Repository: zcemycl/TF2DeepFloorplan
Branch: main
Commit: b5860f2976cb
Files: 71
Total size: 156.0 KB
Directory structure:
gitextract__92euf85/
├── .gitattributes
├── .github/
│ └── workflows/
│ ├── main.yml
│ └── release.yml
├── .gitignore
├── .isort.cfg
├── .pre-commit-config.yaml
├── CITATION.cff
├── Dockerfile
├── LICENSE
├── README.md
├── deepfloorplan.ipynb
├── docs/
│ ├── app.toml
│ ├── cfg_test.md
│ ├── experiments/
│ │ ├── mobilenetv1/
│ │ │ ├── exp1/
│ │ │ │ ├── compress.toml
│ │ │ │ ├── deploy.toml
│ │ │ │ └── train.toml
│ │ │ └── exp2/
│ │ │ ├── compress.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ ├── mobilenetv2/
│ │ │ └── exp1/
│ │ │ └── train.toml
│ │ ├── resnet50/
│ │ │ └── exp1/
│ │ │ └── train.toml
│ │ └── vgg16/
│ │ ├── exp1/
│ │ │ ├── compress.toml
│ │ │ ├── compress_log.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ ├── exp2/
│ │ │ ├── compress.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ └── exp3/
│ │ ├── compress.toml
│ │ ├── deploy.toml
│ │ └── train.toml
│ ├── game.toml
│ ├── notebook.toml
│ └── pytest.md
├── install/
│ └── environment.yml
├── mypy.ini
├── pyproject.toml
├── requirements.txt
├── setup.cfg
├── setup.py
├── src/
│ └── dfp/
│ ├── __init__.py
│ ├── app.py
│ ├── convert2tflite.py
│ ├── data.py
│ ├── deploy.py
│ ├── game/
│ │ ├── __init__.py
│ │ ├── __main__.py
│ │ ├── controller.py
│ │ ├── model.py
│ │ └── view.py
│ ├── loss.py
│ ├── net.py
│ ├── net_func.py
│ ├── train.py
│ └── utils/
│ ├── __init__.py
│ ├── legend.py
│ ├── rgb_ind_convertor.py
│ ├── settings.py
│ └── util.py
└── tests/
├── __init__.py
├── test_app.py
├── test_convert2tflite.py
├── test_data.py
├── test_deploy.py
├── test_loss.py
├── test_net.py
├── test_train.py
└── utils/
├── __init__.py
├── test_legend.py
├── test_rgb_ind_convertor.py
└── test_util.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitattributes
================================================
*.ipynb linguist-vendored
================================================
FILE: .github/workflows/main.yml
================================================
# This is a basic workflow to help you get started with Actions
name: Python CI
# Controls when the workflow will run
on:
# Triggers the workflow on push or pull request events but only for the main branch
push:
branches:
- '*'
pull_request:
branches:
- '*'
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
# This workflow contains a single job called "build"
build:
# The type of runner that the job will run on
runs-on: ubuntu-latest
# Steps represent a sequence of tasks that will be executed as part of the job
steps:
# Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
- uses: actions/checkout@v2
- name: Set up Python 3.8
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e .[tfcpu,api,dev,testing,linting,game]
- name: Precommit check
run: |
pre-commit install
SKIP=pytest-check pre-commit run
SKIP=pytest-check pre-commit run --all-files
# Test python scripts
- name: Test with pytest
run: |
pip install python-coveralls
python -m pytest --cov=./dfp --cov-report lcov:lcov.info
# Coveralls
- name: Coveralls
uses: coverallsapp/github-action@master
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
path-to-lcov: lcov.info
- name: Install deep floorplan package
run: |
pip install -e .[tfcpu,api]
================================================
FILE: .github/workflows/release.yml
================================================
name: Tag Release
on:
push:
tags:
- "v*"
jobs:
tagged-release:
name: "Tagged Release"
runs-on: "ubuntu-latest"
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v3
with:
python-version: 3.8
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e .[tfcpu,api,dev,testing,linting,game]
- name: Test with pytest
run: |
pip install python-coveralls
python -m pytest --cov=./dfp --cov-report lcov:lcov.info
- name: Build
run: |
pip install setuptools sdist wheel twine
pip install -e .[tfcpu,api,game]
python setup.py sdist bdist_wheel
# - name: Publish distribution 📦 to PyPI
# uses: pypa/gh-action-pypi-publish@master
# with:
# password: ${{ secrets.PYPI_API_TOKEN }}
- uses: "marvinpinto/action-automatic-releases@latest"
with:
repo_token: "${{ secrets.GITHUB_TOKEN }}"
prerelease: false
files: |
./dist/*tar.gz
./dist/*.whl
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
*.pyc
*.pyo
*.swp
*.swo
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
log/
model/
*.tfrecords
cov.info
lcov.info
*.h5
out.jpg
.DS_Store
================================================
FILE: .isort.cfg
================================================
[settings]
profile=black
line_length=79
src_paths=src,tests
skip=.tox,.nox,venv,build,dist,resources,model,log
known_setuptools=setuptools,pkg_resources
known_test=pytest
known_first_party=Deep_floorplan
sections=FUTURE,STDLIB,SETUPTOOLS,TEST,THIRDPARTY,FIRSTPARTY,LOCALFOLDER
================================================
FILE: .pre-commit-config.yaml
================================================
exclude: '^(build|docs|resources|log|model)'
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.3.0
hooks:
- id: trailing-whitespace
- id: check-added-large-files
- id: check-json
- id: check-merge-conflict
- id: check-xml
- id: check-yaml
- id: debug-statements
- id: end-of-file-fixer
- repo: https://github.com/pycqa/flake8
rev: 4.0.1
hooks:
- id: flake8
always_run: true
verbose: true
- repo: http://github.com/timothycrosley/isort
rev: 5.12.0
hooks:
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entry: isort
args: [.]
always_run: true
verbose: true
- repo: https://github.com/ambv/black
rev: 22.3.0
hooks:
- id: black
entry: black
args: [.]
language: system
always_run: true
verbose: true
- repo: https://github.com/pre-commit/mirrors-mypy
rev: 'v0.961'
hooks:
- id: mypy
entry: mypy
always_run: true
args: [--show-error-codes]
additional_dependencies: ['types-requests']
verbose: true
- repo: local
hooks:
- id: pytest-check
name: pytest-check
entry: pytest
language: system
pass_filenames: false
always_run: true
verbose: true
================================================
FILE: CITATION.cff
================================================
cff-version: 1.2.0
message: "If you use this software, please star and cite it as below. "
authors:
- family-names: Leung
given-names: Yui Chun
title: "TF2DeepFloorplan"
version: 0.0.0
date-released: 2022-11-12
repository-code: "https://github.com/zcemycl/TF2DeepFloorplan"
================================================
FILE: Dockerfile
================================================
FROM tensorflow/tensorflow:latest-gpu
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
RUN apt-get -y update
RUN apt-get install -y python3-pip software-properties-common wget ffmpeg
COPY requirements.txt /
ADD src src
COPY setup.cfg /
COPY setup.py /
COPY pyproject.toml /
RUN pip install --upgrade pip setuptools wheel
WORKDIR /
ENV AM_I_IN_A_DOCKER_CONTAINER Yes
RUN pip install opencv-python==4.4.0.44
RUN pip install cmake
RUN pip install -e .[tfgpu,api]
# RUN gdown https://drive.google.com/uc?id=1czUSFvk6Z49H-zRikTc67g2HUUz4imON
# RUN unzip log.zip
# RUN rm log.zip
COPY docs/app.toml /docs/app.toml
ADD log/store log/store
COPY resources /usr/local/resources
RUN mv /usr/local/resources .
CMD ["python","-m","dfp.app"]
EXPOSE 1111
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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================================================
FILE: README.md
================================================
# TF2DeepFloorplan [](https://www.gnu.org/licenses/gpl-3.0) [<img src="https://colab.research.google.com/assets/colab-badge.svg" >](https://colab.research.google.com/github/zcemycl/TF2DeepFloorplan/blob/master/deepfloorplan.ipynb)  [](https://coveralls.io/github/zcemycl/TF2DeepFloorplan?branch=main)[](https://hits.seeyoufarm.com)
This repo contains a basic procedure to train and deploy the DNN model suggested by the paper ['Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'](https://arxiv.org/abs/1908.11025). It rewrites the original codes from [zlzeng/DeepFloorplan](https://github.com/zlzeng/DeepFloorplan) into newer versions of Tensorflow and Python.
<br>
Network Architectures from the paper, <br>
<img src="resources/dfpmodel.png" width="50%"><img src="resources/features.png" width="50%">
### Additional feature (pygame)
## Requirements
Depends on different applications, the following installation methods can
|OS|Hardware|Application|Command|
|---|---|---|---|
|Ubuntu|CPU|Model Development|`pip install -e .[tfcpu,dev,testing,linting]`|
|Ubuntu|GPU|Model Development|`pip install -e .[tfgpu,dev,testing,linting]`|
|MacOS|M1 Chip|Model Development|`pip install -e .[tfmacm1,dev,testing,linting]`|
|Ubuntu|GPU|Model Deployment API|`pip install -e .[tfgpu,api]`|
|Ubuntu|GPU|Everything|`pip install -e .[tfgpu,api,dev,testing,linting,game]`|
|Agnostic|...|Docker|(to be updated)|
|Ubuntu|GPU|Notebook|`pip install -e .[tfgpu,jupyter]`|
|Ubuntu|GPU|Game|`pip install -e .[tfgpu,game]`|
## How to run?
1. Install packages.
```
# Option 1
python -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel
# Option 2 (Preferred)
conda create -n venv python=3.8 cudatoolkit=10.1 cudnn=7.6.5
conda activate venv
# common install
pip install -e .[tfgpu,api,dev,testing,linting]
```
2. According to the original repo, please download r3d dataset and transform it to tfrecords `r3d.tfrecords`. Friendly reminder: there is another dataset r2v used to train their original repo's model, I did not use it here cos of limited access. Please see the link here [https://github.com/zlzeng/DeepFloorplan/issues/17](https://github.com/zlzeng/DeepFloorplan/issues/17).
3. Run the `train.py` file to initiate the training, model checkpoint is stored as `log/store/G` and weight is in `model/store`,
```
python -m dfp.train [--batchsize 2][--lr 1e-4][--epochs 1000]
[--logdir 'log/store'][--modeldir 'model/store']
[--save-tensor-interval 10][--save-model-interval 20]
[--tfmodel 'subclass'/'func'][--feature-channels 256 128 64 32]
[--backbone 'vgg16'/'mobilenetv1'/'mobilenetv2'/'resnet50']
[--feature-names block1_pool block2_pool block3_pool block4_pool block5_pool]
```
- for example,
```
python -m dfp.train --batchsize=4 --lr=5e-4 --epochs=100
--logdir=log/store --modeldir=model/store
```
4. Run Tensorboard to view the progress of loss and images via,
```
tensorboard --logdir=log/store
```
5. Convert model to tflite via `convert2tflite.py`.
```
python -m dfp.convert2tflite [--modeldir model/store]
[--tflitedir model/store/model.tflite]
[--loadmethod 'log'/'none'/'pb']
[--quantize][--tfmodel 'subclass'/'func']
[--feature-channels 256 128 64 32]
[--backbone 'vgg16'/'mobilenetv1'/'mobilenetv2'/'resnet50']
[--feature-names block1_pool block2_pool block3_pool block4_pool block5_pool]
```
6. Download and unzip model from google drive,
```
gdown https://drive.google.com/uc?id=1czUSFvk6Z49H-zRikTc67g2HUUz4imON # log files 112.5mb
unzip log.zip
gdown https://drive.google.com/uc?id=1tuqUPbiZnuubPFHMQqCo1_kFNKq4hU8i # pb files 107.3mb
unzip model.zip
gdown https://drive.google.com/uc?id=1B-Fw-zgufEqiLm00ec2WCMUo5E6RY2eO # tfilte file 37.1mb
unzip tflite.zip
```
7. Deploy the model via `deploy.py`, please be aware that load method parameter should match with weight input.
```
python -m dfp.deploy [--image 'path/to/image']
[--postprocess][--colorize][--save 'path/to/output_image']
[--loadmethod 'log'/'pb'/'tflite']
[--weight 'log/store/G'/'model/store'/'model/store/model.tflite']
[--tfmodel 'subclass'/'func']
[--feature-channels 256 128 64 32]
[--backbone 'vgg16'/'mobilenetv1'/'mobilenetv2'/'resnet50']
[--feature-names block1_pool block2_pool block3_pool block4_pool block5_pool]
```
- for example,
```
python -m dfp.deploy --image floorplan.jpg --weight log/store/G
--postprocess --colorize --save output.jpg --loadmethod log
```
8. Play with pygame.
```
python -m dfp.game
```
## Docker for API
1. Build and run docker container. (Please train your weight, google drive does not work currently due to its update.)
```
docker build -t tf_docker -f Dockerfile .
docker run -d -p 1111:1111 tf_docker:latest
docker run --gpus all -d -p 1111:1111 tf_docker:latest
# special for hot reloading flask
docker run -v ${PWD}/src/dfp/app.py:/src/dfp/app.py -v ${PWD}/src/dfp/deploy.py:/src/dfp/deploy.py -d -p 1111:1111 tf_docker:latest
docker logs `docker ps | grep "tf_docker:latest" | awk '{ print $1 }'` --follow
```
2. Call the api for output.
```
curl -H "Content-Type: application/json" --request POST \
-d '{"uri":"https://cdn.cnn.com/cnnnext/dam/assets/200212132008-04-london-rental-market-intl-exlarge-169.jpg","colorize":1,"postprocess":0}' \
http://0.0.0.0:1111/uri --output /tmp/tmp.jpg
curl --request POST -F "file=@resources/30939153.jpg" \
-F "postprocess=0" -F "colorize=0" http://0.0.0.0:1111/upload --output out.jpg
```
3. If you run `app.py` without docker, the second curl for file upload will not work.
## Google Colab
1. Click on [<img src="https://colab.research.google.com/assets/colab-badge.svg" >](https://colab.research.google.com/github/zcemycl/TF2DeepFloorplan/blob/master/deepfloorplan.ipynb) and authorize access.
2. Run the first 2 code cells for installation.
3. Go to Runtime Tab, click on Restart runtime. This ensures the packages installed are enabled.
4. Run the rest of the notebook.
## How to Contribute?
1. Git clone this repo.
2. Install required packages and pre-commit-hooks.
```
pip install -e .[tfgpu,api,dev,testing,linting]
pre-commit install
pre-commit run
pre-commit run --all-files
# pre-commit uninstall/ pip uninstall pre-commit
```
3. Create issues. Maintainer will decide if it requires branch. If so,
```
git fetch origin
git checkout xx-features
```
4. Stage your files, Commit and Push to branch.
5. After pull and merge requests, the issue is solved and the branch is deleted. You can,
```
git checkout main
git pull
git remote prune origin
git branch -d xx-features
```
## Results
- From `train.py` and `tensorboard`.
|Compare Ground Truth (top)<br> against Outputs (bottom)|Total Loss|
|:-------------------------:|:-------------------------:|
|<img src="resources/epoch60.png" width="400">|<img src="resources/Loss.png" width="400">|
|Boundary Loss|Room Loss|
|<img src="resources/LossB.png" width="400">|<img src="resources/LossR.png" width="400">|
- From `deploy.py` and `utils/legend.py`.
