New:

1. Create pipeline [yolov5, yolov8]

ODRS/train_utils/config/custom_config.yaml

@@ -1,10 +1,10 @@
-BATCH_SIZE: 20
+BATCH_SIZE: [25, 20]
 CLASSES: classes.txt
-DATA_PATH: /media/space/ssd_1_tb_evo_sumsung/Work/Warp-D
-EPOCHS: 2
+DATA_PATH: Warp-D
+EPOCHS: [4, 5]
 GPU_COUNT: 1
-IMG_SIZE: 300
-MODEL: ssd
+IMG_SIZE: [300, 400]
+MODEL: [yolov5l, yolov5s]
 SELECT_GPU: 0
 
 CONFIG_PATH: dataset.yaml

ODRS/train_utils/custom_train_all.py

@@ -66,14 +66,7 @@ def fit_model(DATA_PATH, CLASSES, IMG_SIZE, BATCH_SIZE, EPOCHS, MODEL, CONFIG_PA
         train_ssd(CONFIG_PATH)
 
 
-def run():
-    """
-    Create config, run learning functions.
-
-    """
-    config_path = Path(ROOT) / 'ODRS' / 'train_utils' / 'config' / 'custom_config.yaml'
-    config = loadConfig(config_path)
-
+def prepare_to_train(config, list_parameters):
     DATA_PATH = config['DATA_PATH']
     CLASSES = config['CLASSES']
     IMG_SIZE = config['IMG_SIZE']
@@ -86,8 +79,46 @@ def run():
     GPU_COUNT = config['GPU_COUNT']
     SELECT_GPU = config['SELECT_GPU']
 
-    fit_model(DATA_PATH, CLASSES, IMG_SIZE, BATCH_SIZE, EPOCHS, MODEL, CONFIG_PATH, SPLIT_TRAIN_VALUE,
-              SPLIT_VAL_VALUE, GPU_COUNT, SELECT_GPU)
+    if list_parameters:
+        for i in range(len(list_parameters[list(list_parameters.keys())[0]])):
+            current_params = {
+                'DATA_PATH': DATA_PATH[i] if isinstance(DATA_PATH, list) else DATA_PATH,
+                'CLASSES': CLASSES[i] if isinstance(CLASSES, list) else CLASSES,
+                'IMG_SIZE': IMG_SIZE[i] if isinstance(IMG_SIZE, list) else IMG_SIZE,
+                'BATCH_SIZE': BATCH_SIZE[i] if isinstance(BATCH_SIZE, list) else BATCH_SIZE,
+                'EPOCHS': EPOCHS[i] if isinstance(EPOCHS, list) else EPOCHS,
+                'MODEL': MODEL[i] if isinstance(MODEL, list) else MODEL,
+                'CONFIG_PATH': CONFIG_PATH[i] if isinstance(CONFIG_PATH, list) else CONFIG_PATH,
+                'SPLIT_TRAIN_VALUE': SPLIT_TRAIN_VALUE[i] if isinstance(SPLIT_TRAIN_VALUE, list) else SPLIT_TRAIN_VALUE,
+                'SPLIT_VAL_VALUE': SPLIT_VAL_VALUE[i] if isinstance(SPLIT_VAL_VALUE, list) else SPLIT_VAL_VALUE,
+                'GPU_COUNT': GPU_COUNT[i] if isinstance(GPU_COUNT, list) else GPU_COUNT,
+                'SELECT_GPU': SELECT_GPU[i] if isinstance(SELECT_GPU, list) else SELECT_GPU
+            }
+            fit_model(**current_params)
+
+    else:
+        fit_model(DATA_PATH, CLASSES, IMG_SIZE, BATCH_SIZE, EPOCHS, MODEL, CONFIG_PATH, SPLIT_TRAIN_VALUE,
+                SPLIT_VAL_VALUE, GPU_COUNT, SELECT_GPU)
+
+
+def check_dict_arrays_sizes(dictionary):
+    for key, value in dictionary.items():
+        if isinstance(value, list):
+            first_array = next(iter(dictionary.values()))
+            first_array_size = len(first_array)
+            current_array_size = len(value)
+            if current_array_size != first_array_size:
+                raise ValueError(f"Size mismatch for key '{key}'. Expected size: {first_array_size}, actual size: {current_array_size}")
+
+
+def run():
+    config_path = Path(ROOT) / 'ODRS' / 'train_utils' / 'config' / 'custom_config.yaml'
+    config = loadConfig(config_path)
+
+    list_parameters = {key: value for key, value in config.items() if isinstance(value, list)}
+    check_dict_arrays_sizes(list_parameters)
+    prepare_to_train(config, list_parameters)
+
 
 if __name__ == "__main__":
     run()

ODRS/train_utils/train_model/scripts/yolov5_train.py

@@ -1,4 +1,6 @@
 import os
+import subprocess
+import time
 from pathlib import Path
 from ODRS.train_utils.train_model.models.yolov5 import train
 
@@ -44,5 +46,4 @@ def train_V5(IMG_SIZE, BATCH_SIZE, EPOCHS, CONFIG_PATH, MODEL_PATH, GPU_COUNT, S
         f" --project {CONFIG_PATH.parent}" +
         f" --name exp"
     )
-
-    os.system(full_command)
+    os.system(full_command)

ODRS/utils/utils.py

@@ -9,6 +9,7 @@
 
 file = Path(__file__).resolve()
 
+
 def loadConfig(config_file):
     with open(config_file) as f:
         return load(f, Loader=FullLoader)