def prepare_omz_model(openvino_ref, model, omz_repo, omz_cache_dir, tmpdir):
"""
Download and convert Open Model Zoo model to Intermediate Representation,
get path to model XML.
"""
# Step 1: downloader
omz_log = logging.getLogger("prepare_omz_model")
python_executable = sys.executable
downloader_path = omz_repo / "tools" / "downloader" / "downloader.py"
model_path_root = tmpdir
cmd = [
f'{python_executable}', f'{downloader_path}', '--name',
f'{model["name"]}', f'--precisions={model["precision"]}',
'--num_attempts', f'{OMZ_NUM_ATTEMPTS}', '--output_dir',
f'{model_path_root}'
]
if omz_cache_dir:
cmd.append('--cache_dir')
cmd.append(f'{omz_cache_dir}')
return_code, output = cmd_exec(cmd, log=omz_log)
assert return_code == 0, "Downloading OMZ models has failed!"
# Step 2: converter
converter_path = omz_repo / "tools" / "downloader" / "converter.py"
ir_path = model_path_root / "_IR"
# Note: remove --precisions if both precisions (FP32 & FP16) are required
cmd = [
f'{python_executable}', f'{converter_path}', '--name',
f'{model["name"]}', '-p', f'{python_executable}',
f'--precisions={model["precision"]}', '--output_dir', f'{ir_path}',
'--download_dir', f'{model_path_root}', '--mo',
f'{openvino_ref / "tools"/ "model_optimizer" / "mo.py"}'
]
return_code, output = cmd_exec(cmd,
env=get_openvino_environment(openvino_ref),
log=omz_log)
assert return_code == 0, "Converting OMZ models has failed!"
# Step 3: info_dumper
info_dumper_path = omz_repo / "tools" / "downloader" / "info_dumper.py"
cmd = [
f'{python_executable}', f'{info_dumper_path}', '--name',
f'{model["name"]}'
]
return_code, output = cmd_exec(cmd, log=omz_log)
assert return_code == 0, "Getting information about OMZ models has failed!"
model_info = json.loads(output)[0]
# Step 4: form model_path
model_path = ir_path / model_info["subdirectory"] / model[
"precision"] / f'{model_info["name"]}.xml'
return model_path
def run_infer(model, out_file, install_dir):
""" Function running inference
"""
return_code, output = cmd_exec(
[sys.executable,
infer_tool,
"-d=CPU", f"-m={model}", f"-r={out_file}"
],
env=get_openvino_environment(install_dir),
)
return return_code, output
def test_infer(test_id, model, artifacts):
""" Test inference with conditional compiled binaries
"""
install_prefix = artifacts / test_id / "install_pkg"
exe_suffix = ".exe" if sys.platform == "win32" else ""
benchmark_app = install_prefix / "bin" / f"benchmark_app{exe_suffix}"
returncode, _ = cmd_exec(
[str(benchmark_app), "-d=CPU", f"-m={model}", "-niter=1", "-nireq=1"],
env=get_openvino_environment(install_prefix),
)
assert returncode == 0, f"Command exited with non-zero status {returncode}"
def run_infer(models, out_dir, install_dir):
""" Function running inference
"""
out_dir.mkdir(parents=True, exist_ok=True)
return_code, output = cmd_exec(
[
sys.executable, infer_tool, "-d=CPU",
*[f"-m={model}" for model in models], f"-r={out_dir}"
],
env=get_openvino_environment(install_dir),
)
return return_code, output
def run_infer(model, out_file, install_dir):
""" Function running inference
"""
sys_executable = os.path.join(sys.prefix, 'python.exe') if sys.platform == "win32" \
else os.path.join(sys.prefix, 'bin', 'python')
return_code, output = cmd_exec(
[
sys_executable, infer_tool, "-d=CPU", f"-m={model}",
f"-r={out_file}"
],
env=get_openvino_environment(install_dir),
)
return return_code, output
def prepare_omz_model(openvino_ref, model, omz_repo, omz_cache_dir, tmpdir):
"""
Download and convert Open Model Zoo model to Intermediate Representation,
get path to model XML.
"""
# Step 1: downloader
omz_log = logging.getLogger("prepare_omz_model")
python_executable = sys.executable
downloader_path = omz_repo / "tools" / "downloader" / "downloader.py"
model_path_root = tmpdir
cmd = f'{python_executable} {downloader_path} --name {model["name"]}' \
f' --precisions={model["precision"]}' \
f' --num_attempts {OMZ_NUM_ATTEMPTS}' \
f' --output_dir {model_path_root}'
if omz_cache_dir:
cmd += f' --cache_dir {omz_cache_dir}'
cmd_exec(cmd, log=omz_log)
# Step 2: converter
converter_path = omz_repo / "tools" / "downloader" / "converter.py"
ir_path = model_path_root / "_IR"
# Note: remove --precisions if both precisions (FP32 & FP16) are required
cmd = f'{python_executable} {converter_path} --name {model["name"]}' \
f' -p {python_executable}' \
f' --precisions={model["precision"]}' \
f' --output_dir {ir_path}' \
f' --download_dir {model_path_root}' \
f' --mo {Path("../../model-optimizer/mo.py").resolve()}'
cmd_exec(cmd, env=get_openvino_environment(openvino_ref), log=omz_log)
# Step 3: info_dumper
info_dumper_path = omz_repo / "tools" / "downloader" / "info_dumper.py"
cmd = f'"{python_executable}" "{info_dumper_path}" --name {model["name"]}'
return_code, output = cmd_exec(cmd, log=omz_log)
model_info = json.loads(output)[0]
# Step 4: form model_path
model_path = ir_path / model_info["subdirectory"] / model[
"precision"] / f'{model_info["name"]}.xml'
return model_path
