Utils

catalyst.utils.argparse.boolean_flag(parser: argparse.ArgumentParser, name: str, default: Optional[bool] = False, help: str = None, shorthand: str = None) → None

Add a boolean flag to a parser inplace.

Parameters

• parser (argparse.ArgumentParser) – parser to add the flag to

• name (str) – argument name –<name> will enable the flag, while –no-<name> will disable it

• default (bool, optional) – default value of the flag

• help (str) – help string for the flag

• shorthand (str) – shorthand string for the argument

Examples

>>> parser = argparse.ArgumentParser()
>>> boolean_flag(
>>>     parser, "flag", default=False, help="some flag", shorthand="f"
>>> )

catalyst.utils.checkpoint.load_checkpoint(filepath)

catalyst.utils.checkpoint.pack_checkpoint(model=None, criterion=None, optimizer=None, scheduler=None, **kwargs)

catalyst.utils.checkpoint.save_checkpoint(checkpoint: Dict, logdir: Union[pathlib.Path, str], suffix: str, is_best: bool = False, is_last: bool = False, special_suffix: str = '')

catalyst.utils.checkpoint.unpack_checkpoint(checkpoint, model=None, criterion=None, optimizer=None, scheduler=None)

catalyst.utils.compression.compress(data)

catalyst.utils.compression.compress_if_needed(data)

catalyst.utils.compression.decompress(data)

catalyst.utils.compression.decompress_if_needed(data)

catalyst.utils.compression.is_compressed(data)

catalyst.utils.config.load_ordered_yaml(stream, Loader=<class 'yaml.loader.Loader'>, object_pairs_hook=<class 'collections.OrderedDict'>)

Loads yaml config into OrderedDict

Parameters

• stream – opened file with yaml

• Loader – base class for yaml Loader

• object_pairs_hook – type of mapping

Returns

configuration

Return type

dict

catalyst.utils.config.get_environment_vars() → Dict[str, Any]

Creates a dictionary with environment variables

Returns

environment variables

Return type

dict

catalyst.utils.config.dump_environment(experiment_config: Dict, logdir: str, configs_path: List[str] = None) → None

Saves config, environment variables and package list in JSON into logdir

Parameters

• experiment_config (dict) – experiment config

• logdir (str) – path to logdir

• configs_path – path(s) to config

catalyst.utils.config.parse_config_args(*, config, args, unknown_args)

catalyst.utils.config.parse_args_uargs(args, unknown_args)

Function for parsing configuration files

Parameters

• args – recognized arguments

• unknown_args – unrecognized arguments

Returns

updated arguments, dict with config

Return type

tuple

catalyst.utils.dataset.balance_classes(dataframe: pandas.core.frame.DataFrame, class_column: str = 'label', random_state: int = 42, how: str = 'downsampling') → pandas.core.frame.DataFrame

Balance classes in dataframe by class_column

See also catalyst.data.sampler.BalanceClassSampler

Parameters

• dataframe – a dataset

• class_column – which column to use for split

• random_state – seed for random shuffle

• how – strategy to sample must be one on [“downsampling”, “upsampling”]

Returns

new dataframe with balanced class_column

Return type

pd.DataFrame

catalyst.utils.dataset.column_fold_split(dataframe: pandas.core.frame.DataFrame, column: str, random_state: int = 42, n_folds: int = 5) → pandas.core.frame.DataFrame

Splits DataFrame into N folds.

Parameters

• dataframe – a dataset

• column – which column to use

• random_state – seed for random shuffle

• n_folds – number of result folds

Returns

new dataframe with fold column

Return type

pd.DataFrame

catalyst.utils.dataset.create_dataframe(dataset: Dict[str, object], **dataframe_args) → pandas.core.frame.DataFrame

