Updates to PyTorch 2.7

.github/workflows/style.yml

@@ -16,15 +16,13 @@ jobs:
     steps:
       - name: Checkout source code
         uses: actions/checkout@v4
-        with:
-          submodules: true
       - name: Set up Python version
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
       - name: Install dependencies
         run: |
-          python -m pip install -U clang-format==10.0.1.1 yapf==0.30.0 nbformat pydocstyle==6.0.0
+          python -m pip install -U clang-format==18.* yapf==0.43.* nbformat==5.10.* pydocstyle==6.0.0
       - name: Run style check
         run: |
           python ci/check_style.py --verbose

.github/workflows/ubuntu.yml

@@ -4,7 +4,7 @@ permissions: {}
 on:
   workflow_dispatch:
   push:
-    branches: [main, dev]
+    branches: [main]
   pull_request:
     types: [opened, reopened, synchronize]
 
@@ -19,7 +19,7 @@ jobs:
   ubuntu:
     permissions:
       contents: read
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
     steps:
@@ -41,7 +41,7 @@ jobs:
       - name: Set up Python version
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.12"
       # Pre-installed  packages: https://github.com/actions/runner-images/tree/main/images
       - name: Install ccache
         run: |

ci/check_style.py

@@ -6,39 +6,11 @@
 from functools import partial
 import time
 import sys
-
-# Yapf requires python 3.6+
-if not (sys.version_info.major == 3 and sys.version_info.minor >= 6):
-    raise RuntimeError(
-        "Requires Python 3.6+, currently using Python {}.{}.".format(
-            sys.version_info.major, sys.version_info.minor))
-
-# Check and import yapf
-# > not found: throw exception
-# > version mismatch: throw exception
-try:
-    import yapf
-except:
-    raise ImportError(
-        "yapf not found. Install with `pip install yapf==0.30.0`.")
-if yapf.__version__ != "0.30.0":
-    raise RuntimeError(
-        "yapf 0.30.0 required. Install with `pip install yapf==0.30.0`.")
-print("Using yapf version {}".format(yapf.__version__))
-
-# Check and import nbformat
-# > not found: throw exception
-try:
-    import nbformat
-except:
-    raise ImportError(
-        "nbformat not found. Install with `pip install nbformat`.")
-print("Using nbformat version {}".format(nbformat.__version__))
+import yapf
+import nbformat
 
 PYTHON_FORMAT_DIRS = ["."]
-
 JUPYTER_FORMAT_DIRS = ["."]
-
 # Note: also modify CPP_FORMAT_DIRS in check_cpp_style.cmake.
 CPP_FORMAT_DIRS = ["."]
 
@@ -86,8 +58,8 @@ def _apply_style(file_path, style_config):
                                                    style_config=style_config,
                                                    in_place=True)
 
-    def run(self, do_apply_style, no_parallel, verbose):
-        if do_apply_style:
+    def run(self, apply, no_parallel, verbose):
+        if apply:
             print("Applying Python style...")
         else:
             print("Checking Python style...")
@@ -112,7 +84,7 @@ def run(self, do_apply_style, no_parallel, verbose):
         for is_valid, file_path in zip(is_valid_files, self.file_paths):
             if not is_valid:
                 changed_files.append(file_path)
-                if do_apply_style:
+                if apply:
                     self._apply_style(file_path, self.style_config)
         print("Formatting takes {:.2f}s".format(time.time() - start_time))
 
@@ -126,7 +98,7 @@ def __init__(self, file_paths, style_config):
         self.style_config = style_config
 
     @staticmethod
-    def _check_or_apply_style(file_path, style_config, do_apply_style):
+    def _check_or_apply_style(file_path, style_config, apply):
         """Returns true if style is valid.
 
         Since there are common code for check and apply style, the two functions
@@ -142,27 +114,39 @@ def _check_or_apply_style(file_path, style_config, do_apply_style):
             if cell["cell_type"] != "code":
                 continue
             src = cell["source"]
+            if not src.strip():  # Ignore empty cells.
+                continue
             lines = src.split("\n")
-            if len(lines) <= 0 or "# noqa" in lines[0]:
+            if "# noqa" in lines[0]:
                 continue
+            # Ignore cells starting with shell commands or jupyter magics.
+            # These are not valid python code and yapf will fail.
+            if src.lstrip().startswith(('!', '%')):
+                continue
+
             # yapf will puts a `\n` at the end of each cell, and if this is the
             # only change, cell_changed is still False.
-            formatted_src, cell_changed = yapf.yapflib.yapf_api.FormatCode(
-                src, style_config=style_config)
+            try:
+                formatted_src, cell_changed = yapf.yapflib.yapf_api.FormatCode(
+                    src, style_config=style_config)
+            except Exception:
+                # This may happen for cells with valid python and magics/shell
+                # commands mixed. We will just ignore formatting for these cells.
+                continue
             if formatted_src.endswith("\n"):
                 formatted_src = formatted_src[:-1]
             if cell_changed:
                 cell["source"] = formatted_src
                 changed = True
 
