpu = backward_convolutional_layer_gpu;
l.update_gpu = update_convolutional_layer_gpu;
@ -319,27 +322,27 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
#endif
}
#endif
l.workspace_size = get_workspace_size(l);
l.activation = activation;
l.workspace_size = get_workspace_size(l);
l.activation = activation;
fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.);
fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.);
return l;
return l;
}
void denormalize_convolutional_layer(convolutional_layer l)
{
int i, j;
for(i = 0; i < l.n; ++i){
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){
l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
l.scales[i] = 1;
l.rolling_mean[i] = 0;
l.rolling_variance[i] = 1;
}
int i, j;
for(i = 0; i < l.n; ++i){
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){
l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
l.scales[i] = 1;
l.rolling_mean[i] = 0;
l.rolling_variance[i] = 1;
}
}
/*
@ -369,26 +372,26 @@ void test_convolutional_layer()
void resize_convolutional_layer(convolutional_layer *l, int w, int h)
{
l->w = w;
l->h = h;
int out_w = convolutional_out_width(*l);
int out_h = convolutional_out_height(*l);
l->w = w;
l->h = h;
int out_w = convolutional_out_width(*l);
int out_h = convolutional_out_height(*l);
l->out_w = out_w;
l->out_h = out_h;
l->out_w = out_w;
l->out_h = out_h;
l->outputs = l->out_h * l->out_w * l->out_c;
l->inputs = l->w * l->h * l->c;
l->outputs = l->out_h * l->out_w * l->out_c;
l->inputs = l->w * l->h * l->c;
l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
if(l->batch_normalize){
l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
}
l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
if(l->batch_normalize){
l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
}
#ifdef GPU
cuda_free(l->delta_gpu);
cuda_free(l->delta_gpu);
cuda_free(l->output_gpu);
l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
@ -405,218 +408,218 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
cudnn_convolutional_setup(l);
#endif
#endif
l->workspace_size = get_workspace_size(*l);
l->workspace_size = get_workspace_size(*l);
}
void add_bias(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] += biases[i];
}
}
}
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] += biases[i];
}
}
}
}
void scale_bias(float *output, float *scales, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] *= scales[i];
}
}
}
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] *= scales[i];
}
}
}
}
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
{
int i,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
bias_updates[i] += sum_array(delta+size*(i+b*n), size);
}
}
int i,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
bias_updates[i] += sum_array(delta+size*(i+b*n), size);
}
}
}
void forward_convolutional_layer(convolutional_layer l, network net)
{
int i, j;
int i, j;
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
if(l.xnor){
binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights);
swap_binary(&l);
binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input);
net.input = l.binary_input;
}
if(l.xnor){
binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights);
swap_binary(&l);
binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input);
net.input = l.binary_input;
}
int m = l.n/l.groups;
int k = l.size*l.size*l.c/l.groups;
int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.weights + j*l.nweights/l.groups;
float *b = net.workspace;
float *c = l.output + (i*l.groups + j)*n*m;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
int m = l.n/l.groups;
int k = l.size*l.size*l.c/l.groups;
int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.weights + j*l.nweights/l.groups;
float *b = net.workspace;
float *c = l.output + (i*l.groups + j)*n*m;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
if (l.size == 1) {
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
}
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
if (l.size == 1) {
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
}
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
if(l.batch_normalize){
forward_batchnorm_layer(l, net);
} else {
add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w);
}
if(l.batch_normalize){
forward_batchnorm_layer(l, net);
} else {
add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w);
}
activate_array(l.output, l.outputs*l.batch, l.activation);
if(l.binary || l.xnor) swap_binary(&l);
activate_array(l.output, l.outputs*l.batch, l.activation);
if(l.binary || l.xnor) swap_binary(&l);
}
void backward_convolutional_layer(convolutional_layer l, network net)
{
int i, j;
int m = l.n/l.groups;
int n = l.size*l.size*l.c/l.groups;
int k = l.out_w*l.out_h;
int i, j;
int m = l.n/l.groups;
int n = l.size*l.size*l.c/l.groups;
int k = l.out_w*l.out_h;
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
if(l.batch_normalize){
backward_batchnorm_layer(l, net);
} else {
backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
}
if(l.batch_normalize){
backward_batchnorm_layer(l, net);
} else {
backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
}
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.delta + (i*l.groups + j)*m*k;
float *b = net.workspace;
float *c = l.weight_updates + j*l.nweights/l.groups;
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.delta + (i*l.groups + j)*m*k;
