SSD 安装、训练、测试（ubuntu14.04+cuda7.5+openvc2.4.9）
安裝步驟
1.安裝git，下載SSD源碼包
sudo apt-get install git
git clone https://github.com/weiliu89/caffe.git
cd caffe
git checkout ssd
以下幾條命令是驗證相應(yīng)的包是否齊全
sudo apt-get install python-pip
sudo apt-get install python-numpy
sudo apt-get install python-scipy
pip install cython -ihttp://pypi.douban.com/simple
pip install eaydict
也可以指定git clone 存放地址（到指定目錄下，運行以上命令就行了）
2.修改Makefile.config文件
復(fù)制根目錄下的Makefile.config.example為Makefile.config
根據(jù)本機環(huán)境，調(diào)整以下參數(shù)：
CUDA_ARCH：
BLAS：
MATLAB_DIR:（可選）
PYTHON_INCLUDE：
3.編譯
在源碼包的根目錄下運行以下命令：
make -j8
make py
make test -j8
make runtest -j8(可選)
4.編譯錯誤分析
注意：cuda8.0要將gcc升級到5.0，否則就會出現(xiàn)上圖的錯誤。錯誤鏈接https://github.com/weiliu89/caffe/issues/237
The problem was that to make caffe with CUDA 8 it is necessary a 5.3 or 5.4 GCC version.
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-5 g++-5
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 60 --slave /usr/bin/g++ g++ /usr/bin/g++-5
錯誤1
如果有多GPU，運行make runtest 出錯
解決方案：export CUDA_VISIBLE_DEVICES=0; makeruntest -j8
如果出現(xiàn)錯誤： check failed :error == cudasuccess(10 vs. 0) invaliddevice ordinal
解決方案：首先需要確保使用的是特定的GPU，或者嘗試
unsetCUDA_VISIBLE_DEVICES
錯誤2
使用caffe時編譯出錯
include and lib
使用自己機器編譯的include和lib (caffe/build/lib, caffe/include)
caffe.pb.h丟失問題：
/home/xxx/caffe/include/caffe/blob.hpp:9:34:fatal error: caffe/proto/caffe.pb.h: No such file or directory
?#include "caffe/proto/caffe.pb.h"
解決方法： 用protoc從caffe/src/caffe/proto/caffe.proto生成caffe.pb.h和caffe.pb.cc
li@li:~/caffe/src/caffe/proto$protoc --cpp_out=/home/xxx/caffe/include/caffe/ caffe.proto
錯誤3
stdc++
linker error:
/usr/bin/ld:caffe_cnn_handler.o: undefined reference to symbol'_ZNSs4_Rep10_M_destroyERKSaIcE@@GLIBCXX_3.4'
//usr/lib/x86_64-linux-gnu/libstdc++.so.6:error adding symbols: DSO missing from command line
解決方案：是找不到libstdc++.so.6，解決方法是在Makefile中加入：
LIBS +=-L/usr/lib/x86_64-linux-gnu -lstdc++
測試步驟
1.下載官網(wǎng)提供的模型，解壓放到/caffe/models/
比如：models_VGGNet_VOC0712_SSD_300x300.tar.gz，
解壓出來的是models文件夾，把這個文件夾里面的VGGNet拷貝放到caffe/models/下
2.測試
源碼包根目錄下運行：?
pythonexamples/ssd/score_ssd_pascal.py (數(shù)值在0.718左右)（老版本使用）
pythonexamles/ssd/ssd_pascal_webcam.py
pythonexamles/ssd/ssd_pascal_video.py
3.錯誤解析
錯誤1
提示：no module named caffe
在score_ssd_pascal.py/ssd_pascal_webcam.py/ssd_pascal_video.py等對應(yīng)腳本中添加
import sys
sys.path.insert(0,'/home/xxx/caffe/python')
訓(xùn)練步驟
1.制作自己的數(shù)據(jù)集（與faster rcnn類似）可參考我的另一篇博文：faster rcnn的安裝、訓(xùn)練、調(diào)試
①新建
(1)data/VOCdevkit/VOC2007新建 Annotations；ImageSets/Main;JPEGImages
說明：
Annotations：保存標簽txt轉(zhuǎn)換的xml文件
JPEGImages： 圖片文件
ImageSets/Main：文件名列表（不含后綴）
訓(xùn)練集： ? ? train.txt
訓(xùn)練驗證集： trainval.txt
測試集： ? ? test.txt
驗證集： ? ? val.txt
②拷貝
將data/VOC0712下面的create_list.sh、create_data.sh、labelmap_voc.prototxt拷貝到data/VOCdevkit2007/VOC2007/
③修改接口
**create_list.sh**：修改3處
1.root_dir=$HOME/data/VOCdevkit/? ? ? ? ? ? ? ?
