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bf_sdf.py
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bf_sdf.py
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# -----------------------------------------------------------------------------
# SPDX-License-Identifier: MIT
# This file is part of the RDF project.
# Copyright (c) 2023 Idiap Research Institute <[email protected]>
# Contributor: Yimming Li <[email protected]>
# -----------------------------------------------------------------------------
import torch
import os
import numpy as np
np.set_printoptions(threshold=np.inf)
import glob
import trimesh
import utils
import mesh_to_sdf
import skimage
from panda_layer.panda_layer import PandaLayer
import argparse
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
class BPSDF():
def __init__(self, n_func,domain_min,domain_max,robot,device):
self.n_func = n_func
self.domain_min = domain_min
self.domain_max = domain_max
self.device = device
self.robot = robot
self.model_path = os.path.join(CUR_DIR, 'models')
def binomial_coefficient(self, n, k):
return torch.exp(torch.lgamma(n + 1) - torch.lgamma(k + 1) - torch.lgamma(n - k + 1))
def build_bernstein_t(self,t, use_derivative=False):
# t is normalized to [0,1]
t =torch.clamp(t, min=1e-4, max=1-1e-4)
n = self.n_func - 1
i = torch.arange(self.n_func, device=self.device)
comb = self.binomial_coefficient(torch.tensor(n, device=self.device), i)
phi = comb * (1 - t).unsqueeze(-1) ** (n - i) * t.unsqueeze(-1) ** i
if not use_derivative:
return phi.float(),None
else:
dphi = -comb * (n - i) * (1 - t).unsqueeze(-1) ** (n - i - 1) * t.unsqueeze(-1) ** i + comb * i * (1 - t).unsqueeze(-1) ** (n - i) * t.unsqueeze(-1) ** (i - 1)
dphi = torch.clamp(dphi, min=-1e4, max=1e4)
return phi.float(),dphi.float()
def build_basis_function_from_points(self,p,use_derivative=False):
N = len(p)
p = ((p - self.domain_min)/(self.domain_max-self.domain_min)).reshape(-1)
phi,d_phi = self.build_bernstein_t(p,use_derivative)
phi = phi.reshape(N,3,self.n_func)
phi_x = phi[:,0,:]
phi_y = phi[:,1,:]
phi_z = phi[:,2,:]
phi_xy = torch.einsum("ij,ik->ijk",phi_x,phi_y).view(-1,self.n_func**2)
phi_xyz = torch.einsum("ij,ik->ijk",phi_xy,phi_z).view(-1,self.n_func**3)
if use_derivative ==False:
return phi_xyz,None
else:
d_phi = d_phi.reshape(N,3,self.n_func)
d_phi_x_1D= d_phi[:,0,:]
d_phi_y_1D = d_phi[:,1,:]
d_phi_z_1D = d_phi[:,2,:]
d_phi_x = torch.einsum("ij,ik->ijk",torch.einsum("ij,ik->ijk",d_phi_x_1D,phi_y).view(-1,self.n_func**2),phi_z).view(-1,self.n_func**3)
d_phi_y = torch.einsum("ij,ik->ijk",torch.einsum("ij,ik->ijk",phi_x,d_phi_y_1D).view(-1,self.n_func**2),phi_z).view(-1,self.n_func**3)
d_phi_z = torch.einsum("ij,ik->ijk",phi_xy,d_phi_z_1D).view(-1,self.n_func**3)
d_phi_xyz = torch.cat((d_phi_x.unsqueeze(-1),d_phi_y.unsqueeze(-1),d_phi_z.unsqueeze(-1)),dim=-1)
return phi_xyz,d_phi_xyz
def train_bf_sdf(self,epoches=200):
# represent SDF using basis functions
mesh_path = os.path.join(CUR_DIR,"panda_layer/meshes/voxel_128/*")
mesh_files = glob.glob(mesh_path)
mesh_files = sorted(mesh_files)[1:] #except finger
mesh_dict = {}
for i,mf in enumerate(mesh_files):
mesh_name = mf.split('/')[-1].split('.')[0]
mesh = trimesh.load(mf)
offset = mesh.bounding_box.centroid
