Commit
•
48069eb
1
Parent(s):
afbf9c4
add script
Browse files- test_verifications.py +297 -0
test_verifications.py
ADDED
@@ -0,0 +1,297 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
Loading script for the Food Vision 199 classes dataset.
|
3 |
+
|
4 |
+
See the template: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
|
5 |
+
See the example for Food101: https://huggingface.co/datasets/food101/blob/main/food101.py
|
6 |
+
See another example: https://huggingface.co/datasets/davanstrien/encyclopedia_britannica/blob/main/encyclopedia_britannica.py
|
7 |
+
"""
|
8 |
+
|
9 |
+
import datasets
|
10 |
+
import os
|
11 |
+
import requests
|
12 |
+
|
13 |
+
import pandas as pd
|
14 |
+
|
15 |
+
from datasets.tasks import ImageClassification
|
16 |
+
|
17 |
+
# Print datasets version
|
18 |
+
print(f"Datasets version: {datasets.__version__}")
|
19 |
+
|
20 |
+
# Set verbosity to 10
|
21 |
+
datasets.logging.set_verbosity(10)
|
22 |
+
print(f"Verbosity level: {datasets.logging.get_verbosity()}")
|
23 |
+
|
24 |
+
_HOMEPAGE = "https://www.nutrify.app"
|
25 |
+
_LICENSE = "TODO"
|
26 |
+
_CITATION = "TODO"
|
27 |
+
_DESCRIPTION = "Images of 199 food classes from the Nutrify app."
|
28 |
+
|
29 |
+
# # Download class_names.txt and read it
|
30 |
+
# url = "https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/class_names.txt"
|
31 |
+
# r = requests.get(url, allow_redirects=True)
|
32 |
+
# open("class_names.txt", "wb").write(r.content)
|
33 |
+
# with open("class_names.txt", "r") as f:
|
34 |
+
# _NAMES = f.read().splitlines()
|
35 |
+
|
36 |
+
# Create list of class names
|
37 |
+
_NAMES = ['almond_butter',
|
38 |
+
'almonds',
|
39 |
+
'apple',
|
40 |
+
'apricot',
|
41 |
+
'asparagus',
|
42 |
+
'avocado',
|
43 |
+
'bacon',
|
44 |
+
'bacon_and_egg_burger',
|
45 |
+
'bagel',
|
46 |
+
'baklava',
|
47 |
+
'banana',
|
48 |
+
'banana_bread',
|
49 |
+
'barbecue_sauce',
|
50 |
+
'beans',
|
51 |
+
'beef',
|
52 |
+
'beef_curry',
|
53 |
+
'beef_mince',
|
54 |
+
'beef_stir_fry',
|
55 |
+
'beer',
|
56 |
+
'beetroot',
|
57 |
+
'biltong',
|
58 |
+
'blackberries',
|
59 |
+
'blueberries',
|
60 |
+
'bok_choy',
|
61 |
+
'bread',
|
62 |
+
'broccoli',
|
63 |
+
'broccolini',
|
64 |
+
'brownie',
|
65 |
+
'brussel_sprouts',
|
66 |
+
'burrito',
|
67 |
+
'butter',
|
68 |
+
'cabbage',
|
69 |
+
'calamari',
|
70 |
+
'candy',
|
71 |
+
'capsicum',
|
72 |
+
'carrot',
|
73 |
+
'cashews',
|
74 |
+
'cauliflower',
|
75 |
+
'celery',
|
76 |
+
'cheese',
|
77 |
+
'cheeseburger',
|
78 |
+
'cherries',
|
79 |
+
'chicken_breast',
|
80 |
+
'chicken_thighs',
|
81 |
+
'chicken_wings',
|
82 |
+
'chilli',
|
83 |
+
'chimichurri',
|
84 |
+
'chocolate',
|
85 |
+
'chocolate_cake',
|
86 |
+
'coconut',
|
87 |
+
'coffee',
|
88 |
+
