Spaces:
Runtime error
Runtime error
File size: 10,519 Bytes
1bb1365 |
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 |
# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# --------------------------------------------------------
# Main test function
# --------------------------------------------------------
import argparse
import os
import pickle
import numpy as np
import torch
import utils.misc as misc
from models.croco_downstream import CroCoDownstreamBinocular
from models.head_downstream import PixelwiseTaskWithDPT
from PIL import Image
from stereoflow.criterion import *
from stereoflow.datasets_flow import flowToColor, get_test_datasets_flow
from stereoflow.datasets_stereo import get_test_datasets_stereo, vis_disparity
from stereoflow.engine import tiled_pred
from torch.utils.data import DataLoader
from tqdm import tqdm
def get_args_parser():
parser = argparse.ArgumentParser("Test CroCo models on stereo/flow", add_help=False)
# important argument
parser.add_argument(
"--model", required=True, type=str, help="Path to the model to evaluate"
)
parser.add_argument(
"--dataset",
required=True,
type=str,
help="test dataset (there can be multiple dataset separated by a +)",
)
# tiling
parser.add_argument(
"--tile_conf_mode",
type=str,
default="",
help="Weights for the tiling aggregation based on confidence (empty means use the formula from the loaded checkpoint",
)
parser.add_argument(
"--tile_overlap", type=float, default=0.7, help="overlap between tiles"
)
# save (it will automatically go to <model_path>_<dataset_str>/<tile_str>_<save>)
parser.add_argument(
"--save",
type=str,
nargs="+",
default=[],
help="what to save: \
metrics (pickle file), \
pred (raw prediction save as torch tensor), \
visu (visualization in png of each prediction), \
err10 (visualization in png of the error clamp at 10 for each prediction), \
submission (submission file)",
)
# other (no impact)
parser.add_argument("--num_workers", default=4, type=int)
return parser
def _load_model_and_criterion(model_path, do_load_metrics, device):
print("loading model from", model_path)
assert os.path.isfile(model_path)
ckpt = torch.load(model_path, "cpu")
ckpt_args = ckpt["args"]
task = ckpt_args.task
tile_conf_mode = ckpt_args.tile_conf_mode
num_channels = {"stereo": 1, "flow": 2}[task]
with_conf = eval(ckpt_args.criterion).with_conf
if with_conf:
num_channels += 1
print("head: PixelwiseTaskWithDPT()")
head = PixelwiseTaskWithDPT()
head.num_channels = num_channels
print("croco_args:", ckpt_args.croco_args)
model = CroCoDownstreamBinocular(head, **ckpt_args.croco_args)
msg = model.load_state_dict(ckpt["model"], strict=True)
model.eval()
model = model.to(device)
if do_load_metrics:
if task == "stereo":
metrics = StereoDatasetMetrics().to(device)
else:
metrics = FlowDatasetMetrics().to(device)
else:
metrics = None
return model, metrics, ckpt_args.crop, with_conf, task, tile_conf_mode
def _save_batch(
pred, gt, pairnames, dataset, task, save, outdir, time, submission_dir=None
):
for i in range(len(pairnames)):
pairname = (
eval(pairnames[i]) if pairnames[i].startswith("(") else pairnames[i]
) # unbatch pairname
fname = os.path.join(outdir, dataset.pairname_to_str(pairname))
os.makedirs(os.path.dirname(fname), exist_ok=True)
predi = pred[i, ...]
if gt is not None:
gti = gt[i, ...]
