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encode & decode onnx model to qr code
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| import argparse | |
| import binascii | |
| import tarfile | |
| from io import BytesIO | |
| import qrcode | |
| parser = argparse.ArgumentParser("xx") | |
| parser.add_argument("--input_file", type=str, required=True, help="input file") | |
| args = parser.parse_args() | |
| fp = args.input_file | |
| with open(fp, "rb") as fh: | |
| onnx_buffer = BytesIO(fh.read()) | |
| onnx_buffer.seek(0) | |
| # save to tar buffer | |
| tar_buffer = BytesIO() | |
| # mode = 'w:gz' | |
| mode = 'w:xz' | |
| # mode = 'w:bz2' | |
| tar = tarfile.open(fileobj=tar_buffer, mode=mode) | |
| tarinfo = tarfile.TarInfo(fp) | |
| tarinfo.size = len(onnx_buffer.getvalue()) | |
| tar.addfile(tarinfo, onnx_buffer) | |
| tar.close() | |
| tar_buffer.seek(0) | |
| tar_content = tar_buffer.read() | |
| xx_ = binascii.hexlify(tar_content) | |
| print(f"{mode}: {len(xx_)}") | |
| # debug | |
| # b = binascii.a2b_hex(xx_) | |
| MAX_LEN = 2300 | |
| for i in range(0, len(xx_), MAX_LEN): | |
| xx = xx_[i : i + MAX_LEN] | |
| fp = f'qr_{i}.png' | |
| print(f"encode to {fp} , len {len(xx)}") | |
| qrcode.make(xx, version=40).save(fp) |
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| import argparse | |
| import binascii | |
| import tarfile | |
| import numpy as np | |
| from io import BytesIO | |
| import onnx | |
| import qrcode | |
| def clean_onnx(fp): | |
| onnx_model = onnx.load_model(fp) | |
| for init in onnx_model.graph.initializer: | |
| num_elements = np.prod(init.dims) | |
| if num_elements > 10: | |
| init.ClearField('int32_data') | |
| init.ClearField('int64_data') | |
| init.ClearField('float_data') | |
| init.ClearField('double_data') | |
| init.ClearField('raw_data') | |
| return onnx_model | |
| def clean_onnx_no_init(fp): | |
| onnx_model = onnx.load_model(fp) | |
| init = onnx_model.graph.initializer | |
| # TODO: keep axis/step/.. param for Gather/Slice and other Nodes | |
| for idx in range(len(init)): | |
| init.pop() | |
| return onnx_model | |
| parser = argparse.ArgumentParser("xx") | |
| parser.add_argument("--input_file", type=str, required=True, help="input file") | |
| args = parser.parse_args() | |
| # test | |
| # wget https://github.com/onnx/models/raw/main/vision/classification/resnet/model/resnet50-v1-7.onnx | |
| # fp = 'resnet50-v1-7.onnx' | |
| fp = args.input_file | |
| onnx_model = clean_onnx(fp) | |
| # save onnx to buffer | |
| onnx_buffer = BytesIO() | |
| onnx.save_model(onnx_model, onnx_buffer) | |
| onnx_buffer.seek(0) | |
| # save to tar buffer | |
| tar_buffer = BytesIO() | |
| # mode = 'w:gz' | |
| mode = 'w:xz' | |
| # mode = 'w:bz2' | |
| tar = tarfile.open(fileobj=tar_buffer, mode=mode) | |
| tarinfo = tarfile.TarInfo(f"{fp}.clean.onnx") | |
| tarinfo.size = len(onnx_buffer.getvalue()) | |
| tar.addfile(tarinfo, onnx_buffer) | |
| tar.close() | |
| tar_buffer.seek(0) | |
| tar_content = tar_buffer.read() | |
| xx_ = binascii.hexlify(tar_content) | |
| print(f"{mode}: {len(xx_)}") | |
| # debug | |
| # b = binascii.a2b_hex(xx_) | |
| MAX_LEN = 2300 | |
| for i in range(0, len(xx_), MAX_LEN): | |
| xx = xx_[i : i + MAX_LEN] | |
| fp = f'qr_{i}.png' | |
| print(f"encode to {fp} , len {len(xx)}") | |
| qrcode.make(xx, version=40).save(fp) |
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| import binascii | |
| import glob | |
| import os | |
| import tarfile | |
| from io import BytesIO | |
| from PIL import Image | |
| from pyzbar.pyzbar import decode | |
| fps = glob.glob('qr_*.png') | |
| fps = sorted(fps, key=lambda x: int(x.split('_')[1].split('.')[0])) | |
| bytes = b'' | |
| for fp in fps: | |
| x = decode(Image.open(fp)) | |
| xxx = x[0].data | |
| print(f"dencode from {fp} , len {len(x[0].data)}") | |
| bytes += xxx | |
| he = binascii.a2b_hex(bytes) | |
| tar_buffer = BytesIO() | |
| tar_buffer.write(he) | |
| tar_buffer.seek(0) | |
| # mode = 'r:gz' | |
| mode = 'r:xz' | |
| # mode = 'r:bz2' | |
| tar = tarfile.open(fileobj=tar_buffer, mode=mode) | |
| tar.extractall('.') |
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| import onnx | |
| import numpy as np | |
| import onnx.helper | |
| import onnx.numpy_helper | |
| import numpy as np | |
| np.random.seed(1984) | |
| def fill_onnx(fp): | |
| onnx_model = onnx.load_model(fp) | |
| for init in onnx_model.graph.initializer: | |
| num_elements = np.prod(init.dims) | |
| if num_elements > 10: | |
| print(f"{init.name}: {init.dims}") | |
| np_dtype = onnx.helper.tensor_dtype_to_np_dtype(init.data_type) | |
| array = np.random.uniform(-1, 1, size=init.dims).astype(np_dtype) | |
| init.raw_data = array.tobytes() | |
| return onnx_model | |
| fp = 'clean.onnx' | |
| onnx_model = fill_onnx(fp) | |
| fp_restored = f"{fp}.restored.onnx" | |
| onnx.save(onnx_model, fp_restored) |
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3rds
for encode
onnx
qrcode
https://pypi.org/project/qrcode/
https://github.com/lincolnloop/python-qrcode
for decode
PIL
pyzbar
https://pypi.org/project/pyzbar/
https://github.com/NaturalHistoryMuseum/pyzbar