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python_version < "3.8" +Requires-Dist: greenlet>=1; platform_machine == "aarch64" or (platform_machine == "ppc64le" or (platform_machine == "x86_64" or (platform_machine == "amd64" or (platform_machine == "AMD64" or (platform_machine == "win32" or platform_machine == "WIN32"))))) +Requires-Dist: typing-extensions>=4.6.0 +Provides-Extra: asyncio +Requires-Dist: greenlet>=1; extra == "asyncio" +Provides-Extra: mypy +Requires-Dist: mypy>=0.910; extra == "mypy" +Provides-Extra: mssql +Requires-Dist: pyodbc; extra == "mssql" +Provides-Extra: mssql-pymssql +Requires-Dist: pymssql; extra == "mssql-pymssql" +Provides-Extra: mssql-pyodbc +Requires-Dist: pyodbc; extra == "mssql-pyodbc" +Provides-Extra: mysql +Requires-Dist: mysqlclient>=1.4.0; extra == "mysql" +Provides-Extra: mysql-connector +Requires-Dist: mysql-connector-python; extra == "mysql-connector" +Provides-Extra: mariadb-connector +Requires-Dist: mariadb!=1.1.10,!=1.1.2,!=1.1.5,>=1.0.1; extra == "mariadb-connector" +Provides-Extra: oracle +Requires-Dist: cx_oracle>=8; 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extra == "aiomysql" +Provides-Extra: aioodbc +Requires-Dist: greenlet>=1; extra == "aioodbc" +Requires-Dist: aioodbc; extra == "aioodbc" +Provides-Extra: asyncmy +Requires-Dist: greenlet>=1; extra == "asyncmy" +Requires-Dist: asyncmy!=0.2.4,!=0.2.6,>=0.2.3; extra == "asyncmy" +Provides-Extra: aiosqlite +Requires-Dist: greenlet>=1; extra == "aiosqlite" +Requires-Dist: aiosqlite; extra == "aiosqlite" +Requires-Dist: typing_extensions!=3.10.0.1; extra == "aiosqlite" +Provides-Extra: sqlcipher +Requires-Dist: sqlcipher3_binary; extra == "sqlcipher" +Dynamic: license-file + +SQLAlchemy +========== + +|PyPI| |Python| |Downloads| + +.. |PyPI| image:: https://img.shields.io/pypi/v/sqlalchemy + :target: https://pypi.org/project/sqlalchemy + :alt: PyPI + +.. |Python| image:: https://img.shields.io/pypi/pyversions/sqlalchemy + :target: https://pypi.org/project/sqlalchemy + :alt: PyPI - Python Version + +.. |Downloads| image:: https://static.pepy.tech/badge/sqlalchemy/month + :target: https://pepy.tech/project/sqlalchemy + :alt: PyPI - Downloads + + +The Python SQL Toolkit and Object Relational Mapper + +Introduction +------------- + +SQLAlchemy is the Python SQL toolkit and Object Relational Mapper +that gives application developers the full power and +flexibility of SQL. SQLAlchemy provides a full suite +of well known enterprise-level persistence patterns, +designed for efficient and high-performing database +access, adapted into a simple and Pythonic domain +language. + +Major SQLAlchemy features include: + +* An industrial strength ORM, built + from the core on the identity map, unit of work, + and data mapper patterns. These patterns + allow transparent persistence of objects + using a declarative configuration system. + Domain models + can be constructed and manipulated naturally, + and changes are synchronized with the + current transaction automatically. +* A relationally-oriented query system, exposing + the full range of SQL's capabilities + explicitly, including joins, subqueries, + correlation, and most everything else, + in terms of the object model. + Writing queries with the ORM uses the same + techniques of relational composition you use + when writing SQL. While you can drop into + literal SQL at any time, it's virtually never + needed. +* A comprehensive and flexible system + of eager loading for related collections and objects. + Collections are cached within a session, + and can be loaded on individual access, all + at once using joins, or by query per collection + across the full result set. +* A Core SQL construction system and DBAPI + interaction layer. The SQLAlchemy Core is + separate from the ORM and is a full database + abstraction layer in its own right, and includes + an extensible Python-based SQL expression + language, schema metadata, connection pooling, + type coercion, and custom types. +* All primary and foreign key constraints are + assumed to be composite and natural. Surrogate + integer primary keys are of course still the + norm, but SQLAlchemy never assumes or hardcodes + to this model. +* Database introspection and generation. Database + schemas can be "reflected" in one step into + Python structures representing database metadata; + those same structures can then generate + CREATE statements right back out - all within + the Core, independent of the ORM. + +SQLAlchemy's philosophy: + +* SQL databases behave less and less like object + collections the more size and performance start to + matter; object collections behave less and less like + tables and rows the more abstraction starts to matter. + SQLAlchemy aims to accommodate both of these + principles. +* An ORM doesn't need to hide the "R". A relational + database provides rich, set-based functionality + that should be fully exposed. SQLAlchemy's + ORM provides an open-ended set of patterns + that allow a developer to construct a custom + mediation layer between a domain model and + a relational schema, turning the so-called + "object relational impedance" issue into + a distant memory. +* The developer, in all cases, makes all decisions + regarding the design, structure, and naming conventions + of both the object model as well as the relational + schema. SQLAlchemy only provides the means + to automate the execution of these decisions. +* With SQLAlchemy, there's no such thing as + "the ORM generated a bad query" - you + retain full control over the structure of + queries, including how joins are organized, + how subqueries and correlation is used, what + columns are requested. Everything SQLAlchemy + does is ultimately the result of a developer-initiated + decision. +* Don't use an ORM if the problem doesn't need one. + SQLAlchemy consists of a Core and separate ORM + component. The Core offers a full SQL expression + language that allows Pythonic construction + of SQL constructs that render directly to SQL + strings for a target database, returning + result sets that are essentially enhanced DBAPI + cursors. +* Transactions should be the norm. With SQLAlchemy's + ORM, nothing goes to permanent storage until + commit() is called. SQLAlchemy encourages applications + to create a consistent means of delineating + the start and end of a series of operations. +* Never render a literal value in a SQL statement. + Bound parameters are used to the greatest degree + possible, allowing query optimizers to cache + query plans effectively and making SQL injection + attacks a non-issue. + +Documentation +------------- + +Latest documentation is at: + +https://www.sqlalchemy.org/docs/ + +Installation / Requirements +--------------------------- + +Full documentation for installation is at +`Installation `_. + +Getting Help / Development / Bug reporting +------------------------------------------ + +Please refer to the `SQLAlchemy Community Guide `_. + +Code of Conduct +--------------- + +Above all, SQLAlchemy places great emphasis on polite, thoughtful, and +constructive communication between users and developers. +Please see our current Code of Conduct at +`Code of Conduct `_. + +License +------- + +SQLAlchemy is distributed under the `MIT license +`_. + diff --git a/.cache/pip/http-v2/a/a/0/d/7/aa0d704d97a2f85d8db67bd1a027229056e18fcc8fef4bddcbbb3312 b/.cache/pip/http-v2/a/a/0/d/7/aa0d704d97a2f85d8db67bd1a027229056e18fcc8fef4bddcbbb3312 new file mode 100644 index 0000000000000000000000000000000000000000..16eb19115419c45f286481105b7707baef53d68d Binary files /dev/null and b/.cache/pip/http-v2/a/a/0/d/7/aa0d704d97a2f85d8db67bd1a027229056e18fcc8fef4bddcbbb3312 differ diff --git a/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a b/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a new file mode 100644 index 0000000000000000000000000000000000000000..ddd3ab6c15d15c78607a54565cc337188151c1e0 Binary files /dev/null and b/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a differ diff --git a/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a.body b/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a.body new file mode 100644 index 0000000000000000000000000000000000000000..c3d4a170d762f718b0babe6b9cbb2e5b060ab9cd Binary files /dev/null and b/.cache/pip/http-v2/a/f/5/3/d/af53d10b2a0401eb083b4e24ccf3518cc83ed2a451d87928b17de49a.body differ diff --git a/.cache/pip/http-v2/d/2/5/3/1/d2531450b00c0f876e3ca84ca161003746b7b3d143f1136ba3f06223 b/.cache/pip/http-v2/d/2/5/3/1/d2531450b00c0f876e3ca84ca161003746b7b3d143f1136ba3f06223 new file mode 100644 index 0000000000000000000000000000000000000000..bf6c133101a5cd4e539eec1f3ebfd46849484bf6 Binary files /dev/null and b/.cache/pip/http-v2/d/2/5/3/1/d2531450b00c0f876e3ca84ca161003746b7b3d143f1136ba3f06223 differ diff --git a/.cache/pip/http-v2/d/3/2/9/d/d329d269473dcb26bc2fcf82e354619e8463ba8855d5a3b77637c124 b/.cache/pip/http-v2/d/3/2/9/d/d329d269473dcb26bc2fcf82e354619e8463ba8855d5a3b77637c124 new file mode 100644 index 0000000000000000000000000000000000000000..1e36c46ef6404684827cd6608fe5477a50003160 Binary files /dev/null and b/.cache/pip/http-v2/d/3/2/9/d/d329d269473dcb26bc2fcf82e354619e8463ba8855d5a3b77637c124 differ diff --git a/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365 b/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365 new file mode 100644 index 0000000000000000000000000000000000000000..c5d374a983f6acada7056b8b3c098f78c24184bc Binary files /dev/null and b/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365 differ diff --git a/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365.body b/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365.body new file mode 100644 index 0000000000000000000000000000000000000000..a7ba0c9f0edd33ab4157300f5afb66fb1262dc38 --- /dev/null +++ b/.cache/pip/http-v2/d/b/c/4/1/dbc4126a106958ce0e89d6c950a7727e6eaafca25d91f254f998a365.body @@ -0,0 +1,109 @@ +Metadata-Version: 2.4 +Name: pip +Version: 26.1.2 +Summary: The PyPA recommended tool for installing Python packages. +Author-email: The pip developers +Requires-Python: >=3.10 +Description-Content-Type: text/x-rst +License-Expression: MIT +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Topic :: Software Development :: Build Tools +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +License-File: AUTHORS.txt +License-File: LICENSE.txt +License-File: src/pip/_vendor/cachecontrol/LICENSE.txt +License-File: src/pip/_vendor/certifi/LICENSE +License-File: src/pip/_vendor/distlib/LICENSE.txt +License-File: src/pip/_vendor/distro/LICENSE +License-File: src/pip/_vendor/idna/LICENSE.md +License-File: src/pip/_vendor/msgpack/COPYING +License-File: src/pip/_vendor/packaging/LICENSE +License-File: src/pip/_vendor/packaging/LICENSE.APACHE +License-File: src/pip/_vendor/packaging/LICENSE.BSD +License-File: src/pip/_vendor/pkg_resources/LICENSE +License-File: src/pip/_vendor/platformdirs/LICENSE +License-File: src/pip/_vendor/pygments/LICENSE +License-File: src/pip/_vendor/pyproject_hooks/LICENSE +License-File: src/pip/_vendor/requests/LICENSE +License-File: src/pip/_vendor/resolvelib/LICENSE +License-File: src/pip/_vendor/rich/LICENSE +License-File: src/pip/_vendor/tomli/LICENSE +License-File: src/pip/_vendor/tomli_w/LICENSE +License-File: src/pip/_vendor/truststore/LICENSE +License-File: src/pip/_vendor/urllib3/LICENSE.txt +Project-URL: Changelog, https://pip.pypa.io/en/stable/news/ +Project-URL: Documentation, https://pip.pypa.io +Project-URL: Homepage, https://pip.pypa.io/ +Project-URL: Source, https://github.com/pypa/pip + +pip - The Python Package Installer +================================== + +.. |pypi-version| image:: https://img.shields.io/pypi/v/pip.svg + :target: https://pypi.org/project/pip/ + :alt: PyPI + +.. |python-versions| image:: https://img.shields.io/pypi/pyversions/pip + :target: https://pypi.org/project/pip + :alt: PyPI - Python Version + +.. |docs-badge| image:: https://readthedocs.org/projects/pip/badge/?version=latest + :target: https://pip.pypa.io/en/latest + :alt: Documentation + +|pypi-version| |python-versions| |docs-badge| + +pip is the `package installer`_ for Python. You can use pip to install packages from the `Python Package Index`_ and other indexes. + +Please take a look at our documentation for how to install and use pip: + +* `Installation`_ +* `Usage`_ + +We release updates regularly, with a new version every 3 months. Find more details in our documentation: + +* `Release notes`_ +* `Release process`_ + +If you find bugs, need help, or want to talk to the developers, please use our mailing lists or chat rooms: + +* `Issue tracking`_ +* `Discourse channel`_ +* `User IRC`_ + +If you want to get involved, head over to GitHub to get the source code, look at our development documentation and feel free to jump on the developer mailing lists and chat rooms: + +* `GitHub page`_ +* `Development documentation`_ +* `Development IRC`_ + +Code of Conduct +--------------- + +Everyone interacting in the pip project's codebases, issue trackers, chat +rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_. + +.. _package installer: https://packaging.python.org/guides/tool-recommendations/ +.. _Python Package Index: https://pypi.org +.. _Installation: https://pip.pypa.io/en/stable/installation/ +.. _Usage: https://pip.pypa.io/en/stable/ +.. _Release notes: https://pip.pypa.io/en/stable/news.html +.. _Release process: https://pip.pypa.io/en/latest/development/release-process/ +.. _GitHub page: https://github.com/pypa/pip +.. _Development documentation: https://pip.pypa.io/en/latest/development +.. _Issue tracking: https://github.com/pypa/pip/issues +.. _Discourse channel: https://discuss.python.org/c/packaging +.. _User IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa +.. _Development IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa-dev +.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md + diff --git a/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b b/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b new file mode 100644 index 0000000000000000000000000000000000000000..c3bbb6b1781032a18b53203fb324189e4e81eca1 Binary files /dev/null and b/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b differ diff --git a/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b.body b/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b.body new file mode 100644 index 0000000000000000000000000000000000000000..3eee724166b8ba6132c01f452f7df3c1975a5b2c --- /dev/null +++ b/.cache/pip/http-v2/d/d/2/0/6/dd206ee8d449d4bec512242e8f8ebadb5a808c682766018f3921f62b.body @@ -0,0 +1,51 @@ +Metadata-Version: 2.1 +Name: et_xmlfile +Version: 2.0.0 +Summary: An implementation of lxml.xmlfile for the standard library +Home-page: https://foss.heptapod.net/openpyxl/et_xmlfile +Author: See AUTHORS.txt +Author-email: charlie.clark@clark-consulting.eu +License: MIT +Project-URL: Documentation, https://openpyxl.pages.heptapod.net/et_xmlfile/ +Project-URL: Source, https://foss.heptapod.net/openpyxl/et_xmlfile +Project-URL: Tracker, https://foss.heptapod.net/openpyxl/et_xmfile/-/issues +Classifier: Development Status :: 5 - Production/Stable +Classifier: Operating System :: MacOS :: MacOS X +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX +Classifier: License :: OSI Approved :: MIT License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Requires-Python: >=3.8 +License-File: LICENCE.python +License-File: LICENCE.rst +License-File: AUTHORS.txt + +.. image:: https://foss.heptapod.net/openpyxl/et_xmlfile/badges/branch/default/coverage.svg + :target: https://coveralls.io/bitbucket/openpyxl/et_xmlfile?branch=default + :alt: coverage status + +et_xmfile +========= + +XML can use lots of memory, and et_xmlfile is a low memory library for creating large XML files +And, although the standard library already includes an incremental parser, `iterparse` it has no equivalent when writing XML. Once an element has been added to the tree, it is written to +the file or stream and the memory is then cleared. + +This module is based upon the `xmlfile module from lxml `_ with the aim of allowing code to be developed that will work with both libraries. +It was developed initially for the openpyxl project, but is now a standalone module. + +The code was written by Elias Rabel as part of the `Python Düsseldorf `_ openpyxl sprint in September 2014. + +Proper support for incremental writing was provided by Daniel Hillier in 2024 + +Note on performance +------------------- + +The code was not developed with performance in mind, but turned out to be faster than the existing SAX-based implementation but is generally slower than lxml's xmlfile. +There is one area where an optimisation for lxml may negatively affect the performance of et_xmfile and that is when using the `.element()` method on the xmlfile context manager. 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A migrations tool +offers the following functionality: + +* Can emit ALTER statements to a database in order to change + the structure of tables and other constructs +* Provides a system whereby "migration scripts" may be constructed; + each script indicates a particular series of steps that can "upgrade" a + target database to a new version, and optionally a series of steps that can + "downgrade" similarly, doing the same steps in reverse. +* Allows the scripts to execute in some sequential manner. + +The goals of Alembic are: + +* Very open ended and transparent configuration and operation. A new + Alembic environment is generated from a set of templates which is selected + among a set of options when setup first occurs. The templates then deposit a + series of scripts that define fully how database connectivity is established + and how migration scripts are invoked; the migration scripts themselves are + generated from a template within that series of scripts. The scripts can + then be further customized to define exactly how databases will be + interacted with and what structure new migration files should take. +* Full support for transactional DDL. The default scripts ensure that all + migrations occur within a transaction - for those databases which support + this (Postgresql, Microsoft SQL Server), migrations can be tested with no + need to manually undo changes upon failure. +* Minimalist script construction. Basic operations like renaming + tables/columns, adding/removing columns, changing column attributes can be + performed through one line commands like alter_column(), rename_table(), + add_constraint(). There is no need to recreate full SQLAlchemy Table + structures for simple operations like these - the functions themselves + generate minimalist schema structures behind the scenes to achieve the given + DDL sequence. +* "auto generation" of migrations. While real world migrations are far more + complex than what can be automatically determined, Alembic can still + eliminate the initial grunt work in generating new migration directives + from an altered schema. The ``--autogenerate`` feature will inspect the + current status of a database using SQLAlchemy's schema inspection + capabilities, compare it to the current state of the database model as + specified in Python, and generate a series of "candidate" migrations, + rendering them into a new migration script as Python directives. The + developer then edits the new file, adding additional directives and data + migrations as needed, to produce a finished migration. Table and column + level changes can be detected, with constraints and indexes to follow as + well. +* Full support for migrations generated as SQL scripts. Those of us who + work in corporate environments know that direct access to DDL commands on a + production database is a rare privilege, and DBAs want textual SQL scripts. + Alembic's usage model and commands are oriented towards being able to run a + series of migrations into a textual output file as easily as it runs them + directly to a database. Care must be taken in this mode to not invoke other + operations that rely upon in-memory SELECTs of rows - Alembic tries to + provide helper constructs like bulk_insert() to help with data-oriented + operations that are compatible with script-based DDL. +* Non-linear, dependency-graph versioning. Scripts are given UUID + identifiers similarly to a DVCS, and the linkage of one script to the next + is achieved via human-editable markers within the scripts themselves. + The structure of a set of migration files is considered as a + directed-acyclic graph, meaning any migration file can be dependent + on any other arbitrary set of migration files, or none at + all. Through this open-ended system, migration files can be organized + into branches, multiple roots, and mergepoints, without restriction. + Commands are provided to produce new branches, roots, and merges of + branches automatically. +* Provide a library of ALTER constructs that can be used by any SQLAlchemy + application. The DDL constructs build upon SQLAlchemy's own DDLElement base + and can be used standalone by any application or script. +* At long last, bring SQLite and its inability to ALTER things into the fold, + but in such a way that SQLite's very special workflow needs are accommodated + in an explicit way that makes the most of a bad situation, through the + concept of a "batch" migration, where multiple changes to a table can + be batched together to form a series of instructions for a single, subsequent + "move-and-copy" workflow. You can even use "move-and-copy" workflow for + other databases, if you want to recreate a table in the background + on a busy system. + +Documentation and status of Alembic is at https://alembic.sqlalchemy.org/ + +The SQLAlchemy Project +====================== + +Alembic is part of the `SQLAlchemy Project `_ and +adheres to the same standards and conventions as the core project. + +Development / Bug reporting / Pull requests +___________________________________________ + +Please refer to the +`SQLAlchemy Community Guide `_ for +guidelines on coding and participating in this project. + +Code of Conduct +_______________ + +Above all, SQLAlchemy places great emphasis on polite, thoughtful, and +constructive communication between users and developers. +Please see our current Code of Conduct at +`Code of Conduct `_. + +License +======= + +Alembic is distributed under the `MIT license +`_. diff --git a/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c b/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c new file mode 100644 index 0000000000000000000000000000000000000000..e5c89cabe91a798395452e4a9f8ee05055b6af91 Binary files /dev/null and b/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c differ diff --git a/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c.body b/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c.body new file mode 100644 index 0000000000000000000000000000000000000000..68529fbd400e902db54175b9286d9f24eb6976e1 --- /dev/null +++ b/.cache/pip/http-v2/e/d/e/d/1/eded1062df3a835b1d654db11016327d0de7276866caf329f6f27b1c.body @@ -0,0 +1,94 @@ +Metadata-Version: 2.1 +Name: contourpy +Version: 1.3.3 +Summary: Python library for calculating contours of 2D quadrilateral grids +Author-Email: Ian Thomas +License: BSD 3-Clause License + + Copyright (c) 2021-2025, ContourPy Developers. + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + 3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: C++ +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Topic :: Scientific/Engineering :: Information Analysis +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Visualization +Project-URL: Homepage, https://github.com/contourpy/contourpy +Project-URL: Changelog, https://contourpy.readthedocs.io/en/latest/changelog.html +Project-URL: Documentation, https://contourpy.readthedocs.io +Project-URL: Repository, https://github.com/contourpy/contourpy +Requires-Python: >=3.11 +Requires-Dist: numpy>=1.25 +Provides-Extra: docs +Requires-Dist: furo; extra == "docs" +Requires-Dist: sphinx>=7.2; extra == "docs" +Requires-Dist: sphinx-copybutton; extra == "docs" +Provides-Extra: bokeh +Requires-Dist: bokeh; extra == "bokeh" +Requires-Dist: selenium; extra == "bokeh" +Provides-Extra: mypy +Requires-Dist: contourpy[bokeh,docs]; extra == "mypy" +Requires-Dist: bokeh; extra == "mypy" +Requires-Dist: docutils-stubs; extra == "mypy" +Requires-Dist: mypy==1.17.0; extra == "mypy" +Requires-Dist: types-Pillow; extra == "mypy" +Provides-Extra: test +Requires-Dist: contourpy[test-no-images]; extra == "test" +Requires-Dist: matplotlib; extra == "test" +Requires-Dist: Pillow; extra == "test" +Provides-Extra: test-no-images +Requires-Dist: pytest; extra == "test-no-images" +Requires-Dist: pytest-cov; extra == "test-no-images" +Requires-Dist: pytest-rerunfailures; extra == "test-no-images" +Requires-Dist: pytest-xdist; extra == "test-no-images" +Requires-Dist: wurlitzer; extra == "test-no-images" +Description-Content-Type: text/markdown + +ContourPy + +ContourPy is a Python library for calculating contours of 2D quadrilateral grids. It is written in C++11 and wrapped using pybind11. + +It contains the 2005 and 2014 algorithms used in Matplotlib as well as a newer algorithm that includes more features and is available in both serial and multithreaded versions. It provides an easy way for Python libraries to use contouring algorithms without having to include Matplotlib as a dependency. + + * **Documentation**: https://contourpy.readthedocs.io + * **Source code**: https://github.com/contourpy/contourpy + +| | | +| --- | --- | +| Latest release | [![PyPI version](https://img.shields.io/pypi/v/contourpy.svg?label=pypi&color=fdae61)](https://pypi.python.org/pypi/contourpy) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/contourpy.svg?label=conda-forge&color=a6d96a)](https://anaconda.org/conda-forge/contourpy) | +| Downloads | [![PyPi downloads](https://img.shields.io/pypi/dm/contourpy?label=pypi&style=flat&color=fdae61)](https://pepy.tech/project/contourpy) | +| Python version | [![Platforms](https://img.shields.io/pypi/pyversions/contourpy?color=fdae61)](https://pypi.org/project/contourpy/) | +| Coverage | [![Codecov](https://img.shields.io/codecov/c/gh/contourpy/contourpy?color=fdae61&label=codecov)](https://app.codecov.io/gh/contourpy/contourpy) | diff --git a/ablations/abl_ab1_no_critic.py b/ablations/abl_ab1_no_critic.py new file mode 100644 index 0000000000000000000000000000000000000000..d294acef5e3d650bc60492683d361b55a784d427 --- /dev/null +++ b/ablations/abl_ab1_no_critic.py @@ -0,0 +1,13598 @@ +from __future__ import annotations +import csv +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from datetime import datetime, timezone +import gc +import hashlib +import importlib +import inspect +import json +import math +import os +import random +import shutil +import sqlite3 +import subprocess +import sys +import tarfile +import tempfile +import threading +import time +import traceback +import weakref +from collections import Counter +from contextlib import contextmanager, nullcontext +from pathlib import Path +from typing import Any, Iterator, Literal + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import segmentation_models_pytorch as smp +os.environ.setdefault("NNPACK_DISABLE", "1") +import torch +torch.backends.nnpack.enabled = False +import torch.nn as nn +import torch.nn.functional as F +from optuna.storages import RDBStorage +from PIL import Image as PILImage +from scipy import ndimage +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +"""============================================================================= +EDIT ME +============================================================================= +""" + +PROJECT_DIR = Path(__file__).resolve().parent.parent # ABLATION: repo root (this copy lives in ablations/) +TRANSUNET_REPO_DIR = PROJECT_DIR / "TransUNet" +TRANSUNET_VIT_NAME = "R50-ViT-B_16" +TRANSUNET_N_SKIP = 3 +TRANSUNET_PRETRAINED_PATH = PROJECT_DIR / "model" / "vit_checkpoint" / "imagenet21k" / "R50+ViT-B_16.npz" + +RUNS_ROOT = PROJECT_DIR / "runs" +HARD_CODED_PARAM_DIR = PROJECT_DIR +MODEL_NAME = "Segformer_B0_revamped_nt_2" + +EXPERIMENT_MODE = "repeated_holdout" # "single_run" or "repeated_holdout" +SUPPORTED_EXPERIMENT_MODES = ("single_run", "repeated_holdout") +SPLIT_GENERATION_MODE = "fixed_stratified_phases_8_1_1" # "repeated_holdout" or "fixed_stratified_phases_8_1_1" +SUPPORTED_SPLIT_GENERATION_MODES = ("repeated_holdout", "fixed_stratified_phases_8_1_1") +NUM_STRATIFIED_SPLIT_REPEATS = 5 +NUM_PHASES = 10 +PHASE_VAL_OFFSET = 1 +DATASET_PERCENT_REPEAT_COUNTS: dict[int, int] = { + # 5: 4, + # 15: 3, + # 30: 3, + # 50: 2, + 100: 1, +} +PERCENT_SAMPLING_MODE = "incremental" # "independent" or "incremental" +SUPPORTED_PERCENT_SAMPLING_MODES = ("independent", "incremental") +PERCENT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PERCENT_EXECUTION_MODES = ("auto", "manual") +SELECTED_DATASET_PERCENTS: list[int] = [100] +SPLIT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_SPLIT_EXECUTION_MODES = ("auto", "manual") +SELECTED_SPLIT_INDICES: list[int] = [1] + +PHASE_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PHASE_EXECUTION_MODES = ("auto", "manual") +SELECTED_PHASES: list[int] = [1] # used only when PHASE_EXECUTION_MODE="manual" + +REPEAT_EXECUTION_MODE = "auto" # "auto" or "manual" +SUPPORTED_REPEAT_EXECUTION_MODES = ("auto", "manual") +SELECTED_REPEAT_INDICES: list[int] = [1] + +FOLDS_EXPERIMENT_NAME = "stratified_holdout_v1" +RESUME_FOLDS = False +ASYNC_REPO_BACKUP_AFTER_PHASE = False +# Hugging Face dataset repo to mirror the project into. Set via env so nothing is +# hardcoded: export HF_REPO_ID="your-username/ADVAI24JUN-backup" and HF_TOKEN=... +HF_REPO_ID = os.environ.get("HF_REPO_ID", "") +HF_REPO_TYPE = "dataset" +# Only upload after every Nth phase (boundary), so we don't hammer HF every phase. +HF_BACKUP_EVERY_N_PHASES = 1 +HF_BACKUP_MAX_RETRIES = 5 +# Run one synchronous backup BEFORE training starts: it creates the repo and uploads +# the current project state, proving the whole backup pipeline works before we commit +# hours of compute. Phase backups later refresh this same repo. +HF_BACKUP_ON_START = False +# Glob patterns excluded from the upload (matched against repo-relative paths). +HF_IGNORE_PATTERNS = ( + "**/.git/**", + "**/__pycache__/**", + "**/.ipynb_checkpoints/**", + "**/.cache/**", + "**/.venv/**", + "*.pyc", + ".DS_Store", +) + +DATASET_NAME = "BUSI_with_classes" # "BUSI" or "BUSI_with_classes" +SUPPORTED_DATASET_NAMES = ("BUSI", "BUSI_with_classes") +DATA_ROOT = PROJECT_DIR / DATASET_NAME +BUSI_WITH_CLASSES_SPLIT_POLICY = "stratified" # "balanced_train" or "stratified" +SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES = ("balanced_train", "stratified") + +SUPPORTED_STRATEGIES: tuple[int, ...] = (2,3) +STRATEGIES = [2,3] +DATASET_PERCENTS = [] #ignored in the folding [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1.0] #, 0.5, 1.0] #, 0.5, 1.0] +SPLIT_TYPE = "80_10_10" +SUPPORTED_SPLIT_TYPES = ("80_10_10", "70_10_20") +DATASET_SPLITS_JSON = PROJECT_DIR / "dataset_splits.json" +DATASET_SPLITS_VERSION = 1 +TRAIN_SUBSET_VARIANT = 1 # 0 uses the persisted subset; >0 deterministically resamples only the train subset from the frozen base train split. +NUM_TRIALS = 30 +STUDY_DIRECTION = "maximize" +BEST_CHECKPOINT_METRICS = { + 2: "val_iou", + 3: "val_refine_score", +} +OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR = "best_observed_objective" +OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR = "best_observed_objective_name" +SUPPORTED_CHECKPOINT_METRICS = { + "val_loss", + "val_dice", + "val_iou", + "val_biou", + "val_refine_score", + "val_decoder_dice", + "val_decoder_iou", + "val_decoder_biou", + "val_dice_gain", + "val_iou_gain", + "val_biou_gain", + "val_actor_loss", + "val_critic_loss", + "val_ce_loss", + "val_dice_loss", + "val_reward", + "val_entropy", +} + +SEED = 42 +IMG_SIZE = 128 +# d = 0 -> auto (floor(0.02 * diag)); any positive int overrides. +# Recommended: 0 (auto) -> resolves to ~4 px for IMG_SIZE=128. +BOUNDARY_IOU_D: int = 0 +BATCH_SIZE = 16 # Recommended to prevent OOM +NUM_WORKERS = 128 # Recommended with RAM-preloaded datasets to avoid worker RAM duplication. +USE_PIN_MEMORY = True +USE_PERSISTENT_WORKERS = True +PRELOAD_TO_RAM = True + +SMP_ENCODER_NAME = "efficientnet-b0" +SMP_ENCODER_WEIGHTS = "imagenet" +SMP_ENCODER_DEPTH = 5 +SMP_ENCODER_PROJ_DIM = 256 +SMP_DECODER_TYPE = "Unet" +BACKBONE_FAMILY = "smp" # "smp" or "custom_vgg" +ENABLE_CUSTOM_VGG_BACKBONE = False +VGG_FEATURE_SCALES = 4 +VGG_FEATURE_DILATION = 1 + +USE_IMAGENET_NORM = True +REPLACE_BN_WITH_GN = True +GN_NUM_GROUPS = 8 +NUM_ACTIONS = 2 + +STRATEGY_1_MAX_EPOCHS = 100 +STRATEGY_2_MAX_EPOCHS = 100 +STRATEGY_3_MAX_EPOCHS = 120 +STRATEGY_4_MAX_EPOCHS = 100 +STRATEGY_5_MAX_EPOCHS = 100 +VALIDATE_EVERY_N_EPOCHS = 1 +CHECKPOINT_EVERY_N_EPOCHS = 0 +SAVE_LATEST_EVERY_EPOCH = True +SAVE_HISTORY_INCREMENTALLY = False +EARLY_STOPPING_PATIENCE = 0 +VERBOSE_EPOCH_LOG = False + +DEFAULT_HEAD_LR = 1e-4 +DEFAULT_ENCODER_LR = 1e-5 +DEFAULT_WEIGHT_DECAY = 1e-4 +DEFAULT_TMAX = 5 +TEST_ITERATION_CONTROL = False # If True, validation/evaluation/inference uses TEST_ITERATION_T instead of full tmax. +TEST_ITERATION_T = 1 # Applied only when TEST_ITERATION_CONTROL=True. Clamped to [1, tmax]. +DEFAULT_GAMMA = 0.95 +DEFAULT_CRITIC_LOSS_WEIGHT = 0.5 +DEFAULT_ENTROPY_ALPHA_INIT = 0.2 +DEFAULT_ENTROPY_TARGET_RATIO = 0.25 +DEFAULT_ENTROPY_LR = 3e-4 +DEFAULT_CE_WEIGHT = 0.5 +DEFAULT_DICE_WEIGHT = 0.5 +DEFAULT_DROPOUT_P = 0.2 +DEFAULT_GRAD_CLIP_NORM = 6.0 +DEFAULT_MASK_UPDATE_STEP = 0.1 +DEFAULT_FOREGROUND_REWARD_WEIGHT = 0.0 +DEFAULT_RECALL_REWARD_WEIGHT = 1.0 +DEFAULT_DICE_REWARD_WEIGHT = 0.35 +DEFAULT_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_PRIOR_REWARD_WEIGHT = 0.01 +DEFAULT_DECODER_GAIN_REWARD_WEIGHT = 0.5 +DEFAULT_REWARD_SCALE = 1.0 +DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION = True +DEFAULT_STRATEGY3_VARIANT = "lite" +DEFAULT_STRATEGY3_NUM_ACTIONS = 3 +DEFAULT_REFINE_DELTA_SMALL = 0.03 +DEFAULT_REFINE_DELTA_LARGE = 0.08 +DEFAULT_STRATEGY3_AUX_CE_WEIGHT = 0.40 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH = 25 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS = 15 +DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION = 0.10 +DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE = 30 +DEFAULT_STRATEGY3_PROBE_MODE = "rolling_random" +DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM = 0.25 +DEFAULT_STRATEGY3_RL_LOSS_SCALE = 10.0 +DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED = True +DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES = 8 +DEFAULT_STRATEGY3_MC_DROPOUT_P = 0.2 +DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_READ = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE = True +DEFAULT_STRATEGY3_DELTA_MAX = 0.10 +DEFAULT_STRATEGY3_SAM_ATTENTION_GRID = 64 +DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT = 1.0 +DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE = False +DEFAULT_BIOU_REWARD_WEIGHT = 1.0 +DEFAULT_IOU_REWARD_WEIGHT = 1.0 +DEFAULT_KEEP_CORRECT_REWARD_WEIGHT = 0.05 +DEFAULT_STRATEGY3_A3C_ENTROPY_COEFF = 0.0 +DEFAULT_STRATEGY3_ENTROPY_TARGET_RATIO = 0.20 +DEFAULT_STRATEGY3_ENTROPY_ALPHA_INIT = 0.005 +DEFAULT_STRATEGY3_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_EARLY_STOPPING_MONITOR = "auto" +DEFAULT_EARLY_STOPPING_MODE = "auto" +DEFAULT_EARLY_STOPPING_MIN_DELTA = 0.0 +DEFAULT_EARLY_STOPPING_START_EPOCH = 30 +DEFAULT_EXPLORATION_EPS = 0.1 +EXPLORATION_EPS_EPOCHS = 20 +ATTENTION_MAX_TOKENS = 1024 +ATTENTION_MIN_POOL_SIZE = 16 + +_STRATEGY3_MC_DROPOUT_WARNED = False +_STRATEGY3_SAM_GRID_WARNED: set[int] = set() +_STRATEGY3_MC_CACHE_SCHEMA_VERSION = 1 +_STRATEGY3_MC_FILE_SHA256_CACHE: dict[str, str] = {} + +SCHEDULER_FACTOR = 0.5 +SCHEDULER_PATIENCE = 5 +SCHEDULER_THRESHOLD = 1e-3 +SCHEDULER_MIN_LR = 1e-5 + +HEAD_LR_RANGE = (1e-5, 3e-3) +ENCODER_LR_RANGE = (1e-6, 3e-3) +WEIGHT_DECAY_RANGE = (1e-6, 1e-2) +TMAX_RANGE = (3, 10) +ENTROPY_LR_RANGE = (1e-5, 1e-3) +DROPOUT_P_RANGE = (0.0, 0.5) + +USE_TRIAL_PRUNING = True +TRIAL_PRUNER_WARMUP_STEPS = 80 +TRIAL_PRUNER_PATIENCE_STEPS = 40 +LOAD_EXISTING_STUDIES = False +SKIP_EXISTING_FINALS = False +RUN_OPTUNA = False +RESET_ALL_STUDIES_EACH_RUN = False +USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF = False + +EXECUTION_MODE = "train_eval" # "train_eval" or "eval_only" +EVAL_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +EVAL_SPECIFIC_CHECKPOINT = "" +STRATEGY2_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = { + # Non-phase mode — keyed by dataset percent (float): + # 0.1: "runs/EfficientNet_Strategy2_New/pct_10/strategy_2/final/checkpoints/epoch_0089.pt", + # 0.5: "Strategy2_Checkpoints/strat2_50_best.pt", + # 1.0: "runs/EfficientNet_Strategy2_New/pct_100/strategy_2/final/checkpoints/best.pt", + # Phase mode — keyed by phase index (int): + 1: "/content/UNET_REVAMP/best_strat2.pt", + 2: "/content/UNET_REVAMP/best_strat2_2.pt", + 3: "/content/UNET_REVAMP/best_strat2_3.pt", +} +STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 = True +TRAIN_RESUME_MODE = "off" # "off", "latest", "best", or "specific" +TRAIN_RESUME_SPECIFIC_CHECKPOINT = "" +OPTUNA_HEARTBEAT_INTERVAL = 60 +OPTUNA_HEARTBEAT_GRACE_PERIOD = 180 + +USE_AMP = True +AMP_DTYPE = "bfloat16" # "auto", "bfloat16", or "float16" +USE_CHANNELS_LAST = True +USE_TORCH_COMPILE = True +STEPWISE_BACKWARD = True +ALLOW_TF32 = True + +RUN_SMOKE_TEST = False +SMOKE_TEST_SAMPLE_INDEX = 0 +RUN_OVERFIT_TEST = False +OVERFIT_N_BATCHES = 2 +OVERFIT_N_EPOCHS = 100 +OVERFIT_HEAD_LR = 1e-3 +OVERFIT_ENCODER_LR = 1e-4 +OVERFIT_PRINT_EVERY = 5 +WRITE_EPOCH_DIAGNOSTIC = True +EPOCH_DIAGNOSTIC_TRAIN_BATCHES = 2 +EPOCH_DIAGNOSTIC_VAL_BATCHES = 2 +CONTROLLED_MASK_THRESHOLD = 0.50 + +REQUIRED_HPARAM_KEYS = ("head_lr", "encoder_lr", "weight_decay", "dropout_p", "tmax", "entropy_lr") + +_TRANSUNET_REQUIRED_NPZ_KEYS: tuple[str, ...] = ( + "embedding/kernel", + "embedding/bias", + "Transformer/encoder_norm/scale", + "Transformer/encoder_norm/bias", + "Transformer/posembed_input/pos_embedding", + "conv_root/kernel", + "gn_root/scale", + "gn_root/bias", + "Transformer/encoderblock_0/MultiHeadDotProductAttention_1/query/kernel", +) +_TRANSUNET_ENCODER_ALIASES: set[str] = {"vitb16r50", "r50vitb16"} +_TRANSUNET_VISION_TRANSFORMER: Any | None = None +_TRANSUNET_CONFIGS: dict[str, Any] | None = None +_TEST_ITERATION_NOTICE_CACHE: set[tuple[str, int, int]] = set() + +"""============================================================================= +IF OPTUNA IS OFF --> USE ME +============================================================================= +""" + +# Key format: ":" +# Each value is a JSON filename in HARD_CODED_PARAM_DIR containing the required hyperparameters. +MANUAL_HPARAMS_IF_OPTUNA_OFF: dict[str, str] = { + "2:100": "param_segformer/best_params_strat2.json", + "3:100": "param_unet/best_params_strat3.json", +} + +# ===================== ABLATION HARNESS OVERRIDE ===================== +# Auto-generated. Outputs go to a separate MODEL_NAME subtree; strategy 3 +# only; the frozen strategy-2 base is reused from the original run tree. +MODEL_NAME = "Unet_B0_AB1_no_critic" +STRATEGIES = [3] +STRATEGY2_CHECKPOINT_MODE = "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = {1: str(PROJECT_DIR / "best_unet.pt")} +MANUAL_HPARAMS_IF_OPTUNA_OFF = {**MANUAL_HPARAMS_IF_OPTUNA_OFF, "3:100": "param_unet/best_params_strat3.json"} +# ===================================================================== + +"""============================================================================= +RUNTIME SETUP +============================================================================= +""" + +torch.set_float32_matmul_precision("high") +if torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32 + torch.backends.cudnn.allow_tf32 = ALLOW_TF32 +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = True + +def select_runtime_device() -> tuple[torch.device, str]: + if torch.cuda.is_available(): + return torch.device("cuda"), "cuda" + + mps_backend = getattr(torch.backends, "mps", None) + if mps_backend is not None and mps_backend.is_available(): + try: + _probe = torch.zeros(1, device="mps") + del _probe + return torch.device("mps"), "mps" + except Exception as exc: + print(f"[Device] MPS detected but failed to initialize ({exc}). Falling back to CPU.") + + return torch.device("cpu"), "cpu" + +DEVICE, DEVICE_FALLBACK_SOURCE = select_runtime_device() +CURRENT_JOB_PARAMS: dict[str, Any] = {} + +@dataclass(frozen=True) +class RuntimeModelConfig: + backbone_family: str + smp_encoder_name: str + smp_encoder_weights: str | None + smp_encoder_depth: int + smp_encoder_proj_dim: int + smp_decoder_type: str + vgg_feature_scales: int + vgg_feature_dilation: int + + @classmethod + def from_globals(cls) -> RuntimeModelConfig: + return cls( + backbone_family=str(BACKBONE_FAMILY).strip().lower(), + smp_encoder_name=str(SMP_ENCODER_NAME), + smp_encoder_weights=SMP_ENCODER_WEIGHTS, + smp_encoder_depth=int(SMP_ENCODER_DEPTH), + smp_encoder_proj_dim=int(SMP_ENCODER_PROJ_DIM), + smp_decoder_type=str(SMP_DECODER_TYPE), + vgg_feature_scales=int(VGG_FEATURE_SCALES), + vgg_feature_dilation=int(VGG_FEATURE_DILATION), + ) + + @classmethod + def from_payload(cls, payload: dict[str, Any] | None) -> RuntimeModelConfig: + payload = payload or {} + return cls( + backbone_family=str(payload.get("backbone_family", "smp")).strip().lower(), + smp_encoder_name=str(payload.get("smp_encoder_name", SMP_ENCODER_NAME)), + smp_encoder_weights=payload.get("smp_encoder_weights", SMP_ENCODER_WEIGHTS), + smp_encoder_depth=int(payload.get("smp_encoder_depth", SMP_ENCODER_DEPTH)), + smp_encoder_proj_dim=int(payload.get("smp_encoder_proj_dim", SMP_ENCODER_PROJ_DIM)), + smp_decoder_type=str(payload.get("smp_decoder_type", SMP_DECODER_TYPE)), + vgg_feature_scales=int(payload.get("vgg_feature_scales", VGG_FEATURE_SCALES)), + vgg_feature_dilation=int(payload.get("vgg_feature_dilation", VGG_FEATURE_DILATION)), + ) + + def validate(self) -> RuntimeModelConfig: + if self.backbone_family not in {"smp", "custom_vgg"}: + raise ValueError(f"BACKBONE_FAMILY must be 'smp' or 'custom_vgg', got {self.backbone_family!r}") + if self.vgg_feature_scales not in {3, 4}: + raise ValueError(f"VGG_FEATURE_SCALES must be 3 or 4, got {self.vgg_feature_scales}") + if self.vgg_feature_dilation < 1: + raise ValueError(f"VGG_FEATURE_DILATION must be >= 1, got {self.vgg_feature_dilation}") + if self.smp_encoder_depth < 1: + raise ValueError(f"SMP_ENCODER_DEPTH must be >= 1, got {self.smp_encoder_depth}") + if self.smp_encoder_proj_dim < 0: + raise ValueError(f"SMP_ENCODER_PROJ_DIM must be >= 0, got {self.smp_encoder_proj_dim}") + if _normalized_model_token(self.smp_decoder_type) == "transunet": + if _normalized_model_token(self.smp_encoder_name) not in _TRANSUNET_ENCODER_ALIASES: + print( + "[RuntimeModelConfig] Warning: SMP_DECODER_TYPE='TransUNet' is wired for " + "SMP_ENCODER_NAME='ViTB16R50' (or 'R50ViTB16'). " + f"Received {self.smp_encoder_name!r}." + ) + if IMG_SIZE % 16 != 0: + raise ValueError( + f"TransUNet requires IMG_SIZE divisible by 16, got IMG_SIZE={IMG_SIZE}." + ) + return self + + def to_payload(self) -> dict[str, Any]: + return { + "backbone_family": self.backbone_family, + "smp_encoder_name": self.smp_encoder_name, + "smp_encoder_weights": self.smp_encoder_weights, + "smp_encoder_depth": self.smp_encoder_depth, + "smp_encoder_proj_dim": self.smp_encoder_proj_dim, + "smp_decoder_type": self.smp_decoder_type, + "vgg_feature_scales": self.vgg_feature_scales, + "vgg_feature_dilation": self.vgg_feature_dilation, + } + + def