Updated config model placeholders and added all new trained fid models

autoafids/config/snakebids.yml

@@ -287,35 +287,35 @@ afids_inference:
     afid_01: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'
     afid_02: 'resources/afids_cnn_ckpts/afid-02_epoch-964_mae-0.0007.ckpt'
     afid_03: 'resources/afids_cnn_ckpts/afid-03_epoch-778_mae-0.0008.ckpt'
-    afid_04: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_05: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_06: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_07: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_08: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_09: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_10: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_11: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_12: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_13: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_14: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_15: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_16: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_17: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_18: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_19: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_20: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_21: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_22: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_23: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_24: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_25: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_26: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_27: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_28: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_29: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_30: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_31: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
-    afid_32: 'resources/afids_cnn_ckpts/afid-01_epoch-820_mae-0.0002.ckpt'  # placeholder
+    afid_04: 'resources/afids_cnn_ckpts/afid-04_epoch-529_mae-0.0007.ckpt'  
+    afid_05: 'resources/afids_cnn_ckpts/afid-05_epoch-628_mae-0.0007.ckpt'  
+    afid_06: 'resources/afids_cnn_ckpts/afid-06_epoch-485_mae-0.0009.ckpt'  
+    afid_07: 'resources/afids_cnn_ckpts/afid-07_epoch-599_mae-0.0008.ckpt'  
+    afid_08: 'resources/afids_cnn_ckpts/afid-08_epoch-455_mae-0.0013.ckpt' 
+    afid_09: 'resources/afids_cnn_ckpts/afid-09_epoch-535_mae-0.0010.ckpt'  
+    afid_10: 'resources/afids_cnn_ckpts/afid-10_epoch-431_mae-0.0009.ckpt'  
+    afid_11: 'resources/afids_cnn_ckpts/afid-11_epoch-735_mae-0.0005.ckpt'  
+    afid_12: 'resources/afids_cnn_ckpts/afid-12_epoch-694_mae-0.0007.ckpt'  
+    afid_13: 'resources/afids_cnn_ckpts/afid-13_epoch-763_mae-0.0006.ckpt'  
+    afid_14: 'resources/afids_cnn_ckpts/afid-14_epoch-405_mae-0.0011.ckpt'  
+    afid_15: 'resources/afids_cnn_ckpts/afid-15_epoch-371_mae-0.0013.ckpt'  
+    afid_16: 'resources/afids_cnn_ckpts/afid-16_epoch-454_mae-0.0012.ckpt'  
+    afid_17: 'resources/afids_cnn_ckpts/afid-17_epoch-291_mae-0.0023.ckpt'  
+    afid_18: 'resources/afids_cnn_ckpts/afid-18_epoch-233_mae-0.0016.ckpt'  
+    afid_19: 'resources/afids_cnn_ckpts/afid-19_epoch-626_mae-0.0011.ckpt'  
+    afid_20: 'resources/afids_cnn_ckpts/afid-20_epoch-720_mae-0.0011.ckpt'  
+    afid_21: 'resources/afids_cnn_ckpts/afid-21_epoch-672_mae-0.0009.ckpt'  
+    afid_22: 'resources/afids_cnn_ckpts/afid-22_epoch-647_mae-0.0008.ckpt'   
+    afid_23: 'resources/afids_cnn_ckpts/afid-23_epoch-656_mae-0.0010.ckpt'   
+    afid_24: 'resources/afids_cnn_ckpts/afid-24_epoch-689_mae-0.0007.ckpt'   
+    afid_25: 'resources/afids_cnn_ckpts/afid-25_epoch-513_mae-0.0013.ckpt'   
+    afid_26: 'resources/afids_cnn_ckpts/afid-26_epoch-784_mae-0.0009.ckpt'  
+    afid_27: 'resources/afids_cnn_ckpts/afid-27_epoch-300_mae-0.0014.ckpt'  
+    afid_28: 'resources/afids_cnn_ckpts/afid-28_epoch-351_mae-0.0009.ckpt'  
+    afid_29: 'resources/afids_cnn_ckpts/afid-29_epoch-394_mae-0.0014.ckpt'  
+    afid_30: 'resources/afids_cnn_ckpts/afid-30_epoch-618_mae-0.0012.ckpt'  
+    afid_31: 'resources/afids_cnn_ckpts/afid-31_epoch-573_mae-0.0009.ckpt'  
+    afid_32: 'resources/afids_cnn_ckpts/afid-32_epoch-561_mae-0.0007.ckpt'  
   patch_size: 64      # cubic patch size in voxels
   device: cuda:0       # cpu recommended for parallel mode; cuda:0 for sequential
   overlap: 0.5        # sliding-window overlap for --detect_without_prior (0.0–0.75)

autoafids/workflow/Snakefile

@@ -355,13 +355,16 @@ rule mni2subfids:
 