改寫為 root_dir=$HOME/caffe/data/VOCdevkit/
2.for name inVOC2007 VOC2012 ? ? ? ? ? ? ? ???
改寫為 for name in VOC2007
3.$bash_dir/../../build/tools/get_image_size? ??
改寫為?$HOME/caffe/build/tools/get_image_size
?
**create_data.sh**修改5處
1.root_dir=$cur_dir/../..? ? ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ?
改寫為?root_dir=$HOME/caffe
2.data_root_dir="$HOME/data/VOCdevkit"? ??
改寫為?data_root_dir="$HOME/caffe/data/VOCdevkit"
3.dataset_name="VOC0712"? ? ? ? ? ? ? ? ? ? ?? ??
改寫為 dataset_name="VOC2007"
4.mapfile="$root_dir/data/$dataset_name/labelmap_voc.prototxt"
改寫為?mapfile="$root_dir/data/VOCdevkit/$dataset_name/labelmap_voc.prototxt"
?
?
5.python$root_dir/scripts/create_annoset.py --anno-type=$anno_type--label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim--resize-width=$width --resize-height=$height --check-label $extra_cmd$data_root_dir $root_dir/data/$dataset_name/$subset.txt$data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$dbexamples/$dataset_name
改寫為
python$root_dir/scripts/create_annoset.py --anno-type=$anno_type--label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim--resize-width=$width --resize-height=$height --check-label $extra_cmd$data_root_dir $root_dir/data/VOCdevkit/$dataset_name/$subset.txt$data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$dbexamples/$dataset_name
?
**labelmap_voc.prototxt**
需要注意是label需要小寫，刪除多余的label,保留label=0的背景，以及自己數(shù)據(jù)的name和label
例如：
item {
? name: "none_of_the_above"
? label: 0
? display_name: "background"
}
item {
? name: "face"
? label: 1
? display_name: "face"
}
item {
? name: "pedestrian"
? label: 2
? display_name: "pedestrian"
}
2. 轉(zhuǎn)換成 LMDB文件
到 caffe/examples 路徑下新建VOC2007文件夾，用于創(chuàng)建LMDB文件軟連接
然后到根目錄下運行已經(jīng)修改的sh文件
./data/VOCdevkit/VOC2007/create_list.sh
./data/VOCdevkit/VOC2007/create_data.sh
如果出現(xiàn)： ? ?nomoudle named caffe/caffe-proto,
則在終端輸入：exportPYTHONPATH=$PYTHONPATH:/home/**(服務(wù)器的名字)/caffe/python
?
如果依然不行，打開 ./scripts/creta_annosetpy
在import sys后添加以下代碼：
import os.path asosp
defadd_path(path):
??? if path not in sys.path:
??????? sys.path.insert(0,path)
caffe_path =osp.join('/home/****/caffe/python')
add_path(caffe_path)
3. 如果是直接使用他人已經(jīng)制作好的LMDB 文件，則只需創(chuàng)建鏈接文件
到 ./scripts 創(chuàng)建 create_link.py 文件，并粘貼如下代碼：
import argparse
import os
import shutil
import subprocess
import sys
from caffe.protoimport caffe_pb2
fromgoogle.protobuf import text_format
example_dir ='/home/li/caffe/examples/VOC2007'
out_dir ='/home/***/caffe/data/VOCdevkit/VOC2007/lmdb'
lmdb_name =['VOC2007_test_lmdb', 'VOC2007_trainval_lmdb']
# checkexample_dir is exist
if notos.path.exists(example_dir):
??? os.makedirs(example_dir)
for lmdb_sub inlmdb_name:
??? link_dir = os.path.join(example_dir,lmdb_sub)
??? # check lin_dir is exist
??? if os.path.exists(link_dir):
??????? os.unlink(link_dir)
??? os.symlink(os.path.join(out_dir,lmdb_sub),link_dir)
?
