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- import torch.nn as nn
- import torch
- import torch.nn.functional as F
-
-
- class GoogLeNet(nn.Module):
- def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
- super(GoogLeNet, self).__init__()
- self.aux_logits = aux_logits
-
- self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
- self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
-
- self.conv2 = BasicConv2d(64, 64, kernel_size=1)
- self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
- self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
-
- self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
- self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
- self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
-
- self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
- self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
- self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
- self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
- self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
- self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
-
- self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
- self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
-
- if self.aux_logits:
- self.aux1 = InceptionAux(512, num_classes)
- self.aux2 = InceptionAux(528, num_classes)
-
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.dropout = nn.Dropout(0.4)
- self.fc = nn.Linear(1024, num_classes)
- if init_weights:
- self._initialize_weights()
-
- def forward(self, x):
- # N x 3 x 224 x 224
- x = self.conv1(x)
- # N x 64 x 112 x 112
- x = self.maxpool1(x)
- # N x 64 x 56 x 56
- x = self.conv2(x)
- # N x 64 x 56 x 56
- x = self.conv3(x)
- # N x 192 x 56 x 56
- x = self.maxpool2(x)
-
- # N x 192 x 28 x 28
- x = self.inception3a(x)
- # N x 256 x 28 x 28
- x = self.inception3b(x)
- # N x 480 x 28 x 28
- x = self.maxpool3(x)
- # N x 480 x 14 x 14
- x = self.inception4a(x)
- # N x 512 x 14 x 14
- if self.training and self.aux_logits: # eval model lose this layer
- aux1 = self.aux1(x)
-
- x = self.inception4b(x)
- # N x 512 x 14 x 14
- x = self.inception4c(x)
- # N x 512 x 14 x 14
- x = self.inception4d(x)
- # N x 528 x 14 x 14
- if self.training and self.aux_logits: # eval model lose this layer
- aux2 = self.aux2(x)
-
- x = self.inception4e(x)
- # N x 832 x 14 x 14
- x = self.maxpool4(x)
- # N x 832 x 7 x 7
- x = self.inception5a(x)
- # N x 832 x 7 x 7
- x = self.inception5b(x)
- # N x 1024 x 7 x 7
-
- x = self.avgpool(x)
- # N x 1024 x 1 x 1
- x = torch.flatten(x, 1)
- # N x 1024
- x = self.dropout(x)
- x = self.fc(x)
- # N x 1000 (num_classes)
- if self.training and self.aux_logits: # eval model lose this layer
- return x, aux2, aux1
- return x
-
- def _initialize_weights(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- if m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- nn.init.constant_(m.bias, 0)
-
-
- class Inception(nn.Module):
- def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
- super(Inception, self).__init__()
-
- self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
-
- self.branch2 = nn.Sequential(
- BasicConv2d(in_channels, ch3x3red, kernel_size=1),
- BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1) # 保证输出大小等于输入大小
- )
-
- self.branch3 = nn.Sequential(
- BasicConv2d(in_channels, ch5x5red, kernel_size=1),
- # 在官方的实现中,其实是3x3的kernel并不是5x5,这里我也懒得改了,具体可以参考下面的issue
- # Please see https://github.com/pytorch/vision/issues/906 for details.
- BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2) # 保证输出大小等于输入大小
- )
-
- self.branch4 = nn.Sequential(
- nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
- BasicConv2d(in_channels, pool_proj, kernel_size=1)
- )
-
- def forward(self, x):
- branch1 = self.branch1(x)
- branch2 = self.branch2(x)
- branch3 = self.branch3(x)
- branch4 = self.branch4(x)
-
- outputs = [branch1, branch2, branch3, branch4]
- return torch.cat(outputs, 1)
-
-
- class InceptionAux(nn.Module):
- def __init__(self, in_channels, num_classes):
- super(InceptionAux, self).__init__()
- self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
- self.conv = BasicConv2d(in_channels, 128, kernel_size=1) # output[batch, 128, 4, 4]
-
- self.fc1 = nn.Linear(2048, 1024)
- self.fc2 = nn.Linear(1024, num_classes)
-
- def forward(self, x):
- # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
- x = self.averagePool(x)
- # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
- x = self.conv(x)
- # N x 128 x 4 x 4
- x = torch.flatten(x, 1)
- x = F.dropout(x, 0.5, training=self.training)
- # N x 2048
- x = F.relu(self.fc1(x), inplace=True)
- x = F.dropout(x, 0.5, training=self.training)
- # N x 1024
- x = self.fc2(x)
- # N x num_classes
- return x
-
-
- class BasicConv2d(nn.Module):
- def __init__(self, in_channels, out_channels, **kwargs):
- super(BasicConv2d, self).__init__()
- self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.relu(x)
- return x
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