update readme

PixelsPointsPolygons.py

@@ -0,0 +1,170 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import json
+import os
+import datasets
+
+_CITATION = """\
+@InProceedings{arxiv,
+title = {The P3 dataset: Pixels, Points and Polygons <br> for Multimodal Building Vectorization},
+author={Raphael Sulzer},
+year={2025}
+}
+"""
+
+_DESCRIPTION = """\
+The P3 dataset is 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.
+"""
+
+_HOMEPAGE = "https://github.com/raphaelsulzer/PixelsPointsPolygons"
+
+_LICENSE = "cc-by-4.0"
+
+
+# _URLS = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+class PixelsPointsPolygons(datasets.GeneratorBasedBuilder):
+    """The P3 dataset is a large-scale multimodal benchmark for building vectorization."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="all", version=VERSION, description="Data from all countries (CH, NY, NZ)"),
+        datasets.BuilderConfig(name="CH", version=VERSION, description="Data from Switzerland (CH) only"),
+        datasets.BuilderConfig(name="NY", version=VERSION, description="Data from New York State, US (NY) only"),
+        datasets.BuilderConfig(name="NZ", version=VERSION, description="Data from New Zealand (NZ) only"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        #     features = datasets.Features(
+        #         {
+        #             "sentence": datasets.Value("string"),
+        #             "option1": datasets.Value("string"),
+        #             "answer": datasets.Value("string")
+        #             # These are the features of your dataset like images, labels ...
+        #         }
+        #     )
+        # else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+        #     features = datasets.Features(
+        #         {
+        #             "sentence": datasets.Value("string"),
+        #             "option2": datasets.Value("string"),
+        #             "second_domain_answer": datasets.Value("string")
+        #             # These are the features of your dataset like images, labels ...
+        #         }
+        #     )
+        features = datasets.Features(
+            {
+                "images": datasets.Value("uint8"),
+                "lidar": datasets.Value("float32"),
+                "polygon": datasets.Value("float32"),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _get_urls(self):
+        
+        
+    
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.jsonl"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.jsonl"),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "first_domain":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option1": data["option1"],
+                        "answer": "" if split == "test" else data["answer"],
+                    }
+                else:
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "option2": data["option2"],
+                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
+                    }

README.md

@@ -20,44 +20,42 @@ tags:
 - Vectorization
 language:
 - en
-configs:
-- config_name: all
-  data_files:
-  - split: train
-    path: "data/224/annotations/annotations_all_train.json"
-  - split: val
-    path: "data/224/annotations/annotations_all_val.json"
-  - split: test
-    path: "data/224/annotations/annotations_all_test.json"
-- config_name: CH
-  data_files:
-  - split: train
-    path: "data/224/annotations/annotations_CH_train.json"
-  - split: val
-    path: "data/224/annotations/annotations_CH_val.json"
-  - split: test
-    path: "data/224/annotations/annotations_CH_test.json"
-- config_name: NY
-  data_files:
-  - split: train
-    path: "data/224/annotations/annotations_NY_train.json"
-  - split: val
-    path: "data/224/annotations/annotations_NY_val.json"
-  - split: test
-    path: "data/224/annotations/annotations_NY_test.json"
-- config_name: NZ
-  data_files:
-  - split: train
-    path: "data/224/annotations/annotations_NZ_train.json"
-  - split: val
-    path: "data/224/annotations/annotations_NZ_val.json"
-  - split: test
-    path: "data/224/annotations/annotations_NZ_test.json"
+# configs:
+# - config_name: all
+#   data_files:
+#   - split: train
+#     path: "data/224/annotations/annotations_all_train.json"
+#   - split: val
+#     path: "data/224/annotations/annotations_all_val.json"
+#   - split: test
+#     path: "data/224/annotations/annotations_all_test.json"
+# - config_name: CH
+#   data_files:
+#   - split: train
+#     path: "data/224/annotations/annotations_CH_train.json"
+#   - split: val
+#     path: "data/224/annotations/annotations_CH_val.json"
+#   - split: test
+#     path: "data/224/annotations/annotations_CH_test.json"
+# - config_name: NY
+#   data_files:
+#   - split: train
+#     path: "data/224/annotations/annotations_NY_train.json"
+#   - split: val
+#     path: "data/224/annotations/annotations_NY_val.json"
+#   - split: test
+#     path: "data/224/annotations/annotations_NY_test.json"
+# - config_name: NZ
+#   data_files:
+#   - split: train
+#     path: "data/224/annotations/annotations_NZ_train.json"
+#   - split: val
+#     path: "data/224/annotations/annotations_NZ_val.json"
+#   - split: test
+#     path: "data/224/annotations/annotations_NZ_test.json"
 ---
 
