mmseg/models/necks/featurepyramid.py

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer

from mmseg.registry import MODELS


@MODELS.register_module()
class Feature2Pyramid(nn.Module):
    """Feature2Pyramid.

    A neck structure connect ViT backbone and decoder_heads.

    Args:
        embed_dims (int): Embedding dimension.
        rescales (list[float]): Different sampling multiples were
            used to obtain pyramid features. Default: [4, 2, 1, 0.5].
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='SyncBN', requires_grad=True).
    """

    def __init__(self,
                 embed_dim,
                 rescales=[4, 2, 1, 0.5],
                 norm_cfg=dict(type='SyncBN', requires_grad=True)):
        super().__init__()
        self.rescales = rescales
        self.upsample_4x = None
        for k in self.rescales:
            if k == 4:
                self.upsample_4x = nn.Sequential(
                    nn.ConvTranspose2d(
                        embed_dim, embed_dim, kernel_size=2, stride=2),
                    build_norm_layer(norm_cfg, embed_dim)[1],
                    nn.GELU(),
                    nn.ConvTranspose2d(
                        embed_dim, embed_dim, kernel_size=2, stride=2),
                )
            elif k == 2:
                self.upsample_2x = nn.Sequential(
                    nn.ConvTranspose2d(
                        embed_dim, embed_dim, kernel_size=2, stride=2))
            elif k == 1:
                self.identity = nn.Identity()
            elif k == 0.5:
                self.downsample_2x = nn.MaxPool2d(kernel_size=2, stride=2)
            elif k == 0.25:
                self.downsample_4x = nn.MaxPool2d(kernel_size=4, stride=4)
            else:
                raise KeyError(f'invalid {k} for feature2pyramid')

    def forward(self, inputs):
        assert len(inputs) == len(self.rescales)
        outputs = []
        if self.upsample_4x is not None:
            ops = [
                self.upsample_4x, self.upsample_2x, self.identity,
                self.downsample_2x
            ]
        else:
            ops = [
                self.upsample_2x, self.identity, self.downsample_2x,
                self.downsample_4x
            ]
        for i in range(len(inputs)):
            outputs.append(ops[i](inputs[i]))
        return tuple(outputs)