Models and Criteria#

Pre-defined neural network model architectures

This package contains everything related to implementing data encoders and the loss functions applied to the feature spaces. cebra.models.criterions contains the implementations of InfoNCE and other contrastive losses. All additions regarding how data is encoded and losses are computed should be added to this package.

This module is a registry and currently contains the options [‘offset10-model’, ‘offset10-model-mse’, ‘offset5-model’, ‘offset1-model-mse’, ‘offset1-model’, ‘offset1-model-v2’, ‘offset1-model-v3’, ‘offset1-model-v4’, ‘offset1-model-v5’, ‘offset40-model-4x-subsample’].

To retrieve a list of options, call:

>>> print(cebra.models.get_options())
['offset10-model', 'offset10-model-mse', 'offset5-model', ...]

To obtain an initialized instance, call cebra.models.init, defined in cebra.registry.add_helper_functions(). The first parameter to provide is the models name to use, which is one of the available options presented above. Then the required positional arguments specific to the module are provided, if needed.

You can register additional options by defining and registering classes with a name. To do that, you can add a decorator on top of it: @cebra.models.register("my-cebra-models").

Later, initialize your class similarly to the pre-defined options, using cebra.models.init with the models name set to my-cebra-models.

Note that these customized options will not be automatically added to this docstring.

Registration and initialization#

cebra.models.init(name, *args, **kwargs)#

Initialize an instance from the registry with the specified arguments.

Parameters:

  • name (str) – The to identify the registered class

  • args – Arguments and keyword arguments to pass to the constructor while instantiating the selected type.

  • kwargs – Arguments and keyword arguments to pass to the constructor while instantiating the selected type.

Returns:

An instance of the specified class.

cebra.models.get_options(pattern=None, limit=None, expand_parametrized=True)#

Retrieve a list of registered names, optionally filtered.

Parameters:

  • pattern (Optional[str]) – A glob-like pattern (supporting wildcards * and ? to filter the options. Optional argument, defaults to no filtering.

  • limit (Optional[int]) – An optional maximum amount of options to return, in the order of finding them with the given query.

  • expand_parametrized (bool) – Whether to list classes registered with the parametrize decorator in the options.

Return type:

List[str]

Returns:

All matching names. If a limit was specified, the maximum length is given by the limit.

cebra.models.register(name, base=None, override=False, deprecated=False)#

Decorator to add a new class type to the registry.

cebra.models.parametrize(pattern, *, kwargs=[], **all_kwargs)#

Decorator to add parametrizations of a new class to the registry.

The specified keyword arguments will be passed as default arguments to the constructor of the class.

Models#

Neural network models and criterions for training CEBRA models.

class cebra.models.model.Model(*, num_input, num_output, offset=None)#

Bases: Module

Base model for CEBRA experiments.

The model is a pytorch nn.Module. Features can be computed by calling the forward() or __call__ method. This class should not be directly instantiated, and instead used as the base class for CEBRA models.

Parameters:

  • num_input (int) – The number of input dimensions. The tensor passed to the forward method will have shape (batch, num_input, in_time).

  • num_output (int) – The number of output dimensions. The tensor returned by the forward method will have shape (batch, num_output, out_time).

  • offset (Optional[Offset]) – A specification of the offset to the left and right of the signal due to the network’s receptive field. The offset specifies the relation between the input and output times, in_time - out_time = len(offset).

num_input#

The input dimensionality (of the input signal). When calling forward, this is the dimensionality expected for the input argument. In typical applications of CEBRA, the input dimension corresponds to the number of neurons for neural data analysis, number of keypoints for kinematik analysis, or can also be the dimension of a feature space in case preprocessing happened before feeding the data into the model.

num_output#

The output dimensionality (of the embedding space). This is the feature dimension of value returned by forward. Note that for models using normalization, the output dimension should be at least 3D, and 2D without normalization to learn meaningful embeddings. The output dimensionality is typically smaller than num_input, but this is not enforced.

abstract get_offset()#

Offset between input and output sequence caused by the receptive field.

The offset specifies the relation between the length of the input and output time sequences. The output sequence is len(offset) steps shorter than the input sequence. For input sequences of shape (*, *, len(offset)), the model should return an output sequence that drops the last dimension (which would be 1).

