MyTesting.py

import torch
import torch.nn.functional as F
import numpy as np
import os, argparse
from scipy import misc
import cv2
from backbone.PLCamo import MyNet
from utils.dataloader import My_test_dataset
import time



parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=704, help='testing size default 352')
parser.add_argument('--pth_path', type=str, default='./ckpt/Net_epoch_best.pth')
opt = parser.parse_args()

# for _data_name in ['CAMO', 'COD10K', 'CHAMELEON',NC4K]:
for _data_name in ['PlantCAMO1250']:
    data_path = './datasets/PlantCAMO1250/test'
    save_path = './results1/PlantCAMO1250/PlantCAMO1250/'
    model = MyNet()
    model.load_state_dict(torch.load(opt.pth_path))
    model.cuda()
    model.eval()

    os.makedirs(save_path, exist_ok=True)
    image_root = '{}/rgb/'.format(data_path)
    gt_root = '{}/gt/'.format(data_path)
    print('root',image_root,gt_root)
    test_loader = My_test_dataset(image_root, gt_root, opt.testsize)
    print('****',test_loader.size)
    T1 = time.perf_counter()
    for i in range(test_loader.size):
        image, gt, name = test_loader.load_data()
        print('***name',name)
        gt = np.asarray(gt, np.float32)
        gt /= (gt.max() + 1e-8)
        image = image.cuda()

        _, P2 = model(image)
        # P2 = model(image)
        res = F.upsample( P2[-1], size=gt.shape, mode='bilinear', align_corners=False)
        res = res.sigmoid().data.cpu().numpy().squeeze()
        res = (res - res.min()) / (res.max() - res.min() + 1e-8)
        print('> {} - {}'.format(_data_name, name))
        # misc.imsave(save_path+name, res)
        # If `mics` not works in your environment, please comment it and then use CV2
        cv2.imwrite(save_path+name,res*255)
    T2 = time.perf_counter()
    print("Finish! Average Time Is {}ms".format(((T2-T1)*1000)/test_loader.size))