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haesleinhuepfnapari-aydin's Issues
Unclear terminal output: looping loop
Hi @AhmetCanSolak ,
I'm just testing napari-aydin and have some questions about the terminal output. First of all, the predicted time works well, that's super cool! I'm just wondering about the progress loss/quality-wise and number of iterations.
Used dataset: C2-DrosophilaEggChamber-small.zip
(Source: Image data adapted from Sarah Machado, Vincent Mercier, & Nicolas Chiaruttini. (2018). LimeSeg Test Datasets [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1472859 Shared under CC-BY 4.0 )
Used code/napari-aydin version: haesleinhuepf@62b98bd
To reproduce setup an environment like this:
mamba create --name aydin-env python=3.9 napari=0.4.15 -c conda-forge
conda activate aydin-env
cd napari-aydin
pip install -e .
napari
Then, load the tif image linked above and click the menu Tools Filtering / noise removal > noise2self (fgr-catboost, aydin) and Run.
The output on the terminal looks like this:
0: learn: 0.0732909 test: 0.0729163 best: 0.0729163 (0) total: 102ms remaining: 6m 55s
50: learn: 0.0369519 test: 0.0369845 best: 0.0369845 (50) total: 2.59s remaining: 3m 25s
100: learn: 0.0367526 test: 0.0368248 best: 0.0368248 (100) total: 5.28s remaining: 3m 28s
150: learn: 0.0366542 test: 0.0367643 best: 0.0367641 (149) total: 8.01s remaining: 3m 29s
200: learn: 0.0365809 test: 0.0367242 best: 0.0367242 (200) total: 10.8s remaining: 3m 29s
250: learn: 0.0365174 test: 0.0366979 best: 0.0366979 (250) total: 13.7s remaining: 3m 29s
300: learn: 0.0364652 test: 0.0366801 best: 0.0366801 (300) total: 16.5s remaining: 3m 27s
350: learn: 0.0364226 test: 0.0366740 best: 0.0366735 (336) total: 19.3s remaining: 3m 25s
400: learn: 0.0363853 test: 0.0366663 best: 0.0366662 (398) total: 22.1s remaining: 3m 23s
450: learn: 0.0363446 test: 0.0366621 best: 0.0366621 (449) total: 25s remaining: 3m 21s
500: learn: 0.0363036 test: 0.0366576 best: 0.0366576 (500) total: 27.9s remaining: 3m 20s
550: learn: 0.0362705 test: 0.0366541 best: 0.0366534 (546) total: 30.7s remaining: 3m 17s
600: learn: 0.0362322 test: 0.0366508 best: 0.0366504 (598) total: 33.5s remaining: 3m 15s
bestTest = 0.03664927588
bestIteration = 608
Shrink model to first 609 iterations.
0: learn: 0.0632866 test: 0.0630592 best: 0.0630592 (0) total: 105ms remaining: 7m 8s
50: learn: 0.0382550 test: 0.0382955 best: 0.0378260 (35) total: 2.94s remaining: 3m 52s
bestTest = 0.03782599262
bestIteration = 35
Shrink model to first 36 iterations.
0: learn: 0.0700986 test: 0.0697696 best: 0.0697696 (0) total: 114ms remaining: 7m 48s
50: learn: 0.0370615 test: 0.0370972 best: 0.0370943 (49) total: 2.89s remaining: 3m 48s
bestTest = 0.03709432256
bestIteration = 49
Shrink model to first 50 iterations.
