File size: 15,046 Bytes
4f6b78d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 |
import os
import math
import cv2
import numpy as np
import torch
from dust3r.utils.vo_eval import load_traj, eval_metrics, plot_trajectory, save_trajectory_tum_format, process_directory, calculate_averages
import croco.utils.misc as misc
import torch.distributed as dist
from tqdm import tqdm
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
from dust3r.utils.image import load_images, rgb, enlarge_seg_masks
from dust3r.image_pairs import make_pairs
from dust3r.inference import inference
# from dust3r.demo import get_3D_model_from_scene
import dust3r.eval_metadata
from dust3r.eval_metadata import dataset_metadata
def eval_pose_estimation(args, model, device, save_dir=None):
metadata = dataset_metadata.get(args.eval_dataset, dataset_metadata['sintel'])
img_path = metadata['img_path']
mask_path = metadata['mask_path']
ate_mean, rpe_trans_mean, rpe_rot_mean, outfile_list, bug = eval_pose_estimation_dist(
args, model, device, save_dir=save_dir, img_path=img_path, mask_path=mask_path
)
return ate_mean, rpe_trans_mean, rpe_rot_mean, outfile_list, bug
def eval_pose_estimation_dist(args, model, device, img_path, save_dir=None, mask_path=None):
metadata = dataset_metadata.get(args.eval_dataset, dataset_metadata['sintel'])
anno_path = metadata.get('anno_path', None)
silent = args.silent
seq_list = args.seq_list
if seq_list is None:
if metadata.get('full_seq', False):
args.full_seq = True
else:
seq_list = metadata.get('seq_list', [])
if args.full_seq:
seq_list = os.listdir(img_path)
seq_list = [seq for seq in seq_list if os.path.isdir(os.path.join(img_path, seq))]
seq_list = sorted(seq_list)
if save_dir is None:
save_dir = args.output_dir
# Split seq_list across processes
if misc.is_dist_avail_and_initialized():
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
total_seqs = len(seq_list)
seqs_per_proc = (total_seqs + world_size - 1) // world_size # Ceiling division
start_idx = rank * seqs_per_proc
end_idx = min(start_idx + seqs_per_proc, total_seqs)
seq_list = seq_list[start_idx:end_idx]
ate_list = []
rpe_trans_list = []
rpe_rot_list = []
outfile_list = []
load_img_size = 512
error_log_path = f'{save_dir}/_error_log_{rank}.txt' # Unique log file per process
bug = False
for seq in tqdm(seq_list):
try:
dir_path = metadata['dir_path_func'](img_path, seq)
# Handle skip_condition
skip_condition = metadata.get('skip_condition', None)
if skip_condition is not None and skip_condition(save_dir, seq):
continue
mask_path_seq_func = metadata.get('mask_path_seq_func', lambda mask_path, seq: None)
mask_path_seq = mask_path_seq_func(mask_path, seq)
filelist = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
filelist.sort()
if args.evaluate_davis:
filelist = filelist[:50]
filelist = filelist[::args.pose_eval_stride]
max_winsize = max(1, math.ceil((len(filelist)-1)/2))
scene_graph_type = args.scene_graph_type
if int(scene_graph_type.split('-')[1]) > max_winsize:
scene_graph_type = f'{args.scene_graph_type.split("-")[0]}-{max_winsize}'
if len(scene_graph_type.split("-")) > 2:
scene_graph_type += f'-{args.scene_graph_type.split("-")[2]}'
imgs = load_images(
filelist, size=load_img_size, verbose=False,
dynamic_mask_root=mask_path_seq, crop=not args.no_crop
)
print(f'Processing {seq} with {len(imgs)} images')
if args.eval_dataset == 'davis' and len(imgs) > 95:
# use swinstride-4
scene_graph_type = scene_graph_type.replace('5', '4')
pairs = make_pairs(
imgs, scene_graph=scene_graph_type, prefilter=None, symmetrize=True
)
#
output = inference(pairs, model, device, batch_size=1, verbose=not silent)
torch.cuda.empty_cache()
with torch.enable_grad():
if len(imgs) > 2:
mode = GlobalAlignerMode.PointCloudOptimizer
scene = global_aligner(
output, device=device, mode=mode, verbose=not silent,
shared_focal=not args.not_shared_focal and not args.use_gt_focal,
