浅谈tensorflow语义分割api的使用(deeplab训练cityscapes)

安装教程：

cityscapes训练：

遇到的坑：

1. 环境：

– tensorflow1.8+CUDA9.0+cudnn7.0+annaconda3+py3.5

– 使用最新的tensorflow1.12或者1.10都不行，报错：报错不造卷积算法（convolution algorithm…）

2. 数据集转换

# Exit immediately if a command exits with a non-zero status.
set -e
CURRENT_DIR=$(pwd)
WORK_DIR="."
# Root path for Cityscapes dataset.
CITYSCAPES_ROOT="${WORK_DIR}/cityscapes"
# Create training labels.
python "${CITYSCAPES_ROOT}/cityscapesscripts/preparation/createTrainIdLabelImgs.py"
# Build TFRecords of the dataset.
# First, create output directory for storing TFRecords.
OUTPUT_DIR="${CITYSCAPES_ROOT}/tfrecord"
mkdir -p "${OUTPUT_DIR}"
BUILD_SCRIPT="${CURRENT_DIR}/build_cityscapes_data.py"
echo "Converting Cityscapes dataset..."
python "${BUILD_SCRIPT}" \

  --cityscapes_root="${CITYSCAPES_ROOT}" \

  --output_dir="${OUTPUT_DIR}" \

– 首先当前conda环境下安装cityscapesScripts模块，要支持py3.5才行；

– 由于cityscapesscripts/preparation/createTrainIdLabelImgs.py里面默认会把数据集gtFine下面的test,train,val文件夹json文件都转为TrainIdlandelImgs.png;然而在test文件下有很多json文件编码格式是错误的，大约十几张，每次报错，然后将其剔除！！！

– 然后执行build_cityscapes_data.py将img,lable转换为tfrecord格式。

3. 训练cityscapes代码

– 将训练代码写成脚本文件：train_deeplab_cityscapes.sh

#!/bin/bash
# CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone resnet --lr 0.01 --workers 4 --epochs 40 --batch-size 16 --gpu-ids 0,1,2,3 --checkname deeplab-resnet --eval-interval 1 --dataset coco

PATH_TO_INITIAL_CHECKPOINT='/home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/model.ckpt'
PATH_TO_TRAIN_DIR='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/'
PATH_TO_DATASET='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/tfrecord'
WORK_DIR='/home/rjw/tf-models/research/deeplab'
# From tensorflow/models/research/
python "${WORK_DIR}"/train.py \

    --logtostderr \

    --training_number_of_steps=40000 \

    --train_split="train" \

    --model_variant="xception_65" \

    --atrous_rates=6 \

    --atrous_rates=12 \

    --atrous_rates=18 \

    --output_stride=16 \

    --decoder_output_stride=4 \

    --train_crop_size=513 \

    --train_crop_size=513 \

    --train_batch_size=1 \

    --fine_tune_batch_norm=False \

    --dataset="cityscapes" \

    --tf_initial_checkpoint=${PATH_TO_INITIAL_CHECKPOINT} \

    --train_logdir=${PATH_TO_TRAIN_DIR} \

    --dataset_dir=${PATH_TO_DATASET}

参数分析：

training_number_of_steps：训练迭代次数；

train_crop_size：训练图片的裁剪大小，因为我的GPU只有8G，故我将这个设置为513了；

train_batch_size: 训练的batchsize，也是因为硬件条件，故保持1；

fine_tune_batch_norm=False ：是否使用batch_norm，官方建议，如果训练的batch_size小于12的话，须将该参数设置为False，这个设置很重要，否则的话训练时会在2000步左右报错

tf_initial_checkpoint：预训练的初始checkpoint，这里设置的即是前面下载的../research/deeplab/backbone/deeplabv3_cityscapes_train/model.ckpt.index

train_logdir: 保存训练权重的目录，注意在开始的创建工程目录的时候就创建了，这里设置为”../research/deeplab/exp/train_on_train_set/train/”

dataset_dir:数据集的地址，前面创建的TFRecords目录。这里设置为”../dataset/cityscapes/tfrecord”

