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import math
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .decode_head import BaseDecodeHead
def reduce_mean(tensor):
"""Reduce mean when distributed training."""
if not (dist.is_available() and dist.is_initialized()):
return tensor
tensor = tensor.clone()
dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
return tensor
class EMAModule(nn.Module):
"""Expectation Maximization Attention Module used in EMANet.
Args:
channels (int): Channels of the whole module.
num_bases (int): Number of bases.
num_stages (int): Number of the EM iterations.
"""
def __init__(self, channels, num_bases, num_stages, momentum):
super(EMAModule, self).__init__()
assert num_stages >= 1, 'num_stages must be at least 1!'
self.num_bases = num_bases
self.num_stages = num_stages
self.momentum = momentum
bases = torch.zeros(1, channels, self.num_bases)
bases.normal_(0, math.sqrt(2. / self.num_bases))
# [1, channels, num_bases]
bases = F.normalize(bases, dim=1, p=2)
self.register_buffer('bases', bases)
def forward(self, feats):
"""Forward function."""
batch_size, channels, height, width = feats.size()
# [batch_size, channels, height*width]
feats = feats.view(batch_size, channels, height * width)
# [batch_size, channels, num_bases]
bases = self.bases.repeat(batch_size, 1, 1)
with torch.no_grad():
for i in range(self.num_stages):
# [batch_size, height*width, num_bases]
attention = torch.einsum('bcn,bck->bnk', feats, bases)
attention = F.softmax(attention, dim=2)
# l1 norm
attention_normed = F.normalize(attention, dim=1, p=1)
# [batch_size, channels, num_bases]
bases = torch.einsum('bcn,bnk->bck', feats, attention_normed)
# l2 norm
bases = F.normalize(bases, dim=1, p=2)
feats_recon = torch.einsum('bck,bnk->bcn', bases, attention)
feats_recon = feats_recon.view(batch_size, channels, height, width)
if self.training:
bases = bases.mean(dim=0, keepdim=True)
bases = reduce_mean(bases)
# l2 norm
bases = F.normalize(bases, dim=1, p=2)
self.bases = (1 -
self.momentum) * self.bases + self.momentum * bases
return feats_recon
@HEADS.register_module()
class EMAHead(BaseDecodeHead):
"""Expectation Maximization Attention Networks for Semantic Segmentation.
This head is the implementation of `EMANet
<https://arxiv.org/abs/1907.13426>`_.
Args:
ema_channels (int): EMA module channels
num_bases (int): Number of bases.
num_stages (int): Number of the EM iterations.
concat_input (bool): Whether concat the input and output of convs
before classification layer. Default: True
momentum (float): Momentum to update the base. Default: 0.1.
"""
def __init__(self,
ema_channels,
num_bases,
num_stages,
concat_input=True,
momentum=0.1,
**kwargs):
super(EMAHead, self).__init__(**kwargs)
self.ema_channels = ema_channels
self.num_bases = num_bases
self.num_stages = num_stages
self.concat_input = concat_input
self.momentum = momentum
self.ema_module = EMAModule(self.ema_channels, self.num_bases,
self.num_stages, self.momentum)
self.ema_in_conv = ConvModule(
self.in_channels,
self.ema_channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
# project (0, inf) -> (-inf, inf)
self.ema_mid_conv = ConvModule(
self.ema_channels,
self.ema_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=None,
act_cfg=None)
for param in self.ema_mid_conv.parameters():
param.requires_grad = False
self.ema_out_conv = ConvModule(
self.ema_channels,
self.ema_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=None)
self.bottleneck = ConvModule(
self.ema_channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
if self.concat_input:
self.conv_cat = ConvModule(
self.in_channels + self.channels,
self.channels,
kernel_size=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)
feats = self.ema_in_conv(x)
identity = feats
feats = self.ema_mid_conv(feats)
recon = self.ema_module(feats)
recon = F.relu(recon, inplace=True)
recon = self.ema_out_conv(recon)
output = F.relu(identity + recon, inplace=True)
output = self.bottleneck(output)
if self.concat_input:
output = self.conv_cat(torch.cat([x, output], dim=1))
output = self.cls_seg(output)
return output
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