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Config

catalyst.utils.config.load_config(path: Union[str, pathlib.Path], ordered: bool = False, data_format: Optional[str] = None, encoding: str = 'utf-8')Union[Dict, List][source]

Loads config by giving path. Supports YAML and JSON files.

Examples

>>> load(path="./config.yml", ordered=True)
Parameters
  • path – path to config file (YAML or JSON)

  • ordered – if true the config will be loaded as OrderedDict

  • data_formatyaml, yml or json.

  • encoding – encoding to read the config

Returns

config

Return type

Union[Dict, List]

Raises

ValueError – if path path doesn’t exists or file format is not YAML or JSON

Adapted from https://github.com/TezRomacH/safitty/blob/v1.2.0/safitty/parser.py#L63 which was adapted from https://github.com/catalyst-team/catalyst/blob/v19.03/catalyst/utils/config.py#L10

catalyst.utils.config.save_config(config: Union[Dict, List], path: Union[str, pathlib.Path], data_format: Optional[str] = None, encoding: str = 'utf-8', ensure_ascii: bool = False, indent: int = 2)None[source]

Saves config to file. Path must be either YAML or JSON.

Parameters
  • config (Union[Dict, List]) – config to save

  • path (Union[str, Path]) – path to save

  • data_formatyaml, yml or json.

  • encoding – Encoding to write file. Default is utf-8

  • ensure_ascii – Used for JSON, if True non-ASCII

  • are escaped in JSON strings. (characters) –

  • indent – Used for JSON

Adapted from https://github.com/TezRomacH/safitty/blob/v1.2.0/safitty/parser.py#L110 which was adapted from https://github.com/catalyst-team/catalyst/blob/v19.03/catalyst/utils/config.py#L38

Data

catalyst.utils.data.get_loader(data_source: Iterable[dict], open_fn: Callable, dict_transform: Optional[Callable] = None, sampler=None, collate_fn: Callable = <function default_collate>, batch_size: int = 32, num_workers: int = 4, shuffle: bool = False, drop_last: bool = False)[source]

Creates a DataLoader from given source and its open/transform params.

Parameters
  • data_source – and iterable containing your data annotations, (for example path to images, labels, bboxes, etc)

  • open_fn – function, that can open your annotations dict and transfer it to data, needed by your network (for example open image by path, or tokenize read string)

  • dict_transform – transforms to use on dict (for example normalize image, add blur, crop/resize/etc)

  • sampler (Sampler, optional) – defines the strategy to draw samples from the dataset

  • collate_fn (callable, optional) – merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset

  • batch_size (int, optional) – how many samples per batch to load

  • num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process

  • shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).

  • drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)

Returns

DataLoader with catalyst.data.ListDataset

catalyst.utils.data.get_loaders_from_params(batch_size: int = 1, num_workers: int = 0, drop_last: bool = False, per_gpu_scaling: bool = False, loaders_params: Dict[str, Any] = None, samplers_params: Dict[str, Any] = None, initial_seed: int = 42, datasets_fn: Callable = None, **data_params)OrderedDict[str, DataLoader][source]

Creates pytorch dataloaders from datasets and additional parameters.

Parameters
  • batch_sizebatch_size parameter from torch.utils.data.DataLoader

  • num_workersnum_workers parameter from torch.utils.data.DataLoader

  • drop_lastdrop_last parameter from torch.utils.data.DataLoader

  • per_gpu_scaling – boolean flag, if True, scales batch_size in proportion to the number of GPUs

  • loaders_params (Dict[str, Any]) – additional loaders parameters

  • samplers_params (Dict[str, Any]) – additional sampler parameters

  • initial_seed – initial seed for torch.utils.data.DataLoader workers

  • datasets_fn (Callable) – callable function to get dictionary with torch.utils.data.Datasets

  • **data_params – additional data parameters or dictionary with torch.utils.data.Datasets to use for pytorch dataloaders creation

Returns

dictionary with

torch.utils.data.DataLoader

Return type

OrderedDict[str, DataLoader]

Raises
  • NotImplementedError – if datasource is out of Dataset or dict

  • ValueError – if batch_sampler option is mutually exclusive with distributed

Distributed

catalyst.utils.distributed.all_gather(data: Any)List[Any][source]

Run all_gather on arbitrary picklable data (not necessarily tensors).

Note

if data on different devices then data in resulted list will be on the same devices. Source: https://github.com/facebookresearch/detr/blob/master/util/misc.py#L88-L128

Parameters

data – any picklable object

Returns

list of data gathered from each process.

catalyst.utils.distributed.get_distributed_params()[source]

Returns distributed params for experiment run.