scale = np.max(np.linalg.norm(mesh.vertices-offset, axis=1))
mesh = mesh_to_sdf.scale_to_unit_sphere(mesh)
mesh_dict[i] = {}
mesh_dict[i]['mesh_name'] = mesh_name
# load data
data = np.load(f'./data/sdf_points/voxel_128_{mesh_name}.npy',allow_pickle=True).item()
point_near_data = data['near_points']
sdf_near_data = data['near_sdf']
point_random_data = data['random_points']
sdf_random_data = data['random_sdf']
sdf_random_data[sdf_random_data <-1] = -sdf_random_data[sdf_random_data <-1]
wb = torch.zeros(self.n_func**3).float().to(self.device)
B = (torch.eye(self.n_func**3)/1e-4).float().to(self.device)
# loss_list = []
for iter in range(epoches):
choice_near = np.random.choice(len(point_near_data),1024,replace=False)
p_near,sdf_near = torch.from_numpy(point_near_data[choice_near]).float().to(self.device),torch.from_numpy(sdf_near_data[choice_near]).float().to(self.device)
choice_random = np.random.choice(len(point_random_data),256,replace=False)
p_random,sdf_random = torch.from_numpy(point_random_data[choice_random]).float().to(self.device),torch.from_numpy(sdf_random_data[choice_random]).float().to(self.device)
p = torch.cat([p_near,p_random],dim=0)
sdf = torch.cat([sdf_near,sdf_random],dim=0)
phi_xyz, _ = self.build_basis_function_from_points(p.float().to(self.device),use_derivative=False)
K = torch.matmul(B,phi_xyz.T).matmul(torch.linalg.inv((torch.eye(len(p)).float().to(self.device)+torch.matmul(torch.matmul(phi_xyz,B),phi_xyz.T))))
B -= torch.matmul(K,phi_xyz).matmul(B)
delta_wb = torch.matmul(K,(sdf - torch.matmul(phi_xyz,wb)).squeeze())
# loss = torch.nn.functional.mse_loss(torch.matmul(phi_xyz,wb).squeeze(), sdf, reduction='mean').item()
# loss_list.append(loss)
wb += delta_wb
print(f'mesh name {mesh_name} finished!')
mesh_dict[i] ={
'mesh_name': mesh_name,
'weights': wb,
'offset': torch.from_numpy(offset),
'scale': scale,
}
if os.path.exists(self.model_path) is False:
os.mkdir(self.model_path)
torch.save(mesh_dict,f'{self.model_path}/BP_{self.n_func}.pt') # save the robot sdf model
print(f'{self.model_path}/BP_{self.n_func}.pt model saved!')
def sdf_to_mesh(self, model, nbData,use_derivative=False):
verts_list, faces_list, mesh_name_list = [], [], []
for i, k in enumerate(model.keys()):
mesh_dict = model[k]
mesh_name = mesh_dict['mesh_name']
print(f'{mesh_name}')
mesh_name_list.append(mesh_name)
weights = mesh_dict['weights'].to(self.device)
domain = torch.linspace(self.domain_min,self.domain_max,nbData).to(self.device)
grid_x, grid_y, grid_z= torch.meshgrid(domain,domain,domain)
grid_x, grid_y, grid_z = grid_x.reshape(-1,1), grid_y.reshape(-1,1), grid_z.reshape(-1,1)
p = torch.cat([grid_x, grid_y, grid_z],dim=1).float().to(self.device)
# split data to deal with memory issues
p_split = torch.split(p, 10000, dim=0)
d =[]
for p_s in p_split:
phi_p,d_phi_p = self.build_basis_function_from_points(p_s,use_derivative)
d_s = torch.matmul(phi_p,weights)
d.append(d_s)
d = torch.cat(d,dim=0)
verts, faces, normals, values = skimage.measure.marching_cubes(
d.view(nbData,nbData,nbData).detach().cpu().numpy(), level=0.0, spacing=np.array([(self.domain_max-self.domain_min)/nbData] * 3)
)
verts = verts - [1,1,1]
verts_list.append(verts)
faces_list.append(faces)
return verts_list, faces_list,mesh_name_list
