add loading script

Caltech-101.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+_CITATION = """\
+@article{FeiFei2004LearningGV,
+  title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
+  author={Li Fei-Fei and Rob Fergus and Pietro Perona},
+  journal={Computer Vision and Pattern Recognition Workshop},
+  year={2004},
+}
+"""
+
+_DESCRIPTION = """\
+Pictures of objects belonging to 101 categories. 
+About 40 to 800 images per category. 
+Most categories have about 50 images. 
+Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. 
+The size of each image is roughly 300 x 200 pixels. 
+"""
+
+_HOMEPAGE = "https://data.caltech.edu/records/20086"
+
+_LICENSE = "CC BY 4.0"
+
+_DATA_URL = "brand_new_data/caltech-101.zip"
+# _DATA_URL = "brand_new_data/caltech-101/101_ObjectCategories.tar.gz"
+
+_NAMES = [
+    "accordion",
+    "airplanes",
+    "anchor",
+    "ant",
+    "background_google",
+    "barrel",
+    "bass",
+    "beaver",
+    "binocular",
+    "bonsai",
+    "brain",
+    "brontosaurus",
+    "buddha",
+    "butterfly",
+    "camera",
+    "cannon",
+    "car_side",
+    "ceiling_fan",
+    "cellphone",
+    "chair",
+    "chandelier",
+    "cougar_body",
+    "cougar_face",
+    "crab",
+    "crayfish",
+    "crocodile",
+    "crocodile_head",
+    "cup",
+    "dalmatian",
+    "dollar_bill",
+    "dolphin",
+    "dragonfly",
+    "electric_guitar",
+    "elephant",
+    "emu",
+    "euphonium",
+    "ewer",
+    "faces",
+    "faces_easy",
+    "ferry",
+    "flamingo",
+    "flamingo_head",
+    "garfield",
+    "gerenuk",
+    "gramophone",
+    "grand_piano",
+    "hawksbill",
+    "headphone",
+    "hedgehog",
+    "helicopter",
+    "ibis",
+    "inline_skate",
+    "joshua_tree",
+    "kangaroo",
+    "ketch",
+    "lamp",
+    "laptop",
+    "leopards",
+    "llama",
+    "lobster",
+    "lotus",
+    "mandolin",
+    "mayfly",
+    "menorah",
+    "metronome",
+    "minaret",
+    "motorbikes",
+    "nautilus",
+    "octopus",
+    "okapi",
+    "pagoda",
+    "panda",
+    "pigeon",
+    "pizza",
+    "platypus",
+    "pyramid",
+    "revolver",
+    "rhino",
+    "rooster",
+    "saxophone",
+    "schooner",
+    "scissors",
+    "scorpion",
+    "sea_horse",
+    "snoopy",
+    "soccer_ball",
+    "stapler",
+    "starfish",
+    "stegosaurus",
+    "stop_sign",
+    "strawberry",
+    "sunflower",
+    "tick",
+    "trilobite",
+    "umbrella",
+    "watch",
+    "water_lilly",
+    "wheelchair",
+    "wild_cat",
+    "windsor_chair",
+    "wrench",
+    "yin_yang",
+]
+
+_TRAIN_POINTS_PER_CLASS = 30
+
+
+class Caltech101(datasets.GeneratorBasedBuilder):
+    """Caltech 101 dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    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=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=ImageClassification(
+                image_column="img", label_column="label"
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        # ----- Work in progress here -----
+        data_dir = dl_manager.download_and_extract(_DATA_URL)
+        files = dl_manager.iter_files(data_dir)
+        # ---------------------------------
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,  # TODO: change accordingly
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,  # TODO: change accordingly
+                    "split": "test",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO
+        pass