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"mask": sample["mask"]["stage1"],
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"errormap": (depth_est - depth_gt).abs() * mask}
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if is_distributed:
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scalar_outputs = reduce_scalar_outputs(scalar_outputs)
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return tensor2float(scalar_outputs["loss"]), tensor2float(scalar_outputs), tensor2numpy(image_outputs)
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def profile():
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warmup_iter = 5
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iter_dataloader = iter(TestImgLoader)
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@make_nograd_func
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def do_iteration():
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torch.cuda.synchronize()
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torch.cuda.synchronize()
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start_time = time.perf_counter()
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test_sample_depth(next(iter_dataloader), detailed_summary=True)
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torch.cuda.synchronize()
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end_time = time.perf_counter()
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return end_time - start_time
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for i in range(warmup_iter):
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t = do_iteration()
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print('WarpUp Iter {}, time = {:.4f}'.format(i, t))
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with torch.autograd.profiler.profile(enabled=True, use_cuda=True) as prof:
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for i in range(5):
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t = do_iteration()
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print('Profile Iter {}, time = {:.4f}'.format(i, t))
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time.sleep(0.02)
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if prof is not None:
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# print(prof)
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trace_fn = 'chrome-trace.bin'
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prof.export_chrome_trace(trace_fn)
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print("chrome trace file is written to: ", trace_fn)
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if __name__ == '__main__':
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# parse arguments and check
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args = parser.parse_args()
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# using sync_bn by using nvidia-apex, need to install apex.
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if args.sync_bn:
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assert args.using_apex, "must set using apex and install nvidia-apex"
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if args.using_apex:
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try:
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from apex.parallel import DistributedDataParallel as DDP
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from apex.fp16_utils import *
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from apex import amp, optimizers
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from apex.multi_tensor_apply import multi_tensor_applier
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")
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if args.resume:
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assert args.mode == "train"
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assert args.loadckpt is None
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if args.testpath is None:
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args.testpath = args.trainpath
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if is_distributed:
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torch.cuda.set_device(args.local_rank)
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torch.distributed.init_process_group(
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backend="nccl", init_method="env://"
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)
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synchronize()
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set_random_seed(args.seed)
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# device = torch.device(args.device)
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device = torch.device(args.local_rank)
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if (not is_distributed) or (dist.get_rank() == 0):
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# create logger for mode "train" and "testall"
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if args.mode == "train":
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if not os.path.isdir(args.logdir):
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os.makedirs(args.logdir)
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current_time_str = str(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))
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print("current time", current_time_str)
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print("creating new summary file")
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logger = SummaryWriter(args.logdir)
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print("argv:", sys.argv[1:])
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print_args(args)
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# model, optimizer
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model = CascadeMVSNet(refine=False, ndepths=[int(nd) for nd in args.ndepths.split(",") if nd],
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depth_interals_ratio=[float(d_i) for d_i in args.depth_inter_r.split(",") if d_i],
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share_cr=args.share_cr,
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cr_base_chs=[int(ch) for ch in args.cr_base_chs.split(",") if ch],
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grad_method=args.grad_method)
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model.to(device)
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# using kl loss
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model_loss = cas_mvsnet_loss_kl
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if args.sync_bn:
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import apex
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print("using apex synced BN")
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model = apex.parallel.convert_syncbn_model(model)
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