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| # import sonnet as snt | |
| # from tensor2tensor.layers import common_attention | |
| # from tensor2tensor.layers import common_layers | |
| # import tensorflow.compat.v1 as tf | |
| # from tensorflow.python.framework import function | |
| # import tensorflow_probability as tfp | |
| import numpy as np | |
| import torch.nn as nn | |
| # import layer_utils | |
| import torch | |
| # import data_utils_torch as data_utils | |
| import math ## | |
| import os | |
| # from options.options import opt | |
| # import model_util | |
| # | |
| ### smoothness | |
| ### whether in the object -> using vertices and using joints ### | |
| ### | |
| import trimesh | |
| def batched_index_select_ours(values, indices, dim = 1): | |
| value_dims = values.shape[(dim + 1):] | |
| values_shape, indices_shape = map(lambda t: list(t.shape), (values, indices)) | |
| indices = indices[(..., *((None,) * len(value_dims)))] | |
| indices = indices.expand(*((-1,) * len(indices_shape)), *value_dims) | |
| value_expand_len = len(indices_shape) - (dim + 1) | |
| values = values[(*((slice(None),) * dim), *((None,) * value_expand_len), ...)] | |
| value_expand_shape = [-1] * len(values.shape) | |
| expand_slice = slice(dim, (dim + value_expand_len)) | |
| value_expand_shape[expand_slice] = indices.shape[expand_slice] | |
| values = values.expand(*value_expand_shape) | |
| dim += value_expand_len | |
| return values.gather(dim, indices) | |
| def calculate_joint_smoothness(joint_seq): | |
| # joint_seq: nf x nnjoints x 3 | |
| disp_seq = joint_seq[1:] - joint_seq[:-1] # (nf - 1) x nnjoints x 3 # | |
| disp_seq = np.sum(disp_seq ** 2, axis=-1) | |
| disp_seq = np.mean(disp_seq) | |
| # disp_seq = np. | |
| disp_seq = disp_seq.item() | |
| return disp_seq | |
| def calculate_penetration_depth(subj_seq, obj_verts, obj_faces): | |
| # obj_verts: nn_verts x 3 -> numpy array | |
| # obj_faces: nn_faces x 3 -> numpy array | |
| # obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| # process=False, use_embree=True) | |
| obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| ) | |
| # subj_seq: nf x nn_subj_pts x 3 # | |
| tot_penetration_depth = [] | |
| for i_f in range(subj_seq.shape[0]): ## total sequence length ## | |
| # for i_f in range(10): | |
| cur_subj_seq = subj_seq[i_f] | |
| cur_subj_seq_in_obj = obj_mesh.contains(cur_subj_seq) # nn_subj_pts # | |
| dist_cur_subj_to_obj_verts = np.sum( # nn_subj_pts x nn_obj_pts # | |
| (np.reshape(cur_subj_seq, (cur_subj_seq.shape[0], 1, 3)) - np.reshape(obj_verts, (1, obj_verts.shape[0], 3))) ** 2, axis=-1 | |
| ) | |
| # dist_cur_subj_to_obj_verts | |
| nearest_obj_dist = np.min(dist_cur_subj_to_obj_verts, axis=-1) # nn_subj_pts | |
| nearest_obj_dist = np.sqrt(nearest_obj_dist) | |
| cur_pene_depth = np.zeros_like(nearest_obj_dist) | |
| cur_pene_depth[cur_subj_seq_in_obj] = nearest_obj_dist[cur_subj_seq_in_obj] | |
| tot_penetration_depth.append(cur_pene_depth) | |
| # tot_penetration_depth = max(tot_penetration_depth) | |
| tot_penetration_depth = np.stack(tot_penetration_depth, axis=0) # nf x nn_subj_pts | |
| tot_penetration_depth = np.mean(tot_penetration_depth).item() | |
| return tot_penetration_depth | |
| def calculate_max_penetration_depth(subj_seq, obj_verts, obj_faces): | |
| # obj_verts: nn_verts x 3 -> numpy array | |
| # obj_faces: nn_faces x 3 -> numpy array | |
| # obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| # process=False, use_embree=True) | |
| obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| ) | |
| # subj_seq: nf x nn_subj_pts x 3 # | |
| tot_penetration_depth = [] | |
| for i_f in range(subj_seq.shape[0]): ## total sequence length ## | |
| # for i_f in range(10): | |
| cur_subj_seq = subj_seq[i_f] | |
| cur_subj_seq_in_obj = obj_mesh.contains(cur_subj_seq) # nn_subj_pts # | |
| dist_cur_subj_to_obj_verts = np.sum( # nn_subj_pts x nn_obj_pts # | |
| (np.reshape(cur_subj_seq, (cur_subj_seq.shape[0], 1, 3)) - np.reshape(obj_verts, (1, obj_verts.shape[0], 3))) ** 2, axis=-1 | |
| ) | |
| # dist_cur_subj_to_obj_verts | |
| nearest_obj_dist = np.min(dist_cur_subj_to_obj_verts, axis=-1) # nn_subj_pts | |
| nearest_obj_dist = np.sqrt(nearest_obj_dist) | |
| cur_pene_depth = np.zeros_like(nearest_obj_dist) | |
| cur_pene_depth[cur_subj_seq_in_obj] = nearest_obj_dist[cur_subj_seq_in_obj] | |
| cur_pene_depth = float(np.max(cur_pene_depth)) | |
| tot_penetration_depth.append(cur_pene_depth) | |
| tot_penetration_depth = sum(tot_penetration_depth) / float(len(tot_penetration_depth)) | |
| # tot_penetration_depth = np.stack(tot_penetration_depth, axis=0) # nf x nn_subj_pts | |
| # tot_penetration_depth = np.mean(tot_penetration_depth).item() | |
| return tot_penetration_depth | |
| def calculate_proximity_dist(subj_seq, subj_seq_gt, obj_verts, obj_faces): | |
| # obj_verts: nn_verts x 3 -> numpy array | |
| # obj_faces: nn_faces x 3 -> numpy array | |
| # obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| # process=False, use_embree=True) | |
