shunk031 commited on
Commit
1d9dac8
1 Parent(s): d3f4182

Initialize (#1)

Browse files

* add files

* update files

* add pytest-xdist for parallel testing

* add settings for CI

* update

* update README

* update README.md

* update

.github/workflows/ci.yaml ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: CI
2
+
3
+ on:
4
+ push:
5
+ branches: [ main ]
6
+ pull_request:
7
+ branches: [ main ]
8
+ paths-ignore:
9
+ - 'README.md'
10
+
11
+ jobs:
12
+ test:
13
+ runs-on: ubuntu-latest
14
+ strategy:
15
+ matrix:
16
+ python-version: [ '3.9', '3.10' ]
17
+
18
+ steps:
19
+ - uses: actions/checkout@v3
20
+
21
+ - name: Set up Python ${{ matrix.python-version }}
22
+ uses: actions/setup-python@v4
23
+ with:
24
+ python-version: ${{ matrix.python-version }}
25
+
26
+ - name: Install dependencies
27
+ run: |
28
+ pip install -U pip setuptools wheel poetry
29
+ poetry install
30
+
31
+ - name: Format
32
+ run: |
33
+ poetry run black --check .
34
+
35
+ - name: Lint
36
+ run: |
37
+ poetry run ruff .
38
+
39
+ - name: Type check
40
+ run: |
41
+ poetry run mypy . \
42
+ --ignore-missing-imports \
43
+ --no-strict-optional \
44
+ --no-site-packages \
45
+ --cache-dir=/dev/null
46
+
47
+ # - name: Run tests
48
+ # run: |
49
+ # poetry run pytest --color=yes -rf
.github/workflows/push_to_hub.yaml ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Sync to Hugging Face Hub
2
+
3
+ on:
4
+ workflow_run:
5
+ workflows:
6
+ - CI
7
+ branches:
8
+ - main
9
+ types:
10
+ - completed
11
+
12
+ jobs:
13
+ push_to_hub:
14
+ runs-on: ubuntu-latest
15
+
16
+ steps:
17
+ - name: Checkout repository
18
+ uses: actions/checkout@v3
19
+
20
+ - name: Push to Huggingface hub
21
+ env:
22
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
23
+ HF_USERNAME: ${{ secrets.HF_USERNAME }}
24
+ run: |
25
+ git fetch --unshallow
26
+ # git lfs fetch --all origin main
27
+ git push --force https://${HF_USERNAME}:${HF_TOKEN}@huggingface.co/datasets/${HF_USERNAME}/MSCOCO main
.gitignore ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Created by https://www.toptal.com/developers/gitignore/api/python
2
+ # Edit at https://www.toptal.com/developers/gitignore?templates=python
3
+
4
+ ### Python ###
5
+ # Byte-compiled / optimized / DLL files
6
+ __pycache__/
7
+ *.py[cod]
8
+ *$py.class
9
+
10
+ # C extensions
11
+ *.so
12
+
13
+ # Distribution / packaging
14
+ .Python
15
+ build/
16
+ develop-eggs/
17
+ dist/
18
+ downloads/
19
+ eggs/
20
+ .eggs/
21
+ lib/
22
+ lib64/
23
+ parts/
24
+ sdist/
25
+ var/
26
+ wheels/
27
+ share/python-wheels/
28
+ *.egg-info/
29
+ .installed.cfg
30
+ *.egg
31
+ MANIFEST
32
+
33
+ # PyInstaller
34
+ # Usually these files are written by a python script from a template
35
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
36
+ *.manifest
37
+ *.spec
38
+
39
+ # Installer logs
40
+ pip-log.txt
41
+ pip-delete-this-directory.txt
42
+
43
+ # Unit test / coverage reports
44
+ htmlcov/
45
+ .tox/
46
+ .nox/
47
+ .coverage
48
+ .coverage.*
49
+ .cache
50
+ nosetests.xml
51
+ coverage.xml
52
+ *.cover
53
+ *.py,cover
54
+ .hypothesis/
55
+ .pytest_cache/
56
+ cover/
57
+
58
+ # Translations
59
+ *.mo
60
+ *.pot
61
+
62
+ # Django stuff:
63
+ *.log
64
+ local_settings.py
65
+ db.sqlite3
66
+ db.sqlite3-journal
67
+
68
+ # Flask stuff:
69
+ instance/
70
+ .webassets-cache
71
+
72
+ # Scrapy stuff:
73
+ .scrapy
74
+
75
+ # Sphinx documentation
76
+ docs/_build/
77
+
78
+ # PyBuilder
79
+ .pybuilder/
80
+ target/
81
+
82
+ # Jupyter Notebook
83
+ .ipynb_checkpoints
84
+
85
+ # IPython
86
+ profile_default/
87
+ ipython_config.py
88
+
89
+ # pyenv
90
+ # For a library or package, you might want to ignore these files since the code is
91
+ # intended to run in multiple environments; otherwise, check them in:
92
+ .python-version
93
+
94
+ # pipenv
95
+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
96
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
97
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
98
+ # install all needed dependencies.
99
+ #Pipfile.lock
100
+
101
+ # poetry
102
+ # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
103
+ # This is especially recommended for binary packages to ensure reproducibility, and is more
104
+ # commonly ignored for libraries.
105
+ # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
106
+ #poetry.lock
107
+
108
+ # pdm
109
+ # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
110
+ #pdm.lock
111
+ # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
112
+ # in version control.
