Merge remote-tracking branch 'origin/master' into main

.dockerignore

+.git
+.cache
+.mypy_cache
+.gitignore
+*.py[cod]
+*$py.class
+*.vscode
+*.so
+*.sif
+
+__pycache__/
+
+/nndet.egg-info
+/build
+/notebooks
+/projects
+/singularity

.github/ISSUE_TEMPLATE/bug.md

+---
+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]
+```

.github/ISSUE_TEMPLATE/question.md

+---
+name: Question
+about: Ask a question
+title: "[Question]"
+labels: Question
+assignees: ''
+
+---
+
+### :question: Question
+<!-- Please provide a short and cleaer question-->

.gitignore

@@ -2,12 +2,16 @@
 __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
@@ -58,8 +55,6 @@ coverage.xml
 # Django stuff:
 *.log
 local_settings.py
-db.sqlite3
-db.sqlite3-journal
 
 # Flask stuff:
 instance/
@@ -74,56 +69,49 @@ docs/_build/
 # 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/*
+*.nii.gz
+*.nii
+*.tif
+*.bmp
+*.pkl
+*.xml
+*.pkl
+*.pdf
+*.jpg
+*.jpeg
+
+*.model
+
+.DS_Store

Dockerfile

+#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 .

LICENSE

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+      outstanding shares, or (iii) beneficial ownership of such entity.
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+      "You" (or "Your") shall mean an individual or Legal Entity
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+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [2019] [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.
+
+
+################################################################################
+
+Some parts of nndet/core/boxes/anchors.py, coder.py, matcher.py are derived 
+from torchvision and thus licensed under:
+
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016, 
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+"""

MANIFEST.in

+include README.md
+include LICENSE
+
+global-exclude *.pyc
+global-exclude __pycache__
+
+recursive-include nndet/conf *

docs/results/nnDetectionV001.md

+# 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/source/nnDetectionFunctionalDetails.svg

+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="1198px" height="948px" viewBox="0 0 1198 948" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+    <title>Group 33</title>
+    <defs>
+        <path d="M0,0 L1198,0 L1198,47 L0,47 L0,0 Z" id="path-1"></path>
+        <mask id="mask-2" maskContentUnits="userSpaceOnUse" maskUnits="objectBoundingBox" x="0" y="0" width="1198" height="47" fill="white">
+            <use xlink:href="#path-1"></use>
+        </mask>
+    </defs>
+    <g id="Page-1" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
+        <g id="Group-27">
+            <g id="Group-19">
+                <g id="Group-15" transform="translate(0.000000, 65.000000)">
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" x="185.5" y="1.5" width="406" height="102"></rect>
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" fill="#BEEEB8" x="1.5" y="1.5" width="184" height="102"></rect>
+                    <text id="Resampling-Strategy" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
+                        <tspan x="44.4775391" y="18">Resampling</tspan>
+                        <tspan x="58.4829102" y="40">Strategy</tspan>
+                    </text>
+                    <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>
+                    </text>
+                </g>
+                <g id="Group-15">
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" x="185.5" y="1.5" width="406" height="65"></rect>
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" fill="#FFFFFF" x="1.5" y="1.5" width="184" height="65"></rect>
+                    <text id="Parameter" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
+                        <tspan x="49.9711914" y="18">Parameter</tspan>
+                    </text>
+                    <text id="Description" font-family="Helvetica-Bold, Helvetica" font-size="18" font-weight="bold" fill="#000000">
+                        <tspan x="340.491211" y="18">Description</tspan>
+                    </text>
+                </g>
+                <g id="Group-15" transform="translate(0.000000, 167.000000)">
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" x="185.5" y="1.5" width="406" height="214"></rect>
+                    <rect id="Rectangle" stroke="#979797" stroke-width="3" fill="#BEEEB8" x="1.5" y="1.5" width="184" height="214"></rect>
+                    <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>
+                    </text>
+                    <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>
+                    </text>
+                </g>
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+                    <text id="Anchor-Optimization" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
+                        <tspan x="63.4838867" y="18">Anchor</tspan>
+                        <tspan x="41.9814453" y="40">Optimization</tspan>
+                    </text>
+                    <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>
+                    </text>
+                    <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>
+                    </text>
+                    <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>
+                    </text>
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+                    <text id="Architecture-Templat" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
+                        <tspan x="43.980957" y="18">Architecture</tspan>
+                        <tspan x="55.4814453" y="40">Template</tspan>
+                    </text>
+                    <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>
+                        <tspan x="195" y="113">of three convolutions with group norm.</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>
+                    </text>
+                    <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>
+                    </text>
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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>
+                    </text>
+                    <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>
+                    </text>
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+                    <text id="Anchor-Matching" font-family="Helvetica" font-size="18" font-weight="normal" fill="#000000">
+                        <tspan x="24.4648438" y="18">Anchor Matching</tspan>
+                    </text>
+                    <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>
+                    </text>
+                    <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>
+                    </text>
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+                        <tspan x="67.4873047" y="18">Model</tspan>
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+                    </text>
+                    <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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nndet/arch/abstract.py

+"""
+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

+from nndet.arch.blocks.basic import AbstractBlock, StackedConvBlock, \
+    StackedResidualBlock, StackedConvBlock2
+from nndet.arch.blocks.res import ResBasic, ResBottleneck