淺談tensorflow語義分割api的使用(deeplab訓練cityscapes)

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淺談tensorflow語義分割api的使用(deeplab訓練cityscapes)

安裝教程：

cityscapes訓練：

遇到的坑：

1. 環境：

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

– 使用最新的tensorflow1.12或者1.10都不行，報錯：報錯不造卷積算法（convolution algorithm…）

2. 數據集轉換

# 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}" \

– 首先當前conda環境下安裝cityscapesScripts模塊，要支持py3.5才行；

– 由於cityscapesscripts/preparation/createTrainIdLabelImgs.py裡面默認會把數據集gtFine下面的test,train,val文件夾json文件都轉為TrainIdlandelImgs.png;然而在test文件下有很多json文件編碼格式是錯誤的，大約十幾張，每次報錯，然後將其剔除！！！

– 然後執行build_cityscapes_data.py將img,lable轉換為tfrecord格式。

3. 訓練cityscapes代碼

– 將訓練代碼寫成腳本文件：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}

參數分析：

training_number_of_steps： 訓練迭代次數；

train_crop_size：訓練圖片的裁剪大小，因為我的GPU隻有8G，故我將這個設置為513瞭；

train_batch_size: 訓練的batchsize，也是因為硬件條件，故保持1；

fine_tune_batch_norm=False ：是否使用batch_norm，官方建議，如果訓練的batch_size小於12的話，須將該參數設置為False，這個設置很重要，否則的話訓練時會在2000步左右報錯

tf_initial_checkpoint：預訓練的初始checkpoint，這裡設置的即是前面下載的../research/deeplab/backbone/deeplabv3_cityscapes_train/model.ckpt.index

train_logdir: 保存訓練權重的目錄，註意在開始的創建工程目錄的時候就創建瞭，這裡設置為”../research/deeplab/exp/train_on_train_set/train/”

dataset_dir:數據集的地址，前面創建的TFRecords目錄。這裡設置為”../dataset/cityscapes/tfrecord”

4.驗證測試

– 驗證腳本：

#!/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為在初始化模型上訓練40000次迭代的模型；後面用初始化模型測試miou_1.0還是很低，不知道是不是有什麼參數設置的問題！！！

– 註意，如果使用官方提供的checkpoint，壓縮包中是沒有checkpoint文件的，需要手動添加一個checkpoint文件；初始化模型中是沒有提供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.可視化測試

– 在vis目錄下生成分割結果圖

#!/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}

以上為個人經驗，希望能給大傢一個參考，也希望大傢多多支持WalkonNet。

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