'coleslaw',
|
89 |
+
'cookies',
|
90 |
+
'coriander',
|
91 |
+
'corn',
|
92 |
+
'corn_chips',
|
93 |
+
'cream',
|
94 |
+
'croissant',
|
95 |
+
'crumbed_chicken',
|
96 |
+
'cucumber',
|
97 |
+
'cupcake',
|
98 |
+
'daikon_radish',
|
99 |
+
'dates',
|
100 |
+
'donuts',
|
101 |
+
'dragonfruit',
|
102 |
+
'eggplant',
|
103 |
+
'eggs',
|
104 |
+
'enoki_mushroom',
|
105 |
+
'fennel',
|
106 |
+
'figs',
|
107 |
+
'french_toast',
|
108 |
+
'fried_rice',
|
109 |
+
'fries',
|
110 |
+
'fruit_juice',
|
111 |
+
'garlic',
|
112 |
+
'garlic_bread',
|
113 |
+
'ginger',
|
114 |
+
'goji_berries',
|
115 |
+
'granola',
|
116 |
+
'grapefruit',
|
117 |
+
'grapes',
|
118 |
+
'green_beans',
|
119 |
+
'green_onion',
|
120 |
+
'guacamole',
|
121 |
+
'guava',
|
122 |
+
'gyoza',
|
123 |
+
'ham',
|
124 |
+
'honey',
|
125 |
+
'hot_chocolate',
|
126 |
+
'ice_coffee',
|
127 |
+
'ice_cream',
|
128 |
+
'iceberg_lettuce',
|
129 |
+
'jerusalem_artichoke',
|
130 |
+
'kale',
|
131 |
+
'karaage_chicken',
|
132 |
+
'kimchi',
|
133 |
+
'kiwi_fruit',
|
134 |
+
'lamb_chops',
|
135 |
+
'leek',
|
136 |
+
'lemon',
|
137 |
+
'lentils',
|
138 |
+
'lettuce',
|
139 |
+
'lime',
|
140 |
+
'mandarin',
|
141 |
+
'mango',
|
142 |
+
'maple_syrup',
|
143 |
+
'mashed_potato',
|
144 |
+
'mayonnaise',
|
145 |
+
'milk',
|
146 |
+
'miso_soup',
|
147 |
+
'mushrooms',
|
148 |
+
'nectarines',
|
149 |
+
'noodles',
|
150 |
+
'nuts',
|
151 |
+
'olive_oil',
|
152 |
+
'olives',
|
153 |
+
'omelette',
|
154 |
+
'onion',
|
155 |
+
'orange',
|
156 |
+
'orange_juice',
|
157 |
+
'oysters',
|
158 |
+
'pain_au_chocolat',
|
159 |
+
'pancakes',
|
160 |
+
'papaya',
|
161 |
+
'parsley',
|
162 |
+
'parsnips',
|
163 |
+
'passionfruit',
|
164 |
+
'pasta',
|
165 |
+
'pawpaw',
|
166 |
+
'peach',
|
167 |
+
'pear',
|
168 |
+
'peas',
|
169 |
+
'pickles',
|
170 |
+
'pineapple',
|
171 |
+
'pizza',
|
172 |
+
'plum',
|
173 |
+
'pomegranate',
|
174 |
+
'popcorn',
|
175 |
+
'pork_belly',
|
176 |
+
'pork_chop',
|
177 |
+
'pork_loins',
|
178 |
+
'porridge',
|
179 |
+
'potato_bake',
|
180 |
+
'potato_chips',
|
181 |
+
'potato_scallop',
|
182 |
+
'potatoes',
|
183 |
+
'prawns',
|
184 |
+
'pumpkin',
|
185 |
+
'radish',
|
186 |
+
'ramen',
|
187 |
+
'raspberries',
|
188 |
+
'red_onion',
|
189 |
+
'red_wine',
|
190 |
+
'rhubarb',
|
191 |
+
'rice',
|
192 |
+
'roast_beef',
|
193 |
+
'roast_pork',
|
194 |
+
'roast_potatoes',
|
195 |
+
'rockmelon',
|
196 |
+
'rosemary',
|
197 |
+
'salad',
|
198 |
+
'salami',
|
199 |
+
'salmon',
|
200 |
+
'salsa',
|
201 |
+
'salt',
|
202 |
+
'sandwich',
|
203 |
+
'sardines',
|
204 |
+
'sausage_roll',
|
205 |
+
'sausages',
|
206 |
+
'scrambled_eggs',
|
207 |
+
'seaweed',
|
208 |
+
'shallots',
|
209 |
+
'snow_peas',
|
210 |
+
'soda',
|
211 |
+
'soy_sauce',
|
212 |
+
