Delete pages/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