| obj_mesh = trimesh.Trimesh(vertices=obj_verts, faces=obj_faces, | |
| ) | |
| # subj_seq: nf x nn_subj_pts x 3 # | |
| tot_penetration_depth = [] | |
| # nf x nn_subj_pts x 3 # # nf x nn_subj_pts x nn_obj_pts | |
| dist_subj_seq_to_obj_verts_gt = np.sum( | |
| (np.reshape(subj_seq_gt, (subj_seq_gt.shape[0], subj_seq_gt.shape[1], 1, 3)) - np.reshape(obj_verts, (1, 1, obj_verts.shape[0], 3))) ** 2, axis=-1 | |
| ) | |
| minn_dist_subj_seq_to_obj_verts_gt = np.min(dist_subj_seq_to_obj_verts_gt, axis=-1) # nf x nn_subj_pts | |
| # nf x nn_subj_pts x 3 # # nf x nn_subj_pts x nn_obj_pts | |
| dist_subj_seq_to_obj_verts = np.sum( | |
| (np.reshape(subj_seq, (subj_seq.shape[0], subj_seq.shape[1], 1, 3)) - np.reshape(obj_verts, (1, 1, obj_verts.shape[0], 3))) ** 2, axis=-1 | |
| ) | |
| minn_dist_subj_seq_to_obj_verts = np.min(dist_subj_seq_to_obj_verts, axis=-1) # nf x nn_subj_pts | |
| dist_minn_dist = np.mean( | |
| (minn_dist_subj_seq_to_obj_verts_gt[46:][..., -5:-3] - minn_dist_subj_seq_to_obj_verts[46:][..., -5:-3]) ** 2 | |
| ).item() | |
| # dist_minn_dist = np.sum( | |
| # (minn_dist_subj_seq_to_obj_verts_gt[46:][..., -5:-3] - minn_dist_subj_seq_to_obj_verts[46:][..., -5:-3]) ** 2, axis=-1 | |
| # ) | |
| # dist_minn_dist = np.mean( | |
| # (minn_dist_subj_seq_to_obj_verts_gt - minn_dist_subj_seq_to_obj_verts) ** 2 | |
| # ).item() | |
| return dist_minn_dist | |
| for i_f in range(subj_seq.shape[0]): | |
| cur_subj_seq = subj_seq[i_f] | |
| cur_subj_seq_in_obj = obj_mesh.contains(cur_subj_seq) # nn_subj_pts # | |
| dist_cur_subj_to_obj_verts = np.sum( # nn_subj_pts x nn_obj_pts # | |
| (np.reshape(cur_subj_seq, (cur_subj_seq.shape[0], 1, 3)) - np.reshape(obj_verts, (1, obj_verts.shape[0], 3))) ** 2, axis=-1 | |
| ) | |
| # dist_cur_subj_to_obj_verts | |
| nearest_obj_dist = np.min(dist_cur_subj_to_obj_verts, axis=-1) # nn_subj_pts | |
| nearest_obj_dist = np.sqrt(nearest_obj_dist) | |
| cur_pene_depth = np.zeros_like(nearest_obj_dist) | |
| cur_pene_depth[cur_subj_seq_in_obj] = nearest_obj_dist[cur_subj_seq_in_obj] | |
| tot_penetration_depth.append(cur_pene_depth) | |
| tot_penetration_depth = np.stack(tot_penetration_depth, axis=0) # nf x nn_subj_pts | |
| tot_penetration_depth = np.mean(tot_penetration_depth).item() | |
| return tot_penetration_depth | |
| def calculate_moving_consistency(base_pts_trans, joints_trans): | |
| # base_pts_trans: nf x nn_base_pts x 3 # | |
| # joints_trans: nf x nn_jts x 3 # | |
| base_pts_trans = torch.from_numpy(base_pts_trans).float() | |
| joints_trans = torch.from_numpy(joints_trans).float() | |
| # dist_joints_to_base_pts = np.sum | |
| dist_joints_to_base_pts = torch.sum( | |
| (joints_trans.unsqueeze(2) - base_pts_trans.unsqueeze(1)) ** 2, dim=-1 # nf x nn_jts x nn_base_pts # | |
| ) | |
| dist_joints_to_base_pts = torch.sqrt(dist_joints_to_base_pts) | |
| dist_joints_to_base_pts, joints_to_base_pts_minn_idxes = torch.min(dist_joints_to_base_pts, dim=-1) | |
| minn_dist_joints_to_base_pts_across_joints, minn_dist_joints_to_base_pts_idxes = torch.min(dist_joints_to_base_pts, dim=-1) # (nf - 1) | |
| minn_dist_joints_to_base_pts_idxes = minn_dist_joints_to_base_pts_idxes[:-1] | |
| disp_joints_to_base_pts_minn_idxes = joints_to_base_pts_minn_idxes[:-1] | |
| disp_base_pts = base_pts_trans[1:] - base_pts_trans[:-1] | |
| disp_joints = joints_trans[1:] - joints_trans[:-1] # (nf - 1) x nn_jts x 3 | |
| dist_joints_to_base_pts = dist_joints_to_base_pts[:-1] | |
| k_f = 100. | |
| k = torch.exp( | |
| -k_f * dist_joints_to_base_pts | |
| ) | |
| disp_joints_base_pts = batched_index_select_ours(disp_base_pts, indices=disp_joints_to_base_pts_minn_idxes, dim=1) # (nf - 1) x nn_jts x 3 | |
| nearest_joints_disp = batched_index_select_ours(disp_joints_base_pts, indices=minn_dist_joints_to_base_pts_idxes.unsqueeze(-1), dim=1) # (nf - 1) x 1 | |
| nearest_joints_disp = nearest_joints_disp.squeeze(1) # (nf - 1) x 3 # | |
| disp_joints = batched_index_select_ours(disp_joints, indices=minn_dist_joints_to_base_pts_idxes.unsqueeze(-1), dim=1).squeeze(1) # (nf - 1) x 3 | |
| nearest_k = batched_index_select_ours(k, indices=minn_dist_joints_to_base_pts_idxes.unsqueeze(-1), dim=1).squeeze(1) # (nf - 1) | |
| ##### use k for weighting disp ##### # disp_joints for the joints # the delta distance # | |
| disp_joints_to_nearest_base_pts = disp_joints * nearest_k.unsqueeze(-1) # ### (nf - 1 ) x 3 | |
| diff_disp_joints_to_nearest_base_pts_disp = torch.sum( | |
| (disp_joints_to_nearest_base_pts - nearest_joints_disp) ** 2, dim=-1 # squared joint jdistance pairs # | |
| ) | |
| diff_disp_joints_base_pts = diff_disp_joints_to_nearest_base_pts_disp.mean() | |
| ##### use k for weighting disp ##### | |
| # diff_disp_joints_base_pts = torch.sum( | |
| # ) | |
| ##### use k for weighting diff ##### | |
| # diff_disp_joints_base_pts = torch.sum( | |
| # (disp_joints - disp_joints_base_pts) ** 2, dim=-1 # (nf - 1) x nn_jts | |
| # ) | |
| # diff_disp_joints_base_pts = torch.sqrt(diff_disp_joints_base_pts) * k | |
