Merge pull request #564 from isl-org/dev_to_master_0.16

Dev to master 0.16

Author: ssheorey

.github/workflows/ubuntu.yml

@@ -17,8 +17,6 @@ jobs:
     steps:
       - name: Checkout source code
         uses: actions/checkout@v2
-        with:
-          submodules: true
       - name: Setup cache
         uses: actions/cache@v2
         with:
@@ -35,7 +33,7 @@ jobs:
       - name: Set up Python version
         uses: actions/setup-python@v2
         with:
-          python-version: 3.6
+          python-version: "3.10"
       # Pre-installed 18.04 packages: https://git.io/JfHmW
       - name: Install ccache
         run: |

ci/run_ci.sh

@@ -3,10 +3,14 @@
 # The following environment variables are required:
 # - NPROC
 #
-TENSORFLOW_VER="2.5.2"
-TORCH_GLNX_VER="1.8.2+cpu"
+TENSORFLOW_VER="2.8.2"
+TORCH_GLNX_VER="1.12.0+cpu"
+# OPENVINO_DEV_VER="2021.4.2"  # Numpy version conflict with TF 2.8.2
+PIP_VER="21.1.1"
+WHEEL_VER="0.37.1"
+STOOLS_VER="50.3.2"
 YAPF_VER="0.30.0"
-PYTEST_VER="6.0.1"
+PYTEST_VER="7.1.2"
 PYTEST_RANDOMLY_VER="3.8.0"
 
 set -euo pipefail
@@ -16,7 +20,14 @@ echo
 export PATH_TO_OPEN3D_ML=$(pwd)
 # the build system of the main repo expects a master branch. make sure master exists
 git checkout -b master || true
-pip install -r requirements.txt
+python -m pip install -U pip==$PIP_VER \
+	wheel=="$WHEEL_VER" \
+	setuptools=="$STOOLS_VER" \
+	yapf=="$YAPF_VER" \
+	pytest=="$PYTEST_VER" \
+	pytest-randomly=="$PYTEST_RANDOMLY_VER"
+
+python -m pip install -r requirements.txt
 echo $PATH_TO_OPEN3D_ML
 cd ..
 python -m pip install -U Cython
@@ -26,28 +37,25 @@ echo
 git clone --recursive --branch master https://github.com/isl-org/Open3D.git
 
 ./Open3D/util/install_deps_ubuntu.sh assume-yes
-python -m pip install -U tensorflow-cpu==$TENSORFLOW_VER
-python -m pip install -U torch==${TORCH_GLNX_VER} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-python -m pip install -U pytest=="$PYTEST_VER" \
-    pytest-randomly=="$PYTEST_RANDOMLY_VER"
-python -m pip install -U yapf=="$YAPF_VER"
-python -m pip install -U openvino-dev==2021.4.2
+python -m pip install -U tensorflow-cpu==$TENSORFLOW_VER \
+	torch==${TORCH_GLNX_VER} --extra-index-url https://download.pytorch.org/whl/cpu/
+# openvino-dev=="$OPENVINO_DEV_VER"
 
 echo 3. Configure for bundling the Open3D-ML part
 echo
 mkdir Open3D/build
 pushd Open3D/build
 cmake -DBUNDLE_OPEN3D_ML=ON \
-    -DOPEN3D_ML_ROOT=$PATH_TO_OPEN3D_ML \
-    -DGLIBCXX_USE_CXX11_ABI=OFF \
-    -DBUILD_TENSORFLOW_OPS=ON \
-    -DBUILD_PYTORCH_OPS=ON \
-    -DBUILD_GUI=OFF \
-    -DBUILD_RPC_INTERFACE=OFF \
-    -DBUILD_UNIT_TESTS=OFF \
-    -DBUILD_BENCHMARKS=OFF \
-    -DBUILD_EXAMPLES=OFF \
-    ..
+	-DOPEN3D_ML_ROOT=$PATH_TO_OPEN3D_ML \
+	-DGLIBCXX_USE_CXX11_ABI=OFF \
+	-DBUILD_TENSORFLOW_OPS=ON \
+	-DBUILD_PYTORCH_OPS=ON \
+	-DBUILD_GUI=OFF \
+	-DBUILD_RPC_INTERFACE=OFF \
+	-DBUILD_UNIT_TESTS=OFF \
+	-DBUILD_BENCHMARKS=OFF \
+	-DBUILD_EXAMPLES=OFF \
+	..
 
