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