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

NVIDIA Release 22.09 (build 44877844)
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: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
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: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
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.

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Accessing lmdb file: /tmp/tmp77h3mnri/monai_cache.lmdb.
----------
Epoch 1/6
1/10, train_loss:0.6621
2/10, train_loss:0.5984
3/10, train_loss:0.5553
4/10, train_loss:0.5258
5/10, train_loss:0.6387
6/10, train_loss:0.4434
7/10, train_loss:0.5204
8/10, train_loss:0.4882
9/10, train_loss:0.4858
10/10, train_loss:0.5107
epoch 1 average loss:0.5429
----------
Epoch 2/6
1/10, train_loss:0.5180
2/10, train_loss:0.5113
3/10, train_loss:0.4100
4/10, train_loss:0.5068
5/10, train_loss:0.4367
6/10, train_loss:0.6062
7/10, train_loss:0.4657
8/10, train_loss:0.4265
9/10, train_loss:0.3888
10/10, train_loss:0.4633
epoch 2 average loss:0.4733
saved new best metric model
current epoch 2 current mean dice: 0.8960 best mean dice: 0.8960 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4727
2/10, train_loss:0.4422
3/10, train_loss:0.5306
4/10, train_loss:0.4464
5/10, train_loss:0.4212
6/10, train_loss:0.4589
7/10, train_loss:0.4084
8/10, train_loss:0.3681
9/10, train_loss:0.4585
10/10, train_loss:0.4754
epoch 3 average loss:0.4482
----------
Epoch 4/6
1/10, train_loss:0.4336
2/10, train_loss:0.3964
3/10, train_loss:0.3937
4/10, train_loss:0.5268
5/10, train_loss:0.4489
6/10, train_loss:0.4493
7/10, train_loss:0.4220
8/10, train_loss:0.4657
9/10, train_loss:0.5309
10/10, train_loss:0.3853
epoch 4 average loss:0.4453
saved new best metric model
current epoch 4 current mean dice: 0.9264 best mean dice: 0.9264 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4024
2/10, train_loss:0.4756
3/10, train_loss:0.4313
4/10, train_loss:0.4372
5/10, train_loss:0.5133
6/10, train_loss:0.4053
7/10, train_loss:0.4140
8/10, train_loss:0.4154
9/10, train_loss:0.3792
10/10, train_loss:0.4164
epoch 5 average loss:0.4290
----------
Epoch 6/6
1/10, train_loss:0.4760
2/10, train_loss:0.3921
3/10, train_loss:0.4284
4/10, train_loss:0.6420
5/10, train_loss:0.3869
6/10, train_loss:0.4096
7/10, train_loss:0.3746
8/10, train_loss:0.4030
9/10, train_loss:0.4116
10/10, train_loss:0.4350
epoch 6 average loss:0.4359
saved new best metric model
current epoch 6 current mean dice: 0.9330 best mean dice: 0.9330 at epoch 6
train completed, best_metric: 0.9330  at epoch: 6
2023-02-10 06:42:32,632 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img0/img0_seg.nii.gz
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2023-02-10 06:42:45,826 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img39/img39_seg.nii.gz
2023-02-10 06:42:46,219 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img4/img4_seg.nii.gz
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2023-02-10 06:42:47,413 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img7/img7_seg.nii.gz
2023-02-10 06:42:47,810 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img8/img8_seg.nii.gz
2023-02-10 06:42:48,204 INFO image_writer.py:197 - writing: /tmp/tmp77h3mnri/output/img9/img9_seg.nii.gz
losses [0.5428896486759186, 0.47332107126712797, 0.4482243746519089, 0.44526649713516236, 0.4289955377578735, 0.43592421114444735]
best metric 0.9330238103866577
infer metric 0.9330325126647949
[0.14221418110696976, 0.15208585182377798, 0.1513897058205168, 0.13967962601912887, 0.18842722322448288, 0.16946449114556605, 0.14666448241871227, 0.1681556678159737, 0.15700726088045847, 0.17919478363108776, 0.16260375535105126, 0.16814699068513195, 0.14436604062597075, 0.11326929669037776, 0.1618907044396175, 0.20103438554573408, 0.17609589027233832, 0.0971353473692992, 0.19394589782420674, 0.2032719334386539, 0.19631397190397853, 0.2085800298746335, 0.1619660769362055, 0.13194106267129266, 0.14814064746136182, 0.14242913546879304, 0.2318148304738647, 0.16099277990981092, 0.14840797220896973, 0.10264401461133835, 0.1185961010722464, 0.12947038940604966, 0.1137130476919966, 0.15194059564840892, 0.16326019107581102, 0.1940761647525908, 0.22307824674270305, 0.18091674446467965, 0.19041087517922825, 0.07408664840526798]
matched 0 result of integration_segmentation_3d, losses, 0.001.
