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.git
.cache
.mypy_cache
.gitignore
*.py[cod]
*$py.class
*.vscode
*.so
*.sif
__pycache__/
/nndet.egg-info
/build
/notebooks
/projects
/singularity
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---
name: Bug
about: Report a bug
title: "[Bug]"
labels: Bug
assignees: ''
---
### :skull: Bug
<!-- Please provide a short and cleaer describtion of the bug. What happens? What should happen? If applicable, please include the full error message. -->
### Environment
Please provide some information about the used environment.
<!--
Please run the following command to collect environment information and paste them below:
If nnDetection is already installed:
```bash
nndet_env
```
otherwise execute the script directly from the nndetection source folder:
```bash
python scripts/utils.py
```
-->
How was nnDetection installed [`docker` | `source`]:
Environment Information:
```text
[paste here]
```
If necessary, please provide the used run command with all overwrites:
```text
[paste here]
```
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### :question: Question
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__pycache__/
*.py[cod]
*$py.class
*.vscode
*.simg
*.sif
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
......@@ -19,13 +23,9 @@ lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
......@@ -40,16 +40,13 @@ pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
*,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
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# PyBuilder
target/
# Jupyter Notebook
# IPython Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
# celery beat schedule file
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
# dotenv
.env
.venv
env/
# virtualenv
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
*.memmap
*.png
*.zip
*.npz
*.npy
*.jpg
*.jpeg
.idea
*.txt
.idea/*
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*.nii
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*.bmp
*.pkl
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*.pdf
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#Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
# Contains pytorch, torchvision, cuda, cudnn
FROM nvcr.io/nvidia/pytorch:20.12-py3
ARG env_det_num_threads=6
ARG env_det_verbose=1
# Setup environment variables
ENV det_data=/opt/data det_models=/opt/models det_num_threads=$env_det_num_threads det_verbose=$env_det_verbose OMP_NUM_THREADS=1
# Install some tools
RUN apt-get update && export DEBIAN_FRONTEND=noninteractive && apt-get install -y \
git \
cmake \
make \
wget \
gnupg \
build-essential \
software-properties-common \
gdb \
ninja-build
RUN pip install numpy
# Install own code
COPY ./requirements.txt .
RUN mkdir ${det_data} \
&& mkdir ${det_models} \
&& mkdir -p /opt/code/nndet \
&& pip install -r requirements.txt \
&& pip install hydra-core --upgrade --pre \
&& pip install git+https://github.com/mibaumgartner/pytorch_model_summary.git
WORKDIR /opt/code/nndet
COPY . .
RUN FORCE_CUDA=1 pip install -v -e .
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################################################################################
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include README.md
include LICENSE
global-exclude *.pyc
global-exclude __pycache__
recursive-include nndet/conf *
README.md
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<div align="center">
<img src=docs/source/nnDetection.svg width="600px">
![Version](https://img.shields.io/badge/nnDetection-v0.1-blue)
![Python](https://img.shields.io/badge/python-3.8+-orange)
![CUDA](https://img.shields.io/badge/CUDA-10.1%2F10.2%2F11.0-green)
![license](https://img.shields.io/badge/License-Apache%202.0-red.svg)
</div>
# What is nnDetection?
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions depend on rating of objects rather than e.g. pixels.
For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck.
Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success.
Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection.
The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art.
We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 10 further public data sets for a comprehensive evaluation of medical object detection methods.
# Installation
## Docker
The easiest way to get started with nnDetection is the provided is to build a Docker Container with the provided Dockerfile.
Please install docker and [nvidia-docker2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) before continuing.
All projects which are based on nnDetection assume that the base image was built with the following tagging scheme `nnDetection:[version]`.
To build a container (nnDetection Version 0.1) run the following command from the base directory:
```bash
docker build -t nndetection:0.1 --build-arg env_det_num_threads=6 --build-arg env_det_verbose=1 .
```
(`--build-arg env_det_num_threads=6` and `--build-arg env_det_verbose=1` are optional and are used to overwrite the provided default parameters)
The docker container expects data and models in its own `/opt/data` and `/opt/models` directories respectively.
The directories need to be mounted via docker `-v`. For simplicity and speed, the ENV variables `det_data` and `det_models` can be set in the host system to point to the desired directories. To run:
```bash
docker run --gpus all -v ${det_data}:/opt/data -v ${det_models}:/opt/models -it --shm-size=24gb nndetection:0.1 /bin/bash
```
Warning:
When running a training inside the container it is necessary to [increase the shared memory](https://stackoverflow.com/questions/30210362/how-to-increase-the-size-of-the-dev-shm-in-docker-container) (via --shm-size).
