format
Browse files- Caltech-101.py +27 -19
Caltech-101.py
CHANGED
@@ -1,4 +1,4 @@
|
|
1 |
-
# Copyright
|
2 |
#
|
3 |
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
# you may not use this file except in compliance with the License.
|
@@ -11,18 +11,14 @@
|
|
11 |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
# See the License for the specific language governing permissions and
|
13 |
# limitations under the License.
|
14 |
-
|
15 |
-
"""TODO: Add a description here."""
|
16 |
|
17 |
|
18 |
-
import csv
|
19 |
-
import json
|
20 |
-
import os
|
21 |
from pathlib import Path
|
22 |
|
23 |
import datasets
|
24 |
-
from datasets.tasks import ImageClassification
|
25 |
import numpy as np
|
|
|
26 |
|
27 |
_CITATION = """\
|
28 |
@article{FeiFei2004LearningGV,
|
@@ -180,31 +176,35 @@ class Caltech101(datasets.GeneratorBasedBuilder):
|
|
180 |
|
181 |
def _split_generators(self, dl_manager):
|
182 |
data_root_dir = dl_manager.download_and_extract(_DATA_URL)
|
183 |
-
compress_folder_path = [
|
|
|
|
|
|
|
|
|
184 |
data_dir = dl_manager.extract(compress_folder_path)
|
185 |
return [
|
186 |
datasets.SplitGenerator(
|
187 |
name=datasets.Split.TRAIN,
|
188 |
-
# These kwargs will be passed to _generate_examples
|
189 |
gen_kwargs={
|
190 |
-
"filepath": data_dir,
|
191 |
"split": "train",
|
192 |
},
|
193 |
),
|
194 |
datasets.SplitGenerator(
|
195 |
name=datasets.Split.TEST,
|
196 |
-
# These kwargs will be passed to _generate_examples
|
197 |
gen_kwargs={
|
198 |
-
"filepath": data_dir,
|
199 |
"split": "test",
|
200 |
},
|
201 |
),
|
202 |
]
|
203 |
|
204 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
205 |
def _generate_examples(self, filepath, split):
|
206 |
-
# Same stratagy as the one proposed in TF datasets
|
207 |
-
|
|
|
|
|
|
|
208 |
data_dir = Path(filepath) / "101_ObjectCategories"
|
209 |
# Sets random seed so the random partitioning of files is the same when
|
210 |
# called for the train and test splits.
|
@@ -212,14 +212,22 @@ class Caltech101(datasets.GeneratorBasedBuilder):
|
|
212 |
np.random.seed(1234)
|
213 |
|
214 |
for class_dir in data_dir.iterdir():
|
215 |
-
fnames = [
|
|
|
|
|
|
|
|
|
216 |
# _TRAIN_POINTS_PER_CLASS datapoints are sampled for the train split,
|
217 |
# the others constitute the test split.
|
218 |
if _TRAIN_POINTS_PER_CLASS > len(fnames):
|
219 |
-
raise ValueError(
|
220 |
-
|
|
|
|
|
|
|
221 |
train_fnames = np.random.choice(
|
222 |
-
fnames, _TRAIN_POINTS_PER_CLASS, replace=False
|
|
|
223 |
test_fnames = set(fnames).difference(train_fnames)
|
224 |
fnames_to_emit = train_fnames if is_train_split else test_fnames
|
225 |
|
|
|
1 |
+
# Copyright 2022 The HuggingFace Datasets Authors.
|
2 |
#
|
3 |
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
# you may not use this file except in compliance with the License.
|
|
|
11 |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
# See the License for the specific language governing permissions and
|
13 |
# limitations under the License.
|
14 |
+
"""Caltech 101 loading script"""
|
|
|
15 |
|
16 |
|
|
|
|
|
|
|
17 |
from pathlib import Path
|
18 |
|
19 |
import datasets
|
|
|
20 |
import numpy as np
|
21 |
+
from datasets.tasks import ImageClassification
|
22 |
|
23 |
_CITATION = """\
|
24 |
@article{FeiFei2004LearningGV,
|
|
|
176 |
|
177 |
def _split_generators(self, dl_manager):
|
178 |
data_root_dir = dl_manager.download_and_extract(_DATA_URL)
|
179 |
+
compress_folder_path = [
|
180 |
+
file
|
181 |
+
for file in dl_manager.iter_files(data_root_dir)
|
182 |
+
if Path(file).name == "101_ObjectCategories.tar.gz"
|
183 |
+
][0]
|
184 |
data_dir = dl_manager.extract(compress_folder_path)
|
185 |
return [
|
186 |
datasets.SplitGenerator(
|
187 |
name=datasets.Split.TRAIN,
|
|
|
188 |
gen_kwargs={
|
189 |
+
"filepath": data_dir,
|
190 |
"split": "train",
|
191 |
},
|
192 |
),
|
193 |
datasets.SplitGenerator(
|
194 |
name=datasets.Split.TEST,
|
|
|
195 |
gen_kwargs={
|
196 |
+
"filepath": data_dir,
|
197 |
"split": "test",
|
198 |
},
|
199 |
),
|
200 |
]
|
201 |
|
|
|
202 |
def _generate_examples(self, filepath, split):
|
203 |
+
# Same stratagy as the one proposed in TF datasets: 30 random examples from each class are added to the train
|
204 |
+
# split, and the remainder are added to the test split.
|
205 |
+
# Source: https://github.com/tensorflow/datasets/blob/1106d587f97c4fca68c5b593dc7dc48c790ffa8c/tensorflow_datasets/image_classification/caltech.py#L88-L140
|
206 |
+
|
207 |
+
is_train_split = split == "train"
|
208 |
data_dir = Path(filepath) / "101_ObjectCategories"
|
209 |
# Sets random seed so the random partitioning of files is the same when
|
210 |
# called for the train and test splits.
|
|
|
212 |
np.random.seed(1234)
|
213 |
|
214 |
for class_dir in data_dir.iterdir():
|
215 |
+
fnames = [
|
216 |
+
image_path
|
217 |
+
for image_path in class_dir.iterdir()
|
218 |
+
if image_path.name.endswith(".jpg")
|
219 |
+
]
|
220 |
# _TRAIN_POINTS_PER_CLASS datapoints are sampled for the train split,
|
221 |
# the others constitute the test split.
|
222 |
if _TRAIN_POINTS_PER_CLASS > len(fnames):
|
223 |
+
raise ValueError(
|
224 |
+
"Fewer than {} ({}) points in class {}".format(
|
225 |
+
_TRAIN_POINTS_PER_CLASS, len(fnames), class_dir.name
|
226 |
+
)
|
227 |
+
)
|
228 |
train_fnames = np.random.choice(
|
229 |
+
fnames, _TRAIN_POINTS_PER_CLASS, replace=False
|
230 |
+
)
|
231 |
test_fnames = set(fnames).difference(train_fnames)
|
232 |
fnames_to_emit = train_fnames if is_train_split else test_fnames
|
233 |
|