4. 下載預(yù)訓(xùn)練模型
下載預(yù)訓(xùn)練模型VGG_ILSVRC_16_layers_fc_reduced.caffemodel，放在 ./models/VGGNet/路徑下
5. 修改./examples/ssd/ssd_pascal.py腳本
需要修改的地方在對應(yīng)行之后用######標注出來了
from __future__import print_function
import sys######
sys.path.insert(0,'/XXX/caffe/python')######添加路徑SSD/caffe/python路徑，防止找不到caffe
import caffe
fromcaffe.model_libs import *
fromgoogle.protobuf import text_format
import math
import os
import shutil
import stat
import subprocess
# Add extra layerson top of a "base" network (e.g. VGGNet or Inception).
defAddExtraLayers(net, use_batchnorm=True, lr_mult=1):
??? use_relu = True
??? # Add additional convolutional layers.
??? # 19 x 19
??? from_layer = net.keys()[-1]
??? # TODO(weiliu89): Construct the name usingthe last layer to avoid duplication.
??? # 10 x 10
??? out_layer = "conv6_1"
??? ConvBNLayer(net, from_layer, out_layer, use_batchnorm,use_relu, 256, 1, 0, 1,
??????? lr_mult=lr_mult)
??? from_layer = out_layer
??? out_layer = "conv6_2"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 512, 3, 1, 2,
??????? lr_mult=lr_mult)
??? # 5 x 5
??? from_layer = out_layer
??? out_layer = "conv7_1"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 128, 1, 0, 1,
????? lr_mult=lr_mult)
??? from_layer = out_layer
??? out_layer = "conv7_2"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 256, 3, 1, 2,
????? lr_mult=lr_mult)
??? # 3 x 3
??? from_layer = out_layer
??? out_layer = "conv8_1"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 128, 1, 0, 1,
????? lr_mult=lr_mult)
??? from_layer = out_layer
??? out_layer = "conv8_2"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 256, 3, 0, 1,
????? lr_mult=lr_mult)
??? # 1 x 1
??? from_layer = out_layer
??? out_layer = "conv9_1"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 128, 1, 0, 1,
????? lr_mult=lr_mult)
??? from_layer = out_layer
??? out_layer = "conv9_2"
??? ConvBNLayer(net, from_layer, out_layer,use_batchnorm, use_relu, 256, 3, 0, 1,
????? lr_mult=lr_mult)
??? return net
### Modify thefollowing parameters accordingly ###
# The directorywhich contains the caffe code.
# We assume youare running the script at the CAFFE_ROOT.
caffe_root =os.getcwd()
