#!/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": "vww", "resources": [{"name": "vww4_128_128_INT8.tflite", "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite"}, {"name": "ifm0.npy", "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/testing_input/input/0.npy"}, {"name": "ofm0.npy", "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_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)