-
Notifications
You must be signed in to change notification settings - Fork 138
/
SMIL_torch_batch.py
249 lines (200 loc) · 10.6 KB
/
SMIL_torch_batch.py
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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
# https://github.com/CalciferZh/SMPL
import torch
import torch.nn as nn
import pickle
import numpy as np
import scipy.sparse
class SMIL(nn.Module):
def with_zeros(self, x):
"""
Append a [0, 0, 0, 1] vector to a batch of [3, 4] matrices.
Parameter:
---------
x: Tensor to be appended of shape [N, 3, 4]
Return:
------
Tensor after appending of shape [N, 4, 4]
"""
ret = torch.cat([x, self.e4.expand(x.shape[0], 1, -1)], dim=1)
return ret
def pack(self, x):
"""
Append zero tensors of shape [4, 3] to a batch of [4, 1] shape tensors.
Parameter:
----------
x: A tensor of shape [batch_size, 4, 1]
Return:
------
A tensor of shape [batch_size, 4, 4] after appending.
"""
ret = torch.cat(
(torch.zeros((x.shape[0], x.shape[1], 4, 3), dtype=x.dtype, device=x.device), x),
dim=3
)
return ret
def rodrigues(self, r):
"""
Rodrigues' rotation formula that turns axis-angle tensor into rotation
matrix in a batch-ed manner.
Parameter:
----------
r: Axis-angle rotation tensor of shape [N, 1, 3].
Return:
-------
Rotation matrix of shape [N, 3, 3].
"""
theta = torch.norm(r, dim=(1, 2), keepdim=True)
# avoid division by zero
torch.max(theta, theta.new_full((1,), torch.finfo(theta.dtype).eps), out=theta)
#The .tiny has to be uploaded to GPU, but self.regress_joints is such a big bottleneck it is not felt.
r_hat = r / theta
z_stick = torch.zeros_like(r_hat[:, 0, 0])
m = torch.stack(
(z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1],
r_hat[:, 0, 2], z_stick, -r_hat[:, 0, 0],
-r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick), dim=1)
m = m.reshape(-1, 3, 3)
dot = torch.bmm(r_hat.transpose(1, 2), r_hat) # Batched outer product.
# torch.matmul or torch.stack([torch.ger(r, r) for r in r_hat.squeeze(1)] works too.
cos = theta.cos()
R = cos * self.eye + (1 - cos) * dot + theta.sin() * m
return R
def __init__(self, model_path='./model.pkl', sparse=True):
super().__init__()
self.parent = None
self.model_path = None
if model_path is not None:
with open(model_path, 'rb') as f:
self.model_path = model_path
params = pickle.load(f)
# The first three can be added simply:
registerbuffer = lambda name: self.register_buffer(name,
torch.as_tensor(params[name]))
registerbuffer('weights')
registerbuffer('posedirs')
registerbuffer('v_template')
registerbuffer('shapedirs')
# Now for the more difficult...:
# We have to convert f from uint32 to int32. (This is the indexbuffer)
self.register_buffer('f', torch.as_tensor(params['f'].astype(np.int32)))
self.register_buffer('kintree_table', torch.as_tensor(params['kintree_table'].astype(np.int32)))
# J_regressor is a sparse tensor. This is (experimentally) supported in PyTorch.
J_regressor = params['J_regressor']
if scipy.sparse.issparse(J_regressor):
# If tensor is sparse (Which it is with SMPL/SMIL)
J_regressor = J_regressor.tocoo()
J_regressor = torch.sparse_coo_tensor([J_regressor.row, J_regressor.col],
J_regressor.data,
J_regressor.shape)
if not sparse:
J_regressor = J_regressor.to_dense()
else:
J_regressor = torch.as_tensor(J_regressor)
self.register_buffer('J_regressor', J_regressor)
self.register_buffer('e4', self.posedirs.new_tensor([0, 0, 0, 1])) # Cache this. (Saves a lot of time)
self.register_buffer('eye', torch.eye(3, dtype=self.e4.dtype, device=self.e4.device)) # And this.
self.set_parent()
# Make sure the tree map is reconstructed if/when model is loaded.
self._register_state_dict_hook(self.set_parent)
def set_parent(self, *args, **kwargs):
# Get kintree_table from state dict.
# Make kinematic tree relations.
id_to_col = {self.kintree_table[1, i].item(): i for i in range(self.kintree_table.shape[1])}
self.parent = {
i: id_to_col[self.kintree_table[0, i].item()]
for i in range(1, self.kintree_table.shape[1])
}
# Must return None, since only a state dict or None return value is permitted for state dict hooks.
def save_obj(self, verts, obj_mesh_name):
with open(obj_mesh_name, 'w') as fp:
for v in verts:
fp.write('v %f %f %f\n' % (v[0], v[1], v[2]))
for f in self.f: # Faces are 1-based, not 0-based in obj files
fp.write('f %d %d %d\n' % (f[0] + 1, f[1] + 1, f[2] + 1))
def regress_joints(self, vertices):
"""The J_regressor matrix transforms vertices to joints."""
