convention folder naming

Author: solivr

evaluation.py

@@ -6,11 +6,10 @@
 from glob import glob
 
 import click
-from tf_crnn.callbacks import CustomLoaderCallback
-from tf_crnn.config import Params
+from tf_crnn.callbacks import CustomLoaderCallback, FOLDER_SAVED_MODEL
+from tf_crnn.config import Params, CONST
 from tf_crnn.data_handler import dataset_generator
 from tf_crnn.preprocessing import preprocess_csv
-
 from tf_crnn.model import get_model_train
 
 
@@ -23,7 +22,7 @@ def evaluation(csv_filename: str,
     config_filename = os.path.join(model_dir, 'config.json')
     parameters = Params.from_json_file(config_filename)
 
-    saving_dir = os.path.join(parameters.output_model_dir, 'saving')
+    saving_dir = os.path.join(parameters.output_model_dir, FOLDER_SAVED_MODEL)
 
     # Callback for model weights loading
     last_time_stamp = max([int(p.split(os.path.sep)[-1].split('-')[0])
@@ -32,7 +31,7 @@ def evaluation(csv_filename: str,
     ld_callback = CustomLoaderCallback(loading_dir)
 
     # Preprocess csv data
-    csv_evaluation_file = os.path.join(parameters.output_model_dir, 'preprocessed', 'evaluation_data.csv')
+    csv_evaluation_file = os.path.join(parameters.output_model_dir, CONST.PREPROCESSING_FOLDER, 'evaluation_data.csv')
     n_samples = preprocess_csv(csv_filename,
                                parameters,
                                csv_evaluation_file)
@@ -50,4 +49,4 @@ def evaluation(csv_filename: str,
 
 
 if __name__ == '__main__':
-    evaluation()
+    evaluation()

prediction.py

@@ -9,7 +9,6 @@
 from tf_crnn.callbacks import CustomPredictionSaverCallback, FOLDER_SAVED_MODEL
 from tf_crnn.config import Params
 from tf_crnn.data_handler import dataset_generator
-
 from tf_crnn.model import get_model_inference
 
 
@@ -37,4 +36,4 @@ def prediction(csv_filename: str,
 
 
 if __name__ == '__main__':
-    prediction()
+    prediction()

tf_crnn/config.py

@@ -14,6 +14,7 @@
 
 class CONST:
     DIMENSION_REDUCTION_W_POOLING = 2*2  # 2x2 pooling in dimension W on layer 1 and 2
+    PREPROCESSING_FOLDER = 'preprocessed'
 
 
 class Alphabet:

tf_crnn/preprocessing.py

@@ -5,7 +5,7 @@
 import re
 import numpy as np
 import os
-from .config import Params
+from .config import Params, CONST
 import pandas as pd
 from typing import List, Tuple
 from taputapu.io.image import get_image_shape_without_loading
@@ -131,7 +131,7 @@ def data_preprocessing(params: Params) -> (str, str, int, int):
     :param params: parameters of the experiment (``Params``)
     :return: output path files, number of samples (for train and evaluation data)
     """
-    output_dir = os.path.join(params.output_model_dir, 'preprocessed')
+    output_dir = os.path.join(params.output_model_dir, CONST.PREPROCESSING_FOLDER)
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
     else: