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# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# ---------------------------------------------------------------
import torch.nn as nn
import torch
from mmcv.cnn import ConvModule
from additional_modules.segformer.ops import resize
from .base_decode_head import BaseDecodeHead
class MLP(nn.Module):
"""
Linear Embedding
"""
def __init__(self, input_dim=2048, embed_dim=768):
super().__init__()
self.proj = nn.Linear(input_dim, embed_dim)
def forward(self, x):
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
return x
class SegFormerHead(BaseDecodeHead):
"""
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
"""
def __init__(self, feature_strides, **kwargs):
super(SegFormerHead, self).__init__(input_transform='multiple_select', **kwargs)
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides
c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels
decoder_params = kwargs['decoder_params']
embedding_dim = decoder_params['embed_dim']
self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=embedding_dim)
self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=embedding_dim)
self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=embedding_dim)
self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=embedding_dim)
self.linear_fuse = ConvModule(
in_channels=embedding_dim*4,
out_channels=embedding_dim,
kernel_size=1,
norm_cfg=dict(type='SyncBN', requires_grad=True)
)
self.linear_pred = nn.Conv2d(embedding_dim, self.num_classes, kernel_size=1)
def forward(self, inputs):
x = self._transform_inputs(inputs) # len=4, 1/4,1/8,1/16,1/32
c1, c2, c3, c4 = x
############## MLP decoder on C1-C4 ###########
n, _, h, w = c4.shape
_c4 = self.linear_c4(c4).permute(0,2,1).reshape(n, -1, c4.shape[2], c4.shape[3])
_c4 = resize(_c4, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c3 = self.linear_c3(c3).permute(0,2,1).reshape(n, -1, c3.shape[2], c3.shape[3])
_c3 = resize(_c3, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c2 = self.linear_c2(c2).permute(0,2,1).reshape(n, -1, c2.shape[2], c2.shape[3])
_c2 = resize(_c2, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c1 = self.linear_c1(c1).permute(0,2,1).reshape(n, -1, c1.shape[2], c1.shape[3])
_c = self.linear_fuse(torch.cat([_c4, _c3, _c2, _c1], dim=1))
x = self.dropout(_c)
x = self.linear_pred(x)
return x
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