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 layercd 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 ..

#!/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 imageif [ ! -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 imageif [ ! -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)