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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmseg.registry import MODELS
from ..decode_heads.psp_head import PPM
from ..utils import resize
@MODELS.register_module()
class ICNet(BaseModule):
"""ICNet for Real-Time Semantic Segmentation on High-Resolution Images.
This backbone is the implementation of
`ICNet <https://arxiv.org/abs/1704.08545>`_.
Args:
backbone_cfg (dict): Config dict to build backbone. Usually it is
ResNet but it can also be other backbones.
in_channels (int): The number of input image channels. Default: 3.
layer_channels (Sequence[int]): The numbers of feature channels at
layer 2 and layer 4 in ResNet. It can also be other backbones.
Default: (512, 2048).
light_branch_middle_channels (int): The number of channels of the
middle layer in light branch. Default: 32.
psp_out_channels (int): The number of channels of the output of PSP
module. Default: 512.
out_channels (Sequence[int]): The numbers of output feature channels
at each branches. Default: (64, 256, 256).
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module. Default: (1, 2, 3, 6).
conv_cfg (dict): Dictionary to construct and config conv layer.
Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN').
act_cfg (dict): Dictionary to construct and config act layer.
Default: dict(type='ReLU').
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(self,
backbone_cfg,
in_channels=3,
layer_channels=(512, 2048),
light_branch_middle_channels=32,
psp_out_channels=512,
out_channels=(64, 256, 256),
pool_scales=(1, 2, 3, 6),
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='ReLU'),
align_corners=False,
init_cfg=None):
if backbone_cfg is None:
raise TypeError('backbone_cfg must be passed from config file!')
if init_cfg is None:
init_cfg = [
dict(type='Kaiming', mode='fan_out', layer='Conv2d'),
dict(type='Constant', val=1, layer='_BatchNorm'),
dict(type='Normal', mean=0.01, layer='Linear')
]
super().__init__(init_cfg=init_cfg)
self.align_corners = align_corners
self.backbone = MODELS.build(backbone_cfg)
# Note: Default `ceil_mode` is false in nn.MaxPool2d, set
# `ceil_mode=True` to keep information in the corner of feature map.
self.backbone.maxpool = nn.MaxPool2d(
kernel_size=3, stride=2, padding=1, ceil_mode=True)
self.psp_modules = PPM(
pool_scales=pool_scales,
in_channels=layer_channels[1],
channels=psp_out_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
align_corners=align_corners)
self.psp_bottleneck = ConvModule(
layer_channels[1] + len(pool_scales) * psp_out_channels,
psp_out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.conv_sub1 = nn.Sequential(
ConvModule(
in_channels=in_channels,
out_channels=light_branch_middle_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg),
ConvModule(
in_channels=light_branch_middle_channels,
out_channels=light_branch_middle_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg),
ConvModule(
in_channels=light_branch_middle_channels,
out_channels=out_channels[0],
kernel_size=3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
self.conv_sub2 = ConvModule(
layer_channels[0],
out_channels[1],
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
self.conv_sub4 = ConvModule(
psp_out_channels,
out_channels[2],
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
def forward(self, x):
output = []
# sub 1
output.append(self.conv_sub1(x))
# sub 2
x = resize(
x,
scale_factor=0.5,
mode='bilinear',
align_corners=self.align_corners)
x = self.backbone.stem(x)
x = self.backbone.maxpool(x)
x = self.backbone.layer1(x)
x = self.backbone.layer2(x)
output.append(self.conv_sub2(x))
# sub 4
x = resize(
x,
scale_factor=0.5,
mode='bilinear',
align_corners=self.align_corners)
x = self.backbone.layer3(x)
x = self.backbone.layer4(x)
psp_outs = self.psp_modules(x) + [x]
psp_outs = torch.cat(psp_outs, dim=1)
x = self.psp_bottleneck(psp_outs)
output.append(self.conv_sub4(x))
return output
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