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from abc import ABCMeta, abstractmethod
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from typing import Dict
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import torch.nn as nn
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from detectron2.layers import ShapeSpec
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__all__ = ["Backbone"]
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class Backbone(nn.Module, metaclass=ABCMeta):
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"""
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Abstract base class for network backbones.
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"""
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def __init__(self):
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"""
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The `__init__` method of any subclass can specify its own set of arguments.
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"""
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super().__init__()
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@abstractmethod
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def forward(self):
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"""
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Subclasses must override this method, but adhere to the same return type.
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Returns:
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dict[str->Tensor]: mapping from feature name (e.g., "res2") to tensor
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"""
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pass
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@property
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def size_divisibility(self) -> int:
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"""
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Some backbones require the input height and width to be divisible by a
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specific integer. This is typically true for encoder / decoder type networks
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with lateral connection (e.g., FPN) for which feature maps need to match
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dimension in the "bottom up" and "top down" paths. Set to 0 if no specific
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input size divisibility is required.
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"""
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return 0
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@property
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def padding_constraints(self) -> Dict[str, int]:
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"""
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This property is a generalization of size_divisibility. Some backbones and training
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recipes require specific padding constraints, such as enforcing divisibility by a specific
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integer (e.g., FPN) or padding to a square (e.g., ViTDet with large-scale jitter
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in :paper:vitdet). `padding_constraints` contains these optional items like:
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{
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"size_divisibility": int,
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"square_size": int,
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# Future options are possible
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}
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`size_divisibility` will read from here if presented and `square_size` indicates the
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square padding size if `square_size` > 0.
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TODO: use type of Dict[str, int] to avoid torchscipt issues. The type of padding_constraints
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could be generalized as TypedDict (Python 3.8+) to support more types in the future.
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"""
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return {}
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def output_shape(self):
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"""
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Returns:
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dict[str->ShapeSpec]
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"""
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return {
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name: ShapeSpec(
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channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
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)
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for name in self._out_features
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}
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