update readme

README.md

@@ -18,6 +18,7 @@ tags:
 - pointcloud
 - multimodal
 ---
+
 <div align="center">
     <h1 align="center">The P<sup>3</sup> dataset: Pixels, Points and Polygons <br> for Multimodal Building Vectorization</h1>
     <h3><align="center">Raphael Sulzer<sup>1,2</sup> &nbsp;&nbsp;&nbsp; Liuyun Duan<sup>1</sup>
@@ -27,7 +28,7 @@ tags:
     <b>Figure 1</b>: A view of our dataset of Zurich, Switzerland
 </div>
 
-## Abstract:
+## Abstract
 
 <div align="justify">
 We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. The dataset contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 cm. While many existing datasets primarily focus on the image modality, P<sup>3</sup> offers a complementary perspective by also incorporating dense 3D information. We demonstrate that LiDAR point clouds serve as a robust modality for predicting building polygons, both in hybrid and end-to-end learning frameworks. Moreover, fusing aerial LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P<sup>3</sup> dataset is publicly available, along with code and pretrained weights of three state-of-the-art models for building polygon prediction at https://github.com/raphaelsulzer/PixelsPointsPolygons.
@@ -35,28 +36,425 @@ We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for bui
 
 ## Highlights
 
-- A global, multimodal dataset of aerial images, aerial lidar point clouds and building polygons
-- A library for training and evaluating state-of-the-art deep learning methods on the dataset
+- A global, multimodal dataset of aerial images, aerial LiDAR point clouds and building outline polygons, available at [huggingface.co/datasets/rsi/PixelsPointsPolygons](https://huggingface.co/datasets/rsi/PixelsPointsPolygons) 
+- A library for training and evaluating state-of-the-art deep learning methods on the dataset, available at [github.com/raphaelsulzer/PixelsPointsPolygons](https://github.com/raphaelsulzer/PixelsPointsPolygons)
+- Pretrained model weights, available at [huggingface.co/rsi/PixelsPointsPolygons](https://huggingface.co/rsi/PixelsPointsPolygons) 
 
 
 ## Dataset
 
-### Download
-
-You can download the dataset at [huggingface.co/datasets/rsi/PixelsPointsPolygons](https://huggingface.co/datasets/rsi/PixelsPointsPolygons) .
-
-
 ### Overview
 
