def lift_2d_to_3d(records, model, stats, template, cuda=True):
"""
Foward-pass of the lifter model.
"""
for path in records.keys():
data = np.concatenate(records[path]['kpts_2d_pred'], axis=0)
data = nop.normalize_1d(data, stats['mean_in'], stats['std_in'])
data = data.astype(np.float32)
data = torch.from_numpy(data)
if cuda:
data = data.cuda()
prediction = model(data)
prediction = nop.unnormalize_1d(prediction.data.cpu().numpy(),
stats['mean_out'], stats['std_out'])
records[path]['kpts_3d_pred'] = prediction.reshape(
len(prediction), -1, 3)
return records
def unnormalize(self, data, mean, std):
return nop.unnormalize_1d(data, mean, std)