Upload run_experiments.py

run_experiments.py

@@ -0,0 +1,77 @@
+import subprocess
+import argparse
+
+# python run_experiments.py --dataset "Birds-Nest" --model yolov10n yolov10s yolov10m yolov10l
+
+def run_experiment(base_command, run_mode, use_pretrained):
+    """
+    Constructs and runs a single experiment command.
+    """
+    command = base_command + ["--run", run_mode]
+    if use_pretrained:
+        command.append("--pretrained")
+
+    print("="*80)
+    print(f"Starting run: {run_mode}")
+    print(f"Command: {' '.join(command)}")
+    print("="*80)
+
+    try:
+        # subprocess.run is a blocking call, ensuring sequential execution.
+        subprocess.run(command, check=True)
+        print(f"\nSUCCESS: Run '{run_mode}' completed.\n")
+    except subprocess.CalledProcessError as e:
+        print(f"\nERROR: Run '{run_mode}' failed with exit code {e.returncode}.\n")
+        # Decide if you want to stop all subsequent runs on failure
+        # raise e # Uncomment to stop the entire sequence on error
+
+def main():
+    """
+    Parses arguments and launches a sequence of training experiments for each specified model.
+    """
+    parser = argparse.ArgumentParser(description="Run a sequence of YOLO training experiments.")
+
+    # Define arguments that will be common to all training runs
+    parser.add_argument('--dataset', type=str, required=True, choices=["Birds-Nest", "Common-VALID", "Electric-Substation", "InsPLAD-det"], help='Dataset name to be used.')
+    parser.add_argument("--model", nargs='+', required=True, choices=["yolov8n", "yolov8s", "yolov8m", "yolov8l", "yolov10n", "yolov10s", "yolov10m", "yolov10l"], help="One or more models to use for the experiments.")
+    parser.add_argument("--epochs", type=int, default=1000, help="Number of epochs.")
+    parser.add_argument("--batch", type=int, default=16, help="Batch size.")
+    parser.add_argument("--plots", action="store_true", default=True, help="Generate plots for all runs.")
+
+    args = parser.parse_args()
+
+    # Define the sequence of experiments to run for each model
+    # Each tuple is (run_mode, use_pretrained_flag)
+    experiment_sequence = [
+        ("From_Scratch",    False),
+        ("Finetuning",      True),
+        ("freeze_[P1-P3]",  True),
+        ("freeze_Backbone", True),
+        ("freeze_[P1-23]",  True)
+    ]
+
+    # Iterate over each specified model variant
+    for model_name in args.model:
+        print(f"\n{'='*25} Starting Experiments for Model: {model_name.upper()} {'='*25}\n")
+
+        # Base command list, specific to the current model
+        base_command = [
+            "python", "main.py",
+            "--dataset", args.dataset,
+            "--epochs", str(args.epochs),
+            "--batch", str(args.batch),
+            "--model", model_name
+        ]
+        if args.plots:
+            base_command.append("--plots")
+
+        # Execute each experiment in sequence for the current model
+        for run_mode, use_pretrained in experiment_sequence:
+            run_experiment(base_command, run_mode, use_pretrained)
+
+    print("="*80)
+    print("All experiments for all specified models have been completed.")
+    print("="*80)
+
+if __name__ == "__main__":
+    main()