repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
Delgan/loguru | import pickle
import re
import sys
import time
import pytest
from loguru import logger
from .conftest import default_threading_excepthook
def test_enqueue():
x = []
def sink(message):
time.sleep(0.1)
x.append(message)
logger.add(sink, format="{message}", enqueue=True)
logger.debug(... | 0 | assert | numeric_literal | tests/test_add_option_enqueue.py | test_enqueue | 61 | null | |
Delgan/loguru | import pytest
from loguru import logger
def test_multiple_activations():
def n():
return len(logger._core.activation_list)
assert n() == | 0 | assert | numeric_literal | tests/test_activation.py | test_multiple_activations | 83 | null | |
Delgan/loguru | import asyncio
import contextlib
import datetime
import logging
import pickle
import pytest
from loguru import logger
from .conftest import parse
def print_(message):
print(message, end="")
async def async_print(msg):
print_(msg)
def copied_logger_though_pickle(logger):
pickled = pickle.dumps(logger)
... | "" | assert | string_literal | tests/test_pickling.py | test_pickling_function_handler | 109 | null | |
Delgan/loguru | import gc
import pickle
import sys
import threading
import time
import pytest
from loguru import logger
def _remove_cyclic_references():
"""Prevent cyclic isolate finalizers bleeding into other tests."""
try:
yield
finally:
gc.collect()
def test_no_deadlock_if_logger_used_inside_sink_wit... | err | assert | variable | tests/test_locks.py | test_no_deadlock_if_logger_used_inside_sink_with_catch | 81 | null | |
Delgan/loguru | import itertools
import time
from threading import Barrier, Thread
from loguru import logger
def test_safe_adding_while_logging(writer):
barrier = Barrier(2)
counter = itertools.count()
sink_1 = NonSafeSink(1)
sink_2 = NonSafeSink(1)
logger.add(sink_1, format="{message}", catch=False)
def th... | "ccc1ddd\n" | assert | string_literal | tests/test_threading.py | test_safe_adding_while_logging | 82 | null | |
Delgan/loguru | import sys
import _init
from somelib import assertionerror
from loguru import logger
def test(*, backtrace, colorize, diagnose):
logger.remove()
logger.add(sys.stderr, format="", colorize=colorize, backtrace=backtrace, diagnose=diagnose)
try:
a, b = 1, 2
assert a == | b | assert | variable | tests/exceptions/source/ownership/assertion_from_local.py | test | 15 | null | |
Delgan/loguru | import os
import sys
import threading
import time
from unittest.mock import Mock
import pytest
from loguru import logger
from .conftest import check_dir
@pytest.mark.parametrize("delay", [True, False])
def test_exception_during_compression_at_rotation_not_caught(freeze_time, tmp_path, capsys, delay):
with freez... | OSError, match="^Compression error$") | pytest.raises | complex_expr | tests/test_filesink_compression.py | test_exception_during_compression_at_rotation_not_caught | 222 | null | |
Delgan/loguru | import sys
import pytest
from loguru import logger
def test_extra(writer):
extra = {"a": 1, "b": 9}
logger.add(writer, format="{extra[a]} {extra[b]}")
logger.configure(extra=extra)
logger.debug("")
assert writer.read() == | "1 9\n" | assert | string_literal | tests/test_configure.py | test_extra | 46 | null | |
Delgan/loguru | import json
import re
import sys
from loguru import logger
def test_serialize_with_record_option():
sink = JsonSink()
logger.add(sink, format="{message}", serialize=True, catch=False)
logger.opt(record=True).info("Test", foo=123)
assert sink.json["text"] == "Test\n"
assert sink.dict["extra"] ==... | {"foo": 123} | assert | collection | tests/test_add_option_serialize.py | test_serialize_with_record_option | 134 | null | |
Delgan/loguru | import pytest
from colorama import Back, Fore, Style
from .conftest import parse
@pytest.mark.parametrize(
("text", "expected"),
[
("<bg red>1</bg red>", Back.RED + "1" + Style.RESET_ALL),
("<bg BLACK>1</bg BLACK>", Back.BLACK + "1" + Style.RESET_ALL),
("<bg light-green>1</bg light-gre... | expected | assert | variable | tests/test_ansimarkup_extended.py | test_background_colors | 17 | null | |
Delgan/loguru | import copy
from loguru import logger
def print_(message):
print(message, end="")
def test_remove_from_copy(capsys):
logger.add(print_, format="{message}", catch=False)
logger_ = copy.deepcopy(logger)
logger_.remove()
logger_.info("A")
logger.info("B")
out, err = capsys.readouterr()
... | "B\n" | assert | string_literal | tests/test_deepcopy.py | test_remove_from_copy | 60 | null | |
Delgan/loguru | import datetime
import os
import sys
from time import strftime
from unittest.mock import Mock
import freezegun
import pytest
import loguru
from loguru import logger
def _expected_fallback_time_zone():