# Set true if youwant to start training right after generating all files.
run_soon = True
# Set true if youwant to load from most recently saved snapshot.
# Otherwise, wewill load from the pretrain_model defined below.
resume_training =True
# If true, Removeold model files.
remove_old_models= False
# The databasefile for training data. Created by data/VOC0712/create_data.sh
train_data ="examples/VOC2007/VOC2007_trainval_lmdb"######
# The databasefile for testing data. Created by data/VOC0712/create_data.sh
test_data ="examples/VOC2007/VOC2007_test_lmdb"######
# Specify thebatch sampler.
resize_width = 300######
resize_height =300######
resize ="{}x{}".format(resize_width, resize_height)
batch_sampler = [
??????? {
??????????????? 'sampler': {
??????????????????????? },
??????????????? 'max_trials': 1,
??????????????? 'max_sample': 1,
??????? },
??????? {
?????????????? ?'sampler': {
??????????????????????? 'min_scale': 0.3,
??????????????????????? 'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????????????????????? },
??????????????? 'sample_constraint': {
??????????????????????? 'min_jaccard_overlap':0.1,
??????????????????????? },
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? {
??????????????? 'sampler': {
??????????????????????? 'min_scale': 0.3,
??????????? ????????????'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????????????????????? },
??????????????? 'sample_constraint': {
??????????????????????? 'min_jaccard_overlap':0.3,
????????? ??????????????},
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? {
??????????????? 'sampler': {
??????????????????????? 'min_scale': 0.3,
??????????????????????? 'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????????????????????? },
??????????????? 'sample_constraint': {
??????????????????????? 'min_jaccard_overlap':0.5,
??????????????????????? },
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? {
??????????????? 'sampler': {
??????????????????????? 'min_scale': 0.3,
??????????????????????? 'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????? ????????????????},
??????????????? 'sample_constraint': {
??????????????????????? 'min_jaccard_overlap':0.7,
??????????????????????? },
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? {
??????????????? 'sampler': {
? ??????????????????????'min_scale': 0.3,
??????????????????????? 'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????????????????????? },
??????????????? 'sample_constraint': {
????????? ??????????????'min_jaccard_overlap': 0.9,
??????????????????????? },
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? {
??????????????? 'sampler': {
??????????????????????? 'min_scale': 0.3,
??????????????????????? 'max_scale': 1.0,
??????????????????????? 'min_aspect_ratio':0.5,
??????????????????????? 'max_aspect_ratio':2.0,
??????????????????????? },
??????????????? 'sample_constraint': {
??????????????????????? 'max_jaccard_overlap':1.0,
??????????????????????? },
??????????????? 'max_trials': 50,
??????????????? 'max_sample': 1,
??????? },
??????? ]
train_transform_param= {
??????? 'mirror': True,
??????? 'mean_value': [104, 117, 123],
??????? 'resize_param': {
??????????????? 'prob': 1,
??????????????? 'resize_mode': P.Resize.WARP,
??????????????? 'height': resize_height,
??????????????? 'width': resize_width,
??????????????? 'interp_mode': [
??????????????????????? P.Resize.LINEAR,
??????????????????????? P.Resize.AREA,
??????????????????????? P.Resize.NEAREST,
??????????????????????? P.Resize.CUBIC,
??????????????????????? P.Resize.LANCZOS4,
??????????????????????? ],
??????????????? },
??????? 'distort_param': {
??????????????? 'brightness_prob': 0.5,
??????????????? 'brightness_delta': 32,
??????????????? 'contrast_prob': 0.5,
??????????????? 'contrast_lower': 0.5,
??????????????? 'contrast_upper': 1.5,
??????????????? 'hue_prob': 0.5,
??????????????? 'hue_delta': 18,
??????????????? 'saturation_prob': 0.5,
??????????????? 'saturation_lower': 0.5,
???????????? ???'saturation_upper': 1.5,
??????????????? 'random_order_prob': 0.0,
??????????????? },
??????? 'expand_param': {
??????????????? 'prob': 0.5,
??????????????? 'max_expand_ratio': 4.0,
??????????????? },
??????? 'emit_constraint': {
??????????? 'emit_type':caffe_pb2.EmitConstraint.CENTER,
??????????? }
??????? }
test_transform_param= {
??????? 'mean_value': [104, 117, 123],
??????? 'resize_param': {
??????????????? 'prob': 1,
??????????????? 'resize_mode': P.Resize.WARP,
??????????????? 'height': resize_height,
??????????????? 'width': resize_width,
??????????????? 'interp_mode':[P.Resize.LINEAR],
??????????????? },
??????? }
# If true, usebatch norm for all newly added layers.
# Currently onlythe non batch norm version has been tested.
use_batchnorm =False
lr_mult = 1
# Use differentinitial learning rate.
if use_batchnorm:
??? base_lr = 0.0004
else:
??? # A learning rate for batch_size = 1,num_gpus = 1.
