Add dataset

.gitattributes

@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.fits filter=lfs diff=lfs merge=lfs -text

GBI-16-2D-Legacy.py

@@ -0,0 +1,130 @@
+import os
+import random
+from glob import glob
+import json
+from huggingface_hub import hf_hub_download
+
+
+from astropy.io import fits
+import datasets
+from datasets import DownloadManager
+from fsspec.core import url_to_fs
+
+_DESCRIPTION = (
+"GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression "
+"dataset assembled by Maireles-González et al. (Publications of the "
+"Astronomical Society of the Pacific, 135:094502, 2023 September; doi: "
+"[https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/"
+"acf6e0)). It contains 226 FITS images from 5 different ground-based "
+"telescope/cameras with a varying amount of entropy per image."
+)
+
+_HOMEPAGE = "https://google.github.io/AstroCompress"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://huggingface.co/datasets/AstroCompress/GBI-16-2D-Legacy/resolve/main/"
+
+_URLS = {
+    "tiny": {
+        "train": "./splits/tiny_train.jsonl",
+        "test": "./splits/tiny_test.jsonl",
+    },
+    "full": {
+        "train": "./splits/full_train.jsonl",
+        "test": "./splits/full_test.jsonl",
+    }
+}
+
+_REPO_ID = "AstroCompress/GBI-16-2D-Legacy"
+
+class GBI_16_2D_Legacy(datasets.GeneratorBasedBuilder):
+    """GBI-16-2D-Legacy Dataset"""
+
+    VERSION = datasets.Version("1.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="tiny",
+            version=VERSION,
+            description="A small subset of the data, to test downsteam workflows.",
+        ),
+        datasets.BuilderConfig(
+            name="full",
+            version=VERSION,
+            description="The full dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "tiny"
+
+    def __init__(self, **kwargs):
+        super().__init__(version=self.VERSION, **kwargs)
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    # Images are variable size across the dataset
+                    # so use the Image type here, returning as
+                    # numpy uint16
+                    "image": datasets.Image(decode=True, mode="I;16"),
+                    "telescope": datasets.Value("string"),
+                    "image_id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation="TBD",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+
+        ret = []
+        base_path = dl_manager._base_path
+        locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) 
+        _, path = url_to_fs(base_path)
+
+        for split in ["train", "test"]:
+            if locally_run:
+                split_file_location = os.path.normpath(os.path.join(path, _URLS[self.config.name][split]))
+                split_file = dl_manager.download_and_extract(split_file_location)
+            else:
+                split_file = hf_hub_download(repo_id=_REPO_ID, filename=_URLS[self.config.name][split], repo_type="dataset")
+            with open(split_file, encoding="utf-8") as f:
+                data_filenames = []
+                data_metadata = []
+                for line in f:
+                    item = json.loads(line)
+                    data_filenames.append(item["image"])
+                    data_metadata.append({"telescope": item["telescope"],
+                                          "image_id": item["image_id"]})
+                if locally_run:
+                    data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames]
+                    data_files = [dl_manager.download(data_url) for data_url in data_urls]
+                else:
+                    data_urls = data_filenames
+                    data_files = [hf_hub_download(repo_id=_REPO_ID, filename=data_url, repo_type="dataset") for data_url in data_urls]
+            ret.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,
+                    gen_kwargs={"filepaths": data_files,
+                                "split_file": split_file,
+                                "split": split, 
+                                "data_metadata": data_metadata},
+                ),
+            )
+        return ret
+
+    def _generate_examples(self, filepaths, split_file, split, data_metadata):
+        """Generate GBI-16-2D-Legacy examples"""
+
+        for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
+            task_instance_key = f"{self.config.name}-{split}-{idx}"
+            with fits.open(filepath, memmap=False) as hdul:
+                # this data is natively formatted like (1, 4200, 2154)
+                # just use the 2D image
+                image_data = hdul[0].data[0,:,:].tolist()
+                yield task_instance_key, {**{"image": image_data}, **item}