Create pd.DataFrame from dict like {key: [values]}

Parameters

• dataset – dict like {key: [values]}

• **dataframe_args –

  indexIndex or array-like

  Index to use for resulting frame. Will default to np.arange(n) if no indexing information part of input data and no index provided

  columnsIndex or array-like

  Column labels to use for resulting frame. Will default to np.arange(n) if no column labels are provided

  dtypedtype, default None

  Data type to force, otherwise infer

Returns

dataframe from giving dataset

Return type

pd.DataFrame

catalyst.utils.dataset.create_dataset(dirs: str, extension: str = None, process_fn: Callable[[str], object] = None, recursive: bool = False) → Dict[str, object]

Create dataset (dict like {key: [values]}) from vctk-like dataset:

dataset/
    cat/
        *.ext
    dog/
        *.ext

Parameters

• dirs (str) – path to dirs, for example /home/user/data/**

• extension (str) – data extension you are looking for

• process_fn (Callable[[str], object]) – function(path_to_file) -> object process function for found files, by default

• recursive (bool) – enables recursive globbing

Returns

dataset

Return type

dict

catalyst.utils.dataset.default_fold_split(dataframe: pandas.core.frame.DataFrame, random_state: int = 42, n_folds: int = 5) → pandas.core.frame.DataFrame

Splits DataFrame into N folds.

Parameters

• dataframe – a dataset

• random_state – seed for random shuffle

• n_folds – number of result folds

Returns

new dataframe with fold column

Return type

pd.DataFrame

catalyst.utils.dataset.prepare_dataset_labeling(dataframe: pandas.core.frame.DataFrame, class_column: str) → Dict[str, int]

Prepares a mapping using unique values from class_column

{
    "class_name_0": 0,
    "class_name_1": 1,
    ...
    "class_name_N": N
}

Parameters

• dataframe – a dataset

• class_column – which column to use

Returns

mapping from tag to labels

Return type

Dict[str, int]

catalyst.utils.dataset.separate_tags(dataframe: pandas.core.frame.DataFrame, tag_column: str = 'label', tag_delim: str = '-') → pandas.core.frame.DataFrame

Separates values in class_column column

Parameters

• dataframe – a dataset

• tag_column – column name to separate values

• tag_delim – delimiter to separate values

Returns

new dataframe

Return type

pd.DataFrame

catalyst.utils.dataset.split_dataframe(dataframe: pandas.core.frame.DataFrame, **train_test_split_args) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]

Split dataframe in train and test part.

Parameters

• dataframe – pd.DataFrame to split

• **train_test_split_args –

  test_sizefloat, int, or None (default is None)

  If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25.

  train_sizefloat, int, or None (default is None)

  If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

  random_stateint or RandomState

  Pseudo-random number generator state used for random sampling.

  stratifyarray-like or None (default is None)

  If not None, data is split in a stratified fashion, using this as the class labels.

Returns

train and test DataFrames

PS. It exist cause sklearn split is overcomplicated.

catalyst.utils.dataset.split_dataset(dataset: pandas.core.frame.DataFrame, **train_test_split_args) → Tuple[Dict[str, object], Dict[str, object]]

Split dataset in train and test parts.

Parameters

• dataset – dict like dataset

• **train_test_split_args –

  test_sizefloat, int, or None (default is None)

  If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25.

  train_sizefloat, int, or None (default is None)

  If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

  random_stateint or RandomState

  Pseudo-random number generator state used for random sampling.

  stratifyarray-like or None (default is None)

  If not None, data is split in a stratified fashion, using this as the class labels.

Returns

train and test dicts

catalyst.utils.dataset.stratified_fold_split(dataframe: pandas.core.frame.DataFrame, class_column: str, random_state: int = 42, n_folds: int = 5) → pandas.core.frame.DataFrame

Splits DataFrame into N stratified folds.

Also see catalyst.data.sampler.BalanceClassSampler

Parameters

• dataframe – a dataset

• class_column – which column to use for split

• random_state – seed for random shuffle

• n_folds – number of result folds

Returns

new dataframe with fold column

Return type

pd.DataFrame

catalyst.utils.ddp.get_real_module(model: torch.nn.modules.module.Module) → torch.nn.modules.module.Module

Return a real model from a torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, or apex.parallel.DistributedDataParallel.

