Merge pull request #121 from anwai98/main

Add Argument - Target Domain

experiments/probabilistic_domain_adaptation/README.md

@@ -23,6 +23,7 @@ python unet_mean_teacher.py -p [check / train / evaluate]
                             -i <PATH-TO-DATA>
                             -s <PATH-TO-SAVE-MODEL-WEIGHTS>
                             -o <PATH-FOR-SAVING-PREDICTIONS>
+                            [(optional) --target_ct <TARGET-DOMAIN-CELL-TYPE>]
                             [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
 ```
 
@@ -33,6 +34,7 @@ python unet_adamt.py -p [check / train / evaluate]
                      -i <PATH-TO-DATA>
                      -s <PATH-TO-SAVE-MODEL-WEIGHTS>
                      -o <PATH-FOR-SAVING-PREDICTIONS>
+                     [(optional) --target_ct <TARGET-DOMAIN-CELL-TYPE>]
                      [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
 ```
 
@@ -43,6 +45,7 @@ python unet_fixmatch.py -p [check / train / evaluate]
                         -i <PATH-TO-DATA>
                         -s <PATH-TO-SAVE-MODEL-WEIGHTS>
                         -o <PATH-FOR-SAVING-PREDICTIONS>
+                        [(optional) --target_ct <TARGET-DOMAIN-CELL-TYPE>]
                         [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
                         [(optional) --distribution_alignment <ACTIVATES-DISTRIBUTION-ALIGNMENT>]
 ```
@@ -54,6 +57,7 @@ python unet_adamatch.py -p [check / train / evaluate]
                         -i <PATH-TO-DATA>
                         -s <PATH-TO-SAVE-MODEL-WEIGHTS>
                         -o <PATH-FOR-SAVING-PREDICTIONS>
+                        [(optional) --target_ct <TARGET-DOMAIN-CELL-TYPE>]
                         [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
                         [(optional) --distribution_alignment <ACTIVATES-DISTRIBUTION-ALIGNMENT>]
 ```

experiments/probabilistic_domain_adaptation/livecell/common.py

@@ -282,5 +282,6 @@ def get_parser(default_batch_size=8, default_iterations=int(1e5)):
     parser.add_argument("-n", "--n_iterations", default=default_iterations, type=int)
     parser.add_argument("-s", "--save_root")
     parser.add_argument("-c", "--cell_types", nargs="+", default=CELL_TYPES)
+    parser.add_argument("--target_ct", nargs="+", default=None)
     parser.add_argument("-o", "--output")
     return parser

experiments/probabilistic_domain_adaptation/livecell/unet_adamatch.py

@@ -84,15 +84,21 @@ def _train_source_target(args, source_cell_type, target_cell_type):
 
 
 def _train_source(args, cell_type):
-    for target_cell_type in common.CELL_TYPES:
+    if args.target_ct is None:
+        target_cell_list = common.CELL_TYPES
+    else:
+        target_cell_list = args.target_ct
+
+    for target_cell_type in target_cell_list:
+        print("Training on target cell type:", target_cell_type)
         if target_cell_type == cell_type:
             continue
         _train_source_target(args, cell_type, target_cell_type)
 
 
 def run_training(args):
     for cell_type in args.cell_types:
-        print("Start training for cell type:", cell_type)
+        print("Start training for source cell type:", cell_type)
         _train_source(args, cell_type)
 
 

experiments/probabilistic_domain_adaptation/livecell/unet_adamt.py

@@ -71,15 +71,21 @@ def _train_source_target(args, source_cell_type, target_cell_type):
 
 
 def _train_source(args, cell_type):
-    for target_cell_type in common.CELL_TYPES:
+    if args.target_ct is None:
+        target_cell_list = common.CELL_TYPES
+    else:
+        target_cell_list = args.target_ct
+
+    for target_cell_type in target_cell_list:
+        print("Training on target cell type:", target_cell_type)
         if target_cell_type == cell_type:
             continue
         _train_source_target(args, cell_type, target_cell_type)
 
 
 def run_training(args):
     for cell_type in args.cell_types:
-        print("Start training for cell type:", cell_type)
+        print("Start training for source cell type:", cell_type)
         _train_source(args, cell_type)
 
 

experiments/probabilistic_domain_adaptation/livecell/unet_fixmatch.py

@@ -90,15 +90,21 @@ def _train_source_target(args, source_cell_type, target_cell_type):
 
 
 def _train_source(args, cell_type):
-    for target_cell_type in common.CELL_TYPES:
+    if args.target_ct is None:
+        target_cell_list = common.CELL_TYPES
+    else:
+        target_cell_list = args.target_ct
+
+    for target_cell_type in target_cell_list:
+        print("Training on target cell type:", target_cell_type)
         if target_cell_type == cell_type:
             continue
         _train_source_target(args, cell_type, target_cell_type)
 
 
 def run_training(args):
     for cell_type in args.cell_types:
-        print("Start training for cell type:", cell_type)
+        print("Start training for source cell type:", cell_type)
         _train_source(args, cell_type)
 
 

experiments/probabilistic_domain_adaptation/livecell/unet_mean_teacher.py

@@ -75,15 +75,21 @@ def _train_source_target(args, source_cell_type, target_cell_type):
 
 
 def _train_source(args, cell_type):
-    for target_cell_type in common.CELL_TYPES:
+    if args.target_ct is None:
+        target_cell_list = common.CELL_TYPES
+    else:
+        target_cell_list = args.target_ct
+
+    for target_cell_type in target_cell_list:
+        print("Training on target cell type:", target_cell_type)
         if target_cell_type == cell_type:
             continue
         _train_source_target(args, cell_type, target_cell_type)
 
 
 def run_training(args):
     for cell_type in args.cell_types:
-        print("Start training for cell type:", cell_type)
+        print("Start training for source cell type:", cell_type)
         _train_source(args, cell_type)