Convert dataset to Parquet (#6)

- Convert dataset to Parquet (e025e6419806783509e14ac5648d777c9de2df0f)
- Delete loading script (3c990e07c1af87bb8db3439efce68199575ea40f)
- Delete legacy dataset_infos.json (2b98fdb5ca486858b980072b9b45e02115f5f3d5)

README.md

@@ -19,6 +19,7 @@ task_ids: []
 paperswithcode_id: cifar-10
 pretty_name: Cifar10
 dataset_info:
+  config_name: plain_text
   features:
   - name: img
     dtype: image
@@ -36,16 +37,23 @@ dataset_info:
           '7': horse
           '8': ship
           '9': truck
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 113854600
+    num_bytes: 113648310.0
     num_examples: 50000
   - name: test
-    num_bytes: 22772838
+    num_bytes: 22731580.0
     num_examples: 10000
-  download_size: 170498071
-  dataset_size: 136627438
+  download_size: 143646105
+  dataset_size: 136379890.0
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  - split: test
+    path: plain_text/test-*
+  default: true
 ---
 
 # Dataset Card for CIFAR-10

cifar10.py

@@ -1,121 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""CIFAR-10 Data Set"""
-
-
-import pickle
-
-import numpy as np
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_CITATION = """\
-@TECHREPORT{Krizhevsky09learningmultiple,
-    author = {Alex Krizhevsky},
-    title = {Learning multiple layers of features from tiny images},
-    institution = {},
-    year = {2009}
-}
-"""
-
-_DESCRIPTION = """\
-The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
-per class. There are 50000 training images and 10000 test images.
-"""
-
-_DATA_URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
-
-_NAMES = [
-    "airplane",
-    "automobile",
-    "bird",
-    "cat",
-    "deer",
-    "dog",
-    "frog",
-    "horse",
-    "ship",
-    "truck",
-]
-
-
-class Cifar10(datasets.GeneratorBasedBuilder):
-    """CIFAR-10 Data Set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of CIFAR-10 Data Set",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "img": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES),
-                }
-            ),
-            supervised_keys=("img", "label"),
-            homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
-            citation=_CITATION,
-            task_templates=ImageClassification(image_column="img", label_column="label"),
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_DATA_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
-            ),
-        ]
-
-    def _generate_examples(self, files, split):
-        """This function returns the examples in the raw (text) form."""
-
-        if split == "train":
-            batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
-
-        if split == "test":
-            batches = ["test_batch"]
-        batches = [f"cifar-10-batches-py/{filename}" for filename in batches]
-
-        for path, fo in files:
-
-            if path in batches:
-                dict = pickle.load(fo, encoding="bytes")
-
-                labels = dict[b"labels"]
-                images = dict[b"data"]
-
-                for idx, _ in enumerate(images):
-
-                    img_reshaped = np.transpose(np.reshape(images[idx], (3, 32, 32)), (1, 2, 0))
-
-                    yield f"{path}_{idx}", {
-                        "img": img_reshaped,
-                        "label": labels[idx],
-                    }

dataset_infos.json

@@ -1 +0,0 @@
-{"plain_text": {"description": "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images\nper class. There are 50000 training images and 10000 test images.\n", "citation": "@TECHREPORT{Krizhevsky09learningmultiple,\n    author = {Alex Krizhevsky},\n    title = {Learning multiple layers of features from tiny images},\n    institution = {},\n    year = {2009}\n}\n", "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html", "license": "", "features": {"img": {"id": null, "_type": "Image"}, "label": {"num_classes": 10, "names": ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "img", "output": "label"}, "task_templates": [{"task": "image-classification", "image_column": "img", "label_column": "label", "labels": ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]}], "builder_name": "cifar10", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 113854600, "num_examples": 50000, "dataset_name": "cifar10"}, "test": {"name": "test", "num_bytes": 22772838, "num_examples": 10000, "dataset_name": "cifar10"}}, "download_checksums": {"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz": {"num_bytes": 170498071, "checksum": "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"}}, "download_size": 170498071, "post_processing_size": null, "dataset_size": 136627438, "size_in_bytes": 307125509}}

plain_text/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:841389e6f2d64f28bf17310e430aebac20ec3ba611a3c5e231dc93c645ce84de
+size 23940850

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8428b53a88a11ac374111006708df51469e315a22ac6d66470afd9c78d2ae883
+size 119705255