113
+ # https://pdm.fming.dev/#use-with-ide
114
+ .pdm.toml
115
+
116
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
117
+ __pypackages__/
118
+
119
+ # Celery stuff
120
+ celerybeat-schedule
121
+ celerybeat.pid
122
+
123
+ # SageMath parsed files
124
+ *.sage.py
125
+
126
+ # Environments
127
+ .env
128
+ .venv
129
+ env/
130
+ venv/
131
+ ENV/
132
+ env.bak/
133
+ venv.bak/
134
+
135
+ # Spyder project settings
136
+ .spyderproject
137
+ .spyproject
138
+
139
+ # Rope project settings
140
+ .ropeproject
141
+
142
+ # mkdocs documentation
143
+ /site
144
+
145
+ # mypy
146
+ .mypy_cache/
147
+ .dmypy.json
148
+ dmypy.json
149
+
150
+ # Pyre type checker
151
+ .pyre/
152
+
153
+ # pytype static type analyzer
154
+ .pytype/
155
+
156
+ # Cython debug symbols
157
+ cython_debug/
158
+
159
+ # PyCharm
160
+ # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
161
+ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
162
+ # and can be added to the global gitignore or merged into this file. For a more nuclear
163
+ # option (not recommended) you can uncomment the following to ignore the entire idea folder.
164
+ #.idea/
165
+
166
+ ### Python Patch ###
167
+ # Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
168
+ poetry.toml
169
+
170
+ # ruff
171
+ .ruff_cache/
172
+
173
+ # LSP config files
174
+ pyrightconfig.json
175
+
176
+ # End of https://www.toptal.com/developers/gitignore/api/python
MSCOCO.py ADDED
@@ -0,0 +1,969 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import logging
3
+ import os
4
+ from collections import defaultdict
5
+ from dataclasses import asdict, dataclass
6
+ from typing import (
7
+ Any,
8
+ Dict,
9
+ Final,
10
+ Iterator,
11
+ List,
12
+ Literal,
13
+ Optional,
14
+ Sequence,
15
+ Tuple,
16
+ TypedDict,
17
+ Union,
18
+ get_args,
19
+ )
20
+
21
+ import datasets as ds
22
+ import numpy as np
23
+ from datasets.data_files import DataFilesDict
24
+ from PIL import Image
25
+ from PIL.Image import Image as PilImage
26
+ from pycocotools import mask as cocomask
27
+ from tqdm.auto import tqdm
28
+
29
+ logger = logging.getLogger(__name__)
30
+
31
+ JsonDict = Dict[str, Any]
32
+ ImageId = int
33
+ AnnotationId = int
34
+ LicenseId = int
35
+ CategoryId = int
36
+ Bbox = Tuple[float, float, float, float]
37
+
38
+ MscocoSplits = Literal["train", "val", "test"]
39
+
40
+ KEYPOINT_STATE: Final[List[str]] = ["unknown", "invisible", "visible"]
41
+
42
+
43
+ _CITATION = """
44
+ """
45
+
46
+ _DESCRIPTION = """
47
+ """
48
+
49
+ _HOMEPAGE = """
50
+ """
51
+
52
+ _LICENSE = """
53
+ """
54
+
55
+ _URLS = {
56
+ "2014": {
57
+ "images": {
58
+ "train": "http://images.cocodataset.org/zips/train2014.zip",
59
+ "validation": "http://images.cocodataset.org/zips/val2014.zip",
60
+ "test": "http://images.cocodataset.org/zips/test2014.zip",
61
+ },
62
+ "annotations": {
63
+ "train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip",
64
+ "test_image_info": "http://images.cocodataset.org/annotations/image_info_test2014.zip",
65
+ },
66
+ },
67
+ "2015": {
68
+ "images": {
69
+ "test": "http://images.cocodataset.org/zips/test2015.zip",
70
+ },
71
+ "annotations": {
72
+ "test_image_info": "http://images.cocodataset.org/annotations/image_info_test2015.zip",
73
+ },
74
+ },
75
+ "2017": {
76
+ "images": {
77
+ "train": "http://images.cocodataset.org/zips/train2017.zip",
78
+ "validation": "http://images.cocodataset.org/zips/val2017.zip",
79
+ "test": "http://images.cocodataset.org/zips/test2017.zip",
80
+ "unlabeled": "http://images.cocodataset.org/zips/unlabeled2017.zip",
81
+ },
82
+ "annotations": {
83
+ "train_validation": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip",
84
+ "stuff_train_validation": "http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip",
85
+ "panoptic_train_validation": "http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip",
86
+ "test_image_info": "http://images.cocodataset.org/annotations/image_info_test2017.zip",
87
+ "unlabeled": "http://images.cocodataset.org/annotations/image_info_unlabeled2017.zip",
88
+ },
89
+ },
90
+ }
91
+
92
+
93
+ @dataclass
94
+ class AnnotationInfo(object):
95
+ description: str
96
+ url: str
97
+ version: str
98
+ year: str
99
+ contributor: str
100
+ date_created: str
101
+
102
+ @classmethod
103
+ def from_dict(cls, json_dict: JsonDict) -> "AnnotationInfo":
104
+ return cls(**json_dict)
105
+
106
+
107
+ @dataclass
108
+ class LicenseData(object):
109
+ url: str
110
+ license_id: LicenseId
111
+ name: str
112
+
113
+ @classmethod
114
+ def from_dict(cls, json_dict: JsonDict) -> "LicenseData":
115
+ return cls(
116
+ license_id=json_dict["id"],
117
+ url=json_dict["url"],
