(monoscene) [email protected]:~/workplace/MonoScene$ python monoscene/scripts/train_monoscene.py dataset=kitti enable_log=true kitti_root=$KITTI_ROOT kitti_preprocess_root=$KITTI_PREPROCESS kitti_logdir=$KITTI_LOG n_gpus=2 batch_size=2 ^[[Dexp_kitti_1_FrusSize_8_nRelations4_WD0.0001_lr0.0001_CEssc_geoScalLoss_semScalLoss_fpLoss_CERel_3DCRP_Proj_2_4_8 n_relations (32, 32, 4) Traceback (most recent call last): File "monoscene/scripts/train_monoscene.py", line 118, in main class_weights=class_weights, File "/home/ruidong/workplace/MonoScene/monoscene/models/monoscene.py", line 80, in init context_prior=context_prior, File "/home/ruidong/workplace/MonoScene/monoscene/models/unet3d_kitti.py", line 62, in init self.feature * 4, self.feature * 4, size_l3, bn_momentum=bn_momentum File "/home/ruidong/workplace/MonoScene/monoscene/models/CRP3D.py", line 15, in init self.flatten_size = size[0] * size[1] * size[2] TypeError: 'int' object is not subscriptable

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Dear authors, thanks for your great works! In your paper, you say that "the losses are computed only where y is defined". I wonder if this means you do not add supervision on non-occupied voxels and only use multi-class classification loss on occupied voxels ? If this holds true, why the model can identify which voxels are occupied ?

FileNotFoundError: [Errno 2] No such file or directory: '/home/ruidong/workplace/MonoScene/trained_models/monoscene_kitti.ckpt'

the last printing of trainning is： Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2325/2325 [1:06:52<00:00, 1.73s/it, loss=3.89, v_num=]

Dear author, you said that Use smaller 2D backbone by chaning the basemodel_name and num_features The pretrained model name is here. You can try the efficientnet B5 can reduces the memory, I want to know the B5 weight and the value of num_features?

Hi @anhquancao,

Thanks so much for your paper and your implementation. Do you have your pretrained model on the NuScenes? If yes, could you share it? The reason is that I want to build upon your work on the NuScenes dataset but there exists a large domain gap between the two (SemanticKITTI and NuScenes) so the pretrained on SemanticKITTI works does not well on the NuScenes.

Thanks!

When I try to run this script, it crashed without giving any information: python monoscene/scripts/generate_output.py +output_path=$MONOSCENE_OUTPUT dataset=kitti_360 +kitti_360_root=$KITTI_360_ROOT +kitti_360_sequence=2013_05_28_drive_0028_sync n_gpus=1 batch_size=1

Any suggestion will be much appreciated.

PS F:\Studying\CY-Workspace\MonoScene-master> python monoscene/scripts/eval_monoscene.py dataset=kitti kitti_root=$KITTI_ROOT kitti_preprocess_root=$KITTI_PREPROCESS n_gpus=1 batch_size= 1 GPU available: True, used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs n_relations 4 Using cache found in C:\Users\DELL/.cache\torch\hub\rwightman_gen-efficientnet-pytorch_master Loading base model ()...Done. Removing last two layers (global_pool & classifier). Building Encoder-Decoder model..Done. Traceback (most recent call last): File "monoscene/scripts/eval_monoscene.py", line 71, in main data_module.setup() File "F:\anaconda\envs\monoscene\lib\site-packages\pytorch_lightning\core\datamodule.py", line 440, in wrapped_fn fn(*args, **kwargs) File "F:\Studying\CY-Workspace\MonoScene-master\monoscene\scripts/../..\monoscene\data\semantic_kitti\kitti_dm.py", line 34, in setup color_jitter=(0.4, 0.4, 0.4), File "F:\Studying\CY-Workspace\MonoScene-master\monoscene\scripts/../..\monoscene\data\semantic_kitti\kitti_dataset.py", line 60, in init os.path.join(self.root, "dataset", "sequences", sequence, "calib.txt") File "F:\Studying\CY-Workspace\MonoScene-master\monoscene\scripts/../..\monoscene\data\semantic_kitti\kitti_dataset.py", line 193, in read_calib with open(calib_path, "r") as f: FileNotFoundError: [Errno 2] No such file or directory: 'dataset\sequences\00\calib.txt'

(monoscene) [email protected]:~/workplace/MonoScene$ python monoscene/scripts/visualization/kitti_vis_pred.py +file=/home/ruidong/workplace/MonoScene/outputs/kitti/08/000000.pkl +dataset=kitt monoscene/scripts/visualization/kitti_vis_pred.py:23: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations coords_grid = coords_grid.astype(np.float) Traceback (most recent call last): File "monoscene/scripts/visualization/kitti_vis_pred.py", line 196, in main d=7, File "monoscene/scripts/visualization/kitti_vis_pred.py", line 75, in draw grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T AttributeError: 'tuple' object has no attribute 'T'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Hello author, first of all thank you for your great work. I want to directly apply your work to the nuscenes dataset, is it possible? Does the nuscenes dataset need point cloud data to assist in generating voxel data?

Hello！ Last year, when you reproduced the code SISC（https://github.com/OPEN-AIR-SUN/SISC）, you found a bug and solve it！ Now, I get the same problem too，can you tell me how to solve it ！ Thank you very much！

there is something wrong with my machine and I reinstall my ubuntu. I re-gitclone the code and just keep the data.but when I follow the readme to do installation,it print:

(monoscene) [email protected]:~/workplace/MonoScene$ pip install -e ./ Obtaining file:///home/potato/workplace/MonoScene Installing collected packages: monoscene Running setup.py develop for monoscene Successfully installed monoscene-0.0.0 (monoscene) [email protected]:~/workplace/MonoScene$ python monoscene/scripts/train_monoscene.py dataset=kitti enable_log=true kitti_root=$KITTI_ROOT kitti_preprocess_root=$KITTI_PREPROCESS kitti_logdir=$KITTI_LOG n_gpus=1 batch_size=1 sem_scal_loss=False Traceback (most recent call last): File "monoscene/scripts/train_monoscene.py", line 1, in from monoscene.data.semantic_kitti.kitti_dm import KittiDataModule File "/home/potato/workplace/MonoScene/monoscene/data/semantic_kitti/kitti_dm.py", line 3, in import pytorch_lightning as pl File "/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/pytorch_lightning/init.py", line 20, in from pytorch_lightning import metrics # noqa: E402 File "/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/pytorch_lightning/metrics/init.py", line 15, in from pytorch_lightning.metrics.classification import ( # noqa: F401 File "/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/pytorch_lightning/metrics/classification/init.py", line 14, in from pytorch_lightning.metrics.classification.accuracy import Accuracy # noqa: F401 File "/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/pytorch_lightning/metrics/classification/accuracy.py", line 18, in from pytorch_lightning.metrics.utils import deprecated_metrics, void File "/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/pytorch_lightning/metrics/utils.py", line 22, in from torchmetrics.utilities.data import get_num_classes as _get_num_classes ImportError: cannot import name 'get_num_classes' from 'torchmetrics.utilities.data' (/home/potato/anaconda3/envs/monoscene/lib/python3.7/site-packages/torchmetrics/utilities/data.py)