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depth_gt_ms = sample_cuda["depth"]
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mask_ms = sample_cuda["mask"]
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sigma_ms = sample_cuda["sigma"]
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num_stage = len([int(nd) for nd in args.ndepths.split(",") if nd])
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depth_gt = depth_gt_ms["stage{}".format(num_stage)]
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mask = mask_ms["stage{}".format(num_stage)]
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try:
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outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
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depth_est = outputs["depth"]
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loss, depth_loss, kl, approx_kl, depth_wta = model_loss(outputs, depth_gt_ms,
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sigma_ms, mask_ms, dlossw=[float(e) for e in args.dlossw.split(",") if e])
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if np.isnan(loss.item()):
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raise NanError
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if is_distributed and args.using_apex:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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optimizer.step()
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except NanError:
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print(f'nan error occur!!')
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gc.collect()
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torch.cuda.empty_cache()
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scalar_outputs = {"loss": loss,
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"depth_loss": depth_loss,
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"kl_loss": kl,
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"approx_kl": approx_kl,
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"abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5),
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"thres2mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 2),
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"thres4mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 4),
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"thres8mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 8),}
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image_outputs = {"depth_est": depth_est * mask,
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"depth_est_nomask": depth_est,
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"depth_gt": sample["depth"]["stage1"],
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"ref_img": sample["imgs"][:, 0],
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"mask": sample["mask"]["stage1"],
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"errormap": (depth_est - depth_gt).abs() * mask,
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}
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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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@make_nograd_func
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def test_sample_depth(model, model_loss, sample, args):
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if is_distributed:
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model_eval = model.module
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else:
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model_eval = model
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model_eval.eval()
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sample_cuda = tocuda(sample)
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depth_gt_ms = sample_cuda["depth"]
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mask_ms = sample_cuda["mask"]
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sigma_ms = sample_cuda["sigma"]
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num_stage = len([int(nd) for nd in args.ndepths.split(",") if nd])
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depth_gt = depth_gt_ms["stage{}".format(num_stage)]
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mask = mask_ms["stage{}".format(num_stage)]
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outputs = model_eval(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
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depth_est = outputs["depth"]
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loss, depth_loss, kl, approx_kl, depth_wta = model_loss(outputs, depth_gt_ms,
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sigma_ms, mask_ms, dlossw=[float(e) for e in args.dlossw.split(",") if e])
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scalar_outputs = {"loss": loss,
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"depth_loss": depth_loss,
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"kl_loss": kl,
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"approx_kl": approx_kl,
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"abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5),
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"thres2mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 2),
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"thres4mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 4),
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"thres8mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 8),
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"thres14mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 14),
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"thres20mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 20),
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"thres2mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [0, 2.0]),
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"thres4mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [2.0, 4.0]),
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"thres8mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [4.0, 8.0]),
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"thres14mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [8.0, 14.0]),
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"thres20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [14.0, 20.0]),
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"thres>20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [20.0, 1e5]),
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}
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image_outputs = {"depth_est": depth_est * mask,
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"depth_est_nomask": depth_est,
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"depth_gt": sample["depth"]["stage1"],
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"ref_img": sample["imgs"][:, 0],
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