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pid_head.py
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pid_head.py
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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
from mmengine.model import BaseModule
from torch import Tensor
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
from mmseg.models.losses import accuracy
from mmseg.models.utils import resize
from mmseg.registry import MODELS
from mmseg.utils import OptConfigType, SampleList
class BasePIDHead(BaseModule):
"""Base class for PID head.
Args:
in_channels (int): Number of input channels.
channels (int): Number of output channels.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU', inplace=True).
init_cfg (dict or list[dict], optional): Init config dict.
Default: None.
"""
def __init__(self,
in_channels: int,
channels: int,
norm_cfg: OptConfigType = dict(type='BN'),
act_cfg: OptConfigType = dict(type='ReLU', inplace=True),
init_cfg: OptConfigType = None):
super().__init__(init_cfg)
self.conv = ConvModule(
in_channels,
channels,
kernel_size=3,
padding=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
order=('norm', 'act', 'conv'))
_, self.norm = build_norm_layer(norm_cfg, num_features=channels)
self.act = build_activation_layer(act_cfg)
def forward(self, x: Tensor, cls_seg: Optional[nn.Module]) -> Tensor:
"""Forward function.
Args:
x (Tensor): Input tensor.
cls_seg (nn.Module, optional): The classification head.
Returns:
Tensor: Output tensor.
"""
x = self.conv(x)
x = self.norm(x)
x = self.act(x)
if cls_seg is not None:
x = cls_seg(x)
return x
@MODELS.register_module()
class PIDHead(BaseDecodeHead):
"""Decode head for PIDNet.
Args:
in_channels (int): Number of input channels.
channels (int): Number of output channels.
num_classes (int): Number of classes.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU', inplace=True).
"""
def __init__(self,
in_channels: int,
channels: int,
num_classes: int,
norm_cfg: OptConfigType = dict(type='BN'),
act_cfg: OptConfigType = dict(type='ReLU', inplace=True),
**kwargs):
super().__init__(
in_channels,
channels,
num_classes=num_classes,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
**kwargs)
self.i_head = BasePIDHead(in_channels, channels, norm_cfg, act_cfg)
self.p_head = BasePIDHead(in_channels // 2, channels, norm_cfg,
act_cfg)
self.d_head = BasePIDHead(
in_channels // 2,
in_channels // 4,
norm_cfg,
)
self.p_cls_seg = nn.Conv2d(channels, self.out_channels, kernel_size=1)
self.d_cls_seg = nn.Conv2d(in_channels // 4, 1, kernel_size=1)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(
self,
inputs: Union[Tensor,
Tuple[Tensor]]) -> Union[Tensor, Tuple[Tensor]]:
"""Forward function.
Args:
inputs (Tensor | tuple[Tensor]): Input tensor or tuple of
Tensor. When training, the input is a tuple of three tensors,
(p_feat, i_feat, d_feat), and the output is a tuple of three
tensors, (p_seg_logit, i_seg_logit, d_seg_logit).
When inference, only the head of integral branch is used, and
input is a tensor of integral feature map, and the output is
the segmentation logit.
Returns:
Tensor | tuple[Tensor]: Output tensor or tuple of tensors.
"""
if self.training:
x_p, x_i, x_d = inputs
x_p = self.p_head(x_p, self.p_cls_seg)
x_i = self.i_head(x_i, self.cls_seg)
x_d = self.d_head(x_d, self.d_cls_seg)
return x_p, x_i, x_d
else:
return self.i_head(inputs, self.cls_seg)
def _stack_batch_gt(self, batch_data_samples: SampleList) -> Tuple[Tensor]:
gt_semantic_segs = [
data_sample.gt_sem_seg.data for data_sample in batch_data_samples
]
gt_edge_segs = [
data_sample.gt_edge_map.data for data_sample in batch_data_samples
]
gt_sem_segs = torch.stack(gt_semantic_segs, dim=0)
gt_edge_segs = torch.stack(gt_edge_segs, dim=0)
return gt_sem_segs, gt_edge_segs
def loss_by_feat(self, seg_logits: Tuple[Tensor],
batch_data_samples: SampleList) -> dict:
loss = dict()
p_logit, i_logit, d_logit = seg_logits
sem_label, bd_label = self._stack_batch_gt(batch_data_samples)
p_logit = resize(
input=p_logit,
size=sem_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
i_logit = resize(
input=i_logit,
size=sem_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
d_logit = resize(
input=d_logit,
size=bd_label.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
sem_label = sem_label.squeeze(1)
bd_label = bd_label.squeeze(1)
loss['loss_sem_p'] = self.loss_decode[0](
p_logit, sem_label, ignore_index=self.ignore_index)
loss['loss_sem_i'] = self.loss_decode[1](i_logit, sem_label)
loss['loss_bd'] = self.loss_decode[2](d_logit, bd_label)
filler = torch.ones_like(sem_label) * self.ignore_index
sem_bd_label = torch.where(
torch.sigmoid(d_logit[:, 0, :, :]) > 0.8, sem_label, filler)
loss['loss_sem_bd'] = self.loss_decode[3](i_logit, sem_bd_label)
loss['acc_seg'] = accuracy(
i_logit, sem_label, ignore_index=self.ignore_index)
return loss