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test.py
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test.py
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import torch
import torch.nn.functional as F
import numpy as np
import os
import argparse
from lib.model import CFANet
from utils.dataloader import get_loader,test_dataset
from utils.eva_funcs import eval_Smeasure,eval_mae,numpy2tensor
import scipy.io as scio
import cv2
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--pth_path', type=str, default='./checkpoint/CFANet.pth')
for _data_name in ['CVC-300', 'CVC-ClinicDB', 'Kvasir', 'CVC-ColonDB', 'ETIS-LaribPolypDB']:
print('-----------strating -------------')
data_path = '/test/Polpy/Dataset/TestDataset/{}/'.format(_data_name)
save_path = './Snapshot/seg_maps/{}/'.format(_data_name)
opt = parser.parse_args()
model = CFANet(channel=64).cuda()
model.load_state_dict(torch.load(opt.pth_path))
model.cuda()
model.eval()
os.makedirs(save_path, exist_ok=True)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
test_loader = test_dataset(image_root, gt_root, opt.testsize)
for i in range(test_loader.size):
print(['--------------processing-------------', i])
image, gt, name = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
_,_,_,res = model(image)
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)
cv2.imwrite(save_path+name, res*255)