float *b = net.workspace;
float *c = l.weight_updates + j*l.nweights/l.groups;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
if(l.size == 1){
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
if(l.size == 1){
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
}
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if (net.delta) {
a = l.weights + j*l.nweights/l.groups;
b = l.delta + (i*l.groups + j)*m*k;
c = net.workspace;
if (l.size == 1) {
c = imd;
}
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
if (net.delta) {
a = l.weights + j*l.nweights/l.groups;
b = l.delta + (i*l.groups + j)*m*k;
c = net.workspace;
if (l.size == 1) {
c = imd;
}
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
if (l.size != 1) {
col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
}
if (l.size != 1) {
col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
}
}
}
}
}
}
}
void update_convolutional_layer(convolutional_layer l, update_args a)
{
float learning_rate = a.learning_rate*l.learning_rate_scale;
float momentum = a.momentum;
float decay = a.decay;
int batch = a.batch;
float learning_rate = a.learning_rate*l.learning_rate_scale;
float momentum = a.momentum;
float decay = a.decay;
int batch = a.batch;
axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
scal_cpu(l.n, momentum, l.bias_updates, 1);
axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
scal_cpu(l.n, momentum, l.bias_updates, 1);
if(l.scales){
axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
scal_cpu(l.n, momentum, l.scale_updates, 1);
}
if(l.scales){
axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
scal_cpu(l.n, momentum, l.scale_updates, 1);
}
axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1);
axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
scal_cpu(l.nweights, momentum, l.weight_updates, 1);
axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1);
axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
scal_cpu(l.nweights, momentum, l.weight_updates, 1);
}
image get_convolutional_weight(convolutional_layer l, int i)
{
int h = l.size;
int w = l.size;
int c = l.c/l.groups;
return float_to_image(w,h,c,l.weights+i*h*w*c);
int h = l.size;
int w = l.size;
int c = l.c/l.groups;
return float_to_image(w,h,c,l.weights+i*h*w*c);
}
void rgbgr_weights(convolutional_layer l)
{
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
rgbgr_image(im);
}
}
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
rgbgr_image(im);
}
}
}
void rescale_weights(convolutional_layer l, float scale, float trans)
{
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
scale_image(im, scale);
float sum = sum_array(im.data, im.w*im.h*im.c);
l.biases[i] += sum*trans;
}
}
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
scale_image(im, scale);
float sum = sum_array(im.data, im.w*im.h*im.c);
l.biases[i] += sum*trans;
}
}
}
image *get_weights(convolutional_layer l)
{
image *weights = calloc(l.n, sizeof(image));
int i;
for(i = 0; i < l.n; ++i){
weights[i] = copy_image(get_convolutional_weight(l, i));
normalize_image(weights[i]);
/*
char buff[256];
sprintf(buff, "filter%d", i);
save_image(weights[i], buff);
*/
}
//error("hey");
return weights;
image *weights = calloc(l.n, sizeof(image));
int i;
for(i = 0; i < l.n; ++i){
weights[i] = copy_image(get_convolutional_weight(l, i));
normalize_image(weights[i]);
/*
char buff[256];
sprintf(buff, "filter%d", i);
save_image(weights[i], buff);
*/
}
//error("hey");
return weights;
}
image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_weights)
{
image *single_weights = get_weights(l);
show_images(single_weights, l.n, window);
image *single_weights = get_weights(l);
show_images(single_weights, l.n, window);
image delta = get_convolutional_image(l);
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);
return single_weights;
image delta = get_convolutional_image(l);
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);
return single_weights;
}

0
src/activation_kernels.cu → src/cuda/activation_kernels.cu
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0
src/avgpool_layer_kernels.cu → src/cuda/avgpool_layer_kernels.cu
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0
src/blas_kernels.cu → src/cuda/blas_kernels.cu
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0
src/col2im_kernels.cu → src/cuda/col2im_kernels.cu
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0
src/convolutional_kernels.cu → src/cuda/convolutional_kernels.cu
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0
src/crop_layer_kernels.cu → src/cuda/crop_layer_kernels.cu
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src/deconvolutional_kernels.cu → src/cuda/deconvolutional_kernels.cu
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0
src/dropout_layer_kernels.cu → src/cuda/dropout_layer_kernels.cu
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0
src/im2col_kernels.cu → src/cuda/im2col_kernels.cu
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0
src/maxpool_layer_kernels.cu → src/cuda/maxpool_layer_kernels.cu
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2
src/parser.c
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@ -739,7 +739,7 @@ int is_network(section *s)
|| strcmp(s->type, "[network]")==0);
}
network *parse_network_cfg(char *filename)
network* parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
node *n = sections->front;