flow_loss_weight=args.flow_loss_weight, flow_loss_fn=args.flow_loss_fn,
depth_regularize_weight=args.depth_regularize_weight,
num_total_iter=args.n_iter, temporal_smoothing_weight=args.temporal_smoothing_weight,
motion_mask_thre=args.motion_mask_thre,
flow_loss_start_epoch=args.flow_loss_start_epoch, flow_loss_thre=args.flow_loss_thre, translation_weight=args.translation_weight,
sintel_ckpt=args.eval_dataset == 'sintel', use_gt_mask = args.use_gt_mask, use_pred_mask = args.use_pred_mask, sam2_mask_refine=args.sam2_mask_refine,
empty_cache=len(filelist) > 72, pxl_thre=args.pxl_thresh, batchify=not args.not_batchify
)
if args.use_gt_focal:
focal_path = os.path.join(
img_path.replace('final', 'camdata_left'), seq, 'focal.txt'
)
focals = np.loadtxt(focal_path)
focals = focals[::args.pose_eval_stride]
original_img_size = cv2.imread(filelist[0]).shape[:2]
resized_img_size = tuple(imgs[0]['img'].shape[-2:])
focals = focals * max(
(resized_img_size[0] / original_img_size[0]),
(resized_img_size[1] / original_img_size[1])
)
scene.preset_focal(focals, requires_grad=False) # TODO: requires_grad=False
lr = 0.01
loss = scene.compute_global_alignment(
init='mst', niter=args.n_iter, schedule=args.pose_schedule, lr=lr,
)
else:
mode = GlobalAlignerMode.PairViewer
scene = global_aligner(output, device=device, mode=mode, verbose=not silent)
if args.save_pose_qualitative:
outfile = get_3D_model_from_scene(
outdir=save_dir, silent=silent, scene=scene, min_conf_thr=2, as_pointcloud=True, mask_sky=False,
clean_depth=True, transparent_cams=False, cam_size=0.01, save_name=seq
)
else:
outfile = None
pred_traj = scene.get_tum_poses()
os.makedirs(f'{save_dir}/{seq}', exist_ok=True)
scene.clean_pointcloud()
scene.save_tum_poses(f'{save_dir}/{seq}/pred_traj.txt')
scene.save_focals(f'{save_dir}/{seq}/pred_focal.txt')
scene.save_intrinsics(f'{save_dir}/{seq}/pred_intrinsics.txt')
scene.save_depth_maps(f'{save_dir}/{seq}')
scene.save_dynamic_masks(f'{save_dir}/{seq}')
scene.save_dyna_maps(f'{save_dir}/{seq}')
scene.save_conf_maps(f'{save_dir}/{seq}')
scene.save_init_conf_maps(f'{save_dir}/{seq}')
scene.save_rgb_imgs(f'{save_dir}/{seq}')
enlarge_seg_masks(f'{save_dir}/{seq}', kernel_size=5 if args.use_gt_mask else 3)
gt_traj_file = metadata['gt_traj_func'](img_path, anno_path, seq)
traj_format = metadata.get('traj_format', None)
if args.eval_dataset == 'sintel':
gt_traj = load_traj(gt_traj_file=gt_traj_file, stride=args.pose_eval_stride)
elif traj_format is not None:
gt_traj = load_traj(gt_traj_file=gt_traj_file, traj_format=traj_format)
else:
gt_traj = None
if gt_traj is not None:
ate, rpe_trans, rpe_rot = eval_metrics(
pred_traj, gt_traj, seq=seq, filename=f'{save_dir}/{seq}_eval_metric.txt'
)
plot_trajectory(
pred_traj, gt_traj, title=seq, filename=f'{save_dir}/{seq}.png'
)
else:
ate, rpe_trans, rpe_rot = 0, 0, 0
outfile = None
bug = True
ate_list.append(ate)
rpe_trans_list.append(rpe_trans)
rpe_rot_list.append(rpe_rot)
outfile_list.append(outfile)
# Write to error log after each sequence
with open(error_log_path, 'a') as f:
f.write(f'{args.eval_dataset}-{seq: <16} | ATE: {ate:.5f}, RPE trans: {rpe_trans:.5f}, RPE rot: {rpe_rot:.5f}\n')
f.write(f'{ate:.5f}\n')
f.write(f'{rpe_trans:.5f}\n')
f.write(f'{rpe_rot:.5f}\n')
except Exception as e:
if 'out of memory' in str(e):
# Handle OOM
torch.cuda.empty_cache() # Clear the CUDA memory
with open(error_log_path, 'a') as f:
f.write(f'OOM error in sequence {seq}, skipping this sequence.\n')
print(f'OOM error in sequence {seq}, skipping...')
elif 'Degenerate covariance rank' in str(e) or 'Eigenvalues did not converge' in str(e):
# Handle Degenerate covariance rank exception and Eigenvalues did not converge exception
with open(error_log_path, 'a') as f:
f.write(f'Exception in sequence {seq}: {str(e)}\n')
print(f'Traj evaluation error in sequence {seq}, skipping.')
