test.py

#!/usr/bin/python3
#coding=utf-8

import os
import sys
#sys.path.insert(0, '../')
sys.dont_write_bytecode = True

import cv2
import numpy as np
import matplotlib.pyplot as plt
plt.ion()

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
#from tensorboardX import SummaryWriter
from lib import dataset
from net import GCPANet
import time
import logging as logger

TAG = "GCPANet"
SAVE_PATH = TAG
GPU_ID=0
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_ID)

logger.basicConfig(level=logger.INFO, format='%(levelname)s %(asctime)s %(filename)s: %(lineno)d] %(message)s', datefmt='%Y-%m-%d %H:%M:%S', \
                           filename="test_%s.log"%(TAG), filemode="w")


DATASETS = ['./data/SOD', './data/PASCAL-S', './data/ECSSD', './data/HKU-IS',
            './data/DUT-OMRON', './data/DUTS']

class Test(object):
    def __init__(self, Dataset, datapath, Network):
        ## dataset
        self.datapath = datapath.split("/")[-1]
        print("Testing on %s"%self.datapath)
        self.cfg = Dataset.Config(datapath = datapath, snapshot=sys.argv[1], mode='test')
        self.data   = Dataset.Data(self.cfg)
        self.loader = DataLoader(self.data, batch_size=1, shuffle=True, num_workers=8)
        ## network
        self.net    = Network(self.cfg)
        self.net.train(False)
        self.net.cuda()
        self.net.eval()

    def accuracy(self):
        with torch.no_grad():
            mae, fscore, cnt, number   = 0, 0, 0, 256
            mean_pr, mean_re, threshod = 0, 0, np.linspace(0, 1, number, endpoint=False)
            cost_time = 0
            for image, mask, (H, W), maskpath in self.loader:
                image, mask            = image.cuda().float(), mask.cuda().float()
                start_time = time.time()
                out2, out3, out4, out5 = self.net(image)
                pred                   = torch.sigmoid(out2)
                torch.cuda.synchronize()
                end_time = time.time()
                cost_time += end_time - start_time

                ## MAE
                cnt += 1
                mae += (pred-mask).abs().mean()
                ## F-Score
                precision = torch.zeros(number)
                recall    = torch.zeros(number)
                for i in range(number):
                    temp         = (pred >= threshod[i]).float()
                    precision[i] = (temp*mask).sum()/(temp.sum()+1e-12)
                    recall[i]    = (temp*mask).sum()/(mask.sum()+1e-12)
                mean_pr += precision
                mean_re += recall
                fscore   = mean_pr*mean_re*(1+0.3)/(0.3*mean_pr+mean_re+1e-12)
                if cnt % 20 == 0:
                    fps = image.shape[0] / (end_time - start_time)
                    print('MAE=%.6f, F-score=%.6f, fps=%.4f'%(mae/cnt, fscore.max()/cnt, fps))
            fps = len(self.loader.dataset) / cost_time
            msg = '%s MAE=%.6f, F-score=%.6f, len(imgs)=%s, fps=%.4f'%(self.datapath, mae/cnt, fscore.max()/cnt, len(self.loader.dataset), fps)
            print(msg)
            logger.info(msg)

    def save(self):
        with torch.no_grad():
            for image, mask, (H, W), name in self.loader:
                out2, out3, out4, out5 = self.net(image.cuda().float())
                out2     = F.interpolate(out2, size=(H,W), mode='bilinear')
                pred     = (torch.sigmoid(out2[0,0])*255).cpu().numpy()
                head     = './pred_maps/{}/'.format(TAG) + self.cfg.datapath.split('/')[-1]
                if not os.path.exists(head):
                    os.makedirs(head)
                cv2.imwrite(head+'/'+name[0],np.uint8(pred))


if __name__=='__main__':
    for e in DATASETS:
        t =Test(dataset, e, GCPANet)
        t.accuracy()
        t.save()