 echo 4. Build and install wheel
 echo
@@ -60,12 +68,12 @@ popd
 mkdir test_workdir
 pushd test_workdir
 mv $PATH_TO_OPEN3D_ML/tests .
-echo Add --rondomly-seed=SEED to the test command to reproduce test order.
+echo Add --randomly-seed=SEED to the test command to reproduce test order.
 python -m pytest tests
 
 echo ... now do the same but in dev mode by setting OPEN3D_ML_ROOT
 export OPEN3D_ML_ROOT=$PATH_TO_OPEN3D_ML
-echo Add --rondomly-seed=SEED to the test command to reproduce test order.
+echo Add --randomly-seed=SEED to the test command to reproduce test order.
 python -m pytest tests
 unset OPEN3D_ML_ROOT
 

docs/tensorboard.md

@@ -252,7 +252,7 @@ order.
 
 Now you can visualize the data in TensorBoard as before. The web interface
 allows showing and hiding points with different classes, changing their colors,
-and exploring predictions and intermediate network features.  Scalar network 
+and exploring predictions and intermediate network features.  Scalar network
 features can be visualized with custom user editable colormaps, and 3D features
 can be visualized as RGB colors. Here is a video showing the different ways in
 which semantic segmentation summary data can be visualized in TensorBoard.
@@ -376,3 +376,14 @@ for step in range(len(val_split)):  # one pointcloud per step
                   step,
                   label_to_names=dset.get_label_to_names())
 ```
+
+Troubleshooting
+---------------
+
+If you cannot interact with the 3D model, or use controls in the WebRTC widget,
+make sure that Allow Autoplay is enabled for the Tensorboard web site and reload.
+
+<img src=https://user-images.githubusercontent.com/41028320/180485249-5233b65e-11b1-44ff-bfc4-35f390ef51f2.png
+title="Allow Autoplay for correct behavior."
+alt="Allow Autoplay for correct behavior."
+style="width:80%;display:block;margin:auto"></img>

ml3d/configs/pointpillars_waymo.yml

@@ -2,7 +2,7 @@ dataset:
   name: Waymo
   dataset_path: # path/to/your/dataset
   cache_dir: ./logs/cache
-  steps_per_epoch_train: 5000
+  steps_per_epoch_train: 4000
 
 model:
   name: PointPillars
@@ -31,7 +31,7 @@ model:
     max_voxels: [32000, 32000]
 
   voxel_encoder:
-    in_channels: 5
+    in_channels: 4
     feat_channels: [64]
     voxel_size: *vsize
 
@@ -43,7 +43,7 @@ model:
     in_channels: 64
     out_channels: [64, 128, 256]
     layer_nums: [3, 5, 5]
-    layer_strides: [2, 2, 2]
+    layer_strides: [1, 2, 2]
 
   neck:
     in_channels: [64, 128, 256]
@@ -62,17 +62,18 @@ model:
       [-74.88, -74.88, 0, 74.88, 74.88, 0],
     ]
     sizes: [
-      [2.08, 4.73, 1.77],  # car
-      [0.84, 1.81, 1.77],  # cyclist
-      [0.84, 0.91, 1.74]   # pedestrian
+      [2.08, 4.73, 1.77],  # VEHICLE
+      [0.84, 1.81, 1.77],  # CYCLIST
+      [0.84, 0.91, 1.74]   # PEDESTRIAN
     ]
     dir_offset: 0.7854
     rotations: [0, 1.57]
     iou_thr: [[0.4, 0.55], [0.3, 0.5], [0.3, 0.5]]
 
   augment:
     PointShuffle: True
-    ObjectRangeFilter: True
+    ObjectRangeFilter:
+      point_cloud_range: [-74.88, -74.88, -2, 74.88, 74.88, 4]
     ObjectSample:
       min_points_dict:
         VEHICLE: 5
@@ -88,7 +89,7 @@ pipeline:
   name: ObjectDetection
   test_compute_metric: true
   batch_size: 6
-  val_batch_size: 1
+  val_batch_size: 6
   test_batch_size: 1
   save_ckpt_freq: 5
   max_epoch: 200
@@ -102,7 +103,7 @@ pipeline:
     weight_decay: 0.01
 
   # evaluation properties
-  overlaps: [0.5, 0.5, 0.7]
+  overlaps: [0.5, 0.5, 0.5]
