From 1581094141a2eb8892fa58b09de7fe8500e73e08 Mon Sep 17 00:00:00 2001
From: Robert Sachunsky <sachunsky@informatik.uni-leipzig.de>
Date: Wed, 4 Feb 2026 17:35:12 +0100
Subject: [PATCH] training: extend `index_start` to tasks classification and RO

---
 src/eynollah/training/train.py | 32 ++++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index de8cccd..168884a 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -423,11 +423,15 @@ def run(_config,
         #model.save(dir_output+'/'+'model'+'.h5')
 
     elif task=='classification':
-        model = resnet50_classifier(n_classes,
-                                    input_height,
-                                    input_width,
-                                    weight_decay,
-                                    pretraining)
+        if continue_training:
+            model = load_model(dir_of_start_model, compile=False)
+        else:
+            index_start = 0
+            model = resnet50_classifier(n_classes,
+                                        input_height,
+                                        input_width,
+                                        weight_decay,
+                                        pretraining)
 
         model.compile(loss='categorical_crossentropy',
                       optimizer=Adam(learning_rate=0.001), # rs: why not learning_rate?
@@ -453,7 +457,8 @@ def run(_config,
                             verbose=1,
                             epochs=n_epochs,
                             metrics=[F1Score(average='macro', name='f1')],
-                            callbacks=callbacks)
+                            callbacks=callbacks,
+                            initial_epoch=index_start)
 
         usable_checkpoints = np.flatnonzero(np.array(history['val_f1']) > f1_threshold_classification)
         if len(usable_checkpoints) >= 1:
@@ -481,8 +486,15 @@ def run(_config,
             _log.info("ensemble model saved under '%s'", cp_path)
             
     elif task=='reading_order':
-        model = machine_based_reading_order_model(
-            n_classes, input_height, input_width, weight_decay, pretraining)
+        if continue_training:
+            model = load_model(dir_of_start_model, compile=False)
+        else:
+            index_start = 0
+            model = machine_based_reading_order_model(n_classes,
+                                                      input_height,
+                                                      input_width,
+                                                      weight_decay,
+                                                      pretraining)
         
         dir_flow_train_imgs = os.path.join(dir_train, 'images')
         dir_flow_train_labels = os.path.join(dir_train, 'labels')
@@ -495,7 +507,6 @@ def run(_config,
         #ls_test = os.listdir(dir_flow_train_labels)
 
         #f1score_tot = [0]
-        indexer_start = 0
         model.compile(loss="binary_crossentropy",
                       #optimizer=SGD(learning_rate=0.01, momentum=0.9),
                       optimizer=Adam(learning_rate=0.0001), # rs: why not learning_rate?
@@ -515,7 +526,8 @@ def run(_config,
                             steps_per_epoch=num_rows / n_batch,
                             verbose=1,
                             epochs=n_epochs,
-                            callbacks=callbacks)
+                            callbacks=callbacks,
+                            initial_epoch=index_start)
         '''
         if f1score>f1score_tot[0]:
             f1score_tot[0] = f1score