??? base_lr = 0.000004######
# Modify the jobname if you want.
job_name ="SSD_{}".format(resize)
# The name of themodel. Modify it if you want.
model_name ="VGG_VOC2007_{}".format(job_name)######
# Directory whichstores the model .prototxt file.
save_dir ="models/VGGNet/VOC2007/{}".format(job_name)######
# Directory whichstores the snapshot of models.
snapshot_dir ="models/VGGNet/VOC2007/{}".format(job_name)######
# Directory whichstores the job script and log file.
job_dir ="jobs/VGGNet/VOC2007/{}".format(job_name)######
# Directory whichstores the detection results.
output_result_dir= "{}/data/VOCdevkit/results/VOC2007/{}/Main".format(os.environ['HOME'],job_name)######
# model definitionfiles.
train_net_file ="{}/train.prototxt".format(save_dir)
test_net_file ="{}/test.prototxt".format(save_dir)
deploy_net_file ="{}/deploy.prototxt".format(save_dir)
solver_file ="{}/solver.prototxt".format(save_dir)
# snapshot prefix.
snapshot_prefix ="{}/{}".format(snapshot_dir, model_name)
# job script path.
job_file ="{}/{}.sh".format(job_dir, model_name)
# Stores the testimage names and sizes. Created by data/VOC0712/create_list.sh
name_size_file ="data/VOCdevkit/VOC2007/test_name_size.txt"######
# The pretrainedmodel. We use the Fully convolutional reduced (atrous) VGGNet.
pretrain_model ="models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel"######
# StoresLabelMapItem.
label_map_file ="data/VOCdevkit/VOC2007/labelmap_voc.prototxt"######
# MultiBoxLossparameters.
num_classes = 2######
share_location =True
background_label_id=0
train_on_diff_gt =True
normalization_mode= P.Loss.VALID
code_type =P.PriorBox.CENTER_SIZE
ignore_cross_boundary_bbox= False
mining_type =P.MultiBoxLoss.MAX_NEGATIVE
neg_pos_ratio = 3.
loc_weight =(neg_pos_ratio + 1.) / 4.
multibox_loss_param= {
??? 'loc_loss_type': P.MultiBoxLoss.SMOOTH_L1,
??? 'conf_loss_type': P.MultiBoxLoss.SOFTMAX,
??? 'loc_weight': loc_weight,
??? 'num_classes': num_classes,
??? 'share_location': share_location,
??? 'match_type':P.MultiBoxLoss.PER_PREDICTION,
??? 'overlap_threshold': 0.5,
??? 'use_prior_for_matching': True,
??? 'background_label_id': background_label_id,
??? 'use_difficult_gt': train_on_diff_gt,
??? 'mining_type': mining_type,
??? 'neg_pos_ratio': neg_pos_ratio,
??? 'neg_overlap': 0.5,
??? 'code_type': code_type,
??? 'ignore_cross_boundary_bbox':ignore_cross_boundary_bbox,
??? }
loss_param = {
??? 'normalization': normalization_mode,
??? }
# parameters forgenerating priors.
# minimumdimension of input image
min_dim = 300
# conv4_3 ==>38 x 38
# fc7 ==> 19 x19
# conv6_2 ==>10 x 10
# conv7_2 ==> 5x 5
# conv8_2 ==> 3x 3
# conv9_2 ==> 1x 1
mbox_source_layers= ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2']
# in percent %
min_ratio = 20
max_ratio = 90
step =int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2)))
min_sizes = []
max_sizes = []
for ratio inxrange(min_ratio, max_ratio + 1, step):
? min_sizes.append(min_dim * ratio / 100.)