|Input|Legend|Output|
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img src="resources/30939153.jpg" width="250">|<img src="resources/legend.png" width="180">|<img src="resources/output.jpg" width="250">|
|`--colorize`|`--postprocess`|`--colorize`<br>`--postprocess`|
|<img src="resources/color.jpg" width="250">|<img src="resources/post.jpg" width="250">|<img src="resources/postcolor.jpg" width="250">|
## Optimization
- Backbone Comparison in Size
|Backbone|log|pb|tflite|toml|
|---|---|---|---|---|
|VGG16|130.5Mb|119Mb|45.3Mb|[link](docs/experiments/vgg16/exp1)|
|MobileNetV1|102.1Mb|86.7Mb|50.2Mb|[link](docs/experiments/mobilenetv1/exp1)|
|MobileNetV2|129.3Mb|94.4Mb|57.9Mb|[link](docs/experiments/mobilenetv2/exp1)|
|ResNet50|214Mb|216Mb|107.2Mb|[link](docs/experiments/resnet50/exp1)|
- Feature Selection Comparison in Size
|Backbone|Feature Names|log|pb|tflite|toml|
|---|---|---|---|---|---|
|MobileNetV1|"conv_pw_1_relu", <br>"conv_pw_3_relu", <br>"conv_pw_5_relu", <br>"conv_pw_7_relu", <br>"conv_pw_13_relu"|102.1Mb|86.7Mb|50.2Mb|[link](docs/experiments/mobilenetv1/exp1)|
|MobileNetV1|"conv_pw_1_relu", <br>"conv_pw_3_relu", <br>"conv_pw_5_relu", <br>"conv_pw_7_relu", <br>"conv_pw_12_relu"|84.5Mb|82.3Mb|49.2Mb|[link](docs/experiments/mobilenetv1/exp2)|
- Feature Channels Comparison in Size
|Backbone|Channels|log|pb|tflite|toml|
|---|---|---|---|---|---|
|VGG16|[256,128,64,32]|130.5Mb|119Mb|45.3Mb|[link](docs/experiments/vgg16/exp1)|
|VGG16|[128,64,32,16]|82.4Mb|81.6Mb|27.3Mb||
|VGG16|[32,32,32,32]|73.2Mb|67.5Mb|18.1Mb|[link](docs/experiments/vgg16/exp2)|
- tfmot
- Pruning (not working)
- Clustering (not working)
- Post training Quantization (work the best)
- Training aware Quantization (not supported by the version)
================================================
FILE: deepfloorplan.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation\n",
"1. Run the first 2 cells\n",
"2. Restart runtime\n",
"3. Run the rest of the jupyter notebooks (do not run the first 2 cells again)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YPHHCUKZn89j",
"outputId": "0748f66f-8128-4e89-a845-482efb2d0c8c"
},
"outputs": [],
"source": [
"!git clone -b main https://github.com/zcemycl/TF2DeepFloorplan.git\n",
"!pip install gdown\n",
"!pip install --upgrade --no-cache-dir gdown\n",
"!gdown https://drive.google.com/uc?id=1czUSFvk6Z49H-zRikTc67g2HUUz4imON\n",
"!unzip log.zip\n",
"!rm log.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# gpu\n",
"# !cd TF2DeepFloorplan && pip install -e .[tfgpu]\n",
"# cpu\n",
"!cd TF2DeepFloorplan && pip install -e .[tfcpu]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Main Script"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "C4VRom9mqBPT",
"outputId": "74d58dfd-60cb-44a2-b992-94fff7cc83f6"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import sys\n",
"from dfp.net import *\n",
"from dfp.data import *\n",
"import matplotlib.image as mpimg\n",
"import matplotlib.pyplot as plt\n",
"from argparse import Namespace\n",
"import os\n",
"import gc\n",
"os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
"from dfp.utils.rgb_ind_convertor import *\n",
"from dfp.utils.util import *\n",
"from dfp.utils.legend import *\n",
"from dfp.utils.settings import *\n",
"from dfp.deploy import *\n",
"print(tf.test.is_gpu_available())\n",
"print(tf.config.list_physical_devices('GPU'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "30DTDbxbwm3O"
},
"outputs": [],
"source": [
"img_path = './TF2DeepFloorplan/resources/30939153.jpg'\n",
"inp = mpimg.imread(img_path)\n",
"args = parse_args(\"--tomlfile ./TF2DeepFloorplan/docs/notebook.toml\".split())\n",
"args = overwrite_args_with_toml(args)\n",
"args.image = img_path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xzqbdPC0uJNc",
"outputId": "e57d885d-2f31-4077-cfbe-8ef738c5466c"
},
"outputs": [],
"source": [
"result = main(args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "2xVIt5LEusqf",
"outputId": "cb197ad8-6971-420e-aae7-3d4a9142cf8c"
},
"outputs": [],
"source": [
"plt.subplot(1,2,1)\n",
"plt.imshow(inp); plt.xticks([]); plt.yticks([]);\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(result); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Jto5H5cXypOD"
},
"source": [
"## Breakdown of postprocessing (step by step)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "57rg5h7XywwU",
"outputId": "91f8d2d0-e32d-466d-e830-010607016fec"
},
"outputs": [],
"source": [
"model,img,shp = init(args)\n",
"logits_cw,logits_r = predict(model,img,shp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "2aUCqpT6zPmv",
"outputId": "1b3901fa-a7a0-4d68-d9e2-21455c4dc26f"
},
"outputs": [],
"source": [
"logits_r = tf.image.resize(logits_r,shp[:2])\n",
"logits_cw = tf.image.resize(logits_cw,shp[:2])\n",
"r = convert_one_hot_to_image(logits_r)[0].numpy()\n",
"cw = convert_one_hot_to_image(logits_cw)[0].numpy()\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(r.squeeze()); plt.xticks([]); plt.yticks([]);\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(cw.squeeze()); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "UYf4WVVCzgqj",
"outputId": "423422f1-b292-4b0e-cfc7-e639db1115ca"
},
"outputs": [],
"source": [
"r_color,cw_color = colorize(r.squeeze(),cw.squeeze())\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(r_color); plt.xticks([]); plt.yticks([]);\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(cw_color); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"id": "LTm_qYqa0HGc",
"outputId": "83a183c7-2a6a-4144-8462-2a95aabdaed3"
},
"outputs": [],
"source": [
"newr,newcw = post_process(r,cw,shp)\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(newr.squeeze()); plt.xticks([]); plt.yticks([]);\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(newcw.squeeze()); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "S5MCeHF30ygl",
"outputId": "bb88248d-331a-496e-8b08-78c7a0306fa4"
},
"outputs": [],
"source": [
"newr_color,newcw_color = colorize(newr.squeeze(),newcw.squeeze())\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(newr_color); plt.xticks([]); plt.yticks([]);\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(newcw_color); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "18UYo3rz0918",
"outputId": "2a2319a4-668e-40b9-837d-964421f87c14"
},
"outputs": [],
"source": [
"plt.imshow(newr_color+newcw_color); plt.xticks([]); plt.yticks([]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"id": "dydb1kWl13hL",
"outputId": "16e9a5bc-3809-41bb-9a1a-f49c7deb5aa4"
},
"outputs": [],
"source": [
"over255 = lambda x: [p/255 for p in x]\n",
"colors2 = [over255(rgb) for rgb in list(floorplan_fuse_map.values())]\n",
"colors = [\"background\", \"closet\", \"bathroom\",\n",
" \"living room\\nkitchen\\ndining room\",\n",
" \"bedroom\",\"hall\",\"balcony\",\"not used\",\"not used\",\n",
" \"door/window\",\"wall\"]\n",
"f = lambda m,c: plt.plot([],[],marker=m, color=c, ls=\"none\")[0]\n",
"handles = [f(\"s\", colors2[i]) for i in range(len(colors))]\n",
"labels = colors\n",
"legend = plt.legend(handles, labels, loc=3,framealpha=1, frameon=True)\n",
"\n",
"fig = legend.figure\n",
"fig.canvas.draw()\n",
"plt.xticks([]); plt.yticks([]);\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iy8nx0WZ2QGS"
},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "deepfloorplan.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: docs/app.toml
================================================
tfmodel = 'subclass'
image = ''
postprocess = 1
colorize = 1
save = ''
weight = 'log/store/G'
loadmethod = 'log'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/cfg_test.md
================================================
## Some coding tests
- black (pyproject.toml)
- isort (pyproject.toml, isort.cfg)
- flake8
- pytest (setup.cfg)
- pytest-cov (setup.cfg)
- mypy (mypy.ini)
- Other Configuration files: tox.ini, .editorconfig
### References
1. https://pycqa.github.io/isort/docs/configuration/config_files.html
2. https://stackoverflow.com/questions/14399534/reference-requirements-txt-for-the-install-requires-kwarg-in-setuptools-setup-py
3. https://ianhopkinson.org.uk/2022/02/understanding-setup-py-setup-cfg-and-pyproject-toml-in-python/
4. https://towardsdatascience.com/pre-commit-hooks-you-must-know-ff247f5feb7e
5. https://mypy.readthedocs.io/en/stable/running_mypy.html
6. https://mypy.readthedocs.io/en/stable/config_file.html#config-file
7. https://pre-commit.com/
================================================
FILE: docs/experiments/mobilenetv1/exp1/compress.toml
================================================
tfmodel = 'func'
modeldir = 'model/store_mobilenetv1_exp1'
tflitedir = 'model/store_mobilenetv1_exp1_model.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'pb'
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_13_relu",]
================================================
FILE: docs/experiments/mobilenetv1/exp1/deploy.toml
================================================
tfmodel = 'func'
image = 'resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
weight = 'log/store_mobilenetv1_exp1/G'
loadmethod = 'log'
# weight = 'model/store_mobilenetv1_exp1'
# loadmethod = 'pb'
# weight = 'model/store_mobilenetv1_exp1_model.tflite'
# loadmethod = 'tflite'
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_13_relu",]
================================================
FILE: docs/experiments/mobilenetv1/exp1/train.toml
================================================
tfmodel = 'func'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_mobilenetv1_exp1'
modeldir = 'model/store_mobilenetv1_exp1'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_13_relu",]
================================================
FILE: docs/experiments/mobilenetv1/exp2/compress.toml
================================================
tfmodel = 'func'
modeldir = 'model/store_mobilenetv1_exp2'
tflitedir = 'model/store_mobilenetv1_exp2_model.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'pb'
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_12_relu",]
================================================
FILE: docs/experiments/mobilenetv1/exp2/deploy.toml
================================================
tfmodel = 'func'
image = 'resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
weight = 'log/store_mobilenetv1_exp2/G'
loadmethod = 'log'
# weight = 'model/store_mobilenetv1_exp2'
# loadmethod = 'pb'
# weight = 'model/store_mobilenetv1_exp2_model.tflite'
# loadmethod = 'tflite'
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_12_relu",]
================================================
FILE: docs/experiments/mobilenetv1/exp2/train.toml
================================================
tfmodel = 'func'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_mobilenetv1_exp2'
modeldir = 'model/store_mobilenetv1_exp2'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv1'
feature_names = ["conv_pw_1_relu", "conv_pw_3_relu", "conv_pw_5_relu", "conv_pw_7_relu", "conv_pw_12_relu",]
================================================
FILE: docs/experiments/mobilenetv2/exp1/train.toml
================================================
tfmodel = 'func'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_mobilenetv2_exp1'
modeldir = 'model/store_mobilenetv2_exp1'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'mobilenetv2'
feature_names = ["block_1_expand_relu", "block_3_expand_relu", "block_5_expand_relu", "block_13_expand_relu", "out_relu",]
================================================
FILE: docs/experiments/resnet50/exp1/train.toml
================================================
tfmodel = 'func'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_resnet50_exp1'
modeldir = 'model/store_resnet50_exp1'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'resnet50'
feature_names = ["conv1_relu", "conv2_block3_out", "conv3_block4_out", "conv4_block6_out", "conv5_block3_out",]
================================================
FILE: docs/experiments/vgg16/exp1/compress.toml
================================================
tfmodel = 'subclass'
modeldir = 'model/store_vgg16_exp1'
tflitedir = 'model/store_vgg16_exp1_model.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'pb'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp1/compress_log.toml
================================================
tfmodel = 'subclass'
modeldir = 'log/store_vgg16_exp1/G'
tflitedir = 'model/store_vgg16_exp1_model_log.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'log'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp1/deploy.toml
================================================
tfmodel = 'subclass'
image = 'resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
# weight = 'log/store_vgg16_exp1/G'
# loadmethod = 'log'
# weight = 'model/store_vgg16_exp1'
# loadmethod = 'pb'
weight = 'model/store_vgg16_exp1_model.tflite'
loadmethod = 'tflite'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp1/train.toml
================================================
tfmodel = 'subclass'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_vgg16_exp1'
modeldir = 'model/store_vgg16_exp1'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp2/compress.toml
================================================
tfmodel = 'subclass'
modeldir = 'model/store_vgg16_exp2'
tflitedir = 'model/store_vgg16_exp2_model.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'pb'
feature_channels = [32, 32, 32, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp2/deploy.toml
================================================
tfmodel = 'subclass'
image = 'resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
weight = 'log/store_vgg16_exp2/G'
loadmethod = 'log'
# weight = 'model/store_vgg16_exp2'
# loadmethod = 'pb'
# weight = 'model/store_vgg16_exp2_model.tflite'
# loadmethod = 'tflite'
feature_channels = [32, 32, 32, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp2/train.toml
================================================
tfmodel = 'subclass'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_vgg16_exp2'
modeldir = 'model/store_vgg16_exp2'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [32, 32, 32, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp3/compress.toml
================================================
tfmodel = 'func'
modeldir = 'model/store_vgg16_exp3'
tflitedir = 'model/store_vgg16_exp3_model.tflite'
quantize = 1
compress_mode = 'quantization'
loadmethod = 'pb'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp3/deploy.toml
================================================
tfmodel = 'func'
image = 'resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
# weight = 'log/store_vgg16_exp3/G'
# loadmethod = 'log'
weight = 'model/store_vgg16_exp3'
loadmethod = 'pb'
# weight = 'model/store_vgg16_exp3_model.tflite'
# loadmethod = 'tflite'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/experiments/vgg16/exp3/train.toml
================================================
tfmodel = 'func'
batchsize = 8
lr = 1e-4
wd = 1e-5
epochs = 100
logdir = 'log/store_vgg16_exp3'
modeldir = 'model/store_vgg16_exp3'
weight = ''
save_tensor_interval = 10
save_model_interval = 20
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/game.toml
================================================
tfmodel = 'subclass'
# image = 'resources/123.jpg'
# image = 'resources/example4.png'
image = 'resources/example5.jpg'
CASTED_RAYS = 150
postprocess = 1
colorize = 0
save = ''
weight = 'model/store_vgg16_exp2_model.tflite'
loadmethod = 'tflite'
feature_channels = [32, 32, 32, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/notebook.toml
================================================
tfmodel = 'subclass'
image = './TF2DeepFloorplan/resources/30939153.jpg'
postprocess = 1
colorize = 1
save = ''
weight = './log/store/G'
loadmethod = 'log'
feature_channels = [256, 128, 64, 32]
backbone = 'vgg16'
feature_names = ["block1_pool", "block2_pool", "block3_pool", "block4_pool", "block5_pool",]
================================================
FILE: docs/pytest.md
================================================
## Some coding tests
- pytest (setup.cfg)
- pytest-cov (setup.cfg)
- pytest-mock
## Keywords
- Fixture (share data across test cases)
- Unit Test (test for indepedent component)
- Mock Test (test for depedent component)
- Parametrizing (various test cases for same component)
- Integration Test (end-to-end)
### References
1. https://blogs.sap.com/2022/02/16/how-to-write-independent-unit-test-with-pytest-and-mock-techniques/
================================================
FILE: install/environment.yml
================================================
channels:
- defaults
dependencies:
- python=3.8
- cudatoolkit=10.1
- cudnn=7.6.5
- pip:
- -e .[tfgpu,api,dev,testing,linting]
================================================
FILE: mypy.ini
================================================
[mypy]
ignore_missing_imports = True
files = src/, tests/
exclude = (?x)(
^app\.py$ # files named "one.py"
| app\.py$ # or files ending with "two.pyi"
| ^three\. # or files starting with "three."
