pytorch判断类型
【摘要】
class ResNet(nn.Module): def __init__(self, block, layers, use_se=True): self.inplanes = 64 self.use_se = use_se super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel...
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class ResNet(nn.Module):
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def __init__(self, block, layers, use_se=True):
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self.inplanes = 64
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self.use_se = use_se
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super(ResNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.bn2 = nn.BatchNorm2d(512)
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self.dropout = nn.Dropout()
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self.conv2 = nn.Conv2d(512, 512, kernel_size=7, stride=1, groups=512)
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self.linear = Linear(10240, 512, bias=False)
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self.bn3 = nn.BatchNorm1d(512)
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for m in self.mo
文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/jacke121/article/details/103254815
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