# For some reason, Python versions and interepreters return different time zones here.
return strftime("%Z")... | "" | assert | string_literal | tests/test_datetime.py | test_stdout_formatting | 172 | null | |
Delgan/loguru | from loguru import logger
import asyncio
import sys
logger.remove()
logger.add(lambda m: None, format="", diagnose=True, backtrace=True, colorize=True)
def test_decorate_async_generator():
@logger.catch(reraise=True)
async def generator(x, y):
yield x
yield y
async def coro():
ou... | [1, 2] | assert | collection | tests/exceptions/source/modern/decorate_async_generator.py | test_decorate_async_generator | 24 | null | |
Delgan/loguru | import pytest
from loguru import logger
def test_log_before_enable(writer):
logger.add(writer, format="{message}")
logger.disable("")
logger.debug("nope")
logger.enable("tests")
logger.debug("yes")
result = writer.read()
assert result == | "yes\n" | assert | string_literal | tests/test_activation.py | test_log_before_enable | 66 | null | |
Delgan/loguru | import re
import sys
import time
import pytest
from loguru import logger
from .conftest import default_threading_excepthook
def broken_sink(m):
raise ValueError("Error!")
def test_catch_is_false(capsys):
logger.add(broken_sink, catch=False)
with pytest.raises(ValueError, match="Error!"):
logger... | err | assert | variable | tests/test_add_option_catch.py | test_catch_is_false | 29 | null | |
Delgan/loguru | import asyncio
import logging
import os
import pathlib
import sys
import pytest
from loguru import logger
message = "test message"
expected = message + "\n"
repetitions = pytest.mark.parametrize("rep", [0, 1, 2])
def log(sink, rep=1):
logger.debug("This shouldn't be printed.")
i = logger.add(sink, format="... | "Test\n" | assert | string_literal | tests/test_add_sinks.py | test_custom_sink_invalid_flush | 194 | null | |
Delgan/loguru | import re
import pytest
from loguru import logger
@pytest.mark.parametrize(
"filter",
[
None,
lambda _: True,
{},
{None: 0},
{"": False},
{"tests": False, None: True},
{"unrelated": 100},
{None: "INFO", "": "WARNING"},
],
)
def test_filtered... | "It's ok\n" | assert | string_literal | tests/test_add_option_filter.py | test_filtered_in_incomplete_frame_context | 74 | null | |
Delgan/loguru | import datetime
import time
from loguru import logger
from .conftest import check_dir
def test_file_delayed(tmp_path):
file = tmp_path / "test.log"
logger.add(file, format="{message}", delay=True)
assert not file.exists()
logger.debug("Delayed")
assert file.read_text() == | "Delayed\n" | assert | string_literal | tests/test_filesink_delay.py | test_file_delayed | 22 | null | |
Delgan/loguru | import sys
import loguru
from loguru._get_frame import load_get_frame_function
def test_without_sys_getframe(monkeypatch):
with monkeypatch.context() as context:
context.delattr(sys, "_getframe")
assert load_get_frame_function() == | loguru._get_frame.get_frame_fallback | assert | complex_expr | tests/test_get_frame.py | test_without_sys_getframe | 19 | null | |
Delgan/loguru | import sys
from unittest.mock import MagicMock
import pytest
from loguru import logger
from .conftest import parse
def test_before_bind(writer):
logger.add(writer, format="{message}")
logger.opt(record=True).bind(key="value").info("{record[level]}")
assert writer.read() == | "INFO\n" | assert | string_literal | tests/test_opt.py | test_before_bind | 578 | null | |
Delgan/loguru | import os
from stat import S_IMODE
import pytest
from loguru import logger
def set_umask():
default = os.umask(0)
yield
os.umask(default)
@pytest.mark.parametrize("permissions", [0o777, 0o766, 0o744, 0o700, 0o611])
def test_rotation_permissions(tmp_path, permissions, set_umask):
def file_permission_... | 2 | assert | numeric_literal | tests/test_filesink_permissions.py | test_rotation_permissions | 40 | null | |
Delgan/loguru | import io
import pathlib
import re
from datetime import datetime
import pytest
from loguru import logger
TEXT = "This\nIs\nRandom\nText\n123456789\nABC!DEF\nThis Is The End\n"
def fileobj():
with io.StringIO(TEXT) as file:
yield file
def test_parse_file(tmp_path):
file = tmp_path / "test.log"
f... | dict(num="123456789") | assert | func_call | tests/test_parse.py | test_parse_file | 23 | null | |
Delgan/loguru | import json
import re
import sys
from loguru import logger
def test_serialize_exception_without_context():
sink = JsonSink()
logger.add(sink, format="{message}", serialize=True, catch=False)
logger.exception("No Error")
lines = sink.json["text"].splitlines()
assert lines[0] == | "No Error" | assert | string_literal | tests/test_add_option_serialize.py | test_serialize_exception_without_context | 67 | null | |
Delgan/loguru | import sys
from unittest.mock import MagicMock
