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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import torch.nn as nn | |
| from detectron2.modeling import ShapeSpec | |
| __all__ = ["Backbone"] | |
| class Backbone(nn.Module): | |
| """ | |
| Abstract base class for network backbones. | |
| """ | |
| def __init__(self): | |
| """ | |
| The `__init__` method of any subclass can specify its own set of arguments. | |
| """ | |
| super().__init__() | |
| 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 | |
| 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 | |
| def output_shape(self): | |
| """ | |
| Returns: | |
| dict[str->ShapeSpec] | |
| """ | |
| # this is a backward-compatible default | |
| return { | |
| name: ShapeSpec( | |
| channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] | |
| ) | |
| for name in self._out_features | |
| } | |