 <div align="left">
     <img src="./worldmap.jpg" width=60% height=50%>
 </div>
 
+### Download
 
-<!-- ### Prepare custom tile size
+```
+git lfs install
+git clone https://huggingface.co/datasets/rsi/PixelsPointsPolygons $DATA_ROOT
+```
+
+### Structure
+
+<details>
+<summary>📁 Click to expand folder structure</summary -->
+
+```text
+PixelsPointsPolygons/data/224
+├── annotations
+│   ├── annotations_all_test.json
+│   ├── annotations_all_train.json
+│   └── annotations_all_val.json
+│       ... (24 files total)
+├── images
+│   ├── train
+│   │   ├── CH
+│   │   │   ├── 0
+│   │   │   │   ├── image0_CH_train.tif
+│   │   │   │   ├── image1000_CH_train.tif
+│   │   │   │   └── image1001_CH_train.tif
+│   │   │   │       ... (5000 files total)
+│   │   │   ├── 5000
+│   │   │   │   ├── image5000_CH_train.tif
+│   │   │   │   ├── image5001_CH_train.tif
+│   │   │   │   └── image5002_CH_train.tif
+│   │   │   │       ... (5000 files total)
+│   │   │   └── 10000
+│   │   │       ├── image10000_CH_train.tif
+│   │   │       ├── image10001_CH_train.tif
+│   │   │       └── image10002_CH_train.tif
+│   │   │           ... (5000 files total)
+│   │   │       ... (11 dirs total)
+│   │   ├── NY
+│   │   │   ├── 0
+│   │   │   │   ├── image0_NY_train.tif
+│   │   │   │   ├── image1000_NY_train.tif
+│   │   │   │   └── image1001_NY_train.tif
+│   │   │   │       ... (5000 files total)
+│   │   │   ├── 5000
+│   │   │   │   ├── image5000_NY_train.tif
+│   │   │   │   ├── image5001_NY_train.tif
+│   │   │   │   └── image5002_NY_train.tif
+│   │   │   │       ... (5000 files total)
+│   │   │   └── 10000
+│   │   │       ├── image10000_NY_train.tif
+│   │   │       ├── image10001_NY_train.tif
+│   │   │       └── image10002_NY_train.tif
+│   │   │           ... (5000 files total)
+│   │   │       ... (11 dirs total)
+│   │   └── NZ
+│   │       ├── 0
+│   │       │   ├── image0_NZ_train.tif
+│   │       │   ├── image1000_NZ_train.tif
+│   │       │   └── image1001_NZ_train.tif
+│   │       │       ... (5000 files total)
+│   │       ├── 5000
+│   │       │   ├── image5000_NZ_train.tif
+│   │       │   ├── image5001_NZ_train.tif
+│   │       │   └── image5002_NZ_train.tif
+│   │       │       ... (5000 files total)
+│   │       └── 10000
+│   │           ├── image10000_NZ_train.tif
+│   │           ├── image10001_NZ_train.tif
+│   │           └── image10002_NZ_train.tif
+│   │               ... (5000 files total)
+│   │           ... (11 dirs total)
+│   ├── val
+│   │   ├── CH
+│   │   │   └── 0
+│   │   │       ├── image0_CH_val.tif
+│   │   │       ├── image100_CH_val.tif
+│   │   │       └── image101_CH_val.tif
+│   │   │           ... (529 files total)
+│   │   ├── NY
+│   │   │   └── 0
+│   │   │       ├── image0_NY_val.tif
+│   │   │       ├── image100_NY_val.tif
+│   │   │       └── image101_NY_val.tif
+│   │   │           ... (529 files total)
+│   │   └── NZ
+│   │       └── 0
+│   │           ├── image0_NZ_val.tif
+│   │           ├── image100_NZ_val.tif
+│   │           └── image101_NZ_val.tif
+│   │               ... (529 files total)
+│   └── test
+│       ├── CH
+│       │   ├── 0
+│       │   │   ├── image0_CH_test.tif
+│       │   │   ├── image1000_CH_test.tif
+│       │   │   └── image1001_CH_test.tif
+│       │   │       ... (5000 files total)
+│       │   ├── 5000
+│       │   │   ├── image5000_CH_test.tif
+│       │   │   ├── image5001_CH_test.tif
+│       │   │   └── image5002_CH_test.tif
+│       │   │       ... (5000 files total)
+│       │   └── 10000
+│       │       ├── image10000_CH_test.tif