import pytest
from loguru import logger
from .conftest import parse
def test_logging_within_lazy_function(writer):
logger.add(writer, level=20, format="{message}")
def laziness():
logger.trace("Nope")
logger.warning("Yes Warn")
logger.opt(... | "" | assert | string_literal | tests/test_opt.py | test_logging_within_lazy_function | 181 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def test_copy_no... | 1 | assert | numeric_literal | tests/theseus_tests/core/test_objective.py | test_copy_no_duplicate_cost_weights | 451 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | f"ManifoldGaussian__{t._id}" | assert | string_literal | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_init | 54 | null | |
facebookresearch/theseus | import pytest
import torch
import theseus as th
from tests.theseus_tests.decorators import run_if_baspacho
from theseus.utils import numeric_grad
torch.manual_seed(0)
@run_if_baspacho()
@pytest.mark.parametrize(
"linear_solver_cls",
[
th.CholeskyDenseSolver,
th.CholmodSparseSolver,
t... | 1.5 | assert | numeric_literal | tests/theseus_tests/optimizer/nonlinear/test_backwards.py | test_backwards_quartic | 214 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | new_var.mean[j] | assert | complex_expr | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_copy | 93 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
import theseus as th
@pytest.mark.parametrize("batch_size", [1, 10])
def test_track_best_solution_matrix_vars(batch_size):
x = th.SO3(name="x")
y = th.SO3(name="y")
objective = th.Objective()
objective.add(th.Difference(x, y, th.ScaleCostWeight(1.0), name="cf")... | (batch_size, 3, 3) | assert | collection | tests/theseus_tests/optimizer/nonlinear/test_info.py | test_track_best_solution_matrix_vars | 49 | null | |
facebookresearch/theseus | import numpy as np
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.theseus_layer import _DLMPerturbation
from theseus.utils import numeric_jacobian
def _original_dlm_perturbation(optim_vars, aux_vars):
v = optim_vars[0]
g = aux_vars[0]
epsilon... | original_jac[0]) | assert_* | complex_expr | tests/theseus_tests/test_dlm_perturbation.py | test_dlm_perturbation_jacobian | 72 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | ValueError) | pytest.raises | variable | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_update | 125 | null | |
facebookresearch/theseus | import pytest
import torch
import torchlie as lie
import torchlie.functional.se3_impl as se3_impl
import torchlie.functional.so3_impl as so3_impl
from .functional.common import get_test_cfg, sample_inputs
def rng():
rng_ = torch.Generator(device="cuda:0" if torch.cuda.is_available() else "cpu")
rng_.manual_se... | [impl_jac, impl_out]) | assert_* | collection | tests/torchlie_tests/test_lie_tensor.py | test_op | 118 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def test_to_dtyp... | dtype | assert | variable | tests/theseus_tests/core/test_objective.py | test_to_dtype | 547 | null | |
facebookresearch/theseus | import torch
import theseus as th
from theseus.utils.examples.bundle_adjustment.util import random_small_quaternion
def test_residual():
# unit test for Cost term
torch.manual_seed(0)
batch_size = 4
cam_rot = torch.cat(
[
random_small_quaternion(max_degrees=20).unsqueeze(0)
... | 5e-5 | assert | numeric_literal | tests/theseus_tests/embodied/measurements/test_reprojection.py | test_residual | 94 | null | |
facebookresearch/theseus | import pytest # noqa
import torch
import theseus as th
from tests.theseus_tests.core.common import (
BATCH_SIZES_TO_TEST,
check_another_theseus_function_is_copy,
check_another_theseus_tensor_is_copy,
check_another_torch_tensor_is_copy,
)
from theseus.utils import numeric_jacobian
from .utils import r... | cost_function2 | assert | variable | tests/theseus_tests/embodied/collision/test_collision_factor.py | test_collision2d_copy | 63 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | out_expand_flat.reshape(broadcast_size + t2_size)) | assert_* | func_call | tests/torchlie_tests/functional/common.py | check_binary_op_broadcasting | 301 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.constants import EPS
from tests.theseus_tests.core.common import check_copy_var
from .common import (
BATCH_SIZES_TO_TEST,
check_jacobian_for_local,
check_projection_for_exp_map,
check_projection_for_log_map,... | (i, k) | assert | collection | tests/theseus_tests/geometry/test_vector.py | test_matmul | 86 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.core.cost_function import AutogradMode
from theseus.core.cost_weight import ScaleCostWeight
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