 
-
-
 <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>
@@ -67,6 +65,16 @@ configs:
     <b>Figure 1</b>: A view of our dataset of Zurich, Switzerland
 </div>
 
+## Table of Contents
+
+- [Abstract](#abstract)
+- [Highlights](#highlights)
+- [Dataset](#dataset)
+- [Pretrained model weights](#pretrained-model-weights)
+- [Code](#code)
+- [Citation](#citation)
+- [Acknowledgements](#acknowledgements)
+
 ## Abstract
 
 <div align="justify">
@@ -79,7 +87,6 @@ We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for bui
 - 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
 
 ### Overview
@@ -90,15 +97,26 @@ We present the P<sup>3</sup> dataset, a large-scale multimodal benchmark for bui
 
 ### Download
 
+The recommended and fastest way to download the dataset is to run
+
+```
+pip install huggingface_hub
+python scripts/download_dataset.py --dataset-root $DATA_ROOT
+```
+
+Optionally you can also download the dataset by running
+
 ```
 git lfs install
 git clone https://huggingface.co/datasets/rsi/PixelsPointsPolygons $DATA_ROOT
 ```
 
+Both options will download the full dataset, including aerial images (as .tif), aerial lidar point clouds (as .copc.laz) and building polygon annotaions (as MS-COCO .json) into `$DATA_ROOT` . The size of the dataset is around 163GB.
+
 ### Structure
 
 <details>
-<summary>📁 Click to expand folder structure</summary -->
+<summary>📁 Click to expand dataset folder structure</summary -->
 
 ```text
 PixelsPointsPolygons/data/224
@@ -490,12 +508,21 @@ PixelsPointsPolygons/data/224
 
 ### Download
 
+The recommended and fastest way to download the pretrained model weights is to run
+
+```
+python scripts/download_pretrained.py --model-root $MODEL_ROOT
+```
+
+Optionally you can also download the weights by running
+
 ```
-git lfs install
 git clone https://huggingface.co/rsi/PixelsPointsPolygons $MODEL_ROOT
 ```
 
-## Code 
+Both options will download all checkpoints (as .pth) and results presented in the paper (as MS-COCO .json) into `$MODEL_ROOT` .
+
+## Code
 
 ### Download
 
@@ -536,7 +563,7 @@ pip install .
 
 ### Setup
 
-The project supports hydra configuration which allows to modify any parameter either from a `.yaml` file of directly from the command line.
+The project supports hydra configuration which allows to modify any parameter either from a `.yaml` file or 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`.
 
@@ -570,7 +597,7 @@ python scripts/predict_demo.py
 
 ### Reproduce paper results
 
-To reproduce the results from the paper you can run any of the following commands
+To reproduce the results from the paper you can run the following commands
 
 ```
 python scripts/modality_ablation.py
@@ -580,15 +607,17 @@ python scripts/all_countries.py
 
 ### Custom training, prediction and evaluation
 
-We recommend to first setup a custom `$EXP_FILE` in `config/experiment` following the structure of one of the existing experiment files, e.g. `ffl_fusion.yaml`. You can then run:
+We recommend to first setup a custom experiment file `$EXP_FILE` in `config/experiment/` following the structure of one of the existing files, e.g. `ffl_fusion.yaml`. You can then run
 
 ```
 # 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=$NUM_GPU scripts/predict.py evaluation=test checkpoint=best_val_iou
+torchrun --nproc_per_node=$NUM_GPU scripts/predict.py experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou
+
 # evaluate your prediction of the test set
-python scripts/evaluate.py model=<model> evaluation=test checkpoint=best_val_iou
+python scripts/evaluate.py model=<model> experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou
 ```
 
 You could also continue training from a provided pretrained model with