0: learn: 0.0741147 test: 0.0737288 best: 0.0737288 (0) total: 123ms remaining: 8m 22s
50: learn: 0.0370428 test: 0.0370653 best: 0.0370653 (50) total: 2.88s remaining: 3m 48s
100: learn: 0.0367941 test: 0.0368586 best: 0.0368586 (100) total: 5.61s remaining: 3m 41s
150: learn: 0.0366860 test: 0.0367834 best: 0.0367834 (150) total: 8.39s remaining: 3m 39s
200: learn: 0.0366146 test: 0.0367420 best: 0.0367420 (200) total: 11.3s remaining: 3m 38s
250: learn: 0.0365580 test: 0.0367152 best: 0.0367152 (250) total: 14.1s remaining: 3m 36s
300: learn: 0.0365142 test: 0.0366978 best: 0.0366978 (300) total: 16.9s remaining: 3m 32s
350: learn: 0.0364692 test: 0.0366818 best: 0.0366818 (350) total: 19.7s remaining: 3m 30s
400: learn: 0.0364306 test: 0.0366710 best: 0.0366710 (400) total: 22.6s remaining: 3m 28s
450: learn: 0.0363936 test: 0.0366628 best: 0.0366628 (450) total: 25.7s remaining: 3m 27s
500: learn: 0.0363594 test: 0.0366551 best: 0.0366551 (500) total: 28.6s remaining: 3m 25s
550: learn: 0.0363260 test: 0.0366506 best: 0.0366503 (544) total: 31.5s remaining: 3m 22s
600: learn: 0.0362964 test: 0.0366482 best: 0.0366482 (600) total: 34.3s remaining: 3m 19s
650: learn: 0.0362628 test: 0.0366435 best: 0.0366435 (650) total: 37.3s remaining: 3m 17s
700: learn: 0.0362308 test: 0.0366365 best: 0.0366365 (700) total: 40.2s remaining: 3m 14s
750: learn: 0.0362042 test: 0.0366361 best: 0.0366357 (736) total: 43s remaining: 3m 11s
800: learn: 0.0361744 test: 0.0366333 best: 0.0366328 (788) total: 45.8s remaining: 3m 8s
850: learn: 0.0361499 test: 0.0366327 best: 0.0366325 (824) total: 48.6s remaining: 3m 5s
bestTest = 0.03663248625
bestIteration = 824
Shrink model to first 825 iterations.
0: learn: 0.0762177 test: 0.0758036 best: 0.0758036 (0) total: 105ms remaining: 7m 11s
50: learn: 0.0396266 test: 0.0396529 best: 0.0396529 (50) total: 2.99s remaining: 3m 57s
100: learn: 0.0370520 test: 0.0370753 best: 0.0370753 (100) total: 5.63s remaining: 3m 42s
150: learn: 0.0368759 test: 0.0369205 best: 0.0369205 (150) total: 8.38s remaining: 3m 39s
200: learn: 0.0367917 test: 0.0368522 best: 0.0368522 (200) total: 11.2s remaining: 3m 36s
250: learn: 0.0367278 test: 0.0368030 best: 0.0368030 (250) total: 14s remaining: 3m 34s
300: learn: 0.0366841 test: 0.0367743 best: 0.0367743 (300) total: 16.8s remaining: 3m 31s
350: learn: 0.0366454 test: 0.0367499 best: 0.0367499 (350) total: 19.6s remaining: 3m 29s
400: learn: 0.0366116 test: 0.0367287 best: 0.0367287 (400) total: 22.5s remaining: 3m 27s
450: learn: 0.0365797 test: 0.0367121 best: 0.0367121 (450) total: 25.3s remaining: 3m 24s
500: learn: 0.0365525 test: 0.0367000 best: 0.0367000 (500) total: 28.2s remaining: 3m 22s
550: learn: 0.0365254 test: 0.0366885 best: 0.0366885 (550) total: 31s remaining: 3m 19s
600: learn: 0.0365019 test: 0.0366795 best: 0.0366795 (600) total: 33.9s remaining: 3m 17s
650: learn: 0.0364785 test: 0.0366721 best: 0.0366721 (650) total: 36.9s remaining: 3m 15s
700: learn: 0.0364572 test: 0.0366655 best: 0.0366655 (700) total: 39.7s remaining: 3m 12s
750: learn: 0.0364370 test: 0.0366599 best: 0.0366598 (748) total: 42.7s remaining: 3m 10s
800: learn: 0.0364155 test: 0.0366526 best: 0.0366525 (798) total: 45.6s remaining: 3m 7s
850: learn: 0.0363972 test: 0.0366481 best: 0.0366481 (850) total: 48.5s remaining: 3m 4s
900: learn: 0.0363786 test: 0.0366429 best: 0.0366429 (900) total: 51.4s remaining: 3m 2s
950: learn: 0.0363597 test: 0.0366384 best: 0.0366384 (944) total: 54.4s remaining: 2m 59s
1000: learn: 0.0363409 test: 0.0366353 best: 0.0366353 (1000) total: 57.2s remaining: 2m 56s
1050: learn: 0.0363238 test: 0.0366313 best: 0.0366313 (1050) total: 1m remaining: 2m 54s
1100: learn: 0.0363075 test: 0.0366281 best: 0.0366280 (1099) total: 1m 3s remaining: 2m 51s
1150: learn: 0.0362911 test: 0.0366255 best: 0.0366254 (1149) total: 1m 5s remaining: 2m 48s
1200: learn: 0.0362727 test: 0.0366219 best: 0.0366219 (1200) total: 1m 8s remaining: 2m 45s
1250: learn: 0.0362578 test: 0.0366192 best: 0.0366192 (1250) total: 1m 11s remaining: 2m 42s
1300: learn: 0.0362428 test: 0.0366172 best: 0.0366171 (1299) total: 1m 14s remaining: 2m 39s
1350: learn: 0.0362274 test: 0.0366141 best: 0.0366141 (1350) total: 1m 20s remaining: 2m 43s