check_another_theseus_function_is_copy... | reps | assert | variable | tests/theseus_tests/core/test_cost_function.py | test_default_name_and_ids | 57 | null | |
facebookresearch/theseus | import numpy as np
import torch
import theseus as th
from tests.theseus_tests.core.common import (
BATCH_SIZES_TO_TEST,
check_another_theseus_function_is_copy,
check_another_theseus_tensor_is_copy,
)
from theseus.utils import numeric_jacobian
def evaluate_numerical_jacobian_local_cost_fn(Group, tol):
... | "new_name" | assert | string_literal | tests/theseus_tests/embodied/misc/test_variable_difference.py | test_copy_local_cost_fn | 53 | null | |
facebookresearch/theseus | from unittest import mock
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.core.vectorizer import _CostFunctionWrapper
def test_correct_schemas_and_shared_vars():
v1 = th.Vector(1)
v2 = th.Vector(1)
... | 2 | assert | numeric_literal | tests/theseus_tests/core/test_vectorizer.py | test_correct_schemas_and_shared_vars | 128 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from tests.theseus_tests.embodied.collision.utils import (
random_origin,
random_scalar,
random_sdf_data,
)
from tests.theseus_tests.geometry.test_se2 import create_ran... | eff_radius | assert | variable | tests/theseus_tests/embodied/collision/test_eff_obj_contact.py | test_eff_obj_variable_type | 175 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | dtype | assert | variable | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_to | 80 | null | |
facebookresearch/theseus | from unittest import mock
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.core.vectorizer import _CostFunctionWrapper
def _check_vectorized_wrappers(vectorization, objective):
for w in vectorization._cos... | objective.error()) | assert_* | func_call | tests/theseus_tests/core/test_vectorizer.py | test_vectorized_error | 256 | null | |
facebookresearch/theseus | import copy
import torch
import theseus as th
BATCH_SIZES_TO_TEST = (1, 10)
def create_mock_cost_functions(tensor=None, cost_weight=NullCostWeight()):
len_data = 1 if tensor is None else tensor.shape[1]
var1 = MockVar(len_data, tensor=tensor, name="var1")
var2 = MockVar(len_data, tensor=tensor, name="va... | "new" | assert | string_literal | tests/theseus_tests/core/common.py | check_copy_var | 216 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.core.cost_function import AutogradMode
from theseus.core.cost_weight import ScaleCostWeight
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
check_another_theseus_function_is_copy... | (batch_size, err_dim, 3) | assert | collection | tests/theseus_tests/core/test_cost_function.py | test_autodiff_cost_function_error_and_jacobians_shape_on_SO3 | 326 | null | |
facebookresearch/theseus | import pytest # noqa
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.utils import numeric_jacobian
from .utils import random_sdf
def test_sdf_2d_shapes():
generator = torch.Generator()
generator.manual_seed(0)
for batch_size in BATCH_SIZES_T... | (batch_size, num_points) | assert | collection | tests/theseus_tests/embodied/collision/test_signed_distance_field.py | test_sdf_2d_shapes | 26 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import MockVar
def test_update():
for _ in range(10):
for length in range(1, 10):
var = MockVar(length)
batch_size = np.random.randint(1, 10)
# check update from torch tensor
... | new_data_good | assert | variable | tests/theseus_tests/core/test_variable.py | test_update | 54 | null | |
facebookresearch/theseus | import pytest
import torch
import theseus as th
from tests.theseus_tests.decorators import run_if_baspacho
from theseus.utils import numeric_grad
torch.manual_seed(0)
@pytest.mark.parametrize(
"linear_solver_cls", [th.CholeskyDenseSolver, th.CholmodSparseSolver]
)
def test_backwards_quad_fit(linear_solver_cls):... | da_dx_truncated) | assert_* | variable | tests/theseus_tests/optimizer/nonlinear/test_backwards.py | test_backwards_quad_fit | 128 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.constants import TEST_EPS
from tests.theseus_tests.core.common import check_copy_var
from theseus.utils import numeric_jacobian
from .common import (
BATCH_SIZES_TO_TEST,
check_adjoint,
check_compose,
check_e... | expected_jac[1].shape | assert | complex_expr | tests/theseus_tests/geometry/test_se2.py | test_transform_from_and_to | 159 | null | |
facebookresearch/theseus | import pytest # noqa
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.utils import numeric_jacobian
from .utils import random_sdf
def test_signed_distance_2d_jacobian():
for batch_size in BATCH_SIZES_TO_TEST:
sdf = random_sdf(batch_size, 10, ... | 1e-5 | assert | numeric_literal | tests/theseus_tests/embodied/collision/test_signed_distance_field.py | test_signed_distance_2d_jacobian | 111 | null | |
facebookresearch/theseus | from unittest import mock
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.core.vectorizer import _CostFunctionWrapper
def _check_vectorized_wrappers(vectorization, objective):
for w in vectorization._cos... | exp_jac) | assert_* | variable | tests/theseus_tests/core/test_vectorizer.py | _check_vectorized_wrappers | 211 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import scipy.sparse
import torch
import torch.nn as nn
import theseus as th
import theseus.utils as thutils
def _check_sparse_mv_and_mtv(batch_size, num_rows, num_cols, fill, device):
A_col_ind, A_row_ptr, A_val, _ = thutils.random_sparse_matrix(
batch_size,
... | 1e-8 | assert | numeric_literal | tests/theseus_tests/utils/test_utils.py | _check_sparse_mv_and_mtv | 140 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
import theseus as th
def _check_info(info, batch_size, max_iterations, initial_error, objective):
assert info.err_history.shape == (batch_size, max_iterations + 1)
assert info.err_history[:, 0].allclose(initial_error)
assert info.err_history.argmin(dim=1).allclose(... | RuntimeError) | pytest.raises | variable | tests/theseus_tests/optimizer/nonlinear/common.py | _check_optimizer_returns_fail_status_on_singular | 272 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch # needed for import of Torch C++ extensions to work
from scipy.sparse import csr_matrix
from theseus.utils import random_sparse_matrix
def check_lu_solver(
init_batch_size, batch_size, num_rows, num_cols, fill, verbose=False
):
# this is necessary a... | num_cols | assert | variable | tests/theseus_tests/extlib/test_cusolver_lu_solver.py | check_lu_solver | 20 | null | |
facebookresearch/theseus | import warnings
import pytest # noqa: F401
import torch
import theseus as th
from theseus.constants import EPS
from .common import (
BATCH_SIZES_TO_TEST,
check_jacobian_for_local,
check_projection_for_exp_map,
check_projection_for_log_map,
)
def test_operations_mypy_cast():
# mypy is optional i... | 0 | assert | numeric_literal | tests/theseus_tests/geometry/test_point_types.py | test_operations_mypy_cast | 80 | null | |
facebookresearch/theseus | import pytest
import torch
from omegaconf import OmegaConf
import examples.pose_graph.pose_graph_synthetic as pgo
from tests.theseus_tests.decorators import run_if_baspacho
def default_cfg():
cfg = OmegaConf.load("examples/configs/pose_graph/pose_graph_synthetic.yaml")
cfg.outer_optim.num_epochs = 1
cfg.... | pytest.approx(expected_loss, rel=1e-10, abs=1e-10) | assert | func_call | tests/theseus_tests/test_pgo_benchmark.py | test_pgo_losses | 60 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def _check_v... | v1_data | assert | variable | tests/theseus_tests/core/test_objective.py | _check_variables | 259 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import MockCostFunction, MockCostWeight
def test_theseus_function_init():
all_ids = []
variables = [th.Variable(torch.ones(1, 1), name="var_1")]
aux_vars = [th.Variable(torch.ones(1, 1), name="aux_1")]
for i... | cost_function.name | assert | complex_expr | tests/theseus_tests/core/test_theseus_function.py | test_theseus_function_init | 28 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
import theseus as th
def _create_linear_system(batch_size=32, matrix_size=10):
A = torch.randn((batch_size, matrix_size, matrix_size))
AtA = torch.empty((batch_size, matrix_size, matrix_size))
Atb = torch.empty((batch_size, matrix_size, 1))
x = torch.randn((bat... | 0 | assert | numeric_literal | tests/theseus_tests/optimizer/linear/test_dense_solver.py | test_handle_singular | 103 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | log_map_ref) | assert_* | variable | tests/torchlie_tests/functional/common.py | check_log_map_passt | 342 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
import theseus as th
@pytest.mark.parametrize(
"var_type", [(th.Vector, 1), (th.Vector, 2), (th.SE2, None), (th.SE3, None)]
)
@pytest.mark.parametrize("batch_size", [1, 10])
def test_state_history(var_type, batch_size):
cls_, dof = var_type
rand_args = (batch_size... | info.state_history | assert | complex_expr | tests/theseus_tests/optimizer/nonlinear/test_info.py | test_state_history | 34 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import MockVar
def test_variable_init():
all_ids = []
for i in range(100):
if np.random.random() < 0.5:
name = f"name_{i}"
else:
name = None
data = torch.rand(1, 1)
... | t.name | assert | complex_expr | tests/theseus_tests/core/test_variable.py | test_variable_init | 26 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | grad_ref) | assert_* | variable | tests/torchlie_tests/functional/common.py | check_log_map_passt | 344 | null | |
facebookresearch/theseus | import pytest
import torch
