def evaluate(self):
for k, v in self.evaluation_accumulators.items():
self.evaluation_accumulators[k] = np.vstack(v)
pred_j3ds = self.evaluation_accumulators['pred_j3d']
target_j3ds = self.evaluation_accumulators['target_j3d']
pred_j3ds = torch.from_numpy(pred_j3ds).float()
target_j3ds = torch.from_numpy(target_j3ds).float()
print(f'Evaluating on {pred_j3ds.shape[0]} number of poses...')
pred_pelvis = (pred_j3ds[:, [2], :] + pred_j3ds[:, [3], :]) / 2.0
target_pelvis = (target_j3ds[:, [2], :] + target_j3ds[:, [3], :]) / 2.0
pred_j3ds -= pred_pelvis
target_j3ds -= target_pelvis
errors = torch.sqrt(
((pred_j3ds -
target_j3ds)**2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
S1_hat = batch_compute_similarity_transform_torch(
pred_j3ds, target_j3ds)
errors_pa = torch.sqrt(
((S1_hat -
target_j3ds)**2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
pred_verts = self.evaluation_accumulators['pred_verts']
target_theta = self.evaluation_accumulators['target_theta']
m2mm = 1000
pve = np.mean(
compute_error_verts(target_theta=target_theta,
pred_verts=pred_verts)) * m2mm
accel = np.mean(compute_accel(pred_j3ds)) * m2mm
accel_err = np.mean(
compute_error_accel(joints_pred=pred_j3ds,
joints_gt=target_j3ds)) * m2mm
mpjpe = np.mean(errors) * m2mm
pa_mpjpe = np.mean(errors_pa) * m2mm
eval_dict = {
'mpjpe': mpjpe,
'pa-mpjpe': pa_mpjpe,
'accel': accel,
'pve': pve,
'accel_err': accel_err
}
log_str = f'Epoch {self.epoch}, '
log_str += ' '.join(
[f'{k.upper()}: {v:.4f},' for k, v in eval_dict.items()])
logger.info(log_str)
for k, v in eval_dict.items():
self.writer.add_scalar(f'error/{k}', v, global_step=self.epoch)
# return accel_err
return pa_mpjpe
def evaluate(self):
for k, v in self.evaluation_accumulators.items():
self.evaluation_accumulators[k] = np.vstack(v)
pred_j3ds = self.evaluation_accumulators['pred_j3d']
target_j3ds = self.evaluation_accumulators['target_j3d']
pred_j3ds = torch.from_numpy(pred_j3ds).float()
target_j3ds = torch.from_numpy(target_j3ds).float()
print(f'Evaluating on {pred_j3ds.shape[0]} number of poses...')
pred_pelvis = (pred_j3ds[:, [2], :] + pred_j3ds[:, [3], :]) / 2.0
target_pelvis = (target_j3ds[:, [2], :] + target_j3ds[:, [3], :]) / 2.0
pred_j3ds -= pred_pelvis
target_j3ds -= target_pelvis
# Absolute error (MPJPE)
errors = torch.sqrt(
((pred_j3ds -
target_j3ds)**2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
S1_hat = batch_compute_similarity_transform_torch(
pred_j3ds, target_j3ds)
errors_pa = torch.sqrt(
((S1_hat -
target_j3ds)**2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
pred_verts = self.evaluation_accumulators['pred_verts']
target_theta = self.evaluation_accumulators['target_theta']
# pk.dump(target_theta, open("3dpw_thetas.pkl", "wb"))
# np.savez("amass_eval_test.npz", pred_j3ds = pred_j3ds.numpy(), target_j3ds = target_j3ds.numpy())
m2mm = 1000
pve = np.mean(
compute_error_verts(target_theta=target_theta,
pred_verts=pred_verts)) * m2mm
accel = np.mean(compute_accel(pred_j3ds)) * m2mm
accel_err = np.mean(
compute_error_accel(joints_pred=pred_j3ds,
joints_gt=target_j3ds)) * m2mm
mpjpe = np.mean(errors) * m2mm
pa_mpjpe = np.mean(errors_pa) * m2mm
eval_dict = {
'mpjpe': mpjpe,
'pa-mpjpe': pa_mpjpe,
'pve': pve,
'accel': accel,
'accel_err': accel_err
}
log_str = ' '.join(
[f'{k.upper()}: {v:.4f},' for k, v in eval_dict.items()])
print(log_str)