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daformer_head.py
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daformer_head.py
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# ---------------------------------------------------------------
# Copyright (c) 2021-2022 ETH Zurich, Lukas Hoyer. All rights reserved.
# Licensed under the Apache License, Version 2.0
# ---------------------------------------------------------------
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmseg.models.decode_heads.isa_head import ISALayer
from mmseg.ops import resize
from ..builder import HEADS
from .aspp_head import ASPPModule
from .decode_head import BaseDecodeHead
from .segformer_head import MLP
from .sep_aspp_head import DepthwiseSeparableASPPModule
class ASPPWrapper(nn.Module):
def __init__(self,
in_channels,
channels,
sep,
dilations,
pool,
norm_cfg,
act_cfg,
align_corners,
context_cfg=None):
super(ASPPWrapper, self).__init__()
assert isinstance(dilations, (list, tuple))
self.dilations = dilations
self.align_corners = align_corners
if pool:
self.image_pool = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
ConvModule(
in_channels,
channels,
1,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
else:
self.image_pool = None
if context_cfg is not None:
self.context_layer = build_layer(in_channels, channels,
**context_cfg)
else:
self.context_layer = None
ASPP = {True: DepthwiseSeparableASPPModule, False: ASPPModule}[sep]
self.aspp_modules = ASPP(
dilations=dilations,
in_channels=in_channels,
channels=channels,
norm_cfg=norm_cfg,
conv_cfg=None,
act_cfg=act_cfg)
self.bottleneck = ConvModule(
(len(dilations) + int(pool) + int(bool(context_cfg))) * channels,
channels,
kernel_size=3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def forward(self, x):
"""Forward function."""
aspp_outs = []
if self.image_pool is not None:
aspp_outs.append(
resize(
self.image_pool(x),
size=x.size()[2:],
mode='bilinear',
align_corners=self.align_corners))
if self.context_layer is not None:
aspp_outs.append(self.context_layer(x))
aspp_outs.extend(self.aspp_modules(x))
aspp_outs = torch.cat(aspp_outs, dim=1)
output = self.bottleneck(aspp_outs)
return output
def build_layer(in_channels, out_channels, type, **kwargs):
if type == 'id':
return nn.Identity()
elif type == 'mlp':
return MLP(input_dim=in_channels, embed_dim=out_channels)
elif type == 'sep_conv':
return DepthwiseSeparableConvModule(
in_channels=in_channels,
out_channels=out_channels,
padding=kwargs['kernel_size'] // 2,
**kwargs)
elif type == 'conv':
return ConvModule(
in_channels=in_channels,
out_channels=out_channels,
padding=kwargs['kernel_size'] // 2,
**kwargs)
elif type == 'aspp':
return ASPPWrapper(
in_channels=in_channels, channels=out_channels, **kwargs)
elif type == 'rawconv_and_aspp':
kernel_size = kwargs.pop('kernel_size')
return nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=kernel_size // 2),
ASPPWrapper(
in_channels=out_channels, channels=out_channels, **kwargs))
elif type == 'isa':
return ISALayer(
in_channels=in_channels, channels=out_channels, **kwargs)
else:
raise NotImplementedError(type)
@HEADS.register_module()
class DAFormerHead(BaseDecodeHead):
def __init__(self, **kwargs):
super(DAFormerHead, self).__init__(
input_transform='multiple_select', **kwargs)
assert not self.align_corners
decoder_params = kwargs['decoder_params']
embed_dims = decoder_params['embed_dims']
if isinstance(embed_dims, int):
embed_dims = [embed_dims] * len(self.in_index)
embed_cfg = decoder_params['embed_cfg']
embed_neck_cfg = decoder_params['embed_neck_cfg']
if embed_neck_cfg == 'same_as_embed_cfg':
embed_neck_cfg = embed_cfg
fusion_cfg = decoder_params['fusion_cfg']
for cfg in [embed_cfg, embed_neck_cfg, fusion_cfg]:
if cfg is not None and 'aspp' in cfg['type']:
cfg['align_corners'] = self.align_corners
self.embed_layers = {}
for i, in_channels, embed_dim in zip(self.in_index, self.in_channels,
embed_dims):
if i == self.in_index[-1]:
self.embed_layers[str(i)] = build_layer(
in_channels, embed_dim, **embed_neck_cfg)
else:
self.embed_layers[str(i)] = build_layer(
in_channels, embed_dim, **embed_cfg)
self.embed_layers = nn.ModuleDict(self.embed_layers)
self.fuse_layer = build_layer(
sum(embed_dims), self.channels, **fusion_cfg)
def forward(self, inputs):
x = inputs
n, _, h, w = x[-1].shape
# for f in x:
# mmcv.print_log(f'{f.shape}', 'mmseg')
os_size = x[0].size()[2:]
_c = {}
for i in self.in_index:
# mmcv.print_log(f'{i}: {x[i].shape}', 'mmseg')
_c[i] = self.embed_layers[str(i)](x[i])
if _c[i].dim() == 3:
_c[i] = _c[i].permute(0, 2, 1).contiguous()\
.reshape(n, -1, x[i].shape[2], x[i].shape[3])
# mmcv.print_log(f'_c{i}: {_c[i].shape}', 'mmseg')
if _c[i].size()[2:] != os_size:
# mmcv.print_log(f'resize {i}', 'mmseg')
_c[i] = resize(
_c[i],
size=os_size,
mode='bilinear',
align_corners=self.align_corners)
x = self.fuse_layer(torch.cat(list(_c.values()), dim=1))
x = self.cls_seg(x)
return x