PadddleSeg_padddleseg github

我主要玩儿了pp_liteseg。

做法如下：

一、配置文件

 
batch_size: 6  # total: 4*6
iters: 10000
 
train_dataset:
  type: Dataset
  dataset_root: ./paddleseg/datasets/custom_dataset
  num_classes: 11
  mode: train
  train_path: ./paddleseg/datasets/custom_dataset/train.txt
  transforms:
#    - type: ResizeStepScaling
#      min_scale_factor: 0.9
#      max_scale_factor: 1.1
#      scale_step_size: 0.25
#    - type: RandomPaddingCrop
#      crop_size: [960, 720]
#    - type: RandomHorizontalFlip
#    - type: RandomDistort
#      brightness_range: 0.5
#      contrast_range: 0.5
#      saturation_range: 0.5
#    - type: Normalize
    - type: Resize                      # 送入网络之前需要进行resize
      target_size: [ 512, 512 ]           # 将原图 resize 成 512*512 再送入网络
val_dataset:
  type: Dataset
  dataset_root: ./paddleseg/datasets/custom_dataset
  num_classes: 11
  mode: val
  val_path: ./paddleseg/datasets/custom_dataset/val.txt
  transforms:
    - type: Resize                      # 送入网络之前需要进行resize
      target_size: [ 512, 512 ]           # 将原图 resize 成 512*512 再送入网络
 
optimizer:
  type: sgd
  momentum: 0.9
  weight_decay: 5.0e-4
 
lr_scheduler:
  type: PolynomialDecay
  learning_rate: 0.01
  end_lr: 0
  power: 0.9
  warmup_iters: 200
  warmup_start_lr: 1.0e-5
 
loss:
  types:
    - type: OhemCrossEntropyLoss
      min_kept: 250000   # batch_size * 960 * 720 // 16
    - type: OhemCrossEntropyLoss
      min_kept: 250000
    - type: OhemCrossEntropyLoss
      min_kept: 250000
  coef: [1, 1, 1]
 
model:
  type: PPLiteSeg
  backbone:
    type: STDC1
    pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
  arm_out_chs: [32, 64, 128]
  seg_head_inter_chs: [32, 64, 64]

二、自定义训练数据

这些数据怎么弄的？

如果你原来的图像是一堆bmp，现在想转化为一堆jpg,怎么办？因为这里必须用jpg!

代码如下：

 
# coding:utf-8
import os
from PIL import Image
 
 
# bmp 转换为jpg
def bmpToJpg(file_path):
    for fileName in os.listdir(file_path):
        # print(fileName)
        newFileName = fileName[0:fileName.find(".")]+".jpg"
        print(newFileName)
        im = Image.open(file_path+"\\"+fileName)
        im.save(file_path+"\\"+newFileName)
 
 
# 删除原来的位图
def deleteImages(file_path, imageFormat):
    command = "del "+file_path+"\\*."+imageFormat
    os.system(command)
 
 
def main():
    file_path = "D:\\BaiduNetdiskDownload\\PaddleSeg-release-2.8\\paddleseg\\datasets\\custom_dataset\\bmp"
    bmpToJpg(file_path)
    #deleteImages(file_path, "bmp")
 
 
if __name__ == '__main__':
    main()
 

 
python tools/data/split_dataset_list.py D:/BaiduNetdiskDownload/PaddleSeg-release-2.8/paddleseg/datasets/custom_dataset/  images labels

三、训练

 
python tools/train.py   --config configs\pp_liteseg\gao.yml --do_eval    --use_vdl    --save_interval 500   --save_dir     output

竟然提示我，：Could not locate zlibwapi.dll.

Installation Guide - NVIDIA Docs

在这里，下载一个zlib，主要是那个dll,我放在了c:/windows/system32下面

四、预测

 
python tools/predict.py   --config configs\pp_liteseg\gao.yml     --model_path output/iter_4500/model.pdparams       --image_path paddleseg/datasets/custom_dataset/images/aWriteRectangle2ByWireEnd000.jpg       --save_dir output/result

五、模型导出（从动态到静态）

 
python tools/export.py    --config configs/pp_liteseg/gao.yml     --model_path output/iter_30000/model.pdparams --save_dir output/inference_model --output_op none

注意最后这个--with_softmax

没他，最后一层输出的就是整数，也就是类别。

有了他，如果11类，那么会输出共11维，第0维说的，第0类在每个像素点的概率，是浮点型小数。

下面是全部命令，注意正反斜杠。

python tools/data/split_dataset_list.py /home/gao/PaddleSeg/paddleseg/datasets/custom_dataset/  images labels
 
python tools/train.py   --config configs/pp_liteseg/gao.yml --do_eval    --use_vdl    --save_interval 500   --save_dir     output
 
python tools/predict.py   --config configs/pp_liteseg/gao.yml     --model_path output/iter_30000/model.pdparams       --image_path paddleseg/datasets/custom_dataset/test       --save_dir output/result
 
python tools/export.py    --config configs/pp_liteseg/gao.yml     --model_path output/iter_30000/model.pdparams --save_dir output/inference_model --output_op none