=============
== PyTorch ==
=============

NVIDIA Release 22.10 (build 46164382)
PyTorch Version 1.13.0a0+d0d6b1f

Container image Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Copyright (c) 2014-2022 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

MONAI version: 1.1.0+66.g0b8aa84c.dirty
Numpy version: 1.22.2
Pytorch version: 1.13.0a0+d0d6b1f
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 0b8aa84c115c3ea8fb5dc9becf3ea45fc7b6083b
MONAI __file__: /workspace/monai/MONAI/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.10
ITK version: 5.2.1
Nibabel version: 5.0.0
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.1
gdown version: 4.6.0
TorchVision version: 0.14.0a0
tqdm version: 4.64.1
lmdb version: 1.3.0
psutil version: 5.9.2
pandas version: 1.4.4
einops version: 0.6.0
transformers version: 4.21.3
mlflow version: 2.1.1
pynrrd version: 1.0.0

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies

monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.

  0% 0/20 [00:00<?, ?it/s]
  5% 1/20 [00:00<00:09,  2.06it/s]
 10% 2/20 [00:01<00:09,  1.83it/s]
 15% 3/20 [00:01<00:09,  1.83it/s]
 20% 4/20 [00:02<00:08,  1.83it/s]
 25% 5/20 [00:02<00:09,  1.63it/s]
 30% 6/20 [00:03<00:08,  1.57it/s]
 35% 7/20 [00:04<00:08,  1.51it/s]
 40% 8/20 [00:05<00:08,  1.46it/s]
 45% 9/20 [00:05<00:07,  1.44it/s]
 50% 10/20 [00:06<00:07,  1.42it/s]
 55% 11/20 [00:07<00:06,  1.42it/s]
 60% 12/20 [00:07<00:05,  1.39it/s]
 65% 13/20 [00:08<00:05,  1.40it/s]
 70% 14/20 [00:09<00:04,  1.40it/s]
 75% 15/20 [00:10<00:03,  1.40it/s]
 80% 16/20 [00:10<00:02,  1.40it/s]
 85% 17/20 [00:11<00:02,  1.40it/s]
 90% 18/20 [00:12<00:01,  1.40it/s]
 95% 19/20 [00:12<00:00,  1.40it/s]
100% 20/20 [00:13<00:00,  1.40it/s]
100% 20/20 [00:13<00:00,  1.47it/s]
Accessing lmdb file: /tmp/tmpe42nh72z/monai_cache.lmdb.
----------
Epoch 1/6
1/10, train_loss:0.6724
2/10, train_loss:0.6119
3/10, train_loss:0.5548
4/10, train_loss:0.5189
5/10, train_loss:0.6292
6/10, train_loss:0.4268
7/10, train_loss:0.4796
8/10, train_loss:0.4762
9/10, train_loss:0.4960
10/10, train_loss:0.4965
epoch 1 average loss:0.5362
----------
Epoch 2/6
1/10, train_loss:0.4777
2/10, train_loss:0.4672
3/10, train_loss:0.4604
4/10, train_loss:0.5214
5/10, train_loss:0.4138
6/10, train_loss:0.5920
7/10, train_loss:0.4559
8/10, train_loss:0.4458
9/10, train_loss:0.3994
10/10, train_loss:0.4713
epoch 2 average loss:0.4705
saved new best metric model
current epoch 2 current mean dice: 0.8815 best mean dice: 0.8815 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4523
2/10, train_loss:0.4289
3/10, train_loss:0.4841
4/10, train_loss:0.4318
5/10, train_loss:0.4177
6/10, train_loss:0.4343
7/10, train_loss:0.3755
8/10, train_loss:0.3605
9/10, train_loss:0.5042
10/10, train_loss:0.4461
epoch 3 average loss:0.4335
----------
Epoch 4/6
1/10, train_loss:0.4624
2/10, train_loss:0.3803
3/10, train_loss:0.3904
4/10, train_loss:0.5515
5/10, train_loss:0.4798
6/10, train_loss:0.4610
7/10, train_loss:0.4472
8/10, train_loss:0.4244
9/10, train_loss:0.5064
10/10, train_loss:0.4039
epoch 4 average loss:0.4507
saved new best metric model
current epoch 4 current mean dice: 0.9259 best mean dice: 0.9259 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4832
2/10, train_loss:0.4743
3/10, train_loss:0.4534
4/10, train_loss:0.4321
5/10, train_loss:0.5644
6/10, train_loss:0.4092
7/10, train_loss:0.3841
8/10, train_loss:0.4387
9/10, train_loss:0.4356
10/10, train_loss:0.4437
epoch 5 average loss:0.4519
----------
Epoch 6/6
1/10, train_loss:0.4854
2/10, train_loss:0.3835
3/10, train_loss:0.4443
4/10, train_loss:0.6366
5/10, train_loss:0.3965
6/10, train_loss:0.3845
7/10, train_loss:0.4001
8/10, train_loss:0.3966
9/10, train_loss:0.3942
10/10, train_loss:0.4415
epoch 6 average loss:0.4363
saved new best metric model
current epoch 6 current mean dice: 0.9332 best mean dice: 0.9332 at epoch 6
train completed, best_metric: 0.9332  at epoch: 6
2023-02-10 06:50:41,681 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img0/img0_seg.nii.gz
2023-02-10 06:50:42,020 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img1/img1_seg.nii.gz
2023-02-10 06:50:42,492 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img10/img10_seg.nii.gz
2023-02-10 06:50:42,816 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img11/img11_seg.nii.gz
2023-02-10 06:50:43,251 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img12/img12_seg.nii.gz
2023-02-10 06:50:43,589 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img13/img13_seg.nii.gz
2023-02-10 06:50:44,002 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img14/img14_seg.nii.gz
2023-02-10 06:50:44,332 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img15/img15_seg.nii.gz