matched 0 result of integration_segmentation_3d, best_metric, 0.01.
matched 0 result of integration_segmentation_3d, infer_metric, 0.01.
matched 0 result of integration_segmentation_3d, output_sums, 0.01.
.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: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
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: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
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:0.6621
2/10, train_loss:0.5984
3/10, train_loss:0.5553
4/10, train_loss:0.5258
5/10, train_loss:0.6387
6/10, train_loss:0.4434
7/10, train_loss:0.5204
8/10, train_loss:0.4882
9/10, train_loss:0.4858
10/10, train_loss:0.5107
epoch 1 average loss:0.5429
----------
Epoch 2/6
1/10, train_loss:0.5180
2/10, train_loss:0.5113
3/10, train_loss:0.4100
4/10, train_loss:0.5068
5/10, train_loss:0.4367
6/10, train_loss:0.6062
7/10, train_loss:0.4657
8/10, train_loss:0.4265
9/10, train_loss:0.3888
10/10, train_loss:0.4633
epoch 2 average loss:0.4733
saved new best metric model
current epoch 2 current mean dice: 0.8960 best mean dice: 0.8960 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4727
2/10, train_loss:0.4422
3/10, train_loss:0.5306
4/10, train_loss:0.4464
5/10, train_loss:0.4212
6/10, train_loss:0.4589
7/10, train_loss:0.4084
8/10, train_loss:0.3681
9/10, train_loss:0.4585
10/10, train_loss:0.4754
epoch 3 average loss:0.4482
----------
Epoch 4/6
1/10, train_loss:0.4336
2/10, train_loss:0.3964
3/10, train_loss:0.3937
4/10, train_loss:0.5268
5/10, train_loss:0.4489
6/10, train_loss:0.4493
7/10, train_loss:0.4220
8/10, train_loss:0.4657
9/10, train_loss:0.5309
10/10, train_loss:0.3853
epoch 4 average loss:0.4453
saved new best metric model
current epoch 4 current mean dice: 0.9264 best mean dice: 0.9264 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4024
2/10, train_loss:0.4756
3/10, train_loss:0.4313
4/10, train_loss:0.4372
5/10, train_loss:0.5133
6/10, train_loss:0.4053
7/10, train_loss:0.4140
8/10, train_loss:0.4154
9/10, train_loss:0.3792
10/10, train_loss:0.4164
epoch 5 average loss:0.4290
----------
Epoch 6/6
1/10, train_loss:0.4760
2/10, train_loss:0.3921
3/10, train_loss:0.4284
4/10, train_loss:0.6420
5/10, train_loss:0.3869
6/10, train_loss:0.4096
7/10, train_loss:0.3746
8/10, train_loss:0.4030
9/10, train_loss:0.4116
10/10, train_loss:0.4350
epoch 6 average loss:0.4359
saved new best metric model
current epoch 6 current mean dice: 0.9330 best mean dice: 0.9330 at epoch 6
train completed, best_metric: 0.9330  at epoch: 6
2023-02-10 06:43:54,717 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img0/img0_seg.nii.gz
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2023-02-10 06:43:55,864 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img11/img11_seg.nii.gz
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2023-02-10 06:43:56,766 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img13/img13_seg.nii.gz
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2023-02-10 06:43:57,445 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img15/img15_seg.nii.gz
2023-02-10 06:43:57,894 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img16/img16_seg.nii.gz
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2023-02-10 06:44:05,201 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img33/img33_seg.nii.gz
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2023-02-10 06:44:05,990 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img35/img35_seg.nii.gz
2023-02-10 06:44:06,388 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img36/img36_seg.nii.gz
2023-02-10 06:44:06,778 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img37/img37_seg.nii.gz
2023-02-10 06:44:07,256 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img38/img38_seg.nii.gz
2023-02-10 06:44:07,666 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img39/img39_seg.nii.gz
2023-02-10 06:44:08,063 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img4/img4_seg.nii.gz
2023-02-10 06:44:08,462 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img5/img5_seg.nii.gz
2023-02-10 06:44:08,845 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img6/img6_seg.nii.gz
2023-02-10 06:44:09,247 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img7/img7_seg.nii.gz
2023-02-10 06:44:09,635 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img8/img8_seg.nii.gz