## Source
1. Install CUDA (>10.1) and cudnn (make sure to select [compatible versions](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html)!)
2. [Optional] Depending on your GPU you might need to set `TORCH_CUDA_ARCH_LIST`, check [compute capabilities](https://developer.nvidia.com/cuda-gpus) here.
3. Install [torch](https://pytorch.org/) (make sure to match the pytorch and CUDA versions!) (requires pytorch >1.7+)
4. Install [torchvision](https://github.com/pytorch/vision) (make sure to match the versions!)
5. Clone nnDetection, `cd [path_to_repo]` and `pip install -e .`
6. Upgrade hydra to next release: `pip install hydra-core --upgrade --pre`
7. Set environment variables (more info can be found below):
- `det_data`: [required] Path to the source directory where all the data will be located
- `det_models`: [required] Path to directory where all models will be saved
- `OMP_NUM_THREADS=1` : [required] Needs to be set! Otherwise bad things will happen... Refer to batchgenerators documentation.
- `det_num_threads`: [recommended] Number processes to use for augmentation (at least 6, default 12)
- `det_verbose`: [optional] Can be used to deactivate progress bars (activated by default)
- `MLFLOW_TRACKING_URI`: [optional] Specify the logging directory of mlflow. Refer to the [mlflow documentation](https://www.mlflow.org/docs/latest/tracking.html) for more information.
Note: nnDetection was developed on Linux => Windows is not supported.
<details close>
<summary>Test Installation</summary>
<br>
Run the following command in the terminal (!not! in pytorch root folder) to verify that the compilation of the C++/CUDA code was successfull:
```bash
python -c "import torch; import nndet._C; import nndet"
```
To test the whole installation please run the Toy Data set example.
</details>
<details close>
<summary>Maximising Training Speed</summary>
<br>
To get the best possible performance we recommend using CUDA 11.0+ with cuDNN 8.1.X+ and a (!)locally compiled version(!) of Pytorch 1.7+
</details>
# nnDetection
Stay tuned, will be released very soon :)
<div align="center">
<img src=docs/source/nnDetectionFunctional.svg width="600px">
</div>
<details close>
<summary>nnDetection Module Overview</summary>
<br>
<div align="center">
<img src=docs/source/nnDetectionModule.svg width="600px">
</div>
nnDetection uses multiple Registries to keep track of different modules and easily switch between them via the config files.
***Config Files***
nnDetection uses [Hydra](https://hydra.cc/) to dynamically configure and compose configurations.
The configuration files are located in `nndet.conf` and can be overwritten to customize the behavior of the pipeline.
***AUGMENTATION_REGISTRY***
The augmentation registry can be imported from `nndet.io.augmentation` and contains different augmentation configurations. Examples can be found in `nndet.io.augmentation.bg_aug`.
***DATALOADER_REGISTRY***
The dataloader registry contains different dataloader classes to customize the IO of nnDetection.
It can be imported from `nndet.io.datamodule` and examples can be found in `nndet.io.datamodule.bg_loader`.
***PLANNER_REGISTRY***
New plans can be registered via the planner registry which contains classes to define and perform different architecture and preprocessing schemes.
It can be imported from `nndet.planning.experiment` and examples can be found in `nndet.planning.experiment.v001`.
***MODULE_REGISTRY***
The module registry contains the core modules of nnDetection which inherits from the [Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) Module.
It is the main module which is used for training and inference and contains all the necessary steps to build the final models.
It can be imported from `nndet.ptmodule` and examples can be found in `nndet.ptmodule.retinaunet`.
</details>
<details close>
<summary>nnDetection Functional Details</summary>
<br>
<div align="center">
<img src=docs/source/nnDetectionFunctionalDetails.svg width="600px">
</div>
</details>
# Experiments & Data
The data sets used for our experiments are not hosted or maintained by us, please give credit to the authors of the data sets.
Some of the labels were corrected in data sets which we converted and can be downloaded (links can be found in the guides).
The `Experiments` section contains multiple guides which explain the preparation of the data sets via the provided scripts.
## Toy Data set
Running `nndet_example` will automatically generate an example data set with 3D squares and sqaures with holes which can be used to test the installation or experiment with prototype code (it is still necessary to run the other nndet commands to process/train/predict the data set).
```bash
# create data to test installation/environment (10 train 10 test)
nndet_example
# create full data set for prototyping (1000 train 1000 test)
nndet_example --full [--num_processes]
```
The `full` problem is very easy and the final results should be near perfect.
After running the generation script follow the `Planning`, `Training` and `Inference` instructions below to construct the whole nnDetection pipeline.
## Experiments
Besides the self-configuring method, nnDetection acts as a standard interface for many data sets.