118
+ name=json_dict["name"],
119
+ )
120
+
121
+
122
+ @dataclass
123
+ class ImageData(object):
124
+ image_id: ImageId
125
+ license_id: LicenseId
126
+ file_name: str
127
+ coco_url: str
128
+ height: int
129
+ width: int
130
+ date_captured: str
131
+ flickr_url: str
132
+
133
+ @classmethod
134
+ def from_dict(cls, json_dict: JsonDict) -> "ImageData":
135
+ return cls(
136
+ image_id=json_dict["id"],
137
+ license_id=json_dict["license"],
138
+ file_name=json_dict["file_name"],
139
+ coco_url=json_dict["coco_url"],
140
+ height=json_dict["height"],
141
+ width=json_dict["width"],
142
+ date_captured=json_dict["date_captured"],
143
+ flickr_url=json_dict["flickr_url"],
144
+ )
145
+
146
+ @property
147
+ def shape(self) -> Tuple[int, int]:
148
+ return (self.height, self.width)
149
+
150
+
151
+ @dataclass
152
+ class CategoryData(object):
153
+ category_id: int
154
+ name: str
155
+ supercategory: str
156
+
157
+ @classmethod
158
+ def from_dict(cls, json_dict: JsonDict) -> "CategoryData":
159
+ return cls(
160
+ category_id=json_dict["id"],
161
+ name=json_dict["name"],
162
+ supercategory=json_dict["supercategory"],
163
+ )
164
+
165
+
166
+ @dataclass
167
+ class AnnotationData(object):
168
+ annotation_id: AnnotationId
169
+ image_id: ImageId
170
+
171
+
172
+ @dataclass
173
+ class CaptionsAnnotationData(AnnotationData):
174
+ caption: str
175
+
176
+ @classmethod
177
+ def from_dict(cls, json_dict: JsonDict) -> "CaptionsAnnotationData":
178
+ return cls(
179
+ annotation_id=json_dict["id"],
180
+ image_id=json_dict["image_id"],
181
+ caption=json_dict["caption"],
182
+ )
183
+
184
+
185
+ class UncompressedRLE(TypedDict):
186
+ counts: List[int]
187
+ size: Tuple[int, int]
188
+
189
+
190
+ class CompressedRLE(TypedDict):
191
+ counts: bytes
192
+ size: Tuple[int, int]
193
+
194
+
195
+ @dataclass
196
+ class InstancesAnnotationData(AnnotationData):
197
+ segmentation: Union[np.ndarray, CompressedRLE]
198
+ area: float
199
+ iscrowd: bool
200
+ bbox: Tuple[float, float, float, float]
201
+ category_id: int
202
+
203
+ @classmethod
204
+ def compress_rle(
205
+ cls,
206
+ segmentation: Union[List[List[float]], UncompressedRLE],
207
+ iscrowd: bool,
208
+ height: int,
209
+ width: int,
210
+ ) -> CompressedRLE:
211
+ if iscrowd:
212
+ rle = cocomask.frPyObjects(segmentation, h=height, w=width)
213
+ else:
214
+ rles = cocomask.frPyObjects(segmentation, h=height, w=width)
215
+ rle = cocomask.merge(rles)
216
+
217
+ return rle # type: ignore
218
+
219
+ @classmethod
220
+ def rle_segmentation_to_binary_mask(
221
+ cls, segmentation, iscrowd: bool, height: int, width: int
222
+ ) -> np.ndarray:
223
+ rle = cls.compress_rle(
224
+ segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
225
+ )
226
+ return cocomask.decode(rle) # type: ignore
227
+
228
+ @classmethod
229
+ def rle_segmentation_to_mask(
230
+ cls,
231
+ segmentation: Union[List[List[float]], UncompressedRLE],
232
+ iscrowd: bool,
233
+ height: int,
234
+ width: int,
235
+ ) -> np.ndarray:
236
+ binary_mask = cls.rle_segmentation_to_binary_mask(
237
+ segmentation=segmentation, iscrowd=iscrowd, height=height, width=width
238
+ )
239
+ return binary_mask * 255
240
+
241
+ @classmethod
242
+ def from_dict(
243
+ cls,
244
+ json_dict: JsonDict,
245
+ images: Dict[ImageId, ImageData],
246
+ decode_rle: bool,
247
+ ) -> "InstancesAnnotationData":
248
+ segmentation = json_dict["segmentation"]
249
+ image_id = json_dict["image_id"]
250
+ image_data = images[image_id]
251
+ iscrowd = bool(json_dict["iscrowd"])
252
+
253
+ if decode_rle:
254
+ segmentation_mask = cls.rle_segmentation_to_mask(
255
+ segmentation=segmentation,
256
+ iscrowd=iscrowd,
257
+ height=image_data.height,
258
+ width=image_data.width,
259
+ )
260
+ assert segmentation_mask.shape == image_data.shape
261
+ else:
262
+ segmentation_mask = cls.compress_rle(
263
+ segmentation=segmentation,
264
+ iscrowd=iscrowd,
265
+ height=image_data.height,
266
+ width=image_data.width,
267
+ )
268
+ return cls(
269
+ #
270
+ # for AnnotationData
271
+ #
272
+ annotation_id=json_dict["id"],
273
+ image_id=image_id,
274
+ #
275
+ # for InstancesAnnotationData
276
+ #
277
+ segmentation=segmentation_mask,
278
+ area=json_dict["area"],
279
+ iscrowd=iscrowd,
280
+ bbox=json_dict["bbox"],
281
+ category_id=json_dict["category_id"],
282
+ )
283
+
284
+
285
+ @dataclass
286
+ class PersonKeypoint(object):
287
+ x: int
288
+ y: int
289
+ v: int
290
+ state: str
291
+
292
+
293
+ @dataclass
294
+ class PersonKeypointsAnnotationData(InstancesAnnotationData):
295
+ num_keypoints: int
296
+ keypoints: List[PersonKeypoint]
297
+
298
+ @classmethod
299