if "pred" in save:
torch.save(predi.squeeze(0).cpu(), fname + "_pred.pth")
if "visu" in save:
if task == "stereo":
disparity = predi.permute((1, 2, 0)).squeeze(2).cpu().numpy()
m, M = None
if gt is not None:
mask = torch.isfinite(gti)
m = gt[mask].min()
M = gt[mask].max()
img_disparity = vis_disparity(disparity, m=m, M=M)
Image.fromarray(img_disparity).save(fname + "_pred.png")
else:
# normalize flowToColor according to the maxnorm of gt (or prediction if not available)
flowNorm = (
torch.sqrt(
torch.sum((gti if gt is not None else predi) ** 2, dim=0)
)
.max()
.item()
)
imgflow = flowToColor(
predi.permute((1, 2, 0)).cpu().numpy(), maxflow=flowNorm
)
Image.fromarray(imgflow).save(fname + "_pred.png")
if "err10" in save:
assert gt is not None
L2err = torch.sqrt(torch.sum((gti - predi) ** 2, dim=0))
valid = torch.isfinite(gti[0, :, :])
L2err[~valid] = 0.0
L2err = torch.clamp(L2err, max=10.0)
red = (L2err * 255.0 / 10.0).to(dtype=torch.uint8)[:, :, None]
zer = torch.zeros_like(red)
imgerr = torch.cat((red, zer, zer), dim=2).cpu().numpy()
Image.fromarray(imgerr).save(fname + "_err10.png")
if "submission" in save:
assert submission_dir is not None
predi_np = (
predi.permute(1, 2, 0).squeeze(2).cpu().numpy()
) # transform into HxWx2 for flow or HxW for stereo
dataset.submission_save_pairname(pairname, predi_np, submission_dir, time)
def main(args):
# load the pretrained model and metrics
device = (
torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
)
(
model,
metrics,
cropsize,
with_conf,
task,
tile_conf_mode,
) = _load_model_and_criterion(args.model, "metrics" in args.save, device)
if args.tile_conf_mode == "":
args.tile_conf_mode = tile_conf_mode
# load the datasets
datasets = (
get_test_datasets_stereo if task == "stereo" else get_test_datasets_flow
)(args.dataset)
dataloaders = [
DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
for dataset in datasets
]
# run
for i, dataloader in enumerate(dataloaders):
dataset = datasets[i]
dstr = args.dataset.split("+")[i]
outdir = args.model + "_" + misc.filename(dstr)
if "metrics" in args.save and len(args.save) == 1:
fname = os.path.join(
outdir, f"conf_{args.tile_conf_mode}_overlap_{args.tile_overlap}.pkl"
)
if os.path.isfile(fname) and len(args.save) == 1:
print(" metrics already compute in " + fname)
with open(fname, "rb") as fid:
results = pickle.load(fid)
for k, v in results.items():
print("{:s}: {:.3f}".format(k, v))
continue
if "submission" in args.save:
dirname = (
f"submission_conf_{args.tile_conf_mode}_overlap_{args.tile_overlap}"
)
submission_dir = os.path.join(outdir, dirname)
else:
submission_dir = None
print("")
print("saving {:s} in {:s}".format("+".join(args.save), outdir))
print(repr(dataset))
if metrics is not None:
metrics.reset()
for data_iter_step, (image1, image2, gt, pairnames) in enumerate(
tqdm(dataloader)
):
do_flip = (
task == "stereo"
and dstr.startswith("Spring")
and any("right" in p for p in pairnames)
) # we flip the images and will flip the prediction after as we assume img1 is on the left
image1 = image1.to(device, non_blocking=True)
image2 = image2.to(device, non_blocking=True)
gt = (
gt.to(device, non_blocking=True) if gt.numel() > 0 else None
) # special case for test time
if do_flip:
assert all("right" in p for p in pairnames)
image1 = image1.flip(
dims=[3]
) # this is already the right frame, let's flip it
image2 = image2.flip(dims=[3])
gt = gt # that is ok
with torch.inference_mode():
pred, _, _, time = tiled_pred(
model,
None,
image1,
image2,
None if dataset.name == "Spring" else gt,
conf_mode=args.tile_conf_mode,
overlap=args.tile_overlap,
crop=cropsize,
with_conf=with_conf,
return_time=True,
)
if do_flip:
pred = pred.flip(dims=[3])
if metrics is not None:
metrics.add_batch(pred, gt)
if any(k in args.save for k in ["pred", "visu", "err10", "submission"]):
_save_batch(
pred,
gt,
pairnames,
dataset,
task,
args.save,
outdir,
time,
submission_dir=submission_dir,
)
# print
if metrics is not None:
results = metrics.get_results()
for k, v in results.items():
print("{:s}: {:.3f}".format(k, v))
# save if needed
if "metrics" in args.save:
os.makedirs(os.path.dirname(fname), exist_ok=True)
with open(fname, "wb") as fid:
pickle.dump(results, fid)
print("metrics saved in", fname)
# finalize submission if needed
if "submission" in args.save:
dataset.finalize_submission(submission_dir)
if __name__ == "__main__":
args = get_args_parser()
args = args.parse_args()
main(args)
|