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run_x_cube_ai.py
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run_x_cube_ai.py
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# coding=utf-8
'''
@ Summary: support analyze|validate(option) generate mode
@ Update:
@ file: run_x_cube_ai.py
@ version: 1.0.0
@ Author: Lebhoryi@gmail.com
@ Date: 2020/12/9 16:58
@ Update: repair --model_path include space
@ Date: 2021/08/02
'''
import os
import re
import subprocess
import shutil
import logging
from pathlib import Path
def readonly_handler(func, path, execinfo):
# Change the mode of file, to make it could be used of shutil.rmtree
os.chmod(path, 128)
func(path)
def excute_cmd(cmd):
""" Returnning string after the command is executed """
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout
return p.read().split(b"\r\n")
def by_read_line(filename):
""" Read a report, yield line """
f = open(filename, "r+")
line = f.readline()
while line:
yield line
line = f.readline()
f.close()
def is_stm32ai_success(c_model_name, output, mode, result):
"""check the stm23ai:ana|val|gen is running successfully """
def is_mode_success(result, mode, report_path):
if b"elapsed time" not in result:
if not os.path.isfile(report_path):
raise Exception("Failed to {}, pls check the params".format(mode))
else:
reader = by_read_line(report_path)
for line in reader:
if re.findall(r"^error", line):
line = line.strip().split(": ")
raise Exception("{}: {}".format(line[1], line[2]))
return True
# saved strs after cmd excuted.
flags = [False for _ in range(len(mode))]
# three modes
modes_list = ["analyze", "validate", "generate"]
for i in range(len(modes_list)):
if mode[i]:
report_file_name = f"{c_model_name}_{modes_list[i]}_report.txt"
report_file_path = os.path.join(output[i], report_file_name)
flags[i] = is_mode_success(result[i], modes_list[i], report_file_path)
return flags
def stm32ai(model, stm_out, c_model_name, sup_modes, ai_params):
""" convert model to c-model by x-cube-ai:stm32ai
Args:
model: indicates the original model file paths, str
stm_out: indicates the output directory for the generated C-files and report files, str
c_model_name: indicates the C-name (C-string type) of the imported model, str
sup_modes: suppport modes(analyze, validate, generate), {"001", "011", "101", "111"}
ai_params: [workspace, compress, batches, mode, val_data], list
workspace: indicates a working/temporary directory for the intermediate/temporary files
compress: indicates the expected global factor of compression which will be applied, int
batches: indicates how many random data sample is generated (default: 10), int
mode: chooses which cmd to excution, list
val_date: indicates the custom test data set which must be used.
Returns:
flag: return the True, list, len(list) == the number of true modes
"""
def analyze(model, output, model_name, compress):
analyze_cmd = "stm32ai analyze -m \"{}\" -o {} -w {} -n {} -c {}".format(
model, output, workspace, model_name, compress)
return analyze_cmd
def validate(model, output, model_name, batches, val_date):
if val_date:
# using random {batches} val data
validate_cmd = "stm32ai validate -m \"{}\" -o {} -w {} -n {} -vi {} " \
"--validate.batch_mode {} -b {}".format(model,
output, workspace, model_name, val_date, "random", batches)
else:
validate_cmd = "stm32ai validate -m \"{}\" -o {} -w {} -n {} -b {}".format(
model, output, workspace, model_name, batches)
return validate_cmd
def generate(model, output, model_name, compress):
# generate the dir "X-CUBE-AI/App"
generate_cmd = "stm32ai generate -m \"{}\" -o {} -w {} -n {} -c {}".format(
model, output, workspace, model_name, compress)
return generate_cmd
workspace = ai_params[0]
compress = ai_params[1]
batches = ai_params[2]
mode = ai_params[3]
val_date = ai_params[4]
# if the mode is valid: {'001', '011', '101', '111'}
assert mode in sup_modes, Exception("Wrong mode???")
mode = list(map(int, list(mode))) # str to list
# save generate mode files
sub_path = Path(stm_out) / "X-CUBE-AI/App"
# analyze, validate, generate
output_list = [stm_out, stm_out, str(sub_path)]
# stm32ai command for different mode
commands = list()
commands.append(analyze(model, output_list[0], c_model_name, compress))
commands.append(validate(model, output_list[1], c_model_name, batches, val_date))
commands.append(generate(model, output_list[2], c_model_name, compress))
# Store the returned string after the command is executed
result = [excute_cmd(commands[index]) if elem else list()
for index, elem in enumerate(mode)]
flags = is_stm32ai_success(c_model_name, output_list, mode, result)
logging.info("Model convert to c-model successfully...")
# remove workspace
if os.path.exists(workspace):
shutil.rmtree(workspace, onerror=readonly_handler)
return flags
if __name__ == "__main__":
from prepare_work import pre_sconscript
from plugin_init import set_env
logging.getLogger().setLevel(logging.INFO)
# 1. set env
ext_tools = r"D:\Program Files (x86)\stm32ai-windows-5.2.0\windows"
_ = set_env(ext_tools)
# 2. prepare tmp output
aitools_out, dir_names, scons_path = 'tmp_cwd', ["Middlewares", "X-CUBE-AI"], "./Sconscripts"
_ = pre_sconscript(aitools_out, scons_path, dir_names)
# 3. test stm32ai
class Opt():
def __init__(self):
self.workspace = "./stm32ai_ws"
self.compress = 1
self.batches = 10
self.mode = "111"
self.val_data = ''
opt = Opt()
model_path = "../../Model/keras_mnist.h5"
stm_out = "tmp_cwd"
c_model_name = "network"
sup_modes = ["001", "011", "101", "111"]
ai_params = [opt.workspace, opt.compress, opt.batches, opt.mode, opt.val_data]
_ = stm32ai(model_path, stm_out, c_model_name, sup_modes, ai_params)
print("u a right...")