| venv
| resources
| model
| log
| src/app.py
)
================================================
FILE: pyproject.toml
================================================
[tool.black]
line-length = 79
include = '''
/(
src
| tests
)/
'''
exclude = '''
/(
\.git
| \.mypy_cache
| \.nox
| __pycache__
| \.pyc$
| \.ipynb$
| \.md$
| build
| venv
| dist
| \.eggs
)/
'''
[tool.isort]
profile = "black"
src_paths = [".","src", "tests"]
skip=[".tox",".nox","venv","build","dist","resources","model","log"]
skip_glob=["venv/*","build/*","dist/*","resources/*"]
sections="FUTURE,STDLIB,SETUPTOOLS,TEST,THIRDPARTY,FIRSTPARTY,LOCALFOLDER"
known_first_party = "src"
================================================
FILE: requirements.txt
================================================
matplotlib
numpy
opencv-python
pdbpp
scipy
Pillow
gdown
protobuf==3.20.0
chardet
types-requests
pytype
dynaconf
================================================
FILE: setup.cfg
================================================
[metadata]
name = dfp
description = Deep Floorplan (dfp)
author = Leo Leung
[options]
zip_safe = False
packages = find:
include_package_data = True
setup_requires = setuptools_scm
package_dir =
= src
[options.packages.find]
where = src
exclude =
tests
[options.extras_require]
tfcpu =
tensorflow-cpu
tensorboard
tensorflow-model-optimization
tfgpu =
tensorflow-gpu==2.3.0
tensorboard==2.3.0
tensorflow-model-optimization==0.5.0
tfmacm1 =
tensorflow-macos
tensorflow-metal
tensorboard
tensorflow-model-optimization
api =
Flask
Flask-Bcrypt
Flask-Classful
Flask-Cors
Flask-Ext
Flask-Jsonpify
Flask-Markdown
Flask-WTF
dev =
pre-commit
testing =
pytest
pytest-cov
pytest-mock
pytest-flask
linting =
black==22.3.0
isort==5.10.1
flake8==4.0.1
mypy==0.961
jupyter =
jupyterlab
notebook
game =
pygame
numba
[tool:pytest]
testpaths =
tests
addopts =
--cov src
--cov-report term-missing
--disable-warnings
--verbose
norecursedirs =
dist
build
.tox
resources
log
model
venv
[tool.setuptools_scm]
version_scheme = guess-next-dev
[bdist_wheel]
universal = 1
[flake8]
ignore = E203 W503 W291 W293
max-line-length = 79
exclude =
.tox
dist
.eggs
venv
log
model
resources
================================================
FILE: setup.py
================================================
import os
from setuptools import setup
with open("requirements.txt") as f:
required = f.read().splitlines()
if __name__ == "__main__":
use_scm_version = not os.environ.get("AM_I_IN_A_DOCKER_CONTAINER", False)
setup(
use_scm_version=use_scm_version,
install_requires=required,
test_suite="tests",
)
================================================
FILE: src/dfp/__init__.py
================================================
================================================
FILE: src/dfp/app.py
================================================
import multiprocessing as mp
import os
import random
from argparse import Namespace
import matplotlib.image as mpimg
import numpy as np
import requests
from flask import Flask, request, send_file
from werkzeug.datastructures import FileStorage
from .deploy import main
from .utils.settings import overwrite_args_with_toml
app = Flask(__name__)
app.config["UPLOAD_EXTENSIONS"] = [".jpg", ".png", ".jpeg"]
args = Namespace(tomlfile="docs/app.toml")
args = overwrite_args_with_toml(args)
finname = "resources/30939153.jpg"
output = "/tmp"
def saveStreamFile(stream: FileStorage, fnum: str):
stream.save(fnum + ".jpg")
def saveStreamURI(stream: bytes, fnum: str):
with open(fnum + ".jpg", "wb") as handler:
handler.write(stream)
@app.route("/")
def home():
return {"message": "Hello Flask!"}
@app.route("/upload", methods=["POST"])
def dummy():
finname = "resources/30939153.jpg"
fnum = str(random.randint(0, 10000))
foutname = fnum + "-out.jpg"
if "file" in request.files:
saveStreamFile(request.files["file"], fnum)
finname = fnum + ".jpg"
postprocess = (
False
if "postprocess" not in request.form.keys()
else bool(int(request.form.getlist("postprocess")[0]))
)
colorize = (
False
if "colorize" not in request.form.keys()
else bool(int(request.form.getlist("colorize")[0]))
)
args.image = finname
args.postprocess = postprocess
args.colorize = colorize
args.save = os.path.join(output, foutname)
app.logger.info(args)
with mp.Pool() as pool:
result = pool.map(main, [args])[0]
app.logger.info(f"Output Image shape: {np.array(result).shape}")
if args.save:
mpimg.imsave(args.save, np.array(result).astype(np.uint8))
try:
callback = send_file(
os.path.join(output, foutname), mimetype="image/jpg"
)
return callback, 200
except Exception:
return {"message": "send error"}, 400
finally:
os.system("rm " + os.path.join(output, foutname))
if finname != "resources/30939153.jpg":
os.system("rm " + finname)
return {"message": "hello"}
@app.route("/uri", methods=["POST"])
def process_image():
fnum = str(random.randint(0, 10000))
finname = "resources/30939153.jpg"
foutname = fnum + "-out.jpg"
postprocess = (
bool(request.json["postprocess"])
if request.json and "postprocess" in request.json.keys()
else False
)
colorize = (
bool(request.json["colorize"])
if request.json and "colorize" in request.json.keys()
else False
)
# input image: uri
if request.json and "uri" in request.json.keys():
app.logger.info("URI mode...")
uri = request.json["uri"]
try:
data = requests.get(uri).content
saveStreamURI(data, fnum)
finname = fnum + ".jpg"
except Exception:
return {"message": "input error"}, 400
args.image = finname
args.postprocess = postprocess
args.colorize = colorize
args.save = os.path.join(output, foutname)
app.logger.info(args)
with mp.Pool() as pool:
result = pool.map(main, [args])[0]
app.logger.info(f"Output Image shape: {np.array(result).shape}")
if args.save:
mpimg.imsave(args.save, np.array(result).astype(np.uint8))
try:
callback = send_file(
os.path.join(output, foutname), mimetype="image/jpg"
)
return callback, 200
except Exception:
return {"message": "send error"}, 400
finally:
os.system("rm " + os.path.join(output, foutname))
if finname != "resources/30939153.jpg":
os.system("rm " + finname)
if __name__ == "__main__":
app.run(debug=True, host="0.0.0.0", port=1111)
================================================
FILE: src/dfp/convert2tflite.py
================================================
import argparse
import os
import sys
import tempfile
from typing import List
import tensorflow as tf
import tensorflow_model_optimization as tfmot
from tqdm import tqdm
from .data import decodeAllRaw, loadDataset, preprocess
from .net import deepfloorplanModel
from .net_func import deepfloorplanFunc
from .train import train_step
from .utils.settings import overwrite_args_with_toml
from .utils.util import (
print_model_weight_clusters,
print_model_weights_sparsity,
)
def model_init(config: argparse.Namespace) -> tf.keras.Model:
if config.loadmethod == "log":
if config.tfmodel == "subclass":
base_model = deepfloorplanModel(config=config)
base_model.build((1, 512, 512, 3))
assert True, "subclass and log are not convertible to tflite."
elif config.tfmodel == "func":
base_model = deepfloorplanFunc(config=config)
base_model.load_weights(config.modeldir)
elif config.loadmethod == "pb":
base_model = tf.keras.models.load_model(config.modeldir)
# need changes later to frozen backbone and constant kernel
for layer in base_model.layers:
layer.trainable = False
return base_model
def converter(config: argparse.Namespace):
# model = tf.keras.models.load_model(config.modeldir)
model = model_init(config)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
if config.quantize:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# converter.target_spec.supported_ops = [
# tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
# tf.lite.OpsSet.SELECT_TF_OPS, # enable TensorFlow ops.
# tf.float16
# ]
converter.experimental_new_converter = True
tflite_model = converter.convert()
with open(config.tflitedir, "wb") as f:
f.write(tflite_model)
def parse_args(args: List[str]) -> argparse.Namespace:
p = argparse.ArgumentParser()
p.add_argument(
"--tfmodel", type=str, default="subclass", choices=["subclass", "func"]
)
p.add_argument("--modeldir", type=str, default="model/store")
p.add_argument("--tflitedir", type=str, default="model/store/model.tflite")
p.add_argument("--quantize", action="store_true")
p.add_argument(
"--compress-mode",
type=str,
default="quantization",
choices=["quantization", "prune", "cluster"],
)
p.add_argument(
"--loadmethod",
type=str,
default="log",
choices=["log", "pb", "none"],
) # log,tflite,pb
p.add_argument(
"--feature-channels",
type=int,
action="store",
default=[256, 128, 64, 32],
nargs=4,
)
p.add_argument(
"--backbone",
type=str,
default="vgg16",
choices=["vgg16", "resnet50", "mobilenetv1", "mobilenetv2"],
)
p.add_argument(
"--feature-names",
type=str,
action="store",
nargs=5,
default=[
"block1_pool",
"block2_pool",
"block3_pool",
"block4_pool",
"block5_pool",
],
)
p.add_argument("--tomlfile", type=str, default=None)
return p.parse_args(args)
def prune(config: argparse.Namespace):
base_model = model_init(config)
print(base_model.summary())
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
print(model_for_pruning.summary())
dataset = loadDataset()
optimizer = tf.keras.optimizers.Adam()
log_dir = tempfile.mkdtemp()
unused_arg = -1
epochs = 4
batches = 8
model_for_pruning.optimizer = optimizer
step_callback = tfmot.sparsity.keras.UpdatePruningStep()
step_callback.set_model(model_for_pruning)
log_callback = tfmot.sparsity.keras.PruningSummaries(
log_dir=log_dir
) # Log sparsity and other metrics in Tensorboard.
log_callback.set_model(model_for_pruning)
step_callback.on_train_begin() # run pruning callback
for _ in range(epochs):
log_callback.on_epoch_begin(epoch=unused_arg) # run pruning callback
for data in tqdm(list(dataset.batch(batches))):
step_callback.on_train_batch_begin(
batch=unused_arg
) # run pruning callback
img, bound, room = decodeAllRaw(data)
img, bound, room, hb, hr = preprocess(img, bound, room)
_, _, loss_value, _, _ = train_step(
model_for_pruning, optimizer, img, hr, hb
)
step_callback.on_epoch_end(batch=unused_arg) # run pruning callback
# model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(base_model)
print(f"log directory: {log_dir}...")
model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)
print_model_weights_sparsity(model_for_export)
model_for_export.save(log_dir + "/prune")
converter = tf.lite.TFLiteConverter.from_keras_model(model_for_export)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
pruned_tflite_model = converter.convert()
os.system(f"mkdir -p {log_dir}/tflite")
with open(log_dir + "/tflite/model.tflite", "wb") as f:
f.write(pruned_tflite_model)
def cluster(config: argparse.Namespace):
base_model = model_init(config)
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization
clustering_params = {
"number_of_clusters": 8,
"cluster_centroids_init": CentroidInitialization.DENSITY_BASED,
}
def apply_clustering_to_conv2d(layer):
if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(
layer, tf.keras.layers.Conv2DTranspose
):
return cluster_weights(layer, **clustering_params)
return layer
clustered_model = tf.keras.models.clone_model(
base_model,
clone_function=apply_clustering_to_conv2d,
)
print(clustered_model.summary())
dataset = loadDataset()
optimizer = tf.keras.optimizers.Adam()
for epoch in range(4):
print("[INFO] Epoch {}".format(epoch))
for data in tqdm(list(dataset.batch(8))):
img, bound, room = decodeAllRaw(data)
img, bound, room, hb, hr = preprocess(img, bound, room)
_, _, loss_value, _, _ = train_step(
clustered_model, optimizer, img, hr, hb
)
log_dir = tempfile.mkdtemp()
print("log directory: " + log_dir)
final_model = tfmot.clustering.keras.strip_clustering(clustered_model)
print_model_weight_clusters(final_model)
final_model.save(log_dir + "/cluster")
converter = tf.lite.TFLiteConverter.from_keras_model(final_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
clustered_tflite_model = converter.convert()
os.system(f"mkdir -p {log_dir}/tflite")
with open(log_dir + "/tflite/model.tflite", "wb") as f:
f.write(clustered_tflite_model)
def quantization_aware_training(config: argparse.Namespace):
base_model = model_init(config)
def apply_quantization_to_conv2D(layer):
if isinstance(layer, tf.keras.layers.Conv2D):
return tfmot.quantization.keras.quantize_annotate_layer(layer)
return layer
raise Exception("Not supported")
annotated_model = tf.keras.models.clone_model(
base_model,
clone_function=apply_quantization_to_conv2D,
)
quant_aware_model = tfmot.quantization.keras.quantize_apply(
annotated_model
)
# quant_aware_model = tfmot.quantization.keras.quantize_model(base_model)
print(quant_aware_model.summary())
dataset = loadDataset()
optimizer = tf.keras.optimizers.Adam()
for epoch in range(4):
print("[INFO] Epoch {}".format(epoch))
for data in tqdm(list(dataset.batch(8))):
img, bound, room = decodeAllRaw(data)
img, bound, room, hb, hr = preprocess(img, bound, room)
_, _, loss_value, _, _ = train_step(
quant_aware_model, optimizer, img, hr, hb
)
log_dir = tempfile.mkdtemp()
print("log directory: " + log_dir)
converter = tf.lite.TFLiteConverter.from_keras_model(quant_aware_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized_tflite_model = converter.convert()
os.system(f"mkdir -p {log_dir}/tflite")
with open(log_dir + "/tflite/model.tflite", "wb") as f:
f.write(quantized_tflite_model)
if __name__ == "__main__":
args = parse_args(sys.argv[1:])
args = overwrite_args_with_toml(args)
print(args)
if args.compress_mode == "quantization" or args.quantize:
# quantization_aware_training(args)
converter(args)
elif args.tfmodel == "func":
if args.compress_mode == "prune":
prune(args)
elif args.compress_mode == "cluster":
cluster(args)
elif args.tfmodel == "subclass":
raise Exception(
"Pruning or Clustering for Subclass Model are not available."
)
================================================
FILE: src/dfp/data.py
================================================
from typing import Dict, Tuple
import matplotlib.pyplot as plt
import tensorflow as tf
def convert_one_hot_to_image(
one_hot: tf.Tensor, dtype: str = "float", act: str = None
) -> tf.Tensor:
if act == "softmax":
one_hot = tf.keras.activations.softmax(one_hot)
[n, h, w, c] = one_hot.shape.as_list()
im = tf.reshape(tf.keras.backend.argmax(one_hot, axis=-1), [n, h, w, 1])
if dtype == "int":
im = tf.cast(im, dtype=tf.uint8)
else:
im = tf.cast(im, dtype=tf.float32)
return im
def _parse_function(example_proto: bytes) -> Dict[str, tf.Tensor]:
feature = {
"image": tf.io.FixedLenFeature([], tf.string),
"boundary": tf.io.FixedLenFeature([], tf.string),
"room": tf.io.FixedLenFeature([], tf.string),
"door": tf.io.FixedLenFeature([], tf.string),
}
return tf.io.parse_single_example(example_proto, feature)
def decodeAllRaw(
x: Dict[str, tf.Tensor]
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
image = tf.io.decode_raw(x["image"], tf.uint8)
boundary = tf.io.decode_raw(x["boundary"], tf.uint8)
room = tf.io.decode_raw(x["room"], tf.uint8)
return image, boundary, room
def preprocess(
img: tf.Tensor, bound: tf.Tensor, room: tf.Tensor, size: int = 512
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]:
img = tf.cast(img, dtype=tf.float32)
img = tf.reshape(img, [-1, size, size, 3]) / 255
bound = tf.reshape(bound, [-1, size, size])
room = tf.reshape(room, [-1, size, size])
hot_b = tf.one_hot(bound, 3, axis=-1)
hot_r = tf.one_hot(room, 9, axis=-1)
return img, bound, room, hot_b, hot_r
def loadDataset(size: int = 512) -> tf.data.Dataset:
raw_dataset = tf.data.TFRecordDataset("r3d.tfrecords")
parsed_dataset = raw_dataset.map(_parse_function)
return parsed_dataset
def plotData(data: Dict[str, tf.Tensor]):
img, bound, room = decodeAllRaw(data)
img, bound, room, hb, hr = preprocess(img, bound, room)
plt.subplot(1, 3, 1)
plt.imshow(img[0].numpy())
plt.subplot(1, 3, 2)
plt.imshow(bound[0].numpy())
plt.subplot(1, 3, 3)
plt.imshow(convert_one_hot_to_image(hb)[0].numpy())
