import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import HEADS
[docs]@HEADS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_channels (int, optional): number of input channels. Default: 256.
conv_out_channels (int, optional): number of output channels before
classification layer. Default: 256.
roi_feat_size (int, optional): roi feat size at box head. Default: 7.
scale_factor (int, optional): scale factor to match roi feat size
at mask head. Default: 2.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
in_channels=1024,
out_conv_channels=256,
roi_feat_size=7,
scale_factor=2,
init_cfg=dict(type='Kaiming', layer='Linear')):
super(FeatureRelayHead, self).__init__(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)
[docs] @auto_fp16()
def forward(self, x):
"""Forward function."""
N, in_C = 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