File size: 4,997 Bytes
ce2759c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c3d8ff6
60ac70f
 
cb85080
ce2759c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cb85080
 
ce2759c
 
 
 
cb85080
c1bc82d
ce2759c
 
 
 
 
cb85080
c1bc82d
ce2759c
 
 
 
 
c1bc82d
ce2759c
315d061
5eb5d3d
c4764fb
ce2759c
5eb5d3d
ce2759c
 
 
 
 
43fd754
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
# coding=utf-8
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""High-Level dataset."""


import json
from pathlib import Path

import datasets


_CITATION = """\
@misc{}
"""

_DESCRIPTION = """\
High-level Dataset
"""

# github link
_HOMEPAGE = ""

_LICENSE = "Apache 2.0"

_IMG = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/images.tar.gz"
_TRAIN = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/train.jsonl"
_TEST = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/test.jsonl"



class HL(datasets.GeneratorBasedBuilder):
    """High Level Dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "file_name": datasets.Value("string"),
                "image": datasets.Image(),
                "scene": datasets.Sequence(datasets.Value("string")),
                "action": datasets.Sequence(datasets.Value("string")),
                "rationale": datasets.Sequence(datasets.Value("string")),
                "object": datasets.Sequence(datasets.Value("string")),
                # "captions": {
                #         "scene": datasets.Sequence(datasets.Value("string")),
                #         "action": datasets.Sequence(datasets.Value("string")),
                #         "rationale": datasets.Sequence(datasets.Value("string")),
                #         "object": datasets.Sequence(datasets.Value("string")),
                #     },
                "confidence": {
                        "scene": datasets.Sequence(datasets.Value("float32")),
                        "action": datasets.Sequence(datasets.Value("float32")),
                        "rationale": datasets.Sequence(datasets.Value("float32")),
                    }
                # "purity": {
                #     "scene": datasets.Sequence(datasets.Value("float32")),
                #     "action": datasets.Sequence(datasets.Value("float32")),
                #     "rationale": datasets.Sequence(datasets.Value("float32")),
                # },
                # "diversity": {
                #     "scene": datasets.Value("float32"),
                #     "action": datasets.Value("float32"),
                #     "rationale": datasets.Value("float32"),
                # },
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        image_files = dl_manager.download(_IMG)
        annotation_files = dl_manager.download_and_extract([_TRAIN, _TEST])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotation_file_path": annotation_files[0],
                    "images": dl_manager.iter_archive(image_files),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "annotation_file_path": annotation_files[1],
                    "images": dl_manager.iter_archive(image_files),
                },
            ),
        ]

    def _generate_examples(self, annotation_file_path, images):
        
        idx = 0

        #assert Path(annotation_file_path).suffix == ".jsonl"
        
        with open(annotation_file_path, "r") as fp:
            metadata = {json.loads(item)['file_name']: json.loads(item) for item in fp}

        # This loop relies on the ordering of the files in the archive:
        # Annotation files come first, then the images.
        for img_file_path, img_obj in images:

            if img_file_path in metadata:
                file_name = Path(img_file_path).name
    
                yield idx, {
                        "file_name": file_name,
                        "image": {"path": img_file_path, "bytes": img_obj.read()},
                        "scene": metadata[file_name]['captions']['scene'],
                        "action": metadata[file_name]['captions']['action'],
                        "rationale": metadata[file_name]['captions']['rationale'],
                        "object": metadata[file_name]['captions']['object'],
                        "confidence": metadata[file_name]['confidence'],
                    }
                idx += 1