We provide guides to prepare all data sets from our evaluation to the correct and make it easy to reproduce our resutls.
Furthermore, we provide pretrained models which can be used without investing large amounts of compute to rerun our experiments (see Section `Pretrained Models`).
<div align="center">
### Results
| <!-- --> | <!-- --> | <!-- --> |
|:--------:|:--------:|:--------:|
| | [nnDetection v0.1](./docs/results/nnDetectionV001.md) | |
</div>
<div align="center">
### Guides
| <!-- --> | <!-- --> | <!-- --> |
|:----------------------------------------------------------------:|:-------------------------------------------------:|:---------------------------------------:|
| [Task 003 Liver](./projects/Task001_Decathlon/README.md) | [Task 011 Kits](./projects/Task011_Kits/README.md) | [Task 020 RibFrac](./projects/Task020_RibFrac/README.md) |
| [Task 007 Pancreas](./projects/Task001_Decathlon/README.md) | [Task 012 LIDC](./projects/Task012_LIDC/README.md) | [Task 021 ProstateX](./projects/Task021_ProstateX/README.md) |
| [Task 008 Hepatic Vessel](./projects/Task001_Decathlon/README.md) | [Task 017 CADA](./projects/Task017_CADA/README.md) | [Task 025 LymphNodes](./projects/Task025_LymphNodes/README.md) |
| [Task 010 Colon](./projects/Task001_Decathlon/README.md) | [Task 019 ADAM](./projects/Task019_ADAM/README.md) | [Task 016 Luna](./projects/Task016_Luna/README.md) |
</div>
## Adding New Data sets
nnDetection relies on a standardized input format which is very similar to [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) and allows easy integration of new data sets.
More details about the format can be found below.
### Folders
All data sets should reside inside `Task[Number]_[Name]` folders inside the specified detection data folder (the path to this folder can be set via the `det_data` environment flag).
To avoid conflicts with our provided pretrained models we recommend to use task numbers starting from 100.
An overview is provided below ([Name] symbolise folders, `-` symbolise files, indents refer to substructures)
```text
${det_data}
[Task000_Example]
- dataset.yaml # dataset.json works too
[raw_splitted_data]
[imagesTr]
- case0000_0000.nii.gz # case0000 modality 0
- case0000_0001.nii.gz # case0000 modality 1
- case0001_0000.nii.gz # case0001 modality 0
- case0000_0001.nii.gz # case0001 modality 1
[labelsTr]
- case0000.nii.gz # instance segmentation case0000
- case0000.json # properties of case0000
- case0001.nii.gz # instance segmentation case0001
- case0001.json # properties of case0001
[imagesTs] # optional, same structure as imagesTr
...
[labelsTs] # optional, same structure as labelsTr
...
[Task001_Example1]
...
```
### Data set Info
`dataset.yaml` or `dataset.json` provides general information about the data set:
Note: [Important] Classes and modalities start with index 0!
```yaml
task: Task000D3_Example
name: "Example" # [Optional]
dim: 3 # number of spatial dimensions of the data
# Note: need to use integer value which is defined below of target class!
target_class: 1 # [Optional] define class of interest for patient level evaluations
test_labels: True # manually splitted test set
labels: # classes of data set; need to start at 0
"0": "Square"
"1": "SquareHole"
modalities: # modalities of data set; need to start at 0
"0": "CT"
```
### Image Format
nnDetection uses the same image format as nnU-Net.
Each case consists of at least one 3D nifty file with a single modality and are saved in the `images` folders.
If multiple modalities are available, each modality uses a separate file and the sequence number at the end of the name indicates the modality (these need to correspond to the numbers specified in the data set file and be consistent across the whole data set).
An example with two modalities could look like this:
```text
- case001_0000.nii.gz # Case ID: case001; Modality: 0
- case001_0001.nii.gz # Case ID: case001; Modality: 1
- case002_0000.nii.gz # Case ID: case002; Modality: 0
- case002_0001.nii.gz # Case ID: case002; Modality: 1
```
If multiple modalities are available, please check beforehand if they need to be registered and perform registration befor nnDetection preprocessing. nnDetection does (!)not(!) include automatic registration of multiple modalities.
### Label Format
Labels are encoded with two files per case: one nifty file which contains the instance segmentation and one json file which includes the "meta" information of each instance.
The nifty file should contain all annotated instances where each instance has a unique number and are in consecutive order (e.g. 0 ALWAYS refers to background, 1 refers to the first instance, 2 refers to the second instance ...)
`case[XXXX].json` label files need to provide the class of every instance in the segmentation. In this example the first isntance is assigned to class `0` and the second instance is assigned to class `1`:
```json
{
"instances": {
"1": 0,
"2": 1
}
}
```
Each label file needs a corresponding json file to define the classes.