Parameters

model – A model, or DataParallel wrapper.

Returns

A model

catalyst.utils.ddp.is_wrapped_with_ddp(model: torch.nn.modules.module.Module) → bool

Checks whether model is wrapped with DataParallel/DistributedDataParallel.

class catalyst.utils.frozen.FrozenClass

Class which prohibit __setattr__ on existing attributes

Examples

>>> class RunnerState(FrozenClass):

catalyst.utils.hash.get_hash(obj: Any) → str

Creates unique hash from object following way: - Represent obj as sting recursively - Hash this string with sha256 hash function - encode hash with url-safe base64 encoding

Parameters

obj – object to hash

Returns

base64-encoded string

catalyst.utils.hash.get_short_hash(o) → str

catalyst.utils.image.has_image_extension(uri) → bool

Check that file has image extension

Parameters

uri (Union[str, pathlib.Path]) – The resource to load the file from

Returns

True if file has image extension, False otherwise

Return type

bool

catalyst.utils.image.imread(uri, grayscale: bool = False, expand_dims: bool = True, rootpath: Union[str, pathlib.Path] = None, **kwargs)

Parameters

• uri – {str, pathlib.Path, bytes, file}

• resource to load the image from, e.g. a filename, pathlib.Path, (The) –

• address or file object, see the docs for more info. (http) –

• grayscale –

• expand_dims –

• rootpath –

Returns:

catalyst.utils.image.mask_to_overlay_image(image: numpy.ndarray, masks: List[numpy.ndarray], threshold: float = 0, mask_strength: float = 0.5) → numpy.ndarray

Draws every mask for with some color over image

Parameters

• image (np.ndarray) – RGB image used as underlay for masks

• masks (List[np.ndarray]) – list of masks

• threshold (float) – threshold for masks binarization

• mask_strength (float) – opacity of colorized masks

Returns

HxWx3 image with overlay

Return type

np.ndarray

catalyst.utils.image.mimread(uri, clip_range: Tuple[int, int] = None, expand_dims: bool = True, rootpath: Union[str, pathlib.Path] = None, **kwargs)

Parameters

• uri – {str, pathlib.Path, bytes, file}

• resource to load the mask from, e.g. a filename, pathlib.Path, (The) –

• address or file object, see the docs for more info. (http) –

• clip_range (Tuple[int, int]) – lower and upper interval edges, image values outside the interval are clipped to the interval edges

• expand_dims (bool) – if True, append channel axis to grayscale images

• rootpath (Union[str, pathlib.Path]) – path to an image (allows to use relative path)

Returns

Image

Return type

np.ndarray

catalyst.utils.image.mimwrite_with_meta(uri, ims, meta, **kwargs)

catalyst.utils.image.tensor_from_rgb_image(image: numpy.ndarray) → torch.Tensor

catalyst.utils.image.tensor_to_ndimage(images: torch.Tensor, mean: Tuple[float, float, float] = (0.485, 0.456, 0.406), std: Tuple[float, float, float] = (0.229, 0.224, 0.225), dtype=<class 'numpy.float32'>) → numpy.ndarray

Convert float image(s) with standard normalization to np.ndarray with [0..1] when dtype is np.float32 and [0..255] when dtype is np.uint8.

Parameters

• images – [B]xCxHxW float tensor

• mean – mean to add

• std – std to multiply

• dtype – result ndarray dtype. Only float32 and uint8 are supported.

Returns

[B]xHxWxC np.ndarray of dtype

catalyst.utils.initialization.bias_init_with_prob(prior_prob)

Initialize conv/fc bias value according to giving probablity

catalyst.utils.initialization.constant_init(module, val, bias=0)

Initialize the module with constant value

catalyst.utils.initialization.create_optimal_inner_init(nonlinearity: torch.nn.modules.module.Module, **kwargs) → Callable[[torch.nn.modules.module.Module], None]

Create initializer for inner layers based on their activation function (nonlinearity).