Returns

The offset of the network. See cebra.data.datatypes.Offset for full documentation.

Return type:

Offset

class cebra.models.model.ConvolutionalModelMixin#

Bases: object

Mixin for models that support operating on a time-series.

The input for convolutional models should be batch, dim, time and the convolution will be applied across the last dimension.

class cebra.models.model.ResampleModelMixin#

Bases: object

Mixin for models that re-sample the signal over time.

property resample_factor: float#

The factor by which the signal is downsampled.

Return type:

float

class cebra.models.model.HasFeatureEncoder#

Bases: object

Networks with an explicitly defined feature encoder.

class cebra.models.model.ClassifierModel(*, num_input, num_output, offset=None)#

Bases: Model, HasFeatureEncoder

Base model for classifiers.

Adds an additional classifier layer to the model which is lazily initialized after calling set_output_num().

Parameters:

  • num_input (int) – The number of input units

  • num_output (int) – The number of output units

  • offset (Optional[Offset]) – The offset introduced by the model’s receptive field

features_encoder#

The feature encoder to map the input tensor (2d or 3d depending on the exact model implementation) into a feature space of same dimension

classifier#

Map from the feature space to class scores

abstract get_offset()#

See get_offset()

Return type:

Offset

set_output_num(label_num, override=False)#

Set the number of output classes.

Parameters:

  • label_num (int) – The number of labels to be added to the classifier layer.

  • override (bool) – If True, override an existing classifier layer. If you passed the parameters of this model to an optimizer, make sure to correctly handle the replacement of the classifier there.

forward(inputs)#

See ClassifierModel.

Return type:

Tensor

class cebra.models.model._OffsetModel(*args: Any, **kwargs: Any)#

Bases: Model, HasFeatureEncoder

forward(inp)#

Compute the embedding given the input signal.

Parameters:

inp – The input tensor of shape num_samples x self.num_input x time

Returns:

The output tensor of shape num_samples x self.num_output x (time - receptive field).

Based on the parameters used for initializing, the output embedding is normalized to the hypersphere (normalize = True).

class cebra.models.model.ParameterCountMixin#

Bases: object

Add a parameter counter to a torch.nn.Module.

property num_parameters: int#

Total parameter count of the model.

Return type:

int

property num_trainable_parameters: int#

Number of trainable parameters.

Return type:

int

class cebra.models.model.Offset10ModelMSE(*args: Any, **kwargs: Any)#

Bases: Offset10Model

Symmetric model with 10 sample receptive field, without normalization.

Suitable for use with InfoNCE metrics for Euclidean space.

class cebra.models.model.Offset5Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 5 sample receptive field and output normalization.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0ModelMSE(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, without output normalization.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, with output normalization.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0Modelv2(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, with output normalization.

This is a variant of Offset0Model.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0Modelv3(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, with output normalization.

This is a variant of Offset0Model.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0Modelv4(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, with output normalization.

This is a variant of Offset0Model.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0Modelv5(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, with output normalization.

This is a variant of Offset0Model.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.ResampleModel(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin, ResampleModelMixin

CEBRA model with 40 sample receptive field, output normalization and 4x subsampling.

property resample_factor#

The factor by which the signal is downsampled.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Resample5Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin, ResampleModelMixin

CEBRA model with 20 sample receptive field, output normalization and 4x subsampling.

property resample_factor#

The factor by which the signal is downsampled.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Resample1Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ResampleModelMixin

CEBRA model with 4 sample receptive field, output normalization and 2x subsampling.

This model is not convolutional, and needs to be applied to fixed (N, d, 4) inputs.

property resample_factor#

The factor by which the signal is downsampled.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.SupervisedNN10(*args: Any, **kwargs: Any)#

Bases: ClassifierModel

A supervised model with 10 sample receptive field.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.SupervisedNN1(*args: Any, **kwargs: Any)#

Bases: ClassifierModel

A supervised model with single sample receptive field.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset36(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 10 sample receptive field.

get_offset()#

See :py:meth:Model.get_offset

Return type:

Offset

class cebra.models.model.Offset36Dropout(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 10 sample receptive field.

