Spaces:
Sleeping
Sleeping
franzi2505
commited on
Commit
β’
9d444ef
1
Parent(s):
1c14bfe
first commit
Browse files- README.md +39 -6
- app.py +6 -0
- gitattributes +35 -0
- pq.py +180 -0
- requirements.txt +3 -0
README.md
CHANGED
@@ -1,13 +1,46 @@
|
|
1 |
---
|
2 |
title: PanopticQuality
|
3 |
-
|
4 |
-
|
5 |
-
|
|
|
|
|
6 |
sdk: gradio
|
7 |
-
sdk_version:
|
8 |
app_file: app.py
|
9 |
pinned: false
|
10 |
-
|
11 |
---
|
12 |
|
13 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
title: PanopticQuality
|
3 |
+
tags:
|
4 |
+
- evaluate
|
5 |
+
- metric
|
6 |
+
description: >-
|
7 |
+
PanopticQuality score
|
8 |
sdk: gradio
|
9 |
+
sdk_version: 3.19.1
|
10 |
app_file: app.py
|
11 |
pinned: false
|
12 |
+
emoji: π΅οΈ
|
13 |
---
|
14 |
|
15 |
+
# SEA-AI/PanopticQuality
|
16 |
+
|
17 |
+
This hugging face metric uses `seametrics.segmentation.PanopticQuality` under the hood to compute a panoptic quality score. It is a wrapper class for the torchmetrics class [`torchmetrics.detection.PanopticQuality`](https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html).
|
18 |
+
|
19 |
+
## Getting Started
|
20 |
+
|
21 |
+
To get started with PanopticQuality, make sure you have the necessary dependencies installed. This metric relies on the `evaluate`, `seametrics` and `seametrics[segmentation]`libraries for metric calculation and integration with FiftyOne datasets.
|
22 |
+
|
23 |
+
### Installation
|
24 |
+
|
25 |
+
First, ensure you have Python 3.8 or later installed. Then, install det-metrics using pip:
|
26 |
+
|
27 |
+
```sh
|
28 |
+
pip install evaluate git+https://github.com/SEA-AI/seametrics@develop
|
29 |
+
```
|
30 |
+
|
31 |
+
### Basic Usage
|
32 |
+
|
33 |
+
|
34 |
+
## Metric Settings
|
35 |
+
|
36 |
+
## Output Values
|
37 |
+
|
38 |
+
## Further References
|
39 |
+
|
40 |
+
- **seametrics Library**: Explore the [seametrics GitHub repository](https://github.com/SEA-AI/seametrics/tree/main) for more details on the underlying library.
|
41 |
+
- **Torchmetrics**: https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html
|
42 |
+
- **Understanding Metrics**: The Panoptic Segmentation task, as well as Panoptic Quality as the evaluation metric, were introduced [in this paper](https://arxiv.org/pdf/1801.00868.pdf).
|
43 |
+
|
44 |
+
## Contribution
|
45 |
+
|
46 |
+
Your contributions are welcome! If you'd like to improve SEA-AI/PanopticQuality or add new features, please feel free to fork the repository, make your changes, and submit a pull request.
|
app.py
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import evaluate
|
2 |
+
from evaluate.utils import launch_gradio_widget
|
3 |
+
|
4 |
+
|
5 |
+
module = evaluate.load("SEA-AI/PanopticQuality")
|
6 |
+
launch_gradio_widget(module)
|
gitattributes
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
pq.py
ADDED
@@ -0,0 +1,180 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
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.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
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 |
+
"""TODO: Add a description here."""
|
15 |
+
|
16 |
+
from typing import Set
|
17 |
+
from deprecated import deprecated
|
18 |
+
|
19 |
+
import evaluate
|
20 |
+
import datasets
|
21 |
+
import numpy as np
|
22 |
+
|
23 |
+
from seametrics.segmentation import PanopticQuality
|
24 |
+
|
25 |
+
_CITATION = """\
|
26 |
+
@inproceedings{DBLP:conf/cvpr/KirillovHGRD19,
|
27 |
+
author = {Alexander Kirillov and
|
28 |
+
Kaiming He and
|
29 |
+
Ross B. Girshick and
|
30 |
+
Carsten Rother and
|
31 |
+
Piotr Doll{\'{a}}r},
|
32 |
+
title = {Panoptic Segmentation},
|
33 |
+
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
|
34 |
+
2019, Long Beach, CA, USA, June 16-20, 2019},
|
35 |
+
pages = {9404--9413},
|
36 |
+
publisher = {Computer Vision Foundation / {IEEE}},
|
37 |
+
year = {2019},
|
38 |
+
url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Kirillov\_Panoptic\_Segmentation\_CVPR\_2019\_paper.html
|
39 |
+
}
|
40 |
+
"""
|
41 |
+
|
42 |
+
_DESCRIPTION = """\
|
43 |
+
This evaluation metric calculates Panoptic Quality (PQ) for panoptic segmentation masks.
