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test_cls_uae_rs.py
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test_cls_uae_rs.py
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import os
import os.path as osp
import numpy as np
import argparse
from tools.utils import *
from torch import nn
from dataset.scene_dataset import *
from torch.utils import data
import tools.model as models
def main(args):
if args.dataID==1:
DataName = 'UCM'
num_classes = 21
classname = ('agricultural','airplane','baseballdiamond',
'beach','buildings','chaparral',
'denseresidential','forest','freeway',
'golfcourse','harbor','intersection',
'mediumresidential','mobilehomepark','overpass',
'parkinglot','river','runway',
'sparseresidential','storagetanks','tenniscourt')
elif args.dataID==2:
DataName = 'AID'
num_classes = 30
classname = ('airport','bareland','baseballfield',
'beach','bridge','center',
'church','commercial','denseresidential',
'desert','farmland','forest',
'industrial','meadow','mediumresidential',
'mountain','parking','park',
'playground','pond','port',
'railwaystation','resort','river',
'school','sparseresidential','square',
'stadium','storagetanks','viaduct')
adv_root_dir = osp.join(args.root_dir,'UAE-RS',DataName)+'/'
composed_transforms = transforms.Compose([
transforms.Resize(size=(args.crop_size,args.crop_size)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])
adv_loader = data.DataLoader(
scene_dataset(root_dir=adv_root_dir,pathfile='./dataset/'+DataName+'_test.txt', transform=composed_transforms, mode='adv'),
batch_size=args.val_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True)
clean_loader = data.DataLoader(
scene_dataset(root_dir=args.root_dir,pathfile='./dataset/'+DataName+'_test.txt', transform=composed_transforms),
batch_size=args.val_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True)
###################Target target_model Definition###################
if args.target_model=='alexnet':
target_model = models.alexnet(pretrained=False)
target_model.classifier._modules['6'] = nn.Linear(4096, num_classes)
elif args.target_model=='vgg11':
target_model = models.vgg11(pretrained=False)
target_model.classifier._modules['6'] = nn.Linear(4096, num_classes)
elif args.target_model=='vgg16':
target_model = models.vgg16(pretrained=False)
target_model.classifier._modules['6'] = nn.Linear(4096, num_classes)
elif args.target_model=='vgg19':
target_model = models.vgg19(pretrained=False)
target_model.classifier._modules['6'] = nn.Linear(4096, num_classes)
elif args.target_model=='resnet18':
target_model = models.resnet18(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='resnet50':
target_model = models.resnet50(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='resnet101':
target_model = models.resnet101(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='resnext50_32x4d':
target_model = models.resnext50_32x4d(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='resnext101_32x8d':
target_model = models.resnext101_32x8d(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='densenet121':
target_model = models.densenet121(pretrained=False)
target_model.classifier = nn.Linear(1024, num_classes)
elif args.target_model=='densenet169':
target_model = models.densenet169(pretrained=False)
target_model.classifier = nn.Linear(1664, num_classes)
elif args.target_model=='densenet201':
target_model = models.densenet201(pretrained=False)
target_model.classifier = nn.Linear(1920, num_classes)
elif args.target_model=='inception':
target_model = models.inception_v3(pretrained=True, aux_logits=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='regnet_x_400mf':
target_model = models.regnet_x_400mf(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='regnet_x_8gf':
target_model = models.regnet_x_8gf(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
elif args.target_model=='regnet_x_16gf':
target_model = models.regnet_x_16gf(pretrained=False)
target_model.fc = torch.nn.Linear(target_model.fc.in_features, num_classes)
dirpath = args.save_path_prefix+DataName+'/Pretrain/'+args.target_model+'/'
model_path = os.listdir(dirpath)
for filename in model_path:
filepath = os.path.join(dirpath, filename)
if os.path.isfile(filepath) and filename.lower().endswith('.pth'):
print(os.path.join(dirpath, filename))
model_path_resume = os.path.join(dirpath, filename)
saved_state_dict = torch.load(model_path_resume)
new_params = target_model.state_dict().copy()
for i,j in zip(saved_state_dict,new_params):
new_params[j] = saved_state_dict[i]
target_model.load_state_dict(new_params)
target_model = torch.nn.DataParallel(target_model).cuda()
target_model.eval()
OA_clean,_ = test_acc(target_model,classname, clean_loader, 1,num_classes,print_per_batches=10)
OA_adv,_ = test_acc(target_model,classname, adv_loader, 1,num_classes,print_per_batches=10)
print('Clean Test Set OA:',OA_clean*100)
print('UAE-RS Test Set OA:',OA_adv*100)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataID', type=int, default=1)
parser.add_argument('--root_dir', type=str, default='/iarai/home/yonghao.xu/Data/',help='dataset path.')
parser.add_argument('--target_model', type=str, default='inception',
help='alexnet,vgg11,vgg16,vgg19,inception,resnet18,resnet50,resnet101,resnext50_32x4d,resnext101_32x8d,densenet121,densenet169,densenet201,regnet_x_400mf,regnet_x_8gf,regnet_x_16gf')
parser.add_argument('--save_path_prefix', type=str,default='./')
parser.add_argument('--crop_size', type=int, default=256)
parser.add_argument('--val_batch_size', type=int, default=32)
parser.add_argument('--num_workers', type=int, default=1)
main(parser.parse_args())