docker安装caffe
docker构建容器，新建文件Dockerfile，复制进下面的内容，然后运行build构建容器，读条完毕后CPU版的docker Caffe就安装好了。
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| docker build -t caffe:1.0 .
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Dockerfile
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| FROM ubuntu:16.04
LABEL maintainer caffe-maint@googlegroups.com
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
cmake \
git \
wget \
libatlas-base-dev \
libboost-all-dev \
libgflags-dev \
libgoogle-glog-dev \
libhdf5-serial-dev \
libleveldb-dev \
liblmdb-dev \
libopencv-dev \
libprotobuf-dev \
libsnappy-dev \
protobuf-compiler \
python-dev \
python-numpy \
python-pip \
python-setuptools \
python-scipy && \
rm -rf /var/lib/apt/lists/*
ENV CAFFE_ROOT=/opt/caffe
WORKDIR $CAFFE_ROOT
ENV CLONE_TAG=1.0
RUN git clone -b ${CLONE_TAG} --depth 1 https://gitee.com/mirrors/caffe.git . && \
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pip -U && \
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \
cd python && for req in $(cat requirements.txt) pydot; do pip install $req; done && cd .. && \
mkdir build && cd build && \
cmake -DCPU_ONLY=1 .. && \
make -j"$(nproc)"
ENV PYCAFFE_ROOT $CAFFE_ROOT/python
ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
WORKDIR /workspace
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样本分类和转化样本数据
运行python脚本，把样本数据装化成caffe可以识别的格式。
注意样本数据名称不能有空格如果有空格的话，需要运行下边这个脚本给样本批量重命名。
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| @echo off&setlocal EnableDelayedExpansion
set a=1
for /f "delims=" %%i in ('dir /b *.bmp') do (
if not "%%~ni"=="%~n0" (
if !a! LSS 10 (ren "%%i" "!a!.bmp") else ren "%%i" "!a!.bmp"
set/a a+=1
)
)
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样本分类脚本
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|
import os
import random
image_path = '../trainImages'
txt_path = '../'
if __name__ == '__main__':
val_percent = 0.2
train_percent = 0.8
labels = os.listdir(image_path)
image_list = [os.path.join(root, fn) for root, dirs, files in os.walk(image_path) for fn in files]
image_count = len(image_list)
list = range(image_count)
tv = int(image_count * val_percent)
tr = int(image_count * train_percent)
val = random.sample(list, tv)
file_train = open(txt_path + '/train.txt', 'wt')
file_val = open(txt_path + '/val.txt', 'wt')
for i in list:
name = image_list[i]
sub_name = name[:name.rindex("\\")]
file_name = name[name.rindex("\\") + 1:]
label = sub_name[sub_name.rindex("\\") + 1:]
index = labels.index(label)
if i in val:
print('样本 (' + name + ') 标签 (' + str(index) + ') 标为验证集')
file_val.write(label + '/' + file_name + ' ' + str(index) + '\n')
else:
print('样本 (' + name + ') 标签 (' + str(index) + ') 标为训练集')
file_train.write(label + '/' + file_name + ' ' + str(index) + '\n')
file_train.close()
file_val.close()
print('完成！')
print('标签种类:')
print(labels)
print('样本总数:' + str(image_count))
print('训练集总数:' + str(tr))
print('验证集总数:' + str(tv))
exit(1)
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生成完txt后，txt里的数据应该是这个样的。
工程结构是这个样的
开始训练
创建生成好的容器
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| docker run -id --name caffe caffe:1.0
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输入下边脚本进入容器，并把整个工程复制到初始的/workspace目录下。
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| sudo docker exec -it 容器号 /bin/bash
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| docker cp 本地文件的路径 container_id:<docker容器内的路径>
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模型文件
