dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
sampler=sampler_batch, num_workers=args.num_workers)
# initilize the tensor holder here.
im_left_data = Variable(torch.FloatTensor(1).cuda())
im_right_data = Variable(torch.FloatTensor(1).cuda())
im_info = Variable(torch.FloatTensor(1).cuda())
num_boxes = Variable(torch.LongTensor(1).cuda())
gt_boxes_left = Variable(torch.FloatTensor(1).cuda())
gt_boxes_right = Variable(torch.FloatTensor(1).cuda())
gt_boxes_merge = Variable(torch.FloatTensor(1).cuda())
gt_dim_orien = Variable(torch.FloatTensor(1).cuda())
gt_kpts = Variable(torch.FloatTensor(1).cuda())
# initilize the network here.
stereoRCNN = resnet(imdb.classes, 101, pretrained=True)
stereoRCNN.create_architecture()
lr = cfg.TRAIN.LEARNING_RATE
uncert = Variable(torch.rand(6).cuda(), requires_grad=True)
torch.nn.init.constant(uncert, -1.0)
params = []
for key, value in dict(stereoRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
params += [{'params':[value],'lr':lr*(cfg.TRAIN.DOUBLE_BIAS + 1), \
'weight_decay': cfg.TRAIN.BIAS_DECAY and cfg.TRAIN.WEIGHT_DECAY or 0}]
else:
args = parse_args()
np.random.seed(cfg.RNG_SEED)
input_dir = args.load_dir + "/"
if not os.path.exists(input_dir):
raise Exception(
'There is no input directory for loading network from ' +
input_dir)
load_name = os.path.join(
input_dir, 'stereo_rcnn_{}_{}.pth'.format(args.checkepoch,
args.checkpoint))
kitti_classes = np.asarray(['__background__', 'Car'])
# initilize the network here.
stereoRCNN = resnet(kitti_classes, 101, pretrained=False)
stereoRCNN.create_architecture()
print("load checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
stereoRCNN.load_state_dict(checkpoint['model'])
print('load model successfully!')
with torch.no_grad():
# initilize the tensor holder here.
im_left_data = Variable(torch.FloatTensor(1).cuda())
im_right_data = Variable(torch.FloatTensor(1).cuda())
im_info = Variable(torch.FloatTensor(1).cuda())
num_boxes = Variable(torch.LongTensor(1).cuda())
gt_boxes = Variable(torch.FloatTensor(1).cuda())