Parameters

nonlinearity – non-linear activation

catalyst.utils.initialization.kaiming_init(module, mode='fan_out', nonlinearity='relu', bias=0, distribution='normal')

Initialize the module with he initialization

catalyst.utils.initialization.normal_init(module, mean=0, std=1, bias=0)

Initialize the module with normal distribution

catalyst.utils.initialization.outer_init(layer: torch.nn.modules.module.Module) → None

Initialization for output layers of policy and value networks typically used in deep reinforcement learning literature.

catalyst.utils.initialization.uniform_init(module, a=0, b=1, bias=0)

Initialize the module with uniform distribution

catalyst.utils.initialization.xavier_init(module, gain=1, bias=0, distribution='normal')

Initialize the module with xavier initialization

catalyst.utils.misc.append_dict(dict1, dict2)

Appends dict2 with the same keys as dict1 to dict1

catalyst.utils.misc.args_are_not_none(*args: Optional[Any]) → bool

Check that all arguments are not None :param *args: values :type *args: Any

Returns

True if all value were not None, False otherwise

Return type

bool

catalyst.utils.misc.copy_directory(input_dir: pathlib.Path, output_dir: pathlib.Path) → None

Recursively copies the input directory

Parameters

• input_dir (Path) – input directory

• output_dir (Path) – output directory

catalyst.utils.misc.flatten_dict(dictionary: Dict[str, Any], parent_key: str = '', separator: str = '/') → collections.OrderedDict

Make the given dictionary flatten

Parameters

• dictionary (dict) – giving dictionary

• parent_key (str, optional) – prefix nested keys with string parent_key

• separator (str, optional) – delimiter between parent_key and key to use

Returns

ordered dictionary with flatten keys

Return type

collections.OrderedDict

catalyst.utils.misc.format_metric(name: str, value: float) → str

Format metric. Metric will be returned in the scientific format if 4 decimal chars are not enough (metric value lower than 1e-4)

Parameters

• name (str) – metric name

• value (float) – value of metric

catalyst.utils.misc.get_utcnow_time(format: str = None) → str

Return string with current utc time in chosen format

Parameters

format (str) – format string. if None “%y%m%d.%H%M%S” will be used.

Returns

formatted utc time string

Return type

str

catalyst.utils.misc.is_exception(ex: Any) → bool

Check if the argument is of Exception type

catalyst.utils.misc.make_tuple(tuple_like)

Creates a tuple if given tuple_like value isn’t list or tuple

Returns

tuple or list

catalyst.utils.misc.maybe_recursive_call(object_or_dict, method: str, recursive_args=None, recursive_kwargs=None, **kwargs)

Calls the method recursively for the object_or_dict

Parameters

• object_or_dict (Any) – some object or a dictinary of objects

• method (str) – method name to call

• recursive_args – list of arguments to pass to the method

• recursive_kwargs – list of key-arguments to pass to the method

• **kwargs – Arbitrary keyword arguments

catalyst.utils.misc.merge_dicts(*dicts: dict) → dict

Recursive dict merge. Instead of updating only top-level keys, merge_dicts recurses down into dicts nested to an arbitrary depth, updating keys.

Parameters

*dicts – several dictionaries to merge

Returns

deep-merged dictionary

Return type

dict

catalyst.utils.misc.pairwise(iterable: Iterable[Any]) → Iterable[Any]

Iterate sequences by pairs

Parameters

iterable – Any iterable sequence

Returns

pairwise iterator

Examples

>>> for i in pairwise([1, 2, 5, -3]):
>>>     print(i)
(1, 2)
(2, 5)
(5, -3)

catalyst.utils.numpy.dict2structed(array: Dict)

catalyst.utils.numpy.geometric_cumsum(alpha, x)

Calculate future accumulated sums for each element in a list with an exponential factor.

