Update imagenette.py

imagenette.py

@@ -1,4 +1,5 @@
 import os
+import json
 from pathlib import Path
 
 import datasets
@@ -15,117 +16,161 @@ _CITATION = """
 """
 
 _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.
+# ImageNette
+
+Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+'Imagenette' is pronounced just like 'Imagenet', except with a corny inauthentic French accent.
+If you've seen Peter Sellars in The Pink Panther, then think something like that.
+It's important to ham up the accent as much as possible, otherwise people might not be sure whether you're refering to "Imagenette" or "Imagenet".
+(Note to native French speakers: to avoid confusion, be sure to use a corny inauthentic American accent when saying "Imagenet".
+Think something like the philosophy restaurant skit from Monty Python's The Meaning of Life.)
+
 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).
+
+The '320 px' and '160 px' versions have their shortest side resized to that size, with their aspect ratio maintained.
+
+
+Too easy for you? In that case, you might want to try Imagewoof.
+
+# Imagewoof
+Imagewoof is a subset of 10 classes from Imagenet that aren't so easy to classify, since they're all dog breeds.
+The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog.
+(No we will not enter in to any discussion in to whether a dingo is in fact a dog.
+Any suggestions to the contrary are un-Australian. Thank you for your cooperation.)
+
+Full size download;
+320 px download;
+160 px download.
 """
 
-_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"
-]
+_LABELS = {
+    "imagenette": [
+        "cassette_player",
+        "chain_saw",
+        "church",
+        "English_springer",
+        "French_horn",
+        "garbage_truck",
+        "gas_pump",
+        "golf_ball",
+        "parachute",
+        "tench",
+    ],
+    "imagewoof": [
+        "Australian_terrier",
+        "beagle",
+        "Border_terrier",
+        "dingo",
+        "English_foxhound",
+        "golden_retriever",
+        "Old_English_sheepdog",
+        "Rhodesian_ridgeback",
+        "Samoyed",
+        "Shih-Tzu",
+    ],
+}
+
+
+_NAME_TO_DIR = {
+    "imagenette-full-res": "imagenette2",
+    "imagenette-320px": "imagenette2-320",
+    "imagenette-160px": "imagenette2-160",
+    "imagewoof-full-res": "imagewoof2",
+    "imagewoof-320px": "imagewoof2-320",
+    "imagewoof-160px": "imagewoof2-160",
+}
+
 
 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]
+    """BuilderConfig for Imagenette."""
+
+    def __init__(self, name, **kwargs):
+        super(ImagenetteConfig, self).__init__(
+            name=name, description="{} version.".format(name), **kwargs
+        )
+
+        self.dataset = name.split("-")[0]
+        self.labels = _LABELS[self.dataset]
+        self.name = name
+
+        with open("imagenet_refs.json", "r") as f:
+            self.imagenet_refs = json.load(f)
+            self.ref_to_labels = {}
 
 
 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
+    return [ImagenetteConfig(name) for name in _NAME_TO_DIR]
 
 
 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_column="image_file_path", 
-                label_column="labels",            
+    """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": datasets.Image(),
+                    "labels": datasets.ClassLabel(names=self.config.labels),
+                }
+            ),
+            supervised_keys=("path", "labels"),
+            homepage="https://github.com/fastai/imagenette",
+            citation=_CITATION,
+            task_templates=[
+                ImageClassification(
+                    image_column="path",
+                    label_column="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
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        print(self.__dict__.keys())
+        print(self.config)
+        name = self.config.name
+        dirname = _NAME_TO_DIR[name]
+        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."""
+        imagenet_refs = self.config.imagenet_refs
+        for path in Path(datapath).glob("**/*.JPEG"):
+            record = {
+                # In Imagenette, the parent folder of the file is
+                # the imagenet reference to the label name.
+                "image": str(path),
+                "labels": imagenet_refs[path.parent.name],
+            }
+            yield path.name, record