[Bug]: 4 tests fail on `ppc` (perhaps precision issue)
barracuda156 opened this issue · 4 comments
What Operating System(s) are you seeing this problem on?
Other (plase, specify in the Steps to Reproduce)
dlib version
19.24.3
Python version
3.11
Compiler
GCC 13.2.0
Expected Behavior
Hopefully an appropriate precision threshold is used for 32-bit platforms, so that tests pass without failures.
Current Behavior
---> Testing py311-dlib
Executing: cd "/opt/local/var/macports/build/_opt_PPCSnowLeopardPorts_math_dlib/py311-dlib/work/dlib-19.24.3" && py.test-3.11 -o addopts=''
============================= test session starts ==============================
platform darwin -- Python 3.11.8, pytest-7.4.3, pluggy-1.3.0
rootdir: /opt/local/var/macports/build/_opt_PPCSnowLeopardPorts_math_dlib/py311-dlib/work/dlib-19.24.3
plugins: flaky-3.7.0, cov-4.1.0
collected 75 items
tools/python/test/test_array.py .............. [ 18%]
tools/python/test/test_chinese_whispers.py ... [ 22%]
tools/python/test/test_global_optimization.py F.F [ 26%]
tools/python/test/test_matrix.py ........ [ 37%]
tools/python/test/test_numpy_returns.py ... [ 41%]
tools/python/test/test_point.py .... [ 46%]
tools/python/test/test_range.py ..... [ 53%]
tools/python/test/test_rgb_pixel.py . [ 54%]
tools/python/test/test_sparse_vector.py .... [ 60%]
tools/python/test/test_svm_c_trainer.py ....FF...... [ 76%]
tools/python/test/test_vector.py .................. [100%]
=================================== FAILURES ===================================
________________________ test_global_optimization_nargs ________________________
def test_global_optimization_nargs():
w0 = find_max_global(lambda *args: sum(args), [0, 0, 0], [1, 1, 1], 10)
w1 = find_min_global(lambda *args: sum(args), [0, 0, 0], [1, 1, 1], 10)
> assert w0 == ([1, 1, 1], 3)
E assert ([0.0, 0.0, 0.0], -inf) == ([1, 1, 1], 3)
E At index 0 diff: [0.0, 0.0, 0.0] != [1, 1, 1]
E Use -v to get more diff
tools/python/test/test_global_optimization.py:10: AssertionError
_____________________________ test_on_holder_table _____________________________
def test_on_holder_table():
x,y = find_min_global(holder_table,
[-10,-10],
[10,10],
200)
> assert (y - -19.2085025679) < 1e-7
E assert (inf - -19.2085025679) < 1e-07
tools/python/test/test_global_optimization.py:69: AssertionError
__________ test_trainers[svm_c_trainer_linear-1.0-0.7666666666666667] __________
training_data = <[AttributeError("'_dlib_pybind11.array' object has no attribute 'typecode'") raised in repr()] tuple object at 0x139f1128>
trainer = <class '_dlib_pybind11.svm_c_trainer_linear'>, class1_accuracy = 1.0
class2_accuracy = 0.7666666666666667
@pytest.mark.parametrize('trainer, class1_accuracy, class2_accuracy', [
(svm_c_trainer_radial_basis, 1.0, 1.0),
(svm_c_trainer_sparse_radial_basis, 1.0, 1.0),
(svm_c_trainer_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_sparse_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_linear, 1.0, 23 / 30),
(svm_c_trainer_sparse_linear, 1.0, 23 / 30),
(rvm_trainer_radial_basis, 1.0, 1.0),
(rvm_trainer_sparse_radial_basis, 1.0, 1.0),
(rvm_trainer_histogram_intersection, 1.0, 1.0),
(rvm_trainer_sparse_histogram_intersection, 1.0, 1.0),
(rvm_trainer_linear, 1.0, 0.6),
(rvm_trainer_sparse_linear, 1.0, 0.6)
])
def test_trainers(training_data, trainer, class1_accuracy, class2_accuracy):
predictors, sparse_predictors, response = training_data
if 'sparse' in trainer.__name__:
predictors = sparse_predictors
cv = cross_validate_trainer(trainer(), predictors, response, folds=10)
assert cv.class1_accuracy == pytest.approx(class1_accuracy)
> assert cv.class2_accuracy == pytest.approx(class2_accuracy)
E assert 0.0 == 0.7666666666666667 ± 7.7e-07