? max_sizes.append(min_dim * (ratio + step) /100.)
min_sizes =[min_dim * 10 / 100.] + min_sizes
max_sizes =[min_dim * 20 / 100.] + max_sizes
steps = [8, 16,32, 64, 100, 300]
aspect_ratios =[[2], [2, 3], [2, 3], [2, 3], [2], [2]]
# L2 normalizeconv4_3.
normalizations =[20, -1, -1, -1, -1, -1]
# variance used toencode/decode prior bboxes.
if code_type ==P.PriorBox.CENTER_SIZE:
? prior_variance = [0.1, 0.1, 0.2, 0.2]
else:
? prior_variance = [0.1]
flip = True
clip = False
# Solverparameters.
# Defining whichGPUs to use.
gpus ="0"######
gpulist =gpus.split(",")
num_gpus =len(gpulist)
# Divide themini-batch to different GPUs.
batch_size = 32######
accum_batch_size =32######
iter_size =accum_batch_size / batch_size
solver_mode =P.Solver.CPU
device_id = 0
batch_size_per_device= batch_size
if num_gpus >0:
? batch_size_per_device =int(math.ceil(float(batch_size) / num_gpus))
? iter_size =int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
? solver_mode = P.Solver.GPU
? device_id = int(gpulist[0])
ifnormalization_mode == P.Loss.NONE:
? base_lr /= batch_size_per_device
elifnormalization_mode == P.Loss.VALID:
? base_lr *= 25. / loc_weight
elifnormalization_mode == P.Loss.FULL:
? # Roughly there are 2000 prior bboxes perimage.
? # TODO(weiliu89): Estimate the exact # ofpriors.
? base_lr *= 2000.
# Evaluate onwhole test set.
num_test_image =15439######
test_batch_size =8######
test_iter =num_test_image / test_batch_size
solver_param = {
??? # Train parameters
??? 'base_lr': base_lr,
??? 'weight_decay': 0.0005,
??? 'lr_policy': "multistep",
??? 'stepvalue': [80000, 100000, 120000],
??? 'gamma': 0.1,
??? 'momentum': 0.9,
??? 'iter_size': iter_size,
??? 'max_iter': 120000,
??? 'snapshot': 80000,
??? 'display': 10,
??? 'average_loss': 10,
??? 'type': "SGD",
??? 'solver_mode': solver_mode,
??? 'device_id': device_id,
??? 'debug_info': False,
??? 'snapshot_after_train': True,
??? # Test parameters
??? 'test_iter': [test_iter],
??? 'test_interval': 10000,
??? 'eval_type': "detection",
??? 'ap_version': "11point",
??? 'test_initialization': False,
??? }
?
# parameters forgenerating detection output.
det_out_param = {
??? 'num_classes': num_classes,
??? 'share_location': share_location,
??? 'background_label_id': background_label_id,
??? 'nms_param': {'nms_threshold': 0.45,'top_k': 400},
??? 'save_output_param': {
??????? 'output_directory': output_result_dir,
??????? 'output_name_prefix':"comp4_det_test_",
??????? 'output_format': "VOC",
??????? 'label_map_file': label_map_file,
??????? 'name_size_file': name_size_file,
??????? 'num_test_image': num_test_image,
??????? },
??? 'keep_top_k': 200,
??? 'confidence_threshold': 0.01,
??? 'code_type': code_type,
??? }
# parameters forevaluating detection results.
det_eval_param = {
??? 'num_classes': num_classes,
??? 'background_label_id': background_label_id,
??? 'overlap_threshold': 0.5,
??? 'evaluate_difficult_gt': False,
??? 'name_size_file': name_size_file,
??? }
### Hopefully youdon't need to change the following ###
# Check file.
check_if_exist(train_data)
check_if_exist(test_data)
check_if_exist(label_map_file)
check_if_exist(pretrain_model)
make_if_not_exist(save_dir)
make_if_not_exist(job_dir)
make_if_not_exist(snapshot_dir)
# Create trainnet.
net =caffe.NetSpec()
net.data,net.label = CreateAnnotatedDataLayer(train_data,batch_size=batch_size_per_device,
??????? train=True, output_label=True,label_map_file=label_map_file,
??????? transform_param=train_transform_param,batch_sampler=batch_sampler)
VGGNetBody(net,from_layer='data', fully_conv=True, reduced=True, dilated=True,
??? dropout=False)
AddExtraLayers(net,use_batchnorm, lr_mult=lr_mult)
mbox_layers =CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,
??????? use_batchnorm=use_batchnorm,min_sizes=min_sizes, max_sizes=max_sizes,
??????? aspect_ratios=aspect_ratios,steps=steps, normalizations=normalizations,
??????? num_classes=num_classes,share_location=share_location, flip=flip, clip=clip,
??????? prior_variance=prior_variance,kernel_size=3, pad=1, lr_mult=lr_mult)
