mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 22:16:35 +00:00
Oleksandr Bezdieniezhnykh
18b88ba9bf
- Updated `.gitignore` to remove committed test fixture data exclusions. - Increased batch size in `config.test.yaml` from 4 to 128 for training. - Simplified directory structure in `config.yaml` by removing unnecessary data paths. - Adjusted paths in `augmentation.py`, `dataset-visualiser.py`, and `exports.py` to align with the new configuration structure. - Enhanced `annotation_queue_handler.py` to utilize the updated configuration for directory management. - Added CSV logging of test results in `conftest.py` for better test reporting. These changes streamline the configuration management and enhance the testing framework, ensuring better organization and clarity in the project.
65 lines
1.5 KiB
Python
65 lines
1.5 KiB
Python
import shutil
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import time
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from os import path as osp
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from pathlib import Path
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import pytest
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import constants as c_mod
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def _prepare_form_dataset(
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monkeypatch,
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tmp_path,
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constants_patch,
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fixture_images_dir,
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fixture_labels_dir,
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count,
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corrupt_stems,
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):
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constants_patch(tmp_path)
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import train
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data_img = Path(c_mod.config.images_dir)
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data_lbl = Path(c_mod.config.labels_dir)
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data_img.mkdir(parents=True, exist_ok=True)
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data_lbl.mkdir(parents=True, exist_ok=True)
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imgs = sorted(fixture_images_dir.glob("*.jpg"))[:count]
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for p in imgs:
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stem = p.stem
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shutil.copy2(fixture_images_dir / f"{stem}.jpg", data_img / f"{stem}.jpg")
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dst = data_lbl / f"{stem}.txt"
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shutil.copy2(fixture_labels_dir / f"{stem}.txt", dst)
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if stem in corrupt_stems:
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dst.write_text("0 1.5 0.5 0.1 0.1\n", encoding="utf-8")
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today_ds = osp.join(c_mod.config.datasets_dir, train.today_folder)
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return train, today_ds
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@pytest.mark.performance
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def test_pt_dsf_01_dataset_formation_under_thirty_seconds(
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monkeypatch,
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tmp_path,
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constants_patch,
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fixture_images_dir,
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fixture_labels_dir,
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):
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# Arrange
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train, today_ds = _prepare_form_dataset(
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monkeypatch,
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tmp_path,
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constants_patch,
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fixture_images_dir,
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fixture_labels_dir,
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100,
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set(),
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)
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# Act
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t0 = time.perf_counter()
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train.form_dataset()
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elapsed = time.perf_counter() - t0
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# Assert
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assert elapsed <= 30.0
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