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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
from mmseg.registry import MODELS
from ..utils import resize
@MODELS.register_module()
class SegformerHead(BaseDecodeHead):
"""The all mlp Head of segformer.
This head is the implementation of
`Segformer <https://arxiv.org/abs/2105.15203>` _.
Args:
interpolate_mode: The interpolate mode of MLP head upsample operation.
Default: 'bilinear'.
"""
def __init__(self, interpolate_mode='bilinear', **kwargs):
super().__init__(input_transform='multiple_select', **kwargs)
self.interpolate_mode = interpolate_mode
num_inputs = len(self.in_channels)
assert num_inputs == len(self.in_index)
self.convs = nn.ModuleList()
for i in range(num_inputs):
self.convs.append(
ConvModule(
in_channels=self.in_channels[i],
out_channels=self.channels,
kernel_size=1,
stride=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
self.fusion_conv = ConvModule(
in_channels=self.channels * num_inputs,
out_channels=self.channels,
kernel_size=1,
norm_cfg=self.norm_cfg)
def forward(self, inputs):
# Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
inputs = self._transform_inputs(inputs)
outs = []
for idx in range(len(inputs)):
x = inputs[idx]
conv = self.convs[idx]
outs.append(
resize(
input=conv(x),
size=inputs[0].shape[2:],
mode=self.interpolate_mode,
align_corners=self.align_corners))
out = self.fusion_conv(torch.cat(outs, dim=1))
out = self.cls_seg(out)
return out
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