(model, feats, graph, ego_graph_nodes, max_epoch, device, use_scheduler, lr, weight_decay, batch_size=512, sampling_method="lc", optimizer="adam", drop_edge_rate=0)
| 88 | |
| 89 | |
| 90 | def pretrain(model, feats, graph, ego_graph_nodes, max_epoch, device, use_scheduler, lr, weight_decay, batch_size=512, sampling_method="lc", optimizer="adam", drop_edge_rate=0): |
| 91 | logging.info("start training..") |
| 92 | |
| 93 | model = model.to(device) |
| 94 | optimizer = create_optimizer(optimizer, model, lr, weight_decay) |
| 95 | |
| 96 | dataloader = setup_training_dataloder( |
| 97 | sampling_method, ego_graph_nodes, graph, feats, batch_size=batch_size, drop_edge_rate=drop_edge_rate) |
| 98 | |
| 99 | logging.info(f"After creating dataloader: Memory: {show_occupied_memory():.2f} MB") |
| 100 | if use_scheduler and max_epoch > 0: |
| 101 | logging.info("Use scheduler") |
| 102 | scheduler = lambda epoch :( 1 + np.cos((epoch) * np.pi / max_epoch) ) * 0.5 |
| 103 | scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler) |
| 104 | else: |
| 105 | scheduler = None |
| 106 | |
| 107 | for epoch in range(max_epoch): |
| 108 | epoch_iter = tqdm(dataloader) |
| 109 | losses = [] |
| 110 | # assert (graph.in_degrees() > 0).all(), "after loading" |
| 111 | |
| 112 | for batch_g in epoch_iter: |
| 113 | model.train() |
| 114 | if drop_edge_rate > 0: |
| 115 | batch_g, targets, _, node_idx, drop_g1, drop_g2 = batch_g |
| 116 | batch_g = batch_g.to(device) |
| 117 | drop_g1 = drop_g1.to(device) |
| 118 | drop_g2 = drop_g2.to(device) |
| 119 | x = batch_g.x |
| 120 | loss = model(batch_g, x, targets, epoch, drop_g1, drop_g2) |
| 121 | else: |
| 122 | batch_g, targets, _, node_idx = batch_g |
| 123 | batch_g = batch_g.to(device) |
| 124 | x = batch_g.x |
| 125 | loss = model(batch_g, x, targets, epoch) |
| 126 | |
| 127 | optimizer.zero_grad() |
| 128 | loss.backward() |
| 129 | torch.nn.utils.clip_grad_norm_(model.parameters(), 3) |
| 130 | optimizer.step() |
| 131 | |
| 132 | epoch_iter.set_description(f"train_loss: {loss.item():.4f}, Memory: {show_occupied_memory():.2f} MB") |
| 133 | losses.append(loss.item()) |
| 134 | |
| 135 | if scheduler is not None: |
| 136 | scheduler.step() |
| 137 | |
| 138 | torch.save(model.state_dict(), os.path.join(model_dir, model_name)) |
| 139 | |
| 140 | print(f"# Epoch {epoch} | train_loss: {np.mean(losses):.4f}, Memory: {show_occupied_memory():.2f} MB") |
| 141 | |
| 142 | return model |
| 143 | |
| 144 | |
| 145 | if __name__ == "__main__": |
no test coverage detected