   difficulties: [0, 1, 2]
   summary:
     record_for: []

ml3d/datasets/augment/augmentation.py

@@ -493,7 +493,7 @@ def ObjectSample(self, data, db_boxes_dict, sample_dict):
             sampled_points = np.concatenate(
                 [box.points_inside_box for box in sampled], axis=0)
             points = remove_points_in_boxes(points, sampled)
-            points = np.concatenate([sampled_points, points], axis=0)
+            points = np.concatenate([sampled_points[:, :4], points], axis=0)
 
         return {
             'point': points,

ml3d/datasets/utils/operations.py

@@ -4,7 +4,7 @@
 import math
 from scipy.spatial import ConvexHull
 
-from ...metrics import iou_bev
+from open3d.ml.contrib import iou_bev_cpu as iou_bev
 
 
 def create_3D_rotations(axis, angle):

ml3d/datasets/waymo.py

@@ -25,7 +25,6 @@ def __init__(self,
                  name='Waymo',
                  cache_dir='./logs/cache',
                  use_cache=False,
-                 val_split=3,
                  **kwargs):
         """Initialize the function by passing the dataset and other details.
 
@@ -34,7 +33,6 @@ def __init__(self,
             name: The name of the dataset (Waymo in this case).
             cache_dir: The directory where the cache is stored.
             use_cache: Indicates if the dataset should be cached.
-            val_split: The split value to get a set of images for training, validation, for testing.
 
         Returns:
             class: The corresponding class.
@@ -43,7 +41,6 @@ def __init__(self,
                          name=name,
                          cache_dir=cache_dir,
                          use_cache=use_cache,
-                         val_split=val_split,
                          **kwargs)
 
         cfg = self.cfg
@@ -52,22 +49,27 @@ def __init__(self,
         self.dataset_path = cfg.dataset_path
         self.num_classes = 4
         self.label_to_names = self.get_label_to_names()
+        self.shuffle = kwargs.get('shuffle', False)
 
         self.all_files = sorted(
             glob(join(cfg.dataset_path, 'velodyne', '*.bin')))
         self.train_files = []
         self.val_files = []
+        self.test_files = []
 
         for f in self.all_files:
-            idx = Path(f).name.replace('.bin', '')[:3]
-            idx = int(idx)
-            if idx < cfg.val_split:
+            if 'train' in f:
                 self.train_files.append(f)
-            else:
+            elif 'val' in f:
                 self.val_files.append(f)
-
-        self.test_files = glob(
-            join(cfg.dataset_path, 'testing', 'velodyne', '*.bin'))
+            elif 'test' in f:
+                self.test_files.append(f)
+            else:
+                log.warning(
+                    f"Skipping {f}, prefix must be one of train, test or val.")
+        if self.shuffle:
+            log.info("Shuffling training files...")
+            self.rng.shuffle(self.train_files)
 
     @staticmethod
     def get_label_to_names():
@@ -90,18 +92,21 @@ def read_lidar(path):
         """Reads lidar data from the path provided.
 
         Returns:
-            A data object with lidar information.
+            pc: pointcloud data with shape [N, 6], where
+                the format is xyzRGB.
         """
-        assert Path(path).exists()
-
         return np.fromfile(path, dtype=np.float32).reshape(-1, 6)
 
     @staticmethod
     def read_label(path, calib):