| # diff_disp_joints_base_pts = diff_disp_joints_base_pts.mean() # # mean of the base_pts # | |
| ##### use k for weighting diff ##### | |
| return diff_disp_joints_base_pts.item() | |
| # static grasping stability -> the gravity direction and the linear combination of contact directions | |
| # dynamic grasping stability -> 1) rotation and acceleration dynamics of the object 2) forces added by contact points | |
| def calculate_grasping_stability(hand_verts, obj_verts, obj_normals, obj_grav_dirs=None): # obj_grav_dir: nf x 3 --> negative to the object gravity dir here # | |
| # hand_verts: nf x nn_verts x 3 # | |
| # obj_verts: nf x nn_obj_verts x 3 # | |
| # obj_normals: nf x nn_obj_normals x 3 # | |
| # obj_grav_dirs: nf x 3 # --> for object gravity directions # | |
| contact_thres = 0.002 # 2mm # | |
| # gravity_dir = np.zeros((3,),dtype=np.float32) | |
| if obj_grav_dirs is None: | |
| gravity_dir = np.array([0., 1., 0.], dtype=np.float32) # (3,) for the gravity direction # # negative to the gravity direction # | |
| # concert to | |
| # if not isinstance(hand_verts, torch.Tensor): | |
| # hand_verts = torch.from_numpy(hand_verts).float().cuda() | |
| # # dir_dim x nn_candidate_dirs xxxx nn_candidate_dirs x 1 --> dir_dim x 1 # a leasts square problem? | |
| nn_hand_verts = hand_verts.shape[1] # nf x nn_hand_verts x 3 # | |
| nn_obj_verts = obj_verts.shape[1] # nf x nn_obj_verts x 3 # | |
| nn_frames = hand_verts.shape[0] | |
| dist_hand_verts_to_obj_verts = np.sum( | |
| (hand_verts.reshape(nn_frames, nn_hand_verts, 1, 3) - obj_verts.reshape(nn_frames, 1, nn_obj_verts, 3)) ** 2, axis=-1 # nf x nn_hand_verts x nn_obj_verts | |
| ) | |
| minn_dist_hand_verts_to_obj_verts_idxes = np.argmin(dist_hand_verts_to_obj_verts, axis=-1) # nf x nn_hand_verts | |
| minn_dist_hand_verts_to_obj_verts = np.min(dist_hand_verts_to_obj_verts, axis=-1) # nf x nn_hand_verts # | |
| # minn_dist_hand_vert | |
| hand_verts_in_contact_mask = minn_dist_hand_verts_to_obj_verts <= contact_thres ## nf x nn_hand_verts # | |
| # nf x nn_hand_verts # | |
| obj_normals_th = torch.from_numpy(obj_normals).float() | |
| minn_dist_hand_verts_to_obj_verts_idxes_th = torch.from_numpy(minn_dist_hand_verts_to_obj_verts_idxes).long() | |
| hand_verts_in_contact_obj_normals_th = batched_index_select_ours(obj_normals_th, minn_dist_hand_verts_to_obj_verts_idxes_th, dim=1) # nf x nn_hand_verts x 3 | |
| hand_verts_in_contact_obj_normals = hand_verts_in_contact_obj_normals_th.numpy() | |
| # hand_verts_in_contact_obj_normals = obj_normals[ minn_dist_hand_verts_to_obj_verts_idxes] # nf x nn_hand_verts x 3 # for obj normals in contact with hand verts # | |
| # print(f"Selected hand_verts_in_contact_obj_normals: {hand_verts_in_contact_obj_normals.shape}") | |
| hand_verts_in_contact_obj_normals = hand_verts_in_contact_obj_normals * -1.0 | |
| diff_cloest_dir_to_gravity_dir = [] | |
| for i_f in range(nn_frames): | |
| if obj_grav_dirs is not None: | |
| gravity_dir = obj_grav_dirs[i_f] | |
| cur_fr_hand_verts_in_contact_mask = hand_verts_in_contact_mask[i_f] # nn_hnad_verts # | |
| cur_fr_hand_verts_in_contact_obj_normals = hand_verts_in_contact_obj_normals[i_f] # nn_hand_verts x 3 # | |
| if np.sum(cur_fr_hand_verts_in_contact_mask.astype(np.float32)).item() == 0: | |
| cur_diff_cloest_dir_to_gravity_dir = np.sqrt(np.sum(gravity_dir ** 2, axis=0)).item() | |
| diff_cloest_dir_to_gravity_dir.append(cur_diff_cloest_dir_to_gravity_dir) | |
| continue | |
| # print(f"cur_fr_hand_verts_in_contact_obj_normals: {cur_fr_hand_verts_in_contact_obj_normals.shape}, cur_fr_hand_verts_in_contact_mask: {cur_fr_hand_verts_in_contact_mask.shape}") | |
| cur_fr_hand_verts_in_contact_obj_normals = cur_fr_hand_verts_in_contact_obj_normals[cur_fr_hand_verts_in_contact_mask] ## nn_in_contact_pts x 3 # | |
| in_contact_coeff, res, _, _ = np.linalg.lstsq(cur_fr_hand_verts_in_contact_obj_normals.T, gravity_dir.reshape(3, 1)) # nn_in_contact_pts x 1 as the combination coefficients # | |
| # print(f"in_contact_coeff: {in_contact_coeff.shape}") | |
| # print() | |
| combined_in_contact_dir = np.matmul( | |
| cur_fr_hand_verts_in_contact_obj_normals.T, in_contact_coeff | |
| ) | |
| combined_in_contact_dir = combined_in_contact_dir.reshape(3,) | |
| diff_combined_in_contact_dir_to_gravity_dir = np.sum( | |
| (combined_in_contact_dir - gravity_dir) ** 2, axis=-1 | |
| ).item() # for the in_contact direction | |
| diff_cloest_dir_to_gravity_dir.append(diff_combined_in_contact_dir_to_gravity_dir) | |
| diff_cloest_dir_to_gravity_dir = sum(diff_cloest_dir_to_gravity_dir) / float(len(diff_cloest_dir_to_gravity_dir)) | |
| return diff_cloest_dir_to_gravity_dir | |
| ### metrics ### # avg acc metrics ## | |
| def get_acc_metrics(outputs, gt_joints): | |
| # | |
| # outputs: ws x nn_jts x 3 # | |
| # gt_joints: ws x nn_jts x 3 # | |
| dist_outputs_gt_joints = np.sqrt(np.sum((outputs - gt_joints) ** 2, axis=-1)) # ws x nn_jts # | |
| avg_dist_outputs_gt_joints = np.mean(dist_outputs_gt_joints).item() | |