Returns

dictionary with distributed params

catalyst.utils.distributed.get_nn_from_ddp_module(model: torch.nn.modules.module.Module)torch.nn.modules.module.Module[source]

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.distributed.get_rank()int[source]

Returns the rank of the current worker.

Returns

rank if torch.distributed is initialized, otherwise -1

Return type

int

catalyst.utils.distributed.mean_reduce(tensor: torch.Tensor, world_size: int)torch.Tensor[source]

Reduce tensor to all processes and compute mean value.

Parameters
  • tensor – tensor to reduce.

  • world_size – number of processes in DDP setup.

Returns

reduced tensor

catalyst.utils.distributed.sum_reduce(tensor: torch.Tensor)torch.Tensor[source]

Reduce tensor to all processes and compute total (sum) value.

Parameters

tensor – tensor to reduce.

Returns

reduced tensor

Misc

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

Check that all arguments are not None.

Parameters

*args – values # noqa: RST213

Returns

True if all value were not None, False otherwise

Return type

bool

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

Add a boolean flag to a parser inplace.

Examples

>>> parser = argparse.ArgumentParser()
>>> boolean_flag(
>>>     parser, "flag", default=False, help="some flag", shorthand="f"
>>> )
Parameters
  • parser – parser to add the flag to

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

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

  • help – help string for the flag

  • shorthand – shorthand string for the argument

catalyst.utils.misc.convert_labels2list(labels: Union[torch.Tensor, List[int]])List[int][source]

This function allows to work with 2 types of indexing: using a integer tensor and a list of indices.

Parameters

labels – labels of batch samples

Returns

labels of batch samples in the aligned format

Raises

TypeError – if type of input labels is not tensor and list

catalyst.utils.misc.find_value_ids(it: Iterable[Any], value: Any)List[int][source]
Parameters
  • it – list of any

  • value – query element

Returns

indices of the all elements equal x0

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

Make the given dictionary flatten.

Parameters
  • dictionary – 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.get_attr(obj: Any, key: str, inner_key: Optional[str] = None)Any[source]

Alias for python getattr method. Useful for Callbacks preparation and cases with multi-criterion, multi-optimizer setup. For example, when you would like to train multi-task classification.

Used to get a named attribute from a IRunner by key keyword; for example

get_attr(runner, "criterion")
# is equivalent to
runner.criterion

get_attr(runner, "optimizer")
# is equivalent to
runner.optimizer

get_attr(runner, "scheduler")
# is equivalent to
runner.scheduler

With inner_key usage, it suppose to find a dictionary under key and would get inner_key from this dict; for example,

get_attr(runner, "criterion", "bce")
# is equivalent to
runner.criterion["bce"]

get_attr(runner, "optimizer", "adam")
# is equivalent to
runner.optimizer["adam"]

get_attr(runner, "scheduler", "adam")
# is equivalent to
runner.scheduler["adam"]
Parameters
  • obj – object of interest

  • key – name for attribute of interest, like criterion, optimizer, scheduler

  • inner_key – name of inner dictionary key

Returns

inner attribute

catalyst.utils.misc.get_by_keys(dict_: dict, *keys: Any, default: Optional[T] = None)T[source]

@TODO: docs.

catalyst.utils.misc.get_dictkey_auto_fn(key: Optional[Union[str, List[str]]])Callable[source]

Function generator for sub-dict preparation from dict based on predefined keys.

Parameters

key – keys

Returns

function

Raises

NotImplementedError – if key is out of str, tuple, list, dict, None

catalyst.utils.misc.get_fn_argsnames(fn: Callable[[], Any], exclude: Optional[List[str]] = None)[source]

Return parameter names of Callable.

Parameters
  • fn (Callable[.., Any]) – target Callable

  • exclude – exclude list of parameters

Returns

contains parameter names of fn

Return type

list

catalyst.utils.misc.get_fn_default_params(fn: Callable[[], Any], exclude: Optional[List[str]] = None)[source]

Return default parameters of Callable.

Parameters
  • fn (Callable[.., Any]) – target Callable

  • exclude – exclude list of parameters

Returns

contains default parameters of fn

Return type

dict

catalyst.utils.misc.get_hash(obj: Any)str[source]

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.misc.get_short_hash(obj)str[source]

Creates unique short hash from object.

Parameters

obj – object to hash

Returns

short base64-encoded string (6 chars)

catalyst.utils.misc.get_utcnow_time(format: Optional[str] = None)str[source]

Return string with current utc time in chosen format.

Parameters

format – 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[source]

Check if the argument is of Exception type.

catalyst.utils.misc.make_tuple(tuple_like)[source]

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

Parameters

tuple_like – tuple like object - list or tuple

Returns

tuple or list

catalyst.utils.misc.maybe_recursive_call(object_or_dict, method: Union[str, Callable], recursive_args=None, recursive_kwargs=None, **kwargs)[source]

Calls the method recursively for the object_or_dict.

