Spaces:
Sleeping
Sleeping
File size: 6,517 Bytes
4409449 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
# -*- 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©2020 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
import contextlib
from typing import Optional
import torch
from einops import rearrange
from torch import Tensor
from mGPT.utils.joints import smplh_to_mmm_scaling_factor
from mGPT.utils.joints import smplh2mmm_indexes
from .base import Rots2Joints
def slice_or_none(data, cslice):
if data is None:
return data
else:
return data[cslice]
class SMPLH(Rots2Joints):
def __init__(self,
path: str,
jointstype: str = "mmm",
input_pose_rep: str = "matrix",
batch_size: int = 512,
gender="neutral",
**kwargs) -> None:
super().__init__(path=None, normalization=False)
self.batch_size = batch_size
self.input_pose_rep = input_pose_rep
self.jointstype = jointstype
self.training = False
from smplx.body_models import SMPLHLayer
import os
# rel_p = path.split('/')
# rel_p = rel_p[rel_p.index('data'):]
# rel_p = '/'.join(rel_p)
# Remove annoying print
with contextlib.redirect_stdout(None):
self.smplh = SMPLHLayer(path, ext="pkl", gender=gender).eval()
self.faces = self.smplh.faces
for p in self.parameters():
p.requires_grad = False
def train(self, *args, **kwargs):
return self
def forward(self,
smpl_data: dict,
jointstype: Optional[str] = None,
input_pose_rep: Optional[str] = None,
batch_size: Optional[int] = None) -> Tensor:
# Take values from init if not specified there
jointstype = self.jointstype if jointstype is None else jointstype
batch_size = self.batch_size if batch_size is None else batch_size
input_pose_rep = self.input_pose_rep if input_pose_rep is None else input_pose_rep
if input_pose_rep == "xyz":
raise NotImplementedError(
"You should use identity pose2joints instead")
poses = smpl_data.rots
trans = smpl_data.trans
from functools import reduce
import operator
save_shape_bs_len = poses.shape[:-3]
nposes = reduce(operator.mul, save_shape_bs_len, 1)
if poses.shape[-3] == 52:
nohands = False
elif poses.shape[-3] == 22:
nohands = True
else:
raise NotImplementedError("Could not parse the poses.")
# Convert any rotations to matrix
# from temos.tools.easyconvert import to_matrix
# matrix_poses = to_matrix(input_pose_rep, poses)
matrix_poses = poses
# Reshaping
matrix_poses = matrix_poses.reshape((nposes, *matrix_poses.shape[-3:]))
global_orient = matrix_poses[:, 0]
if trans is None:
trans = torch.zeros((*save_shape_bs_len, 3),
dtype=poses.dtype,
device=poses.device)
trans_all = trans.reshape((nposes, *trans.shape[-1:]))
body_pose = matrix_poses[:, 1:22]
if nohands:
left_hand_pose = None
right_hand_pose = None
else:
hand_pose = matrix_poses[:, 22:]
left_hand_pose = hand_pose[:, :15]
right_hand_pose = hand_pose[:, 15:]
n = len(body_pose)
outputs = []
for chunk in range(int((n - 1) / batch_size) + 1):
chunk_slice = slice(chunk * batch_size, (chunk + 1) * batch_size)
smpl_output = self.smplh(
global_orient=slice_or_none(global_orient, chunk_slice),
body_pose=slice_or_none(body_pose, chunk_slice),
left_hand_pose=slice_or_none(left_hand_pose, chunk_slice),
right_hand_pose=slice_or_none(right_hand_pose, chunk_slice),
transl=slice_or_none(trans_all, chunk_slice))
if jointstype == "vertices":
output_chunk = smpl_output.vertices
else:
joints = smpl_output.joints
output_chunk = joints
outputs.append(output_chunk)
outputs = torch.cat(outputs)
outputs = outputs.reshape((*save_shape_bs_len, *outputs.shape[1:]))
# Change topology if needed
outputs = smplh_to(jointstype, outputs, trans)
return outputs
def inverse(self, joints: Tensor) -> Tensor:
raise NotImplementedError("Cannot inverse SMPLH layer.")
def smplh_to(jointstype, data, trans):
from mGPT.utils.joints import get_root_idx
if "mmm" in jointstype:
from mGPT.utils.joints import smplh2mmm_indexes
indexes = smplh2mmm_indexes
data = data[..., indexes, :]
# make it compatible with mmm
if jointstype == "mmm":
from mGPT.utils.joints import smplh_to_mmm_scaling_factor
data *= smplh_to_mmm_scaling_factor
if jointstype == "smplmmm":
pass
elif jointstype in ["mmm", "mmmns"]:
# swap axis
data = data[..., [1, 2, 0]]
# revert left and right
data[..., 2] = -data[..., 2]
elif jointstype == "smplnh":
from mGPT.utils.joints import smplh2smplnh_indexes
indexes = smplh2smplnh_indexes
data = data[..., indexes, :]
elif jointstype == "smplh":
pass
elif jointstype == "vertices":
pass
else:
raise NotImplementedError(f"SMPLH to {jointstype} is not implemented.")
if jointstype != "vertices":
# shift the output in each batch
# such that it is centered on the pelvis/root on the first frame
root_joint_idx = get_root_idx(jointstype)
shift = trans[..., 0, :] - data[..., 0, root_joint_idx, :]
data += shift[..., None, None, :]
return data
|