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#!/usr/bin/env python3
"""
Export YOLOv8/v11 model to ONNX format for optimized inference.
Supports dynamic axes, batch size, and different opset versions.
"""
import argparse
import os
import sys
from pathlib import Path
import yaml
from ultralytics import YOLO
def load_config(config_path="config.yaml"):
"""Load configuration from YAML file."""
if not os.path.exists(config_path):
print(f"Warning: Config file {config_path} not found. Using defaults.")
return {}
with open(config_path, 'r') as f:
return yaml.safe_load(f)
def export_to_onnx(
model_path,
output_path=None,
imgsz=640,
batch=1,
dynamic=False,
simplify=True,
opset=12,
half=False
):
"""
Export YOLO model to ONNX format.
Args:
model_path: Path to .pt model file
output_path: Output .onnx file path (optional, auto-generated if None)
imgsz: Input image size
batch: Batch size (1 for static, -1 for dynamic)
dynamic: Enable dynamic axes (batch, height, width)
simplify: Apply ONNX simplifier
opset: ONNX opset version
half: FP16 quantization
"""
print(f"Loading model from {model_path}")
model = YOLO(model_path)
# Determine output path
if output_path is None:
model_name = Path(model_path).stem
output_dir = Path(model_path).parent / "exports"
output_dir.mkdir(exist_ok=True)
output_path = str(output_dir / f"{model_name}.onnx")
# Create output directory if needed
output_dir = os.path.dirname(output_path)
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
# Prepare export arguments
export_args = {
'format': 'onnx',
'imgsz': imgsz,
'batch': batch,
'simplify': simplify,
'opset': opset,
'half': half,
}
if dynamic:
export_args['dynamic'] = True
print(f"Exporting to ONNX with args: {export_args}")
try:
# Export model
exported_path = model.export(**export_args)
# The exported file will be in the same directory as the model
# Find the .onnx file that was just created
exported_files = list(Path(model_path).parent.glob("*.onnx"))
if exported_files:
latest_onnx = max(exported_files, key=os.path.getctime)
# Move to desired output path if different
if str(latest_onnx) != output_path:
import shutil
shutil.move(str(latest_onnx), output_path)
print(f"Model moved to {output_path}")
else:
print(f"Model exported to {output_path}")
else:
# Try to find the exported file in the current directory
exported_files = list(Path('.').glob("*.onnx"))
if exported_files:
latest_onnx = max(exported_files, key=os.path.getctime)
if str(latest_onnx) != output_path:
import shutil
shutil.move(str(latest_onnx), output_path)
print(f"Model moved to {output_path}")
else:
print(f"Model exported to {output_path}")
else:
print(f"Warning: Could not locate exported ONNX file.")
print(f"Expected at: {output_path}")
return None
# Print model info
size_mb = os.path.getsize(output_path) / (1024 * 1024)
print(f"✅ ONNX export successful!")
print(f" Output: {output_path}")
print(f" Size: {size_mb:.2f} MB")
print(f" Input shape: {batch if batch > 0 else 'dynamic'}x3x{imgsz}x{imgsz}")
print(f" Opset: {opset}")
print(f" Dynamic: {dynamic}")
print(f" FP16: {half}")
return output_path
except Exception as e:
print(f"❌ Error during ONNX export: {e}")
import traceback
traceback.print_exc()
return None
def main():
parser = argparse.ArgumentParser(description='Export YOLO model to ONNX format')
parser.add_argument('--model', type=str, default='yolov8n.pt',
help='Path to YOLO model (.pt file)')
parser.add_argument('--output', type=str, default=None,
help='Output ONNX file path (default: model/exports/<model_name>.onnx)')
parser.add_argument('--img-size', type=int, default=640,
help='Input image size (default: 640)')
parser.add_argument('--batch', type=int, default=1,
help='Batch size (default: 1, use -1 for dynamic)')
parser.add_argument('--dynamic', action='store_true',
help='Enable dynamic axes (batch, height, width)')
parser.add_argument('--no-simplify', action='store_true',
help='Disable ONNX simplifier')
parser.add_argument('--opset', type=int, default=12,
help='ONNX opset version (default: 12)')
parser.add_argument('--half', action='store_true',
help='Use FP16 quantization')
parser.add_argument('--config', type=str, default='config.yaml',
help='Path to config file')
args = parser.parse_args()
# Load config
config = load_config(args.config)
# Use model from config if not specified
if args.model == 'yolov8n.pt' and config:
models_config = config.get('models', {})
detection_config = models_config.get('detection', {})
default_model = detection_config.get('strawberry_yolov8n', 'yolov8n.pt')
if os.path.exists(default_model):
args.model = default_model
else:
# Check for other available models
available_models = ['yolov8n.pt', 'yolov8s.pt', 'yolov8m.pt',
'model/weights/strawberry_yolov11n.pt',
'model/weights/ripeness_detection_yolov11n.pt']
for model in available_models:
if os.path.exists(model):
args.model = model
print(f"Using available model: {model}")
break
# Export model
success = export_to_onnx(
model_path=args.model,
output_path=args.output,
imgsz=args.img_size,
batch=args.batch,
dynamic=args.dynamic,
simplify=not args.no_simplify,
opset=args.opset,
half=args.half
)
if success:
print(f"\n✅ Export completed successfully!")
print(f"\nNext steps:")
print(f"1. Test the ONNX model with ONNX Runtime:")
print(f" python -m onnxruntime.tools.onnx_model_test {success}")
print(f"2. Convert to TensorFlow Lite for edge deployment:")
print(f" python export_tflite_int8.py --model {success}")
print(f"3. Use in your application with ONNX Runtime")
else:
print("\n❌ Export failed.")
sys.exit(1)
if __name__ == '__main__':
main() |