def main(argv):
try:
if (len(argv) < 9):
eprint("usage: PythonRunner test_type module_path input_filepath " +\
"output_dir minimum_measurable_time nruns_F nruns_J time_limit [-rep]\n")
return 1
test_type = argv[1]
module_path = os.path.normpath(argv[2])
input_filepath = os.path.normpath(argv[3])
output_prefix = os.path.normpath(argv[4])
minimum_measurable_time = float(argv[5])
nruns_F = int(argv[6])
nruns_J = int(argv[7])
time_limit = float(argv[8])
# read only 1 point and replicate it?
replicate_point = (len(argv) > 9 and str(argv[9]) == "-rep")
# If the given prefix is a directory then add a separator to its end
# thus we can just use concatenation further
if os.path.isdir(output_prefix):
output_prefix += os.path.sep
if test_type == "GMM":
# read gmm input
_input = input_utils.read_gmm_instance(input_filepath, replicate_point)
elif test_type == "BA":
# read ba input
_input = input_utils.read_ba_instance(input_filepath)
elif test_type == "HAND":
model_dir = os.path.join(filepath_to_dirname(input_filepath), "model")
# read hand input
_input = input_utils.read_hand_instance(model_dir, input_filepath, False)
elif test_type == "HAND-COMPLICATED":
model_dir = os.path.join(filepath_to_dirname(input_filepath), "model")
# read hand complicated input
_input = input_utils.read_hand_instance(model_dir, input_filepath, True)
elif test_type == "LSTM":
_input = input_utils.read_lstm_instance(input_filepath)
else:
raise RuntimeError("Python runner doesn't support tests of " + test_type + " type")
run_benchmark(
module_path,
input_filepath,
_input,
output_prefix,
minimum_measurable_time,
nruns_F,
nruns_J,
time_limit
)
except RuntimeError as ex:
eprint("Runtime exception caught: ", ex)
except Exception as ex:
eprint("An exception caught: ", ex)
return 0
def test_jacobian_runs_multiple_times(self):
'''Checks if jacobian can be calculated multiple times.'''
input = read_ba_instance(TEST_INPUT_FILE_NAME)
self.test.prepare(input)
func = self.test.calculate_jacobian
self.assertTrue(utils.can_objective_run_multiple_times(func))
def objective_calculation_correctness(self, times):
'''Checks objective calculation correctness running calculation
several times.'''
input = read_ba_instance(TEST_INPUT_FILE_NAME)
self.test.prepare(input)
self.test.calculate_objective(times)
output = self.test.output()
expected_weight_err = np.full(10, 8.26092651515999976e-01)
expected_reproj_err = np.array(
[-2.69048849235189402e-01, 2.59944792677901881e-01] * 10)
self.assertFloatArrayEqual(expected_reproj_err, output.reproj_err,
self.params["tolerance"])
self.assertFloatArrayEqual(expected_weight_err, output.w_err,
self.params["tolerance"])
def jacobian_calculation_correctness(self, times):
'''Checks jacobian calculation correctness running calculation
several times.'''
input = read_ba_instance(TEST_INPUT_FILE_NAME)
self.test.prepare(input)
self.test.calculate_jacobian(times)
output = self.test.output()
# check jacobian shape
self.assertEqual(30, output.J.nrows)
self.assertEqual(62, output.J.ncols)
self.assertEqual(31, len(output.J.rows))
self.assertEqual(310, len(output.J.cols))
self.assertEqual(310, len(output.J.vals))
# check jacobian values
expected_J_values = [
2.28877202208246757e+02, 6.34574811495545418e+02,
-7.82222866259340549e+02, 2.42892615607159668e+00,
-1.17828079628011313e+01, 2.54169312487743460e+00,
-1.03657084958518086e+00, 4.17022e-01, 0.0,
-3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, 2.28877202208246757e+02,
6.34574811495545418e+02, -7.82222866259340549e+02,
2.42892615607159668e+00, -1.17828079628011313e+01,
2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01,
0.0, -3.50739521096005205e+02, -9.12107773668008576e+02,
-2.42892615607159668e+00, 1.17828079628011313e+01,
-2.54169312487743460e+00, -6.45167039712987389e-01,
-1.20542435994996879e+02, -3.85673240766460424e+02,
9.75476291403326456e+01, -1.78372108529576567e+00,
4.15466799433126077e+00, 2.04025718029898906e+00,
3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02,
3.07250108960343255e+02, 1.78372108529576567e+00,
-4.15466799433126077e+00, -2.04025718029898906e+00,
6.23335921553064054e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01,
-8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01,
-8.34044e-01, -8.34044e-01, -8.34044e-01
]
self.assertFloatArrayEqual(expected_J_values, output.J.vals,
self.params["tolerance"])