mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 11:26:36 +00:00
Oleksandr Bezdieniezhnykh
222f552a10
- Add TensorRT export tests with graceful skip when no GPU available - Add AC test coverage verification step (Step 8) to implement skill - Add test coverage gap analysis to new-task skill - Move exported_models fixture to conftest.py as session-scoped (shared across modules) - Reorder tests: e2e training runs first so images/labels are available for all tests - Consolidate teardown into single session-level cleanup in conftest.py - Fix infrastructure tests to count files dynamically instead of hardcoded 20 Made-with: Cursor
111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
import os
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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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_TESTS_DIR = Path(__file__).resolve().parent
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_TEST_ROOT = _TESTS_DIR / "root"
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_DATASET_IMAGES = _TEST_ROOT / "data" / "images"
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_CONFIG_TEST = _TESTS_DIR.parent / "config.test.yaml"
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_SOURCE_DATASET = _TESTS_DIR.parent / "_docs" / "00_problem" / "input_data" / "dataset"
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def _hardlink_tree(src_dir: Path, dst_dir: Path):
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dst_dir.mkdir(parents=True, exist_ok=True)
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for f in src_dir.iterdir():
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if f.is_file():
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target = dst_dir / f.name
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if not target.exists():
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os.link(f, target)
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@pytest.fixture(scope="module")
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def e2e_result():
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# Arrange
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src_images = _SOURCE_DATASET / "images"
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src_labels = _SOURCE_DATASET / "labels"
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if not src_images.is_dir() or not src_labels.is_dir():
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pytest.skip("source dataset not found")
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(_TEST_ROOT))
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dst_images = Path(c.config.images_dir)
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dst_labels = Path(c.config.labels_dir)
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for d in (dst_images, dst_labels, c.config.datasets_dir, c.config.models_dir, c.config.corrupted_dir):
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shutil.rmtree(str(d), ignore_errors=True)
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_hardlink_tree(src_images, dst_images)
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_hardlink_tree(src_labels, dst_labels)
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linked_count = len(list(dst_images.glob("*.jpg")))
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Path(c.config.models_dir).mkdir(parents=True, exist_ok=True)
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# Act
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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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"linked_count": linked_count,
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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_dataset_formed(self, e2e_result):
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# Assert
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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 0 < total <= e2e_result["linked_count"]
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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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