backbone_tag(self) -> str: + return self.backbone_family + + def backbone_display_name(self) -> str: + if self.backbone_family == "custom_vgg": + return f"Custom VGG (scales={self.vgg_feature_scales}, dilation={self.vgg_feature_dilation})" + return f"SMP {self.smp_encoder_name}" + +def current_model_config() -> RuntimeModelConfig: + return RuntimeModelConfig.from_globals().validate() + +"""============================================================================= +UTILITIES +============================================================================= +""" + +def _normalized_model_token(value: str | None) -> str: + return "".join(ch for ch in str(value or "") if ch.isalnum()).lower() + + +def _is_transunet_selection( + model_config: RuntimeModelConfig | None = None, + *, + encoder_name: str | None = None, + decoder_type: str | None = None, +) -> bool: + if model_config is not None: + encoder_name = model_config.smp_encoder_name + decoder_type = model_config.smp_decoder_type + enc = _normalized_model_token(encoder_name) + dec = _normalized_model_token(decoder_type) + return dec == "transunet" and enc in _TRANSUNET_ENCODER_ALIASES + + +def _resolve_test_iteration_tmax(tmax: int, *, context: str) -> int: + effective_tmax = max(int(tmax), 1) + if not TEST_ITERATION_CONTROL: + return effective_tmax + + requested_t = int(TEST_ITERATION_T) + if requested_t < 1: + raise ValueError( + f"TEST_ITERATION_T must be >= 1 when TEST_ITERATION_CONTROL=True, got {requested_t}." + ) + + effective_tmax = min(effective_tmax, requested_t) + cache_key = (context, int(tmax), effective_tmax) + if cache_key not in _TEST_ITERATION_NOTICE_CACHE: + if requested_t > int(tmax): + print( + f"[Test Iteration Control] {context}: TEST_ITERATION_T={requested_t} exceeds tmax={int(tmax)}; " + f"using t={effective_tmax}." + ) + else: + print( + f"[Test Iteration Control] {context}: overriding test rollout steps " + f"from tmax={int(tmax)} to t={effective_tmax}." + ) + _TEST_ITERATION_NOTICE_CACHE.add(cache_key) + return effective_tmax + +def banner(title: str) -> None: + line = "=" * 80 + print(f"\n{line}\n{title}\n{line}") + +def section(title: str) -> None: + print(f"\n{'-' * 80}\n{title}\n{'-' * 80}") + +def ensure_dir(path: str | Path) -> Path: + path = Path(path).expanduser().resolve() + path.mkdir(parents=True, exist_ok=True) + return path + +def save_json(path: str | Path, payload: Any) -> None: + path = Path(path) + ensure_dir(path.parent) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + +def load_json(path: str | Path) -> Any: + with Path(path).open("r", encoding="utf-8") as f: + return json.load(f) + +def _format_history_log_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key == "lr" or key.endswith("_lr"): + return f"{value:.6e}" + return json.dumps(value) + return json.dumps(value) + +def format_history_log_row(row: dict[str, Any]) -> str: + return ", ".join(f"{key}={_format_history_log_value(key, value)}" for key, value in row.items()) + +def _format_epoch_metric(value: Any, *, scientific: bool = False) -> str: + if value is None: + return "null" + if isinstance(value, (float, int, np.floating, np.integer)): + value = float(value) + return f"{value:.6e}" if scientific else f"{value:.4f}" + return str(value) + +def format_concise_epoch_log( + row: dict[str, Any], + *, + best_metric_name: str, + best_metric_value: float, +) -> str: + fields: list[tuple[str, Any, bool]] = [ + ("train_loss", row.get("train_loss"), False), + ("train_iou", row.get("train_iou"), False), + ("train_entropy", row.get("train_entropy"), False), + ("val_loss", row.get("val_loss"), False), + ("val_iou", row.get("val_iou"), False), + ("val_dice", row.get("val_dice"), False), + ("val_iou_gain", row.get("val_iou_gain"), False), + ("val_biou_gain", row.get("val_biou_gain"), False), + ("head_lr", row.get("lr"), True), + ("encoder_lr", row.get("encoder_lr"), True), + (best_metric_name, best_metric_value, False), + ("study_best", row.get("study_best_objective"), False), + ] + parts = [ + f"{name}={_format_epoch_metric(value, scientific=scientific)}" + for name, value, scientific in fields + if value is not None + ] + early_monitor_name = row.get("early_stopping_monitor_name") + if early_monitor_name: + parts.append(f"es_monitor={early_monitor_name}") + if row.get("early_stopping_monitor_value") is not None: + parts.append(f"es_value={_format_epoch_metric(row.get('early_stopping_monitor_value'))}") + if row.get("early_stopping_best_value") is not None: + parts.append(f"es_best={_format_epoch_metric(row.get('early_stopping_best_value'))}") + if row.get("early_stopping_wait") is not None and row.get("early_stopping_patience") is not None: + parts.append( + f"es_wait={int(row.get('early_stopping_wait'))}/{int(row.get('early_stopping_patience'))}" + ) + if row.get("early_stopping_active") is not None: + parts.append(f"es_active={bool(row.get('early_stopping_active'))}") + if row.get("strategy3_freeze_active") is not None: + parts.append(f"s3_frozen={bool(row.get('strategy3_freeze_active'))}") + if row.get("study_best_trial") is not None: + parts.append(f"study_best_trial={int(row.get('study_best_trial'))}") + return ", ".join(parts) + +def _optuna_direction_is_maximize(direction: Any) -> bool: + direction_name = str(getattr(direction, "name", direction)).lower() + return direction_name.endswith("maximize") + +def _optuna_value_is_better( + candidate: float | None, + current: float | None, + *, + direction: Any, +) -> bool: + if candidate is None: + return False + if current is None: + return True + return float(candidate) > float(current) if _optuna_direction_is_maximize(direction) else float(candidate) < float(current) + +def _optuna_trial_state_name(trial: Any) -> str: + state = getattr(trial, "state", None) + return str(getattr(state, "name", state)).upper() + +def _optuna_trial_user_attr_float(trial: Any, attr_name: str) -> float | None: + user_attrs = getattr(trial, "user_attrs", None) + if not isinstance(user_attrs, dict): + return None + value = user_attrs.get(attr_name) + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + +def _optuna_trial_best_intermediate_value( + trial: Any, + *, + direction: Any, +) -> float | None: + best_value: float | None = None + for value in getattr(trial, "intermediate_values", {}).values(): + if value is None: + continue + candidate_value = float(value) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_value = candidate_value + return best_value + +def _optuna_trial_best_observed_value( + trial: Any, + *, + direction: Any, + current_best_value: float | None = None, +) -> float | None: + if current_best_value is not None: + return float(current_best_value) + + best_observed = _optuna_trial_user_attr_float(trial, OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR) + if best_observed is not None: + return best_observed + + state_name = _optuna_trial_state_name(trial) + if state_name == "COMPLETE": + value = getattr(trial, "value", None) + return None if value is None else float(value) + + best_intermediate = _optuna_trial_best_intermediate_value(trial, direction=direction) + if best_intermediate is not None: + return best_intermediate + + value = getattr(trial, "value", None) + return None if value is None else float(value) + +def _current_optuna_study_best_candidate( + study: optuna.study.Study, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> tuple[Any | None, float | None]: + direction = getattr(study, "direction", STUDY_DIRECTION) + best_trial: Any | None = None + best_value: float | None = None + + for study_trial in getattr(study, "trials", []): + if _optuna_trial_state_name(study_trial) not in {"COMPLETE", "PRUNED"}: + continue + candidate_value = _optuna_trial_best_observed_value(study_trial, direction=direction) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_trial = study_trial + best_value = candidate_value + + if current_trial is not None: + live_trial_best = _optuna_trial_best_observed_value( + current_trial, + direction=direction, + current_best_value=current_best_value, + ) + if _optuna_value_is_better(live_trial_best, best_value, direction=direction): + best_trial = current_trial + best_value = live_trial_best + + return best_trial, best_value + +def _current_optuna_study_best_snapshot( + trial: optuna.trial.Trial | None, + *, + current_best_value: float | None = None, +) -> tuple[float | None, int | None]: + if trial is None: + return None, None + study = getattr(trial, "study", None) + if study is None: + return None, None + + best_trial, best_value = _current_optuna_study_best_candidate( + study, + current_trial=trial, + current_best_value=current_best_value, + ) + best_trial_number = None if best_trial is None else int(getattr(best_trial, "number", -1)) + return best_value, best_trial_number + +def set_global_seed(seed: int = 42) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def stable_int_from_text(text: str) -> int: + value = 0 + for byte in text.encode("utf-8"): + value = (value * 131 + byte) % (2 ** 31 - 1) + return value + +def seed_worker(worker_id: int) -> None: + del worker_id + worker_seed = torch.initial_seed() % (2 ** 32) + random.seed(worker_seed) + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + +def make_seeded_generator(seed: int, tag: str) -> torch.Generator: + generator = torch.Generator() + generator.manual_seed(seed + stable_int_from_text(tag)) + return generator + +def cuda_memory_snapshot() -> str: + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return "allocated=0.00 GB, reserved=0.00 GB, peak=0.00 GB" + allocated = torch.cuda.memory_allocated(device=DEVICE) / (1024 ** 3) + reserved = torch.cuda.memory_reserved(device=DEVICE) / (1024 ** 3) + peak = torch.cuda.max_memory_allocated(device=DEVICE) / (1024 ** 3) + return f"allocated={allocated:.2f} GB, reserved={reserved:.2f} GB, peak={peak:.2f} GB" + +def run_cuda_cleanup(context: str | None = None) -> None: + gc.collect() + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return + try: + torch.cuda.synchronize(device=DEVICE) + except Exception: + pass + try: + torch.cuda.empty_cache() + except Exception: + pass + try: + torch.cuda.ipc_collect() + except Exception: + pass + if context is not None: + print(f"[CUDA Cleanup] {context}: {cuda_memory_snapshot()}") + try: + torch.cuda.reset_peak_memory_stats(device=DEVICE) + except Exception: + pass + +def prune_directory_except(root: Path, keep_file_names: set[str]) -> None: + if not root.exists(): + return + keep_paths = {root / name for name in keep_file_names} + for path in sorted((p for p in root.rglob("*") if p.is_file()), reverse=True): + if path not in keep_paths: + path.unlink() + for path in sorted((p for p in root.rglob("*") if p.is_dir()), reverse=True): + if path != root: + try: + path.rmdir() + except OSError: + pass + +def prune_optuna_trial_dir(trial_dir: Path) -> None: + if trial_dir.exists(): + shutil.rmtree(trial_dir, ignore_errors=True) + +def prune_optuna_study_dir(study_root: Path) -> None: + prune_directory_except(study_root, {"best_params.json", "summary.json", "study.sqlite3"}) + +def to_device(batch: Any, device: torch.device) -> Any: + if torch.is_tensor(batch): + return batch.to(device, non_blocking=True) + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, list): + return [to_device(v, device) for v in batch] + if isinstance(batch, tuple): + return tuple(to_device(v, device) for v in batch) + return batch + +def _normalized_decimal_text(value: Decimal) -> str: + normalized = value.normalize() + text = format(normalized, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text or "0" + +def _fraction_decimal(value: Any, *, field_name: str) -> Decimal: + if isinstance(value, bool): + raise TypeError(f"{field_name} must be a real number in (0, 1], got boolean {value!r}.") + try: + decimal_value = Decimal(str(value).strip()) + except (InvalidOperation, ValueError) as exc: + raise ValueError(f"{field_name} must be a real number in (0, 1], got {value!r}.") from exc + if not decimal_value.is_finite(): + raise ValueError(f"{field_name} must be finite, got {value!r}.") + if decimal_value <= 0 or decimal_value > 1: + raise ValueError(f"{field_name} must be in the interval (0, 1], got {value!r}.") + return decimal_value + +def _percent_decimal(value: Any, *, field_name: str = "dataset percent") -> Decimal: + return _fraction_decimal(value, field_name=field_name) * Decimal("100") + +def normalize_dataset_percents(values: list[float] | tuple[float, ...]) -> list[float]: + if not values: + raise ValueError("DATASET_PERCENTS must contain at least one fraction in (0, 1].") + normalized: dict[str, float] = {} + for value in values: + fraction = _fraction_decimal(value, field_name="DATASET_PERCENTS entry") + normalized[_normalized_decimal_text(fraction)] = float(fraction) + return [normalized[key] for key in sorted(normalized, key=Decimal)] + +def percent_label(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)).replace(".", "p") + +def percent_display(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)) + +def percent_text(percent: float) -> str: + return f"{percent_display(percent)}%" + + +def run_identity_parts( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> list[str]: + parts: list[str] = [] + payload = split_payload or {} + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase={int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split={int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split={split_type}") + + if subset_repeat_index is not None: + parts.append(f"repeat={int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant={int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy={int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct={percent_text(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial={int(trial_number):03d}") + + return parts + + +def run_identity_label( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + parts = run_identity_parts( + strategy=strategy, + percent=percent, + trial_number=trial_number, + split_payload=split_payload, + ) + return " | ".join(parts) if parts else "run" + + +def run_identity_slug( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + payload = split_payload or {} + parts: list[str] = [] + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase_{int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split_{int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split_{str(split_type)}") + + if subset_repeat_index is not None: + parts.append(f"repeat_{int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant_{int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy_{int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct_{percent_label(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial_{int(trial_number):03d}") + + return "__".join(parts) if parts else "run" + + +def current_dataset_name() -> str: + dataset_name = str(DATASET_NAME).strip() + if dataset_name not in SUPPORTED_DATASET_NAMES: + raise ValueError(f"DATASET_NAME must be one of {SUPPORTED_DATASET_NAMES}, got {dataset_name!r}") + return dataset_name + +def current_busi_with_classes_split_policy() -> str: + split_policy = str(BUSI_WITH_CLASSES_SPLIT_POLICY).strip().lower() + if split_policy not in SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES: + raise ValueError( + f"BUSI_WITH_CLASSES_SPLIT_POLICY must be one of {SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES}, " + f"got {split_policy!r}" + ) + return split_policy + +def current_dataset_splits_json_path() -> Path: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATASET_SPLITS_JSON + return PROJECT_DIR / f"dataset_splits_{dataset_name.lower()}_{current_busi_with_classes_split_policy()}.json" + +def current_dataset_dirs() -> tuple[Path, Path]: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATA_ROOT / "images", DATA_ROOT / "annotations" + return DATA_ROOT / "all_images", DATA_ROOT / "all_masks" + +def current_pipeline_check_path() -> Path | None: + if current_dataset_name() != "BUSI_with_classes": + return None + return DATA_ROOT / "pipeline_check.json" + +def normalization_cache_tag() -> str: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return "BUSI" + return f"{dataset_name}_{current_busi_with_classes_split_policy()}" + +def resolve_amp_dtype(key: str) -> torch.dtype: + key = key.lower().strip() + if key == "auto": + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + return torch.float16 + if key in {"float16", "fp16", "half"}: + return torch.float16 + if key in {"bfloat16", "bf16"}: + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + print("[AMP] bfloat16 requested but unsupported here. Falling back to float16.") + return torch.float16 + raise ValueError(f"Unsupported AMP_DTYPE: {key}") + +def amp_autocast_enabled(device: torch.device) -> bool: + return USE_AMP and device.type in {"cuda", "mps"} + +def autocast_ctx(enabled: bool, device: torch.device, amp_dtype: torch.dtype): + if not enabled: + return nullcontext() + return torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=True) + +def make_grad_scaler(enabled: bool, amp_dtype: torch.dtype, device: torch.device): + if not enabled or device.type != "cuda" or amp_dtype == torch.bfloat16: + return None + try: + return torch.amp.GradScaler("cuda", enabled=True, init_scale=8192.0) + except Exception: + return torch.cuda.amp.GradScaler(enabled=True, init_scale=8192.0) + +def format_seconds(seconds: float) -> str: + seconds = int(seconds) + h, rem = divmod(seconds, 3600) + m, s = divmod(rem, 60) + return f"{h:02d}:{m:02d}:{s:02d}" + +def tensor_bytes(t: torch.Tensor) -> int: + return t.numel() * t.element_size() + +def bytes_to_gb(num_bytes: int) -> float: + return num_bytes / (1024 ** 3) + +def set_current_job_params(payload: dict[str, Any] | None = None) -> None: + CURRENT_JOB_PARAMS.clear() + if payload: + CURRENT_JOB_PARAMS.update(dict(payload)) + +def _job_param(name: str, default: Any) -> Any: + return CURRENT_JOB_PARAMS.get(name, default) + +def _alpha_log_floor() -> float: + return math.log(max(float(_job_param("min_alpha", math.exp(-5.0))), 1e-6)) + +def _keep_action_index(action_count: int) -> int: + action_count = max(int(action_count), 1) + if action_count >= 3: + return action_count // 2 + return action_count - 1 + +def _strategy3_variant() -> str: + raw = str(_job_param("strategy3_variant", DEFAULT_STRATEGY3_VARIANT)).strip().lower() + return raw or DEFAULT_STRATEGY3_VARIANT + +def _strategy3_annealed_weight( + base_weight: float, + *, + current_epoch: int, + anneal_start_epoch: int = 1, + anneal_epochs: int, +) -> float: + base_weight = float(base_weight) + if base_weight <= 0.0: + return 0.0 + anneal_start_epoch = max(int(anneal_start_epoch), 1) + anneal_epochs = max(int(anneal_epochs), 0) + if current_epoch < anneal_start_epoch: + return 0.0 + if anneal_epochs <= 0: + return base_weight + progress = min( + max((float(current_epoch) - float(anneal_start_epoch)) / float(anneal_epochs), 0.0), + 1.0, + ) + floor_fraction = float( + _job_param( + "strategy3_aux_ce_floor_fraction", + DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + ) + ) + floor_fraction = min(max(floor_fraction, 0.0), 1.0) + fraction = max(1.0 - progress, floor_fraction) + return base_weight * fraction + +def _strategy3_annealed_aux_ce_weight(current_epoch: int) -> float: + return _strategy3_annealed_weight( + float(_job_param("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT)), + current_epoch=int(current_epoch), + anneal_start_epoch=int( + _job_param( + "strategy3_aux_ce_anneal_start_epoch", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + ) + ), + anneal_epochs=int( + _job_param( + "strategy3_aux_ce_anneal_epochs", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + ) + ), + ) + +def _strategy3_exploration_eps(current_epoch: int) -> float: + base_eps = max(float(_job_param("strategy3_exploration_eps", DEFAULT_EXPLORATION_EPS)), 0.0) + decay_epochs = max(int(_job_param("strategy3_exploration_eps_epochs", EXPLORATION_EPS_EPOCHS)), 0) + if base_eps <= 0.0: + return 0.0 + if decay_epochs <= 0: + return base_eps + progress = min(max((float(current_epoch) - 1.0) / float(decay_epochs), 0.0), 1.0) + return base_eps * (1.0 - progress) + +def _bootstrap_value_target(model: nn.Module, value_next: torch.Tensor) -> torch.Tensor: + neighborhood_value = getattr(model, "neighborhood_value", None) + if callable(neighborhood_value): + return neighborhood_value(value_next) + return value_next + +def _strategy3_delta_max() -> float: + return float(_job_param("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX)) + +def _strategy3_policy_delta(policy_raw: torch.Tensor) -> torch.Tensor: + return torch.tanh(policy_raw.float()) * _strategy3_delta_max() + +def _strategy3_apply_delta(seg: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: + seg_f = seg.float() + delta_f = delta.float() + return (seg_f + delta_f).clamp(0.0, 1.0).to(dtype=seg.dtype) + +def _strategy3_advantage_normalize_enabled() -> bool: + return bool(_job_param("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE)) + +def _normalize_strategy3_advantage_map(advantage_map: torch.Tensor) -> torch.Tensor: + if not _strategy3_advantage_normalize_enabled(): + return advantage_map + if advantage_map.ndim < 4: + mean = advantage_map.mean() + std = advantage_map.std(unbiased=False) + return (advantage_map - mean) / (std + 1e-6) + mean = advantage_map.mean(dim=(2, 3), keepdim=True) + std = advantage_map.std(dim=(2, 3), unbiased=False, keepdim=True) + return (advantage_map - mean) / (std + 1e-6) + +def _strategy3_actor_advantage( + reward_map: torch.Tensor, + value_t: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + advantage_map = reward_map + float(gamma) * value_next.detach() - value_t.detach() + return _normalize_strategy3_advantage_map(advantage_map) + +def _strategy3_critic_target( + reward_map: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + return reward_map.detach() + float(gamma) * value_next.detach() + +def _strategy3_delta_distribution(delta_map: torch.Tensor) -> dict[str, float]: + delta_f = delta_map.detach().float() + abs_delta = delta_f.abs() + return { + "mean_delta": float(delta_f.mean().item()), + "mean_abs_delta": float(abs_delta.mean().item()), + "positive_pct": float((delta_f > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((delta_f < -1e-6).float().mean().item() * 100.0), + "near_zero_pct": float((abs_delta <= 1e-6).float().mean().item() * 100.0), + "max_abs_delta": float(abs_delta.max().item()), + } + +def _bernoulli_predictive_entropy(prob: torch.Tensor) -> torch.Tensor: + prob_f = prob.float().clamp(1e-6, 1.0 - 1e-6) + return -(prob_f * torch.log(prob_f) + (1.0 - prob_f) * torch.log1p(-prob_f)) + +def _iter_strategy3_dropout_modules(model: nn.Module) -> Iterator[nn.Module]: + for module in _unwrap_compiled(model).modules(): + if isinstance(module, (nn.Dropout, nn.Dropout2d)): + yield module + +@contextmanager +def _strategy3_mc_dropout_scope(model: nn.Module) -> Iterator[None]: + global _STRATEGY3_MC_DROPOUT_WARNED + + requested_p = float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)) + if requested_p <= 0.0 and not _STRATEGY3_MC_DROPOUT_WARNED: + print("[Strategy3] MC-dropout requested with non-positive dropout p; variance maps may collapse to zero.") + _STRATEGY3_MC_DROPOUT_WARNED = True + + saved_states: list[tuple[nn.Module, bool, float | None]] = [] + for module in _iter_strategy3_dropout_modules(model): + saved_states.append((module, bool(module.training), getattr(module, "p", None))) + module.train(True) + if hasattr(module, "p") and requested_p > 0.0: + module.p = requested_p + try: + yield + finally: + for module, was_training, saved_p in saved_states: + module.train(was_training) + if saved_p is not None and hasattr(module, "p"): + module.p = saved_p + +def _strategy3_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_fn: Any, +) -> tuple[torch.Tensor, torch.Tensor]: + if not bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)): + with torch.no_grad(): + zeros = torch.zeros_like(decoder_prob.float()) + pred_entropy = _bernoulli_predictive_entropy(decoder_prob) + return zeros, pred_entropy + + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + with torch.no_grad(): + mc_probs: list[torch.Tensor] = [] + with _strategy3_mc_dropout_scope(model): + for _ in range(samples): + logits = sample_fn() + mc_probs.append(torch.sigmoid(logits).float()) + stacked = torch.stack(mc_probs, dim=0) + mean_prob = stacked.mean(dim=0) + variance = stacked.var(dim=0, unbiased=False) + pred_entropy = _bernoulli_predictive_entropy(mean_prob) + return variance.to(dtype=decoder_prob.dtype), pred_entropy.to(dtype=decoder_prob.dtype) + +def _strategy3_mc_config_hash() -> tuple[bool, int, float]: + enabled = bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)) + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + dropout_p = round(float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)), 8) + return enabled, samples, dropout_p + +def _strategy3_mc_disk_cache_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED)) + +def _strategy3_mc_disk_cache_read_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ)) + +def _strategy3_mc_disk_cache_write_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE)) + +def _strategy3_normalize_sample_ids( + sample_ids: list[str] | tuple[str, ...] | None, + *, + batch_size: int, +) -> list[str] | None: + if sample_ids is None: + return None + normalized = [str(item) for item in sample_ids] + if len(normalized) != int(batch_size): + raise ValueError( + f"Strategy 3 eval MC cache expected {batch_size} sample_ids, got {len(normalized)}." + ) + return normalized + +def _strategy3_ensure_mc_cache_state(model: nn.Module) -> nn.Module: + raw_model = getattr(model, "_orig_mod", model) + if not hasattr(raw_model, "_strategy3_mc_cache"): + raw_model._strategy3_mc_cache = {} + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint"): + raw_model._strategy3_mc_cache_fingerprint = "" + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint_sources"): + raw_model._strategy3_mc_cache_fingerprint_sources = {} + if not hasattr(raw_model, "_strategy3_strategy2_checkpoint_path"): + raw_model._strategy3_strategy2_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_eval_checkpoint_path"): + raw_model._strategy3_eval_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_mc_cache_stats"): + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return raw_model + +def _strategy3_reset_mc_cache_stats(model: nn.Module) -> None: + raw_model = _strategy3_ensure_mc_cache_state(model) + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + +def _strategy3_get_mc_cache_stats(model: nn.Module) -> dict[str, int]: + raw_model = _strategy3_ensure_mc_cache_state(model) + stats = raw_model._strategy3_mc_cache_stats + return { + "ram_hits": int(stats.get("ram_hits", 0)), + "disk_hits": int(stats.get("disk_hits", 0)), + "misses": int(stats.get("misses", 0)), + "writes": int(stats.get("writes", 0)), + } + +def _strategy3_checkpoint_sha256(path: str | Path | None) -> str | None: + if not path: + return None + resolved = str(Path(path).expanduser().resolve()) + cached = _STRATEGY3_MC_FILE_SHA256_CACHE.get(resolved) + if cached is not None: + return cached + checkpoint_path = Path(resolved) + if not checkpoint_path.is_file(): + return None + digest = hashlib.sha256() + with checkpoint_path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + checksum = digest.hexdigest() + _STRATEGY3_MC_FILE_SHA256_CACHE[resolved] = checksum + return checksum + +def _strategy3_decoder_state_hash(model: nn.Module) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + modules: list[tuple[str, nn.Module]] = [] + if isinstance(raw_model, PixelDRLMG_WithDecoder): + modules = [ + ("smp_model.encoder", raw_model.smp_model.encoder), + ("smp_model.decoder", raw_model.smp_model.decoder), + ("smp_model.segmentation_head", raw_model.smp_model.segmentation_head), + ] + elif isinstance(raw_model, PixelDRLMG_VGGWithDecoder): + modules = [ + ("encoder", raw_model.encoder), + ("segmentation_head", raw_model.segmentation_head), + ] + else: + return stable_hash(raw_model.__class__.__name__) + + digest = hashlib.sha256() + for prefix, module in modules: + for name, tensor in sorted(module.state_dict().items()): + tensor_cpu = tensor.detach().cpu().contiguous() + digest.update(prefix.encode("utf-8")) + digest.update(b"\0") + digest.update(name.encode("utf-8")) + digest.update(b"\0") + digest.update(str(tensor_cpu.dtype).encode("utf-8")) + digest.update(b"\0") + digest.update(json.dumps(list(tensor_cpu.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(tensor_cpu.numpy().tobytes()) + return digest.hexdigest() + +def _strategy3_resolve_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> tuple[str, dict[str, Any]]: + raw_model = _strategy3_ensure_mc_cache_state(model) + strategy2_path = ( + str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if strategy2_checkpoint_path + else getattr(raw_model, "_strategy3_strategy2_checkpoint_path", None) + ) + eval_path = ( + str(Path(eval_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path + else getattr(raw_model, "_strategy3_eval_checkpoint_path", None) + ) + decoder_hash = _strategy3_decoder_state_hash(raw_model) + sources: dict[str, Any] = {"decoder_state_hash": decoder_hash} + if strategy2_path: + sources["strategy2_checkpoint"] = strategy2_path + strategy2_sha = _strategy3_checkpoint_sha256(strategy2_path) + if strategy2_sha is not None: + sources["strategy2_sha256"] = strategy2_sha + if eval_path: + sources["eval_checkpoint"] = eval_path + eval_sha = _strategy3_checkpoint_sha256(eval_path) + if eval_sha is not None: + sources["eval_checkpoint_sha256"] = eval_sha + return decoder_hash, sources + +def _strategy3_bump_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + if strategy2_checkpoint_path is not None: + raw_model._strategy3_strategy2_checkpoint_path = str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path is not None: + raw_model._strategy3_eval_checkpoint_path = str(Path(eval_checkpoint_path).expanduser().resolve()) + fingerprint, sources = _strategy3_resolve_mc_cache_fingerprint( + raw_model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + eval_checkpoint_path=eval_checkpoint_path, + ) + fingerprint_changed = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) != str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint = str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint_sources = dict(sources) + if fingerprint_changed: + clear_cache = getattr(raw_model, "clear_strategy3_mc_cache", None) + if callable(clear_cache): + clear_cache() + else: + raw_model._strategy3_mc_cache.clear() + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return str(fingerprint) + +def _strategy3_mc_disk_cache_root(run_dir: Path | None) -> Path | None: + if run_dir is None or not _strategy3_mc_disk_cache_enabled(): + return None + return Path(run_dir) / "mc_cache" + +def _strategy3_mc_disk_entry_path( + root: Path, + fingerprint: str, + split: str, + sample_id: str, +) -> Path: + safe_sample_id = str(sample_id).replace(os.sep, "__").replace("/", "__") + return Path(root) / str(fingerprint)[:16] / str(split) / f"{safe_sample_id}.pt" + +def _strategy3_load_mc_maps_from_disk( + path: Path, + *, + sample_id: str, + fingerprint: str, + mc_config_hash: tuple[bool, int, float], + split: str, +) -> tuple[torch.Tensor, torch.Tensor] | None: + if not path.is_file(): + return None + try: + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + payload = torch.load(path, map_location="cpu", weights_only=False) + except Exception: + return None + + if not isinstance(payload, dict): + return None + if int(payload.get("schema_version", -1)) != int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION): + return None + if str(payload.get("sample_id", "")) != str(sample_id): + return None + if str(payload.get("split", "")) != str(split): + return None + if str(payload.get("fingerprint", "")) != str(fingerprint): + return None + if tuple(payload.get("mc_config_hash", ())) != tuple(mc_config_hash): + return None + if int(payload.get("img_size", -1)) != int(IMG_SIZE): + return None + + variance = payload.get("mc_variance") + pred_entropy = payload.get("pred_entropy") + if not (torch.is_tensor(variance) and torch.is_tensor(pred_entropy)): + return None + if variance.ndim != 4 or pred_entropy.ndim != 4: + return None + return ( + variance.detach().to(device="cpu", dtype=torch.float32).contiguous(), + pred_entropy.detach().to(device="cpu", dtype=torch.float32).contiguous(), + ) + +def _strategy3_save_mc_maps_to_disk(path: Path, payload: dict[str, Any]) -> None: + atomic_torch_save(path, payload) + +def _strategy3_write_mc_cache_manifest( + run_dir: Path | None, + *, + fingerprint: str, + fingerprint_sources: dict[str, Any], + mc_config_hash: tuple[bool, int, float], + split: str, + split_write_count: int, +) -> None: + if run_dir is None: + return + manifest_path = Path(run_dir) / "mc_cache" / "manifest.json" + existing: dict[str, Any] = {} + if manifest_path.exists(): + try: + loaded = load_json(manifest_path) + except Exception: + loaded = {} + if isinstance(loaded, dict): + existing = loaded + existing_fingerprint = str(existing.get("fingerprint", "")) + existing_config = tuple(existing.get("mc_config_hash", ())) + if existing_fingerprint != str(fingerprint) or existing_config != tuple(mc_config_hash): + existing = {} + splits = dict(existing.get("splits", {})) if isinstance(existing.get("splits", {}), dict) else {} + splits[str(split)] = int(splits.get(str(split), 0)) + int(max(split_write_count, 0)) + payload = { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "fingerprint": str(fingerprint), + "fingerprint_sources": dict(fingerprint_sources), + "mc_config_hash": list(mc_config_hash), + "img_size": int(IMG_SIZE), + "dataset_name": current_dataset_name(), + "splits": splits, + } + atomic_save_json(manifest_path, payload) + +def _strategy3_mc_disk_mode( + *, + split: str | None, + run_dir: Path | None, +) -> tuple[bool, bool, Path | None, str | None]: + normalized_split = str(split).strip().lower() if split is not None else None + if normalized_split not in (None, "train", "val", "test"): + raise ValueError(f"Unsupported Strategy 3 MC cache split {split!r}.") + if normalized_split == "train": + return False, False, None, normalized_split + root = _strategy3_mc_disk_cache_root(run_dir) + can_use_disk = normalized_split in {"val", "test"} and root is not None + return ( + bool(can_use_disk and _strategy3_mc_disk_cache_read_enabled()), + bool(can_use_disk and _strategy3_mc_disk_cache_write_enabled()), + root, + normalized_split, + ) + +def _strategy3_prepare_cached_sample_pair( + sample_variance: torch.Tensor, + sample_entropy: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + return ( + sample_variance.detach().to(device="cpu", dtype=torch.float32).clone(), + sample_entropy.detach().to(device="cpu", dtype=torch.float32).clone(), + ) + +def _strategy3_cached_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_ids: list[str] | tuple[str, ...] | None, + compute_sample_maps: Any, + mc_cache_split: str | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + normalized_sample_ids = _strategy3_normalize_sample_ids(sample_ids, batch_size=int(decoder_prob.shape[0])) + if normalized_sample_ids is None: + raise ValueError("Strategy 3 eval MC cache requires non-empty sample_ids.") + + raw_model = _strategy3_ensure_mc_cache_state(model) + fingerprint = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) or _strategy3_bump_mc_cache_fingerprint(raw_model) + cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = raw_model._strategy3_mc_cache + stats = raw_model._strategy3_mc_cache_stats + mc_hash = _strategy3_mc_config_hash() + disk_read_enabled, disk_write_enabled, disk_root, normalized_split = _strategy3_mc_disk_mode( + split=mc_cache_split, + run_dir=mc_cache_run_dir, + ) + manifest_write_count = 0 + variance_samples: list[torch.Tensor] = [] + entropy_samples: list[torch.Tensor] = [] + + for sample_index, sample_id in enumerate(normalized_sample_ids): + cache_key = (fingerprint, sample_id, mc_hash) + cached_pair = cache.get(cache_key) + if cached_pair is not None: + stats["ram_hits"] = int(stats.get("ram_hits", 0)) + 1 + else: + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_read_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + cached_pair = _strategy3_load_mc_maps_from_disk( + disk_path, + sample_id=sample_id, + fingerprint=fingerprint, + mc_config_hash=mc_hash, + split=normalized_split, + ) + if cached_pair is not None: + cache[cache_key] = _strategy3_prepare_cached_sample_pair(*cached_pair) + stats["disk_hits"] = int(stats.get("disk_hits", 0)) + 1 + else: + sample_variance, sample_entropy = compute_sample_maps(sample_index) + cached_pair = _strategy3_prepare_cached_sample_pair(sample_variance, sample_entropy) + cache[cache_key] = cached_pair + stats["misses"] = int(stats.get("misses", 0)) + 1 + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_write_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + entry_exists = disk_path.exists() + _strategy3_save_mc_maps_to_disk( + disk_path, + { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "sample_id": str(sample_id), + "split": str(normalized_split), + "fingerprint": str(fingerprint), + "mc_config_hash": tuple(mc_hash), + "img_size": int(IMG_SIZE), + "dtype": "float32", + "created_at": datetime.now(timezone.utc).isoformat(), + "mc_variance": cached_pair[0], + "pred_entropy": cached_pair[1], + }, + ) + stats["writes"] = int(stats.get("writes", 0)) + 1 + if not entry_exists: + manifest_write_count += 1 + + cached_variance, cached_entropy = cached_pair + variance_samples.append(cached_variance.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + entropy_samples.append(cached_entropy.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + + if manifest_write_count > 0 and normalized_split is not None: + _strategy3_write_mc_cache_manifest( + mc_cache_run_dir, + fingerprint=fingerprint, + fingerprint_sources=dict(getattr(raw_model, "_strategy3_mc_cache_fingerprint_sources", {})), + mc_config_hash=mc_hash, + split=normalized_split, + split_write_count=manifest_write_count, + ) + + return torch.cat(variance_samples, dim=0), torch.cat(entropy_samples, dim=0) + +def _require_supported_strategy(strategy: int) -> int: + strategy = int(strategy) + if strategy not in SUPPORTED_STRATEGIES: + raise ValueError( + f"Unsupported strategy {strategy}. Supported strategies are {list(SUPPORTED_STRATEGIES)}." + ) + return strategy + +def _resolve_checkpoint_metric_name(metric_name: Any, *, strategy: int) -> str: + if not isinstance(metric_name, str) or not metric_name.strip(): + raise KeyError( + f"No best-checkpoint metric configured for strategy {strategy}. " + f"Set BEST_CHECKPOINT_METRICS[{strategy}] or best_checkpoint_metric_name to a non-empty metric name." + ) + metric_name = metric_name.strip() + if metric_name not in SUPPORTED_CHECKPOINT_METRICS: + raise KeyError( + f"Unsupported best-checkpoint metric {metric_name!r} for strategy {strategy}. " + f"Supported metrics: {sorted(SUPPORTED_CHECKPOINT_METRICS)}." + ) + return metric_name + +def _strategy_selection_metric_name(strategy: int) -> str: + strategy = _require_supported_strategy(strategy) + metric_name = _job_param( + f"strategy{strategy}_best_checkpoint_metric_name", + _job_param("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS.get(strategy)), + ) + return _resolve_checkpoint_metric_name(metric_name, strategy=strategy) + +def _strategy_selection_metric_value(strategy: int, metrics: dict[str, Any]) -> float: + metric_name = _strategy_selection_metric_name(strategy) + value = metrics.get(metric_name) + if value is None: + raise KeyError( + f"Configured best-checkpoint metric {metric_name!r} for strategy {strategy} " + f"is missing from metrics payload keys={sorted(metrics.keys())}." + ) + return float(value) + +def _early_stopping_monitor_name(strategy: int) -> str: + raw = str(_job_param("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR)).strip() + if not raw or raw.lower() == "auto": + return _strategy_selection_metric_name(strategy) + return raw + +def _early_stopping_mode(strategy: int, monitor_name: str | None = None) -> str: + raw = str(_job_param("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE)).strip().lower() + if raw in {"min", "max"}: + return raw + if raw != "auto": + raise ValueError(f"Unsupported early_stopping_mode={raw!r}. Expected 'auto', 'min', or 'max'.") + monitor_name = monitor_name or _early_stopping_monitor_name(strategy) + lowered = monitor_name.lower() + if "loss" in lowered or lowered.startswith("hd") or lowered.endswith("error"): + return "min" + return "max" + +def _early_stopping_min_delta() -> float: + return max(float(_job_param("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA)), 0.0) + +def _early_stopping_start_epoch() -> int: + return max(int(_job_param("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH)), 1) + +def _early_stopping_patience() -> int: + return max(int(_job_param("early_stopping_patience", EARLY_STOPPING_PATIENCE)), 0) + +def _early_stopping_monitor_value( + metrics: dict[str, Any], + *, + strategy: int, + monitor_name: str, +) -> float | None: + value = metrics.get(monitor_name) + if value is None and monitor_name == _strategy_selection_metric_name(strategy): + value = _strategy_selection_metric_value(strategy, metrics) + if value is None: + return None + return float(value) + +def _early_stopping_improved( + current_value: float, + best_value: float | None, + *, + mode: str, + min_delta: float, +) -> bool: + if best_value is None: + return True + if mode == "min": + return current_value < (best_value - min_delta) + if mode == "max": + return current_value > (best_value + min_delta) + raise ValueError(f"Unsupported early stopping comparison mode: {mode!r}") + +def _strategy3_requested_bootstrap_freeze() -> bool: + return bool( + _job_param( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + ) + +def _module_freeze_state(module: nn.Module | None) -> str: + if not isinstance(module, nn.Module): + return "n/a" + requires_grad_flags = [bool(param.requires_grad) for param in module.parameters()] + if not requires_grad_flags: + return "n/a" + if all(not flag for flag in requires_grad_flags): + return "frozen" + if all(requires_grad_flags): + return "trainable" + return "mixed" + +def _strategy3_bootstrap_freeze_status(model: nn.Module) -> dict[str, Any]: + raw = _raw_decoder_rl_model(model) + status = { + "bootstrap_loaded": False, + "freeze_requested": False, + "freeze_active": False, + "encoder_state": "n/a", + "decoder_state": "n/a", + "segmentation_head_state": "n/a", + } + if raw is None: + return status + + status["bootstrap_loaded"] = bool(getattr(raw, "strategy2_bootstrap_loaded", False)) + status["freeze_requested"] = bool(getattr(raw, "freeze_bootstrapped_segmentation", False)) + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + status["encoder_state"] = _module_freeze_state(getattr(smp_model, "encoder", None)) + status["decoder_state"] = _module_freeze_state(getattr(smp_model, "decoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(smp_model, "segmentation_head", None)) + else: + status["encoder_state"] = _module_freeze_state(getattr(raw, "encoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(raw, "segmentation_head", None)) + + relevant_states = [ + state + for state in ( + status["encoder_state"], + status["decoder_state"], + status["segmentation_head_state"], + ) + if state != "n/a" + ] + status["freeze_active"] = bool( + status["bootstrap_loaded"] and relevant_states and all(state == "frozen" for state in relevant_states) + ) + return status + +def _strategy3_decoder_is_frozen(model: nn.Module) -> bool: + return bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]) + +def _strategy3_loss_weights( + model: nn.Module, + *, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + decoder_ce_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(ce_weight) + decoder_dice_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(dice_weight) + return { + "decoder_ce": float(_job_param("strategy3_decoder_ce_weight", decoder_ce_default)), + "decoder_dice": float(_job_param("strategy3_decoder_dice_weight", decoder_dice_default)), + } + +def _strategy3_keep_frozen_modules_in_eval(model: nn.Module) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]): + return + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + module_names = ("encoder", "decoder", "segmentation_head") + module_root = smp_model + else: + module_names = ("encoder", "segmentation_head") + module_root = raw + for module_name in module_names: + module = getattr(module_root, module_name, None) + if isinstance(module, nn.Module): + module.eval() + +def _strategy3_apply_rollout_step( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + return apply_actions( + seg, + actions, + num_actions=int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)), + ).to(dtype=seg.dtype) + +def _refinement_deltas( + *, + action_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + small = float(_job_param("refine_delta_small", DEFAULT_REFINE_DELTA_SMALL)) + large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + if action_count == 3: + values = (-large, 0.0, small) + elif action_count == 4: + values = (-large, -small, 0.0, small) + elif action_count == 5: + values = (-large, -small, 0.0, small, large) + else: + raise ValueError( + f"Unsupported Strategy 3 action count {action_count}. " + "Expected one of {3, 4, 5}." + ) + return torch.tensor(values, device=device, dtype=dtype) + +def threshold_binary_mask(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).to(dtype=mask.dtype) + +def threshold_binary_long(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).long() + +"""============================================================================= +BUSI SPLIT + NORMALIZATION +============================================================================= +""" + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) +IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) + +def validate_image_mask_consistency(images_dir: Path, annotations_dir: Path): + image_files = {f for f in os.listdir(images_dir) if not f.startswith(".") and f.lower().endswith(".png")} + mask_files = {f for f in os.listdir(annotations_dir) if not f.startswith(".") and f.lower().endswith(".png")} + matched = sorted(image_files & mask_files) + missing_masks = sorted(image_files - mask_files) + missing_images = sorted(mask_files - image_files) + return matched, missing_masks, missing_images + +def parse_busi_with_classes_label(filename: str) -> str: + upper_name = str(filename).upper() + if upper_name.endswith("_B.PNG"): + return "benign" + if upper_name.endswith("_M.PNG"): + return "malignant" + raise ValueError( + f"BUSI_with_classes filename must end with '_B.png' or '_M.png', got {filename!r}" + ) + +def _candidate_report_dicts(payload: dict[str, Any]) -> list[dict[str, Any]]: + candidates = [payload] + for key in ("counts", "summary", "dataset", "report", "metadata"): + value = payload.get(key) + if isinstance(value, dict): + candidates.append(value) + return candidates + +def _extract_report_int(payload: dict[str, Any], keys: tuple[str, ...]) -> int | None: + for candidate in _candidate_report_dicts(payload): + for key in keys: + value = candidate.get(key) + if isinstance(value, bool): + continue + if isinstance(value, (int, np.integer)): + return int(value) + if isinstance(value, float) and float(value).is_integer(): + return int(value) + return None + +def _extract_report_filenames(payload: dict[str, Any]) -> set[str] | None: + for candidate in _candidate_report_dicts(payload): + filenames = candidate.get("filenames") + if isinstance(filenames, list) and all(isinstance(item, str) for item in filenames): + return set(filenames) + + pairs = candidate.get("pairs") + if isinstance(pairs, list): + extracted = {item["filename"] for item in pairs if isinstance(item, dict) and isinstance(item.get("filename"), str)} + if extracted: + return extracted + return None + +def validate_busi_with_classes_pipeline_report(report_path: Path, sample_records: list[dict[str, str]]) -> None: + if not report_path.exists(): + return + + payload = load_json(report_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict payload in {report_path}, found {type(payload).