start adding documentation for toothfairy2

documentation/competitions/Toothfairy2.md

@@ -0,0 +1,177 @@
+# Introduction
+
+This document describes our submission to the [Toothfairy2 Challenge](https://toothfairy2.grand-challenge.org/toothfairy2/). 
+Our model is essentially a nnU-Net ResEnc L with the patch size upscaled to 160x320x320 pixels. We disable left/right 
+mirroring and train for 1500 instead of the standard 1000 epochs. Training was either done on 2xA100 40GB or one GH200 96GB.
+
+# Dataset Conversion
+
+# Experiment Planning and Preprocessing
+
+## Extract fingerprint:
+`nnUNetv2_extract_fingerprint -d 119 -np 48`
+
+## Run planning:
+`nnUNetv2_plan_experiment -d 119 -pl nnUNetPlannerResEncL_torchres`
+
+This planner not only uses the ResEncL configuration but also replaces the default resampling scheme with one that is 
+faster (but less precise). Since all images in the challenge (train and test) should already have 0.3x0.3x0.3 spacing 
+resampling is not required. This is just here as a safety measure. The speed is needed at inference time because grand 
+challenge imposes a limit of 10 minutes per case.
+
+## Edit the plans files
+Add the following configuration to the generated plans file:
+
+```json
+ "3d_fullres_torchres_ps160x320x320_bs2": {
+ "inherits_from": "3d_fullres",
+ "patch_size": [
+ 160,
+ 320,
+ 320
+ ],
+ "architecture": {
+ "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+ "arch_kwargs": {
+ "n_stages": 7,
+ "features_per_stage": [
+ 32,
+ 64,
+ 128,
+ 256,
+ 320,
+ 320,
+ 320
+ ],
+ "conv_op": "torch.nn.modules.conv.Conv3d",
+ "kernel_sizes": [
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ],
+ [
+ 3,
+ 3,
+ 3
+ ]
+ ],
+ "strides": [
+ [
+ 1,
+ 1,
+ 1
+ ],
+ [
+ 2,
+ 2,
+ 2
+ ],
+ [
+ 2,
+ 2,
+ 2
+ ],
+ [
+ 2,
+ 2,
+ 2
+ ],
+ [
+ 2,
+ 2,
+ 2
+ ],
+ [
+ 2,
+ 2,
+ 2
+ ],
+ [
+ 1,
+ 2,
+ 2
+ ]
+ ],
+ "n_blocks_per_stage": [
+ 1,
+ 3,
+ 4,
+ 6,
+ 6,
+ 6,
+ 6
+ ],
+ "n_conv_per_stage_decoder": [
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1
+ ],
+ "conv_bias": true,
+ "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
+ "norm_op_kwargs": {
+ "eps": 1e-05,
+ "affine": true
+ },
+ "dropout_op": null,
+ "dropout_op_kwargs": null,
+ "nonlin": "torch.nn.LeakyReLU",
+ "nonlin_kwargs": {
+ "inplace": true
+ }
+ },
+ "_kw_requires_import": [
+ "conv_op",
+ "norm_op",
+ "dropout_op",
+ "nonlin"
+ ]
+ } 
+ }
+```
+Aside from changing the patch size this makes the architecture one stage deeper (one more pooling + res blocks), enabling
+it to make effective use of the larger input
+
+# Training
+We train two models on all training cases:
+
+```bash
+nnUNetv2_train 119 3d_fullres_torchres_ps160x320x320_bs2 all -p nnUNetResEncUNetLPlans -tr nnUNetTrainer_onlyMirror01_1500ep
+nnUNet_results=${nnUNet_results}_2 nnUNetv2_train 119 3d_fullres_torchres_ps160x320x320_bs2 all -p nnUNetResEncUNetLPlans -tr nnUNetTrainer_onlyMirror01_1500ep
+```
+Note how in the second line we overwrite the nnUNet_results variable in order to be able to train the same model twice without overwriting the results
+
+# Inference
+We ensemble the two models from above. On a technical level we copy the two fold_all folders into one training output 
+directory and rename them to fold_0 and fold_1. This lets us use nnU-Net's cross-validation ensembling strategy which 
+is more computationally efficient (needed for time limit on grand-challenge.org).
+
+Run inference with the inference script

nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerNoMirroring.py

@@ -1,6 +1,7 @@
 from typing import Union, Tuple, List
 
 import numpy as np
+import torch
 from batchgeneratorsv2.helpers.scalar_type import RandomScalar
 from batchgeneratorsv2.transforms.base.basic_transform import BasicTransform
 from batchgeneratorsv2.transforms.intensity.brightness import MultiplicativeBrightnessTransform
@@ -51,6 +52,13 @@ def configure_rotation_dummyDA_mirroring_and_inital_patch_size(self):
  return rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes
 
 
+class nnUNetTrainer_onlyMirror01_1500ep(nnUNetTrainer_onlyMirror01):
+ def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool = True,
+ device: torch.device = torch.device('cuda')):
+ super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device)
+ self.num_epochs = 1500
+
+
 class nnUNetTrainer_onlyMirror01_DASegOrd0(nnUNetTrainer_onlyMirror01):
  @staticmethod
  def get_training_transforms(