def main(dataset: tf.data.Dataset):
for ite in range(2):
for data in list(dataset.shuffle(400).batch(1)):
plotData(data)
plt.show()
break
if __name__ == "__main__":
dataset = loadDataset()
main(dataset)
================================================
FILE: src/dfp/deploy.py
================================================
import argparse
import gc
import os
import sys
from typing import List, Tuple
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from .data import convert_one_hot_to_image
from .net import deepfloorplanModel
from .net_func import deepfloorplanFunc
from .utils.rgb_ind_convertor import (
floorplan_boundary_map,
floorplan_fuse_map,
ind2rgb,
)
from .utils.settings import overwrite_args_with_toml
from .utils.util import fill_break_line, flood_fill, refine_room_region
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
def init(
config: argparse.Namespace,
) -> Tuple[tf.keras.Model, tf.Tensor, np.ndarray]:
if config.tfmodel == "subclass":
model = deepfloorplanModel(config=config)
elif config.tfmodel == "func":
model = deepfloorplanFunc(config=config)
if config.loadmethod == "log":
model.load_weights(config.weight)
elif config.loadmethod == "pb":
model = tf.keras.models.load_model(config.weight)
elif config.loadmethod == "tflite":
model = tf.lite.Interpreter(model_path=config.weight)
model.allocate_tensors()
img = mpimg.imread(config.image)[:, :, :3]
shp = img.shape
img = tf.convert_to_tensor(img, dtype=tf.uint8)
img = tf.image.resize(img, [512, 512])
img = tf.cast(img, dtype=tf.float32)
img = tf.reshape(img, [-1, 512, 512, 3])
if tf.math.reduce_max(img) > 1.0:
img /= 255
if config.loadmethod == "tflite":
return model, img, shp
model.trainable = False
if config.tfmodel == "subclass":
model.vgg16.trainable = False
return model, img, shp
def predict(
model: tf.keras.Model, img: tf.Tensor, shp: np.ndarray
) -> Tuple[tf.Tensor, tf.Tensor]:
features = []
feature = img
for layer in model.vgg16.layers:
feature = layer(feature)
if layer.name.find("pool") != -1:
features.append(feature)
x = feature
features = features[::-1]
del model.vgg16
gc.collect()
featuresrbp = []
for i in range(len(model.rbpups)):
x = model.rbpups[i](x) + model.rbpcv1[i](features[i + 1])
x = model.rbpcv2[i](x)
featuresrbp.append(x)
logits_cw = tf.keras.backend.resize_images(
model.rbpfinal(x), 2, 2, "channels_last"
)
x = features.pop(0)
nLays = len(model.rtpups)
for i in range(nLays):
rs = model.rtpups.pop(0)
r1 = model.rtpcv1.pop(0)
r2 = model.rtpcv2.pop(0)
f = features.pop(0)
x = rs(x) + r1(f)
x = r2(x)
a = featuresrbp.pop(0)
x = model.non_local_context(a, x, i)
del featuresrbp
logits_r = tf.keras.backend.resize_images(
model.rtpfinal(x), 2, 2, "channels_last"
)
del model.rtpfinal
return logits_cw, logits_r
def post_process(
rm_ind: np.ndarray, bd_ind: np.ndarray, shp: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
hard_c = (bd_ind > 0).astype(np.uint8)
# region from room prediction
rm_mask = np.zeros(rm_ind.shape)
rm_mask[rm_ind > 0] = 1
# region from close wall line
cw_mask = hard_c
# regine close wall mask by filling the gap between bright line
cw_mask = fill_break_line(cw_mask)
cw_mask = np.reshape(cw_mask, (*shp[:2], -1))
fuse_mask = cw_mask + rm_mask
fuse_mask[fuse_mask >= 1] = 255
# refine fuse mask by filling the hole
fuse_mask = flood_fill(fuse_mask)
fuse_mask = fuse_mask // 255
# one room one label
new_rm_ind = refine_room_region(cw_mask, rm_ind)
# ignore the background mislabeling
new_rm_ind = fuse_mask.reshape(*shp[:2], -1) * new_rm_ind
new_bd_ind = fill_break_line(bd_ind).squeeze()
return new_rm_ind, new_bd_ind
def colorize(r: np.ndarray, cw: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
cr = ind2rgb(r, color_map=floorplan_fuse_map)
ccw = ind2rgb(cw, color_map=floorplan_boundary_map)
return cr, ccw
def main(config: argparse.Namespace) -> np.ndarray:
model, img, shp = init(config)
if config.loadmethod == "tflite":
input_details = model.get_input_details()
output_details = model.get_output_details()
model.set_tensor(input_details[0]["index"], img)
model.invoke()
ri, cwi = 0, 1
if config.tfmodel == "func":
ri, cwi = 1, 0
logits_r = model.get_tensor(output_details[ri]["index"])
logits_cw = model.get_tensor(output_details[cwi]["index"])
logits_cw = tf.convert_to_tensor(logits_cw)
logits_r = tf.convert_to_tensor(logits_r)
else:
if config.tfmodel == "func":
logits_r, logits_cw = model.predict(img)
elif config.tfmodel == "subclass":
if config.loadmethod == "log":
logits_cw, logits_r = predict(model, img, shp)
elif config.loadmethod == "pb" or config.loadmethod == "none":
logits_r, logits_cw = model(img)
logits_r = tf.image.resize(logits_r, shp[:2])
logits_cw = tf.image.resize(logits_cw, shp[:2])
r = convert_one_hot_to_image(logits_r)[0].numpy()
cw = convert_one_hot_to_image(logits_cw)[0].numpy()
if not config.colorize and not config.postprocess:
cw[cw == 1] = 9
cw[cw == 2] = 10
r[cw != 0] = 0
return (r + cw).squeeze()
elif config.colorize and not config.postprocess:
r_color, cw_color = colorize(r.squeeze(), cw.squeeze())
return r_color + cw_color
newr, newcw = post_process(r, cw, shp)
if not config.colorize and config.postprocess:
newcw[newcw == 1] = 9
newcw[newcw == 2] = 10
newr[newcw != 0] = 0
return newr.squeeze() + newcw
newr_color, newcw_color = colorize(newr.squeeze(), newcw.squeeze())
result = newr_color + newcw_color
print(shp, result.shape)
if config.save:
mpimg.imsave(config.save, result.astype(np.uint8))
return result
def parse_args(args: List[str]) -> argparse.Namespace:
p = argparse.ArgumentParser()
p.add_argument(
"--tfmodel", type=str, default="subclass", choices=["subclass", "func"]
)
p.add_argument("--image", type=str, default="resources/30939153.jpg")
p.add_argument("--weight", type=str, default="log/store/G")
p.add_argument("--postprocess", action="store_true")
p.add_argument("--colorize", action="store_true")
p.add_argument(
"--loadmethod",
type=str,
default="log",
choices=["log", "tflite", "pb", "none"],
) # log,tflite,pb
p.add_argument("--save", type=str)
p.add_argument(
"--feature-channels",
type=int,
action="store",
default=[256, 128, 64, 32],
nargs=4,
)
p.add_argument(
"--backbone",
type=str,
default="vgg16",
choices=["vgg16", "resnet50", "mobilenetv1", "mobilenetv2"],
)
p.add_argument(
"--feature-names",
type=str,
action="store",
nargs=5,
default=[
"block1_pool",
"block2_pool",
"block3_pool",
"block4_pool",
"block5_pool",
],
)
p.add_argument("--tomlfile", type=str, default=None)
return p.parse_args(args)
def deploy_plot_res(result: np.ndarray):
print(result.shape)
plt.imshow(result)
plt.xticks([])
plt.yticks([])
plt.grid(False)
if __name__ == "__main__":
args = parse_args(sys.argv[1:])
args = overwrite_args_with_toml(args)
result = main(args)
deploy_plot_res(result)
plt.show()
================================================
FILE: src/dfp/game/__init__.py
================================================
================================================
FILE: src/dfp/game/__main__.py
================================================
import logging
import math
import sys
import time
from typing import Tuple, Union
import numpy as np
import pygame
from numba import jit, njit
from .controller import Controller
from .model import Model
from .view import View
logging.basicConfig(level=logging.INFO)
@njit
def ray_cast_dda(
x: float,
y: float,
angle: float,
depth: Union[float, int],
w: int,
h: int,
binary_map: np.ndarray,
) -> Tuple[float, float, bool]:
vPlayer = np.array([x, y])
vRayStart = vPlayer.copy()
vMouseCell = np.array(
[x - math.sin(angle) * depth, y + math.cos(angle) * depth]
)
vRayDir = (vMouseCell - vPlayer) / np.linalg.norm((vMouseCell - vPlayer))
vRayUnitStepSize = np.array(
[
np.sqrt(1 + (vRayDir[1] / (vRayDir[0] + 1e-10)) ** 2),
np.sqrt(1 + (vRayDir[0] / (vRayDir[1] + 1e-10)) ** 2),
]
)
vMapCheck = vRayStart.copy()
vRayLength1D = np.array([0.0, 0.0])
vStep = np.array([0, 0])
for i in range(2):
vStep[i] = -1 if vRayDir[i] < 0 else 1
# vRayLength1D[i] = vRayUnitStepSize[i]
# vRayLength1D[i] = (
# (vRayStart[i] - vMapCheck[i]) * vRayUnitStepSize[i]
# if vRayDir[i] < 0
# else ((vMapCheck[i] + 1) - vRayStart[i]) * vRayUnitStepSize[i]
# )
bTileFound = False
fMaxDistance = depth
fDistance = 0
while not bTileFound and fDistance < fMaxDistance:
if vRayLength1D[0] < vRayLength1D[1]:
vMapCheck[0] += vStep[0]
fDistance = vRayLength1D[0]
vRayLength1D[0] += vRayUnitStepSize[0]
elif vRayLength1D[0] > vRayLength1D[1]:
vMapCheck[1] += vStep[1]
fDistance = vRayLength1D[1]
vRayLength1D[1] += vRayUnitStepSize[1]
elif vRayLength1D[0] == vRayLength1D[1]:
vMapCheck += vStep
vRayLength1D += vRayUnitStepSize
if 0 <= vMapCheck[0] < w and 0 <= vMapCheck[1] < h:
if binary_map[int(vMapCheck[1]), int(vMapCheck[0])] == 1:
bTileFound = True
else:
break
return angle, fDistance, bTileFound
@jit(nopython=True)
def loop_rays(x, y, angle, depth, w, h, binary_map, STEP_ANGLE, CASTED_RAYS):
return [
ray_cast_dda(x, y, angle + i * STEP_ANGLE, depth, w, h, binary_map)
for i in range(CASTED_RAYS)
]
class Game:
def __init__(self, tomlfile: str = "docs/game.toml"):
start = time.time()
self.model = Model(tomlfile=tomlfile)
end = time.time()
logging.info(f"DFP takes {end-start} s to output map.")
self._initialise_game_engine()
self.view = View(self.model).register_window(self.win)
self.control = Controller(self.model)
def _initialise_game_engine(self):
pygame.init()
self.win = pygame.display.set_mode(
(self.model.SCREEN_WIDTH, self.model.SCREEN_HEIGHT)
)
pygame.display.set_caption("Deep Floorplan Raycasting")
self.clock = pygame.time.Clock()
def run(self):
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit(0)
# 3d background
self.view.draw_3d_env()
self.control.player_control()
self.view.draw_dfp()
self.view.show_fps(str(int(self.clock.get_fps())))
self.view.draw_player_loc()
# calculate player rays
wallangles = loop_rays(
self.model.player_x,
self.model.player_y,
self.model.player_angle - self.model.HALF_FOV,
self.model.MAX_DEPTH,
self.model.w,
self.model.h,
self.model.result,
self.model.STEP_ANGLE,
self.model.args.casted_rays,
)
for idx, (angle, fdist, iswall) in enumerate(wallangles):
self.view.draw_player_rays(angle, fdist)
wall_color = int(
255 * (1 - (3 * fdist / self.model.MAX_DEPTH) ** 2)
)
wall_color = max(min(wall_color, 255), 30)
fdist *= math.cos(self.model.player_angle - angle)
wall_height = 21000 / (fdist + 0.00001)
if iswall:
self.view.draw_3d_wall(idx, wall_color, wall_height)
pygame.display.flip()
self.clock.tick()
pygame.quit()
if __name__ == "__main__":
env = Game()
env.run()
================================================
FILE: src/dfp/game/controller.py
================================================
import math
import sys
import pygame
from .model import Model
class Controller:
def __init__(self, model: Model):
self.model = model
def player_control(self):
keys = pygame.key.get_pressed()
angle, x, y = (
self.model.player_angle,
self.model.player_x,
self.model.player_y,
)
origx, origy = x, y
if keys[pygame.K_LEFT]:
angle -= self.model.dangle
if keys[pygame.K_RIGHT]:
angle += self.model.dangle
if keys[pygame.K_w]:
x -= math.sin(angle) * self.model.dpos
y += math.cos(angle) * self.model.dpos
if keys[pygame.K_s]:
x += math.sin(angle) * self.model.dpos
y -= math.cos(angle) * self.model.dpos
if keys[pygame.K_d]:
x += math.sin(angle - math.pi / 2) * self.model.dpos
y -= math.cos(angle - math.pi / 2) * self.model.dpos
if keys[pygame.K_a]:
x -= math.sin(angle - math.pi / 2) * self.model.dpos
y += math.cos(angle - math.pi / 2) * self.model.dpos
if keys[pygame.K_q]:
pygame.quit()
sys.exit(0)
if x < 0:
x = 0
if x >= self.model.w:
x = self.model.w - 1
if y < 0:
y = 0
if y >= self.model.h:
y = self.model.h - 1
if self.model.result[int(y), int(x)] == 1:
(
self.model.player_angle,
self.model.player_x,
self.model.player_y,
) = (
angle,
origx,
origy,
)
return
self.model.player_angle, self.model.player_x, self.model.player_y = (
angle,
x,
y,
)
================================================
FILE: src/dfp/game/model.py
================================================
import math
import random
from argparse import Namespace
import numpy as np
import pygame
from ..deploy import main
from ..utils.settings import overwrite_args_with_toml
class Model:
FOV = math.pi / 3
HALF_FOV = FOV / 2
WALL_COLOR = [100, 100, 100]
FLOOR_COLOR = [200, 200, 200]
RAY_COLOR = (0, 255, 0)
SKY_COLOR = (0, 150, 200)
GRD_COLOR = (100, 100, 100)
PLAYER_COLOR = (255, 0, 0)
TILE_SIZE = 1
dangle = 0.01
dpos = 1
GAME_TEXT_COLOR = pygame.Color("coral")
# surf = None
def __init__(self, tomlfile: str = "docs/game.toml"):
self.tomlfile = tomlfile
args = Namespace(tomlfile=tomlfile)
self.args = overwrite_args_with_toml(args)
self.STEP_ANGLE = self.FOV / self.args.casted_rays
self._initialise_map()
self._initialise_player_pose()
def _initialise_map(self):
self.result = main(self.args)
self.h, self.w = self.result.shape
self.bg = self.rgb_im = np.zeros((self.h, self.w, 3))
self.bg[self.result != 10] = self.FLOOR_COLOR
self.bg[self.result == 10] = self.WALL_COLOR
self.result[self.result != 10] = 0
self.result[self.result == 10] = 1
self.bg = np.transpose(self.bg, (1, 0, 2))
self.MAX_DEPTH = max(self.h, self.w)
self.SCREEN_HEIGHT = self.h
self.SCREEN_WIDTH = self.w * 2
self.SCALE = (self.SCREEN_WIDTH / 2) / self.args.casted_rays
self.surf = pygame.surfarray.make_surface(self.bg)
def _initialise_player_pose(self):
posy, posx = np.where(self.result != 1)
posidx = random.randint(0, len(posy) - 1)
self.player_x, self.player_y = posx[posidx], posy[posidx]
self.player_angle = math.pi
================================================
FILE: src/dfp/game/view.py
================================================
from __future__ import annotations
import math
from typing import Union
import pygame
from .model import Model
class View:
def __init__(self, model: Model):
self.model = model
def register_window(self, win: pygame.Surface) -> View:
self.win = win
return self
def draw_dfp(self):
self.win.blit(self.model.surf, (0, 0))
def show_fps(self, fps: str):
font = pygame.font.SysFont("Arial", 18)
fps_text = font.render(fps, 1, self.model.GAME_TEXT_COLOR)
self.win.blit(fps_text, (10, 0))
def draw_player_loc(self):
pygame.draw.circle(
self.win,
self.model.PLAYER_COLOR,
(self.model.player_x, self.model.player_y),
8,
)
def draw_player_rays(
self,
angle: float,
depth: Union[float, int],
):
pygame.draw.line(
self.win,
self.model.RAY_COLOR,
(self.model.player_x, self.model.player_y),
(
self.model.player_x - math.sin(angle) * depth,
self.model.player_y + math.cos(angle) * depth,
),
3,
)
def draw_3d_env(self):
pygame.draw.rect(
self.win,
self.model.GRD_COLOR,
(self.model.w, self.model.h / 2, self.model.w, self.model.h),
)
pygame.draw.rect(
self.win,
self.model.SKY_COLOR,
(self.model.w, -self.model.h / 2, self.model.w, self.model.h),
)
def draw_3d_wall(
self, idx: int, wall_color: int, wall_height: Union[int, float]
):
pygame.draw.rect(
self.win,
(wall_color, wall_color, wall_color),
(
int(self.model.w + idx * self.model.SCALE),
int((self.model.h / 2) - wall_height / 2),
self.model.SCALE,
wall_height,
),
)
================================================
FILE: src/dfp/loss.py
================================================
from typing import List, Tuple
import tensorflow as tf
def cross_two_tasks_weight(
y1: tf.Tensor, y2: tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor]:
p1, p2 = tf.keras.backend.sum(y1), tf.keras.backend.sum(y2)
w1, w2 = p2 / (p1 + p2), p1 / (p1 + p2)
return w1, w2
def balanced_entropy(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
eps = 1e-6
z = tf.keras.activations.softmax(x)
cliped_z = tf.keras.backend.clip(z, eps, 1 - eps)
log_z = tf.keras.backend.log(cliped_z)
num_classes = y.shape.as_list()[-1]
ind = tf.keras.backend.argmax(y, axis=-1)
total = tf.keras.backend.sum(y)
m_c: List[int] = []
n_c: List[int] = []
loss = 0
for c_ in range(num_classes):
