Utilities¶
Configurations¶
-
monai.config.deviceconfig.
get_system_info
()[source]¶ Get system info as an ordered dictionary.
- Return type
OrderedDict
-
monai.config.deviceconfig.
print_config
(file=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶ Print the package versions to file.
- Parameters
file – print() text stream file. Defaults to sys.stdout.
-
monai.config.deviceconfig.
print_debug_info
(file=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶ Print config (installed dependencies, etc.) and system info for debugging.
- Parameters
file – print() text stream file. Defaults to sys.stdout.
- Return type
None
Module utils¶
-
exception
monai.utils.module.
InvalidPyTorchVersionError
(required_version, name)[source]¶ Raised when called function or method requires a more recent PyTorch version than that installed.
-
exception
monai.utils.module.
OptionalImportError
[source]¶ Could not import APIs from an optional dependency.
-
monai.utils.module.
exact_version
(the_module, version_str='')[source]¶ Returns True if the module’s __version__ matches version_str
- Return type
bool
-
monai.utils.module.
export
(modname)[source]¶ Make the decorated object a member of the named module. This will also add the object under its aliases if it has a __aliases__ member, thus this decorator should be before the alias decorator to pick up those names. Alias names which conflict with package names or existing members will be ignored.
-
monai.utils.module.
get_package_version
(dep_name, default='NOT INSTALLED or UNKNOWN VERSION.')[source]¶ Try to load package and get version. If not found, return default.
-
monai.utils.module.
get_torch_version_tuple
()[source]¶ - Returns
tuple of ints represents the pytorch major/minor version.
-
monai.utils.module.
has_option
(obj, keywords)[source]¶ Return a boolean indicating whether the given callable obj has the keywords in its signature.
- Return type
bool
-
monai.utils.module.
load_submodules
(basemod, load_all=True, exclude_pattern='(.*[tT]est.*)|(_.*)')[source]¶ Traverse the source of the module structure starting with module basemod, loading all packages plus all files if load_all is True, excluding anything whose name matches exclude_pattern.
-
monai.utils.module.
min_version
(the_module, min_version_str='')[source]¶ Convert version strings into tuples of int and compare them.
Returns True if the module’s version is greater or equal to the ‘min_version’. When min_version_str is not provided, it always returns True.
- Return type
bool
-
monai.utils.module.
optional_import
(module, version='', version_checker=<function min_version>, name='', descriptor='{}', version_args=None, allow_namespace_pkg=False)[source]¶ Imports an optional module specified by module string. Any importing related exceptions will be stored, and exceptions raise lazily when attempting to use the failed-to-import module.
- Parameters
module (
str
) – name of the module to be imported.version (
str
) – version string used by the version_checker.version_checker (
Callable
[…,bool
]) – a callable to check the module version, Defaults to monai.utils.min_version.name (
str
) – a non-module attribute (such as method/class) to import from the imported module.descriptor (
str
) – a format string for the final error message when using a not imported module.version_args – additional parameters to the version checker.
allow_namespace_pkg (
bool
) – whether importing a namespace package is allowed. Defaults to False.
- Return type
Tuple
[Any
,bool
]- Returns
The imported module and a boolean flag indicating whether the import is successful.
Examples:
>>> torch, flag = optional_import('torch', '1.1') >>> print(torch, flag) <module 'torch' from 'python/lib/python3.6/site-packages/torch/__init__.py'> True >>> the_module, flag = optional_import('unknown_module') >>> print(flag) False >>> the_module.method # trying to access a module which is not imported OptionalImportError: import unknown_module (No module named 'unknown_module'). >>> torch, flag = optional_import('torch', '42', exact_version) >>> torch.nn # trying to access a module for which there isn't a proper version imported OptionalImportError: import torch (requires version '42' by 'exact_version'). >>> conv, flag = optional_import('torch.nn.functional', '1.0', name='conv1d') >>> print(conv) <built-in method conv1d of type object at 0x11a49eac0> >>> conv, flag = optional_import('torch.nn.functional', '42', name='conv1d') >>> conv() # trying to use a function from the not successfully imported module (due to unmatched version) OptionalImportError: from torch.nn.functional import conv1d (requires version '42' by 'min_version').
Aliases¶
This module is written for configurable workflow, not currently in use.
-
monai.utils.aliases.
alias
(*names)[source]¶ Stores the decorated function or class in the global aliases table under the given names and as the __aliases__ member of the decorated object. This new member will contain all alias names declared for that object.
-
monai.utils.aliases.
resolve_name
(name)[source]¶ Search for the declaration (function or class) with the given name. This will first search the list of aliases to see if it was declared with this aliased name, then search treating name as a fully qualified name, then search the loaded modules for one having a declaration with the given name. If no declaration is found, raise ValueError.
- Raises
ValueError – When the module is not found.
ValueError – When the module does not have the specified member.
ValueError – When multiple modules with the declaration name are found.
ValueError – When no module with the specified member is found.
