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This file contains tests for the API of your model. You can run these tests by installing test requirements:
```bash
pip install -r requirements-test.txt
```
"""
import pytest
import json
from label_studio_ml.utils import compare_nested_structures
from .test_common import client
label_configs = [
# test 1: one control tag with rectangle labels
"""
<View>
<Image name="image" value="$image"/>
<RectangleLabels name="label" toName="image" model_score_threshold="0.25">
<Label value="Airplane" background="green"/>
<Label value="Car" background="blue" predicted_values="car, truck"/>
</RectangleLabels>
</View>
""",
# test 2: two control tags with rectangle labels and two images
"""
<View>
<Image name="image" value="$image"/>
<RectangleLabels name="label" toName="image" model_score_threshold="0.30">
<Label value="Airplane" background="green"/>
<Label value="Car" background="blue" predicted_values="car, truck"/>
</RectangleLabels>
<Image name="image2" value="$image2"/>
<RectangleLabels name="label2" toName="image2" model_score_threshold="0.90">
<Label value="Person" background="green"/>
<Label value="Animal" background="blue" predicted_values="cat,dog"/>
</RectangleLabels>
</View>
""",
]
tasks = [
# test 1: one control tag with rectangle labels
{
"data": {
"image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
}
},
# test 2: two control tags with rectangle labels and two images
{
"data": {
"image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg",
"image2": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg",
}
},
]
expected = [
# test 1: one control tag with rectangle labels
[
{
"model_version": "yolo",
"result": [
{
"from_name": "label",
"score": 0.5791077017784119,
"to_name": "image",
"type": "rectanglelabels",
"value": {
"height": 77.13761925697327,
"rectanglelabels": ["Car"],
"width": 69.33701038360596,
"x": 21.9377338886261,
"y": 7.984769344329834,
},
},
{
"from_name": "label",
"score": 0.31354132294654846,
"to_name": "image",
"type": "rectanglelabels",
"value": {
"height": 25.369155406951904,
"rectanglelabels": ["Car"],
"width": 18.623733520507812,
"x": 81.27312660217285,
"y": 0.10521858930587769,
},
},
],
"score": 0.44632451236248016,
}
],
# test 2: two control tags with rectangle labels and two images
[
{
"model_version": "yolo",
"result": [
{
"from_name": "label",
"score": 0.5791077017784119,
"to_name": "image",
"type": "rectanglelabels",
"value": {
"height": 77.13761925697327,
"rectanglelabels": ["Car"],
"width": 69.33701038360596,
"x": 21.9377338886261,
"y": 7.984769344329834,
},
},
{
"from_name": "label",
"score": 0.31354132294654846,
"to_name": "image",
"type": "rectanglelabels",
"value": {
"height": 25.369155406951904,
"rectanglelabels": ["Car"],
"width": 18.623733520507812,
"x": 81.27312660217285,
"y": 0.10521858930587769,
},
},
{
"from_name": "label2",
"score": 0.9059886932373047,
"to_name": "image2",
"type": "rectanglelabels",
"value": {
"height": 39.60925042629242,
"rectanglelabels": ["Person"],
"width": 10.503808408975601,
"x": 89.45398144423962,
"y": 6.985808908939362,
},
},
],
"score": 0.5995459059874216,
}
],
]
@pytest.mark.parametrize(
"label_config, task, expect", zip(label_configs, tasks, expected)
)
def test_rectanglelabels_predict(client, label_config, task, expect):
data = {"schema": label_config, "project": "42"}
response = client.post(
"/setup", data=json.dumps(data), content_type="application/json"
)
assert response.status_code == 200, "Error while setup: " + str(response.content)
data = {"tasks": [task], "label_config": label_config}
response = client.post(
"/predict", data=json.dumps(data), content_type="application/json"
)
assert response.status_code == 200, "Error while predict"
data = response.json
compare_nested_structures(data["results"], expect)
|