Fixed a bug: model is now correctly passed to predict function.

pysegcnn/core/trainer.py

@@ -955,9 +955,9 @@ class LogConfig(BaseConfig):
             The string to write to the model log file.
 
         """
-        LOGGER.info(80 * '-')
+        LOGGER.info(200 * '-')
         LOGGER.info('{}: '.format(LogConfig.now()) + init_str)
-        LOGGER.info(80 * '-')
+        LOGGER.info(200 * '-')
 
 
 @dataclasses.dataclass
@@ -2459,6 +2459,11 @@ class NetworkInference(BaseConfig):
     def predict(self, model):
         """Classify the samples of the target dataset.
 
+        Parameters
+        ----------
+        model : :py:class:`pysegcnn.core.models.Network`
+            The model to evaluate on the target dataset.
+
         Returns
         -------
         output : `dict` [`str`, `dict`]
@@ -2581,7 +2586,7 @@ class NetworkInference(BaseConfig):
             # initialize logging
             log = LogConfig(state)
             dictConfig(log_conf(log.log_file))
-            log.init_log('Evaluating model: {}.'.format(state.name))
+            log.init_log('Evaluating model: {}.'.format(state))
 
             # check whether model was already evaluated
             if self.eval_file(state).exists():
@@ -2607,7 +2612,7 @@ class NetworkInference(BaseConfig):
             model, _ = Network.load_pretrained_model(state)
 
             # evaluate the model on the target dataset
-            output = self.predict()
+            output = self.predict(model)
 
             # check whether to calculate confusion matrix
             if self.cm: