def test_model(modname='alexnet', pm_ch='both', bs=16):
# hyperparameters
batch_size = bs
# device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# determine number of input channels
nch = 2
if pm_ch != 'both':
nch = 1
# restore model
model = None
if modname == 'alexnet':
model = alexnet(num_classes=3, in_ch=nch).to(device)
elif modname == 'densenet':
model = DenseNet(num_classes=3, in_ch=nch).to(device)
elif modname == 'inception':
model = inception_v3(num_classes=3, in_ch=nch).to(device)
elif modname == 'resnet':
model = resnet18(num_classes=3, in_ch=nch).to(device)
elif modname == 'squeezenet':
model = squeezenet1_1(num_classes=3, in_ch=nch).to(device)
elif modname == 'vgg':
model = vgg19_bn(in_ch=nch, num_classes=3).to(device)
else:
print('Model {} not defined.'.format(modname))
return
# retrieve trained model
# load path
load_path = '../../../data/two_views/saved_models/{}/{}'.format(
modname, pm_ch)
model_pathname = os.path.join(load_path, 'model.ckpt')
if not os.path.exists(model_pathname):
print('Trained model file {} does not exist. Abort.'.format(
model_pathname))
return
model.load_state_dict(torch.load(model_pathname))
# load test dataset
test_dataset = PixelMapDataset('test_file_list.txt', pm_ch)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
# test the model
model.eval(
) # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
correct = 0
total = 0
correct_cc_or_bkg = 0
ws_total = 0
ws_correct = 0
for view1, view2, labels in test_loader:
view1 = view1.float().to(device)
if modname == 'inception':
view1 = nn.ZeroPad2d((0, 192, 102, 101))(view1)
else:
view1 = nn.ZeroPad2d((0, 117, 64, 64))(view1)
labels = labels.to(device)
outputs = model(view1)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
for i in range(len(predicted)):
if (predicted[i] < 2
and labels[i] < 2) or (predicted[i] == 2
and labels[i] == 2):
correct_cc_or_bkg += 1
if labels[i] < 2:
ws_total += 1
if (predicted[i] == labels[i]):
ws_correct += 1
print('Model Performance:')
print('Model:', modname)
print('Channel:', pm_ch)
print(
'3-class Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(correct, total, 100 * correct / total))
print(
'2-class Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(correct_cc_or_bkg, total, 100 * correct_cc_or_bkg / total))
print(
'Wrong-sign Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(ws_correct, ws_total, 100 * ws_correct / ws_total))
tie_weights=helper.params['tied'])
else:
net = Net()
if helper.params.get('multi_gpu', False):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.info(f"Let's use {torch.cuda.device_count()} GPUs!")
net = nn.DataParallel(net)
net.to(device)
if helper.params.get('resumed_model', False):
logger.info('Resuming training...')
loaded_params = torch.load(
f"saved_models/{helper.params['resumed_model']}")
net.load_state_dict(loaded_params['state_dict'])
helper.start_epoch = loaded_params['epoch']
# helper.params['lr'] = loaded_params.get('lr', helper.params['lr'])
logger.info(
f"Loaded parameters from saved model: LR is"
f" {helper.params['lr']} and current epoch is {helper.start_epoch}"
)
else:
helper.start_epoch = 1
logger.info(
f'Total number of params for model {helper.params["model"]}: {sum(p.numel() for p in net.parameters() if p.requires_grad)}'
)
if dp:
criterion = nn.CrossEntropyLoss(reduction='none')
else:
net = DenseNet(growth_rate=args.growth_rate,
theta=args.theta,
num_layers=[12, 12, 12],
num_classes=10)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print('The number of parameters of model is', num_params)
if args.resume is not None:
checkpoint = torch.load('./save_model/' + args.resume)
net.load_state_dict(checkpoint['net'])
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
decay_epoch = [150, 225]
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=decay_epoch,
gamma=0.1)
writer = SummaryWriter(args.logdir)
