| #!/usr/bin/env python3 |
| |
| # Copyright (c) 2022 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, ArgumentTypeError |
| from urllib.error import URLError |
| from collections import namedtuple |
| |
| |
| 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": "object_detection", |
| "resources": [{"name": "yolo-fastest_192_face_v4.tflite", |
| "url": "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/yolo-fastest_192_face_v4.tflite?raw=true"}] |
| }, |
| { |
| "use_case_name": "kws", |
| "resources": [{"name": "ifm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"}, |
| {"name": "ofm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"}, |
| {"name": "kws_micronet_m.tflite", |
| "url": " https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}] |
| }, |
| { |
| "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"}, |
| {"sub_folder": "kws", "name": "ifm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"}, |
| {"sub_folder": "kws", "name": "ofm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"}, |
| {"name": "kws_micronet_m.tflite", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}] |
| }, |
| { |
| "use_case_name": "noise_reduction", |
| "resources": [{"name": "rnnoise_INT8.tflite", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite"}, |
| {"name": "ifm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/main_input_int8/0.npy"}, |
| {"name": "ifm1.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/vad_gru_prev_state_int8/0.npy"}, |
| {"name": "ifm2.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/noise_gru_prev_state_int8/0.npy"}, |
| {"name": "ifm3.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/denoise_gru_prev_state_int8/0.npy"}, |
| {"name": "ofm0.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_int8/0.npy"}, |
| {"name": "ofm1.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_1_int8/0.npy"}, |
| {"name": "ofm2.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_2_int8/0.npy"}, |
| {"name": "ofm3.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_3_int8/0.npy"}, |
| {"name": "ofm4.npy", |
| "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_4_int8/0.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"} |
| ] |
| },] |
| |
| # Valid NPU configurations: |
| valid_npu_config_names = [ |
| 'ethos-u55-32', 'ethos-u55-64', |
| 'ethos-u55-128', 'ethos-u55-256', |
| 'ethos-u65-256','ethos-u65-512'] |
| |
| # Default NPU configurations (these are always run when the models are optimised) |
| default_npu_config_names = [valid_npu_config_names[2], valid_npu_config_names[4]] |
| |
| # NPU config named tuple |
| NPUConfig = namedtuple('NPUConfig',['config_name', |
| 'memory_mode', |
| 'system_config', |
| 'ethos_u_npu_id', |
| 'ethos_u_config_id', |
| 'arena_cache_size']) |
| |
| # The internal SRAM size for Corstone-300 implementation on MPS3 specified by AN552 |
| mps3_max_sram_sz = 2 * 1024 * 1024 # 2 MiB (2 banks of 1 MiB each) |
| |
| |
| 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 get_default_npu_config_from_name(config_name: str, arena_cache_size: int = 0) -> NPUConfig: |
| """ |
| Gets the file suffix for the tflite file from the |
| `accelerator_config` string. |
| |
| Parameters: |
| ---------- |
| config_name (str): Ethos-U NPU configuration from valid_npu_config_names |
| |
| arena_cache_size (int): Specifies arena cache size in bytes. If a value |
| greater than 0 is provided, this will be taken |
| as the cache size. If 0, the default values, as per |
| the NPU config requirements, are used. |
| |
| Returns: |
| ------- |
| NPUConfig: An NPU config named tuple populated with defaults for the given |
| config name |
| """ |
| if config_name not in valid_npu_config_names: |
| raise ValueError(f""" |
| Invalid Ethos-U NPU configuration. |
| Select one from {valid_npu_config_names}. |
| """) |
| |
| strings_ids = ["ethos-u55-", "ethos-u65-"] |
| processor_ids = ["U55", "U65"] |
| prefix_ids = ["H", "Y"] |
| memory_modes = ["Shared_Sram", "Dedicated_Sram"] |
| system_configs = ["Ethos_U55_High_End_Embedded", "Ethos_U65_High_End"] |
| memory_modes_arena = { |
| # For shared SRAM memory mode, we use the MPS3 SRAM size by default |
| "Shared_Sram" : mps3_max_sram_sz if arena_cache_size <= 0 else arena_cache_size, |
