MMSegmentation 模型训练结果批量推理及结果保存脚本

import os
import torch
import cv2
import argparse
import numpy as np 
from pprint import pprint
from tqdm import tqdm
from mmseg.apis import init_model, inference_model


DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# 测试图像所在文件夹
IMAGE_FILE_PATH = r"dataset\test\images"
# 模型训练结果的config配置文件路径
CONFIG = r'work_dir\dmnet_r50\dmnet_r50-d8_4xb4-160k_ade20k-512x512.py'
# 模型训练结果的权重文件路径
CHECKPOINT = r'work_dir\dmnet_r50\best_mIoU_iter_25600.pth'
# 模型推理测试结果的保存路径，每个模型的推理结果都保存在`{save_dir}/{模型config同名文件夹}`下，如文末图片所示。
SAVE_DIR = r"work_dir\infer_results"

def parse_args():
    parser = argparse.ArgumentParser(description='Visualize CAM')
    parser.add_argument('--img', default=IMAGE_FILE_PATH, help='Image file')
    parser.add_argument('--config', default=CONFIG ,help='Config file')
    parser.add_argument('--checkpoint', default=CHECKPOINT, help='Checkpoint file')
    parser.add_argument('--device', default=DEVICE, help='device')
    parser.add_argument('--save_dir', default=SAVE_DIR, help='save_dir')

    args = parser.parse_args()
    return args

def make_full_path(root_list, root_path):
    file_full_path_list = []
    for filename in root_list:
        file_full_path = os.path.join(root_path, filename)
        file_full_path_list.append(file_full_path)
    return file_full_path_list


def read_filepath(root):
    from natsort import natsorted
    test_image_list = natsorted(os.listdir(root))
    test_image_full_path_list = make_full_path(test_image_list, root)
    return test_image_full_path_list

def main():
    args = parse_args()
    
    model_mmseg = init_model(args.config, args.checkpoint, device=args.device)
    
    for imgs in tqdm(read_filepath(args.img)):
        result= inference_model(model_mmseg, imgs)
        pred_mask = result.pred_sem_seg.data.squeeze(0).detach().cpu().numpy().astype(np.uint8)
        pred_mask[pred_mask == 1] =255
        save_path = os.path.join(args.save_dir, f"{
      
      os.path.basename(args.config).split('.')[0]}")
        
        if not os.path.exists(save_path):
            os.makedirs(save_path)
            
        cv2.imwrite(os.path.join(save_path, f"{
      
      os.path.basename(result.img_path).split('.')[0]}.png"), pred_mask, [cv2.IMWRITE_PNG_COMPRESSION, 0])


if __name__ == '__main__':
    main()

结果保存格式如下：每个模型的推理结果都保存在{save_dir}/{模型config同名文件夹}下