README.md

@@ -0,0 +1,123 @@
+---
+license: cc-by-4.0
+pretty_name: Ground-based 2d images assembled in Maireles-González et al.
+tags:
+- astronomy
+- compression
+- images
+dataset_info:
+- config_name: full
+  features:
+  - name: image
+    dtype:
+      image:
+        mode: I;16
+  - name: telescope
+    dtype: string
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3509045373
+    num_examples: 120
+  - name: test
+    num_bytes: 970120060
+    num_examples: 32
+  download_size: 2240199274
+  dataset_size: 4479165433
+- config_name: tiny
+  features:
+  - name: image
+    dtype:
+      image:
+        mode: I;16
+  - name: telescope
+    dtype: string
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 307620695
+    num_examples: 10
+  - name: test
+    num_bytes: 168984698
+    num_examples: 5
+  download_size: 238361934
+  dataset_size: 476605393
+---
+
+# GBI-16-2D-Legacy Dataset
+
+GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression dataset assembled by Maireles-González et al. (Publications of the Astronomical Society of the Pacific, 135:094502, 2023 September; doi: [https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/acf6e0)). It contains 226 FITS images from 5 different ground-based telescope/cameras with a varying amount of entropy per image.
+
+# Usage
+
+You first need to install the `datasets` and `astropy` packages:
+
+```bash
+pip install datasets astropy PIL
+```
+
+There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 5 2D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.
+
+## Local Use (RECOMMENDED)
+
+You can clone this repo and use directly without connecting to hf:
+
+```bash
+git clone https://huggingface.co/datasets/AstroCompress/GBI-16-2D-Legacy
+```
+
+```bash
+git lfs pull
+```
+
+Then `cd GBI-16-2D-Legacy` and start python like:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("./GBI-16-2D-Legacy.py", "tiny", data_dir="./data/", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np")
+```
+
+Now you should be able to use the `ds` variable like:
+
+```python
+ds["test"][0]["image"].shape # -> (4200, 2154)
+```
+
+Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk. If you run into issues with downloading the `full` dataset, try changing `num_proc` in `load_dataset` to >1 (e.g. 5). You can also set the `writer_batch_size` to ~10-20.
+
+
+
+## Use from Huggingface Directly
+
+This method may only be an option when trying to access the "tiny" version of the dataset.
+
+To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:
+
+```bash
+huggingface-cli login
+```
+
+or
+
+```
+import huggingface_hub
+huggingface_hub.login(token=token)
+```
+
+Then in your python script:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("AstroCompress/GBI-16-2D-Legacy", "tiny", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np")
+```
+
+
+## Demo Colab Notebook
+We provide a demo collab notebook to get started on using the dataset [here](https://colab.research.google.com/drive/1SuFBPZiYZg9LH4pqypc_v8Sp99lShJqZ?usp=sharing).
+
+## Utils scripts
+Note that utils scripts such as `eval_baselines.py` must be run from the parent directory of `utils`, i.e. `python utils/eval_baselines.py`.

