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| from .metrics import * | |
| from .evaluators import EvaluatorBase | |
| from lib.body_models.smpl_utils.reality import eval_rot_delta as smpl_eval_rot_delta | |
| class HSMR3DEvaluator(EvaluatorBase): | |
| def eval(self, **kwargs): | |
| # Get the predictions and ground truths. | |
| pd, gt = kwargs['pd'], kwargs['gt'] | |
| v3d_pd, v3d_gt = pd['v3d_pose'], gt['v3d_pose'] | |
| j3d_pd, j3d_gt = pd['j3d_pose'], gt['j3d_pose'] | |
| # Compute the metrics. | |
| mpjpe = eval_MPxE(j3d_pd, j3d_gt) | |
| pa_mpjpe = eval_PA_MPxE(j3d_pd, j3d_gt) | |
| mpve = eval_MPxE(v3d_pd, v3d_gt) | |
| pa_mpve = eval_PA_MPxE(v3d_pd, v3d_gt) | |
| # Append the results. | |
| self.accumulator['MPJPE'].append(mpjpe.detach().cpu()) | |
| self.accumulator['PA-MPJPE'].append(pa_mpjpe.detach().cpu()) | |
| self.accumulator['MPVE'].append(mpve.detach().cpu()) | |
| self.accumulator['PA-MPVE'].append(pa_mpve.detach().cpu()) | |
| class SMPLRealityEvaluator(EvaluatorBase): | |
| def eval(self, **kwargs): | |
| # Get the predictions and ground truths. | |
| pd = kwargs['pd'] | |
| body_pose = pd['body_pose'] # (..., 23, 3) | |
| # Compute the metrics. | |
| vio_d0 = smpl_eval_rot_delta(body_pose, tol_deg=0 ) # {k: (N, 3)} | |
| vio_d5 = smpl_eval_rot_delta(body_pose, tol_deg=5 ) # {k: (N, 3)} | |
| vio_d10 = smpl_eval_rot_delta(body_pose, tol_deg=10) # {k: (N, 3)} | |
| vio_d20 = smpl_eval_rot_delta(body_pose, tol_deg=20) # {k: (N, 3)} | |
| vio_d30 = smpl_eval_rot_delta(body_pose, tol_deg=30) # {k: (N, 3)} | |
| # Append the results. | |
| parts = vio_d5.keys() | |
| for part in parts: | |
| self.accumulator[f'VD0_{part}' ].append(vio_d0[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD5_{part}' ].append(vio_d5[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD10_{part}'].append(vio_d10[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD20_{part}'].append(vio_d20[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD30_{part}'].append(vio_d30[part].max(-1)[0].detach().cpu()) # (N,) | |
| def get_results(self, chosen_metric=None): | |
| ''' Get the current mean results. ''' | |
| # Only chosen metrics will be compacted and returned. | |
| compacted = self._compact_accumulator(chosen_metric) | |
| ret = {} | |
| for k, v in compacted.items(): | |
| vio_max = v.max() | |
| vio_mean = v.mean() | |
| vio_median = v.median() | |
| tot_cnt = len(v) | |
| vio_cnt = (v > 0).float().sum() | |
| vio_p = vio_cnt / tot_cnt | |
| ret[f'{k}_max'] = vio_max.item() | |
| ret[f'{k}_mean'] = vio_mean.item() | |
| ret[f'{k}_median'] = vio_median.item() | |
| ret[f'{k}_percentage'] = vio_p.item() | |
| return ret | |
| from lib.body_models.skel_utils.reality import eval_rot_delta as skel_eval_rot_delta | |
| class SKELRealityEvaluator(SMPLRealityEvaluator): | |
| def eval(self, **kwargs): | |
| # Get the predictions and ground truths. | |
| pd = kwargs['pd'] | |
| poses = pd['poses'] # (..., 46) | |
| # Compute the metrics. | |
| vio_d0 = skel_eval_rot_delta(poses, tol_deg=0 ) # {k: (N, 3)} | |
| vio_d5 = skel_eval_rot_delta(poses, tol_deg=5 ) # {k: (N, 3)} | |
| vio_d10 = skel_eval_rot_delta(poses, tol_deg=10) # {k: (N, 3)} | |
| vio_d20 = skel_eval_rot_delta(poses, tol_deg=20) # {k: (N, 3)} | |
| vio_d30 = skel_eval_rot_delta(poses, tol_deg=30) # {k: (N, 3)} | |
| # Append the results. | |
| parts = vio_d5.keys() | |
| for part in parts: | |
| self.accumulator[f'VD0_{part}' ].append(vio_d0[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD5_{part}' ].append(vio_d5[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD10_{part}'].append(vio_d10[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD20_{part}'].append(vio_d20[part].max(-1)[0].detach().cpu()) # (N,) | |
| self.accumulator[f'VD30_{part}'].append(vio_d30[part].max(-1)[0].detach().cpu()) # (N,) | |