File size: 3,414 Bytes
4e11427 6f96ffc 4e11427 6f96ffc c11db92 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 6f96ffc 4e11427 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
# 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
_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(_DATA_URL)
mask_archives = dl_manager.download(_MASK_DATA_URL)
return [
datasets.SplitGenerator(
name="SMD",
gen_kwargs={
"archives": [dl_manager.iter_archive(archive) for archive in archives["smd"]],
"mask_archives": [dl_manager.iter_archive(archive) for archive in mask_archives["smd_masks"]],
},
),
]
def _generate_examples(self, archives, mask_archives):
"""Yields examples."""
idx = 0
for archive, mask_archive in zip(archives, mask_archives):
mask_files = {path: np.load(file) for path, file in mask_archive if path.endswith(".npy")}
for path, file in archive:
if path.endswith(".png"):
synset_id = os.path.basename(os.path.dirname(path))
label = IMAGENET2012_CLASSES[synset_id]
mask_file_path = path.replace(".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 |