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| """
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| @Author : Peike Li
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| @Contact : peike.li@yahoo.com
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| @File : aspp.py
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| @Time : 8/4/19 3:36 PM
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| @Desc :
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| @License : This source code is licensed under the license found in the
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| LICENSE file in the root directory of this source tree.
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| """
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| import torch
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| import torch.nn as nn
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| from torch.nn import functional as F
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| from modules import InPlaceABNSync
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| class ASPPModule(nn.Module):
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| """
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| Reference:
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| Chen, Liang-Chieh, et al. *"Rethinking Atrous Convolution for Semantic Image Segmentation."*
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| """
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| def __init__(self, features, out_features=512, inner_features=256, dilations=(12, 24, 36)):
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| super(ASPPModule, self).__init__()
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| self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
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| nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1,
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| bias=False),
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| InPlaceABNSync(inner_features))
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| self.conv2 = nn.Sequential(
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| nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False),
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| InPlaceABNSync(inner_features))
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| self.conv3 = nn.Sequential(
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| nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),
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| InPlaceABNSync(inner_features))
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| self.conv4 = nn.Sequential(
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| nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),
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| InPlaceABNSync(inner_features))
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| self.conv5 = nn.Sequential(
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| nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),
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| InPlaceABNSync(inner_features))
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| self.bottleneck = nn.Sequential(
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| nn.Conv2d(inner_features * 5, out_features, kernel_size=1, padding=0, dilation=1, bias=False),
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| InPlaceABNSync(out_features),
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| nn.Dropout2d(0.1)
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| )
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| def forward(self, x):
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| _, _, h, w = x.size()
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| feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
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| feat2 = self.conv2(x)
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| feat3 = self.conv3(x)
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| feat4 = self.conv4(x)
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| feat5 = self.conv5(x)
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| out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)
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| bottle = self.bottleneck(out)
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| return bottle |
