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

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)