test_MyNet.py

import torch
import torch.nn.functional as F

import numpy as np
import argparse

import imageio
import time
from PIL import Image

from model.DCPNet import DCPNet
from data import test_dataset
import os

parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
opt = parser.parse_args()

dataset_path = r'D:\DataSets\SOD'

model = DCPNet()
model.load_state_dict(torch.load('EORSSD.pth'))

model.cuda()
model.eval()

test_datasets = ['EORSSD_aug']
# test_datasets = ['ORS-4199_aug']

for dataset in test_datasets:
    save_path = 'result/predict_img/' + dataset + '/'
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    print(dataset)
    image_root = dataset_path + '/' + dataset + '/' + 'test/image/'
    gt_root = dataset_path + '/' + dataset + '/' + 'test/GT/'
    test_loader = test_dataset(image_root, gt_root, opt.testsize)
    time_sum = 0
    for i in range(test_loader.size):
        image, gt, name = test_loader.load_data()
        gt = np.asarray(gt, np.float32)
        gt /= (gt.max() + 1e-8)
        image = image.cuda()

        time_start = time.time()
        res, _ = model(image)

        time_end = time.time()
        time_sum = time_sum + (time_end - time_start)
        res = F.upsample(res, 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)
        res = res * 255
        res = res.astype(np.uint8)

        imageio.imsave(save_path + name, res)
        if i == test_loader.size - 1:
            print('Running time {:.5f}'.format(time_sum / test_loader.size))
            print('FPS {:.5f}'.format(test_loader.size / time_sum))