E comparison failed
E Obtained: 0.0
E Expected: 0.7666666666666667 ± 7.7e-07
tools/python/test/test_svm_c_trainer.py:59: AssertionError
______ test_trainers[svm_c_trainer_sparse_linear-1.0-0.7666666666666667] _______
training_data = <[AttributeError("'_dlib_pybind11.array' object has no attribute 'typecode'") raised in repr()] tuple object at 0x139f10c8>
trainer = <class '_dlib_pybind11.svm_c_trainer_sparse_linear'>
class1_accuracy = 1.0, class2_accuracy = 0.7666666666666667
@pytest.mark.parametrize('trainer, class1_accuracy, class2_accuracy', [
(svm_c_trainer_radial_basis, 1.0, 1.0),
(svm_c_trainer_sparse_radial_basis, 1.0, 1.0),
(svm_c_trainer_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_sparse_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_linear, 1.0, 23 / 30),
(svm_c_trainer_sparse_linear, 1.0, 23 / 30),
(rvm_trainer_radial_basis, 1.0, 1.0),
(rvm_trainer_sparse_radial_basis, 1.0, 1.0),
(rvm_trainer_histogram_intersection, 1.0, 1.0),
(rvm_trainer_sparse_histogram_intersection, 1.0, 1.0),
(rvm_trainer_linear, 1.0, 0.6),
(rvm_trainer_sparse_linear, 1.0, 0.6)
])
def test_trainers(training_data, trainer, class1_accuracy, class2_accuracy):
predictors, sparse_predictors, response = training_data
if 'sparse' in trainer.__name__:
predictors = sparse_predictors
cv = cross_validate_trainer(trainer(), predictors, response, folds=10)
assert cv.class1_accuracy == pytest.approx(class1_accuracy)
> assert cv.class2_accuracy == pytest.approx(class2_accuracy)
E assert 0.0 == 0.7666666666666667 ± 7.7e-07
E comparison failed
E Obtained: 0.0
E Expected: 0.7666666666666667 ± 7.7e-07
tools/python/test/test_svm_c_trainer.py:59: AssertionError
=========================== short test summary info ============================
FAILED tools/python/test/test_global_optimization.py::test_global_optimization_nargs
FAILED tools/python/test/test_global_optimization.py::test_on_holder_table - ...
FAILED tools/python/test/test_svm_c_trainer.py::test_trainers[svm_c_trainer_linear-1.0-0.7666666666666667]
FAILED tools/python/test/test_svm_c_trainer.py::test_trainers[svm_c_trainer_sparse_linear-1.0-0.7666666666666667]
========================= 4 failed, 71 passed in 5.16s =========================
Command failed: cd "/opt/local/var/macports/build/_opt_PPCSnowLeopardPorts_math_dlib/py311-dlib/work/dlib-19.24.3" && py.test-3.11 -o addopts=''
Exit code: 1
Error: Failed to test py311-dlib: command execution failed
Steps to Reproduce
Run tests on ppc arch. Presumably BSD or Linux will do as well, though I can only confirm this for Darwin.
Anything else?
macOS 10.6 / powerpc (32-bit)
I don't have a machine to test this on. Can you post a PR with some adjustments to handle this?
Warning: this issue has been inactive for 35 days and will be automatically closed on 2024-05-15 if there is no further activity.
If you are waiting for a response but haven't received one it's possible your question is somehow inappropriate. E.g. it is off topic, you didn't follow the issue submission instructions, or your question is easily answerable by reading the FAQ, dlib's official compilation instructions, dlib's API documentation, or a Google search.
Warning: this issue has been inactive for 43 days and will be automatically closed on 2024-05-15 if there is no further activity.
If you are waiting for a response but haven't received one it's possible your question is somehow inappropriate. E.g. it is off topic, you didn't follow the issue submission instructions, or your question is easily answerable by reading the FAQ, dlib's official compilation instructions, dlib's API documentation, or a Google search.
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