Create quickdraw.py

quickdraw.py

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+"""Quick, Draw! Data Set"""
+
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_CITATION = """\
+@article{DBLP:journals/corr/HaE17,
+  author    = {David Ha and
+               Douglas Eck},
+  title     = {A Neural Representation of Sketch Drawings},
+  journal   = {CoRR},
+  volume    = {abs/1704.03477},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1704.03477},
+  archivePrefix = {arXiv},
+  eprint    = {1704.03477},
+  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/HaE17},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!.
+"""
+
+_URL = "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/"
+_CLASSES = [
+    "aircraft carrier",
+    "airplane",
+    "alarm clock",
+    "ambulance",
+    "angel",
+    "animal migration",
+    "ant",
+    "anvil",
+    "apple",
+    "arm",
+    "asparagus",
+    "axe",
+    "backpack",
+    "banana",
+    "bandage",
+    "barn",
+    "baseball",
+    "baseball bat",
+    "basket",
+    "basketball",
+    "bat",
+    "bathtub",
+    "beach",
+    "bear",
+    "beard",
+    "bed",
+    "bee",
+    "belt",
+    "bench",
+    "bicycle",
+    "binoculars",
+    "bird",
+    "birthday cake",
+    "blackberry",
+    "blueberry",
+    "book",
+    "boomerang",
+    "bottlecap",
+    "bowtie",
+    "bracelet",
+    "brain",
+    "bread",
+    "bridge",
+    "broccoli",
+    "broom",
+    "bucket",
+    "bulldozer",
+    "bus",
+    "bush",
+    "butterfly",
+    "cactus",
+    "cake",
+    "calculator",
+    "calendar",
+    "camel",
+    "camera",
+    "camouflage",
+    "campfire",
+    "candle",
+    "cannon",
+    "canoe",
+    "car",
+    "carrot",
+    "castle",
+    "cat",
+    "ceiling fan",
+    "cello",
+    "cell phone",
+    "chair",
+    "chandelier",
+    "church",
+    "circle",
+    "clarinet",
+    "clock",
+    "cloud",
+    "coffee cup",
+    "compass",
+    "computer",
+    "cookie",
+    "cooler",
+    "couch",
+    "cow",
+    "crab",
+    "crayon",
+    "crocodile",
+    "crown",
+    "cruise ship",
+    "cup",
+    "diamond",
+    "dishwasher",
+    "diving board",
+    "dog",
+    "dolphin",
+    "donut",
+    "door",
+    "dragon",
+    "dresser",
+    "drill",
+    "drums",
+    "duck",
+    "dumbbell",
+    "ear",
+    "elbow",
+    "elephant",
+    "envelope",
+    "eraser",
+    "eye",
+    "eyeglasses",
+    "face",
+    "fan",
+    "feather",
+    "fence",
+    "finger",
+    "fire hydrant",
+    "fireplace",
+    "firetruck",
+    "fish",
+    "flamingo",
+    "flashlight",
+    "flip flops",
+    "floor lamp",
+    "flower",
+    "flying saucer",
+    "foot",
+    "fork",
+    "frog",
+    "frying pan",
+    "garden",
+    "garden hose",
+    "giraffe",
+    "goatee",
+    "golf club",
+    "grapes",
+    "grass",
+    "guitar",
+    "hamburger",
+    "hammer",
+    "hand",
+    "harp",
+    "hat",
+    "headphones",
+    "hedgehog",
+    "helicopter",
+    "helmet",
+    "hexagon",
+    "hockey puck",
+    "hockey stick",
+    "horse",
+    "hospital",
+    "hot air balloon",
+    "hot dog",
+    "hot tub",
+    "hourglass",
+    "house",
+    "house plant",
+    "hurricane",
+    "ice cream",
+    "jacket",
+    "jail",
+    "kangaroo",
+    "key",
+    "keyboard",
+    "knee",
+    "knife",
+    "ladder",
+    "lantern",
+    "laptop",
+    "leaf",
+    "leg",
+    "light bulb",
+    "lighter",
+    "lighthouse",
+    "lightning",
+    "line",
+    "lion",
+    "lipstick",
+    "lobster",
+    "lollipop",
+    "mailbox",
+    "map",
+    "marker",
+    "matches",
+    "megaphone",
+    "mermaid",
+    "microphone",
+    "microwave",
+    "monkey",
+    "moon",
