/
uper_head.py
139 lines (121 loc) · 4.39 KB
/
uper_head.py
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmseg.registry import MODELS
from ..utils import resize
from .decode_head import BaseDecodeHead
from .psp_head import PPM
@MODELS.register_module()
class UPerHead(BaseDecodeHead):
"""Unified Perceptual Parsing for Scene Understanding.
This head is the implementation of `UPerNet
<https://arxiv.org/abs/1807.10221>`_.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module applied on the last feature. Default: (1, 2, 3, 6).
"""
def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
super().__init__(input_transform='multiple_select', **kwargs)
# PSP Module
self.psp_modules = PPM(
pool_scales,
self.in_channels[-1],
self.channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
align_corners=self.align_corners)
self.bottleneck = ConvModule(
self.in_channels[-1] + 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)
# FPN Module
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
l_conv = ConvModule(
in_channels,
self.channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
inplace=False)
fpn_conv = ConvModule(
self.channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
inplace=False)
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
self.fpn_bottleneck = ConvModule(
len(self.in_channels) * self.channels,
self.channels,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def psp_forward(self, inputs):
"""Forward function of PSP module."""
x = inputs[-1]
psp_outs = [x]
psp_outs.extend(self.psp_modules(x))
psp_outs = torch.cat(psp_outs, dim=1)
output = self.bottleneck(psp_outs)
return output
def _forward_feature(self, inputs):
"""Forward function for feature maps before classifying each pixel with
``self.cls_seg`` fc.
Args:
inputs (list[Tensor]): List of multi-level img features.
Returns:
feats (Tensor): A tensor of shape (batch_size, self.channels,
H, W) which is feature map for last layer of decoder head.
"""
inputs = self._transform_inputs(inputs)
# build laterals
laterals = [
lateral_conv(inputs[i])
for i, lateral_conv in enumerate(self.lateral_convs)
]
laterals.append(self.psp_forward(inputs))
# build top-down path
used_backbone_levels = len(laterals)
for i in range(used_backbone_levels - 1, 0, -1):
prev_shape = laterals[i - 1].shape[2:]
laterals[i - 1] = laterals[i - 1] + resize(
laterals[i],
size=prev_shape,
mode='bilinear',
align_corners=self.align_corners)
# build outputs
fpn_outs = [
self.fpn_convs[i](laterals[i])
for i in range(used_backbone_levels - 1)
]
# append psp feature
fpn_outs.append(laterals[-1])
for i in range(used_backbone_levels - 1, 0, -1):
fpn_outs[i] = resize(
fpn_outs[i],
size=fpn_outs[0].shape[2:],
mode='bilinear',
align_corners=self.align_corners)
fpn_outs = torch.cat(fpn_outs, dim=1)
feats = self.fpn_bottleneck(fpn_outs)
return feats
def forward(self, inputs):
"""Forward function."""
output = self._forward_feature(inputs)
output = self.cls_seg(output)
return output