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