m_c.append(
tf.keras.backend.cast(
tf.keras.backend.equal(ind, c_), dtype=tf.int32
)
)
n_c.append(
tf.keras.backend.cast(
tf.keras.backend.sum(m_c[-1]), dtype=tf.float32
)
)
c: List[int] = []
for i in range(num_classes):
c.append(total - n_c[i])
tc = tf.math.add_n(c)
for i in range(num_classes):
w = c[i] / tc
m_c_one_hot = tf.one_hot((i * m_c[i]), num_classes, axis=-1)
y_c = m_c_one_hot * y
loss += w * tf.keras.backend.mean(
-tf.keras.backend.sum(y_c * log_z, axis=1)
)
return loss / num_classes
================================================
FILE: src/dfp/net.py
================================================
import argparse
import os
from typing import Tuple
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Model
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
# print('Is gpu available: ',tf.test.is_gpu_available());
def conv2d(
dim: int,
size: int = 3,
stride: int = 1,
rate: int = 1,
pad: str = "same",
act: str = "relu",
) -> tf.keras.Sequential:
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2D(
dim, size, strides=stride, padding=pad, dilation_rate=rate
)
)
if act == "leaky":
result.add(tf.keras.layers.LeakyReLU())
elif act == "relu":
result.add(tf.keras.layers.ReLU())
return result
def max_pool2d(
size: int = 2, stride: int = 2, pad: str = "valid"
) -> tf.keras.Sequential:
result = tf.keras.Sequential()
result.add(
tf.keras.layers.MaxPool2D(pool_size=size, strides=stride, padding=pad)
)
return result
def upconv2d(
dim: int,
size: int = 4,
stride: int = 2,
pad: str = "same",
act: str = "relu",
) -> tf.keras.Sequential:
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(dim, size, strides=stride, padding=pad)
)
if act == "relu":
result.add(tf.keras.layers.ReLU())
return result
def up_bilinear(dim: int) -> tf.keras.Sequential:
result = tf.keras.Sequential()
result.add(conv2d(dim, size=1, act="linear"))
return result
class deepfloorplanModel(Model):
def __init__(self, config: argparse.Namespace = None):
super(deepfloorplanModel, self).__init__()
self.config = config
dimlist = [256, 128, 64, 32]
self.feature_names = [
"block1_pool",
"block2_pool",
"block3_pool",
"block4_pool",
"block5_pool",
]
if config is not None:
dimlist = config.feature_channels
assert (
config.backbone == "vgg16"
), "subclass backbone must be vgg16"
self.feature_names = config.feature_names
self._vgg16init()
# room boundary prediction (rbp)
self.rbpups = [upconv2d(dim=d, act="linear") for d in dimlist]
self.rbpcv1 = [conv2d(dim=d, act="linear") for d in dimlist]
self.rbpcv2 = [conv2d(dim=d) for d in dimlist]
self.rbpfinal = up_bilinear(3)
# room type prediction (rtp)
self.rtpups = [upconv2d(dim=d, act="linear") for d in dimlist]
self.rtpcv1 = [conv2d(dim=d, act="linear") for d in dimlist]
self.rtpcv2 = [conv2d(dim=d) for d in dimlist]
# attention map
self.atts1 = [conv2d(dim=dimlist[i]) for i in range(len(dimlist))]
self.atts2 = [conv2d(dim=dimlist[i]) for i in range(len(dimlist))]
self.atts3 = [
conv2d(dim=1, size=1, act="sigmoid") for i in range(len(dimlist))
]
# reduce the tensor depth
self.xs1 = [conv2d(dim=d) for d in dimlist]
self.xs2 = [conv2d(dim=1, size=1, act="linear") for d in dimlist]
# context conv2d
dak = [9, 17, 33, 65] # kernel_shape=[h,v,inc,outc]
# horizontal
self.hs = [self.constant_kernel((d, 1, 1, 1)) for d in dak]
self.hf = [
tf.keras.layers.Conv2D(
1,
[dak[i], 1],
strides=1,
padding="same",
trainable=False,
use_bias=False,
weights=[self.hs[i]],
)
for i in range(len(dak))
]
# vertical
self.vs = [self.constant_kernel((1, d, 1, 1)) for d in dak]
self.vf = [
tf.keras.layers.Conv2D(
1,
[1, dak[i]],
strides=1,
padding="same",
trainable=False,
use_bias=False,
weights=[self.vs[i]],
)
for i in range(len(dak))
]
# diagonal
self.ds = [self.constant_kernel((d, d, 1, 1), diag=True) for d in dak]
self.df = [
tf.keras.layers.Conv2D(
1,
dak[i],
strides=1,
padding="same",
trainable=False,
use_bias=False,
weights=[self.ds[i]],
)
for i in range(len(dak))
]
# diagonal flip
self.dfs = [
self.constant_kernel((d, d, 1, 1), diag=True, flip=True)
for d in dak
]
self.dff = [
tf.keras.layers.Conv2D(
1,
dak[i],
strides=1,
padding="same",
trainable=False,
use_bias=False,
weights=[self.dfs[i]],
)
for i in range(len(dak))
]
# expand dim
self.ed = [conv2d(dim=d, size=1, act="linear") for d in dimlist]
# learn rich feature
self.lrf = [conv2d(dim=d) for d in dimlist]
# final
self.rtpfinal = up_bilinear(9)
def _vgg16init(self):
self.vgg16 = VGG16(
weights="imagenet", include_top=False, input_shape=(512, 512, 3)
)
for layer in self.vgg16.layers:
layer.trainable = False
def constant_kernel(
self,
shape: Tuple[int, int, int, int],
val: int = 1,
diag: bool = False,
flip: bool = False,
) -> np.ndarray:
k = np.array([]).astype(int)
if not diag:
k = val * np.ones(shape)
else:
w = np.eye(shape[0], shape[1])
if flip:
w = w.reshape((shape[0], shape[1], 1))
w = np.flip(w, 1)
k = w.reshape(shape)
return k
def non_local_context(
self, t1: tf.Tensor, t2: tf.Tensor, idx: int, stride: int = 4
) -> tf.Tensor:
N, H, W, C = t1.shape.as_list()
hs = H // stride if (H // stride) > 1 else (stride - 1)
vs = W // stride if (W // stride) > 1 else (stride - 1)
hs = hs if (hs % 2 != 0) else hs + 1
vs = hs if (vs % 2 != 0) else vs + 1
a = t1
x = t2
a = self.atts1[idx](a)
a = self.atts2[idx](a)
a = self.atts3[idx](a)
a = tf.keras.activations.sigmoid(a)
x = self.xs1[idx](x)
x = self.xs2[idx](x)
x = a * x
h = self.hf[idx](x)
v = self.vf[idx](x)
d = self.df[idx](x)
f = self.dff[idx](x)
c1 = a * (h + v + d + f)
c1 = self.ed[idx](c1)
features = tf.concat([t2, c1], axis=3)
out = self.lrf[idx](features)
return out
def call(self, x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
features = []
feature = x
for layer in self.vgg16.layers:
feature = layer(feature)
if layer.name in self.feature_names:
features.append(feature)
x = feature
features = features[::-1]
featuresrbp = []
for i in range(len(self.rbpups)):
x = self.rbpups[i](x) + self.rbpcv1[i](features[i + 1])
x = self.rbpcv2[i](x)
featuresrbp.append(x)
logits_cw = tf.keras.backend.resize_images(
self.rbpfinal(x), 2, 2, "channels_last"
)
x = feature
for i in range(len(self.rtpups)):
x = self.rtpups[i](x) + self.rtpcv1[i](features[i + 1])
x = self.rtpcv2[i](x)
x = self.non_local_context(featuresrbp[i], x, i)
logits_r = tf.keras.backend.resize_images(
self.rtpfinal(x), 2, 2, "channels_last"
)
return logits_r, logits_cw
================================================
FILE: src/dfp/net_func.py
================================================
import argparse
from typing import Tuple
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import (
Add,
Concatenate,
Conv2D,
Conv2DTranspose,
Input,
Multiply,
ReLU,
)
from tensorflow.keras.models import Model
def resnet50_backbone(x, feature_names):
backbone = ResNet50(weights="imagenet", include_top=False, input_tensor=x)
backbone = Model(
inputs=x, outputs=backbone.get_layer(feature_names[-1]).output
)
for layer in backbone.layers:
layer.trainable = False
features = []
for layer in feature_names:
features.append(backbone.get_layer(layer).output)
features = features[::-1]
return features
def mobilenet_backbone(x, feature_names):
backbone = MobileNet(weights="imagenet", include_top=False, input_tensor=x)
backbone = Model(
inputs=x, outputs=backbone.get_layer(feature_names[-1]).output
)
for layer in backbone.layers:
layer.trainable = False
features = []
for layer in feature_names:
features.append(backbone.get_layer(layer).output)
features = features[::-1]
return features
def mobilenetv2_backbone(x, feature_names):
backbone = MobileNetV2(
weights="imagenet", include_top=False, input_tensor=x
)
backbone = Model(
inputs=x, outputs=backbone.get_layer(feature_names[-1]).output
)
for layer in backbone.layers:
layer.trainable = False
features = []
for layer in feature_names:
features.append(backbone.get_layer(layer).output)
features = features[::-1]
return features
def vgg16_backbone(x, feature_names):
backbone = VGG16(weights="imagenet", include_top=False, input_tensor=x)
backbone = Model(
inputs=x, outputs=backbone.get_layer(feature_names[-1]).output
)
for layer in backbone.layers:
layer.trainable = False
features = []
for layer in feature_names:
features.append(backbone.get_layer(layer).output)
features = features[::-1]
return features
def vertical_horizontal_filters(
h: int, w: int
) -> Tuple[np.ndarray, np.ndarray]:
return np.ones([1, h, 1, 1]), np.ones([w, 1, 1, 1])
def diagonal_filters(h: int, w: int) -> Tuple[np.ndarray, np.ndarray]:
d = np.eye(h, w)
dr = np.eye(h, w)
dr = dr.reshape([h, w, 1])
dr = np.flip(dr, 1)
return d.reshape((h, w, 1, 1)), dr.reshape((h, w, 1, 1))
def non_local_context(xf, x_, rbdim, stride=4):
config_non_trainable = {
"filters": 1,
"trainable": False,
"padding": "same",
"use_bias": False,
}
_, H, W, _ = xf.shape
hs = H // stride if (H // stride) > 1 else (stride - 1)
vs = W // stride if (W // stride) > 1 else (stride - 1)
hs = hs if (hs % 2 != 0) else hs + 1
vs = hs if (vs % 2 != 0) else vs + 1
vf, hf = vertical_horizontal_filters(vs, hs)
df, dfr = diagonal_filters(vs, hs)
v = Conv2D(
kernel_size=[1, vs],
**config_non_trainable,
weights=[vf],
)(x_)
h = Conv2D(
kernel_size=[hs, 1],
**config_non_trainable,
weights=[hf],
)(x_)
d = Conv2D(
kernel_size=[hs, vs],
**config_non_trainable,
weights=[df],
)(x_)
dr = Conv2D(
kernel_size=[hs, vs],
**config_non_trainable,
weights=[dfr],
)(x_)
c = Add()([v, h, d, dr])
c = Multiply()([xf, c])
c = Conv2D(rbdim, 1, strides=1, padding="same", dilation_rate=1)(c)
c = Concatenate(axis=3)([x_, c])
x = Conv2D(rbdim, 3, strides=1, padding="same", dilation_rate=1)(c)
return x
def attention(xf, x_, rbdim):
xf = Conv2D(rbdim, 3, strides=1, padding="same", dilation_rate=1)(xf)
xf = ReLU()(xf)
xf = Conv2D(rbdim, 3, strides=1, padding="same", dilation_rate=1)(xf)
xf = ReLU()(xf)
xf = Conv2D(1, 1, strides=1, padding="same", dilation_rate=1)(xf)
xf = tf.keras.activations.sigmoid(xf)
x_ = Conv2D(rbdim, 3, strides=1, padding="same", dilation_rate=1)(x_)
x_ = ReLU()(x_)
x_ = Conv2D(1, 1, strides=1, padding="same", dilation_rate=1)(x_)
x_ = Multiply()([xf, x_])
return non_local_context(xf, x_, rbdim)
def deepfloorplanFunc(config: argparse.Namespace = None):
inp = Input([512, 512, 3])
if config is None:
rbdims = [256, 128, 64, 32]
features = vgg16_backbone(
inp,
[
"block1_pool",
"block2_pool",
"block3_pool",
"block4_pool",
"block5_pool",
],
)
elif config is not None:
rbdims = config.feature_channels
if config.backbone == "resnet50":
features = resnet50_backbone(inp, config.feature_names)
elif config.backbone == "vgg16":
features = vgg16_backbone(inp, config.feature_names)
elif config.backbone == "mobilenetv1":
features = mobilenet_backbone(inp, config.feature_names)
elif config.backbone == "mobilenetv2":
features = mobilenetv2_backbone(inp, config.feature_names)
assert len(features) == 5, "Not enough 5 features..."
features_room_boundary = []
x = features[0]
for i in range(len(rbdims)):
x = Conv2DTranspose(rbdims[i], 4, strides=2, padding="same")(x)
xf = Conv2D(rbdims[i], 3, strides=1, padding="same", dilation_rate=1)(
features[i + 1]
)
x = Add()([x, xf])
x = Conv2D(rbdims[i], 3, strides=1, padding="same", dilation_rate=1)(x)
x = ReLU()(x)
features_room_boundary.append(x)
x = Conv2D(3, 1, strides=1, padding="same", dilation_rate=1)(x)
logits_cw = tf.keras.backend.resize_images(x, 2, 2, "channels_last")
x = features[0]
for i in range(len(rbdims)):
x = Conv2DTranspose(rbdims[i], 4, strides=2, padding="same")(x)
xf = Conv2D(rbdims[i], 3, strides=1, padding="same", dilation_rate=1)(
features[i + 1]
)
x = Add()([x, xf])
x = Conv2D(rbdims[i], 3, strides=1, padding="same", dilation_rate=1)(x)
x = ReLU()(x)
# attention and contexture
x = attention(features_room_boundary[i], x, rbdims[i])
x = Conv2D(9, 1, strides=1, padding="same", dilation_rate=1)(x)
logits_r = tf.keras.backend.resize_images(x, 2, 2, "channels_last")
print(logits_cw.shape, logits_r.shape)
return Model(inputs=inp, outputs=[logits_r, logits_cw])
if __name__ == "__main__":
model = deepfloorplanFunc()
print(model.summary())
for layer in model.layers:
print(layer.name, layer.trainable)
model.save("/tmp/tmp")
================================================
FILE: src/dfp/train.py
================================================
import argparse
import io
import os
import sys
from typing import List, Tuple
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
from tqdm import tqdm
from .data import (
convert_one_hot_to_image,
decodeAllRaw,
loadDataset,
preprocess,
)
from .loss import balanced_entropy, cross_two_tasks_weight
from .net import deepfloorplanModel
from .net_func import deepfloorplanFunc
from .utils.settings import overwrite_args_with_toml
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
def init(
config: argparse.Namespace,
) -> Tuple[tf.data.Dataset, tf.keras.Model, tf.keras.optimizers.Optimizer]:
dataset = loadDataset()
if config.tfmodel == "subclass":
model = deepfloorplanModel(config=config)
elif config.tfmodel == "func":
model = deepfloorplanFunc(config=config)
os.system(f"mkdir -p {config.modeldir}")
if config.weight:
model.load_weights(config.weight)
# optim = tf.keras.optimizers.AdamW(learning_rate=config.lr,
# weight_decay=config.wd)
optim = tf.keras.optimizers.Adam(learning_rate=config.lr)
return dataset, model, optim
def plot_to_image(figure: matplotlib.figure.Figure) -> tf.Tensor:
buf = io.BytesIO()
plt.savefig(buf, format="png")
plt.close(figure)
buf.seek(0)
image = tf.image.decode_png(buf.getvalue(), channels=4)
image = tf.expand_dims(image, 0)
return image
def image_grid(
img: tf.Tensor,
bound: tf.Tensor,
room: tf.Tensor,
logr: tf.Tensor,
logcw: tf.Tensor,
) -> matplotlib.figure.Figure:
figure = plt.figure()
plt.subplot(2, 3, 1)
plt.imshow(img[0].numpy())
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.subplot(2, 3, 2)
plt.imshow(bound[0].numpy())
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.subplot(2, 3, 3)
plt.imshow(room[0].numpy())
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.subplot(2, 3, 5)
plt.imshow(convert_one_hot_to_image(logcw)[0].numpy().squeeze())
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.subplot(2, 3, 6)
plt.imshow(convert_one_hot_to_image(logr)[0].numpy().squeeze())
plt.xticks([])
plt.yticks([])
plt.grid(False)
return figure
@tf.function
def train_step(
model: tf.keras.Model,
optim: tf.keras.optimizers.Optimizer,
img: tf.Tensor,
hr: tf.Tensor,
hb: tf.Tensor,
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]:
# forward
with tf.GradientTape() as tape:
logits_r, logits_cw = model(img)
loss1 = balanced_entropy(logits_r, hr)
loss2 = balanced_entropy(logits_cw, hb)
w1, w2 = cross_two_tasks_weight(hr, hb)
loss = w1 * loss1 + w2 * loss2
# backward
grads = tape.gradient(loss, model.trainable_weights)
optim.apply_gradients(zip(grads, model.trainable_weights))
return logits_r, logits_cw, loss, loss1, loss2
def main(config: argparse.Namespace):
# initialization
writer = tf.summary.create_file_writer(config.logdir)
pltiter = 0
dataset, model, optim = init(config)
# training loop
for epoch in range(config.epochs):
print("[INFO] Epoch {}".format(epoch))
for data in tqdm(list(dataset.shuffle(400).batch(config.batchsize))):
img, bound, room = decodeAllRaw(data)
img, bound, room, hb, hr = preprocess(img, bound, room)
logits_r, logits_cw, loss, loss1, loss2 = train_step(
model, optim, img, hr, hb
)
# plot progress
if pltiter % config.save_tensor_interval == 0:
f = image_grid(img, bound, room, logits_r, logits_cw)
im = plot_to_image(f)
with writer.as_default():
tf.summary.scalar("Loss", loss.numpy(), step=pltiter)
tf.summary.scalar("LossR", loss1.numpy(), step=pltiter)
tf.summary.scalar("LossB", loss2.numpy(), step=pltiter)
tf.summary.image("Data", im, step=pltiter)
writer.flush()
pltiter += 1
# save model
if epoch % config.save_model_interval == 0:
model.save_weights(config.logdir + "/G")
model.save(config.modeldir)
print("[INFO] Saving Model ...")