def create_surface_mesh(self,model, nbData,vis =False, save_mesh_name=None):
verts_list, faces_list,mesh_name_list = self.sdf_to_mesh(model, nbData)
for verts, faces,mesh_name in zip(verts_list, faces_list,mesh_name_list):
rec_mesh = trimesh.Trimesh(verts,faces)
if vis:
rec_mesh.show()
if save_mesh_name != None:
save_path = os.path.join(CUR_DIR,"output_meshes")
if os.path.exists(save_path) is False:
os.mkdir(save_path)
trimesh.exchange.export.export_mesh(rec_mesh, os.path.join(save_path,f"{save_mesh_name}_{mesh_name}.stl"))
def get_whole_body_sdf_batch(self,x,pose,theta,model,use_derivative = True, used_links = [0,1,2,3,4,5,6,7,8],return_index=False):
B = len(theta)
N = len(x)
K = len(used_links)
offset = torch.cat([model[i]['offset'].unsqueeze(0) for i in used_links],dim=0).to(self.device)
offset = offset.unsqueeze(0).expand(B,K,3).reshape(B*K,3).float()
scale = torch.tensor([model[i]['scale'] for i in used_links],device=self.device)
scale = scale.unsqueeze(0).expand(B,K).reshape(B*K).float()
trans_list = self.robot.get_transformations_each_link(pose,theta)
fk_trans = torch.cat([t.unsqueeze(1) for t in trans_list],dim=1)[:,used_links,:,:].reshape(-1,4,4) # B,K,4,4
x_robot_frame_batch = utils.transform_points(x.float(),torch.linalg.inv(fk_trans).float(),device=self.device) # B*K,N,3
x_robot_frame_batch_scaled = x_robot_frame_batch - offset.unsqueeze(1)
x_robot_frame_batch_scaled = x_robot_frame_batch_scaled/scale.unsqueeze(-1).unsqueeze(-1) #B*K,N,3
x_bounded = torch.where(x_robot_frame_batch_scaled>1.0-1e-2,1.0-1e-2,x_robot_frame_batch_scaled)
x_bounded = torch.where(x_bounded<-1.0+1e-2,-1.0+1e-2,x_bounded)
res_x = x_robot_frame_batch_scaled - x_bounded
if not use_derivative:
phi,_ = self.build_basis_function_from_points(x_bounded.reshape(B*K*N,3), use_derivative=False)
phi = phi.reshape(B,K,N,-1).transpose(0,1).reshape(K,B*N,-1) # K,B*N,-1
weights_near = torch.cat([model[i]['weights'].unsqueeze(0) for i in used_links],dim=0).to(self.device)
# sdf
sdf = torch.einsum('ijk,ik->ij',phi,weights_near).reshape(K,B,N).transpose(0,1).reshape(B*K,N) # B,K,N
sdf = sdf + res_x.norm(dim=-1)
sdf = sdf.reshape(B,K,N)
sdf = sdf*scale.reshape(B,K).unsqueeze(-1)
sdf_value, idx = sdf.min(dim=1)
if return_index:
return sdf_value, None, idx
return sdf_value, None
else:
phi,dphi = self.build_basis_function_from_points(x_bounded.reshape(B*K*N,3), use_derivative=True)
phi_cat = torch.cat([phi.unsqueeze(-1),dphi],dim=-1)
phi_cat = phi_cat.reshape(B,K,N,-1,4).transpose(0,1).reshape(K,B*N,-1,4) # K,B*N,-1,4
weights_near = torch.cat([model[i]['weights'].unsqueeze(0) for i in used_links],dim=0).to(self.device)
output = torch.einsum('ijkl,ik->ijl',phi_cat,weights_near).reshape(K,B,N,4).transpose(0,1).reshape(B*K,N,4)
sdf = output[:,:,0]
gradient = output[:,:,1:]
# sdf
sdf = sdf + res_x.norm(dim=-1)
sdf = sdf.reshape(B,K,N)
sdf = sdf*(scale.reshape(B,K).unsqueeze(-1))
sdf_value, idx = sdf.min(dim=1)
# derivative
gradient = res_x + torch.nn.functional.normalize(gradient,dim=-1)
gradient = torch.nn.functional.normalize(gradient,dim=-1).float()
# gradient = gradient.reshape(B,K,N,3)
fk_rotation = fk_trans[:,:3,:3]
gradient_base_frame = torch.einsum('ijk,ikl->ijl',fk_rotation,gradient.transpose(1,2)).transpose(1,2).reshape(B,K,N,3)
