MyTest.py

import torch
import torch.nn.functional as F
import numpy as np
import os, argparse
from scipy import misc
from lib.Fuzzy_Res2Net import FuzzyNet
from utils.dataloader import test_dataset
import imageio


def mean_iou_np(y_true, y_pred, **kwargs):
    """
    compute mean iou for binary segmentation map via numpy
    """
    axes = (0, 1) 
    intersection = np.sum(np.abs(y_pred * y_true), axis=axes) 
    mask_sum = np.sum(np.abs(y_true), axis=axes) + np.sum(np.abs(y_pred), axis=axes)
    union = mask_sum  - intersection 
    
    smooth = .001
    iou = (intersection + smooth) / (union + smooth)
    return iou

parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--pth_path', type=str, default='./snapshots/PraNet_Res2Net/PraNet-39.pth')

for _data_name in ['CVC-300', 'CVC-ClinicDB', 'Kvasir', 'CVC-ColonDB', 'ETIS-LaribPolypDB']:
    data_path = '/home/kpatel/projects/def-wangcs/kpatel/cross_transformer/data_poly/TestDataset/{}/'.format(_data_name)
    save_path = './results/PraNet/{}/'.format(_data_name)
    opt = parser.parse_args()
    model = FuzzyNet()
    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)
 
    DSC = 0
    IOU = 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()

        res5, res4, res3, res2 = model(image)
        res = res2
        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)

        input = res
        target = np.array(gt)
        N = gt.shape
        smooth = 1
        input_flat = np.reshape(input, (-1))
        target_flat = np.reshape(target, (-1))
        intersection = (input_flat * target_flat)

        #union = np.logical_or(input_flat, target_flat)
        dice = (2 * intersection.sum() + smooth) / (input.sum() + target.sum() + smooth)
        dice = '{:.4f}'.format(dice)
        dice = float(dice)
        DSC = DSC + dice
   
        iou = (intersection.sum() + smooth) / (input.sum() + target.sum() + smooth - intersection.sum())
        IOU = IOU + float(iou)
        #mean_loss.append(np.mean(loss_bank))


        #misc.imsave(save_path+name, res)
        #imageio.imwrite(save_path+name, res)
    print(_data_name)
    print(DSC / test_loader.size)
    print(IOU / test_loader.size)
    #print(np.mean(iou_bank))