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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Qingping Zheng
@Contact : qingpingzheng2014@gmail.com
@File : dml_csr.py
@Time : 10/01/21 00:00 PM
@Desc :
@License : Licensed under the Apache License, Version 2.0 (the "License");
@Copyright : Copyright 2015 The Authors. All Rights Reserved.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn as nn
from torch.nn import functional as F
from inplace_abn import InPlaceABNSync
from .modules.ddgcn import DDualGCNHead
from .modules.parsing import Parsing
from .modules.edges import Edges
from .modules.util import Bottleneck
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class DML_CSR(nn.Module):
def __init__(self,
num_classes,
abn=InPlaceABNSync,
trained=True):
super().__init__()
self.inplanes = 128
self.is_trained = trained
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = abn(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = abn(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = abn(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layers = [3, 4, 23, 3]
self.abn = abn
strides = [1, 2, 1, 1]
dilations = [1, 1, 1, 2]
self.layer1 = self._make_layer(Bottleneck, 64, self.layers[0], stride=strides[0], dilation=dilations[0])
self.layer2 = self._make_layer(Bottleneck, 128, self.layers[1], stride=strides[1], dilation=dilations[1])
self.layer3 = self._make_layer(Bottleneck, 256, self.layers[2], stride=strides[2], dilation=dilations[2])
self.layer4 = self._make_layer(Bottleneck, 512, self.layers[3], stride=strides[3], dilation=dilations[3], multi_grid=(1,1,1))
# Context Aware
self.context = DDualGCNHead(2048, 512, abn)
self.layer6 = Parsing(512, 256, num_classes, abn)
# edge
if self.is_trained:
self.edge_layer = Edges(abn, out_fea=num_classes)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
self.abn(planes * block.expansion, affine=True))
layers = []
generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1
layers.append(block(self.inplanes, planes, stride, abn=self.abn, dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, abn=self.abn, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))
return nn.Sequential(*layers)
def forward(self, x):
input = x
x = self.relu1(self.bn1(self.conv1(x)))
x = self.relu2(self.bn2(self.conv2(x)))
x1 = self.relu3(self.bn3(self.conv3(x)))
x = self.maxpool(x1)
x2 = self.layer1(x) # 119 x 119
x3 = self.layer2(x2) # 60 x 60
x4 = self.layer3(x3) # 60 x 60
x5 = self.layer4(x4) # 60 x 60
x = self.context(x5)
seg, x = self.layer6(x, x2)
if self.is_trained:
binary_edge, semantic_edge, edge_fea = self.edge_layer(x2,x3,x4)
return seg, binary_edge, semantic_edge
return seg
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