From 623a707be9aa8ec30a4bf1a7ee54829711bea489 Mon Sep 17 00:00:00 2001
From: oscarkramer <oscar.kramer@radiantsolutions.com>
Date: Tue, 19 Mar 2019 00:41:22 -0400
Subject: [PATCH] Initial fork and modified to use cmake

---
 .gitignore                                |   2 +
 .idea/codeStyles/Project.xml              |  29 +
 .idea/darknet.iml                         |   2 +
 .idea/misc.xml                            |   7 +
 .idea/modules.xml                         |   8 +
 .idea/vcs.xml                             |   6 +
 .idea/workspace.xml                       | 759 ++++++++++++++++++++++
 CMakeLists.txt                            |  55 ++
 Makefile                                  | 105 ---
 examples/CMakeLists.txt                   |   6 +
 examples/attention.c                      |   4 +-
 examples/{dice.c => dice.c.HIDE}          |   0
 examples/swag.c                           |  18 +-
 examples/voxel.c                          |  24 +-
 examples/writing.c                        |  34 +-
 src/CMakeLists.txt                        |  15 +
 src/{compare.c => compare.c.HIDE}         |   0
 src/convolutional_layer.c                 | 579 +++++++++--------
 src/{ => cuda}/activation_kernels.cu      |   0
 src/{ => cuda}/avgpool_layer_kernels.cu   |   0
 src/{ => cuda}/blas_kernels.cu            |   0
 src/{ => cuda}/col2im_kernels.cu          |   0
 src/{ => cuda}/convolutional_kernels.cu   |   0
 src/{ => cuda}/crop_layer_kernels.cu      |   0
 src/{ => cuda}/deconvolutional_kernels.cu |   0
 src/{ => cuda}/dropout_layer_kernels.cu   |   0
 src/{ => cuda}/im2col_kernels.cu          |   0
 src/{ => cuda}/maxpool_layer_kernels.cu   |   0
 src/parser.c                              |   2 +-
 29 files changed, 1221 insertions(+), 434 deletions(-)
 create mode 100644 .idea/codeStyles/Project.xml
 create mode 100644 .idea/darknet.iml
 create mode 100644 .idea/misc.xml
 create mode 100644 .idea/modules.xml
 create mode 100644 .idea/vcs.xml
 create mode 100644 .idea/workspace.xml
 create mode 100644 CMakeLists.txt
 delete mode 100644 Makefile
 create mode 100644 examples/CMakeLists.txt
 rename examples/{dice.c => dice.c.HIDE} (100%)
 create mode 100644 src/CMakeLists.txt
 rename src/{compare.c => compare.c.HIDE} (100%)
 rename src/{ => cuda}/activation_kernels.cu (100%)
 rename src/{ => cuda}/avgpool_layer_kernels.cu (100%)
 rename src/{ => cuda}/blas_kernels.cu (100%)
 rename src/{ => cuda}/col2im_kernels.cu (100%)
 rename src/{ => cuda}/convolutional_kernels.cu (100%)
 rename src/{ => cuda}/crop_layer_kernels.cu (100%)
 rename src/{ => cuda}/deconvolutional_kernels.cu (100%)
 rename src/{ => cuda}/dropout_layer_kernels.cu (100%)
 rename src/{ => cuda}/im2col_kernels.cu (100%)
 rename src/{ => cuda}/maxpool_layer_kernels.cu (100%)

diff --git a/.gitignore b/.gitignore
index bea19ff4..eb15c135 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,6 +16,8 @@ convnet/
 decaf/
 submission/
 cfg/
+weights/
+build/
 darknet
 .fuse*
 
diff --git a/.idea/codeStyles/Project.xml b/.idea/codeStyles/Project.xml
new file mode 100644
index 00000000..30aa626c
--- /dev/null
+++ b/.idea/codeStyles/Project.xml
@@ -0,0 +1,29 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <Objective-C-extensions>
+      <file>
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Import" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Macro" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Typedef" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Enum" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Constant" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Global" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Struct" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="FunctionPredecl" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Function" />
+      </file>
+      <class>
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Property" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="Synthesize" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InitMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="StaticMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="InstanceMethod" />
+        <option name="com.jetbrains.cidr.lang.util.OCDeclarationKind" value="DeallocMethod" />
+      </class>
+      <extensions>
+        <pair source="cpp" header="h" fileNamingConvention="NONE" />
+        <pair source="c" header="h" fileNamingConvention="NONE" />
+      </extensions>
+    </Objective-C-extensions>
+  </code_scheme>
+</component>
\ No newline at end of file
diff --git a/.idea/darknet.iml b/.idea/darknet.iml
new file mode 100644
index 00000000..f08604bb
--- /dev/null
+++ b/.idea/darknet.iml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module classpath="CMake" type="CPP_MODULE" version="4" />
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 00000000..8822db8f
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 00000000..33597b7b
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/darknet.iml" filepath="$PROJECT_DIR$/.idea/darknet.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 00000000..94a25f7f
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 00000000..7aa27d78
