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SMIL_torch_batch.py
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SMIL_torch_batch.py
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# 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)