| |
| |
| """ |
| @Author : Qingping Zheng |
| @Contact : qingpingzheng2014@gmail.com |
| @File : parsing.py |
| @Time : 10/01/21 00:00 PM |
| @Desc : |
| @License : Licensed under the Apache License, Version 2.0 (the "License"); |
| @Copyright : Copyright 2022 The Authors. All Rights Reserved. |
| """ |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import torch |
| import torch.nn.functional as F |
| import torch.nn as nn |
|
|
| from inplace_abn import InPlaceABNSync |
|
|
|
|
| class Parsing(nn.Module): |
| def __init__(self, in_plane1, in_plane2, num_classes, abn=InPlaceABNSync): |
| super(Parsing, self).__init__() |
| self.conv1 = nn.Sequential( |
| nn.Conv2d(in_plane1, 256, kernel_size=1, padding=0, dilation=1, bias=False), |
| abn(256) |
| ) |
| self.conv2 = nn.Sequential( |
| nn.Conv2d(in_plane2, 48, kernel_size=1, stride=1, padding=0, dilation=1, bias=False), |
| abn(48) |
| ) |
| self.conv3 = nn.Sequential( |
| nn.Conv2d(304, 256, kernel_size=1, padding=0, dilation=1, bias=False), |
| abn(256), |
| nn.Conv2d(256, 256, kernel_size=1, padding=0, dilation=1, bias=False), |
| abn(256) |
| ) |
| self.conv4 = nn.Conv2d(256, num_classes, kernel_size=1, padding=0, dilation=1, bias=True) |
|
|
| def forward(self, xt, xl): |
| _, _, h, w = xl.size() |
|
|
| xt = F.interpolate(self.conv1(xt), size=(h, w), mode='bilinear', align_corners=True) |
| xl = self.conv2(xl) |
| x = torch.cat([xt, xl], dim=1) |
| x = self.conv3(x) |
| seg = self.conv4(x) |
| return seg, x |
| |
|
|
