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Parametrize offset models #265

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45f2e94
Parametrize offset models
CeliaBenquet Aug 22, 2025
31ee9dc
Merge branch 'main' into celia/simplify-offset-models
CeliaBenquet Dec 9, 2025
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183 changes: 33 additions & 150 deletions cebra/models/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,59 +260,62 @@ def num_trainable_parameters(self) -> int:
param.numel() for param in self.parameters() if param.requires_grad)


@register("offset10-model")
class Offset10Model(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 10 sample receptive field."""
@parametrize("offset{n_offset}-model",
n_offset=(5, 10, 15, 18, 20, 31, 36, 40, 50))
class OffsetNModel(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a `n_offset` sample receptive field.

def __init__(self, num_neurons, num_units, num_output, normalize=True):
n_offset: The size of the receptive field.
"""

def __init__(self,
num_neurons,
num_units,
num_output,
n_offset,
normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)

self.n_offset = n_offset

def _compute_num_layers(n_offset):
"""Compute the number of layers to add on top of the first and last conv layers."""
return (n_offset - 4) // 2 + self.n_offset % 2
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The function parameter n_offset is passed but self.n_offset is used in the return statement. This creates inconsistency - either use the parameter consistently or remove it. The function should use n_offset (the parameter) instead of self.n_offset in the calculation.

Suggested change
return (n_offset - 4) // 2 + self.n_offset % 2
return (n_offset - 4) // 2 + n_offset % 2

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last_layer_kernel = 3 if (self.n_offset % 2) == 0 else 2
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, num_layers=3),
nn.Conv1d(num_units, num_output, 3),
*self._make_layers(num_units,
num_layers=_compute_num_layers(self.n_offset)),
nn.Conv1d(num_units, num_output, last_layer_kernel),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See :py:meth:`~.Model.get_offset`"""
return cebra.data.Offset(5, 5)
"""See `:py:meth:Model.get_offset`"""
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The docstring reference syntax is incorrect. It should be :py:meth:~.Model.get_offset (with tilde and dot prefix) to match the format used elsewhere in the codebase, such as on line 770.

Suggested change
"""See `:py:meth:Model.get_offset`"""
"""See :py:meth:`~.Model.get_offset`"""

Copilot uses AI. Check for mistakes.
return cebra.data.Offset(self.n_offset // 2,
self.n_offset // 2 + self.n_offset % 2)


@register("offset10-model-mse")
class Offset10ModelMSE(Offset10Model):
class Offset10ModelMSE(OffsetNModel):
"""Symmetric model with 10 sample receptive field, without normalization.

Suitable for use with InfoNCE metrics for Euclidean space.
"""

def __init__(self, num_neurons, num_units, num_output, normalize=False):
super().__init__(num_neurons, num_units, num_output, normalize)


@register("offset5-model")
class Offset5Model(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 5 sample receptive field and output normalization."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
cebra_layers._Skip(nn.Conv1d(num_units, num_units, 3), nn.GELU()),
nn.Conv1d(num_units, num_output, 2),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See :py:meth:`~.Model.get_offset`"""
return cebra.data.Offset(2, 3)
super().__init__(num_neurons,
num_units,
num_output,
n_offset=10,
normalize=normalize)


@register("offset1-model-mse")
Expand Down Expand Up @@ -666,30 +669,6 @@ def get_offset(self) -> cebra.data.datatypes.Offset:
return cebra.data.Offset(0, 1)


@register("offset36-model")
class Offset36(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 10 sample receptive field."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, num_layers=16),
nn.Conv1d(num_units, num_output, 3),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See `:py:meth:Model.get_offset`"""
return cebra.data.Offset(18, 18)


@_register_conditionally("offset36-model-dropout")
class Offset36Dropout(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 10 sample receptive field.
Expand Down Expand Up @@ -767,102 +746,6 @@ def get_offset(self) -> cebra.data.datatypes.Offset:
return cebra.data.Offset(18, 18)


@register("offset40-model")
class Offset40(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 40 samples receptive field."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, 18),
nn.Conv1d(num_units, num_output, 3),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See `:py:meth:Model.get_offset`"""
return cebra.data.Offset(20, 20)


@register("offset50-model")
class Offset50(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a sample receptive field."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, 23),
nn.Conv1d(num_units, num_output, 3),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See `:py:meth:Model.get_offset`"""
return cebra.data.Offset(25, 25)


@register("offset15-model")
class Offset15Model(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 15 sample receptive field."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, num_layers=6),
nn.Conv1d(num_units, num_output, 2),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See `:py:meth:Model.get_offset`"""
return cebra.data.Offset(7, 8)


@register("offset20-model")
class Offset20Model(_OffsetModel, ConvolutionalModelMixin):
"""CEBRA model with a 15 sample receptive field."""

def __init__(self, num_neurons, num_units, num_output, normalize=True):
if num_units < 1:
raise ValueError(
f"Hidden dimension needs to be at least 1, but got {num_units}."
)
super().__init__(
nn.Conv1d(num_neurons, num_units, 2),
nn.GELU(),
*self._make_layers(num_units, num_layers=8),
nn.Conv1d(num_units, num_output, 3),
num_input=num_neurons,
num_output=num_output,
normalize=normalize,
)

def get_offset(self) -> cebra.data.datatypes.Offset:
"""See `:py:meth:Model.get_offset`"""
return cebra.data.Offset(10, 10)


@register("offset10-model-mse-tanh")
class Offset10Model(_OffsetModel, ConvolutionalModelMixin):
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There is a naming conflict - Offset10Model is used here for the 'offset10-model-mse-tanh' architecture, but the same class name may conflict with models generated by the parametrize decorator. This class should be renamed to Offset10ModelMSETanh to avoid confusion and match the pattern used in line 775 (Offset0ModelMSETanH).

Suggested change
class Offset10Model(_OffsetModel, ConvolutionalModelMixin):
class Offset10ModelMSETanh(_OffsetModel, ConvolutionalModelMixin):

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"""CEBRA model with a 10 sample receptive field."""
Expand Down
2 changes: 1 addition & 1 deletion docs/source/usage.rst
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Expand Up @@ -175,7 +175,7 @@ We provide a set of pre-defined models. You can access (and search) a list of av

.. testoutput::

['offset10-model', 'offset10-model-mse', 'offset5-model', 'offset1-model-mse']
['offset5-model', 'offset10-model', 'offset15-model', 'offset18-model']

Then, you can choose the one that fits best with your needs and provide it to the CEBRA model as the :py:attr:`~.CEBRA.model_architecture` parameter.

Expand Down
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