-        if do_apply_style:
+        if apply:
             with open(file_path, "w") as f:
                 nbformat.write(notebook, f, version=nbformat.NO_CONVERT)
 
         return not changed
 
-    def run(self, do_apply_style, no_parallel, verbose):
-        if do_apply_style:
+    def run(self, apply, no_parallel, verbose):
+        if apply:
             print("Applying Jupyter style...")
         else:
             print("Checking Jupyter style...")
@@ -177,22 +161,22 @@ def run(self, do_apply_style, no_parallel, verbose):
             is_valid_files = map(
                 partial(self._check_or_apply_style,
                         style_config=self.style_config,
-                        do_apply_style=False), self.file_paths)
+                        apply=False), self.file_paths)
         else:
             with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
                 is_valid_files = pool.map(
                     partial(self._check_or_apply_style,
                             style_config=self.style_config,
-                            do_apply_style=False), self.file_paths)
+                            apply=False), self.file_paths)
 
         changed_files = []
         for is_valid, file_path in zip(is_valid_files, self.file_paths):
             if not is_valid:
                 changed_files.append(file_path)
-                if do_apply_style:
+                if apply:
                     self._check_or_apply_style(file_path,
                                                style_config=self.style_config,
-                                               do_apply_style=True)
+                                               apply=True)
         print("Formatting takes {:.2f}s".format(time.time() - start_time))
 
         return changed_files
@@ -207,8 +191,8 @@ def run(self, do_apply_style, no_parallel, verbose):
 
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--do_apply_style",
-        dest="do_apply_style",
+        "--apply",
+        dest="apply",
         action="store_true",
         default=False,
         help="Apply style to files in-place.",
@@ -242,16 +226,16 @@ def run(self, do_apply_style, no_parallel, verbose):
 
     changed_files = []
     changed_files.extend(
-        python_formatter.run(do_apply_style=args.do_apply_style,
+        python_formatter.run(apply=args.apply,
                              no_parallel=args.no_parallel,
                              verbose=args.verbose))
     changed_files.extend(
-        jupyter_formatter.run(do_apply_style=args.do_apply_style,
+        jupyter_formatter.run(apply=args.apply,
                               no_parallel=args.no_parallel,
                               verbose=args.verbose))
 
     if len(changed_files) != 0:
-        if args.do_apply_style:
+        if args.apply:
             print("Style applied to the following files:")
             print("\n".join(changed_files))
         else:

ci/run_ci.sh

@@ -3,7 +3,7 @@
 set -euo pipefail
 
 NPROC=${NPROC:?'env var must be set to number of available CPUs.'}
-PIP_VER="23.2.1"
+PIP_VER="24.3.1"
 
 echo 1. Prepare the Open3D-ML repo and install dependencies
 echo
@@ -13,15 +13,15 @@ echo "$PATH_TO_OPEN3D_ML"
 git checkout -b main || true
 python -m pip install -U pip==$PIP_VER
 python -m pip install -r requirements.txt \
-    -r requirements-torch.txt
-# -r requirements-tensorflow.txt # TF disabled on Linux (Open3D PR#6288)
+    -r requirements-torch.txt \
+    -r requirements-tensorflow.txt
 # -r requirements-openvino.txt # Numpy version conflict with TF 2.8.2
 cd ..
 python -m pip install -U Cython
 
 echo 2. clone Open3D and install dependencies
 echo
-git clone --branch main https://github.com/isl-org/Open3D.git
+git clone --branch main --depth 1 https://github.com/isl-org/Open3D.git
 
 ./Open3D/util/install_deps_ubuntu.sh assume-yes
 python -m pip install -r Open3D/python/requirements.txt \
@@ -32,11 +32,9 @@ echo 3. Configure for bundling the Open3D-ML part
 echo
 mkdir Open3D/build
 pushd Open3D/build
-# TF disabled on Linux (Open3D PR#6288)
 cmake -DBUNDLE_OPEN3D_ML=ON \
     -DOPEN3D_ML_ROOT="${PATH_TO_OPEN3D_ML}" \
-    -DGLIBCXX_USE_CXX11_ABI=OFF \
-    -DBUILD_TENSORFLOW_OPS=OFF \
+    -DBUILD_TENSORFLOW_OPS=ON \
     -DBUILD_PYTORCH_OPS=ON \
     -DBUILD_GUI=ON \
     -DBUILD_UNIT_TESTS=OFF \

ml3d/datasets/utils/dataprocessing.py

@@ -234,10 +234,10 @@ def remove_outside_points(points, world_cam, cam_img, image_shape):
         pts_cam = DataProcessing.world2cam(points[:, :3], world_cam)
         pts_img, depth = DataProcessing.cam2img(pts_cam, cam_img)
 