def parse_args(args: List[str]) -> argparse.Namespace:
p = argparse.ArgumentParser()
p.add_argument(
"--tfmodel", type=str, default="subclass", choices=["subclass", "func"]
)
p.add_argument("--batchsize", type=int, default=2)
p.add_argument("--lr", type=float, default=1e-4)
p.add_argument("--wd", type=float, default=1e-5)
p.add_argument("--epochs", type=int, default=1000)
p.add_argument("--logdir", type=str, default="log/store")
p.add_argument("--modeldir", type=str, default="model/store")
p.add_argument("--weight", type=str)
p.add_argument("--save-tensor-interval", type=int, default=10)
p.add_argument("--save-model-interval", type=int, default=20)
p.add_argument("--tomlfile", type=str, default=None)
p.add_argument(
"--feature-channels",
type=int,
action="store",
default=[256, 128, 64, 32],
nargs=4,
)
p.add_argument(
"--backbone",
type=str,
default="vgg16",
choices=["vgg16", "resnet50", "mobilenetv1", "mobilenetv2"],
)
p.add_argument(
"--feature-names",
type=str,
action="store",
nargs=5,
default=[
"block1_pool",
"block2_pool",
"block3_pool",
"block4_pool",
"block5_pool",
],
)
return p.parse_args(args)
if __name__ == "__main__":
args = parse_args(sys.argv[1:])
args = overwrite_args_with_toml(args)
print(args)
main(args)
================================================
FILE: src/dfp/utils/__init__.py
================================================
================================================
FILE: src/dfp/utils/legend.py
================================================
from typing import List
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from .rgb_ind_convertor import floorplan_fuse_map
def export_legend(
legend: matplotlib.legend.Legend,
filename: str = "legend.png",
expand: List[int] = [-5, -5, 5, 5],
):
fig = legend.figure
fig.canvas.draw()
bbox = legend.get_window_extent()
bbox = bbox.from_extents(*(bbox.extents + np.array(expand)))
bbox = bbox.transformed(fig.dpi_scale_trans.inverted())
fig.savefig(filename, dpi="figure", bbox_inches=bbox)
def norm255to1(x: List[int]) -> List[float]:
return [p / 255 for p in x]
def handle(m: str, c: List[float]):
return plt.plot([], [], marker=m, color=c, ls="none")[0]
def main():
colors = [
"background",
"closet",
"bathroom",
"living room\nkitchen\ndining room",
"bedroom",
"hall",
"balcony",
"not used",
"not used",
"door/window",
"wall",
]
colors2 = [norm255to1(rgb) for rgb in list(floorplan_fuse_map.values())]
handles = [handle("s", colors2[i]) for i in range(len(colors))]
labels = colors
legend = plt.legend(handles, labels, loc=3, framealpha=1, frameon=True)
export_legend(legend)
if __name__ == "__main__":
main()
================================================
FILE: src/dfp/utils/rgb_ind_convertor.py
================================================
from typing import Dict, List
import numpy as np
# use for index 2 rgb
floorplan_room_map = {
0: [0, 0, 0], # background
1: [192, 192, 224], # closet
2: [192, 255, 255], # bathroom/washroom
3: [224, 255, 192], # livingroom/kitchen/diningroom
4: [255, 224, 128], # bedroom
5: [255, 160, 96], # hall
6: [255, 224, 224], # balcony
7: [224, 224, 224], # not used
8: [224, 224, 128], # not used
}
# boundary label
floorplan_boundary_map = {
0: [0, 0, 0], # background
1: [255, 60, 128], # opening (door&window)
2: [255, 255, 255], # wall line
}
# boundary label for presentation
floorplan_boundary_map_figure = {
0: [255, 255, 255], # background
1: [255, 60, 128], # opening (door&window)
2: [0, 0, 0], # wall line
}
# merge all label into one multi-class label
floorplan_fuse_map = {
0: [0, 0, 0], # background
1: [192, 192, 224], # closet
2: [192, 255, 255], # batchroom/washroom
3: [224, 255, 192], # livingroom/kitchen/dining room
4: [255, 224, 128], # bedroom
5: [255, 160, 96], # hall
6: [255, 224, 224], # balcony
7: [224, 224, 224], # not used
8: [224, 224, 128], # not used
9: [255, 60, 128], # extra label for opening (door&window)
10: [255, 255, 255], # extra label for wall line
}
# invert the color of wall line and background for presentation
floorplan_fuse_map_figure = {
0: [255, 255, 255], # background
1: [192, 192, 224], # closet
2: [192, 255, 255], # batchroom/washroom
3: [224, 255, 192], # livingroom/kitchen/dining room
4: [255, 224, 128], # bedroom
5: [255, 160, 96], # hall
6: [255, 224, 224], # balcony
7: [224, 224, 224], # not used
8: [224, 224, 128], # not used
9: [255, 60, 128], # extra label for opening (door&window)
10: [0, 0, 0], # extra label for wall line
}
def rgb2ind(
im: np.ndarray, color_map: Dict[int, List[int]] = floorplan_room_map
) -> np.ndarray:
ind = np.zeros((im.shape[0], im.shape[1]))
for i, rgb in color_map.items():
ind[(im == rgb).all(2)] = i
# return ind.astype(int) # int => int64
return ind.astype(np.uint8) # force to uint8
def ind2rgb(
ind_im: np.ndarray, color_map: Dict[int, List[int]] = floorplan_room_map
) -> np.ndarray:
rgb_im = np.zeros((ind_im.shape[0], ind_im.shape[1], 3))
for i, rgb in color_map.items():
rgb_im[(ind_im == i)] = rgb
return rgb_im.astype(int)
================================================
FILE: src/dfp/utils/settings.py
================================================
import argparse
from argparse import Namespace
from dynaconf import Dynaconf
def overwrite_args_with_toml(config: argparse.Namespace) -> argparse.Namespace:
if config.tomlfile is None:
return config
settings = Dynaconf(
envvar_prefix="DYNACONF", settings_files=[config.tomlfile]
)
settings = dict((k.lower(), v) for k, v in settings.as_dict().items())
return Namespace(**settings)
================================================
FILE: src/dfp/utils/util.py
================================================
import cv2
import numpy as np
import tensorflow as tf
from scipy import ndimage
def fast_hist(im: np.ndarray, gt: np.ndarray, n: int = 9) -> np.ndarray:
"""
n is num_of_classes
"""
k = (gt >= 0) & (gt < n)
return np.bincount(
n * gt[k].astype(int) + im[k], minlength=n**2
).reshape(n, n)
def flood_fill(test_array: np.ndarray, h_max: int = 255) -> np.ndarray:
"""
fill in the hole
"""
test_array = test_array.squeeze()
input_array = np.copy(test_array)
el = ndimage.generate_binary_structure(2, 2).astype(int)
inside_mask = ndimage.binary_erosion(~np.isnan(input_array), structure=el)
output_array = np.copy(input_array)
output_array[inside_mask] = h_max
output_old_array = np.copy(input_array)
output_old_array.fill(0)
el = ndimage.generate_binary_structure(2, 1).astype(int)
while not np.array_equal(output_old_array, output_array):
output_old_array = np.copy(output_array)
output_array = np.maximum(
input_array,
ndimage.grey_erosion(output_array, size=(3, 3), footprint=el),
)
return output_array
def fill_break_line(cw_mask: np.ndarray) -> np.ndarray:
broken_line_h = np.array(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
broken_line_h2 = np.array(
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1, 1, 0, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
broken_line_v = np.transpose(broken_line_h)
broken_line_v2 = np.transpose(broken_line_h2)
cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_h)
cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_v)
cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_h2)
cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_v2)
return cw_mask
def refine_room_region(cw_mask: np.ndarray, rm_ind: np.ndarray) -> np.ndarray:
label_rm, num_label = ndimage.label((1 - cw_mask))
new_rm_ind = np.zeros(rm_ind.shape)
for j in range(1, num_label + 1):
mask = (label_rm == j).astype(np.uint8)
ys, xs, _ = np.where(mask != 0)
area = (np.amax(xs) - np.amin(xs)) * (np.amax(ys) - np.amin(ys))
if area < 100:
continue
else:
room_types, type_counts = np.unique(
mask * rm_ind, return_counts=True
)
if len(room_types) > 1:
room_types = room_types[1:]
# ignore background type which is zero
type_counts = type_counts[1:]
# ignore background count
new_rm_ind += mask * room_types[np.argmax(type_counts)]
return new_rm_ind
def print_model_weights_sparsity(model):
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Wrapper):
weights = layer.trainable_weights
else:
weights = layer.weights
for weight in weights:
if "kernel" not in weight.name or "centroid" in weight.name:
continue
weight_size = weight.numpy().size
zero_num = np.count_nonzero(weight == 0)
print(
f"{weight.name}: {zero_num/weight_size:.2%} sparsity ",
f"({zero_num}/{weight_size})",
)
def print_model_weight_clusters(model):
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Wrapper):
weights = layer.trainable_weights
else:
weights = layer.weights
for weight in weights:
# ignore auxiliary quantization weights
if "quantize_layer" in weight.name:
continue
if "kernel" in weight.name:
unique_count = len(np.unique(weight))
print(f"{layer.name}/{weight.name}: {unique_count} clusters ")
================================================
FILE: tests/__init__.py
================================================
================================================
FILE: tests/test_app.py
================================================
import os
from argparse import Namespace
from types import TracebackType
from typing import Any, Dict, List, Optional, Type
import pytest
import numpy as np
from flask.testing import FlaskClient
from pytest_mock import MockFixture
from dfp.app import app as create_app
class fakeMultiprocessing:
def Pool(self):
return self
def map(self, *args: str, **kwargs: int) -> np.ndarray:
return np.zeros([1, 32, 32, 3])
def __enter__(self) -> object:
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
):
pass
def __getitem__(self) -> np.ndarray:
return np.zeros([1, 32, 32, 3])
class fakeForm:
def __init__(self, data: Dict[str, str]):
self.data = data
def keys(self):
return self.data.keys()
def getlist(self, key: str) -> List[str]:
return [self.data[key]]
def __getitem__(self, key: str) -> str:
return self.data[key]
class fakeRequest:
def __init__(self):
self.form = fakeForm(
{"postprocess": "0", "colorize": "0", "output": "/tmp"}
)
# self.files = []
self.json = fakeForm({})
@pytest.fixture
def client(mocker: MockFixture) -> FlaskClient:
mp = fakeMultiprocessing()
args = Namespace(
image="resources/30939153.jpg",
weight="",
loadmethod="none",
postprocess=False,
colorize=False,
save="/tmp/tmp.jpg",
)
content = Namespace(content=None)
mocker.patch("dfp.app.mp.Pool", return_value=mp)
mocker.patch("dfp.app.Namespace", return_value=args)
mocker.patch("dfp.app.saveStreamFile", return_value=None)
mocker.patch("dfp.app.saveStreamURI", return_value=None)
mocker.patch("dfp.app.requests.get", return_value=content)
mocker.patch("dfp.app.os.system", return_value=None)
mocker.patch("dfp.app.mpimg.imsave", return_value=None)
mocker.patch("dfp.app.send_file", return_value={"message": "success!"})
return create_app.test_client()
def test_app_home(client: FlaskClient):
resp = client.get("/")
assert resp.status_code == 200
assert isinstance(resp.json, dict)
assert resp.json.get("message", "Hello Flask!")
def test_app_process_image(client: FlaskClient):
resp = client.post("/upload")
assert resp.status_code == 200
def test_app_mock_process_empty(client: FlaskClient):
headers: Dict[Any, Any] = {}
data: Dict[Any, Any] = {}
resp = client.post("/uri", headers=headers, json=data)
assert resp.status_code == 200
assert resp.json.get("message", "success!")
def test_app_mock_process_uri(client: FlaskClient):
headers: Dict[Any, Any] = {}
data = {
"uri": "",
"postprocess": 1,
"colorize": 1,
"output": "/tmp",
}
resp = client.post("/uri", headers=headers, json=data)
os.system("rm *.jpg")
assert resp.status_code == 200
assert resp.json.get("message", "success!")
