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testing_model_Seting1.py
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testing_model_Seting1.py
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import time,argparse,sys,os
import torch,math,random
import numpy as np
from tqdm import tqdm
from torch.utils.data import Dataset,DataLoader
from torch.autograd import Variable
from datasets.dataset_pairs_wRandomSample import my_dataset_eval
import torchvision.transforms as transforms
import matplotlib.image as img
from utils.UTILS import compute_psnr,compute_ssim
sys.path.append(os.getcwd())
# 设置随机数种子
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
setup_seed(20)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
parser = argparse.ArgumentParser()
parser.add_argument('--eval_in_path_Haze', type=str,default= '/gdata2/zhuyr/Weather/Data/RainDrop/test_a/test_a/data-re/')
parser.add_argument('--eval_gt_path_Haze', type=str,default= '/gdata2/zhuyr/Weather/Data/RainDrop/test_a/test_a/gt-re/')
parser.add_argument('--eval_in_path_Rain', type=str,default= '/gdata2/zhuyr/Weather/Data/Rain/HeavyRain/Test/in/')
parser.add_argument('--eval_gt_path_Rain', type=str,default= '/gdata2/zhuyr/Weather/Data/Rain/HeavyRain/Test/gt_re/')
parser.add_argument('--eval_in_path_L', type=str,default= '/gdata2/zhuyr/Weather/Data/Snow/test/Snow100K-L/synthetic/')
parser.add_argument('--eval_gt_path_L', type=str,default= '/gdata2/zhuyr/Weather/Data/Snow/test/Snow100K-L/gt/')
parser.add_argument('--eval_in_path_realSnow', type=str,default= '/gdata2/zhuyr/Weather/Data/Snow/test_realistic/')
parser.add_argument('--eval_in_path_realRain', type=str,default= '/gdata2/zhuyr/Weather/Data/RealRain300/')
parser.add_argument('--eval_in_path_realRainDrop', type=str,default= '/gdata2/zhuyr/Weather/Data/RainDS/RainDS/RainDS_real/test_set/raindrop/')
parser.add_argument('--eval_gt_path_realRainDrop', type=str,default= '/gdata2/zhuyr/Weather/Data/RainDS/RainDS/RainDS_real/test_set/gt/')
parser.add_argument('--model_path', type=str,default= '/ghome/zhuyr/WGWSNet/ckpt/')
parser.add_argument('--model_name', type=str,default= 'Setting1_K1.pth')
parser.add_argument('--save_path', type=str,default= '/ghome/zhuyr/WGWSNet/results/')
#training setting
parser.add_argument('--flag', type=str, default= 's1')
parser.add_argument('--base_channel', type = int, default= 18)
parser.add_argument('--num_block', type=int, default= 6)
args = parser.parse_args()
trans_eval = transforms.Compose(
[
transforms.ToTensor()
])
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
def get_eval_data(val_in_path=args.eval_in_path_L,val_gt_path =args.eval_gt_path_L ,trans_eval=trans_eval):
eval_data = my_dataset_eval(
root_in=val_in_path, root_label =val_gt_path, transform=trans_eval,fix_sample= 20000 )
eval_loader = DataLoader(dataset=eval_data, batch_size=1, num_workers=4)
return eval_loader
def test(net,eval_loader,Dname = 'S',flag = [1,0,0], model_flag= args.flag, save_results_path=args.save_path):
net.to('cuda:0')
net.eval()
st = time.time()
with torch.no_grad():
eval_output_psnr = 0.0
eval_input_psnr = 0.0
eval_output_ssim = 0.0
eval_input_ssim = 0.0
final_save_path = save_results_path + '-setting1-'+ Dname+'/'
if not os.path.exists(final_save_path):
os.mkdir(final_save_path)
for index, (data_in, label, name) in enumerate(tqdm(eval_loader), 0):# enumerate(eval_loader, 0):
inputs = Variable(data_in).to('cuda:0')
labels = Variable(label).to('cuda:0')
if model_flag == 'S1':
outputs = net(inputs)
else:
outputs = net(inputs, flag=flag)
eval_input_psnr += compute_psnr(inputs, labels)
eval_output_psnr += compute_psnr(outputs, labels)
eval_input_ssim += compute_ssim(inputs, labels)
eval_output_ssim += compute_ssim(outputs, labels)
out_eval_np = np.squeeze(torch.clamp(outputs, 0., 1.).cpu().detach().numpy()).transpose((1,2,0))
img.imsave(final_save_path+ name[0], np.uint8(out_eval_np * 255.))
