pems with index-batching for a3tgcn

Author: OckermanSethGVSU

examples/indexBatching/A3TGCN/pems_main.py

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+import numpy as np
+import time
+import csv
+import torch
+import torch.nn.functional as F
+from torch_geometric.nn import GCNConv
+from torch_geometric_temporal.nn.recurrent import A3TGCN2
+from torch_geometric_temporal.dataset import PemsDatasetLoader
+import argparse
+from utils import *
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Demo of index batching with PemsBay dataset")
+
+    parser.add_argument(
+        "-e", "--epochs", type=int, default=100, help="The desired number of training epochs"
+    )
+    parser.add_argument(
+        "-bs", "--batch-size", type=int, default=64, help="The desired batch size"
+    )
+    parser.add_argument(
+        "-g", "--gpu", type=str, default="False", help="Should data be preprocessed and migrated directly to the GPU"
+    )
+    parser.add_argument(
+        "-d", "--debug", type=str, default="False", help="Print values for debugging"
+    )
+    return parser.parse_args()
+
+# Making the model 
+class TemporalGNN(torch.nn.Module):
+    def __init__(self, node_features, periods, batch_size):
+        super(TemporalGNN, self).__init__()
+        # Attention Temporal Graph Convolutional Cell
+        self.tgnn = A3TGCN2(in_channels=node_features,  out_channels=32, periods=periods,batch_size=batch_size) # node_features=2, periods=12
+        # Equals single-shot prediction
+        self.linear = torch.nn.Linear(32, periods)
+
+    def forward(self, x, edge_index):
+        """
+        x = Node features for T time steps
+        edge_index = Graph edge indices
+        """
+        h = self.tgnn(x, edge_index) # x [b, 207, 2, 12]  returns h [b, 207, 12]
+        h = F.relu(h) 
+        h = self.linear(h)
+        return h
+
+
+
+def train(train_dataloader, val_dataloader, batch_size, epochs, edges, DEVICE, allGPU=False, debug=False):
+    
+    # Create model and optimizers
+    model = TemporalGNN(node_features=2, periods=12, batch_size=batch_size).to(DEVICE)
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+    loss_fn = torch.nn.MSELoss()
+    
+    stats = []
+    t_mse = []
+    v_mse = []
+
+    
+    edges = edges.to(DEVICE)
+    for epoch in range(epochs):
+        step = 0
+        loss_list = []
+        t1 = time.time()
+        i = 1
+        total = len(train_dataloader)
+        mae_total = 0
+        for batch in train_dataloader:
+            X_batch, y_batch = batch
+            
+            # Need to permute based on expected input shape for ATGCN
+            if allGPU:
+                X_batch = X_batch.permute(0, 2, 3, 1)
+                y_batch = y_batch[...,0].permute(0, 2, 1)
+            else:
+                X_batch = X_batch.permute(0, 2, 3, 1).to(DEVICE).float()
+                y_batch = y_batch[...,0].permute(0, 2, 1).to(DEVICE).float()
+            
+
+
+            y_hat = model(X_batch, edges)         # Get model predictions
+            loss = loss_fn(y_hat, y_batch) # Mean squared error #loss = torch.mean((y_hat-labels)**2)  sqrt to change it to rmse
+            
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            step= step+ 1
+            loss_list.append(loss.item())
+
+            if debug:
+                print(f"Train Batch: {i}/{total}", end="\r")
+                i+=1
+
+
+        model.eval()
+        step = 0
+        # Store for analysis
+        total_loss = []
+        i = 1
+        total = len(val_dataloader)
+        if debug:
+            print("                      ", end="\r")
+        with torch.no_grad():
+            for batch in val_dataloader:
+                X_batch, y_batch = batch
+
+                
+                # Need to permute based on expected input shape for ATGCN
+                if allGPU:
+                    X_batch = X_batch.permute(0, 2, 3, 1)
+                    y_batch = y_batch[...,0].permute(0, 2, 1)
+                else:
+                    X_batch = X_batch.permute(0, 2, 3, 1).to(DEVICE).float()
+                    y_batch = y_batch[...,0].permute(0, 2, 1).to(DEVICE).float()
+                
+                # Get model predictions
+                y_hat = model(X_batch, edges)
+                # Mean squared error
+                loss = loss_fn(y_hat, y_batch)
+                total_loss.append(loss.item())
+
+                mae_total += masked_mae_loss(y_hat, y_batch)
+                
+                if debug:
+                    print(f"Val Batch: {i}/{total}", end="\r")
+                    i += 1
+                
+        mae = mae_total / len(val_dataloader)   
+        t2 = time.time()
+        print("Epoch {} time: {:.4f} train RMSE: {:.4f} Test MSE: {:.4f} Test MAE: {:.4f}".format(epoch,t2 - t1, sum(loss_list)/len(loss_list), sum(total_loss)/len(total_loss), mae))
+        stats.append([epoch, t2-t1, sum(loss_list)/len(loss_list), sum(total_loss)/len(total_loss)])
+        t_mse.append(sum(loss_list)/len(loss_list))
+        v_mse.append(sum(total_loss)/len(total_loss))
+    return min(t_mse), min(v_mse)
+        
+
+  
+
+
+
+
+
+def main():
+    args = parse_arguments()
+    allGPU = args.gpu.lower() in ["true", "y", "t", "yes"]
+    debug = args.debug.lower() in ["true", "y", "t", "yes"]
+    batch_size = args.batch_size
+    epochs = args.epochs
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    shuffle= True
+
+
+    start = time.time()
+    p1 = time.time() 
+    indexLoader = PemsDatasetLoader(index=True)
+    if allGPU:
+        train_dataloader, val_dataloader, test_dataloader, edges, edge_weights, mean, std = indexLoader.get_index_dataset(batch_size=batch_size, shuffle=shuffle, allGPU=0) 
+    else:
+        train_dataloader, val_dataloader, test_dataloader, edges, edge_weights, mean, std = indexLoader.get_index_dataset(batch_size=batch_size, shuffle=shuffle) 
+    p2 = time.time() 
+    t_mse, v_mse = train(train_dataloader, val_dataloader, batch_size, epochs, edges, device, debug=debug)
+    end = time.time()
+
+    print(f"Runtime: {round(end - start,2)}; T-MSE: {round(t_mse, 3)}; V-MSE: {round(v_mse, 3)}")
+
+if __name__ == "__main__":
+    main()