浅谈tensorflow语义分割api的使用(deeplab训练cityscapes)
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}
(资源库 www.zyku.net)
原文链接:https://www.cnblogs.com/ranjiewen/p/10134108.html
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