+│       │       ├── image10001_CH_test.tif
+│       │       └── image10002_CH_test.tif
+│       │           ... (4400 files total)
+│       ├── NY
+│       │   ├── 0
+│       │   │   ├── image0_NY_test.tif
+│       │   │   ├── image1000_NY_test.tif
+│       │   │   └── image1001_NY_test.tif
+│       │   │       ... (5000 files total)
+│       │   ├── 5000
+│       │   │   ├── image5000_NY_test.tif
+│       │   │   ├── image5001_NY_test.tif
+│       │   │   └── image5002_NY_test.tif
+│       │   │       ... (5000 files total)
+│       │   └── 10000
+│       │       ├── image10000_NY_test.tif
+│       │       ├── image10001_NY_test.tif
+│       │       └── image10002_NY_test.tif
+│       │           ... (4400 files total)
+│       └── NZ
+│           ├── 0
+│           │   ├── image0_NZ_test.tif
+│           │   ├── image1000_NZ_test.tif
+│           │   └── image1001_NZ_test.tif
+│           │       ... (5000 files total)
+│           ├── 5000
+│           │   ├── image5000_NZ_test.tif
+│           │   ├── image5001_NZ_test.tif
+│           │   └── image5002_NZ_test.tif
+│           │       ... (5000 files total)
+│           └── 10000
+│               ├── image10000_NZ_test.tif
+│               ├── image10001_NZ_test.tif
+│               └── image10002_NZ_test.tif
+│                   ... (4400 files total)
+├── lidar
+│   ├── train
+│   │   ├── CH
+│   │   │   ├── 0
+│   │   │   │   ├── lidar0_CH_train.copc.laz
+│   │   │   │   ├── lidar1000_CH_train.copc.laz
+│   │   │   │   └── lidar1001_CH_train.copc.laz
+│   │   │   │       ... (5000 files total)
+│   │   │   ├── 5000
+│   │   │   │   ├── lidar5000_CH_train.copc.laz
+│   │   │   │   ├── lidar5001_CH_train.copc.laz
+│   │   │   │   └── lidar5002_CH_train.copc.laz
+│   │   │   │       ... (5000 files total)
+│   │   │   └── 10000
+│   │   │       ├── lidar10000_CH_train.copc.laz
+│   │   │       ├── lidar10001_CH_train.copc.laz
+│   │   │       └── lidar10002_CH_train.copc.laz
+│   │   │           ... (5000 files total)
+│   │   │       ... (11 dirs total)
+│   │   ├── NY
+│   │   │   ├── 0
+│   │   │   │   ├── lidar0_NY_train.copc.laz
+│   │   │   │   ├── lidar10_NY_train.copc.laz
+│   │   │   │   └── lidar1150_NY_train.copc.laz
+│   │   │   │       ... (1071 files total)
+│   │   │   ├── 5000
+│   │   │   │   ├── lidar5060_NY_train.copc.laz
+│   │   │   │   ├── lidar5061_NY_train.copc.laz
+│   │   │   │   └── lidar5062_NY_train.copc.laz
+│   │   │   │       ... (2235 files total)
+│   │   │   └── 10000
+│   │   │       ├── lidar10000_NY_train.copc.laz
+│   │   │       ├── lidar10001_NY_train.copc.laz
+│   │   │       └── lidar10002_NY_train.copc.laz
+│   │   │           ... (4552 files total)
+│   │   │       ... (11 dirs total)
+│   │   └── NZ
+│   │       ├── 0
+│   │       │   ├── lidar0_NZ_train.copc.laz
+│   │       │   ├── lidar1000_NZ_train.copc.laz
+│   │       │   └── lidar1001_NZ_train.copc.laz
+│   │       │       ... (5000 files total)
+│   │       ├── 5000
+│   │       │   ├── lidar5000_NZ_train.copc.laz
+│   │       │   ├── lidar5001_NZ_train.copc.laz
+│   │       │   └── lidar5002_NZ_train.copc.laz
+│   │       │       ... (5000 files total)
+│   │       └── 10000
+│   │           ├── lidar10000_NZ_train.copc.laz
+│   │           ├── lidar10001_NZ_train.copc.laz
+│   │           └── lidar10002_NZ_train.copc.laz
+│   │               ... (4999 files total)
+│   │           ... (11 dirs total)
+│   ├── val
+│   │   ├── CH
+│   │   │   └── 0
+│   │   │       ├── lidar0_CH_val.copc.laz
+│   │   │       ├── lidar100_CH_val.copc.laz
