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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 | |