| # For dedicated SRAM memory mode, we do no override the arena size. This is expected to |
| # be defined in the vela configuration file instead. |
| "Dedicated_Sram" : None if arena_cache_size <= 0 else arena_cache_size |
| } |
| |
| for i in range(len(strings_ids)): |
| if config_name.startswith(strings_ids[i]): |
| npu_config_id = config_name.replace(strings_ids[i], prefix_ids[i]) |
| return NPUConfig(config_name=config_name, |
| memory_mode=memory_modes[i], |
| system_config=system_configs[i], |
| ethos_u_npu_id=processor_ids[i], |
| ethos_u_config_id=npu_config_id, |
| arena_cache_size=memory_modes_arena[memory_modes[i]]) |
| |
| return None |
| |
| |
| def set_up_resources(run_vela_on_models: bool = False, |
| additional_npu_config_names: list = (), |
| arena_cache_size: int = 0): |
| """ |
| Helpers function that retrieve the output from a command. |
| |
| Parameters: |
| ---------- |
| run_vela_on_models (bool): Specifies if run vela on downloaded models. |
| additional_npu_config_names(list): list of strings of Ethos-U NPU configs. |
| arena_cache_size (int): Specifies arena cache size in bytes. If a value |
| greater than 0 is provided, this will be taken |
| as the cache size. If 0, the default values, as per |
| the NPU config requirements, are used. |
| """ |
| 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==3.2.0"] |
| 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: kws_micronet_m.tflite |
| # after vela model: kws_micronet_m_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] |
| |
| # Consolidate all config names while discarding duplicates: |
| config_names = list(set(default_npu_config_names + additional_npu_config_names)) |
| |
| # Get npu config tuple for each config name in a list: |
| npu_configs = [get_default_npu_config_from_name(name, arena_cache_size) for name in config_names] |
| |
| logging.info(f'All models will be optimised for these configs:') |
| for config in npu_configs: |
| logging.info(config) |
| |
| optimisation_skipped = False |
| |
| 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") |
| |
| for config in npu_configs: |
| vela_command_arena_cache_size = "" |
| |
| if config.arena_cache_size: |
| vela_command_arena_cache_size = f"--arena-cache-size={config.arena_cache_size}" |
| |
| vela_command = (f". {env_activate} && vela {model} " + |
| f"--accelerator-config={config.config_name} " + |
| "--optimise Performance " + |
| f"--config {config_file} " + |
| f"--memory-mode={config.memory_mode} " + |
| f"--system-config={config.system_config} " + |
| f"--output-dir={output_dir} " + |
| f"{vela_command_arena_cache_size}") |
| |
| # we want the name to include the configuration suffix. For example: vela_H128, |
| # vela_Y512 etc. |
| new_suffix = "_vela_" + config.ethos_u_config_id + '.tflite' |
| new_vela_optimised_model_path = ( |
| vela_optimised_model_path.replace("_vela.tflite", new_suffix)) |
| |
| if os.path.isfile(new_vela_optimised_model_path): |
| logging.info(f"File {new_vela_optimised_model_path} exists, skipping optimisation.") |
| optimisation_skipped = True |
| continue |
| |
| call_command(vela_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 any optimisation was skipped, show how to regenerate: |
| if optimisation_skipped: |
| logging.warning("One or more optimisations were skipped.") |
| logging.warning(f"To optimise all the models, please remove the directory {download_dir}.") |
| |
| |
| if __name__ == '__main__': |
| parser = ArgumentParser() |
| parser.add_argument("--skip-vela", |
| help="Do not run Vela optimizer on downloaded models.", |
| action="store_true") |
| parser.add_argument("--additional-ethos-u-config-name", |
| help=f"""Additional (non-default) configurations for Vela: |
| {valid_npu_config_names}""", |
| default=[], action="append") |
| parser.add_argument("--arena-cache-size", |
| help="Arena cache size in bytes (if overriding the defaults)", |
| type=int, |
| default=0) |
| args = parser.parse_args() |
| |
| if args.arena_cache_size < 0: |
| raise ArgumentTypeError('Arena cache size cannot not be less than 0') |
| |
| logging.basicConfig(filename='log_build_default.log', level=logging.DEBUG) |
| logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) |
| |
| set_up_resources(not args.skip_vela, |
| args.additional_ethos_u_config_name, |
| args.arena_cache_size) |