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graph_class_gnn.py
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graph_class_gnn.py
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import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.nn import GCNConv,SSGConv
from torch_geometric.nn import global_mean_pool
from torch_geometric.datasets import QM9,TUDataset
from torch_geometric.loader import DataLoader
import graph_generation.island_graphs as island_graphs
import graph_generation.RedRatioGraphs as RedRatioGraphs
class Graph_Classification_GCN(torch.nn.Module):
def __init__(self, input_nodes=3, output_nodes=2):
super().__init__()
self.conv1 = GCNConv(input_nodes, 16)
self.conv2 = GCNConv(16, 16)
self.conv3 = GCNConv(16, 16)
# self.conv4 = GCNConv(16, 16)
# self.conv5 = GCNConv(16, 16)
self.lin = Linear(16, output_nodes)
def forward(self, x, edge_index, batch=None, edge_weight = None):
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv2(x, edge_index)
x = F.relu(x)
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv3(x, edge_index)
x = F.relu(x)
x = F.dropout(x, p=0.5, training=self.training)
# x = self.conv4(x, edge_index)
# x = F.relu(x)
# x = F.dropout(x, p=0.5, training=self.training)
# x = self.conv5(x, edge_index)
# x = F.relu(x)
x = global_mean_pool(x,batch)
# x = F.dropout(x, p=0.5, training=self.training)
x = self.lin(x)
return F.log_softmax(x, dim=1)
def model_optimizer_setup(model_constr,device, input_nodes=3, output_nodes=2):
model = model_constr(input_nodes,output_nodes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0015, weight_decay=5e-4)
return model, optimizer
def train(model, optimizer, loader):
model.train()
for data in loader: # Iterate in batches over the training dataset.
out = model(data.x, data.edge_index, data.batch) # Perform a single forward pass.
loss = torch.nn.CrossEntropyLoss()(out, data.y) # Compute the loss.
loss.backward() # Derive gradients.
optimizer.step() # Update parameters based on gradients.
optimizer.zero_grad() # Clear gradients.
def test(loader, model):
model.eval()
correct = 0
for data in loader: # Iterate in batches over the training/test dataset.
out = model(data.x, data.edge_index, data.batch)
pred = out.argmax(dim=1) # Use the class with highest probability.
correct += int((pred == data.y).sum()) # Check against ground-truth labels.
return correct / len(loader.dataset) # Derive ratio of correct predictions.
if __name__=='__main__':
dataset = RedRatioGraphs.RedRatioGraphs(10000).getDataset()
# dataset = island_graphs.DatasetCreator(10000, 16,32).getDataset()
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device('cpu')
train_test_split = 0.8
train_idx = int(len(dataset)*0.8)
print(train_idx)
train_dataset = dataset[:train_idx]
test_dataset = dataset[train_idx:]
print("dataset downloaded")
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
# pre_train_loader = PrefetchLoader(train_loader, device)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# pre_test_loader = PrefetchLoader(test_loader, device)
print("batches created")
print("done")
model, optimizer = model_optimizer_setup(Graph_Classification_GCN, device)
for epoch in range(1, 30):
train(model, optimizer, train_loader)
train_acc = test(train_loader, model)
test_acc = test(test_loader, model)
print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')
torch.save(model.state_dict(), "model/pgexp_model_red_ratio.pt")
torch.save(train_loader, "model/pgexp_test_loader_red_ratio.pt")