| return avg_dist_outputs_gt_joints #### m -> the average ### | |
| # smoothness: 4.6728710003662854e-05, average penetration depth: 6.894875681965049e-05, minn_dist_dist: 4.546074229087353e-07 | |
| # smoothness: 4.832542617805302e-05, average penetration depth: 4.242679380969064e-05, minn_dist_dist: 3.059734870003453e-07 | |
| # jts and rel: smoothness: 4.6728710003662854e-05, average penetration depth: 6.894875681965049e-05, minn_dist_dist: 4.624256727994665e-07 | |
| # jts: smoothness: 4.832542617805302e-05, average penetration depth: 4.242679380969064e-05, minn_dist_dist: 4.4718556466597084e-07 | |
| # | |
| # T = 400 | |
| # smoothness: 3.935288259526715e-05, average penetration depth: 0.000366439988587091, minn_dist_dist: 8.311451183183466e-07 | |
| # smoothness: 0.00011870301386807114, average penetration depth: 0.0002747326595483027, minn_dist_dist: 8.44164813606991e-07 | |
| # smoothness: 6.404393207048997e-05, average penetration depth: 0.00037923554579558723, minn_dist_dist: 1.3080925943256124e-06 # | |
| # smoothness: 3.935288259526715e-05, average penetration depth: 0.000366439988587091, minn_dist_dist: 8.311451183183466e-07 # 3 | |
| # # 3.0236743775145877e-07, 5.490092818343169e-07 | |
| # the examples on different noise scales # | |
| # T = 300 | |
| # smoothness: 4.595715654431842e-05, average penetration depth: 0.00021960893150693885, minn_dist_dist: 5.6245558029369e-07 -- jts only | |
| # smoothness: 0.00011870301386807114, average penetration depth: 0.0002747326595483027, minn_dist_dist: 8.44164813606991e-07 | |
| # | |
| from manopth.manolayer import ManoLayer | |
| def get_mano_model(): | |
| mano_path = "/data1/sim/mano_models/mano/models" | |
| mano_layer = ManoLayer( | |
| flat_hand_mean=True, | |
| side='right', | |
| mano_root=mano_path, # mano_root # | |
| ncomps=24, | |
| use_pca=True, | |
| root_rot_mode='axisang', | |
| joint_rot_mode='axisang' | |
| ) | |
| return mano_layer | |
| def load_grab_clip_data_clean_subj(clip_seq_idx, split = "train", pert=False, more_pert=False, other_noise=False): | |
| mano_model = get_mano_model() | |
| grab_path = "/data1/sim/GRAB_extracted" # extracted | |
| # split = "test" | |
| window_size = 60 | |
| singe_seq_path = f"/data1/sim/GRAB_processed/{split}/{clip_seq_idx}.npy" | |
| clip_clean = np.load(singe_seq_path) | |
| subj_root_path = '/data1/sim/GRAB_processed_wsubj' | |
| subj_seq_path = f"{clip_seq_idx}_subj.npy" | |
| subj_params_fn = os.path.join(subj_root_path, split, subj_seq_path) | |
| subj_params = np.load(subj_params_fn, allow_pickle=True).item() | |
| window_size = min(window_size, subj_params["rhand_transl"].shape[0]) | |
| rhand_transl = subj_params["rhand_transl"][:window_size].astype(np.float32) | |
| rhand_betas = subj_params["rhand_betas"].astype(np.float32) | |
| rhand_pose = clip_clean['f2'][:window_size].astype(np.float32) ## rhand pose ## # | |
| rhand_global_orient = clip_clean['f1'][:window_size].astype(np.float32) | |
| # rhand_pose = clip_clean['f2'][:window_size].astype(np.float32) | |
| rhand_global_orient_var = torch.from_numpy(rhand_global_orient).float() | |
| rhand_pose_var = torch.from_numpy(rhand_pose).float() | |
| rhand_beta_var = torch.from_numpy(rhand_betas).float() | |
| rhand_transl_var = torch.from_numpy(rhand_transl).float() | |
| aug_trans, aug_rot, aug_pose = 0.01, 0.05, 0.3 | |
| # aug_trans, aug_rot, aug_pose = 0.01, 0.1, 0.5 | |
| # aug_trans, aug_rot, aug_pose = 0.001, 0.05, 0.3 | |
| # aug_trans, aug_rot, aug_pose = 0.000, 0.05, 0.3 | |
| if pert: | |
| if more_pert: | |
| aug_trans, aug_rot, aug_pose = 0.04, 0.2, 0.8 | |
| if other_noise: | |
| dist_beta = torch.distributions.beta.Beta(torch.tensor([8.]), torch.tensor([2.])) | |
| print(f"here!") | |
| aug_pose_var = dist_beta.sample(rhand_pose_var.size()).squeeze(-1) * aug_pose | |
| aug_global_orient_var = dist_beta.sample(rhand_global_orient_var.size()).squeeze(-1) * aug_rot | |
| print(f"aug_pose_var: {aug_pose_var.size()}, aug_global_orient_var: {aug_global_orient_var.size()}") | |
| aug_transl_var = dist_beta.sample(rhand_transl_var.size()).squeeze(-1) * aug_trans | |
| else: | |
| aug_global_orient_var = torch.randn_like(rhand_global_orient_var) * aug_rot ### sigma = aug_rot | |
| aug_pose_var = torch.randn_like(rhand_pose_var) * aug_pose ### sigma = aug_pose | |
| aug_transl_var = torch.randn_like(rhand_transl_var) * aug_trans ### sigma = aug_trans | |
| rnd_aug_global_orient_var = rhand_global_orient_var + aug_global_orient_var | |
| rnd_aug_pose_var = rhand_pose_var + aug_pose_var | |
| rnd_aug_transl_var = rhand_transl_var + aug_transl_var ### aug transl ### | |
| else: | |
| rnd_aug_global_orient_var = rhand_global_orient_var | |
| rnd_aug_pose_var = rhand_pose_var | |
| rnd_aug_transl_var = rhand_transl_var ### aug transl | |
| rhand_verts, rhand_joints = mano_model( | |
| torch.cat([rnd_aug_global_orient_var, rnd_aug_pose_var], dim=-1), | |
| rhand_beta_var.unsqueeze(0).repeat(window_size, 1).view(-1, 10), rnd_aug_transl_var | |
| ) | |
| ### rhand_joints: for joints ### | |