2023-02-10 06:50:44,780 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img16/img16_seg.nii.gz
2023-02-10 06:50:45,113 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img17/img17_seg.nii.gz
2023-02-10 06:50:45,480 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img18/img18_seg.nii.gz
2023-02-10 06:50:45,877 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img19/img19_seg.nii.gz
2023-02-10 06:50:46,273 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img2/img2_seg.nii.gz
2023-02-10 06:50:46,675 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img20/img20_seg.nii.gz
2023-02-10 06:50:47,052 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img21/img21_seg.nii.gz
2023-02-10 06:50:47,453 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img22/img22_seg.nii.gz
2023-02-10 06:50:48,031 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img23/img23_seg.nii.gz
2023-02-10 06:50:48,434 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img24/img24_seg.nii.gz
2023-02-10 06:50:48,870 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img25/img25_seg.nii.gz
2023-02-10 06:50:49,282 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img26/img26_seg.nii.gz
2023-02-10 06:50:49,665 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img27/img27_seg.nii.gz
2023-02-10 06:50:50,110 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img28/img28_seg.nii.gz
2023-02-10 06:50:50,456 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img29/img29_seg.nii.gz
2023-02-10 06:50:50,883 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img3/img3_seg.nii.gz
2023-02-10 06:50:51,252 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img30/img30_seg.nii.gz
2023-02-10 06:50:51,650 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img31/img31_seg.nii.gz
2023-02-10 06:50:52,016 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img32/img32_seg.nii.gz
2023-02-10 06:50:52,409 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img33/img33_seg.nii.gz
2023-02-10 06:50:52,818 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img34/img34_seg.nii.gz
2023-02-10 06:50:53,232 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img35/img35_seg.nii.gz
2023-02-10 06:50:53,625 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img36/img36_seg.nii.gz
2023-02-10 06:50:54,025 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img37/img37_seg.nii.gz
2023-02-10 06:50:54,417 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img38/img38_seg.nii.gz
2023-02-10 06:50:54,803 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img39/img39_seg.nii.gz
2023-02-10 06:50:55,191 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img4/img4_seg.nii.gz
2023-02-10 06:50:55,589 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img5/img5_seg.nii.gz
2023-02-10 06:50:55,970 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img6/img6_seg.nii.gz
2023-02-10 06:50:56,369 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img7/img7_seg.nii.gz
2023-02-10 06:50:56,757 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img8/img8_seg.nii.gz
2023-02-10 06:50:57,145 INFO image_writer.py:197 - writing: /tmp/tmpe42nh72z/output/img9/img9_seg.nii.gz
losses [0.5362188071012497, 0.4704934895038605, 0.4335438013076782, 0.45073508024215697, 0.4518702685832977, 0.43633219599723816]
best metric 0.9332388639450073
infer metric 0.9333451390266418
[0.1419206098299633, 0.15202653102411912, 0.1512304792935884, 0.1395774206866045, 0.18837926785891068, 0.16912123286383246, 0.14654133236254155, 0.16802208278641106, 0.15692209517257702, 0.179013512622859, 0.1624336824966475, 0.1678829004568564, 0.14431809901961565, 0.11337207798573065, 0.1615752771576913, 0.20085344298051, 0.17578151561740576, 0.0972033878521958, 0.1936855488212249, 0.20301129974178245, 0.195989400890454, 0.20819782935015016, 0.16181660206660825, 0.1317880711497551, 0.148228376670616, 0.14229968462376025, 0.23135579771057072, 0.1606368946276316, 0.14813350117122623, 0.10257333536872124, 0.11863681666605838, 0.12953277288580237, 0.11377857720427251, 0.15208983809708881, 0.1630664676562511, 0.19361134237752603, 0.22270260737095976, 0.18079758359027048, 0.19009478755156461, 0.07431135235781589]
EMONAI version: 1.1.0+66.g0b8aa84c.dirty
Numpy version: 1.22.2
Pytorch version: 1.13.0a0+d0d6b1f
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 0b8aa84c115c3ea8fb5dc9becf3ea45fc7b6083b
MONAI __file__: /workspace/monai/MONAI/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.10
ITK version: 5.2.1
Nibabel version: 5.0.0
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.1
gdown version: 4.6.0
TorchVision version: 0.14.0a0
tqdm version: 4.64.1
lmdb version: 1.3.0
psutil version: 5.9.2
pandas version: 1.4.4
einops version: 0.6.0
transformers version: 4.21.3
mlflow version: 2.1.1
pynrrd version: 1.0.0