# Create theMultiBoxLossLayer.
name ="mbox_loss"
mbox_layers.append(net.label)
net[name] =L.MultiBoxLoss(*mbox_layers, multibox_loss_param=multibox_loss_param,
??????? loss_param=loss_param,include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),
??????? propagate_down=[True, True, False,False])
withopen(train_net_file, 'w') as f:
??? print('name:"{}_train"'.format(model_name), file=f)
??? print(net.to_proto(), file=f)
shutil.copy(train_net_file,job_dir)
# Create test net.
net =caffe.NetSpec()
net.data,net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size,
??????? train=False, output_label=True,label_map_file=label_map_file,
??????? transform_param=test_transform_param)
VGGNetBody(net,from_layer='data', fully_conv=True, reduced=True, dilated=True,
??? dropout=False)
AddExtraLayers(net,use_batchnorm, lr_mult=lr_mult)
mbox_layers =CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,
??????? use_batchnorm=use_batchnorm,min_sizes=min_sizes, max_sizes=max_sizes,
??????? aspect_ratios=aspect_ratios,steps=steps, normalizations=normalizations,
??????? num_classes=num_classes,share_location=share_location, flip=flip, clip=clip,
??????? prior_variance=prior_variance,kernel_size=3, pad=1, lr_mult=lr_mult)
conf_name ="mbox_conf"
ifmultibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.SOFTMAX:
? reshape_name ="{}_reshape".format(conf_name)
? net[reshape_name] = L.Reshape(net[conf_name],shape=dict(dim=[0, -1, num_classes]))
? softmax_name ="{}_softmax".format(conf_name)
? net[softmax_name] =L.Softmax(net[reshape_name], axis=2)
? flatten_name ="{}_flatten".format(conf_name)
? net[flatten_name] =L.Flatten(net[softmax_name], axis=1)
? mbox_layers[1] = net[flatten_name]
elifmultibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.LOGISTIC:
? sigmoid_name ="{}_sigmoid".format(conf_name)
? net[sigmoid_name] = L.Sigmoid(net[conf_name])
? mbox_layers[1] = net[sigmoid_name]
net.detection_out= L.DetectionOutput(*mbox_layers,
??? detection_output_param=det_out_param,
???include=dict(phase=caffe_pb2.Phase.Value('TEST')))
net.detection_eval= L.DetectionEvaluate(net.detection_out, net.label,
??? detection_evaluate_param=det_eval_param,
???include=dict(phase=caffe_pb2.Phase.Value('TEST')))
withopen(test_net_file, 'w') as f:
??? print('name:"{}_test"'.format(model_name), file=f)
??? print(net.to_proto(), file=f)
shutil.copy(test_net_file,job_dir)
# Create deploynet.
# Remove the firstand last layer from test net.
deploy_net = net
withopen(deploy_net_file, 'w') as f:
??? net_param = deploy_net.to_proto()
??? # Remove the first (AnnotatedData) and last(DetectionEvaluate) layer from test net.
??? del net_param.layer[0]
??? del net_param.layer[-1]
??? net_param.name ='{}_deploy'.format(model_name)
??? net_param.input.extend(['data'])
??? net_param.input_shape.extend([
??????? caffe_pb2.BlobShape(dim=[1, 3,resize_height, resize_width])])
??? print(net_param, file=f)
shutil.copy(deploy_net_file,job_dir)
# Create solver.
solver =caffe_pb2.SolverParameter(
??????? train_net=train_net_file,
??????? test_net=[test_net_file],
??????? snapshot_prefix=snapshot_prefix,
??????? **solver_param)
withopen(solver_file, 'w') as f:
??? print(solver, file=f)
shutil.copy(solver_file,job_dir)
max_iter = 0
# Find most recentsnapshot.
for file inos.listdir(snapshot_dir):
? if file.endswith(".solverstate"):
??? basename = os.path.splitext(file)[0]
??? iter =int(basename.split("{}_iter_".format(model_name))[1])
??? if iter > max_iter:
????? max_iter = iter
train_src_param ='--weights="{}" \\\n'.format(pretrain_model)
ifresume_training:
? if max_iter > 0:
??? train_src_param ='--snapshot="{}_iter_{}.solverstate" \\\n'.format(snapshot_prefix,max_iter)
ifremove_old_models:
? # Remove any snapshots smaller than max_iter.
