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import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ACM(nn.Module):
"""Adaptive Context Module used in APCNet.
Args:
pool_scale (int): Pooling scale used in Adaptive Context
Module to extract region features.
fusion (bool): Add one conv to fuse residual 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, pool_scale, fusion, in_channels, channels, conv_cfg,
norm_cfg, act_cfg):
super(ACM, self).__init__()
self.pool_scale = pool_scale
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.pooled_redu_conv = ConvModule(
self.in_channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
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)
self.global_info = ConvModule(
self.channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)
self.residual_conv = ConvModule(
self.channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=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."""
pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale)
# [batch_size, channels, h, w]
x = self.input_redu_conv(x)
# [batch_size, channels, pool_scale, pool_scale]
pooled_x = self.pooled_redu_conv(pooled_x)
batch_size = x.size(0)
# [batch_size, pool_scale * pool_scale, channels]
pooled_x = pooled_x.view(batch_size, self.channels,
-1).permute(0, 2, 1).contiguous()
# [batch_size, h * w, pool_scale * pool_scale]
affinity_matrix = self.gla(x + resize(
self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])
).permute(0, 2, 3, 1).reshape(
batch_size, -1, self.pool_scale**2)
affinity_matrix = F.sigmoid(affinity_matrix)
# [batch_size, h * w, channels]
z_out = torch.matmul(affinity_matrix, pooled_x)
# [batch_size, channels, h * w]
z_out = z_out.permute(0, 2, 1).contiguous()
# [batch_size, channels, h, w]
z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3))
z_out = self.residual_conv(z_out)
z_out = F.relu(z_out + x)
if self.fusion:
z_out = self.fusion_conv(z_out)
return z_out
@HEADS.register_module()
class APCHead(BaseDecodeHead):
"""Adaptive Pyramid Context Network for Semantic Segmentation.
This head is the implementation of
`APCNet <https://openaccess.thecvf.com/content_CVPR_2019/papers/\
He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_\
CVPR_2019_paper.pdf>`_.
Args:
pool_scales (tuple[int]): Pooling scales used in Adaptive Context
Module. Default: (1, 2, 3, 6).
fusion (bool): Add one conv to fuse residual feature.
"""
def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
super(APCHead, self).__init__(**kwargs)
assert isinstance(pool_scales, (list, tuple))
self.pool_scales = pool_scales
self.fusion = fusion
acm_modules = []
for pool_scale in self.pool_scales:
acm_modules.append(
ACM(pool_scale,
self.fusion,
self.in_channels,
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
self.acm_modules = nn.ModuleList(acm_modules)
self.bottleneck = ConvModule(
self.in_channels + len(pool_scales) * 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)
acm_outs = [x]
for acm_module in self.acm_modules:
acm_outs.append(acm_module(x))
acm_outs = torch.cat(acm_outs, dim=1)
output = self.bottleneck(acm_outs)
output = self.cls_seg(output)
return output
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