def test_app_mock_process_file(client: FlaskClient):
files = {"file": (open("resources/30939153.jpg", "rb"), "30939153.jpg")}
resp = client.post("/upload", data=files)
os.system("rm *.jpg")
assert resp.status_code == 200
================================================
FILE: tests/test_convert2tflite.py
================================================
from argparse import Namespace
from types import TracebackType
from typing import Any, List, Optional, Type
from pytest_mock import MockFixture
from dfp.convert2tflite import converter, parse_args
class fakeConverter:
def __init__(self):
self.optimizations = []
self.experimental_new_converter = False
def convert(self):
return None
class fakeFile:
def write(self, *args: str, **kwargs: int):
pass
def __enter__(self, *args: str, **kwargs: int):
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
):
pass
class fakeModel:
layers: List[Any] = []
def test_parse_args():
args = parse_args(["--quantize"])
assert args.quantize is True
def test_converter(mocker: MockFixture):
model = fakeModel()
con = fakeConverter()
f = fakeFile()
mocker.patch(
"dfp.convert2tflite.tf.keras.models.load_model", return_value=model
)
mocker.patch(
"dfp.convert2tflite.tf.lite.TFLiteConverter.from_keras_model",
return_value=con,
)
mocker.patch("dfp.convert2tflite.open", return_value=f)
args = Namespace(
quantize=True,
tflitedir="model/store/model.tflite",
modeldir="model/store",
compress_mode="quantization",
tfmodel="subclass",
loadmethod="pb",
)
converter(args)
================================================
FILE: tests/test_data.py
================================================
import numpy as np
import tensorflow as tf
from pytest_mock import MockFixture
from dfp.data import (
_parse_function,
convert_one_hot_to_image,
decodeAllRaw,
main,
plotData,
preprocess,
)
def _bytes_feature(value: bytes) -> tf.train.Feature:
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = (
value.numpy()
) # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
class TestDataCase:
def test_convert_one_hot2img(self):
hot = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
hot = hot / tf.reduce_sum(hot, axis=-1, keepdims=True)
res = convert_one_hot_to_image(hot, dtype="int", act="softmax")
assert res.numpy().shape == (1, 16, 16, 1)
def test_preprocess(self):
img = tf.random.normal((26, 26, 3))
b = tf.random.uniform(
shape=(26, 26), minval=0, maxval=2, dtype=tf.int32
)
r = tf.random.uniform(
shape=(26, 26), minval=0, maxval=8, dtype=tf.int32
)
img_, b_, r_, hb, hr = preprocess(img, b, r, size=26)
assert img_.numpy().shape == (1, 26, 26, 3)
assert b_.numpy().shape == (1, 26, 26)
assert r_.numpy().shape == (1, 26, 26)
assert hb.numpy().shape == (1, 26, 26, 3)
assert hr.numpy().shape == (1, 26, 26, 9)
def test_decodeAllRaw(self):
inp = tf.constant("1")
x = {"image": inp, "boundary": inp, "room": inp}
i, b, r = decodeAllRaw(x)
assert i.shape == (1,)
assert b.shape == (1,)
assert r.shape == (1,)
def test_parse_function(self, mocker: MockFixture):
encoded_image_string = np.array([[1, 2], [3, 4]]).tobytes()
image = tf.compat.as_bytes(encoded_image_string)
image = _bytes_feature(image)
feature = {
"image": image,
"boundary": image,
"room": image,
"door": image,
}
tf_example = tf.train.Example(
features=tf.train.Features(feature=feature)
).SerializeToString()
res = _parse_function(tf_example)
assert len(list(res.keys())) == 4
def test_plotData(self, mocker: MockFixture):
img = tf.random.normal((1, 26, 26, 3))
b = tf.random.uniform(
shape=(1, 26, 26), minval=0, maxval=2, dtype=tf.int32
)
r = tf.random.uniform(
shape=(1, 26, 26), minval=0, maxval=8, dtype=tf.int32
)
hb = tf.random.uniform(shape=(1, 26, 26, 3), minval=0, maxval=1)
hr = tf.random.uniform(shape=(1, 26, 26, 9), minval=0, maxval=1)
mocker.patch("dfp.data.decodeAllRaw", return_value=(img, b, r))
mocker.patch("dfp.data.preprocess", return_value=(img, b, r, hb, hr))
eg = tf.convert_to_tensor(np.array([[1, 2], [3, 4]]).tobytes())
plotData({"image": eg})
def test_main(self, mocker: MockFixture):
mocker.patch("dfp.data.plotData", return_value=None)
mocker.patch("dfp.data.plt.show", return_value=None)
arr = np.array([[1, 2], [3, 4]]).tobytes()
arr = tf.data.Dataset.from_tensor_slices([arr])
ds = tf.data.Dataset.zip((arr, arr))
main(ds)
================================================
FILE: tests/test_deploy.py
================================================
from argparse import Namespace
from typing import Dict, List, Tuple
import pytest
import numpy as np
import tensorflow as tf
from pytest_mock import MockFixture
from dfp.deploy import (
colorize,
deploy_plot_res,
init,
main,
parse_args,
post_process,
predict,
)
class fakeLayer:
def __init__(self):
self.name = "pool"
def __call__(self, x: tf.Tensor) -> tf.Tensor:
return x
class fakeVGG16:
def __init__(self):
self.trainable = True
self.layer = fakeLayer()
self.layers = [self.layer, self.layer]
class fakeModel:
def __init__(self):
self.trainable = True
self.vgg16 = fakeVGG16()
self.rtpfinal = fakeLayer()
self.rbpups = [self.rbpfinal]
self.rbpcv1 = [self.rbpfinal]
self.rbpcv2 = [self.rbpfinal]
self.rtpups = [self.rtpfinal]
self.rtpcv1 = [self.rtpfinal]
self.rtpcv2 = [self.rtpfinal]
def rbpfinal(self, x: tf.Tensor) -> tf.Tensor:
return x
def non_local_context(
self, x: tf.Tensor, *args: int, **kwargs: str
) -> tf.Tensor:
return x
def predict(self, x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
a = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
b = tf.random.uniform((1, 16, 16, 9), minval=0, maxval=1)
return b / tf.reduce_sum(b, axis=-1, keepdims=True), a / tf.reduce_sum(
a, axis=-1, keepdims=True
)
def invoke(self):
pass
def get_input_details(self) -> List[Dict[str, Tuple[int, int, int]]]:
return [{"index": (512, 512, 3)}]
def get_output_details(self) -> List[Dict[str, Tuple[int, int, int]]]:
return [{"index": (512, 512, 9)}, {"index": (512, 512, 3)}]
def set_tensor(self, ind: Tuple[int, int, int], img: tf.Tensor):
pass
def get_tensor(self, ind: Tuple[int, int, int]):
if ind[2] == 3:
a = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
return a / tf.reduce_sum(a, axis=-1, keepdims=True)
elif ind[2] == 9:
b = tf.random.uniform((1, 16, 16, 9), minval=0, maxval=1)
return b / tf.reduce_sum(b, axis=-1, keepdims=True)
def load_weights(self, x: str):
pass
def allocate_tensors(self):
pass
@pytest.fixture
def model_img() -> Tuple[fakeModel, tf.Tensor]:
model = fakeModel()
img = tf.random.normal((1, 16, 16, 3))
return model, img
@pytest.mark.parametrize(
"h,w,c", [(8, 8, 3), (16, 16, 3), (32, 32, 3), (64, 64, 3)]
)
def test_colorize(h: int, w: int, c: int, mocker: MockFixture):
inp = np.random.randint(2, size=(h, w))
out = np.zeros((h, w, c))
m = mocker.patch("dfp.deploy.ind2rgb")
m.return_value = out
r, cw = colorize(inp, inp)
assert r.shape == (h, w, c) and cw.shape == (h, w, c)
@pytest.mark.parametrize(
"h,w,c", [(8, 8, 3), (16, 16, 3), (32, 32, 3), (64, 64, 3)]
)
def test_post_process(h: int, w: int, c: int):
inp = np.ones((h, w, 1))
r, cw = post_process(inp, inp, np.array([h, w, c]))
assert r.shape == (h, w, 1) and cw.shape == (h, w)
def test_parse_args():
args = parse_args(
["--postprocess", "--loadmethod", "tflite", "--tfmodel", "subclass"]
)
assert args.postprocess is True
assert args.loadmethod == "tflite"
def test_init_none(mocker: MockFixture):
model = fakeModel()
mocker.patch("dfp.deploy.deepfloorplanModel", return_value=model)
mocker.patch("dfp.deploy.mpimg.imread", return_value=np.zeros([16, 16, 3]))
args = parse_args(
'--loadmethod none --image "" --tfmodel subclass'.split()
)
model_, img, shp = init(args)
assert shp == (16, 16, 3)
def test_init_log(mocker: MockFixture):
model = fakeModel()
mocker.patch("dfp.deploy.deepfloorplanModel", return_value=model)
mocker.patch("dfp.deploy.mpimg.imread", return_value=np.zeros([16, 16, 3]))
args = parse_args(
"""--loadmethod log --image ""
--weight log/store/G --tfmodel subclass""".split()
)
model_, img, shp = init(args)
assert shp == (16, 16, 3)
def test_init_pb(mocker: MockFixture):
model = fakeModel()
mocker.patch("dfp.deploy.deepfloorplanModel", return_value=model)
mocker.patch("dfp.deploy.mpimg.imread", return_value=np.zeros([16, 16, 3]))
mocker.patch("dfp.deploy.tf.keras.models.load_model", return_value=model)
args = parse_args(
"""--loadmethod pb --image ""
--weight model/store --tfmodel subclass""".split()
)
model_, img, shp = init(args)
assert shp == (16, 16, 3)
def test_init_tflite(mocker: MockFixture):
model = fakeModel()
mocker.patch("dfp.deploy.deepfloorplanModel", return_value=model)
mocker.patch("dfp.deploy.mpimg.imread", return_value=np.zeros([16, 16, 3]))
mocker.patch("dfp.deploy.tf.lite.Interpreter", return_value=model)
args = parse_args(
"""--loadmethod tflite --image \"\"
--weight model/store/model.tflite
--tfmodel subclass""".split()
)
model_, img, shp = init(args)
assert shp == (16, 16, 3)
@pytest.mark.parametrize(
"colorize,postprocess,expected",
[
(True, True, (16, 16, 3)),
(False, False, (16, 16)),
(True, False, (16, 16, 3)),
(False, True, (16, 16)),
],
)
def test_main(
colorize: bool,
postprocess: bool,
expected: Tuple[int, int, int],
model_img: Tuple[fakeModel, tf.Tensor],
mocker: MockFixture,
):
args = Namespace(
loadmethod="none",
image="",
colorize=colorize,
postprocess=postprocess,
save=True,
tfmodel="subclass",
)
model, img = model_img
mocker.patch(
"dfp.deploy.init", return_value=(model.predict, img, [16, 16, 3])
)
mocker.patch("dfp.deploy.mpimg.imsave", return_value=None)
res = main(args)
assert res.shape == expected
def test_main_tflite(
model_img: Tuple[fakeModel, tf.Tensor], mocker: MockFixture
):
args = Namespace(
loadmethod="tflite",
image="",
colorize=True,
postprocess=True,
save=False,
tfmodel="subclass",
)
model, img = model_img
mocker.patch("dfp.deploy.init", return_value=(model, img, [16, 16, 3]))
res = main(args)
assert res.shape == (16, 16, 3)
def test_main_log(model_img: Tuple[fakeModel, tf.Tensor], mocker: MockFixture):
args = Namespace(
loadmethod="log",
image="",
colorize=True,
postprocess=True,
save=False,
tfmodel="subclass",
)
model, img = model_img
mocker.patch("dfp.deploy.init", return_value=(model, img, [16, 16, 3]))
mocker.patch(
"dfp.deploy.predict",
return_value=(
tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1),
tf.random.uniform((1, 16, 16, 9), minval=0, maxval=1),
),
)
res = main(args)
assert res.shape == (16, 16, 3)
def test_deploy_plot_res():
a = np.zeros((16, 16, 3))
deploy_plot_res(a)
def test_predict(model_img: Tuple[fakeModel, tf.Tensor], mocker: MockFixture):
model, img = model_img
shp = np.array([16, 16, 3])
a, b = predict(model, img, shp)
assert a.numpy().shape == (1, 32, 32, 3)
================================================
FILE: tests/test_loss.py
================================================
import unittest
import tensorflow as tf
from dfp.loss import balanced_entropy, cross_two_tasks_weight
class TestLossCase(unittest.TestCase):
randx = tf.random.normal((1, 32, 32, 3), 0, 1)
randy = tf.random.normal((1, 32, 32, 9), 0, 1)
def test_balanced_entropy(self):
loss = balanced_entropy(self.__class__.randx, self.__class__.randx)
self.assertEqual(loss.dtype, tf.float32)
def test_weight(self):
w1, w2 = cross_two_tasks_weight(
self.__class__.randx, self.__class__.randy
)
w = w1 + w2
self.assertLessEqual(w.numpy(), 2.0)
if __name__ == "__main__":
unittest.main()
================================================
FILE: tests/test_net.py
================================================
import unittest
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input
from dfp.net import (
conv2d,
deepfloorplanModel,
max_pool2d,
up_bilinear,
upconv2d,
)
class TestNetCase(unittest.TestCase):
model = deepfloorplanModel()
randx = np.random.randn(1, 512, 512, 3)
def test_deepfloorplan_forward(self):
with tf.device("/cpu:0"):
x = preprocess_input(self.__class__.randx)
logits_r, logits_cw = self.__class__.model(x)
shpr, shpcw = logits_r.numpy().shape, logits_cw.numpy().shape
self.assertEqual((shpr, shpcw), ((1, 512, 512, 9), (1, 512, 512, 3)))
def test_vgg16(self):
gt = [
(1, 256, 256, 64),
(1, 128, 128, 128),
(1, 64, 64, 256),
(1, 32, 32, 512),
(1, 16, 16, 512),
]
vgg16 = self.__class__.model.vgg16
out = []
x = self.__class__.randx
for lay in vgg16.layers:
x = lay(x)
if lay.name.find("pool") != -1:
out.append(x.shape)
self.assertEqual(out, gt)
def test_conv2d(self):
lay = conv2d(1, act="leaky")
x = np.random.randn(1, 32, 32, 2)
x = lay(x)
self.assertEqual(x.numpy().shape, (1, 32, 32, 1))
def test_upconv2d(self):
lay = upconv2d(1, act="relu")
x = np.random.randn(1, 16, 16, 2)
x = lay(x)
self.assertEqual(x.numpy().shape, (1, 32, 32, 1))
def test_maxpool2d(self):
lay = max_pool2d()
x = np.random.randn(1, 32, 32, 1)
x = lay(x)
self.assertEqual(x.numpy().shape, (1, 16, 16, 1))
def test_upbilinear(self):
lay = up_bilinear(2)
x = np.random.randn(1, 16, 16, 1)
x = lay(x)
self.assertEqual(x.numpy().shape, (1, 16, 16, 2))
if __name__ == "__main__":
unittest.main()
================================================
FILE: tests/test_train.py
================================================
from argparse import Namespace
from types import TracebackType
from typing import Any, List, Optional, Tuple, Type
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
from pytest_mock import MockFixture
from dfp.train import image_grid, init, parse_args, plot_to_image, train_step
class fakeModel:
def __init__(self):
self.trainable_weights = []
def load_weights(self, weights: str):
pass
def __call__(
self, *args: str, **kwargs: int
) -> Tuple[tf.Tensor, tf.Tensor]:
a = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
b = tf.random.uniform((1, 16, 16, 9), minval=0, maxval=1)
return a / tf.reduce_sum(a, axis=-1, keepdims=True), b / tf.reduce_sum(
b, axis=-1, keepdims=True
)
class fakeOptim:
def apply_gradients(self, *args: str, **kwargs: int):
pass
class fakeTape:
def GradientTape(self) -> object:
return self
def gradient(self, *args: str, **kwargs: int) -> List[Any]:
return []
def __enter__(self) -> object:
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_value: Optional[BaseException],
traceback: Optional[TracebackType],
):
pass
class TestTrainCase:
def test_image_grid(self):
inp123 = tf.random.normal([1, 32, 32, 3])
inp123 = tf.clip_by_value(inp123, 0, 1)
inp45 = inp123 / tf.reduce_sum(inp123, axis=-1, keepdims=True)
f = image_grid(inp123, inp123, inp123, inp45, inp45)
assert isinstance(f, matplotlib.figure.Figure)
def test_plot_to_image(self):
f = plt.figure()
img = plot_to_image(f)
assert img.numpy().ndim == 4
def test_parse_args(self):
args = parse_args(["--lr", "1e-3", "--epochs", "300"])
assert args.lr == 1e-3
assert args.epochs == 300
def test_init(self, mocker: MockFixture):
model = fakeModel()
mocker.patch("dfp.train.loadDataset", return_value=None)
mocker.patch("dfp.train.deepfloorplanModel", return_value=model)
args = Namespace(
weight="fakepath", lr=1e-4, tfmodel="subclass", modeldir=None
)
ds, model, opt = init(args)
assert isinstance(opt, tf.keras.optimizers.Optimizer)
def test_trainstep(self, mocker: MockFixture):
model = fakeModel()
tape = fakeTape()
optim = fakeOptim()
img = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
hr = tf.random.uniform((1, 16, 16, 3), minval=0, maxval=1)
hb = tf.random.uniform((1, 16, 16, 9), minval=0, maxval=1)
mocker.patch("dfp.train.tf.GradientTape", return_value=tape)
logits_r, logits_cw, loss, loss1, loss2 = train_step.__wrapped__(
model, optim, img, hr, hb
)
assert logits_r.numpy().shape == (1, 16, 16, 3)
assert logits_cw.numpy().shape == (1, 16, 16, 9)
================================================
FILE: tests/utils/__init__.py
================================================
================================================
FILE: tests/utils/test_legend.py
================================================
import os
import unittest
import matplotlib
import matplotlib.pyplot as plt
from dfp.utils.legend import export_legend, handle, main, norm255to1
from dfp.utils.rgb_ind_convertor import floorplan_fuse_map
class TestLegendCase(unittest.TestCase):
rgbs = list(floorplan_fuse_map.values())
colors = [
"background",
"closet",
"bathroom",
"living room\nkitchen\ndining room",
"bedroom",
"hall",
"balcony",
"not used",
"not used",
"door/window",
"wall",
]
rgbs01 = [norm255to1(rgb) for rgb in rgbs]
def test_norm255to1(self):
self.assertEqual(len(self.__class__.rgbs01[0]), 3)
def test_handle(self):
self.__class__.handles = [
handle("s", self.__class__.rgbs01[i])
for i in range(len(self.__class__.colors))
]
self.assertIsInstance(
self.__class__.handles[0], matplotlib.lines.Line2D
)
def test_export_legend(self):
handles = [handle("s", [0.5, 0.5, 0.5])]
legend = plt.legend(
handles,
"Leo",
loc=3,
framealpha=1,
frameon=True,
)
export_legend(legend, filename="tmp.png")
ans = os.path.isfile("tmp.png")
os.system("rm tmp.png")
self.assertTrue(ans)
def test_main(self):
main()
ans = os.path.isfile("legend.png")
os.system("rm legend.png")
self.assertTrue(ans)
if __name__ == "__main__":
unittest.main()
================================================
FILE: tests/utils/test_rgb_ind_convertor.py
================================================
import unittest
import numpy as np
from dfp.utils.rgb_ind_convertor import floorplan_fuse_map, ind2rgb, rgb2ind
class TestRgbIndConvertorCase(unittest.TestCase):
def test_rgb2ind(self):
inp = np.array([[[255, 60, 128], [192, 255, 255]]])
expected = [9, 2]
out = rgb2ind(inp, floorplan_fuse_map)
out = list(out[0])
self.assertListEqual(out, expected)
def test_ind2rgb(self):
inp = np.array([[9, 2]])
expected = np.array([[[255, 60, 128], [192, 255, 255]]])
out = ind2rgb(inp, floorplan_fuse_map)
self.assertSequenceEqual(out.tolist(), expected.tolist())
if __name__ == "__main__":
unittest.main()
================================================
FILE: tests/utils/test_util.py
================================================
from typing import List
import pytest
import numpy as np
from dfp.utils.util import (
fast_hist,
fill_break_line,
flood_fill,
refine_room_region,
)
@pytest.mark.parametrize("shape", [[16, 16], [32, 32]])
class TestUtilCase:
def test_fill_break_line(self, shape: List[int]):
inp = np.ones(shape)
out = fill_break_line(inp)
assert out.shape == tuple(shape)
def test_flood_fill(self, shape: List[int]):
inp = np.ones(shape)
inp = np.reshape(inp, (*shape, -1))
out = flood_fill(inp)
assert out.shape == tuple(shape)
def test_refine_room_region(self, shape: List[int]):
inp = np.random.randint(10, size=shape)
inp = np.reshape(inp, (*shape, -1))
out = refine_room_region(inp, inp)
assert out.shape == (*shape, 1)
def test_fast_hist(self, shape: List[int]):
inp = np.random.randint(2, size=shape)
out = fast_hist(inp, inp)
assert out.shape == (9, 9)
gitextract__92euf85/
├── .gitattributes
├── .github/
│ └── workflows/
│ ├── main.yml
│ └── release.yml
├── .gitignore
├── .isort.cfg
├── .pre-commit-config.yaml
├── CITATION.cff
├── Dockerfile
├── LICENSE
├── README.md
├── deepfloorplan.ipynb
├── docs/
│ ├── app.toml
│ ├── cfg_test.md
│ ├── experiments/
│ │ ├── mobilenetv1/
│ │ │ ├── exp1/
│ │ │ │ ├── compress.toml
│ │ │ │ ├── deploy.toml
│ │ │ │ └── train.toml
│ │ │ └── exp2/
│ │ │ ├── compress.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ ├── mobilenetv2/
│ │ │ └── exp1/
│ │ │ └── train.toml
│ │ ├── resnet50/
│ │ │ └── exp1/
│ │ │ └── train.toml
│ │ └── vgg16/
│ │ ├── exp1/
│ │ │ ├── compress.toml
│ │ │ ├── compress_log.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ ├── exp2/
│ │ │ ├── compress.toml
│ │ │ ├── deploy.toml
│ │ │ └── train.toml
│ │ └── exp3/
│ │ ├── compress.toml
│ │ ├── deploy.toml
│ │ └── train.toml
│ ├── game.toml
│ ├── notebook.toml
│ └── pytest.md
├── install/
│ └── environment.yml
├── mypy.ini
├── pyproject.toml
├── requirements.txt
├── setup.cfg
├── setup.py
├── src/
│ └── dfp/
│ ├── __init__.py
│ ├── app.py
│ ├── convert2tflite.py
│ ├── data.py
│ ├── deploy.py
│ ├── game/
│ │ ├── __init__.py
│ │ ├── __main__.py
│ │ ├── controller.py
│ │ ├── model.py
│ │ └── view.py
│ ├── loss.py
│ ├── net.py
│ ├── net_func.py
│ ├── train.py
│ └── utils/
│ ├── __init__.py
│ ├── legend.py
│ ├── rgb_ind_convertor.py
│ ├── settings.py
│ └── util.py
└── tests/
├── __init__.py
├── test_app.py
├── test_convert2tflite.py
├── test_data.py
├── test_deploy.py
├── test_loss.py
├── test_net.py
├── test_train.py
└── utils/
├── __init__.py
├── test_legend.py
├── test_rgb_ind_convertor.py
└── test_util.py
SYMBOL INDEX (194 symbols across 26 files)
FILE: src/dfp/app.py
function saveStreamFile (line 24) | def saveStreamFile(stream: FileStorage, fnum: str):
function saveStreamURI (line 28) | def saveStreamURI(stream: bytes, fnum: str):
function home (line 34) | def home():
function dummy (line 39) | def dummy():
function process_image (line 85) | def process_image():
FILE: src/dfp/convert2tflite.py
function model_init (line 22) | def model_init(config: argparse.Namespace) -> tf.keras.Model:
function converter (line 39) | def converter(config: argparse.Namespace):
function parse_args (line 56) | def parse_args(args: List[str]) -> argparse.Namespace:
function prune (line 106) | def prune(config: argparse.Namespace):
function cluster (line 156) | def cluster(config: argparse.Namespace):
function quantization_aware_training (line 207) | def quantization_aware_training(config: argparse.Namespace):
FILE: src/dfp/data.py
function convert_one_hot_to_image (line 7) | def convert_one_hot_to_image(
function _parse_function (line 21) | def _parse_function(example_proto: bytes) -> Dict[str, tf.Tensor]:
function decodeAllRaw (line 31) | def decodeAllRaw(
function preprocess (line 40) | def preprocess(
function loadDataset (line 52) | def loadDataset(size: int = 512) -> tf.data.Dataset:
function plotData (line 58) | def plotData(data: Dict[str, tf.Tensor]):
function main (line 69) | def main(dataset: tf.data.Dataset):
FILE: src/dfp/deploy.py
function init (line 26) | def init(
function predict (line 56) | def predict(
function post_process (line 100) | def post_process(
function colorize (line 128) | def colorize(r: np.ndarray, cw: np.ndarray) -> Tuple[np.ndarray, np.ndar...