+ def v_keypoint_to_state(cls, keypoint_v: int) -> str:
300
+ return KEYPOINT_STATE[keypoint_v]
301
+
302
+ @classmethod
303
+ def get_person_keypoints(
304
+ cls, flatten_keypoints: List[int], num_keypoints: int
305
+ ) -> List[PersonKeypoint]:
306
+ keypoints_x = flatten_keypoints[0::3]
307
+ keypoints_y = flatten_keypoints[1::3]
308
+ keypoints_v = flatten_keypoints[2::3]
309
+ assert len(keypoints_x) == len(keypoints_y) == len(keypoints_v)
310
+
311
+ keypoints = [
312
+ PersonKeypoint(x=x, y=y, v=v, state=cls.v_keypoint_to_state(v))
313
+ for x, y, v in zip(keypoints_x, keypoints_y, keypoints_v)
314
+ ]
315
+ assert len([kp for kp in keypoints if kp.state != "unknown"]) == num_keypoints
316
+ return keypoints
317
+
318
+ @classmethod
319
+ def from_dict(
320
+ cls,
321
+ json_dict: JsonDict,
322
+ images: Dict[ImageId, ImageData],
323
+ decode_rle: bool,
324
+ ) -> "PersonKeypointsAnnotationData":
325
+ segmentation = json_dict["segmentation"]
326
+ image_id = json_dict["image_id"]
327
+ image_data = images[image_id]
328
+ iscrowd = bool(json_dict["iscrowd"])
329
+
330
+ if decode_rle:
331
+ segmentation_mask = cls.rle_segmentation_to_mask(
332
+ segmentation=segmentation,
333
+ iscrowd=iscrowd,
334
+ height=image_data.height,
335
+ width=image_data.width,
336
+ )
337
+ assert segmentation_mask.shape == image_data.shape
338
+ else:
339
+ segmentation_mask = cls.compress_rle(
340
+ segmentation=segmentation,
341
+ iscrowd=iscrowd,
342
+ height=image_data.height,
343
+ width=image_data.width,
344
+ )
345
+
346
+ flatten_keypoints = json_dict["keypoints"]
347
+ num_keypoints = json_dict["num_keypoints"]
348
+ keypoints = cls.get_person_keypoints(flatten_keypoints, num_keypoints)
349
+
350
+ return cls(
351
+ #
352
+ # for AnnotationData
353
+ #
354
+ annotation_id=json_dict["id"],
355
+ image_id=image_id,
356
+ #
357
+ # for InstancesAnnotationData
358
+ #
359
+ segmentation=segmentation_mask,
360
+ area=json_dict["area"],
361
+ iscrowd=iscrowd,
362
+ bbox=json_dict["bbox"],
363
+ category_id=json_dict["category_id"],
364
+ #
365
+ # PersonKeypointsAnnotationData
366
+ #
367
+ num_keypoints=num_keypoints,
368
+ keypoints=keypoints,
369
+ )
370
+
371
+
372
+ class LicenseDict(TypedDict):
373
+ license_id: LicenseId
374
+ name: str
375
+ url: str
376
+
377
+
378
+ class BaseExample(TypedDict):
379
+ image_id: ImageId
380
+ image: PilImage
381
+ file_name: str
382
+ coco_url: str
383
+ height: int
384
+ width: int
385
+ date_captured: str
386
+ flickr_url: str
387
+ license_id: LicenseId
388
+ license: LicenseDict
389
+
390
+
391
+ class CaptionAnnotationDict(TypedDict):
392
+ annotation_id: AnnotationId
393
+ caption: str
394
+
395
+
396
+ class CaptionExample(BaseExample):
397
+ annotations: List[CaptionAnnotationDict]
398
+
399
+
400
+ def generate_captions_examples(
401
+ image_dir: str,
402
+ images: Dict[ImageId, ImageData],
403
+ annotations: Dict[ImageId, List[CaptionsAnnotationData]],
404
+ licenses: Dict[LicenseId, LicenseData],
405
+ ) -> Iterator[Tuple[int, CaptionExample]]:
406
+ for idx, image_id in enumerate(images.keys()):
407
+ image_data = images[image_id]
408
+ image_anns = annotations[image_id]
409
+
410
+ assert len(image_anns) > 0
411
+
412
+ image = _load_image(
413
+ image_path=os.path.join(image_dir, image_data.file_name),
414
+ )
415
+ example = asdict(image_data)
416
+ example["image"] = image
417
+ example["license"] = asdict(licenses[image_data.license_id])
418
+
419
+ example["annotations"] = []
420
+ for ann in image_anns:
421
+ example["annotations"].append(asdict(ann))
422
+
423
+ yield idx, example # type: ignore
424
+
425
+
426
+ class CategoryDict(TypedDict):
427
+ category_id: CategoryId
428
+ name: str
429
+ supercategory: str
430
+
431
+
432
+ class InstanceAnnotationDict(TypedDict):
433
+ annotation_id: AnnotationId
434
+ area: float
435
+ bbox: Bbox
436
+ image_id: ImageId
437
+ category_id: CategoryId
438
+ category: CategoryDict
439
+ iscrowd: bool
440
+ segmentation: np.ndarray
441
+
442
+
443
+ class InstanceExample(BaseExample):
444
+ annotations: List[InstanceAnnotationDict]
445
+
446
+
447
+ def generate_instances_examples(
448
+ image_dir: str,
449
+ images: Dict[ImageId, ImageData],
450
+ annotations: Dict[ImageId, List[InstancesAnnotationData]],
451
+ licenses: Dict[LicenseId, LicenseData],
452
+ categories: Dict[CategoryId, CategoryData],
453
+ ) -> Iterator[Tuple[int, InstanceExample]]:
454
+ for idx, image_id in enumerate(images.keys()):
455
+ image_data = images[image_id]
456
+ image_anns = annotations[image_id]
457
+
458
+ if len(image_anns) < 1:
459
+ logger.warning(f"No annotation found for image id: {image_id}.")