+│   │   │       └── lidar101_CH_val.copc.laz
+│   │   │           ... (529 files total)
+│   │   ├── NY
+│   │   │   └── 0
+│   │   │       ├── lidar0_NY_val.copc.laz
+│   │   │       ├── lidar100_NY_val.copc.laz
+│   │   │       └── lidar101_NY_val.copc.laz
+│   │   │           ... (529 files total)
+│   │   └── NZ
+│   │       └── 0
+│   │           ├── lidar0_NZ_val.copc.laz
+│   │           ├── lidar100_NZ_val.copc.laz
+│   │           └── lidar101_NZ_val.copc.laz
+│   │               ... (529 files total)
+│   └── test
+│       ├── CH
+│       │   ├── 0
+│       │   │   ├── lidar0_CH_test.copc.laz
+│       │   │   ├── lidar1000_CH_test.copc.laz
+│       │   │   └── lidar1001_CH_test.copc.laz
+│       │   │       ... (5000 files total)
+│       │   ├── 5000
+│       │   │   ├── lidar5000_CH_test.copc.laz
+│       │   │   ├── lidar5001_CH_test.copc.laz
+│       │   │   └── lidar5002_CH_test.copc.laz
+│       │   │       ... (5000 files total)
+│       │   └── 10000
+│       │       ├── lidar10000_CH_test.copc.laz
+│       │       ├── lidar10001_CH_test.copc.laz
+│       │       └── lidar10002_CH_test.copc.laz
+│       │           ... (4400 files total)
+│       ├── NY
+│       │   ├── 0
+│       │   │   ├── lidar0_NY_test.copc.laz
+│       │   │   ├── lidar1000_NY_test.copc.laz
+│       │   │   └── lidar1001_NY_test.copc.laz
+│       │   │       ... (4964 files total)
+│       │   ├── 5000
+│       │   │   ├── lidar5000_NY_test.copc.laz
+│       │   │   ├── lidar5001_NY_test.copc.laz
+│       │   │   └── lidar5002_NY_test.copc.laz
+│       │   │       ... (4953 files total)
+│       │   └── 10000
+│       │       ├── lidar10000_NY_test.copc.laz
+│       │       ├── lidar10001_NY_test.copc.laz
+│       │       └── lidar10002_NY_test.copc.laz
+│       │           ... (4396 files total)
+│       └── NZ
+│           ├── 0
+│           │   ├── lidar0_NZ_test.copc.laz
+│           │   ├── lidar1000_NZ_test.copc.laz
+│           │   └── lidar1001_NZ_test.copc.laz
+│           │       ... (5000 files total)
+│           ├── 5000
+│           │   ├── lidar5000_NZ_test.copc.laz
+│           │   ├── lidar5001_NZ_test.copc.laz
+│           │   └── lidar5002_NZ_test.copc.laz
+│           │       ... (5000 files total)
+│           └── 10000
+│               ├── lidar10000_NZ_test.copc.laz
+│               ├── lidar10001_NZ_test.copc.laz
+│               └── lidar10002_NZ_test.copc.laz
+│                   ... (4400 files total)
+└── ffl
+    ├── train
+    │   ├── CH
+    │   │   ├── 0
+    │   │   │   ├── image0_CH_train.pt
+    │   │   │   ├── image1000_CH_train.pt
+    │   │   │   └── image1001_CH_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   ├── 5000
+    │   │   │   ├── image5000_CH_train.pt
+    │   │   │   ├── image5001_CH_train.pt
+    │   │   │   └── image5002_CH_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   └── 10000
+    │   │       ├── image10000_CH_train.pt
+    │   │       ├── image10001_CH_train.pt
+    │   │       └── image10002_CH_train.pt
+    │   │           ... (5000 files total)
+    │   │       ... (11 dirs total)
+    │   ├── NY
+    │   │   ├── 0
+    │   │   │   ├── image0_NY_train.pt
+    │   │   │   ├── image1000_NY_train.pt
+    │   │   │   └── image1001_NY_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   ├── 5000
+    │   │   │   ├── image5000_NY_train.pt
+    │   │   │   ├── image5001_NY_train.pt
+    │   │   │   └── image5002_NY_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   └── 10000
+    │   │       ├── image10000_NY_train.pt