-        """Reads labels of bound boxes.
+        """Reads labels of bounding boxes.
+
+        Args:
+            path: The path to the label file.
+            calib: Calibration as returned by read_calib().
 
         Returns:
-            The data objects with bound boxes information.
+            The data objects with bounding boxes information.
         """
         if not Path(path).exists():
             return None
@@ -131,24 +136,22 @@ def read_calib(path):
         Returns:
             The camera and the camera image used in calibration.
         """
-        assert Path(path).exists()
-
         with open(path, 'r') as f:
             lines = f.readlines()
         obj = lines[0].strip().split(' ')[1:]
-        P0 = np.array(obj, dtype=np.float32)
+        unused_P0 = np.array(obj, dtype=np.float32)
 
         obj = lines[1].strip().split(' ')[1:]
-        P1 = np.array(obj, dtype=np.float32)
+        unused_P1 = np.array(obj, dtype=np.float32)
 
         obj = lines[2].strip().split(' ')[1:]
         P2 = np.array(obj, dtype=np.float32)
 
         obj = lines[3].strip().split(' ')[1:]
-        P3 = np.array(obj, dtype=np.float32)
+        unused_P3 = np.array(obj, dtype=np.float32)
 
         obj = lines[4].strip().split(' ')[1:]
-        P4 = np.array(obj, dtype=np.float32)
+        unused_P4 = np.array(obj, dtype=np.float32)
 
         obj = lines[5].strip().split(' ')[1:]
         R0 = np.array(obj, dtype=np.float32).reshape(3, 3)
@@ -162,7 +165,7 @@ def read_calib(path):
         Tr_velo_to_cam = Waymo._extend_matrix(Tr_velo_to_cam)
 
         world_cam = np.transpose(rect_4x4 @ Tr_velo_to_cam)
-        cam_img = np.transpose(P2)
+        cam_img = np.transpose(np.vstack((P2.reshape(3, 4), [0, 0, 0, 1])))
 
         return {'world_cam': world_cam, 'cam_img': cam_img}
 
@@ -209,7 +212,7 @@ def get_split_list(self, split):
         else:
             raise ValueError("Invalid split {}".format(split))
 
-    def is_tested():
+    def is_tested(attr):
         """Checks if a datum in the dataset has been tested.
 
         Args:
@@ -219,16 +222,16 @@ def is_tested():
             If the datum attribute is tested, then return the path where the
                 attribute is stored; else, returns false.
         """
-        pass
+        raise NotImplementedError()
 
-    def save_test_result():
+    def save_test_result(results, attr):
         """Saves the output of a model.
 
         Args:
             results: The output of a model for the datum associated with the attribute passed.
             attr: The attributes that correspond to the outputs passed in results.
         """
-        pass
+        raise NotImplementedError()
 
 
 class WaymoSplit():
@@ -273,11 +276,9 @@ def get_attr(self, idx):
 
 
 class Object3d(BEVBox3D):
-    """The class stores details that are object-specific, such as bounding box
-    coordinates, occlusion and so on.
-    """
 
     def __init__(self, center, size, label, calib):
+        # ground truth files doesn't have confidence value.
         confidence = float(label[15]) if label.__len__() == 16 else -1.0
 
         world_cam = calib['world_cam']

ml3d/tf/pipelines/object_detection.py

@@ -290,7 +290,7 @@ def run_train(self):
 
             self.save_logs(writer, epoch)
 
-            if epoch % cfg.save_ckpt_freq == 0:
+            if epoch % cfg.save_ckpt_freq == 0 or epoch == cfg.max_epoch:
                 self.save_ckpt(epoch)
 
     def get_3d_summary(self,