Given input data \(x_1, \dots, x_n\) # noqa: E501, W605 and exponential factor \(lpha\in [0, 1]\), # noqa: E501, W605 it returns an array \(y\) with the same length and each element is calculated as following

\[y_i = x_i + lpha x_{i+1} + lpha^2 x_{i+2} + \dots + lpha^{n-i-1}x_{n-1} + lpha^{n-i}x_{n} # noqa: E501, W605\]

Note

To gain the optimal runtime speed, we use scipy.signal.lfilter

Example

>>> geometric_cumsum(0.1, [[1, 1], [2, 2], [3, 3], [4, 4]])
array([[1.234, 1.234], [2.34 , 2.34 ], [3.4  , 3.4  ], [4.   , 4.   ]])

Parameters

• alpha (float) – exponential factor between zero and one.

• x (np.ndarray) – input data, [trajectory_len, num_atoms]

Returns

calculated data

Return type

out (np.ndarray)

source: https://github.com/zuoxingdong/lagom

catalyst.utils.numpy.get_one_hot(label: int, num_classes: int, smoothing: float = None) → numpy.ndarray

Applies OneHot vectorization to a giving scalar, optional with label smoothing from https://arxiv.org/abs/1812.01187

Parameters

• label (int) – scalar value to be vectorized

• num_classes (int) – total number of classes

• smoothing (float, optional) – if specified applies label smoothing from Bag of Tricks for Image Classification with Convolutional Neural Networks paper

Returns

a one-hot vector with shape (num_classes,)

Return type

np.ndarray

catalyst.utils.numpy.np_softmax(x)

catalyst.utils.numpy.structed2dict(array: numpy.ndarray)

catalyst.utils.pandas.dataframe_to_list(dataframe: pandas.core.frame.DataFrame) → List[dict]

Converts dataframe to a list of rows (without indexes)

Parameters

dataframe (DataFrame) – input dataframe

Returns

list of rows

Return type

(List[dict])

catalyst.utils.pandas.folds_to_list(folds: Union[list, str, pandas.core.series.Series]) → List[int]

This function formats string or either list of numbers into a list of unique int

Parameters

folds (Union[list, str, pd.Series]) – Either list of numbers or one string with numbers separated by commas or pandas series

Returns

list of unique ints

Return type

List[int]

Examples

>>> folds_to_list("1,2,1,3,4,2,4,6")
[1, 2, 3, 4, 6]
>>> folds_to_list([1, 2, 3.0, 5])
[1, 2, 3, 5]

Raises

ValueError – if value in string or array cannot be casted to int

catalyst.utils.pandas.map_dataframe(dataframe: pandas.core.frame.DataFrame, tag_column: str, class_column: str, tag2class: Dict[str, int], verbose: bool = False) → pandas.core.frame.DataFrame

This function maps tags from tag_column to ints into class_column Using tag2class dictionary

Parameters

• dataframe (pd.DataFrame) – input dataframe

• tag_column (str) – column with tags

• class_column (str) –

• tag2class (Dict[str, int]) – mapping from tags to class labels

• verbose – flag if true, uses tqdm

Returns

updated dataframe with class_column

Return type

pd.DataFrame

catalyst.utils.pandas.merge_multiple_fold_csv(fold_name: str, paths: Optional[str]) → pandas.core.frame.DataFrame

Reads csv into one DataFrame with column fold :param fold_name: current fold name :type fold_name: str :param paths: paths to csv separated by commas :type paths: str

Returns

merged dataframes with column fold == fold_name

Return type

pd.DataFrame

catalyst.utils.pandas.read_csv_data(in_csv: str = None, train_folds: Optional[List[int]] = None, valid_folds: Optional[List[int]] = None, infer_folds: Optional[List[int]] = None, seed: int = 42, n_folds: int = 5, in_csv_train: str = None, in_csv_valid: str = None, in_csv_infer: str = None, tag2class: Optional[Dict[str, int]] = None, class_column: str = None, tag_column: str = None) → Tuple[pandas.core.frame.DataFrame, List[dict], List[dict], List[dict]]

From giving path in_csv reads a dataframe and split it to train/valid/infer folds or from several paths in_csv_train, in_csv_valid, in_csv_infer reads independent folds.

Note

This function can be used with different combinations of params.

