Utilities Module Tests⚓︎
Overview⚓︎
The utilities module tests (test_utils.py) verify the various utility functions, including string processing, tensor handling, and system utilities.
Test Cases⚓︎
test_get_timestamp⚓︎
Tests the timestamp generation function.
def test_get_timestamp(self):
"""Test get_timestamp function."""
timestamp = utils.get_timestamp()
assert isinstance(timestamp, str)
assert len(timestamp) > 0
# Test with custom format
timestamp_custom = utils.get_timestamp("%Y-%m-%d")
assert isinstance(timestamp_custom, str)
assert len(timestamp_custom) > 0
test_format_pair⚓︎
Verifies the key-value pair formatting function.
def test_format_pair(self):
"""Test format_pair function."""
# Test with integer
result = utils.format_pair("test", 5)
assert result == "test=5"
# Test with float
result = utils.format_pair("test", 5.123456)
assert result == "test=5.123456"
# Test with custom precision
result = utils.format_pair("test", 5.123456, precision=2)
assert result == "test=5.12"
test_summarize_dict⚓︎
Tests the dictionary summarization function.
def test_summarize_dict(self):
"""Test summarize_dict function."""
test_dict = {"a": 1, "b": 2.5, "c": True}
result = utils.summarize_dict(test_dict)
assert isinstance(result, str)
assert "a=1" in result
assert "b=2.5" in result
assert "c=True" in result
test_normalize⚓︎
Verifies the string normalization function.
def test_normalize(self):
"""Test normalize function."""
# Test with dashes
result = utils.normalize("test-name")
assert result == "test-name"
# Test with underscores
result = utils.normalize("test_name")
assert result == "test-name"
# Test with dots
result = utils.normalize("test.name")
assert result == "test-name"
# Test with mixed
result = utils.normalize("test_name.sub-name")
assert result == "test-name-sub-name"
test_grab_tensor⚓︎
Tests the tensor conversion utility function.
def test_grab_tensor(self):
"""Test grab_tensor function."""
# Test with numpy array
np_array = np.array([1, 2, 3])
result = utils.grab_tensor(np_array)
assert np.array_equal(result, np_array)
# Test with torch tensor
torch_tensor = torch.tensor([1, 2, 3])
result = utils.grab_tensor(torch_tensor)
assert isinstance(result, np.ndarray)
assert np.array_equal(result, torch_tensor.numpy())
# Test with scalar
scalar = 5
result = utils.grab_tensor(scalar)
assert result == scalar
# Test with list
test_list = [1, 2, 3]
result = utils.grab_tensor(test_list)
assert isinstance(result, np.ndarray)
assert np.array_equal(result, np.array(test_list))
# Test with None
result = utils.grab_tensor(None)
assert result is None
test_breakpoint⚓︎
Verifies the debugging breakpoint function.
def test_breakpoint(self):
"""Test breakpoint function (mocked)."""
# This is a bit tricky to test since it's a debugging function
# We'll just make sure it doesn't raise an exception
# In a real test environment, we would mock the distributed setup
pass
test_get_max_memory_functions⚓︎
Tests the memory monitoring functions.
def test_get_max_memory_functions(self):
"""Test memory functions."""
# These functions require specific hardware to test properly
# We'll just make sure they exist and can be called
assert hasattr(utils, "get_max_memory_allocated")
assert hasattr(utils, "get_max_memory_reserved")