__name__}.") + + benign_count = sum(1 for record in sample_records if record.get("class_label") == "benign") + malignant_count = sum(1 for record in sample_records if record.get("class_label") == "malignant") + expected_counts = { + "total_pairs": len(sample_records), + "benign": benign_count, + "malignant": malignant_count, + } + report_counts = { + "total_pairs": _extract_report_int(payload, ("total_pairs", "pair_count", "num_pairs", "total")), + "benign": _extract_report_int(payload, ("benign", "benign_count", "num_benign")), + "malignant": _extract_report_int(payload, ("malignant", "malignant_count", "num_malignant")), + } + for key, expected_value in expected_counts.items(): + report_value = report_counts[key] + if report_value is not None and report_value != expected_value: + raise RuntimeError( + f"pipeline_check mismatch for {key}: discovered={expected_value}, report={report_value} ({report_path})" + ) + + report_filenames = _extract_report_filenames(payload) + if report_filenames is not None: + discovered_filenames = {record["filename"] for record in sample_records} + if report_filenames != discovered_filenames: + missing_from_report = sorted(discovered_filenames - report_filenames)[:10] + extra_in_report = sorted(report_filenames - discovered_filenames)[:10] + raise RuntimeError( + f"pipeline_check filenames mismatch for {report_path}: " + f"missing_from_report={missing_from_report}, extra_in_report={extra_in_report}" + ) + + print(f"[Pipeline Check] Validated BUSI_with_classes metadata from {report_path}") + +def check_data_leakage(splits: dict[str, list[str]]) -> dict[str, list[str]]: + leaks: dict[str, list[str]] = {} + split_names = list(splits.keys()) + for i, lhs in enumerate(split_names): + for rhs in split_names[i + 1 :]: + overlap = sorted(set(splits[lhs]) & set(splits[rhs])) + if overlap: + leaks[f"{lhs} ∩ {rhs}"] = overlap + return leaks + +def _project_relative_path(path: Path) -> str: + resolved = Path(path).resolve() + try: + return str(resolved.relative_to(PROJECT_DIR.resolve())) + except ValueError: + return str(resolved) + +def resolve_dataset_root_from_registry(split_registry: dict[str, Any]) -> Path: + dataset_root = Path(split_registry["dataset_root"]) + if dataset_root.is_absolute(): + return dataset_root + return (PROJECT_DIR / dataset_root).resolve() + +def make_sample_record( + filename: str, + images_subdir: str, + annotations_subdir: str, + *, + class_label: str | None = None, +) -> dict[str, str]: + record = { + "filename": filename, + "image_rel_path": str(Path(images_subdir) / filename), + "mask_rel_path": str(Path(annotations_subdir) / filename), + } + if class_label is not None: + record["class_label"] = class_label + return record + +def build_sample_records( + filenames: list[str], + *, + images_subdir: str, + annotations_subdir: str, + dataset_name: str, +) -> list[dict[str, str]]: + records = [] + for filename in sorted(filenames): + class_label = parse_busi_with_classes_label(filename) if dataset_name == "BUSI_with_classes" else None + records.append( + make_sample_record( + filename, + images_subdir, + annotations_subdir, + class_label=class_label, + ) + ) + return records + +def split_ratios_for_type(split_type: str) -> tuple[float, float]: + if split_type == "80_10_10": + return 0.80, 0.10 + if split_type == "70_10_20": + return 0.70, 0.10 + raise ValueError(f"Unsupported split_type: {split_type}") + +def deterministic_shuffle_records(records: list[dict[str, str]], *, seed: int, tag: str) -> list[dict[str, str]]: + rng = random.Random(seed + stable_int_from_text(tag)) + shuffled = [dict(record) for record in records] + rng.shuffle(shuffled) + return shuffled + +def train_subset_variant_suffix(variant: int | None = None) -> str: + variant_value = int(TRAIN_SUBSET_VARIANT if variant is None else variant) + return "" if variant_value <= 0 else f"_variant{variant_value:02d}" + +def group_records_by_class(sample_records: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + grouped: dict[str, list[dict[str, str]]] = {} + for record in sample_records: + class_label = record.get("class_label") + if class_label is None: + raise RuntimeError("Expected class_label in sample record for class-aware splitting.") + grouped.setdefault(class_label, []).append(dict(record)) + return grouped + +def allocate_counts_by_ratio(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + total_available = sum(available_counts.values()) + if total_available <= 0: + return allocation + + exact = {label: total_size * available_counts[label] / total_available for label in available_counts} + for label in available_counts: + allocation[label] = min(available_counts[label], int(math.floor(exact[label]))) + + remaining = min(total_size, total_available) - sum(allocation.values()) + order = sorted( + available_counts.keys(), + key=lambda label: (exact[label] - math.floor(exact[label]), available_counts[label], label), + reverse=True, + ) + while remaining > 0: + progressed = False + for label in order: + if allocation[label] < available_counts[label]: + allocation[label] += 1 + remaining -= 1 + progressed = True + if remaining == 0: + break + if not progressed: + break + return allocation + +def allocate_balanced_counts(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + labels = sorted(available_counts.keys()) + half = total_size // 2 + for label in labels: + allocation[label] = min(available_counts[label], half) + + remaining = min(total_size, sum(available_counts.values())) - sum(allocation.values()) + while remaining > 0: + candidates = [label for label in labels if allocation[label] < available_counts[label]] + if not candidates: + break + best_label = max( + candidates, + key=lambda label: ( + available_counts[label] - allocation[label], + 1 if label == "benign" else 0, + label, + ), + ) + allocation[best_label] += 1 + remaining -= 1 + return allocation + +def build_unstratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + records = deterministic_shuffle_records(sample_records, seed=seed, tag=f"base::{split_type}") + + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + return { + "train": records[:train_end], + "val": records[train_end:val_end], + "test": records[val_end:], + } + +def build_stratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + grouped = group_records_by_class(sample_records) + splits = {"train": [], "val": [], "test": []} + + for class_label in sorted(grouped.keys()): + records = deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"base::{split_type}::{class_label}", + ) + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + splits["train"].extend(records[:train_end]) + splits["val"].extend(records[train_end:val_end]) + splits["test"].extend(records[val_end:]) + + for split_name in splits: + splits[split_name] = deterministic_shuffle_records( + splits[split_name], + seed=seed, + tag=f"base::{split_type}::{split_name}", + ) + return splits + +def build_balanced_train_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + test_ratio = 1.0 - train_ratio - val_ratio + grouped = group_records_by_class(sample_records) + if sorted(grouped.keys()) != ["benign", "malignant"]: + raise RuntimeError( + f"balanced_train split policy expects benign/malignant classes, found {sorted(grouped.keys())}" + ) + + shuffled = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"base::{split_type}::balanced_train::{class_label}", + ) + for class_label, records in grouped.items() + } + + nominal_train_size = int(len(sample_records) * train_ratio) + per_class_train = min( + nominal_train_size // 2, + *(len(records) for records in shuffled.values()), + ) + + train_records: list[dict[str, str]] = [] + remaining_by_class: dict[str, list[dict[str, str]]] = {} + for class_label in sorted(shuffled.keys()): + records = shuffled[class_label] + train_records.extend(records[:per_class_train]) + remaining_by_class[class_label] = records[per_class_train:] + + remainder_val_fraction = val_ratio / max(val_ratio + test_ratio, 1e-8) + val_records: list[dict[str, str]] = [] + test_records: list[dict[str, str]] = [] + for class_label in sorted(remaining_by_class.keys()): + records = remaining_by_class[class_label] + val_count = int(len(records) * remainder_val_fraction) + val_records.extend(records[:val_count]) + test_records.extend(records[val_count:]) + + return { + "train": deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::train", + ), + "val": deterministic_shuffle_records( + val_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::val", + ), + "test": deterministic_shuffle_records( + test_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::test", + ), + } + +def build_nested_train_subsets( + train_records: list[dict[str, str]], + train_fractions: list[float], + *, + split_type: str, + seed: int, + split_policy: str | None = None, + subset_variant: int = 0, +) -> dict[str, list[dict[str, str]]]: + if not train_records: + return {} + + variant_tag = "" if int(subset_variant) <= 0 else f"::variant::{int(subset_variant)}" + ordered_records = deterministic_shuffle_records(train_records, seed=seed, tag=f"subset::{split_type}{variant_tag}") + use_class_labels = any("class_label" in record for record in train_records) + if not use_class_labels: + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + subsets[subset_key] = [dict(record) for record in ordered_records[:subset_size]] + return subsets + + grouped = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{class_label}{variant_tag}", + ) + for class_label, records in group_records_by_class(train_records).items() + } + available_counts = {class_label: len(records) for class_label, records in grouped.items()} + + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + if split_policy == "balanced_train": + class_counts = allocate_balanced_counts(subset_size, available_counts) + else: + class_counts = allocate_counts_by_ratio(subset_size, available_counts) + + subset_records: list[dict[str, str]] = [] + for class_label in sorted(grouped.keys()): + subset_records.extend([dict(record) for record in grouped[class_label][: class_counts[class_label]]]) + subsets[subset_key] = deterministic_shuffle_records( + subset_records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{subset_key}{variant_tag}", + ) + return subsets + +def train_fraction_from_subset_key(subset_key: str) -> float: + subset_text = str(subset_key).strip().lower() + if not subset_text: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") + try: + percent = Decimal(subset_text.replace("p", ".")) + except InvalidOperation as exc: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") from exc + if not percent.is_finite() or percent <= 0 or percent > 100: + raise RuntimeError(f"Train subset key {subset_key!r} must represent a percentage in the range (0, 100].") + return float(percent / Decimal("100")) + +def validate_persisted_split_no_leakage(split_type: str, split_entry: dict[str, Any], *, source: str) -> None: + base_splits = split_entry["base_splits"] + base_filenames: dict[str, list[str]] = {} + for split_name, records in base_splits.items(): + filenames = [record["filename"] for record in records] + if len(filenames) != len(set(filenames)): + raise RuntimeError(f"Duplicate filenames detected inside {split_name} for split_type={split_type}.") + base_filenames[split_name] = filenames + + leaks = check_data_leakage(base_filenames) + if leaks: + raise RuntimeError(f"Data leakage detected for split_type={split_type}: {list(leaks.keys())}") + + base_train = set(base_filenames["train"]) + previous_subset: set[str] = set() + for subset_key in sorted(split_entry["train_subsets"].keys(), key=train_fraction_from_subset_key): + subset_filenames = [record["filename"] for record in split_entry["train_subsets"][subset_key]] + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames detected inside train subset {subset_key} for split_type={split_type}." + ) + subset_set = set(subset_filenames) + missing = sorted(subset_set - base_train) + if missing: + raise RuntimeError( + f"Train subset {subset_key} contains files outside the base train split for split_type={split_type}." + ) + if previous_subset and not previous_subset.issubset(subset_set): + raise RuntimeError( + f"Train subsets are not nested for split_type={split_type}." + ) + previous_subset = subset_set + + print(f"[Split Check] No data leakage detected for split_type={split_type} ({source}).") + +def repair_persisted_train_subsets( + split_registry: dict[str, Any], + requested_train_fractions: list[float], + *, + split_json_path: Path, + seed: int, +) -> bool: + split_entries = split_registry.get("split_types", {}) + requested_fractions = normalize_dataset_percents(requested_train_fractions) + combined_fractions = {float(value) for value in split_registry.get("train_fractions", [])} + combined_fractions.update(requested_fractions) + dataset_name = str(split_registry.get("dataset_name", "BUSI")) + split_policy = split_registry.get("split_policy") if dataset_name == "BUSI_with_classes" else None + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + for subset_key in train_subsets.keys(): + combined_fractions.add(train_fraction_from_subset_key(subset_key)) + + combined_fractions_list = normalize_dataset_percents(list(combined_fractions)) + requested_keys = {percent_label(fraction) for fraction in requested_fractions} + registry_seed = int(split_registry.get("seed", seed)) + repaired = False + + if split_registry.get("train_fractions") != combined_fractions_list: + split_registry["train_fractions"] = combined_fractions_list + repaired = True + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + missing_requested_keys = sorted(requested_keys - set(train_subsets.keys()), key=train_fraction_from_subset_key) + if not missing_requested_keys: + continue + + split_entry["train_subsets"] = build_nested_train_subsets( + split_entry["base_splits"]["train"], + combined_fractions_list, + split_type=split_type, + seed=registry_seed, + split_policy=split_policy, + ) + print( + f"[Splits] Rebuilt missing train subsets {missing_requested_keys} " + f"for split_type={split_type} in {split_json_path}" + ) + repaired = True + + if repaired: + save_json(split_json_path, split_registry) + print(f"[Splits] Updated persisted dataset splits at {split_json_path}") + return repaired + +def load_or_create_dataset_splits( + images_dir: Path, + annotations_dir: Path, + split_json_path: Path, + train_fractions: list[float], + seed: int, +) -> tuple[dict[str, Any], str]: + train_fractions = normalize_dataset_percents(train_fractions) + images_dir = Path(images_dir).resolve() + annotations_dir = Path(annotations_dir).resolve() + split_json_path = Path(split_json_path).resolve() + dataset_name = current_dataset_name() + split_policy = current_busi_with_classes_split_policy() if dataset_name == "BUSI_with_classes" else None + if split_json_path.exists(): + split_registry = load_json(split_json_path) + if split_registry.get("version") != DATASET_SPLITS_VERSION: + raise RuntimeError( + f"Unsupported dataset_splits.json version in {split_json_path}. " + f"Expected version={DATASET_SPLITS_VERSION}." + ) + persisted_dataset_name = str(split_registry.get("dataset_name", "BUSI")) + if persisted_dataset_name != dataset_name: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets dataset_name={persisted_dataset_name!r}, " + f"but current DATASET_NAME={dataset_name!r}." + ) + persisted_split_policy = split_registry.get("split_policy") + if dataset_name == "BUSI_with_classes" and persisted_split_policy != split_policy: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets split_policy={persisted_split_policy!r}, " + f"but current BUSI_WITH_CLASSES_SPLIT_POLICY={split_policy!r}." + ) + split_entries = split_registry.get("split_types") + if not isinstance(split_entries, dict): + raise RuntimeError(f"Invalid split_types payload in {split_json_path}.") + for split_type in SUPPORTED_SPLIT_TYPES: + if split_type not in split_entries: + raise RuntimeError( + f"dataset_splits.json is missing split_type={split_type}. Delete it to regenerate cleanly." + ) + repaired = repair_persisted_train_subsets( + split_registry, + train_fractions, + split_json_path=split_json_path, + seed=seed, + ) + source = "repaired" if repaired else "loaded" + if dataset_name == "BUSI_with_classes": + sample_records = build_sample_records( + validate_image_mask_consistency(images_dir, annotations_dir)[0], + images_subdir=split_registry["images_subdir"], + annotations_subdir=split_registry["annotations_subdir"], + dataset_name=dataset_name, + ) + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + for split_type in SUPPORTED_SPLIT_TYPES: + validate_persisted_split_no_leakage(split_type, split_entries[split_type], source=source) + if repaired: + print(f"[Splits] Loaded and repaired persisted dataset splits from {split_json_path}") + else: + print(f"[Splits] Loaded persisted dataset splits from {split_json_path}") + return split_registry, source + + matched, missing_masks, missing_images = validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + images_subdir = images_dir.relative_to(dataset_root).as_posix() + annotations_subdir = annotations_dir.relative_to(dataset_root).as_posix() + sample_records = build_sample_records( + matched, + images_subdir=images_subdir, + annotations_subdir=annotations_subdir, + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + split_registry = { + "version": DATASET_SPLITS_VERSION, + "dataset_name": dataset_name, + "split_policy": split_policy, + "dataset_root": _project_relative_path(dataset_root), + "images_subdir": images_subdir, + "annotations_subdir": annotations_subdir, + "seed": seed, + "train_fractions": list(train_fractions), + "split_types": {}, + } + + for split_type in SUPPORTED_SPLIT_TYPES: + if dataset_name == "BUSI_with_classes": + if split_policy == "balanced_train": + base_splits = build_balanced_train_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_stratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_unstratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + train_subsets = build_nested_train_subsets( + base_splits["train"], + train_fractions, + split_type=split_type, + seed=seed, + split_policy=split_policy, + ) + split_entry = { + "split_type": split_type, + "base_splits": base_splits, + "train_subsets": train_subsets, + } + validate_persisted_split_no_leakage(split_type, split_entry, source="created") + split_registry["split_types"][split_type] = split_entry + + save_json(split_json_path, split_registry) + print(f"[Splits] Created persisted dataset splits at {split_json_path}") + return split_registry, "created" + +def select_persisted_split( + split_registry: dict[str, Any], + split_type: str, + train_fraction: float, +) -> dict[str, Any]: + if split_type not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"Unsupported split_type: {split_type}") + + split_entries = split_registry.get("split_types", {}) + if split_type not in split_entries: + raise KeyError( + f"Requested split_type={split_type} is not available in dataset_splits.json. " + "Delete the JSON file to regenerate it with the new configuration." + ) + + subset_key = percent_label(train_fraction) + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + if subset_key not in train_subsets: + raise KeyError( + f"Requested train fraction={train_fraction} (key={subset_key}) is not available in dataset_splits.json." + ) + + return { + "dataset_root": resolve_dataset_root_from_registry(split_registry), + "split_type": split_type, + "train_fraction": float(train_fraction), + "train_subset_key": subset_key, + "train_subset_variant": 0, + "train_subset_source": "persisted", + "base_train_records": split_entry["base_splits"]["train"], + "train_records": train_subsets[subset_key], + "val_records": split_entry["base_splits"]["val"], + "test_records": split_entry["base_splits"]["test"], + } + +def apply_train_subset_variant( + selected_split: dict[str, Any], + split_registry: dict[str, Any], + *, + subset_variant: int, +) -> dict[str, Any]: + variant = int(subset_variant) + if variant <= 0 or float(selected_split["train_fraction"]) >= 1.0: + return selected_split + + split_policy = split_registry.get("split_policy") if current_dataset_name() == "BUSI_with_classes" else None + variant_subsets = build_nested_train_subsets( + selected_split["base_train_records"], + [float(selected_split["train_fraction"])], + split_type=str(selected_split["split_type"]), + seed=int(split_registry.get("seed", SEED)), + split_policy=split_policy, + subset_variant=variant, + ) + subset_key = str(selected_split["train_subset_key"]) + updated_split = dict(selected_split) + updated_split["train_records"] = variant_subsets[subset_key] + updated_split["train_subset_variant"] = variant + updated_split["train_subset_source"] = "variant_override" + return updated_split + +def export_selected_split_manifest( + pct_root: Path, + *, + percent: float, + split_source: str, + selected_split: dict[str, Any], +) -> Path: + variant_suffix = train_subset_variant_suffix(int(selected_split.get("train_subset_variant", 0))) + manifest_path = pct_root / ( + f"selected_split_{selected_split['split_type']}_{percent_label(percent)}pct{variant_suffix}.json" + ) + payload = { + "dataset_name": current_dataset_name(), + "dataset_root": str(Path(selected_split["dataset_root"]).resolve()), + "dataset_percent": float(percent), + "dataset_percent_label": percent_label(percent), + "split_source": split_source, + "split_type": str(selected_split["split_type"]), + "train_fraction": float(selected_split["train_fraction"]), + "train_subset_key": str(selected_split["train_subset_key"]), + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(manifest_path.resolve()), + "base_train_records": [dict(record) for record in selected_split["base_train_records"]], + "train_records": [dict(record) for record in selected_split["train_records"]], + "val_records": [dict(record) for record in selected_split["val_records"]], + "test_records": [dict(record) for record in selected_split["test_records"]], + } + save_json(manifest_path, payload) + return manifest_path + +def compute_busi_statistics( + dataset_root: Path, + sample_records: list[dict[str, str]], + cache_path: Path, +) -> tuple[float, float, str]: + filenames = [record["filename"] for record in sample_records] + if cache_path.exists(): + stats = load_json(cache_path) + if stats.get("filenames") == filenames: + print(f"[Normalization] Loaded cached normalization stats from {cache_path}") + return float(stats["global_mean"]), float(stats["global_std"]), "loaded_from_cache" + + total_sum = np.float64(0.0) + total_sq_sum = np.float64(0.0) + total_pixels = 0 + + for record in tqdm(sample_records, desc="Computing BUSI train mean/std", leave=False): + image_path = dataset_root / record["image_rel_path"] + img = np.array(PILImage.open(image_path)).astype(np.float64) + total_sum += img.sum() + total_sq_sum += (img ** 2).sum() + total_pixels += img.size + + global_mean = float(total_sum / total_pixels) + global_std = float(np.sqrt(total_sq_sum / total_pixels - global_mean ** 2)) + if global_std < 1e-6: + global_std = 1.0 + + save_json( + cache_path, + { + "global_mean": global_mean, + "global_std": global_std, + "total_pixels": int(total_pixels), + "num_images": len(sample_records), + "filenames": filenames, + }, + ) + print(f"[Normalization] Computed and saved normalization stats to {cache_path}") + return global_mean, global_std, "computed_fresh" + +def compute_class_distribution(sample_records: list[dict[str, str]]) -> dict[str, int] | None: + if not sample_records or not any("class_label" in record for record in sample_records): + return None + return { + "benign": sum(1 for record in sample_records if record.get("class_label") == "benign"), + "malignant": sum(1 for record in sample_records if record.get("class_label") == "malignant"), + } + +def format_class_distribution(class_distribution: dict[str, int] | None) -> str: + if class_distribution is None: + return "unavailable" + benign = int(class_distribution.get("benign", 0)) + malignant = int(class_distribution.get("malignant", 0)) + total = benign + malignant + return f"benign={benign}, malignant={malignant}, total={total}" + +def print_loaded_class_distribution( + *, + split_type: str, + train_subset_key: str, + base_train_records: list[dict[str, str]], + train_records: list[dict[str, str]], + val_records: list[dict[str, str]], + test_records: list[dict[str, str]], +) -> None: + if not any("class_label" in record for record in train_records): + return + section(f"Loaded Class Distribution | {split_type} | {train_subset_key}%") + print(f"Base train classes : {format_class_distribution(compute_class_distribution(base_train_records))}") + print(f"Train subset classes : {format_class_distribution(compute_class_distribution(train_records))}") + print(f"Validation classes : {format_class_distribution(compute_class_distribution(val_records))}") + print(f"Test classes : {format_class_distribution(compute_class_distribution(test_records))}") + +def print_split_summary(payload: dict[str, Any]) -> None: + unit_name = "Phase" if payload.get("phase_index") is not None else "Split" + section(f"{unit_name} Summary | {payload['split_type']} | {payload['train_subset_key']}%") + print(f"Dataset name : {payload['dataset_name']}") + if payload.get("dataset_split_policy") is not None: + print(f"Dataset split policy : {payload['dataset_split_policy']}") + print(f"Dataset splits JSON : {payload['dataset_splits_path']}") + print(f"Split source : {payload['split_source']}") + print(f"Split type used : {payload['split_type']}") + if payload.get("split_generation_mode") is not None: + print(f"Split generation mode : {payload['split_generation_mode']}") + if payload.get("phase_index") is not None: + print(f"Phase index : {payload['phase_index']}") + print(f"Phase val/test folds : val={payload['phase_val_fold_index']}, test={payload['phase_test_fold_index']}") + if payload.get("percent_sampling_mode") is not None: + print(f"Percent sampling mode : {payload['percent_sampling_mode']}") + print(f"Train fraction : {payload['train_subset_key']}% of frozen base train") + print(f"Train subset variant : {payload.get('train_subset_variant', 0)}") + print(f"Train subset source : {payload.get('train_subset_source', 'persisted')}") + if payload.get("sampling_chain_dataset_percents") is not None: + print(f"Sampling chain percents: {payload['sampling_chain_dataset_percents']}") + print(f"Base train samples : {payload['base_train_count']}") + print(f"Train subset samples : {payload['train_count']}") + print(f"Validation samples : {payload['val_count']}") + print(f"Test samples : {payload['test_count']}") + if payload.get("base_train_class_distribution") is not None: + print(f"Base train classes : {format_class_distribution(payload['base_train_class_distribution'])}") + print(f"Train subset classes : {format_class_distribution(payload['train_class_distribution'])}") + print(f"Validation classes : {format_class_distribution(payload['val_class_distribution'])}") + print(f"Test classes : {format_class_distribution(payload['test_class_distribution'])}") + print(f"Validation/Test frozen : {payload['val_test_frozen']}") + print(f"Leakage check : {payload['leakage_check']}") + +def print_normalization_summary(payload: dict[str, Any]) -> None: + mode = "ImageNet mean/std" if USE_IMAGENET_NORM else "Dataset train mean/std" + print(f"Dataset name : {payload['dataset_name']}") + print(f"Normalization mode : {mode}") + print(f"Stats cache path : {payload['normalization_cache_path']}") + print(f"Stats source : {payload['normalization_source']}") + print(f"Split type used : {payload['split_type']}") + variant_suffix = train_subset_variant_suffix(int(payload.get("train_subset_variant", 0))) + print( + f"Stats computed from : {payload['train_count']} train samples " + f"({payload['train_subset_key']}%{variant_suffix})" + ) +# ============================================================================= +# IMAGE PREPARATION + DATASETS +# ============================================================================= + +def _to_three_channels(image: np.ndarray) -> np.ndarray: + if image.ndim == 2: + image = image[..., None] + if image.shape[2] == 1: + image = np.repeat(image, 3, axis=2) + elif image.shape[2] > 3: + image = image[..., :3] + return image + +def _prepare_image(raw: np.ndarray, global_mean: float, global_std: float) -> np.ndarray: + img = raw.astype(np.float32) + img = _to_three_channels(img) + if IMG_SIZE > 0 and (img.shape[0] != IMG_SIZE or img.shape[1] != IMG_SIZE): + img = np.array( + PILImage.fromarray(img.astype(np.uint8)).resize((IMG_SIZE, IMG_SIZE), PILImage.BILINEAR) + ).astype(np.float32) + if USE_IMAGENET_NORM: + if img.max() > 1.0: + img = img / 255.0 + img = (img - IMAGENET_MEAN) / IMAGENET_STD + else: + img = (img - global_mean) / global_std + return np.transpose(img, (2, 0, 1)).copy() + +def _prepare_mask(raw: np.ndarray) -> np.ndarray: + mask = raw.astype(np.uint8) + if mask.ndim == 3: + mask = mask[..., 0] + if IMG_SIZE > 0 and (mask.shape[0] != IMG_SIZE or mask.shape[1] != IMG_SIZE): + pil_mask = PILImage.fromarray(mask) + if pil_mask.mode != "L": + pil_mask = pil_mask.convert("L") + mask = np.array(pil_mask.resize((IMG_SIZE, IMG_SIZE), PILImage.NEAREST)) + return ((mask > 0).astype(np.float32))[None, ...].copy() + +def print_imagenet_normalization_status() -> bool: + uses_imagenet_norm = bool(USE_IMAGENET_NORM) + if uses_imagenet_norm: + print("✅🖼️ ImageNet normalization is ACTIVE in `_prepare_image`.") + else: + print("⚠️🧪 ImageNet normalization is NOT active in `_prepare_image`.") + print("⚠️📊 Using dataset global mean/std normalization instead.") + if SMP_ENCODER_WEIGHTS == "imagenet" and not uses_imagenet_norm: + print("⚠️🚨 Encoder weights are set to ImageNet, but ImageNet normalization is disabled.") + return uses_imagenet_norm + +def _gaussian_kernel1d( + sigma: float, + *, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + if sigma <= 0: + return torch.ones(1, device=device, dtype=dtype) + radius = max(int(math.ceil(3.0 * sigma)), 1) + coords = torch.arange(-radius, radius + 1, device=device, dtype=dtype) + kernel = torch.exp(-(coords.square()) / max(2.0 * sigma * sigma, 1e-6)) + return kernel / kernel.sum().clamp_min(1e-12) + +def _smooth_displacement_field(field: torch.Tensor, sigma: float) -> torch.Tensor: + kernel = _gaussian_kernel1d(sigma, device=field.device, dtype=field.dtype) + if kernel.numel() == 1: + return field + radius = kernel.numel() // 2 + kernel_y = kernel.view(1, 1, -1, 1) + kernel_x = kernel.view(1, 1, 1, -1) + field = F.conv2d(field, kernel_y, padding=(radius, 0)) + field = F.conv2d(field, kernel_x, padding=(0, radius)) + return field + +def _apply_elastic_deformation( + image: torch.Tensor, + mask: torch.Tensor, + *, + alpha: float = 8.0, + sigma: float = 4.0, +) -> tuple[torch.Tensor, torch.Tensor]: + _, h, w = image.shape + if h < 2 or w < 2: + return image.contiguous(), mask.contiguous() + + device = image.device + dtype = image.dtype + dx = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + dy = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + + yy, xx = torch.meshgrid( + torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype), + torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype), + indexing="ij", + ) + grid = torch.stack((xx, yy), dim=-1).unsqueeze(0) + grid[..., 0] = grid[..., 0] + dx.squeeze(0).squeeze(0) * (2.0 / max(w - 1, 1)) + grid[..., 1] = grid[..., 1] + dy.squeeze(0).squeeze(0) * (2.0 / max(h - 1, 1)) + grid = grid.clamp(-1.25, 1.25) + + image_out = F.grid_sample( + image.unsqueeze(0), + grid, + mode="bilinear", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + mask_out = F.grid_sample( + mask.unsqueeze(0), + grid, + mode="nearest", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + return image_out.contiguous(), mask_out.clamp(0.0, 1.0).contiguous() + +def _apply_minimal_train_aug(image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(2,)) + mask = torch.flip(mask, dims=(2,)) + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(1,)) + mask = torch.flip(mask, dims=(1,)) + if torch.rand(1).item() < 0.5: + k = 1 if torch.rand(1).item() < 0.5 else 3 + image = torch.rot90(image, k=k, dims=(1, 2)) + mask = torch.rot90(mask, k=k, dims=(1, 2)) + elastic_aug_prob = float(_job_param("elastic_aug_prob", 0.0)) + if elastic_aug_prob > 0 and torch.rand(1).item() < elastic_aug_prob: + image, mask = _apply_elastic_deformation(image, mask) + return image.contiguous(), mask.contiguous() + +class BUSIDataset(Dataset): + def __init__( + self, + sample_records: list[dict[str, str]], + dataset_root: Path, + global_mean: float, + global_std: float, + *, + preload: bool, + augment: bool, + split_name: str, + ) -> None: + super().__init__() + self.sample_records = [dict(record) for record in sample_records] + self.dataset_root = Path(dataset_root) + self.global_mean = float(global_mean) + self.global_std = float(global_std) + self.preload = preload + self.augment = augment + self.split_name = split_name + self._images: list[torch.Tensor] = [] + self._masks: list[torch.Tensor] = [] + self._raw_cache_bytes = 0 + + if not self.preload: + raise ValueError("PRELOAD_TO_RAM is mandatory in this RunPod runner.") + self._preload_to_ram() + + def _preload_to_ram(self) -> None: + desc = f"Preloading {self.split_name} ({len(self.sample_records)} samples) to RAM" + for record in tqdm(self.sample_records, desc=desc, leave=False): + raw_img = np.array(PILImage.open(self.dataset_root / record["image_rel_path"])) + raw_mask = np.array(PILImage.open(self.dataset_root / record["mask_rel_path"])) + if raw_img.shape[:2] != raw_mask.shape[:2]: + raise RuntimeError( + f"Image/mask spatial size mismatch for {record['filename']}: " + f"image={raw_img.shape[:2]}, mask={raw_mask.shape[:2]}" + ) + image = torch.from_numpy(_prepare_image(raw_img, self.global_mean, self.global_std)) + mask = torch.from_numpy(_prepare_mask(raw_mask)) + self._raw_cache_bytes += tensor_bytes(image) + tensor_bytes(mask) + self._images.append(image) + self._masks.append(mask) + + def __len__(self) -> int: + return len(self.sample_records) + + def __getitem__(self, index: int) -> dict[str, Any]: + image = self._images[index].clone() + mask = self._masks[index].clone() + if self.augment: + image, mask = _apply_minimal_train_aug(image, mask) + return { + "image": image, + "mask": mask, + "sample_id": Path(self.sample_records[index]["filename"]).stem, + "dataset": current_dataset_name(), + } + + @property + def cache_bytes(self) -> int: + return self._raw_cache_bytes + +class CUDAPrefetcher: + def __init__(self, loader: DataLoader, device: torch.device) -> None: + self.loader = loader + self.device = device + self._use_cuda = device.type == "cuda" + self._iter = None + self._stream = None + self._next_batch = None + + def __len__(self) -> int: + return len(self.loader) + + def __iter__(self): + self._iter = iter(self.loader) + self._stream = torch.cuda.Stream(device=self.device) if self._use_cuda else None + self._next_batch = None + self._preload() + return self + + def close(self) -> None: + self._next_batch = None + self._iter = None + self._stream = None + + def _preload(self) -> None: + if self._iter is None: + self._next_batch = None + return + try: + self._next_batch = next(self._iter) + except StopIteration: + self._next_batch = None + return + if self._use_cuda: + assert self._stream is not None + with torch.cuda.stream(self._stream): + self._next_batch = to_device(self._next_batch, self.device) + else: + self._next_batch = to_device(self._next_batch, self.device) + + def __next__(self): + if self._next_batch is None: + self.close() + raise StopIteration + if self._use_cuda: + assert self._stream is not None + torch.cuda.current_stream(self.device).wait_stream(self._stream) + batch = self._next_batch + self._preload() + if self._next_batch is None: + self._iter = None + self._stream = None + return batch + +class DataBundle: + def __init__( + self, + *, + percent: float, + split_payload: dict[str, Any], + train_ds: BUSIDataset, + val_ds: BUSIDataset, + test_ds: BUSIDataset, + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + ) -> None: + self.percent = percent + self.split_payload = split_payload + self.train_ds = train_ds + self.val_ds = val_ds + self.test_ds = test_ds + self.train_loader = train_loader + self.val_loader = val_loader + self.test_loader = test_loader + + @property + def global_mean(self) -> float: + return float(self.split_payload["global_mean"]) + + @property + def global_std(self) -> float: + return float(self.split_payload["global_std"]) + + @property + def total_cache_bytes(self) -> int: + return self.train_ds.cache_bytes + self.val_ds.cache_bytes + self.test_ds.cache_bytes + +def make_loader(dataset: Dataset, shuffle: bool, *, loader_tag: str) -> DataLoader: + num_workers = NUM_WORKERS + persistent_workers = USE_PERSISTENT_WORKERS and num_workers > 0 + pin_memory = USE_PIN_MEMORY and DEVICE.type == "cuda" + generator = make_seeded_generator(SEED, loader_tag) + return DataLoader( + dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, + persistent_workers=persistent_workers, + worker_init_fn=seed_worker, + generator=generator, + ) + +def build_data_bundle(percent: float, split_registry: dict[str, Any], split_source: str) -> DataBundle: + pct_label = percent_label(percent) + pct_text = percent_text(percent) + selected_split = select_persisted_split(split_registry, SPLIT_TYPE, percent) + selected_split = apply_train_subset_variant( + selected_split, + split_registry, + subset_variant=TRAIN_SUBSET_VARIANT, + ) + pct_root = ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{pct_label}") + split_manifest_path = export_selected_split_manifest( + pct_root, + percent=percent, + split_source=split_source, + selected_split=selected_split, + ) + stats_cache_path = pct_root / ( + f"norm_stats_{normalization_cache_tag()}_{SPLIT_TYPE}_{pct_label}pct" + f"{train_subset_variant_suffix(int(selected_split.get('train_subset_variant', 0)))}.json" + ) + base_train_class_distribution = compute_class_distribution(selected_split["base_train_records"]) + train_class_distribution = compute_class_distribution(selected_split["train_records"]) + val_class_distribution = compute_class_distribution(selected_split["val_records"]) + test_class_distribution = compute_class_distribution(selected_split["test_records"]) + print_loaded_class_distribution( + split_type=selected_split["split_type"], + train_subset_key=selected_split["train_subset_key"], + base_train_records=selected_split["base_train_records"], + train_records=selected_split["train_records"], + val_records=selected_split["val_records"], + test_records=selected_split["test_records"], + ) + dataset_root = Path(selected_split["dataset_root"]).resolve() + global_mean, global_std, normalization_source = compute_busi_statistics( + dataset_root=dataset_root, + sample_records=selected_split["train_records"], + cache_path=stats_cache_path, + ) + + split_payload = { + "dataset_name": current_dataset_name(), + "dataset_split_policy": split_registry.get("split_policy"), + "dataset_splits_path": str(current_dataset_splits_json_path().resolve()), + "dataset_root": str(dataset_root), + "split_source": split_source, + "split_type": SPLIT_TYPE, + "dataset_percent": percent, + "train_subset_key": selected_split["train_subset_key"], + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(split_manifest_path.resolve()), + "base_train_count": len(selected_split["base_train_records"]), + "train_count": len(selected_split["train_records"]), + "val_count": len(selected_split["val_records"]), + "test_count": len(selected_split["test_records"]), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(stats_cache_path.resolve()), + "normalization_source": normalization_source, + } + + print_split_summary(split_payload) + print_normalization_summary(split_payload) + + train_ds = BUSIDataset( + selected_split["train_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=True, + split_name=f"train {SPLIT_TYPE} {pct_text}", + ) + val_ds = BUSIDataset( + selected_split["val_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"val {SPLIT_TYPE}", + ) + test_ds = BUSIDataset( + selected_split["test_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"test {SPLIT_TYPE}", + ) + + bundle = DataBundle( + percent=percent, + split_payload=split_payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=make_loader(train_ds, shuffle=True, loader_tag=f"{SPLIT_TYPE}:{pct_label}:train"), + val_loader=make_loader(val_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:val"), + test_loader=make_loader(test_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:test"), + ) + print_preload_summary(bundle) + return bundle + +def print_preload_summary(bundle: DataBundle) -> None: + section( + f"RAM Preload Summary | {bundle.split_payload['split_type']} | {int(bundle.percent * 100)}%" + ) + print(f"Train samples : {len(bundle.train_ds)}") + print(f"Val samples : {len(bundle.val_ds)}") + print(f"Test samples : {len(bundle.test_ds)}") + print(f"Train batches : {len(bundle.train_loader)}") + print(f"Val batches : {len(bundle.val_loader)}") + print(f"Test batches : {len(bundle.test_loader)}") + print(f"Global mean : {bundle.global_mean:.6f}") + print(f"Global std : {bundle.global_std:.6f}") + first = bundle.train_ds[0] + print(f"Sample image shape : {tuple(first['image'].shape)}") + print(f"Sample mask shape : {tuple(first['mask'].shape)}") + print(f"Sample image dtype : {first['image'].dtype}") + print(f"Sample mask dtype : {first['mask'].dtype}") + print(f"Estimated RAM preload : {bytes_to_gb(bundle.total_cache_bytes):.3f} GB") + +"""============================================================================= +MODEL DEFINITIONS +============================================================================= +""" + +def strategy_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + strategy = _require_supported_strategy(strategy) + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + if strategy == 2: + return "Strategy 2: Custom VGG + Segmentation Head (Supervised)" + if strategy == 3: + return "Strategy 3 Lite: Custom VGG + Segmentation Head + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + + if strategy == 2: + return f"Strategy 2: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) supervised" + if strategy == 3: + return f"Strategy 3 Lite: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + +def _apply_omega_conv(omega_conv: nn.Conv2d, value_next: torch.Tensor) -> torch.Tensor: + weight = omega_conv.weight + value_next = value_next.to(device=weight.device, dtype=weight.dtype) + return omega_conv(value_next) + +def _conv3x3(in_ch: int, out_ch: int, dilation: int = 1) -> nn.Conv2d: + return nn.Conv2d( + in_ch, + out_ch, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=True, + ) + +class _ConvBlock(nn.Module): + def __init__( + self, + in_ch: int, + out_ch: int, + dilation: int = 1, + *, + num_groups: int = 0, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.conv = _conv3x3(in_ch, out_ch, dilation=dilation) + self.norm = _group_norm(out_ch, num_groups=num_groups) if num_groups > 0 else nn.Identity() + self.act = nn.ReLU(inplace=True) + self.drop = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.act(self.norm(self.conv(x)))) + +def _group_norm(num_channels: int, *, num_groups: int = GN_NUM_GROUPS) -> nn.GroupNorm: + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + return nn.GroupNorm(groups, num_channels) + + +class _MultiScaleRefineBranch(nn.Module): + """Processes raw encoder features at each scale independently, then fuses + them into a single feature map. This gives the refinement head access to + multi-resolution spatial cues (edges at low levels, semantics at high + levels) that the 1x1 projection squashes away.""" + + def __init__( + self, + encoder_channels: list[int] | tuple[int, ...], + out_channels: int, + per_scale_channels: int = 32, + ) -> None: + super().