Parameters
  • object_or_dict – some object or a dictionary of objects

  • method – 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

Returns

result of method call

catalyst.utils.misc.merge_dicts(*dicts: dict)dict[source]

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[T])Iterable[Tuple[T, T]][source]

Iterate sequences by pairs.

Examples

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

iterable – Any iterable sequence

Returns

pairwise iterator

catalyst.utils.misc.set_global_seed(seed: int)None[source]

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

Parameters

seed – random seed

Numpy

catalyst.utils.numpy.get_one_hot(label: int, num_classes: int, smoothing: Optional[float] = None)numpy.ndarray[source]

Applies OneHot vectorization to a giving scalar, optional with label smoothing as described in Bag of Tricks for Image Classification with Convolutional Neural Networks.

Parameters
  • label – scalar value to be vectorized

  • num_classes – 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

Onnx

catalyst.utils.onnx.onnx_export(model: torch.nn.modules.module.Module, batch: torch.Tensor, file: str, method_name: str = 'forward', input_names: Iterable = None, output_names: List[str] = None, dynamic_axes: Union[Dict[str, int], Dict[str, Dict[str, int]]] = None, opset_version: int = 9, do_constant_folding: bool = False, return_model: bool = False, verbose: bool = False)Union[None, onnx][source]

Converts model to onnx runtime.

Parameters
  • model (torch.nn.Module) – model

  • batch (Tensor) – inputs

  • file (str, optional) – file to save. Defaults to “model.onnx”.

  • method_name (str, optional) – Forward pass method to be converted. Defaults to “forward”.

  • input_names (Iterable, optional) – name of inputs in graph. Defaults to None.

  • output_names (List[str], optional) – name of outputs in graph. Defaults to None.

  • dynamic_axes (Union[Dict[str, int], Dict[str, Dict[str, int]]], optional) – axes with dynamic shapes. Defaults to None.

  • opset_version (int, optional) – Defaults to 9.

  • do_constant_folding (bool, optional) – If True, the constant-folding optimization is applied to the model during export. Defaults to False.

  • return_model (bool, optional) – If True then returns onnxruntime model (onnx required). Defaults to False.

  • verbose (bool, default False) – if specified, we will print out a debug description of the trace being exported.

Example

import torch

from catalyst.utils import convert_to_onnx

class LinModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lin1 = torch.nn.Linear(10, 10)
        self.lin2 = torch.nn.Linear(2, 10)

    def forward(self, inp_1, inp_2):
        return self.lin1(inp_1), self.lin2(inp_2)

    def first_only(self, inp_1):
        return self.lin1(inp_1)

lin_model = LinModel()
convert_to_onnx(
    model, batch=torch.randn((1, 10)), file="model.onnx", method_name="first_only"
)
Raises

ImportError – when return_model is True, but onnx is not installed.

Returns

onnx model if return_model set to True.

Return type

Union[None, “onnx”]

catalyst.utils.onnx.quantize_onnx_model(onnx_model_path: Union[pathlib.Path, str], quantized_model_path: Union[pathlib.Path, str], qtype: str = 'qint8', verbose: bool = False)None[source]

Takes model converted to onnx runtime and applies pruning.

Parameters
  • onnx_model_path (Union[Path, str]) – path to onnx model.

  • quantized_model_path (Union[Path, str]) – path to quantized model.

  • qtype (str, optional) – Type of weights in quantized model. Can be quint8 or qint8. Defaults to “qint8”.

  • verbose (bool, optional) – If set to True prints model size before and after quantization. Defaults to False.

Raises

ValueError – If qtype is not understood.

Pruning

catalyst.utils.pruning.get_pruning_fn(pruning_fn: Union[str, Callable], dim: Optional[int] = None, l_norm: Optional[int] = None)Callable[source]

[summary]

Parameters
  • pruning_fn (Union[str, Callable]) – function from torch.nn.utils.prune module or your based on BasePruningMethod. Can be string e.g. “l1_unstructured”. See pytorch docs for more details.

  • dim (int, optional) – if you are using structured pruning method you need to specify dimension. Defaults to None.

  • l_norm (int, optional) – if you are using ln_structured you need to specify l_norm. Defaults to None.

Raises

ValueError – If dim or l_norm is not defined when it’s required.

Returns

pruning_fn

Return type

Callable

catalyst.utils.pruning.prune_model(model: torch.nn.modules.module.Module, pruning_fn: Union[Callable, str], amount: Union[float, int], keys_to_prune: Optional[List[str]] = None, layers_to_prune: Optional[List[str]] = None, dim: Optional[int] = None, l_norm: Optional[int] = None)None[source]

Prune model function can be used for pruning certain tensors in model layers.

