mirror of
https://github.com/openmv/openmv.git
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tools/mkromfs: Add support for stedgeai.
Signed-off-by: iabdalkader <i.abdalkader@gmail.com>
This commit is contained in:
parent
3d79d5cf33
commit
946808e4c1
@ -13,7 +13,8 @@ import os
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import json
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import argparse
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import struct
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from tflite2c import vela_compile
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from modelc import vela_compile
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from modelc import stedge_compile
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from haar2c import cascade_binary_universal
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CG = '\033[92m'
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@ -89,6 +90,10 @@ def romfs_build(romfs_cfg, p, args):
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vela_args = args.vela_args + " --optimise " + entry["optimize"]
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vela_compile(file_path, args.build_dir, vela_args.split())
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file_path = os.path.join(args.build_dir, file_name + ".tflite")
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if args.stedge_args:
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# Compile the model using Vela.
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stedge_compile(file_path, args.build_dir, entry["profile"], args.stedge_args.split())
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file_path = os.path.join(args.build_dir, file_name + ".tflite")
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# If tflite has a labels file add it as a new entry
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labels_path = os.path.splitext(_file_path)[0] + ".txt"
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if os.path.exists(labels_path):
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@ -146,6 +151,7 @@ def main():
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parser.add_argument('--out-dir', action = 'store', help='Output directory', required=True)
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parser.add_argument('--build-dir', action = 'store', help='Build directory', required=True)
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parser.add_argument('--vela-args', action = 'store', help='Vela compiler args', default='')
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parser.add_argument('--stedge-args', action = 'store', help='STEdgeAI compiler args', default='')
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parser.add_argument('--partition', action = 'store', help = 'romfs partition to build. Default=all.', default=None)
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# Parse arguments
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147
tools/modelc.py
Executable file
147
tools/modelc.py
Executable file
@ -0,0 +1,147 @@
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#!/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) 2025 OpenMV, LLC.
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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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# AI models converter.
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import sys
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import os
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import csv
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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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import re
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C_GREEN = '\033[92m'
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C_RED = '\033[91m'
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C_BLUE = '\033[94m'
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C_RESET = '\033[0m'
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def vela_compile(model_path, build_dir, vela_args):
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vela_ini = os.path.dirname(os.path.abspath(__file__))
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model = os.path.basename(os.path.splitext(model_path)[0])
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# Construct the command
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command = [
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'vela',
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*vela_args,
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'--output-dir', build_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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except subprocess.CalledProcessError as e:
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print(e.stderr, file=sys.stderr)
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print(vela_args, file=sys.stderr)
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csv_file_path = glob.glob(f"{build_dir}/{model}_summary_*.csv")[0]
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with open(csv_file_path, mode='r') as file:
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row = next(csv.DictReader(file))
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stoi = lambda x, d=1: str(int(float(x) / d))
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color = lambda c,x: c + x + C_RESET
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summary = {
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C_BLUE + "Network:": C_BLUE + row["network"],
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C_BLUE + "Accelerator Configuration:": C_GREEN + row["accelerator_configuration"],
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C_BLUE + "System Configuration:": C_BLUE + row["system_config"],
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C_BLUE + "Memory Mode:": C_BLUE + row["memory_mode"],
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C_BLUE + "Compiler Mode: ": C_RED + vela_args[-1],
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C_BLUE + "Accelerator Clock:": C_BLUE + stoi(row["core_clock"], 10**6) + " MHz",
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C_BLUE + "SRAM Usage:": C_RED + stoi(row["sram_memory_used"]) + " KiB",
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C_BLUE + "Flash Usage:": C_RED + stoi(row["off_chip_flash_memory_used"]) + " KiB",
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C_BLUE + "Inference Time:": C_GREEN + "%.2f ms, %.2f inferences/s"%
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(float(row["inference_time"]) * 1000, float(row["inferences_per_second"])),
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}
