62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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from abc import abstractmethod
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from dataclasses import asdict, dataclass
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from typing import Optional, Type, TypeVar
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import torch
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"""
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Feature maps allow for a given query or key to be encoded in a different space.
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"""
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Self = TypeVar("Self", bound="FeatureMap")
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@dataclass
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class FeatureMapConfig:
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name: str
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dim_features: int
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iter_before_redraw: Optional[int]
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normalize_inputs: Optional[bool]
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epsilon: Optional[float]
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class FeatureMap(torch.nn.Module):
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def __init__(
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self,
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dim_features: int,
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iter_before_redraw: Optional[int] = None,
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normalize_inputs: bool = False,
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epsilon: float = 1e-6,
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):
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super().__init__()
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self.dim_features = dim_features
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self.dim_feature_map = dim_features
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self.iter_before_redraw = iter_before_redraw
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self.features: Optional[torch.Tensor] = None
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self.epsilon = epsilon
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self.normalize_inputs = normalize_inputs
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self._iter_counter = 0
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@abstractmethod
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def _get_feature_map(self, dim_input: int, dim_features: int, device: torch.device):
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raise NotImplementedError()
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@classmethod
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def from_config(cls: Type[Self], config: FeatureMapConfig) -> Self:
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# Generate the class inputs from the config
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fields = asdict(config)
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# Skip all Nones so that default values are used
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fields = {k: v for k, v in fields.items() if v is not None}
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return cls(**fields)
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