IndexError: tensors used as indices must be long, byte or bool tensors

[Seeseallllll]

Hi. when I

python -m mhmocap.predict_mupots --configs_yml D:\Seesea\human\sa-3d-mh\configs\predict_mupots.yml --ts_id 1 --num_iter 100 --output_path D:\Seesea\human\sa-3d-mh\data\mupots-3d-eval\TS1\output\result_output

i got this error:

Info: writing output to D:\Seesea\human\sa-3d-mh\data\mupots-3d-eval\TS1\output\result_output\TS1 DEBUG:: joint_confidence_thr>> 0.5 DEBUG:: erode_segmentation_iters>> 0 DEBUG:: erode_backmask_iters>> 5 DEBUG:: renormalize_depth>> True DEBUG:: post_process_depth>> True DEBUG:: H3DHCustomSequenceData DEBUG:: erode_segmentation_iters 0 DEBUG:: erode_backmask_iters 5 DEBUG:: use_hrnet_pose False DEBUG:: joint_coef_thr 0.5 DEBUG:: max_num_people None Images_path: ./data/mupots-3d-eval/TS1\images Image data: (201, 256, 256, 3) 0 255 Depth data: (201, 256, 256) 0.0 1.0 Segmentation data: (201, 256, 256) 0 4 Background mask data: (201, 256, 256) 0 1 ROMP predictions: 201 dict_keys(['cam', 'poses', 'betas']) Found 201 images with predictions from AlphaPose with idx: [1, 2, 3, 4, 5, 6] AlphaPose:: found max 4 predictions per frame from AlphaPose! AlphaPose data: (201, 4, 17, 3) DEBUG:: pvis [1. 1. 1. 0.] threshold is 0.125 ROMP predictions (final): 201 dict_keys(['cam', 'poses', 'betas', 'valid']) Filtering 2D poses with One-Euro filter. DEBUG:: H3DHCustomSequenceData: using cam: {'K': array([[1.8784500e+02, 0.0000000e+00, 1.2877887e+02], [0.0000000e+00, 1.8923062e+02, 1.2986162e+02], [0.0000000e+00, 0.0000000e+00, 1.2500000e-01]], dtype=float32), 'fov': 68.54204142245207, 'Kd': None, 'image_size': (256, 256)} 0%| | 0/201 [00:00<?, ?it/s] 0%| | 0/100 [00:09<?, ?it/s] Traceback (most recent call last): File "D:\Anaconda3\lib\runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "D:\Anaconda3\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "D:\Seesea\human\sa-3d-mh\mhmocap\predict_mupots.py", line 100, in log = predictor.run() File "D:\Seesea\human\sa-3d-mh\mhmocap\predict.py", line 343, in run log = self.optim_smpl.fit(self.dataloader, num_iter=self.num_iter, verbose=True) File "D:\Seesea\human\sa-3d-mh\mhmocap\optimizer.py", line 401, in fit idx_var = self.__eval_batch_optimized_variables(idx_data['idxs']) File "D:\Seesea\human\sa-3d-mh\mhmocap\optimizer.py", line 683, in __eval_batch_optimized_variables idx_var['min_z'] = softplus(self.zmin_lin[idxs]) IndexError: tensors used as indices must be long, byte or bool tensors

Then I try to change the code in optimizer.py,like this

def __eval_batch_optimized_variables(self, idxs):
    idx_var = {} # Indexed variables
    batch_size = idxs.shape[0]
    idx_var['scale_factor'] = torch.pow(1.1, self.xscale_factor)

    #修改idx_var['min_z'] = softplus(self.zmin_lin[idxs])
    idx_var['min_z'] = softplus(self.zmin_lin[idxs.long()])


    idx_var['max_z'] = (
        idx_var['min_z'].detach().clone()
        + self.min_delta_z
        #修改+ softplus(self.zmax_lin[idxs])
        + softplus(self.zmax_lin[idxs.long()])
     ) # (batch, 1, 1)