First block is used to get dataset from one csv:

in_csv, train_folds, valid_folds, infer_folds, seed, n_folds

Second includes paths to different csv for train/valid and infer parts:

in_csv_train, in_csv_valid, in_csv_infer

The other params (tag2class, tag_column, class_column) are optional

for any previous block

Parameters

• in_csv (str) – paths to whole dataset

• train_folds (List[int]) – train folds

• valid_folds (List[int], optional) – valid folds. If none takes all folds not included in train_folds

• infer_folds (List[int], optional) – infer folds. If none takes all folds not included in train_folds and valid_folds

• seed (int) – seed for split

• n_folds (int) – number of folds

• in_csv_train (str) – paths to train csv separated by commas

• in_csv_valid (str) – paths to valid csv separated by commas

• in_csv_infer (str) – paths to infer csv separated by commas

• tag2class (Dict[str, int]) – mapping from label names into ints

• tag_column (str) – column with label names

• class_column (str) – column to use for split

Returns

tuple with 4 elements (whole dataframe, list with train data, list with valid data and list with infer data)

Return type

(Tuple[pd.DataFrame, List[dict], List[dict], List[dict]])

catalyst.utils.pandas.read_multiple_dataframes(in_csv_train: str = None, in_csv_valid: str = None, in_csv_infer: str = None, tag2class: Optional[Dict[str, int]] = None, class_column: str = None, tag_column: str = None) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]

This function reads train/valid/infer dataframes from giving paths :param in_csv_train: paths to train csv separated by commas :type in_csv_train: str :param in_csv_valid: paths to valid csv separated by commas :type in_csv_valid: str :param in_csv_infer: paths to infer csv separated by commas :type in_csv_infer: str :param tag2class: mapping from label names into int :type tag2class: Dict[str, int], optional :param tag_column: column with label names :type tag_column: str, optional :param class_column: column to use for split :type class_column: str, optional

Returns

tuple with 4 dataframes

whole dataframe, train part, valid part and infer part

Return type

(tuple)

catalyst.utils.pandas.split_dataframe(dataframe: pandas.core.frame.DataFrame, train_folds: List[int], valid_folds: Optional[List[int]] = None, infer_folds: Optional[List[int]] = None, tag2class: Optional[Dict[str, int]] = None, tag_column: str = None, class_column: str = None, seed: int = 42, n_folds: int = 5) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]

Split a Pandas DataFrame into folds.

Parameters

• dataframe (pd.DataFrame) – input dataframe

• train_folds (List[int]) – train folds

• valid_folds (List[int], optional) – valid folds. If none takes all folds not included in train_folds

• infer_folds (List[int], optional) – infer folds. If none takes all folds not included in train_folds and valid_folds

• tag2class (Dict[str, int], optional) – mapping from label names into int

• tag_column (str, optional) – column with label names

• class_column (str, optional) – column to use for split

• seed (int) – seed for split

• n_folds (int) – number of folds

Returns

tuple with 4 dataframes

whole dataframe, train part, valid part and infer part

Return type

(tuple)

catalyst.utils.plotly.plot_tensorboard_log(logdir: Union[str, pathlib.Path], step: Optional[str] = 'batch', metrics: Optional[List[str]] = None, height: Optional[int] = None, width: Optional[int] = None) → None

catalyst.utils.scripts.import_module(expdir: pathlib.Path)

catalyst.utils.scripts.dump_code(expdir, logdir)

catalyst.utils.scripts.dump_python_files(src, dst)

class catalyst.utils.seed.Seeder(init_seed: int = 0, max_seed: int = None)

A random seed generator.

Given an initial seed, the seeder can be called continuously to sample a single or a batch of random seeds.

Note

The seeder creates an independent RandomState to generate random numbers. It does not affect the RandomState in np.random.

Example::

>>> seeder = Seeder(init_seed=0)
>>> seeder(size=5)
[209652396, 398764591, 924231285, 1478610112, 441365315]

__init__(init_seed: int = 0, max_seed: int = None)

Initialize the seeder.

