mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 10:26:36 +00:00
Oleksandr Bezdieniezhnykh
243b69656b
- Modified `.gitignore` to reflect the new path for test results. - Updated `docker-compose.test.yml` to mount the correct test results directory. - Adjusted `Dockerfile.test` to set the `PYTHONPATH` and ensure test results are saved in the updated location. - Added `boto3` and `netron` to `requirements-test.txt` to support new functionalities. - Updated `pytest.ini` to include the new `pythonpath` for test discovery. These changes streamline the testing process and ensure compatibility with the updated directory structure.
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
import shutil
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from os import path
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from pathlib import Path
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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 train as train_mod
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import exports as exports_mod
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_PROJECT_ROOT = Path(__file__).resolve().parent.parent
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_DATASET_IMAGES = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/images"
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_DATASET_LABELS = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/labels"
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_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"
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@pytest.fixture(scope="module")
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def e2e_result(tmp_path_factory):
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base = tmp_path_factory.mktemp("e2e")
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(base / "azaion"))
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data_img = Path(c.config.data_images_dir)
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data_lbl = Path(c.config.data_labels_dir)
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data_img.mkdir(parents=True)
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data_lbl.mkdir(parents=True)
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Path(c.config.models_dir).mkdir(parents=True)
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for img in sorted(_DATASET_IMAGES.glob("*.jpg")):
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shutil.copy2(img, data_img / img.name)
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lbl = _DATASET_LABELS / f"{img.stem}.txt"
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if lbl.exists():
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shutil.copy2(lbl, data_lbl / lbl.name)
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from augmentation import Augmentator
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Augmentator().augment_annotations()
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train_mod.train_dataset()
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exports_mod.export_onnx(c.config.current_pt_model)
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exports_mod.export_coreml(c.config.current_pt_model)
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today_ds = path.join(c.config.datasets_dir, train_mod.today_folder)
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yield {
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"today_dataset": today_ds,
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}
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c.config = old_config
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@pytest.mark.e2e
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class TestTrainingPipeline:
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def test_augmentation_produced_output(self, e2e_result):
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proc = Path(c.config.processed_images_dir)
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assert len(list(proc.glob("*.jpg"))) == 800
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def test_dataset_formed(self, e2e_result):
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base = Path(e2e_result["today_dataset"])
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for split in ("train", "valid", "test"):
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assert (base / split / "images").is_dir()
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assert (base / split / "labels").is_dir()
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total = sum(
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len(list((base / s / "images").glob("*.jpg")))
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for s in ("train", "valid", "test")
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)
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assert total == 800
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def test_data_yaml_created(self, e2e_result):
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yaml_path = Path(e2e_result["today_dataset"]) / "data.yaml"
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assert yaml_path.exists()
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content = yaml_path.read_text()
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assert "nc: 80" in content
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assert "train:" in content
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assert "val:" in content
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def test_training_produces_pt(self, e2e_result):
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pt = Path(c.config.current_pt_model)
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assert pt.exists()
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assert pt.stat().st_size > 0
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def test_export_onnx(self, e2e_result):
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p = Path(c.config.current_onnx_model)
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assert p.exists()
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assert p.suffix == ".onnx"
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assert p.stat().st_size > 0
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def test_export_coreml(self, e2e_result):
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pkgs = list(Path(c.config.models_dir).glob("*.mlpackage"))
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assert len(pkgs) >= 1
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def test_onnx_inference(self, e2e_result):
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onnx_model = YOLO(c.config.current_onnx_model)
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img = sorted(_DATASET_IMAGES.glob("*.jpg"))[0]
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results = onnx_model.predict(source=str(img), imgsz=c.config.export.onnx_imgsz, verbose=False)
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assert len(results) == 1
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assert results[0].boxes is not None
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