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136 lines
5.2 KiB
Python
Executable File
136 lines
5.2 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 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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import subprocess
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def run_vela(model_path, model_name, args):
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vela_dir = f'{args.build_dir}/{model_name}'
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vela_ini = os.path.dirname(os.path.abspath(__file__))
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# Construct the command
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command = [
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'vela',
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*args.vela_args.split(),
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'--output-dir', vela_dir,
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'--config', f'{vela_ini}/vela.ini',
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model_path
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]
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# Call the command and capture the output
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try:
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result = subprocess.run(command, check=True, text=True, capture_output=True)
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keywords = [
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"Network summary for",
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"Accelerator configuration",
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"System configuration",
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"Memory mode",
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"Accelerator clock",
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"CPU operators",
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"NPU operators",
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"Batch Inference time"
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]
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output = result.stdout.split("\n")
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output = [line for line in output if any(keyword in line for keyword in keywords)]
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print(f'VELA {model_name}.tflite\n{"\n".join(output)}\n', file=sys.stderr)
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except subprocess.CalledProcessError as e:
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print(e.stderr, file=sys.stderr)
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return f'{vela_dir}/{model_name}_vela.tflite'
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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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parser.add_argument('--build-dir', action = 'store', help='Build directory', default='build', )
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parser.add_argument('--vela-args', action = 'store', help='Vela compiler args', default='')
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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_path in models_list:
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model_size = os.path.getsize(model_path)
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model_name = os.path.basename(os.path.splitext(model_path)[0])
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labels_file = os.path.splitext(model_path)[0]+'.txt'
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if (args.vela_args):
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# Compile the model using Vela and switch path to the new model.
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model_path = run_vela(model_path, model_name, args)
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model_size = os.path.getsize(model_path)
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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[] = {{{: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[] __attribute__((aligned(16))) = {{'.format(model_name))
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with open(model_path, '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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