/
smpl_torch.py
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/
smpl_torch.py
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import numpy as np
import pickle
import torch
from torch.nn import Module
import os
class SMPLModel(Module):
def __init__(self, device=None, model_path='./model.pkl'):
super(SMPLModel, self).__init__()
with open(model_path, 'rb') as f:
params = pickle.load(f)
self.J_regressor = torch.from_numpy(
np.array(params['J_regressor'].todense())
).type(torch.float64)
self.weights = torch.from_numpy(params['weights']).type(torch.float64)
self.posedirs = torch.from_numpy(params['posedirs']).type(torch.float64)
self.v_template = torch.from_numpy(params['v_template']).type(torch.float64)
self.shapedirs = torch.from_numpy(params['shapedirs']).type(torch.float64)
self.kintree_table = params['kintree_table']
self.faces = params['f']
self.device = device if device is not None else torch.device('cpu')
for name in ['J_regressor', 'weights', 'posedirs', 'v_template', 'shapedirs']:
_tensor = getattr(self, name)
setattr(self, name, _tensor.to(device))
@staticmethod
def rodrigues(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 [batch_size, 1, 3].
Return:
-------
Rotation matrix of shape [batch_size, 3, 3].
"""
#r = r.to(self.device)
eps = r.clone().normal_(std=1e-8)
theta = torch.norm(r + eps, dim=(1, 2), keepdim=True) # dim cannot be tuple
theta_dim = theta.shape[0]
r_hat = r / theta
cos = torch.cos(theta)
z_stick = torch.zeros(theta_dim, dtype=torch.float64).to(r.device)
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 = torch.reshape(m, (-1, 3, 3))
i_cube = (torch.eye(3, dtype=torch.float64).unsqueeze(dim=0) \
+ torch.zeros((theta_dim, 3, 3), dtype=torch.float64)).to(r.device)
A = r_hat.permute(0, 2, 1)
dot = torch.matmul(A, r_hat)
R = cos * i_cube + (1 - cos) * dot + torch.sin(theta) * m
return R
@staticmethod
def with_zeros(x):
"""
Append a [0, 0, 0, 1] tensor to a [3, 4] tensor.
Parameter:
---------
x: Tensor to be appended.
Return:
------
Tensor after appending of shape [4,4]
"""
ones = torch.tensor([[0.0, 0.0, 0.0, 1.0]], dtype=torch.float64).to(x.device)
ret = torch.cat((x, ones), dim=0)
return ret
@staticmethod
def pack(x):
"""
Append zero tensors of shape [4, 3] to a batch of [4, 1] shape tensor.
Parameter:
----------
x: A tensor of shape [batch_size, 4, 1]
Return:
------
A tensor of shape [batch_size, 4, 4] after appending.
"""
zeros43 = torch.zeros((x.shape[0], 4, 3), dtype=torch.float64).to(x.device)
ret = torch.cat((zeros43, x), dim=2)
return ret
def write_obj(self, verts, file_name):
with open(file_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.faces + 1:
fp.write('f %d %d %d\n' % (f[0], f[1], f[2]))
def forward(self, betas, pose, trans, simplify=False):
"""
Construct a compute graph that takes in parameters and outputs a tensor as
model vertices. Face indices are also returned as a numpy ndarray.
Prameters:
---------
pose: Also known as 'theta', a [24,3] tensor indicating child joint rotation
relative to parent joint. For root joint it's global orientation.
Represented in a axis-angle format.
betas: Parameter for model shape. A tensor of shape [10] as coefficients of
PCA components. Only 10 components were released by SMPL author.
trans: Global translation tensor of shape [3].
Return:
------
A tensor for vertices, and a numpy ndarray as face indices.
"""
id_to_col = {self.kintree_table[1, i]: i
for i in range(self.kintree_table.shape[1])}
parent = {
i: id_to_col[self.kintree_table[0, i]]
for i in range(1, self.kintree_table.shape[1])
}
v_shaped = torch.tensordot(self.shapedirs, betas, dims=([2], [0])) + self.v_template
J = torch.matmul(self.J_regressor, v_shaped)
R_cube_big = self.rodrigues(pose.view(-1, 1, 3))
if simplify:
v_posed = v_shaped
else:
R_cube = R_cube_big[1:]
I_cube = (torch.eye(3, dtype=torch.float64).unsqueeze(dim=0) + \
torch.zeros((R_cube.shape[0], 3, 3), dtype=torch.float64)).to(self.device)
lrotmin = torch.reshape(R_cube - I_cube, (-1, 1)).squeeze()
v_posed = v_shaped + torch.tensordot(self.posedirs, lrotmin, dims=([2], [0]))
results = []
results.append(
self.with_zeros(torch.cat((R_cube_big[0], torch.reshape(J[0, :], (3, 1))), dim=1))
)
for i in range(1, self.kintree_table.shape[1]):
results.append(
torch.matmul(
results[parent[i]],
self.with_zeros(
torch.cat(
(R_cube_big[i], torch.reshape(J[i, :] - J[parent[i], :], (3, 1))),
dim=1
)
)
)
)
stacked = torch.stack(results, dim=0)
results = stacked - \
self.pack(
torch.matmul(
stacked,
torch.reshape(
torch.cat((J, torch.zeros((24, 1), dtype=torch.float64).to(self.device)), dim=1),
(24, 4, 1)
)
)
)
T = torch.tensordot(self.weights, results, dims=([1], [0]))
rest_shape_h = torch.cat(
(v_posed, torch.ones((v_posed.shape[0], 1), dtype=torch.float64).to(self.device)), dim=1
)
v = torch.matmul(T, torch.reshape(rest_shape_h, (-1, 4, 1)))
v = torch.reshape(v, (-1, 4))[:, :3]
result = v + torch.reshape(trans, (1, 3))
return result
def test_gpu(gpu_id=[0]):
if len(gpu_id) > 0 and torch.cuda.is_available():
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id[0])
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(device)
pose_size = 72
beta_size = 10
np.random.seed(9608)
pose = torch.from_numpy((np.random.rand(pose_size) - 0.5) * 0.4)\
.type(torch.float64).to(device)
betas = torch.from_numpy((np.random.rand(beta_size) - 0.5) * 0.06) \
.type(torch.float64).to(device)
trans = torch.from_numpy(np.zeros(3)).type(torch.float64).to(device)
outmesh_path = './smpl_torch.obj'
model = SMPLModel(device=device)
result = model(betas, pose, trans)
model.write_obj(result, outmesh_path)
if __name__ == '__main__':
test_gpu([1])