Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #11 from sebftw/patch-1
Faster pytorch batched version.
- Loading branch information
Showing
1 changed file
with
249 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,249 @@ | ||
# 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).tiny), 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) | ||
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) | ||
Jtr = self.regress_joints(v) | ||
|
||
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) | ||
|