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies

monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.
----------
Epoch 1/6
1/10, train_loss:0.6724
2/10, train_loss:0.6119
3/10, train_loss:0.5548
4/10, train_loss:0.5189
5/10, train_loss:0.6292
6/10, train_loss:0.4268
7/10, train_loss:0.4796
8/10, train_loss:0.4762
9/10, train_loss:0.4960
10/10, train_loss:0.4965
epoch 1 average loss:0.5362
----------
Epoch 2/6
1/10, train_loss:0.4777
2/10, train_loss:0.4672
3/10, train_loss:0.4604
4/10, train_loss:0.5214
5/10, train_loss:0.4138
6/10, train_loss:0.5920
7/10, train_loss:0.4559
8/10, train_loss:0.4458
9/10, train_loss:0.3994
10/10, train_loss:0.4713
epoch 2 average loss:0.4705
saved new best metric model
current epoch 2 current mean dice: 0.8815 best mean dice: 0.8815 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4523
2/10, train_loss:0.4289
3/10, train_loss:0.4841
4/10, train_loss:0.4318
5/10, train_loss:0.4177
6/10, train_loss:0.4343
7/10, train_loss:0.3755
8/10, train_loss:0.3605
9/10, train_loss:0.5042
10/10, train_loss:0.4461
epoch 3 average loss:0.4335
----------
Epoch 4/6
1/10, train_loss:0.4624
2/10, train_loss:0.3803
3/10, train_loss:0.3904
4/10, train_loss:0.5515
5/10, train_loss:0.4798
6/10, train_loss:0.4610
7/10, train_loss:0.4472
8/10, train_loss:0.4244
9/10, train_loss:0.5064
10/10, train_loss:0.4039
epoch 4 average loss:0.4507
saved new best metric model
current epoch 4 current mean dice: 0.9259 best mean dice: 0.9259 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4832
2/10, train_loss:0.4743
3/10, train_loss:0.4534
4/10, train_loss:0.4321
5/10, train_loss:0.5644
6/10, train_loss:0.4092
7/10, train_loss:0.3841
8/10, train_loss:0.4387
9/10, train_loss:0.4356
10/10, train_loss:0.4437
epoch 5 average loss:0.4519
----------
Epoch 6/6
1/10, train_loss:0.4854
2/10, train_loss:0.3835
3/10, train_loss:0.4443
4/10, train_loss:0.6366
5/10, train_loss:0.3965
6/10, train_loss:0.3845
7/10, train_loss:0.4001
8/10, train_loss:0.3966
9/10, train_loss:0.3942
10/10, train_loss:0.4415
epoch 6 average loss:0.4363
saved new best metric model
current epoch 6 current mean dice: 0.9332 best mean dice: 0.9332 at epoch 6
train completed, best_metric: 0.9332  at epoch: 6
2023-02-10 06:52:02,801 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img0/img0_seg.nii.gz
2023-02-10 06:52:03,215 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img1/img1_seg.nii.gz
2023-02-10 06:52:03,555 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img10/img10_seg.nii.gz
2023-02-10 06:52:03,947 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img11/img11_seg.nii.gz
2023-02-10 06:52:04,412 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img12/img12_seg.nii.gz
2023-02-10 06:52:04,795 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img13/img13_seg.nii.gz
2023-02-10 06:52:05,148 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img14/img14_seg.nii.gz
2023-02-10 06:52:05,532 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img15/img15_seg.nii.gz
2023-02-10 06:52:05,934 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img16/img16_seg.nii.gz
2023-02-10 06:52:06,338 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img17/img17_seg.nii.gz
2023-02-10 06:52:06,745 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img18/img18_seg.nii.gz
2023-02-10 06:52:07,151 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img19/img19_seg.nii.gz
2023-02-10 06:52:07,559 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img2/img2_seg.nii.gz
2023-02-10 06:52:07,969 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img20/img20_seg.nii.gz
2023-02-10 06:52:08,363 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img21/img21_seg.nii.gz
2023-02-10 06:52:08,750 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img22/img22_seg.nii.gz
2023-02-10 06:52:09,136 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img23/img23_seg.nii.gz
2023-02-10 06:52:09,538 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img24/img24_seg.nii.gz
2023-02-10 06:52:09,933 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img25/img25_seg.nii.gz
