Upload 4 files
Browse files- data.zip +3 -0
- my_dataset.py +103 -0
- train.txt +7070 -0
- validation.txt +3030 -0
data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3818336252c0b6f722ec0c1899b8bbe65bd66b8620cc6da27d7d7eab16129b1
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size 1500352279
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my_dataset.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from os.path import (
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basename
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, listdir
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)
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import random
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import scipy.io
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# from datasets import (
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# GeneratorBasedBuilder
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# , DatasetInfo
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# , Features
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# , ImageClassification
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# , Image
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# , ClassLabel
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# )
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import datasets
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_HOMEPAGE = ""
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_CITATION = ""
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_DESCRIPTION = ""
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_LICENSE = ""
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_IMAGES_DIR = "images"
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_ANNOTATIONS_DIR = "annotations"
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_METADATA_URLS = [
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"train.txt"
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, "validation.txt"
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]
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_BASE_URL = "data"
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class MultiMMOD2(datasets.GeneratorBasedBuilder):
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"""Food-101 Images dataset"""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"image": datasets.Image(),
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"label": datasets.Array2D() # datasets.ClassLabel(names=_NAMES),
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}
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),
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supervised_keys=("image", "label"),
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homepage=_HOMEPAGE,
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citation=_CITATION,
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license=_LICENSE,
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task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
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)
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def _split_generators(self, dl_manager):
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archive_path = dl_manager.download(_BASE_URL)
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split_metadata_paths = dl_manager.download(_METADATA_URLS)
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data_dir = dl_manager.download_and_extract(_BASE_URL)
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with open(split_metadata_paths["train"], encoding="utf-8") as f:
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train_files = set(f.read().split("\n"))
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with open(split_metadata_paths["validation"], encoding="utf-8") as f:
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validation_files = set(f.read().split("\n"))
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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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"split_key": "train",
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"images": train_files,
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"metadata_path": split_metadata_paths["train"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"split_key": "validation",
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"images": validation_files,
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"metadata_path": split_metadata_paths["test"],
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},
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),
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]
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def _generate_examples(self, images, metadata_path):
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"""Generate images and labels for splits."""
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with open(metadata_path, encoding="utf-8") as f:
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files_to_keep = set(f.read().split("\n"))
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for file_path, file_obj in images:
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# get file name without extension
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file_basename = basename(file_path)[: -len(".jpg")]
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if file_basename in files_to_keep:
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annotation_file = f"{_ANNOTATIONS_DIR}/{file_basename}.mat"
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annotation = scipy.io.loadmat(f"{_ANNOTATIONS_DIR}/{file_basename}.mat")["annotations"][0][0][0]
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label = file_path.split("/")[2]
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yield file_path, {
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"image": {"path": file_path, "bytes": file_obj.read()},
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"label": annotation, # { "path": annotation_file, "bytes": annotation},
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}
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train.txt
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|
validation.txt
ADDED
|
@@ -0,0 +1,3030 @@
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