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on
L40S
Running
on
L40S
File size: 2,588 Bytes
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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from .utils import to_tensor
class VertexJointSelector(nn.Module):
def __init__(self,
vertex_ids=None,
use_hands=True,
use_feet_keypoints=True,
**kwargs):
super(VertexJointSelector, self).__init__()
extra_joints_idxs = []
face_keyp_idxs = np.array([
vertex_ids['nose'], vertex_ids['reye'], vertex_ids['leye'],
vertex_ids['rear'], vertex_ids['lear']
],
dtype=np.int64)
extra_joints_idxs = np.concatenate([extra_joints_idxs, face_keyp_idxs])
if use_feet_keypoints:
feet_keyp_idxs = np.array([
vertex_ids['LBigToe'], vertex_ids['LSmallToe'],
vertex_ids['LHeel'], vertex_ids['RBigToe'],
vertex_ids['RSmallToe'], vertex_ids['RHeel']
],
dtype=np.int32)
extra_joints_idxs = np.concatenate(
[extra_joints_idxs, feet_keyp_idxs])
if use_hands:
self.tip_names = ['thumb', 'index', 'middle', 'ring', 'pinky']
tips_idxs = []
for hand_id in ['l', 'r']:
for tip_name in self.tip_names:
tips_idxs.append(vertex_ids[hand_id + tip_name])
extra_joints_idxs = np.concatenate([extra_joints_idxs, tips_idxs])
self.register_buffer('extra_joints_idxs',
to_tensor(extra_joints_idxs, dtype=torch.long))
def forward(self, vertices, joints):
extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs)
joints = torch.cat([joints, extra_joints], dim=1)
return joints
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