add test_models.py script

test_models.py

@@ -0,0 +1,120 @@
+# Testing & Debugging Script for Seg2Map
+# This script is designed for testing and debugging the Seg2Map application. It runs multiple models on a provided input directory containing RGB images,
+# using the specified implementation and model types.The results are logged and saved for analysis and evaluation.
+# The script allows for command-line arguments to specify the input directory path.
+#
+# # Author: Sharon Fitzpatrick
+# Date: 7/18/2023
+#
+# To run this script, follow these steps:
+# Make sure you have Python installed and the necessary dependencies for the script.
+# 1. Open a command prompt or terminal.
+# 2. Replace <your path here> in the command below with the path to the ROI's RGB directory:
+#    python test_models.py -P "<your path here>"" -I "BEST"
+# 2. Execute the command. For example, if the RGB directory is located at C:\development\doodleverse\seg2map\seg2map\data\new_data, the command would be:
+#    python test_models.py -P "C:\development\doodleverse\seg2map\seg2map\data\new_data" -I "BEST"
+
+
+import argparse
+from seg2map import log_maker
+from seg2map.zoo_model import ZooModel
+
+# from transformers import TFSegformerForSemanticSegmentation
+# import tensorflow as tf
+
+# alternatively you can hard code your own variables
+# INPUT_DIRECTORY = r"C:\development\doodleverse\seg2map\seg2map\data\new_data"
+INPUT_DIRECTORY = r"C:\development\doodleverse\seg2map\seg2map\data\download_group1"
+
+IMPLEMENTATION = "BEST"  # "ENSEMBLE" or "BEST"
+
+
+def parse_arguments():
+    """Parse command-line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Run models on provided input directory."
+    )
+    parser.add_argument(
+        "-P",
+        "--path",
+        type=str,
+        help="Path to an ROI's RGB directory from the data directory",
+    )
+    parser.add_argument(
+        "-I",
+        "--implementation",
+        type=str,
+        help="BEST or ENSEMBLE",
+    )
+    return parser.parse_args()
+
+
+def print_model_info(model_selected, session_name, input_directory):
+    """Print information about the selected model."""
+    print(f"Running model {model_selected}")
+    print(f"session_name: {session_name}")
+    print(f"model_selected: {model_selected}")
+    print(f"sample_directory: {input_directory}")
+
+
+def run_model(model_dict):
+    """Run the Seg2Map model with given parameters."""
+    zoo_model_instance = ZooModel()
+    zoo_model_instance.run_model(
+        model_dict["implementation"],
+        model_dict["session_name"],
+        model_dict["sample_direc"],
+        model_id=model_dict["model_type"],
+        use_GPU="0",
+        use_otsu=model_dict["otsu"],
+        use_tta=model_dict["tta"],
+    )
+
+
+def main():
+    args = parse_arguments()
+
+    # Get input directory and implementation from command-line arguments or use default values
+    input_directory = args.path or INPUT_DIRECTORY
+    implementation = args.implementation or IMPLEMENTATION
+
+    print(f"Using input_directory: {input_directory}")
+    print(f"Using implementation: {implementation}")
+
+    # List of models that will be tested
+    available_models = [
+        "OpenEarthNet_RGB_9class_7576894",
+        "DeepGlobe_RGB_7class_7576898",
+        "EnviroAtlas_RGB_6class_7576909",
+        "AAAI-Buildings_RGB_2class_7607895",
+        "aaai_floodedbuildings_RGB_2class_7622733",
+        "xbd_building_RGB_2class_7613212",
+        "xbd_damagedbuilding_RGB_4class_7613175",
+        "chesapeake_RGB_7class_7576904",
+        "orthoCT_RGB_2class_7574784",
+        "orthoCT_RGB_5class_7566992",
+        "orthoCT_RGB_5class_segformer_7641708",
+        "orthoCT_RGB_8class_7570583",
+        "orthoCT_RGB_8class_segformer_7641724",
+        "chesapeake_7class_segformer_7677506",
+    ]
+
+    for model_selected in available_models:
+        session_name = model_selected + "_" + implementation + "_" + "session"
+        print_model_info(model_selected, session_name, input_directory)
+
+        # Load the basic zoo_model settings
+        model_dict = {
+            "sample_direc": input_directory,
+            "session_name": session_name,
+            "use_GPU": "0",
+            "implementation": implementation,
+            "model_type": model_selected,
+            "otsu": False,
+            "tta": False,
+        }
+        run_model(model_dict)
+
+
+if __name__ == "__main__":
+    main()