modeling/vision/backbone/backbone.py

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright (c) Facebook, Inc. and its affiliates.
import torch.nn as nn

from detectron2.modeling import ShapeSpec

# from ..layers 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

    @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

    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
        }