Fix pytorch 1.13 error due to 2D weights (#601)

Tested change works with both PyTorch 1.13.1 and Tensorflow 2.8.4

Author: ssheorey

.github/workflows/style.yml

@@ -9,20 +9,19 @@ on:
 
 jobs:
   style-check:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     steps:
       - name: Checkout source code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           submodules: true
       - name: Set up Python version
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: 3.6
       - name: Install dependencies
         run: |
-          sudo apt-get install --yes clang-format-10
-          python -m pip install -U yapf==0.30.0 nbformat pydocstyle==6.0.0
+          python -m pip install -U clang-format==10.0.1.1 yapf==0.30.0 nbformat pydocstyle==6.0.0
       - name: Run style check
         run: |
           python ci/check_style.py

.github/workflows/ubuntu.yml

@@ -9,16 +9,16 @@ on:
 
 jobs:
   ubuntu:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     strategy:
       fail-fast: false
     env:
       NPROC: 2
     steps:
       - name: Checkout source code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
       - name: Setup cache
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           # Ref: https://github.com/apache/incubator-mxnet/pull/18459/files
           path: ~/.ccache
@@ -31,10 +31,10 @@ jobs:
           restore-keys: |
             ${{ runner.os }}-ccache
       - name: Set up Python version
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
-      # Pre-installed 18.04 packages: https://git.io/JfHmW
+      # Pre-installed  packages: https://github.com/actions/runner-images/tree/main/images
       - name: Install ccache
         run: |
           sudo apt-get --yes install ccache

ml3d/datasets/utils/dataprocessing.py

@@ -170,7 +170,7 @@ def get_class_weights(num_per_class):
         weight = num_per_class / float(sum(num_per_class))
         ce_label_weight = 1 / (weight + 0.02)
 
-        return np.expand_dims(ce_label_weight, axis=0)
+        return ce_label_weight
 
     @staticmethod
     def invT(T):