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Add unit tests for detectors' forward function #3447

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May 3, 2024
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Add unit tests for detectors' forward function #3447

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28 changes: 27 additions & 1 deletion tests/unit/algo/detection/test_atss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,11 @@
# SPDX-License-Identifier: Apache-2.0
"""Test of OTX SSD architecture."""

from otx.algo.detection.atss import MobileNetV2ATSS
import pytest
import torch
from otx.algo.detection.atss import MobileNetV2ATSS, ResNeXt101ATSS
from otx.algo.utils.support_otx_v1 import OTXv1Helper
from otx.core.data.entity.detection import DetBatchPredEntity
from otx.core.exporter.native import OTXModelExporter
from otx.core.types.export import TaskLevelExportParameters

Expand All @@ -17,3 +20,26 @@ def test(self, mocker) -> None:

assert isinstance(model._export_parameters, TaskLevelExportParameters)
assert isinstance(model._exporter, OTXModelExporter)

@pytest.mark.parametrize("model", [MobileNetV2ATSS(3), ResNeXt101ATSS(3)])
def test_loss(self, model, fxt_data_module):
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
output = model(data)
assert "loss_cls" in output
assert "loss_bbox" in output
assert "loss_centerness" in output

@pytest.mark.parametrize("model", [MobileNetV2ATSS(3), ResNeXt101ATSS(3)])
def test_predict(self, model, fxt_data_module):
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
model.eval()
output = model(data)
assert isinstance(output, DetBatchPredEntity)

@pytest.mark.parametrize("model", [MobileNetV2ATSS(3), ResNeXt101ATSS(3)])
def test_export(self, model):
model.eval()
output = model.forward_for_tracing(torch.randn(1, 3, 32, 32))
assert len(output) == 2
24 changes: 24 additions & 0 deletions tests/unit/algo/detection/test_ssd.py
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Expand Up @@ -5,8 +5,10 @@
from pathlib import Path

import pytest
import torch
from lightning import Trainer
from otx.algo.detection.ssd import SSD
from otx.core.data.entity.detection import DetBatchPredEntity


class TestSSD:
Expand Down Expand Up @@ -36,3 +38,25 @@ def test_save_and_load_anchors(self, fxt_checkpoint) -> None:

assert loaded_model.model.bbox_head.anchor_generator.widths[0][0] == 40
assert loaded_model.model.bbox_head.anchor_generator.heights[0][0] == 50

def test_loss(self, fxt_data_module):
model = SSD(3)
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
output = model(data)
assert "loss_cls" in output
assert "loss_bbox" in output

def test_predict(self, fxt_data_module):
model = SSD(3)
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
model.eval()
output = model(data)
assert isinstance(output, DetBatchPredEntity)

def test_export(self):
model = SSD(3)
model.eval()
output = model.forward_for_tracing(torch.randn(1, 3, 32, 32))
assert len(output) == 2
28 changes: 27 additions & 1 deletion tests/unit/algo/detection/test_yolox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,13 @@
# SPDX-License-Identifier: Apache-2.0
"""Test of OTX YOLOX architecture."""

import pytest
import torch
from otx.algo.detection.backbones.csp_darknet import CSPDarknet
from otx.algo.detection.heads.yolox_head import YOLOXHead
from otx.algo.detection.necks.yolox_pafpn import YOLOXPAFPN
from otx.algo.detection.yolox import YOLOXL, YOLOXTINY
from otx.algo.detection.yolox import YOLOXL, YOLOXS, YOLOXTINY, YOLOXX
from otx.core.data.entity.detection import DetBatchPredEntity
from otx.core.exporter.native import OTXNativeModelExporter


Expand All @@ -32,3 +35,26 @@ def test_exporter(self) -> None:
otx_yolox_tiny_exporter = otx_yolox_tiny._exporter
assert isinstance(otx_yolox_tiny_exporter, OTXNativeModelExporter)
assert otx_yolox_tiny_exporter.swap_rgb is False

@pytest.mark.parametrize("model", [YOLOXTINY(3), YOLOXS(3), YOLOXL(3), YOLOXX(3)])
def test_loss(self, model, fxt_data_module):
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
output = model(data)
assert "loss_cls" in output
assert "loss_bbox" in output
assert "loss_obj" in output

@pytest.mark.parametrize("model", [YOLOXTINY(3), YOLOXS(3), YOLOXL(3), YOLOXX(3)])
def test_predict(self, model, fxt_data_module):
data = next(iter(fxt_data_module.train_dataloader()))
data.images = [torch.randn(3, 32, 32), torch.randn(3, 48, 48)]
model.eval()
output = model(data)
assert isinstance(output, DetBatchPredEntity)

@pytest.mark.parametrize("model", [YOLOXTINY(3), YOLOXS(3), YOLOXL(3), YOLOXX(3)])
def test_export(self, model):
model.eval()
output = model.forward_for_tracing(torch.randn(1, 3, 32, 32))
assert len(output) == 2
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