text stringlengths 1 93.6k |
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if epoch_log % args.eval_freq == 0:
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loss_val, mIoU_val, mAcc_val, allAcc_val = validate(
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val_data_loader, model, val_criterion)
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if main_process():
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if args.use_tensorboard:
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writer.add_scalar('loss_val', loss_val, epoch_log)
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writer.add_scalar('mIoU_val', mIoU_val, epoch_log)
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writer.add_scalar('mAcc_val', mAcc_val, epoch_log)
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writer.add_scalar('allAcc_val', allAcc_val, epoch_log)
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# else:
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# metrics_dict = {'loss_val': loss_val,
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# 'mIoU_val': mIoU_val,
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# 'mAcc_val': mAcc_val,
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# 'allAcc_val': allAcc_val}
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is_best = mIoU_val > best_iou
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best_iou = max(best_iou, mIoU_val)
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if (epoch_log % args.save_freq == 0) and main_process():
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if not os.path.exists(args.file_dir + "/model/"):
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os.makedirs(args.file_dir + "/model/")
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filename = args.file_dir + '/model/model_last.pth'
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logger.info('Saving checkpoint to : ' + filename)
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logger.info('best IoU sofar: %.3f' % (best_iou))
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torch.save({'epoch': epoch_log,
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'state_dict': model.state_dict(),
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'optimizer': optimizer.state_dict(),
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'scheduler': scheduler.state_dict(),
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'best_iou': best_iou,
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'is_best': is_best},
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filename)
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if is_best:
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shutil.copyfile(
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filename,
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args.file_dir +
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'/model/model_best.pth')
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if main_process():
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writer.close()
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logger.info('===>Training done!\nBest IoU: %.3f' % (best_iou))
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def train(train_loader, model, criterion, optimizer, epoch, scheduler):
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batch_time = AverageMeter()
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data_time = AverageMeter()
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loss_meter = AverageMeter()
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intersection_meter = AverageMeter()
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union_meter = AverageMeter()
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target_meter = AverageMeter()
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model.train()
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end = time.time()
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max_iter = args.total_epoches * len(train_loader)
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if 'accum_iter' in args:
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accum_iter = args.accum_iter
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else:
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accum_iter = 1
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for i, data in enumerate(train_loader):
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features, pointclouds, edges_self, edges_forward, edges_propagate, target, norms = data
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# print('maximum points: ', max([feat.shape[0] for feat in features]))
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features, pointclouds, edges_self, edges_forward, edges_propagate, target, norms = to_device(
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features, non_blocking=True), to_device(
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pointclouds, non_blocking=True), to_device(
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edges_self, non_blocking=True), to_device(
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edges_forward, non_blocking=True), to_device(
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edges_propagate, non_blocking=True), to_device(
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target, non_blocking=True), to_device(
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norms, non_blocking=True)
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data_time.update(time.time() - end)
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pred = model(
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features,
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pointclouds,
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edges_self,
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edges_forward,
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edges_propagate,
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norms)
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pred = pred.contiguous().view(-1, args.num_classes)
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target = target.view(-1, 1)[:, 0]
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loss = criterion(pred, target)
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loss /= accum_iter
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 10)
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if ((i + 1) % accum_iter == 0) or ((i + 1) == len(train_loader)):
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optimizer.step()
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optimizer.zero_grad(set_to_none=True)
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if args.scheduler_update == 'step':
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scheduler.step()
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output = pred.max(1)[1]
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n = pred.size(0)
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loss *= n
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count = target.new_tensor([n], dtype=torch.long)
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if args.DDP:
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dist.all_reduce(loss)
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dist.all_reduce(count)
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