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https://github.com/azaion/ai-training.git
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zxsanny
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import shutil
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from os import path, scandir, makedirs
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from pathlib import Path
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import random
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import netron
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import yaml
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from ultralytics import YOLO
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from constants import datasets_dir, processed_images_dir
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def export_rknn(model_path):
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model = YOLO(model_path)
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model.export(format="rknn", name="rk3588", simplify=True)
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model_stem = Path(model_path).stem
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folder_name = f'{model_stem}_rknn_model'
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shutil.move(path.join(folder_name, f'{Path(model_path).stem}-rk3588.rknn'), f'{model_stem}.rknn')
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shutil.rmtree(folder_name)
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pass
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def export_onnx(model_path):
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model = YOLO(model_path)
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model.export(
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format="onnx",
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imgsz=1280,
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batch=2,
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simplify=True,
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nms=True)
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return Path(model_path).stem + '.onnx'
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def export_tensorrt(model_path):
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YOLO(model_path).export(
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format='engine',
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batch=4,
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half=True,
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simplify=True,
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nms=True
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)
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def form_data_sample(destination_path, size=500, write_txt_log=False):
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images = []
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with scandir(processed_images_dir) as imd:
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for image_file in imd:
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if not image_file.is_file():
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continue
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images.append(image_file)
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print('shuffling images')
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random.shuffle(images)
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images = images[:size]
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shutil.rmtree(destination_path, ignore_errors=True)
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makedirs(destination_path, exist_ok=True)
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lines = []
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for image in images:
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shutil.copy(image.path, path.join(destination_path, image.name))
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lines.append(f'./{image.name}')
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if write_txt_log:
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with open(path.join(destination_path, 'azaion_subset.txt'), 'w', encoding='utf-8') as f:
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f.writelines([f'{line}\n' for line in lines])
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def show_model(model: str = None):
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netron.start(model)
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if __name__ == '__main__':
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export_tensorrt('azaion-2025-03-10.pt')
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# export_rknn('azaion-2025-03-10.pt')
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# export_onnx('azaion-2025-03-10.pt') |