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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch.nn as nn
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig
@MODELS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_channels (int): number of input channels. Defaults to 256.
conv_out_channels (int): number of output channels before
classification layer. Defaults to 256.
roi_feat_size (int): roi feat size at box head. Default: 7.
scale_factor (int): scale factor to match roi feat size
at mask head. Defaults to 2.
init_cfg (:obj:`ConfigDict` or dict or list[dict] or
list[:obj:`ConfigDict`]): Initialization config dict. Defaults to
dict(type='Kaiming', layer='Linear').
"""
def __init__(
self,
in_channels: int = 1024,
out_conv_channels: int = 256,
roi_feat_size: int = 7,
scale_factor: int = 2,
init_cfg: MultiConfig = dict(type='Kaiming', layer='Linear')
) -> None:
super().__init__(init_cfg=init_cfg)
assert isinstance(roi_feat_size, int)
self.in_channels = in_channels
self.out_conv_channels = out_conv_channels
self.roi_feat_size = roi_feat_size
self.out_channels = (roi_feat_size**2) * out_conv_channels
self.scale_factor = scale_factor
self.fp16_enabled = False
self.fc = nn.Linear(self.in_channels, self.out_channels)
self.upsample = nn.Upsample(
scale_factor=scale_factor, mode='bilinear', align_corners=True)
def forward(self, x: Tensor) -> Optional[Tensor]:
"""Forward function.
Args:
x (Tensor): Input feature.
Returns:
Optional[Tensor]: Output feature. When the first dim of input is
0, None is returned.
"""
N, _ = x.shape
if N > 0:
out_C = self.out_conv_channels
out_HW = self.roi_feat_size
x = self.fc(x)
x = x.reshape(N, out_C, out_HW, out_HW)
x = self.upsample(x)
return x
return None
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