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test_RAGRNet.py
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test_RAGRNet.py
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import torch
import torch.nn.functional as F
import numpy as np
import os, argparse
import time
import imageio
from skimage import img_as_ubyte
from model.RAGRNet import RAGRNet
from utils.data import test_dataset
torch.cuda.set_device(0)
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=256, help='testing size') # The test size of SEINet_ResNet50 and SEINet2_ResNet50 is 352
opt = parser.parse_args()
dataset_path = '/Your_File/SOD/test_dataset/'
model = RAGRNet()
model.load_state_dict(torch.load('/Your_pth.pth'))
model.cuda()
model.eval()
# test_datasets = ['ORSSD']
test_datasets = ['EORSSD']
for dataset in test_datasets:
save_path = './results/' + dataset + '/'
if not os.path.exists(save_path):
os.makedirs(save_path)
image_root = dataset_path + dataset + '/Images/'
print(dataset)
gt_root = dataset_path + dataset + '/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, s1_sig, e1, s2, s2_sig, e2, s3, s3_sig, e3, s4, s4_sig, e4, edgesal, graphsal, graphsal_sig, g1sal, g1sal_sig = 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)
imageio.imsave(save_path+name, img_as_ubyte(res))
if i == test_loader.size-1:
print('Running time {:.5f}'.format(time_sum/test_loader.size))
print('Average speed: {:.4f} fps'.format(test_loader.size/time_sum))