v0.1.3

docs/source/conf.py

@@ -24,7 +24,7 @@
 
 project = "Vis4D"
 copyright = "2022, ETH Zurich"
-author = "Tobias Fischer"
+author = "Vis4D Team"
 
 
 # -- General configuration ---------------------------------------------------

docs/source/datasets.rst

@@ -0,0 +1,3 @@
+********
+Datasets
+********

docs/source/dev_guide/cli.rst

@@ -1,17 +1,19 @@
 ###
 CLI
 ###
+
 We provide a command line interface for training and evaluating your models.
 Assuming you have installed the package using pip, you can use the command `vis4d` to access the CLI.
 
-Alternatively, you can run the CLI using `python -m vis4d.engine.cli` or `python -m vis4d.pl.cli` if you want to use the PyTorch Lightning version.
+Alternatively, you can run the CLI using `python -m vis4d.engine.run` or `python -m vis4d.pl.run` if you want to use the PyTorch Lightning version.
 
 The CLI relies on a configuration file to specify each experiment. We use `ml_collections <https://github.com/google/ml_collections>`_ as underlying framework to define the configuration files.
 You can read up on our configuration files in the `Config System <configuration_files>`_ section.
 
 =============
 CLI Interface
 =============
+
 The provided examples assume that the experiment configuration file is located at `path_to_experiment_cfg.py`.
 You can read up on our configuration files in the `Config System <configuration_files>`_ section.
 
@@ -20,6 +22,7 @@ We support both, our own training engine as well as `PyTorch Lightning <https://
 ------------
 CLI Commands
 ------------
+
 .. code-block:: bash
 
   vis4d [fit | test] --config path_to_experiment_cfg.py
@@ -54,16 +57,14 @@ Quick Start
 
 .. code-block:: bash
 
-   vis4d fit --config path_to_experiment_cfg.py
+   vis4d test --config path_to_experiment_cfg.py
 
 **Overwrite Config Parameters**
 
 .. code-block:: bash
 
    vis4d fit --config path_to_experiment_cfg.py --config.my_field=2 --config.my.nested.field="test"
 
-
-
 **Perform Parameter Sweeps**
 
 .. code-block:: bash
@@ -87,6 +88,7 @@ Note that misstyping a config parameter
    vis4d fit --config path_to_experiment_cfg.py --config.params.lrs=0.01
 
 will result in the following error:
+
 .. code-block:: bash
 
-   AttributeError: Did you mean "lr" instead of "lrw"?'
+   AttributeError: Did you mean "lr" instead of "lrs"?'

docs/source/faq/trouble.rst

@@ -2,10 +2,6 @@
 Troubleshooting
 ***************
 
-================
-Training crashes
-================
-
 -----------
 File limits
 -----------

docs/source/user_guide/3D_visualization.ipynb

@@ -20,7 +20,7 @@
     "os.environ[\"WEBRTC_IP\"] = \"127.0.0.1\"\n",
     "\n",
     "import pickle\n",
-    "from vis4d.vis.functional import show_points\n",
+    "from vis4d.vis.pointcloud.functional import show_points\n",
     "import numpy as np"
    ]
   },

docs/source/user_guide/faster_rcnn_example.py

@@ -6,7 +6,7 @@
 
 import lightning.pytorch as pl
 import numpy as np
-from torch.optim import SGD
+from torch.optim.sgd import SGD
 from torch.optim.lr_scheduler import LinearLR, MultiStepLR
 
 from vis4d.config import class_config

docs/source/user_guide/getting_started.ipynb

@@ -251,7 +251,7 @@
     "from vis4d.model.detect.faster_rcnn import FasterRCNN\n",
     "\n",
     "from vis4d.data.const import CommonKeys as K\n",
-    "from vis4d.vis.functional.image import imshow_bboxes\n",
+    "from vis4d.vis.image.functional import imshow_bboxes\n",
     "\n",
     "from vis4d.config import instantiate_classes\n",
     "from vis4d.zoo.base.datasets.coco import get_coco_detection_cfg"

docs/source/user_guide/visualization.ipynb

@@ -15,22 +15,12 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
-      "[Open3D INFO] WebRTC GUI backend enabled.\n",
-      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from __future__ import annotations\n",
     "\n",
     "from vis4d.common.typing import NDArrayF64, NDArrayI64\n",
-    "from vis4d.vis.functional import imshow_bboxes, imshow_masks, imshow_topk_bboxes, imshow, draw_bboxes, draw_masks, imshow_track_matches\n",
+    "from vis4d.vis.image.functional import imshow_bboxes, imshow_masks, imshow_topk_bboxes, imshow, draw_bboxes, draw_masks, imshow_track_matches\n",
     "\n",
     "import pickle\n",
     "import numpy as np"
@@ -453,7 +443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.9"
   },
   "vscode": {
    "interpreter": {

pyproject.toml

@@ -14,6 +14,7 @@ line_length = 79
 
 [tool.pyright]
 include = ["vis4d"]
+typeCheckingMode = "off"
 