Misc¶
-
monai.utils.misc.
copy_to_device
(obj, device, non_blocking=True, verbose=False)[source]¶ Copy object or tuple/list/dictionary of objects to
device
.- Parameters
obj (
Any
) – object or tuple/list/dictionary of objects to move todevice
.device (
Union
[str
,device
,None
]) – moveobj
to this device. Can be a string (e.g.,cpu
,cuda
,cuda:0
, etc.) or of typetorch.device
.non_blocking_transfer – when True, moves data to device asynchronously if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.
verbose (
bool
) – when True, will print a warning for any elements of incompatible type not copied todevice
.
- Return type
Any
- Returns
- Same as input, copied to
device
where possible. Original input will be unchanged.
- Same as input, copied to
-
monai.utils.misc.
dtype_numpy_to_torch
(dtype)[source]¶ Convert a numpy dtype to its torch equivalent.
-
monai.utils.misc.
dtype_torch_to_numpy
(dtype)[source]¶ Convert a torch dtype to its numpy equivalent.
-
monai.utils.misc.
ensure_tuple_rep
(tup, dim)[source]¶ Returns a copy of tup with dim values by either shortened or duplicated input.
- Raises
ValueError – When
tup
is a sequence andtup
length is notdim
.
Examples:
>>> ensure_tuple_rep(1, 3) (1, 1, 1) >>> ensure_tuple_rep(None, 3) (None, None, None) >>> ensure_tuple_rep('test', 3) ('test', 'test', 'test') >>> ensure_tuple_rep([1, 2, 3], 3) (1, 2, 3) >>> ensure_tuple_rep(range(3), 3) (0, 1, 2) >>> ensure_tuple_rep([1, 2], 3) ValueError: Sequence must have length 3, got length 2.
- Return type
Tuple
[Any
, …]
-
monai.utils.misc.
ensure_tuple_size
(tup, dim, pad_val=0)[source]¶ Returns a copy of tup with dim values by either shortened or padded with pad_val as necessary.
- Return type
Tuple
[Any
, …]
-
monai.utils.misc.
fall_back_tuple
(user_provided, default, func=<function <lambda>>)[source]¶ Refine user_provided according to the default, and returns as a validated tuple.
The validation is done for each element in user_provided using func. If func(user_provided[idx]) returns False, the corresponding default[idx] will be used as the fallback.
Typically used when user_provided is a tuple of window size provided by the user, default is defined by data, this function returns an updated user_provided with its non-positive components replaced by the corresponding components from default.
- Parameters
user_provided (
Any
) – item to be validated.default (
Union
[Sequence
,ndarray
]) – a sequence used to provided the fallbacks.func (
Callable
) – a Callable to validate every components of user_provided.
Examples:
>>> fall_back_tuple((1, 2), (32, 32)) (1, 2) >>> fall_back_tuple(None, (32, 32)) (32, 32) >>> fall_back_tuple((-1, 10), (32, 32)) (32, 10) >>> fall_back_tuple((-1, None), (32, 32)) (32, 32) >>> fall_back_tuple((1, None), (32, 32)) (1, 32) >>> fall_back_tuple(0, (32, 32)) (32, 32) >>> fall_back_tuple(range(3), (32, 64, 48)) (32, 1, 2) >>> fall_back_tuple([0], (32, 32)) ValueError: Sequence must have length 2, got length 1.
- Return type
Tuple
[Any
, …]
-
monai.utils.misc.
first
(iterable, default=None)[source]¶ Returns the first item in the given iterable or default if empty, meaningful mostly with ‘for’ expressions.
-
monai.utils.misc.
issequenceiterable
(obj)[source]¶ Determine if the object is an iterable sequence and is not a string.
- Return type
bool
-
monai.utils.misc.
list_to_dict
(items)[source]¶ To convert a list of “key=value” pairs into a dictionary. For examples: items: [“a=1”, “b=2”, “c=3”], return: {“a”: “1”, “b”: “2”, “c”: “3”}. If no “=” in the pair, use None as the value, for example: [“a”], return: {“a”: None}. Note that it will remove the blanks around keys and values.
-
monai.utils.misc.
progress_bar
(index, count, desc=None, bar_len=30, newline=False)[source]¶ print a progress bar to track some time consuming task.
- Parameters
index (
int
) – current status in progress.count (
int
) – total steps of the progress.desc (
Optional
[str
]) – description of the progress bar, if not None, show before the progress bar.bar_len (
int
) – the total length of the bar on screen, default is 30 char.newline (
bool
) – whether to print in a new line for every index.
- Return type
None
-
monai.utils.misc.
set_determinism
(seed=4294967295, additional_settings=None)[source]¶ Set random seed for modules to enable or disable deterministic training.
- Parameters
seed (
Optional
[int
]) – the random seed to use, default is np.iinfo(np.int32).max. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed. if set to None, will disable deterministic training.additional_settings (
Union
[Sequence
[Callable
[[int
],Any
]],Callable
[[int
],Any
],None
]) – additional settings that need to set random seed.
- Return type
None
Profiling¶
-
class
monai.utils.profiling.
PerfContext
[source]¶ Context manager for tracking how much time is spent within context blocks. This uses time.perf_counter to accumulate the total amount of time in seconds in the attribute total_time over however many context blocks the object is used in.
-
monai.utils.profiling.
torch_profiler_full
(func)[source]¶ A decorator which will run the torch profiler for the decorated function, printing the results in full. Note: Enforces a gpu sync point which could slow down pipelines.