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