Update I/O and configs

Configs/train_1photon.yaml

@@ -1,10 +1,10 @@
 # configs/train_1photon.yaml
 experiment:
-  name: "1photon_12keV"
+  name: "1photon_15keV"
 
 data:
   sample_folder: "/mnt/sls_det_storage/moench_data/MLXID/Samples/Simulation/Moench040"
-  energy: 12 ### in keV
+  energy: 15 ### in keV
   noise_keV: 0.13
   noise_threshold: 0.0 ### set values below (noise * noise_threshold) to zero
   num_aug_ops: 1
@@ -22,7 +22,7 @@ model:
   version: "260511" # 251022
 
 training:
-  epochs: 150
+  epochs: 500
   learning_rate: 1.0e-3
   scheduler_factor: 0.7
   scheduler_patience: 3

Configs/train_2photon.yaml

@@ -1,10 +1,10 @@
 # configs/train_2photon.yaml
 experiment:
-  name: "2photon_12keV"
+  name: "2photon_15keV"
 
 data:
-  sample_folder: "/home/xie_x1/MLXID/DataProcess/Samples/SimuPileupSample/"
+  sample_folder: "/home/xie_x1/MLXID/DataProcess/Samples/15keV_SimuPileupSample/"
-  energy: 12 ### in keV
+  energy: 15 ### in keV
   sample_ratio: 1.0
   noise_keV: 0.13
 
@@ -18,7 +18,7 @@ data:
   n_size: 6 ### size of sub-images containing 2 photons
 
 model:
-  version: "260608" ## 260608
+  version: "260610" ### 260610 is the best one as of 260622
 
 training:
   epochs: 1000

Configs/train_3photon.yaml

@@ -1,12 +1,12 @@
 # configs/train_3photon.yaml
 experiment:
-  name: "3photon_12keV"
+  name: "3photon_15keV"
 
 data:
   sample_folder: "/home/xie_x1/MLXID/DeepLearning/PileupSample"
-  energy: 12 ### in keV
+  energy: 15 ### in keV
 
-  batch_size_train: 4096
+  batch_size_train: 512
   batch_size_val: 8192
   batch_size_test: 8192
   num_workers: 16

Train_1Photon.py

@@ -31,13 +31,13 @@ def prepare_output_folder(conf):
     # find the next index for experiment name
     exp_index = 0
     while True:
-        exp_name = f'{date}_1ph_{conf.data.energy}keV_v{conf.model.version}_{exp_index:02d}'
+        exp_name = f'{date}_{conf.data.energy}keV_v{conf.model.version}_{exp_index:02d}'
-        if not Path(f'Results/{exp_name}').exists():
+        if not Path(f'Results/1ph/{exp_name}').exists():
             break
         exp_index += 1
-    Path(f'Results/{exp_name}').mkdir(parents=True, exist_ok=True)
+    Path(f'Results/1ph/{exp_name}').mkdir(parents=True, exist_ok=True)
-    Path(f'Results/{exp_name}/Models').mkdir(parents=True, exist_ok=True)
+    Path(f'Results/1ph/{exp_name}/Models').mkdir(parents=True, exist_ok=True)
-    Path(f'Results/{exp_name}/Plots').mkdir(parents=True, exist_ok=True)
+    Path(f'Results/1ph/{exp_name}/Plots').mkdir(parents=True, exist_ok=True)
     OmegaConf.save(conf, f'Results/{exp_name}/config.yaml')
     return exp_name
 

Train_2Photon.py

@@ -181,7 +181,7 @@ def get_dataloaders(conf):
     file_range_keys = ['train_file_range', 'val_file_range', 'test_file_range']
 
     for split, key, batch_key, file_range_key in zip(splits, keys, batch_keys, file_range_keys):
-        files = [f"{conf.data.sample_folder}/pileupOf2phs_sample_{i}.npz" for i in range(conf.data[file_range_key][0], conf.data[file_range_key][1] + 1)]
+        files = [f"{conf.data.sample_folder}/{conf.data.energy}keV_Noise{conf.data.noise_keV}keV_pileupOf2phs_sample_{i}.npz" for i in range(conf.data[file_range_key][0], conf.data[file_range_key][1] + 1)]
 
         datasets[split] = doublePhotonDataset(
             files,