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Add test for mixed-type dataset concatenation. #705

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Aug 14, 2024
Merged

Add test for mixed-type dataset concatenation. #705

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2 changes: 2 additions & 0 deletions api/valor_api/backend/metrics/detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -787,6 +787,8 @@ def _convert_annotations_to_common_type(
source_type = core.get_annotation_type(
db=db, dataset=dataset, task_type=enums.TaskType.OBJECT_DETECTION
)
if target_type > source_type:
continue
core.convert_geometry(
db=db,
dataset=dataset,
Expand Down
349 changes: 347 additions & 2 deletions integration_tests/client/metrics/test_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,11 @@

import random

import numpy as np
import pytest
import requests
from geoalchemy2.functions import ST_Area
from sqlalchemy import select
from sqlalchemy import func, select
from sqlalchemy.orm import Session

from valor import (
Expand All @@ -23,7 +24,7 @@
)
from valor.enums import AnnotationType, EvaluationStatus, MetricType, TaskType
from valor.exceptions import ClientException
from valor.schemas import Box
from valor.schemas import Box, Polygon, Raster
from valor_api.backend import models


Expand Down Expand Up @@ -3237,3 +3238,347 @@ def test_evaluate_detection_model_with_no_predictions(
assert all([metric["value"] == 0 for metric in computed_metrics])
assert all([metric in computed_metrics for metric in expected_metrics])
assert all([metric in expected_metrics for metric in computed_metrics])


def test_evaluate_mixed_annotations(
db: Session,
client: Client,
dataset_name: str,
model_name: str,
image_height: int,
image_width: int,
):
"""Test the automatic conversion to rasters."""
datum = Datum(uid="datum1")

xmin, xmax, ymin, ymax = 11, 45, 37, 102
h, w = image_height, image_width
mask = np.zeros((h, w), dtype=bool)
mask[ymin:ymax, xmin:xmax] = True

pts = [
(xmin, ymin),
(xmin, ymax),
(xmax, ymax),
(xmax, ymin),
(xmin, ymin),
]
poly = Polygon([pts])
raster = Raster.from_numpy(mask)
box = Box.from_extrema(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)

gt_box = GroundTruth(
datum=datum,
annotations=[
Annotation(
bounding_box=box,
labels=[Label(key="box", value="value")],
is_instance=True,
)
],
)
gt_polygon = GroundTruth(
datum=datum,
annotations=[
Annotation(
polygon=poly,
labels=[Label(key="polygon", value="value")],
is_instance=True,
)
],
)
gt_raster = GroundTruth(
datum=datum,
annotations=[
Annotation(
raster=raster,
labels=[Label(key="raster", value="value")],
is_instance=True,
)
],
)

pd_box = Prediction(
datum=datum,
annotations=[
Annotation(
raster=raster,
labels=[Label(key="box", value="value", score=0.88)],
is_instance=True,
)
],
)
pd_polygon = Prediction(
datum=datum,
annotations=[
Annotation(
raster=raster,
labels=[Label(key="polygon", value="value", score=0.89)],
is_instance=True,
)
],
)
pd_raster = Prediction(
datum=datum,
annotations=[
Annotation(
raster=raster,
labels=[Label(key="raster", value="value", score=0.9)],
is_instance=True,
)
],
)

dset_box = Dataset.create(f"{dataset_name}_box")
dset_polygon = Dataset.create(f"{dataset_name}_polygon")
dset_raster = Dataset.create(f"{dataset_name}_raster")

dset_box.add_groundtruth(gt_box)
dset_polygon.add_groundtruth(gt_polygon)
dset_raster.add_groundtruth(gt_raster)

dset_box.finalize()
dset_polygon.finalize()
dset_raster.finalize()

model = Model.create(model_name)

model.add_prediction(dset_box, pd_box)
model.add_prediction(dset_polygon, pd_polygon)
model.add_prediction(dset_raster, pd_raster)

assert db.scalar(select(func.count(models.Annotation.id))) == 6
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.box.isnot(None)
)
)
== 1
)
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.polygon.isnot(None)
)
)
== 1
)
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.raster.isnot(None)
)
)
== 4
)

eval_job = model.evaluate_detection(
[dset_box, dset_polygon, dset_raster],
iou_thresholds_to_compute=[0.1, 0.6],
iou_thresholds_to_return=[0.1, 0.6],
metrics_to_return=[
"AP",
],
)
eval_job.wait_for_completion()

# show that all 6 annotations have a box now since it is the common type.
assert db.scalar(select(func.count(models.Annotation.id))) == 6
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.box.isnot(None)
)
)
== 6
)
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.polygon.isnot(None)
)
)
== 1
)
assert (
db.scalar(
select(func.count(models.Annotation.id)).where(
models.Annotation.raster.isnot(None)
)
)
== 4
)

expected = [
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "box", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "box", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
]

for m in eval_job.metrics:
assert m in expected
for m in expected:
assert m in eval_job.metrics

eval_job_raster = model.evaluate_detection(
[dset_box, dset_polygon, dset_raster],
iou_thresholds_to_compute=[0.1, 0.6],
iou_thresholds_to_return=[0.1, 0.6],
metrics_to_return=[
"AP",
],
convert_annotations_to_type=AnnotationType.RASTER,
)
eval_job_raster.wait_for_completion()

expected = [
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
]

for m in eval_job_raster.metrics:
assert m in expected
for m in expected:
assert m in eval_job_raster.metrics

eval_job_poly = model.evaluate_detection(
[dset_box, dset_polygon, dset_raster],
iou_thresholds_to_compute=[0.1, 0.6],
iou_thresholds_to_return=[0.1, 0.6],
metrics_to_return=[
"AP",
],
convert_annotations_to_type=AnnotationType.POLYGON,
)
eval_job_poly.wait_for_completion()

expected = [
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
]

for m in eval_job_poly.metrics:
assert m in expected
for m in expected:
assert m in eval_job_poly.metrics

eval_job_box = model.evaluate_detection(
[dset_box, dset_polygon, dset_raster],
iou_thresholds_to_compute=[0.1, 0.6],
iou_thresholds_to_return=[0.1, 0.6],
metrics_to_return=[
"AP",
],
convert_annotations_to_type=AnnotationType.BOX,
)
eval_job_box.wait_for_completion()

expected = [
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "raster", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "box", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "box", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.1},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
{
"type": "AP",
"parameters": {"iou": 0.6},
"value": 1.0,
"label": {"key": "polygon", "value": "value"},
},
]

for m in eval_job_box.metrics:
assert m in expected
for m in expected:
assert m in eval_job_box.metrics
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