 # ── Detection mode: choose rules to include ──────────────────────────────
 if config.get("detect_mode", "prior") == "nnlm":
+
     # nnLandmark mode: single-pass whole-volume detection
     include: "rules/nnlm.smk"
+
 else:
     # Legacy CNN modes (prior / noprior)
     # If running on GPU, automatically enable sequential inference to avoid spawning 32 parallel jobs
     if config.get("afids_inference", {}).get("device") == "cuda:0":
         config["enable_sequential_inference"] = True
+
     include: "rules/cnn.smk"
 
 
@@ -377,7 +380,6 @@ if config["LEAD_DBS_DIR"] or config["FMRIPREP_DIR"]:
     include: "rules/regqc.smk"
 
 
-
 # Select the correct FCSV output descriptor based on inference mode.
 # prior   → applyfidmodel_gather          → desc="afidscnn"
 # noprior → applyfidmodel_noprior_gather  → desc="afidscnn-noprior"
@@ -445,4 +447,3 @@ rule all:
             else []
         ),
     default_target: True
-

autoafids/workflow/rules/cnn.smk

@@ -1,5 +1,6 @@
 # populate the AUTOAFIDS_CACHE_DIR folder as needed
 
+
 rule download_cnn_model:
     params:
         url=config["resource_urls"][config["model"]],
@@ -15,9 +16,11 @@ rule download_cnn_model:
         " unzip -q -d {output.unzip_dir} model.zip && "
         " rm model.zip"
 
+
 _AFIDS = [f"{i:02d}" for i in range(1, 33)]  # ["01", "02", ..., "32"]
 
 if config.get("enable_sequential_inference", False):
+
     rule applyfidmodel_all:
         """Run 5-patch inference for ALL 32 AFIDs using MNI-registered prior location"""
         input:
@@ -27,7 +30,7 @@ if config.get("enable_sequential_inference", False):
                     datatype="normalize",
                     desc=chosen_norm_method,
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
                 if config["modality"] != "T1w"
                 else bids(
@@ -36,7 +39,7 @@ if config.get("enable_sequential_inference", False):
                     desc=chosen_norm_method,
                     res=config["res"],
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
             ),
             prior=bids(
@@ -55,7 +58,8 @@ if config.get("enable_sequential_inference", False):
                     afid=afid,
                     suffix="coord.txt",
                     **inputs[config["modality"]].wildcards,
-                ) for afid in _AFIDS
+                )
+                for afid in _AFIDS
             ],
             probs=[
                 bids(
@@ -64,7 +68,8 @@ if config.get("enable_sequential_inference", False):
                     afid=afid,
                     suffix="probmap.nii.gz",
                     **inputs[config["modality"]].wildcards,
-                ) for afid in _AFIDS
+                )
+                for afid in _AFIDS
             ],
             fcsv=bids(
                 root=root,
@@ -83,14 +88,15 @@ if config.get("enable_sequential_inference", False):
             ckpts=lambda wildcards: {
                 key: str(Path(workflow.basedir).parent / path)
                 for key, path in config["afids_inference"]["checkpoints"].items()
-            }
+            },
         threads: 1
-
         conda:
             "../envs/pytorch.yaml"
         script:
             "../scripts/apply_with_prior_all.py"
+
 else:
+
     # ── WITH PRIOR (SINGLE) ──────────────────────────────────────────────────────
     rule applyfidmodel_single:
         """Run 5-patch inference for ONE AFID using MNI-registered prior location."""
@@ -101,7 +107,7 @@ else:
                     datatype="normalize",
                     desc=chosen_norm_method,
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
                 if config["modality"] != "T1w"
                 else bids(
@@ -110,7 +116,7 @@ else:
                     desc=chosen_norm_method,
                     res=config["res"],
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
             ),
             prior=bids(
@@ -148,10 +154,11 @@ else:
         params:
             ckpt_path=lambda wildcards: str(
                 Path(workflow.basedir).parent
-                / config["afids_inference"]["checkpoints"][f"afid_{int(wildcards.afid):02d}"]
+                / config["afids_inference"]["checkpoints"][
+                    f"afid_{int(wildcards.afid):02d}"
+                ]
             ),
         threads: 1
-
         conda:
             "../envs/pytorch.yaml"
         script:
@@ -166,7 +173,10 @@ else:
                     datatype="afids-cnn",
                     afid="{afid}",
                     suffix="coord.txt",
-                    **{k: getattr(wildcards, k) for k in inputs[config["modality"]].wildcards},
+                    **{
+                        k: getattr(wildcards, k)
+                        for k in inputs[config["modality"]].wildcards
+                    },
                 ),
                 afid=_AFIDS,
             ),
@@ -190,9 +200,11 @@ else:
         script:
             "../scripts/gather_afids.py"
 