2023-02-10 06:44:10,029 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img9/img9_seg.nii.gz
losses [0.5428896486759186, 0.47332107126712797, 0.4482243746519089, 0.44526649713516236, 0.4289955377578735, 0.43592421114444735]
best metric 0.9330238103866577
infer metric 0.9330325126647949
[0.14221418110696976, 0.15208585182377798, 0.1513897058205168, 0.13967962601912887, 0.18842722322448288, 0.16946449114556605, 0.14666448241871227, 0.1681556678159737, 0.15700726088045847, 0.17919478363108776, 0.16260375535105126, 0.16814699068513195, 0.14436604062597075, 0.11326929669037776, 0.1618907044396175, 0.20103438554573408, 0.17609589027233832, 0.0971353473692992, 0.19394589782420674, 0.2032719334386539, 0.19631397190397853, 0.2085800298746335, 0.1619660769362055, 0.13194106267129266, 0.14814064746136182, 0.14242913546879304, 0.2318148304738647, 0.16099277990981092, 0.14840797220896973, 0.10264401461133835, 0.1185961010722464, 0.12947038940604966, 0.1137130476919966, 0.15194059564840892, 0.16326019107581102, 0.1940761647525908, 0.22307824674270305, 0.18091674446467965, 0.19041087517922825, 0.07408664840526798]
matched 0 result of integration_segmentation_3d, losses, 0.001.
matched 0 result of integration_segmentation_3d, best_metric, 0.01.
matched 0 result of integration_segmentation_3d, infer_metric, 0.01.
matched 0 result of integration_segmentation_3d, output_sums, 0.01.
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: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
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: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
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

----------
Epoch 1/6
1/10, train_loss:0.6621
2/10, train_loss:0.5984
3/10, train_loss:0.5553
4/10, train_loss:0.5258
5/10, train_loss:0.6387
6/10, train_loss:0.4434
7/10, train_loss:0.5204
8/10, train_loss:0.4882
9/10, train_loss:0.4858
10/10, train_loss:0.5107
epoch 1 average loss:0.5429
----------
Epoch 2/6
1/10, train_loss:0.5180
2/10, train_loss:0.5113
3/10, train_loss:0.4100
4/10, train_loss:0.5068
5/10, train_loss:0.4367
6/10, train_loss:0.6062
7/10, train_loss:0.4657
8/10, train_loss:0.4265
9/10, train_loss:0.3888
10/10, train_loss:0.4633
epoch 2 average loss:0.4733
saved new best metric model
current epoch 2 current mean dice: 0.8960 best mean dice: 0.8960 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4727
2/10, train_loss:0.4422
3/10, train_loss:0.5306
4/10, train_loss:0.4464
5/10, train_loss:0.4212
6/10, train_loss:0.4589
7/10, train_loss:0.4084
8/10, train_loss:0.3681
9/10, train_loss:0.4585
10/10, train_loss:0.4754
epoch 3 average loss:0.4482
----------
Epoch 4/6
1/10, train_loss:0.4336
2/10, train_loss:0.3964
3/10, train_loss:0.3937
4/10, train_loss:0.5268
5/10, train_loss:0.4489
6/10, train_loss:0.4493
7/10, train_loss:0.4220
8/10, train_loss:0.4657
9/10, train_loss:0.5309
10/10, train_loss:0.3853
epoch 4 average loss:0.4453
saved new best metric model
current epoch 4 current mean dice: 0.9264 best mean dice: 0.9264 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4024
2/10, train_loss:0.4756
3/10, train_loss:0.4313
4/10, train_loss:0.4372
5/10, train_loss:0.5133
6/10, train_loss:0.4053
7/10, train_loss:0.4140
8/10, train_loss:0.4154
9/10, train_loss:0.3792
10/10, train_loss:0.4164
epoch 5 average loss:0.4290
----------
Epoch 6/6
1/10, train_loss:0.4760
2/10, train_loss:0.3921
3/10, train_loss:0.4284
4/10, train_loss:0.6420
5/10, train_loss:0.3869
6/10, train_loss:0.4096
7/10, train_loss:0.3746
8/10, train_loss:0.4030
9/10, train_loss:0.4116
10/10, train_loss:0.4350
epoch 6 average loss:0.4359
saved new best metric model
current epoch 6 current mean dice: 0.9330 best mean dice: 0.9330 at epoch 6
train completed, best_metric: 0.9330  at epoch: 6
2023-02-10 06:45:20,927 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img0/img0_seg.nii.gz
2023-02-10 06:45:21,281 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img1/img1_seg.nii.gz
2023-02-10 06:45:21,683 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img10/img10_seg.nii.gz
2023-02-10 06:45:22,075 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img11/img11_seg.nii.gz
2023-02-10 06:45:22,476 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img12/img12_seg.nii.gz
2023-02-10 06:45:22,842 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img13/img13_seg.nii.gz
2023-02-10 06:45:23,237 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img14/img14_seg.nii.gz
