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""" |
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@Author : Peike Li |
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@Contact : [email protected] |
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@File : psp.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 PSPModule(nn.Module): |
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""" |
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Reference: |
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Zhao, Hengshuang, et al. *"Pyramid scene parsing network."* |
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""" |
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def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)): |
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super(PSPModule, self).__init__() |
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self.stages = [] |
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self.stages = nn.ModuleList([self._make_stage(features, out_features, size) for size in sizes]) |
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self.bottleneck = nn.Sequential( |
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nn.Conv2d(features + len(sizes) * out_features, out_features, kernel_size=3, padding=1, dilation=1, |
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bias=False), |
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InPlaceABNSync(out_features), |
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) |
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def _make_stage(self, features, out_features, size): |
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prior = nn.AdaptiveAvgPool2d(output_size=(size, size)) |
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conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False) |
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bn = InPlaceABNSync(out_features) |
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return nn.Sequential(prior, conv, bn) |
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def forward(self, feats): |
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h, w = feats.size(2), feats.size(3) |
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priors = [F.interpolate(input=stage(feats), size=(h, w), mode='bilinear', align_corners=True) for stage in |
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self.stages] + [feats] |
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bottle = self.bottleneck(torch.cat(priors, 1)) |
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return bottle |