__init__() + self._valid_indices: list[int] = [i for i, c in enumerate(encoder_channels) if c > 0] + self.scale_convs = nn.ModuleList() + for i in self._valid_indices: + self.scale_convs.append(nn.Sequential( + nn.Conv2d(encoder_channels[i], per_scale_channels, kernel_size=1, bias=False), + _group_norm(per_scale_channels), + nn.ReLU(inplace=True), + )) + total_ch = per_scale_channels * len(self._valid_indices) + self.fuse = nn.Sequential( + nn.Conv2d(total_ch, out_channels, kernel_size=3, padding=1, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2, dilation=2, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + ) + self._init_small() + + def _init_small(self) -> None: + """Small-magnitude init so the branch starts as a near-zero residual.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + m.weight.data.mul_(0.1) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + parts: list[torch.Tensor] = [] + for idx, conv in zip(self._valid_indices, self.scale_convs): + out = conv(encoder_features[idx]) + if out.shape[-2] != h or out.shape[-1] != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + parts.append(out) + return self.fuse(torch.cat(parts, dim=1)) + + +class SelfAttentionModule(nn.Module): + def __init__(self, channels: int) -> None: + super().__init__() + mid = max(channels // 8, 1) + self.query = nn.Conv2d(channels, mid, 1) + self.key = nn.Conv2d(channels, mid, 1) + self.value = nn.Conv2d(channels, channels, 1) + self.gamma = nn.Parameter(torch.tensor([0.1], dtype=torch.float32)) + + def forward(self, f: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + b, c, h, w = f.shape + pooled = f + target_grid = max(int(_job_param("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID)), 1) + if target_grid < max(h, w): + stride_h = max(1, math.ceil(h / target_grid)) + stride_w = max(1, math.ceil(w / target_grid)) + pooled = F.avg_pool2d(f, kernel_size=(stride_h, stride_w), stride=(stride_h, stride_w)) + if target_grid >= 64 and target_grid not in _STRATEGY3_SAM_GRID_WARNED: + print( + "[Strategy3] Self-attention grid " + f"{target_grid}x{target_grid} requested; this implies a much heavier attention matrix " + "(for example 64x64 -> 4096 tokens). Lower strategy3_sam_attention_grid if this is too slow." + ) + _STRATEGY3_SAM_GRID_WARNED.add(target_grid) + + ph, pw = pooled.shape[-2:] + q = self.query(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + k = self.key(pooled).view(b, -1, ph * pw) + v = self.value(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + attn = torch.softmax(q @ k / (q.shape[-1] ** 0.5), dim=-1) + out = (attn @ v).permute(0, 2, 1).view(b, c, ph, pw) + if ph != h or pw != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + return f + self.gamma * out, attn + + def forward_features(self, f: torch.Tensor) -> torch.Tensor: + out, _ = self.forward(f) + return out + +class DilatedPolicyHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.classifier = nn.Conv2d(64, NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + bias = torch.full((NUM_ACTIONS,), -2.0, dtype=torch.float32) + keep_index = NUM_ACTIONS // 2 if NUM_ACTIONS >= 3 else NUM_ACTIONS - 1 + bias[keep_index] = 2.0 + with torch.no_grad(): + self.classifier.bias.copy_(bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class DilatedValueHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.readout = nn.Conv2d(64, 1, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.body(x) + return self.readout(features) + +def replace_bn_with_gn(model: nn.Module, num_groups: int = 8) -> nn.Module: + for name, module in model.named_children(): + if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)): + num_channels = module.num_features + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + setattr(model, name, nn.GroupNorm(groups, num_channels, eps=module.eps, affine=module.affine)) + else: + replace_bn_with_gn(module, num_groups=num_groups) + return model + +def _ensure_transunet_repo_on_path() -> None: + repo_dir = TRANSUNET_REPO_DIR.resolve() + if not repo_dir.is_dir(): + raise FileNotFoundError( + f"TransUNet repo not found at {repo_dir}. Expected the official repo in " + f"{TRANSUNET_REPO_DIR}." + ) + repo_str = str(repo_dir) + if repo_str not in sys.path: + sys.path.insert(0, repo_str) + + +def _load_transunet_components() -> tuple[Any, dict[str, Any]]: + global _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + if _TRANSUNET_VISION_TRANSFORMER is None or _TRANSUNET_CONFIGS is None: + _ensure_transunet_repo_on_path() + try: + vit_module = importlib.import_module("networks.vit_seg_modeling") + except Exception as exc: + raise RuntimeError( + "Unable to import the official TransUNet modules. Ensure the TransUNet repo is present " + "and dependencies such as ml_collections, scipy, and torch are installed." + ) from exc + _TRANSUNET_VISION_TRANSFORMER = getattr(vit_module, "VisionTransformer") + _TRANSUNET_CONFIGS = getattr(vit_module, "CONFIGS") + return _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + + +def _transunet_tensor_changed(before: torch.Tensor, after: torch.Tensor) -> bool: + return not torch.equal(before, after) + + +def _load_and_verify_transunet_checkpoint( + vit_model: nn.Module, + *, + pretrained_path: Path, + img_size: int, + n_skip: int, +) -> dict[str, Any]: + checkpoint_path = Path(pretrained_path).expanduser().resolve() + if not checkpoint_path.is_file(): + raise FileNotFoundError( + f"TransUNet checkpoint not found at {checkpoint_path}. " + f"Expected ImageNet weights at {TRANSUNET_PRETRAINED_PATH.resolve()}." + ) + + weights = np.load(checkpoint_path, allow_pickle=False) + try: + missing_keys = [key for key in _TRANSUNET_REQUIRED_NPZ_KEYS if key not in weights] + if missing_keys: + raise RuntimeError( + f"TransUNet checkpoint {checkpoint_path} is missing required arrays: {missing_keys}" + ) + + position_embeddings = vit_model.transformer.embeddings.position_embeddings + root_conv = vit_model.transformer.embeddings.hybrid_model.root.conv.weight + block0_query = vit_model.transformer.encoder.layer[0].attn.query.weight + + pos_before = position_embeddings.detach().cpu().clone() + root_before = root_conv.detach().cpu().clone() + query_before = block0_query.detach().cpu().clone() + + posemb_source_shape = tuple(weights["Transformer/posembed_input/pos_embedding"].shape) + posemb_target_shape = tuple(position_embeddings.shape) + array_count = len(getattr(weights, "files", [])) + file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024) + + vit_model.load_from(weights=weights) + + pos_after = position_embeddings.detach().cpu() + root_after = root_conv.detach().cpu() + query_after = block0_query.detach().cpu() + + updated = { + "PosEmbed updated": _transunet_tensor_changed(pos_before, pos_after), + "ResNet root conv updated": _transunet_tensor_changed(root_before, root_after), + "ViT block-0 query updated": _transunet_tensor_changed(query_before, query_after), + } + + section("TransUNet Checkpoint Verification") + print("[TransUNet] OK Loaded R50+ViT-B/16 ImageNet checkpoint") + print(f"[TransUNet] File : {checkpoint_path} ({file_size_mb:.1f} MB)") + print(f"[TransUNet] NPZ arrays : {array_count}") + print( + "[TransUNet] PosEmbed shape : " + f"src {posemb_source_shape} -> tgt {posemb_target_shape}" + f"{' (interpolated)' if posemb_source_shape != posemb_target_shape else ''}" + ) + for label, status in updated.items(): + print(f"[TransUNet] {label:<22}: {status}") + print( + "[TransUNet] " + f"img_size={img_size}, patches.grid=({img_size // 16}, {img_size // 16}), " + f"n_skip={n_skip}, n_classes=1" + ) + + failed = [label for label, status in updated.items() if not status] + if failed: + raise RuntimeError( + "TransUNet checkpoint load verification failed. The following tensors were unchanged after " + f"load_from(...): {failed}. Training was stopped to avoid using a randomly initialized model." + ) + + return { + "checkpoint_path": str(checkpoint_path), + "array_count": array_count, + "file_size_mb": file_size_mb, + "posemb_source_shape": posemb_source_shape, + "posemb_target_shape": posemb_target_shape, + "updated": updated, + } + finally: + close_fn = getattr(weights, "close", None) + if callable(close_fn): + close_fn() + + +class _TransUNetEncoder(nn.Module): + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.transformer = transformer + self.out_channels = (3, 64, 256, 512, 768) + self._vit_token_cache: torch.Tensor | None = None + self._decoder_skip_cache: list[torch.Tensor] | None = None + + def _clear_cache(self) -> None: + self._vit_token_cache = None + self._decoder_skip_cache = None + + def decoder_inputs(self) -> tuple[torch.Tensor, list[torch.Tensor]]: + if self._vit_token_cache is None or self._decoder_skip_cache is None: + raise RuntimeError( + "TransUNet decoder was called before the encoder cache was populated. " + "Call the encoder first in the current forward pass." + ) + return self._vit_token_cache, self._decoder_skip_cache + + def forward(self, x: torch.Tensor) -> list[torch.Tensor]: + self._clear_cache() + if x.shape[1] == 1: + model_input = x.repeat(1, 3, 1, 1) + elif x.shape[1] == 3: + model_input = x + else: + raise ValueError(f"TransUNet expects 1 or 3 input channels, got {x.shape[1]}.") + + embedding_output, hybrid_features = self.transformer.embeddings(model_input) + hidden_states, _ = self.transformer.encoder(embedding_output) + if hybrid_features is None or len(hybrid_features) < 3: + raise RuntimeError( + "TransUNet hybrid ResNet features were not produced as expected." + ) + + deepest_skip, mid_skip, shallow_skip = hybrid_features[:3] + batch_size, n_patch, hidden_dim = hidden_states.shape + side = math.isqrt(n_patch) + if side * side != n_patch: + raise RuntimeError( + f"TransUNet token grid is not square: n_patch={n_patch}." + ) + vit_out = hidden_states.permute(0, 2, 1).contiguous().view(batch_size, hidden_dim, side, side) + + self._vit_token_cache = hidden_states + self._decoder_skip_cache = [deepest_skip, mid_skip, shallow_skip] + return [model_input, shallow_skip, mid_skip, deepest_skip, vit_out] + + +class _TransUNetDecoder(nn.Module): + def __init__(self, decoder_core: nn.Module, encoder: _TransUNetEncoder) -> None: + super().__init__() + self.decoder_core = decoder_core + self._encoder_ref = weakref.ref(encoder) + + def _encoder(self) -> _TransUNetEncoder: + encoder = self._encoder_ref() + if encoder is None: + raise RuntimeError("TransUNet encoder reference is no longer available.") + return encoder + + def forward(self, *features: torch.Tensor) -> torch.Tensor: + del features + hidden_states, skip_features = self._encoder().decoder_inputs() + return self.decoder_core(hidden_states, features=skip_features) + + +class TransUNetSMPAdapter(nn.Module): + def __init__(self, *, img_size: int, pretrained_path: Path) -> None: + super().__init__() + if img_size % 16 != 0: + raise ValueError(f"TransUNet requires img_size divisible by 16, got {img_size}.") + + vision_transformer_cls, configs = _load_transunet_components() + if TRANSUNET_VIT_NAME not in configs: + raise KeyError( + f"TransUNet config {TRANSUNET_VIT_NAME!r} not found in the official repo." + ) + + config_vit = copy.deepcopy(configs[TRANSUNET_VIT_NAME]) + config_vit.n_classes = 1 + config_vit.n_skip = TRANSUNET_N_SKIP + config_vit.classifier = "seg" + config_vit.patches.grid = (img_size // 16, img_size // 16) + + vit_model = vision_transformer_cls(config_vit, img_size=img_size, num_classes=1) + self.checkpoint_summary = _load_and_verify_transunet_checkpoint( + vit_model, + pretrained_path=pretrained_path, + img_size=img_size, + n_skip=TRANSUNET_N_SKIP, + ) + self.encoder = _TransUNetEncoder(vit_model.transformer) + self.decoder = _TransUNetDecoder(vit_model.decoder, self.encoder) + self.segmentation_head = vit_model.segmentation_head + self.classification_head = None + self.transunet_config = config_vit + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.encoder(x) + decoder_output = run_smp_decoder(self.decoder, encoder_features) + logits = self.segmentation_head(decoder_output) + return logits + +class HalfVGG16DilatedExtractor(nn.Module): + def __init__(self, *, dilation: int = 1, num_scales: int = 3) -> None: + super().__init__() + self.num_scales = num_scales + deep_dropout = 0.1 + + self.conv1_1 = _ConvBlock(3, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv1_2 = _ConvBlock(32, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv2_1 = _ConvBlock(32, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv2_2 = _ConvBlock(64, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv3_1 = _ConvBlock(64, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_2 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_3 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.conv4_1 = _ConvBlock(128, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_2 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_3 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + @property + def out_channels(self) -> int: + return (32 + 64 + 128) if self.num_scales == 3 else (32 + 64 + 128 + 256) + + @property + def pyramid_channels(self) -> list[int]: + return [32, 64, 128] if self.num_scales == 3 else [32, 64, 128, 256] + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + x = self.conv1_1(x) + src1 = self.conv1_2(x) + x = self.pool(src1) + + x = self.conv2_1(x) + src2 = self.conv2_2(x) + x = self.pool(src2) + + x = self.conv3_1(x) + x = self.conv3_2(x) + src3 = self.conv3_3(x) + + if self.num_scales == 3: + return [src1, src2, src3] + + x = self.pool(src3) + x = self.conv4_1(x) + x = self.conv4_2(x) + src4 = self.conv4_3(x) + return [src1, src2, src3, src4] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + pyramid = self.forward_pyramid(x) + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + +class CustomVGGEncoderWrapper(nn.Module): + def __init__(self, *, num_scales: int, dilation: int) -> None: + super().__init__() + self.encoder = HalfVGG16DilatedExtractor(dilation=dilation, num_scales=num_scales) + self.projection = None + + @property + def out_channels(self) -> int: + return self.encoder.out_channels + + @property + def pyramid_channels(self) -> list[int]: + return self.encoder.pyramid_channels + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + return self.encoder.forward_pyramid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.encoder(x) + +class SMPEncoderWrapper(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + depth: int, + in_channels: int, + proj_dim: int, + ) -> None: + super().__init__() + self.encoder = smp.encoders.get_encoder( + encoder_name, + in_channels=in_channels, + depth=depth, + weights=encoder_weights, + ) + if REPLACE_BN_WITH_GN: + replace_bn_with_gn(self.encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + self._out_channels = proj_dim + else: + self.projection = None + self._out_channels = raw_channels + + @property + def out_channels(self) -> int: + return self._out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + features = self.encoder(x) + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + +class VGGDecoderBlock(nn.Module): + def __init__(self, *, in_channels: int, skip_channels: int, out_channels: int) -> None: + super().__init__() + self.block = nn.Sequential( + _ConvBlock(in_channels + skip_channels, out_channels, num_groups=GN_NUM_GROUPS), + _ConvBlock(out_channels, out_channels, num_groups=GN_NUM_GROUPS), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = F.interpolate(x, size=skip.shape[-2:], mode="bilinear", align_corners=False) + return self.block(torch.cat([x, skip], dim=1)) + +class VGGSegmentationHead(nn.Module): + def __init__(self, *, pyramid_channels: list[int], dropout_p: float) -> None: + super().__init__() + if len(pyramid_channels) not in {3, 4}: + raise ValueError(f"Expected 3 or 4 VGG pyramid channels, got {pyramid_channels}") + + self.dropout = nn.Dropout2d(p=dropout_p) + self.num_scales = len(pyramid_channels) + + deepest = pyramid_channels[-1] + self.bridge = _ConvBlock(deepest, deepest, num_groups=GN_NUM_GROUPS) + if self.num_scales == 4: + self.up3 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[2], out_channels=128) + self.up2 = VGGDecoderBlock(in_channels=128, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + else: + self.up2 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + self.out_conv = nn.Conv2d(32, 1, kernel_size=1) + + def forward(self, pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + features = list(pyramid) + x = self.bridge(self.dropout(features[-1])) + if self.num_scales == 4: + x = self.up3(x, features[2]) + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + else: + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + return self.out_conv(x) + +class PixelDRLMG_SMP(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = SMPEncoderWrapper( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + depth=encoder_depth, + in_channels=3, + proj_dim=proj_dim, + ) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + features = self.extractor(x) + return self.sam(features) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class PixelDRLMG_VGG(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.extractor(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class SupervisedSMPModel(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int = 5, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + self.dropout = nn.Dropout2d(p=dropout_p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.smp_encoder(x) + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + decoder_output = self.dropout(decoder_output) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + +class SupervisedVGGModel(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + +class RefinementPolicyHead(nn.Module): + A3C_NUM_ACTIONS = 1 + + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 256, dilation=1, num_groups=GN_NUM_GROUPS), + _ConvBlock(256, 128, dilation=2, num_groups=GN_NUM_GROUPS), + _ConvBlock(128, 64, dilation=3, num_groups=GN_NUM_GROUPS), + ) + self.classifier = nn.Conv2d(64, self.A3C_NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + if self.classifier.bias is not None: + nn.init.zeros_(self.classifier.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class PixelDRLMG_WithDecoder(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.smp_encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + ch = proj_dim + else: + self.projection = None + ch = raw_channels + + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.multi_scale_refine = _MultiScaleRefineBranch( + encoder_channels=list(self.smp_encoder.out_channels), + out_channels=ch, + per_scale_channels=32, + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + self.multi_scale_refine.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "decoder", "segmentation_head"): + module = getattr(self.smp_model, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_from_features( + self, + features: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + + def _encoder_concat(self, x: torch.Tensor) -> torch.Tensor: + return self._concat_from_features(self.smp_encoder(x), output_size=x.shape[-2:]) + + def forward_decoder_from_features( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + ) -> torch.Tensor: + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.smp_model(x) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, Any]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + encoder_features = self.smp_encoder(x) + decoder_logits = self.forward_decoder_from_features(encoder_features) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_features=[feat[sample_index:sample_index + 1] for feat in encoder_features]: self.forward_decoder_from_features(sample_features), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.forward_decoder_from_features(encoder_features), + ) + return { + "base_features": self._concat_from_features(encoder_features, output_size=x.shape[-2:]), + "encoder_features": list(encoder_features), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + if encoder_features is not None: + ms_feat = self.multi_scale_refine(encoder_features, output_size=fused.shape[-2:]) + fused = fused + ms_feat + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + concat_feat = self._encoder_concat(x) + return self.sam(concat_feat) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +class PixelDRLMG_VGGWithDecoder(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + ch = self.encoder.out_channels + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "segmentation_head"): + module = getattr(self, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_pyramid( + self, + pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, torch.Tensor]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + pyramid = self.encoder.forward_pyramid(x) + decoder_logits = self.segmentation_head(pyramid) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_pyramid=[feat[sample_index:sample_index + 1] for feat in pyramid]: self.segmentation_head(sample_pyramid), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.segmentation_head(pyramid), + ) + return { + "base_features": self._concat_pyramid(pyramid, output_size=x.shape[-2:]), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + del encoder_features + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.encoder(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +def run_smp_decoder(decoder: nn.Module, encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + signature = inspect.signature(decoder.forward) + parameters = list(signature.parameters.values()) + if any(param.kind == inspect.Parameter.VAR_POSITIONAL for param in parameters): + return decoder(*encoder_features) + if len(parameters) == 1: + return decoder(encoder_features) + return decoder(*encoder_features) + +def checkpoint_run_config_payload(payload: dict[str, Any]) -> dict[str, Any]: + return payload.get("run_config") or payload.get("config") or {} + +def _raw_decoder_rl_model( + model: nn.Module, +) -> PixelDRLMG_WithDecoder | PixelDRLMG_VGGWithDecoder | None: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return raw + return None + +def _uses_refinement_runtime(model: nn.Module, *, strategy: int | None = None) -> bool: + raw = _raw_decoder_rl_model(model) + if raw is None: + return False + if strategy is not None and strategy != 3: + return False + return bool(getattr(raw, "use_refinement", False)) + +def _policy_action_count_from_state_dict(state_dict: dict[str, Any]) -> int | None: + for key in ( + "policy_head.classifier.weight", + "policy_head.classifier.bias", + "policy_head.net.4.weight", + "policy_head.net.4.bias", + ): + tensor = state_dict.get(key) + if torch.is_tensor(tensor): + return int(tensor.shape[0]) + return None + +def _model_policy_action_count(model: nn.Module) -> int | None: + raw = _unwrap_compiled(model) + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d): + return int(classifier.out_channels) + return None + +def _set_model_policy_action_count(model: nn.Module, action_count: int) -> bool: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) == 1: + return False + policy_head = getattr(raw, "policy_head", None) + classifier = getattr(policy_head, "classifier", None) + if not isinstance(classifier, nn.Conv2d): + return False + if int(classifier.out_channels) == int(action_count): + return False + + new_classifier = nn.Conv2d( + classifier.in_channels, + int(action_count), + kernel_size=classifier.kernel_size, + stride=classifier.stride, + padding=classifier.padding, + dilation=classifier.dilation, + groups=classifier.groups, + bias=classifier.bias is not None, + padding_mode=classifier.padding_mode, + ).to(device=classifier.weight.device, dtype=classifier.weight.dtype) + nn.init.xavier_uniform_(new_classifier.weight) + if new_classifier.bias is not None: + nn.init.zeros_(new_classifier.bias) + policy_head.classifier = new_classifier + return True + +def _configure_policy_head_compatibility( + model: nn.Module, + state_dict: dict[str, Any], + *, + source: str, +) -> int | None: + action_count = _policy_action_count_from_state_dict(state_dict) + if action_count is None: + return None + if _set_model_policy_action_count(model, action_count): + print(f"[Policy Compatibility] source={source} num_actions={action_count}") + return action_count + +def _strategy3_checkpoint_layout_info( + state_dict: dict[str, Any], + run_config: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = run_config or {} + strategy = run_config.get("strategy") + has_legacy_policy_head = any(key.startswith("policy_head.net.") for key in state_dict) + has_new_policy_body = any(key.startswith("policy_head.body.") for key in state_dict) + has_new_policy_classifier = any(key.startswith("policy_head.classifier.") for key in state_dict) + has_refinement_adapter = any(key.startswith("refinement_adapter.") for key in state_dict) + is_strategy3_decoder_checkpoint = bool( + strategy == 3 + or has_legacy_policy_head + or has_new_policy_body + or has_new_policy_classifier + or has_refinement_adapter + ) + use_refinement = bool( + has_refinement_adapter or ((has_new_policy_body or has_new_policy_classifier) and not has_legacy_policy_head) + ) + return { + "strategy": strategy, + "is_strategy3_decoder_checkpoint": is_strategy3_decoder_checkpoint, + "has_legacy_policy_head": has_legacy_policy_head, + "has_new_policy_head": bool(has_new_policy_body or has_new_policy_classifier), + "has_refinement_adapter": has_refinement_adapter, + "requires_policy_remap": has_legacy_policy_head, + "policy_action_count": _policy_action_count_from_state_dict(state_dict), + "use_refinement": use_refinement, + "compatibility_mode": "refinement" if use_refinement else "legacy", + } + +def inspect_strategy3_checkpoint_compatibility(path: str | Path) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + layout = _strategy3_checkpoint_layout_info(payload.get("model_state_dict", {}), checkpoint_run_config_payload(payload)) + layout["path"] = str(checkpoint_path) + return layout + +def _configure_strategy3_model_compatibility( + model: nn.Module, + layout: dict[str, Any], + *, + source: str, +) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not layout.get("is_strategy3_decoder_checkpoint"): + return + raw.set_refinement_mode(bool(layout["use_refinement"])) + if not bool(layout["use_refinement"]): + raw.refinement_adapter.eval() + if hasattr(raw, "multi_scale_refine"): + raw.multi_scale_refine.eval() + print( + "[Strategy3 Compatibility] " + f"source={source} mode={layout['compatibility_mode']} " + f"legacy_policy_head={layout['has_legacy_policy_head']} " + f"refinement_adapter={layout['has_refinement_adapter']}" + ) + +def _ensure_strategy3_refinement_adapter_compatible( + model: nn.Module, + state_dict: dict[str, Any], + *, + checkpoint_path: str | Path, +) -> None: + raw_model = _unwrap_compiled(model) + if not isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return + target_state = raw_model.state_dict() + mismatched: list[str] = [] + for key, value in state_dict.items(): + if not key.startswith(("refinement_adapter.", "policy_head.classifier.")): + continue + target_value = target_state.get(key) + if target_value is None: + continue + if tuple(target_value.shape) != tuple(value.shape): + mismatched.append( + f"{key}: checkpoint={tuple(value.shape)} model={tuple(target_value.shape)}" + ) + if mismatched: + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected after the continuous Strategy 3 redesign. " + f"Checkpoint={Path(checkpoint_path).resolve()} mismatches={mismatched[:4]}. " + "Resume/eval from legacy S3 checkpoints is not supported; retrain Strategy 3 from the Strategy 2 bootstrap checkpoint." + ) + +def _configure_model_from_checkpoint_path( + model: nn.Module, + checkpoint_path: str | Path, +) -> dict[str, Any]: + checkpoint_path = Path(checkpoint_path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state_dict = payload.get("model_state_dict", {}) + _configure_policy_head_compatibility(model, state_dict, source=str(checkpoint_path)) + layout = _strategy3_checkpoint_layout_info(state_dict, checkpoint_run_config_payload(payload)) + if layout.get("is_strategy3_decoder_checkpoint") and layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected: the checkpoint uses a discrete multi-action " + f"policy head with out_channels={layout.get('policy_action_count')}. " + "The current Strategy 3 implementation requires a continuous 1-channel delta head." + ) + _configure_strategy3_model_compatibility(model, layout, source=str(checkpoint_path)) + layout["path"] = str(checkpoint_path) + return layout + +def _remap_legacy_policy_head_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]: + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + if key.startswith("policy_head.net."): + suffix = key[len("policy_head.net."):] + layer_idx, dot, rest = suffix.partition(".") + if dot: + if layer_idx in {"0", "1", "2", "3"}: + remapped[f"policy_head.body.{layer_idx}.{rest}"] = value + continue + if layer_idx == "4": + remapped[f"policy_head.classifier.{rest}"] = value + continue + remapped[key] = value + return remapped + +def _load_strategy2_checkpoint_payload( + path: str | Path, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False) + saved_config = checkpoint_run_config_payload(ckpt) + if saved_config: + saved_model_config = RuntimeModelConfig.from_payload(saved_config).validate() + if saved_model_config.backbone_family != model_config.backbone_family: + raise ValueError( + f"Strategy 2 checkpoint backbone family mismatch: requested {model_config.backbone_family!r}, " + f"checkpoint has {saved_model_config.backbone_family!r} at {checkpoint_path}." + ) + return ckpt + +def _preview_state_keys(keys: list[str], *, limit: int = 8) -> str: + if not keys: + return "none" + preview = ", ".join(keys[:limit]) + if len(keys) > limit: + preview += ", ..." + return preview + +def _strict_load_strategy2_submodule( + target_module: nn.Module, + *, + checkpoint_state_dict: dict[str, Any], + checkpoint_prefix: str, + checkpoint_path: str | Path, + target_name: str, +) -> None: + extracted = { + key[len(checkpoint_prefix):]: value + for key, value in checkpoint_state_dict.items() + if key.startswith(checkpoint_prefix) + } + if not extracted: + raise RuntimeError( + f"Strategy 2 bootstrap failed for {target_name}: no checkpoint keys found with prefix " + f"{checkpoint_prefix!r} in {Path(checkpoint_path).resolve()}." + ) + + target_state = target_module.state_dict() + missing = sorted(set(target_state.keys()) - set(extracted.keys())) + unexpected = sorted(set(extracted.keys()) - set(target_state.keys())) + if missing or unexpected: + section(f"Strategy 2 Bootstrap Mismatch | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + print(f"Missing keys ({len(missing)}) : {_preview_state_keys(missing)}") + print(f"Unexpected keys ({len(unexpected)}): {_preview_state_keys(unexpected)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap failed for {target_name} from {Path(checkpoint_path).resolve()}. " + f"Missing keys={len(missing)}, unexpected keys={len(unexpected)}." + ) + + try: + load_result = target_module.load_state_dict(extracted, strict=True) + except Exception as exc: + section(f"Strategy 2 Bootstrap Strict Load Failure | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap load failed for {target_name} from " + f"{Path(checkpoint_path).resolve()}: {exc}" + ) from exc + + post_missing = list(getattr(load_result, "missing_keys", [])) + post_unexpected = list(getattr(load_result, "unexpected_keys", [])) + if post_missing or post_unexpected: + raise RuntimeError( + f"Strict Strategy 2 bootstrap reported residual mismatches for {target_name}: " + f"missing={post_missing}, unexpected={post_unexpected}" + ) + + section(f"Strategy 2 Bootstrap OK | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Loaded tensors : {len(extracted)}") + print("Strict load : passed") + +def _use_channels_last_for_run(model_config: RuntimeModelConfig | None = None) -> bool: + model_config = (model_config or current_model_config()).validate() + if not USE_CHANNELS_LAST: + return False + if DEVICE.type != "cuda": + return False + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + if ( + model_config.backbone_family == "smp" + and "efficientnet" in model_config.smp_encoder_name.lower() + and USE_AMP + and amp_dtype in {torch.float16, torch.bfloat16} + ): + print("[MemoryFormat] Disabling channels_last for EfficientNet + AMP stability.") + return False + return True + +def build_model( + strategy: int, + dropout_p: float, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[nn.Module, str, bool]: + strategy = _require_supported_strategy(strategy) + model_config = model_config.validate() + if model_config.backbone_family == "custom_vgg": + if not bool(ENABLE_CUSTOM_VGG_BACKBONE): + raise RuntimeError( + "The legacy custom VGG backbone is feature-flagged off. " + "Set ENABLE_CUSTOM_VGG_BACKBONE=True to opt into the unused VGG code path." + ) + if strategy == 2: + model = SupervisedVGGModel( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_VGGWithDecoder( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.encoder, + checkpoint_state_dict=s2_state, + checkpoint_prefix="encoder.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.encoder", + ) + _strict_load_strategy2_submodule( + model.segmentation_head, + checkpoint_state_dict=s2_state, + checkpoint_prefix="segmentation_head.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.segmentation_head", + ) + if freeze_bootstrapped_segmentation: + model.encoder.requires_grad_(False) + model.segmentation_head.requires_grad_(False) + if hasattr(model.encoder, "projection") and model.encoder.projection is not None: + model.encoder.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + else: + if strategy == 2: + model = SupervisedSMPModel( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_WithDecoder( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + proj_dim=model_config.smp_encoder_proj_dim, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.smp_model, + checkpoint_state_dict=s2_state, + checkpoint_prefix="smp_model.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.smp_model", + ) + if freeze_bootstrapped_segmentation: + model.smp_model.encoder.requires_grad_(False) + model.smp_model.decoder.requires_grad_(False) + if hasattr(model.smp_model, "segmentation_head"): + model.smp_model.segmentation_head.requires_grad_(False) + if hasattr(model, "projection") and model.projection is not None: + model.