Lenet5 and ResNet18 example

Author: bat67

convolutional-neural-network(CNN) 卷积神经网络/Lenet5-and-ResNet18.py

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+import  torch
+from    torch.utils.data import DataLoader
+from    torchvision import datasets
+from    torchvision import transforms
+from    torch import nn, optim
+
+from    lenet5 import Lenet5
+from    resnet import ResNet18
+
+def main():
+    batchsz = 32
+
+    cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor()
+    ]), download=True)
+    cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)
+
+    cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor()
+    ]), download=True)
+    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)
+
+
+    x, label = iter(cifar_train).next()
+    print('x:', x.shape, 'label:', label.shape)
+
+    device = torch.device('cuda')
+    # model = Lenet5().to(device)
+    model = ResNet18().to(device)
+
+    criteon = nn.CrossEntropyLoss().to(device)
+    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+    print(model)
+
+    for epoch in range(1000):
+
+        model.train()
+        for batchidx, (x, label) in enumerate(cifar_train):
+            # [b, 3, 32, 32]
+            # [b]
+            x, label = x.to(device), label.to(device)
+
+            logits = model(x)
+            # logits: [b, 10]
+            # label:  [b]
+            # loss: tensor scalar
+            loss = criteon(logits, label)
+
+            # backprop
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+
+        #
+        print(epoch, 'loss:', loss.item())
+
+
+        model.eval()
+        with torch.no_grad():
+            # test
+            total_correct = 0
+            total_num = 0
+            for x, label in cifar_test:
+                # [b, 3, 32, 32]
+                # [b]
+                x, label = x.to(device), label.to(device)
+
+                # [b, 10]
+                logits = model(x)
+                # [b]
+                pred = logits.argmax(dim=1)
+                # [b] vs [b] => scalar tensor
+                correct = torch.eq(pred, label).float().sum().item()
+                total_correct += correct
+                total_num += x.size(0)
+                # print(correct)
+
+            acc = total_correct / total_num
+            print(epoch, 'acc:', acc)
+
+
+
+if __name__ == '__main__':
+    main()

convolutional-neural-network(CNN) 卷积神经网络/lenet5.py

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+import  torch
+from    torch import nn
+from    torch.nn import functional as F
+
+
+
+
+class Lenet5(nn.Module):
+    """
+    for cifar10 dataset.
+    """
+    def __init__(self):
+        super(Lenet5, self).__init__()
+
+        self.conv_unit = nn.Sequential(
+            # x: [b, 3, 32, 32] => [b, 6, ]
+            nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),
+            nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+            #
+            nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),
+            nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+            #
+        )
+        # flatten
+        # fc unit
+        self.fc_unit = nn.Sequential(
+            nn.Linear(16*5*5, 32),
+            nn.ReLU(),
+            # nn.Linear(120, 84),
+            # nn.ReLU(),
+            nn.Linear(32, 10)
+        )
+
+
+        # [b, 3, 32, 32]
+        tmp = torch.randn(2, 3, 32, 32)
+        out = self.conv_unit(tmp)
+        # [b, 16, 5, 5]
+        print('conv out:', out.shape)
+
+        # # use Cross Entropy Loss
+        # self.criteon = nn.CrossEntropyLoss()
+
+
+
+    def forward(self, x):
+        """
+
+        :param x: [b, 3, 32, 32]
+        :return:
+        """
+        batchsz = x.size(0)
+        # [b, 3, 32, 32] => [b, 16, 5, 5]
+        x = self.conv_unit(x)
+        # [b, 16, 5, 5] => [b, 16*5*5]
+        x = x.view(batchsz, 16*5*5)
+        # [b, 16*5*5] => [b, 10]
+        logits = self.fc_unit(x)
+
+        # # [b, 10]
+        # pred = F.softmax(logits, dim=1)
+        # loss = self.criteon(logits, y)
+
+        return logits
+
+def main():
+
+    net = Lenet5()
+
+    tmp = torch.randn(2, 3, 32, 32)
+    out = net(tmp)
+    print('lenet out:', out.shape)
+
+
+
+
+if __name__ == '__main__':
+    main()

convolutional-neural-network(CNN) 卷积神经网络/resnet.py

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+import  torch
+from    torch import  nn
+from    torch.nn import functional as F
+
+
+
+class ResBlk(nn.Module):
+    """
+    resnet block
+    """
+
+    def __init__(self, ch_in, ch_out):
+        """
+
+        :param ch_in:
+        :param ch_out:
+        """
+        super(ResBlk, self).__init__()
+
+        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
+        self.bn1 = nn.BatchNorm2d(ch_out)
+        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
+        self.bn2 = nn.BatchNorm2d(ch_out)
+
+        self.extra = nn.Sequential()
+        if ch_out != ch_in:
+            # [b, ch_in, h, w] => [b, ch_out, h, w]
+            self.extra = nn.Sequential(
+                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1),
+                nn.BatchNorm2d(ch_out)
+            )
+
+
+    def forward(self, x):
+        """
+
+        :param x: [b, ch, h, w]
+        :return:
+        """
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        # short cut.
+        # extra module: [b, ch_in, h, w] => [b, ch_out, h, w]
+        # element-wise add:
+        out = self.extra(x) + out
+
+        return out
+
+
+
+
+class ResNet18(nn.Module):
+
+    def __init__(self):
+        super(ResNet18, self).__init__()
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(16)
+        )
+        # followed 4 blocks
+        # [b, 64, h, w] => [b, 128, h ,w]
+        self.blk1 = ResBlk(16, 16)
+        # [b, 128, h, w] => [b, 256, h, w]
+        self.blk2 = ResBlk(16, 32)
+        # # [b, 256, h, w] => [b, 512, h, w]
+        # self.blk3 = ResBlk(128, 256)
+        # # [b, 512, h, w] => [b, 1024, h, w]
+        # self.blk4 = ResBlk(256, 512)
+
+        self.outlayer = nn.Linear(32*32*32, 10)
+
+    def forward(self, x):
+        """
+
+        :param x:
+        :return:
+        """
+        x = F.relu(self.conv1(x))
+
+        # [b, 64, h, w] => [b, 1024, h, w]
+        x = self.blk1(x)
+        x = self.blk2(x)
+        # x = self.blk3(x)
+        # x = self.blk4(x)
+
+        # print(x.shape)
+        x = x.view(x.size(0), -1)
+        x = self.outlayer(x)
+
+
+        return x
+
+
+
+def main():
+    blk = ResBlk(64, 128)
+    tmp = torch.randn(2, 64,32, 32)
+    out = blk(tmp)
+    print('blkk', out.shape)
+
+
+    model = ResNet18()
+    tmp = torch.randn(2, 3, 32, 32)
+    out = model(tmp)
+    print('resnet:', out.shape)
+
+
+if __name__ == '__main__':
+    main()