mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 10:46:35 +00:00
Oleksandr Bezdieniezhnykh
123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
from pathlib import Path
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import cv2
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import numpy as np
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import onnxruntime as ort
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import pytest
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from ultralytics import YOLO
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import constants as c
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import exports as exports_mod
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_TESTS_DIR = Path(__file__).resolve().parent
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_CONFIG_TEST = _TESTS_DIR.parent / "config.test.yaml"
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_DATASET_IMAGES = _TESTS_DIR / "root" / "data" / "images"
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@pytest.fixture(scope="module")
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def exported_models(tmp_path_factory):
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# Arrange
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tmp = tmp_path_factory.mktemp("export")
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model_dir = tmp / "models"
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model_dir.mkdir()
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pt_path = str(model_dir / "test.pt")
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YOLO("yolo11n.pt").save(pt_path)
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(tmp))
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# Act
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exports_mod.export_onnx(pt_path)
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exports_mod.export_coreml(pt_path)
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yield {
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"onnx": str(next(model_dir.glob("*.onnx"))),
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"model_dir": model_dir,
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}
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c.config = old_config
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class TestOnnxExport:
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def test_onnx_file_created(self, exported_models):
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# Assert
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p = Path(exported_models["onnx"])
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assert p.exists()
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assert p.stat().st_size > 0
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def test_onnx_batch_dimension_is_dynamic(self, exported_models):
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# Arrange
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session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
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batch_dim = session.get_inputs()[0].shape[0]
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# Assert
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assert isinstance(batch_dim, str) or batch_dim == -1
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def test_onnx_inference_batch_1(self, exported_models):
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# Arrange
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session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
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meta = session.get_inputs()[0]
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imgsz = c.config.export.onnx_imgsz
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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blob = cv2.dnn.blobFromImage(
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cv2.imread(str(imgs[0])), 1.0 / 255.0, (imgsz, imgsz), (0, 0, 0), swapRB=True, crop=False,
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)
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# Act
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out = session.run(None, {meta.name: blob})
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# Assert
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assert out[0].shape[0] == 1
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def test_onnx_inference_batch_multiple(self, exported_models):
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# Arrange
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session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
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meta = session.get_inputs()[0]
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imgsz = c.config.export.onnx_imgsz
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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single = cv2.dnn.blobFromImage(
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cv2.imread(str(imgs[0])), 1.0 / 255.0, (imgsz, imgsz), (0, 0, 0), swapRB=True, crop=False,
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)
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batch = np.concatenate([single] * 4, axis=0)
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# Act
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out = session.run(None, {meta.name: batch})
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# Assert
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assert out[0].shape[0] == 4
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class TestCoremlExport:
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def test_coreml_package_created(self, exported_models):
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# Assert
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pkgs = list(exported_models["model_dir"].glob("*.mlpackage"))
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assert len(pkgs) >= 1
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def test_coreml_package_has_model(self, exported_models):
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# Assert
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pkgs = list(exported_models["model_dir"].glob("*.mlpackage"))
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assert len(pkgs) >= 1
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model_file = pkgs[0] / "Data" / "com.apple.CoreML" / "model.mlmodel"
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assert model_file.exists()
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def test_coreml_inference_produces_detections(self, exported_models):
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# Arrange
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pkgs = list(exported_models["model_dir"].glob("*.mlpackage"))
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assert len(pkgs) >= 1
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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model = YOLO(str(pkgs[0]))
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# Act
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results = model.predict(source=str(imgs[0]), imgsz=c.config.export.onnx_imgsz, verbose=False)
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# Assert
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assert len(results) == 1
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assert results[0].boxes is not None
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