飞桨常规赛：遥感影像地块分割 - 10月第5名方案

# 解压PaddleSeg套件!unzip -oq /home/aistudio/data/data114849/PaddleSeg-release-2.3.zip

# 为方便后期操作将文件夹改名!mv PaddleSeg-release-2.3 PaddleSeg

# 安装依赖，AIStudio实验环境已有相关依赖#!pip install -r PaddleSeg/requirements.txt

!unzip -oq /home/aistudio/data/data77571/train_and_label.zip -d data/
!unzip -oq /home/aistudio/data/data77571/img_test.zip -d data/

import os
img_train_dir = os.listdir('./data/img_train')
train_val_ratio = 0.9 #划分训练集和验证集的比例train_img_num = int(len(img_train_dir)*train_val_ratio)
val_img_num = len(img_train_dir) - train_img_num
train_list_origin = img_train_dir[:train_img_num]
val_list_origin = img_train_dir[train_img_num:]print(len(train_list_origin),len(val_list_origin)) #输出训练集和验证集数量#生成trainlistwith open('train_list.txt','w') as f:    for i in range(len(train_list_origin)):
        train_img_name = 'img_train/' + train_list_origin[i]
        train_lab_name = 'lab_train/' + train_list_origin[i].split('.')[0] + '.png'
        f.write(train_img_name + ' ' + train_lab_name + '\n')#生成vallistwith open('val_list.txt','w') as f:    for i in range(len(val_list_origin)):
        val_img_name = 'img_train/' + val_list_origin[i]
        val_lab_name = 'lab_train/' + val_list_origin[i].split('.')[0] + '.png'
        f.write(val_img_name + ' ' + val_lab_name + '\n')

!python PaddleSeg/train.py \
       --config swin.yml \
       --do_eval \
       --use_vdl \
       --save_interval 4000 \
       --save_dir output

!python PaddleSeg/predict.py \
       --config swin0.yml \
       --model_path output/best_model/model.pdparams \
       --image_path data/img_testA \
       --save_dir ./result