optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',
factor=0.5,
patience=25,
verbose=True)
dur = []
total_load = 0
total_fp = 0
total_bp = 0
total_up = 0
total_ot = 0
for epoch in range(1, 201):
t1 = time.time()
train_loss, train_acc, optimizer= train_dgl('sage', model, optimizer, device, train_loader)
dur.append(time.time() - t1)
print(
'load Time: {:.4f},forward Time: {:.4f}, backward Time: {:.4f}, update Time: {:.4f}, batch Time: {:.4f}'.format(
epoch_load_time,
epoch_forward_time,
epoch_backward_time,
epoch_update_time,
epoch_batch_time))
if epoch > 50 and epoch < 151:
total_load = total_load + epoch_load_time
total_fp = total_fp + epoch_forward_time
total_bp = total_bp + epoch_backward_time
total_up = total_up + epoch_update_time
total_ot = epoch_batch_time - epoch_forward_time - epoch_backward_time - epoch_update_time + total_ot
factor=0.5,
patience=25,
verbose=True)
dur = []
total_load = 0
total_fp = 0
total_bp = 0
total_up = 0
total_ot = 0
for epoch in range(1, 201):
torch.cuda.synchronize()
t1 = time.time()
train_loss, epoch_train_acc, optimizer = train_dgl('gcn', model, optimizer,
device, train_loader)
# gc.collect()
torch.cuda.synchronize()
dur.append(time.time() - t1)
print(
'load Time: {:.4f},forward Time: {:.4f}, backward Time: {:.4f}, update Time: {:.4f}, batch Time: {:.4f}'
.format(epoch_load_time, epoch_forward_time, epoch_backward_time,
epoch_update_time, epoch_batch_time))
if epoch > 50 and epoch < 151:
total_load = total_load + epoch_load_time
total_fp = total_fp + epoch_forward_time
total_bp = total_bp + epoch_backward_time
total_up = total_up + epoch_update_time
total_ot = epoch_batch_time - epoch_forward_time - epoch_backward_time - epoch_update_time + total_ot