460
+ continue
461
+
462
+ image = _load_image(
463
+ image_path=os.path.join(image_dir, image_data.file_name),
464
+ )
465
+ example = asdict(image_data)
466
+ example["image"] = image
467
+ example["license"] = asdict(licenses[image_data.license_id])
468
+
469
+ example["annotations"] = []
470
+ for ann in image_anns:
471
+ ann_dict = asdict(ann)
472
+ category = categories[ann.category_id]
473
+ ann_dict["category"] = asdict(category)
474
+ example["annotations"].append(ann_dict)
475
+
476
+ yield idx, example # type: ignore
477
+
478
+
479
+ class KeypointDict(TypedDict):
480
+ x: int
481
+ y: int
482
+ v: int
483
+ state: str
484
+
485
+
486
+ class PersonKeypointAnnotationDict(InstanceAnnotationDict):
487
+ num_keypoints: int
488
+ keypoints: List[KeypointDict]
489
+
490
+
491
+ class PersonKeypointExample(BaseExample):
492
+ annotations: List[PersonKeypointAnnotationDict]
493
+
494
+
495
+ def generate_person_keypoints_examples(
496
+ image_dir: str,
497
+ images: Dict[ImageId, ImageData],
498
+ annotations: Dict[ImageId, List[PersonKeypointsAnnotationData]],
499
+ licenses: Dict[LicenseId, LicenseData],
500
+ categories: Dict[CategoryId, CategoryData],
501
+ ) -> Iterator[Tuple[int, PersonKeypointExample]]:
502
+ for idx, image_id in enumerate(images.keys()):
503
+ image_data = images[image_id]
504
+ image_anns = annotations[image_id]
505
+
506
+ if len(image_anns) < 1:
507
+ # If there are no persons in the image,
508
+ # no keypoint annotations will be assigned.
509
+ continue
510
+
511
+ image = _load_image(
512
+ image_path=os.path.join(image_dir, image_data.file_name),
513
+ )
514
+ example = asdict(image_data)
515
+ example["image"] = image
516
+ example["license"] = asdict(licenses[image_data.license_id])
517
+
518
+ example["annotations"] = []
519
+ for ann in image_anns:
520
+ ann_dict = asdict(ann)
521
+ category = categories[ann.category_id]
522
+ ann_dict["category"] = asdict(category)
523
+ example["annotations"].append(ann_dict)
524
+
525
+ yield idx, example # type: ignore
526
+
527
+
528
+ class MsCocoConfig(ds.BuilderConfig):
529
+ YEARS: Tuple[int, ...] = (
530
+ 2014,
531
+ 2017,
532
+ )
533
+ TASKS: Tuple[str, ...] = (
534
+ "captions",
535
+ "instances",
536
+ "person_keypoints",
537
+ )
538
+
539
+ def __init__(
540
+ self,
541
+ year: int,
542
+ coco_task: Union[str, Sequence[str]],
543
+ version: Optional[Union[ds.Version, str]],
544
+ decode_rle: bool = False,
545
+ data_dir: Optional[str] = None,
546
+ data_files: Optional[DataFilesDict] = None,
547
+ description: Optional[str] = None,
548
+ ) -> None:
549
+ super().__init__(
550
+ name=self.config_name(year=year, task=coco_task),
551
+ version=version,
552
+ data_dir=data_dir,
553
+ data_files=data_files,
554
+ description=description,
555
+ )
556
+ self._check_year(year)
557
+ self._check_task(coco_task)
558
+
559
+ self._year = year
560
+ self._task = coco_task
561
+ self.decode_rle = decode_rle
562
+
563
+ def _check_year(self, year: int) -> None:
564
+ assert year in self.YEARS, year
565
+
566
+ def _check_task(self, task: Union[str, Sequence[str]]) -> None:
567
+ if isinstance(task, str):
568
+ assert task in self.TASKS, task
569
+ elif isinstance(task, list) or isinstance(task, tuple):
570
+ for t in task:
571
+ assert self.TASKS, task
572
+ else:
573
+ raise ValueError(f"Invalid task: {task}")
574
+
575
+ @property
576
+ def year(self) -> int:
577
+ return self._year
578
+
579
+ @property
580
+ def task(self) -> str:
581
+ if isinstance(self._task, str):
582
+ return self._task
583
+ elif isinstance(self._task, list) or isinstance(self._task, tuple):
584
+ return "-".join(sorted(self._task))
585
+ else:
586
+ raise ValueError(f"Invalid task: {self._task}")
587
+
588
+ @classmethod
589
+ def config_name(cls, year: int, task: Union[str, Sequence[str]]) -> str:
590
+ if isinstance(task, str):
591
+ return f"{year}-{task}"
592
+ elif isinstance(task, list) or isinstance(task, tuple):
593
+ task = "-".join(task)
594
+ return f"{year}-{task}"
595
+ else:
596
+ raise ValueError(f"Invalid task: {task}")
597
+
598
+
599
+ def _load_image(image_path: str) -> PilImage:
600
+ return Image.open(image_path)
601
+
602
+
603
+ def _load_annotation_json(ann_file_path: str) -> JsonDict:
604
+ logger.info(f"Load annotation json from {ann_file_path}")
605
+ with open(ann_file_path, "r") as rf:
606
+ ann_json = json.load(rf)
607
+ return ann_json
608
+
609
+
610
+ def _load_licenses_data(license_dicts: List[JsonDict]) -> Dict[LicenseId, LicenseData]:
611
+ licenses = {}
