淺談tensorflow語義分割api的使用(deeplab訓(xùn)練cityscapes)
安裝教程:
cityscapes訓(xùn)練:
遇到的坑:
1. 環(huán)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(shù)據(jù)集轉(zhuǎn)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="${OUTPUT_DIR}" \
- 首先當(dāng)前conda環(huán)境下安裝cityscapesScripts模塊,要支持py3.5才行;
- 由于cityscapesscripts/preparation/createTrainIdLabelImgs.py里面默認(rèn)會把數(shù)據(jù)集gtFine下面的test,train,val文件夾json文件都轉(zhuǎn)為TrainIdlandelImgs.png;然而在test文件下有很多json文件編碼格式是錯誤的,大約十幾張,每次報錯,然后將其剔除?。?!
- 然后執(zhí)行build_cityscapes_data.py將img,lable轉(zhuǎn)換為tfrecord格式。
3. 訓(xùn)練cityscapes代碼
- 將訓(xùn)練代碼寫成腳本文件:train_deeplab_cityscapes.sh
#!/bin/bash
# CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone resnet --lr 0.01 --workers 4 --epochs 40 --batch-size 16 --gpu-ids 0,1,2,3 --checkname deeplab-resnet --eval-interval 1 --dataset coco
PATH_TO_INITIAL_CHECKPOINT='/home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/model.ckpt'
PATH_TO_TRAIN_DIR='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/'
PATH_TO_DATASET='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/tfrecord'
WORK_DIR='/home/rjw/tf-models/research/deeplab'
# From tensorflow/models/research/
python "${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=${PATH_TO_DATASET}
參數(shù)分析:
training_number_of_steps: 訓(xùn)練迭代次數(shù);
train_crop_size:訓(xùn)練圖片的裁剪大小,因為我的GPU只有8G,故我將這個設(shè)置為513了;
train_batch_size: 訓(xùn)練的batchsize,也是因為硬件條件,故保持1;
fine_tune_batch_norm=False :是否使用batch_norm,官方建議,如果訓(xùn)練的batch_size小于12的話,須將該參數(shù)設(shè)置為False,這個設(shè)置很重要,否則的話訓(xùn)練時會在2000步左右報錯
tf_initial_checkpoint:預(yù)訓(xùn)練的初始checkpoint,這里設(shè)置的即是前面下載的../research/deeplab/backbone/deeplabv3_cityscapes_train/model.ckpt.index
train_logdir: 保存訓(xùn)練權(quán)重的目錄,注意在開始的創(chuàng)建工程目錄的時候就創(chuàng)建了,這里設(shè)置為"../research/deeplab/exp/train_on_train_set/train/"
dataset_dir:數(shù)據(jù)集的地址,前面創(chuàng)建的TFRecords目錄。這里設(shè)置為"../dataset/cityscapes/tfrecord"
4.驗證測試
- 驗證腳本:
#!/bin/bash
# CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --backbone resnet --lr 0.01 --workers 4 --epochs 40 --batch-size 16 --gpu-ids 0,1,2,3 --checkname deeplab-resnet --eval-interval 1 --dataset coco
PATH_TO_INITIAL_CHECKPOINT='/home/rjw/tf-models/research/deeplab/pretrain_models/deeplabv3_cityscapes_train/'
PATH_TO_CHECKPOINT='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/train/'
PATH_TO_EVAL_DIR='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/exp/train_on_train_set/eval/'
PATH_TO_DATASET='/home/rjw/tf-models/research/deeplab/datasets/cityscapes/tfrecord'
WORK_DIR='/home/rjw/tf-models/research/deeplab'
# From tensorflow/models/research/
python "${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(xùn)練40000次迭代的模型;后面用初始化模型測試miou_1.0還是很低,不知道是不是有什么參數(shù)設(shè)置的問題!??!
- 注意,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(jié)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(jīng)驗,希望能給大家一個參考,也希望大家多多支持腳本之家。
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