+    │   │       ├── image10001_NY_train.pt
+    │   │       └── image10002_NY_train.pt
+    │   │           ... (5000 files total)
+    │   │       ... (11 dirs total)
+    │   ├── NZ
+    │   │   ├── 0
+    │   │   │   ├── image0_NZ_train.pt
+    │   │   │   ├── image1000_NZ_train.pt
+    │   │   │   └── image1001_NZ_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   ├── 5000
+    │   │   │   ├── image5000_NZ_train.pt
+    │   │   │   ├── image5001_NZ_train.pt
+    │   │   │   └── image5002_NZ_train.pt
+    │   │   │       ... (5000 files total)
+    │   │   └── 10000
+    │   │       ├── image10000_NZ_train.pt
+    │   │       ├── image10001_NZ_train.pt
+    │   │       └── image10002_NZ_train.pt
+    │   │           ... (5000 files total)
+    │   │       ... (11 dirs total)
+    │   ├── processed-flag-all
+    │   ├── processed-flag-CH
+    │   └── processed-flag-NY
+    │       ... (8 files total)
+    ├── val
+    │   ├── CH
+    │   │   └── 0
+    │   │       ├── image0_CH_val.pt
+    │   │       ├── image100_CH_val.pt
+    │   │       └── image101_CH_val.pt
+    │   │           ... (529 files total)
+    │   ├── NY
+    │   │   └── 0
+    │   │       ├── image0_NY_val.pt
+    │   │       ├── image100_NY_val.pt
+    │   │       └── image101_NY_val.pt
+    │   │           ... (529 files total)
+    │   ├── NZ
+    │   │   └── 0
+    │   │       ├── image0_NZ_val.pt
+    │   │       ├── image100_NZ_val.pt
+    │   │       └── image101_NZ_val.pt
+    │   │           ... (529 files total)
+    │   ├── processed-flag-all
+    │   ├── processed-flag-CH
+    │   └── processed-flag-NY
+    │       ... (8 files total)
+    └── test
+        ├── CH
+        │   ├── 0
+        │   │   ├── image0_CH_test.pt
+        │   │   ├── image1000_CH_test.pt
+        │   │   └── image1001_CH_test.pt
+        │   │       ... (5000 files total)
+        │   ├── 5000
+        │   │   ├── image5000_CH_test.pt
+        │   │   ├── image5001_CH_test.pt
+        │   │   └── image5002_CH_test.pt
+        │   │       ... (5000 files total)
+        │   └── 10000
+        │       ├── image10000_CH_test.pt
+        │       ├── image10001_CH_test.pt
+        │       └── image10002_CH_test.pt
+        │           ... (4400 files total)
+        ├── NY
+        │   ├── 0
+        │   │   ├── image0_NY_test.pt
+        │   │   ├── image1000_NY_test.pt
+        │   │   └── image1001_NY_test.pt
+        │   │       ... (5000 files total)
+        │   ├── 5000
+        │   │   ├── image5000_NY_test.pt
+        │   │   ├── image5001_NY_test.pt
+        │   │   └── image5002_NY_test.pt
+        │   │       ... (5000 files total)
+        │   └── 10000
+        │       ├── image10000_NY_test.pt
+        │       ├── image10001_NY_test.pt
+        │       └── image10002_NY_test.pt
+        │           ... (4400 files total)
+        ├── NZ
+        │   ├── 0
+        │   │   ├── image0_NZ_test.pt
+        │   │   ├── image1000_NZ_test.pt
+        │   │   └── image1001_NZ_test.pt
+        │   │       ... (5000 files total)
+        │   ├── 5000
+        │   │   ├── image5000_NZ_test.pt
+        │   │   ├── image5001_NZ_test.pt
+        │   │   └── image5002_NZ_test.pt
+        │   │       ... (5000 files total)
+        │   └── 10000
+        │       ├── image10000_NZ_test.pt
+        │       ├── image10001_NZ_test.pt
+        │       └── image10002_NZ_test.pt
+        │           ... (4400 files total)
+        ├── processed-flag-all
+        ├── processed-flag-CH
+        └── processed-flag-NY
+            ... (8 files total)
+```
 
