enhanced_transfer_learning / run_experiments.py

Upload run_experiments.py

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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()

	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()