MMScan devkit

.dev_scripts/covignore.cfg

@@ -4,3 +4,4 @@
 # .*/utils.py
 
 .*/__init__.py
+models/

.dev_scripts/diff_coverage_test.sh

@@ -35,7 +35,7 @@ done
 if [ ! -z "${PY_FILES}" ]
 then
     if [ "$REUSE_COVERAGE_REPORT" == "0" ]; then
-        coverage run --branch --source embodiedscan -m pytest tests/
+        coverage run --branch --source mmscan -m pytest tests/
     fi
     coverage report --fail-under 80 -m $PY_FILES
     interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 95 $PY_FILES

.dev_scripts/linter.sh

@@ -1,3 +1,3 @@
-yapf -r -i embodiedscan/ configs/ tests/ tools/
-isort embodiedscan/ configs/ tests/ tools/
+yapf -r -i mmscan/ data_preparation/
+isort mmscan/ data_preparation/
 flake8 .

.github/workflows/lint.yml

@@ -24,4 +24,4 @@ jobs:
       - name: Check docstring coverage
         run: |
           pip install interrogate
-          interrogate -v --ignore-init-method --ignore-magic --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 embodiedscan
+          interrogate -v --ignore-init-method --ignore-magic --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 mmscan

.github/workflows/merge_stage_test.yml

@@ -38,11 +38,11 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests
+          coverage run --branch --source mmscan -m pytest tests
           coverage xml
           coverage report -m
 
@@ -67,10 +67,10 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests
+          coverage run --branch --source mmscan -m pytest tests
           coverage xml
           coverage report -m

.github/workflows/pr_stage_test.yml

@@ -34,10 +34,10 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests/
+          coverage run --branch --source mmscan -m pytest tests/
           coverage xml
           coverage report -m

.github/workflows/test_mim.yml

@@ -41,4 +41,4 @@ jobs:
       - name: Build and install
         run: rm -rf .eggs && mim install -e .
       - name: test commands of mim
-        run: mim search embodiedscan
+        run: mim search mmscan

.gitignore

@@ -14,8 +14,7 @@ dist/
 downloads/
 eggs/
 .eggs/
-lib/
-lib64/
+
 parts/
 sdist/
 var/
@@ -113,7 +112,6 @@ venv.bak/
 
 # demo
 *.jpg
-*.png
 *.obj
 *.ply
 demo/data/*
@@ -127,7 +125,7 @@ data/3rscan
 data/matterport3d
 data/arkitscenes
 data/*.pkl
-data/*.json
+
 exps/
 todo.md
 
@@ -140,4 +138,3 @@ tools/*.sh
 
 # test submission results
 *.pkl
-*.json

.pre-commit-config.yaml

@@ -3,6 +3,13 @@ repos:
     rev: 5.0.4
     hooks:
       - id: flake8
+        exclude: '^models/.*'
+  # - repo: https://github.com/ambv/black
+  #   rev: 23.9.1
+  #   hooks:
+  #     - id: black
+  #       args: [--line-length=79]
+  #       exclude: '^models/.*'
   - repo: https://github.com/PyCQA/isort
     rev: 5.11.5
     hooks:

data_preparation/README.md

@@ -0,0 +1,51 @@
+### Prepare MMscan info files.
+
+Given the licenses of respective raw datasets, we recommend users download the raw data from their official websites and then organize them following the below guide.
+Detailed steps are shown as follows.
+
+1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Link or move the folder to this level of directory.
+
+2. Download 3RScan data [HERE](https://github.com/WaldJohannaU/3RScan). Link or move the folder to this level of directory.
+
+3. Download Matterport3D data [HERE](https://github.com/niessner/Matterport). Link or move the folder to this level of directory.
+
+4. Organize the file structure. You are recommanded to create a soft link to the raw data folder under `mmscan_data/embodiedscan_split/data`.
+
+   ```
+   mmscan_data/embodiedscan_split/data/
+   ├── scannet/
+   │   ├── scans
+   │   │   ├── <scene_id>
+   │   │   ├── ...
+   ├── 3rscan/
+   │   ├── <scene_id>
+   │   ├── ...
+   ├── matterport3d/
+   │   ├── <scene_id>
+   │   ├── ...
+   ```
+
+   Additionally, create a `process_pcd` folder under `mmscan_data/embodiedscan_split` to store the results. Similarly, we recommend using a symbolic link, as the total file size might be a little large (approximately 21GB)
+
+   PS: If you have followed the embodiedscan tutorial to organize the data, you can skip these steps and link or copy the `data` folder to
+   `mmscan_data/embodiedscan_split`.
+
+   After all the raw data is organized, the directory structure should be as below:
+
+   ```
+   mmscan_data
+   ├── embodiedscan_split/
+   │   ├── data/
+   │   ├── process_pcd/
+   │   ├── embodiedscan-v1/
+   │   ├── embodiedscan-v2/
+   ├── MMScan-beta-release
+
+   ```
+
+5. Read raw files and generate processed point cloud files, by running the following scripts.
+
+   ```bash
+   python process_all_scan.py --nproc 8
+   # If your various file directories do not match the configuration settings, define them using --
+   ```

