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Parametrize offset models #265

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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

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`"""
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):
"""CEBRA model with a 10 sample receptive field."""
Expand Down
2 changes: 1 addition & 1 deletion docs/source/usage.rst
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Original file line number Diff line number Diff line change
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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