GraphPlanner: Floorplanning with Graph Neural Network
Introduction
In this article, I will guide you on how to implement "GraphPlanner: Floorplanning with Graph Neural Network" as a beginner developer. We will go through the step-by-step process along with the code snippets and explanations. Let's get started!
Steps and Code
| Step | Description | Code |
|---|---|---|
| Step 1 | Import necessary libraries | import torch<br>import torch.nn as nn<br>import torch.optim as optim<br>import torch.nn.functional as F<br>import dgl |
| Step 2 | Define the Graph Neural Network model | class GraphPlanner(nn.Module):<br>def __init__(self, input_dim, hidden_dim, output_dim):<br>super(GraphPlanner, self).__init__()<br>self.conv1 = dgl.nn.GraphConv(input_dim, hidden_dim)<br>self.conv2 = dgl.nn.GraphConv(hidden_dim, hidden_dim)<br>self.fc = nn.Linear(hidden_dim, output_dim) |
| Step 3 | Implement the forward pass in the model | def forward(self, g, features):<br>x = F.relu(self.conv1(g, features))<br>x = F.relu(self.conv2(g, x))<br>x = self.fc(x)<br>return x |
| Step 4 | Create the dataset | # Assume dataset is available as a list<br>dataset = [...]<br># Convert the dataset to DGLGraphs<br>graphs = [dgl.from_networkx(g) for g in dataset] |
| Step 5 | Split the dataset into train and test sets | train_set = graphs[:80]<br>test_set = graphs[80:] |
| Step 6 | Prepare the data for training | def collate(samples):<br>graphs, labels = map(list, zip(*samples))<br>batched_graph = dgl.batch(graphs)<br>return batched_graph, torch.tensor(labels)<br>train_loader = DataLoader(train_set, batch_size=32, shuffle=True, collate_fn=collate) |
| Step 7 | Initialize the model and optimizer | model = GraphPlanner(input_dim, hidden_dim, output_dim)<br>optimizer = optim.Adam(model.parameters(), lr=0.01) |
| Step 8 | Train the model | def train(model, optimizer, data_loader):<br>model.train()<br>for epoch in range(num_epochs):<br>for batched_graph, labels in data_loader:<br>logits = model(batched_graph, batched_graph.ndata['feat'])<br>loss = F.cross_entropy(logits, labels)<br>optimizer.zero_grad()<br>loss.backward()<br>optimizer.step() |
| Step 9 | Evaluate the model | def evaluate(model, data_loader):<br>model.eval（)<br>total_correct = 0<br>total_tests = 0<br>for batched_graph, labels in data_loader:<br>logits = model(batched_graph, batched_graph.ndata['feat'])<br>predicted_labels = logits.argmax(1)<br>total_correct += (predicted_labels == labels).sum().item()<br>total_tests += len(labels)<br>accuracy = total_correct / total_tests<br>return accuracy |
| Step 10 | Train and evaluate the model | num_epochs = 10<br>for epoch in range(num_epochs):<br>train(model, optimizer, train_loader)<br>train_accuracy = evaluate(model, train_loader)<br>test_accuracy = evaluate(model, test_loader)<br>print(f"Epoch: {epoch}, Train Accuracy: {train_accuracy}, Test Accuracy: {test_accuracy}") |
That's it! By following the above steps and code snippets, you should be able to implement "GraphPlanner: Floorplanning with Graph Neural Network" successfully. Remember to fill in the missing details such as input dimensions, hidden dimensions, output dimensions, and dataset before running the code.
Good luck with your implementation!
