merge

documentation/competitions/AutoPETII.md

@@ -46,7 +46,7 @@ Add the following to the 'configurations' dict in 'nnUNetPlans.json':
 ```json
         "3d_fullres_resenc": {
             "inherits_from": "3d_fullres",
-            "UNet_class_name": "ResidualEncoderUNet",
+            "network_arch_class_name": "ResidualEncoderUNet",
             "n_conv_per_stage_encoder": [
                 1,
                 3,

documentation/explanation_plans_files.md

@@ -74,7 +74,7 @@ nnunetv2.preprocessing.resampling
 resampling function must be callable(data, current_spacing, new_spacing, **kwargs). It must be located in 
 nnunetv2.preprocessing.resampling
 - `resampling_fn_seg_kwargs`: kwargs for resampling_fn_seg
-- `UNet_class_name`: UNet class name, can be used to integrate custom dynamic architectures
+- `network_arch_class_name`: UNet class name, can be used to integrate custom dynamic architectures
 - `UNet_base_num_features`: The number of starting features for the UNet architecture. Default is 32. Default: Features
 are doubled with each downsampling 
 - `unet_max_num_features`: Maximum number of features (default: capped at 320 for 3D and 512 for 2d). The purpose is to 

nnunetv2/batch_running/collect_results_custom_Decathlon.py

@@ -94,21 +94,19 @@ def summarize(input_file, output_file, folds: Tuple[int, ...], configs: Tuple[st
 
 if __name__ == '__main__':
     use_these_trainers = {
-        'nnUNetTrainer': ('nnUNetPlans',),
-        'nnUNetTrainerDiceCELoss_noSmooth': ('nnUNetPlans',),
-        'nnUNetTrainer_DASegOrd0': ('nnUNetPlans',),
+        'nnUNetTrainer': ('nnUNetPlans', 'nnUNetResEncUNetPlans', 'nnUNetResEncUNet2Plans', 'nnUNetResBottleneckEncUNetPlans', 'nnUNetResUNetPlans', 'nnUNetResUNet2Plans', 'nnUNetResUNet3Plans', 'nnUNetDeeperResBottleneckEncUNetPlans'),
      }
     all_results_file= join(nnUNet_results, 'customDecResults.csv')
-    datasets = [2, 3, 4, 17, 20, 24, 27, 38, 55, 64, 82]
+    datasets = [2, 3, 4, 17, 24, 27, 38, 55, 137, 217, 220, 221, 223] # amos post challenge, kits2023
     collect_results(use_these_trainers, datasets, all_results_file)
 
     folds = (0, 1, 2, 3, 4)
-    configs = ("3d_fullres", "3d_lowres")
+    configs = ("3d_fullres", )
     output_file = join(nnUNet_results, 'customDecResults_summary5fold.csv')
     summarize(all_results_file, output_file, folds, configs, datasets, use_these_trainers)
 
     folds = (0, )
-    configs = ("3d_fullres", "3d_lowres")
+    configs = ("3d_fullres", )
     output_file = join(nnUNet_results, 'customDecResults_summaryfold0.csv')
     summarize(all_results_file, output_file, folds, configs, datasets, use_these_trainers)
 

nnunetv2/batch_running/generate_lsf_runs_customDecathlon.py

@@ -21,18 +21,18 @@ def merge(dict1, dict2):
     # after the Nature Methods paper we switch our evaluation to a different (more stable/high quality) set of
     # datasets for evaluation and future development
     configurations_all = {
-        2: ("3d_fullres", "2d"),
-        3: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
-        4: ("2d", "3d_fullres"),
+        # 2: ("3d_fullres", "2d"),
+        # 3: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
+        # 4: ("2d", "3d_fullres"),
         17: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
-        20: ("2d", "3d_fullres"),
-        24: ("2d", "3d_fullres"),
-        27: ("2d", "3d_fullres"),
-        38: ("2d", "3d_fullres"),
-        55: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
-        64: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
-        82: ("2d", "3d_fullres"),
-        # 83: ("2d", "3d_fullres"),
+        # 24: ("2d", "3d_fullres"),
+        # 27: ("2d", "3d_fullres"),
+        # 38: ("2d", "3d_fullres"),
+        # 55: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
+        137: ("2d", "3d_fullres"),
+        220: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
+        # 221: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
+        223: ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
     }
 
     configurations_3d_fr_only = {
@@ -52,25 +52,23 @@ def merge(dict1, dict2):
     }
 
     num_gpus = 1
-    exclude_hosts = "-R \"select[hname!='e230-dgx2-2']\" -R \"select[hname!='e230-dgx2-1']\" -R \"select[hname!='e230-dgx1-1']\" -R \"select[hname!='e230-dgxa100-1']\" -R \"select[hname!='e230-dgxa100-2']\" -R \"select[hname!='e230-dgxa100-3']\" -R \"select[hname!='e230-dgxa100-4']\""
-    resources = "-R \"tensorcore\""
+    exclude_hosts = "-R \"select[hname!='e230-dgx2-2']\" -R \"select[hname!='e230-dgx2-1']\""
+    resources = ""
     gpu_requirements = f"-gpu num={num_gpus}:j_exclusive=yes:gmem=33G"
-    queue = "-q gpu-lowprio"
-    preamble = "-L /bin/bash \"source ~/load_env_cluster4.sh && "
-    train_command = 'nnUNet_results=/dkfz/cluster/gpu/checkpoints/OE0441/isensee/nnUNet_results_remake_release nnUNetv2_train'
+    queue = "-q gpu"
+    preamble = "-L /bin/bash \"source ~/load_env_mamba_slumber.sh && "
+    train_command = 'nnUNetv2_train'
 
-    folds = (0, )
+    folds = (1, 2, 3, 4)
     # use_this = configurations_2d_only
-    use_this = merge(configurations_3d_fr_only, configurations_3d_lr_only)
+    use_this = configurations_3d_fr_only
     # use_this = merge(use_this, configurations_3d_c_only)
 
     use_these_modules = {
-        'nnUNetTrainer': ('nnUNetPlans',),
-        'nnUNetTrainerDiceCELoss_noSmooth': ('nnUNetPlans',),
-        # 'nnUNetTrainer_DASegOrd0': ('nnUNetPlans',),
+        'nnUNetTrainer': ('nnUNetPlans', 'nnUNetResEncUNetMPlans', 'nnUNetResEncUNetLPlans', 'nnUNetResEncUNetXLPlans'),
     }
 
-    additional_arguments = f'--disable_checkpointing -num_gpus {num_gpus}'  # ''
+    additional_arguments = f' -num_gpus {num_gpus}'  # ''
 
     output_file = "/home/isensee/deleteme.txt"
     with open(output_file, 'w') as f: