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coslam_mp.py
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coslam_mp.py
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import os
#os.environ['TCNN_CUDA_ARCHITECTURES'] = '86'
# Package imports
import torch
import torch.optim as optim
import numpy as np
import random
import torch.nn.functional as F
import argparse
import shutil
import json
import time
from torch.utils.data import DataLoader
from tqdm import tqdm
# Local imports
import config
from model.scene_rep import JointEncoding
from model.keyframe import KeyFrameDatabase
from datasets.dataset import get_dataset
from utils import coordinates, extract_mesh
from tools.eval_ate import pose_evaluation
from optimization.utils import at_to_transform_matrix, qt_to_transform_matrix, matrix_to_axis_angle, matrix_to_quaternion
# Multiprocessing imports
import torch.multiprocessing as mp
mp.set_sharing_strategy('file_system')
from mp_slam.tracker import Tracker
from mp_slam.mapper import Mapper
class CoSLAM():
def __init__(self, config):
self.config = config
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.dataset = get_dataset(config)
self.create_bounds()
self.create_pose_data()
self.get_pose_representation()
try:
mp.set_start_method('spawn', force=True)
except RuntimeError:
pass
self.create_share_data()
self.keyframeDatabase = self.create_kf_database(config)
self.model = JointEncoding(config, self.bounding_box).to(self.device).share_memory()
self.create_optimizer()
self.tracker = Tracker(config, self)
self.mapper = Mapper(config, self)
def pose_eval_func(self):
return pose_evaluation
def create_share_data(self):
self.create_pose_data()
self.mapping_first_frame = torch.zeros((1)).int().share_memory_()
self.mapping_idx = torch.zeros((1)).share_memory_()
self.tracking_idx = torch.zeros((1)).share_memory_()
def seed_everything(self, seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def get_pose_representation(self):
'''
Get the pose representation axis-angle or quaternion
'''
if self.config['training']['rot_rep'] == 'axis_angle':
self.matrix_to_tensor = matrix_to_axis_angle
self.matrix_from_tensor = at_to_transform_matrix
elif self.config['training']['rot_rep'] == "quat":
print("Using quaternion as rotation representation")
self.matrix_to_tensor = matrix_to_quaternion
self.matrix_from_tensor = qt_to_transform_matrix
else:
raise NotImplementedError
def create_pose_data(self):
'''
Create the pose data
'''
num_frames = self.dataset.num_frames
self.est_c2w_data = torch.zeros((num_frames, 4, 4)).to(self.device).share_memory_()
self.est_c2w_data_rel = torch.zeros((num_frames, 4, 4)).to(self.device).share_memory_()
self.load_gt_pose()
def create_bounds(self):
'''
Get the pre-defined bounds for the scene
'''
self.bounding_box = torch.from_numpy(np.array(self.config['mapping']['bound'])).to(torch.float32).to(self.device)
self.marching_cube_bound = torch.from_numpy(np.array(self.config['mapping']['marching_cubes_bound'])).to(torch.float32).to(self.device)
def create_kf_database(self, config):
'''
Create the keyframe database
'''
num_kf = int(self.dataset.num_frames // self.config['mapping']['keyframe_every'] + 1)
print('#kf:', num_kf)
print('#Pixels to save:', self.dataset.num_rays_to_save)
return KeyFrameDatabase(config,
self.dataset.H,
self.dataset.W,
num_kf,
self.dataset.num_rays_to_save,
self.device)
def load_gt_pose(self):
'''
Load the ground truth pose
'''
self.pose_gt = torch.zeros((self.dataset.num_frames, 4, 4))
