File size: 3,406 Bytes
2310c93 | 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import pandas as pd
from huggingface_hub import hf_hub_url
import datasets
import os
_VERSION = datasets.Version("0.0.1")
_DESCRIPTION = "TODO"
_HOMEPAGE = "TODO"
_LICENSE = "TODO"
_CITATION = "TODO"
_FEATURES = datasets.Features(
{
"flawless": datasets.Image(),
"mask": datasets.Image(),
"reference": datasets.Image(),
"prompt": datasets.Value("string"),
},
)
METADATA_URL = hf_hub_url(
"NegarMov/DF_segmented_mask",
filename="train.jsonl",
repo_type="dataset",
)
FLAWLESS_URL = hf_hub_url(
"NegarMov/DF_segmented_mask",
filename="flawless.zip",
repo_type="dataset",
)
MASK_URL = hf_hub_url(
"NegarMov/DF_segmented_mask",
filename="mask.zip",
repo_type="dataset",
)
reference_URL = hf_hub_url(
"NegarMov/DF_segmented_mask",
filename="reference.zip",
repo_type="dataset",
)
_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
class DF_segmented_mask(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [_DEFAULT_CONFIG]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
metadata_path = dl_manager.download(METADATA_URL)
flawless_dir = dl_manager.download_and_extract(
FLAWLESS_URL
)
mask_dir = dl_manager.download_and_extract(
MASK_URL
)
reference_dir = dl_manager.download_and_extract(
reference_URL
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"metadata_path": metadata_path,
"flawless_dir": flawless_dir,
"mask_dir": mask_dir,
"reference_dir": reference_dir,
},
),
]
def _generate_examples(self, metadata_path, flawless_dir, mask_dir, reference_dir):
metadata = pd.read_json(metadata_path, lines=True)
for _, row in metadata.iterrows():
prompt = row["prompt"]
flawless_path = row["flawless"]
flawless_path = os.path.join(flawless_dir, flawless_path)
flawless = open(flawless_path, "rb").read()
mask_path = row["mask"]
mask_path = os.path.join(
mask_dir, row["mask"]
)
mask = open(mask_path, "rb").read()
reference_path = row["reference"]
reference_path = os.path.join(
reference_dir, row["reference"]
)
reference = open(reference_path, "rb").read()
yield row["flawless"], {
"prompt": prompt,
"flawless": {
"path": flawless_path,
"bytes": flawless,
},
"mask": {
"path": mask_path,
"bytes": mask,
},
"reference": {
"path": reference_path,
"bytes": reference,
},
}
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