print("\n Time taken to load Images: %s " % (time.time() - start_time))
print("\n Test Dataset Shape: {shape}".format(shape=np.shape(depth_dataset)))
# ### Importing the Model
from Mobile_model import Model
model = Model().cuda()
model = nn.DataParallel(model)
# Import the Pre-trained Model
model.load_state_dict(torch.load(pretrained_path))
print("\n Loaded MobileNet U-Net Weights successfully\n")
model.eval()
# ### Model Variables (state_dict)
# print("\n\nModel's state_dict:\n\n")
# for param_tensor in model.state_dict():
# print(param_tensor, "\t", model.state_dict()[param_tensor].size())
# ## Generating Depth Images
start_time = time.time()
for i, sample_batched1 in enumerate(train_loader):
input_image = torch.autograd.Variable(sample_batched1['image'].cuda())
help='Image size of network input')
parser.add_argument('--data_dir',
default='comarision_datasets\input',
type=str,
help='Data path')
parser.add_argument(
'--result_dir',
default='demo_results',
type=str,
help='Directory for saving results, default: demo_results')
parser.add_argument('--gpu_id',
default=0,
type=int,
help='GPU id, default:0')
args = parser.parse_args()
if not os.path.exists(args.result_dir):
os.makedirs(args.result_dir)
gpu_id = args.gpu_id
torch.cuda.device(gpu_id)
net = Model().cuda()
net.load_state_dict(torch.load('weights_model.pth'))
net.eval()
print('Begin to test ...')
with torch.no_grad():
demo(net, args)
print('Finished!')