function main (line 134) | def main(config: argparse.Namespace) -> np.ndarray:
function parse_args (line 186) | def parse_args(args: List[str]) -> argparse.Namespace:
function deploy_plot_res (line 232) | def deploy_plot_res(result: np.ndarray):
FILE: src/dfp/game/__main__.py
function ray_cast_dda (line 19) | def ray_cast_dda(
function loop_rays (line 79) | def loop_rays(x, y, angle, depth, w, h, binary_map, STEP_ANGLE, CASTED_R...
class Game (line 86) | class Game:
method __init__ (line 87) | def __init__(self, tomlfile: str = "docs/game.toml"):
method _initialise_game_engine (line 96) | def _initialise_game_engine(self):
method run (line 104) | def run(self):
FILE: src/dfp/game/controller.py
class Controller (line 9) | class Controller:
method __init__ (line 10) | def __init__(self, model: Model):
method player_control (line 13) | def player_control(self):
FILE: src/dfp/game/model.py
class Model (line 12) | class Model:
method __init__ (line 27) | def __init__(self, tomlfile: str = "docs/game.toml"):
method _initialise_map (line 35) | def _initialise_map(self):
method _initialise_player_pose (line 50) | def _initialise_player_pose(self):
FILE: src/dfp/game/view.py
class View (line 11) | class View:
method __init__ (line 12) | def __init__(self, model: Model):
method register_window (line 15) | def register_window(self, win: pygame.Surface) -> View:
method draw_dfp (line 19) | def draw_dfp(self):
method show_fps (line 22) | def show_fps(self, fps: str):
method draw_player_loc (line 27) | def draw_player_loc(self):
method draw_player_rays (line 35) | def draw_player_rays(
method draw_3d_env (line 51) | def draw_3d_env(self):
method draw_3d_wall (line 63) | def draw_3d_wall(
FILE: src/dfp/loss.py
function cross_two_tasks_weight (line 6) | def cross_two_tasks_weight(
function balanced_entropy (line 14) | def balanced_entropy(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
FILE: src/dfp/net.py
function conv2d (line 15) | def conv2d(
function max_pool2d (line 36) | def max_pool2d(
function upconv2d (line 46) | def upconv2d(
function up_bilinear (line 62) | def up_bilinear(dim: int) -> tf.keras.Sequential:
class deepfloorplanModel (line 68) | class deepfloorplanModel(Model):
method __init__ (line 69) | def __init__(self, config: argparse.Namespace = None):
method _vgg16init (line 177) | def _vgg16init(self):
method constant_kernel (line 184) | def constant_kernel(
method non_local_context (line 202) | def non_local_context(
method call (line 231) | def call(self, x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
FILE: src/dfp/net_func.py
function resnet50_backbone (line 22) | def resnet50_backbone(x, feature_names):
function mobilenet_backbone (line 37) | def mobilenet_backbone(x, feature_names):
function mobilenetv2_backbone (line 52) | def mobilenetv2_backbone(x, feature_names):
function vgg16_backbone (line 69) | def vgg16_backbone(x, feature_names):
function vertical_horizontal_filters (line 84) | def vertical_horizontal_filters(
function diagonal_filters (line 90) | def diagonal_filters(h: int, w: int) -> Tuple[np.ndarray, np.ndarray]:
function non_local_context (line 98) | def non_local_context(xf, x_, rbdim, stride=4):
function attention (line 143) | def attention(xf, x_, rbdim):
function deepfloorplanFunc (line 159) | def deepfloorplanFunc(config: argparse.Namespace = None):
FILE: src/dfp/train.py
function init (line 26) | def init(
function plot_to_image (line 43) | def plot_to_image(figure: matplotlib.figure.Figure) -> tf.Tensor:
function image_grid (line 53) | def image_grid(
function train_step (line 90) | def train_step(
function main (line 110) | def main(config: argparse.Namespace):
function parse_args (line 144) | def parse_args(args: List[str]) -> argparse.Namespace:
FILE: src/dfp/utils/legend.py
function export_legend (line 10) | def export_legend(
function norm255to1 (line 23) | def norm255to1(x: List[int]) -> List[float]:
function handle (line 27) | def handle(m: str, c: List[float]):
function main (line 31) | def main():
FILE: src/dfp/utils/rgb_ind_convertor.py
function rgb2ind (line 63) | def rgb2ind(
function ind2rgb (line 75) | def ind2rgb(
FILE: src/dfp/utils/settings.py
function overwrite_args_with_toml (line 7) | def overwrite_args_with_toml(config: argparse.Namespace) -> argparse.Nam...
FILE: src/dfp/utils/util.py
function fast_hist (line 7) | def fast_hist(im: np.ndarray, gt: np.ndarray, n: int = 9) -> np.ndarray:
function flood_fill (line 17) | def flood_fill(test_array: np.ndarray, h_max: int = 255) -> np.ndarray:
function fill_break_line (line 39) | def fill_break_line(cw_mask: np.ndarray) -> np.ndarray:
function refine_room_region (line 70) | def refine_room_region(cw_mask: np.ndarray, rm_ind: np.ndarray) -> np.nd...
function print_model_weights_sparsity (line 93) | def print_model_weights_sparsity(model):
function print_model_weight_clusters (line 110) | def print_model_weight_clusters(model):
FILE: tests/test_app.py
class fakeMultiprocessing (line 15) | class fakeMultiprocessing:
method Pool (line 16) | def Pool(self):
method map (line 19) | def map(self, *args: str, **kwargs: int) -> np.ndarray:
method __enter__ (line 22) | def __enter__(self) -> object:
method __exit__ (line 25) | def __exit__(
method __getitem__ (line 33) | def __getitem__(self) -> np.ndarray:
class fakeForm (line 37) | class fakeForm:
method __init__ (line 38) | def __init__(self, data: Dict[str, str]):
method keys (line 41) | def keys(self):
method getlist (line 44) | def getlist(self, key: str) -> List[str]:
method __getitem__ (line 47) | def __getitem__(self, key: str) -> str:
class fakeRequest (line 51) | class fakeRequest:
method __init__ (line 52) | def __init__(self):
function client (line 61) | def client(mocker: MockFixture) -> FlaskClient:
function test_app_home (line 83) | def test_app_home(client: FlaskClient):
function test_app_process_image (line 90) | def test_app_process_image(client: FlaskClient):
function test_app_mock_process_empty (line 95) | def test_app_mock_process_empty(client: FlaskClient):
function test_app_mock_process_uri (line 103) | def test_app_mock_process_uri(client: FlaskClient):
function test_app_mock_process_file (line 117) | def test_app_mock_process_file(client: FlaskClient):
FILE: tests/test_convert2tflite.py
class fakeConverter (line 10) | class fakeConverter:
method __init__ (line 11) | def __init__(self):
method convert (line 15) | def convert(self):
class fakeFile (line 19) | class fakeFile:
method write (line 20) | def write(self, *args: str, **kwargs: int):
method __enter__ (line 23) | def __enter__(self, *args: str, **kwargs: int):
method __exit__ (line 26) | def __exit__(
class fakeModel (line 35) | class fakeModel:
function test_parse_args (line 39) | def test_parse_args():
function test_converter (line 44) | def test_converter(mocker: MockFixture):
FILE: tests/test_data.py
function _bytes_feature (line 15) | def _bytes_feature(value: bytes) -> tf.train.Feature:
class TestDataCase (line 24) | class TestDataCase:
method test_convert_one_hot2img (line 25) | def test_convert_one_hot2img(self):
method test_preprocess (line 31) | def test_preprocess(self):
method test_decodeAllRaw (line 46) | def test_decodeAllRaw(self):
method test_parse_function (line 54) | def test_parse_function(self, mocker: MockFixture):
method test_plotData (line 71) | def test_plotData(self, mocker: MockFixture):
method test_main (line 86) | def test_main(self, mocker: MockFixture):
FILE: tests/test_deploy.py
class fakeLayer (line 21) | class fakeLayer:
method __init__ (line 22) | def __init__(self):
method __call__ (line 25) | def __call__(self, x: tf.Tensor) -> tf.Tensor:
class fakeVGG16 (line 29) | class fakeVGG16:
method __init__ (line 30) | def __init__(self):
class fakeModel (line 36) | class fakeModel:
method __init__ (line 37) | def __init__(self):
method rbpfinal (line 48) | def rbpfinal(self, x: tf.Tensor) -> tf.Tensor:
method non_local_context (line 51) | def non_local_context(
method predict (line 56) | def predict(self, x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
method invoke (line 63) | def invoke(self):
method get_input_details (line 66) | def get_input_details(self) -> List[Dict[str, Tuple[int, int, int]]]:
method get_output_details (line 69) | def get_output_details(self) -> List[Dict[str, Tuple[int, int, int]]]:
method set_tensor (line 72) | def set_tensor(self, ind: Tuple[int, int, int], img: tf.Tensor):
method get_tensor (line 75) | def get_tensor(self, ind: Tuple[int, int, int]):
method load_weights (line 83) | def load_weights(self, x: str):
method allocate_tensors (line 86) | def allocate_tensors(self):
function model_img (line 91) | def model_img() -> Tuple[fakeModel, tf.Tensor]:
function test_colorize (line 100) | def test_colorize(h: int, w: int, c: int, mocker: MockFixture):
function test_post_process (line 112) | def test_post_process(h: int, w: int, c: int):
function test_parse_args (line 118) | def test_parse_args():
function test_init_none (line 126) | def test_init_none(mocker: MockFixture):
function test_init_log (line 137) | def test_init_log(mocker: MockFixture):
function test_init_pb (line 149) | def test_init_pb(mocker: MockFixture):
function test_init_tflite (line 162) | def test_init_tflite(mocker: MockFixture):
function test_main (line 185) | def test_main(
function test_main_tflite (line 210) | def test_main_tflite(
function test_main_log (line 227) | def test_main_log(model_img: Tuple[fakeModel, tf.Tensor], mocker: MockFi...
function test_deploy_plot_res (line 249) | def test_deploy_plot_res():
function test_predict (line 254) | def test_predict(model_img: Tuple[fakeModel, tf.Tensor], mocker: MockFix...
FILE: tests/test_loss.py
class TestLossCase (line 8) | class TestLossCase(unittest.TestCase):
method test_balanced_entropy (line 12) | def test_balanced_entropy(self):
method test_weight (line 16) | def test_weight(self):
FILE: tests/test_net.py
class TestNetCase (line 16) | class TestNetCase(unittest.TestCase):
method test_deepfloorplan_forward (line 20) | def test_deepfloorplan_forward(self):
method test_vgg16 (line 27) | def test_vgg16(self):
method test_conv2d (line 44) | def test_conv2d(self):
method test_upconv2d (line 50) | def test_upconv2d(self):
method test_maxpool2d (line 56) | def test_maxpool2d(self):
method test_upbilinear (line 62) | def test_upbilinear(self):
FILE: tests/test_train.py
class fakeModel (line 13) | class fakeModel:
method __init__ (line 14) | def __init__(self):
method load_weights (line 17) | def load_weights(self, weights: str):
method __call__ (line 20) | def __call__(
class fakeOptim (line 30) | class fakeOptim:
method apply_gradients (line 31) | def apply_gradients(self, *args: str, **kwargs: int):
class fakeTape (line 35) | class fakeTape:
method GradientTape (line 36) | def GradientTape(self) -> object:
method gradient (line 39) | def gradient(self, *args: str, **kwargs: int) -> List[Any]:
method __enter__ (line 42) | def __enter__(self) -> object:
method __exit__ (line 45) | def __exit__(
class TestTrainCase (line 54) | class TestTrainCase:
method test_image_grid (line 55) | def test_image_grid(self):
method test_plot_to_image (line 62) | def test_plot_to_image(self):
method test_parse_args (line 67) | def test_parse_args(self):
method test_init (line 72) | def test_init(self, mocker: MockFixture):
method test_trainstep (line 82) | def test_trainstep(self, mocker: MockFixture):
FILE: tests/utils/test_legend.py
class TestLegendCase (line 11) | class TestLegendCase(unittest.TestCase):
method test_norm255to1 (line 28) | def test_norm255to1(self):
method test_handle (line 31) | def test_handle(self):
method test_export_legend (line 41) | def test_export_legend(self):
method test_main (line 55) | def test_main(self):
FILE: tests/utils/test_rgb_ind_convertor.py
class TestRgbIndConvertorCase (line 8) | class TestRgbIndConvertorCase(unittest.TestCase):
method test_rgb2ind (line 9) | def test_rgb2ind(self):
method test_ind2rgb (line 16) | def test_ind2rgb(self):
FILE: tests/utils/test_util.py
class TestUtilCase (line 16) | class TestUtilCase:
method test_fill_break_line (line 17) | def test_fill_break_line(self, shape: List[int]):
method test_flood_fill (line 22) | def test_flood_fill(self, shape: List[int]):
method test_refine_room_region (line 28) | def test_refine_room_region(self, shape: List[int]):
method test_fast_hist (line 34) | def test_fast_hist(self, shape: List[int]):
Condensed preview — 71 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (171K chars).
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"path": "tests/utils/test_legend.py",
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"path": "tests/utils/test_rgb_ind_convertor.py",
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"path": "tests/utils/test_util.py",
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]
About this extraction
This page contains the full source code of the zcemycl/TF2DeepFloorplan GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 71 files (156.0 KB), approximately 45.4k tokens, and a symbol index with 194 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.