We also wrote an [Detection Annotation Guide](https://www.notion.so/Object-Detection-Annotation-Guide-5318f090091c4e3db7e046a5990bd03c) which includes a dedicated section of the nnDetection format with additional visualizations :)
## Using nnDetection
The following paragrah provides an high level overview of the functionality of nnDetection and which commands are available.
A typical flow of commands would look like this:
```text
nndet_prep -> nndet_unpack -> nndet_train -> nndet_consolidate -> nndet_predict
```
Eachs of this commands is explained below and more detailt information can be obtained by running `nndet_[command] -h` in the terminal.
### Planning & Preprocessing
Before training the networks, nnDetection needs to preprocess and analyze the data.
The preprocessing stage normalizes and resamples the data while the analyzed properties are used to create a plan which will be used for configuring the training.
nnDetectionV0 requires a GPU with approximately the same amount of VRAM you are planning to use for training (we used a RTX2080TI; no monitor attached to it) to perform live estimation of the VRAM used by the network.
Future releases aim at improving this process...
```bash
nndet_prep [tasks] [-o / --overwrites] [-np / --num_processes] [-npp / --num_processes_preprocessing] [--full_check]
# Example
nndet_prep 000
# Script
# /scripts/preprocess.py - main()
```
`-o` option can be used to overwrite parameters for planning and preprocessing (refer to the config files to see all parameters). The number of processes used for cropping and analysis can be adjusted by using `-np` and the number of processes used for resampling can be set via `-npp`. The current values are fairly save if 64GB of RAM is available.
The `--full_check` will iterate over the data before starting any preprocessing and check correct formatting of the data and labels.
If any problems occur during preprocessing please run the full check to make sure that the format is correct.
After planning and preprocessing the resulting data folder structure should look like this:
```text
[Task000_Example]
[raw_splitted]
[raw_cropped] # only needed for different resampling strategies
[imagesTr] # stores cropped image data; contains npz files
[labelsTr] # stores labels
[preprocessed]
[analysis] # some plots to visualize properties of the underlying data set
[properties] # sufficient for new plans
[labelsTr] # labels in original format (original spacing)
[labelsTs] # optional
[Data identifier; e.g. D3V001_3d]
[imagesTr] # preprocessed data
[labelsTr] # preprocessed labels (resampled spacing)
- {name of plan}.pkl e.g. D3V001_3d.pkl
```
Befor starting the training copy the data (Task Folder, data set info and preprocessed folder are needed) to a SSD (highly recommended) and unpack the image data with
```bash
nndet_unpack [path] [num_processes]
# Example (unpack example with 6 processes)
nndet_unpack ${det_data}/Task000D3_Example/preprocessed/D3V001_3d/imagesTr 6
# Script
# /scripts/utils.py - unpack()
```
### Training and Evaluation
After the planning and preprocessing stage is finished the training phase can be started.
The default setup of nnDetection is trained in a 5 fold cross-validation scheme.
First, check which plans were generated during planning by checking the preprocessing folder and look for the pickled plan files.
In most cases only the defaul plan will be generated (`D3V001_3d`) but there might be instances (e.g. Kits) where the low resolution plan will be generated too (`D3V001_3dlr1`).
```bash
nndet_train [task] [-o / --overwrites] [--sweep]
# Example (train default plan D3V001_3d and search best inference parameters)
nndet_train 000 --sweep
# Script
# /scripts/train.py - train()
```
Use `-o exp.fold=X` to overwrite the trained fold, this should be run for all folds `X = 0, 1, 2, 3, 4`!
The `--sweep` option tells nnDetection to look for the best hyparameters for inference by empirically evaluating them on the validation set.
Sweeping can also be performed later by running the following command:
```bash
nndet_sweep [task] [model] [fold]
# Example (sweep Task 000 of model RetinaUNetV001_D3V001_3d in fold 0)
nndet_sweep 000 RetinaUNetV001_D3V001_3d 0
# Script
# /experiments/train.py - sweep()
```
Evaluation can be invoked by the following command (requires access to the model and preprocessed data):
```bash
nndet_eval [task] [model] [fold] [--test] [--case] [--boxes] [--seg] [--instances] [--analyze_boxes]
# Example (evaluate and analyze box predictions of default model)
nndet_eval 000 RetinaUNetV001_D3V001_3d 0 --boxes --analyze_boxes
# Script
# /scripts/train.py - evaluate()
# Note: --test invokes evaluation of the test set
# Note: --seg, --instances are placeholders for future versions and not working yet
```
### Inference
After running all folds it is time to collect the models and creat a unified inference plan.