Parameters

init_seed (int, optional) – Initial seed for generating random seeds. Default: 0.

catalyst.utils.seed.set_global_seed(seed: int) → None

Sets random seed into PyTorch, TensorFlow, Numpy and Random.

Parameters

seed – random seed

catalyst.utils.serialization.deserialize(data)

Deserialize bytes into an object using pickle

Parameters

bytes – a bytes object containing serialized with pickle data.

Returns

Returns a value deserialized from the bytes-like object.

catalyst.utils.serialization.pickle_deserialize(data)

Deserialize bytes into an object using pickle

Parameters

bytes – a bytes object containing serialized with pickle data.

Returns

Returns a value deserialized from the bytes-like object.

catalyst.utils.serialization.pickle_serialize(data)

Serialize the data into bytes using pickle

Parameters

data – a value

Returns

Returns a bytes object serialized with pickle data.

catalyst.utils.serialization.pyarrow_deserialize(data)

Deserialize bytes into an object using pyarrow

Parameters

bytes – a bytes object containing serialized with pyarrow data.

Returns

Returns a value deserialized from the bytes-like object.

catalyst.utils.serialization.pyarrow_serialize(data)

Serialize the data into bytes using pyarrow

Parameters

data – a value

Returns

Returns a bytes object serialized with pyarrow data.

catalyst.utils.serialization.serialize(data)

Serialize the data into bytes using pickle

Parameters

data – a value

Returns

Returns a bytes object serialized with pickle data.

exception catalyst.utils.tensorboard.EventReadingError

An exception that correspond to an event file reading error

class catalyst.utils.tensorboard.EventsFileReader(events_file: BinaryIO)

An iterator over a Tensorboard events file

__init__(events_file: BinaryIO)

Initialize an iterator over an events file

Parameters

events_file – An opened file-like object.

class catalyst.utils.tensorboard.SummaryItem(tag, step, wall_time, value, type)

property step

Alias for field number 1

property tag

Alias for field number 0

property type

Alias for field number 4

property value

Alias for field number 3

property wall_time

Alias for field number 2

class catalyst.utils.tensorboard.SummaryReader(logdir: Union[str, pathlib.Path], tag_filter: Optional[collections.abc.Iterable] = None, types: collections.abc.Iterable = ('scalar',))

Iterates over events in all the files in the current logdir. Only scalars and images are supported at the moment.

__init__(logdir: Union[str, pathlib.Path], tag_filter: Optional[collections.abc.Iterable] = None, types: collections.abc.Iterable = ('scalar',))

Initalize new summary reader

Parameters

• logdir – A directory with Tensorboard summary data

• tag_filter – A list of tags to leave (None for all)

• types – A list of types to get.

• "scalar" and "image" types are allowed at the moment. (Only) –

catalyst.utils.torch.ce_with_logits(logits, target)

Returns cross entropy for giving logits

catalyst.utils.torch.log1p_exp(x)

Computationally stable function for computing log(1+exp(x)).

catalyst.utils.torch.normal_sample(mu, sigma)

Sample from multivariate Gaussian distribution z ~ N(z|mu,sigma) while supporting backpropagation through its mean and variance.

catalyst.utils.torch.normal_logprob(mu, sigma, z)

Probability density function of multivariate Gaussian distribution N(z|mu,sigma).

catalyst.utils.torch.soft_update(target, source, tau)

Updates the target data with smoothing by tau

catalyst.utils.torch.get_optimizable_params(model_or_params)

Returns all the parameters that requires gradients

catalyst.utils.torch.get_optimizer_momentum(optimizer: torch.optim.optimizer.Optimizer) → float

Get momentum of current optimizer.