Note

Requires torch>=1.12.

get_offset()#

See :py:meth:Model.get_offset

Return type:

Offset

class cebra.models.model.Offset36Dropoutv2(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 10 sample receptive field.

Note

Requires torch>=1.12.

get_offset()#

See :py:meth:Model.get_offset

Return type:

Offset

class cebra.models.model.Offset15Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 15 sample receptive field.

get_offset()#

See :py:meth:Model.get_offset

Return type:

Offset

class cebra.models.model.Offset20Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 15 sample receptive field.

get_offset()#

See :py:meth:Model.get_offset

Return type:

Offset

class cebra.models.model.Offset10Model(*args: Any, **kwargs: Any)#

Bases: _OffsetModel, ConvolutionalModelMixin

CEBRA model with a 10 sample receptive field.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0ModelMSETanH(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, without output normalization.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0ModelMSEClip(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, without output normalization.

forward(inputs)#

Compute the embedding given the input signal.

Parameters:

inp – The input tensor of shape num_samples x self.num_input x time

Returns:

The output tensor of shape num_samples x self.num_output x (time - receptive field).

Based on the parameters used for initializing, the output embedding is normalized to the hypersphere (normalize = True).

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0ModelMSETanHv2(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, without output normalization.

get_offset()#

See get_offset()

Return type:

Offset

class cebra.models.model.Offset0ModelResNetTanH(*args: Any, **kwargs: Any)#

Bases: _OffsetModel

CEBRA model with a single sample receptive field, without output normalization.

get_offset()#

See get_offset()

Return type:

Offset

Criterions#

Criterions for contrastive learning

Different criterions can be used for learning embeddings with CEBRA. The common interface of criterions implementing the generalized InfoNCE metric is given by BaseInfoNCE.

Criterions are available for fixed and learnable temperatures, as well as different similarity measures.

Note that criterions can have trainable parameters, which are automatically handled by the training loops implemented in cebra.solver.base.Solver classes.

cebra.models.criterions.dot_similarity(ref, pos, neg)#

Cosine similarity the ref, pos and negative pairs

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The similarity between reference samples and positive samples of shape (n,), and the similarities between reference samples and negative samples of shape (n, n).

cebra.models.criterions.euclidean_similarity(ref, pos, neg)#

Negative L2 distance between the ref, pos and negative pairs

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The similarity between reference samples and positive samples of shape (n,), and the similarities between reference samples and negative samples of shape (n, n).

cebra.models.criterions.infonce(pos_dist, neg_dist)#

InfoNCE implementation

See BaseInfoNCE for reference.

Note

  • The behavior of this function changed beginning in CEBRA 0.3.0. The InfoNCE implementation is numerically stabilized.

Return type:

Tuple[Tensor, Tensor, Tensor]

class cebra.models.criterions.ContrastiveLoss(*args, **kwargs)#

Bases: Module

Base class for contrastive losses.

Note

  • Added in 0.0.2.

forward(ref, pos, neg)#

Compute the contrastive loss.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor, Tensor]

class cebra.models.criterions.BaseInfoNCE(*args, **kwargs)#

Bases: ContrastiveLoss

Base class for all InfoNCE losses.

Given a similarity measure \(\phi\) which will be implemented by the subclasses of this class, the generalized InfoNCE loss is computed as

\[\sum_{i=1}^n - \phi(x_i, y^{+}_i) + \log \sum_{j=1}^{n} e^{\phi(x_i, y^{-}_{ij})}\]

where \(n\) is the batch size, \(x\) are the reference samples (ref), \(y^{+}\) are the positive samples (pos) and \(y^{-}\) are the negative samples (neg).

_distance(ref, pos, neg)#

The similarity measure.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor]

Returns:

The distance between reference samples and positive samples of shape (n,), and the distances between reference samples and negative samples of shape (n, n).

forward(ref, pos, neg)#

Compute the InfoNCE loss.

Parameters:

  • ref – The reference samples of shape (n, d).

  • pos – The positive samples of shape (n, d).

  • neg – The negative samples of shape (n, d).

See also

BaseInfoNCE.