1400: learn: 0.0362117 test: 0.0366126 best: 0.0366123 (1394) total: 1m 30s remaining: 2m 54s
1450: learn: 0.0361944 test: 0.0366110 best: 0.0366110 (1450) total: 1m 42s remaining: 3m 6s
1500: learn: 0.0361792 test: 0.0366101 best: 0.0366098 (1492) total: 1m 57s remaining: 3m 22s
1550: learn: 0.0361657 test: 0.0366087 best: 0.0366087 (1550) total: 2m 11s remaining: 3m 35s
1600: learn: 0.0361493 test: 0.0366078 best: 0.0366075 (1592) total: 2m 25s remaining: 3m 47s
1650: learn: 0.0361345 test: 0.0366075 best: 0.0366074 (1624) total: 2m 40s remaining: 3m 57s
bestTest = 0.0366073658
bestIteration = 1624
Shrink model to first 1625 iterations.
Two questions:
- Would it be possible to clarify the output? In the block
bestTest, bestIteration: could it print out how often it will do this? It appears training is running in a loop. A novice user couuld conclude that the algorithm runs in an endless loop. - Would it be possible to limit the number of iterations? The
learn/testoutput above seems not improving much after the first 150 iterations. Can we specify150as max?
And for completeness, a screenshot. The results look fantasti!
Thanks!
Best,
Robert
Running napari fails with napari-aydin installed
Hi @AhmetCanSolak ,
I just tried to run the plugin from this branch.
I set up a new conda environment and only installed pip install -e . The installation worked ok, but when starting napari, I receive this error: DLL load failed while importing shell: The specified procedure could not be found. Not sure which DLL it is trying to load.
Any hint is appreciated!
Here are more details:
(aydin) C:\structure\code\napari-aydin>napari
Traceback (most recent call last):
File "C:\Users\rober\miniconda3\envs\aydin\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\rober\miniconda3\envs\aydin\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\rober\miniconda3\envs\aydin\Scripts\napari.exe\__main__.py", line 7, in <module>
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\__main__.py", line 431, in main
_run()
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\__main__.py", line 230, in _run
from napari import run, view_path
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\_lazy.py", line 47, in __getattr__
submod = import_module(f'{module_name}.{attr_to_modules[name]}')
File "C:\Users\rober\miniconda3\envs\aydin\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\view_layers.py", line 17, in <module>
from .viewer import Viewer
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\viewer.py", line 5, in <module>
from .components.viewer_model import ViewerModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\components\__init__.py", line 17, in <module>
from .camera import Camera
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\components\camera.py", line 6, in <module>
from ..utils.events import EventedModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\__init__.py", line 2, in <module>
from .colormaps import Colormap
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\colormaps\__init__.py", line 2, in <module>
from .colormap import Colormap
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\colormaps\colormap.py", line 7, in <module>
from ..events import EventedModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\events\__init__.py", line 1, in <module>
from .event import EmitterGroup, Event, EventEmitter # isort:skip
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\events\event.py", line 71, in <module>
from ..translations import trans
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\translations.py", line 14, in <module>
from ._base import _DEFAULT_CONFIG_PATH, _DEFAULT_LOCALE
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\_base.py", line 14, in <module>
_DEFAULT_CONFIG_PATH = user_config_dir(_APPNAME, _APPAUTHOR, _FILENAME)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 196, in user_config_dir
path = user_data_dir(appname, appauthor, None, roaming)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 81, in user_data_dir
path = os.path.normpath(_get_win_folder(const))
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 480, in _get_win_folder_with_pywin32
from win32com.shell import shellcon, shell
ImportError: DLL load failed while importing shell: The specified procedure could not be found.
