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""" |
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@Author : Qingping Zheng |
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@Contact : [email protected] |
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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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