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| import torch | |
| def flow_loss_func(flow_preds, flow_gt, valid, | |
| gamma=0.9, | |
| max_flow=400, | |
| **kwargs, | |
| ): | |
| n_predictions = len(flow_preds) | |
| flow_loss = 0.0 | |
| # exlude invalid pixels and extremely large diplacements | |
| mag = torch.sum(flow_gt ** 2, dim=1).sqrt() # [B, H, W] | |
| valid = (valid >= 0.5) & (mag < max_flow) | |
| for i in range(n_predictions): | |
| i_weight = gamma ** (n_predictions - i - 1) | |
| i_loss = (flow_preds[i] - flow_gt).abs() | |
| flow_loss += i_weight * (valid[:, None] * i_loss).mean() | |
| epe = torch.sum((flow_preds[-1] - flow_gt) ** 2, dim=1).sqrt() | |
| if valid.max() < 0.5: | |
| pass | |
| epe = epe.view(-1)[valid.view(-1)] | |
| metrics = { | |
| 'epe': epe.mean().item(), | |
| '1px': (epe > 1).float().mean().item(), | |
| '3px': (epe > 3).float().mean().item(), | |
| '5px': (epe > 5).float().mean().item(), | |
| } | |
| return flow_loss, metrics | |