(aydin) C:\structure\code\napari-aydin>napari
Traceback (most recent call last):
File "C:\Users\rober\miniconda3\envs\aydin\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\rober\miniconda3\envs\aydin\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\rober\miniconda3\envs\aydin\Scripts\napari.exe\__main__.py", line 7, in <module>
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\__main__.py", line 431, in main
_run()
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\__main__.py", line 230, in _run
from napari import run, view_path
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\_lazy.py", line 47, in __getattr__
submod = import_module(f'{module_name}.{attr_to_modules[name]}')
File "C:\Users\rober\miniconda3\envs\aydin\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\view_layers.py", line 17, in <module>
from .viewer import Viewer
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\viewer.py", line 5, in <module>
from .components.viewer_model import ViewerModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\components\__init__.py", line 17, in <module>
from .camera import Camera
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\components\camera.py", line 6, in <module>
from ..utils.events import EventedModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\__init__.py", line 2, in <module>
from .colormaps import Colormap
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\colormaps\__init__.py", line 2, in <module>
from .colormap import Colormap
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\colormaps\colormap.py", line 7, in <module>
from ..events import EventedModel
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\events\__init__.py", line 1, in <module>
from .event import EmitterGroup, Event, EventEmitter # isort:skip
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\events\event.py", line 71, in <module>
from ..translations import trans
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\translations.py", line 14, in <module>
from ._base import _DEFAULT_CONFIG_PATH, _DEFAULT_LOCALE
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\napari\utils\_base.py", line 14, in <module>
_DEFAULT_CONFIG_PATH = user_config_dir(_APPNAME, _APPAUTHOR, _FILENAME)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 196, in user_config_dir
path = user_data_dir(appname, appauthor, None, roaming)
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 81, in user_data_dir
path = os.path.normpath(_get_win_folder(const))
File "C:\Users\rober\miniconda3\envs\aydin\lib\site-packages\appdirs.py", line 480, in _get_win_folder_with_pywin32
from win32com.shell import shellcon, shell
ImportError: DLL load failed while importing shell: The specified procedure could not be found.
(aydin) C:\structure\code\napari-aydin>
That's my conda list:
# packages in environment at C:\Users\rober\miniconda3\envs\aydin:
#
# Name Version Build Channel
absl-py 1.1.0 pypi_0 pypi
alabaster 0.7.12 pypi_0 pypi
appdirs 1.4.4 pypi_0 pypi
asciitree 0.3.3 pypi_0 pypi
asttokens 2.0.5 pypi_0 pypi
astunparse 1.6.3 pypi_0 pypi
attrs 21.4.0 pypi_0 pypi
aydin 0.1.14 pypi_0 pypi
babel 2.10.3 pypi_0 pypi
backcall 0.2.0 pypi_0 pypi
beautifulsoup4 4.11.1 pypi_0 pypi
ca-certificates 2022.4.26 haa95532_0
cachetools 5.2.0 pypi_0 pypi
cachey 0.2.1 pypi_0 pypi
catboost 1.0.5 pypi_0 pypi
certifi 2022.6.15 py39haa95532_0
charset-normalizer 2.0.12 pypi_0 pypi
click 8.0.3 pypi_0 pypi
cloudpickle 2.1.0 pypi_0 pypi
colorama 0.4.5 pypi_0 pypi