4.验证测试

– 验证脚本：

#!/bin/bash
# CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone resnet --lr 0.01 --workers 4 --epochs 40 --batch-size 16 --gpu-ids 0,1,2,3 --checkname deeplab-resnet --eval-interval 1 --dataset coco
PATH_TO_INITIAL_CHECKPOINT='/home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/'
PATH_TO_CHECKPOINT='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/'
PATH_TO_EVAL_DIR='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/eval/'
PATH_TO_DATASET='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/tfrecord'
WORK_DIR='/home/rjw/tf-models/research/deeplab'
# From tensorflow/models/research/
python "${WORK_DIR}"/eval.py \

    --logtostderr \

    --eval_split="val" \

    --model_variant="xception_65" \

    --atrous_rates=6 \

    --atrous_rates=12 \

    --atrous_rates=18 \

    --output_stride=16 \

    --decoder_output_stride=4 \

    --eval_crop_size=1025 \

    --eval_crop_size=2049 \

    --dataset="cityscapes" \

    --checkpoint_dir=${PATH_TO_INITIAL_CHECKPOINT} \

    --eval_logdir=${PATH_TO_EVAL_DIR} \

    --dataset_dir=${PATH_TO_DATASET}

– rusult:model.ckpt-40000为在初始化模型上训练40000次迭代的模型；后面用初始化模型测试miou_1.0还是很低，不知道是不是有什么参数设置的问题！！！

– 注意，如果使用官方提供的checkpoint，压缩包中是没有checkpoint文件的，需要手动添加一个checkpoint文件；初始化模型中是没有提供chekpoint文件的。

INFO:tensorflow:Restoring parameters from /home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/model.ckpt-40000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting evaluation at 2018-12-18-07:13:08
INFO:tensorflow:Evaluation [50/500]
INFO:tensorflow:Evaluation [100/500]
INFO:tensorflow:Evaluation [150/500]
INFO:tensorflow:Evaluation [200/500]
INFO:tensorflow:Evaluation [250/500]
INFO:tensorflow:Evaluation [300/500]
INFO:tensorflow:Evaluation [350/500]
INFO:tensorflow:Evaluation [400/500]
INFO:tensorflow:Evaluation [450/500]
miou_1.0[0.478293568]
INFO:tensorflow:Waiting for new checkpoint at /home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/
INFO:tensorflow:Found new checkpoint at /home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/model.ckpt
INFO:tensorflow:Graph was finalized.
2018-12-18 15:18:05.210957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-12-18 15:18:05.211047: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-12-18 15:18:05.211077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0 
2018-12-18 15:18:05.211100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N 
2018-12-18 15:18:05.211645: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9404 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
INFO:tensorflow:Restoring parameters from /home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/model.ckpt
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting evaluation at 2018-12-18-07:18:06
INFO:tensorflow:Evaluation [50/500]
INFO:tensorflow:Evaluation [100/500]
INFO:tensorflow:Evaluation [150/500]
INFO:tensorflow:Evaluation [200/500]
INFO:tensorflow:Evaluation [250/500]
INFO:tensorflow:Evaluation [300/500]
INFO:tensorflow:Evaluation [350/500]
INFO:tensorflow:Evaluation [400/500]
INFO:tensorflow:Evaluation [450/500]
miou_1.0[0.496331513]

5.可视化测试

– 在vis目录下生成分割结果图

#!/bin/bash
# CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone resnet --lr 0.01 --workers 4 --epochs 40 --batch-size 16 --gpu-ids 0,1,2,3 --checkname deeplab-resnet --eval-interval 1 --dataset coco

PATH_TO_CHECKPOINT='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/'
PATH_TO_VIS_DIR='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/vis/'
PATH_TO_DATASET='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/tfrecord'
WORK_DIR='/home/rjw/tf-models/research/deeplab'

# From tensorflow/models/research/
python "${WORK_DIR}"/vis.py \

    --logtostderr \

    --vis_split="val" \

    --model_variant="xception_65" \

    --atrous_rates=6 \

    --atrous_rates=12 \

    --atrous_rates=18 \

    --output_stride=16 \

    --decoder_output_stride=4 \

    --vis_crop_size=1025 \

    --vis_crop_size=2049 \

    --dataset="cityscapes" \

    --colormap_type="cityscapes" \

    --checkpoint_dir=${PATH_TO_CHECKPOINT} \

    --vis_logdir=${PATH_TO_VIS_DIR} \

    --dataset_dir=${PATH_TO_DATASET}

以上为个人经验，希望能给大家一个参考，也希望大家多多支持免费资源网。

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浅谈tensorflow语义分割api的使用(deeplab训练cityscapes)