Parameters
  • model – Model to be pruned.

  • pruning_fn – Pruning function with API same as in torch.nn.utils.pruning. pruning_fn(module, name, amount).

  • keys_to_prune – list of strings. Determines which tensor in modules will be pruned.

  • amount – quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune.

  • layers_to_prune – list of strings - module names to be pruned. If None provided then will try to prune every module in model.

  • dim (int, optional) – if you are using structured pruning method you need to specify dimension. Defaults to None.

  • l_norm (int, optional) – if you are using ln_structured you need to specify l_norm. Defaults to None.

Example

pruned_model = prune_model(model, pruning_fn="l1_unstructured")
Raises
  • AttributeError – If layers_to_prune is not None, but there is no layers with specified name. OR

  • ValueError – if no layers have specified keys.

catalyst.utils.pruning.remove_reparametrization(model: torch.nn.modules.module.Module, keys_to_prune: List[str], layers_to_prune: Optional[List[str]] = None)None[source]

Removes pre-hooks and pruning masks from the model.

Parameters
  • model – model to remove reparametrization.

  • keys_to_prune – list of strings. Determines which tensor in modules have already been pruned.

  • layers_to_prune – list of strings - module names have already been pruned. If None provided then will try to prune every module in model.

Quantization

catalyst.utils.quantization.quantize_model(model: torch.nn.modules.module.Module, qconfig_spec: Optional[Dict] = None, dtype: Optional[Union[str, torch.dtype]] = 'qint8')torch.nn.modules.module.Module[source]

Function to quantize model weights.

Parameters
  • model – model to be quantized

  • qconfig_spec (Dict, optional) – quantization config in PyTorch format. Defaults to None.

  • dtype (Union[str, Optional[torch.dtype]], optional) – Type of weights after quantization. Defaults to “qint8”.

Returns

quantized model

Return type

Model

Stochastic Weights Averaging (SWA)

catalyst.utils.swa.average_weights(state_dicts: List[dict])collections.OrderedDict[source]

Averaging of input weights.

Parameters

state_dicts – Weights to average

Raises

KeyError – If states do not match

Returns

Averaged weights

catalyst.utils.swa.get_averaged_weights_by_path_mask(path_mask: str, logdir: Optional[Union[str, pathlib.Path]] = None)collections.OrderedDict[source]

Averaging of input weights and saving them.

Parameters
  • path_mask – globe-like pattern for models to average

  • logdir – Path to logs directory

Returns

Averaged weights

Sys

catalyst.utils.sys.dump_code(logdir: Union[str, pathlib.Path], expdir: Optional[Union[str, pathlib.Path]] = None)[source]

Dumps Catalyst code for reproducibility.

Parameters
  • logdir – logging dir path

  • expdir – experiment dir path

catalyst.utils.sys.dump_environment(logdir: str, config: Optional[Any] = None, configs_path: Optional[List[str]] = None)None[source]

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

Parameters
  • logdir – path to logdir

  • config – experiment config

  • configs_path – path(s) to config

catalyst.utils.sys.get_config_runner(expdir: pathlib.Path, config: Dict)[source]

Imports and creates ConfigRunner instance.

Parameters
  • expdir – experiment directory path

  • config – dictionary with experiment Config

Returns

ConfigRunner instance

catalyst.utils.sys.import_module(expdir: Union[str, pathlib.Path])[source]

Imports python module by path.

Parameters

expdir – path to python module.

Returns

Imported module.

Torch

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

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 – target device ids

Returns

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

catalyst.utils.torch.detach_tensor(tensor: torch.Tensor)numpy.ndarray[source]

Detach a pytorch tensor from graph and convert it to numpy array

Parameters

tensor – PyTorch tensor

Returns

numpy ndarray

catalyst.utils.torch.get_available_engine(fp16: bool = False, ddp: bool = False, amp: bool = False, apex: bool = False)IEngine[source]

Returns available engine based on given arguments.

Parameters
  • fp16 (bool) – option to use fp16 for training. Default is False.

  • ddp (bool) – option to use DDP for training. Default is False.

  • amp (bool) – option to use APEX for training. Default is False.

  • apex (bool) – option to use APEX for training. Default is False.

Returns

IEngine which match requirements.

catalyst.utils.torch.get_available_gpus()[source]

Array of available GPU ids.

