print('+' * 80)
# Fire the engines
# Fire the engines
if args.clf == 'fcn':
print('Initializing FCN...')
model = FCN(args.input_size, args.output_size)
elif args.clf == 'svm':
print('Initializing SVM...')
model = SVM(args.input_size, args.output_size)
elif args.clf == 'resnet18':
print('Initializing ResNet18...')
model = resnet.resnet18(num_channels=args.num_channels,
num_classes=args.output_size)
model.load_state_dict(torch.load(init_path))
print('Load init: {}'.format(init_path))
model = nn.DataParallel(model.to(device), device_ids=args.device_id)
if 'sgd' in args.paradigm:
optim = optim.SGD(params=model.parameters(), lr=args.lr)
elif 'adam' in args.paradigm:
optim = optim.Adam(params=model.parameters(), lr=args.lr)
loss_fn = multiClassHingeLoss() if args.clf == 'svm' else F.nll_loss
loss_type = 'hinge' if args.clf == 'svm' else 'nll'
print('+' * 80)
best = 0
padding0 = 32 - w % 32
padding1 = 32 - h % 32
image = cv2.copyMakeBorder(image,
padding0 // 2,
padding0 - padding0 // 2,
padding1 // 2,
padding1 - padding1 // 2,
cv2.BORDER_REFLECT)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
image = transform(image).unsqueeze(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
state_dict = torch.load(path, map_location='cpu')
model = FCN(21).to(device).eval()
model.load_state_dict(state_dict)
del state_dict
with torch.no_grad():
image = image.to(device)
output = model(image)
predict = output.data.cpu().squeeze(0).numpy()
predict = np.argmax(predict, axis=0)
predict = colormap[predict]
cv2.imwrite("res.png", predict)