The following command will copy all the models and predictions from the folds. By adding the `sweep_` options, the empiricaly hyperparameter optimization across all folds can be started.
This will generate a unified plan for all models which will be used during inference.
```bash
nndet_consolidate [task] [model] [--overwrites] [--consolidate] [--num_folds] [--no_model] [--sweep_boxes] [--sweep_instances]
# Example
nndet_consolidate 000 RetinaUNetV001_D3V001_3d --sweep_boxes
# Script
# /scripts/consolidate.py - main()
```
For the final test set predictions simply select the best model according to the validation scores and run the prediction command below.
Data which is located in `raw_splitted/imagesTs` will be automatically preprocessed and predicted by running the following command:
```bash
nndet_predict [task] [model] [--fold] [--num_tta] [--no_preprocess] [--check] [-npp / --num_processes_preprocessing] [--force_args]
# Example
nndet_predict 000 RetinaUNetV001_D3V001_3d --fold -1
# Script
# /scripts/predict.py - main()
```
If a self-made test set was used, evaluation can be performed by invoking `nndet_eval` with `--test` as described above.
## nnU-Net for Detection
Besides nnDetection we also include the scripts to prepare and evaluate nnU-Net in the context of obejct detection.
Both frameworks need to be configured correctly before running the scripts to assure correctness.
After preparing the data set in the nnDetection format (which is a superset of the nnU-Net format) it is possible to export it to nnU-Net via `scripts/nnunet/nnunet_export.py`. Since nnU-Net needs task ids without any additions it may be necessary to overwrite the task name via the `-nt` option for some dataets (e.g. `Task019FG_ADAM` needs to be renamed to `Task019_ADAM`).
Follow the usual nnU-Net preprocessing and training pipeline to generate the needed models.
Use the `--npz` option during training to save the predicted probabilities which are needed to generate the detection results.
After determining the best ensemble configuration from nnU-Net pass all paths to `scripts/nnunet/nnunet_export.py` which will ensemble and postprocess the predictions for object detection.
Per default the `nnU-Net Plus` scheme will be used which incorporates the empirical parameter optimization step.
Use `--simple` flag to switch to the `nnU-Net` basic configuration.
## Pretrained models
**Coming Soon**
# FAQ
<details close>
<summary>GPU requirements</summary>
<br>
nnDetection v0.1 was developed for GPUs with at least 11GB of VRAM (e.g. RTX2080TI, TITAN RTX).
All of our experiments were conducted with a RTX2080TI.
While the memory can be adjusted by manipulating the correct setting we recommend using the default values for now.
Future releases will refactor the planning stage to improve the VRAM estimation and add support for different memory budgets.
</details>
<details close>
<summary>Error: Undefined CUDA symbols when importing `nndet._C`</summary>
<br>
Please double check CUDA version of your PC, pytorch, torchvision and nnDetection build!
Follow the installation instruction at the beginning!
</details>
<details close>
<summary>Error: No kernel image is available for execution"</summary>
<br>
You are probably executing the build on a machine with a GPU architecture which was not present/set during the build.
Please check [link](https://developer.nvidia.com/cuda-gpus) to find the correct SM architecture and set `TORCH_CUDA_ARCH_LIST`
approriately (e.g. check Dockefile for example).
Make sure to delete all caches before rebulding!
</details>
<details close>
<summary>Training with bounding boxes</summary>
<br>
The first release of nnDetection focuses on 3d medical images and Retina U-Net.
As a consequence training (specifically planning and augmentation) requrie segmentation annotations.
In many cases this limitation can be circumvented by converting the bounding boxes into segmentations.
</details>
<details close>
<summary>Mask RCNN and 2D Data sets</summary>
<br>
2D data sets and Mask R-CNN are not supported in the first release.
We hope to provide these in the future.
</details>
<details close>
<summary>Multi GPU Training</summary>
<br>
Multi GPU training is not officially supported yet.
Inference and the metric computation are not properly designed to support these usecases!
</details>
<details close>
<summary>Prebuild package</summary>
<br>
We are planning to provide prebuild wheels in the future but no prebuild wheels are available right now.
Please use the provided Dockerfile or the installation instructions to run nnDetection.
</details>
# Cite
If you use nnDetection for your project/research/work please cite the following paper:
```text
Coming Soon
```
# Acknowledgements
nnDetection combines the information from multiple open source repositores we wish to acknoledge for their awesome work, please check them out!
## [nnU-Net](https://github.com/MIC-DKFZ/nnUNet)
nnU-Net is self-configuring method for semantic segmentation and many steps of nnDetection follow in the footsteps of nnU-Net.