'spaghetti_bolognese',
|
213 |
+
'spinach',
|
214 |
+
'sports_drink',
|
215 |
+
'squash',
|
216 |
+
'starfruit',
|
217 |
+
'steak',
|
218 |
+
'strawberries',
|
219 |
+
'sushi',
|
220 |
+
'sweet_potato',
|
221 |
+
'tacos',
|
222 |
+
'tamarillo',
|
223 |
+
'taro',
|
224 |
+
'tea',
|
225 |
+
'toast',
|
226 |
+
'tofu',
|
227 |
+
'tomato',
|
228 |
+
'tomato_chutney',
|
229 |
+
'tomato_sauce',
|
230 |
+
'turnip',
|
231 |
+
'watermelon',
|
232 |
+
'white_onion',
|
233 |
+
'white_wine',
|
234 |
+
'yoghurt',
|
235 |
+
'zucchini']
|
236 |
+
|
237 |
+
# Create Food199 class
|
238 |
+
class Food199(datasets.GeneratorBasedBuilder):
|
239 |
+
"""Food199 Images dataset"""
|
240 |
+
|
241 |
+
def _info(self):
|
242 |
+
return datasets.DatasetInfo(
|
243 |
+
description=_DESCRIPTION,
|
244 |
+
features=datasets.Features(
|
245 |
+
{
|
246 |
+
"image": datasets.Image(),
|
247 |
+
"label": datasets.ClassLabel(names=_NAMES)
|
248 |
+
}
|
249 |
+
),
|
250 |
+
supervised_keys=("image", "label"),
|
251 |
+
homepage=_HOMEPAGE,
|
252 |
+
citation=_CITATION,
|
253 |
+
license=_LICENSE
|
254 |
+
)
|
255 |
+
|
256 |
+
def _split_generators(self, dl_manager):
|
257 |
+
"""
|
258 |
+
This function returns the logic to split the dataset into different splits as well as labels.
|
259 |
+
"""
|
260 |
+
annotations_csv = dl_manager.download("https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/raw/main/annotations_with_links.csv")
|
261 |
+
print(annotations_csv)
|
262 |
+
|
263 |
+
return [
|
264 |
+
datasets.SplitGenerator(
|
265 |
+
name=datasets.Split.TRAIN,
|
266 |
+
gen_kwargs={
|
267 |
+
"annotations": annotations_csv,
|
268 |
+
"split": "train"
|
269 |
+
}
|
270 |
+
),
|
271 |
+
# datasets.SplitGenerator(
|
272 |
+
# name=datasets.Split.TEST,
|
273 |
+
# gen_kwargs={
|
274 |
+
# "annotations": annotations_csv,
|
275 |
+
# "split": "test"
|
276 |
+
# }
|
277 |
+
# )
|
278 |
+
]
|
279 |
+
|
280 |
+
def _generate_examples(self, annotations, split):
|
281 |
+
"""
|
282 |
+
This function takes in the kwargs from the _split_generators method and can then yield information from them.
|
283 |
+
"""
|
284 |
+
annotations_df = pd.read_csv(annotations, low_memory=False)
|
285 |
+
|
286 |
+
if split == "train":
|
287 |
+
annotations = annotations_df[["image", "label"]][annotations_df["split"] == "train"].to_dict(orient="records")
|
288 |
+
elif split == "test":
|
289 |
+
annotations = annotations_df[["image", "label"]][annotations_df["split"] == "test"].to_dict(orient="records")
|
290 |
+
|
291 |
+
for id_, row in enumerate(annotations):
|
292 |
+
# print(row["image"])
|
293 |
+
row["image"] = str(row.pop("image"))
|
294 |
+
row["label"] = row.pop("label")
|
295 |
+
# print(id_, row)
|
296 |
+
yield id_, row
|
297 |
+
|