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| import numpy as np | |
| from manopth.manolayer import ManoLayer | |
| import os | |
| import torch | |
| def get_mano_models(): | |
| mano_path = "/data1/sim/mano_models/mano/models" | |
| rgt_mano_layer = ManoLayer( | |
| flat_hand_mean=False, | |
| side='right', | |
| mano_root=mano_path, # mano_root # | |
| ncomps=45, | |
| use_pca=False, | |
| # root_rot_mode='axisang', | |
| # joint_rot_mode='axisang' | |
| ) | |
| lft_mano_layer = ManoLayer( | |
| flat_hand_mean=False, | |
| side='left', | |
| mano_root=mano_path, # mano_root # | |
| ncomps=45, | |
| use_pca=False, | |
| # root_rot_mode='axisang', | |
| # joint_rot_mode='axisang' | |
| ) | |
| return rgt_mano_layer, lft_mano_layer | |
| def get_embeddings(): | |
| # /home/xueyi/sim/arctic/prepared_data_s01_box_grab_01_2.npy | |
| # /home/xueyi/sim/arctic/prepared_data_s01_box_grab_01_1.npy | |
| # /home/xueyi/sim/arctic/prepared_data_s01_laptop_use_01_1.npy | |
| # /home/xueyi/sim/arctic/prepared_data_s02_laptop_use_02_2.npy | |
| tags = [ | |
| "s01_box_grab_01", "s01_laptop_use_01", "s02_laptop_use_02" | |
| ] | |
| sv_root = "/home/xueyi/sim/arctic/" | |
| tot_pose_diff_data = [] | |
| tot_gaussian_diff_data = [] | |
| tot_beta_diff_data = [] | |
| ws = 60 | |
| aug_pose = 0.5 | |
| aug_pose_beta = 0.3 | |
| for tag in tags: | |
| for cur_view in range(1, 9): | |
| cur_predicted_data_sv_fn = f"prepared_data_{tag}_{cur_view}.npy" | |
| cur_predicted_data_sv_fn = os.path.join(sv_root, cur_predicted_data_sv_fn) | |
| prepared_data = np.load(cur_predicted_data_sv_fn, allow_pickle=True).item() | |
| # prepared_data = np.load(prepared_data_fn, allow_pickle=True).item() | |
| # print(f"prepared_data: {prepared_data.keys()}") | |
| # tag = "targets" | |
| # tag = "pred" | |
| # rhand_verts = prepared_data[f"{tag}.mano.v3d.cam.r"].detach().cpu().numpy() | |
| # lhand_verts = prepared_data[f"{tag}.mano.v3d.cam.l"].detach().cpu().numpy() | |
| # pred_obj_verts = prepared_data[f'{tag}.object.v.cam'].detach().cpu().numpy() # pred.object.v.cam | |
| # get_mano_model | |
| # rgt_mano_model, lft_mano_layer = get_mano_models(ncomps=45, side='right') ## single path -> and then to the evaluation protocal # | |
| pred_tag = "pred" | |
| target_tag = "targets" | |
| pred_pose_r = prepared_data[f"{pred_tag}.mano.pose.r"] | |
| target_pose_r = prepared_data[f"{target_tag}.mano.pose.r"] | |
| pred_pose_r = pred_pose_r.contiguous().view(pred_pose_r.size(0), -1).contiguous() ### | |
| pred_pose_r = pred_pose_r[:, 3:] | |
| target_pose_r = target_pose_r[:, 3:] | |
| for i in range(0, pred_pose_r.size(0) - 60, ws): | |
| st_idx = i | |
| ed_idx = i + ws | |
| cur_w_pred_pose_r = pred_pose_r[st_idx: ed_idx, 3:] | |
| cur_w_target_pose_r = target_pose_r[st_idx: ed_idx, 3:] | |
| diff_cur_w_target_pred_pose_r = cur_w_pred_pose_r - cur_w_target_pose_r | |
| # diff_cur_w_target_pred_pose_ | |
| tot_pose_diff_data.append(diff_cur_w_target_pred_pose_r.numpy()) | |
| gaussian_rnd_noise = torch.randn_like(diff_cur_w_target_pred_pose_r) * aug_pose | |
| gaussian_rnd_noise = gaussian_rnd_noise.numpy() | |
| tot_gaussian_diff_data.append(gaussian_rnd_noise) | |
| dist_beta = torch.distributions.beta.Beta(torch.tensor([8.]), torch.tensor([2.])) | |
| aug_pose_var = dist_beta.sample(diff_cur_w_target_pred_pose_r.size()).squeeze(-1) * aug_pose_beta | |
| # aug_pose_var = torch.randn_like(torch.from_numpy(diff_cur_w_target_pred_pose_r).float()) * aug_pose | |
| aug_pose_var = aug_pose_var.numpy() | |
| tot_beta_diff_data.append(aug_pose_var) | |
| tot_pose_diff_data = np.stack(tot_pose_diff_data, axis=0) | |
| # tot_pose_diff_data = | |
| tot_pose_diff_data = np.reshape(tot_pose_diff_data, (tot_pose_diff_data.shape[0], -1)) | |
| np.save(f"tot_pose_diff_data.npy", tot_pose_diff_data) | |
| tot_gaussian_diff_data = np.stack(tot_gaussian_diff_data, axis=0) | |
| tot_gaussian_diff_data = np.reshape(tot_gaussian_diff_data, (tot_gaussian_diff_data.shape[0], -1)) | |
| np.save(f"tot_gaussian_diff_data.npy", tot_gaussian_diff_data) | |
| tot_beta_diff_data = np.stack(tot_beta_diff_data, axis=0) | |
| tot_beta_diff_data = np.reshape(tot_beta_diff_data, (tot_beta_diff_data.shape[0], -1)) | |
| np.save(f"tot_beta_diff_data.npy", tot_beta_diff_data) | |
| # pred pose r; target pose r # | |
| # trans_r = prepared_data[f'{tag}.mano.cam_t.r'] | |
| # pose_r = prepared_data[f'{tag}.mano.pose.r'] | |
| # pose_r = pose_r.contiguous().view(pose_r.size(0), -1).contiguous() ### | |
| # psoe_r # | |
| # gt_pose_r = prepared_data[f'targets.mano.pose.r'] | |
| # pred_pose_r = prepared_data[f'{tag}.mano.pose.r'] | |
| # pred_pose_r = pred_pose_r.contiguous().view(pose_r.size(0), -1).contiguous() ### | |
| # print(f"gt_pose_r: {gt_pose_r.shape}, pred_pose_r: {pred_pose_r.shape}") | |
| # pred_pose_pose = pred_pose_r[:, 3:] | |
| # gt_pose_pose = gt_pose_r[:, 3:] | |
| # diff_pred_gt_pose = torch.mean((pred_pose_pose - gt_pose_pose) ** 2) | |
| # # diff_pred_gt_pose = diff_pred_ | |
| # print(diff_pred_gt_pose) | |
| if __name__=='__main__': | |
| get_embeddings() | |