2023-02-10 06:45:23,644 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img15/img15_seg.nii.gz
2023-02-10 06:45:24,036 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img16/img16_seg.nii.gz
2023-02-10 06:45:24,423 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img17/img17_seg.nii.gz
2023-02-10 06:45:24,797 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img18/img18_seg.nii.gz
2023-02-10 06:45:25,187 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img19/img19_seg.nii.gz
2023-02-10 06:45:25,559 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img2/img2_seg.nii.gz
2023-02-10 06:45:25,929 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img20/img20_seg.nii.gz
2023-02-10 06:45:26,316 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img21/img21_seg.nii.gz
2023-02-10 06:45:26,694 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img22/img22_seg.nii.gz
2023-02-10 06:45:27,078 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img23/img23_seg.nii.gz
2023-02-10 06:45:27,464 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img24/img24_seg.nii.gz
2023-02-10 06:45:27,860 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img25/img25_seg.nii.gz
2023-02-10 06:45:28,340 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img26/img26_seg.nii.gz
2023-02-10 06:45:28,728 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img27/img27_seg.nii.gz
2023-02-10 06:45:29,133 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img28/img28_seg.nii.gz
2023-02-10 06:45:29,522 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img29/img29_seg.nii.gz
2023-02-10 06:45:29,909 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img3/img3_seg.nii.gz
2023-02-10 06:45:30,305 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img30/img30_seg.nii.gz
2023-02-10 06:45:30,686 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img31/img31_seg.nii.gz
2023-02-10 06:45:31,088 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img32/img32_seg.nii.gz
2023-02-10 06:45:31,478 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img33/img33_seg.nii.gz
2023-02-10 06:45:31,882 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img34/img34_seg.nii.gz
2023-02-10 06:45:32,278 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img35/img35_seg.nii.gz
2023-02-10 06:45:32,674 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img36/img36_seg.nii.gz
2023-02-10 06:45:33,066 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img37/img37_seg.nii.gz
2023-02-10 06:45:33,461 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img38/img38_seg.nii.gz
2023-02-10 06:45:33,849 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img39/img39_seg.nii.gz
2023-02-10 06:45:34,227 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img4/img4_seg.nii.gz
2023-02-10 06:45:34,598 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img5/img5_seg.nii.gz
2023-02-10 06:45:34,991 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img6/img6_seg.nii.gz
2023-02-10 06:45:35,382 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img7/img7_seg.nii.gz
2023-02-10 06:45:35,773 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img8/img8_seg.nii.gz
2023-02-10 06:45:36,170 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img9/img9_seg.nii.gz
losses [0.5428896486759186, 0.47332107126712797, 0.4482243746519089, 0.44526649713516236, 0.4289955377578735, 0.43592421114444735]
best metric 0.9330238103866577
infer metric 0.9330325126647949
[0.14221418110696976, 0.15208585182377798, 0.1513897058205168, 0.13967962601912887, 0.18842722322448288, 0.16946449114556605, 0.14666448241871227, 0.1681556678159737, 0.15700726088045847, 0.17919478363108776, 0.16260375535105126, 0.16814699068513195, 0.14436604062597075, 0.11326929669037776, 0.1618907044396175, 0.20103438554573408, 0.17609589027233832, 0.0971353473692992, 0.19394589782420674, 0.2032719334386539, 0.19631397190397853, 0.2085800298746335, 0.1619660769362055, 0.13194106267129266, 0.14814064746136182, 0.14242913546879304, 0.2318148304738647, 0.16099277990981092, 0.14840797220896973, 0.10264401461133835, 0.1185961010722464, 0.12947038940604966, 0.1137130476919966, 0.15194059564840892, 0.16326019107581102, 0.1940761647525908, 0.22307824674270305, 0.18091674446467965, 0.19041087517922825, 0.07408664840526798]
matched 0 result of integration_segmentation_3d, losses, 0.001.
