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import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(self, pool_scales=(1, 3, 6, 8)):
super(PPMConcat, self).__init__(
[nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales])
def forward(self, feats):
"""Forward function."""
ppm_outs = []
for ppm in self:
ppm_out = ppm(feats)
ppm_outs.append(ppm_out.view(*feats.shape[:2], -1))
concat_outs = torch.cat(ppm_outs, dim=2)
return concat_outs
class SelfAttentionBlock(_SelfAttentionBlock):
"""Make a ANN used SelfAttentionBlock.
Args:
low_in_channels (int): Input channels of lower level feature,
which is the key feature for self-attention.
high_in_channels (int): Input channels of higher level feature,
which is the query feature for self-attention.
channels (int): Output channels of key/query transform.
out_channels (int): Output channels.
share_key_query (bool): Whether share projection weight between key
and query projection.
query_scale (int): The scale of query feature map.
key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module of key feature.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict|None): Config of activation layers.
"""
def __init__(self, low_in_channels, high_in_channels, channels,
out_channels, share_key_query, query_scale, key_pool_scales,
conv_cfg, norm_cfg, act_cfg):
key_psp = PPMConcat(key_pool_scales)
if query_scale > 1:
query_downsample = nn.MaxPool2d(kernel_size=query_scale)
else:
query_downsample = None
super(SelfAttentionBlock, self).__init__(
key_in_channels=low_in_channels,
query_in_channels=high_in_channels,
channels=channels,
out_channels=out_channels,
share_key_query=share_key_query,
query_downsample=query_downsample,
key_downsample=key_psp,
key_query_num_convs=1,
key_query_norm=True,
value_out_num_convs=1,
value_out_norm=False,
matmul_norm=True,
with_out=True,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
class AFNB(nn.Module):
"""Asymmetric Fusion Non-local Block(AFNB)
Args:
low_in_channels (int): Input channels of lower level feature,
which is the key feature for self-attention.
high_in_channels (int): Input channels of higher level feature,
which is the query feature for self-attention.
channels (int): Output channels of key/query transform.
out_channels (int): Output channels.
and query projection.
query_scales (tuple[int]): The scales of query feature map.
Default: (1,)
key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module of key feature.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict|None): Config of activation layers.
"""
def __init__(self, low_in_channels, high_in_channels, channels,
out_channels, query_scales, key_pool_scales, conv_cfg,
norm_cfg, act_cfg):
super(AFNB, self).__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
SelfAttentionBlock(
low_in_channels=low_in_channels,
high_in_channels=high_in_channels,
channels=channels,
out_channels=out_channels,
share_key_query=False,
query_scale=query_scale,
key_pool_scales=key_pool_scales,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.bottleneck = ConvModule(
out_channels + high_in_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
def forward(self, low_feats, high_feats):
"""Forward function."""
priors = [stage(high_feats, low_feats) for stage in self.stages]
context = torch.stack(priors, dim=0).sum(dim=0)
output = self.bottleneck(torch.cat([context, high_feats], 1))
return output
class APNB(nn.Module):
"""Asymmetric Pyramid Non-local Block (APNB)
Args:
in_channels (int): Input channels of key/query feature,
which is the key feature for self-attention.
channels (int): Output channels of key/query transform.
out_channels (int): Output channels.
query_scales (tuple[int]): The scales of query feature map.
Default: (1,)
key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module of key feature.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict|None): Config of activation layers.
"""
def __init__(self, in_channels, channels, out_channels, query_scales,
key_pool_scales, conv_cfg, norm_cfg, act_cfg):
super(APNB, self).__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
SelfAttentionBlock(
low_in_channels=in_channels,
high_in_channels=in_channels,
channels=channels,
out_channels=out_channels,
share_key_query=True,
query_scale=query_scale,
key_pool_scales=key_pool_scales,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.bottleneck = ConvModule(
2 * in_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def forward(self, feats):
"""Forward function."""
priors = [stage(feats, feats) for stage in self.stages]
context = torch.stack(priors, dim=0).sum(dim=0)
output = self.bottleneck(torch.cat([context, feats], 1))
return output
@HEADS.register_module()
class ANNHead(BaseDecodeHead):
"""Asymmetric Non-local Neural Networks for Semantic Segmentation.
This head is the implementation of `ANNNet
<https://arxiv.org/abs/1908.07678>`_.
Args:
project_channels (int): Projection channels for Nonlocal.
query_scales (tuple[int]): The scales of query feature map.
Default: (1,)
key_pool_scales (tuple[int]): The pooling scales of key feature map.
Default: (1, 3, 6, 8).
"""
def __init__(self,
project_channels,
query_scales=(1, ),
key_pool_scales=(1, 3, 6, 8),
**kwargs):
super(ANNHead, self).__init__(
input_transform='multiple_select', **kwargs)
assert len(self.in_channels) == 2
low_in_channels, high_in_channels = self.in_channels
self.project_channels = project_channels
self.fusion = AFNB(
low_in_channels=low_in_channels,
high_in_channels=high_in_channels,
out_channels=high_in_channels,
channels=project_channels,
query_scales=query_scales,
key_pool_scales=key_pool_scales,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.bottleneck = ConvModule(
high_in_channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.context = APNB(
in_channels=self.channels,
out_channels=self.channels,
channels=project_channels,
query_scales=query_scales,
key_pool_scales=key_pool_scales,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def forward(self, inputs):
"""Forward function."""
low_feats, high_feats = self._transform_inputs(inputs)
output = self.fusion(low_feats, high_feats)
output = self.dropout(output)
output = self.bottleneck(output)
output = self.context(output)
output = self.cls_seg(output)
return output
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