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()
[]
Returns

available GPU ids

Return type

iterable

catalyst.utils.torch.get_device()torch.device[source]

Simple returning the best available device (TPU > GPU > CPU).

catalyst.utils.torch.get_network_output(net: torch.nn.modules.module.Module, *input_shapes_args, **input_shapes_kwargs)[source]

For each input shape returns an output tensor

Examples

>>> net = nn.Linear(10, 5)
>>> utils.get_network_output(net, (1, 10))
tensor([[[-0.2665,  0.5792,  0.9757, -0.5782,  0.1530]]])
Parameters
  • net – the model

  • *input_shapes_args – variable length argument list of shapes

  • **input_shapes_kwargs – key-value arguemnts of shapes

Returns

tensor with network output

catalyst.utils.torch.get_optimal_inner_init(nonlinearity: torch.nn.modules.module.Module, **kwargs)Callable[[torch.nn.modules.module.Module], None][source]

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

Parameters
  • nonlinearity – non-linear activation

  • **kwargs – extra kwargs

Returns

optimal initialization function

Raises

NotImplementedError – if nonlinearity is out of sigmoid, tanh, relu, `leaky_relu

catalyst.utils.torch.get_optimizable_params(model_or_params)[source]

Returns all the parameters that requires gradients.

catalyst.utils.torch.get_optimizer_momentum(optimizer: torch.optim.optimizer.Optimizer)float[source]

Get momentum of current optimizer.

Parameters

optimizer – PyTorch optimizer

Returns

momentum at first param group

Return type

float

catalyst.utils.torch.get_optimizer_momentum_list(optimizer: torch.optim.optimizer.Optimizer)List[Optional[float]][source]

Get list of optimizer momentums (for each param group)

Parameters

optimizer – PyTorch optimizer

Returns

momentum for each param group

Return type

momentum_list (List[Union[float, None]])

catalyst.utils.torch.get_requires_grad(model: torch.nn.modules.module.Module)[source]

Gets the requires_grad value for all model parameters.

Example:

>>> model = SimpleModel()
>>> requires_grad = get_requires_grad(model)
Parameters

model – model

Returns

value

Return type

requires_grad

catalyst.utils.torch.load_checkpoint(path: str)[source]

Load checkpoint from path.

Parameters

path – checkpoint file to load

Returns

checkpoint content

catalyst.utils.torch.outer_init(layer: torch.nn.modules.module.Module)None[source]

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

Parameters

layer – torch nn.Module instance

catalyst.utils.torch.pack_checkpoint(model: Optional[Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]]] = None, criterion: Optional[Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]]] = None, optimizer: Optional[Union[torch.optim.optimizer.Optimizer, Dict[str, torch.optim.optimizer.Optimizer]]] = None, scheduler: Optional[Union[torch.optim.lr_scheduler._LRScheduler, Dict[str, torch.optim.lr_scheduler._LRScheduler]]] = None, **kwargs)Dict[source]

Packs model, criterion, optimizer, scheduler and some extra info **kwargs to torch-based checkpoint.

Parameters
  • model – torch model

  • criterion – torch criterion

  • optimizer – torch optimizer

  • scheduler – torch scheduler

  • **kwargs – some extra info to pack

Returns

torch-based checkpoint with model_state_dict, criterion_state_dict, optimizer_state_dict, scheduler_state_dict keys.

catalyst.utils.torch.prepare_cudnn(deterministic: Optional[bool] = None, benchmark: Optional[bool] = None)None[source]

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

Parameters
  • deterministic – deterministic mode if running in CuDNN backend.

  • benchmark – 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: Optional[Dict[str, dict]] = None, no_bias_weight_decay: bool = True, lr_scaling: float = 1.0)List[Union[torch.nn.parameter.Parameter, dict]][source]

Gains model parameters for torch.optim.Optimizer.

Parameters
  • model – Model to process

  • layerwise_params – 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 – If true, removes weight_decay for all bias parameters in the model

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

Returns

parameters for an optimizer

Return type

iterable

Example:

>>> 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.reset_weights_if_possible(module: torch.nn.modules.module.Module)[source]

Resets module parameters if possible.

Parameters

module – Module to reset.

catalyst.utils.torch.save_checkpoint(checkpoint: Mapping[str, Any], path: str)[source]

Saves checkpoint to a file.

Parameters
  • checkpoint – data to save.

  • path – filepath where checkpoint should be stored.

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

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

Parameters
  • optimizer – PyTorch optimizer

  • value – new value of momentum

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

catalyst.utils.torch.set_requires_grad(model: torch.nn.modules.module.Module, requires_grad: Union[bool, Dict[str, bool]])[source]

Sets the requires_grad value for all model parameters.

Example:

>>> model = SimpleModel()
>>> set_requires_grad(model, requires_grad=True)
>>> # or
>>> model = SimpleModel()
>>> set_requires_grad(model, requires_grad={""})
Parameters
  • model – model

  • requires_grad – value

catalyst.utils.torch.trim_tensors(tensors: torch.Tensor)List[torch.Tensor][source]

Trim padding off of a batch of tensors to the smallest possible length. Should be used with catalyst.data.DynamicLenBatchSampler.