    #修改idx_var['poses_smpl'] = self.poses_smpl[idxs].view(-1, 72)
    idx_var['poses_smpl'] = self.poses_smpl[idxs.long()].view(-1, 72)
    idx_var['betas_smpl'] = self.betas_smpl.tile((batch_size, 1, 1)).view(-1, 10)
    #修改idx_var['valid_smpl'] = self.valid_smpl[idxs]
    idx_var['valid_smpl'] = self.valid_smpl[idxs.long()]

    results = self.SMPLPY(betas=idx_var['betas_smpl'], poses=idx_var['poses_smpl'])
    verts = results['verts'].view(batch_size, self.num_people, -1, 3)
    joints_smpl24 = results[self.smpl_sparse_joints_key].view(batch_size, self.num_people, -1, 3)

    idx_var['poses_smpl'] = idx_var['poses_smpl'].view(batch_size, self.num_people, 72)
    idx_var['betas_smpl'] = idx_var['betas_smpl'].view(batch_size, self.num_people, 10)
    #修改idx_var['poses_T'] = self.poses_T[idxs]
    idx_var['poses_T'] = self.poses_T[idxs.long()]

    idx_var['verts_smpl_abs'] = idx_var['scale_factor'] * verts + idx_var['poses_T'] # (batch, N, V, 3)
    idx_var['joints_smpl_abs'] = idx_var['scale_factor'] * joints_smpl24 + idx_var['poses_T'] # (batch, N, J, 3)

    #修改idx_var['intrinsics'] = torch.tile(self.cam_intrinsics[idxs], (1, self.num_people, 1, 1)).view(-1, 3, 3)
    idx_var['intrinsics'] = torch.tile(self.cam_intrinsics[idxs.long()], (1, self.num_people, 1, 1)).view(-1, 3, 3)

after that, i got this

DEBUG:: H3DHCustomSequenceData DEBUG:: erode_segmentation_iters 0 DEBUG:: erode_backmask_iters 5 DEBUG:: use_hrnet_pose False DEBUG:: joint_coef_thr 0.5 DEBUG:: max_num_people None Images_path: ./data/mupots-3d-eval/TS1\images Image data: (201, 256, 256, 3) 0 255 Depth data: (201, 256, 256) 0.0 1.0 Segmentation data: (201, 256, 256) 0 4 Background mask data: (201, 256, 256) 0 1 ROMP predictions: 201 dict_keys(['cam', 'poses', 'betas']) Found 201 images with predictions from AlphaPose with idx: [1, 2, 3, 4, 5, 6] AlphaPose:: found max 4 predictions per frame from AlphaPose! AlphaPose data: (201, 4, 17, 3) DEBUG:: pvis [1. 1. 1. 0.] threshold is 0.125 ROMP predictions (final): 201 dict_keys(['cam', 'poses', 'betas', 'valid']) Filtering 2D poses with One-Euro filter. DEBUG:: H3DHCustomSequenceData: using cam: {'K': array([[1.8784500e+02, 0.0000000e+00, 1.2877887e+02], [0.0000000e+00, 1.8923062e+02, 1.2986162e+02], [0.0000000e+00, 0.0000000e+00, 1.2500000e-01]], dtype=float32), 'fov': 68.54204142245207, 'Kd': None, 'image_size': (256, 256)} WARNING: Variable number of images in the batches. len(dataset)=201, batch_size=10 0%| | 0/201 [00:00<?, ?it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [43:21<00:00, 26.01s/it] D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:157: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(loss_depth), c='b', label='Depth loss') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:159: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_vel), c='darkorange', label='Reg. 3D Pose Velocity') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:160: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_filter_verts), c='darkgreen', label='Reg. 3D Vert. Smooth') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:161: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_ref_poses), c='m', label='Reg. Ref. Poses') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:162: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_scale), c='y', label='Reg. Scale') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:163: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_contact), c='k', label='Reg. Contact') D:\Seesea\human\sa-3d-mh\mhmocap\predict.py:164: RuntimeWarning: divide by zero encountered in log axs.plot(np.log(reg_foot_sliding), c='gold', label='Reg. Food Slid.')

DEBUG:: >> stage1_optvar: >> scale_factor: [[[nan]]

[[nan]]

[[nan]]] >> scene_depth_min / scene_depth_max: [[0.6931472]] [[1.3862944]] 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 201/201 [00:40<00:00, 4.94it/s]

Then I try to vis the result python -m mhmocap.visualization --input_path data\mupots-3d-eval\TS1\output\result_output\TS1 --output_path data\mupots-3d-eval\TS1\output\vis_output

I got this

No human, only scene.

I tried to reinstall it, but the results didn't change. I don't know what the problem is.