|
44 |
+
"""
|
45 |
+
|
46 |
+
|
47 |
+
_KWARGS_DESCRIPTION = """
|
48 |
+
Calculates PQ-score given predicted and ground truth panoptic segmentation masks.
|
49 |
+
Args:
|
50 |
+
predictions: a 4-d array of shape (batch_size, img_height, img_width, 2).
|
51 |
+
The last dimension should hold the category index at position 0, and
|
52 |
+
the instance ID at position 1.
|
53 |
+
references: a 4-d array of shape (batch_size, img_height, img_width, 2).
|
54 |
+
The last dimension should hold the category index at position 0, and
|
55 |
+
the instance ID at position 1.
|
56 |
+
Returns:
|
57 |
+
A single float number in range [0, 1] that represents the PQ score.
|
58 |
+
1 is perfect panoptic segmentation, 0 is worst possible panoptic segmentation.
|
59 |
+
Examples:
|
60 |
+
>>> import evaluate
|
61 |
+
>>> from seametrics.fo_utils.utils import fo_to_payload
|
62 |
+
>>> MODEL_FIELD = ["maskformer-27k-100ep"]
|
63 |
+
>>> payload = fo_to_payload("SAILING_PANOPTIC_DATASET_QA",
|
64 |
+
>>> gt_field="ground_truth_det",
|
65 |
+
>>> models=MODEL_FIELD,
|
66 |
+
>>> sequence_list=["Trip_55_Seq_2", "Trip_197_Seq_1", "Trip_197_Seq_68"],
|
67 |
+
>>> excluded_classes=[""])
|
68 |
+
>>> module = evaluate.load("SEA-AI/PanopticQuality")
|
69 |
+
>>> module.add_payload(payload, model_name=MODEL_FIELD[0])
|
70 |
+
>>> module.compute()
|
71 |
+
100%|ββββββββββ| 3/3 [00:03<00:00, 1.30s/it]
|
72 |
+
Added data ...
|
73 |
+
Start computing ...
|
74 |
+
Finished!
|
75 |
+
tensor(0.2082, dtype=torch.float64)
|
76 |
+
"""
|
77 |
+
|
78 |
+
|
79 |
+
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
|
80 |
+
class PQMetric(evaluate.Metric):
|
81 |
+
def __init__(
|
82 |
+
self,
|
83 |
+
label2id: dict = None,
|
84 |
+
stuff: list = None,
|
85 |
+
**kwargs
|
86 |
+
):
|
87 |
+
super().__init__(**kwargs)
|
88 |
+
DEFAULT_LABEL2ID = {'WATER': 0,
|
89 |
+
'SKY': 1,
|
90 |
+
'LAND': 2,
|
91 |
+
'MOTORBOAT': 3,
|
92 |
+
'FAR_AWAY_OBJECT': 4,
|
93 |
+
'SAILING_BOAT_WITH_CLOSED_SAILS': 5,
|
94 |
+
'SHIP': 6,
|
95 |
+
'WATERCRAFT': 7,
|
96 |
+
'SPHERICAL_BUOY': 8,
|
97 |
+
'CONSTRUCTION': 9,
|
98 |
+
'FLOTSAM': 10,
|
99 |
+
'SAILING_BOAT_WITH_OPEN_SAILS': 11,
|
100 |
+
'CONTAINER': 12,
|
101 |
+
'PILLAR_BUOY': 13}
|
102 |
+
DEFAULT_STUFF = ["WATER", "SKY", "LAND", "CONSTRUCTION", "ICE", "OWN_BOAT"]
|
103 |
+
self.label2id = label2id if label2id is not None else DEFAULT_LABEL2ID
|
104 |
+
self.stuff = stuff if stuff is not None else DEFAULT_STUFF
|
105 |
+
self.pq_metric = PanopticQuality(
|
106 |
+
things=set([self.label2id[label] for label in self.label2id.keys() if label not in self.stuff]),
|
107 |
+
stuffs=set([self.label2id[label] for label in self.label2id.keys() if label in self.stuff])
|
108 |
+
)
|
109 |
+
|
110 |
+
def _info(self):
