Spaces:
Configuration error
Configuration error
File size: 8,547 Bytes
9d31508 |
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 |
import os
import sys
import numpy as np
import torch
import argparse
from accelerate import PartialState
from tqdm import tqdm
from PIL import Image
import imageio.v2 as iio
from stream3r.models.stream3r import STream3R
from stream3r.stream_session import StreamSession
from stream3r.dust3r.utils.image import load_images_for_eval as load_images
from stream3r.dust3r.utils.device import collate_with_cat
from stream3r.utils.utils import ImgDust3r2Stream3r
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from eval.video_depth.metadata import dataset_metadata
from eval.video_depth.utils import colorize
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.cuda.matmul.allow_tf32 = True
# avoid high cpu usage
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
torch.set_num_threads(1)
# ===========================================
def save_depth_maps(pts3ds_self, path, conf_self=None, depth_maps=None):
if depth_maps is None:
depth_maps = torch.stack([pts3d_self[..., -1] for pts3d_self in pts3ds_self], 0)
min_depth = depth_maps.min() # float(torch.quantile(out, 0.01))
max_depth = depth_maps.max() # float(torch.quantile(out, 0.99))
colored_depth = colorize(
depth_maps,
cmap_name="Spectral_r",
range=(min_depth, max_depth),
append_cbar=True,
)
images = []
if conf_self is not None:
conf_selfs = torch.concat(conf_self, 0)
min_conf = torch.log(conf_selfs.min()) # float(torch.quantile(out, 0.01))
max_conf = torch.log(conf_selfs.max()) # float(torch.quantile(out, 0.99))
colored_conf = colorize(
torch.log(conf_selfs),
cmap_name="jet",
range=(min_conf, max_conf),
append_cbar=True,
)
for i, depth_map in enumerate(colored_depth):
# Apply color map to depth map
img_path = f"{path}/frame_{(i):04d}.png"
if conf_self is None:
to_save = (depth_map * 255).detach().cpu().numpy().astype(np.uint8)
else:
to_save = torch.cat([depth_map, colored_conf[i]], dim=1)
to_save = (to_save * 255).detach().cpu().numpy().astype(np.uint8)
iio.imwrite(img_path, to_save)
images.append(Image.open(img_path))
np.save(f"{path}/frame_{(i):04d}.npy", depth_maps[i].detach().cpu().numpy())
return depth_maps
def get_args_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--device", type=str, default="cuda", help="pytorch device")
parser.add_argument(
"--output_dir",
type=str,
default="",
help="value for outdir",
)
parser.add_argument(
"--no_crop", type=bool, default=True, help="whether to crop input data"
)
parser.add_argument(
"--eval_dataset",
type=str,
default="sintel",
choices=list(dataset_metadata.keys()),
)
parser.add_argument("--size", type=int, default="512")
parser.add_argument(
"--pose_eval_stride", default=1, type=int, help="stride for pose evaluation"
)
parser.add_argument(
"--full_seq",
action="store_true",
default=False,
help="use full sequence for pose evaluation",
)
parser.add_argument(
"--seq_list",
nargs="+",
default=None,
help="list of sequences for pose evaluation",
)
parser.add_argument(
"--checkpoint_dir",
type=str,
default="",
help="path to the checkpoint directory",
)
return parser
def eval_pose_estimation(args, model, save_dir=None):
metadata = dataset_metadata.get(args.eval_dataset)
img_path = metadata["img_path"]
mask_path = metadata["mask_path"]
ate_mean, rpe_trans_mean, rpe_rot_mean = eval_pose_estimation_dist(
args, model, save_dir=save_dir, img_path=img_path, mask_path=mask_path)
return ate_mean, rpe_trans_mean, rpe_rot_mean
def eval_pose_estimation_dist(args,
model,
img_path,
save_dir=None,
mask_path=None):
metadata = dataset_metadata.get(args.eval_dataset)
model.eval()
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
distributed_state = PartialState()
model.to(distributed_state.device)
device = distributed_state.device
with distributed_state.split_between_processes(seq_list) as seqs:
error_log_path = f"{save_dir}/_error_log_{distributed_state.process_index}.txt" # Unique log file per process
for seq in tqdm(seqs):
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()
filelist = filelist[::args.pose_eval_stride]
images = load_images(
filelist,
size=518,
verbose=True,
crop=False,
patch_size=14,
)
images = collate_with_cat([tuple(images)])
images = torch.stack([view["img"] for view in images], dim=1)
images = ImgDust3r2Stream3r(images).to(device)
with torch.no_grad():
session = StreamSession(model, mode="causal")
for i in range(images.shape[1]):
image = images[:, i:i+1]
predictions = session.forward_stream(image)
print(
f"Finished depth estmation of {len(filelist)} images"
)
os.makedirs(f"{save_dir}/{seq}", exist_ok=True)
save_depth_maps(None,
f"{save_dir}/{seq}",
conf_self=None,
depth_maps=predictions['depth'].squeeze().cpu())
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
return None, None, None
def main():
args = get_args_parser()
args = args.parse_args()
if args.eval_dataset == "sintel":
args.full_seq = True
else:
args.full_seq = False
args.no_crop = True
model = STream3R.from_pretrained("yslan/STream3R").to(args.device)
model.eval()
eval_pose_estimation(args, model, save_dir=args.output_dir)
if __name__ == "__main__":
main()
|