From 5d0c26b629dc0f7368c7d2058a2efbd0ac27a911 Mon Sep 17 00:00:00 2001
From: Robert Sachunsky <sachunsky@informatik.uni-leipzig.de>
Date: Thu, 5 Feb 2026 12:02:58 +0100
Subject: [PATCH] training.train: use std Keras data loader for classification

(much more efficient, works with std F1 metric)
---
 src/eynollah/training/train.py | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index 0f8d0e9..7cf7536 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -23,8 +23,6 @@ from eynollah.training.models import (
 from eynollah.training.utils import (
     data_gen,
     generate_arrays_from_folder_reading_order,
-    generate_data_from_folder_evaluation,
-    generate_data_from_folder_training,
     get_one_hot,
     preprocess_imgs,
     return_number_of_total_training_data
@@ -37,6 +35,7 @@ from tensorflow.keras.optimizers import SGD, Adam
 from tensorflow.keras.metrics import MeanIoU, F1Score
 from tensorflow.keras.models import load_model
 from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
+from tensorflow.keras.utils import image_dataset_from_directory
 from sacred import Experiment
 from sacred.config import create_captured_function
 from tqdm import tqdm
@@ -430,13 +429,13 @@ def run(_config,
                       metrics=['accuracy', F1Score(average='macro', name='f1')])
 
         list_classes = list(classification_classes_name.values())
-        trainXY = generate_data_from_folder(
-            dir_train, n_batch, input_height, input_width, n_classes, list_classes, shuffle=True)
-        testXY = generate_data_from_folder(
-            dir_eval, n_batch, input_height, input_width, n_classes, list_classes)
-        epoch_size_train = return_number_of_total_training_data(dir_train)
-        epoch_size_eval = return_number_of_total_training_data(dir_eval)
-
+        data_args = dict(label_mode="categorical",
+                         class_names=list_classes,
+                         batch_size=n_batch,
+                         image_size=(input_height, input_width),
+                         interpolation="nearest")
+        trainXY = image_dataset_from_directory(dir_train, shuffle=True, **data_args)
+        testXY = image_dataset_from_directory(dir_eval, shuffle=False, **data_args)
         callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False),
                      SaveWeightsAfterSteps(0, dir_output, _config,
                                            monitor='val_f1',
@@ -444,10 +443,8 @@ def run(_config,
                                            mode='max')]
         
         history = model.fit(trainXY,
-                            steps_per_epoch=epoch_size_train // n_batch,
                             #class_weight=weights)
                             validation_data=testXY,
-                            validation_steps=epoch_size_eval // n_batch,
                             verbose=1,
                             epochs=n_epochs,
                             callbacks=callbacks,