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + + use_channels_last_now = _use_channels_last_for_run(model_config) + if strategy == 3: + classifier = getattr(getattr(_unwrap_compiled(model), "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) != 1: + raise RuntimeError("Strategy 3 expects a continuous 1-channel policy head.") + _strategy3_bump_mc_cache_fingerprint( + model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + model = model.to(DEVICE) + if use_channels_last_now: + model = model.to(memory_format=torch.channels_last) + + compiled = False + if USE_TORCH_COMPILE and hasattr(torch, "compile"): + try: + model = torch.compile(model, mode="max-autotune") + compiled = True + except Exception as exc: + print(f"[Compile] torch.compile skipped: {exc}") + return model, strategy_name(strategy, model_config), compiled + +def _unwrap_compiled(model: nn.Module) -> nn.Module: + return getattr(model, "_orig_mod", model) + +def count_parameters(module: nn.Module | None, *, only_trainable: bool = False) -> int: + if module is None: + return 0 + if only_trainable: + return sum(p.numel() for p in module.parameters() if p.requires_grad) + return sum(p.numel() for p in module.parameters()) + +def print_model_parameter_summary( + *, + model: nn.Module, + description: str, + strategy: int, + model_config: RuntimeModelConfig, + dropout_p: float, + amp_dtype: torch.dtype, + compiled: bool, +) -> None: + raw = _unwrap_compiled(model) + total_params = count_parameters(raw) + trainable_params = count_parameters(raw, only_trainable=True) + frozen_params = total_params - trainable_params + bn_count = sum(1 for m in raw.modules() if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d))) + gn_count = sum(1 for m in raw.modules() if isinstance(m, nn.GroupNorm)) + + section(f"Model Parameter Summary | {description}") + print(f"Strategy : {strategy}") + print(f"Model : {description}") + print(f"Dropout p : {dropout_p:.4f}") + print(f"Total params : {total_params:,}") + print(f"Trainable params : {trainable_params:,}") + print(f"Frozen params : {frozen_params:,}") + print(f"BN layers : {bn_count}") + print(f"GN layers : {gn_count}") + print(f"channels_last : {_use_channels_last_for_run(model_config)}") + print(f"AMP dtype : {amp_dtype}") + print(f"torch.compile : {compiled}") + print(f"Backbone family : {model_config.backbone_family}") + if strategy == 3: + print(f"S3 variant : {_strategy3_variant()}") + freeze_status = _strategy3_bootstrap_freeze_status(model) + print(f"S3 bootstrap loaded : {freeze_status['bootstrap_loaded']}") + print(f"S3 freeze requested : {freeze_status['freeze_requested']}") + print(f"S3 frozen now : {freeze_status['freeze_active']}") + print(f"S3 encoder state : {freeze_status['encoder_state']}") + if freeze_status["decoder_state"] != "n/a": + print(f"S3 decoder state : {freeze_status['decoder_state']}") + if freeze_status["segmentation_head_state"] != "n/a": + print(f"S3 seg head state : {freeze_status['segmentation_head_state']}") + + block_counts: dict[str, int] = {} + if strategy == 2: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + block_counts["dropout"] = count_parameters(getattr(raw, "dropout", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + elif strategy == 3: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + block_counts["sam"] = count_parameters(getattr(raw, "sam", None)) + block_counts["policy_head"] = count_parameters(getattr(raw, "policy_head", None)) + block_counts["value_head"] = count_parameters(getattr(raw, "value_head", None)) + + for name, value in block_counts.items(): + print(f"{name:22s}: {value:,}") + +"""============================================================================= +METRICS + CHECKPOINTS +============================================================================= +""" + +_EPS = 1e-4 + +def _as_bool(mask: np.ndarray) -> np.ndarray: + return (mask[0] if mask.ndim == 3 else mask).astype(bool) + +def _tp_fp_fn(pred: np.ndarray, target: np.ndarray): + p, t = _as_bool(pred), _as_bool(target) + tp = float((p & t).sum()) + fp = float((p & ~t).sum()) + fn = float((~p & t).sum()) + return tp, fp, fn, float(t.sum()), float(p.sum()) + +def dice_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (2 * tp + _EPS) / (t + p + _EPS) + +def ppv_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, fp, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fp + _EPS) + +def sensitivity_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, fn, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fn + _EPS) + +def iou_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (tp + _EPS) / (t + p - tp + _EPS) + +def _boundary_1px(mask: np.ndarray) -> np.ndarray: + m = _as_bool(mask) + if not m.any(): + return m + return m ^ ndimage.binary_erosion(m, iterations=1, border_value=0) + +def boundary_iou_contour_score(pred: np.ndarray, target: np.ndarray) -> float: + pb, tb = _boundary_1px(pred), _boundary_1px(target) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _biou_d(img_hw: tuple[int, int]) -> int: + """Resolve the Boundary IoU dilation width d for a given image size.""" + d = int(BOUNDARY_IOU_D) + if d > 0: + return d + height, width = img_hw + return max(1, int(round(0.02 * math.hypot(height, width)))) + +def _boundary_band(mask: np.ndarray, d: int) -> np.ndarray: + """Return the d-pixel inner boundary band used by paper-standard BIoU.""" + m = _as_bool(mask) + if not m.any(): + return m + eroded = ndimage.binary_erosion(m, iterations=max(int(d), 1), border_value=0) + return m & ~eroded + +def boundary_iou_score(pred: np.ndarray, target: np.ndarray) -> float: + """Boundary IoU from Cheng et al. CVPR 2021 using a d-pixel inner band.""" + height, width = _as_bool(target).shape + d = _biou_d((height, width)) + pb = _boundary_band(pred, d) + tb = _boundary_band(target, d) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _surf_dist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _as_bool(a), _as_bool(b) + if not a.any() and not b.any(): + return np.array([0.0], dtype=np.float32) + if not a.any() or not b.any(): + return np.array([np.inf], dtype=np.float32) + ba, bb = _boundary_1px(a), _boundary_1px(b) + return ndimage.distance_transform_edt(~bb)[ba].astype(np.float32) + +def hd95_score(pred: np.ndarray, target: np.ndarray) -> float: + distances = np.concatenate([_surf_dist(pred, target), _surf_dist(target, pred)]) + if np.isinf(distances).any(): + height, width = _as_bool(target).shape + return float(math.hypot(height, width)) + return float(np.percentile(distances, 95)) + +def compute_all_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + return { + "dice": dice_score(pred, target), + "ppv": ppv_score(pred, target), + "sen": sensitivity_score(pred, target), + "iou": iou_score(pred, target), + "biou": boundary_iou_score(pred, target), + "biou_contour": boundary_iou_contour_score(pred, target), + "hd95": hd95_score(pred, target), + } + +def checkpoint_manifest_path(path: Path) -> Path: + path = Path(path) + return path.with_name(f"{path.name}.meta.json") + +def checkpoint_history_path(run_dir: Path, run_type: str) -> Path: + if run_type == "overfit": + return Path(run_dir) / "overfit_history.json" + return Path(run_dir) / "history.json" + +def checkpoint_state_presence(payload: dict[str, Any]) -> dict[str, bool]: + tracked = [ + "model_state_dict", + "optimizer_state_dict", + "scheduler_state_dict", + "scaler_state_dict", + "log_alpha", + "alpha_optimizer_state_dict", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + "history", + "resume_source", + ] + return {name: name in payload for name in tracked} + +def write_checkpoint_manifest( + path: Path, + payload: dict[str, Any], + *, + extra: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = checkpoint_run_config_payload(payload) + manifest = { + "checkpoint_path": str(Path(path).resolve()), + "run_type": payload.get("run_type", "unknown"), + "epoch": int(payload.get("epoch", 0)), + "strategy": run_config.get("strategy"), + "dataset_percent": run_config.get("dataset_percent"), + "backbone_family": run_config.get("backbone_family", "smp"), + "saved_keys": sorted(payload.keys()), + "state_presence": checkpoint_state_presence(payload), + } + if "resume_source" in payload: + manifest["resume_source"] = payload["resume_source"] + if extra: + manifest.update(extra) + save_json(checkpoint_manifest_path(path), manifest) + return manifest + +def checkpoint_required_keys( + *, + optimizer: torch.optim.Optimizer | None, + scheduler: CosineAnnealingLR | None, + scaler: Any | None, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + require_run_metadata: bool, +) -> list[str]: + keys = ["epoch", "model_state_dict"] + if require_run_metadata: + keys.extend( + [ + "run_type", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + ] + ) + if optimizer is not None: + keys.append("optimizer_state_dict") + if scheduler is not None: + keys.append("scheduler_state_dict") + if scaler is not None: + keys.append("scaler_state_dict") + if log_alpha is not None: + keys.append("log_alpha") + if alpha_optimizer is not None: + keys.append("alpha_optimizer_state_dict") + return keys + +def validate_checkpoint_payload( + path: Path, + payload: dict[str, Any], + *, + required_keys: list[str], + expected_run_type: str | None = None, +) -> None: + missing = [name for name in required_keys if name not in payload] + if missing: + raise KeyError(f"Checkpoint {path} is missing required keys: {missing}") + if expected_run_type is not None and payload.get("run_type") != expected_run_type: + raise ValueError( + f"Checkpoint {path} run_type mismatch: expected {expected_run_type!r}, " + f"got {payload.get('run_type')!r}." + ) + +def save_checkpoint( + path: Path, + *, + run_type: str, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: ReduceLROnPlateau | None, + scaler: Any | None, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": _unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + validate_checkpoint_payload( + path, + payload, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + torch.save(payload, path) + write_checkpoint_manifest(path, payload) + +def load_checkpoint( + path: Path, + *, + model: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: ReduceLROnPlateau | None = None, + scaler: Any | None = None, + device: torch.device, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + expected_run_type: str | None = None, + require_run_metadata: bool = False, +) -> dict[str, Any]: + ckpt = torch.load(path, map_location=device, weights_only=False) + validate_checkpoint_payload( + path, + ckpt, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=require_run_metadata, + ), + expected_run_type=expected_run_type, + ) + + raw_model = _unwrap_compiled(model) + state_dict = ckpt["model_state_dict"] + load_strict = True + compat_layout: dict[str, Any] | None = None + _configure_policy_head_compatibility(model, state_dict, source=str(path)) + if any(key.startswith("policy_head.net.") for key in state_dict): + state_dict = _remap_legacy_policy_head_state_dict(state_dict) + if isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + compat_layout = _strategy3_checkpoint_layout_info(ckpt["model_state_dict"], checkpoint_run_config_payload(ckpt)) + if compat_layout["is_strategy3_decoder_checkpoint"]: + if compat_layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected during restore. " + f"Checkpoint={path} policy_head_out_channels={compat_layout.get('policy_action_count')}. " + "Resume/eval from pre-redesign Strategy 3 checkpoints is not supported." + ) + _configure_strategy3_model_compatibility(model, compat_layout, source=str(path)) + _ensure_strategy3_refinement_adapter_compatible(model, state_dict, checkpoint_path=path) + load_strict = bool(compat_layout["use_refinement"]) + + incompatible = raw_model.load_state_dict(state_dict, strict=load_strict) + if hasattr(raw_model, "clear_strategy3_mc_cache"): + _strategy3_bump_mc_cache_fingerprint(raw_model, eval_checkpoint_path=path) + if not load_strict: + missing_keys = [key for key in incompatible.missing_keys if not key.startswith(("refinement_adapter.", "multi_scale_refine."))] + unexpected_keys = list(incompatible.unexpected_keys) + if missing_keys or unexpected_keys: + print( + "[Checkpoint Restore] Non-strict legacy Strategy 3 load " + f"missing={missing_keys} unexpected={unexpected_keys}" + ) + + if optimizer is not None and "optimizer_state_dict" in ckpt: + try: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + except ValueError: + if compat_layout is None or compat_layout.get("compatibility_mode") != "legacy": + raise + print( + f"[Checkpoint Restore] Skipping optimizer state for legacy Strategy 3 checkpoint at {path} " + "because the parameter layout differs from the refinement-capable model." + ) + if scheduler is not None and "scheduler_state_dict" in ckpt: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler is not None and "scaler_state_dict" in ckpt: + scaler.load_state_dict(ckpt["scaler_state_dict"]) + if log_alpha is not None and "log_alpha" in ckpt: + with torch.no_grad(): + log_alpha.fill_(float(ckpt["log_alpha"])) + if alpha_optimizer is not None and "alpha_optimizer_state_dict" in ckpt: + alpha_optimizer.load_state_dict(ckpt["alpha_optimizer_state_dict"]) + restored = checkpoint_state_presence(ckpt) + restore_info = { + "restored_keys": restored, + "restored_at_epoch": int(ckpt.get("epoch", 0)), + "expected_run_type": expected_run_type, + } + write_checkpoint_manifest(path, ckpt, extra={"last_restore": restore_info}) + print( + f"[Checkpoint Restore] path={path} epoch={ckpt.get('epoch')} " + f"run_type={ckpt.get('run_type', 'unknown')} " + f"backbone={checkpoint_run_config_payload(ckpt).get('backbone_family', 'unknown')}" + ) + return ckpt + +"""============================================================================= +TRAINING + VALIDATION +============================================================================= +""" + +def _policy_log_probs_and_entropy(policy_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs = F.log_softmax(logits, dim=1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=1).mean() + return log_probs, entropy + +def _log_prob_for_actions(log_probs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor: + return log_probs.gather(1, actions.unsqueeze(1)) + +def sample_actions( + policy_logits: torch.Tensor, + stochastic: bool, + exploration_eps: float = 0.0, + keep_action_index: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs, entropy = _policy_log_probs_and_entropy(policy_logits) + if stochastic: + uniform = torch.rand_like(logits) + gumbel = -torch.log(-torch.log(uniform + 1e-8) + 1e-8) + actions = (logits + gumbel).argmax(dim=1) + if exploration_eps > 0: + keep_index = _keep_action_index(logits.shape[1]) if keep_action_index is None else int(keep_action_index) + keep_actions = torch.full_like(actions, keep_index) + random_mask = torch.rand(actions.shape, device=actions.device) < exploration_eps + actions = torch.where(random_mask, keep_actions, actions) + else: + actions = logits.argmax(dim=1) + log_prob = _log_prob_for_actions(log_probs, actions) + return actions, log_prob, entropy + +def apply_actions( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + num_actions = int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)) + if num_actions >= 3: + deltas = _refinement_deltas(action_count=num_actions, device=seg.device, dtype=seg.dtype) + delta = deltas[actions.long()].unsqueeze(1) + return (seg + delta).clamp_(0.0, 1.0) + action_map = actions.unsqueeze(1) + return seg * (action_map == 1).to(dtype=seg.dtype) + +def _soft_dice_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + denom = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) + return (2.0 * inter + 1e-6) / (denom + 1e-6) + +def _soft_iou_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - inter + return (inter + 1e-6) / (union + 1e-6) + +def _soft_recall_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + true_positive = (pred * target).sum(dim=(1, 2, 3)) + positives = target.sum(dim=(1, 2, 3)) + return (true_positive + 1e-6) / (positives + 1e-6) + +def _soft_boundary(mask: torch.Tensor) -> torch.Tensor: + return (mask - F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1)).abs() + +def _differentiable_boundary(mask: torch.Tensor, kernel_size: int = 3) -> torch.Tensor: + padding = kernel_size // 2 + mask_f = mask.float().clamp(0.0, 1.0) + eroded = 1.0 - F.max_pool2d(1.0 - mask_f, kernel_size, stride=1, padding=padding) + return (mask_f - eroded).clamp(0.0, 1.0) + +def _soft_iou_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + pred_f = pred.float().clamp(0.0, 1.0) + target_f = target.float().clamp(0.0, 1.0) + inter = (pred_f * target_f).sum(dim=(2, 3), keepdim=True) + union = (pred_f + target_f - pred_f * target_f).sum(dim=(2, 3), keepdim=True) + return inter / (union + 1e-6) + +def differentiable_biou_loss( + pred: torch.Tensor, + target: torch.Tensor, + kernel_size: int | None = None, + *, + reduction: str = "mean", +) -> torch.Tensor: + if kernel_size is None: + height, width = int(pred.shape[-2]), int(pred.shape[-1]) + d = _biou_d((height, width)) + kernel_size = 2 * d + 1 + kernel_size = max(int(kernel_size), 1) + if kernel_size % 2 == 0: + kernel_size += 1 + pred_boundary = _differentiable_boundary(pred, kernel_size=kernel_size) + target_boundary = _differentiable_boundary(target, kernel_size=kernel_size) + inter = (pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + union = (pred_boundary + target_boundary - pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + loss = 1.0 - (inter + 1e-6) / (union + 1e-6) + if reduction == "none": + return loss + if reduction == "mean": + return loss.mean() + raise ValueError(f"Unsupported differentiable_biou_loss reduction {reduction!r}.") + +def _strategy3_expected_next_mask( + policy_logits: torch.Tensor, + base_seg: torch.Tensor, + *, + action_count: int, +) -> tuple[torch.Tensor, torch.Tensor]: + logits_f = policy_logits.float() + base_seg_f = base_seg.float() + probs = F.softmax(logits_f, dim=1) + deltas = _refinement_deltas(action_count=action_count, device=logits_f.device, dtype=logits_f.dtype) + expected_delta = (probs * deltas.view(1, -1, 1, 1)).sum(dim=1, keepdim=True) + predicted_next = (base_seg_f + expected_delta).clamp(1e-4, 1.0 - 1e-4) + return predicted_next, deltas + +def _strategy3_action_targets( + seg_mask: torch.Tensor, + gt_mask: torch.Tensor, + deltas: torch.Tensor, +) -> torch.Tensor: + target_delta = gt_mask.float() - seg_mask.float() + return (target_delta - deltas.view(1, -1, 1, 1)).abs().argmin(dim=1) + +def compute_refinement_reward( + seg: torch.Tensor, + seg_next: torch.Tensor, + gt_mask: torch.Tensor, + *, + return_details: bool = False, +) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]: + seg_f = seg.float() + seg_next_f = seg_next.float() + gt_f = gt_mask.float().clamp(0.0, 1.0) + + r1_weight = float(_job_param("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT)) + biou_reward_weight = float(_job_param("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT)) + + target_dir = 2.0 * gt_f - 1.0 + progress = target_dir * (seg_next_f - seg_f) + room = gt_f * (1.0 - seg_f) + (1.0 - gt_f) * seg_f + r1 = r1_weight * progress * room + + biou_before = 1.0 - differentiable_biou_loss(seg_f, gt_f, reduction="none") + biou_next = 1.0 - differentiable_biou_loss(seg_next_f, gt_f, reduction="none") + biou_delta = biou_next - biou_before + r3 = biou_reward_weight * biou_delta.expand_as(seg_next_f) + + reward = (r1 + r3).clamp(-3.0, 3.0) + if not return_details: + return reward + return reward, { + "biou_before": biou_before, + "biou_next": biou_next, + "biou_delta": biou_delta, + } + +def compute_strategy1_aux_segmentation_loss( + policy_logits: torch.Tensor, + gt_mask: torch.Tensor, + *, + ce_weight: float, + dice_weight: float, + seg_mask: torch.Tensor | None = None, +) -> tuple[torch.Tensor, float, float]: + logits_f = policy_logits.float() + gt_mask_f = gt_mask.float() + num_actions = int(logits_f.shape[1]) + + if num_actions >= 3: + base_seg = seg_mask.float() if seg_mask is not None else torch.full_like(gt_mask_f, 0.5) + predicted_next, deltas = _strategy3_expected_next_mask( + policy_logits, + base_seg, + action_count=num_actions, + ) + action_targets = _strategy3_action_targets(base_seg, gt_mask_f, deltas) + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + boundary_dice_w = float(_job_param("strategy3_aux_boundary_dice_weight", 0.0)) + + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) + delta_large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + hard_margin = delta_large * 1.5 + with torch.no_grad(): + dist_to_thresh = (base_seg - threshold).abs() + hard_mask = (dist_to_thresh < hard_margin).squeeze(1) + + if ce_weight > 0: + action_ce_mix = float(_job_param("aux_action_ce_mix", 0.75)) + action_ce_mix = min(max(action_ce_mix, 0.0), 1.0) + + if hard_mask.any(): + logits_hw = policy_logits.float().permute(0, 2, 3, 1) + targets_hw = action_targets + action_ce = F.cross_entropy(logits_hw[hard_mask], targets_hw[hard_mask]) + else: + action_ce = F.cross_entropy(policy_logits.float(), action_targets) + + predicted_next_logits = torch.logit(predicted_next) + if hard_mask.any(): + gt_hard = gt_mask_f.squeeze(1)[hard_mask] + pred_hard = predicted_next_logits.squeeze(1)[hard_mask] + bce = F.binary_cross_entropy_with_logits(pred_hard, gt_hard) + else: + bce = F.binary_cross_entropy_with_logits(predicted_next_logits, gt_mask_f) + + ce_term = action_ce_mix * action_ce + (1.0 - action_ce_mix) * bce + aux_loss = aux_loss + ce_weight * ce_term + ce_loss_value = float(ce_term.detach().item()) + + if dice_weight > 0: + inter = (predicted_next * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (predicted_next.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + if boundary_dice_w > 0: + bd_loss = differentiable_biou_loss(predicted_next, gt_mask_f) + aux_loss = aux_loss + boundary_dice_w * bd_loss + + return aux_loss, ce_loss_value, dice_loss_value + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + + if ce_weight > 0: + if num_actions >= 3: + if seg_mask is None: + seg_mask = torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(seg_mask).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + ce_loss_value = float(ce_loss.detach().item()) + + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + inter = (probs_fg * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + return aux_loss, ce_loss_value, dice_loss_value + +def make_optimizer( + model: nn.Module, + strategy: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + rl_lr: float | None = None, +): + raw = _unwrap_compiled(model) + encoder_params = [] + decoder_params = [] + rl_params = [] + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + if ( + name.startswith("extractor.encoder.") + or name.startswith("encoder.") + or name.startswith("smp_model.encoder.") + or name.startswith("smp_encoder.") + ): + encoder_params.append(param) + elif "decoder" in name or "segmentation_head" in name: + decoder_params.append(param) + else: + rl_params.append(param) + + decoder_lr = float(_job_param("decoder_lr", head_lr)) + rl_group_lr = float(_job_param("rl_lr", rl_lr if rl_lr is not None else head_lr)) + + param_groups: list[dict[str, Any]] = [] + + if encoder_params: + param_groups.append({"params": encoder_params, "lr": encoder_lr}) + + if decoder_params: + param_groups.append({"params": decoder_params, "lr": decoder_lr}) + + if rl_params: + param_groups.append({"params": rl_params, "lr": rl_group_lr}) + + try: + optimizer = AdamW(param_groups, weight_decay=weight_decay, fused=DEVICE.type == "cuda") + except Exception: + optimizer = AdamW(param_groups, weight_decay=weight_decay) + + return optimizer + +def infer_segmentation_mask( + model: nn.Module, + image: torch.Tensor, + tmax: int, + *, + strategy: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> torch.Tensor: + model.eval() + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + return threshold_binary_mask(torch.sigmoid(logits)).float() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="infer_segmentation_mask") + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta) + return threshold_binary_mask(seg.float()).float() + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + policy_logits = model.forward_policy_only(x_t) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + return seg.float() + +def train_step( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor, + alpha_optimizer: torch.optim.Optimizer, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + initial_mask: torch.Tensor | None = None, + decoder_loss_extra: torch.Tensor | None = None, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + if initial_mask is not None: + seg = initial_mask.to(device=image.device, dtype=image.dtype) + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + alpha = log_alpha.exp() + + total_actor = 0.0 + total_critic = 0.0 + total_loss = 0.0 + total_reward = 0.0 + total_entropy = 0.0 + total_ce_loss = 0.0 + total_dice_loss = 0.0 + accum_tensor = None + alpha_loss_accum = torch.tensor(0.0, device=image.device, dtype=torch.float32) + aux_fused = False + + optimizer.zero_grad(set_to_none=True) + + for _ in range(tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=True) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + + neighborhood_next = _bootstrap_value_target(model, value_next) + target = reward + gamma * neighborhood_next + advantage = target - value_t + critic_loss = F.smooth_l1_loss(value_t, target) + actor_loss = -(log_prob * advantage.detach()).mean() + actor_loss = actor_loss - alpha.detach() * entropy + step_loss = (actor_loss + critic_loss_weight * critic_loss) / float(tmax) + alpha_loss_accum = alpha_loss_accum + (log_alpha * (entropy.detach() - target_entropy)) / float(tmax) + + if not aux_fused and initial_mask is None and (ce_weight > 0 or dice_weight > 0): + aux_loss, ce_loss_value, dice_loss_value = compute_strategy1_aux_segmentation_loss( + policy_logits, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + if ce_weight > 0: + total_ce_loss = ce_loss_value + if dice_weight > 0: + total_dice_loss = dice_loss_value + step_loss = step_loss + aux_loss + aux_fused = True + + total_actor += float(actor_loss.detach().item()) + total_critic += float(critic_loss.detach().item()) + total_loss += float(step_loss.detach().item()) + total_reward += float(reward.detach().mean().item()) + total_entropy += float(entropy.detach().item()) + + if stepwise_backward: + if scaler is not None: + scaler.scale(step_loss).backward() + else: + step_loss.backward() + else: + accum_tensor = step_loss if accum_tensor is None else accum_tensor + step_loss + + seg = seg_next.detach() + + if not stepwise_backward and accum_tensor is not None: + if scaler is not None: + scaler.scale(accum_tensor).backward() + else: + accum_tensor.backward() + + if not aux_fused and (ce_weight > 0 or dice_weight > 0): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + logits_f = policy_aux.float() + aux_loss = torch.zeros(1, device=image.device, dtype=torch.float32) + if ce_weight > 0: + num_actions = int(logits_f.shape[1]) + if num_actions >= 3: + init_seg = initial_mask if initial_mask is not None else torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(init_seg).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + total_ce_loss = float(ce_loss.detach().item()) + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + gt_f = gt_mask.float() + inter = (probs_fg * gt_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + total_dice_loss = float(dice_loss.detach().item()) + if decoder_loss_extra is not None: + aux_loss = aux_loss + decoder_loss_extra + if scaler is not None: + scaler.scale(aux_loss).backward() + else: + aux_loss.backward() + total_loss += float(aux_loss.detach().item()) + elif decoder_loss_extra is not None: + if scaler is not None: + scaler.scale(decoder_loss_extra).backward() + else: + decoder_loss_extra.backward() + total_loss += float(decoder_loss_extra.detach().item()) + + if scaler is not None: + scaler.unscale_(optimizer) + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + alpha_optimizer.zero_grad(set_to_none=True) + alpha_loss_accum.backward() + alpha_optimizer.step() + with torch.no_grad(): + log_alpha.clamp_(min=_alpha_log_floor(), max=5.0) + + return { + "loss": total_loss, + "actor_loss": total_actor / tmax, + "critic_loss": total_critic / tmax, + "mean_reward": total_reward / tmax, + "entropy": total_entropy / tmax, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": seg.detach(), + } + +def train_step_strategy3( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + current_epoch: int, + max_epochs: int, +) -> dict[str, Any]: + if not _uses_refinement_runtime(model, strategy=3): + raise RuntimeError( + "Legacy non-refinement Strategy 3 training is not supported after the continuous Strategy 3 redesign." + ) + + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + annealed_aux_ce_weight = _strategy3_annealed_aux_ce_weight(current_epoch) + del stepwise_backward, max_epochs, log_alpha, alpha_optimizer, target_entropy + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context(image, mc_mode="train") + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"] + base_features = refinement_context["base_features"] + encoder_features = refinement_context.get("encoder_features") + mc_variance = refinement_context["mc_variance"] + pred_entropy = refinement_context["pred_entropy"] + seg = decoder_prob.detach().to(device=image.device, dtype=image.dtype) + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + + decoder_loss = torch.zeros((), device=image.device, dtype=torch.float32) + decoder_ce_loss_value = 0.0 + decoder_dice_loss_value = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + decoder_ce = F.binary_cross_entropy_with_logits(dl_f, gt_f) + decoder_loss = decoder_loss + loss_weights["decoder_ce"] * decoder_ce + decoder_ce_loss_value = float(decoder_ce.detach().item()) + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + decoder_dice = 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + decoder_loss = decoder_loss + loss_weights["decoder_dice"] * decoder_dice + decoder_dice_loss_value = float(decoder_dice.detach().item()) + + optimizer.zero_grad(set_to_none=True) + + a3c_grad_clip = float(_job_param("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM)) + + refinement_base_features = base_features + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = mc_variance.detach() + detached_pred_entropy = pred_entropy.detach() + detached_encoder_features = [f.detach() for f in encoder_features] if encoder_features is not None else None + + step_action_hists: list[dict[str, float]] = [] + step_mask_deltas: list[float] = [] + step_reward_means: list[float] = [] + step_reward_pos_pcts: list[float] = [] + step_reward_zero_pcts: list[float] = [] + step_biou_deltas: list[float] = [] + advantage_maps: list[torch.Tensor] = [] + critic_targets: list[torch.Tensor] = [] + value_maps: list[torch.Tensor] = [] + + actor_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + critic_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + total_actor = 0.0 + total_critic = 0.0 + total_reward = 0.0 + total_ce_loss = decoder_ce_loss_value + total_dice_loss = decoder_dice_loss_value + effective_steps = max(int(tmax), 1) + final_refined_seg_for_aux: torch.Tensor | None = None + + for _ in range(effective_steps): + seg_before = seg.detach() + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg_before, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg_before.dtype) + seg_next = _strategy3_apply_delta(seg_before, delta) + reward_map, reward_details = compute_refinement_reward( + seg_before, + seg_next, + gt_mask.float(), + return_details=True, + ) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next.detach(), + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + value_next = model.value_from_state(state_next).detach() + + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + step_actor = -actor_advantage.mean() + step_critic = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + actor_loss_tensor = actor_loss_tensor + step_actor / float(effective_steps) + critic_loss_tensor = critic_loss_tensor + step_critic / float(effective_steps) + total_actor += float(step_actor.detach().item()) + total_critic += float(step_critic.detach().item()) + total_reward += float(reward_map.detach().mean().item()) + + step_action_hists.append(_strategy3_delta_distribution(delta)) + step_mask_deltas.append(float(delta.detach().abs().mean().item())) + step_reward_means.append(float(reward_map.detach().mean().item())) + step_reward_pos_pcts.append(float((reward_map.detach() > 0).float().mean().item() * 100.0)) + step_reward_zero_pcts.append(float((reward_map.detach().abs() < 1e-8).float().mean().item() * 100.0)) + step_biou_deltas.append(float(reward_details["biou_delta"].detach().mean().item())) + advantage_maps.append(actor_advantage.detach()) + critic_targets.append(critic_target.detach()) + value_maps.append(value_t.detach()) + final_refined_seg_for_aux = seg_next + seg = seg_next.detach() + + aux_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + aux_ce_loss_value = 0.0 + aux_dice_loss_value = 0.0 + if annealed_aux_ce_weight > 0.0 and final_refined_seg_for_aux is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refined_prob = final_refined_seg_for_aux.float().clamp(1e-6, 1.0 - 1e-6) + gt_f = gt_mask.float() + supervised_aux = torch.zeros((), device=image.device, dtype=torch.float32) + if ce_weight > 0: + refined_logits = torch.logit(refined_prob) + aux_ce = F.binary_cross_entropy_with_logits(refined_logits, gt_f) + supervised_aux = supervised_aux + float(ce_weight) * aux_ce + aux_ce_loss_value = float(aux_ce.detach().item()) + if dice_weight > 0: + inter = (refined_prob * gt_f).sum() + aux_dice = 1.0 - (2.0 * inter + 1e-6) / (refined_prob.sum() + gt_f.sum() + 1e-6) + supervised_aux = supervised_aux + float(dice_weight) * aux_dice + aux_dice_loss_value = float(aux_dice.detach().item()) + aux_loss_tensor = float(annealed_aux_ce_weight) * supervised_aux + total_ce_loss += aux_ce_loss_value + total_dice_loss += aux_dice_loss_value + + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + rl_loss = rl_loss_scale * (actor_loss_tensor + 0.0 * critic_loss_tensor) + total_loss_tensor = decoder_loss + rl_loss + aux_loss_tensor + + if scaler is not None: + scaler.scale(total_loss_tensor).backward() + scaler.unscale_(optimizer) + else: + total_loss_tensor.backward() + + effective_grad_clip = a3c_grad_clip if a3c_grad_clip > 0 else grad_clip_norm + total_grad_norm = ( + float(torch.nn.utils.clip_grad_norm_(model.parameters(), effective_grad_clip).item()) + if effective_grad_clip > 0 + else 0.0 + ) + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + adv_means = [float(a.mean().item()) for a in advantage_maps] + adv_stds = [ + float(a.std(unbiased=False).item()) + for a in advantage_maps + if a.numel() > 1 + ] + value_pred_errors = [ + float((target - value).abs().mean().item()) + for target, value in zip(critic_targets, value_maps) + ] + mean_value_pred = float(torch.stack([value.mean() for value in value_maps]).mean().item()) if value_maps else 0.0 + avg_action_dist: dict[str, float] = {} + if step_action_hists: + all_keys = set() + for h in step_action_hists: + all_keys.update(h.keys()) + for k in sorted(all_keys): + avg_action_dist[k] = float(np.mean([h.get(k, 0.0) for h in step_action_hists])) + + return { + "loss": float(total_loss_tensor.detach().item()), + "actor_loss": total_actor / float(effective_steps), + "critic_loss": total_critic / float(effective_steps), + "mean_reward": total_reward / float(effective_steps), + "entropy": 0.0, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": effective_grad_clip, + "final_mask": threshold_binary_mask(seg.detach().float()).float(), + "effective_steps": effective_steps, + "action_distribution": avg_action_dist, + "mask_delta_mean": float(np.mean(step_mask_deltas)) if step_mask_deltas else 0.0, + "reward_per_step": step_reward_means, + "reward_pos_pct_per_step": step_reward_pos_pcts, + "reward_zeros_pct": float(np.mean(step_reward_zero_pcts)) if step_reward_zero_pcts else 0.0, + "biou_delta_mean": float(np.mean(step_biou_deltas)) if step_biou_deltas else 0.0, + "advantage_mean": float(np.mean(adv_means)), + "advantage_std": float(np.nanmean(adv_stds)) if adv_stds else 0.0, + "value_pred_error_mean": float(np.mean(value_pred_errors)), + "mean_value_pred": mean_value_pred, + "rl_loss_scale_used": float(rl_loss_scale), + "annealed_aux_ce_weight": float(annealed_aux_ce_weight), + "alpha": 0.0, + } + +def train_step_supervised( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + grad_clip_norm: float, + ce_weight: float = 0.5, + dice_weight: float = 0.5, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + optimizer.zero_grad(set_to_none=True) + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + logits_f = logits.float() + gt_f = gt_mask.float() + loss = torch.zeros(1, device=image.device, dtype=torch.float32) + ce_loss_val = 0.0 + dice_loss_val = 0.0 + if ce_weight > 0: + bce = F.binary_cross_entropy_with_logits(logits_f, gt_f, reduction="mean") + loss = loss + ce_weight * bce + ce_loss_val = float(bce.detach().item()) + if dice_weight > 0: + pred_f = torch.sigmoid(logits_f) + inter = (pred_f * gt_f).sum() + dice_l = 1.0 - (2.0 * inter + 1e-6) / (pred_f.sum() + gt_f.sum() + 1e-6) + loss = loss + dice_weight * dice_l + dice_loss_val = float(dice_l.detach().item()) + + if scaler is not None: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + final_mask = threshold_binary_mask(torch.sigmoid(logits_f)).float().detach() + return { + "loss": float(loss.detach().item()), + "actor_loss": 0.0, + "critic_loss": 0.0, + "mean_reward": 0.0, + "entropy": 0.0, + "ce_loss": ce_loss_val, + "dice_loss": dice_loss_val, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": final_mask, + } + +@torch.inference_mode() +def validate( + model: nn.Module, + loader: DataLoader, + *, + run_dir: Path | None, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + gamma: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + strategy = _require_supported_strategy(strategy) + model.eval() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="validate") if strategy != 2 else max(int(tmax), 1) + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + losses: list[float] = [] + dice_scores: list[float] = [] + iou_scores: list[float] = [] + biou_scores: list[float] = [] + entropies: list[float] = [] + rewards: list[float] = [] + actor_losses: list[float] = [] + critic_losses: list[float] = [] + ce_losses: list[float] = [] + dice_losses: list[float] = [] + decoder_dice_scores: list[float] = [] + decoder_iou_scores: list[float] = [] + decoder_biou_scores: list[float] = [] + val_binary_flips_total: list[float] = [] + val_binary_flips_correct: list[float] = [] + val_binary_flips_wrong: list[float] = [] + prefetcher = CUDAPrefetcher(loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(loader), desc="Validating", leave=False): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred_prob = torch.sigmoid(logits).float() + pred = threshold_binary_mask(pred_prob).float() + bce = F.binary_cross_entropy_with_logits(logits.float(), gt_mask.float(), reduction="mean") if ce_weight > 0 else torch.tensor(0.0, device=image.device) + inter_prob = (pred_prob * gt_mask.float()).sum() + dice_loss = 1.0 - (2.0 * inter_prob + 1e-6) / (pred_prob.sum() + gt_mask.sum() + 1e-6) if dice_weight > 0 else torch.tensor(0.0, device=image.device) + losses.append(float((ce_weight * bce + dice_weight * dice_loss).item())) + ce_losses.append(float(bce.item()) if ce_weight > 0 else 0.0) + dice_losses.append(float(dice_loss.item()) if dice_weight > 0 else 0.0) + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"].float() + seg = decoder_prob.float() + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += loss_weights["decoder_ce"] * batch_ce + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += loss_weights["decoder_dice"] * batch_dice + decoder_pred = threshold_binary_mask(decoder_prob.float()).float() + decoder_inter = (decoder_pred * gt_mask.float()).sum(dim=(1, 2, 3)) + decoder_pred_sum = decoder_pred.sum(dim=(1, 2, 3)) + decoder_gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + decoder_dice = (2.0 * decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum + _EPS) + decoder_iou = (decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum - decoder_inter + _EPS) + decoder_dice_scores.extend(decoder_dice.cpu().tolist()) + decoder_iou_scores.extend(decoder_iou.cpu().tolist()) + else: + fg_count = gt_mask.sum().clamp(min=1.0) + bg_count = (gt_mask == 0).sum().clamp(min=1.0) + pos_weight = bg_count / fg_count + _weight_map = torch.where(gt_mask == 1, pos_weight, torch.ones_like(gt_mask)) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + decoder_logits = model.forward_decoder(image) + seg = threshold_binary_mask(torch.sigmoid(decoder_logits)).float() + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if ce_weight > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += ce_weight * batch_ce + if dice_weight > 0: + dm_prob = torch.sigmoid(dl_f) + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += dice_weight * batch_dice + else: + seg = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + + batch_actor = 0.0 + batch_critic = 0.0 + batch_reward = 0.0 + batch_entropy = 0.0 + batch_loss = decoder_loss + decoder_ce_base = batch_ce + decoder_dice_base = batch_dice + aux_ce_total = 0.0 + aux_dice_total = 0.0 + effective_steps = effective_tmax + + if strategy == 3 and refinement_runtime: + refinement_base_features = refinement_context["base_features"] + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = refinement_context["mc_variance"].detach() + detached_pred_entropy = refinement_context["pred_entropy"].detach() + _enc_feats = refinement_context.get("encoder_features") + detached_enc_feats = [f.detach() for f in _enc_feats] if _enc_feats is not None else None + effective_steps = effective_tmax + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + value_next = model.value_from_state(state_next).detach() + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + actor_loss = -actor_advantage.mean() + critic_loss = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward_map.mean().item()) + batch_loss += float((actor_loss + critic_loss_weight * critic_loss).item()) + seg = seg_next + + batch_ce = decoder_ce_base + batch_dice = decoder_dice_base + batch_loss = decoder_loss + rl_loss_scale * ((batch_loss - decoder_loss) / float(max(effective_steps, 1))) + pred = threshold_binary_mask(seg.float()).float() + # Track binary mask flips between decoder and RL-refined prediction + flipped = (decoder_pred != pred) + gt_binary = (gt_mask.float() > 0.5) + correct_flips = flipped & ((pred > 0.5) == gt_binary) + wrong_flips = flipped & ((pred > 0.5) != gt_binary) + total_px = max(pred.numel(), 1) + val_binary_flips_total.append(float(flipped.float().sum().item() / total_px * 100.0)) + val_binary_flips_correct.append(float(correct_flips.float().sum().item() / total_px * 100.0)) + val_binary_flips_wrong.append(float(wrong_flips.float().sum().item() / total_px * 100.0)) + else: + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=False) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = ((seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2)) + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + target = reward + gamma * _bootstrap_value_target(model, value_next) + advantage = target - value_t + actor_loss = -(log_prob * advantage.detach()).mean() + critic_loss = F.smooth_l1_loss(value_t, target) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward.mean().item()) + batch_entropy += float(entropy.item()) + batch_loss += float(((actor_loss + critic_loss_weight * critic_loss) / float(max(effective_tmax, 1))).item()) + seg = seg_next + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + aux_loss, aux_ce, aux_dice = compute_strategy1_aux_segmentation_loss( + policy_aux, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + batch_loss += float(aux_loss.item()) + batch_ce = aux_ce + batch_dice = aux_dice + pred = infer_segmentation_mask( + model, + image, + effective_tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ).float() + + ce_losses.append(batch_ce) + dice_losses.append(batch_dice) + actor_losses.append(batch_actor / float(max(effective_steps, 1))) + critic_losses.append(batch_critic / float(max(effective_steps, 1))) + rewards.append(batch_reward / float(max(effective_steps, 1))) + entropies.append(batch_entropy / float(max(effective_steps, 1))) + losses.append(batch_loss) + + inter = (pred * gt_mask.float()).sum(dim=(1, 2, 3)) + pred_sum = pred.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + dice_scores.extend(dice.cpu().tolist()) + iou_scores.extend(iou.cpu().tolist()) + pred_np = pred.detach().cpu().numpy() + gt_np = gt_mask.float().detach().cpu().numpy() + for idx in range(pred_np.shape[0]): + biou_scores.append(boundary_iou_score(pred_np[idx], gt_np[idx])) + if strategy == 3 and decoder_dice_scores: + decoder_pred_np = decoder_pred.detach().cpu().numpy() + for idx in range(decoder_pred_np.shape[0]): + decoder_biou_scores.append(boundary_iou_score(decoder_pred_np[idx], gt_np[idx])) + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + val_decoder_dice = float(np.mean(decoder_dice_scores)) if decoder_dice_scores else None + val_decoder_iou = float(np.mean(decoder_iou_scores)) if decoder_iou_scores else None + val_decoder_biou = float(np.mean(decoder_biou_scores)) if decoder_biou_scores else None + val_dice = float(np.mean(dice_scores)) if dice_scores else 0.0 + val_iou = float(np.mean(iou_scores)) if iou_scores else 0.0 + val_biou = float(np.mean(biou_scores)) if biou_scores else 0.0 + val_refine_score = 0.5 * (val_iou + val_biou) + + return { + "val_loss": float(np.mean(losses)) if losses else 0.0, + "val_dice": val_dice, + "val_iou": val_iou, + "val_biou": val_biou, + "val_refine_score": val_refine_score, + "val_decoder_dice": val_decoder_dice, + "val_decoder_iou": val_decoder_iou, + "val_decoder_biou": val_decoder_biou, + "val_dice_gain": None if val_decoder_dice is None else val_dice - val_decoder_dice, + "val_iou_gain": None if val_decoder_iou is None else val_iou - val_decoder_iou, + "val_biou_gain": None if val_decoder_biou is None else val_biou - val_decoder_biou, + "val_actor_loss": float(np.mean(actor_losses)) if actor_losses else 0.0, + "val_critic_loss": float(np.mean(critic_losses)) if critic_losses else 0.0, + "val_ce_loss": float(np.mean(ce_losses)) if ce_losses else 0.0, + "val_dice_loss": float(np.mean(dice_losses)) if dice_losses else 0.0, + "val_reward": float(np.mean(rewards)) if rewards else 0.0, + "val_entropy": float(np.mean(entropies)) if entropies else 0.0, + "val_binary_flips_total_pct": float(np.mean(val_binary_flips_total)) if val_binary_flips_total else None, + "val_binary_flips_correct_pct": float(np.mean(val_binary_flips_correct)) if val_binary_flips_correct else None, + "val_binary_flips_wrong_pct": float(np.mean(val_binary_flips_wrong)) if val_binary_flips_wrong else None, + } + +def _save_training_plots(history: list[dict[str, Any]], plots_dir: Path) -> None: + if len(history) < 1: + return + ensure_dir(plots_dir) + epochs = [row["epoch"] for row in history] + plot_specs = [ + ("loss.png", "Loss", [("train_loss", "Train"), ("val_loss", "Val")]), + ("dice.png", "Dice", [("train_dice", "Train"), ("val_dice", "Val")]), + ("iou.png", "IoU", [("train_iou", "Train"), ("val_iou", "Val")]), + ("reward.png", "Reward", [("train_mean_reward", "Train"), ("val_reward", "Val")]), + ("ce_loss.png", "CE Loss", [("train_ce_loss", "Train")]), + ("dice_loss.png", "Dice Loss", [("train_dice_loss", "Train")]), + ("lr.png", "Learning Rate", [("lr", "Head LR"), ("encoder_lr", "Encoder LR")]), + ] + for file_name, title, curves in plot_specs: + fig, ax = plt.subplots(figsize=(8, 4)) + has_data = False + for key, label in curves: + values = [(row["epoch"], row[key]) for row in history if key in row] + if not values: + continue + xs, ys = zip(*values) + ax.plot(xs, ys, label=label, linewidth=1.2) + has_data = True + if has_data: + ax.set_title(title) + ax.set_xlabel("Epoch") + ax.set_ylabel(title) + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(plots_dir / file_name, dpi=110) + plt.close(fig) + +RESUME_IDENTITY_KEYS = ( + "strategy", + "dataset_percent", + "dataset_name", + "dataset_split_policy", + "split_type", + "train_subset_key", + "train_subset_variant", + "best_checkpoint_metric_name", + "backbone_family", + "smp_encoder_name", + "smp_encoder_weights", + "smp_encoder_depth", + "smp_encoder_proj_dim", + "smp_decoder_type", + "vgg_feature_scales", + "vgg_feature_dilation", + "head_lr", + "encoder_lr", + "weight_decay", + "dropout_p", + "tmax", + "entropy_lr", +) + +PORTABLE_RESUME_PATH_KEYS = frozenset( + { + "base_split_manifest_path", + "subset_manifest_path", + } +) + +def _path_parts(value: Any) -> tuple[str, ...]: + if value is None: + return () + return tuple(part for part in Path(str(value)).parts if part not in {"", os.sep}) + +def _portable_path_token(value: Any) -> str: + if value in (None, ""): + return "" + path = Path(str(value)).expanduser() + roots: list[tuple[str, Path]] = [] + experiment_root = globals().get("EXPERIMENT_ROOT") + if experiment_root is not None: + roots.append(("EXPERIMENT_ROOT", Path(experiment_root))) + roots.append(("PROJECT_DIR", PROJECT_DIR)) + for label, root in roots: + try: + rel = path.resolve().relative_to(root.resolve()) + return f"{label}:{rel.as_posix()}" + except (OSError, ValueError): + continue + parts = _path_parts(value) + for marker in ("runs", "repeated_holdout", "manifests", "checkpoints"): + if marker in parts: + return "/".join(parts[parts.index(marker):]) + return "/".join(parts) + +def _resume_path_values_match(current: Any, saved: Any) -> tuple[bool, str]: + current_text = str(current or "") + saved_text = str(saved or "") + if current_text == saved_text: + return True, "exact" + + current_token = _portable_path_token(current_text) + saved_token = _portable_path_token(saved_text) + if current_token and current_token == saved_token: + return True, "portable-token" + + current_parts = _path_parts(current_text) + saved_parts = _path_parts(saved_text) + max_suffix = min(len(current_parts), len(saved_parts)) + for length in range(max_suffix, 2, -1): + if current_parts[-length:] == saved_parts[-length:]: + return True, f"suffix:{length}" + return False, f"current_token={current_token!r}, checkpoint_token={saved_token!r}" + +def _resume_value_matches(current: Any, saved: Any) -> bool: + if isinstance(current, (int, float)) and isinstance(saved, (int, float)) and not isinstance(current, bool): + return math.isclose(float(current), float(saved), rel_tol=1e-9, abs_tol=1e-12) + return current == saved + +def validate_resume_checkpoint_identity( + current_run_config: dict[str, Any], + saved_run_config: dict[str, Any], + *, + checkpoint_path: Path, +) -> None: + mismatches: list[str] = [] + for key in RESUME_IDENTITY_KEYS: + if key not in current_run_config or key not in saved_run_config: + mismatches.append(f"{key}: current={current_run_config.get(key)!r}, checkpoint={saved_run_config.get(key)!r}") + continue + if key in PORTABLE_RESUME_PATH_KEYS: + matches, reason = _resume_path_values_match(current_run_config[key], saved_run_config[key]) + if matches: + if str(current_run_config[key]) != str(saved_run_config[key]): + print( + f"[Resume] Accepted portable path match for {key}: " + f"current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}, reason={reason}." + ) + continue + mismatches.append( + f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r} ({reason})" + ) + continue + if not _resume_value_matches(current_run_config[key], saved_run_config[key]): + mismatches.append(f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}") + + current_s2 = current_run_config.get("strategy2_checkpoint_path") + saved_s2 = saved_run_config.get("strategy2_checkpoint_path") + if current_s2 or saved_s2: + if str(current_s2 or "") != str(saved_s2 or ""): + # Warn but do not abort: when resuming, the model weights are fully restored + # from the resume checkpoint (not re-loaded from the strategy2 checkpoint), + # so a path change (e.g. file moved/renamed) does not affect correctness. + print( + f"[WARN] strategy2_checkpoint_path changed since checkpoint was saved " + f"(current={current_s2!r}, checkpoint={saved_s2!r}). " + f"Resuming anyway — model state comes from the resume checkpoint." + ) + + if mismatches: + raise ValueError( + f"Resume checkpoint identity mismatch for {checkpoint_path}:\n" + "\n".join(f" - {line}" for line in mismatches) + ) + +def load_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> list[dict[str, Any]]: + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + epoch_metrics = checkpoint_payload.get("epoch_metrics") + history: list[dict[str, Any]] = [] + checkpoint_history = checkpoint_payload.get("history") + if isinstance(checkpoint_history, list): + history = [dict(row) for row in checkpoint_history if isinstance(row, dict)] + history = [row for row in history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, list): + raise RuntimeError(f"Expected list history at {history_path}, found {type(payload).__name__}.") + file_history = [dict(row) for row in payload if isinstance(row, dict)] + file_history = [row for row in file_history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if len(file_history) >= len(history): + history = file_history + if not history and isinstance(epoch_metrics, dict): + history = [dict(epoch_metrics)] + elif history and isinstance(epoch_metrics, dict): + if int(history[-1].get("epoch", 0)) < checkpoint_epoch: + history.append(dict(epoch_metrics)) + return history + +def train_model( + *, + run_type: str, + model_config: RuntimeModelConfig, + run_config: dict[str, Any], + model: nn.Module, + description: str, + strategy: int, + run_dir: Path, + bundle: DataBundle, + max_epochs: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + tmax: int, + entropy_lr: float, + entropy_alpha_init: float, + entropy_target_ratio: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, + dropout_p: float, + resume_checkpoint_path: Path | None = None, + trial: optuna.trial.Trial | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=description, + strategy=strategy, + model_config=model_config, + dropout_p=dropout_p, + amp_dtype=amp_dtype, + compiled=hasattr(model, "_orig_mod"), + ) + + strategy3_freeze_status = _strategy3_bootstrap_freeze_status(model) if strategy == 3 else None + strategy3_frozen_decoder = strategy == 3 and _strategy3_decoder_is_frozen(model) + decoder_head_lr = float(_job_param("decoder_lr", 0.0 if strategy3_frozen_decoder else head_lr * 0.1)) + encoder_group_lr = 0.0 if strategy3_frozen_decoder else encoder_lr + rl_group_lr = float(_job_param("rl_lr", head_lr)) + optimizer = make_optimizer( + model, + strategy, + head_lr=decoder_head_lr, + encoder_lr=encoder_group_lr, + weight_decay=weight_decay, + rl_lr=rl_group_lr, + ) + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_epochs, + eta_min=1e-6, # floor + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + + del entropy_alpha_init, entropy_lr, entropy_target_ratio + target_entropy = 0.0 + log_alpha = None + alpha_optimizer = None + + save_artifacts = run_type == "final" + save_history_incrementally = bool(run_config.get("save_history_incrementally", SAVE_HISTORY_INCREMENTALLY)) + write_epoch_diagnostic = bool(run_config.get("write_epoch_diagnostic", WRITE_EPOCH_DIAGNOSTIC)) + ckpt_dir = ensure_dir(run_dir / "checkpoints") if save_artifacts else None + plots_dir = ensure_dir(run_dir / "plots") if save_artifacts else None + history_path = checkpoint_history_path(run_dir, run_type) + diagnostic_path = diagnostic_path_for_run(run_dir) if run_type == "final" and write_epoch_diagnostic else None + history: list[dict[str, Any]] = [] + selection_metric_name = _strategy_selection_metric_name(strategy) + early_stopping_monitor_name = _early_stopping_monitor_name(strategy) + early_stopping_mode = _early_stopping_mode(strategy, early_stopping_monitor_name) + early_stopping_min_delta = _early_stopping_min_delta() + early_stopping_start_epoch = _early_stopping_start_epoch() + early_stopping_patience = _early_stopping_patience() + epoch_probe_mode = str(run_config.get("epoch_probe_mode", "fixed")).strip().lower() + best_model_metric = -float("inf") + patience_counter = 0 + best_early_stopping_metric: float | None = None + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + diagnostic_payload: dict[str, Any] | None = None + train_probe_batches: list[dict[str, Any]] = [] + val_probe_batches: list[dict[str, Any]] = [] + run_label = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ) + if diagnostic_path is not None: + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=0, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=0, + split_tag="val", + ) + else: + train_probe_batches = _fixed_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + ) + val_probe_batches = _fixed_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + ) + diagnostic_payload = empty_epoch_diagnostic_payload( + run_type=run_type, + run_config=run_config, + bundle=bundle, + train_probe_batches=train_probe_batches, + val_probe_batches=val_probe_batches, + ) + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + expected_run_type=run_type, + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + selection_metric_name = str(checkpoint_payload.get("best_metric_name", selection_metric_name)) + best_model_metric = float(checkpoint_payload["best_metric_value"]) + patience_counter = int(checkpoint_payload.get("patience_counter", 0)) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", run_type), + } + epoch_metrics = checkpoint_payload.get("epoch_metrics") + if isinstance(epoch_metrics, dict) and epoch_metrics.get("early_stopping_best_value") is not None: + best_early_stopping_metric = float(epoch_metrics["early_stopping_best_value"]) + if diagnostic_path is not None and diagnostic_payload is not None: + diagnostic_payload = load_epoch_diagnostic_for_resume( + diagnostic_path, + checkpoint_payload, + diagnostic_payload, + ) + print( + f"[Resume] {run_label} | {run_type} continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{max_epochs}." + ) + prev_params = _snapshot_params(model) if diagnostic_path is not None else None + start_time = time.time() + validate_interval = max(int(VALIDATE_EVERY_N_EPOCHS), 1) + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + + for epoch in range(start_epoch, max_epochs + 1): + epoch_losses: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_reward: list[float] = [] + epoch_entropy: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_loss: list[float] = [] + epoch_grad: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_effective_steps: list[float] = [] + epoch_mask_deltas: list[float] = [] + epoch_advantage_means: list[float] = [] + epoch_advantage_stds: list[float] = [] + epoch_value_pred_errors: list[float] = [] + epoch_reward_zero_pcts: list[float] = [] + epoch_biou_deltas: list[float] = [] + epoch_mean_value_preds: list[float] = [] + epoch_annealed_aux_ce_weights: list[float] = [] + epoch_rl_loss_scales: list[float] = [] + epoch_reinforce_losses: list[float] = [] + epoch_entropy_losses: list[float] = [] + epoch_entropy_bonuses_used: list[float] = [] + epoch_alphas: list[float] = [] + epoch_action_dists: list[dict[str, float]] = [] + + prefetcher = CUDAPrefetcher(bundle.train_loader, DEVICE) + progress = tqdm(prefetcher, total=len(bundle.train_loader), desc=f"Epoch {epoch}/{max_epochs}", leave=False) + for batch in progress: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=tmax, + critic_loss_weight=critic_loss_weight, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=ce_weight, + dice_weight=dice_weight, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=max_epochs, + ) + epoch_losses.append(metrics["loss"]) + epoch_actor.append(metrics["actor_loss"]) + epoch_critic.append(metrics["critic_loss"]) + epoch_reward.append(metrics["mean_reward"]) + epoch_entropy.append(metrics["entropy"]) + epoch_ce.append(metrics["ce_loss"]) + epoch_dice_loss.append(metrics["dice_loss"]) + epoch_grad.append(metrics["grad_norm"]) + if "effective_steps" in metrics: + epoch_effective_steps.append(float(metrics["effective_steps"])) + if "mask_delta_mean" in metrics: + epoch_mask_deltas.append(metrics["mask_delta_mean"]) + if "advantage_mean" in metrics: + epoch_advantage_means.append(metrics["advantage_mean"]) + if "advantage_std" in metrics: + epoch_advantage_stds.append(metrics["advantage_std"]) + if "value_pred_error_mean" in metrics: + epoch_value_pred_errors.append(metrics["value_pred_error_mean"]) + if "reward_zeros_pct" in metrics: + epoch_reward_zero_pcts.append(float(metrics["reward_zeros_pct"])) + if "biou_delta_mean" in metrics: + epoch_biou_deltas.append(float(metrics["biou_delta_mean"])) + if "mean_value_pred" in metrics: + epoch_mean_value_preds.append(float(metrics["mean_value_pred"])) + if "annealed_aux_ce_weight" in metrics: + epoch_annealed_aux_ce_weights.append(float(metrics["annealed_aux_ce_weight"])) + if "rl_loss_scale_used" in metrics: + epoch_rl_loss_scales.append(float(metrics["rl_loss_scale_used"])) + if "reinforce_loss" in metrics: + epoch_reinforce_losses.append(float(metrics["reinforce_loss"])) + if "entropy_loss" in metrics: + epoch_entropy_losses.append(float(metrics["entropy_loss"])) + if "entropy_bonus_used" in metrics: + epoch_entropy_bonuses_used.append(float(metrics["entropy_bonus_used"])) + if "alpha" in metrics: + epoch_alphas.append(metrics["alpha"]) + if "action_distribution" in metrics and metrics["action_distribution"]: + epoch_action_dists.append(metrics["action_distribution"]) + + pred_mask = metrics["final_mask"] + gt_mask = batch["mask"].float() + inter = (pred_mask * gt_mask).sum(dim=(1, 2, 3)) + pred_sum = pred_mask.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + epoch_dices.extend(dice.detach().cpu().tolist()) + epoch_ious.extend(iou.detach().cpu().tolist()) + + head_lr_now = float(optimizer.param_groups[-1]["lr"]) + enc_lr_now = float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else head_lr_now + progress.set_postfix(loss=f"{metrics['loss']:.4f}", iou=f"{np.mean(epoch_ious):.4f}", lr=f"{head_lr_now:.2e}") + + should_validate = epoch % validate_interval == 0 or epoch == max_epochs + val_metrics: dict[str, float | None] = { + "val_loss": None, + "val_dice": None, + "val_iou": None, + "val_biou": None, + "val_decoder_dice": None, + "val_decoder_iou": None, + "val_decoder_biou": None, + "val_dice_gain": None, + "val_iou_gain": None, + "val_biou_gain": None, + "val_reward": None, + "val_entropy": None, + } + if should_validate: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: running validation on {len(bundle.val_loader)} batches..." + ) + validated_metrics = validate( + model, + bundle.val_loader, + run_dir=run_dir, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + gamma=DEFAULT_GAMMA, + critic_loss_weight=critic_loss_weight, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + val_metrics.update(validated_metrics) + scheduler.step() # always step up the scheduler + grad_stats: dict[str, Any] = {} + param_stats: dict[str, Any] = {} + if diagnostic_path is not None: + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_epoch_action_dist: dict[str, float] = {} + if epoch_action_dists: + all_act_keys = set() + for d in epoch_action_dists: + all_act_keys.update(d.keys()) + for k in sorted(all_act_keys): + avg_epoch_action_dist[k] = float(np.mean([d.get(k, 0.0) for d in epoch_action_dists])) + + row = { + "epoch": epoch, + "train_loss": float(np.mean(epoch_losses)) if epoch_losses else 0.0, + "train_actor_loss": float(np.mean(epoch_actor)) if epoch_actor else 0.0, + "train_critic_loss": float(np.mean(epoch_critic)) if epoch_critic else 0.0, + "train_mean_reward": float(np.mean(epoch_reward)) if epoch_reward else 0.0, + "train_entropy": float(np.mean(epoch_entropy)) if epoch_entropy else 0.0, + "train_ce_loss": float(np.mean(epoch_ce)) if epoch_ce else 0.0, + "train_dice_loss": float(np.mean(epoch_dice_loss)) if epoch_dice_loss else 0.0, + "train_dice": float(np.mean(epoch_dices)) if epoch_dices else 0.0, + "train_iou": float(np.mean(epoch_ious)) if epoch_ious else 0.0, + "grad_norm": float(np.mean(epoch_grad)) if epoch_grad else 0.0, + "lr": float(optimizer.param_groups[-1]["lr"]), + "encoder_lr": float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else float(optimizer.param_groups[-1]["lr"]), + "alpha": float(log_alpha.exp().detach().item()) if log_alpha is not None else None, + "train_effective_steps": float(np.mean(epoch_effective_steps)) if epoch_effective_steps else 0.0, + "train_mask_delta_mean": float(np.mean(epoch_mask_deltas)) if epoch_mask_deltas else 0.0, + "train_advantage_mean": float(np.mean(epoch_advantage_means)) if epoch_advantage_means else 0.0, + "train_advantage_std": _nanmean_or_default(epoch_advantage_stds, 0.0), + "train_value_pred_error": float(np.mean(epoch_value_pred_errors)) if epoch_value_pred_errors else 0.0, + "train_reward_zeros_pct": float(np.mean(epoch_reward_zero_pcts)) if epoch_reward_zero_pcts else 0.0, + "train_biou_delta_mean": float(np.mean(epoch_biou_deltas)) if epoch_biou_deltas else 0.0, + "train_mean_value_pred": float(np.mean(epoch_mean_value_preds)) if epoch_mean_value_preds else 0.0, + "train_annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "train_rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "train_reinforce_loss": float(np.mean(epoch_reinforce_losses)) if epoch_reinforce_losses else 0.0, + "train_entropy_loss": float(np.mean(epoch_entropy_losses)) if epoch_entropy_losses else 0.0, + "train_entropy_bonus_used": float(np.mean(epoch_entropy_bonuses_used)) if epoch_entropy_bonuses_used else 0.0, + "train_alpha": float(np.mean(epoch_alphas)) if epoch_alphas else 0.0, + "train_action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + "validated_this_epoch": should_validate, + **val_metrics, + } + if strategy == 3: + if row["train_advantage_std"] < 0.005: + low_advantage_std_streak += 1 + else: + low_advantage_std_streak = 0 + if abs(row["train_mean_value_pred"]) < 0.001: + low_mean_value_pred_streak += 1 + else: + low_mean_value_pred_streak = 0 + else: + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + if strategy3_freeze_status is not None: + row["strategy3_bootstrap_loaded"] = bool(strategy3_freeze_status["bootstrap_loaded"]) + row["strategy3_freeze_requested"] = bool(strategy3_freeze_status["freeze_requested"]) + row["strategy3_freeze_active"] = bool(strategy3_freeze_status["freeze_active"]) + row["strategy3_encoder_state"] = str(strategy3_freeze_status["encoder_state"]) + row["strategy3_decoder_state"] = str(strategy3_freeze_status["decoder_state"]) + row["strategy3_segmentation_head_state"] = str(strategy3_freeze_status["segmentation_head_state"]) + history.append(row) + + improved = False + early_stopping_improved_now = False + if should_validate: + selected_metric_value = _strategy_selection_metric_value(strategy, val_metrics) + row["selection_metric_name"] = selection_metric_name + row["selection_metric_value"] = selected_metric_value + improved = selected_metric_value > best_model_metric + if improved: + best_model_metric = selected_metric_value + if trial is not None: + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR, float(best_model_metric)) + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR, selection_metric_name) + early_stopping_monitor_value = _early_stopping_monitor_value( + row, + strategy=strategy, + monitor_name=early_stopping_monitor_name, + ) + early_stopping_active = epoch >= early_stopping_start_epoch + if early_stopping_active and early_stopping_monitor_value is not None: + early_stopping_improved_now = _early_stopping_improved( + early_stopping_monitor_value, + best_early_stopping_metric, + mode=early_stopping_mode, + min_delta=early_stopping_min_delta, + ) + if early_stopping_improved_now: + best_early_stopping_metric = early_stopping_monitor_value + patience_counter = 0 + else: + patience_counter += 1 + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = early_stopping_monitor_value + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = early_stopping_active + row["early_stopping_improved"] = early_stopping_improved_now + row["early_stopping_wait"] = int(patience_counter) + if improved and save_artifacts and ckpt_dir is not None: + save_checkpoint( + ckpt_dir / "best.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + else: + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = None + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = False + row["early_stopping_improved"] = False + row["early_stopping_wait"] = int(patience_counter) + + if save_artifacts and save_history_incrementally: + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + save_json(history_path, history) + + if diagnostic_path is not None and diagnostic_payload is not None: + epoch_alerts = _numerical_health_check(row, prefix=f"epoch[{epoch}]:") + if low_advantage_std_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] advantage_std collapsed - RL gradient near zero") + if low_mean_value_pred_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] critic degenerate - mean value prediction stuck near zero") + if int(grad_stats.get("n_nan", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected NaN gradients") + if int(grad_stats.get("n_inf", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected Inf gradients") + + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="val", + ) + + train_probe = _evaluate_probe_batches( + model, + train_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="train", + mc_cache_run_dir=run_dir, + ) + val_probe = _evaluate_probe_batches( + model, + val_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + epoch_alerts.extend(train_probe.get("alerts", [])) + epoch_alerts.extend(val_probe.get("alerts", [])) + + diagnostic_payload["epochs"].append( + { + "epoch": epoch, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "is_new_best": bool(improved), + "best_metric_name": selection_metric_name, + "best_metric_value_so_far": float(best_model_metric), + "patience_counter": int(patience_counter), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value_so_far": best_early_stopping_metric, + "history_row": dict(row), + "train_batch_summary": { + "loss": _summary_stats(epoch_losses), + "actor_loss": _summary_stats(epoch_actor), + "critic_loss": _summary_stats(epoch_critic), + "reward": _summary_stats(epoch_reward), + "entropy": _summary_stats(epoch_entropy), + "ce_loss": _summary_stats(epoch_ce), + "dice_loss": _summary_stats(epoch_dice_loss), + "grad_norm": _summary_stats(epoch_grad), + "dice": _summary_stats(epoch_dices), + "iou": _summary_stats(epoch_ious), + "effective_steps": _summary_stats(epoch_effective_steps), + "mask_delta": _summary_stats(epoch_mask_deltas), + "advantage_mean": _summary_stats(epoch_advantage_means), + "advantage_std": _summary_stats(epoch_advantage_stds), + "value_pred_error": _summary_stats(epoch_value_pred_errors), + "reward_zeros_pct": _summary_stats(epoch_reward_zero_pcts), + "biou_delta_mean": _summary_stats(epoch_biou_deltas), + "mean_value_pred": _summary_stats(epoch_mean_value_preds), + "annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "reinforce_loss": _summary_stats(epoch_reinforce_losses), + "entropy_loss": _summary_stats(epoch_entropy_losses), + "entropy_bonus_used": _summary_stats(epoch_entropy_bonuses_used), + "alpha": _summary_stats(epoch_alphas), + "action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + }, + "optimizer": { + "param_groups": _optimizer_diagnostics(optimizer), + "scheduler_last_lr": [float(value) for value in scheduler.get_last_lr()], + "target_entropy": float(target_entropy) if log_alpha is not None else 0.0, + }, + "grad_diagnostics": grad_stats, + "param_diagnostics": param_stats, + "probe_batches": { + "mode": epoch_probe_mode, + "train": _probe_batch_id_lists(train_probe_batches), + "val": _probe_batch_id_lists(val_probe_batches), + }, + "probes": { + "train_fixed": train_probe, + "val_fixed": val_probe, + }, + "probe_epoch_summary": { + "train_fixed": _format_probe_deterioration("train", train_probe, tmax), + "val_fixed": _format_probe_deterioration("val", val_probe, tmax), + }, + "alerts": epoch_alerts, + } + ) + save_json(diagnostic_path, diagnostic_payload) + + if save_artifacts and ckpt_dir is not None and SAVE_LATEST_EVERY_EPOCH and run_type != "trial": + save_checkpoint( + ckpt_dir / "latest.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + if save_artifacts and ckpt_dir is not None and CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0 and run_type != "trial": + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + + if trial is not None and should_validate: + reported_metric = row.get("selection_metric_value") + if reported_metric is None: + reported_metric = _strategy_selection_metric_value(strategy, val_metrics) + if reported_metric is None: + reported_metric = float(val_metrics["val_iou"]) + reported_metric = float(reported_metric) + trial.report(reported_metric, step=epoch) + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot( + trial, + current_best_value=best_model_metric, + ) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + if USE_TRIAL_PRUNING and epoch >= TRIAL_PRUNER_WARMUP_STEPS and trial.should_prune(): + raise optuna.TrialPruned( + f"Trial pruned at epoch {epoch} with " + f"{selection_metric_name}={reported_metric:.4f}" + ) + + if trial is not None and row.get("study_best_objective") is None: + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot(trial) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + + if VERBOSE_EPOCH_LOG: + tqdm.write(f"[{run_label}] Epoch {epoch}/{max_epochs}") + tqdm.write(json.dumps(row, indent=2)) + else: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: " + f"{format_concise_epoch_log(row, best_metric_name=selection_metric_name, best_metric_value=best_model_metric)}" + ) + + if should_validate and early_stopping_patience > 0 and patience_counter >= early_stopping_patience: + print( + f"Early stopping triggered at epoch {epoch}: " + f"monitor={early_stopping_monitor_name} mode={early_stopping_mode} " + f"best={best_early_stopping_metric} current={row.get('early_stopping_monitor_value')} " + f"min_delta={early_stopping_min_delta:.6g} wait={patience_counter}/{early_stopping_patience}." + ) + break + + elapsed = elapsed_before_resume + (time.time() - start_time) + if save_artifacts: + save_json(history_path, history) + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + summary = { + "best_model_metric_name": selection_metric_name, + "best_model_metric": float(best_model_metric), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value": best_early_stopping_metric, + "best_val_iou": max((float(r["val_iou"]) for r in history if r.get("val_iou") is not None), default=0.0), + "best_val_dice": max((float(r["val_dice"]) for r in history if r.get("val_dice") is not None), default=0.0), + "best_val_biou": max((float(r["val_biou"]) for r in history if r.get("val_biou") is not None), default=0.0), + "best_val_iou_gain": max((float(r["val_iou_gain"]) for r in history if r.get("val_iou_gain") is not None), default=0.0), + "best_val_biou_gain": max((float(r["val_biou_gain"]) for r in history if r.get("val_biou_gain") is not None), default=0.0), + "final_epoch": int(history[-1]["epoch"]) if history else int(start_epoch - 1), + "elapsed_seconds": elapsed, + "seconds_per_epoch": elapsed / max(len(history), 1), + "device_used": str(DEVICE), + "strategy": strategy, + "run_type": run_type, + "resumed": resume_source is not None, + } + if strategy3_freeze_status is not None: + summary.update( + { + "strategy3_bootstrap_loaded": bool(strategy3_freeze_status["bootstrap_loaded"]), + "strategy3_freeze_requested": bool(strategy3_freeze_status["freeze_requested"]), + "strategy3_freeze_active": bool(strategy3_freeze_status["freeze_active"]), + "strategy3_encoder_state": str(strategy3_freeze_status["encoder_state"]), + "strategy3_decoder_state": str(strategy3_freeze_status["decoder_state"]), + "strategy3_segmentation_head_state": str(strategy3_freeze_status["segmentation_head_state"]), + } + ) + if resume_source is not None: + summary["resume_source"] = resume_source + if save_artifacts: + save_json(run_dir / "summary.json", summary) + return summary, history + +"""============================================================================= +EVALUATION + SMOKE TEST +============================================================================= +""" + +def _save_rgb_panel(image_chw: np.ndarray, pred_hw: np.ndarray, gt_hw: np.ndarray, output_path: Path, title: str) -> None: + img = image_chw.transpose(1, 2, 0) + img = (img - img.min()) / (img.max() - img.min() + 1e-8) + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(img) + axes[0].set_title("Input") + axes[1].imshow(pred_hw, cmap="gray", vmin=0, vmax=1) + axes[1].set_title("Prediction") + axes[2].imshow(gt_hw, cmap="gray", vmin=0, vmax=1) + axes[2].set_title("Ground Truth") + for ax in axes: + ax.axis("off") + fig.suptitle(title) + fig.tight_layout() + fig.savefig(output_path, dpi=120) + plt.close(fig) + + +def _synchronize_device_for_timing(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _write_evaluation_timing_csv( + path: Path, + *, + timing_summary: dict[str, Any], +) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "scope", + "strategy", + "tmax", + "device", + "num_batches", + "num_samples", + "total_inference_ms", + "avg_batch_inference_ms", + "std_batch_inference_ms", + "avg_sample_inference_ms", + "std_sample_inference_ms", + "mean_per_image_inference_ms", + "std_per_image_inference_ms", + "mean_per_image_inference_seconds", + "std_per_image_inference_seconds", + ], + ) + writer.writeheader() + writer.writerow( + { + "scope": str(timing_summary["scope"]), + "strategy": int(timing_summary["strategy"]), + "tmax": int(timing_summary["tmax"]), + "device": str(timing_summary["device"]), + "num_batches": int(timing_summary["num_batches"]), + "num_samples": int(timing_summary["num_samples"]), + "total_inference_ms": f"{float(timing_summary['total_inference_ms']):.6f}", + "avg_batch_inference_ms": f"{float(timing_summary['avg_batch_inference_ms']):.6f}", + "std_batch_inference_ms": f"{float(timing_summary['std_batch_inference_ms']):.6f}", + "avg_sample_inference_ms": f"{float(timing_summary['avg_sample_inference_ms']):.6f}", + "std_sample_inference_ms": f"{float(timing_summary['std_sample_inference_ms']):.6f}", + "mean_per_image_inference_ms": f"{float(timing_summary['mean_per_image_inference_ms']):.6f}", + "std_per_image_inference_ms": f"{float(timing_summary['std_per_image_inference_ms']):.6f}", + "mean_per_image_inference_seconds": f"{float(timing_summary['mean_per_image_inference_seconds']):.9f}", + "std_per_image_inference_seconds": f"{float(timing_summary['std_per_image_inference_seconds']):.9f}", + } + ) + + +def evaluate_model( + *, + model: nn.Module, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + strategy: int, + tmax: int, + best_metric_name: str, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + pred_dir = ensure_dir(run_dir / "predictions") + pred_255_dir = ensure_dir(run_dir / "predictions_255") + + model.eval() + per_metric = {k: [] for k in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95")} + per_sample: list[dict[str, Any]] = [] + inference_total_ms = 0.0 + inference_batch_count = 0 + inference_sample_count = 0 + inference_batch_times_ms: list[float] = [] + inference_sample_times_ms: list[float] = [] + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + + with torch.inference_mode(): + prefetcher = CUDAPrefetcher(bundle.test_loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(bundle.test_loader), desc="Evaluating", leave=False): + image = batch["image"] + gt = batch["mask"] + sample_ids = [str(item) for item in batch["sample_id"]] + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + _synchronize_device_for_timing(DEVICE) + inference_start = time.perf_counter() + pred = infer_segmentation_mask( + model, + image, + tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="test", + mc_cache_run_dir=run_dir, + ).float() + _synchronize_device_for_timing(DEVICE) + inference_elapsed_ms = (time.perf_counter() - inference_start) * 1000.0 + inference_total_ms += inference_elapsed_ms + inference_batch_count += 1 + batch_size = int(pred.shape[0]) + inference_sample_count += batch_size + inference_batch_times_ms.append(float(inference_elapsed_ms)) + per_image_inference_ms = float(inference_elapsed_ms) / float(max(batch_size, 1)) + inference_sample_times_ms.extend([per_image_inference_ms] * batch_size) + pred_np = pred.cpu().numpy().astype(np.uint8) + gt_np = gt.cpu().numpy().astype(np.uint8) + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + for key, value in metrics.items(): + per_metric.setdefault(key, []).append(value) + per_sample.append( + { + "sample_id": sample_ids[idx], + **metrics, + "inference_time_ms": per_image_inference_ms, + "inference_time_seconds": per_image_inference_ms / 1000.0, + } + ) + mask_2d = pred_np[idx].squeeze() + PILImage.fromarray(mask_2d).save(pred_dir / f"{sample_ids[idx]}.png") + PILImage.fromarray((mask_2d * 255).astype(np.uint8)).save(pred_255_dir / f"{sample_ids[idx]}.png") + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate: dict[str, dict[str, float]] = {} + for key, values in per_metric.items(): + values_np = np.array(values, dtype=np.float32) + aggregate[key] = {"mean": float(values_np.mean()), "std": float(values_np.std())} + batch_times_np = np.array(inference_batch_times_ms, dtype=np.float64) + sample_times_np = np.array(inference_sample_times_ms, dtype=np.float64) + timing_summary = { + "scope": "test_set_evaluation", + "strategy": int(strategy), + "tmax": int(tmax), + "device": str(DEVICE), + "num_batches": int(inference_batch_count), + "num_samples": int(inference_sample_count), + "total_inference_ms": float(inference_total_ms), + "avg_batch_inference_ms": float(batch_times_np.mean()) if batch_times_np.size > 0 else 0.0, + "std_batch_inference_ms": float(batch_times_np.std()) if batch_times_np.size > 0 else 0.0, + "avg_sample_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_sample_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_seconds": float(sample_times_np.mean() / 1000.0) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_seconds": float(sample_times_np.std() / 1000.0) if sample_times_np.size > 0 else 0.0, + } + + save_json( + run_dir / "evaluation.json", + { + "strategy": strategy, + "best_metric_name": str(best_metric_name), + "metrics": aggregate, + "per_sample": per_sample, + "timing": timing_summary, + }, + ) + + df_all = pd.DataFrame(per_sample) + avg_row = {} + for column in df_all.columns: + avg_row[column] = df_all[column].mean() if pd.api.types.is_numeric_dtype(df_all[column]) else "AVERAGE" + df_samples = pd.concat([df_all, pd.DataFrame([avg_row])], ignore_index=True) + df_summary = pd.DataFrame(aggregate).T + df_summary.index.name = "metric" + df_low_iou = df_all[df_all["iou"] < 0.01] + history_path = run_dir / "history.json" + df_history = pd.DataFrame(load_json(history_path)) if history_path.exists() else None + + xlsx_path = run_dir / "evaluation_results.xlsx" + with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer: + df_samples.to_excel(writer, sheet_name="Per Sample", index=False) + df_summary.to_excel(writer, sheet_name="Summary") + if not df_low_iou.empty: + df_low_iou.to_excel(writer, sheet_name="Low IoU Samples", index=False) + if df_history is not None: + df_history.to_excel(writer, sheet_name="Training History", index=False) + + csv_rows = [{"sample_id": row["sample_id"]} for row in df_low_iou.to_dict(orient="records")] + save_json(run_dir / "evaluation_summary.json", {"mean_iou": aggregate["iou"]["mean"], "mean_dice": aggregate["dice"]["mean"]}) + pd.DataFrame(csv_rows).to_csv(run_dir / "low_iou_samples.csv", index=False) + _write_evaluation_timing_csv( + run_dir / "timing.csv", + timing_summary=timing_summary, + ) + return aggregate, per_sample + +def percent_root(percent: float) -> Path: + return ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}") + +def strategy_dir_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + return f"strategy_{strategy}_custom_vgg" + return f"strategy_{strategy}" + +def strategy_root_for_percent( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(percent_root(percent) / strategy_dir_name(strategy, model_config)) + +def final_root_for_strategy( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(strategy_root_for_percent(strategy, percent, model_config) / "final") + +def ensure_specific_checkpoint_scope(selector_name: str, selector_mode: str) -> None: + if selector_mode != "specific": + return + # Allow STRATEGY2 checkpoints with dict mapping for dynamic per-percent selection + if "strategy2" in selector_name.lower() and isinstance(STRATEGY2_SPECIFIC_CHECKPOINT, dict): + return + if len(STRATEGIES) != 1 or len(DATASET_PERCENTS) != 1: + raise ValueError( + f"{selector_name}=specific is only supported when exactly one strategy and one dataset percent are selected. " + f"Got STRATEGIES={STRATEGIES} and DATASET_PERCENTS={DATASET_PERCENTS}." + ) + +def resolve_checkpoint_path( + *, + run_dir: Path, + selector_mode: str, + specific_checkpoint: str | Path | dict, + purpose: str, +) -> Path: + run_dir = Path(run_dir) + if selector_mode == "latest": + checkpoint_path = run_dir / "checkpoints" / "latest.pt" + elif selector_mode == "best": + checkpoint_path = run_dir / "checkpoints" / "best.pt" + elif selector_mode == "specific": + ensure_specific_checkpoint_scope(purpose, selector_mode) + if not specific_checkpoint: + raise ValueError(f"{purpose}=specific requires a non-empty specific checkpoint path.") + checkpoint_path = Path(specific_checkpoint).expanduser().resolve() + else: + raise ValueError(f"Unsupported checkpoint selector mode '{selector_mode}' for {purpose}.") + + if not checkpoint_path.exists(): + raise FileNotFoundError(f"Checkpoint for {purpose} not found: {checkpoint_path}") + return checkpoint_path + +def resolve_train_resume_checkpoint_path(run_dir: Path) -> Path | None: + if TRAIN_RESUME_MODE == "off": + return None + return resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=TRAIN_RESUME_MODE, + specific_checkpoint=TRAIN_RESUME_SPECIFIC_CHECKPOINT, + purpose="train_resume_checkpoint", + ) + +def resolve_strategy2_checkpoint_path( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + if strategy != 3: + raise ValueError(f"Strategy 2 dependency checkpoint requested for unsupported strategy {strategy}.") + + specific_checkpoint = STRATEGY2_SPECIFIC_CHECKPOINT + if isinstance(specific_checkpoint, dict): + ctx = globals().get("CURRENT_FOLD_CONTEXT") + _phase_mode_fn = globals().get("using_fixed_phase_mode") + in_phase_mode = callable(_phase_mode_fn) and _phase_mode_fn() + if in_phase_mode and ctx is not None: + # Phase mode: key by phase index (split_repeat_index) + specific_checkpoint = specific_checkpoint.get(ctx.split_repeat_index, "") + else: + # Non-phase mode: key by dataset percent (float) + specific_checkpoint = specific_checkpoint.get(percent, "") + + checkpoint_path = resolve_checkpoint_path( + run_dir=final_root_for_strategy(2, percent, model_config), + selector_mode=STRATEGY2_CHECKPOINT_MODE, + specific_checkpoint=specific_checkpoint, + purpose="strategy2_checkpoint", + ) + + # Print which checkpoint is being used + checkpoint_label = run_identity_label(strategy=strategy, percent=percent) + ctx = globals().get("CURRENT_FOLD_CONTEXT") + if ctx is not None and abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12: + checkpoint_label = run_identity_label( + strategy=strategy, + percent=percent, + split_payload={ + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "dataset_percent": ctx.percent_fraction, + }, + ) + print(f"[Strategy 2 Checkpoint] {checkpoint_label} | Loading: {checkpoint_path}") + + return checkpoint_path + +def load_required_hparams(payload: dict[str, Any], *, source: str, strategy: int, percent: float) -> dict[str, Any]: + missing_keys = [name for name in REQUIRED_HPARAM_KEYS if name not in payload] + if missing_keys: + raise KeyError( + f"Incomplete hyperparameters for strategy={strategy}, percent={percent_text(percent)} from {source}. " + f"Missing keys: {missing_keys}. Required keys: {REQUIRED_HPARAM_KEYS}." + ) + return dict(payload) + +def load_saved_best_params_if_optuna_off( + strategy: int, + percent: float, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + _, study_root, _ = study_paths_for(strategy, percent, model_config) + best_params_path = study_root / "best_params.json" + if not best_params_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=True, but no saved best params were found " + f"for strategy={strategy}, percent={percent_text(percent)} at {best_params_path}." + ) + params = load_json(best_params_path) + params = load_required_hparams( + params, + source=str(best_params_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using saved best parameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {best_params_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def load_manual_hparams_if_optuna_off(strategy: int, percent: float) -> dict[str, Any]: + key = manual_hparams_key(strategy, percent) + if key not in MANUAL_HPARAMS_IF_OPTUNA_OFF: + raise KeyError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but no manual hyperparameter " + f"JSON filename was found for strategy={strategy}, percent={percent_text(percent)} under key '{key}'. " + f"Required keys: {REQUIRED_HPARAM_KEYS}." + ) + manual_filename = MANUAL_HPARAMS_IF_OPTUNA_OFF[key] + manual_path = (HARD_CODED_PARAM_DIR / manual_filename).resolve() + if not manual_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but the manual hyperparameter " + f"JSON file for strategy={strategy}, percent={percent_text(percent)} was not found at {manual_path}. " + f"Configured key='{key}', filename='{manual_filename}'." + ) + params = load_json(manual_path) + params = load_required_hparams( + params, + source=str(manual_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using manual hyperparameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {manual_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def resolve_job_params( + strategy: int, + percent: float, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if RUN_OPTUNA: + banner( + f"OPTUNA STUDY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return run_study( + strategy, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + if USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + return load_saved_best_params_if_optuna_off(strategy, percent, model_config=model_config) + + return load_manual_hparams_if_optuna_off(strategy, percent) + +def read_run_config_for_eval(run_dir: Path, checkpoint_path: Path) -> dict[str, Any]: + run_config_path = Path(run_dir) / "run_config.json" + if run_config_path.exists(): + return load_json(run_config_path) + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + return checkpoint_run_config_payload(ckpt) + +def run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + checkpoint_path = resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=EVAL_CHECKPOINT_MODE, + specific_checkpoint=EVAL_SPECIFIC_CHECKPOINT, + purpose="evaluation_checkpoint", + ) + effective_run_dir = Path(run_dir) + if not (effective_run_dir / "run_config.json").exists() and checkpoint_path.parent.name == "checkpoints": + effective_run_dir = checkpoint_path.parent.parent + print( + f"[Evaluation] {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)} " + f"| checkpoint={checkpoint_path}" + ) + runtime_config = read_run_config_for_eval(effective_run_dir, checkpoint_path) + set_current_job_params(runtime_config) + model_config = RuntimeModelConfig.from_payload(runtime_config).validate() + if strategy == 3 and runtime_config.get("strategy2_checkpoint_path"): + strategy2_checkpoint_path = runtime_config["strategy2_checkpoint_path"] + elif strategy == 3: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + dropout_p = float(runtime_config.get("dropout_p", DEFAULT_DROPOUT_P)) + tmax = int(runtime_config.get("tmax", DEFAULT_TMAX)) + eval_model, _description, _compiled = build_model( + strategy, + dropout_p, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + checkpoint_payload = load_checkpoint( + checkpoint_path, + model=eval_model, + device=DEVICE, + ) + best_metric_name = str( + checkpoint_payload.get("best_metric_name") + or runtime_config.get("best_checkpoint_metric_name") + or _strategy_selection_metric_name(strategy) + ) + aggregate, per_sample = evaluate_model( + model=eval_model, + model_config=model_config, + bundle=bundle, + run_dir=effective_run_dir, + strategy=strategy, + tmax=tmax, + best_metric_name=best_metric_name, + ) + evaluation_json_path = effective_run_dir / "evaluation.json" + evaluation_payload = load_json(evaluation_json_path) + evaluation_payload["checkpoint_mode"] = EVAL_CHECKPOINT_MODE + evaluation_payload["checkpoint_path"] = str(checkpoint_path) + evaluation_payload["best_metric_name"] = best_metric_name + if checkpoint_payload.get("best_metric_value") is not None: + evaluation_payload["best_metric_value"] = float(checkpoint_payload["best_metric_value"]) + save_json(evaluation_json_path, evaluation_payload) + del eval_model + run_cuda_cleanup( + context=f"evaluation {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return aggregate, per_sample + +def run_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + smoke_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + banner( + f"PRE-TRAINING SMOKE TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + smoke_root = ensure_dir(smoke_root) + if RUN_OPTUNA: + set_current_job_params() + elif USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + set_current_job_params( + load_saved_best_params_if_optuna_off(strategy, bundle.percent, model_config=model_config) + ) + else: + set_current_job_params(load_manual_hparams_if_optuna_off(strategy, bundle.percent)) + sample = bundle.test_ds[SMOKE_TEST_SAMPLE_INDEX] + image = sample["image"].unsqueeze(0).to(DEVICE) + raw_image = sample["image"].numpy() + raw_gt = sample["mask"].squeeze(0).numpy() + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + print_model_parameter_summary( + model=model, + description=f"{description} | Smoke Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + pred = infer_segmentation_mask( + model, + image, + DEFAULT_TMAX, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=_use_channels_last_for_run(model_config), + sample_ids=[str(sample["sample_id"])], + mc_cache_split="test", + mc_cache_run_dir=smoke_root, + ).float() + pred_np = pred[0, 0].detach().cpu().numpy() + panel_path = smoke_root / "smoke_panel.png" + raw_mask_path = smoke_root / "smoke_prediction.png" + _save_rgb_panel( + raw_image, + pred_np, + raw_gt, + panel_path, + f"Smoke Test | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}", + ) + PILImage.fromarray((pred_np * 255).astype(np.uint8)).save(raw_mask_path) + del model + run_cuda_cleanup( + context=f"smoke {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + print( + f"[Smoke Test] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} passed. " + f"Saved panel to {panel_path.name} and mask to {raw_mask_path.name}." + ) + +"""============================================================================= +OVERFIT TEST +============================================================================= +""" + +OVERFIT_HISTORY_KEYS = ( + "dice", + "iou", + "loss", + "reward", + "actor_loss", + "critic_loss", + "ce_loss", + "dice_loss", + "entropy", + "grad_norm", + "action_dist", + "reward_pos_pct", + "pred_fg_pct", + "gt_fg_pct", +) + +def empty_overfit_history() -> dict[str, list[Any]]: + return {key: [] for key in OVERFIT_HISTORY_KEYS} + +def load_overfit_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> dict[str, list[Any]]: + history = empty_overfit_history() + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict history at {history_path}, found {type(payload).__name__}.") + for key in OVERFIT_HISTORY_KEYS: + values = payload.get(key, []) + if isinstance(values, list): + history[key] = list(values[:checkpoint_epoch]) + return history + + epoch_metrics = checkpoint_payload.get("epoch_metrics", {}) + if isinstance(epoch_metrics, dict): + for key in OVERFIT_HISTORY_KEYS: + if key in epoch_metrics: + history[key].append(epoch_metrics[key]) + return history + +def _grad_diagnostics(model: nn.Module) -> dict[str, Any]: + raw = _unwrap_compiled(model) + groups: dict[str, list[float]] = {} + total_sq = 0.0 + n_nan = 0 + n_inf = 0 + n_zero = 0 + n_total_params = 0 + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + n_total_params += 1 + if param.grad is None: + n_zero += 1 + continue + grad_norm = float(param.grad.data.norm(2).item()) + if math.isnan(grad_norm): + n_nan += 1 + continue + if math.isinf(grad_norm): + n_inf += 1 + continue + total_sq += grad_norm ** 2 + group_name = name.split(".", 1)[0] + groups.setdefault(group_name, []).append(grad_norm) + + group_stats: dict[str, dict[str, float | int]] = {} + for group_name, norms in groups.items(): + group_stats[group_name] = { + "min": min(norms), + "max": max(norms), + "mean": sum(norms) / len(norms), + "count": len(norms), + } + return { + "global_norm": total_sq ** 0.5, + "groups": group_stats, + "n_nan": n_nan, + "n_inf": n_inf, + "n_zero_grad": n_zero, + "n_total": n_total_params, + } + +def _param_diagnostics(model: nn.Module, prev_params: dict[str, torch.Tensor] | None = None) -> dict[str, dict[str, float]]: + raw = _unwrap_compiled(model) + info: dict[str, dict[str, list[float]]] = {} + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + param_norm = float(param.data.norm(2).item()) + group_name = name.split(".", 1)[0] + entry = info.setdefault(group_name, {"norms": [], "update_ratios": []}) + entry["norms"].append(param_norm) + if prev_params is not None and name in prev_params: + delta = float((param.data - prev_params[name]).norm(2).item()) + entry["update_ratios"].append(delta / max(param_norm, 1e-12)) + + summary: dict[str, dict[str, float]] = {} + for group_name, values in info.items(): + norms = values["norms"] + ratios = values["update_ratios"] + summary[group_name] = { + "p_min": min(norms), + "p_max": max(norms), + "p_mean": sum(norms) / len(norms), + } + if ratios: + summary[group_name]["ur_min"] = min(ratios) + summary[group_name]["ur_max"] = max(ratios) + summary[group_name]["ur_mean"] = sum(ratios) / len(ratios) + return summary + +def _snapshot_params(model: nn.Module) -> dict[str, torch.Tensor]: + raw = _unwrap_compiled(model) + return { + name: param.data.detach().clone() + for name, param in raw.named_parameters() + if param.requires_grad + } + +def _action_distribution( + model: nn.Module, + image: torch.Tensor, + seg: torch.Tensor, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + *, + strategy: int | None = None, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[list[dict[str, float]], torch.Tensor]: + distributions: list[dict[str, float]] = [] + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_action_distribution") if strategy != 2 else max(int(tmax), 1) + refinement_context: dict[str, torch.Tensor] | None = None + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = seg.float() + for _step in range(effective_tmax): + if refinement_context is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _ = model.forward_from_state(state) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta).to(dtype=seg.dtype) + distributions.append(_strategy3_delta_distribution(delta)) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + total_pixels = max(actions.numel(), 1) + step_dist: dict[str, float] = {} + action_count = int(policy_logits.shape[1]) + for action_idx in range(action_count): + step_dist[str(action_idx)] = float((actions == action_idx).sum().item()) / total_pixels * 100.0 + distributions.append(step_dist) + if refinement_context is not None: + return distributions, threshold_binary_mask(seg.float()).float() + return distributions, seg + +def _numerical_health_check(outputs_dict: dict[str, Any], prefix: str = "") -> list[str]: + alerts: list[str] = [] + for name, value in outputs_dict.items(): + if value is None: + continue + if isinstance(value, (int, float)): + if math.isnan(value): + alerts.append(f"{prefix}{name} = NaN") + elif math.isinf(value): + alerts.append(f"{prefix}{name} = Inf") + elif name == "train_reward_zeros_pct" and float(value) > 98.0: + alerts.append(f"{prefix}reward is degenerate (>98% zero-reward pixels)") + continue + if torch.is_tensor(value): + if torch.isnan(value).any(): + alerts.append(f"{prefix}{name} contains NaN") + if torch.isinf(value).any(): + alerts.append(f"{prefix}{name} contains Inf") + return alerts + +def _batch_binary_metrics(pred: torch.Tensor, gt: torch.Tensor) -> tuple[list[float], list[float]]: + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt.detach().cpu().numpy().astype(np.uint8) + dices: list[float] = [] + ious: list[float] = [] + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + dices.append(float(metrics["dice"])) + ious.append(float(metrics["iou"])) + return dices, ious + +def diagnostic_path_for_run(run_dir: Path) -> Path: + return Path(run_dir) / "diagnostic.json" + +def _summary_stats(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "mean": None, "std": None, "min": None, "max": None} + arr = np.asarray(values, dtype=np.float64) + return { + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std()), + "min": float(arr.min()), + "max": float(arr.max()), + } + +def _nanmean_or_default(values: list[float], default: float = 0.0) -> float: + if not values: + return float(default) + arr = np.asarray(values, dtype=np.float64) + if np.isnan(arr).all(): + return float(default) + return float(np.nanmean(arr)) + +def _tensor_stats(tensor: torch.Tensor | None) -> dict[str, Any] | None: + if tensor is None: + return None + data = tensor.detach().float() + flat = data.reshape(-1) + if flat.numel() == 0: + return {"shape": list(data.shape), "dtype": str(tensor.dtype), "numel": 0} + return { + "shape": list(data.shape), + "dtype": str(tensor.dtype), + "numel": int(flat.numel()), + "mean": float(flat.mean().item()), + "std": float(flat.std(unbiased=False).item()), + "min": float(flat.min().item()), + "max": float(flat.max().item()), + } + +def _action_histogram(actions: torch.Tensor, action_count: int) -> dict[str, float]: + total_pixels = max(actions.numel(), 1) + return { + str(action_idx): float((actions == action_idx).sum().item()) / total_pixels * 100.0 + for action_idx in range(action_count) + } + +def _jsonable_action_distribution(distributions: list[dict[int, float]] | list[dict[str, float]]) -> list[dict[str, float]]: + jsonable: list[dict[str, float]] = [] + for step_dist in distributions: + jsonable.append({str(key): float(value) for key, value in step_dist.items()}) + return jsonable + +def _average_action_distributions( + distributions_per_batch: list[list[dict[str, float]]], + steps: int, +) -> list[dict[str, float]]: + averaged: list[dict[str, float]] = [] + if not distributions_per_batch: + return averaged + for step_idx in range(steps): + action_keys = sorted( + { + str(action_idx) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) + for action_idx in batch_dist[step_idx].keys() + } + ) + if not action_keys: + continue + step_summary: dict[str, float] = {} + for action_idx in action_keys: + values = [ + float(batch_dist[step_idx][action_idx]) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) and action_idx in batch_dist[step_idx] + ] + step_summary[str(action_idx)] = float(np.mean(values)) if values else 0.0 + averaged.append(step_summary) + return averaged + +def _trajectory_degradation_summary(step_trace: list[dict[str, Any]]) -> dict[str, Any]: + if not step_trace: + return {} + ious = [float(step.get("iou_mean", 0.0)) for step in step_trace] + dices = [float(step.get("dice_mean", 0.0)) for step in step_trace] + ts = [int(step.get("t", idx)) for idx, step in enumerate(step_trace)] + init_iou = ious[0] + init_dice = dices[0] + best_iou = max(ious) + best_dice = max(dices) + best_iou_t = ts[ious.index(best_iou)] + best_dice_t = ts[dices.index(best_dice)] + first_worse_than_initial_iou_t = next((ts[idx] for idx, value in enumerate(ious[1:], start=1) if value < init_iou - 1e-6), None) + first_worse_than_prev_iou_t = next((ts[idx] for idx in range(1, len(ious)) if ious[idx] < ious[idx - 1] - 1e-6), None) + largest_iou_drop = max(best_iou - value for value in ious) + largest_iou_drop_t = ts[max(range(len(ious)), key=lambda idx: best_iou - ious[idx])] + return { + "steps_recorded": len(step_trace) - 1, + "best_iou_t": best_iou_t, + "best_iou": best_iou, + "best_dice_t": best_dice_t, + "best_dice": best_dice, + "final_t": ts[-1], + "final_iou": ious[-1], + "final_dice": dices[-1], + "delta_final_vs_init_iou": ious[-1] - init_iou, + "delta_final_vs_init_dice": dices[-1] - init_dice, + "delta_final_vs_best_iou": ious[-1] - best_iou, + "delta_final_vs_best_dice": dices[-1] - best_dice, + "first_worse_than_initial_iou_t": first_worse_than_initial_iou_t, + "first_worse_than_prev_iou_t": first_worse_than_prev_iou_t, + "largest_iou_drop_from_best": largest_iou_drop, + "largest_iou_drop_t": largest_iou_drop_t, + } + +def _average_rollout_traces(traces_per_batch: list[list[dict[str, Any]]]) -> list[dict[str, Any]]: + averaged: list[dict[str, Any]] = [] + if not traces_per_batch: + return averaged + max_steps = max(len(trace) for trace in traces_per_batch) + for step_idx in range(max_steps): + present = [trace[step_idx] for trace in traces_per_batch if step_idx < len(trace)] + if not present: + continue + reward_pos_values = [float(step["reward_pos_pct"]) for step in present if step.get("reward_pos_pct") is not None] + value_scores = [float(step["value_score"]) for step in present if step.get("value_score") is not None] + averaged.append( + { + "t": int(np.mean([float(step.get("t", step_idx)) for step in present])), + "dice_mean": float(np.mean([float(step.get("dice_mean", 0.0)) for step in present])), + "iou_mean": float(np.mean([float(step.get("iou_mean", 0.0)) for step in present])), + "pred_fg_pct": float(np.mean([float(step.get("pred_fg_pct", 0.0)) for step in present])), + "reward_pos_pct": float(np.mean(reward_pos_values)) if reward_pos_values else None, + "value_score": float(np.mean(value_scores)) if value_scores else None, + } + ) + return averaged + +def _rollout_probe_trace( + model: nn.Module, + image: torch.Tensor, + gt_mask: torch.Tensor, + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_rollout_probe_trace") if strategy != 2 else max(int(tmax), 1) + rollout_trace: list[dict[str, Any]] = [] + batch_action_dist: list[dict[str, float]] = [] + reward_pos_pct = 0.0 + init_fg_pct = 0.0 + first_action_dist: dict[str, float] | None = None + first_policy_stats: dict[str, Any] | None = None + first_value_stats: dict[str, Any] | None = None + decoder_prob_stats: dict[str, Any] | None = None + first_entropy: float | None = None + selected_t = 0 + + def record_step( + *, + t: int, + seg_tensor: torch.Tensor, + seg_prev: torch.Tensor | None = None, + delta_map: torch.Tensor | None = None, + value_score: float | None = None, + action_distribution: dict[str, float] | None = None, + reward_pos: float | None = None, + reward_map_tensor: torch.Tensor | None = None, + step_entropy: float | None = None, + policy_stats: dict[str, Any] | None = None, + ) -> None: + pred_t = threshold_binary_mask(seg_tensor.float()).float() + dice_vals, iou_vals = _batch_binary_metrics(pred_t, gt_mask.float()) + step_data: dict[str, Any] = { + "t": int(t), + "dice_mean": float(np.mean(dice_vals)) if dice_vals else 0.0, + "iou_mean": float(np.mean(iou_vals)) if iou_vals else 0.0, + "pred_fg_pct": float(pred_t.sum().item()) / max(pred_t.numel(), 1) * 100.0, + "reward_pos_pct": None if reward_pos is None else float(reward_pos), + "value_score": None if value_score is None else float(value_score), + "action_distribution": action_distribution, + "seg_soft_stats": _tensor_stats(seg_tensor), + "entropy": step_entropy, + "policy_logit_stats": policy_stats, + } + if seg_prev is not None: + delta = seg_tensor.float() - seg_prev.float() + abs_delta = delta.abs() + # Binary mask flip tracking: how many pixels actually change in the thresholded output + binary_prev = threshold_binary_mask(seg_prev.float()).float() + binary_curr = threshold_binary_mask(seg_tensor.float()).float() + binary_flipped = (binary_prev != binary_curr) + flipped_to_fg = binary_flipped & (binary_curr > 0.5) + flipped_to_bg = binary_flipped & (binary_curr < 0.5) + gt_binary_local = (gt_mask.float() > 0.5) + correct_flips = binary_flipped & ((binary_curr > 0.5) == gt_binary_local) + wrong_flips = binary_flipped & ((binary_curr > 0.5) != gt_binary_local) + total_px = max(binary_prev.numel(), 1) + step_data["mask_delta"] = { + "mean_abs_change": float(abs_delta.mean().item()), + "max_change": float(abs_delta.max().item()), + "pct_pixels_changed": float((abs_delta > 1e-6).float().mean().item() * 100.0), + "fg_gained_pct": float((delta > 1e-6).float().mean().item() * 100.0), + "fg_lost_pct": float((delta < -1e-6).float().mean().item() * 100.0), + } + step_data["binary_mask_flips"] = { + "total_flipped_pct": float(binary_flipped.float().sum().item() / total_px * 100.0), + "flipped_to_fg_pct": float(flipped_to_fg.float().sum().item() / total_px * 100.0), + "flipped_to_bg_pct": float(flipped_to_bg.float().sum().item() / total_px * 100.0), + "correct_flips_pct": float(correct_flips.float().sum().item() / total_px * 100.0), + "wrong_flips_pct": float(wrong_flips.float().sum().item() / total_px * 100.0), + "flip_accuracy": float(correct_flips.float().sum().item() / max(binary_flipped.float().sum().item(), 1.0) * 100.0), + } + if reward_map_tensor is not None: + step_data["reward_stats"] = { + "mean": float(reward_map_tensor.mean().item()), + "std": float(reward_map_tensor.std().item()), + "min": float(reward_map_tensor.min().item()), + "max": float(reward_map_tensor.max().item()), + "pct_positive": float((reward_map_tensor > 0).float().mean().item() * 100.0), + "pct_negative": float((reward_map_tensor < 0).float().mean().item() * 100.0), + "pct_zero": float((reward_map_tensor.abs() < 1e-8).float().mean().item() * 100.0), + } + if delta_map is not None and seg_prev is not None: + gt_f = gt_mask.float() + ref_pred = threshold_binary_mask(seg_prev.float()).float() + gt_fg = (gt_f > 0.5).squeeze(1) + gt_bg = ~gt_fg + pred_fg = (ref_pred > 0.5).squeeze(1) + tp_mask = pred_fg & gt_fg + tn_mask = (~pred_fg) & gt_bg + fp_mask = pred_fg & gt_bg + fn_mask = (~pred_fg) & gt_fg + delta_squeezed = delta_map.squeeze(1).detach().float() + action_breakdown: dict[str, dict[str, float]] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + if pixel_mask.any(): + class_delta = delta_squeezed[pixel_mask] + action_breakdown[label] = { + "mean_delta": float(class_delta.mean().item()), + "mean_abs_delta": float(class_delta.abs().mean().item()), + "positive_pct": float((class_delta > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((class_delta < -1e-6).float().mean().item() * 100.0), + } + else: + action_breakdown[label] = { + "mean_delta": 0.0, + "mean_abs_delta": 0.0, + "positive_pct": 0.0, + "negative_pct": 0.0, + } + step_data["action_on_class"] = action_breakdown + if reward_map_tensor is not None: + reward_squeezed = reward_map_tensor.squeeze(1) if reward_map_tensor.ndim == 4 else reward_map_tensor + per_class_reward: dict[str, float] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + per_class_reward[label] = float(reward_squeezed[pixel_mask].mean().item()) if pixel_mask.any() else 0.0 + step_data["per_action_reward"] = per_class_reward + rollout_trace.append(step_data) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + record_step(t=0, seg_tensor=torch.sigmoid(logits)) + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": _trajectory_degradation_summary(rollout_trace), + "selected_t": selected_t, + "effective_tmax": effective_tmax, + } + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + decoder_prob_stats = _tensor_stats(refinement_context["decoder_prob"]) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + selected_t = 0 + + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, value_t = model.forward_from_state(state_t) + current_score = float(value_t.detach().mean().item()) + delta = _strategy3_policy_delta(policy_logits).to(dtype=seg.dtype) + + action_dist = _strategy3_delta_distribution(delta) + batch_action_dist.append(action_dist) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_value_stats = _tensor_stats(value_t) + first_entropy = 0.0 + record_step(t=0, seg_tensor=seg, value_score=current_score, step_entropy=first_entropy, policy_stats=first_policy_stats) + + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + step_entropy_val = 0.0 + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + seg_prev=seg, + delta_map=delta, + action_distribution=action_dist, + reward_pos=step_reward_pos, + reward_map_tensor=reward_map, + step_entropy=step_entropy_val, + policy_stats=_tensor_stats(policy_logits), + ) + seg = seg_next + selected_t = step_idx + 1 + + pred = threshold_binary_mask(seg.float()).float() + decoder_pred = threshold_binary_mask(refinement_context["decoder_prob"].float()).float() + decoder_dice_vals, decoder_iou_vals = _batch_binary_metrics(decoder_pred, gt_mask.float()) + decoder_baseline = { + "dice": float(np.mean(decoder_dice_vals)) if decoder_dice_vals else 0.0, + "iou": float(np.mean(decoder_iou_vals)) if decoder_iou_vals else 0.0, + "fg_pct": float(decoder_pred.sum().item()) / max(decoder_pred.numel(), 1) * 100.0, + } + final_dice_vals, final_iou_vals = _batch_binary_metrics(pred, gt_mask.float()) + rl_vs_decoder = { + "decoder_dice": decoder_baseline["dice"], + "decoder_iou": decoder_baseline["iou"], + "final_dice": float(np.mean(final_dice_vals)) if final_dice_vals else 0.0, + "final_iou": float(np.mean(final_iou_vals)) if final_iou_vals else 0.0, + "dice_gain": (float(np.mean(final_dice_vals)) if final_dice_vals else 0.0) - decoder_baseline["dice"], + "iou_gain": (float(np.mean(final_iou_vals)) if final_iou_vals else 0.0) - decoder_baseline["iou"], + } + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = selected_t + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": selected_t, + "effective_tmax": effective_tmax, + "decoder_baseline": decoder_baseline, + "rl_vs_decoder": rl_vs_decoder, + } + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + record_step(t=0, seg_tensor=seg) + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, entropy = sample_actions(policy_logits, stochastic=False) + action_dist = _action_histogram(actions, int(policy_logits.shape[1])) + batch_action_dist.append({str(key): float(value) for key, value in action_dist.items()}) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_entropy = float(entropy.detach().item()) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + reward_map = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + action_distribution=action_dist, + reward_pos=step_reward_pos, + ) + seg = seg_next + pred = seg.float() + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = len(rollout_trace) - 1 + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": len(rollout_trace) - 1, + "effective_tmax": effective_tmax, + } + +def _format_probe_deterioration(label: str, probe_payload: dict[str, Any], tmax: int) -> str: + effective_tmax = int(probe_payload.get("effective_tmax", tmax)) + degradation = probe_payload.get("aggregate", {}).get("degradation", {}) + if not degradation: + return f"{label}: no degradation trace" + first_worse = degradation.get("first_worse_than_initial_iou_t") + first_step_drop = degradation.get("first_worse_than_prev_iou_t") + best_t = degradation.get("best_iou_t") + final_t = degradation.get("final_t") + delta_best = degradation.get("delta_final_vs_best_iou") + worst_t = degradation.get("largest_iou_drop_t") + worst_drop = degradation.get("largest_iou_drop_from_best") + return ( + f"{label}: first_worse={first_worse}/{effective_tmax} " + f"first_drop={first_step_drop}/{effective_tmax} " + f"best={best_t}/{effective_tmax} final={final_t}/{effective_tmax} " + f"final-best_iou={float(delta_best):+.4f} " + f"worst={worst_t}/{effective_tmax} drop={float(worst_drop):+.4f}" + ) + +def _optimizer_diagnostics(optimizer: torch.optim.Optimizer) -> list[dict[str, Any]]: + groups: list[dict[str, Any]] = [] + for group_idx, group in enumerate(optimizer.param_groups): + num_tensors = len(group.get("params", [])) + num_elements = int(sum(param.numel() for param in group.get("params", []))) + groups.append( + { + "index": group_idx, + "lr": float(group.get("lr", 0.0)), + "weight_decay": float(group.get("weight_decay", 0.0)), + "num_tensors": num_tensors, + "num_elements": num_elements, + } + ) + return groups + +def _probe_batches_from_indices( + dataset: BUSIDataset, + *, + indices: list[int], + device: torch.device, +) -> list[dict[str, Any]]: + batches: list[dict[str, Any]] = [] + for start in range(0, len(indices), BATCH_SIZE): + batch_indices = indices[start:start + BATCH_SIZE] + if not batch_indices: + continue + images = torch.stack([dataset._images[idx].clone() for idx in batch_indices], dim=0) + masks = torch.stack([dataset._masks[idx].clone() for idx in batch_indices], dim=0) + sample_ids = [Path(dataset.sample_records[idx]["filename"]).stem for idx in batch_indices] + batches.append( + to_device( + { + "image": images, + "mask": masks, + "sample_id": sample_ids, + "dataset": current_dataset_name(), + }, + device, + ) + ) + return batches + +def _fixed_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + return _probe_batches_from_indices( + dataset, + indices=list(range(max_samples)), + device=device, + ) + +def _rolling_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, + epoch: int, + split_tag: str, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + if max_samples <= 0: + return [] + if max_samples >= len(dataset): + indices = list(range(len(dataset))) + else: + rng = random.Random(SEED + stable_int_from_text(f"probe:{split_tag}:epoch:{int(epoch)}")) + indices = rng.sample(range(len(dataset)), k=max_samples) + return _probe_batches_from_indices(dataset, indices=indices, device=device) + +def _probe_batch_id_lists(fixed_batches: list[dict[str, Any]]) -> list[list[str]]: + return [list(batch.get("sample_id", [])) for batch in fixed_batches] + +def _reference_eval_payloads(project_dir: Path, percent: float) -> dict[str, Any]: + refs: dict[str, Any] = {} + pct = percent_label(percent) + strat2_dir = project_dir / "strat2_history" + strat3_dir = project_dir / "strat3_history_best" + strat2_candidates = [ + strat2_dir / f"evaluation_strat2_pc{pct}.json", + strat2_dir / f"evaluation_strat2_pct{pct}.json", + ] + strat3_candidate = strat3_dir / f"evaluation_{pct} (1)" + for candidate in strat2_candidates: + if candidate.exists(): + refs["strategy2_reference"] = load_json(candidate) + break + if strat3_candidate.exists(): + refs["strategy3_best_reference"] = load_json(strat3_candidate) + return refs + +def empty_epoch_diagnostic_payload( + *, + run_type: str, + run_config: dict[str, Any], + bundle: DataBundle, + train_probe_batches: list[dict[str, Any]], + val_probe_batches: list[dict[str, Any]], +) -> dict[str, Any]: + payload = { + "diagnostic_version": 1, + "run_type": run_type, + "strategy": int(run_config["strategy"]), + "dataset_percent": float(bundle.percent), + "run_config": run_config, + "probe_setup": { + "mode": str(run_config.get("epoch_probe_mode", "fixed")), + "train_probe_batches": _probe_batch_id_lists(train_probe_batches), + "val_probe_batches": _probe_batch_id_lists(val_probe_batches), + "train_probe_batch_count": len(train_probe_batches), + "val_probe_batch_count": len(val_probe_batches), + "tmax": int(run_config.get("tmax", DEFAULT_TMAX)), + }, + "epochs": [], + } + payload.update(_reference_eval_payloads(PROJECT_DIR, bundle.percent)) + return payload + +def load_epoch_diagnostic_for_resume( + path: Path, + checkpoint_payload: dict[str, Any] | None, + default_payload: dict[str, Any], +) -> dict[str, Any]: + payload = dict(default_payload) + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) if checkpoint_payload is not None else 0 + if path.exists(): + loaded = load_json(path) + if isinstance(loaded, dict): + payload.update({k: v for k, v in loaded.items() if k != "epochs"}) + epochs = loaded.get("epochs", []) + if isinstance(epochs, list): + payload["epochs"] = [dict(row) for row in epochs if isinstance(row, dict) and int(row.get("epoch", 0)) <= checkpoint_epoch] + if "epochs" not in payload: + payload["epochs"] = [] + return payload + +def _evaluate_probe_batches( + model: nn.Module, + fixed_batches: list[dict[str, Any]], + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + if not fixed_batches: + return {"n_batches": 0, "batch_details": [], "aggregate": {}, "alerts": []} + + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_evaluate_probe_batches") if strategy != 2 else max(int(tmax), 1) + was_training = model.training + batch_details: list[dict[str, Any]] = [] + alerts: list[str] = [] + action_distributions: list[list[dict[str, float]]] = [] + rollout_traces: list[list[dict[str, Any]]] = [] + metric_lists: dict[str, list[float]] = { + key: [] + for key in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95") + } + reward_pos_values: list[float] = [] + pred_fg_values: list[float] = [] + gt_fg_values: list[float] = [] + init_fg_values: list[float] = [] + decoder_dices: list[float] = [] + decoder_ious: list[float] = [] + iou_gains: list[float] = [] + dice_gains: list[float] = [] + + model.eval() + try: + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + with torch.inference_mode(): + for batch_index, batch in enumerate(fixed_batches): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + + rollout_probe = _rollout_probe_trace( + model, + image, + gt_mask, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + pred = rollout_probe["final_pred"].float() + batch_action_dist = rollout_probe["action_distribution"] + reward_pos_pct = float(rollout_probe["reward_pos_pct"]) + init_fg_pct = float(rollout_probe["init_fg_pct"]) + first_action_dist = rollout_probe["first_action_distribution"] + first_policy_stats = rollout_probe["first_policy_stats"] + first_value_stats = rollout_probe["first_value_stats"] + decoder_prob_stats = rollout_probe["decoder_prob_stats"] + first_entropy = rollout_probe["first_entropy"] + rollout_trace = rollout_probe["rollout_trace"] + rollout_summary = rollout_probe["rollout_summary"] + + if batch_action_dist: + action_distributions.append(batch_action_dist) + if rollout_trace: + rollout_traces.append(rollout_trace) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + reward_pos_values.append(reward_pos_pct) + pred_fg_values.append(pred_fg_pct) + gt_fg_values.append(gt_fg_pct) + init_fg_values.append(init_fg_pct) + rl_vs_dec = rollout_probe.get("rl_vs_decoder") + if rl_vs_dec: + decoder_dices.append(rl_vs_dec["decoder_dice"]) + decoder_ious.append(rl_vs_dec["decoder_iou"]) + iou_gains.append(rl_vs_dec["iou_gain"]) + dice_gains.append(rl_vs_dec["dice_gain"]) + + batch_alerts = _numerical_health_check( + { + "pred": pred, + "gt_mask": gt_mask, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "reward_pos_pct": reward_pos_pct, + "first_entropy": first_entropy if first_entropy is not None else 0.0, + }, + prefix=f"probe[{batch_index}]:", + ) + alerts.extend(batch_alerts) + + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt_mask.detach().cpu().numpy().astype(np.uint8) + per_sample: list[dict[str, Any]] = [] + for sample_index in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[sample_index], gt_np[sample_index]) + per_sample.append( + { + "sample_id": sample_ids[sample_index] if sample_index < len(sample_ids) else f"sample_{sample_index}", + **{key: float(value) for key, value in metrics.items()}, + } + ) + for key, value in metrics.items(): + metric_lists.setdefault(key, []).append(float(value)) + + batch_details.append( + { + "batch_index": batch_index, + "sample_ids": sample_ids, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "init_fg_pct": init_fg_pct, + "reward_pos_pct": reward_pos_pct, + "action_distribution": _jsonable_action_distribution(batch_action_dist), + "first_action_distribution": first_action_dist, + "first_entropy": first_entropy, + "decoder_prob_stats": decoder_prob_stats, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "pred_stats": _tensor_stats(pred), + "rollout_trace": rollout_trace, + "rollout_summary": rollout_summary, + "decoder_baseline": rollout_probe.get("decoder_baseline"), + "rl_vs_decoder": rollout_probe.get("rl_vs_decoder"), + "alerts": batch_alerts, + "per_sample": per_sample, + } + ) + finally: + model.train(was_training) + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate_trace = _average_rollout_traces(rollout_traces) + rl_vs_decoder_aggregate: dict[str, Any] = {} + if decoder_dices: + rl_vs_decoder_aggregate = { + "decoder_dice": _summary_stats(decoder_dices), + "decoder_iou": _summary_stats(decoder_ious), + "iou_gain": _summary_stats(iou_gains), + "dice_gain": _summary_stats(dice_gains), + } + return { + "n_batches": len(fixed_batches), + "batch_details": batch_details, + "aggregate": { + "metrics": {key: _summary_stats(values) for key, values in metric_lists.items()}, + "reward_pos_pct": _summary_stats(reward_pos_values), + "pred_fg_pct": _summary_stats(pred_fg_values), + "gt_fg_pct": _summary_stats(gt_fg_values), + "init_fg_pct": _summary_stats(init_fg_values), + "action_distribution": _average_action_distributions(action_distributions, effective_tmax), + "rollout_trace": aggregate_trace, + "degradation": _trajectory_degradation_summary(aggregate_trace), + "rl_vs_decoder": rl_vs_decoder_aggregate, + }, + "alerts": alerts, + "effective_tmax": effective_tmax, + } + +def run_overfit_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + overfit_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + overfit_root = ensure_dir(overfit_root) + ckpt_dir = ensure_dir(overfit_root / "checkpoints") + history_path = checkpoint_history_path(overfit_root, "overfit") + run_config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": "overfit", + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "max_epochs": OVERFIT_N_EPOCHS, + "head_lr": OVERFIT_HEAD_LR, + "encoder_lr": OVERFIT_ENCODER_LR, + "weight_decay": DEFAULT_WEIGHT_DECAY, + "dropout_p": DEFAULT_DROPOUT_P, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "gamma": DEFAULT_GAMMA, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + if strategy == 3: + run_config.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(run_config["strategy3_freeze_bootstrapped_segmentation"]) + ) + run_config.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + run_config.setdefault("decoder_lr", 0.0 if bootstrap_freeze else OVERFIT_HEAD_LR * 0.1) + run_config.setdefault("rl_lr", OVERFIT_HEAD_LR) + run_config.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + run_config.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + run_config.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + run_config.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + run_config.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + run_config.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + run_config.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + run_config.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + run_config.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + run_config.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + run_config.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + run_config.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + run_config.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + run_config.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + run_config.setdefault("elastic_aug_prob", 0.3) + run_config.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + run_config.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + run_config.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + run_config.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + run_config.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + run_config.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + run_config.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + run_config.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + run_config.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + run_config.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + run_config.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + run_config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + run_config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + save_json(overfit_root / "run_config.json", run_config) + set_current_job_params(run_config) + + banner( + f"OVERFIT TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + effective_test_tmax = _resolve_test_iteration_tmax(DEFAULT_TMAX, context="run_overfit_test") if strategy != 2 else max(int(DEFAULT_TMAX), 1) + print( + f"[Overfit] Fixed batches={OVERFIT_N_BATCHES}, epochs={OVERFIT_N_EPOCHS}, " + f"head_lr={OVERFIT_HEAD_LR:.2e}, encoder_lr={OVERFIT_ENCODER_LR:.2e}" + ) + + fixed_batches: list[dict[str, Any]] = [] + for batch_index, batch in enumerate(bundle.train_loader): + fixed_batches.append(to_device(batch, DEVICE)) + if batch_index + 1 >= OVERFIT_N_BATCHES: + break + if not fixed_batches: + raise RuntimeError("Overfit test could not collect any training batches.") + if len(fixed_batches) < OVERFIT_N_BATCHES: + print(f"[Overfit] Warning: only {len(fixed_batches)} train batch(es) available.") + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(overfit_root) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=f"{description} | Overfit Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + + optimizer = make_optimizer( + model, + strategy, + head_lr=OVERFIT_HEAD_LR, + encoder_lr=OVERFIT_ENCODER_LR, + weight_decay=DEFAULT_WEIGHT_DECAY, + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + log_alpha: torch.Tensor | None = None + alpha_optimizer: Adam | None = None + target_entropy = 0.0 + + history = empty_overfit_history() + prev_loss: float | None = None + best_dice = -1.0 + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + expected_run_type="overfit", + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + best_dice = float(checkpoint_payload["best_metric_value"]) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_overfit_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", "overfit"), + } + print( + f"[Resume] overfit run continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{OVERFIT_N_EPOCHS}." + ) + if history["loss"]: + prev_loss = float(history["loss"][-1]) + prev_params = _snapshot_params(model) + start_time = time.time() + + for epoch in range(start_epoch, OVERFIT_N_EPOCHS + 1): + full_dump = epoch <= 5 or epoch % max(OVERFIT_PRINT_EVERY, 1) == 0 or epoch == OVERFIT_N_EPOCHS + epoch_losses: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_rewards: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_losses: list[float] = [] + epoch_entropy: list[float] = [] + epoch_grad_norms: list[float] = [] + epoch_action_dist: list[list[dict[str, float]]] = [] + epoch_reward_pos_pct: list[float] = [] + epoch_pred_fg_pct: list[float] = [] + epoch_gt_fg_pct: list[float] = [] + epoch_alerts: list[str] = [] + + for batch in fixed_batches: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=DEFAULT_TMAX, + critic_loss_weight=DEFAULT_CRITIC_LOSS_WEIGHT, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=OVERFIT_N_EPOCHS, + ) + + epoch_losses.append(float(metrics["loss"])) + epoch_rewards.append(float(metrics["mean_reward"])) + epoch_actor.append(float(metrics["actor_loss"])) + epoch_critic.append(float(metrics["critic_loss"])) + epoch_ce.append(float(metrics["ce_loss"])) + epoch_dice_losses.append(float(metrics["dice_loss"])) + epoch_entropy.append(float(metrics["entropy"])) + epoch_grad_norms.append(float(metrics["grad_norm"])) + epoch_alerts.extend( + _numerical_health_check( + { + "loss": metrics["loss"], + "actor_loss": metrics["actor_loss"], + "critic_loss": metrics["critic_loss"], + "reward": metrics["mean_reward"], + "entropy": metrics["entropy"], + "grad_norm": metrics["grad_norm"], + "ce_loss": metrics["ce_loss"], + "dice_loss": metrics["dice_loss"], + }, + prefix="train:", + ) + ) + + model.eval() + with torch.inference_mode(): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + )["decoder_prob"].float() + else: + init_mask = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + action_dist, pred = _action_distribution( + model, + image, + init_mask, + effective_test_tmax, + use_amp, + amp_dtype, + strategy=strategy, + sample_ids=sample_ids, + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + epoch_action_dist.append(action_dist) + + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + soft_init_mask = refinement_context["decoder_prob"].float() + state_t = model.forward_refinement_state( + refinement_context["base_features"], + soft_init_mask, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, _ = model.forward_from_state(state_t) + first_delta = _strategy3_policy_delta(policy_logits).to(dtype=soft_init_mask.dtype) + first_seg = _strategy3_apply_delta(soft_init_mask, first_delta) + reward_map = compute_refinement_reward( + soft_init_mask, first_seg, gt_mask.float(), + ) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * init_mask + policy_logits = model.forward_policy_only(masked) + first_actions, _, _ = sample_actions(policy_logits, stochastic=False) + first_seg = apply_actions(init_mask, first_actions, num_actions=policy_logits.shape[1]) + reward_map = (init_mask - gt_mask).pow(2) - (first_seg - gt_mask).pow(2) + epoch_reward_pos_pct.append(float((reward_map > 0).float().mean().item() * 100.0)) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + epoch_pred_fg_pct.append(pred_fg_pct) + epoch_gt_fg_pct.append(gt_fg_pct) + dice_values, iou_values = _batch_binary_metrics(pred.float(), gt_mask.float()) + epoch_dices.extend(dice_values) + epoch_ious.extend(iou_values) + epoch_alerts.extend( + _numerical_health_check( + {"pred": pred, "gt_mask": gt_mask}, + prefix="eval:", + ) + ) + model.train() + + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_loss = float(np.mean(epoch_losses)) if epoch_losses else 0.0 + avg_dice = float(np.mean(epoch_dices)) if epoch_dices else 0.0 + avg_iou = float(np.mean(epoch_ious)) if epoch_ious else 0.0 + avg_reward = float(np.mean(epoch_rewards)) if epoch_rewards else 0.0 + avg_actor = float(np.mean(epoch_actor)) if epoch_actor else 0.0 + avg_critic = float(np.mean(epoch_critic)) if epoch_critic else 0.0 + avg_ce = float(np.mean(epoch_ce)) if epoch_ce else 0.0 + avg_dice_loss = float(np.mean(epoch_dice_losses)) if epoch_dice_losses else 0.0 + avg_entropy = float(np.mean(epoch_entropy)) if epoch_entropy else 0.0 + avg_grad_norm = float(np.mean(epoch_grad_norms)) if epoch_grad_norms else 0.0 + avg_reward_pos = float(np.mean(epoch_reward_pos_pct)) if epoch_reward_pos_pct else 0.0 + avg_pred_fg = float(np.mean(epoch_pred_fg_pct)) if epoch_pred_fg_pct else 0.0 + avg_gt_fg = float(np.mean(epoch_gt_fg_pct)) if epoch_gt_fg_pct else 0.0 + + avg_action_dist = _average_action_distributions(epoch_action_dist, effective_test_tmax) + + history["dice"].append(avg_dice) + history["iou"].append(avg_iou) + history["loss"].append(avg_loss) + history["reward"].append(avg_reward) + history["actor_loss"].append(avg_actor) + history["critic_loss"].append(avg_critic) + history["ce_loss"].append(avg_ce) + history["dice_loss"].append(avg_dice_loss) + history["entropy"].append(avg_entropy) + history["grad_norm"].append(avg_grad_norm) + history["action_dist"].append(avg_action_dist) + history["reward_pos_pct"].append(avg_reward_pos) + history["pred_fg_pct"].append(avg_pred_fg) + history["gt_fg_pct"].append(avg_gt_fg) + save_json(history_path, history) + + loss_delta = avg_loss - prev_loss if prev_loss is not None else 0.0 + prev_loss = avg_loss + if epoch_alerts: + print(f"[Overfit][Epoch {epoch}] Numerical alerts: {' | '.join(epoch_alerts)}") + + if full_dump: + current_alpha = float(log_alpha.exp().detach().item()) if log_alpha is not None else 0.0 + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} (delta={loss_delta:+.6f}) " + f"dice={avg_dice:.4f} iou={avg_iou:.4f} reward={avg_reward:+.6f} " + f"entropy={avg_entropy:.6f} alpha={current_alpha:.4f}" + ) + print( + f"[Overfit][Epoch {epoch:03d}] ce={avg_ce:.6f} dice_l={avg_dice_loss:.6f} " + f"grad_norm={avg_grad_norm:.6f} global_grad={grad_stats['global_norm']:.6f}" + ) + if avg_action_dist: + first = avg_action_dist[0] + last = avg_action_dist[-1] + print( + f"[Overfit][Epoch {epoch:03d}] action step0={first} step_last={last} " + f"reward_pos={avg_reward_pos:.2f}%" + ) + print( + f"[Overfit][Epoch {epoch:03d}] pred_fg={avg_pred_fg:.2f}% gt_fg={avg_gt_fg:.2f}% " + f"param_groups={list(param_stats.keys())}" + ) + else: + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} dice={avg_dice:.4f} " + f"iou={avg_iou:.4f} reward={avg_reward:+.6f}" + ) + + row = { + "epoch": epoch, + "dice": avg_dice, + "iou": avg_iou, + "loss": avg_loss, + "reward": avg_reward, + "actor_loss": avg_actor, + "critic_loss": avg_critic, + "ce_loss": avg_ce, + "dice_loss": avg_dice_loss, + "entropy": avg_entropy, + "grad_norm": avg_grad_norm, + "action_dist": avg_action_dist, + "reward_pos_pct": avg_reward_pos, + "pred_fg_pct": avg_pred_fg, + "gt_fg_pct": avg_gt_fg, + } + if avg_dice > best_dice: + best_dice = avg_dice + save_checkpoint( + ckpt_dir / "best.