read csv only once (#4)
Browse files- only reading the csv once (4f9da14cb7b8a95b227b76886244d3f5fc277a11)
- TID2008.py +17 -18
TID2008.py
CHANGED
@@ -47,34 +47,34 @@ class TID2008(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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data_path = dl_manager.download("data.zip")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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-
"
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"split": "train",
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},
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)
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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,
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reference_paths = (
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df["Reference"]
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.apply(lambda x: os.path.join(data, "reference_images", x))
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.to_list()
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)
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distorted_paths = (
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df["Distorted"]
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.apply(lambda x: os.path.join(data, "distorted_images", x))
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.to_list()
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)
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for key, (ref, dist, m) in enumerate(
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zip(reference_paths, distorted_paths, df["MOS"])
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):
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yield (
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key,
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{
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@@ -83,4 +83,3 @@ class TID2008(datasets.GeneratorBasedBuilder):
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"mos": m,
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},
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)
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-
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def _split_generators(self, dl_manager):
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data_path = dl_manager.download("data.zip")
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+
data_path = dl_manager.download_and_extract(data_path)
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df = pd.read_csv(os.path.join(data_path, "image_pairs_mos.csv"), index_col=0)
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reference_paths = (
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df["Reference"]
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.apply(lambda x: os.path.join(data_path, "reference_images", x))
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.to_list()
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)
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distorted_paths = (
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df["Distorted"]
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.apply(lambda x: os.path.join(data_path, "distorted_images", x))
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.to_list()
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"reference": reference_paths,
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"distorted": distorted_paths,
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"mos": df["MOS"],
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"split": "train",
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},
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)
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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, reference, distorted, mos, split):
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for key, (ref, dist, m) in enumerate(zip(reference, distorted, mos)):
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yield (
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key,
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{
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"mos": m,
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},
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)
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