-See [datasets preprocessing](data_preprocess) for instructions on preparing a dataset with different tile sizes. -->
+</details>
+
+## Pretrained model weights
+
+### Download
 
+```
+git lfs install
+git clone https://huggingface.co/rsi/PixelsPointsPolygons $MODEL_ROOT
+```
 
 ## Code 
 
@@ -66,9 +464,9 @@ See [datasets preprocessing](data_preprocess) for instructions on preparing a da
 git clone https://github.com/raphaelsulzer/PixelsPointsPolygons
 ```
 
-### Requirements
+### Installation
 
-To create a conda environment named `ppp` and install the repository as a python package with all dependencies run
+To create a conda environment named `p3` and install the repository as a python package with all dependencies run
 ```
 bash install.sh
 ```
@@ -97,50 +495,75 @@ pip install .
 | Pix2Poly                  |\<pix2poly>| PointPillars (PP) + ViT   | \<pp_vit>             |       | ✅    | 0.80      | 0.88      |
 | Pix2Poly                  |\<pix2poly>| PP+ViT \& ViT             | \<fusion_vit>         | ✅    |✅     | 0.78      | 0.85      | -->
 
-### Configuration
+### Setup
+
+The project supports hydra configuration which allows to modify any parameter either from a `.yaml` file of directly from the command line.
+
+To setup the project structure we recommend to specify your `$DATA_ROOT` and `$MODEL_ROOT` in `config/host/default.yaml`.
 
-The project supports hydra configuration which allows to modify any parameter from the command line, such as the model and encoder types from the table above.
-To view all available options run
+To view all available configuration options run
 ```
-python train.py --help
+python scripts/train.py --help
 ```
 
-### Training
 
-Start training with the following command:
 
-```
-torchrun --nproc_per_node=<num GPUs> train.py model=<model> encoder=<encoder> model.batch_size=<batch size> ...
+<!-- The most important parameters are described below:
+<details>
+<summary>CLI Parameters</summary>
 
+```text
+        ├── processed-flag-all
+        ├── processed-flag-CH
+        └── processed-flag-NY
+            ... (8 files total)
 ```
 
-### Prediction
+</details> -->
 
-```
-torchrun --nproc_per_node=<num GPUs> predict.py model=<model> checkpoint=best_val_iou ...
+### Predict a single tile
+
+TODO
 
 ```
+python scripts/predict_demo.py
+```
+
+### Reproduce paper results
 
-### Evaluation
+To reproduce the results from the paper you can run any of the following commands
 
