nnUNet | Image datasets | Machine Learning library
kandi X-RAY | nnUNet Summary
kandi X-RAY | nnUNet Summary
[2020_10_21] Update: We now have documentation for common questions and common issues. We now also provide reference epoch times for several datasets and tips on how to identify bottlenecks.
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Top functions reviewed by kandi - BETA
- Predict cases with AMOS2022
- Load postprocessing
- Perform a single step
- Restore a trained model from a checkpoint
- Load model and checkpoint files
- Score model based on rank and rank
- Convert labels back to Brats
- Apply threshold to folder_in to folder_out
- Evaluate BTS folder
- Plan the experiment
- Compute the properties for a stage
- Get default augmentation
- Summarize a list of models
- Restore the trained model from a checkpoint
- Ensembles the training folder
- Performs a single step of the optimizer
- Resample data using preprocessor
- Predict a model from a folder
- Compute properties for a given stage
- Sets preprocessing configuration
- Run training
- Validate the model
- Verify that the dataset is valid
- Collect and prepare and prepare the results for each experiment
- Validate the network
- Validate the dataset
- Generate training batch
- Generate a training batch
nnUNet Key Features
nnUNet Examples and Code Snippets
Lines of Code : 27License : Permissive (Apache-2.0)
CopynnUNet_train 2d nnUNetTrainerV2 Task600_Thyroid2D 0
nnUNet_train 2d nnUNetTrainerV2 Task600_Thyroid2D 1
nnUNet_train 2d nnUNetTrainerV2 Task600_Thyroid2D 2
nnUNet_train 2d nnUNetTrainerV2 Task600_Thyroid2D 3
nnUNet_train 2d nnUNetTrainerV2 Task600_Th python get_flops.py -m CONFIGURATION
from fvcore import FlopCountAnalysis
# The input_size of the baseline model is 1*1*80*192*160
inputs = (torch.randn(input_size),)
flops = FlopCountAnalysis(model, inputs)
python get_params.py -m CONFIGURATION
from torchsummary import summary
# The input_size of the baseline model is 1*80*192*160
summary(model, input_size)
os.mkdir(os.path.join("/", *splits[:i+1]))
base = "/home/pere/Tortuosity/nnUNet_base"
Community Discussions
Trending Discussions on nnUNet
QUESTION
I try to run my python3 based Singularity image on a remote machine, but I get the following error that I do not get with other machines:
...ANSWER
Answered 2020-Aug-28 at 08:20This is often due to environmental variables being passed on, or not passed on, to the container without noticing. To ensure this is not an issue, you can use -e or --cleanenv. This will prevent any variables not prefixed with SINGULARITYENV_ from being loaded into the container.
That said, the warning WARNING: skipping mount of sysfs: no such file or directory is also concerning: singularity was unable to mount /sys into the image because it doesn't exist on the host server. That particular python error also seems to be specific to windows 10. Singularity doesn't currently support windows, even with the magic of WSL2.
QUESTION
I try to extend the pytorch docker image with my definition file nnunet.def:
ANSWER
Answered 2020-Aug-28 at 08:08Short answer: the value of $PATH in %post is different from when you're running in shell, so it doesn't know where to look.
If you look at where pip is located (which pip) in either the docker or singularity image, it is at /opt/conda/bin/pip. The default path used in %post is /bin:/sbin:/usr/bin:/usr/sbin:/usr/local/bin:/usr/local/sbin.
When you see an error stating that a command is not available when run as part of a script but it is when you run interactively, it is almost always an environmental issue and PATH, PYTHONPATH, PERL5LIB, etc. are the frequent culprits.
If you add export PATH=/opt/conda/bin:$PATH to the beginning of the %post block it should solve this issue.
QUESTION
I am trying to get a deep learning network (https://github.com/MIC-DKFZ/nnUNet) to work with my own dataset and I am having trouble with the paths. I have used several approaches to define my paths. The authors import the following packages for this issue:
import os
from batchgenerators.utilities.file_and_folder_operations import maybe_mkdir_p, join
With this, I have tried the following lines, separately:
base = os.environ["nnUNet_base"]
base = join("Tortuosity", "nnUNet_base")
base = "Tortuosity/nnUNet_base"
I have the nnUNet_base directory inside the Tortuositydirectory. With the first approach it seems that it is not registering the directory correctly (I ask for print("base =", base) and in return I get None. For the second and third approaches, I obtain the following error:
ANSWER
Answered 2020-Jan-09 at 12:22The error says this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install nnUNet
Install PyTorch. You need at least version 1.6
Install nnU-Net depending on your use case: For use as standardized baseline, out-of-the-box segmentation algorithm or for running inference with pretrained models: pip install nnunet For use as integrative framework (this will create a copy of the nnU-Net code on your computer so that you can modify it as needed): git clone https://github.com/MIC-DKFZ/nnUNet.git cd nnUNet pip install -e .
nnU-Net needs to know where you intend to save raw data, preprocessed data and trained models. For this you need to set a few of environment variables. Please follow the instructions here.
(OPTIONAL) Install hiddenlayer. hiddenlayer enables nnU-net to generate plots of the network topologies it generates (see Model training). To install hiddenlayer, run the following commands: pip install --upgrade git+https://github.com/FabianIsensee/hiddenlayer.git@more_plotted_details#egg=hiddenlayer
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