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# This script was modified from the imagenet-1k HF dataset repo: https://huggingface.co/datasets/imagenet-1k
import os
import numpy as np
import datasets
from datasets.tasks import ImageClassification
from .classes import IMAGENET2012_CLASSES
from io import BytesIO
_CITATION = """\
@article{BibTeX
}
"""
_HOMEPAGE = "https://arielnlee.github.io/PatchMixing/"
_DESCRIPTION = """\
SMD is an occluded ImageNet-1K validation set, created to be an additional way to evaluate the impact of occlusion on model performance. This experiment used a variety of occluder objects that are not in the ImageNet-1K label space and are unambiguous in relationship to objects that reside in the label space.
"""
_DATA_URL = {
"smd": [
f"https://huggingface.co/datasets/ariellee/Superimposed-Masked-Dataset/resolve/main/smd_{i}.tar.gz"
for i in range(1, 41)
]
}
_MASK_DATA_URL = {
"smd_masks": [
f"https://huggingface.co/datasets/ariellee/Superimposed-Masked-Dataset/resolve/main/SMD_masks.tar.gz"
]
}
class SMD(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_WRITER_BATCH_SIZE = 1000
def _info(self):
assert len(IMAGENET2012_CLASSES) == 1000
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=list(IMAGENET2012_CLASSES.values())),
"segmentation": datasets.Sequence(datasets.Array2D(shape=(None, None), dtype="float32"))
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archives = dl_manager.download_and_extract(_DATA_URL)
mask_archives = dl_manager.download_and_extract(_MASK_DATA_URL)
return [
datasets.SplitGenerator(
name="SMD",
gen_kwargs={
"archives": archives["smd"],
"mask_archives": mask_archives["smd_masks"],
},
),
]
def _generate_examples(self, archives, mask_archives):
"""Yields examples."""
idx = 0
mask_files = {}
for mask_archive in mask_archives:
for path, file in dl_manager.iter_archive(mask_archive):
if path.endswith(".npy"):
mask_files[path] = np.load(BytesIO(file.read()))
for archive in archives:
for path, file in dl_manager.iter_archive(archive):
if path.endswith(".png"):
synset_id = os.path.basename(os.path.dirname(path))
label = IMAGENET2012_CLASSES[synset_id]
mask_file_path = path.replace("_occluded.png", "_mask.npy")
segmentation_mask = mask_files.get(mask_file_path, None)
if segmentation_mask is not None:
ex = {
"image": {"path": path, "bytes": file.read()},
"label": label,
"segmentation": segmentation_mask.tolist() # Convert numpy array to list
}
yield idx, ex
idx += 1
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