## [Medical Detection Toolkit](https://github.com/MIC-DKFZ/medicaldetectiontoolkit)
The Medical Detection Toolkit introduced the first codebase for 3D Object Detection and multiple tricks were transferred to nnDetection to assure optimal configuration for medical object detection.
## [Torchvision](https://github.com/pytorch/vision)
nnDetection tried to follow the interfaces of torchvision to make it easy to understand for everyone coming from the 2D (and video) detection scene. As a result we used based our implementations of some of the core modules of the torchvision implementation.
docs/results/nnDetectionV001.md 0 → 100644
View file @ 7246044d
# nnDetection v0.1
## Pool v0.1
<div align="center">
<img src=./source/v001/results_full_final.svg width="800px">
&nbsp;
### Train Pool [AP @ IoU 0.1]
5 Fold Cross Validation
| Model | LIDC | RibFrac | CADA | Kits19 |
|:-----------:|:--------------------:|:--------:|:--------:|:--------:|
| nnDetection | 0.605 | 0.765 | 0.924 | 0.923 |
| nnUNetPlus | 0.385<sup>*</sup> | 0.700 | 0.955 | 0.935 |
| nnUNetBasic | 0.346<sup>*</sup> | 0.667 | 0.930 | 0.908 |
<sup>*</sup> results with corrected numerical values in softdice loss. Out of the box results: nnUNetPlus 0.304 and nnUNetBasic 0.232
&nbsp;
### Validation Pool [AP @ IoU 0.1]
5 Fold Cross Validation
| Model | ADAM | ProstateX | Pancreas | Hepatic Vessel | Colon | Liver |
|:-----------:|:--------:|:----------:|:--------:|:----------------:|:-----:|:-----:|
| nnDetection | 0.780 | 0.300 | 0.766 | 0.727 | 0.662 | 0.628 |
| nnUNetPlus | 0.720 | 0.197 | 0.721 | 0.721 | 0.579 | 0.678 |
| nnUNetBasic | 0.657 | 0.204 | 0.691 | 0.699 | 0.509 | 0.567 |
&nbsp;
Test Split
| Model | ProstateX | Pancreas | Hepatic Vessel | Colon | Liver |
|:-----------:|:----------:|:--------:|:----------------:|:-----:|:-----:|
| nnDetection | 0.221 | 0.791 | 0.664 | 0.696 | 0.790 |
| nnUNetPlus | 0.078 | 0.704 | 0.684 | 0.731 | 0.760 |
ADAM Results are listed under Benchmarks
&nbsp;
### Test Pool [AP @ IoU 0.1]
5 Fold Cross Validation
| Model | Lymph Nodes |
|:-----------:|:-----------:|
| nnDetection | 0.205 |
| nnUNetPlus | 0.162 |
| nnUNetBasic | 0.159 |
&nbsp;
Test Split
| Model | Lymph Nodes |
|:-----------:|:-----------:|
| nnDetection | 0.270 |
| nnUNetPlus | 0.169 |
Luna results are listed under Benchmarks
&nbsp;
</div>
## Benchmarks
### Luna
<div align="center">
<img src=./source/v001/luna.png width="800px">
| Methods | 1/8 | 1/4 | 1/2 | 1 | 2 | 4 | 8 | CPM |
|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| nnDetection | 0.812 | 0.885 | 0.927 | 0.950 | 0.969 | 0.979 | 0.985 | 0.930 |
&nbsp;
</div>
### ADAM Live Leaderboard
<div align="center">
<img src=./source/v001/adam.svg width="300">
| Model | Sens | FP |
|:-----------:|:----:|:---:|
| nnDetection | 0.64 | 0.3 |
</div>
docs/results/source/v001/adam.svg 0 → 100644
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docs/results/source/v001/results_full_final.svg 0 → 100644
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<title>Group 33</title>
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<tspan x="44.4775391" y="18">Resampling</tspan>
<tspan x="58.4829102" y="40">Strategy</tspan>
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<text id="Image:-We-use-the-sa" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
<tspan x="196" y="27">Image</tspan>
<tspan x="248.022461" y="27" font-family="Helvetica" font-weight="normal">: We use the same image resampling </tspan>
<tspan x="196" y="49" font-family="Helvetica" font-weight="normal">procedure as nnU-Net</tspan>
<tspan x="196" y="71">Annotation</tspan>
<tspan x="290.974609" y="71" font-family="Helvetica" font-weight="normal">: Annotations are resampled with </tspan>
<tspan x="196" y="93" font-family="Helvetica" font-weight="normal">nearest neighbor</tspan>
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<text id="Parameter" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
<tspan x="49.9711914" y="18">Parameter</tspan>
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<text id="Description" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
<tspan x="340.491211" y="18">Description</tspan>
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<text id="Network-Topology-&amp;-F" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="12.1206055" y="18">Network Topology &amp;</tspan>
<tspan x="36.9804688" y="40">FPN Levels &amp;</tspan>
<tspan x="48.9775391" y="62">Patch Size</tspan>
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<text id="The-anisotric-axis-o" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="27">The anisotric axis of the patch size is initialized </tspan>
<tspan x="195" y="49">with the median shape of the anisotropic axis of </tspan>
<tspan x="195" y="71">the dataset. The isotropic axes are initialized </tspan>