+    "mosquito",
+    "motorbike",
+    "mountain",
+    "mouse",
+    "moustache",
+    "mouth",
+    "mug",
+    "mushroom",
+    "nail",
+    "necklace",
+    "nose",
+    "ocean",
+    "octagon",
+    "octopus",
+    "onion",
+    "oven",
+    "owl",
+    "paintbrush",
+    "paint can",
+    "palm tree",
+    "panda",
+    "pants",
+    "paper clip",
+    "parachute",
+    "parrot",
+    "passport",
+    "peanut",
+    "pear",
+    "peas",
+    "pencil",
+    "penguin",
+    "piano",
+    "pickup truck",
+    "picture frame",
+    "pig",
+    "pillow",
+    "pineapple",
+    "pizza",
+    "pliers",
+    "police car",
+    "pond",
+    "pool",
+    "popsicle",
+    "postcard",
+    "potato",
+    "power outlet",
+    "purse",
+    "rabbit",
+    "raccoon",
+    "radio",
+    "rain",
+    "rainbow",
+    "rake",
+    "remote control",
+    "rhinoceros",
+    "rifle",
+    "river",
+    "roller coaster",
+    "rollerskates",
+    "sailboat",
+    "sandwich",
+    "saw",
+    "saxophone",
+    "school bus",
+    "scissors",
+    "scorpion",
+    "screwdriver",
+    "sea turtle",
+    "see saw",
+    "shark",
+    "sheep",
+    "shoe",
+    "shorts",
+    "shovel",
+    "sink",
+    "skateboard",
+    "skull",
+    "skyscraper",
+    "sleeping bag",
+    "smiley face",
+    "snail",
+    "snake",
+    "snorkel",
+    "snowflake",
+    "snowman",
+    "soccer ball",
+    "sock",
+    "speedboat",
+    "spider",
+    "spoon",
+    "spreadsheet",
+    "square",
+    "squiggle",
+    "squirrel",
+    "stairs",
+    "star",
+    "steak",
+    "stereo",
+    "stethoscope",
+    "stitches",
+    "stop sign",
+    "stove",
+    "strawberry",
+    "streetlight",
+    "string bean",
+    "submarine",
+    "suitcase",
+    "sun",
+    "swan",
+    "sweater",
+    "swing set",
+    "sword",
+    "syringe",
+    "table",
+    "teapot",
+    "teddy-bear",
+    "telephone",
+    "television",
+    "tennis racquet",
+    "tent",
+    "The Eiffel Tower",
+    "The Great Wall of China",
+    "The Mona Lisa",
+    "tiger",
+    "toaster",
+    "toe",
+    "toilet",
+    "tooth",
+    "toothbrush",
+    "toothpaste",
+    "tornado",
+    "tractor",
+    "traffic light",
+    "train",
+    "tree",
+    "triangle",
+    "trombone",
+    "truck",
+    "trumpet",
+    "t-shirt",
+    "umbrella",
+    "underwear",
+    "van",
+    "vase",
+    "violin",
+    "washing machine",
+    "watermelon",
+    "waterslide",
+    "whale",
+    "wheel",
+    "windmill",
+    "wine bottle",
+    "wine glass",
+    "wristwatch",
+    "yoga",
+    "zebra",
+    "zigzag",
+]
+
+
+class QuickDraw(datasets.GeneratorBasedBuilder):
+    """QuickDraw Data Set"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="quickdraw",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_CLASSES),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/googlecreativelab/quickdraw-dataset",
+            citation=_CITATION,
+            task_templates=[
+                ImageClassification(
+                    image_column="image",
+                    label_column="label",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = {c: _URL + c + ".npy" for c in _CLASSES}
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": [downloaded_files[c] for c in _CLASSES],
+                    "labels": _CLASSES
+                }
+            )
+        ]
+
+    def _generate_examples(self, filepaths, labels):
+        """This function returns the examples in the raw form."""
+
+        for filepath, label in zip(filepaths, labels):
+            data = np.load(filepath, mmap_mode='r')
+
+            for i, ex in enumerate(data):
+                yield i, {"image": ex.reshape(28, 28), "label": label}