| rhand_verts = rhand_verts * 0.001 | |
| rhand_joints = rhand_joints * 0.001 | |
| # return rhand_verts | |
| # return rhand_joints.detach().cpu().numpy() | |
| return rhand_verts.detach().cpu().numpy() | |
| def get_resplit_test_idxes(): | |
| test_split_mesh_nm_to_seq_idxes = "/home/xueyi/sim/motion-diffusion-model/test_mesh_nm_to_test_seqs.npy" | |
| test_split_mesh_nm_to_seq_idxes = np.load(test_split_mesh_nm_to_seq_idxes, allow_pickle=True).item() | |
| tot_test_seq_idxes = [] | |
| for tst_nm in test_split_mesh_nm_to_seq_idxes: | |
| tot_test_seq_idxes = tot_test_seq_idxes + test_split_mesh_nm_to_seq_idxes[tst_nm] | |
| return tot_test_seq_idxes | |
| # 8.121088892826279e-07 | |
| # 2.951481956519773e-07 | |
| # smoothness: 6.213585584191605e-05, average penetration depth: 4.278580710096256e-05, minn_dist_dist: 7.753524560089469e-07 | |
| # smoothness: 5.4274405556498095e-05, average penetration depth: 3.388783391933507e-05, minn_dist_dist: 5.682716970124003e-07 | |
| if __name__=='__main__': | |
| # predicted_info_fn = "/home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos.npy" | |
| # predicted_infos_jtsonly.npy # predicted infos jts ## | |
| tot_APD = [] | |
| tot_smoothness = [] | |
| tot_proximity_error = [] | |
| tot_consistency_value = [] | |
| tot_stability = [] | |
| st_idx = 0 | |
| ed_idx = 38 | |
| st_idx = 58 | |
| ed_idx = 111 # | |
| st_idx = 0 | |
| ed_idx = 20 # | |
| st_idx = 139 | |
| ed_idx = 158 # | |
| st_idx = 139 | |
| ed_idx = 190 # | |
| use_toch = False | |
| use_toch = True | |
| st_idx = 0 | |
| ed_idx = 246 # | |
| # ed_idx = 160 | |
| tot_seq_nn = 0 | |
| maxx_seq_nn = 246 | |
| all_seq_tot_metrics = [] | |
| all_seqs_jts_acc = [] | |
| all_seqs_sv_data = [] | |
| avg_max_penetration_depth = [] | |
| resplit = True | |
| tot_test_seq_idxes = range(st_idx, ed_idx, 1) | |
| if resplit: | |
| tot_test_seq_idxes = get_resplit_test_idxes() | |
| seq_root = "/data1/sim/GRAB_processed/train" | |
| ## cotnact map iou ## | |
| # st_idx = 58 | |
| # ed_idx = 111 | |
| # st_idx = 0 | |
| # ed_idx = 246 | |
| # for test_seq_idx in range(1, 102, 10): | |
| # for test_seq_idx in range(1, 102, 1): | |
| # for test_seq_idx in range(1, 11, 1): | |
| for test_seq_idx in tot_test_seq_idxes: | |
| if tot_seq_nn >= maxx_seq_nn: | |
| break | |
| tot_jts_acc = [] | |
| seed_to_metrics = {} | |
| tot_metrics = [] | |
| if use_toch: ## load grab clip ## | |
| # cur_seq_gt_joints = load_grab_clip_data_clean_subj(test_seq_idx, split = "test", pert=False, more_pert=False, other_noise=False) | |
| cur_seq_gt_verts = load_grab_clip_data_clean_subj(test_seq_idx, split = "test", pert=False, more_pert=False, other_noise=False) | |
| if use_toch: | |
| seed_rng = range(0, 12, 11) | |
| seed_rng = range(0, 122, 11) | |
| else: | |
| seed_rng = range(0, 122, 11) | |
| # for i_seed in range(0, 122, 11): | |
| # for i_seed in range(0, 12, 11): | |
| for i_seed in seed_rng: | |
| seed = 31 | |
| seed = 77 | |
| test_tag = "cond_jtsobj" | |
| # test_tag = "jts_only" | |
| # test_tag = "rep_only" # | |
| test_tag = "rep_only_real" | |
| test_tag = "rep_only_real_sel_base_0" | |
| test_tag = "jts_only" | |
| test_tag = "jts_rep_28_cbd" | |
| test_tag = "rep_only_real_mean_" | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_37_seed_77_tag_jts_only_gaussian_hoi4d_t_300_.npy | |
| test_tag = "jts_only_gaussian_hoi4d_t_300_" | |
| # sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| # sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_rep_only_real_sel_base_0.npy" | |
| # /home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos_seq_101_seed_31_tag_rep_only_real_sel_base_mean.npy | |
| # sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_rep_only_real_sel_base_mean.npy" | |
| sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| # /home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos_seq_101_seed_77_tag_rep_only_real_sel_base_mean.npy | |
| # predicted_infos_seq_101_seed_77_tag_jts_rep_28_cbd.npy | |
| # sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_jts_only.npy" | |
| test_tag = "rep_only_real_sel_base_mean" | |
| seed = 77 | |
| test_tag = "jts_only" | |
| seed = 77 | |
| sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| test_tag = "jts_rep_28_cbd" | |
| seed = 77 | |
| # /home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos_seq_101_seed_77_tag_jts_rep_55_cbd.npy | |
| test_tag = "jts_rep_55_cbd" | |
| seed = 77 | |
| test_tag = "jts_rep_28_cbd_t_400" | |
| seed = 77 | |
| test_tag = "jts_only_t_400" | |
| test_tag = "jts_rep_28_cbd_t_400_real" | |
| test_tag = "jts_rep_19_cbd_t_400_real" | |
| test_tag = "rep_only_real_sel_base_0_t_400" | |
| test_tag = "jts_rep_19_cbd_t_300_real" | |
| test_tag = "jts_rep_19_cbd_t_300_real" | |
| test_tag = "jts_only_gaussian_hoi4d_t_300_" | |
| sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| # /home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos_seq_101_seed_77_tag_jts_rep_55_cbd.npy | |