 [tool.coverage]
     [tool.coverage.report]

requirements/install.txt

@@ -7,7 +7,7 @@ h5py
 jsonargparse[signatures]
 lightning
 ml_collections==0.1.1 # Config interface. Need exact version since we overwrite internal functions
-numpy>=1.21.0
+numpy>=1.21.0,<2.0.0
 opencv-python
 pandas
 pillow

requirements/viewer.txt

@@ -1 +1,2 @@
 open3d
+matplotlib>3.9

tests/data/io/to_hdf5_test.py

@@ -4,7 +4,7 @@
 import unittest
 
 from tests.util import get_test_data
-from vis4d.data.io.hdf5 import convert_dataset
+from vis4d.data.io.to_hdf5 import convert_dataset
 
 
 class TestHDF5(unittest.TestCase):

tests/engine/callbacks/checkpoint_test.py

@@ -4,7 +4,7 @@
 import tempfile
 import unittest
 
-from torch.optim import SGD
+from torch.optim.sgd import SGD
 
 from tests.util import MOCKLOSS, MockModel
 from vis4d.config import class_config

tests/engine/optim/scheduler_test.py

@@ -5,8 +5,8 @@
 
 import torch
 import torch.nn.functional as F
-from torch import optim
 from torch.optim.lr_scheduler import LRScheduler
+from torch.optim.sgd import SGD
 from torch.testing import assert_close
 
 from vis4d.engine.optim.scheduler import ConstantLR, PolyLR, QuadraticLRWarmup
@@ -68,7 +68,7 @@ def setUp(self) -> None:
         model = ToyModel()
         self.lr = 0.05
         self.l2_mult = 10
-        self.optimizer = optim.SGD(
+        self.optimizer = SGD(
             [
                 {"params": model.conv1.parameters()},
                 {

tests/model/detect/mask_rcnn_test.py

@@ -3,7 +3,7 @@
 import unittest
 
 import torch
-from torch import optim
+from torch.optim.sgd import SGD
 
 from tests.util import get_test_data, get_test_file
 from vis4d.common.ckpt import load_model_checkpoint
@@ -120,7 +120,7 @@ def test_train(self):
             ]
         )
 
-        optimizer = optim.SGD(mask_rcnn.parameters(), lr=0.001, momentum=0.9)
+        optimizer = SGD(mask_rcnn.parameters(), lr=0.001, momentum=0.9)
 
         dataset = COCO(get_test_data("coco_test"), split="train")
         train_loader = get_train_dataloader(dataset, 2, (256, 256))

tests/model/detect/retinanet_test.py

@@ -3,7 +3,7 @@
 import unittest
 
 import torch
-from torch import optim
+from torch.optim.sgd import SGD
 
 from tests.util import get_test_data, get_test_file
 from vis4d.common.ckpt import load_model_checkpoint
@@ -75,7 +75,7 @@ def test_train(self) -> None:
             retina_net.retinanet_head.box_sampler,
         )
 
-        optimizer = optim.SGD(retina_net.parameters(), lr=0.001, momentum=0.9)
+        optimizer = SGD(retina_net.parameters(), lr=0.001, momentum=0.9)
 
         dataset = COCO(get_test_data("coco_test"), split="train")
         train_loader = get_train_dataloader(dataset, 2, (256, 256))

tests/model/seg/fcn_resnet_test.py

@@ -5,7 +5,7 @@
 import unittest
 
 import torch
-from torch import optim
+from torch.optim.sgd import SGD
 
 from tests.util import get_test_data, get_test_file
 from vis4d.common.ckpt import load_model_checkpoint
@@ -46,7 +46,7 @@ def test_train(self) -> None:
         """Test FCNResNet training."""
         model = FCNResNet(base_model="resnet50", resize=(64, 64))
         loss_fn = MultiLevelSegLoss(feature_idx=(4, 5), weights=[0.5, 1])
-        optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+        optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
         dataset = COCO(
             get_test_data("coco_test"), split="train", use_pascal_voc_cats=True
         )

tests/model/seg/semantic_fpn_test.py

@@ -5,7 +5,7 @@
 import unittest
 
 import torch
-from torch import optim
+from torch.optim.sgd import SGD
 
 from tests.util import get_test_data, get_test_file
 from vis4d.data.const import CommonKeys as K
@@ -59,7 +59,7 @@ def test_train(self) -> None:
         """Test SemanticFPN training."""
         model = SemanticFPN(num_classes=21)
         loss_fn = SegCrossEntropyLoss()
-        optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+        optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
         train_loader = get_train_dataloader(self.dataset, 2)
         model.train()
 

tests/pl/trainer_test.py

@@ -7,7 +7,7 @@
 import unittest
 
 from ml_collections import ConfigDict
-from torch import optim
+from torch.optim.sgd import SGD
 from torch.utils.data import DataLoader, Dataset
 
 from tests.util import get_test_data
@@ -85,7 +85,7 @@ def get_training_module(model_cfg: ConfigDict):
         }
     )
 