Final_output_PSNR = eval_output_psnr / len(eval_loader)
Final_input_PSNR = eval_input_psnr / len(eval_loader)
Final_output_SSIM = eval_output_ssim / len(eval_loader)
Final_input_SSIM = eval_input_ssim / len(eval_loader)
print("Dname:{}--------------[Num_eval:{} In_PSNR:{} "
"Out_PSNR:{},In_SSIM:{} Out_SSIM:{}]:-----cost time;{}".format(Dname,len(eval_loader),
round(Final_input_PSNR,5),round(Final_output_PSNR, 5),
round(Final_input_SSIM, 5),round(Final_output_SSIM, 5),time.time() -st))
def print_indictor(indictor):
indictor_list = []
for i in range(len(indictor)):
indictor_list.append(indictor[i].item())
indictor_array = np.array(indictor_list)
print('indictor_array---ori:',list(indictor_array)) #indictor_array)
x = np.zeros_like(indictor_array)
y = np.ones_like(indictor_array)
out = np.where(indictor_array>0.1, y,x)
print('indictor_array---Binary out:',list(out))
if __name__ == '__main__':
if args.flag == 'K1':
from networks.Network_Stage2_K1_Flag import UNet
elif args.flag == 'K3':
from networks.Network_Stage2_K3_Flag import UNet
elif args.flag == 'S1':
from networks.Network_Stage1 import UNet
net = UNet(base_channel=args.base_channel, num_res=args.num_block)
index = 0
model_name = args.model_name
pretrained_model = torch.load(args.model_path + model_name)
net.load_state_dict(pretrained_model, strict=True)
print('----Load successfully!------')
if args.flag != 'S1':
indictor1 = net.getIndicators_B1()
indictor2 = net.getIndicators_B2()
indictor3 = net.getIndicators_B3()
Percent_B1 = torch.mean((torch.tensor(net.getIndicators_B1()) >= .05).float())
Percent_B2 = torch.mean((torch.tensor(net.getIndicators_B2()) >= .05).float())
Percent_B3 = torch.mean((torch.tensor(net.getIndicators_B3()) >= .05).float())
Percent_B1_1 = torch.mean((torch.tensor(net.getIndicators_B1()) >= .1).float())
Percent_B2_1 = torch.mean((torch.tensor(net.getIndicators_B2()) >= .1).float())
Percent_B3_1 = torch.mean((torch.tensor(net.getIndicators_B3()) >= .1).float())
Percent_B1_2 = torch.mean((torch.tensor(net.getIndicators_B1()) >= .2).float())
Percent_B2_2 = torch.mean((torch.tensor(net.getIndicators_B2()) >= .2).float())
Percent_B3_2 = torch.mean((torch.tensor(net.getIndicators_B3()) >= .2).float())
print("Snow (Expansion Ratios) || Percent_B1 0.05: {} | 0.1: {} | 0.15: {} ".format(Percent_B1, Percent_B1_1, Percent_B1_2))
print("Rain (Expansion Ratios)|| Percent_B2 0.05: {} | 0.1: {} | 0.15: {} ".format(Percent_B2, Percent_B2_1, Percent_B2_2))
print("RainDrop (Expansion Ratios) || Percent_B3 0.05: {} | 0.1: {} | 0.15: {} ".format(Percent_B3, Percent_B3_1, Percent_B3_2))
# Datasets of Setting1
eval_loader_Haze = get_eval_data(val_in_path=args.eval_in_path_Haze, val_gt_path=args.eval_gt_path_Haze)
eval_loader_L = get_eval_data(val_in_path=args.eval_in_path_L, val_gt_path=args.eval_gt_path_L)
eval_loader_Rain = get_eval_data(val_in_path=args.eval_in_path_Rain, val_gt_path=args.eval_gt_path_Rain)
#Datasets of Real-world Scenes
eval_loader_RealRain = get_eval_data(val_in_path=args.eval_in_path_realRain, val_gt_path=args.eval_in_path_realRain)
eval_loader_RealSnow = get_eval_data(val_in_path=args.eval_in_path_realSnow, val_gt_path=args.eval_in_path_realSnow)
eval_loader_RealRainDrop = get_eval_data(val_in_path=args.eval_in_path_realRainDrop, val_gt_path=args.eval_gt_path_realRainDrop)
# RainDrop
test(net=net, eval_loader = eval_loader_Haze, Dname= 'RD',flag = [0,0,1],model_flag= args.flag)
# OutDoor-Rain
test(net=net, eval_loader = eval_loader_Rain, Dname= 'HRain',flag = [0,1,0],model_flag= args.flag)
# Snow
#test(net=net, eval_loader = eval_loader_L, Dname= 'L',flag = [1,0,0],model_flag= args.flag)
# test(net=net, eval_loader = eval_loader_RealSnow, Dname= 'RealSnow',flag = [1,0,0],model_flag= args.flag)
# test(net=net, eval_loader = eval_loader_RealRain,Dname= 'RealRain',flag = [0,1,0],model_flag= args.flag)
# test(net=net, eval_loader = eval_loader_RealRainDrop,Dname= 'RealRainDrop',flag = [0,0,1],model_flag= args.flag)