58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import torch.nn as nn
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from torch.nn import init
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from torchvision.models.vgg import vgg16_bn
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import numpy as np
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class ConvNet(nn.Module):
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def __init__(self, num_classes):
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super(ConvNet, self).__init__()
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self.features = nn.Sequential(
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nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=2, stride=2),
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)
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self.classifier = nn.Sequential(
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nn.Dropout(),
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nn.Linear(512*4*4, 1024),
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nn.ReLU(inplace=True),
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nn.Linear(1024, num_classes),
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)
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self.weight_init()
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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 weight_init(self):
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for layer in self.features:
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self._layer_init(layer)
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for layer in self.classifier:
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self._layer_init(layer)
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def _layer_init(self, m):
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# 使用isinstance来判断m属于什么类型
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, np.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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# m中的weight，bias其实都是Variable，为了能学习参数以及后向传播
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.xavier_normal(m.weight)
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