Parameters

optimizer – PyTorch optimizer

Returns

momentum at first param group

Return type

float

catalyst.utils.torch.set_optimizer_momentum(optimizer: torch.optim.optimizer.Optimizer, value: float, index: int = 0)

Set momentum of index ‘th param group of optimizer to value

Parameters

• optimizer – PyTorch optimizer

• value (float) – new value of momentum

• index (int, optional) – integer index of optimizer’s param groups, default is 0

catalyst.utils.torch.assert_fp16_available() → None

Asserts for installed and available Apex FP16

catalyst.utils.torch.get_device() → torch.device

Simple returning the best available device (GPU or CPU)

catalyst.utils.torch.get_available_gpus()

Array of available GPU ids :returns: available GPU ids :rtype: iterable

Examples

>>> os.environ["CUDA_VISIBLE_DEVICES"] = "0,2"
>>> get_available_gpus()
>>> [0, 2]

>>> os.environ["CUDA_VISIBLE_DEVICES"] = "0,-1,1"
>>> get_available_gpus()
>>> [0]

>>> os.environ["CUDA_VISIBLE_DEVICES"] = ""
>>> get_available_gpus()
>>> []

>>> os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
>>> get_available_gpus()
>>> []

catalyst.utils.torch.get_activation_fn(activation: str = None)

Returns the activation function from torch.nn by its name

catalyst.utils.torch.any2device(value, device: Union[str, torch.device])

Move tensor, list of tensors, list of list of tensors, dict of tensors, tuple of tensors to target device.

Parameters

• value – Object to be moved

• device (Device) – target device ids

Returns

Same structure as value, but all tensors and np.arrays moved to device

catalyst.utils.torch.prepare_cudnn(deterministic: bool = None, benchmark: bool = None) → None

Prepares CuDNN benchmark and sets CuDNN to be deterministic/non-deterministic mode

Parameters

• deterministic (bool) – deterministic mode if running in CuDNN backend.

• benchmark (bool) – If True use CuDNN heuristics to figure out which algorithm will be most performant for your model architecture and input. Setting it to False may slow down your training.

catalyst.utils.torch.process_model_params(model: torch.nn.modules.module.Module, layerwise_params: Dict[str, dict] = None, no_bias_weight_decay: bool = True, lr_scaling: float = 1.0) → List[Union[torch.nn.parameter.Parameter, dict]]

Gains model parameters for torch.optim.Optimizer

Parameters

• model (torch.nn.Module) – Model to process

• layerwise_params (Dict) – Order-sensitive dict where each key is regex pattern and values are layer-wise options for layers matching with a pattern

• no_bias_weight_decay (bool) – If true, removes weight_decay for all bias parameters in the model

• lr_scaling (float) – layer-wise learning rate scaling, if 1.0, learning rates will not be scaled

Returns

parameters for an optimizer

Return type

iterable

Examples

>>> model = catalyst.contrib.models.segmentation.ResnetUnet()
>>> layerwise_params = collections.OrderedDict([
>>>     ("conv1.*", dict(lr=0.001, weight_decay=0.0003)),
>>>     ("conv.*", dict(lr=0.002))
>>> ])
>>> params = process_model_params(model, layerwise_params)
>>> optimizer = torch.optim.Adam(params, lr=0.0003)

catalyst.utils.torch.set_requires_grad(model: torch.nn.modules.module.Module, requires_grad: bool)

Sets the requires_grad value for all model parameters.

Parameters

• model (torch.nn.Module) – Model

• requires_grad (bool) – value

Examples

>>> model = SimpleModel()
>>> set_requires_grad(model, requires_grad=True)

catalyst.utils.torch.get_network_output(net: torch.nn.modules.module.Module, *input_shapes)

For each input shape returns an output tensor

Parameters

• net (Model) – the model

• *args – variable length argument list of shapes

catalyst.utils.torch.detach(tensor: torch.Tensor) → numpy.ndarray

Detaches the input tensor to a numpy array

catalyst.utils.visualization.plot_confusion_matrix(cm, class_names=None, normalize=False, title='confusion matrix', fname=None, show=True, figsize=12, fontsize=32, colormap='Blues')

Render the confusion matrix and return matplotlib”s figure with it. Normalization can be applied by setting normalize=True.

catalyst.utils.visualization.render_figure_to_tensor(figure)