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""" |
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@Author : Peike Li |
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@Contact : [email protected] |
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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 |
