| #!/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_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_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==3.0.0"] |
| 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) |