 ```
-python evaluate.py model=<model> checkpoint=best_val_iou
+python scripts/modality_ablation.py
+python scripts/lidar_density_ablation.py
+python scripts/all_countries.py
 ```
-<!-- ## Trained models
 
-asd -->
+### Custom training, prediction and evaluation
 
+We recommend to first setup a custom `$EXP_FILE` in `config/experiment` following the structure of one of the existing experiment files, e.g. `ffl_fusion.yaml`. You can then run:
 
-<!-- ## Results
+```
+# train your model (on multiple GPUs)
+torchrun --nproc_per_node=$NUM_GPU scripts/train.py experiment=$EXP_FILE
+# predict the test set with your model (on multiple GPUs)
+torchrun --nproc_per_node=$NUM_GPU scripts/predict.py evaluation=test checkpoint=best_val_iou
+# evaluate your prediction of the test set
+python scripts/evaluate.py model=<model> evaluation=test checkpoint=best_val_iou
+```
 
-#TODO Put paper main results table here -->
+You could also continue training from a provided pretrained model with
 
+```
+# train your model (on a single GPU)
+python scripts/train.py experiment=p2p_fusion checkpoint=latest
+```
 
 ## Citation
 
 If you find our work useful, please consider citing:
 ```bibtex
-...
+TODO
 ```
 
 ## Acknowledgements

pix2poly/224/v0_all_bs4x16/.hydra/config.yaml

@@ -1,117 +1,32 @@
 host:
-  name: jeanzay
-  data_root: /lustre/fswork/projects/rech/cso/uku93eu/data
-  update_pbar_every: 60
+  name: gin
+  data_root: /data/rsulzer/${..dataset.name}
+  model_root: /data/rsulzer/${..dataset.name}_output
+  multi_gpu: false
+  device: cuda
+  update_pbar_every: 1
+  ldof_exe: /user/rsulzer/home/cpp/line-DOF-metric/build/calculate_DoF
 run_type:
-  name: release
+  name: debug
   batch_size: 16
-  train_subset: null
-  val_subset: null
-  test_subset: null
-  logging: INFO
-  num_workers: 16
-  log_to_wandb: true
-polygonization:
-  method:
-  - acm
-  common_params:
-    init_data_level: 0.5
-  simple_method:
-    data_level: 0.5
-    tolerance:
-    - 1.0
-    seg_threshold: 0.5
-    min_area: 10
-  asm_method:
-    init_method: skeleton
-    data_level: 0.5
-    loss_params:
-      coefs:
-        step_thresholds:
-        - 0
-        - 100
-        - 200
-        - 300
-        data:
-        - 1.0
-        - 0.1
-        - 0.0
-        - 0.0
-        crossfield:
-        - 0.0
-        - 0.05
-        - 0.0
-        - 0.0
-        length:
-        - 0.1
-        - 0.01
-        - 0.0
-        - 0.0
-        curvature:
-        - 0.0
-        - 0.0
-        - 1.0
-        - 0.0
-        corner:
-        - 0.0
-        - 0.0
-        - 0.5
-        - 0.0
-        junction:
-        - 0.0
-        - 0.0
-        - 0.5
-        - 0.0
-      curvature_dissimilarity_threshold: 2
-      corner_angles:
-      - 45
-      - 90
-      - 135
-      corner_angle_threshold: 22.5
-      junction_angles:
-      - 0
-      - 45
-      - 90
-      - 135
-      junction_angle_weights:
-      - 1
-      - 0.01
-      - 0.1
-      - 0.01
-      junction_angle_threshold: 22.5
-    lr: 0.1
-    gamma: 0.995
-    device: cuda
-    tolerance:
-    - 1
-    seg_threshold: 0.5
-    min_area: 10
-  acm_method:
-    steps: 500
-    data_level: 0.5
-    data_coef: 0.1
-    length_coef: 0.4
-    crossfield_coef: 0.5
-    poly_lr: 0.01
-    warmup_iters: 100
-    warmup_factor: 0.1
-    device: cuda
-    tolerance:
-    - 1
-    seg_threshold: 0.5
-    min_area: 10
+  train_subset: 256
+  val_subset: 32
+  test_subset: 32
+  logging: DEBUG
+  num_workers: 0
+  log_to_wandb: false
 dataset:
-  name: lidarpoly
+  name: PixelsPointsPolygons
   size: ${..experiment.encoder.in_size}
-  path: ${host.data_root}/${.name}/${.size}
+  path: ${host.data_root}/data/${.size}
   annotations:
-    train: ${..path}/annotations_${...country}_train.json
-    val: ${..path}/annotations_${...country}_val.json
-    test: ${..path}/annotations_${...country}_test.json
+    train: ${..path}/annotations/annotations_${...experiment.country}_train.json
+    val: ${..path}/annotations/annotations_${...experiment.country}_val.json
+    test: ${..path}/annotations/annotations_${...experiment.country}_test.json
   ffl_stats:
-    train: ${..path}/ffl/train/stats-${...country}.pt
-    val: ${..path}/ffl/val/stats-${...country}.pt
