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