DeepLabV3+训练自己的数据

﹏ヽ暗。殇╰゛Y 2022-02-16 12:18 438阅读 0赞

1.按照官方文档，配置环境：[https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/installation.md][https_github.com_tensorflow_models_blob_master_research_deeplab_g3doc_installation.md]

安装tensorflow-gpu时可能给会出现问题，需要cuda和cudnn的版本都正确才可以。

cd /anaconda3/envs/DeepLab/lib/python3.6/site-packages
    cd tensorflow
    git clone https://github.com/tensorflow/models.git
    cd models
    cd research
    export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
    python deeplab/model_test.py

(2).利用conda安装指定版本的tensorflow  
conda install --channel https://conda.anaconda.org/anaconda tensorflow=1.6.0  
  
(3).python train.py出现错误：段错误（核心已转储），经查找甄别发现是import matplotlib.pyplot as plt导致的，则使用conda uninstall matplotlib将其先卸载之后，再重新安装即可conda install matplotlib

2.导入原始训练测试数据VOC2012

3.导入自己的训练测试数据

制作满足tfrecord格式的数据集，需要借助于源码中的build\_voc2012\_data.py(tensorflow/models/research/deeplab/datasets/build\_voc2012\_data.py)文件, 在目录tensorflow/models/research/deeplab/datasets/data\_pre.sh下，新建脚本文件，内容为：

#!/bin/bash
    
    # Exit immediately if a command exits with a non-zero status.
    set -e
    
    mkdir -p "./pascal_voc_seg/VOCdevkit/mydata0611/tfrecord" #if files are exist, then no errors. if files are not exist, then create them. 
    
    python ./build_voc2012_data.py \
      --image_folder="./pascal_voc_seg/VOCdevkit/mydata0611/full-VOC/JPEGImages" \
      --semantic_segmentation_folder="./pascal_voc_seg/VOCdevkit/mydata0611/full-VOC/SegmentationClass" \
      --list_folder="./pascal_voc_seg/VOCdevkit/mydata0611/full-VOC/ImageSets/Segmentation" \
      --image_format="jpg" \
      --output_dir="./pascal_voc_seg/VOCdevkit/mydata0611/tfrecord"

在已经部署好的conda环境下（DeepLabV3），运行sh data\_pre.sh。

convert完成后，即可以利用数据进行训练。

4. 训练数据

**在训练数据之前，一定要注意修改此处：**

旧版本修改tensorflow/models/research/deeplab/datasets/segmentation\_dataset.py中的内容，

新版本修改models/research/deeplab/datasets/data\_generator.py中的内容。

_PASCAL_VOC_SEG_INFORMATION = DatasetDescriptor(
        splits_to_sizes={
            #'train': 1464,
            #'train_aug': 10582,
            #'trainval': 2913,
            #'val': 1449,
    
            'train': 1303, #pang-add-mydata5 #训练集个数
            #'train_aug': 10582,
            'trainval': 1303, #训练验证集个数
            'val': 326, #验证集个数
    
    
        },
        #num_classes=21,
        #ignore_label=255,
        num_classes=3, #共有三类
        ignore_label=-1, #忽略掉-1的类别，计算IOU时不算
    
    )

训练数据需要借助于源码tensorflow/models/research/deeplab/train.py，则在该路径下新建tensorflow/models/research/deeplab/train.sh脚本文件，脚本内容为：

#!/bin/bash
    
    # Exit immediately if a command exits with a non-zero status.
    set -e
    
    # Move one-level up to tensorflow/models/research directory.
    cd ..
    
    # Update PYTHONPATH.
    export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
    
    #mydata0611
    # From tensorflow/models/research/
    python deeplab/train.py \
        --logtostderr \
        --training_number_of_steps=20000 \
        --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="pascal_voc_seg" \
        --tf_initial_checkpoint='./deeplab/deeplabv3_pascal_train_aug/model.ckpt' \
        --train_logdir='./deeplab/mydata0611/train_logdir' \
        --dataset_dir='./deeplab/datasets/pascal_voc_seg/VOCdevkit/mydata0611/tfrecord'

注意要提前建好文件夹：./deeplab/mydata0611/train\_logdir

应用：在已经部署好的conda环境下（DeepLabV3），运行sh train.sh。

5. 验证数据

验证数据需要借助于源码tensorflow/models/research/deeplab/eval.py，则在该路径下新建tensorflow/models/research/deeplab/eval.sh脚本文件，脚本内容为：

#!/bin/bash
    
    # Exit immediately if a command exits with a non-zero status.
    set -e
    
    # Move one-level up to tensorflow/models/research directory.
    cd ..
    
    # Update PYTHONPATH.
    export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
    
    #man and baby data 
    #mydata0611
    
    python deeplab/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=700 \
        --eval_crop_size=700 \
        --eval_batch_size=1 \
        --dataset="pascal_voc_seg" \
        --checkpoint_dir='./deeplab/mydata0611/train_logdir' \
        --eval_logdir='./deeplab/mydata0611/eval_logdir' \
        --dataset_dir='./deeplab/datasets/pascal_voc_seg/VOCdevkit/mydata0611/tfrecord'

注意要提前建好文件夹：./deeplab/mydata0611/eval\_logdir

应用：在已经部署好的conda环境下（DeepLabV3），运行sh eval.sh。

6.可视化数据

7.使用tensorboard来查看训练过程：

tensorboard --logdir='./mydata0611'  #dir为train，eval，vis的路径

[https_github.com_tensorflow_models_blob_master_research_deeplab_g3doc_installation.md]: https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/installation.md