VGG-M神经网络
【摘要】
目标跟踪论文一般提到VGG-M神经网络，也就是CNN-M神经网络，其出处是论文《Return of the Devil in the Details: Delving Deep into Convolutional Nets》，其定义如下：
其架构包含5个卷积层和3个全连接层，它的特点是第一个卷积层的步幅减小和感受野较小，这在ILSV...
目标跟踪论文一般提到VGG-M神经网络，也就是CNN-M神经网络，其出处是论文《Return of the Devil in the Details:
Delving Deep into Convolutional Nets》，其定义如下：
其架构包含5个卷积层和3个全连接层，它的特点是第一个卷积层的步幅减小和感受野较小，这在ILSVRC数据集上被证明是有益的。同时，conv2使用更大的步幅（stride=2而不是1）来保持合理的计算时间。还在conv4层使用更少的过滤器（512）。
有时候为了神经网络的鲁棒性和泛化性，也会在VGG-M中添加跨通道局部响应归一化（SpatialMapLRN）。具体代码如下：
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class SpatialCrossMapLRN(nn.Module):
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def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1, ACROSS_CHANNELS=True):
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super(SpatialCrossMapLRN, self).__init__()
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self.ACROSS_CHANNELS = ACROSS_CHANNELS
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if ACROSS_CHANNELS:
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self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1),
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stride=1,
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padding=(int((local_size-1.0)/2), 0, 0))
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else:
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self.average=nn.AvgPool2d(kernel_size=local_size,
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stride=1,
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padding=int((local_size-1.0)/2))
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self.alpha = alpha
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self.beta = beta
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self.k = k
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def forward(self, x):
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if self.ACROSS_CHANNELS:
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div = x.pow(2).unsqueeze(1)
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div = self.average(div).squeeze(1)
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div = div.mul(self.alpha).add(self.k).pow(self.beta)
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else:
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div = x.pow(2)
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div = self.average(div)
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div = div.mul(self.alpha).add(self.k).pow(self.beta)
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x = x.div(div)
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return x
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class VGGM(nn.Module):
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def __init__(self, num_classes=1000):
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super(VGGM, self).__init__()
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self.num_classes = num_classes
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self.features = nn.Sequential(
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nn.Conv2d(3, 96, (7, 7), (2, 2)), # conv1
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nn.ReLU(),
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SpatialCrossMapLRN(5, 0.0005, 0.75, 2),
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nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
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nn.Conv2d(96, 256, (5, 5), (2, 2), (1, 1)), # conv2
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nn.ReLU(),
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SpatialCrossMapLRN(5, 0.0005, 0.75, 2),
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nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
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nn.Conv2d(256, 512, (3, 3), (1, 1), (1, 1)), # conv3
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nn.ReLU(),
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nn.Conv2d(512, 512, (3, 3), (1, 1), (1, 1)), # conv4
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nn.ReLU(),
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nn.Conv2d(512, 512, (3, 3), (1, 1), (1, 1)), # conv5
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nn.ReLU(),
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nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True)
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)
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self.classifier = nn.Sequential(
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nn.Linear(18432, 4096),
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nn.ReLU(),
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nn.Dropout(0.5),
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nn.Linear(4096, 4096),
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nn.ReLU(),
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nn.Dropout(0.5),
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nn.Linear(4096, num_classes)
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)
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def forward(self, x):
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x = self.features(x)
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x = x.view(x.size(0), -1)
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x = self.classifier(x)
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return x
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def vggm(num_classes=1000, pretrained='imagenet'):
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if pretrained:
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settings = pretrained_settings['vggm'][pretrained]
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assert num_classes == settings['num_classes'], \
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"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
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model = VGGM(num_classes=num_classes)
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model.load_state_dict(torch.load('../vggm.pth', map_location=lambda storage, loc: storage))
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model.input_space = settings['input_space']
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model.input_size = settings['input_size']
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model.input_range = settings['input_range']
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model.mean = settings['mean']
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model.std = settings['std']
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else:
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model = VGGM(num_classes=num_classes)
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return model
文章来源: blog.csdn.net，作者：小小谢先生，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/xiewenrui1996/article/details/107253152
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