cycler 0.11.0 pypi_0 pypi
czifile 2019.7.2 pypi_0 pypi
dask 2022.6.1 pypi_0 pypi
debugpy 1.6.0 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
docstring-parser 0.13 pypi_0 pypi
docutils 0.18.1 pypi_0 pypi
entrypoints 0.4 pypi_0 pypi
executing 0.8.3 pypi_0 pypi
fasteners 0.17.3 pypi_0 pypi
filelock 3.7.1 pypi_0 pypi
flatbuffers 2.0 pypi_0 pypi
fonttools 4.33.3 pypi_0 pypi
freetype-py 2.3.0 pypi_0 pypi
fsspec 2022.5.0 pypi_0 pypi
gast 0.5.3 pypi_0 pypi
gdown 4.2.0 pypi_0 pypi
git 2.34.1 haa95532_0
google-auth 2.8.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
googledrivedownloader 0.4 pypi_0 pypi
grpcio 1.47.0 pypi_0 pypi
h5py 3.7.0 pypi_0 pypi
heapdict 1.0.1 pypi_0 pypi
hsluv 5.0.3 pypi_0 pypi
idna 3.3 pypi_0 pypi
imagecodecs 2022.2.22 pypi_0 pypi
imageio 2.19.3 pypi_0 pypi
imagesize 1.3.0 pypi_0 pypi
importlib-metadata 4.10.0 pypi_0 pypi
ipykernel 6.15.0 pypi_0 pypi
ipython 8.4.0 pypi_0 pypi
ipython-genutils 0.2.0 pypi_0 pypi
jedi 0.18.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
joblib 1.1.0 pypi_0 pypi
jsonpickle 1.3 pypi_0 pypi
jsonschema 4.6.0 pypi_0 pypi
jupyter-client 7.3.4 pypi_0 pypi
jupyter-core 4.10.0 pypi_0 pypi
keras 2.8.0 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.4.3 pypi_0 pypi
libclang 14.0.1 pypi_0 pypi
lightgbm 3.3.1 pypi_0 pypi
llvmlite 0.38.1 pypi_0 pypi
locket 1.0.0 pypi_0 pypi
magicgui 0.5.1 pypi_0 pypi
markdown 3.3.7 pypi_0 pypi
markupsafe 2.1.1 pypi_0 pypi
matplotlib 3.5.2 pypi_0 pypi
matplotlib-inline 0.1.3 pypi_0 pypi
memoization 0.4.0 pypi_0 pypi
napari 0.4.12 pypi_0 pypi
napari-aydin 0.1.dev5+gc66e472.d20220626 dev_0 <develop>
napari-console 0.0.4 pypi_0 pypi
napari-plugin-engine 0.2.0 pypi_0 pypi
napari-svg 0.1.6 pypi_0 pypi
nd2reader 3.3.0 pypi_0 pypi
nest-asyncio 1.5.5 pypi_0 pypi
networkx 2.8.4 pypi_0 pypi
numba 0.55.1 pypi_0 pypi
numcodecs 0.10.0 pypi_0 pypi
numexpr 2.8.1 pypi_0 pypi
numpy 1.21.6 pypi_0 pypi
numpydoc 1.4.0 pypi_0 pypi
oauthlib 3.2.0 pypi_0 pypi
openssl 1.1.1o h2bbff1b_0
opt-einsum 3.3.0 pypi_0 pypi
packaging 21.3 pypi_0 pypi
pandas 1.4.3 pypi_0 pypi
parso 0.8.3 pypi_0 pypi
partd 1.2.0 pypi_0 pypi
pickleshare 0.7.5 pypi_0 pypi
pillow 9.1.1 pypi_0 pypi
pims 0.6.1 pypi_0 pypi
pint 0.19.2 pypi_0 pypi
pip 21.2.4 py39haa95532_0
plotly 5.9.0 pypi_0 pypi
prompt-toolkit 3.0.29 pypi_0 pypi
protobuf 3.20.1 pypi_0 pypi
psutil 5.9.1 pypi_0 pypi
psygnal 0.3.5 pypi_0 pypi
pure-eval 0.2.2 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pydantic 1.9.1 pypi_0 pypi
pygments 2.12.0 pypi_0 pypi
pynndescent 0.5.5 pypi_0 pypi
pyopengl 3.1.6 pypi_0 pypi
pyparsing 3.0.9 pypi_0 pypi
pyqt5 5.15.6 pypi_0 pypi
pyqt5-qt5 5.15.2 pypi_0 pypi
pyqt5-sip 12.11.0 pypi_0 pypi
pyrsistent 0.18.1 pypi_0 pypi
pysocks 1.7.1 pypi_0 pypi
python 3.9.12 h6244533_0
python-dateutil 2.8.2 pypi_0 pypi
python-graphviz 0.20 pypi_0 pypi
pytz 2022.1 pypi_0 pypi
pywavelets 1.3.0 pypi_0 pypi
pywin32 304 pypi_0 pypi
pyyaml 6.0 pypi_0 pypi
pyzmq 23.2.0 pypi_0 pypi
qdarkstyle 3.0.2 pypi_0 pypi
qtconsole 5.2.2 pypi_0 pypi
qtpy 1.11.2 pypi_0 pypi
requests 2.28.0 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
rsa 4.8 pypi_0 pypi
scikit-image 0.18.3 pypi_0 pypi
scikit-learn 1.1.1 pypi_0 pypi
scipy 1.7.3 pypi_0 pypi
setuptools 61.2.0 py39haa95532_0
six 1.16.0 pypi_0 pypi
slicerator 1.1.0 pypi_0 pypi
snowballstemmer 2.2.0 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
sphinx 5.0.2 pypi_0 pypi
sphinxcontrib-applehelp 1.0.2 pypi_0 pypi
sphinxcontrib-devhelp 1.0.2 pypi_0 pypi
sphinxcontrib-htmlhelp 2.0.0 pypi_0 pypi
