def define_model(is_resnet, is_densenet, is_senet):
if is_resnet:
original_model = resnet.resnet50(pretrained = True)
Encoder = modules.E_resnet(original_model)
model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])
if is_densenet:
original_model = densenet.densenet161(pretrained=True)
Encoder = modules.E_densenet(original_model)
model = net.model(Encoder, num_features=2208, block_channel = [192, 384, 1056, 2208])
if is_senet:
original_model = senet.senet154(pretrained='imagenet')
Encoder = modules.E_senet(original_model)
model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])
return model
def main():
Encoder = modules.E_resnet(resnet.resnet50(pretrained=True))
N = net.model(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048])
N = torch.nn.DataParallel(N).cuda()
N.load_state_dict(torch.load('./models/N'))
N_adv = copy.deepcopy(N)
N_adv.load_state_dict(torch.load('./models/N_adv'))
cudnn.benchmark = True
test_loader = loaddata.getTestingData(8)
#test for N_adv(x*)
test_N_adv(test_loader, N, N_adv, epsilon=0.05, iteration=10)
test_N_adv(test_loader, N, N_adv, epsilon=0.1, iteration=10)
test_N_adv(test_loader, N, N_adv, epsilon=0.15, iteration=10)
test_N_adv(test_loader, N, N_adv, epsilon=0.2, iteration=10)
def define_model(target):
model = None
if target is 'resnet':
original_model = resnet.resnet50(pretrained=True)
encoder = modules.E_resnet(original_model)
model = net.model(encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048])
elif target is 'densenet':
original_model = densenet.densenet161(pretrained=True)
encoder = modules.E_densenet(original_model)
model = net.model(encoder,
num_features=2208,
block_channel=[192, 384, 1056, 2208])
elif target is 'senet':
original_model = senet.senet154(pretrained='imagenet')
encoder = modules.E_senet(original_model)
model = net.model(encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048])
return model
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional
from utils import *
from options.testopt import _get_test_opt
import nyudv2_dataloader
from resnet import resnet18
import modules
import net
args = _get_test_opt
TestImgLoader = nyudv2_dataloader.getTestingData_NYUDV2(
args.batch_size, args.testlist_path, args.root_path)
Encoder = modules.E_resnet(resnet18)
if args.backbone in ['resnet50']:
model = net.model(Encoder,
num_features=2048,
block_channel=[256, 512, 1024, 2048],
refinenet=args.refinenet)
elif args.backbone in ['resnet18', 'resnet34']:
model = net.model(Encoder,
num_features=512,
block_channel=[64, 128, 256, 512],
refinenet=args.refinenet)
model = nn.DataParallel(model).cuda()
if args.loadckpt is not None and args.loadckpt.endswith('.pth.tar'):