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,759 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CMakeRunConfigurationManager" shouldGenerate="true" shouldDeleteObsolete="true" buildAllGenerated="true">
+    <generated>
+      <config projectName="darknet" targetName="DarkNet" />
+      <config projectName="darknet" targetName="darknet" />
+    </generated>
+  </component>
+  <component name="CMakeSettings">
+    <configurations>
+      <configuration PROFILE_NAME="Debug" CONFIG_NAME="Debug" GENERATION_DIR="build" />
+    </configurations>
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="3bfbf356-8fb4-42b7-9cf0-3c2abcc5c096" name="Default Changelist" comment="">
+      <change beforePath="$PROJECT_DIR$/.gitignore" beforeDir="false" afterPath="$PROJECT_DIR$/.gitignore" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Makefile" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/examples/attention.c" beforeDir="false" afterPath="$PROJECT_DIR$/examples/attention.c" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/examples/dice.c" beforeDir="false" afterPath="$PROJECT_DIR$/examples/dice.c.HIDE" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/examples/swag.c" beforeDir="false" afterPath="$PROJECT_DIR$/examples/swag.c" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/examples/voxel.c" beforeDir="false" afterPath="$PROJECT_DIR$/examples/voxel.c" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/examples/writing.c" beforeDir="false" afterPath="$PROJECT_DIR$/examples/writing.c" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/activation_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/avgpool_layer_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/blas_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/col2im_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/compare.c" beforeDir="false" afterPath="$PROJECT_DIR$/src/compare.c.HIDE" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/convolutional_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/convolutional_layer.c" beforeDir="false" afterPath="$PROJECT_DIR$/src/convolutional_layer.c" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/crop_layer_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/deconvolutional_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/dropout_layer_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/im2col_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/maxpool_layer_kernels.cu" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/parser.c" beforeDir="false" afterPath="$PROJECT_DIR$/src/parser.c" afterDir="false" />
+    </list>
+    <ignored path="$PROJECT_DIR$/cmake-build-debug/" />
+    <ignored path="$PROJECT_DIR$/build/" />
+    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="ExecutionTargetManager" SELECTED_TARGET="CMakeBuildProfile:Debug" />
+  <component name="FUSProjectUsageTrigger">
+    <session id="1699346290">
+      <usages-collector id="statistics.lifecycle.project">
+        <counts>
+          <entry key="project.closed" value="2" />
+          <entry key="project.open.time.1" value="1" />
+          <entry key="project.open.time.2" value="1" />
+          <entry key="project.open.time.4" value="1" />
+          <entry key="project.open.time.9" value="1" />
+          <entry key="project.opened" value="4" />
+        </counts>
+      </usages-collector>
+      <usages-collector id="statistics.file.extensions.edit">
+        <counts>
+          <entry key="Makefile" value="1" />
+          <entry key="c" value="62" />
+          <entry key="cu" value="4" />
+          <entry key="gitignore" value="16" />
+          <entry key="h" value="3" />
+          <entry key="txt" value="902" />
+        </counts>
+      </usages-collector>
+      <usages-collector id="statistics.file.types.edit">
+        <counts>
+          <entry key="CMakeLists.txt" value="902" />
+          <entry key="ObjectiveC" value="69" />
+          <entry key="PLAIN_TEXT" value="17" />
+        </counts>
+      </usages-collector>
+      <usages-collector id="statistics.file.extensions.open">
+        <counts>
+          <entry key="HIDE" value="1" />
+          <entry key="Makefile" value="8" />
+          <entry key="OLD" value="2" />
+          <entry key="c" value="40" />
+          <entry key="cfg" value="1" />
+          <entry key="cpp" value="1" />
+          <entry key="cu" value="3" />
+          <entry key="gitignore" value="1" />
+          <entry key="h" value="17" />
+          <entry key="mit" value="1" />
+          <entry key="txt" value="11" />
+        </counts>
+      </usages-collector>
+      <usages-collector id="statistics.file.types.open">
+        <counts>
+          <entry key="CMakeLists.txt" value="11" />
+          <entry key="ObjectiveC" value="62" />
+          <entry key="PLAIN_TEXT" value="13" />
+        </counts>
+      </usages-collector>
+    </session>
+  </component>
+  <component name="FileEditorManager">
+    <splitter split-orientation="horizontal" split-proportion="0.5">
+      <split-first>
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+              </provider>