matched 0 result of integration_segmentation_3d, best_metric, 0.01.
matched 0 result of integration_segmentation_3d, infer_metric, 0.01.
matched 0 result of integration_segmentation_3d, output_sums, 0.01.
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: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
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: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
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

----------
Epoch 1/6
1/10, train_loss:0.6621
2/10, train_loss:0.5984
3/10, train_loss:0.5553
4/10, train_loss:0.5258
5/10, train_loss:0.6387
6/10, train_loss:0.4434
7/10, train_loss:0.5204
8/10, train_loss:0.4882
9/10, train_loss:0.4858
10/10, train_loss:0.5107
epoch 1 average loss:0.5429
----------
Epoch 2/6
1/10, train_loss:0.5180
2/10, train_loss:0.5113
3/10, train_loss:0.4100
4/10, train_loss:0.5068
5/10, train_loss:0.4367
6/10, train_loss:0.6062
7/10, train_loss:0.4657
8/10, train_loss:0.4265
9/10, train_loss:0.3888
10/10, train_loss:0.4633
epoch 2 average loss:0.4733
saved new best metric model
current epoch 2 current mean dice: 0.8960 best mean dice: 0.8960 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4727
2/10, train_loss:0.4422
3/10, train_loss:0.5306
4/10, train_loss:0.4464
5/10, train_loss:0.4212
6/10, train_loss:0.4589
7/10, train_loss:0.4084
8/10, train_loss:0.3681
9/10, train_loss:0.4585
10/10, train_loss:0.4754
epoch 3 average loss:0.4482
----------
Epoch 4/6
1/10, train_loss:0.4336
2/10, train_loss:0.3964
3/10, train_loss:0.3937
4/10, train_loss:0.5268
5/10, train_loss:0.4489
6/10, train_loss:0.4493
7/10, train_loss:0.4220
8/10, train_loss:0.4657
9/10, train_loss:0.5309
10/10, train_loss:0.3853
epoch 4 average loss:0.4453
saved new best metric model
current epoch 4 current mean dice: 0.9264 best mean dice: 0.9264 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4024
2/10, train_loss:0.4756
3/10, train_loss:0.4313
4/10, train_loss:0.4372
5/10, train_loss:0.5133
6/10, train_loss:0.4053
7/10, train_loss:0.4140
8/10, train_loss:0.4154
9/10, train_loss:0.3792
10/10, train_loss:0.4164
epoch 5 average loss:0.4290
----------
Epoch 6/6
1/10, train_loss:0.4760
2/10, train_loss:0.3921
3/10, train_loss:0.4284
4/10, train_loss:0.6420
5/10, train_loss:0.3869
6/10, train_loss:0.4096
7/10, train_loss:0.3746
8/10, train_loss:0.4030
9/10, train_loss:0.4116
10/10, train_loss:0.4350
epoch 6 average loss:0.4359
saved new best metric model
current epoch 6 current mean dice: 0.9330 best mean dice: 0.9330 at epoch 6
train completed, best_metric: 0.9330  at epoch: 6
2023-02-10 06:46:37,179 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img0/img0_seg.nii.gz
2023-02-10 06:46:37,507 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img1/img1_seg.nii.gz
2023-02-10 06:46:37,896 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img10/img10_seg.nii.gz
2023-02-10 06:46:38,296 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img11/img11_seg.nii.gz
2023-02-10 06:46:38,729 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img12/img12_seg.nii.gz
2023-02-10 06:46:39,050 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img13/img13_seg.nii.gz
2023-02-10 06:46:39,436 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img14/img14_seg.nii.gz
2023-02-10 06:46:39,824 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img15/img15_seg.nii.gz
2023-02-10 06:46:40,235 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img16/img16_seg.nii.gz
2023-02-10 06:46:40,646 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img17/img17_seg.nii.gz
