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import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class DCM(nn.Module):
"""Dynamic Convolutional Module used in DMNet.
Args:
filter_size (int): The filter size of generated convolution kernel
used in Dynamic Convolutional Module.
fusion (bool): Add one conv to fuse DCM output feature.
in_channels (int): Input channels.
channels (int): Channels after modules, before conv_seg.
conv_cfg (dict | None): Config of conv layers.
norm_cfg (dict | None): Config of norm layers.
act_cfg (dict): Config of activation layers.
"""
def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
norm_cfg, act_cfg):
super(DCM, self).__init__()
self.filter_size = filter_size
self.fusion = fusion
self.in_channels = in_channels
self.channels = channels
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
0)
self.input_redu_conv = ConvModule(
self.in_channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
if self.norm_cfg is not None:
self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
else:
self.norm = None
self.activate = build_activation_layer(self.act_cfg)
if self.fusion:
self.fusion_conv = ConvModule(
self.channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def forward(self, x):
"""Forward function."""
generated_filter = self.filter_gen_conv(
F.adaptive_avg_pool2d(x, self.filter_size))
x = self.input_redu_conv(x)
b, c, h, w = x.shape
# [1, b * c, h, w], c = self.channels
x = x.view(1, b * c, h, w)
# [b * c, 1, filter_size, filter_size]
generated_filter = generated_filter.view(b * c, 1, self.filter_size,
self.filter_size)
pad = (self.filter_size - 1) // 2
if (self.filter_size - 1) % 2 == 0:
p2d = (pad, pad, pad, pad)
else:
p2d = (pad + 1, pad, pad + 1, pad)
x = F.pad(input=x, pad=p2d, mode='constant', value=0)
# [1, b * c, h, w]
output = F.conv2d(input=x, weight=generated_filter, groups=b * c)
# [b, c, h, w]
output = output.view(b, c, h, w)
if self.norm is not None:
output = self.norm(output)
output = self.activate(output)
if self.fusion:
output = self.fusion_conv(output)
return output
@HEADS.register_module()
class DMHead(BaseDecodeHead):
"""Dynamic Multi-scale Filters for Semantic Segmentation.
This head is the implementation of
`DMNet <https://openaccess.thecvf.com/content_ICCV_2019/papers/\
He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_\
ICCV_2019_paper.pdf>`_.
Args:
filter_sizes (tuple[int]): The size of generated convolutional filters
used in Dynamic Convolutional Module. Default: (1, 3, 5, 7).
fusion (bool): Add one conv to fuse DCM output feature.
"""
def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
super(DMHead, self).__init__(**kwargs)
assert isinstance(filter_sizes, (list, tuple))
self.filter_sizes = filter_sizes
self.fusion = fusion
dcm_modules = []
for filter_size in self.filter_sizes:
dcm_modules.append(
DCM(filter_size,
self.fusion,
self.in_channels,
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
self.dcm_modules = nn.ModuleList(dcm_modules)
self.bottleneck = ConvModule(
self.in_channels + len(filter_sizes) * self.channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def forward(self, inputs):
"""Forward function."""
x = self._transform_inputs(inputs)
dcm_outs = [x]
for dcm_module in self.dcm_modules:
dcm_outs.append(dcm_module(x))
dcm_outs = torch.cat(dcm_outs, dim=1)
output = self.bottleneck(dcm_outs)
output = self.cls_seg(output)
return output
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