baseline_results_2d.csv

@@ -0,0 +1,19 @@
+,JPEG_XL_MAX_EFFORT_BPD,JPEG_XL_MAX_EFFORT_WRITE_RUNTIME,JPEG_XL_MAX_EFFORT_READ_RUNTIME,JPEG_XL_BPD,JPEG_XL_WRITE_RUNTIME,JPEG_XL_READ_RUNTIME,JPEG_2K_BPD,JPEG_2K_WRITE_RUNTIME,JPEG_2K_READ_RUNTIME,JPEG_LS_BPD,JPEG_LS_WRITE_RUNTIME,JPEG_LS_READ_RUNTIME,RICE_BPD,RICE_WRITE_RUNTIME,RICE_READ_RUNTIME
+./data/LCO/ogg2m001.fits,,,,,,,,,,,,,,,
+./data/LCO/coj0m403.fits,,,,,,,,,,,,,,,
+./data/LCO/lsc0m409.fits,,,,,,,,,,,,,,,
+./data/LCO/cpt0m407.fits,,,,,,,,,,,,,,,
+./data/LCO/ogg0m404.fits,,,,,,,,,,,,,,,
+./data/LCO/coj2m002.fits,,,,,,,,,,,,,,,
+./data/LCO/coj0m405.fits,,,,,,,,,,,,,,,
+./data/LCO/lsc0m412.fits,,,,,,,,,,,,,,,
+./data/LCO/elp0m411.fits,,,,,,,,,,,,,,,
+./data/LCO/ogg2m001.fits_hdu0,0.7228008563701923,38.97266483306885,0.5288991928100586,0.7646298922025241,0.4440128803253174,0.30240345001220703,0.7561227651742789,0.8938534259796143,0.662376880645752,0.7517606295072116,0.13510465621948242,0.10557126998901367,0.8046292818509615,0.0588533878326416,0.04409217834472656
+./data/LCO/coj0m403.fits_hdu0,0.6845067483669605,62.80998182296753,0.7949681282043457,0.7279621187963822,0.8048989772796631,0.518756628036499,0.7265686903312462,1.2398710250854492,1.0729033946990967,0.7221424525065708,0.25426721572875977,0.22037935256958008,0.763859698709029,0.106903076171875,0.07623457908630371
+./data/LCO/lsc0m409.fits_hdu0,0.6610539990143784,51.19105768203735,0.685356855392456,0.7128655617076376,0.7473225593566895,0.4433445930480957,0.7006576245555041,1.1074719429016113,0.9230437278747559,0.7085028880160018,0.21909785270690918,0.1813819408416748,0.7456735623937075,0.086578369140625,0.06737279891967773
+./data/LCO/cpt0m407.fits_hdu0,0.6880637730944651,49.86274790763855,0.8803353309631348,0.7368511324984539,0.791924238204956,0.5607116222381592,0.7296890702303649,1.196800708770752,1.13450288772583,0.7261818701202072,0.23211431503295898,0.18094325065612793,0.7707059030998763,0.08548331260681152,0.06668448448181152
+./data/LCO/ogg0m404.fits_hdu0,0.7123647488114564,57.634791135787964,0.5582947731018066,0.7629273744298856,0.6791362762451172,0.45233964920043945,0.7612617223736086,1.3741166591644287,1.0464346408843994,0.7506706695462276,0.20768284797668457,0.16320323944091797,0.797979143717146,0.09054827690124512,0.07115888595581055
+./data/LCO/coj2m002.fits_hdu0,0.48990149864783655,36.07459783554077,0.4525723457336426,0.5265641432542068,0.38028979301452637,0.2625997066497803,0.5202474740835337,0.5612707138061523,0.46169424057006836,0.5321988619290865,0.11610913276672363,0.09262537956237793,0.5791156475360577,0.05453372001647949,0.04210400581359863
+./data/LCO/coj0m405.fits_hdu0,0.7249306682900433,62.591997146606445,1.0271177291870117,0.772575383136209,0.8154196739196777,0.5006818771362305,0.7651275087449753,1.2067930698394775,1.1785037517547607,0.758441196080705,0.2289750576019287,0.17831897735595703,0.8085132756938003,0.0945277214050293,0.07159924507141113
+./data/LCO/lsc0m412.fits_hdu0,0.695435702690167,60.37032628059387,0.762854814529419,0.7426116131822047,0.6952474117279053,0.5152549743652344,0.7424180520929963,1.1946804523468018,1.1025896072387695,0.7327258051658163,0.4584157466888428,0.2643771171569824,0.7762316041473407,0.17229866981506348,0.16986966133117676
+./data/LCO/elp0m411.fits_hdu0,0.6570708983167131,49.1509850025177,0.6957509517669678,0.7090616786487323,0.6933269500732422,0.4518740177154541,0.706205973156308,1.1690149307250977,0.9747042655944824,0.7069195220701917,0.23010897636413574,0.1935582160949707,0.7434930559581787,0.0862727165222168,0.06721353530883789