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test_repo.py

@@ -1,7 +1,6 @@
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
-# TODO: Address all TODOs and remove all explanatory comments
 """TODO: Add a description here."""
 
 import json
@@ -11,9 +10,6 @@ import PIL.Image
 import datasets
 import numpy as np
 
-for _ in range(10):
-    print("LOADING SCRIPT")
-
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -37,28 +33,20 @@ _HOMEPAGE = ""
 # TODO: Add the licence for the dataset here if you can find it
 _LICENSE = ""
 
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
     "8x8": [
+        # Download the original images from the original repo
         "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
+        # Maks and metadata from the current
         "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
         "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
     ]
 }
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class PatchyImagenet(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
     VERSION = datasets.Version("0.0.1")
 
-    # 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.
-
     BUILDER_CONFIGS = [
         # datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
         datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
@@ -74,12 +62,14 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
                 "image": datasets.Image(),
                 "patches": datasets.Features(
                     {
+                        # This would be best but there are too many classes
                         # "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
-                        "categories": datasets.Value("string"),
+                        "categories": datasets.Sequence(datasets.Value("string")),
                         "scores": datasets.Sequence(datasets.Value("float32")),
                         "mask": datasets.Sequence(
                             datasets.Array2D(shape=(224 // 8, 224 // 8), dtype="bool")
                         ),
+                        # Array2D is a bit annoying to use, otherwise use this
                         # "mask": datasets.Sequence(datasets.Image()),
                     }
                 ),
@@ -87,30 +77,19 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
         )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
             features=features,
-            # Homepage of the dataset for documentation
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     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
         url_templates = _URLS[self.config.name]
 
         split_kwargs = {}
         for split in ["train", "test", "val"]:
             urls = [url.format(split=split) for url in url_templates]
             image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
-            # breakpoint()
             split_kwargs[split] = {
                 "meta_path": metadata_file,
                 "image_dir": image_dir, "mask_dir": mask_dir,
@@ -123,25 +102,20 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, meta_path, image_dir, mask_dir, 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(meta_path, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)
                 image_path = os.path.join(image_dir, "images", f"{split}_images", data["file_name"])
                 sample_name, _extension = os.path.splitext(data["file_name"])
                 mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
-                # mask = np.load(mask_file).astype(bool)
-                mask = np.load(mask_file).astype(np.uint8)
-                # breakpoint()
-                pil_image = PIL.Image.open(image_path)
+                mask = np.load(mask_file).astype(bool)
+                # mask = np.load(mask_file).astype(np.uint8)
                 yield key, {
-                    "image": pil_image,
+                    "image": PIL.Image.open(image_path),
                     "patches": {
                         "categories": data["patches"]["categories"],
                         "scores": data["patches"]["scores"],
-                        "mask": list(mask),
+                        "mask": mask,
                     }
                 }