# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from model.networkmodel import NetworkModel
from model.resnet import ResNet
from model.resnet import BasicBlock
import torch.optim as optim
from CTData import dataloader
import time
import numpy as np
if __name__ == "__main__":
resnet18_model = ResNet(BasicBlock, [2, 2, 2, 2])
resnet18_model.load_state_dict(
torch.load("./weights/resnet18-5c106cde.pth"))
model = NetworkModel(pretrained_net=resnet18_model, n_class=1)
fcn_model = model.cuda()
criterion = nn.BCELoss().cuda()
# optimizer = optim.SGD(fcn_model.parameters(), lr=1e-2, momentum=0.7)
optimizer = optim.Adam(fcn_model.parameters(), lr=1e-4)
for epo in range(20):
index = 0
epo_loss = 0
for item in dataloader:
# time_end = time.time()
transform=transform_test)
num_classes = 100
testloader = torch.utils.data.DataLoader(testset,
batch_size=args.bs,
shuffle=False,
num_workers=2)
# Model
print('\n[Phase 2] : Model setup')
print('| Building net type [' + args.net + ']...')
if args.net == 'resnet34':
net = ResNet(34, num_classes, 0.5)
elif args.net == 'densenet':
net = DenseNet3(100, num_classes, 12, 0.5, True, 0.2)
elif args.net == 'vgg16':
net = VGGNet(num_classes, 0.5, False, 2048, True)
else:
print('Error : Network should be either [ResNet34]')
sys.exit(0)
checkpoint = torch.load(args.model_path)
net.load_state_dict(checkpoint['model'])
net.to(device)
avg = 0
for i in range(10):
avg += test(net, testloader)
print(avg / 10)