from torchlie import reset_global_params, set_global_params
from torchlie.functional import SE3, SO3, enable_checks
@pytest.mark.parametrize("dtype", ["float32", "float64"])
def test_global_options(dtype):
rng = torch.Generator()
rng.manual_seed(0)
g = SE3.rand(1, generator=rng,... | ValueError) | pytest.raises | variable | tests/torchlie_tests/test_misc.py | test_global_options | 27 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | flattened_out.reshape(*batch_size, *out.shape[lb:])) | assert_* | func_call | tests/torchlie_tests/functional/common.py | check_lie_group_function | 166 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch # noqa: F401
import theseus as th
from tests.theseus_tests.optimizer.linearization_test_utils import (
build_test_objective_and_linear_system,
)
def test_sparse_linearization():
objective, ordering, A, b = build_test_objective_and_linear_system()
linearization = ... | atb_out) | assert_* | variable | tests/theseus_tests/optimizer/test_sparse_linearization.py | test_sparse_linearization | 35 | null | |
facebookresearch/theseus | import torch
from torch.autograd import grad, gradcheck
def check_grad(solve_func, inputs, eps, atol, rtol):
assert gradcheck(solve_func, inputs, eps=eps, atol=atol)
A_val, b = inputs[0], inputs[1]
# Check that the gradient works correctly for floating point data
out = solve_func(*inputs).sum()
gA... | gb_float.double()) | assert_* | func_call | tests/theseus_tests/optimizer/autograd/common.py | check_grad | 35 | null | |
facebookresearch/theseus | import pytest
import torch
from omegaconf import OmegaConf
import examples.pose_graph.pose_graph_synthetic as pgo
from tests.theseus_tests.decorators import run_if_baspacho
def default_cfg():
cfg = OmegaConf.load("examples/configs/pose_graph/pose_graph_synthetic.yaml")
cfg.outer_optim.num_epochs = 1
cfg.... | expected_loss, rel=1e-10, abs=1e-10) | pytest.approx | complex_expr | tests/theseus_tests/test_pgo_benchmark.py | test_pgo_losses | 60 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
from sksparse.cholmod import analyze_AAt
import theseus as th
def _build_sparse_mat(batch_size):
all_cols = list(range(10))
col_ind = []
row_ptr = [0]
for i in range(12):
start = max(0, i - 2)
end = min(i + 1, 10)
col_ind += all_cols[sta... | 1e-4 | assert | numeric_literal | tests/theseus_tests/optimizer/linear/test_cholmod_sparse_solver.py | _check_correctness | 68 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.core.cost_function import AutogradMode
from theseus.core.cost_weight import ScaleCostWeight
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
check_another_theseus_function_is_copy... | 0 | assert | numeric_literal | tests/theseus_tests/core/test_cost_function.py | error_fn | 178 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | dof | assert | variable | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_init | 55 | null | |
facebookresearch/theseus | import copy
import torch
import theseus as th
BATCH_SIZES_TO_TEST = (1, 10)
def create_mock_cost_functions(tensor=None, cost_weight=NullCostWeight()):
len_data = 1 if tensor is None else tensor.shape[1]
var1 = MockVar(len_data, tensor=tensor, name="var1")
var2 = MockVar(len_data, tensor=tensor, name="va... | new_var.tensor | assert | complex_expr | tests/theseus_tests/core/common.py | check_copy_var | 214 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | jac) | assert_* | variable | tests/torchlie_tests/functional/common.py | check_lie_group_function | 145 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import scipy.sparse
import torch
import torch.nn as nn
import theseus as th
import theseus.utils as thutils
def _check_sparse_mv_and_mtv(batch_size, num_rows, num_cols, fill, device):
A_col_ind, A_row_ptr, A_val, _ = thutils.random_sparse_matrix(
batch_size,
... | 0 | assert | numeric_literal | tests/theseus_tests/utils/test_utils.py | test_timer | 177 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def test_cost_de... | 0 | assert | numeric_literal | tests/theseus_tests/core/test_objective.py | test_cost_delete_and_add | 574 | null | |
facebookresearch/theseus | from typing import Sequence, Union
import pytest
import torch
import torchlie.functional.se3_impl as se3_impl
from torchlie.functional import SE3
from .common import (
BATCH_SIZES_TO_TEST,
TEST_EPS,
check_binary_op_broadcasting,
check_left_project_broadcasting,
check_lie_group_function,
check... | tangent_vector) | assert_* | variable | tests/torchlie_tests/functional/test_se3.py | test_vee | 68 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def test_copy():... | objective | assert | variable | tests/theseus_tests/core/test_objective.py | test_copy | 407 | null | |
facebookresearch/theseus | from typing import Sequence, Union
