47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This source code is licensed under the CC BY-NC 4.0 license found in the
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# LICENSE file in the root directory of this source tree.
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#
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# This file is derived from https://github.com/facebookresearch/flow_matching
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# Licensed under CC BY-NC 4.0: https://creativecommons.org/licenses/by-nc/4.0/
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# pyre-unsafe
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from abc import ABC
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from torch import nn, Tensor
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class ModelWrapper(ABC, nn.Module):
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"""
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This class is used to wrap around another model, adding custom forward pass logic.
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"""
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def __init__(self, model: nn.Module):
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super().__init__()
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self.model = model
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def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor:
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r"""
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This method defines how inputs should be passed through the wrapped model.
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Here, we're assuming that the wrapped model takes both :math:`x` and :math:`t` as input,
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along with any additional keyword arguments.
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Optional things to do here:
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- check that t is in the dimensions that the model is expecting.
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- add a custom forward pass logic.
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- call the wrapped model.
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| given x, t
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| returns the model output for input x at time t, with extra information `extra`.
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Args:
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x (Tensor): input data to the model (batch_size, ...).
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t (Tensor): time (batch_size).
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**extras: additional information forwarded to the model, e.g., text condition.
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Returns:
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Tensor: model output.
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"""
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return self.model(x=x, t=t, **extras)
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