# Given the template + pose blend shapes.
batch_size = vertices.shape[0]
# We could get the result as torch.matmul(self.J_regressor, vertices) or
# torch.stack([self.J_regressor.mm(verts) for verts in vertices]) in case J_regressor is sparse.
# But turns out there is a solution faster than both of the above:
batch_vertices = vertices.transpose(0, 1).reshape(self.J_regressor.shape[1], -1)
batch_results = self.J_regressor.mm(batch_vertices)
batch_results = batch_results.reshape(self.J_regressor.shape[0], batch_size, -1).transpose(0, 1)
return batch_results
def rotate_translate(self, rotation_matrix, translation):
transform = torch.cat((rotation_matrix, translation.unsqueeze(2)), 2)
return self.with_zeros(transform)
def forward(self, beta, pose, trans=None, simplify=False):
"""This module takes betas and poses in a batched manner.
A pose is 3 * K + 3 (= self.kintree_table.shape[1] * 3) parameters, where K is the number of joints.
A beta is a vector of size self.shapedirs.shape[2], that parameterizes the body shape.
Since this is batched, multiple betas and poses should be concatenated along zeroth dimension.
See http://files.is.tue.mpg.de/black/papers/SMPL2015.pdf for more info.
"""
batch_size = beta.shape[0] # Size of zeroth dimension.
# The body shape is decomposed with principal component analysis from many subjects,
# where self.v_template is the average value. Then shapedirs is a subset of the orthogonal directions, and
# a the betas are the values when the subject is projected onto these. v_shaped is the "restored" subject.
v_shaped = torch.tensordot(beta, self.shapedirs, dims=([1], [2])) + self.v_template
# We turn the rotation vectors into rotation matrices.
R_cube = self.rodrigues(pose.reshape(-1, 1, 3)).reshape(batch_size, -1, 3, 3)
J = self.regress_joints(v_shaped) # Joints in T-pose (for limb lengths)
if not simplify:
# Add pose blend shapes. (How joint angles morphs the surface)
# Now calculate how joints affects the body shape.
lrotmin = R_cube[:, 1:] - self.eye
lrotmin = lrotmin.reshape(batch_size, -1)
v_shaped += torch.tensordot(lrotmin, self.posedirs, dims=([1], [2]))
# Now we have the un-posed body shape. Convert to homogeneous coordinates.
rest_shape_h = torch.cat((v_shaped, v_shaped.new_ones(1).expand(*v_shaped.shape[:-1], 1)), 2)
G = [self.rotate_translate(R_cube[:, 0], J[:, 0])]
for i in range(1, self.kintree_table.shape[1]):
G.append(
torch.bmm(
G[self.parent[i]],
self.rotate_translate(R_cube[:, i], J[:, i] - J[:, self.parent[i]])))
G = torch.stack(G, 1)
Jtr = G[..., :4, 3].clone()
G = G - self.pack(torch.matmul(G, torch.cat([J, J.new_zeros(1).expand(*J.shape[:2], 1)], dim=2).unsqueeze(-1)))
# T = torch.tensordot(self.weights, G, dims=([1], [1]))
# v = T.reshape(-1, 4, 4).bmm(rest_shape_h.reshape(-1, 4, 1)).reshape(batch_size, -1, 4)
# Two next lines are a memory bottleneck.
T = torch.tensordot(G, self.weights, dims=([1], [1])).permute(0, 3, 1, 2)
v = torch.matmul(T, torch.reshape(rest_shape_h, (batch_size, -1, 4, 1))).reshape(batch_size, -1, 4)
if trans is not None:
trans = trans.unsqueeze(1)
v[..., :3] += trans
Jtr[..., :3] += trans
return v, Jtr
def time_numpy(body_model, poses):
return [body_model.set_params(pose.pose, pose.beta) for pose in poses]
def time_pytorch(body_model, betas, poses):
for i in range(100):
v = body_model(vbeta, vpose)
if __name__ == '__main__':
from smil_np import SMILModel
import timeit
# The best configurations are:
# device = cuda, dtype = half, sparse = false
# device = cuda, dtype = float, sparse = true
device = torch.device('cuda') # torch.device('cuda')
dtype = torch.float
sparse = True if dtype is not torch.half else False # sparse Half Tensors are not supported (yet).
SMILNP = SMILModel('./model.pkl')
SMILPY = SMIL('./model.pkl', sparse=sparse).to(device)
SMILPY = SMILPY.to(device, dtype, non_blocking=True)
from file_utils import *
poses = find_mini_rgbd(os.path.join('MINI-RGBD_web'))
poses = [MicroRGBD(pose) for pose in poses[:4000]] # If there is a memory error reduce size here.
vbeta = torch.tensor(np.array([pose.beta for pose in poses])).to(device, dtype, non_blocking=True)
vpose = torch.tensor(np.array([pose.pose for pose in poses])).to(device, dtype, non_blocking=True)
vtrans = torch.tensor(np.array([pose.trans for pose in poses])).to(device, dtype, non_blocking=True)
v, Jtr = SMILPY(vbeta, vpose, vtrans) # Do the thing.
time_pytorch(SMILPY, vbeta, vpose)
# SMILNP.set_params(poses[i].pose, poses[i].beta, poses[i].trans)
# print(Jtr.cpu()[i].numpy() - SMILNP.Jtr, Jtr[i].shape, SMILNP.Jtr.shape) # See if there are any rounding errors.
#with torch.cuda.profiler.profile() as prof:
# SMILPY(vbeta, vpose) # Warmup CUDA memory allocator and profiler
# with torch.autograd.profiler.emit_nvtx():
# SMILPY(vbeta, vpose)