openmv/tools/tflite2c.py
iabdalkader 76a17f81c9 tools/tflite2c.py: Update built-in model generator script.
Add support embedding models conditionally using the index file. Models listed
in `models/index.txt` will be generated with an enable macro, which can be defined
per board in `imlib_config.h` files. Other models in models/ will be built-in by
default.
2024-06-28 16:32:04 +02:00

91 lines
3.7 KiB
Python
Executable File

#!/usr/bin/env python3
# This file is part of the OpenMV project.
#
# Copyright (c) 2013-2022 Ibrahim Abdelkader <iabdalkader@openmv.io>
# Copyright (c) 2013-2022 Kwabena W. Agyeman <kwagyeman@openmv.io>
#
# This work is licensed under the MIT license, see the file LICENSE for details.
#
# This script converts tflite models and labels to a C structs.
import sys, os
import glob
import argparse
import binascii
def main():
parser = argparse.ArgumentParser(description='Converts TFLite models to C file.')
parser.add_argument('--input', action = 'store', help = 'Input tflite models directory.', required=True)
parser.add_argument('--header', action = 'store_true', help = 'Generate header file.', required=False, default=False)
args = parser.parse_args()
tflm_builtin_models = []
tflm_builtin_models_index = []
print('/* NOTE: This file is auto-generated. */\n')
with open(os.path.join(args.input, "index.txt"), 'r') as f:
for l in f.readlines():
if not l.startswith("#"):
tflm_builtin_models_index.append(os.path.basename(os.path.splitext(l.strip())[0]))
models_list = glob.glob(os.path.join(args.input, "*tflite"))
if (args.header):
# Generate the header file
print('// Built-in TFLite Models.')
print('typedef struct {')
print(' const char *name;')
print(' const unsigned int n_labels;')
print(' const char **labels;')
print(' const unsigned int size;')
print(' const unsigned char *data;')
print('}tflm_builtin_model_t;\n')
print('extern const tflm_builtin_model_t tflm_builtin_models[];')
else:
# Generate the C file
print('#include "imlib_config.h"')
print('#include "tflm_builtin_models.h"')
for model_file in models_list:
model_size = os.path.getsize(model_file)
model_name = os.path.basename(os.path.splitext(model_file)[0])
labels_file = os.path.splitext(model_file)[0]+'.txt'
# Generate model labels.
labels = []
n_labels = 0
if os.path.exists(labels_file):
with open(labels_file, 'r') as f:
labels = ['"{:s}"'.format(l.strip()) for l in f.readlines()]
n_labels = len(labels)
print('static const char *tflm_{:s}_labels[] __attribute__((aligned(16))) = {{{:s}}};'.format(model_name, ', '.join(labels)))
# Generate model data.
print('static const unsigned char tflm_{:s}_data[] = {{'.format(model_name))
with open(model_file, 'rb') as f:
for chunk in iter(lambda: f.read(12), b''):
print(' ', end='')
print(' '.join(['0x{:02x},'.format(x) for x in chunk]))
print('};')
# Store model info in builtin models table.
tflm_builtin_models.append([
model_name,
n_labels,
'tflm_{:s}_labels'.format(model_name),
model_size,
'tflm_{:s}_data'.format(model_name)]
)
# Generate built-in models table.
print('const tflm_builtin_model_t tflm_builtin_models[] = {')
for model in tflm_builtin_models:
if model[0] in tflm_builtin_models_index:
print(' #if defined(IMLIB_ENABLE_TFLM_BUILTIN_{:s})'.format(model[0].upper()))
print(' {{ "{:s}", {:d}, {:s}, {:d}, {:s} }},'.format(*model))
if model[0] in tflm_builtin_models_index:
print(' #endif')
print(' {0, 0, 0, 0, 0}')
print('};')
if __name__ == '__main__':
main()