2023-02-10 06:46:41,040 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img18/img18_seg.nii.gz
2023-02-10 06:46:41,424 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img19/img19_seg.nii.gz
2023-02-10 06:46:41,820 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img2/img2_seg.nii.gz
2023-02-10 06:46:42,207 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img20/img20_seg.nii.gz
2023-02-10 06:46:42,594 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img21/img21_seg.nii.gz
2023-02-10 06:46:42,984 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img22/img22_seg.nii.gz
2023-02-10 06:46:43,379 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img23/img23_seg.nii.gz
2023-02-10 06:46:43,776 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img24/img24_seg.nii.gz
2023-02-10 06:46:44,187 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img25/img25_seg.nii.gz
2023-02-10 06:46:44,557 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img26/img26_seg.nii.gz
2023-02-10 06:46:44,965 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img27/img27_seg.nii.gz
2023-02-10 06:46:45,344 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img28/img28_seg.nii.gz
2023-02-10 06:46:45,753 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img29/img29_seg.nii.gz
2023-02-10 06:46:46,133 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img3/img3_seg.nii.gz
2023-02-10 06:46:46,522 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img30/img30_seg.nii.gz
2023-02-10 06:46:46,914 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img31/img31_seg.nii.gz
2023-02-10 06:46:47,305 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img32/img32_seg.nii.gz
2023-02-10 06:46:47,692 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img33/img33_seg.nii.gz
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losses [0.5428896486759186, 0.47332107126712797, 0.4482243746519089, 0.44526649713516236, 0.4289955377578735, 0.43592421114444735]
best metric 0.9330238103866577
infer metric 0.9330325126647949
[0.14221418110696976, 0.15208585182377798, 0.1513897058205168, 0.13967962601912887, 0.18842722322448288, 0.16946449114556605, 0.14666448241871227, 0.1681556678159737, 0.15700726088045847, 0.17919478363108776, 0.16260375535105126, 0.16814699068513195, 0.14436604062597075, 0.11326929669037776, 0.1618907044396175, 0.20103438554573408, 0.17609589027233832, 0.0971353473692992, 0.19394589782420674, 0.2032719334386539, 0.19631397190397853, 0.2085800298746335, 0.1619660769362055, 0.13194106267129266, 0.14814064746136182, 0.14242913546879304, 0.2318148304738647, 0.16099277990981092, 0.14840797220896973, 0.10264401461133835, 0.1185961010722464, 0.12947038940604966, 0.1137130476919966, 0.15194059564840892, 0.16326019107581102, 0.1940761647525908, 0.22307824674270305, 0.18091674446467965, 0.19041087517922825, 0.07408664840526798]
matched 0 result of integration_segmentation_3d, losses, 0.001.
matched 0 result of integration_segmentation_3d, best_metric, 0.01.
matched 0 result of integration_segmentation_3d, infer_metric, 0.01.
matched 0 result of integration_segmentation_3d, output_sums, 0.01.
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: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
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: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
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


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Accessing lmdb file: /tmp/tmp7g59k41f/monai_cache.lmdb.