Adapted from Dynamic minibatch trimming to improve BERT training speed.

Parameters

tensors – list of tensors to trim.

Returns

list of trimmed tensors.

Return type

List[torch.tensor]

catalyst.utils.torch.unpack_checkpoint(checkpoint: Dict, model: Optional[Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]]] = None, criterion: Optional[Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]]] = None, optimizer: Optional[Union[torch.optim.optimizer.Optimizer, Dict[str, torch.optim.optimizer.Optimizer]]] = None, scheduler: Optional[Union[torch.optim.lr_scheduler._LRScheduler, Dict[str, torch.optim.lr_scheduler._LRScheduler]]] = None)None[source]

Load checkpoint from file and unpack the content to a model (if not None), criterion (if not None), optimizer (if not None), scheduler (if not None).

Parameters
  • checkpoint – checkpoint to load

  • model – model where should be updated state

  • criterion – criterion where should be updated state

  • optimizer – optimizer where should be updated state

  • scheduler – scheduler where should be updated state

Tracing

catalyst.utils.tracing.trace_model(model: torch.nn.modules.module.Module, batch: Union[Tuple[torch.Tensor], torch.Tensor], method_name: str = 'forward')torch.jit._script.ScriptModule[source]

Traces model using runner and batch.

Parameters
  • model – Model to trace

  • batch – Batch to trace the model

  • method_name – Model’s method name that will be used as entrypoint during tracing

Example

import torch

from catalyst.utils import trace_model

class LinModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lin1 = torch.nn.Linear(10, 10)
        self.lin2 = torch.nn.Linear(2, 10)

    def forward(self, inp_1, inp_2):
        return self.lin1(inp_1), self.lin2(inp_2)

    def first_only(self, inp_1):
        return self.lin1(inp_1)

lin_model = LinModel()
traced_model = trace_model(
    lin_model, batch=torch.randn(1, 10), method_name="first_only"
)
Returns

Traced model

Return type

jit.ScriptModule

Contrib

Compression

catalyst.contrib.utils.compression.pack(data)

Serialize the data into bytes using pickle.

Parameters

data – a value

Returns

Returns a bytes object serialized with pickle data.

catalyst.contrib.utils.compression.pack_if_needed(data)

Serialize the data into bytes using pickle.

Parameters

data – a value

Returns

Returns a bytes object serialized with pickle data.

catalyst.contrib.utils.compression.unpack(bytes)

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.contrib.utils.compression.unpack_if_needed(bytes)

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.

Image

catalyst.contrib.utils.image.has_image_extension(uri)bool[source]

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

Reads an image from the specified file.

Parameters
  • uri (str, pathlib.Path, bytes, file) – the resource to load the image from, e.g. a filename, pathlib.Path, http address or file object, see imageio.imread docs for more info

  • grayscale – if True, make all images grayscale

  • expand_dims – if True, append channel axis to grayscale images rootpath (Union[str, pathlib.Path]): path to the resource with image (allows to use relative path)

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

  • **kwargs – extra params for image read

Returns

image

Return type

np.ndarray

catalyst.contrib.utils.image.imsave(**kwargs)[source]

imwrite(uri, im, format=None, **kwargs)

Write an image to the specified file. Alias for imageio.imsave.

Parameters

**kwargs – parameters for imageio.imsave

Returns

image save result

catalyst.contrib.utils.image.imwrite(**kwargs)[source]

imwrite(uri, im, format=None, **kwargs)

Write an image to the specified file. Alias for imageio.imwrite.

Parameters

**kwargs – parameters for imageio.imwrite

Returns

image save result

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

Reads multiple images from the specified file.

Parameters
  • uri (str, pathlib.Path, bytes, file) – the resource to load the image from, e.g. a filename, pathlib.Path, http address or file object, see imageio.mimread docs for more info

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

  • expand_dims – if True, append channel axis to grayscale images rootpath (Union[str, pathlib.Path]): path to the resource with image (allows to use relative path)

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

  • **kwargs – extra params for image read

Returns

image

Return type

np.ndarray

Pandas

catalyst.contrib.utils.pandas.balance_classes(dataframe: pandas.core.frame.DataFrame, class_column: str = 'label', random_state: int = 42, how: str = 'downsampling')pandas.core.frame.DataFrame[source]

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

Raises

NotImplementedError – if how is not in [“upsampling”, “downsampling”, int]

catalyst.contrib.utils.pandas.create_dataframe(dataset: Dict[str, List[Any]], **dataframe_args)pandas.core.frame.DataFrame[source]

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.contrib.utils.pandas.create_dataset(dirs: str, extension: Optional[str] = None, process_fn: Optional[Callable[[str], object]] = None, recursive: bool = False)Dict[str, List[Any]][source]

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

dataset/
    cat/
        *.ext
    dog/
        *.ext
Parameters
  • dirs – path to dirs, for example /home/user/data/**

  • extension – data extension you are looking for

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

  • recursive – enables recursive globbing

Returns

dataset

Return type

dict

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

Converts dataframe to a list of rows (without indexes).

