torchfunc

class torchfunc.Timer(function: Callable = <built-in function perf_counter>)

Measure execution time of function.

Can be used as context manager or function decorator, perform checkpoints or display absolute time from measurements beginning.

Used as context manager:

with Timer() as timer:
    ... # your operations
    print(timer) # __str__ calls timer.time() internally
    timer.checkpoint() # register checkpoint
    ... # more operations
    print(timer.checkpoint()) # time since last timer.checkpoint() call

... # even more operations
print(timer) # time taken for the block, will not be updated outside of it

When execution leaves the block, timer will be blocked. Last checkpoint and time taken to execute whole block will be returned by checkpoint() and time() methods respectively.

Used as function decorator:

@Timer()
def foo():
    return 42

value, time = foo()

Parameters

function (Callable, optional) – No argument function used to measure time. Default: time.perf_counter

checkpoint()

Time taken since last checkpoint call.

If wasn’t called before, it is the same as as Timer creation time (first call returns the same thing as time())

Returns

Whatever self.function() - self.function() returns, usually fraction of seconds

Return type

time-like

time()

Time taken since the object creation (measurements beginning).

Returns

Whatever self.function() - self.function() returns, usually fraction of seconds

Return type

time-like

torchfunc.info(general: bool = True, cuda: bool = True) → str

Return host related info as string.

This function may help you tailor your module’s architecture to specific environment it will be run on.

For in-depth info regarding possible performance improvements see torchfunc.performance submodule.

Information is divided into two sections:

• general - related to OS, Python version etc.

• cuda - specific to CUDA hardware

Example:

print(torchfunc.info(general=False))

Parameters

• general (bool, optional) – Return general informations. Default: True

• cuda (bool, optional) – Return CUDA related information. Default: True

Returns

Description of system and/or GPU.

Return type

str

torchfunc.installed(module: str) → bool

Return True if module is installed.

Example:

# Check whether mixed precision library available
print(torchfunc.installed("apex"))

Parameters

module (str) – Name of the module to be checked.

Returns

True if installed.

Return type

bool

class torchfunc.seed(value, cuda: bool = False)

Seed PyTorch and numpy.

This code is based on PyTorch’s reproducibility guide: https://pytorch.org/docs/stable/notes/randomness.html Can be used as standard seeding procedure, context manager (seed will be changed only within block) or function decorator.

Standard seed:

torchfunc.Seed(0) # no surprises I guess

Used as context manager:

with Seed(1):
    ... # your operations

print(torch.initial_seed()) # Should be back to seed pre block

Used as function decorator:

@Seed(1) # Seed only within function
def foo():
    return 42

Important: It’s impossible to put original numpy seed after context manager or decorator, hence it will be set to original PyTorch’s seed.

Parameters

• value (int) – Seed value used in np.random_seed and torch.manual_seed. Usually int is provided

• cuda (bool, optional) – Whether to set PyTorch’s cuda backend into deterministic mode (setting cudnn.benchmark to False and cudnn.deterministic to True). If False, consecutive runs may be slightly different. If True, automatic autotuning for convolutions layers with consistent input shape will be turned off. Default: False

torchfunc.sizeof(obj) → int

Get size in bytes of Tensor, torch.nn.Module or standard object.

Specific routines are defined for torch.tensor objects and torch.nn.Module objects. They will calculate how much memory in bytes those object consume.

If another object is passed, sys.getsizeof will be called on it.

This function works similarly to C++’s sizeof operator.

Example:

module = torch.nn.Linear(20, 20)
bias = 20 * 4 # in bytes
weights = 20 * 20 * 4 # in bytes
print(torchfunc.sizeof(model) == bias + weights) # True

Parameters

obj – Object whose size will be measured.

Returns

Size in bytes of the object

Return type

int