Return type:

Tuple[Tensor, Tensor, Tensor]

class cebra.models.criterions.FixedInfoNCE(temperature=1.0)#

Bases: BaseInfoNCE

InfoNCE base loss with a fixed temperature.

temperature#

The softmax temperature

class cebra.models.criterions.LearnableInfoNCE(temperature=1.0, min_temperature=None)#

Bases: BaseInfoNCE

InfoNCE base loss with a learnable temperature.

temperature#

The current value of the learnable temperature parameter.

min_temperature#

The minimum temperature to use. Increase the minimum temperature if you encounter numerical issues during optimization.

_prepare_inverse_temperature()#

Compute the current inverse temperature.

Return type:

Tensor

class cebra.models.criterions.FixedCosineInfoNCE(temperature=1.0)#

Bases: FixedInfoNCE

Cosine similarity function with fixed temperature.

The similarity metric is given as

\[\phi(x, y) = x^\top y / \tau\]

with fixed temperature \(\tau > 0\).

Note that this loss function should typically only be used with normalized. This class itself does not perform any checks. Ensure that \(x\) and \(y\) are normalized.

_distance(ref, pos, neg)#

The similarity measure.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The distance between reference samples and positive samples of shape (n,), and the distances between reference samples and negative samples of shape (n, n).

class cebra.models.criterions.FixedEuclideanInfoNCE(temperature=1.0)#

Bases: FixedInfoNCE

L2 similarity function with fixed temperature.

The similarity metric is given as

\[\phi(x, y) = - \| x - y \| / \tau\]

with fixed temperature \(\tau > 0\).

_distance(ref, pos, neg)#

The similarity measure.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The distance between reference samples and positive samples of shape (n,), and the distances between reference samples and negative samples of shape (n, n).

class cebra.models.criterions.LearnableCosineInfoNCE(temperature=1.0, min_temperature=None)#

Bases: LearnableInfoNCE

Cosine similarity function with a learnable temperature.

Like FixedCosineInfoNCE, but with a learnable temperature parameter \(\tau\).

_distance(ref, pos, neg)#

The similarity measure.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The distance between reference samples and positive samples of shape (n,), and the distances between reference samples and negative samples of shape (n, n).

class cebra.models.criterions.LearnableEuclideanInfoNCE(temperature=1.0, min_temperature=None)#

Bases: LearnableInfoNCE

L2 similarity function with fixed temperature.

Like FixedEuclideanInfoNCE, but with a learnable temperature parameter \(\tau\).

_distance(ref, pos, neg)#

The similarity measure.

Parameters:

  • ref (Tensor) – The reference samples of shape (n, d).

  • pos (Tensor) – The positive samples of shape (n, d).

  • neg (Tensor) – The negative samples of shape (n, d).

Return type:

Tuple[Tensor, Tensor]

Returns:

The distance between reference samples and positive samples of shape (n,), and the distances between reference samples and negative samples of shape (n, n).

cebra.models.criterions.InfoNCE#

alias of FixedCosineInfoNCE

cebra.models.criterions.InfoMSE#

alias of FixedEuclideanInfoNCE

class cebra.models.criterions.NCE(*args: Any, **kwargs: Any)#

Bases: ContrastiveLoss

Noise contrastive estimation (Gutman & Hyvarinen, 2012)

temperature#

The softmax temperature

Type:

float

negative_weight#

Relative weight of the negative samples

Type:

float

reduce#

How to reduce the negative samples. Can be sum or mean.

Type:

str

forward(ref, pos, neg)#

Compute the NCE loss.

Parameters:

  • ref – The reference samples of shape (n, d).

  • pos – The positive samples of shape (n, d).

  • neg – The negative samples of shape (n, d).

See also

NCE.

Layers and model building blocks#

Neural network layers used for building cebra models.

Layers are used in the models defined in model.

class cebra.models.layers.Squeeze(*args, **kwargs)#

Bases: Module

Squeeze 3rd dimension of input tensor, pass through otherwise.

forward(inp)#

Squeeze 3rd dimension of input tensor, pass through otherwise.

Parameters:

inp (Tensor) – 1-3D input tensor

Return type:

Tensor

Returns:

If the third dimension of the input tensor can be squeezed, return the resulting 2D output tensor. If input is 2D or less, return the input.

Multi-objective models#

The multi-objective interface was moved to a separate section beginning with CEBRA 0.6.0. Please see the Multi-objective models section for all details, both on the old and new API interface.