Create imagenette.py

imagenette.py

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+import os
+from pathlib import Path
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_CITATION = """
+@misc{imagenette,
+  author    = "Jeremy Howard",
+  title     = "imagenette",
+  url       = "https://github.com/fastai/imagenette/"
+}
+"""
+
+_DESCRIPTION = """\
+Imagenette is a subset of 10 easily classified classes from the Imagenet
+dataset. It was originally prepared by Jeremy Howard of FastAI. The objective
+behind putting together a small version of the Imagenet dataset was mainly
+because running new ideas/algorithms/experiments on the whole Imagenet take a
+lot of time.
+This version of the dataset allows researchers/practitioners to quickly try out
+ideas and share with others. The dataset comes in three variants:
+  * Full size
+  * 320 px
+  * 160 px
+Note: The v2 config correspond to the new 70/30 train/valid split (released
+in Dec 6 2019).
+"""
+
+_LABELS_FNAME = "image_classification/imagenette_labels.txt"
+_URL_PREFIX = "https://s3.amazonaws.com/fast-ai-imageclas/"
+
+LABELS = [
+    "n01440764",
+    "n02102040",
+    "n02979186",
+    "n03000684",
+    "n03028079",
+    "n03394916",
+    "n03417042",
+    "n03425413",
+    "n03445777",
+    "n03888257"
+]
+
+class ImagenetteConfig(datasets.BuilderConfig):
+  """BuilderConfig for Imagenette."""
+
+  def __init__(self, size, base, **kwargs):
+    super(ImagenetteConfig, self).__init__(
+        # `320px-v2`,...
+        name=size + ("-v2" if base == "imagenette2" else ""),
+        description="{} variant.".format(size),
+        **kwargs)
+    # e.g. `imagenette2-320.tgz`
+    self.dirname = base + {
+        "full-size": "",
+        "320px": "-320",
+        "160px": "-160",
+    }[size]
+
+
+def _make_builder_configs():
+  configs = []
+  for base in ["imagenette2", "imagenette"]:
+    for size in ["full-size", "320px", "160px"]:
+      configs.append(ImagenetteConfig(base=base, size=size))
+  return configs
+
+
+class Imagenette(datasets.GeneratorBasedBuilder):
+  """A smaller subset of 10 easily classified classes from Imagenet."""
+
+  VERSION = datasets.Version("1.0.0")
+
+  BUILDER_CONFIGS = _make_builder_configs()
+
+  def _info(self):
+    return datasets.DatasetInfo(
+        # builder=self,
+        description=_DESCRIPTION,
+        features=datasets.Features({
+            "image_file_path": datasets.Value("string"),
+            "labels": datasets.ClassLabel(names=LABELS)
+        }),
+        supervised_keys=("image_file_path", "labels"),
+        homepage="https://github.com/fastai/imagenette",
+        citation=_CITATION,
+        task_templates=[
+            ImageClassification(
+                image_file_path_column="image_file_path", label_column="labels", labels=LABELS
+            )
+        ],
+    )
+
+  def _split_generators(self, dl_manager):
+    """Returns SplitGenerators."""
+    print(self.__dict__.keys())
+    print(self.config)
+    dirname = self.config.dirname
+    url = _URL_PREFIX + "{}.tgz".format(dirname)
+    path = dl_manager.download_and_extract(url)
+    train_path = os.path.join(path, dirname, "train")
+    val_path = os.path.join(path, dirname, "val")
+    assert os.path.exists(train_path)
+    return [
+        datasets.SplitGenerator(
+            name=datasets.Split.TRAIN,
+            gen_kwargs={
+                "datapath": train_path,
+            },
+        ),
+        datasets.SplitGenerator(
+            name=datasets.Split.VALIDATION,
+            gen_kwargs={
+                "datapath": val_path,
+            },
+        ),
+    ]
+
+  def _generate_examples(self, datapath):
+    """Yields examples."""
+    for path in Path(datapath).glob("**/*.JPEG"):
+        record = {
+            "image_file_path": str(path),
+            "labels": path.parent.name
+        }
+        yield path.name, record