77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import torch.nn as nn
|
|
from mmcv.cnn import ConvModule, xavier_init
|
|
|
|
from mmseg.ops import resize
|
|
from ..builder import NECKS
|
|
|
|
|
|
@NECKS.register_module()
|
|
class MultiLevelNeck(nn.Module):
|
|
"""MultiLevelNeck.
|
|
|
|
A neck structure connect vit backbone and decoder_heads.
|
|
Args:
|
|
in_channels (List[int]): Number of input channels per scale.
|
|
out_channels (int): Number of output channels (used at each scale).
|
|
scales (List[float]): Scale factors for each input feature map.
|
|
Default: [0.5, 1, 2, 4]
|
|
norm_cfg (dict): Config dict for normalization layer. Default: None.
|
|
act_cfg (dict): Config dict for activation layer in ConvModule.
|
|
Default: None.
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
scales=[0.5, 1, 2, 4],
|
|
norm_cfg=None,
|
|
act_cfg=None):
|
|
super(MultiLevelNeck, self).__init__()
|
|
assert isinstance(in_channels, list)
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.scales = scales
|
|
self.num_outs = len(scales)
|
|
self.lateral_convs = nn.ModuleList()
|
|
self.convs = nn.ModuleList()
|
|
for in_channel in in_channels:
|
|
self.lateral_convs.append(
|
|
ConvModule(
|
|
in_channel,
|
|
out_channels,
|
|
kernel_size=1,
|
|
norm_cfg=norm_cfg,
|
|
act_cfg=act_cfg))
|
|
for _ in range(self.num_outs):
|
|
self.convs.append(
|
|
ConvModule(
|
|
out_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
padding=1,
|
|
stride=1,
|
|
norm_cfg=norm_cfg,
|
|
act_cfg=act_cfg))
|
|
|
|
# default init_weights for conv(msra) and norm in ConvModule
|
|
def init_weights(self):
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
xavier_init(m, distribution='uniform')
|
|
|
|
def forward(self, inputs):
|
|
assert len(inputs) == len(self.in_channels)
|
|
inputs = [
|
|
lateral_conv(inputs[i])
|
|
for i, lateral_conv in enumerate(self.lateral_convs)
|
|
]
|
|
# for len(inputs) not equal to self.num_outs
|
|
if len(inputs) == 1:
|
|
inputs = [inputs[0] for _ in range(self.num_outs)]
|
|
outs = []
|
|
for i in range(self.num_outs):
|
|
x_resize = resize(
|
|
inputs[i], scale_factor=self.scales[i], mode='bilinear')
|
|
outs.append(self.convs[i](x_resize))
|
|
return tuple(outs)
|