-        val_flag_1 = np.logical_and(pts_img[:, 0] >= 0,
-                                    pts_img[:, 0] < image_shape[1])
-        val_flag_2 = np.logical_and(pts_img[:, 1] >= 0,
-                                    pts_img[:, 1] < image_shape[0])
+        val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0]
+                                    < image_shape[1])
+        val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1]
+                                    < image_shape[0])
         val_flag_merge = np.logical_and(val_flag_1, val_flag_2)
         valid = np.logical_and(val_flag_merge, depth >= 0)
 

ml3d/metrics/mAP.py

@@ -108,9 +108,9 @@ def precision_3d(pred,
                             axis=1).astype("float32")
 
                 # identify all matches (filtered preds vs filtered targets)
-                match_cond = np.any(
-                    overlap_label[pred_idx][:, target_idx] >= min_overlap[i],
-                    axis=-1)
+                match_cond = np.any(overlap_label[pred_idx][:, target_idx]
+                                    >= min_overlap[i],
+                                    axis=-1)
                 tp = np.zeros((len(pred_idx),))
 
                 # all matches first fp

ml3d/tf/dataloaders/__init__.py

@@ -1,4 +1,5 @@
 """Dataloader for TensorFlow."""
 
 from .tf_dataloader import TFDataloader
+
 __all__ = ['TFDataloader']

ml3d/tf/models/point_pillars.py

@@ -242,8 +242,8 @@ def preprocess(self, data, attr):
         max_val = np.array(self.point_cloud_range[3:])
 
         points = points[np.where(
-            np.all(np.logical_and(points[:, :3] >= min_val,
-                                  points[:, :3] < max_val),
+            np.all(np.logical_and(points[:, :3] >= min_val, points[:, :3]
+                                  < max_val),
                    axis=-1))]
 
         data['point'] = points
@@ -264,8 +264,8 @@ def preprocess(self, data, attr):
             max_val = np.array(self.point_cloud_range[3:])
 
             points = points[np.where(
-                np.all(np.logical_and(points[:, :3] >= min_val,
-                                      points[:, :3] < max_val),
+                np.all(np.logical_and(points[:, :3] >= min_val, points[:, :3]
+                                      < max_val),
                        axis=-1))]
 
             new_data['full_point'] = points

ml3d/tf/models/point_rcnn.py

@@ -388,8 +388,8 @@ def batcher():
                 for lab in labels:
                     max_lab = max(max_lab, lab.shape[0])
 
-                if 'labels' in batch[
-                        0] and labels[0].shape[0] != points.shape[1]:
+                if 'labels' in batch[0] and labels[0].shape[0] != points.shape[
+                        1]:
                     pad_labels = np.ones(
                         (len(labels), max_lab), dtype=np.int32) * (-1)
                     for j in range(len(labels)):
@@ -774,8 +774,8 @@ def __init__(
         self.SA_modules = []
         for i in range(len(SA_config["npoints"])):
             mlps = [in_channels] + SA_config["mlps"][i]
-            npoint = SA_config["npoints"][
-                i] if SA_config["npoints"][i] != -1 else None
+            npoint = SA_config["npoints"][i] if SA_config["npoints"][
+                i] != -1 else None
             self.SA_modules.append(
                 PointnetSAModule(npoint=npoint,
                                  radius=SA_config["radius"][i],

ml3d/tf/models/pvcnn.py

@@ -177,10 +177,10 @@ def preprocess(self, data, attr):
 
         feat = np.concatenate([x, y, z, feat, norm_x, norm_y, norm_z], axis=-1)
 
-        choices = np.random.choice(
-            points.shape[0],
-            self.cfg.num_points,
-            replace=(points.shape[0] < self.cfg.num_points))
+        choices = np.random.choice(points.shape[0],
+                                   self.cfg.num_points,
+                                   replace=(points.shape[0]
+                                            < self.cfg.num_points))
         points = points[choices]
         feat = feat[choices]
         labels = labels[choices]

ml3d/tf/modules/pointnet.py

@@ -44,8 +44,8 @@ def __init__(
         self.FP_modules = []
 
         for i in range(len(fp_mlps)):
-            pre_channel = fp_mlps[
-                i + 1][-1] if i + 1 < len(fp_mlps) else out_channels
+            pre_channel = fp_mlps[i + 1][-1] if i + 1 < len(
+                fp_mlps) else out_channels
             self.FP_modules.append(
                 PointnetFPModule(mlp=[pre_channel + skip_channel_list[i]] +
                                  fp_mlps[i],

ml3d/torch/dataloaders/torch_dataloader.py

@@ -61,7 +61,7 @@ def __init__(self,
                         continue
                     data = dataset.get_data(idx)
                     # cache the data
-                    self.cache_convert(name, data, attr)            
+                    self.cache_convert(name, data, attr)
 
         self.transform = transform