Updated from darknet

Makefile

@@ -3,13 +3,14 @@ CUDNN=0
 OPENCV=0
 NNPACK=1
 ARM_NEON=1
+OPENMP=0
 DEBUG=0
 
-ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \
-      -gencode arch=compute_30,code=sm_30 \
+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
@@ -21,13 +22,17 @@ EXEC=darknet
 OBJDIR=./obj/
 
 CC=gcc
-NVCC=nvcc --compiler-options '-fPIC'
+NVCC=nvcc 
 AR=ar
 ARFLAGS=rcs
 OPTS=-Ofast
 LDFLAGS= -lm -pthread 
 COMMON= -Iinclude/ -Isrc/
-CFLAGS=-Wall -Wfatal-errors -fPIC
+CFLAGS=-Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC
+
+ifeq ($(OPENMP), 1) 
+CFLAGS+= -fopenmp
+endif
 
 ifeq ($(DEBUG), 1) 
 OPTS=-O0 -g
@@ -66,18 +71,19 @@ CFLAGS+= -DARM_NEON -mfpu=neon-vfpv4 -funsafe-math-optimizations -ftree-vectoriz
 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 box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_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
-EXECOBJA=captcha.o lsd.o super.o voxel.o art.o tag.o cifar.o go.o rnn.o rnn_vid.o compare.o segmenter.o regressor.o classifier.o coco.o dice.o yolo.o detector.o  writing.o nightmare.o swag.o darknet.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 attention.o darknet.o
 
-ifeq ($(GPU), 1)
+ifeq ($(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 network_kernels.o avgpool_layer_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 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 backup results $(SLIB) $(ALIB) $(EXEC)
+all: obj  results $(SLIB) $(ALIB) $(EXEC)
 
 
 $(EXEC): $(EXECOBJ) $(ALIB)

README.md

@@ -44,7 +44,7 @@ The weight files can be downloaded from the [YOLO homepage](https://pjreddie.com
 YOLOv2
 ./darknet detector test cfg/coco.data cfg/yolo.cfg yolo.weights data/person.jpg
 Tiny-YOLO
-./darknet detector test cfg/coco.data cfg/tiny-yolo.cfg tiny-yolo.weights data/person.jpg
+./darknet detector test cfg/voc.data cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights data/person.jpg
 ```
 ## Result
 Model | Build Options | Prediction Time (seconds)

cfg/darknet.cfg

@@ -1,6 +1,10 @@
 [net]
-batch=128
+# Train
+batch=1
 subdivisions=1
+# Test
+# batch=1
+# subdivisions=1
 height=224
 width=224
 channels=3