Create 20_ResNet2.py

pages/20_ResNet2.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+from torchvision.datasets import CIFAR10
+from torch.utils.data import DataLoader
+
+# Define the ResNet model
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * planes)
+            )
+
+    def forward(self, x):
+        identity = x
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(identity)
+        out = F.relu(out)
+        return out
+
+class ResNet(nn.Module):
+    def __init__(self, block, num_blocks, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_planes = 64
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.linear = nn.Linear(512 * block.expansion, num_classes)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = F.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+def ResNet18():
+    return ResNet(BasicBlock, [2, 2, 2, 2])
+
+# Define a function to load CIFAR-10 dataset
+def load_data():
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    train_set = CIFAR10(root='./data', train=True, download=True, transform=transform)
+    train_loader = DataLoader(train_set, batch_size=100, shuffle=True, num_workers=2)
+    return train_loader
+
+# Streamlit Interface
+st.title('ResNet with Streamlit')
+st.write("This is an example of integrating a ResNet model with Streamlit.")
+
+# Load data button
+if st.button('Load Data'):
+    st.write("Loading CIFAR-10 data...")
+    train_loader = load_data()
+    st.write("Data loaded successfully!")
+
+# Initialize and test the model
+if st.button('Initialize and Test ResNet18'):
+    net = ResNet18()
+    sample_input = torch.randn(1, 3, 32, 32)
+    output = net(sample_input)
+    st.write("Output size: ", output.size())
+
+# Train the model (for demonstration, we'll just do one epoch)
+if st.button('Train ResNet18'):
+    st.write("Training ResNet18 on CIFAR-10...")
+    net = ResNet18()
+    train_loader = load_data()
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
+
+    net.train()
+    for epoch in range(1):  # Single epoch for demonstration
+        running_loss = 0.0
+        for i, data in enumerate(train_loader, 0):
+            inputs, labels = data
+            optimizer.zero_grad()
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            running_loss += loss.item()
+            if i % 100 == 99:  # Print every 100 mini-batches
+                st.write(f'Epoch [{epoch + 1}], Step [{i + 1}], Loss: {running_loss / 100:.4f}')
+                running_loss = 0.0
+
+    st.write("Training complete!")
+
+# Plotting example (dummy plot for demonstration)
+if st.button('Show Plot'):
+    st.write("Displaying a sample plot...")
+    fig, ax = plt.subplots()
+    ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
+    st.pyplot(fig)
+
+# To run the Streamlit app, use the command below in your terminal:
+# streamlit run your_script_name.py