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for key, value in summary.items():
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print(f"{key:<{35}} {value:<{50}}", file=sys.stderr)
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print(C_RESET, file=sys.stderr)
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os.rename(f"{build_dir}/{model}_vela.tflite", f"{build_dir}/{model}.tflite")
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def stedge_compile(model_path, build_dir, profile, stedge_args=None):
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core_dir = os.path.realpath("tools/st/stedgeai/2.1")
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config = os.path.realpath("tools/st/scripts/neuralart.json")
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model_name = os.path.basename(os.path.splitext(model_path)[0])
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model_ext = os.path.splitext(model_path)[1]
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output_dir = os.path.join(build_dir, model_name)
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# Remove any Make-related variables that could leak
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env = os.environ.copy()
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env["STEDGEAI_CORE_DIR"] = core_dir
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for var in ["RM", "CFLAGS", "CPPFLAGS", "CXXFLAGS", "LDFLAGS", 'MAKEFLAGS']:
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env.pop(var, None)
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print(f"{C_GREEN}Creating relocatable binary model {model_name}{C_RESET}")
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# Step 1: stedgeai generate
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generate_command = [
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os.path.join(core_dir, "Utilities/linux/stedgeai"),
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"generate",
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*stedge_args,
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"--model", model_path,
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"--relocatable",
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"--st-neural-art", f"{profile}@{config}",
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"--no-workspace",
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"--output", os.path.join(output_dir, "gen"),
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"--verbosity", "1",
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]
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try:
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result = subprocess.run(generate_command, check=True, text=True, env=env,
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stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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except subprocess.CalledProcessError as e:
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print(f"stedgeai command failed with exit code {e.returncode}", file=sys.stderr)
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print(" ".join(generate_command), file=sys.stderr)
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raise(e)
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# Step 2: Python relocation script
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reloc_command = [
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sys.executable, # Uses current Python interpreter
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os.path.join(core_dir, "scripts/N6_reloc/npu_driver.py"),
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"--input", os.path.join(output_dir, "gen", "network.c"),
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"--output", output_dir,
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"--verbosity", "1",
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]
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try:
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result = subprocess.run(reloc_command, check=True, text=True, env=env,
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stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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except subprocess.CalledProcessError as e:
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print(f"Relocation script failed with exit code {e.returncode}", file=sys.stderr)
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print(" ".join(reloc_command), file=sys.stderr)
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raise(e)
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match = re.search(
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r"([ \t]+XIP size.*?Table: mempool.*?\n)", result.stdout, re.DOTALL | re.MULTILINE
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)
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if match:
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print(C_BLUE + match.group(1).rstrip() + C_RESET + "\n")
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os.rename(f"{output_dir}/network_rel.bin", f"{build_dir}/{model_name}{model_ext}")
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if __name__ == '__main__':
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# python tools/tflite2c.py --input lib/models/fomo_face_detection.tflite --build-dir /tmp/build_st --stedge-args "--target stm32n6"
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# python tools/tflite2c.py --input lib/models/fomo_face_detection.tflite --build-dir /tmp/build --vela-args "--system-config RTSS_HP_DTCM_MRAM --accelerator-config ethos-u55-256 --memory-mode Shared_Sram"
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parser = argparse.ArgumentParser(description='AI models converter.')
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parser.add_argument('--input', action = 'store', help = 'Input model.', required=True)
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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.')
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parser.add_argument('--stedge-args', action = 'store', help='STEdge AI tools args.')
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parser.add_argument('--stedge-profile', action = 'store', help='STEdge AI tools profile.', default="default")
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args = parser.parse_args()
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if args.vela_args:
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# Compile the model using Vela.
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vela_compile(args.input, args.build_dir, args.vela_args.split())
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elif args.stedge_args:
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# Compile the model using STEdge AI tools.
