training: re-instate index_start, reflect cfg dependency

- `index_start`: re-introduce cfg key, pass to Keras `Model.fit`
  as `initial_epoch`
- make config keys `index_start` and `dir_of_start_model` dependent
  on `continue_training`
- improve description

src/eynollah/training/train.py

@@ -157,10 +157,12 @@ def config_params():
     dir_eval = None  # Directory of validation dataset with subdirectories having the names "images" and "labels".
     dir_output = None  # Directory where the augmented training data and the model checkpoints will be saved.
     pretraining = False  # Set to true to (down)load pretrained weights of ResNet50 encoder.
-    save_interval = None # frequency for writing model checkpoints (nonzero integer for number of batches, or zero for epoch)
-    continue_training = False  # Set to true if you would like to continue training an already trained a model.
-    dir_of_start_model = ''  # Directory containing pretrained encoder to continue training the model.
-    data_is_provided = False  # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output".
+    save_interval = None # frequency for writing model checkpoints (positive integer for number of batches saved under "model_step_{batch:04d}", otherwise epoch saved under "model_{epoch:02d}")
+    continue_training = False  # Whether to continue training an existing model.
+    if continue_training:
+        dir_of_start_model = ''  # Directory of model checkpoint to load to continue training. (E.g. if you already trained for 3 epochs, set "dir_of_start_model=dir_output/model_03".)
+        index_start = 0  #  Epoch counter initial value to continue training. (E.g. if you already trained for 3 epochs, set "index_start=3" to continue naming checkpoints model_04, model_05 etc.)
+    data_is_provided = False  # Whether the preprocessed input data (subdirectories "images" and "labels" in both subdirectories "train" and "eval" of "dir_output") has already been generated (in the first epoch of a previous run).
     if backbone_type == "transformer":
         transformer_patchsize_x = None  # Patch size of vision transformer patches in x direction.
         transformer_patchsize_y = None # Patch size of vision transformer patches in y direction.
@@ -190,6 +192,7 @@ def run(_config,
         weight_decay,
         learning_rate,
         continue_training,
+        index_start,
         dir_of_start_model,
         save_interval,
         augmentation,
@@ -312,6 +315,7 @@ def run(_config,
                                        custom_objects = {"PatchEncoder": PatchEncoder,
                                                          "Patches": Patches})
         else:
+            index_start = 0
             if backbone_type == 'nontransformer':
                 model = resnet50_unet(n_classes,
                                       input_height,
@@ -410,7 +414,8 @@ def run(_config,
             #validation_steps=1, # rs: only one batch??
             validation_steps=steps_val,
             epochs=n_epochs,
-            callbacks=callbacks)
+            callbacks=callbacks,
+            initial_epoch=index_start)
 
         #os.system('rm -rf '+dir_train_flowing)
         #os.system('rm -rf '+dir_eval_flowing)