? for file in os.listdir(snapshot_dir):
??? if file.endswith(".solverstate"):
????? basename = os.path.splitext(file)[0]
????? iter =int(basename.split("{}_iter_".format(model_name))[1])
????? if max_iter > iter:
???????os.remove("{}/{}".format(snapshot_dir, file))
??? if file.endswith(".caffemodel"):
????? basename = os.path.splitext(file)[0]
????? iter =int(basename.split("{}_iter_".format(model_name))[1])
????? if max_iter > iter:
???????os.remove("{}/{}".format(snapshot_dir, file))
# Create job file.
withopen(job_file, 'w') as f:
? f.write('cd {}\n'.format(caffe_root))
? f.write('./build/tools/caffe train \\\n')
? f.write('--solver="{}"\\\n'.format(solver_file))
? f.write(train_src_param)
? if solver_param['solver_mode'] ==P.Solver.GPU:
??? f.write('--gpu {} 2>&1 | tee{}/{}.log\n'.format(gpus, job_dir, model_name))
? else:
??? f.write('2>&1 | tee{}/{}.log\n'.format(job_dir, model_name))
# Copy the pythonscript to job_dir.
py_file =os.path.abspath(__file__)
shutil.copy(py_file,job_dir)
# Run the job.
os.chmod(job_file,stat.S_IRWXU)
if run_soon:
? subprocess.call(job_file, shell=True)
?
?
train\test\deploy\solver.prototxt等都是運行這個腳本自動生成的。
gpus='0,1,2,3',如果有一塊GPU,則刪除123，有兩塊則刪除23
如果沒有GPU,需要注釋以下幾行，程序會以cpu形式訓(xùn)練：（這個是解決 cudasucess(10vs0)的方法）
#ifnum_gpus >0:
#batch_size_per_device=int(math.ceil(float(batch_size)/num_gpus))
#iter_size = int(math.ceil(float(accum_batch_size)/(batch_size_per_device*num_gpus)))
#solver_model=P.Solver.GPU
#device_id=int(gpulist[0])
6. 修改 ./examples/ssd/ssd_pascal_webcam.py腳本
對應(yīng)修改就行了
7. 訓(xùn)練
在根目錄下運行
python ./examples/ssd/ssd_pascal.py 2>&1 | tee ssd_train_log.txt
?
如果出現(xiàn) cudasucess(2vs0):說明顯卡的計算能力有限，需要更改 caffe/examples/sdd/ssd_pascal.py 中的batch_size. 默認的32變小成16、8、4。
8. 測試單張圖片，并顯示框的坐標信息
[python] view plaincopyprint?9.?關(guān)于aspect_ratios問題
SSD算法中aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]，這句話具體是什么意思
[2, 3] means using default box of aspect ratio of 2 and 3. And since we set flip=True at here, it will also use default box of aspect ratio of 1/2 and 1/3.?
舉例說明：[2]表示ar = {1,2,1/2};[2,3]表示ar = {1,2,3,1/2,1/3}。當?shù)扔?的時候會在增加一個默認框。
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]總計有6個元素，每一個元素對應(yīng)相應(yīng)的feature map
第一個元素表示第一個feature map上每個像素點上有多少個box：【2】：表明ar = {1,2,1/2}。等于1的時候會再增加一個（論文中有說明）
第二個元素同理：【2，3】：表明ar={1,2,3,1/2,1/3}.等于1的時候會再增加一個（論文中有說明）
不明白的直接看看prior_box_layer.cpp代碼。里面有具體操作。一看就懂。
具體的
總結(jié)
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