|
111 |
+
return evaluate.MetricInfo(
|
112 |
+
# This is the description that will appear on the modules page.
|
113 |
+
module_type="metric",
|
114 |
+
description=_DESCRIPTION,
|
115 |
+
citation=_CITATION,
|
116 |
+
inputs_description=_KWARGS_DESCRIPTION,
|
117 |
+
# This defines the format of each prediction and reference
|
118 |
+
features=datasets.Features(
|
119 |
+
{
|
120 |
+
"predictions": datasets.Sequence(
|
121 |
+
datasets.Sequence(
|
122 |
+
datasets.Sequence(
|
123 |
+
datasets.Sequence(datasets.Value("float"))
|
124 |
+
)
|
125 |
+
),
|
126 |
+
),
|
127 |
+
"references": datasets.Sequence( # batch
|
128 |
+
datasets.Sequence( # img height
|
129 |
+
datasets.Sequence( # img width
|
130 |
+
datasets.Sequence(datasets.Value("float")) # 2
|
131 |
+
)
|
132 |
+
),
|
133 |
+
),
|
134 |
+
}
|
135 |
+
),
|
136 |
+
# Additional links to the codebase or references
|
137 |
+
codebase_urls=[
|
138 |
+
"https://lightning.ai/docs/torchmetrics/stable/detection/panoptic_quality.html"
|
139 |
+
],
|
140 |
+
)
|
141 |
+
|
142 |
+
def add(self, *, prediction, reference, **kwargs):
|
143 |
+
"""Adds a batch of predictions and references to the metric"""
|
144 |
+
# in case the inputs are lists, convert them to numpy arrays
|
145 |
+
|
146 |
+
self.pq_metric.update(prediction, reference)
|
147 |
+
|
148 |
+
# does not impact the metric, but is required for the interface x_x
|
149 |
+
super(evaluate.Metric, self).add(
|
150 |
+
prediction=self._postprocess(prediction),
|
151 |
+
references=self._postprocess(reference),
|
152 |
+
**kwargs
|
153 |
+
)
|
154 |
+
|
155 |
+
def _compute(self, *, predictions, references, **kwargs):
|
156 |
+
"""Called within the evaluate.Metric.compute() method"""
|
157 |
+
return self.pq_metric.compute()
|
158 |
+
|
159 |
+
def add_payload(self, payload: dict, model_name: str = None):
|
160 |
+
"""Converts the payload to the format expected by the metric"""
|
161 |
+
# import only if needed since fiftyone is not a direct dependency
|
162 |
+
from seametrics.segmentation.utils import payload_to_seg_metric
|
163 |
+
|
164 |
+
predictions, references, label2id = payload_to_seg_metric(payload, model_name, self.label2id)
|
165 |
+
self.label2id = label2id
|
166 |
+
self.add(prediction=predictions, reference=references)
|
167 |
+
|
168 |
+
def _postprocess(self, np_array):
|
169 |
+
"""Converts the numpy arrays to lists for type checking"""
|
170 |
+
return self._np_to_lists(np_array)
|
171 |
+
|
172 |
+
def _np_to_lists(self, d):
|
173 |
+
"""datasets does not support numpy arrays for type checking"""
|
174 |
+
if isinstance(d, np.ndarray):
|
175 |
+
if d.ndim == 1:
|
176 |
+
return d.tolist()
|
177 |
+
else:
|
178 |
+
return [self._np_to_lists(sub_arr) for sub_arr in d]
|
179 |
+
else:
|
180 |
+
return d
|
requirements.txt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
git+https://github.com/huggingface/evaluate@main
|
2 |
+
git+https://github.com/SEA-AI/seametrics@develop
|
3 |
+
fiftyone
|