模型都放在工程目录下的model文件夹里
solver.prototxt
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| net: "/workspace/Caffe/model/train_val.prototxt" #这里时train_val.prototxt文件的路径
test_iter: 4 # x * 50 = 训练集大小
test_interval: 50
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 100
display: 20
max_iter: 1000 #最大训练次数
momentum: 0.9
weight_decay: 0.0005
snapshot: 100 #每x次保存一次模型快照
snapshot_prefix: "/workspace/Caffe/save_model/" #生成快照的路径
solver_mode: CPU
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train_val.prototxt
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| name: "LeNet"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 20
mean_file: "/workspace/Caffe/train_mean.binaryproto"
}
data_param {
source: "/workspace/Caffe/train_lmdb"
batch_size: 32
backend: LMDB
}
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 20
mean_file: "/workspace/Caffe/val_mean.binaryproto"
}
data_param {
source: "/workspace/Caffe/val_lmdb"
batch_size: 50
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 7 #最后输出的分类数量
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "Softmax"
bottom: "ip2"
top: "loss"
}
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deploy.prototxt
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| name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 20 dim: 20} }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 7
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "Softmax"
bottom: "ip2"
top: "loss"
}
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训练脚本
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| EXAMPLE=/workspace/Caffe #工程的路径
TOOLS=/opt/caffe/build/tools #caffe工具路径
TRAIN_DATA_ROOT=$EXAMPLE/trainImages/ #训练集路径
VAL_DATA_ROOT=$EXAMPLE/trainImages/ #测试集路径
RESIZE=true #这里是resize图片，我这里时开启此功能，resize成20*20
if $RESIZE; then
RESIZE_HEIGHT=20
RESIZE_WIDTH=20
else
RESIZE_HEIGHT=0
RESIZE_WIDTH=0
fi
if [ ! -d "$TRAIN_DATA_ROOT" ]; then
echo "Error: 训练集路径错误: $TRAIN_DATA_ROOT"
echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
"where the ImageNet training data is stored."
exit 1
fi
if [ ! -d "$VAL_DATA_ROOT" ]; then
echo "Error: 验证集路径错误: $VAL_DATA_ROOT"
echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \
"where the ImageNet validation data is stored."
exit 1
fi
#生成前需要先删除已有的文件夹 必须，不然会报错
rm -rf $EXAMPLE/train_lmdb
rm -rf $EXAMPLE/val_lmdb
echo "删除已有的lmdb文件夹"
#训练_lmdb
echo "生成训练lmdb"
GLOG_logtostderr=1 $TOOLS/convert_imageset \
--resize_height=$RESIZE_HEIGHT \
--resize_width=$RESIZE_WIDTH \
--shuffle \
$TRAIN_DATA_ROOT \
$EXAMPLE/train.txt \
$EXAMPLE/train_lmdb
#生成的训练lmdb文件所在文件夹，注意train_lmdb是文件夹名称
#测试_lmdb
echo "生成测试lmdb"
GLOG_logtostderr=1 $TOOLS/convert_imageset \
--resize_height=$RESIZE_HEIGHT \
--resize_width=$RESIZE_WIDTH \
--shuffle \
$VAL_DATA_ROOT \
$EXAMPLE/val.txt \
$EXAMPLE/val_lmdb
#生成的测试lmdb文件所在文件夹，注意val_lmdb文件夹名称
#解锁生成的文件夹
echo "解锁lmdb文件夹"
chmod -R 777 $EXAMPLE/train_lmdb
chmod -R 777 $EXAMPLE/val_lmdb
#生成image_mean
$TOOLS/compute_image_mean $EXAMPLE/train_lmdb \
$EXAMPLE/train_mean.binaryproto
$TOOLS/compute_image_mean $EXAMPLE/val_lmdb \
$EXAMPLE/val_mean.binaryproto
#Caffe训练
mkdir -p $EXAMPLE/save_model
chmod -R 777 $EXAMPLE/save_model
caffe train --solver=$EXAMPLE/model/solver.prototxt
chmod -R 777 $EXAMPLE/save_model
echo "完成"
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复制训练模型
训练结束后，可以使用以下命令复制出训练结束的模型到宿主机，
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| docker cp container_id:<docker容器内的路径> <本地保存文件的路径>
|