612
+ for license_dict in license_dicts:
613
+ license_data = LicenseData.from_dict(license_dict)
614
+ licenses[license_data.license_id] = license_data
615
+ return licenses
616
+
617
+
618
+ def _load_images_data(
619
+ image_dicts: List[JsonDict],
620
+ tqdm_desc: str = "Load images",
621
+ ) -> Dict[ImageId, ImageData]:
622
+ images = {}
623
+ for image_dict in tqdm(image_dicts, desc=tqdm_desc):
624
+ image_data = ImageData.from_dict(image_dict)
625
+ images[image_data.image_id] = image_data
626
+ return images
627
+
628
+
629
+ def _load_categories_data(
630
+ category_dicts: List[JsonDict],
631
+ tqdm_desc: str = "Load categories",
632
+ ) -> Dict[CategoryId, CategoryData]:
633
+ categories = {}
634
+ for category_dict in tqdm(category_dicts, desc=tqdm_desc):
635
+ category_data = CategoryData.from_dict(category_dict)
636
+ categories[category_data.category_id] = category_data
637
+ return categories
638
+
639
+
640
+ def _load_captions_data(
641
+ ann_dicts: List[JsonDict],
642
+ tqdm_desc: str = "Load captions data",
643
+ ) -> Dict[ImageId, List[CaptionsAnnotationData]]:
644
+ annotations = defaultdict(list)
645
+ for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
646
+ ann_data = CaptionsAnnotationData.from_dict(ann_dict)
647
+ annotations[ann_data.image_id].append(ann_data)
648
+ return annotations
649
+
650
+
651
+ def _load_instances_data(
652
+ ann_dicts: List[JsonDict],
653
+ images: Dict[ImageId, ImageData],
654
+ decode_rle: bool,
655
+ tqdm_desc: str = "Load instances data",
656
+ ) -> Dict[ImageId, List[InstancesAnnotationData]]:
657
+ annotations = defaultdict(list)
658
+ ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])
659
+
660
+ for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
661
+ ann_data = InstancesAnnotationData.from_dict(
662
+ ann_dict, images=images, decode_rle=decode_rle
663
+ )
664
+ annotations[ann_data.image_id].append(ann_data)
665
+
666
+ return annotations
667
+
668
+
669
+ def _load_person_keypoints_data(
670
+ ann_dicts: List[JsonDict],
671
+ images: Dict[ImageId, ImageData],
672
+ decode_rle: bool,
673
+ tqdm_desc: str = "Load person keypoints data",
674
+ ) -> Dict[ImageId, List[PersonKeypointsAnnotationData]]:
675
+ annotations = defaultdict(list)
676
+ ann_dicts = sorted(ann_dicts, key=lambda d: d["image_id"])
677
+
678
+ for ann_dict in tqdm(ann_dicts, desc=tqdm_desc):
679
+ ann_data = PersonKeypointsAnnotationData.from_dict(
680
+ ann_dict, images=images, decode_rle=decode_rle
681
+ )
682
+ annotations[ann_data.image_id].append(ann_data)
683
+ return annotations
684
+
685
+
686
+ def get_features_base_dict():
687
+ return {
688
+ "image_id": ds.Value("int64"),
689
+ "image": ds.Image(),
690
+ "file_name": ds.Value("string"),
691
+ "coco_url": ds.Value("string"),
692
+ "height": ds.Value("int32"),
693
+ "width": ds.Value("int32"),
694
+ "date_captured": ds.Value("string"),
695
+ "flickr_url": ds.Value("string"),
696
+ "license_id": ds.Value("int32"),
697
+ "license": {
698
+ "url": ds.Value("string"),
699
+ "license_id": ds.Value("int8"),
700
+ "name": ds.Value("string"),
701
+ },
702
+ }
703
+
704
+
705
+ def get_features_instance_dict(decode_rle: bool):
706
+ if decode_rle:
707
+ segmentation_feature = ds.Image()
708
+ else:
709
+ segmentation_feature = {
710
+ "counts": ds.Sequence(ds.Value("int64")),
711
+ "size": ds.Sequence(ds.Value("int32")),
712
+ }
713
+ return {
714
+ "annotation_id": ds.Value("int64"),
715
+ "image_id": ds.Value("int64"),
716
+ "segmentation": segmentation_feature,
717
+ "area": ds.Value("float32"),
718
+ "iscrowd": ds.Value("bool"),
719
+ "bbox": ds.Sequence(ds.Value("float32"), length=4),
720
+ "category_id": ds.Value("int32"),
721
+ "category": {
722
+ "category_id": ds.Value("int32"),
723
+ "name": ds.Value("string"),
724
+ "supercategory": ds.Value("string"),
725
+ },
726
+ }
727
+
728
+
729
+ def get_features_captions() -> ds.Features:
730
+ features_dict = get_features_base_dict()
731
+ annotations = ds.Sequence(
732
+ {
733
+ "annotation_id": ds.Value("int64"),
734
+ "image_id": ds.Value("int64"),
735
+ "caption": ds.Value("string"),
736
+ }
737
+ )
738
+ features_dict.update({"annotations": annotations})
739
+
740
+ return ds.Features(features_dict)
741
+
742
+
743
+ def get_features_instances(decode_rle: bool) -> ds.Features:
744
+ features_dict = get_features_base_dict()
745
+ annotations = ds.Sequence(get_features_instance_dict(decode_rle=decode_rle))
746
+ features_dict.update({"annotations": annotations})
747
