Tensorflow框架下Faster-RCNN实践（二）——用自己制作的数据训练Faster-RCNN网络（附代码）

#!/usr/bin/env bash
TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
echo $TF_INC

CUDA_PATH=/usr/local/cuda/

cd roi_pooling_layer

nvcc -std=c++11 -c -o roi_pooling_op.cu.o roi_pooling_op_gpu.cu.cc \
-I $TF_INC -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_52

## if you install tf using already-built binary, or gcc version 4.x, uncomment the two lines below
#g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o roi_pooling.so roi_pooling_op.cc \
#   roi_pooling_op.cu.o -I $TF_INC -fPIC -lcudart -L $CUDA_PATH/lib64

# for gcc5-built tf
#g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=1 -o roi_pooling.so roi_pooling_op.cc \
#   roi_pooling_op.cu.o -I $TF_INC -fPIC -lcudart -L $CUDA_PATH/lib64
g++ -std=c++11 -shared -o roi_pooling.so roi_pooling_op.cc  -D_GLIBCXX_USE_CXX11_ABI=0  \
roi_pooling_op.cu.o -I $TF_INC  -D GOOGLE_CUDA=1 -fPIC $CXXFLAGS \
-lcudart -L $CUDA_PATH/lib64
cd ..

# add building psroi_pooling layer
cd psroi_pooling_layer
nvcc -std=c++11 -c -o psroi_pooling_op.cu.o psroi_pooling_op_gpu.cu.cc \
-I $TF_INC -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_52

g++ -std=c++11 -shared -o psroi_pooling.so psroi_pooling_op.cc -D_GLIBCXX_USE_CXX11_ABI=0  \
psroi_pooling_op.cu.o -I $TF_INC -D GOOGLE_CUDA=1  -fPIC $CXXFLAGS \
-lcudart -L $CUDA_PATH/lib64

## if you install tf using already-built binary, or gcc version 4.x, uncomment the two lines below
#g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o psroi_pooling.so psroi_pooling_op.cc \
#   psroi_pooling_op.cu.o -I $TF_INC -fPIC -lcudart -L $CUDA_PATH/lib64

cd ..

python ./faster_rcnn/train_net.py --gpu 0 --weights ./data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval --iters 70000 --cfg ./experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train --restore 0 --set EXP_DIR exp_dir

#!/bin/bash

python -m cProfile -o experiments/profiling/profile.out ./faster_rcnn/train_net.py\
--gpu 0 --weights data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval \
--iters 1000 --cfg experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train

# generate an image
if [ ! -f experiments/profiling/gprof2dot.py ]; then
echo "Downloading ... "
wget https://raw.githubusercontent.com/jrfonseca/gprof2dot/master/gprof2dot.py -O experiments/profiling/gprof2dot.py
fi
python experiments/profiling/gprof2dot.py -f pstats experiments/profiling/profile.out | dot -Tpng -o experiments/profiling/profile.png

#!/bin/bash

python -m cProfile -o experiments/profiling/profile.out ./faster_rcnn/train_net.py\
--gpu 0 --weights data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval \
--iters 1000 --restore 0 --cfg experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train

# generate an image
if [ ! -f experiments/profiling/gprof2dot.py ]; then
echo "Downloading ... "
wget https://raw.githubusercontent.com/jrfonseca/gprof2dot/master/gprof2dot.py -O experiments/profiling/gprof2dot.py
fi
python experiments/profiling/gprof2dot.py -f pstats experiments/profiling/profile.out | dot -Tpng -o experiments/profiling/profile.png

EXP_DIR: faster_rcnn_voc_vgg
LOG_DIR: faster_rcnn_voc_vgg
IS_MULTISCALE: False
NET_NAME: VGGnet
ANCHOR_SCALES: [8, 16, 32]
NCLASSES: 2
TRAIN:
OHEM: True
RPN_BATCHSIZE: 2000
BATCH_SIZE: 300
LOG_IMAGE_ITERS: 100
DISPLAY: 100
SNAPSHOT_ITERS: 5000
HAS_RPN: True
LEARNING_RATE: 0.001
MOMENTUM: 0.9
GAMMA: 0.1
STEPSIZE: 60000
IMS_PER_BATCH: 1
BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
RPN_POSITIVE_OVERLAP: 0.7
RPN_BATCHSIZE: 256
PROPOSAL_METHOD: gt
BG_THRESH_LO: 0.0
PRECLUDE_HARD_SAMPLES: True
BBOX_INSIDE_WEIGHTS: [1, 1, 1, 1]
RPN_BBOX_INSIDE_WEIGHTS: [1, 1, 1, 1]
RPN_POSITIVE_WEIGHT: -1.0
FG_FRACTION: 0.3
WEIGHT_DECAY: 0.0005
TEST:
HAS_RPN: True

In train.py: +from tensorflow.core.protobuf import saver_pb2
and in function of "def init":
self.saver = tf.train.Saver(max_to_keep=100,write_version=saver_pb2.SaverDef.V1)