# norm_gradient_base_frame = torch.linalg.norm(gradient_base_frame,dim=-1)
# exit()
# print(norm_gradient_base_frame)
idx_grad = idx.unsqueeze(1).unsqueeze(-1).expand(B,K,N,3)
gradient_value = torch.gather(gradient_base_frame,1,idx_grad)[:,0,:,:]
# gradient_value = None
if return_index:
return sdf_value, gradient_value, idx
return sdf_value, gradient_value
def get_whole_body_sdf_with_joints_grad_batch(self,x,pose,theta,model,used_links = [0,1,2,3,4,5,6,7,8]):
delta = 0.001
B = theta.shape[0]
theta = theta.unsqueeze(1)
d_theta = (theta.expand(B,7,7)+ torch.eye(7,device=self.device).unsqueeze(0).expand(B,7,7)*delta).reshape(B,-1,7)
theta = torch.cat([theta,d_theta],dim=1).reshape(B*8,7)
pose = pose.unsqueeze(1).expand(B,8,4,4).reshape(B*8,4,4)
sdf,_ = self.get_whole_body_sdf_batch(x,pose,theta,model,use_derivative = False, used_links = used_links)
sdf = sdf.reshape(B,8,-1)
d_sdf = (sdf[:,1:,:]-sdf[:,:1,:])/delta
return sdf[:,0,:],d_sdf.transpose(1,2)
def get_whole_body_normal_with_joints_grad_batch(self,x,pose,theta,model,used_links = [0,1,2,3,4,5,6,7,8]):
delta = 0.001
B = theta.shape[0]
theta = theta.unsqueeze(1)
d_theta = (theta.expand(B,7,7)+ torch.eye(7,device=self.device).unsqueeze(0).expand(B,7,7)*delta).reshape(B,-1,7)
theta = torch.cat([theta,d_theta],dim=1).reshape(B*8,7)
pose = pose.unsqueeze(1).expand(B,8,4,4).reshape(B*8,4,4)
sdf, normal = self.get_whole_body_sdf_batch(x,pose,theta,model,use_derivative = True, used_links = used_links)
normal = normal.reshape(B,8,-1,3).transpose(1,2)
return normal # normal size: (B,N,8,3) normal[:,:,0,:] origin normal vector normal[:,:,1:,:] derivatives with respect to joints
if __name__ =='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='cuda', type=str)
parser.add_argument('--domain_max', default=1.0, type=float)
parser.add_argument('--domain_min', default=-1.0, type=float)
parser.add_argument('--n_func', default=8, type=int)
parser.add_argument('--train', action='store_true')
args = parser.parse_args()
panda = PandaLayer(args.device)
bp_sdf = BPSDF(args.n_func,args.domain_min,args.domain_max,panda,args.device)
# # train Bernstein Polynomial model
if args.train:
bp_sdf.train_bf_sdf()
# load trained model
model_path = f'models/BP_{args.n_func}.pt'
model = torch.load(model_path)
# visualize the Bernstein Polynomial model for each robot link
bp_sdf.create_surface_mesh(model,nbData=128,vis=True,save_mesh_name=f'BP_{args.n_func}')
# visualize the Bernstein Polynomial model for the whole body
theta = torch.tensor([0, -0.3, 0, -2.2, 0, 2.0, np.pi/4]).float().to(args.device).reshape(-1,7)
pose = torch.from_numpy(np.identity(4)).to(args.device).reshape(-1, 4, 4).expand(len(theta),4,4).float()
trans_list = panda.get_transformations_each_link(pose,theta)
utils.visualize_reconstructed_whole_body(model, trans_list, tag=f'BP_{args.n_func}')
# run RDF
x = torch.rand(128,3).to(args.device)*2.0 - 1.0
theta = torch.rand(2,7).to(args.device).float()
pose = torch.from_numpy(np.identity(4)).unsqueeze(0).to(args.device).expand(len(theta),4,4).float()
sdf,gradient = bp_sdf.get_whole_body_sdf_batch(x,pose,theta,model,use_derivative=True)
print('sdf:',sdf.shape,'gradient:',gradient.shape)
sdf,joint_grad = bp_sdf.get_whole_body_sdf_with_joints_grad_batch(x,pose,theta,model)
print('sdf:',sdf.shape,'joint gradient:',joint_grad.shape)