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import torch
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, Scale
from torch import nn
from annotator.uniformer.mmseg.core import add_prefix
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PAM(_SelfAttentionBlock):
"""Position Attention Module (PAM)
Args:
in_channels (int): Input channels of key/query feature.
channels (int): Output channels of key/query transform.
"""
def __init__(self, in_channels, channels):
super(PAM, self).__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=1,
key_query_norm=False,
value_out_num_convs=1,
value_out_norm=False,
matmul_norm=False,
with_out=False,
conv_cfg=None,
norm_cfg=None,
act_cfg=None)
self.gamma = Scale(0)
def forward(self, x):
"""Forward function."""
out = super(PAM, self).forward(x, x)
out = self.gamma(out) + x
return out
class CAM(nn.Module):
"""Channel Attention Module (CAM)"""
def __init__(self):
super(CAM, self).__init__()
self.gamma = Scale(0)
def forward(self, x):
"""Forward function."""
batch_size, channels, height, width = x.size()
proj_query = x.view(batch_size, channels, -1)
proj_key = x.view(batch_size, channels, -1).permute(0, 2, 1)
energy = torch.bmm(proj_query, proj_key)
energy_new = torch.max(
energy, -1, keepdim=True)[0].expand_as(energy) - energy
attention = F.softmax(energy_new, dim=-1)
proj_value = x.view(batch_size, channels, -1)
out = torch.bmm(attention, proj_value)
out = out.view(batch_size, channels, height, width)
out = self.gamma(out) + x
return out
@HEADS.register_module()
class DAHead(BaseDecodeHead):
"""Dual Attention Network for Scene Segmentation.
This head is the implementation of `DANet
<https://arxiv.org/abs/1809.02983>`_.
Args:
pam_channels (int): The channels of Position Attention Module(PAM).
"""
def __init__(self, pam_channels, **kwargs):
super(DAHead, self).__init__(**kwargs)
self.pam_channels = pam_channels
self.pam_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.pam = PAM(self.channels, pam_channels)
self.pam_out_conv = ConvModule(
self.channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.pam_conv_seg = nn.Conv2d(
self.channels, self.num_classes, kernel_size=1)
self.cam_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.cam = CAM()
self.cam_out_conv = ConvModule(
self.channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.cam_conv_seg = nn.Conv2d(
self.channels, self.num_classes, kernel_size=1)
def pam_cls_seg(self, feat):
"""PAM feature classification."""
if self.dropout is not None:
feat = self.dropout(feat)
output = self.pam_conv_seg(feat)
return output
def cam_cls_seg(self, feat):
"""CAM feature classification."""
if self.dropout is not None:
feat = self.dropout(feat)
output = self.cam_conv_seg(feat)
return output
def forward(self, inputs):
"""Forward function."""
x = self._transform_inputs(inputs)
pam_feat = self.pam_in_conv(x)
pam_feat = self.pam(pam_feat)
pam_feat = self.pam_out_conv(pam_feat)
pam_out = self.pam_cls_seg(pam_feat)
cam_feat = self.cam_in_conv(x)
cam_feat = self.cam(cam_feat)
cam_feat = self.cam_out_conv(cam_feat)
cam_out = self.cam_cls_seg(cam_feat)
feat_sum = pam_feat + cam_feat
pam_cam_out = self.cls_seg(feat_sum)
return pam_cam_out, pam_out, cam_out
def forward_test(self, inputs, img_metas, test_cfg):
"""Forward function for testing, only ``pam_cam`` is used."""
return self.forward(inputs)[0]
def losses(self, seg_logit, seg_label):
"""Compute ``pam_cam``, ``pam``, ``cam`` loss."""
pam_cam_seg_logit, pam_seg_logit, cam_seg_logit = seg_logit
loss = dict()
loss.update(
add_prefix(
super(DAHead, self).losses(pam_cam_seg_logit, seg_label),
'pam_cam'))
loss.update(
add_prefix(
super(DAHead, self).losses(pam_seg_logit, seg_label), 'pam'))
loss.update(
add_prefix(
super(DAHead, self).losses(cam_seg_logit, seg_label), 'cam'))
return loss
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