Add inference codes for 3-ph category

Co-authored-by: Copilot <copilot@github.com>

Configs/infer_3photon.yaml

@@ -0,0 +1,54 @@
+# configs/infer_1photon.yaml
+experiment:
+  name: "FlatField_3filters_pos0"  # options: SiemenStarLowerLeft, SiemenStarLowerRight
+  task: "3Photon" 
+  output_base: "./InferenceResults"
+
+data:
+  sample_folder: "/home/xie_x1/MLXID/DataProcess/Samples"
+  names:
+    KnifeEdge_3filters_pos0:
+      file_pattern: "2603MaxIV_Edge3Filters_pos0_12keV/3Photon_CS9_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    FlatField_3filters_pos0:
+      file_pattern: "2603MaxIV_FlatField3Filters_pos0_12keV/3Photon_CS9_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    KnifeEdge_2filters:
+      file_pattern: "2603MaxIV_Edge2Filters_12keV/3Photon_CS9_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    FlatField_2filters:
+      file_pattern: "2603MaxIV_Flat2Filters_12keV/3Photon_CS9_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    Uniformity_test:
+      file_pattern: "2603MaxIV_Flat2Filters_12keV/3Photon_CS9_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 1]
+      roi: [0, 101, 0, 101]
+  
+  energy: 12  # keV
+  normalize: false # for inference dataloaders
+  batch_size: 8192
+  num_workers: 16
+
+model:
+  base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/"
+  experiment_name: "260529_3ph_12keV_v260529_01"
+  name: "triplePhoton260529_12keV_E1000.pth"
+
+inference:
+  binning_factor: 10
+  chunk_size: 16  # 
+  num_aug_ops: 1  # augX8 not implemented for 3-photon inference yet

Configs/train_3photon.yaml

@@ -13,14 +13,14 @@ data:
   train_file_range: [0, 12]
   val_file_range: [13, 14]
   test_file_range: [15, 15]
-  sample_ratio: 0.1
+  sample_ratio: 1.0
   n_size: 9 ### size of sub-images containing 3 photons
 
 model:
   version: "260529" 
 
 training:
-  epochs: 100
+  epochs: 1000
   learning_rate: 1.0e-3
   weight_decay: 1.0e-4
   scheduler_factor: 0.7