else:
raise e # Rethrow if it's not an expected exception
# Aggregate results across all processes
if misc.is_dist_avail_and_initialized():
torch.distributed.barrier()
bug_tensor = torch.tensor(int(bug), device=device)
bug = bool(bug_tensor.item())
# Handle outfile_list
outfile_list_all = [None for _ in range(world_size)]
outfile_list_combined = []
for sublist in outfile_list_all:
if sublist is not None:
outfile_list_combined.extend(sublist)
results = process_directory(save_dir)
avg_ate, avg_rpe_trans, avg_rpe_rot = calculate_averages(results)
# Write the averages to the error log (only on the main process)
if rank == 0:
with open(f'{save_dir}/_error_log.txt', 'a') as f:
# Copy the error log from each process to the main error log
for i in range(world_size):
with open(f'{save_dir}/_error_log_{i}.txt', 'r') as f_sub:
f.write(f_sub.read())
f.write(f'Average ATE: {avg_ate:.5f}, Average RPE trans: {avg_rpe_trans:.5f}, Average RPE rot: {avg_rpe_rot:.5f}\n')
return avg_ate, avg_rpe_trans, avg_rpe_rot, outfile_list_combined, bug
def pose_estimation_custom(args, model, device, save_dir=None):
load_img_size = 512
dir_path = args.dir_path
silent = args.silent
filelist = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
filelist.sort()
filelist = filelist[::args.pose_eval_stride]
max_winsize = max(1, math.ceil((len(filelist)-1)/2))
scene_graph_type = args.scene_graph_type
if int(scene_graph_type.split('-')[1]) > max_winsize:
scene_graph_type = f'{args.scene_graph_type.split("-")[0]}-{max_winsize}'
if len(scene_graph_type.split("-")) > 2:
scene_graph_type += f'-{args.scene_graph_type.split("-")[2]}'
imgs = load_images(
filelist, size=load_img_size, verbose=False, crop=not args.no_crop
)
print(f'Processing {args.dir_path} with {len(imgs)} images')
if len(imgs) > 95:
# use swinstride-4
scene_graph_type = scene_graph_type.replace('5', '4')
pairs = make_pairs(
imgs, scene_graph=scene_graph_type, prefilter=None, symmetrize=True
)
output = inference(pairs, model, device, batch_size=1, verbose=not silent)
torch.cuda.empty_cache()
with torch.enable_grad():
if len(imgs) > 2:
mode = GlobalAlignerMode.PointCloudOptimizer
scene = global_aligner(
output, device=device, mode=mode, verbose=not silent,
shared_focal=not args.not_shared_focal and not args.use_gt_focal,
flow_loss_weight=args.flow_loss_weight, flow_loss_fn=args.flow_loss_fn,
depth_regularize_weight=args.depth_regularize_weight,
num_total_iter=args.n_iter, temporal_smoothing_weight=args.temporal_smoothing_weight,
motion_mask_thre=args.motion_mask_thre,
flow_loss_start_epoch=args.flow_loss_start_epoch, flow_loss_thre=args.flow_loss_thre, translation_weight=args.translation_weight,
sintel_ckpt=args.eval_dataset == 'sintel', use_gt_mask = args.use_gt_mask, use_pred_mask = args.use_pred_mask, sam2_mask_refine=args.sam2_mask_refine,
empty_cache=len(filelist) > 72, pxl_thre=args.pxl_thresh, batchify=not args.not_batchify
)
if args.use_gt_focal:
focal_path = args.focal_path
focals = np.loadtxt(focal_path)
focals = focals[::args.pose_eval_stride]
original_img_size = cv2.imread(filelist[0]).shape[:2]
resized_img_size = tuple(imgs[0]['img'].shape[-2:])
focals = focals * max(
(resized_img_size[0] / original_img_size[0]),
(resized_img_size[1] / original_img_size[1])
)
scene.preset_focal(focals, requires_grad=False) # TODO: requires_grad=False
lr = 0.01
loss = scene.compute_global_alignment(
init='mst', niter=args.n_iter, schedule=args.pose_schedule, lr=lr,
)
else:
mode = GlobalAlignerMode.PairViewer
scene = global_aligner(output, device=device, mode=mode, verbose=not silent)
os.makedirs(f'{save_dir}', exist_ok=True)
scene.clean_pointcloud()
scene.save_tum_poses(f'{save_dir}/pred_traj.txt')
scene.save_focals(f'{save_dir}/pred_focal.txt')
scene.save_intrinsics(f'{save_dir}/pred_intrinsics.txt')
scene.save_depth_maps(f'{save_dir}')
scene.save_dynamic_masks(f'{save_dir}')
scene.save_dyna_maps(f'{save_dir}')
scene.save_conf_maps(f'{save_dir}')
scene.save_init_conf_maps(f'{save_dir}')
scene.save_rgb_imgs(f'{save_dir}')
# enlarge_seg_masks(f'{save_dir}', kernel_size=5 if args.use_gt_mask else 3)
|