manuel-delverme
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Browse files- test_repo.py +9 -35
test_repo.py
CHANGED
@@ -1,7 +1,6 @@
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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import json
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@@ -11,9 +10,6 @@ import PIL.Image
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import datasets
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import numpy as np
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for _ in range(10):
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print("LOADING SCRIPT")
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-
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@@ -37,28 +33,20 @@ _HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"8x8": [
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"https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
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"https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
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"https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
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]
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}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class PatchyImagenet(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("0.0.1")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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-
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BUILDER_CONFIGS = [
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# datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
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datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
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@@ -74,12 +62,14 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
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"image": datasets.Image(),
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"patches": datasets.Features(
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{
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# "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
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"categories": datasets.Value("string"),
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"scores": datasets.Sequence(datasets.Value("float32")),
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"mask": datasets.Sequence(
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datasets.Array2D(shape=(224 // 8, 224 // 8), dtype="bool")
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),
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# "mask": datasets.Sequence(datasets.Image()),
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}
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),
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@@ -87,30 +77,19 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features,
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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-
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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url_templates = _URLS[self.config.name]
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split_kwargs = {}
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for split in ["train", "test", "val"]:
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urls = [url.format(split=split) for url in url_templates]
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image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
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# breakpoint()
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split_kwargs[split] = {
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"meta_path": metadata_file,
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"image_dir": image_dir, "mask_dir": mask_dir,
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@@ -123,25 +102,20 @@ class PatchyImagenet(datasets.GeneratorBasedBuilder):
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, meta_path, image_dir, mask_dir, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(meta_path, encoding="utf-8") as f:
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for key, row in enumerate(f):
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data = json.loads(row)
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image_path = os.path.join(image_dir, "images", f"{split}_images", data["file_name"])
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sample_name, _extension = os.path.splitext(data["file_name"])
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mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
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mask = np.load(mask_file).astype(np.uint8)
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# breakpoint()
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pil_image = PIL.Image.open(image_path)
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yield key, {
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"image":
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"patches": {
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"categories": data["patches"]["categories"],
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"scores": data["patches"]["scores"],
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"mask":
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}
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}
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""TODO: Add a description here."""
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import json
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import datasets
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import numpy as np
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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_URLS = {
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"8x8": [
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# Download the original images from the original repo
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"https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
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# Maks and metadata from the current
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"https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
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"https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
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]
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}
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class PatchyImagenet(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.0.1")
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BUILDER_CONFIGS = [
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# datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
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datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
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"image": datasets.Image(),
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"patches": datasets.Features(
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{
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# This would be best but there are too many classes
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# "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
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"categories": datasets.Sequence(datasets.Value("string")),
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"scores": datasets.Sequence(datasets.Value("float32")),
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"mask": datasets.Sequence(
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datasets.Array2D(shape=(224 // 8, 224 // 8), dtype="bool")
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),
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# Array2D is a bit annoying to use, otherwise use this
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# "mask": datasets.Sequence(datasets.Image()),
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}
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),
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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url_templates = _URLS[self.config.name]
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split_kwargs = {}
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for split in ["train", "test", "val"]:
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urls = [url.format(split=split) for url in url_templates]
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image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
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split_kwargs[split] = {
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"meta_path": metadata_file,
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"image_dir": image_dir, "mask_dir": mask_dir,
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
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]
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def _generate_examples(self, meta_path, image_dir, mask_dir, split):
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with open(meta_path, encoding="utf-8") as f:
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for key, row in enumerate(f):
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data = json.loads(row)
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image_path = os.path.join(image_dir, "images", f"{split}_images", data["file_name"])
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sample_name, _extension = os.path.splitext(data["file_name"])
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mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
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mask = np.load(mask_file).astype(bool)
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# mask = np.load(mask_file).astype(np.uint8)
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yield key, {
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"image": PIL.Image.open(image_path),
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"patches": {
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"categories": data["patches"]["categories"],
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"scores": data["patches"]["scores"],
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"mask": mask,
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}
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}
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