-    optimizer_cfg = get_optimizer_cfg(class_config(optim.SGD, lr=0.01))
+    optimizer_cfg = get_optimizer_cfg(class_config(SGD, lr=0.01))
     return TrainingModule(
         model_cfg=model_cfg,
         optimizers_cfg=[optimizer_cfg],

tests/vis/image/bounding_box_visualizer_test.py

@@ -32,8 +32,10 @@ def setUp(self) -> None:
         self.scores: list[NDArrayF64] = testcase_gt["scores"]
         self.tracks = [np.arange(len(b)) for b in self.boxes]
 
+        cat_mapping = {v: k for k, v in COCO_COLOR_MAPPING.items()}
+
         self.vis = BoundingBoxVisualizer(
-            n_colors=20, class_id_mapping=COCO_COLOR_MAPPING, vis_freq=1
+            n_colors=20, cat_mapping=cat_mapping, vis_freq=1
         )
 
     def tearDown(self) -> None:

tests/zoo/cc_3dt_test.py

@@ -96,6 +96,18 @@ def test_nusc_vis(self) -> None:
             )
         )
 
+    def test_nusc_test(self) -> None:
+        """Test the config."""
+        cfg_gt = f"{self.gt_config_path}/cc_3dt_nusc_test.yaml"
+
+        self.assertTrue(
+            compare_configs(
+                f"{self.config_prefix}.cc_3dt_nusc_test",
+                cfg_gt,
+                self.varying_keys,
+            )
+        )
+
     def test_bevformer_base_velo_lstm_nusc(self) -> None:
         """Test the config."""
         cfg_gt = (

vis4d/common/ckpt.py

@@ -205,7 +205,9 @@ def load_from_local(
     filename = osp.expanduser(filename)
     if not osp.isfile(filename):
         raise FileNotFoundError(f"{filename} can not be found.")
-    checkpoint = torch.load(filename, map_location=map_location)
+    checkpoint = torch.load(
+        filename, weights_only=True, map_location=map_location
+    )
     return checkpoint
 
 

vis4d/common/distributed.py

@@ -264,7 +264,7 @@ def all_gather_object_gpu(  # type: ignore
 
 
 def create_tmpdir(
-    rank: int, tmpdir: None | str = None
+    rank: int, tmpdir: None | str = None, use_system_tmp: bool = True
 ) -> str:  # pragma: no cover
     """Create and distribute a temporary directory across all processes."""
     if tmpdir is not None:
@@ -273,10 +273,10 @@ def create_tmpdir(
     if rank == 0:
         # create a temporary directory
         default_tmpdir = tempfile.gettempdir()
-        if default_tmpdir is not None:
+        if default_tmpdir is not None and use_system_tmp:
             dist_tmpdir = os.path.join(default_tmpdir, ".dist_tmp")
         else:
-            dist_tmpdir = ".dist_tmp"
+            dist_tmpdir = os.path.join("vis4d-workspace", ".dist_tmp")
         os.makedirs(dist_tmpdir, exist_ok=True)
         tmpdir = tempfile.mkdtemp(dir=dist_tmpdir)
     else:
@@ -288,13 +288,15 @@ def all_gather_object_cpu(  # type: ignore
     data: Any,
     tmpdir: None | str = None,
     rank_zero_return_only: bool = True,
+    use_system_tmp: bool = True,
 ) -> list[Any] | None:  # pragma: no cover
     """Share arbitrary picklable data via file system caching.
 
     Args:
         data: any picklable object.
         tmpdir: Save path for temporary files. If None, safely create tmpdir.
-        rank_zero_return_only: if results should only be returned on rank 0
+        rank_zero_return_only: if results should only be returned on rank 0.
+        use_system_tmp: if use system tmpdir or not.
 
     Returns:
         list[Any]: list of data gathered from each process.
@@ -304,7 +306,7 @@ def all_gather_object_cpu(  # type: ignore
         return [data]
 
     # make tmp dir
-    tmpdir = create_tmpdir(rank, tmpdir)
+    tmpdir = create_tmpdir(rank, tmpdir, use_system_tmp)
 
     # encode & save
     with open(os.path.join(tmpdir, f"part_{rank}.pkl"), "wb") as f:
@@ -335,7 +337,7 @@ def reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
     if not (dist.is_available() and dist.is_initialized()):
         return tensor
     tensor = tensor.clone()
-    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
+    dist.all_reduce(tensor.div_(get_world_size()), op=dist.ReduceOp.SUM)
     return tensor
 
 

vis4d/common/util.py

@@ -73,7 +73,7 @@ def set_tf32(use_tf32: bool, precision: str) -> None:  # pragma: no cover
 
 def init_random_seed() -> int:
     """Initialize random seed for the experiment."""
-    return np.random.randint(2**31)
+    return int(np.random.randint(2**31))
 
 
 def set_random_seed(seed: int, deterministic: bool = False) -> None: