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#!/usr/bin/env python3
# Copyright (c) 2021 Arm Limited. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os, errno
import urllib.request
import subprocess
import fnmatch
import logging
import sys
from argparse import ArgumentParser
from urllib.error import URLError
json_uc_res = [{
"use_case_name": "ad",
"resources": [{"name": "ad_medium_int8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite"},
{"name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input/0.npy"},
{"name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy"}]
},
{
"use_case_name": "asr",
"resources": [{"name": "wav2letter_pruned_int8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite"},
{"name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
{"name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"}]
},
{
"use_case_name": "img_class",
"resources": [{"name": "mobilenet_v2_1.0_224_INT8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite"},
{"name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_input/tfl.quantize/0.npy"},
{"name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11/0.npy"}]
},
{
"use_case_name": "kws",
"resources": [{"name": "ds_cnn_clustered_int8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
{"name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
{"name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
},
{
"use_case_name": "kws_asr",
"resources": [{"name": "wav2letter_pruned_int8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite"},
{"sub_folder": "asr", "name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
{"sub_folder": "asr", "name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"},
{"name": "ds_cnn_clustered_int8.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/ds_cnn_clustered_int8.tflite"},
{"sub_folder": "kws", "name": "ifm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_input/input_2/0.npy"},
{"sub_folder": "kws", "name": "ofm0.npy",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/ds_cnn_large/tflite_clustered_int8/testing_output/Identity/0.npy"}]
},
{
"use_case_name": "inference_runner",
"resources": [{"name": "dnn_s_quantized.tflite",
"url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/dnn_s_quantized.tflite"}
]
},]
def call_command(command: str) -> str:
"""
Helpers function that call subprocess and return the output.
Parameters:
----------
command (string): Specifies the command to run.
"""
logging.info(command)
proc = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
log = proc.stdout.decode("utf-8")
if proc.returncode == 0:
logging.info(log)
else:
logging.error(log)
proc.check_returncode()
return log
def set_up_resources(run_vela_on_models=False):
"""
Helpers function that retrieve the output from a command.
Parameters:
----------
run_vela_on_models (bool): Specifies if run vela on downloaded models.
"""
current_file_dir = os.path.dirname(os.path.abspath(__file__))
download_dir = os.path.abspath(os.path.join(current_file_dir, "resources_downloaded"))
try:
# 1.1 Does the download dir exist?
os.mkdir(download_dir)
except OSError as e:
if e.errno == errno.EEXIST:
logging.info("'resources_downloaded' directory exists.")
else:
raise
# 1.2 Does the virtual environment exist?
env_python = str(os.path.abspath(os.path.join(download_dir, "env", "bin", "python3")))
env_activate = str(os.path.abspath(os.path.join(download_dir, "env", "bin", "activate")))
if not os.path.isdir(os.path.join(download_dir, "env")):
os.chdir(download_dir)
# Create the virtual environment
command = "python3 -m venv env"
call_command(command)
commands = ["pip install --upgrade pip", "pip install --upgrade setuptools"]
for c in commands:
command = f"{env_python} -m {c}"
call_command(command)
os.chdir(current_file_dir)
# 1.3 Make sure to have all the requirement
requirements = ["ethos-u-vela==3.1.0"]
command = f"{env_python} -m pip freeze"
packages = call_command(command)
for req in requirements:
if req not in packages:
command = f"{env_python} -m pip install {req}"
call_command(command)
# 2. Download models
for uc in json_uc_res:
try:
# Does the usecase_name download dir exist?
os.mkdir(os.path.join(download_dir, uc["use_case_name"]))
except OSError as e:
if e.errno != errno.EEXIST:
logging.error(f"Error creating {uc['use_case_name']} directory.")
raise
for res in uc["resources"]:
res_name = res["name"]
res_url = res["url"]
if "sub_folder" in res:
try:
# Does the usecase_name/sub_folder download dir exist?
os.mkdir(os.path.join(download_dir, uc["use_case_name"], res["sub_folder"]))
except OSError as e:
if e.errno != errno.EEXIST:
logging.error(f"Error creating {uc['use_case_name']} / {res['sub_folder']} directory.")
raise
res_dst = os.path.join(download_dir,
uc["use_case_name"],
res["sub_folder"],
res_name)
else:
res_dst = os.path.join(download_dir,
uc["use_case_name"],
res_name)
if os.path.isfile(res_dst):
logging.info(f"File {res_dst} exists, skipping download.")
else:
try:
g = urllib.request.urlopen(res_url)
with open(res_dst, 'b+w') as f:
f.write(g.read())
logging.info(f"- Downloaded {res_url} to {res_dst}.")
except URLError:
logging.error(f"URLError while downloading {res_url}.")
raise
# 3. Run vela on models in resources_downloaded
# New models will have same name with '_vela' appended.
# For example:
# original model: ds_cnn_clustered_int8.tflite
# after vela model: ds_cnn_clustered_int8_vela_H128.tflite
#
# Note: To avoid to run vela twice on the same model, it's supposed that
# downloaded model names don't contain the 'vela' word.
if run_vela_on_models is True:
config_file = os.path.join(current_file_dir, "scripts", "vela", "default_vela.ini")
models = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(download_dir)
for f in fnmatch.filter(files, '*.tflite') if "vela" not in f]
for model in models:
output_dir = os.path.dirname(model)
# model name after compiling with vela is an initial model name + _vela suffix
vela_optimised_model_path = str(model).replace(".tflite", "_vela.tflite")
# we want it to be initial model name + _vela_H128 suffix which indicates selected MAC config.
new_vela_optimised_model_path = vela_optimised_model_path.replace("_vela.tflite", "_vela_H128.tflite")
if os.path.isfile(new_vela_optimised_model_path):
logging.info(f"File {new_vela_optimised_model_path} exists, skipping optimisation.")
continue
command = (f". {env_activate} && vela {model} " +
"--accelerator-config=ethos-u55-128 " +
"--optimise Performance " +
f"--config {config_file} " +
"--memory-mode=Shared_Sram " +
"--system-config=Ethos_U55_High_End_Embedded " +
f"--output-dir={output_dir}")
call_command(command)
# rename default vela model
os.rename(vela_optimised_model_path, new_vela_optimised_model_path)
logging.info(f"Renaming {vela_optimised_model_path} to {new_vela_optimised_model_path}.")
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("--skip-vela",
help="Do not run Vela optimizer on downloaded models.",
action="store_true")
args = parser.parse_args()
logging.basicConfig(filename='log_build_default.log', level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
set_up_resources(not args.skip_vela)
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