2023-02-10 06:52:10,325 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img26/img26_seg.nii.gz
2023-02-10 06:52:10,719 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img27/img27_seg.nii.gz
2023-02-10 06:52:11,111 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img28/img28_seg.nii.gz
2023-02-10 06:52:11,515 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img29/img29_seg.nii.gz
2023-02-10 06:52:11,922 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img3/img3_seg.nii.gz
2023-02-10 06:52:12,302 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img30/img30_seg.nii.gz
2023-02-10 06:52:12,694 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img31/img31_seg.nii.gz
2023-02-10 06:52:13,186 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img32/img32_seg.nii.gz
2023-02-10 06:52:13,572 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img33/img33_seg.nii.gz
2023-02-10 06:52:13,976 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img34/img34_seg.nii.gz
2023-02-10 06:52:14,366 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img35/img35_seg.nii.gz
2023-02-10 06:52:14,762 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img36/img36_seg.nii.gz
2023-02-10 06:52:15,186 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img37/img37_seg.nii.gz
2023-02-10 06:52:15,711 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img38/img38_seg.nii.gz
2023-02-10 06:52:16,108 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img39/img39_seg.nii.gz
2023-02-10 06:52:16,495 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img4/img4_seg.nii.gz
2023-02-10 06:52:16,895 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img5/img5_seg.nii.gz
2023-02-10 06:52:17,291 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img6/img6_seg.nii.gz
2023-02-10 06:52:17,687 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img7/img7_seg.nii.gz
2023-02-10 06:52:18,070 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img8/img8_seg.nii.gz
2023-02-10 06:52:18,464 INFO image_writer.py:197 - writing: /tmp/tmpt5oxgtm2/output/img9/img9_seg.nii.gz
losses [0.5362188071012497, 0.4704934895038605, 0.4335438013076782, 0.45073508024215697, 0.4518702685832977, 0.43633219599723816]
best metric 0.9332388639450073
infer metric 0.9333451390266418
[0.1419206098299633, 0.15202653102411912, 0.1512304792935884, 0.1395774206866045, 0.18837926785891068, 0.16912123286383246, 0.14654133236254155, 0.16802208278641106, 0.15692209517257702, 0.179013512622859, 0.1624336824966475, 0.1678829004568564, 0.14431809901961565, 0.11337207798573065, 0.1615752771576913, 0.20085344298051, 0.17578151561740576, 0.0972033878521958, 0.1936855488212249, 0.20301129974178245, 0.195989400890454, 0.20819782935015016, 0.16181660206660825, 0.1317880711497551, 0.148228376670616, 0.14229968462376025, 0.23135579771057072, 0.1606368946276316, 0.14813350117122623, 0.10257333536872124, 0.11863681666605838, 0.12953277288580237, 0.11377857720427251, 0.15208983809708881, 0.1630664676562511, 0.19361134237752603, 0.22270260737095976, 0.18079758359027048, 0.19009478755156461, 0.07431135235781589]
E
======================================================================
ERROR: test_timing (__main__.IntegrationSegmentation3D)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/workspace/monai/MONAI/tests/utils.py", line 571, in run_process
    output = func(*args, **kwargs)
  File "/workspace/monai/MONAI/tests/utils.py", line 651, in _call_original_func
    return f(*args, **kwargs)
  File "/workspace/monai/MONAI/tests/test_integration_segmentation_3d.py", line 300, in test_timing
    self.train_and_infer(idx=3)
  File "/workspace/monai/MONAI/tests/test_integration_segmentation_3d.py", line 283, in train_and_infer
    self.assertTrue(test_integration_value(TASK, key="losses", data=results[:6], rtol=1e-3))
  File "/workspace/monai/MONAI/tests/testing_data/integration_answers.py", line 723, in test_integration_value
    raise ValueError(f"no matched results for {test_name}, {key}. {data}.")
ValueError: no matched results for integration_segmentation_3d, losses. [0.5362188071012497, 0.4704934895038605, 0.4335438013076782, 0.45073508024215697, 0.4518702685832977, 0.43633219599723816].

