add script

test_verifications.py

@@ -0,0 +1,297 @@
+"""
+Loading script for the Food Vision 199 classes dataset.
+
+See the template: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
+See the example for Food101: https://huggingface.co/datasets/food101/blob/main/food101.py 
+See another example: https://huggingface.co/datasets/davanstrien/encyclopedia_britannica/blob/main/encyclopedia_britannica.py
+"""
+
+import datasets
+import os
+import requests
+
+import pandas as pd
+
+from datasets.tasks import ImageClassification
+
+# Print datasets version
+print(f"Datasets version: {datasets.__version__}")
+
+# Set verbosity to 10
+datasets.logging.set_verbosity(10)
+print(f"Verbosity level: {datasets.logging.get_verbosity()}")
+
+_HOMEPAGE = "https://www.nutrify.app"
+_LICENSE = "TODO"
+_CITATION = "TODO"
+_DESCRIPTION = "Images of 199 food classes from the Nutrify app."
+
+# # Download class_names.txt and read it
+# url = "https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/class_names.txt"
+# r = requests.get(url, allow_redirects=True)
+# open("class_names.txt", "wb").write(r.content)
+# with open("class_names.txt", "r") as f:
+#     _NAMES = f.read().splitlines()
+
+# Create list of class names
+_NAMES = ['almond_butter',
+ 'almonds',
+ 'apple',
+ 'apricot',
+ 'asparagus',
+ 'avocado',
+ 'bacon',
+ 'bacon_and_egg_burger',
+ 'bagel',
+ 'baklava',
+ 'banana',
+ 'banana_bread',
+ 'barbecue_sauce',
+ 'beans',
+ 'beef',
+ 'beef_curry',
+ 'beef_mince',
+ 'beef_stir_fry',
+ 'beer',
+ 'beetroot',
+ 'biltong',
+ 'blackberries',
+ 'blueberries',
+ 'bok_choy',
+ 'bread',
+ 'broccoli',
+ 'broccolini',
+ 'brownie',
+ 'brussel_sprouts',
+ 'burrito',
+ 'butter',
+ 'cabbage',
+ 'calamari',
+ 'candy',
+ 'capsicum',
+ 'carrot',
+ 'cashews',
+ 'cauliflower',
+ 'celery',
+ 'cheese',
+ 'cheeseburger',
+ 'cherries',
+ 'chicken_breast',
+ 'chicken_thighs',
+ 'chicken_wings',
+ 'chilli',
+ 'chimichurri',
+ 'chocolate',
+ 'chocolate_cake',
+ 'coconut',
+ 'coffee',
+ 'coleslaw',
+ 'cookies',
+ 'coriander',
+ 'corn',
+ 'corn_chips',
+ 'cream',
+ 'croissant',
+ 'crumbed_chicken',
+ 'cucumber',
+ 'cupcake',
+ 'daikon_radish',
+ 'dates',
+ 'donuts',
+ 'dragonfruit',
+ 'eggplant',
+ 'eggs',
+ 'enoki_mushroom',
+ 'fennel',
+ 'figs',
+ 'french_toast',
+ 'fried_rice',
+ 'fries',
+ 'fruit_juice',
+ 'garlic',
+ 'garlic_bread',
+ 'ginger',
+ 'goji_berries',
+ 'granola',
+ 'grapefruit',
+ 'grapes',
+ 'green_beans',
+ 'green_onion',
+ 'guacamole',
+ 'guava',
+ 'gyoza',
+ 'ham',
+ 'honey',
+ 'hot_chocolate',
+ 'ice_coffee',
+ 'ice_cream',
+ 'iceberg_lettuce',
+ 'jerusalem_artichoke',
+ 'kale',
+ 'karaage_chicken',
+ 'kimchi',
+ 'kiwi_fruit',
+ 'lamb_chops',
+ 'leek',
+ 'lemon',
+ 'lentils',
+ 'lettuce',
+ 'lime',
+ 'mandarin',
+ 'mango',
+ 'maple_syrup',
+ 'mashed_potato',
+ 'mayonnaise',
+ 'milk',
+ 'miso_soup',
+ 'mushrooms',
+ 'nectarines',
+ 'noodles',
+ 'nuts',
+ 'olive_oil',
+ 'olives',
+ 'omelette',
+ 'onion',
+ 'orange',
+ 'orange_juice',
+ 'oysters',
+ 'pain_au_chocolat',
+ 'pancakes',
+ 'papaya',
+ 'parsley',
+ 'parsnips',
+ 'passionfruit',
+ 'pasta',
+ 'pawpaw',
+ 'peach',
+ 'pear',
+ 'peas',
+ 'pickles',
+ 'pineapple',
+ 'pizza',
+ 'plum',
+ 'pomegranate',
+ 'popcorn',
+ 'pork_belly',
+ 'pork_chop',
+ 'pork_loins',
+ 'porridge',
+ 'potato_bake',
+ 'potato_chips',
+ 'potato_scallop',
+ 'potatoes',
+ 'prawns',
+ 'pumpkin',
+ 'radish',
+ 'ramen',
+ 'raspberries',
+ 'red_onion',
+ 'red_wine',
+ 'rhubarb',
+ 'rice',
+ 'roast_beef',
+ 'roast_pork',
+ 'roast_potatoes',
+ 'rockmelon',
+ 'rosemary',
+ 'salad',
+ 'salami',
+ 'salmon',
+ 'salsa',
+ 'salt',
+ 'sandwich',
+ 'sardines',
+ 'sausage_roll',
+ 'sausages',
+ 'scrambled_eggs',
+ 'seaweed',
+ 'shallots',
+ 'snow_peas',
+ 'soda',
+ 'soy_sauce',
+ 'spaghetti_bolognese',
+ 'spinach',
+ 'sports_drink',
+ 'squash',
+ 'starfruit',
+ 'steak',
+ 'strawberries',
+ 'sushi',
+ 'sweet_potato',
+ 'tacos',
+ 'tamarillo',
+ 'taro',
+ 'tea',
+ 'toast',
+ 'tofu',
+ 'tomato',
+ 'tomato_chutney',
+ 'tomato_sauce',
+ 'turnip',
+ 'watermelon',
+ 'white_onion',
+ 'white_wine',
+ 'yoghurt',
+ 'zucchini']
+
+# Create Food199 class
+class Food199(datasets.GeneratorBasedBuilder):
+    """Food199 Images dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=_NAMES)
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        """
+        This function returns the logic to split the dataset into different splits as well as labels.
+        """
+        annotations_csv = dl_manager.download("https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/raw/main/annotations_with_links.csv")
+        print(annotations_csv)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations": annotations_csv,
+                    "split": "train"
+                }
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={
+            #         "annotations": annotations_csv,
+            #         "split": "test"
+            #     }
+            # )
+        ]
+
+    def _generate_examples(self, annotations, split):
+        """
+        This function takes in the kwargs from the _split_generators method and can then yield information from them.
+        """
+        annotations_df = pd.read_csv(annotations, low_memory=False)
+
+        if split == "train":
+            annotations = annotations_df[["image", "label"]][annotations_df["split"] == "train"].to_dict(orient="records")
+        elif split == "test":
+            annotations = annotations_df[["image", "label"]][annotations_df["split"] == "test"].to_dict(orient="records")
+
+        for id_, row in enumerate(annotations):
+            # print(row["image"])
+            row["image"] = str(row.pop("image"))
+            row["label"] = row.pop("label")
+            # print(id_, row)
+            yield id_, row
+