--- a
+++ b/medseg_dl/evaluate.py
@@ -0,0 +1,103 @@
+import os
+import logging
+import tensorflow as tf
+from medseg_dl import parameters
+from medseg_dl.utils import utils_data, utils_misc
+from medseg_dl.model import input_fn
+from medseg_dl.model import model_fn
+from medseg_dl.model import evaluation
+
+
+def main(dir_model, non_local = 'disable', attgate = 'disable', device = None):
+
+    # Since this is the evaluation script all ops should switch to prediction
+    b_training = False
+
+    # Load parameters from model file
+    file_params = os.path.join(dir_model, 'params.yaml')
+    assert os.path.isfile(file_params)  # file has to exist!
+    params = parameters.Params(path_yaml=file_params)
+
+    # Set logger
+    utils_misc.set_logger(os.path.join(params.dict['dir_model'], 'eval.log'), params.dict['log_level'])
+
+    # Set random seed
+    if params.dict['b_use_seed']:
+        tf.set_random_seed(params.dict['random_seed'])
+
+    # Set device for graph calc
+    if device:
+        os.environ['CUDA_VISIBLE_DEVICES'] = device
+    else:
+        os.environ['CUDA_VISIBLE_DEVICES'] = params.dict['device']
+
+    """ Fetch data, generate pipeline and model """
+    tf.reset_default_graph()
+
+    # Fetch datasets, atm. saved as json
+    logging.info('Fetching the datasets...')
+    filenames_train, filenames_eval = utils_data.load_sets(params.dict['dir_data'],
+                                                           params.dict['dir_model'],
+                                                           path_parser_cfg=params.dict['path_parser_cfg'],
+                                                           set_split=params.dict['set_split'],
+                                                           b_recreate=True)
+
+    # Create a tf.data pipeline
+    # Note: one can either do evaluation on small little patches or the whole image -> has to be reflected in the model
+    logging.info('Creating the pipeline...')
+    logging.info(f'Evaluating {filenames_eval}')
+    spec_pipeline = input_fn.gen_pipeline_eval_patch(filenames=filenames_eval,
+                                                     shape_image=params.dict['shape_image_eval'],
+                                                     shape_input=params.dict['shape_input'],
+                                                     shape_output=params.dict['shape_output'],
+                                                     size_batch=params.dict['size_batch_eval'],
+                                                     channels_out=params.dict['channels_out'],
+                                                     size_buffer=params.dict['size_buffer'],
+                                                     num_parallel_calls=params.dict['num_parallel_calls'],
+                                                     b_with_labels=params.dict['b_eval_labels_patch'],
+                                                     b_verbose=True)
+
+    spec_pipeline_metrics = input_fn.gen_pipeline_eval_image(filenames=filenames_eval,
+                                                             shape_image=params.dict['shape_image_eval'],
+                                                             channels_out=params.dict['channels_out'],
+                                                             size_batch=1,
+                                                             size_buffer=params.dict['size_buffer'],
+                                                             num_parallel_calls=params.dict['num_parallel_calls'],
+                                                             b_with_labels=params.dict['b_eval_labels_image'],
+                                                             b_verbose=True)
+
+    # Create the model
+    logging.info('Creating the model...')
+    spec_model = model_fn.model_fn(spec_pipeline,
+                                   spec_pipeline_metrics,
+                                   b_training=b_training,
+                                   channels=params.dict['channels'],
+                                   channels_out=params.dict['channels_out'],
+                                   batch_size=params.dict['size_batch_eval'],
+                                   b_dynamic_pos_mid=params.dict['b_dynamic_pos_mid'],
+                                   b_dynamic_pos_end=params.dict['b_dynamic_pos_end'],
+                                   non_local=non_local,
+                                   non_local_num=1,
+                                   attgate=attgate,
+                                   filters=params.dict['filters'],
+                                   dense_layers=params.dict['dense_layers'],
+                                   alpha=params.dict['alpha'],
+                                   dropout_rate=params.dict['rate_dropout'],
+                                   b_verbose=False)
+
+    # Evaluate a saved model
+    logging.info('Starting evaluation...')
+    evaluation.sess_eval(spec_pipeline, spec_pipeline_metrics, spec_model, params, filenames_eval)
+
+
+if __name__ == '__main__':
+
+    model_dir_base = '/home/stage13_realshuffle_attgate_batch24_l2_repeat10_shuffle_eval_reminder_timout6000_K'
+
+    run = 'catalog_in_model_dir_base'
+    model_dir = os.path.join(model_dir_base, run)
+    device = '2'
+    non_local = 'disable'
+    attgate = 'active'
+    main(model_dir, non_local=non_local, attgate=attgate, device=device)
+