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from abc import ABCMeta, abstractmethod |
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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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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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