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人工智能系统实战第三期/实战代码/计算机视觉/CNN/network/GoogLeNet.py

+import torch.nn as nn
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+
+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
+
+class Inception(nn.Module):  # (192, 64, 96, 128, 16, 32, 32)
+    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)  # 在通道的维度上进行concat
+
+
+class InceptionAux(nn.Module):  # 辅助分类器板块
+    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
+
+
+# 1.基础卷积
+# 2.inception实现
+# 3.辅助分类器
+
+
+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)
+
+