for i, pose in enumerate(self.dataset.poses):
self.pose_gt[i] = pose
def save_state_dict(self, save_path):
torch.save(self.model.state_dict(), save_path)
def load(self, load_path):
self.model.load_state_dict(torch.load(load_path))
def save_ckpt(self, save_path):
'''
Save the model parameters and the estimated pose
'''
save_dict = {'pose': self.est_c2w_data,
'pose_rel': self.est_c2w_data_rel,
'model': self.model.state_dict()}
torch.save(save_dict, save_path)
print('Save the checkpoint')
def load_ckpt(self, load_path):
'''
Load the model parameters and the estimated pose
'''
dict = torch.load(load_path)
self.model.load_state_dict(dict['model'])
self.est_c2w_data = dict['pose']
self.est_c2w_data_rel = dict['pose_rel']
def select_samples(self, H, W, samples):
'''
randomly select samples from the image
'''
#indice = torch.randint(H*W, (samples,))
indice = random.sample(range(H * W), int(samples))
indice = torch.tensor(indice)
return indice
def get_loss_from_ret(self, ret, rgb=True, sdf=True, depth=True, fs=True, smooth=False):
'''
Get the training loss
'''
loss = 0
if rgb:
loss += self.config['training']['rgb_weight'] * ret['rgb_loss']
if depth:
loss += self.config['training']['depth_weight'] * ret['depth_loss']
if sdf:
loss += self.config['training']['sdf_weight'] * ret["sdf_loss"]
if fs:
loss += self.config['training']['fs_weight'] * ret["fs_loss"]
if smooth:
loss += self.config['training']['smooth_weight'] * self.smoothness(self.config['training']['smooth_pts'],
self.config['training']['smooth_vox'],
margin=self.config['training']['smooth_margin'])
return loss
def smoothness(self, sample_points=256, voxel_size=0.1, margin=0.05, color=False):
'''
Smoothness loss of feature grid
'''
volume = self.bounding_box[:, 1] - self.bounding_box[:, 0]
grid_size = (sample_points-1) * voxel_size
offset_max = self.bounding_box[:, 1]-self.bounding_box[:, 0] - grid_size - 2 * margin
offset = torch.rand(3).to(offset_max) * offset_max + margin
coords = coordinates(sample_points - 1, 'cpu', flatten=False).float().to(volume)
pts = (coords + torch.rand((1,1,1,3)).to(volume)) * voxel_size + self.bounding_box[:, 0] + offset
if self.config['grid']['tcnn_encoding']:
pts_tcnn = (pts - self.bounding_box[:, 0]) / (self.bounding_box[:, 1] - self.bounding_box[:, 0])
sdf = self.model.query_sdf(pts_tcnn, embed=True)
tv_x = torch.pow(sdf[1:,...]-sdf[:-1,...], 2).sum()
tv_y = torch.pow(sdf[:,1:,...]-sdf[:,:-1,...], 2).sum()
tv_z = torch.pow(sdf[:,:,1:,...]-sdf[:,:,:-1,...], 2).sum()
loss = (tv_x + tv_y + tv_z)/ (sample_points**3)
return loss
def get_rays_from_batch(self, batch, c2w_est, indices):
'''
Get the rays from the batch
Params:
batch['c2w']: [1, 4, 4]
batch['rgb']: [1, H, W, 3]
batch['depth']: [1, H, W, 1]
batch['direction']: [1, H, W, 3]
c2w_est: [4, 4]
indices: [N]
Returns:
rays_o: [N, 3]
rays_d: [N, 3]
target_s: [N, 3]
target_d: [N, 1]
c2w_gt: [4, 4]
'''
rays_d_cam = batch['direction'].reshape(-1, 3)[indices].to(self.device)
target_s = batch['rgb'].reshape(-1, 3)[indices].to(self.device)
target_d = batch['depth'].reshape(-1, 1)[indices].to(self.device)
rays_d = torch.sum(rays_d_cam[..., None, :] * c2w_est[:3, :3], -1)
rays_o = c2w_est[None, :3, -1].repeat(rays_d.shape[0], 1)
c2w_gt = batch['c2w'][0].to(self.device)
if torch.sum(torch.isnan(rays_d_cam)):
print('warning rays_d_cam')
if torch.sum(torch.isnan(c2w_est)):
print('warning c2w_est')
return rays_o, rays_d, target_s, target_d, c2w_gt
def update_pose_array(self, frame_id):
