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author | Isabella Gottardi <isabella.gottardi@arm.com> | 2021-04-07 09:27:38 +0100 |
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committer | Isabella Gottardi <isabella.gottardi@arm.com> | 2021-05-07 12:19:19 +0100 |
commit | 2181d0ac35f30202985a877950c88325ff665f6b (patch) | |
tree | e16c50d41d85945e0c2c864323ac1769b02af64f /set_up_default_resources.py | |
parent | d580eee180be219e118152cedabc9637da8574d6 (diff) | |
download | ml-embedded-evaluation-kit-2181d0ac35f30202985a877950c88325ff665f6b.tar.gz |
MLECO-1766: Default build flow helper scripts added
MLECO-1882: Anomaly Detection use-case default model download added
and tests updated to run with it.
Test data generation cmake logic moved from use-case cmakes to top-level cmake script.
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
Change-Id: Ifde469e3585c37b9a53810236a92ce52d4fbb407
Diffstat (limited to 'set_up_default_resources.py')
-rw-r--r-- | set_up_default_resources.py | 216 |
1 files changed, 216 insertions, 0 deletions
diff --git a/set_up_default_resources.py b/set_up_default_resources.py new file mode 100644 index 0000000..60c1747 --- /dev/null +++ b/set_up_default_resources.py @@ -0,0 +1,216 @@ +#!env/bin/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_int8.tflite", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite"}, + {"name": "ifm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy"}, + {"name": "ofm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_output/Identity_int8/0.npy"}] + }, + { + "use_case_name": "img_class", + "resources": [{"name": "mobilenet_v2_1.0_224_quantized_1_default_1.tflite", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite"}, + {"name": "ifm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_input/input/0.npy"}, + {"name": "ofm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/testing_output/output/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_int8.tflite", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/wav2letter_int8.tflite"}, + {"sub_folder": "asr", "name": "ifm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_int8/testing_input/input_2_int8/0.npy"}, + {"sub_folder": "asr", "name": "ofm0.npy", + "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/speech_recognition/wav2letter/tflite_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.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True) + stdout_log = proc.communicate()[0].decode("utf-8") + logging.info(stdout_log) + return stdout_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")) + logging.basicConfig(filename='log_build_default.log', level=logging.DEBUG) + logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) + + 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==2.1.1"] + 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) + 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.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) + command = (f". {env_activate} && vela {model} " + + "--accelerator-config=ethos-u55-128 " + + "--block-config-limit=0 " + + f"--config {config_file} " + + "--memory-mode=Shared_Sram " + + "--system-config=Ethos_U55_High_End_Embedded " + + f"--output-dir={output_dir}") + call_command(command) + + +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() + set_up_resources(not args.skip_vela) |