ad762e682d
- Suppression de generate_solar (éclairage solaire) des visualisations - Accélération GPU de hillshade, slope, aspect, curvature, depressions, anomalies, roughness, texture GLCM, flow (sink filling) - Nettoyage mémoire GPU entre visualisations (gpu_cleanup) - Correction OOM texture GLCM: calcul entropie bin par bin au lieu d'un tableau 3D massif sur GPU - Correction bug: xp_minimum_filter manquant dans imports visualizations - Option --file accepte plusieurs noms complets sans extension - run.sh affiche l'aide si appelé sans arguments - Option --test pour exécuter les tests unitaires dans Docker - Filtre ReturnNumber>=1 intégré dans le pipeline PDAL (plus d'erreur SMRF) - 60 tests unitaires: GPU, visualisations, rendering, DTM, pipeline, CLI - Ajout pytest au Dockerfile Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
"""Tests for GPU helper module."""
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import numpy as np
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import pytest
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def test_has_gpu_attribute():
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"""HAS_GPU must be a boolean."""
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from lidar_pipeline.gpu import HAS_GPU
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assert isinstance(HAS_GPU, bool)
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def test_to_gpu_returns_array():
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"""to_gpu returns a float64 array with correct values."""
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from lidar_pipeline.gpu import to_gpu, to_cpu, HAS_GPU
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arr = np.array([1.0, 2.0, 3.0])
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result = to_gpu(arr)
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# On GPU: cupy.ndarray, on CPU: numpy.ndarray
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assert result.dtype == np.float64
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# Always bring back to CPU for comparison
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np.testing.assert_array_equal(to_cpu(result), [1.0, 2.0, 3.0])
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def test_to_cpu_noop_numpy():
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"""to_cpu on a numpy array is a no-op."""
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from lidar_pipeline.gpu import to_cpu
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arr = np.array([1.0, 2.0])
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result = to_cpu(arr)
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assert result is arr
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def test_xp_gaussian_filter():
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"""xp_gaussian_filter blurs a point source correctly."""
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from lidar_pipeline.gpu import xp_gaussian_filter
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arr = np.zeros((50, 50), dtype=np.float64)
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arr[25, 25] = 1.0
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result = xp_gaussian_filter(arr, sigma=3)
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assert result.shape == (50, 50)
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# Center should still be the highest value
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center_val = float(np.asarray(result)[25, 25])
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corner_val = float(np.asarray(result)[0, 0])
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assert center_val > corner_val
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assert center_val > 0.01 # Not all energy is lost
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def test_xp_uniform_filter_cpu():
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"""xp_uniform_filter works on CPU arrays."""
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from lidar_pipeline.gpu import xp_uniform_filter
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arr = np.ones((50, 50), dtype=np.float64)
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arr[25, 25] = 100.0
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result = xp_uniform_filter(arr, size=5)
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# Mean should be close to 1 everywhere except near center
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assert result.shape == (50, 50)
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assert result[0, 0] == pytest.approx(1.0, abs=0.01)
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def test_xp_minimum_filter_cpu():
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"""xp_minimum_filter works on CPU arrays."""
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from lidar_pipeline.gpu import xp_minimum_filter
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arr = np.ones((50, 50), dtype=np.float64)
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arr[25, 25] = 0.0
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result = xp_minimum_filter(arr, size=3)
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assert result.shape == (50, 50)
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# Around the minimum, values should be 0
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assert result[25, 25] == 0.0
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assert result[24, 25] == 0.0
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def test_log_gpu_status(caplog):
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"""log_gpu_status emits a log message."""
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import logging
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from lidar_pipeline.gpu import log_gpu_status
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with caplog.at_level(logging.INFO, logger="lidar"):
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log_gpu_status()
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assert any("GPU" in r.message or "CPU" in r.message for r in caplog.records)
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@pytest.mark.skipif(
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not pytest.importorskip("cupy", reason="CuPy not available"),
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reason="Requires GPU + CuPy"
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)
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def test_to_gpu_roundtrip():
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"""to_gpu -> to_cpu preserves data when GPU is available."""
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import cupy as cp
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from lidar_pipeline.gpu import to_gpu, to_cpu, HAS_GPU
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if not HAS_GPU:
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pytest.skip("No GPU available")
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arr = np.array([1.0, 2.0, 3.0], dtype=np.float32)
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gpu_arr = to_gpu(arr)
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assert isinstance(gpu_arr, cp.ndarray)
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result = to_cpu(gpu_arr)
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assert isinstance(result, np.ndarray)
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np.testing.assert_array_almost_equal(result, [1.0, 2.0, 3.0]) |