<tspan x="195" y="93">with the minimum size of the isotropic axes of </tspan>
<tspan x="195" y="115">the dataset. </tspan>
<tspan x="195" y="137">The patch size is decreased while adapting the </tspan>
<tspan x="195" y="159">network architecture and feature pyramid </tspan>
<tspan x="195" y="181">network levels until the memory constrains are </tspan>
<tspan x="195" y="203">fulfilled. The batch size is fixed to four.</tspan>
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<tspan x="63.4838867" y="18">Anchor</tspan>
<tspan x="41.9814453" y="40">Optimization</tspan>
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<text id="The-anchor-sizes-are" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="25">The anchor sizes are determined by maximising </tspan>
<tspan x="195" y="47">the IoU of the best fitting anchor on the given </tspan>
<tspan x="195" y="69">object sizes extracted from the training set. </tspan>
<tspan x="195" y="91">Optimization of three anchor sizes per axis is </tspan>
<tspan x="195" y="113">performed via differential evolution.</tspan>
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<text id="Low-Resolution-Model" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="30.4633789" y="18">Low Resolution</tspan>
<tspan x="67.4873047" y="40">Model</tspan>
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<text id="The-low-resolution-c" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="24">The low resolution configuration will be triggered </tspan>
<tspan x="195" y="46">if the 99.5 percentile of object sizes along any</tspan>
<tspan x="195" y="68">axes exceeds the patch size of the full </tspan>
<tspan x="195" y="90">resolution model. If the low resolution </tspan>
<tspan x="195" y="112">configuration is triggered, the target spacing </tspan>
<tspan x="195" y="134">along each axes will be increased by two to </tspan>
<tspan x="195" y="156">incorporate more contextual information.</tspan>
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<text id="Optimizer-&amp;-Learning" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="44.9916992" y="18">Optimizer &amp;</tspan>
<tspan x="35.4599609" y="40">Learning Rate</tspan>
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<text id="All-configurations-a" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="24">All configurations are trained for 60 epochs with </tspan>
<tspan x="195" y="46">2500 mini batches per epoch and half of the </tspan>
<tspan x="195" y="68">batch is forced to contain at least one object. </tspan>
<tspan x="195" y="90">SGD with Nesterov momentum 0.9 is used.</tspan>
<tspan x="195" y="112">At the beginning of the training the learning rate </tspan>
<tspan x="195" y="134">is linearly ramped up from 1e-6 to 1e-2 over the </tspan>
<tspan x="195" y="156">first 4000 iterations. Poly learning rate schedule </tspan>
<tspan x="195" y="178">is used until epoch 50. The last 10 epochs are </tspan>
<tspan x="195" y="200">trained with a cyclic learning rate fluctuating </tspan>
<tspan x="195" y="222">between 1e-3 and 1e-6 during every epoch.</tspan>
<tspan x="195" y="244">We snapshot the model weights after each </tspan>
<tspan x="195" y="266">epoch for Stochastic Weight Averaging.</tspan>
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<tspan x="43.980957" y="18">Architecture</tspan>
<tspan x="55.4814453" y="40">Template</tspan>
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<text id="Retina-U-Net-with-an" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="25">Retina U-Net with an encoder which consists of </tspan>
<tspan x="195" y="47">plain convolutions, ReLU and instance </tspan>
<tspan x="195" y="69">normalization blocks. The detection heads used </tspan>
<tspan x="195" y="91">for anchor classification and regression consist </tspan>
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<text id="Loss-Functions" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="31.4697266" y="18">Loss Functions</tspan>
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<text id="Detection-Branch:-To" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
<tspan x="195" y="30">Detection Branch</tspan>
<tspan x="344.027344" y="30" font-family="Helvetica" font-weight="normal">: To balance positive and </tspan>
<tspan x="195" y="52" font-family="Helvetica" font-weight="normal">negative anchors, hard negative mining is used </tspan>
<tspan x="195" y="74" font-family="Helvetica" font-weight="normal">while selecting 1/3 positive and 2/3 negative </tspan>
<tspan x="195" y="96" font-family="Helvetica" font-weight="normal">anchors. The classification branch is trained with </tspan>
<tspan x="195" y="118" font-family="Helvetica" font-weight="normal">the Binary Cross-Entropy loss and the </tspan>
<tspan x="195" y="140" font-family="Helvetica" font-weight="normal">Generalized IoU Loss is used for anchor </tspan>