+
 # ---------------------------------------------------------------------------
 
 if config.get("enable_sequential_inference", False):
+
     rule applyfidmodel_noprior_all:
         """Whole-volume sliding-window inference for ALL 32 AFIDs (no prior needed)"""
         input:
@@ -202,7 +214,7 @@ if config.get("enable_sequential_inference", False):
                     datatype="normalize",
                     desc=chosen_norm_method,
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
                 if config["modality"] != "T1w"
                 else bids(
@@ -211,7 +223,7 @@ if config.get("enable_sequential_inference", False):
                     desc=chosen_norm_method,
                     res=config["res"],
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
             ),
         output:
@@ -222,7 +234,8 @@ if config.get("enable_sequential_inference", False):
                     afid=afid,
                     suffix="coord.txt",
                     **inputs[config["modality"]].wildcards,
-                ) for afid in _AFIDS
+                )
+                for afid in _AFIDS
             ],
             probs=[
                 bids(
@@ -231,7 +244,8 @@ if config.get("enable_sequential_inference", False):
                     afid=afid,
                     suffix="probmap.nii.gz",
                     **inputs[config["modality"]].wildcards,
-                ) for afid in _AFIDS
+                )
+                for afid in _AFIDS
             ],
             fcsv=bids(
                 root=root,
@@ -250,13 +264,15 @@ if config.get("enable_sequential_inference", False):
             ckpts=lambda wildcards: {
                 key: str(Path(workflow.basedir).parent / path)
                 for key, path in config["afids_inference"]["checkpoints"].items()
-            }
+            },
         threads: 1
         conda:
             "../envs/pytorch.yaml"
         script:
             "../scripts/apply_noprior_all.py"
+
 else:
+
     # ── WITHOUT PRIOR (SINGLE) ──────────────────────────────────────────────────
     rule applyfidmodel_noprior_single:
         """Whole-volume sliding-window inference for ONE AFID — no prior needed."""
@@ -267,7 +283,7 @@ else:
                     datatype="normalize",
                     desc=chosen_norm_method,
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
                 if config["modality"] != "T1w"
                 else bids(
@@ -276,7 +292,7 @@ else:
                     desc=chosen_norm_method,
                     res=config["res"],
                     suffix="T1w.nii.gz",
-                    **inputs[config["modality"]].wildcards
+                    **inputs[config["modality"]].wildcards,
                 )
             ),
         output:
@@ -306,7 +322,9 @@ else:
         params:
             ckpt_path=lambda wildcards: str(
                 Path(workflow.basedir).parent
-                / config["afids_inference"]["checkpoints"][f"afid_{int(wildcards.afid):02d}"]
+                / config["afids_inference"]["checkpoints"][
+                    f"afid_{int(wildcards.afid):02d}"
+                ]
             ),
         threads: 1
         conda:
@@ -323,7 +341,10 @@ else:
                     datatype="afids-cnn-noprior",
                     afid="{afid}",
                     suffix="coord.txt",
-                    **{k: getattr(wildcards, k) for k in inputs[config["modality"]].wildcards},
+                    **{
+                        k: getattr(wildcards, k)
+                        for k in inputs[config["modality"]].wildcards
+                    },
                 ),
                 afid=_AFIDS,
             ),
@@ -345,4 +366,4 @@ else:
         conda:
             "../envs/pytorch.yaml"
         script:
-            "../scripts/gather_afids.py"
+            "../scripts/gather_afids.py"

autoafids/workflow/rules/nnlm.smk

@@ -147,7 +147,7 @@ rule nnlm_to_fcsv:
             **inputs[config["modality"]].wildcards,
         ),
     params:
-        fcsv_template=str(Path(workflow.basedir).parent / "resources" / "dummy.fcsv")
+        fcsv_template=str(Path(workflow.basedir).parent / "resources" / "dummy.fcsv"),
     conda:
         "../envs/nibabel.yaml"
     script: