eynollah-training: all training CLI into single click group

pyproject.toml

@@ -31,6 +31,7 @@ classifiers = [
 
 [project.scripts]
 eynollah = "eynollah.cli:main"
+eynollah-training = "eynollah.training.cli:main"
 ocrd-eynollah-segment = "eynollah.ocrd_cli:main"
 ocrd-sbb-binarize = "eynollah.ocrd_cli_binarization:main"
 

src/eynollah/training/build_model_load_pretrained_weights_and_save.py

@@ -1,5 +1,5 @@
+import click
 import tensorflow as tf
-from tensorflow.keras.optimizers import *
 
 from .models import resnet50_unet
 
@@ -8,8 +8,8 @@ def configuration():
     gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
     session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
 
-
-if __name__ == '__main__':
+@click.command()
+def build_model_load_pretrained_weights_and_save():
     n_classes = 2
     input_height = 224
     input_width = 448

src/eynollah/training/cli.py

@@ -0,0 +1,26 @@
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+
+import click
+import sys
+
+from .build_model_load_pretrained_weights_and_save import build_model_load_pretrained_weights_and_save
+from .generate_gt_for_training import main as generate_gt_cli
+from .inference import main as inference_cli
+from .train import ex
+
+@click.command(context_settings=dict(
+        ignore_unknown_options=True,
+))
+@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
+def train_cli(sacred_args):
+    ex.run_commandline([sys.argv[0]] + list(sacred_args))
+
+@click.group('training')
+def main():
+    pass
+
+main.add_command(build_model_load_pretrained_weights_and_save)
+main.add_command(generate_gt_cli, 'generate-gt')
+main.add_command(inference_cli, 'inference')
+main.add_command(train_cli, 'train')

src/eynollah/training/generate_gt_for_training.py

@@ -581,6 +581,3 @@ def visualize_ocr_text(xml_file, dir_xml, dir_out):
                     # Draw the text
                     draw.text((text_x, text_y), ocr_texts[index], fill="black", font=font)
         image_text.save(os.path.join(dir_out, f_name+'.png'))
-    
-if __name__ == "__main__":
-    main()

src/eynollah/training/inference.py

@@ -20,7 +20,10 @@ from .gt_gen_utils import (
     resize_image,
     update_list_and_return_first_with_length_bigger_than_one
 )
-from .models import PatchEncoder, Patches
+from .models import (
+    PatchEncoder,
+    Patches
+)
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
@@ -675,9 +678,3 @@ def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_fil
     x=sbb_predict(image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area)
     x.run()
 
-if __name__=="__main__":
-    main()
-
-    
-    
-    

src/eynollah/training/train.py

@@ -2,9 +2,13 @@ import os
 import sys
 import json
 
-from eynollah.training.metrics import soft_dice_loss, weighted_categorical_crossentropy
+import click
 
-from .models import (
+from eynollah.training.metrics import (
+    soft_dice_loss,
+    weighted_categorical_crossentropy
+)
+from eynollah.training.models import (
     PatchEncoder,
     Patches,
     machine_based_reading_order_model,
@@ -13,7 +17,7 @@ from .models import (
     vit_resnet50_unet,
     vit_resnet50_unet_transformer_before_cnn
 )
-from .utils import (
+from eynollah.training.utils import (
     data_gen,
     generate_arrays_from_folder_reading_order,
     generate_data_from_folder_evaluation,
@@ -142,7 +146,6 @@ def config_params():
     dir_rgb_backgrounds = None
     dir_rgb_foregrounds = None
 
-
 @ex.automain
 def run(_config, n_classes, n_epochs, input_height,
         input_width, weight_decay, weighted_loss,