Add application file

.gitignore

@@ -3,7 +3,6 @@ vis_hp
 assets
 images
 backup
-run_training.py
 run_gradio.py
 run_foward.py
 

run_training.py

@@ -0,0 +1,78 @@
+import argparse
+import importlib
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "7"
+os.environ["HDF5_USE_FILE_LOCKING"] = "0"
+import random
+from datetime import date
+from shutil import copyfile
+
+import cv2 as cv
+import numpy as np
+import torch
+import torch.backends.cudnn
+
+import admin.settings as ws_settings
+
+
+def run_training(train_module, train_name, seed, cudnn_benchmark=True):
+    """Run a train scripts in train_settings.
+    args:
+        train_module: Name of module in the "train_settings/" folder.
+        train_name: Name of the train settings file.
+        cudnn_benchmark: Use cudnn benchmark or not (default is True).
+    """
+
+    # This is needed to avoid strange crashes related to opencv
+    cv.setNumThreads(0)
+
+    torch.backends.cudnn.benchmark = cudnn_benchmark
+
+    # dd/mm/YY
+    today = date.today()
+    d1 = today.strftime("%d/%m/%Y")
+    print('Training:  {}  {}\nDate: {}'.format(train_module, train_name, d1))
+
+    settings = ws_settings.Settings()
+    settings.module_name = train_module
+    settings.script_name = train_name
+    settings.project_path = 'train_settings/{}/{}'.format(train_module, train_name)
+    settings.seed = seed
+
+    # will save the checkpoints there
+
+    save_dir = os.path.join(settings.env.workspace_dir, settings.project_path)
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    copyfile(settings.project_path + '.py', os.path.join(save_dir, settings.script_name + '.py'))
+
+    expr_module = importlib.import_module('train_settings.{}.{}'.format(train_module.replace('/', '.'),
+                                                                        train_name.replace('/', '.')))
+    
+    expr_func = getattr(expr_module, 'run')
+
+    expr_func(settings)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.')
+    parser.add_argument('--train_module', type=str, help='Name of module in the "train_settings/" folder.')
+    parser.add_argument('--train_name', type=str, help='Name of the train settings file.')
+    parser.add_argument('--cudnn_benchmark', type=bool, default=True,
+                        help='Set cudnn benchmark on (1) or off (0) (default is on).')
+    parser.add_argument('--seed', type=int, default=1992, help='Pseudo-RNG seed')
+    args = parser.parse_args()
+
+    # args.seed = random.randint(0, 3000000)
+    args.seed = torch.initial_seed() & (2 ** 32 - 1)
+    print('Seed is {}'.format(args.seed))
+    random.seed(int(args.seed))
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed(args.seed)
+
+    run_training(args.train_module, args.train_name, cudnn_benchmark=args.cudnn_benchmark, seed=args.seed)
+
+
+if __name__ == '__main__':
+    main()