+ return ds.Features(features_dict)
748
+
749
+
750
+ def get_features_person_keypoints(decode_rle: bool) -> ds.Features:
751
+ features_dict = get_features_base_dict()
752
+ features_instance_dict = get_features_instance_dict(decode_rle=decode_rle)
753
+ features_instance_dict.update(
754
+ {
755
+ "keypoints": ds.Sequence(
756
+ {
757
+ "state": ds.Value("string"),
758
+ "x": ds.Value("int32"),
759
+ "y": ds.Value("int32"),
760
+ "v": ds.Value("int32"),
761
+ }
762
+ ),
763
+ "num_keypoints": ds.Value("int32"),
764
+ }
765
+ )
766
+ annotations = ds.Sequence(features_instance_dict)
767
+ features_dict.update({"annotations": annotations})
768
+ return ds.Features(features_dict)
769
+
770
+
771
+ def dataset_configs(year: int, version: ds.Version) -> List[MsCocoConfig]:
772
+ return [
773
+ MsCocoConfig(
774
+ year=year,
775
+ coco_task="captions",
776
+ version=version,
777
+ ),
778
+ MsCocoConfig(
779
+ year=year,
780
+ coco_task="instances",
781
+ version=version,
782
+ ),
783
+ MsCocoConfig(
784
+ year=year,
785
+ coco_task="person_keypoints",
786
+ version=version,
787
+ ),
788
+ MsCocoConfig(
789
+ year=year,
790
+ coco_task=("captions", "instances"),
791
+ version=version,
792
+ ),
793
+ MsCocoConfig(
794
+ year=year,
795
+ coco_task=("captions", "person_keypoints"),
796
+ version=version,
797
+ ),
798
+ ]
799
+
800
+
801
+ def configs_2014(version: ds.Version) -> List[MsCocoConfig]:
802
+ return dataset_configs(year=2014, version=version)
803
+
804
+
805
+ def configs_2017(version: ds.Version) -> List[MsCocoConfig]:
806
+ return dataset_configs(year=2017, version=version)
807
+
808
+
809
+ class MsCocoDataset(ds.GeneratorBasedBuilder):
810
+ VERSION = ds.Version("1.0.0")
811
+ BUILDER_CONFIG_CLASS = MsCocoConfig
812
+ BUILDER_CONFIGS = configs_2014(version=VERSION) + configs_2017(version=VERSION)
813
+
814
+ @property
815
+ def year(self) -> int:
816
+ config: MsCocoConfig = self.config # type: ignore
817
+ return config.year
818
+
819
+ @property
820
+ def task(self) -> str:
821
+ config: MsCocoConfig = self.config # type: ignore
822
+ return config.task
823
+
824
+ def _info(self) -> ds.DatasetInfo:
825
+ if self.task == "captions":
826
+ features = get_features_captions()
827
+ elif self.task == "instances":
828
+ features = get_features_instances(
829
+ decode_rle=self.config.decode_rle, # type: ignore
830
+ )
831
+ elif self.task == "person_keypoints":
832
+ features = get_features_person_keypoints(
833
+ decode_rle=self.config.decode_rle, # type: ignore
834
+ )
835
+ else:
836
+ raise ValueError(f"Invalid task: {self.task}")
837
+
838
+ return ds.DatasetInfo(
839
+ description=_DESCRIPTION,
840
+ citation=_CITATION,
841
+ homepage=_HOMEPAGE,
842
+ license=_LICENSE,
843
+ features=features,
844
+ )
845
+
846
+ def _split_generators(self, dl_manager: ds.DownloadManager):
847
+ file_paths = dl_manager.download_and_extract(_URLS[f"{self.year}"])
848
+
849
+ imgs = file_paths["images"] # type: ignore
850
+ anns = file_paths["annotations"] # type: ignore
851
+
852
+ return [
853
+ ds.SplitGenerator(
854
+ name=ds.Split.TRAIN, # type: ignore
855
+ gen_kwargs={
856
+ "base_image_dir": imgs["train"],
857
+ "base_annotation_dir": anns["train_validation"],
858
+ "split": "train",
859
+ },
860
+ ),
861
+ ds.SplitGenerator(
862
+ name=ds.Split.VALIDATION, # type: ignore
863
+ gen_kwargs={
864
+ "base_image_dir": imgs["validation"],
865
+ "base_annotation_dir": anns["train_validation"],
866
+ "split": "val",
867
+ },
868
+ ),
869
+ # ds.SplitGenerator(
870
+ # name=ds.Split.TEST, # type: ignore
871
+ # gen_kwargs={
872
+ # "base_image_dir": imgs["test"],
873
+ # "test_image_info_path": anns["test_image_info"],
874
+ # "split": "test",
875
+ # },
876
+ # ),
877
+ ]
878
+
879
+ def _generate_train_val_examples(
880
+ self, split: str, base_image_dir: str, base_annotation_dir: str
881
+ ):
882
+ image_dir = os.path.join(base_image_dir, f"{split}{self.year}")
883
+
884
+ ann_dir = os.path.join(base_annotation_dir, "annotations")
885
+ ann_file_path = os.path.join(ann_dir, f"{self.task}_{split}{self.year}.json")
886
+
887
+ ann_json = _load_annotation_json(ann_file_path=ann_file_path)
888
+
889
+ # info = AnnotationInfo.from_dict(ann_json["info"])
890
+ licenses = _load_licenses_data(license_dicts=ann_json["licenses"])
891
+ images = _load_images_data(image_dicts=ann_json["images"])
892
+
893
+ category_dicts = ann_json.get("categories")
894
+ categories = (
895
+ _load_categories_data(category_dicts=category_dicts)
896
+ if category_dicts is not None
