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test.py
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test.py
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#!/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 network import Segment
import time
import logging as logger
import argparse
from tqdm import tqdm
DATASETS = ['./data/RGBD_sal/test/DUT',
'./data/RGBD_sal/test/RGBD135',
'./data/RGBD_sal/test/SSD100',
'./data/RGBD_sal/test/LFSD',
'./data/RGBD_sal/test/SIP',
'./data/RGBD_sal/test/NJUD', './data/RGBD_sal/test/NLPR', './data/RGBD_sal/test/STEREO797']
class Test(object):
def __init__(self, conf, Dataset, datapath, Network):
## dataset
#self.cfg = Dataset.Config(datapath='../data/SOD', snapshot='./out/model-30', mode='test')
self.datapath = datapath.split("/")[-1]
print("Testing on %s"%self.datapath)
self.cfg = Dataset.Config(datapath = datapath, snapshot=conf.model, mode='test')
self.tag = conf.tag
self.data = Dataset.RGBDData(self.cfg)
self.loader = DataLoader(self.data, batch_size=1, shuffle=True, num_workers=0)
## network
self.net = Network(backbone='resnet50', cfg=self.cfg, norm_layer=nn.BatchNorm2d)
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, d, mask, (H,W), name in self.loader:
image, d, mask = image.cuda().float(), d.cuda().float(), mask.cuda().float()
start_time = time.time()
out, gate = self.net(image, d)
pred = torch.sigmoid(out)
torch.cuda.synchronize()
end_time = time.time()
cost_time += end_time - start_time
## MAE
#pred = F.interpolate(pred, size=(H,W), mode='bilinear')
#mask = F.interpolate(mask, size=(H,W), mode='bilinear')
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 show(self):
with torch.no_grad():
for image, d, mask, (H, W), maskpath in self.loader:
image, d, mask = image.cuda().float(), d.cuda().float(), mask.cuda().float()
out, out2_1, _, _, _, out2_2, _, _, _, gate = self.net(image, d)
pred = torch.sigmoid(out)
out2_1 = torch.sigmoid(out2_1)
out2_2 = torch.sigmoid(out2_2)
plt.subplot(231)
plt.title("image")
image = image[0].permute(1,2,0).cpu().numpy()*255
plt.imshow(np.uint8(image))
plt.subplot(232)
plt.title("gt")
mask = mask[0, 0].cpu().numpy()
plt.imshow(mask, cmap='gray')
plt.subplot(233)
plt.title("pred-final")
tmp = pred[0, 0].cpu().numpy()
plt.imshow(tmp, cmap='gray')
plt.subplot(234)
plt.title("pred-out1")
out2_1 = out2_1[0].permute(1,2,0).cpu().squeeze().numpy()*255
plt.imshow(np.uint8(out2_1), cmap='gray')
plt.subplot(235)
plt.title("pred-out2")
out2_2 = out2_2[0].permute(1,2,0).cpu().squeeze().numpy()*255
plt.imshow(np.uint8(out2_2), cmap='gray')
plt.show()
input()
def save(self):
with torch.no_grad():
for image, d, mask, (H, W), name in tqdm(self.loader):
image, d = image.cuda().float(), d.cuda().float()
out, gate = self.net(image, d)
out = F.interpolate(out, size=(H,W), mode='bilinear')
pred = (torch.sigmoid(out[0,0])*255).cpu().numpy()
head = './rgbd_pred/{}/'.format(self.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__':
conf = argparse.ArgumentParser(description="train model")
conf.add_argument("--tag", type=str)
conf.add_argument("--gpu", type=int, default=0)
conf.add_argument("--model", type=str)
args = conf.parse_args()
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"%(args.tag), filemode="w")
logger.info("Configuration:{}".format(args))
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
for e in DATASETS:
t =Test(args, dataset, e, Segment)
t.accuracy() # this is not accurate due to the resize operation, please use the matlab code to eval the performance
# t.show()
t.save()