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# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmseg.registry import MODELS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class SelfAttentionBlock(_SelfAttentionBlock):
"""Self-Attention Module.
Args:
in_channels (int): Input channels of key/query feature.
channels (int): Output channels of key/query transform.
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, conv_cfg, norm_cfg, act_cfg):
super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
out_channels=in_channels,
share_key_query=False,
query_downsample=None,
key_downsample=None,
key_query_num_convs=2,
key_query_norm=True,
value_out_num_convs=1,
value_out_norm=False,
matmul_norm=True,
with_out=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.output_project = self.build_project(
in_channels,
in_channels,
num_convs=1,
use_conv_module=True,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def forward(self, x):
"""Forward function."""
context = super().forward(x, x)
return self.output_project(context)
@MODELS.register_module()
class ISAHead(BaseDecodeHead):
"""Interlaced Sparse Self-Attention for Semantic Segmentation.
This head is the implementation of `ISA
<https://arxiv.org/abs/1907.12273>`_.
Args:
isa_channels (int): The channels of ISA Module.
down_factor (tuple[int]): The local group size of ISA.
"""
def __init__(self, isa_channels, down_factor=(8, 8), **kwargs):
super().__init__(**kwargs)
self.down_factor = down_factor
self.in_conv = ConvModule(
self.in_channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.global_relation = SelfAttentionBlock(
self.channels,
isa_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.local_relation = SelfAttentionBlock(
self.channels,
isa_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.out_conv = ConvModule(
self.channels * 2,
self.channels,
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)
x = self.in_conv(x_)
residual = x
n, c, h, w = x.size()
loc_h, loc_w = self.down_factor # size of local group in H- and W-axes
glb_h, glb_w = math.ceil(h / loc_h), math.ceil(w / loc_w)
pad_h, pad_w = glb_h * loc_h - h, glb_w * loc_w - w
if pad_h > 0 or pad_w > 0: # pad if the size is not divisible
padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
pad_h - pad_h // 2)
x = F.pad(x, padding)
# global relation
x = x.view(n, c, glb_h, loc_h, glb_w, loc_w)
# do permutation to gather global group
x = x.permute(0, 3, 5, 1, 2, 4) # (n, loc_h, loc_w, c, glb_h, glb_w)
x = x.reshape(-1, c, glb_h, glb_w)
# apply attention within each global group
x = self.global_relation(x) # (n * loc_h * loc_w, c, glb_h, glb_w)
# local relation
x = x.view(n, loc_h, loc_w, c, glb_h, glb_w)
# do permutation to gather local group
x = x.permute(0, 4, 5, 3, 1, 2) # (n, glb_h, glb_w, c, loc_h, loc_w)
x = x.reshape(-1, c, loc_h, loc_w)
# apply attention within each local group
x = self.local_relation(x) # (n * glb_h * glb_w, c, loc_h, loc_w)
# permute each pixel back to its original position
x = x.view(n, glb_h, glb_w, c, loc_h, loc_w)
x = x.permute(0, 3, 1, 4, 2, 5) # (n, c, glb_h, loc_h, glb_w, loc_w)
x = x.reshape(n, c, glb_h * loc_h, glb_w * loc_w)
if pad_h > 0 or pad_w > 0: # remove padding
x = x[:, :, pad_h // 2:pad_h // 2 + h, pad_w // 2:pad_w // 2 + w]
x = self.out_conv(torch.cat([x, residual], dim=1))
out = self.cls_seg(x)
return out
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