Update for release 2.0.0

CHANGELOG.md

@@ -32,6 +32,11 @@ All notable changes to this project will be documented in this file.
 
 - Fixing some minor issues
 
+### Known issues
+
+- Anomaly task processing times have increased compared with v1.\* version, with anomaly classification experiencing a slowdown of approximately 26%, anomaly detection by approximately 213%, and anomaly segmentation by approximately 78%. [Issue #3592](https://github.com/openvinotoolkit/training_extensions/issues/3592)
+- Post-Training Quantization (PTQ) optimization applied to `maskrcnn_swint` in the instance segmentation task may result in significantly reduced accuracy compared with v1.\* [Issue #3593](https://github.com/openvinotoolkit/training_extensions/issues/3593)
+
 ## \[v1.6.1\]
 
 ### Enhancements

README.md

@@ -240,8 +240,8 @@ Please use [Issues](https://github.com/openvinotoolkit/training_extensions/issue
 
 ## Known limitations
 
-[misc](https://github.com/openvinotoolkit/training_extensions/tree/misc) branch contains training, evaluation, and export scripts for models based on TensorFlow and PyTorch.
-These scripts are not ready for production. They are exploratory and have not been validated.
+- [misc](https://github.com/openvinotoolkit/training_extensions/tree/misc) branch contains training, evaluation, and export scripts for models based on TensorFlow and PyTorch.
+  These scripts are not ready for production. They are exploratory and have not been validated.
 
 ---
 

docs/source/guide/release_notes/index.rst

@@ -8,6 +8,35 @@ Releases
 v2.0.0 (2Q24)
 -------------
 
+.. note::
+  OpenVINO™ Training Extensions which version 2.0.0 has been updated to include refactoring of the overall
+  architecture and functional updates. Users should [install the new environment](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/installation.html).
+
+New features
+^^^^^^^^^^^^
+- Enable New design to provide a more seamless API/CLI that delivers the value of OTX: [Product Design](https://openvinotoolkit.github.io/training_extensions/latest/guide/explanation/product_design.html)
+- Moved away from MMLab's libraries to provide a Lightning-based core and training pipeline
+- Use Lightning-based modules and trainers to deliver APIs/CLIs in a more user-friendly way
+- Support Intel devices for accelerating deep learning model training
+
+Enhancements
+^^^^^^^^^^^^
+- Support more models for each task
+- Improve the API so user can configure efficient training with shorter code
+- Provide more customize settings through the CLI and API
+- Enhance the Auto-Configuration feature and made it available in the API
+
+Bug fixes
+^^^^^^^^^
+- Fixing some minor issues
+
+Known issues
+^^^^^^^^^^^^
+- Anomaly task processing times have increased compared with v1.\* version, with anomaly classification experiencing a slowdown of approximately 26%, anomaly detection by approximately 213%, and anomaly segmentation by approximately 78%.
+  (https://github.com/openvinotoolkit/training_extensions/issues/3592)
+- Post-Training Quantization (PTQ) optimization applied to `maskrcnn_swint` in the instance segmentation task may result in significantly reduced accuracy compared with v1.\*
+  (https://github.com/openvinotoolkit/training_extensions/issues/3593)
+
 v1.6.1 (2024.05)
 ----------------
 

src/otx/__init__.py

@@ -3,7 +3,7 @@
 # Copyright (C) 2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "2.0.0rc0"
+__version__ = "2.0.0"
 
 from otx.core.types import *  # noqa: F403
 

tests/fuzzing/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

tests/fuzzing/assets/cli/commands.dict

@@ -0,0 +1,8 @@
+"install"
+"find"
+"train"
+"test"
+"predict"
+"export"
+"optimize"
+"explain"

tests/fuzzing/cli_fuzzing.py

@@ -0,0 +1,36 @@
+import sys
+
+import atheris
+from helper import FuzzingHelper
+from otx.cli import main as cli_main
+
+
+@atheris.instrument_func
+def fuzz_otx(input_bytes):
+    # create a FuzzingHelper instance to get suitable data type from the randomly generated 'input_bytes'
+    helper = FuzzingHelper(input_bytes)
+    backup_argv = sys.argv
+
+    # get 'operation' arguments from 'input_bytes'
+    operation = helper.get_string()
+    sys.argv = ["otx", operation]
+    try:
+        _ = cli_main()
+    except SystemExit as e:
+        # argparser will throw SystemExit with code 2 when some required arguments are missing
+        if e.code != 2:
+            raise
+    finally:
+        sys.argv = backup_argv
+
+
+def main():
+    # 'sys.argv' used to passing options to atheris.Setup()
+    # available options can be found https://llvm.org/docs/LibFuzzer.html#options
+    atheris.Setup(sys.argv, fuzz_otx)
+    # Fuzz() will
+    atheris.Fuzz()
+
+
+if __name__ == "__main__":
+    main()

tests/fuzzing/helper.py

@@ -0,0 +1,13 @@
+import atheris
+
+
+class FuzzingHelper:
+    """Helper to make required data from input_bytes for the fuzzing tests"""
+
+    def __init__(self, input_bytes):
+        """Init"""
+        self.provider = atheris.FuzzedDataProvider(input_bytes)
+
+    def get_string(self, byte_conut=256):
+        """Consume a string"""
+        return self.provider.ConsumeString(byte_conut)

tox.ini

@@ -105,3 +105,18 @@ allowlist_externals =
     bandit
 commands =
     - bandit -r -c .ci/ipas_default.config {toxinidir}/ -f txt -o {toxworkdir}/bandit-report.txt
+
+
+[testenv:fuzzing]
+deps =
+    .[dev]
+    atheris
+    coverage
+extras = full
+commands_pre =
+    ; [TODO]: Needs to be fixed so that this is not duplicated for each test run
+    otx install -v
+commands =
+    coverage erase
+    - coverage run tests/fuzzing/cli_fuzzing.py {posargs:-artifact_prefix={toxworkdir}/ -print_final_stats=1 -atheris_runs=500000}
+    coverage report --precision=2