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/workspace/monai/MONAI/tests/utils.py", line 619, in _wrapper
    raise RuntimeError(res.traceback) from res
RuntimeError: Traceback (most recent call last):
  File "/workspace/monai/MONAI/tests/utils.py", line 571, in run_process
    output = func(*args, **kwargs)
  File "/workspace/monai/MONAI/tests/utils.py", line 651, in _call_original_func
    return f(*args, **kwargs)
  File "/workspace/monai/MONAI/tests/test_integration_segmentation_3d.py", line 300, in test_timing
    self.train_and_infer(idx=3)
  File "/workspace/monai/MONAI/tests/test_integration_segmentation_3d.py", line 283, in train_and_infer
    self.assertTrue(test_integration_value(TASK, key="losses", data=results[:6], rtol=1e-3))
  File "/workspace/monai/MONAI/tests/testing_data/integration_answers.py", line 723, in test_integration_value
    raise ValueError(f"no matched results for {test_name}, {key}. {data}.")
ValueError: no matched results for integration_segmentation_3d, losses. [0.5362188071012497, 0.4704934895038605, 0.4335438013076782, 0.45073508024215697, 0.4518702685832977, 0.43633219599723816].


======================================================================
ERROR: test_training (__main__.IntegrationSegmentation3D)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "tests/test_integration_segmentation_3d.py", line 292, in test_training
    results = self.train_and_infer(i)
  File "tests/test_integration_segmentation_3d.py", line 283, in train_and_infer
    self.assertTrue(test_integration_value(TASK, key="losses", data=results[:6], rtol=1e-3))
  File "/workspace/monai/MONAI/tests/testing_data/integration_answers.py", line 723, in test_integration_value
    raise ValueError(f"no matched results for {test_name}, {key}. {data}.")
ValueError: no matched results for integration_segmentation_3d, losses. [0.5362188071012497, 0.4704934895038605, 0.4335438013076782, 0.45073508024215697, 0.4518702685832977, 0.43633219599723816].

----------------------------------------------------------------------
Ran 2 tests in 221.260s

FAILED (errors=2)