import pytest
import torch
import torchlie.functional.so3_impl as so3_impl
from torchlie.functional import SO3
from torchlie.global_params import set_global_params
from .common import (
BATCH_SIZES_TO_TEST,
TEST_EPS,
check_binary_op_broadcasting,
check_left_project_... | sa_2.cpu()) | assert_* | func_call | tests/torchlie_tests/functional/test_so3.py | test_sine_axis | 125 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.constants import EPS
from tests.theseus_tests.core.common import check_copy_var
from .common import (
BATCH_SIZES_TO_TEST,
check_jacobian_for_local,
check_projection_for_exp_map,
check_projection_for_log_map,... | torch.norm(t1, p="fro") | assert | func_call | tests/theseus_tests/geometry/test_vector.py | test_norm | 131 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | new_var.precision | assert | complex_expr | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_copy | 94 | null | |
facebookresearch/theseus | import pytest
import torch
import torchlie as lie
import torchlie.functional.se3_impl as se3_impl
import torchlie.functional.so3_impl as so3_impl
from .functional.common import get_test_cfg, sample_inputs
def rng():
rng_ = torch.Generator(device="cuda:0" if torch.cuda.is_available() else "cpu")
rng_.manual_se... | impl_out) | assert_* | variable | tests/torchlie_tests/test_lie_tensor.py | test_op | 88 | null | |
facebookresearch/theseus | import copy
import torch
import theseus as th
BATCH_SIZES_TO_TEST = (1, 10)
def create_mock_cost_functions(tensor=None, cost_weight=NullCostWeight()):
len_data = 1 if tensor is None else tensor.shape[1]
var1 = MockVar(len_data, tensor=tensor, name="var1")
var2 = MockVar(len_data, tensor=tensor, name="va... | other_tensor | assert | variable | tests/theseus_tests/core/common.py | check_another_torch_tensor_is_copy | 228 | null | |
facebookresearch/theseus | import pytest
import theseus as th
import torch
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
def _new_robust_cf(
batch_size,
loss_cls,
generator,
masked_weight=False,
gnc_cost=False,
) -> [th.RobustCostFunction, th.GNCRobustCostFunction]:
v1 = th.rand_se3(batch_size, genera... | lin_flattened.b) | assert_* | complex_expr | tests/theseus_tests/core/test_robust_cost.py | test_flatten_dims | 317 | null | |
facebookresearch/theseus | import pytest
import theseus as th
import torch
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
def _new_robust_cf(
batch_size,
loss_cls,
generator,
masked_weight=False,
gnc_cost=False,
) -> [th.RobustCostFunction, th.GNCRobustCostFunction]:
v1 = th.rand_se3(batch_size, genera... | expected_rho2) | assert_* | variable | tests/theseus_tests/core/test_robust_cost.py | test_robust_cost_weighted_error | 97 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | j2.reshape(broadcast_size + j1.shape[-2:])) | assert_* | func_call | tests/torchlie_tests/functional/common.py | check_binary_op_broadcasting | 306 | null | |
facebookresearch/theseus | from unittest import mock
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.core.vectorizer import _CostFunctionWrapper
def test_costs_vars_and_err_before_vectorization():
for _ in range(20):
objec... | optim_vars | assert | variable | tests/theseus_tests/core/test_vectorizer.py | test_costs_vars_and_err_before_vectorization | 62 | null | |
facebookresearch/theseus | import theseus as th
import torch
from theseus.utils import check_jacobians
def test_hinge_cost():
rng = torch.Generator()
rng.manual_seed(0)
batch_size = 10
how_many = 4
def _rand_chunk():
return torch.rand(batch_size, how_many, generator=rng)
for limit in [0.0, 1.0]:
thresh... | nn_zero | assert | variable | tests/theseus_tests/embodied/motionmodel/test_misc.py | test_hinge_cost | 63 | null | |
facebookresearch/theseus | import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
NullCostWeight,
check_another_theseus_tensor_is_copy,
create_mock_cost_functions,
create_objective_with_mock_cost_functions,
)
def test_update_... | batch_size | assert | variable | tests/theseus_tests/core/test_objective.py | test_update_raises_batch_size_error | 506 | null | |
facebookresearch/theseus | import pytest # noqa: F401
import torch
import theseus as th
from theseus.constants import __FROM_THESEUS_LAYER_TOKEN__
from tests.theseus_tests.optimizer.nonlinear.common import (
run_nonlinear_least_squares_check,
)
def mock_objective():
objective = th.Objective()
v1 = th.Vector(1, name="v1")
v2 =... | RuntimeError) | pytest.raises | variable | tests/theseus_tests/optimizer/nonlinear/test_levenberg_marquardt.py | test_ellipsoidal_damping_compatibility | 53 | null | |
facebookresearch/theseus | import pytest # noqa
import torch
import theseus as th
from tests.theseus_tests.core.common import (
BATCH_SIZES_TO_TEST,
