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train_TUs_graph_classification.py
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"""
Utility functions for training one epoch
and evaluating one epoch
"""
import torch
import torch.nn as nn
import math
from train.metrics import accuracy_TU as accuracy
"""
For GCNs
"""
def train_epoch_sparse(model, optimizer, device, data_loader, epoch):
model.train()
epoch_loss = 0
epoch_train_acc = 0
nb_data = 0
gpu_mem = 0
for iter, (batch_graphs, batch_labels) in enumerate(data_loader):
batch_x = batch_graphs.ndata['feat'].to(device) # num x feat
batch_e = batch_graphs.edata['feat'].to(device)
batch_graphs = batch_graphs.to(device)
batch_labels = batch_labels.to(device)
optimizer.zero_grad()
batch_scores = model.forward(batch_graphs, batch_x, batch_e)
loss = model.loss(batch_scores, batch_labels)
loss.backward()
optimizer.step()
epoch_loss += loss.detach().item()
epoch_train_acc += accuracy(batch_scores, batch_labels)
nb_data += batch_labels.size(0)
epoch_loss /= (iter + 1)
epoch_train_acc /= nb_data
return epoch_loss, epoch_train_acc, optimizer
def evaluate_network_sparse(model, device, data_loader, epoch):
model.eval()
epoch_test_loss = 0
epoch_test_acc = 0
nb_data = 0
with torch.no_grad():
for iter, (batch_graphs, batch_labels) in enumerate(data_loader):
batch_x = batch_graphs.ndata['feat'].to(device)
batch_e = batch_graphs.edata['feat'].to(device)
batch_labels = batch_labels.to(device)
batch_graphs = batch_graphs.to(device)
batch_scores = model.forward(batch_graphs, batch_x, batch_e)
loss = model.loss(batch_scores, batch_labels)
epoch_test_loss += loss.detach().item()
epoch_test_acc += accuracy(batch_scores, batch_labels)
nb_data += batch_labels.size(0)
epoch_test_loss /= (iter + 1)
epoch_test_acc /= nb_data
return epoch_test_loss, epoch_test_acc
"""
For WL-GNNs
"""
def train_epoch_dense(model, optimizer, device, data_loader, epoch, batch_size):
model.train()
epoch_loss = 0
epoch_train_acc = 0
nb_data = 0
gpu_mem = 0
optimizer.zero_grad()
for iter, (x_with_node_feat, labels) in enumerate(data_loader):
x_with_node_feat = x_with_node_feat.to(device)
labels = labels.to(device)
scores = model.forward(x_with_node_feat)
loss = model.loss(scores, labels)
loss.backward()
if not (iter%batch_size):
optimizer.step()
optimizer.zero_grad()
epoch_loss += loss.detach().item()
epoch_train_acc += accuracy(scores, labels)
nb_data += labels.size(0)
epoch_loss /= (iter + 1)
epoch_train_acc /= nb_data
return epoch_loss, epoch_train_acc, optimizer
def evaluate_network_dense(model, device, data_loader, epoch):
model.eval()
epoch_test_loss = 0
epoch_test_acc = 0
nb_data = 0
with torch.no_grad():
for iter, (x_with_node_feat, labels) in enumerate(data_loader):
x_with_node_feat = x_with_node_feat.to(device)
labels = labels.to(device)
scores = model.forward(x_with_node_feat)
loss = model.loss(scores, labels)
epoch_test_loss += loss.detach().item()
epoch_test_acc += accuracy(scores, labels)
nb_data += labels.size(0)
epoch_test_loss /= (iter + 1)
epoch_test_acc /= nb_data
return epoch_test_loss, epoch_test_acc
def check_patience(all_losses, best_loss, best_epoch, curr_loss, curr_epoch, counter):
if curr_loss < best_loss:
counter = 0
best_loss = curr_loss
best_epoch = curr_epoch
else:
counter += 1
return best_loss, best_epoch, counter