| sv_dir = "/home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512" | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_2_seed_77_tag_rep_only_real_sel_base_mean_all_noise_.npy | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_7_seed_77_tag_rep_only_real_mean_.npy | |
| sv_dir = "/data1/sim/mdm/eval_save" | |
| sv_dir_ours = "/data1/sim/mdm/eval_save" | |
| # /data2/sim/eval_save/GRAB | |
| sv_dir = "/data2/sim/eval_save/GRAB" | |
| sv_dir_ours = "/data2/sim/eval_save/GRAB" | |
| sv_dir = "/data1/sim/mdm/eval_save" | |
| sv_dir_ours = "/data1/sim/mdm/eval_save" | |
| # test_tag = "jts_rep_19_cbd_t_300_real" # all noise ## | |
| test_tag = "jts_only" | |
| test_tag = "rep_only_real_mean_" | |
| test_tag = "rep_only_real_mean_same_noise_" | |
| test_tag = "rep_only_real_mean_same_noise_t_400_" | |
| test_tag = "rep_only_real_mean_t_400_" | |
| test_tag = "rep_only_real_mean_t_200_" | |
| test_tag = "rep_only_real_mean_t_400_nores_" | |
| # f"/data1/sim/mdm/eval_save/predicted_infos_seq_{test_seq_idx}_seed_77_tag_jts_only_uniform_t_300_.npy" | |
| test_tag = "jts_only_uniform_t_300_" | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_245_seed_77_tag_jts_repmean_only_uniform_t_200_.npy | |
| test_tag = "jts_repmean_only_uniform_t_200_" | |
| # test_tag = "rep_only_real_mean_same_noise_" | |
| # test_tag = "rep_only_real_mean_" | |
| # test_tag = "rep_only_real_sel_base_mean_all_noise_" | |
| test_tag = "jts_only_gaussian_hoi4d_t_300_" | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_37_seed_77_tag_rep_only_mean_shape_hoi4d_t_200_res_jts_.npy | |
| test_tag = "rep_only_mean_shape_hoi4d_t_200_res_jts_" | |
| test_tag = "rep_res_jts_grab_t_200_scale_1_" | |
| test_tag = "rep_res_jts_grab_t_200_scale_2_" # | |
| test_tag = "jts_grab_t_400_scale_1_" | |
| test_tag = "jts_grab_t_400_scale_2_" | |
| test_tag = "jts_grab_t_400_scale_3_" | |
| # rep_only_mean_shape_hoi4d_t_200_res_jts_ | |
| # # rep_only_mean_shape_hoi4d_t_400_ | |
| # # test_tag = "rep_only_mean_shape_hoi4d_t_400_" | |
| # test_tag = "jts_only_beta_t_300_" | |
| # /data1/sim/mdm/eval_save_toch/14.npy | |
| seed = 77 | |
| seed = 11 | |
| seed = 22 | |
| ### seed, i_seed ### | |
| seed = i_seed | |
| test_tag = "rep_res_jts_grab_t_200_" | |
| test_tag = "jts_grab_t_400_" | |
| test_tag = "repmean_only_beta_t_200_res_jts_" | |
| test_tag = "jts_only_beta_t_300_" | |
| test_tag = "rep_jts_grab_t_200_resplit_res_jts_" | |
| # # /data1/sim/mdm/eval_save/predicted_infos_seq_7_seed_77_tag_jts_only.npy | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_7_seed_77_tag_rep_only_real_mean_.npy | |
| # /data1/sim/mdm/eval_save/predicted_infos_seq_7_seed_77_tag_rep_only_real_mean_same_noise_.npy | |
| sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| sv_predicted_info_fn = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" # sv predicted info fn ## | |
| # predicted_infos_seq_99_seed_77_tag_jts_only_beta_t_300_.npy ### seed and seed and seed ### | |
| sv_predicted_info_fn_ours = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| # predicted_infos_seq_99_seed_77_tag_jts_only_beta_t_300_.npy ### seed and seed and seed ### | |
| sv_predicted_info_fn_ours = f"predicted_infos_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| ### for toch ### | |
| if use_toch: | |
| sv_dir = "/data1/sim/mdm/eval_save_toch" | |
| # other_noise_grab | |
| sv_predicted_info_fn = f"{test_seq_idx}.npy" | |
| # sv_predicted_info_fn = f"other_noise_grab_{test_seq_idx}.npy" | |
| # # # sv_predicted_info_fn = f"other_noise_grab_{test_seq_idx}.npy" | |
| ### for toch ### | |
| sv_predicted_info_fn = os.path.join(sv_dir, sv_predicted_info_fn) | |
| predicted_ours_info_fn = os.path.join(sv_dir_ours, sv_predicted_info_fn_ours) | |
| # if not os.path.exists() | |
| # /data1/sim/mdm/eval_save/optimized_infos_sv_dict_seq_37_seed_77_tag_rep_only_mean_shape_hoi4d_t_200_res_jts_.npy | |
| # sv_optimized_info_fn = f"optimized_infos_sv_dict_seq_{test_seq_idx}_seed_{seed}_tag_{test_tag}.npy" | |
| # sv_optimized_info_fn = os.path.join(sv_dir_ours, sv_optimized_info_fn) # | |
| #### only for predicted info fn #### | |
| # if os.path.exists(sv_predicted_info_fn): | |
| # data = np.load(sv_predicted_info_fn, allow_pickle=True).item() | |
| # # targets = data['targets'] | |
| # outputs = data['outputs'] | |
| # obj_verts = data['obj_verts'][0] | |
| # obj_faces = data['obj_faces'] | |
| # tot_base_pts = data["tot_base_pts"][0] # total base points # # bsz x nnbasepts x 3 # | |
| # gt_joints = data['tot_gt_rhand_joints'][0] # gt rhand joints? # object model; object verts here # | |
| # # tot_rhand_joints = data["tot_rhand_joints"][0] | |
| # print(f"gt_joints: {gt_joints.shape}") | |
| # smoothness = calculate_joint_smoothness(outputs) | |
| # APD = calculate_penetration_depth(outputs, obj_verts, obj_faces) | |
| # dist_dist_avg = calculate_proximity_dist(outputs, gt_joints, obj_verts, obj_faces) | |
| # print(f"smoothness: {smoothness}, average penetration depth: {APD}, minn_dist_dist: {dist_dist_avg}") | |
| # tot_APD.append(APD) | |
