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52 lines
1.7 KiB
Python
52 lines
1.7 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 : parsing.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 2022 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
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import torch.nn.functional as F
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import torch.nn as nn
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from inplace_abn import InPlaceABNSync
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class Parsing(nn.Module):
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def __init__(self, in_plane1, in_plane2, num_classes, abn=InPlaceABNSync):
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super(Parsing, self).__init__()
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self.conv1 = nn.Sequential(
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nn.Conv2d(in_plane1, 256, kernel_size=1, padding=0, dilation=1, bias=False),
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abn(256)
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)
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self.conv2 = nn.Sequential(
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nn.Conv2d(in_plane2, 48, kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
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abn(48)
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)
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self.conv3 = nn.Sequential(
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nn.Conv2d(304, 256, kernel_size=1, padding=0, dilation=1, bias=False),
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abn(256),
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nn.Conv2d(256, 256, kernel_size=1, padding=0, dilation=1, bias=False),
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abn(256)
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)
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self.conv4 = nn.Conv2d(256, num_classes, kernel_size=1, padding=0, dilation=1, bias=True)
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def forward(self, xt, xl):
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_, _, h, w = xl.size()
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xt = F.interpolate(self.conv1(xt), size=(h, w), mode='bilinear', align_corners=True)
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xl = self.conv2(xl)
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x = torch.cat([xt, xl], dim=1)
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x = self.conv3(x)
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seg = self.conv4(x)
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return seg, x
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