from models.GANet_deep import GANet
else:
raise Exception("No suitable model found ...")
cuda = opt.cuda
#cuda = True
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
torch.manual_seed(opt.seed)
if cuda:
torch.cuda.manual_seed(opt.seed)
print('===> Loading datasets')
train_set = get_training_set(opt.data_path, opt.training_list,
[opt.crop_height, opt.crop_width], opt.left_right,
opt.kitti, opt.kitti2015, opt.shift)
# test_set = get_test_set(opt.data_path, opt.val_list, [240, 672], opt.left_right, opt.kitti, opt.kitti2015)
# for full train set
# n_samples = [27, 200, 15]
# for train split
n_samples = [21, 193, 12]
test_set_mid = get_test_set(opt.data_path, opt.val_list_mid, [-1, -1],
opt.left_right, opt.kitti, opt.kitti2015)
test_set_kitti = get_test_set(opt.data_path, opt.val_list_kitti, [-1, -1],
opt.left_right, opt.kitti, opt.kitti2015)
test_set_eth = get_test_set(opt.data_path, opt.val_list_eth, [-1, -1],
opt.left_right, opt.kitti, opt.kitti2015)
probs = [[(1 / len(n_samples)) / (n / sum(n_samples))] * n for n in n_samples]
from dataloader.data import get_training_set, get_test_set
from torch.utils.data import DataLoader
train_set = get_training_set("/media/wserver/D0/Dataset/Kitti/SceneFlow/data_scene_flow/training/",
"./lists/kitti2015_train.list", [240, 528], False, False, True, False)
training_data_loader = DataLoader(dataset=train_set, num_workers=4, batch_size=1, shuffle=True, drop_last=True)
for iter, batch in enumerate(training_data_loader):
print(batch.shape)