| # tot_smoothness.append(smoothness) | |
| # tot_proximity_error.append(dist_dist_avg) | |
| # print(f"for sequence {test_seq_idx}, APD: {APD}, smoothness: {smoothness}") | |
| #### only for predicted info fn #### | |
| # if os.path.exists(sv_predicted_info_fn) and os.path.exists(sv_optimized_info_fn): | |
| ### sv optimized info fn ### | |
| print(f"predicted info fn: {sv_predicted_info_fn}") | |
| ws = 60 | |
| if os.path.exists(sv_predicted_info_fn) and os.path.exists(predicted_ours_info_fn): | |
| print(f"predicted info fn: {sv_predicted_info_fn}") | |
| tot_seq_nn += 1 | |
| # if os.path.exists(sv_predicted_info_fn) and os.path.exists(predicted_ours_info_fn) and os.path.exists(sv_optimized_info_fn): | |
| data = np.load(sv_predicted_info_fn, allow_pickle=True).item() | |
| # targets = data['targets'] | |
| print(f"data: {data.keys()}") | |
| outputs = data['outputs'][:ws] | |
| obj_verts = data['obj_verts'][0] | |
| obj_faces = data['obj_faces'] # groudn-truth joints ## | |
| tot_base_pts = data["tot_base_pts"][0] # total base points # # bsz x nnbasepts x 3 # | |
| # print(f"data: {data.keys()}", len(data['tot_rhand_joints'])) | |
| gt_joints = data['tot_gt_rhand_joints'][0] # total gt rhand joints # | |
| outputs = data['tot_gt_rhand_joints'][:ws] | |
| if use_toch: | |
| # gt_joints = cur_seq_gt_joints | |
| gt_joints = cur_seq_gt_verts | |
| # outputs = data['tot_rhand_joints'][0] # total gt rhand joints # | |
| # outputs = gt_joints.copy() # for the | |
| ### for toch #### | |
| if use_toch: | |
| ours_data = np.load(predicted_ours_info_fn, allow_pickle=True).item() | |
| obj_verts = ours_data['obj_verts'][0] | |
| obj_faces = ours_data['obj_faces'] | |
| # gt_joints = ours_data['tot_gt_rhand_joints'][0] # [0] | |
| # print(f"gt_joints: {gt_joints.shape}") | |
| ### for toch ### | |
| ### for toch ### | |
| ### optimized sv dict ### | |
| ### optimized sv dict ### | |
| # optimized_sv_dict = np.load(sv_optimized_info_fn, allow_pickle=True).item() | |
| # outputs = optimized_sv_dict['optimized_out_hand_joints'] # nf x nn_joints x 3 # | |
| # # outputs_vert = | |
| # tot_obj_rot = ours_data['tot_obj_rot'][0] # ws x 3 x 3 ---> obj_rot; # | |
| # tot_obj_transl = ours_data['tot_obj_transl'][0] # nf x 3 | |
| # outputs = np.matmul( | |
| # outputs - tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]), np.transpose(tot_obj_rot, (0, 2, 1)) # nf x nn_joints x 3 --> as the transformed output joints ## | |
| # ) | |
| # outputs = np.matmul( | |
| # outputs - tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]), np.transpose(tot_obj_rot, (0, 1, 2)) # nf x nn_joints x 3 --> as the transformed output joints ## | |
| # ) | |
| # gt_joints = np.matmul( | |
| # gt_joints , np.transpose(tot_obj_rot, (0, 2, 1)) # nf x nn_joints x 3 --> as the transformed output joints ## | |
| # ) + tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]) | |
| print(f"gt_joints: {gt_joints.shape}") | |
| ### optimized sv dict ### | |
| all_seqs_sv_data.append( | |
| { | |
| "outputs": outputs, | |
| "gt_joints": gt_joints, | |
| } | |
| ) | |
| # jts_acc = calculate | |
| jts_acc = get_acc_metrics(outputs, gt_joints) | |
| tot_jts_acc.append(jts_acc) | |
| # # tot_rhand_joints = data["tot_rhand_joints"][0] | |
| # print(f"gt_joints: {gt_joints.shape}") | |
| # smoothness = calculate_joint_smoothness(outputs) | |
| # APD = calculate_penetration_depth(outputs, obj_verts, obj_faces) | |
| # dist_dist_avg = calculate_proximity_dist(outputs, gt_joints, obj_verts, obj_faces) | |
| # calculate_max_penetration_depth | |
| # APD = calculate_max_penetration_depth(outputs, obj_verts, obj_faces) | |
| APD = 0. | |
| # tot_obj_rot = data['tot_obj_rot'][0] # ws x 3 x 3 ---> obj_rot; # | |
| # tot_obj_transl = data['tot_obj_transl'][0] # nf x 3 | |
| # # outputs = targets | |
| # print(f"outputs: {outputs.shape}") | |
| ### for toch #### | |
| # tot_obj_rot = ours_data['tot_obj_rot'][0] # ws x 3 x 3 ---> obj_rot; # | |
| # tot_obj_transl = ours_data['tot_obj_transl'][0] # nf x 3 | |
| # tot_base_pts = ours_data["tot_base_pts"][0] | |
| ### for toch ### | |
| # calculate_grasping_stability(hand_verts, obj_verts, obj_normals, obj_grav_dirs=None): # obj_grav_dir: nf x 3 --> negative to the object gravity dir here # | |
| #### optimized_sv_dict #### | |
| # outputs_verts = optimized_sv_dict["optimized_out_hand_verts"] # nf x nn_verts x 3 # | |
| # outputs_verts = np.matmul( | |
| # outputs_verts - tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]), np.transpose(tot_obj_rot, (0, 2, 1)) | |
| # ) | |
| ### for toch 3## | |
| # outputs_verts = data["obj_verts"] | |
| # ### for toch ## | |
| # # outputs_verts | |
| # tot_base_pts_exp = np.repeat(tot_base_pts.reshape(1, tot_base_pts.shape[0], 3), repeats=outputs_verts.shape[0], axis=0) # nf x nn_obj_verts x 3 | |
| # tot_base_normals = data['tot_base_normals'][0] | |
| # ### for toch ### | |
| # tot_base_normals = ours_data['tot_base_normals'][0] | |
| # # ### for toch ### | |