897
+ else None
898
+ )
899
+
900
+ config: MsCocoConfig = self.config # type: ignore
901
+ if config.task == "captions":
902
+ yield from generate_captions_examples(
903
+ annotations=_load_captions_data(
904
+ ann_dicts=ann_json["annotations"],
905
+ ),
906
+ image_dir=image_dir,
907
+ images=images,
908
+ licenses=licenses,
909
+ )
910
+ elif config.task == "instances":
911
+ assert categories is not None
912
+ yield from generate_instances_examples(
913
+ annotations=_load_instances_data(
914
+ images=images,
915
+ ann_dicts=ann_json["annotations"],
916
+ decode_rle=self.config.decode_rle, # type: ignore
917
+ ),
918
+ categories=categories,
919
+ image_dir=image_dir,
920
+ images=images,
921
+ licenses=licenses,
922
+ )
923
+ elif config.task == "person_keypoints":
924
+ assert categories is not None
925
+ yield from generate_person_keypoints_examples(
926
+ annotations=_load_person_keypoints_data(
927
+ images=images,
928
+ ann_dicts=ann_json["annotations"],
929
+ decode_rle=self.config.decode_rle, # type: ignore
930
+ ),
931
+ categories=categories,
932
+ image_dir=image_dir,
933
+ images=images,
934
+ licenses=licenses,
935
+ )
936
+ else:
937
+ raise ValueError(f"Invalid task: {config.task}")
938
+
939
+ def _generate_test_examples(self, test_image_info_path: str):
940
+ raise NotImplementedError
941
+
942
+ def _generate_examples(
943
+ self,
944
+ split: MscocoSplits,
945
+ base_image_dir: Optional[str] = None,
946
+ base_annotation_dir: Optional[str] = None,
947
+ test_image_info_path: Optional[str] = None,
948
+ ):
949
+ if split == "test" and test_image_info_path is not None:
950
+ yield from self._generate_test_examples(
951
+ test_image_info_path=test_image_info_path
952
+ )
953
+ elif (
954
+ split in get_args(MscocoSplits)
955
+ and base_image_dir is not None
956
+ and base_annotation_dir is not None
957
+ ):
958
+ yield from self._generate_train_val_examples(
959
+ split=split,
960
+ base_image_dir=base_image_dir,
961
+ base_annotation_dir=base_annotation_dir,
962
+ )
963
+ else:
964
+ raise ValueError(
965
+ f"Invalid arguments: split = {split}, "
966
+ f"base_image_dir = {base_image_dir}, "
967
+ f"base_annotation_dir = {base_annotation_dir}, "
968
+ f"test_image_info_path = {test_image_info_path}",
969
+ )
README.md ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ # Dataset Card for MSCOCO
2
+
3
+ [![CI](https://github.com/shunk031/huggingface-datasets_MSCOCO/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_MSCOCO/actions/workflows/ci.yaml)
poetry.lock ADDED
The diff for this file is too large to render. See raw diff
 
pyproject.toml ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [tool.poetry]
2
+ name = "huggingface-datasets-mscoco"
3
+ version = "0.1.0"
4
+ description = ""
5
+ authors = ["Shunsuke KITADA <[email protected]>"]
6
+ readme = "README.md"
7
+
8
+ [tool.poetry.dependencies]
9
+ python = "^3.9"
10
+ datasets = {extras = ["vision"], version = "^2.14.4"}
11
+ pycocotools = "^2.0.7"
12
+
13
+ [tool.poetry.group.dev.dependencies]
14
+ ruff = "^0.0.286"
15
+ black = "^23.7.0"
16
+ mypy = "^1.5.1"
17
+ pytest = "^7.4.0"
18
+ pytest-xdist = "^3.3.1"
19
+
20
+ [build-system]
21
+ requires = ["poetry-core"]
22
+ build-backend = "poetry.core.masonry.api"
tests/MSCOCO_test.py ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import datasets as ds
2
+ import pytest
3
+
4
+
5
+ @pytest.fixture
6
+ def dataset_path() -> str:
7
+ return "MSCOCO.py"
8
+
9
+
10
+ @pytest.mark.parametrize(
11
+ argnames="decode_rle,",
12
+ argvalues=(
13
+ True,
14
+ False,
15
+ ),
16
+ )
17
+ @pytest.mark.parametrize(
18
+ argnames=(
19
+ "dataset_year",
20
+ "coco_task",
21
+ "expected_num_train",
22
+ "expected_num_validation",
23
+ ),
24
+ argvalues=(
25
+ (2014, "captions", 82783, 40504),
26
+ (2017, "captions", 118287, 5000),
27
+ (2014, "instances", 82081, 40137),
28
+ (2017, "instances", 117266, 4952),
29
+ (2014, "person_keypoints", 45174, 21634),
30
+ (2017, "person_keypoints", 64115, 2693),
31
+ ),
32
+ )
33
+ def test_load_dataset(
34
+ dataset_path: str,
35
+ dataset_year: int,
36
+ coco_task: str,
37
+ decode_rle: bool,
38
+ expected_num_train: int,
39
+ expected_num_validation: int,
40
+ ):
41
+ dataset = ds.load_dataset(
42
+ path=dataset_path,
43
+ year=dataset_year,
44
+ coco_task=coco_task,
45
+ decode_rle=decode_rle,
46
+ )
47
+ assert dataset["train"].num_rows == expected_num_train
48
+ assert dataset["validation"].num_rows == expected_num_validation
tests/__init__.py ADDED
File without changes