----------
Epoch 1/6
1/10, train_loss:0.6621
2/10, train_loss:0.5984
3/10, train_loss:0.5553
4/10, train_loss:0.5258
5/10, train_loss:0.6387
6/10, train_loss:0.4434
7/10, train_loss:0.5204
8/10, train_loss:0.4882
9/10, train_loss:0.4858
10/10, train_loss:0.5107
epoch 1 average loss:0.5429
----------
Epoch 2/6
1/10, train_loss:0.5180
2/10, train_loss:0.5113
3/10, train_loss:0.4100
4/10, train_loss:0.5068
5/10, train_loss:0.4367
6/10, train_loss:0.6062
7/10, train_loss:0.4657
8/10, train_loss:0.4265
9/10, train_loss:0.3888
10/10, train_loss:0.4633
epoch 2 average loss:0.4733
saved new best metric model
current epoch 2 current mean dice: 0.8960 best mean dice: 0.8960 at epoch 2
----------
Epoch 3/6
1/10, train_loss:0.4727
2/10, train_loss:0.4422
3/10, train_loss:0.5306
4/10, train_loss:0.4464
5/10, train_loss:0.4212
6/10, train_loss:0.4589
7/10, train_loss:0.4084
8/10, train_loss:0.3681
9/10, train_loss:0.4585
10/10, train_loss:0.4754
epoch 3 average loss:0.4482
----------
Epoch 4/6
1/10, train_loss:0.4336
2/10, train_loss:0.3964
3/10, train_loss:0.3937
4/10, train_loss:0.5268
5/10, train_loss:0.4489
6/10, train_loss:0.4493
7/10, train_loss:0.4220
8/10, train_loss:0.4657
9/10, train_loss:0.5309
10/10, train_loss:0.3853
epoch 4 average loss:0.4453
saved new best metric model
current epoch 4 current mean dice: 0.9264 best mean dice: 0.9264 at epoch 4
----------
Epoch 5/6
1/10, train_loss:0.4024
2/10, train_loss:0.4756
3/10, train_loss:0.4313
4/10, train_loss:0.4372
5/10, train_loss:0.5133
6/10, train_loss:0.4053
7/10, train_loss:0.4140
8/10, train_loss:0.4154
9/10, train_loss:0.3792
10/10, train_loss:0.4164
epoch 5 average loss:0.4290
----------
Epoch 6/6
1/10, train_loss:0.4760
2/10, train_loss:0.3921
3/10, train_loss:0.4284
4/10, train_loss:0.6420
5/10, train_loss:0.3869
6/10, train_loss:0.4096
7/10, train_loss:0.3746
8/10, train_loss:0.4030
9/10, train_loss:0.4116
10/10, train_loss:0.4350
epoch 6 average loss:0.4359
saved new best metric model
current epoch 6 current mean dice: 0.9330 best mean dice: 0.9330 at epoch 6
train completed, best_metric: 0.9330  at epoch: 6
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2023-02-10 06:48:09,119 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img7/img7_seg.nii.gz
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2023-02-10 06:48:09,887 INFO image_writer.py:197 - writing: /tmp/tmp7g59k41f/output/img9/img9_seg.nii.gz
losses [0.5428896486759186, 0.47332107126712797, 0.4482243746519089, 0.44526649713516236, 0.4289955377578735, 0.43592421114444735]
best metric 0.9330238103866577
infer metric 0.9330325126647949
[0.14221418110696976, 0.15208585182377798, 0.1513897058205168, 0.13967962601912887, 0.18842722322448288, 0.16946449114556605, 0.14666448241871227, 0.1681556678159737, 0.15700726088045847, 0.17919478363108776, 0.16260375535105126, 0.16814699068513195, 0.14436604062597075, 0.11326929669037776, 0.1618907044396175, 0.20103438554573408, 0.17609589027233832, 0.0971353473692992, 0.19394589782420674, 0.2032719334386539, 0.19631397190397853, 0.2085800298746335, 0.1619660769362055, 0.13194106267129266, 0.14814064746136182, 0.14242913546879304, 0.2318148304738647, 0.16099277990981092, 0.14840797220896973, 0.10264401461133835, 0.1185961010722464, 0.12947038940604966, 0.1137130476919966, 0.15194059564840892, 0.16326019107581102, 0.1940761647525908, 0.22307824674270305, 0.18091674446467965, 0.19041087517922825, 0.07408664840526798]
matched 0 result of integration_segmentation_3d, losses, 0.001.
matched 0 result of integration_segmentation_3d, best_metric, 0.01.
matched 0 result of integration_segmentation_3d, infer_metric, 0.01.
matched 0 result of integration_segmentation_3d, output_sums, 0.01.
.
----------------------------------------------------------------------
Ran 2 tests in 465.321s

OK