<tspan x="195" y="162" font-family="Helvetica" font-weight="normal">regression.</tspan>
<tspan x="195" y="184">Segmentation Branch</tspan>
<tspan x="381.029297" y="184" font-family="Helvetica" font-weight="normal">: The segmentation </tspan>
<tspan x="195" y="206" font-family="Helvetica" font-weight="normal">branch is trained with the Dice and Cross-</tspan>
<tspan x="195" y="228" font-family="Helvetica" font-weight="normal">Entropy loss to distinguish foreground and </tspan>
<tspan x="195" y="250" font-family="Helvetica" font-weight="normal">background pixels.</tspan>
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<text id="Data-Augmentation" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="15.4428711" y="18">Data Augmentation</tspan>
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<text id="We-use-the-same-augm" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="22">We use the same augmentation strategy as </tspan>
<tspan x="195" y="44">nnU-Net without simulating low resolution </tspan>
<tspan x="195" y="66">samples.</tspan>
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<tspan x="24.4648438" y="18">Anchor Matching</tspan>
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<text id="Adaptive-Training-Sa" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="24">Adaptive Training Sample Selection (ATSS) is </tspan>
<tspan x="195" y="46">used to match anchors and ground truth boxes. </tspan>
<tspan x="195" y="68">The center of the anchor boxes do not need to </tspan>
<tspan x="195" y="90">lie within the ground truth box.</tspan>
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<text id="Empirical-Parameter" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="10.4770508" y="18">Empirical Parameter</tspan>
<tspan x="41.9814453" y="40">Optimization</tspan>
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<text id="Parameters-which-are" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="23">Parameters which are only required during the </tspan>
<tspan x="195" y="45">inference procedure are empirically optimized </tspan>
<tspan x="195" y="67">by evaluating the performance on the validation </tspan>
<tspan x="195" y="89">set. This includes: the IoU threshold required for </tspan>
<tspan x="195" y="111">the NMS of the model, the IoU threshold </tspan>
<tspan x="195" y="133">required to perform WBC, a minimum probability </tspan>
<tspan x="195" y="155">for predictions of the model, a minimum object </tspan>
<tspan x="195" y="177">size.</tspan>
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<text id="Model-Selection" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="67.4873047" y="18">Model</tspan>
<tspan x="54.9760742" y="40">Selection</tspan>
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<text id="If-the-low-resolutio" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
<tspan x="195" y="30">If the low resolution model was triggered, only </tspan>
<tspan x="195" y="52">the best model as determined by the five fold </tspan>
<tspan x="195" y="74">cross-validation will be used for the test set.</tspan>
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docs/source/nnDetectionModule.svg 0 → 100644
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nndet/arch/abstract.py 0 → 100644
View file @ 7246044d
"""
Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from typing import Dict, Tuple, Any, Optional
import torch
from abc import abstractmethod
from torch import Tensor
class AbstractModel(torch.nn.Module):
@classmethod
@abstractmethod
def from_config_plan(cls,
model_cfg: dict,
plan_arch: dict,
plan_anchors: dict,
log_num_anchors: str = None,
**kwargs,
):
raise NotImplementedError
@abstractmethod
def train_step(self,
images: Tensor,
targets: dict,
evaluation: bool,
batch_num: int,
) -> Tuple[Dict[str, torch.Tensor], Optional[Dict]]:
"""
Perform a single training step
Args:
images: images to process
targets: labels for training
evaluation (bool): compute final predictions which should be used for metric evaluation
batch_num (int): batch index inside epoch
Returns:
torch.Tensor: final loss for back propagation
Dict: predictions for metric calculation
Dict[str, torch.Tensor]: scalars for logging (e.g. individual loss components)
"""
raise NotImplementedError
@abstractmethod
def inference_step(self,
images: Tensor,
*args,
**kwargs,
) -> Dict[str, Any]:
"""
Perform a single training step
Args:
images: images to process
*args: positional arguments
**kwargs: keyword arguments
Returns:
Dict: predictions for metric calculation
"""
raise NotImplementedError
nndet/arch/blocks/__init__.py 0 → 100644
View file @ 7246044d
from nndet.arch.blocks.basic import AbstractBlock, StackedConvBlock, \
StackedResidualBlock, StackedConvBlock2
from nndet.arch.blocks.res import ResBasic, ResBottleneck
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