Parameters

dataframe – input dataframe

Returns

list of rows

Return type

List[dict]

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

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

Raises

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

catalyst.contrib.utils.pandas.get_dataset_labeling(dataframe: pandas.core.frame.DataFrame, tag_column: str)Dict[str, int][source]

Prepares a mapping using unique values from tag_column.

{
    "class_name_0": 0,
    "class_name_1": 1,
    ...
    "class_name_N": N
}
Parameters
  • dataframe – a dataset

  • tag_column – which column to use

Returns

mapping from tag to labels

Return type

Dict[str, int]

catalyst.contrib.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[source]

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

Parameters
  • dataframe – input dataframe

  • tag_column – 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.contrib.utils.pandas.separate_tags(dataframe: pandas.core.frame.DataFrame, tag_column: str = 'tag', tag_delim: str = ',')pandas.core.frame.DataFrame[source]

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.contrib.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: Optional[str] = None, class_column: Optional[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][source]

Split a Pandas DataFrame into folds.

Parameters
  • dataframe – input dataframe

  • train_folds – 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 – seed for split

  • n_folds – number of folds

Returns

tuple with 4 dataframes

whole dataframe, train part, valid part and infer part

Return type

tuple

catalyst.contrib.utils.pandas.split_dataframe_on_column_folds(dataframe: pandas.core.frame.DataFrame, column: str, random_state: int = 42, n_folds: int = 5)pandas.core.frame.DataFrame[source]

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.contrib.utils.pandas.split_dataframe_on_folds(dataframe: pandas.core.frame.DataFrame, random_state: int = 42, n_folds: int = 5)pandas.core.frame.DataFrame[source]

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.contrib.utils.pandas.split_dataframe_on_stratified_folds(dataframe: pandas.core.frame.DataFrame, class_column: str, random_state: int = 42, n_folds: int = 5)pandas.core.frame.DataFrame[source]

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.contrib.utils.pandas.split_dataframe_train_test(dataframe: pandas.core.frame.DataFrame, **train_test_split_args)Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame][source]

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

Note

It exist cause sklearn split is overcomplicated.

catalyst.contrib.utils.pandas.split_dataset_train_test(dataset: pandas.core.frame.DataFrame, **train_test_split_args)Tuple[Dict[str, List[Any]], Dict[str, List[Any]]][source]

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

Parallel

catalyst.contrib.utils.parallel.get_pool(workers: int)Union[multiprocessing.pool.Pool, catalyst.contrib.utils.parallel.DumbPool][source]

@TODO: Docs. Contribution is welcome.

catalyst.contrib.utils.parallel.parallel_imap(func, args, pool: Union[multiprocessing.pool.Pool, catalyst.contrib.utils.parallel.DumbPool])List[T][source]

@TODO: Docs. Contribution is welcome.

catalyst.contrib.utils.parallel.tqdm_parallel_imap(func, args, pool: Union[multiprocessing.pool.Pool, catalyst.contrib.utils.parallel.DumbPool], total: Optional[int] = None, pbar=<class 'tqdm.std.tqdm'>)List[T][source]

@TODO: Docs. Contribution is welcome.

Serialization

catalyst.contrib.utils.serialization.deserialize(bytes)

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.contrib.utils.serialization.serialize(data)

Serialize the data into bytes using pickle.

Parameters

data – a value

Returns

Returns a bytes object serialized with pickle data.

Thresholds

catalyst.contrib.utils.thresholds.get_baseline_thresholds(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float])Tuple[float, List[float]][source]

Returns baseline thresholds for multiclass/multilabel classification.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes]

  • labels – ground truth labels, numpy array with shape [num_examples, num_classes]

  • objective – callable function, metric which we want to maximize

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_best_multiclass_thresholds(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float])Tuple[float, List[float]][source]

Finds best thresholds for multiclass classification task.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model

  • labels – ground truth labels

  • objective – callable function, metric which we want to maximize

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_best_multilabel_thresholds(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float])Tuple[float, List[float]][source]

Finds best thresholds for multilabel classification task.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model

  • labels – ground truth labels

  • objective – callable function, metric which we want to maximize

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_binary_threshold(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float], num_thresholds: int = 100)Tuple[float, float][source]

Finds best threshold for binary classification task based on cross-validation estimates.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ]

  • labels – ground truth labels, numpy array with shape [num_examples, ]