-    test: ${..path}/ffl/test/stats-${...country}.pt
+    train: ${..path}/ffl/train/stats-${...experiment.country}.pt
+    val: ${..path}/ffl/val/stats-${...experiment.country}.pt
+    test: ${..path}/ffl/test/stats-${...experiment.country}.pt
   train_subset: ${..run_type.train_subset}
   val_subset: ${..run_type.val_subset}
   test_subset: ${..run_type.test_subset}
@@ -135,7 +50,7 @@ experiment:
     out_feature_height: 28
     vit:
       type: vit_small_patch${..patch_size}_${..in_size}.dino
-      checkpoint_file: ${....host.data_root}/checkpoints/backbones/dino_deitsmall8_pretrain.pth
+      checkpoint_file: ${....host.model_root}/backbones/dino_deitsmall8_pretrain.pth
       pretrained: true
     patch_size: 8
     patch_feature_size: 28
@@ -185,26 +100,21 @@ experiment:
     weight_decay: 0.0001
   name: v0_all_bs4x16
   group_name: v2_${.model.name}
-output_dir: ${.host.data_root}/${.experiment.model.name}_outputs/${.dataset.name}/${.experiment.encoder.in_size}/${.experiment.name}
-checkpoint: null
-checkpoint_file: null
-save_best: true
-save_latest: true
-save_every: 10
-val_every: 1
-best_val_loss: 10000000.0
-best_val_iou: 0.0
-multi_gpu: true
-device: cuda
-log_to_wandb: true
-num_workers: ${.run_type.num_workers}
-update_pbar_every: ${.host.update_pbar_every}
-country: all
-use_lidar: ${.experiment.encoder.use_lidar}
-use_images: ${.experiment.encoder.use_images}
-eval:
+  country: all
+training:
+  save_best: true
+  save_latest: true
+  save_every: 10
+  val_every: 1
+  best_val_loss: 10000000.0
+  best_val_iou: 0.0
+evaluation:
   split: val
-  pred_file: ${..output_dir}/predictions_${..country}_${.split}/${..checkpoint}.json
+  pred_file: ${..output_dir}/predictions_${..experiment.country}_${.split}/${..checkpoint}.json
   modes:
   - iou
   eval_file: results/metrics
+experiment.name: debug
+output_dir: ${.host.model_root}/${.experiment.model.name}/${.experiment.encoder.in_size}/${.experiment.name}
+checkpoint: null
+num_workers: ${.run_type.num_workers}

pix2poly/224/v0_all_bs4x16/.hydra/hydra.yaml

@@ -112,18 +112,13 @@ hydra:
     hydra:
     - hydra.mode=RUN
     task:
-    - log_to_wandb=true
-    - host=jz
-    - run_type=release
-    - multi_gpu=true
-    - checkpoint=null
-    - experiment=p2p_fusion
-    - experiment.name=v0_all_bs4x16
-    - country=all
+    - run_type=debug
+    - host=gin
+    - run_type.log_to_wandb=false
   job:
     name: train
     chdir: null
-    override_dirname: checkpoint=null,country=all,experiment.name=v0_all_bs4x16,experiment=p2p_fusion,host=jz,log_to_wandb=true,multi_gpu=true,run_type=release
+    override_dirname: host=gin,run_type.log_to_wandb=false,run_type=debug
     id: ???
     num: ???
     config_name: config
@@ -137,26 +132,27 @@ hydra:
   runtime:
     version: 1.3.2
     version_base: '1.3'
-    cwd: /lustre/fswork/projects/rech/cso/uku93eu/python/PixelsPointsPolygons
+    cwd: /run/netsop/u/home-sam/home/rsulzer/remote_python/pixelspointspolygons
     config_sources:
     - path: hydra.conf
       schema: pkg
       provider: hydra
-    - path: /lustre/fswork/projects/rech/cso/uku93eu/python/PixelsPointsPolygons/config
+    - path: /run/netsop/u/home-sam/home/rsulzer/remote_python/pixelspointspolygons/config
       schema: file
       provider: main
     - path: ''
       schema: structured
       provider: schema
-    output_dir: /lustre/fswork/projects/rech/cso/uku93eu/data/pix2poly_outputs/lidarpoly/224/v0_all_bs4x16
+    output_dir: /data/rsulzer/PixelsPointsPolygons_output/pix2poly/224/v0_all_bs4x16
     choices:
+      evaluation: val
+      training: default
       experiment: p2p_fusion
       [email protected]: pix2poly
       [email protected]: early_fusion_vit
-      dataset: lidarpoly
-      polygonization: asm_acm
-      run_type: release
-      host: jz
+      dataset: p3
+      run_type: debug
+      host: gin
       hydra/env: default
       hydra/callbacks: null
       hydra/job_logging: default

pix2poly/224/v0_all_bs4x16/.hydra/overrides.yaml

@@ -1,8 +1,3 @@
-- log_to_wandb=true
-- host=jz
-- run_type=release
-- multi_gpu=true
-- checkpoint=null
-- experiment=p2p_fusion
-- experiment.name=v0_all_bs4x16
-- country=all
+- run_type=debug
+- host=gin
+- run_type.log_to_wandb=false