if torch.sum(torch.isnan(self.est_c2w_data[frame_id])):
print('tracking warning')
self.model.pose_array.add_params(self.est_c2w_data[frame_id].to(self.device), frame_id)
def convert_relative_pose(self):
poses = {}
for i in range(len(self.est_c2w_data)):
if i % self.config['mapping']['keyframe_every'] == 0:
poses[i] = self.est_c2w_data[i]
else:
kf_id = i // self.config['mapping']['keyframe_every']
kf_frame_id = kf_id * self.config['mapping']['keyframe_every']
c2w_key = self.est_c2w_data[kf_frame_id]
delta = self.est_c2w_data_rel[i]
poses[i] = delta @ c2w_key
return poses
def create_optimizer(self):
'''
Create optimizer for mapping
'''
trainable_parameters = [{'params': self.model.decoder.parameters(), 'weight_decay': 1e-6, 'lr': self.config['mapping']['lr_decoder']},
{'params': self.model.embed_fn.parameters(), 'eps': 1e-15, 'lr': self.config['mapping']['lr_embed']}]
if not self.config['grid']['oneGrid']:
trainable_parameters.append({'params': self.model.embed_fn_color.parameters(), 'eps': 1e-15, 'lr': self.config['mapping']['lr_embed_color']})
self.map_optimizer = optim.Adam(trainable_parameters, betas=(0.9, 0.99))
def save_mesh(self, i, voxel_size=0.05):
mesh_savepath = os.path.join(self.config['data']['output'], self.config['data']['exp_name'], 'mesh_track{}.ply'.format(i))
if self.config['mesh']['render_color']:
color_func = self.model.render_surface_color
else:
color_func = self.model.query_color
extract_mesh(self.model.query_sdf,
self.config,
self.bounding_box,
color_func=color_func,
marching_cube_bound=self.marching_cube_bound,
voxel_size=voxel_size,
mesh_savepath=mesh_savepath)
def get_pose_param_optim(self, poses, mapping=True):
task = 'mapping' if mapping else 'tracking'
cur_trans = torch.nn.parameter.Parameter(poses[:, :3, 3])
cur_rot = torch.nn.parameter.Parameter(self.matrix_to_tensor(poses[:, :3, :3]))
pose_optimizer = torch.optim.Adam([{"params": cur_rot, "lr": self.config[task]['lr_rot']},
{"params": cur_trans, "lr": self.config[task]['lr_trans']}])
return cur_rot, cur_trans, pose_optimizer
def tracking(self, rank):
while True:
if self.mapping_first_frame[0] == 1:
print('Start tracking')
break
time.sleep(0.5)
self.tracker.run()
def mapping(self, rank):
self.mapper.run()
def run(self):
processes = []
for rank in range(2):
if rank == 1:
p = mp.Process(target=self.tracking, args=(rank, ))
elif rank == 0:
p = mp.Process(target=self.mapping, args=(rank, ))
time.sleep(2)
p.start()
processes.append(p)
for p in processes:
p.join()
if __name__ == '__main__':
print('Start running...')
parser = argparse.ArgumentParser(
description='Arguments for running the NICE-SLAM/iMAP*.'
)
parser.add_argument('--config', type=str, help='Path to config file.')
parser.add_argument('--input_folder', type=str,
help='input folder, this have higher priority, can overwrite the one in config file')
parser.add_argument('--output', type=str,
help='output folder, this have higher priority, can overwrite the one in config file')
args = parser.parse_args()
cfg = config.load_config(args.config)
if args.output is not None:
cfg['data']['output'] = args.output
print("Saving config and script...")
save_path = os.path.join(cfg["data"]["output"], cfg['data']['exp_name'])
if not os.path.exists(save_path):
os.makedirs(save_path)
shutil.copy("coslam.py", os.path.join(save_path, 'coslam.py'))
with open(os.path.join(save_path, 'config.json'),"w", encoding='utf-8') as f:
f.write(json.dumps(cfg, indent=4))
slam = CoSLAM(cfg)
slam.run()