+            </entry>
+          </file>
+          <file pinned="false" current-in-tab="false">
+            <entry file="file://$PROJECT_DIR$/include/darknet.h">
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+        <leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
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+      <find>&amp;net</find>
+      <find>net.</find>
+      <find>run_yolo</find>
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\ No newline at end of file
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 00000000..153eb842
--- /dev/null
+++ b/CMakeLists.txt
@@ -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 ${LDFLAGS} -lcudnn)
+endif()
+
+add_subdirectory(src)
+add_subdirectory(examples)
+
diff --git a/Makefile b/Makefile
deleted file mode 100644
index 63e15e65..00000000
--- a/Makefile
+++ /dev/null
@@ -1,105 +0,0 @@
-GPU=0
-CUDNN=0
-OPENCV=0
-OPENMP=0
-DEBUG=0
-
-ARCH= -gencode arch=compute_30,code=sm_30 \
-      -gencode arch=compute_35,code=sm_35 \
-      -gencode arch=compute_50,code=[sm_50,compute_50] \
-      -gencode arch=compute_52,code=[sm_52,compute_52]
-#      -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
-
-# This is what I use, uncomment if you know your arch and want to specify
-# ARCH= -gencode arch=compute_52,code=compute_52
-
-VPATH=./src/:./examples
-SLIB=libdarknet.so
-ALIB=libdarknet.a
-EXEC=darknet
-OBJDIR=./obj/
-
-CC=gcc
-CPP=g++
-NVCC=nvcc 
-AR=ar
-ARFLAGS=rcs
-OPTS=-Ofast
-LDFLAGS= -lm -pthread 
-COMMON= -Iinclude/ -Isrc/
-CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
-
-ifeq ($(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)/*
-
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
new file mode 100644
index 00000000..53e15b30
--- /dev/null
+++ b/examples/CMakeLists.txt
@@ -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)
+
diff --git a/examples/attention.c b/examples/attention.c
index cd1e579d..934d922a 100644
--- a/examples/attention.c
+++ b/examples/attention.c
@@ -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);
diff --git a/examples/dice.c b/examples/dice.c.HIDE
similarity index 100%
rename from examples/dice.c
rename to examples/dice.c.HIDE
diff --git a/examples/swag.c b/examples/swag.c
index c22d7855..0dfeb2dc 100644
--- a/examples/swag.c
+++ b/examples/swag.c
@@ -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);
diff --git a/examples/voxel.c b/examples/voxel.c
index 01ea9bb9..aa64a9b4 100644
--- a/examples/voxel.c
+++ b/examples/voxel.c
@@ -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;
diff --git a/examples/writing.c b/examples/writing.c
index 1b6ff83b..623ab8db 100644
--- a/examples/writing.c
+++ b/examples/writing.c
@@ -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();
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 00000000..33124acd
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -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)
diff --git a/src/compare.c b/src/compare.c.HIDE
similarity index 100%
rename from src/compare.c
rename to src/compare.c.HIDE
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 1fb58b09..0bafbf1b 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -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;
 }
 
diff --git a/src/activation_kernels.cu b/src/cuda/activation_kernels.cu
similarity index 100%
rename from src/activation_kernels.cu
rename to src/cuda/activation_kernels.cu
diff --git a/src/avgpool_layer_kernels.cu b/src/cuda/avgpool_layer_kernels.cu
similarity index 100%
rename from src/avgpool_layer_kernels.cu
rename to src/cuda/avgpool_layer_kernels.cu
diff --git a/src/blas_kernels.cu b/src/cuda/blas_kernels.cu
similarity index 100%
rename from src/blas_kernels.cu
rename to src/cuda/blas_kernels.cu
diff --git a/src/col2im_kernels.cu b/src/cuda/col2im_kernels.cu
similarity index 100%
rename from src/col2im_kernels.cu
rename to src/cuda/col2im_kernels.cu
diff --git a/src/convolutional_kernels.cu b/src/cuda/convolutional_kernels.cu
similarity index 100%
rename from src/convolutional_kernels.cu
rename to src/cuda/convolutional_kernels.cu
diff --git a/src/crop_layer_kernels.cu b/src/cuda/crop_layer_kernels.cu
similarity index 100%
rename from src/crop_layer_kernels.cu
rename to src/cuda/crop_layer_kernels.cu
diff --git a/src/deconvolutional_kernels.cu b/src/cuda/deconvolutional_kernels.cu
similarity index 100%
rename from src/deconvolutional_kernels.cu
rename to src/cuda/deconvolutional_kernels.cu
diff --git a/src/dropout_layer_kernels.cu b/src/cuda/dropout_layer_kernels.cu
similarity index 100%
rename from src/dropout_layer_kernels.cu
rename to src/cuda/dropout_layer_kernels.cu
diff --git a/src/im2col_kernels.cu b/src/cuda/im2col_kernels.cu
similarity index 100%
rename from src/im2col_kernels.cu
rename to src/cuda/im2col_kernels.cu
diff --git a/src/maxpool_layer_kernels.cu b/src/cuda/maxpool_layer_kernels.cu
similarity index 100%
rename from src/maxpool_layer_kernels.cu
rename to src/cuda/maxpool_layer_kernels.cu
diff --git a/src/parser.c b/src/parser.c
index c8141c9f..d50a8eca 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -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;