| |
| |
| """ |
| @Author : Qingping Zheng |
| @Contact : qingpingzheng2014@gmail.com |
| @File : util.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.nn as nn |
|
|
| from inplace_abn import InPlaceABNSync |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
| def __init__(self, inplanes, planes, stride=1, abn=InPlaceABNSync, dilation=1, downsample=None, fist_dilation=1, multi_grid=1): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = abn(planes) |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False) |
| self.bn2 = abn(planes) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = abn(planes * 4) |
| self.relu = nn.ReLU(inplace=False) |
| self.relu_inplace = nn.ReLU(inplace=True) |
| self.downsample = downsample |
| self.dilation = dilation |
| self.stride = stride |
|
|
| def forward(self, x): |
| residual = x |
|
|
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu(out) |
|
|
| out = self.conv2(out) |
| out = self.bn2(out) |
| out = self.relu(out) |
|
|
| out = self.conv3(out) |
| out = self.bn3(out) |
|
|
| if self.downsample is not None: |
| residual = self.downsample(x) |
|
|
| out = out + residual |
| out = self.relu_inplace(out) |
|
|
| return out |
|
|
