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for param in net.parameters():
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nb_param += np.prod(list(param.data.size()))
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print('Number of parameters:', nb_param)
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# Create log directory
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log_dir = f"./logs/{config.expt_name}/"
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os.makedirs(log_dir, exist_ok=True)
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json.dump(config, open(f"{log_dir}/config.json", "w"), indent=4)
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writer = SummaryWriter(log_dir) # Define Tensorboard writer
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# Training parameters
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num_nodes = config.num_nodes
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num_neighbors = config.num_neighbors
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max_epochs = config.max_epochs
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val_every = config.val_every
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test_every = config.test_every
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batch_size = config.batch_size
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batches_per_epoch = config.batches_per_epoch
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accumulation_steps = config.accumulation_steps
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learning_rate = config.learning_rate
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decay_rate = config.decay_rate
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val_loss_old = 1e6 # For decaying LR based on validation loss
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best_pred_tour_len = 1e6 # For saving checkpoints
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best_val_loss = 1e6 # For saving checkpoints
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# Define optimizer
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optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
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print(optimizer)
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dataset = DataReader(
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config.num_nodes, config.num_neighbors, config.batch_size,
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config.train_filepath, config.train_filepath_solution,
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do_prep=False
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)
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if 'resume_from_dir' in config:
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if torch.cuda.is_available():
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checkpoint = torch.load(os.path.join(config.resume_from_dir, "last_train_checkpoint.tar"))
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else:
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checkpoint = torch.load(os.path.join(config.resume_from_dir, "last_train_checkpoint.tar"), map_location='cpu')
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# Load network state
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net.load_state_dict(checkpoint['model_state_dict'])
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# Load optimizer state
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optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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# Load other training parameters
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epoch = checkpoint['epoch']
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train_loss = checkpoint['train_loss']
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val_loss = checkpoint['val_loss']
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# Note: the learning_rate was set in load_state_dict,
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# this is just to have the local variable for logging
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for param_group in optimizer.param_groups:
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learning_rate = param_group['lr']
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print(f"Loaded checkpoint from epoch {epoch}")
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else:
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epoch = -1
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epoch_bar = master_bar(range(epoch + 1, max_epochs))
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for epoch in epoch_bar:
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# Log to Tensorboard
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writer.add_scalar('learning_rate', learning_rate, epoch)
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# Train
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train_time, train_loss, train_err_edges, train_err_tour, train_err_tsp, train_pred_tour_len, train_gt_tour_len = train_one_epoch(net, optimizer, config, epoch_bar, dataset=dataset)
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epoch_bar.write('t: ' + metrics_to_str(epoch, train_time, learning_rate, train_loss, train_err_edges, train_err_tour, train_err_tsp, train_pred_tour_len, train_gt_tour_len))
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writer.add_scalar('loss/train_loss', train_loss, epoch)
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writer.add_scalar('pred_tour_len/train_pred_tour_len', train_pred_tour_len, epoch)
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writer.add_scalar('optimality_gap/train_opt_gap', train_pred_tour_len/train_gt_tour_len - 1, epoch)
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if epoch % val_every == 0 or epoch == max_epochs-1:
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# Validate
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val_time, val_loss, val_err_edges, val_err_tour, val_err_tsp, val_pred_tour_len, val_gt_tour_len = test(net, config, epoch_bar, mode='val')
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epoch_bar.write('v: ' + metrics_to_str(epoch, val_time, learning_rate, val_loss, val_err_edges, val_err_tour, val_err_tsp, val_pred_tour_len, val_gt_tour_len))
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writer.add_scalar('loss/val_loss', val_loss, epoch)
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writer.add_scalar('pred_tour_len/val_pred_tour_len', val_pred_tour_len, epoch)
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writer.add_scalar('optimality_gap/val_opt_gap', val_pred_tour_len/val_gt_tour_len - 1, epoch)
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# Save checkpoint
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if val_pred_tour_len < best_pred_tour_len:
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best_pred_tour_len = val_pred_tour_len # Update best val predicted tour length
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torch.save({
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'epoch': epoch,
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'model_state_dict': net.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_loss,
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'val_loss': val_loss,
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}, log_dir+"best_val_tourlen_checkpoint.tar")
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if val_loss < best_val_loss:
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best_val_loss = val_loss # Update best val loss
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torch.save({
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'epoch': epoch,
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'model_state_dict': net.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_loss,
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'val_loss': val_loss,
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}, log_dir+"best_val_loss_checkpoint.tar")
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# Update learning rate
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if val_loss > 0.99 * val_loss_old:
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learning_rate /= decay_rate
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for param_group in optimizer.param_groups:
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