| # tot_base_normals_exp = np.repeat(tot_base_normals.reshape(1, tot_base_normals.shape[0], 3), repeats=outputs_verts.shape[0], axis=0) | |
| # obj_grav_dirs = np.zeros((outputs_verts.shape[0], 3), dtype=np.float32) # obj_grav_dirs # | |
| # obj_grav_dirs[:, 1] = 1. | |
| # cur_avg_stability = calculate_grasping_stability(outputs_verts, tot_base_pts_exp, tot_base_normals_exp, obj_grav_dirs=obj_grav_dirs) | |
| # if cur_avg_stability < 100.: | |
| # tot_stability.append(cur_avg_stability) # for the current stability values # | |
| # print(f"cur_avg_stability: {cur_avg_stability}") | |
| #### optimized_sv_dict #### | |
| # ### get joints_trans and base_pts_trans ### | |
| # joints_trans = np.matmul(outputs, tot_obj_rot) + tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]) # | |
| # base_pts_trans = np.matmul(tot_base_pts.reshape(1, tot_base_pts.shape[0], 3), tot_obj_rot) + tot_obj_transl.reshape(tot_obj_transl.shape[0], 1, tot_obj_transl.shape[1]) | |
| # ### get joints_trans and base_pts_trans ### | |
| # ### calculate moving consistency ### # moving # | |
| # consistency_value = calculate_moving_consistency(base_pts_trans, joints_trans) | |
| # print(f"smoothness: {smoothness}, average penetration depth: {APD}, minn_dist_dist: {dist_dist_avg}, consistency_value: {consistency_value}") | |
| tot_APD.append(APD) | |
| print(f"APD: {APD}") | |
| tot_metrics.append((jts_acc, APD)) | |
| # tot_smoothness.append(smoothness) | |
| # tot_proximity_error.append(dist_dist_avg) | |
| # tot_consistency_value.append(consistency_value) | |
| if use_toch: | |
| break | |
| # print(f"for sequence {test_seq_idx}, APD: {APD}, smoothness: {smoothness}") | |
| # tot_metricstot_metrics # | |
| tot_metrics = sorted(tot_metrics, key=lambda ii: ii[0], reverse=False) | |
| if len(tot_jts_acc) == 0: | |
| continue | |
| minn_acc = tot_metrics[0][0] | |
| # minn_acc = min(tot_jts_acc) | |
| all_seq_tot_metrics.append(tot_metrics) | |
| # minn_acc = sum(tot_jts_acc) / float(len(tot_jts_acc)) | |
| all_seqs_jts_acc.append(minn_acc) | |
| avg_max_penetration_depth.append(tot_metrics[0][1]) | |
| avg_acc = sum(all_seqs_jts_acc) / float(len(all_seqs_jts_acc)) | |
| avg_avg_max_penetration_depth = sum(avg_max_penetration_depth) / float(len(avg_max_penetration_depth)) | |
| print(f"cur_pred_value, cur_seq_acc: {minn_acc}, tot_seq_acc: {avg_acc}, avg_avg_max_penetration_depth: {avg_avg_max_penetration_depth}") | |
| avg_acc = sum(all_seqs_jts_acc) / float(len(all_seqs_jts_acc)) | |
| avg_avg_max_penetration_depth = sum(avg_max_penetration_depth) / float(len(avg_max_penetration_depth)) | |
| print(f"avg_acc: {avg_acc}, avg_avg_max_penetration_depth: {avg_avg_max_penetration_depth}") | |
| # all_seq_tot_metrics # all seq tot metrics ## | |
| # /home/xueyi/sim/motion-diffusion-model/utils/test_utils_bundle_ours_rndseeds.py | |
| np.save(f"all_seq_tot_metrics.npy", all_seq_tot_metrics) # | |
| print(f"tot metrics saved to all_seq_tot_metrics.npy") # # seq_tot_metrics ### | |
| # np.save("all_seqs_sv_data.npy", all_seqs_sv_data) | |
| # avg_APD = sum(tot_APD) / float(len(tot_APD)) ## total penetration depth | |
| # avg_smoothness = sum(tot_smoothness) / float(len(tot_smoothness)) | |
| # avg_proximity_error = sum(tot_proximity_error) / float(len(tot_proximity_error)) | |
| # avg_consistency_value = sum(tot_consistency_value) / float(len(tot_consistency_value)) | |
| # if len(tot_stability) > 0: | |
| # avg_stability = sum(tot_stability) / float(len(tot_stability)) | |
| # print(f"avg_Stability: {avg_stability}") | |
| # print(f"avg_APD: {avg_APD}, avg_smoothness: {avg_smoothness}, avg_proximity_error: {avg_proximity_error}, avg_consistency_value: {avg_consistency_value}") | |
| # print(tot_APD) | |
| # print(tot_smoothness) | |
| # print(tot_proximity_error) | |
| # print(tot_consistency_value) | |
| # print(f"avg_APD: {avg_APD}, avg_smoothness: {avg_smoothness}, avg_proximity_error: {avg_proximity_error}, avg_consistency_value: {avg_consistency_value}") | |
| # /home/xueyi/sim/motion-diffusion-model/utils/test_utils_bundle_ours_rndseeds.py | |
| # /home/xueyi/sim/motion-diffusion-model/utils/test_utils_bundle_only_jts.py | |
| # predicted_info_fn = "/home/xueyi/sim/motion-diffusion-model/save/my_humanml_trans_enc_512/predicted_infos_jts.npy" | |
| # data = np.load(predicted_info_fn, allow_pickle=True).item() | |
| # # targets = data['targets'] | |
| # outputs = data['outputs'] | |
| # obj_verts = data['obj_verts'][0] | |
| # obj_faces = data['obj_faces'] | |
| # tot_base_pts = data["tot_base_pts"][0] # total base points # bsz x nnbasepts x 3 # | |
| # gt_joints = data['tot_gt_rhand_joints'][0] | |
| # # tot_rhand_joints = data["tot_rhand_joints"][0] | |
| # print(f"gt_joints: {gt_joints.shape}") | |
| # smoothness = calculate_joint_smoothness(outputs) | |
| # APD = calculate_penetration_depth(outputs, obj_verts, obj_faces) | |
| # dist_dist_avg = calculate_proximity_dist(outputs, gt_joints, obj_verts, obj_faces) | |
| # print(f"smoothness: {smoothness}, average penetration depth: {APD}, minn_dist_dist: {dist_dist_avg}") | |