data_preparation/meta_data/mp3d_mapping.json

@@ -0,0 +1 @@
+{"ZMojNkEp431": "1mp3d_0000", "uNb9QFRL6hY": "1mp3d_0001", "pRbA3pwrgk9": "1mp3d_0002", "1pXnuDYAj8r": "1mp3d_0003", "ULsKaCPVFJR": "1mp3d_0004", "D7N2EKCX4Sj": "1mp3d_0005", "sT4fr6TAbpF": "1mp3d_0006", "YmJkqBEsHnH": "1mp3d_0007", "VLzqgDo317F": "1mp3d_0008", "EU6Fwq7SyZv": "1mp3d_0009", "s8pcmisQ38h": "1mp3d_0010", "JeFG25nYj2p": "1mp3d_0011", "E9uDoFAP3SH": "1mp3d_0012", "PX4nDJXEHrG": "1mp3d_0013", "Vvot9Ly1tCj": "1mp3d_0014", "5ZKStnWn8Zo": "1mp3d_0015", "S9hNv5qa7GM": "1mp3d_0016", "p5wJjkQkbXX": "1mp3d_0017", "b8cTxDM8gDG": "1mp3d_0018", "mJXqzFtmKg4": "1mp3d_0019", "UwV83HsGsw3": "1mp3d_0020", "wc2JMjhGNzB": "1mp3d_0021", "XcA2TqTSSAj": "1mp3d_0022", "8WUmhLawc2A": "1mp3d_0023", "PuKPg4mmafe": "1mp3d_0024", "jh4fc5c5qoQ": "1mp3d_0025", "oLBMNvg9in8": "1mp3d_0026", "5LpN3gDmAk7": "1mp3d_0027", "WYY7iVyf5p8": "1mp3d_0028", "7y3sRwLe3Va": "1mp3d_0029", "JmbYfDe2QKZ": "1mp3d_0030", "jtcxE69GiFV": "1mp3d_0031", "TbHJrupSAjP": "1mp3d_0032", "kEZ7cmS4wCh": "1mp3d_0033", "q9vSo1VnCiC": "1mp3d_0034", "SN83YJsR3w2": "1mp3d_0035", "sKLMLpTHeUy": "1mp3d_0036", "pa4otMbVnkk": "1mp3d_0037", "QUCTc6BB5sX": "1mp3d_0038", "B6ByNegPMKs": "1mp3d_0039", "vyrNrziPKCB": "1mp3d_0040", "VFuaQ6m2Qom": "1mp3d_0041", "X7HyMhZNoso": "1mp3d_0042", "2azQ1b91cZZ": "1mp3d_0043", "VzqfbhrpDEA": "1mp3d_0044", "i5noydFURQK": "1mp3d_0045", "r1Q1Z4BcV1o": "1mp3d_0046", "Uxmj2M2itWa": "1mp3d_0047", "759xd9YjKW5": "1mp3d_0048", "yqstnuAEVhm": "1mp3d_0049", "r47D5H71a5s": "1mp3d_0050", "YFuZgdQ5vWj": "1mp3d_0051", "HxpKQynjfin": "1mp3d_0052", "rqfALeAoiTq": "1mp3d_0053", "ARNzJeq3xxb": "1mp3d_0054", "RPmz2sHmrrY": "1mp3d_0055", "Vt2qJdWjCF2": "1mp3d_0056", "V2XKFyX4ASd": "1mp3d_0057", "dhjEzFoUFzH": "1mp3d_0058", "zsNo4HB9uLZ": "1mp3d_0059", "aayBHfsNo7d": "1mp3d_0060", "rPc6DW4iMge": "1mp3d_0061", "1LXtFkjw3qL": "1mp3d_0062", "fzynW3qQPVF": "1mp3d_0063", "e9zR4mvMWw7": "1mp3d_0064", "2n8kARJN3HM": "1mp3d_0065", "ur6pFq6Qu1A": "1mp3d_0066", "ac26ZMwG7aT": "1mp3d_0067", "YVUC4YcDtcY": "1mp3d_0068", "gTV8FGcVJC9": "1mp3d_0069", "pLe4wQe7qrG": "1mp3d_0070", "Z6MFQCViBuw": "1mp3d_0071", "VVfe2KiqLaN": "1mp3d_0072", "qoiz87JEwZ2": "1mp3d_0073", "JF19kD82Mey": "1mp3d_0074", "17DRP5sb8fy": "1mp3d_0075", "82sE5b5pLXE": "1mp3d_0076", "5q7pvUzZiYa": "1mp3d_0077", "29hnd4uzFmX": "1mp3d_0078", "EDJbREhghzL": "1mp3d_0079", "gxdoqLR6rwA": "1mp3d_0080", "D7G3Y4RVNrH": "1mp3d_0081", "x8F5xyUWy9e": "1mp3d_0082", "GdvgFV5R1Z5": "1mp3d_0083", "cV4RVeZvu5T": "1mp3d_0084", "8194nk5LbLH": "1mp3d_0085", "2t7WUuJeko7": "1mp3d_0086", "Pm6F8kyY3z2": "1mp3d_0087", "gZ6f7yhEvPG": "1mp3d_0088", "gYvKGZ5eRqb": "1mp3d_0089"}