sphinxcontrib-jsmath 1.0.1 pypi_0 pypi
sphinxcontrib-qthelp 1.0.3 pypi_0 pypi
sphinxcontrib-serializinghtml 1.1.5 pypi_0 pypi
sqlite 3.38.5 h2bbff1b_0
stack-data 0.3.0 pypi_0 pypi
superqt 0.3.2 pypi_0 pypi
tenacity 8.0.1 pypi_0 pypi
tensorboard 2.8.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow 2.8.1 pypi_0 pypi
tensorflow-estimator 2.8.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.26.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tifffile 2022.5.4 pypi_0 pypi
toolz 0.11.2 pypi_0 pypi
torch 1.10.1 pypi_0 pypi
tornado 6.1 pypi_0 pypi
tqdm 4.64.0 pypi_0 pypi
traitlets 5.3.0 pypi_0 pypi
typing-extensions 4.2.0 pypi_0 pypi
tzdata 2022a hda174b7_0
urllib3 1.26.9 pypi_0 pypi
vc 14.2 h21ff451_1
vispy 0.10.0 pypi_0 pypi
vs2015_runtime 14.27.29016 h5e58377_2
wcwidth 0.2.5 pypi_0 pypi
werkzeug 2.1.2 pypi_0 pypi
wheel 0.37.1 pyhd3eb1b0_0
wincertstore 0.2 py39haa95532_2
wrapt 1.14.1 pypi_0 pypi
xmltodict 0.13.0 pypi_0 pypi
zarr 2.4.0 pypi_0 pypi
zipp 3.8.0 pypi_0 pypi
Example data + minimal documentation
Hi @AhmetCanSolak ,
I think it would be good to add some minimal documentation and I'm happy to do this for you if you like. It would be a super short introduction to the tools similar to here. I would also add links to aydin.
Therefore, it would be good to have a small example-dataset, e.g. like the crop of the Drosophila egg chamber I used in #7 . Do you have such a dataset? We could make it part of the plugin and ship it, if it's not too large (< 5 MB?).
Let me know what you thiink!
Best,
Robert
Out-of-memory error / napari crash
Hi @AhmetCanSolak ,
I'm just trying to run napari-aydin on a 40 MB image stack using an RTX 3050 Ti with 4 GB of memory, using Windows 10. It unfortunately crashes after 2 minutes with an out-of-memory error,
You can download the data here: Sarah Machado, Vincent Mercier, & Nicolas Chiaruttini. (2018). LimeSeg Test Datasets [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1472859 Shared under CC-BY 4.0
To reproduce:
- Setup the conda environment as explained in #7
- Open the example data
- Extract one channel using this command in the napari console:
viewer.layers[0].data = viewer.layers[0].data[:,1]
- Apply the menu
Tools > Filtering / noise removal > noise2self (fgr-catboost, aydin), clickRun. - Napari crashes on my machine after about 2 minutes. It produces this output:
Warning: less than 75% gpu memory available for training. Free: 3018.75 Total: 4095.5
Application terminated with error: PyInit__catboost+1853100 (0x7FFFF17187BC)
PyInit__catboost+9801470 (0x7FFFF1EAD00E)
PyInit__catboost+9799327 (0x7FFFF1EAC7AF)
PyInit__catboost+9802031 (0x7FFFF1EAD23F)
PyInit__catboost+9793366 (0x7FFFF1EAB066)
??+0 (0x7FFFF13B1EAB)
??+0 (0x7FFFF13175F9)
??+0 (0x7FFFF12E0830)
PyInit__catboost+9794746 (0x7FFFF1EAB5CA)
PyInit__catboost+9796140 (0x7FFFF1EABB3C)
??+0 (0x7FFFF13B8189)
PyInit__catboost+13330740 (0x7FFFF220AA44)
BaseThreadInitThunk+20 (0x7FF8A0517034)
RtlUserThreadStart+33 (0x7FF8A1982651)
(class NCudaLib::TOutOfMemoryError) C:/Program Files (x86)/Go Agent/pipelines/BuildMaster/catboost.git/catboost/cuda/cuda_lib/memory_pool/stack_like_memory_pool.h:303:
Error: Out of memory. Requested 83.10693359 MB; Free 22.06982422 MB
I'm wondering if it might make sense to
- Limit the training patch to a small size as done here and/or
- Apply the denoiser in patches as done here.
Let me know if I can help!
Thanks again.
Best,
Robert
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