  • objective – callable function, metric which we want to maximize

  • num_thresholds – number of thresholds ot try for each class

Returns

tuple with best found objective score and threshold

catalyst.contrib.utils.thresholds.get_binary_threshold_cv(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float], num_splits: int = 5, num_repeats: int = 1, random_state: int = 42)[source]

Finds best threshold for binary classification task based on cross-validation estimates.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ]

  • labels – ground truth labels, numpy array with shape [num_examples, ]

  • objective – callable function, metric which we want to maximize

  • num_splits – number of splits to use for cross-validation

  • num_repeats – number of repeats to use for cross-validation

  • random_state – random state to use for cross-validation

Returns

tuple with best found objective score and threshold

catalyst.contrib.utils.thresholds.get_multiclass_thresholds(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float])Tuple[List[float], List[float]][source]

Finds best thresholds for multiclass classification task.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes]

  • labels – ground truth labels, numpy array with shape [num_examples, num_classes]

  • objective – callable function, metric which we want to maximize

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_multiclass_thresholds_greedy(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float], num_iterations: int = 100, num_thresholds: int = 100, thresholds: Optional[numpy.ndarray] = None, patience: int = 3, atol: float = 0.01)Tuple[float, List[float]][source]

Finds best thresholds for multiclass classification task with brute-force algorithm.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model

  • labels – ground truth labels

  • objective – callable function, metric which we want to maximize

  • num_iterations – number of iteration for brute-force algorithm

  • num_thresholds – number of thresholds ot try for each class

  • thresholds – baseline thresholds, which we want to optimize

  • patience – maximum number of iteration before early stop exit

  • atol – minimum required improvement per iteration for early stop exit

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_multilabel_thresholds(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float])[source]

Finds best thresholds for multilabel classification task.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes]

  • labels – ground truth labels, numpy array with shape [num_examples, num_classes]

  • objective – callable function, metric which we want to maximize

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_multilabel_thresholds_cv(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float], num_splits: int = 5, num_repeats: int = 1, random_state: int = 42)[source]

Finds best thresholds for multilabel classification task based on cross-validation estimates.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes]

  • labels – ground truth labels, numpy array with shape [num_examples, num_classes]

  • objective – callable function, metric which we want to maximize

  • num_splits – number of splits to use for cross-validation

  • num_repeats – number of repeats to use for cross-validation

  • random_state – random state to use for cross-validation

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_multilabel_thresholds_greedy(scores: numpy.ndarray, labels: numpy.ndarray, objective: Callable[[numpy.ndarray, numpy.ndarray], float], num_iterations: int = 100, num_thresholds: int = 100, thresholds: Optional[numpy.ndarray] = None, patience: int = 3, atol: float = 0.01)Tuple[float, List[float]][source]

Finds best thresholds for multilabel classification task with brute-force algorithm.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model

  • labels – ground truth labels

  • objective – callable function, metric which we want to maximize

  • num_iterations – number of iteration for brute-force algorithm

  • num_thresholds – number of thresholds ot try for each class

  • thresholds – baseline thresholds, which we want to optimize

  • patience – maximum number of iteration before early stop exit

  • atol – minimum required improvement per iteration for early stop exit

Returns

tuple with best found objective score and per-class thresholds

catalyst.contrib.utils.thresholds.get_thresholds_greedy(scores: numpy.ndarray, labels: numpy.ndarray, score_fn: Callable, num_iterations: int = 100, num_thresholds: int = 100, thresholds: Optional[numpy.ndarray] = None, patience: int = 3, atol: float = 0.01)Tuple[float, List[float]][source]

Finds best thresholds for classification task with brute-force algorithm.

Parameters
  • scores – estimated per-class scores/probabilities predicted by the model

  • labels – ground truth labels

  • score_fn – callable function, based on (scores, labels, thresholds)

  • num_iterations – number of iteration for brute-force algorithm

  • num_thresholds – number of thresholds ot try for each class

  • thresholds – baseline thresholds, which we want to optimize

  • patience – maximum number of iteration before early stop exit

  • atol – minimum required improvement per iteration for early stop exit

Returns

tuple with best found objective score and per-class thresholds

Visualization

catalyst.contrib.utils.visualization.plot_confusion_matrix(cm: numpy.ndarray, class_names=None, normalize=False, title='confusion matrix', fname=None, show=True, figsize=12, fontsize=32, colormap='Blues')[source]

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

Parameters
  • cm – numpy confusion matrix

  • class_names – class names

  • normalize – boolean flag to normalize confusion matrix

  • title – title

  • fname – filename to save confusion matrix

  • show – boolean flag for preview

  • figsize – matplotlib figure size

  • fontsize – matplotlib font size

  • colormap – matplotlib color map

Returns

matplotlib figure

catalyst.contrib.utils.visualization.render_figure_to_array(figure)[source]

Renders matplotlib”s figure to tensor.