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stedge_compile(args.input, args.build_dir, args.stedge_profile, args.stedge_args.split())
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else:
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parser.print_help(sys.stderr)
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@ -1,159 +0,0 @@
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#!/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
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import os
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import csv
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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 vela_compile(model_path, build_dir, vela_args):
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vela_ini = os.path.dirname(os.path.abspath(__file__))
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model = os.path.basename(os.path.splitext(model_path)[0])
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# Construct the command
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command = [
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'vela',
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*vela_args,
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'--output-dir', build_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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except subprocess.CalledProcessError as e:
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print(e.stderr, file=sys.stderr)
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print(vela_args, file=sys.stderr)
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C_GREEN = '\033[92m'
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C_RED = '\033[91m'
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C_BLUE = '\033[94m'
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C_RESET = '\033[0m'
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csv_file_path = glob.glob(f"{build_dir}/{model}_summary_*.csv")[0]
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with open(csv_file_path, mode='r') as file:
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row = next(csv.DictReader(file))
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stoi = lambda x, d=1: str(int(float(x) / d))
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color = lambda c,x: c + x + C_RESET
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summary = {
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C_BLUE + "Network:": C_BLUE + row["network"],
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C_BLUE + "Accelerator Configuration:": C_GREEN + row["accelerator_configuration"],
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C_BLUE + "System Configuration:": C_BLUE + row["system_config"],
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C_BLUE + "Memory Mode:": C_BLUE + row["memory_mode"],
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C_BLUE + "Compiler Mode: ": C_RED + vela_args[-1],
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C_BLUE + "Accelerator Clock:": C_BLUE + stoi(row["core_clock"], 10**6) + " MHz",
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C_BLUE + "SRAM Usage:": C_RED + stoi(row["sram_memory_used"]) + " KiB",
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C_BLUE + "Flash Usage:": C_RED + stoi(row["off_chip_flash_memory_used"]) + " KiB",
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C_BLUE + "Inference Time:": C_GREEN + "%.2f ms, %.2f inferences/s"%
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(float(row["inference_time"]) * 1000, float(row["inferences_per_second"])),
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}
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for key, value in summary.items():
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print(f"{key:<{35}} {value:<{50}}", file=sys.stderr)
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print(C_RESET, file=sys.stderr)
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os.rename(f"{build_dir}/{model}_vela.tflite", f"{build_dir}/{model}.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_index = {}
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print('/* NOTE: This file is auto-generated. */\n')
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index_headers = ['model', 'optimise']
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# Open the file and parse it using DictReader
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with open(os.path.join(args.input, "index.csv"), 'r') as file:
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for row in csv.reader((line for line in file if not line.startswith('#'))):
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model = os.path.splitext(row[0])[0]
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tflm_builtin_index[model] = dict(zip(index_headers[1:], row[1:]))
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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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# Add model-specific Vela args.
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if model_name not in tflm_builtin_index:
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args.vela_args += " --optimise Performance"
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else:
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args.vela_args += " --optimise " + tflm_builtin_index[model_name]["optimise"]
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# Compile the model using Vela and switch path to the new model.
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vela_compile(model_path, args.build_dir, args.vela_args.split())
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model_path = os.path.join(args.build_dir, model_name + ".tflite")
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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_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_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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@ -1,34 +0,0 @@
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#!/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) 2024 Ibrahim Abdelkader <iabdalkader@openmv.io>
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# Copyright (c) 2024 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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import tflite
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import struct
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import sys
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with open(sys.argv[1], "rb") as f:
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model = tflite.Model.GetRootAs(f.read())
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opcodes = []
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graph = model.Subgraphs(0)
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for i in range(graph.OperatorsLength()):
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op = graph.Operators(i)
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opcode = model.OperatorCodes(op.OpcodeIndex()).BuiltinCode()
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opcodes.append(opcode)
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# print(tflite.opcode2name(opcode))
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def hash_djb2(s):
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hash = 5381
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for x in s:
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hash = ((hash << 5) + hash) + x
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return hash & 0xFFFFFFFF
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packed = struct.pack("I" * len(opcodes), *opcodes)
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print(hex(hash_djb2(packed)))
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