check_another_theseus_function_is_copy,
check_another_theseus_tensor_is_copy,
check_another_torch_tensor_is_copy,
)
from theseus.utils import numeric_jacobian
from .utils import r... | jac_error.shape | assert | complex_expr | tests/theseus_tests/embodied/collision/test_collision_factor.py | test_collision2d_error_shapes | 41 | null | |
facebookresearch/theseus | import pytest
import torch
import torchlie as lie
import torchlie.functional.se3_impl as se3_impl
import torchlie.functional.so3_impl as so3_impl
from .functional.common import get_test_cfg, sample_inputs
def rng():
rng_ = torch.Generator(device="cuda:0" if torch.cuda.is_available() else "cpu")
rng_.manual_se... | [jac_c, out_c]) | assert_* | collection | tests/torchlie_tests/test_lie_tensor.py | test_op | 123 | null | |
facebookresearch/theseus | from unittest import mock
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
from theseus.core.vectorizer import _CostFunctionWrapper
def _check_vectorized_wrappers(vectorization, objective):
for w in vectorization._cos... | w_err) | assert_* | variable | tests/theseus_tests/core/test_vectorizer.py | _check_vectorized_wrappers | 209 | null | |
facebookresearch/theseus | import theseus as th
import torch
from theseus.utils import check_jacobians
def test_hinge_cost():
rng = torch.Generator()
rng.manual_seed(0)
batch_size = 10
how_many = 4
def _rand_chunk():
return torch.rand(batch_size, how_many, generator=rng)
for limit in [0.0, 1.0]:
thresh... | (batch_size, 3 * how_many, 3 * how_many) | assert | collection | tests/theseus_tests/embodied/motionmodel/test_misc.py | test_hinge_cost | 53 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
def random_manifold_gaussian_params():
manif_types = [th.Point2, th.Point3, th.SE2, th.SE3, th.SO2, th.SO3]
n_vars = np.random.randint(1, 5)
batch_size = np.random.randint(1, 100)
mean = []
dof = 0
f... | "new" | assert | string_literal | tests/theseus_tests/optimizer/test_manifold_gaussian.py | test_copy | 96 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.core.cost_function import AutogradMode
from theseus.core.cost_weight import ScaleCostWeight
from .common import (
MockCostFunction,
MockCostWeight,
MockVar,
check_another_theseus_function_is_copy... | num_optim_vars | assert | variable | tests/theseus_tests/core/test_cost_function.py | test_autodiff_cost_function_error_and_jacobians_shape | 147 | null | |
facebookresearch/theseus | import pytest
import theseus as th
import torch
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
def _new_robust_cf(
batch_size,
loss_cls,
generator,
masked_weight=False,
gnc_cost=False,
) -> [th.RobustCostFunction, th.GNCRobustCostFunction]:
v1 = th.rand_se3(batch_size, genera... | j2) | assert_* | variable | tests/theseus_tests/core/test_robust_cost.py | test_mask_jacobians | 231 | null | |
facebookresearch/theseus | import pytest
import theseus as th
import torch
@pytest.mark.parametrize("dof", [1, 8])
@pytest.mark.parametrize(
"linear_solver_cls",
[th.CholeskyDenseSolver, th.CholmodSparseSolver, th.LUCudaSparseSolver],
)
def test_rho(dof, linear_solver_cls):
device = "cuda:0" if torch.cuda.is_available() else "cpu"
... | torch.ones_like(rho)) | assert_* | func_call | tests/theseus_tests/optimizer/nonlinear/test_trust_region.py | test_rho | 62 | null | |
facebookresearch/theseus | import pytest
import theseus as th
import torch
from tests.theseus_tests.core.common import BATCH_SIZES_TO_TEST
def _new_robust_cf(
batch_size,
loss_cls,
generator,
masked_weight=False,
gnc_cost=False,
) -> [th.RobustCostFunction, th.GNCRobustCostFunction]:
v1 = th.rand_se3(batch_size, genera... | lin_flattened.AtA) | assert_* | complex_expr | tests/theseus_tests/core/test_robust_cost.py | test_flatten_dims | 318 | null | |
facebookresearch/theseus | import copy
import numpy as np
import pytest # noqa: F401
import torch
import theseus as th
from theseus.core import Variable
from tests.theseus_tests.core.common import (
BATCH_SIZES_TO_TEST,
check_another_theseus_function_is_copy,
)
from theseus.utils import numeric_jacobian
def test_gp_motion_model_varia... | dt_v | assert | variable | tests/theseus_tests/embodied/motionmodel/test_double_integrator.py | test_gp_motion_model_variable_type | 87 | null | |
facebookresearch/theseus | from functools import reduce
import torch
from torchlie.global_params import set_global_params
BATCH_SIZES_TO_TEST = [1, 20, (1, 2), (3, 4, 5), tuple()]
TEST_EPS = 5e-7
def get_test_cfg(op_name, dtype, dim, data_shape, module=None):
atol = TEST_EPS
# input_type --> tuple[str, param]
# input_types --> a ... | jac_ref) | assert_* | variable | tests/torchlie_tests/functional/common.py | check_log_map_passt | 343 | null |
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