def __init__(self, opts):
self.opts = opts
self.model_path = opts.model_path
self.output_dir = opts.output_dir
self.gpu_id = opts.gpu_id
self.disp_module = opts.disp_module
self.dataset = opts.dataset
self.batch_size = opts.batch_size
self.train = opts.train
# The data loader
# getting the dataloader ready
if self.dataset == 'kitti':
dataset = Datasets.KittiDataset(self.opts)
elif self.dataset == 'nyu':
dataset = Datasets.NYUDataset(self.opts)
else:
raise NameError('Dataset not found')
self.DataLoader = data.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=8)
print('Data loader made')
# loading the model
disp_module = importlib.import_module(self.disp_module)
self.DispNet = disp_module.DispResNet()
self.DispNet.load_state_dict(torch.load(self.model_path))
if self.gpu_id is not None:
self.device = torch.device('cuda:' + str(self.gpu_id[0]))
self.DispNet = self.DispNet.to(self.device)
if len(self.gpu_id) > 1:
self.DispNet = nn.DataParallel(self.DispNet, self.gpu_id)
else:
self.device = torch.device('cpu')
self.DispNet.eval()
print('Model Loaded')
self.start_test()
def __init__(self, opts):
self.opts = opts
self.epochs = opts.epochs
self.batch_size = opts.batch_size
self.shuffle = opts.shuffle
self.lr = opts.lr
self.beta1 = opts.beta1
self.beta2 = opts.beta2
self.console_out = opts.console_out
self.save_disp = opts.save_disp
self.tboard_out = opts.tboard_out
self.log_tb = opts.log_tensorboard
self.disp_module = opts.disp_module
self.pose_module = opts.pose_module
self.dataset = opts.dataset
self.gpus = opts.gpus
self.tboard_dir = opts.tboard_dir
self.int_result_dir = opts.int_results_dir
self.save_model_dir = opts.save_model_dir
self.save_model_iter = opts.save_model_iter
self.frame_size = opts.frame_size
self.disp_model_path = opts.disp_model_path
self.pose_model_path = opts.pose_model_path
self.start_time = time.time()
# getting the dataloader ready
if self.dataset == 'kitti':
dataset = Datasets.KittiDataset(self.opts)
elif self.dataset == 'nyu':
dataset = Datasets.NYUDataset(self.opts)
else:
raise NameError('Dataset not found')
self.DataLoader = data.DataLoader(dataset, batch_size=self.batch_size, shuffle=self.shuffle,
num_workers=8)
# loading the modules
if len(self.gpus) == 0:
self.device = torch.device('cpu')
else:
self.device = torch.device('cuda:' + str(self.gpus[0]))
disp_module = importlib.import_module(self.disp_module)
pose_module = importlib.import_module(self.pose_module)
self.DispModel = disp_module.DispResNet().to(self.device)
self.PoseModel = pose_module.PoseResNet().to(self.device)
if self.disp_model_path is not None:
self.DispModel.load_state_dict(torch.load(self.disp_model_path))
if self.pose_model_path is not None:
self.PoseModel.load_state_dict(torch.load(self.pose_model_path))
if len(self.gpus) != 0:
self.DispModel = nn.DataParallel(self.DispModel, self.gpus)
self.PoseModel = nn.DataParallel(self.PoseModel, self.gpus)
self.DispModel.to(self.device)
self.PoseModel.to(self.device)
self.Loss = Loss(opts)
# the optimizer
params_dict = [{'params': self.DispModel.parameters()},
{'params': self.PoseModel.parameters()}]
self.optim = torch.optim.Adam(params_dict, lr=self.lr, betas=[self.beta1, self.beta2])
# data logger and output
self.fix_batch = next(iter(self.DataLoader))
self.writer = tb.SummaryWriter(self.tboard_dir)
self.start()
