180 lines
5.9 KiB
Python
180 lines
5.9 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models
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class GoogLeNet(nn.Module):
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def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
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super(GoogLeNet, self).__init__()
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self.aux_logits = aux_logits
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self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
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self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.conv2 = BasicConv2d(64, 64, kernel_size=1)
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self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
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self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
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self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
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self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
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self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
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self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
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self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
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self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
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self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
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self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
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if self.aux_logits:
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self.aux1 = InceptionAux(512, num_classes)
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self.aux2 = InceptionAux(528, num_classes)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.dropout = nn.Dropout(0.4)
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self.fc = nn.Linear(1024, num_classes)
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if init_weights:
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self._initialize_weights()
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def forward(self, x):
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# N x 3 x 224 x 224
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x = self.conv1(x)
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# N x 64 x 112 x 112
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x = self.maxpool1(x)
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# N x 64 x 56 x 56
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x = self.conv2(x)
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# N x 64 x 56 x 56
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x = self.conv3(x)
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# N x 192 x 56 x 56
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x = self.maxpool2(x)
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# N x 192 x 28 x 28
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x = self.inception3a(x)
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# N x 256 x 28 x 28
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x = self.inception3b(x)
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# N x 480 x 28 x 28
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x = self.maxpool3(x)
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# N x 480 x 14 x 14
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x = self.inception4a(x)
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# N x 512 x 14 x 14
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if self.training and self.aux_logits: # eval model lose this layer
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aux1 = self.aux1(x)
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x = self.inception4b(x)
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# N x 512 x 14 x 14
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x = self.inception4c(x)
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# N x 512 x 14 x 14
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x = self.inception4d(x)
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# N x 528 x 14 x 14
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if self.training and self.aux_logits: # eval model lose this layer
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aux2 = self.aux2(x)
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x = self.inception4e(x)
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# N x 832 x 14 x 14
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x = self.maxpool4(x)
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# N x 832 x 7 x 7
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x = self.inception5a(x)
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# N x 832 x 7 x 7
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x = self.inception5b(x)
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# N x 1024 x 7 x 7
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x = self.avgpool(x)
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# N x 1024 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 1024
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x = self.dropout(x)
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x = self.fc(x)
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# N x 1000 (num_classes)
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if self.training and self.aux_logits: # eval model lose this layer
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return x, aux2, aux1
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return x
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def _initialize_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.constant_(m.bias, 0)
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class Inception(nn.Module):
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def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
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super(Inception, self).__init__()
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self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
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self.branch2 = nn.Sequential(
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BasicConv2d(in_channels, ch3x3red, kernel_size=1),
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BasicConv2d(
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ch3x3red, ch3x3, kernel_size=3, padding=1
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), # 保证输出大小等于输入大小
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)
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self.branch3 = nn.Sequential(
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BasicConv2d(in_channels, ch5x5red, kernel_size=1),
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BasicConv2d(
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ch5x5red, ch5x5, kernel_size=5, padding=2
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), # 保证输出大小等于输入大小
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)
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self.branch4 = nn.Sequential(
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
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BasicConv2d(in_channels, pool_proj, kernel_size=1),
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)
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def forward(self, x):
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branch1 = self.branch1(x)
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branch2 = self.branch2(x)
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branch3 = self.branch3(x)
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branch4 = self.branch4(x)
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outputs = [branch1, branch2, branch3, branch4]
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return torch.cat(outputs, 1)
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class InceptionAux(nn.Module):
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def __init__(self, in_channels, num_classes):
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super(InceptionAux, self).__init__()
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self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
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self.conv = BasicConv2d(
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in_channels, 128, kernel_size=1
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) # output[batch, 128, 4, 4]
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self.fc1 = nn.Linear(2048, 1024)
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self.fc2 = nn.Linear(1024, num_classes)
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def forward(self, x):
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# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
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x = self.averagePool(x)
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# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
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x = self.conv(x)
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# N x 128 x 4 x 4
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x = torch.flatten(x, 1)
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x = F.dropout(x, 0.5, training=self.training)
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# N x 2048
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x = F.relu(self.fc1(x), inplace=True)
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x = F.dropout(x, 0.5, training=self.training)
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# N x 1024
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x = self.fc2(x)
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# N x num_classes
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return x
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class BasicConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, **kwargs):
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super(BasicConv2d, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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x = self.conv(x)
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x = self.relu(x)
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return x
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