def test_fail_visual(self):
fig = ImRegBenchmark._visual_image_move_warp_lnds_move_warp({ImRegBenchmark.COL_POINTS_MOVE_WARP: 'abc'})
self.assertIsNone(fig)
fig = ImRegBenchmark._visual_image_move_warp_lnds_ref_warp({ImRegBenchmark.COL_POINTS_REF_WARP: 'abc'})
self.assertIsNone(fig)
fig = ImRegBenchmark.visualise_registration((0, {}))
self.assertIsNone(fig)
def test_benchmark_simple(self):
""" test run in sequence (1 thread) """
self._remove_default_experiment(ImRegBenchmark.__name__)
params = {
'path_table': PATH_CSV_COVER_ANHIR,
'path_dataset': PATH_DATA,
'path_out': self.path_out,
'preprocessing': ['matching-hsv', 'gray'],
'nb_workers': 1,
'visual': True,
'unique': False,
}
benchmark = ImRegBenchmark(params)
benchmark.run()
self.check_benchmark_results(benchmark, final_means=[0., 0.], final_stds=[0., 0.])
def test_benchmark_failing(self):
""" test run in parallel with failing experiment """
params = {
'path_table': PATH_CSV_COVER_MIX,
'path_dataset': PATH_DATA,
'path_out': self.path_out,
'preprocessing': 'nothing',
'nb_workers': 4,
'visual': True,
'unique': True,
}
benchmark = ImRegBenchmark(params)
benchmark.run()
# no landmarks was copy and also no experiment results was produced
list_csv = [len([csv for csv in files if os.path.splitext(csv)[1] == '.csv'])
for _, _, files in os.walk(benchmark.params['path_exp'])]
self.assertEqual(sum(list_csv), 0)
del benchmark
def test_benchmark_parallel(self):
""" test run in parallel (2 threads) """
self._remove_default_experiment(ImRegBenchmark.__name__)
params = {
'path_table': PATH_CSV_COVER_MIX,
'path_out': self.path_out,
'preprocessing': ['gray', 'matching-rgb'],
'nb_workers': 2,
'visual': True,
'unique': False,
}
benchmark = ImRegBenchmark(params)
# run it for the first time, complete experiment
benchmark.run()
# rerun experiment simulated repeating unfinished benchmarks
benchmark.run()
self.check_benchmark_results(benchmark, final_means=[0., 0., 0., 0., 0.], final_stds=[0., 0., 0., 0., 0.])
