format

Caltech-101.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# Copyright 2022 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.
@@ -11,18 +11,14 @@
 # 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."""
+"""Caltech 101 loading script"""
 
 
-import csv
-import json
-import os
 from pathlib import Path
 
 import datasets
-from datasets.tasks import ImageClassification
 import numpy as np
+from datasets.tasks import ImageClassification
 
 _CITATION = """\
 @article{FeiFei2004LearningGV,
@@ -180,31 +176,35 @@ class Caltech101(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         data_root_dir = dl_manager.download_and_extract(_DATA_URL)
-        compress_folder_path = [file for file in dl_manager.iter_files(data_root_dir) if Path(file).name == "101_ObjectCategories.tar.gz"][0]
+        compress_folder_path = [
+            file
+            for file in dl_manager.iter_files(data_root_dir)
+            if Path(file).name == "101_ObjectCategories.tar.gz"
+        ][0]
         data_dir = dl_manager.extract(compress_folder_path)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": data_dir,  # TODO: change accordingly
+                    "filepath": data_dir,
                     "split": "train",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": data_dir,  # TODO: change accordingly
+                    "filepath": data_dir,
                     "split": "test",
                 },
             ),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        # Same stratagy as the one proposed in TF datasets 
-        is_train_split = (split == "train")
+        # Same stratagy as the one proposed in TF datasets: 30 random examples from each class are added to the train
+        # split, and the remainder are added to the test split. 
+        # Source: https://github.com/tensorflow/datasets/blob/1106d587f97c4fca68c5b593dc7dc48c790ffa8c/tensorflow_datasets/image_classification/caltech.py#L88-L140
+
+        is_train_split = split == "train"
         data_dir = Path(filepath) / "101_ObjectCategories"
         # Sets random seed so the random partitioning of files is the same when
         # called for the train and test splits.
@@ -212,14 +212,22 @@ class Caltech101(datasets.GeneratorBasedBuilder):
         np.random.seed(1234)
 
         for class_dir in data_dir.iterdir():
-            fnames = [image_path for image_path in class_dir.iterdir() if image_path.name.endswith(".jpg")]
+            fnames = [
+                image_path
+                for image_path in class_dir.iterdir()
+                if image_path.name.endswith(".jpg")
+            ]
             # _TRAIN_POINTS_PER_CLASS datapoints are sampled for the train split,
             # the others constitute the test split.
             if _TRAIN_POINTS_PER_CLASS > len(fnames):
-                raise ValueError("Fewer than {} ({}) points in class {}".format(
-                    _TRAIN_POINTS_PER_CLASS, len(fnames), class_dir.name))
+                raise ValueError(
+                    "Fewer than {} ({}) points in class {}".format(
+                        _TRAIN_POINTS_PER_CLASS, len(fnames), class_dir.name
+                    )
+                )
             train_fnames = np.random.choice(
-                fnames, _TRAIN_POINTS_PER_CLASS, replace=False)
+                fnames, _TRAIN_POINTS_PER_CLASS, replace=False
+            )
             test_fnames = set(fnames).difference(train_fnames)
             fnames_to_emit = train_fnames if is_train_split else test_fnames