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + if SAVE_LATEST_EVERY_EPOCH: + save_checkpoint( + ckpt_dir / "latest.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + if CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0: + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + peak_dice = max(history["dice"]) if history["dice"] else 0.0 + final_dice = history["dice"][-1] if history["dice"] else 0.0 + summary = { + "run_type": "overfit", + "strategy": strategy, + "peak_dice": peak_dice, + "final_dice": final_dice, + "description": description, + "resumed": resume_source is not None, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "final_epoch": max(len(history["dice"]), start_epoch - 1), + } + if resume_source is not None: + summary["resume_source"] = resume_source + save_json(overfit_root / "summary.json", summary) + print( + f"[Overfit] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} | " + f"peak_dice={peak_dice:.4f}, final_dice={final_dice:.4f}" + ) + + del model + run_cuda_cleanup( + context=f"overfit {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + return {**summary, "history": history} + +def run_configured_overfit_tests( + bundles: dict[float, DataBundle], + *, + model_config: RuntimeModelConfig, +) -> None: + banner("OVERFIT TEST MODE") + for percent in DATASET_PERCENTS: + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=strategy_root_for_percent(strategy, percent, model_config) / "overfit_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + +"""============================================================================= +OPTUNA + ORCHESTRATION +============================================================================= +""" + +def strategy_epochs(strategy: int) -> int: + strategy = _require_supported_strategy(strategy) + if strategy == 2: + return STRATEGY_2_MAX_EPOCHS + if strategy == 3: + return STRATEGY_3_MAX_EPOCHS + raise ValueError(f"Unsupported strategy for epoch selection: {strategy}") + +def suggest_hyperparameters(trial: optuna.trial.Trial, strategy: int) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if strategy == 3: + rl_lr = trial.suggest_float("rl_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + return { + "head_lr": rl_lr, + "encoder_lr": ENCODER_LR_RANGE[0], + "decoder_lr": 0.0, + "strategy3_decoder_ce_weight": 0.0, + "strategy3_decoder_dice_weight": 0.0, + "strategy3_freeze_bootstrapped_segmentation": True, + "strategy3_variant": DEFAULT_STRATEGY3_VARIANT, + "rl_lr": rl_lr, + "weight_decay": trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True), + "dropout_p": trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]), + "tmax": trial.suggest_int("tmax", TMAX_RANGE[0], TMAX_RANGE[1]), + "smp_encoder_proj_dim": trial.suggest_categorical("smp_encoder_proj_dim", [64, 128, 192, 256]), + "critic_loss_weight": trial.suggest_float("critic_loss_weight", 0.10, 1.50), + "strategy3_mc_dropout_enabled": True, + "strategy3_mc_dropout_samples": trial.suggest_categorical("strategy3_mc_dropout_samples", [4, 8, 12]), + "strategy3_mc_dropout_p": trial.suggest_float("strategy3_mc_dropout_p", 0.05, 0.35), + "strategy3_delta_max": trial.suggest_float("strategy3_delta_max", 0.03, 0.20), + "strategy3_sam_attention_grid": trial.suggest_categorical("strategy3_sam_attention_grid", [16, 32, 64]), + "strategy3_r1_progress_weight": trial.suggest_float("strategy3_r1_progress_weight", 0.25, 2.0), + "biou_reward_weight": trial.suggest_float("biou_reward_weight", 0.0, 2.0), + "strategy3_advantage_normalize": trial.suggest_categorical("strategy3_advantage_normalize", [False, True]), + "strategy3_rl_grad_clip_norm": trial.suggest_float("strategy3_rl_grad_clip_norm", 0.5, 4.0), + "strategy3_rl_loss_scale": trial.suggest_float("strategy3_rl_loss_scale", 2.0, 50.0, log=True), + "strategy3_aux_ce_weight": DEFAULT_STRATEGY3_AUX_CE_WEIGHT, + "strategy3_aux_ce_anneal_start_epoch": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + "strategy3_aux_ce_anneal_epochs": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + "strategy3_aux_ce_floor_fraction": DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + "threshold": trial.suggest_float("threshold", 0.35, 0.65), + "elastic_aug_prob": trial.suggest_float("elastic_aug_prob", 0.0, 0.5), + "epoch_probe_mode": DEFAULT_STRATEGY3_PROBE_MODE, + "early_stopping_patience": DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[3], + } + + head_lr = trial.suggest_float("head_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + encoder_lr = trial.suggest_float("encoder_lr", ENCODER_LR_RANGE[0], min(ENCODER_LR_RANGE[1], head_lr), log=True) + weight_decay = trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True) + dropout_p = trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]) + params = { + "head_lr": head_lr, + "encoder_lr": encoder_lr, + "weight_decay": weight_decay, + "dropout_p": dropout_p, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[2], + } + return params + +def _format_hparam_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key in {"head_lr", "encoder_lr", "entropy_lr"}: + return f"{value:.3e}" + return f"{value:.6g}" + return str(value) + +def log_optuna_trial_start( + *, + study_name: str, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + trial_dir: Path, + params: dict[str, Any], + max_epochs: int, +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + lines = [ + "", + "-" * 80, + f"OPTUNA TRIAL START | {run_name}", + "-" * 80, + f"Study name : {study_name}", + f"Run dir : {trial_dir}", + f"Max epochs : {max_epochs}", + f"Objective metric : {_strategy_selection_metric_name(strategy)}", + ] + for key in sorted(params): + lines.append(f"{key:22s}: {_format_hparam_value(key, params[key])}") + tqdm.write("\n".join(lines)) + +def log_optuna_trial_result( + *, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + metric_value: float, + aggregate: dict[str, dict[str, float]], +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + tqdm.write( + f"[{run_name}] completed: {_strategy_selection_metric_name(strategy)}={metric_value:.4f}, " + f"best_test_iou={aggregate['iou']['mean']:.4f}" + ) + +def study_paths_for( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + pct_root = RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}" + strategy_root = pct_root / strategy_dir_name(strategy, model_config) + study_root = strategy_root / "study" + trials_root = strategy_root / "trials" + return strategy_root, study_root, trials_root + +def manual_hparams_key(strategy: int, percent: float) -> str: + return f"{strategy}:{percent_label(percent)}" + +def reset_study_artifacts(strategy: int, percent: float, *, model_config: RuntimeModelConfig) -> None: + strategy_root, study_root, trials_root = study_paths_for(strategy, percent, model_config) + removed_any = False + for path in (study_root, trials_root): + if path.exists(): + shutil.rmtree(path) + removed_any = True + if removed_any: + print( + f"[Optuna Reset] Removed cached study artifacts for strategy={strategy}, " + f"percent={percent_text(percent)} under {strategy_root}." + ) + else: + print( + f"[Optuna Reset] No existing study artifacts found for strategy={strategy}, " + f"percent={percent_text(percent)}." + ) + +class _PlateauPruner(optuna.pruners.BasePruner): + """Prune a trial whose metric has plateaued (no improvement to its + own personal best within a patience window). + + Behaviour: + - During the first *n_warmup_steps* epochs: never prune. + - After warmup, track the trial's own best metric and the epoch + at which it was achieved. + - If *patience_steps* epochs pass without the trial beating its + own best, the trial is pruned (it has stagnated). + """ + + def __init__( + self, + n_warmup_steps: int = 80, + patience_steps: int = 40, + ) -> None: + self._n_warmup_steps = n_warmup_steps + self._patience_steps = patience_steps + + def prune( + self, + study: "optuna.study.Study", + trial: "optuna.trial.FrozenTrial", + ) -> bool: + step = trial.last_step + if step is None or step < self._n_warmup_steps: + return False + + post_warmup = { + s: v for s, v in trial.intermediate_values.items() + if s >= self._n_warmup_steps + } + if not post_warmup: + return False + + best_step = max(post_warmup, key=post_warmup.get) + epochs_since_improvement = step - best_step + return epochs_since_improvement >= self._patience_steps + + +def pruner_for_run() -> optuna.pruners.BasePruner: + if USE_TRIAL_PRUNING: + return _PlateauPruner( + n_warmup_steps=TRIAL_PRUNER_WARMUP_STEPS, + patience_steps=TRIAL_PRUNER_PATIENCE_STEPS, + ) + return optuna.pruners.NopPruner() + +def run_single_job( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + params: dict[str, Any], + max_epochs: int, + trial: optuna.trial.Trial | None, + strategy2_checkpoint_path: str | Path | None = None, + resume_checkpoint_path: Path | None = None, + retrying_from_trial_number: int | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, dict[str, float]]]: + strategy = _require_supported_strategy(strategy) + params = dict(params) + params.setdefault("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS[strategy]) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + if strategy == 3: + params.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(params["strategy3_freeze_bootstrapped_segmentation"]) + ) + params.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + params.setdefault("decoder_lr", 0.0 if bootstrap_freeze else params["head_lr"] * 0.1) + params.setdefault("rl_lr", params["head_lr"]) + params.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + params.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + params.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + params.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + params.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + params.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + params.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + params.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + params.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + params.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + params.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + params.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + params.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + params.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + params.setdefault("elastic_aug_prob", 0.3) + params.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + params.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + params.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + params.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + params.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + params.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + params.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + params.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + params.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + params.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + params.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + set_current_job_params(params) + if "smp_encoder_proj_dim" in params and int(params["smp_encoder_proj_dim"]) != model_config.smp_encoder_proj_dim: + model_config = RuntimeModelConfig.from_payload( + {**model_config.to_payload(), "smp_encoder_proj_dim": int(params["smp_encoder_proj_dim"])} + ).validate() + entropy_target_ratio = float(_job_param("entropy_target_ratio", 0.35)) + entropy_alpha_init = float(_job_param("entropy_alpha_init", 0.12)) + critic_loss_weight = float(_job_param("critic_loss_weight", DEFAULT_CRITIC_LOSS_WEIGHT)) + ensure_dir(run_dir) + run_type = "trial" if trial is not None else "final" + config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": run_type, + "run_name": run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ), + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "train_subset_variant": bundle.split_payload.get("train_subset_variant", 0), + "train_subset_source": bundle.split_payload.get("train_subset_source", "persisted"), + "selected_split_manifest_path": bundle.split_payload.get("selected_split_manifest_path"), + "normalization_cache_path": bundle.split_payload["normalization_cache_path"], + "best_checkpoint_metric_name": _strategy_selection_metric_name(strategy), + "best_checkpoint_metrics": {str(key): value for key, value in BEST_CHECKPOINT_METRICS.items()}, + "save_history_incrementally": bool(SAVE_HISTORY_INCREMENTALLY), + "write_epoch_diagnostic": bool(WRITE_EPOCH_DIAGNOSTIC), + "head_lr": params["head_lr"], + "encoder_lr": params["encoder_lr"], + "weight_decay": params["weight_decay"], + "dropout_p": params["dropout_p"], + "tmax": params["tmax"], + "entropy_lr": params["entropy_lr"], + "max_epochs": max_epochs, + "gamma": DEFAULT_GAMMA, + "critic_loss_weight": critic_loss_weight, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "scheduler_factor": SCHEDULER_FACTOR, + "scheduler_patience": SCHEDULER_PATIENCE, + "scheduler_threshold": SCHEDULER_THRESHOLD, + "scheduler_min_lr": SCHEDULER_MIN_LR, + "execution_mode": EXECUTION_MODE, + "evaluation_checkpoint_mode": EVAL_CHECKPOINT_MODE, + "strategy2_checkpoint_mode": STRATEGY2_CHECKPOINT_MODE, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + config.update({key: value for key, value in params.items() if key not in config}) + config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + if resume_checkpoint_path is not None: + config["resume_checkpoint_path"] = str(Path(resume_checkpoint_path)) + if retrying_from_trial_number is not None: + config["retrying_from_trial_number"] = int(retrying_from_trial_number) + save_json(run_dir / "run_config.json", config) + + model: nn.Module | None = None + try: + model, description, _compiled = build_model( + strategy, + params["dropout_p"], + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + summary, history = train_model( + run_type=run_type, + model_config=model_config, + run_config=config, + model=model, + description=description, + strategy=strategy, + run_dir=run_dir, + bundle=bundle, + max_epochs=max_epochs, + head_lr=params["head_lr"], + encoder_lr=params["encoder_lr"], + weight_decay=params["weight_decay"], + tmax=params["tmax"], + entropy_lr=params["entropy_lr"], + entropy_alpha_init=entropy_alpha_init, + entropy_target_ratio=entropy_target_ratio, + critic_loss_weight=critic_loss_weight, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + dropout_p=params["dropout_p"], + resume_checkpoint_path=resume_checkpoint_path, + trial=trial, + ) + + if trial is not None: + save_json( + run_dir / "summary.json", + { + "params": params, + "best_iou": float(summary["best_val_iou"]), + "best_model_metric_name": str(summary["best_model_metric_name"]), + "best_model_metric": float(summary["best_model_metric"]), + "resumed": bool(resume_checkpoint_path is not None), + "retrying_from_trial_number": retrying_from_trial_number, + }, + ) + return summary, history, {} + + del model + model = None + run_cuda_cleanup() + + aggregate, _per_sample = run_evaluation_for_run( + strategy=strategy, + percent=bundle.percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + return summary, history, aggregate + finally: + if model is not None: + del model + model = None + run_cuda_cleanup() + +def _save_best_params_so_far( + study: optuna.study.Study, + study_root: Path, + strategy: int, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> None: + best, _best_value = _current_optuna_study_best_candidate( + study, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if best is None: + return + params = dict(best.params) + if strategy == 2: + params.setdefault("tmax", DEFAULT_TMAX) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", params) + +def run_study( + strategy: int, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + strategy_root, study_root, trials_root = study_paths_for(strategy, bundle.percent, model_config) + ensure_dir(strategy_root.parent) + strategy_root = ensure_dir(strategy_root) + study_root = ensure_dir(strategy_root / "study") + trials_root = ensure_dir(strategy_root / "trials") + storage_path = study_root / "study.sqlite3" + storage = RDBStorage( + url=f"sqlite:///{storage_path.resolve()}", + heartbeat_interval=OPTUNA_HEARTBEAT_INTERVAL, + grace_period=OPTUNA_HEARTBEAT_GRACE_PERIOD, + ) + sampler = optuna.samplers.TPESampler(seed=SEED) + study_name = ( + f"{MODEL_NAME}_{model_config.backbone_tag()}_{run_identity_slug(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + study = optuna.create_study( + study_name=study_name, + direction=STUDY_DIRECTION, + sampler=sampler, + pruner=pruner_for_run(), + storage=storage, + load_if_exists=LOAD_EXISTING_STUDIES, + ) + existing_trials = [trial for trial in study.trials if trial.state.is_finished()] + if existing_trials: + print( + f"[Optuna Study] Loaded existing study '{study_name}' with " + f"{len(existing_trials)} existing finished trial(s). Running {NUM_TRIALS} new trial(s)." + ) + else: + print(f"[Optuna Study] Starting new study '{study_name}' with {NUM_TRIALS} trial(s).") + + def objective(trial: optuna.trial.Trial) -> float: + trial_dir = ensure_dir(trials_root / f"trial_{trial.number:03d}") + params = suggest_hyperparameters(trial, strategy) + log_optuna_trial_start( + study_name=study_name, + strategy=strategy, + bundle=bundle, + trial=trial, + trial_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + ) + summary: dict[str, Any] | None = None + _history: list[dict[str, Any]] | None = None + aggregate: dict[str, dict[str, float]] | None = None + completed_successfully = False + pruned_by_optuna = False + run_cuda_cleanup() + try: + summary, _history, aggregate = run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + trial=trial, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + metric_value = float(summary["best_model_metric"]) + completed_successfully = True + tqdm.write( + f"[{run_identity_label(strategy=strategy, percent=bundle.percent, trial_number=trial.number, split_payload=bundle.split_payload)}] " + f"completed: {summary['best_model_metric_name']}={metric_value:.4f}" + ) + return metric_value + except optuna.TrialPruned: + pruned_by_optuna = True + raise + finally: + current_trial = trial if (completed_successfully or pruned_by_optuna) else None + current_best_value = None + if summary is not None and summary.get("best_model_metric") is not None: + current_best_value = float(summary["best_model_metric"]) + _save_best_params_so_far( + study, + study_root, + strategy, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if aggregate is not None: + del aggregate + aggregate = None + if _history is not None: + del _history + _history = None + if summary is not None: + del summary + summary = None + prune_optuna_trial_dir(trial_dir) + run_cuda_cleanup(context=f"trial {trial.number:03d} boundary") + + study.optimize(objective, n_trials=NUM_TRIALS, show_progress_bar=True) + + best_trial, best_observed_value = _current_optuna_study_best_candidate(study) + if best_trial is None or best_observed_value is None: + raise RuntimeError( + f"Study '{study_name}' has no trials with recorded best-observed values, so best params cannot be resolved. " + f"Finished trials={len([trial for trial in study.trials if trial.state.is_finished()])}, " + f"configured cap={NUM_TRIALS}." + ) + + best_params = dict(best_trial.params) + if strategy == 2: + best_params.setdefault("tmax", DEFAULT_TMAX) + best_params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", best_params) + optuna_best_value: float | None + try: + optuna_best_value = float(study.best_value) + except Exception: + optuna_best_value = None + save_json( + study_root / "summary.json", + { + "best_params": best_params, + "optimized_param_names": sorted(best_params.keys()), + "best_metric_name": _strategy_selection_metric_name(strategy), + "best_metric_value": float(best_observed_value), + "best_observed_value": float(best_observed_value), + "best_trial_number": int(getattr(best_trial, "number", -1)), + "optuna_best_value": optuna_best_value, + "best_iou": float(best_observed_value) if _strategy_selection_metric_name(strategy) == "val_iou" else None, + "finished_trials": len([trial for trial in study.trials if trial.state.is_finished()]), + "completed_trials": len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + "target_trials": int(NUM_TRIALS), + "ran_trials": int(NUM_TRIALS), + }, + ) + prune_optuna_study_dir(study_root) + if trials_root.exists(): + shutil.rmtree(trials_root, ignore_errors=True) + return best_params + +def run_final_training( + strategy: int, + bundle: DataBundle, + params: dict[str, Any], + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + strategy = _require_supported_strategy(strategy) + final_root = final_root_for_strategy(strategy, bundle.percent, model_config) + if SKIP_EXISTING_FINALS and (final_root / "summary.json").exists(): + print(f"Skipping existing final run: {final_root}") + return + save_json(final_root / "best_params.json", params) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(final_root) + run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=final_root, + params=params, + max_epochs=strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + +def print_environment_summary(model_config: RuntimeModelConfig) -> None: + banner("RUNTIME SUMMARY") + images_dir, annotations_dir = current_dataset_dirs() + print(f"Project dir : {PROJECT_DIR}") + print(f"Data root : {DATA_ROOT}") + print(f"Runs root : {RUNS_ROOT}") + print(f"Dataset name : {current_dataset_name()}") + if current_dataset_name() == "BUSI_with_classes": + print(f"Dataset split policy : {current_busi_with_classes_split_policy()}") + print(f"Images dir : {images_dir}") + print(f"Masks dir : {annotations_dir}") + print(f"Dataset splits json : {current_dataset_splits_json_path()}") + print(f"Split type : {SPLIT_TYPE}") + print(f"Experiment mode : {EXPERIMENT_MODE}") + print(f"Device : {DEVICE}") + print(f"Device source : {DEVICE_FALLBACK_SOURCE}") + print(f"Model name : {MODEL_NAME}") + print(f"Seed : {SEED}") + print(f"PyTorch version : {torch.__version__}") + print(f"Batch size : {BATCH_SIZE}") + print(f"Use AMP : {USE_AMP}") + print(f"Num workers : {NUM_WORKERS}") + print(f"Pin memory : {USE_PIN_MEMORY}") + print(f"CuDNN deterministic : {torch.backends.cudnn.deterministic}") + print(f"CuDNN benchmark : {torch.backends.cudnn.benchmark}") + + if DEVICE.type == "cuda": + props = torch.cuda.get_device_properties(0) + print(f"GPU : {torch.cuda.get_device_name(0)}") + print(f"GPU VRAM : {props.total_memory / (1024 ** 3):.2f} GB") + print(f"AMP dtype : {resolve_amp_dtype(AMP_DTYPE)}") + print(f"Trial pruning : {USE_TRIAL_PRUNING}") + print(f"Backbone family : {model_config.backbone_family}") + if model_config.backbone_family == "custom_vgg": + print(f"VGG feature scales : {model_config.vgg_feature_scales}") + print(f"VGG feature dilation : {model_config.vgg_feature_dilation}") + else: + print(f"SMP encoder : {model_config.smp_encoder_name}") + print(f"SMP encoder depth : {model_config.smp_encoder_depth}") + print(f"SMP encoder proj dim : {model_config.smp_encoder_proj_dim}") + print(f"SMP decoder : {model_config.smp_decoder_type}") + print(f"Strategies : {STRATEGIES}") + print(f"Dataset percents : {[percent_text(value) for value in DATASET_PERCENTS]}") + print(f"Best metrics : {BEST_CHECKPOINT_METRICS}") + print(f"History incremental : {SAVE_HISTORY_INCREMENTALLY}") + print(f"Write diagnostics : {WRITE_EPOCH_DIAGNOSTIC}") + print_imagenet_normalization_status() + print(f"Trials per study : {NUM_TRIALS}") + print(f"Execution mode : {EXECUTION_MODE}") + print(f"Run Optuna : {RUN_OPTUNA}") + print(f"Use saved best params : {USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF}") + print(f"Reset studies/run : {RESET_ALL_STUDIES_EACH_RUN}") + print(f"Load existing studies : {LOAD_EXISTING_STUDIES}") + print(f"Eval ckpt selector : {EVAL_CHECKPOINT_MODE}") + print(f"S2 ckpt selector : {STRATEGY2_CHECKPOINT_MODE}") + print(f"S3 bootstrap from S2 : {STRATEGY3_BOOTSTRAP_FROM_STRATEGY2}") + print(f"S3 freeze default : {DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION}") + print(f"Train resume mode : {TRAIN_RESUME_MODE}") + print(f"Verbose epoch log : {VERBOSE_EPOCH_LOG}") + print(f"Validate every epochs : {VALIDATE_EVERY_N_EPOCHS}") + print(f"Smoke test enabled : {RUN_SMOKE_TEST}") + print(f"Test iter control : {TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {TEST_ITERATION_T}") + print(f"Overfit test enabled : {RUN_OVERFIT_TEST}") + print(f"Overfit batches : {OVERFIT_N_BATCHES}") + print(f"Overfit epochs : {OVERFIT_N_EPOCHS}") + +def maybe_run_strategy_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + if not RUN_SMOKE_TEST: + return + if EXECUTION_MODE == "eval_only": + return + run_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + smoke_root=strategy_root_for_percent(strategy, bundle.percent, model_config) / "smoke_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +"""============================================================================= +REPEATED HOLDOUT INTEGRATION +============================================================================= +""" + +base = sys.modules[__name__] + +REPEATED_HOLDOUT_ROOT = base.RUNS_ROOT / base.MODEL_NAME / "repeated_holdout" +EXPERIMENT_ROOT = REPEATED_HOLDOUT_ROOT / FOLDS_EXPERIMENT_NAME +EXPERIMENT_DB_PATH = EXPERIMENT_ROOT / "experiment_state.sqlite3" +SPLIT_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "splits" +SUBSET_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "subsets" +EXPORTS_DIR = EXPERIMENT_ROOT / "exports" +"""============================================================================= +RUNTIME STATE +============================================================================= +""" + + +@dataclass(frozen=True) +class PercentRepeatSpec: + percent_int: int + fraction: float + repeat_count: int + + +@dataclass(frozen=True) +class FoldRunContext: + split_repeat_index: int + subset_repeat_index: int + percent_int: int + percent_fraction: float + split_seed: int + subset_seed: int + repeat_root: Path + split_manifest_path: Path + subset_manifest_path: Path + + +@dataclass(frozen=True) +class RunKey: + split_repeat_index: int + dataset_percent: int + subset_repeat_index: int + strategy: int + + +CURRENT_FOLD_CONTEXT: FoldRunContext | None = None +LEDGER_CONN: sqlite3.Connection | None = None +PERCENT_SPECS_CACHE: list[PercentRepeatSpec] | None = None + +ORIGINAL_SAVE_JSON = base.save_json +ORIGINAL_PERCENT_ROOT = base.percent_root +ORIGINAL_STRATEGY_ROOT_FOR_PERCENT = base.strategy_root_for_percent +ORIGINAL_FINAL_ROOT_FOR_STRATEGY = base.final_root_for_strategy +ORIGINAL_STUDY_PATHS_FOR = base.study_paths_for +ORIGINAL_SAVE_CHECKPOINT = base.save_checkpoint +ORIGINAL_RUN_EVALUATION_FOR_RUN = base.run_evaluation_for_run + +BASE_RESUME_IDENTITY_KEYS = tuple(base.RESUME_IDENTITY_KEYS) +RUNTIME_ONLY_CONFIG_KEYS = frozenset( + { + "PERCENT_EXECUTION_MODE", + "SELECTED_DATASET_PERCENTS", + "SPLIT_EXECUTION_MODE", + "SELECTED_SPLIT_INDICES", + "PHASE_EXECUTION_MODE", + "SELECTED_PHASES", + "REPEAT_EXECUTION_MODE", + "SELECTED_REPEAT_INDICES", + } +) +PORTABLE_FINGERPRINT_FOLD_KEYS = frozenset( + { + "RESUME_FOLDS", + "REPEATED_HOLDOUT_ROOT", + "EXPERIMENT_ROOT", + "EXPERIMENT_DB_PATH", + } +) + + +"""============================================================================= +UTILITIES +============================================================================= +""" + + +def now_utc_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _jsonify(value: Any) -> Any: + if isinstance(value, Path): + return str(value) + if isinstance(value, dict): + return {str(key): _jsonify(val) for key, val in sorted(value.items(), key=lambda item: str(item[0]))} + if isinstance(value, (list, tuple)): + return [_jsonify(item) for item in value] + if isinstance(value, set): + return [_jsonify(item) for item in sorted(value, key=str)] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return repr(value) + + +def _summary_mean_std(values: list[float]) -> dict[str, float]: + arr = np.array(values, dtype=np.float64) + return { + "mean": float(arr.mean()) if arr.size > 0 else 0.0, + "std": float(arr.std()) if arr.size > 0 else 0.0, + } + + +def _phase_timing_summary_path() -> Path: + return EXPERIMENT_ROOT / "phase_timing_summary.json" + + +def _completed_run_rows_for_phase(phase_index: int) -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + AND split_repeat_index = ? + ORDER BY dataset_percent, subset_repeat_index, strategy + """, + (int(phase_index),), + ).fetchall() + ) + + +def _run_training_elapsed_seconds(row: sqlite3.Row) -> float | None: + run_dir = Path(str(row["run_dir"])) + summary_path = run_dir / "summary.json" + if summary_path.exists(): + try: + summary = base.load_json(summary_path) + if summary.get("elapsed_seconds") is not None: + return float(summary["elapsed_seconds"]) + except Exception as exc: + print(f"[Timing] Could not read {summary_path}: {exc}") + if row["elapsed_seconds"] is not None: + return float(row["elapsed_seconds"]) + return None + + +def write_phase_timing_summary_after_phase(phase_index: int) -> None: + if LEDGER_CONN is None: + return + rows = _completed_run_rows_for_phase(phase_index) + if not rows: + return + + run_entries: list[dict[str, Any]] = [] + phase_values: list[float] = [] + for row in rows: + elapsed = _run_training_elapsed_seconds(row) + entry = { + "phase_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": str(row["run_dir"]), + "training_elapsed_seconds": elapsed, + } + run_entries.append(entry) + if elapsed is not None: + phase_values.append(float(elapsed)) + + phase_stats = _summary_mean_std(phase_values) + existing_payload: dict[str, Any] = {} + summary_path = _phase_timing_summary_path() + if summary_path.exists(): + try: + existing_payload = base.load_json(summary_path) + except Exception as exc: + print(f"[Timing] Could not read existing phase timing summary {summary_path}: {exc}") + + phases_by_index: dict[int, dict[str, Any]] = {} + for phase_payload in existing_payload.get("phases", []): + if isinstance(phase_payload, dict) and phase_payload.get("phase_index") is not None: + phases_by_index[int(phase_payload["phase_index"])] = dict(phase_payload) + phases_by_index[int(phase_index)] = { + "phase_index": int(phase_index), + "completed_strategy_count": len(run_entries), + "completed_strategies": [int(entry["strategy"]) for entry in run_entries], + "runs": run_entries, + "training_elapsed_seconds_mean": phase_stats["mean"], + "training_elapsed_seconds_std": phase_stats["std"], + "updated_at": now_utc_iso(), + } + + phases = [phases_by_index[index] for index in sorted(phases_by_index)] + global_phase_means = [ + float(phase["training_elapsed_seconds_mean"]) + for phase in phases + if phase.get("training_elapsed_seconds_mean") is not None + ] + global_stats = _summary_mean_std(global_phase_means) + payload = { + "scope": "fixed_phase_training_time", + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": len(phases), + "training_elapsed_seconds_mean_across_phases": global_stats["mean"], + "training_elapsed_seconds_std_across_phases": global_stats["std"], + "phases": phases, + "updated_at": now_utc_iso(), + } + atomic_save_json(summary_path, payload) + print( + f"[Timing] Phase {phase_index:03d} training time summary updated -> {summary_path} " + f"(mean={phase_stats['mean']:.2f}s, std={phase_stats['std']:.2f}s)." + ) + + +def validate_hf_backup_settings() -> None: + """Fail fast at startup if backups are enabled but HF env vars are missing. + + Refuses to run rather than discovering hours into training (at the first + backup) that nothing can be uploaded. Disable by setting + ASYNC_REPO_BACKUP_AFTER_PHASE = False if you intentionally want no backups. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + missing = [] + if not repo_id: + missing.append("HF_REPO_ID (target repo, e.g. 'your-username/ADVAI24JUN-backup')") + if not token: + missing.append("HF_TOKEN (Hugging Face write token)") + if missing: + raise RuntimeError( + "Hugging Face backup is enabled (ASYNC_REPO_BACKUP_AFTER_PHASE = True) " + "but required environment variables are not set:\n - " + + "\n - ".join(missing) + + "\n\nSet them before running, e.g.:\n" + " export HF_REPO_ID='your-username/ADVAI24JUN-backup'\n" + " export HF_TOKEN='hf_xxxxxxxxxxxxxxxxxxxxx'\n" + "Or set ASYNC_REPO_BACKUP_AFTER_PHASE = False to run without backups." + ) + + +def _hf_backup_due(phase_index: int) -> bool: + """True only on every HF_BACKUP_EVERY_N_PHASES-th phase (0-indexed boundary).""" + n = max(1, int(HF_BACKUP_EVERY_N_PHASES)) + return (phase_index + 1) % n == 0 + + +def _hf_upload_project(*, label: str) -> bool: + """Create the HF dataset repo if needed and mirror PROJECT_DIR into it. + + Shared by the initial pre-training backup and the per-phase backups. + `upload_large_folder` is resumable and content-addressed: unchanged files are + skipped and an interrupted upload (e.g. a 503) can be safely re-run, so the repo + always converges to the latest project state. Retries with backoff to ride out + transient HF outages. Returns True on a verified successful upload. + """ + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + if not repo_id: + print(f"[Backup] Skipping HF backup ({label}): HF_REPO_ID is not set (export HF_REPO_ID=user/repo).") + return False + if not token: + print(f"[Backup] Skipping HF backup ({label}): HF_TOKEN env var is not set.") + return False + + try: + from huggingface_hub import HfApi + except Exception: + print(f"[Backup] Skipping HF backup ({label}): huggingface_hub not installed (pip install huggingface_hub).") + return False + + api = HfApi(token=token) + try: + api.create_repo(repo_id=repo_id, repo_type=HF_REPO_TYPE, private=True, exist_ok=True) + except Exception as exc: + print(f"[Backup] Could not ensure HF repo {repo_id} exists: {exc}") + + last_exc: Exception | None = None + for attempt in range(1, HF_BACKUP_MAX_RETRIES + 1): + try: + print( + f"[Backup] {label}: uploading project to " + f"hf://{HF_REPO_TYPE}/{repo_id} (attempt {attempt}/{HF_BACKUP_MAX_RETRIES})..." + ) + api.upload_large_folder( + repo_id=repo_id, + repo_type=HF_REPO_TYPE, + folder_path=str(PROJECT_DIR.resolve()), + ignore_patterns=list(HF_IGNORE_PATTERNS), + print_report=True, + ) + print(f"[Backup] {label}: HF backup complete -> {repo_id}.") + return True + except Exception as exc: + last_exc = exc + wait = min(60, 5 * attempt) + print(f"[Backup] {label}: HF upload attempt {attempt} failed: {exc}. Retrying in {wait}s...") + time.sleep(wait) + print(f"[Backup] {label}: HF backup FAILED after {HF_BACKUP_MAX_RETRIES} attempts: {last_exc}") + return False + + +def run_initial_hf_backup() -> None: + """Fresh backup BEFORE any training begins. + + Creates the repo and uploads the current project state synchronously, so the + entire backup pipeline (repo creation, token, upload) is proven before we commit + hours of compute. Later phase backups refresh this same repo. Runs in the + foreground on purpose -- if the first backup cannot complete, we want to know now. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE or not HF_BACKUP_ON_START: + return + print("[Backup] Running initial pre-training backup (this proves the backup pipeline before training)...") + _hf_upload_project(label="Initial backup") + + +def run_repo_backup_after_phase(phase_index: int) -> None: + """Refresh the Hugging Face dataset repo after a training phase. + + Fires only on every HF_BACKUP_EVERY_N_PHASES-th phase so we don't hammer HF. + Runs in a background thread at the call site. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + if not _hf_backup_due(phase_index): + print( + f"[Backup] Phase {phase_index:03d}: skipping HF backup " + f"(uploads every {HF_BACKUP_EVERY_N_PHASES} phases)." + ) + return + _hf_upload_project(label=f"Phase {phase_index:03d}") + + +def atomic_write_text(path: str | Path, text: str) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_write_bytes(path: str | Path, payload: bytes) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_save_json(path: str | Path, payload: Any) -> None: + atomic_write_text(path, json.dumps(payload, indent=2, sort_keys=True)) + + +def atomic_torch_save(path: str | Path, payload: Any) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + torch.save(payload, handle) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def stable_hash(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def stable_int(text: str) -> int: + return base.stable_int_from_text(text) + + +def fold_seed(tag: str) -> int: + return int(base.SEED) + stable_int(tag) + + +def current_split_generation_mode() -> str: + mode = str(SPLIT_GENERATION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_GENERATION_MODES: + raise ValueError( + f"SPLIT_GENERATION_MODE must be one of {SUPPORTED_SPLIT_GENERATION_MODES}, got {mode!r}" + ) + return mode + + +def using_fixed_phase_mode() -> bool: + return current_split_generation_mode() == "fixed_stratified_phases_8_1_1" + + +def primary_unit_name(*, plural: bool = False) -> str: + if using_fixed_phase_mode(): + return "phases" if plural else "phase" + return "splits" if plural else "split" + + +def current_phase_execution_mode() -> str: + mode = str(PHASE_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PHASE_EXECUTION_MODES: + raise ValueError( + f"PHASE_EXECUTION_MODE must be one of {SUPPORTED_PHASE_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def phase_count() -> int: + if isinstance(NUM_PHASES, bool) or int(NUM_PHASES) <= 0: + raise ValueError("NUM_PHASES must be a positive integer.") + return int(NUM_PHASES) + + +def phase_val_offset() -> int: + if isinstance(PHASE_VAL_OFFSET, bool): + raise TypeError("PHASE_VAL_OFFSET must be an integer.") + return int(PHASE_VAL_OFFSET) + + +def phase_indices() -> list[int]: + return list(range(1, phase_count() + 1)) + + +def phase_execution_indices_to_run() -> list[int]: + indices = phase_indices() + if current_phase_execution_mode() == "auto": + return indices + + if not SELECTED_PHASES: + raise ValueError("SELECTED_PHASES must be non-empty when PHASE_EXECUTION_MODE='manual'.") + + selected: list[int] = [] + seen: set[int] = set() + max_index = indices[-1] + for raw_index in SELECTED_PHASES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + f"SELECTED_PHASES entries must be integer phase indices in the range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_PHASES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_PHASES contains duplicate phase index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def phase_fold_indices(phase_index: int) -> tuple[int, int]: + count = phase_count() + if phase_index < 1 or phase_index > count: + raise ValueError(f"Phase index must be in [1, {count}], got {phase_index}.") + val_offset = phase_val_offset() + if val_offset <= 0 or val_offset >= count: + raise ValueError( + f"PHASE_VAL_OFFSET must be in [1, {count - 1}] for {count} phases, got {val_offset}." + ) + test_fold_index = phase_index + val_fold_index = ((phase_index - 1 + val_offset) % count) + 1 + return val_fold_index, test_fold_index + + +def partition_seed() -> int: + if using_fixed_phase_mode(): + return fold_seed(f"phase_partition::{phase_count()}") + return int(base.SEED) + + +def split_generation_display_name() -> str: + if using_fixed_phase_mode(): + return "fixed stratified 10-phase 8/1/1" + return "repeated stratified holdout" + + +def cycle_index_label(index: int) -> str: + return f"{primary_unit_name()}_{int(index):03d}" + + +def cycle_identity_label(index: int) -> str: + return f"{primary_unit_name()}={int(index):03d}" + + +def all_split_repeat_indices() -> list[int]: + if using_fixed_phase_mode(): + return phase_indices() + return list(range(1, int(NUM_STRATIFIED_SPLIT_REPEATS) + 1)) + + +def max_subset_repeat_index() -> int: + return max(int(spec.repeat_count) for spec in percent_specs()) + + +def current_percent_sampling_mode() -> str: + mode = str(PERCENT_SAMPLING_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_SAMPLING_MODES: + raise ValueError( + f"PERCENT_SAMPLING_MODE must be one of {SUPPORTED_PERCENT_SAMPLING_MODES}, got {mode!r}" + ) + return mode + + +def current_split_execution_mode() -> str: + mode = str(SPLIT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_EXECUTION_MODES: + raise ValueError( + f"SPLIT_EXECUTION_MODE must be one of {SUPPORTED_SPLIT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_repeat_execution_mode() -> str: + mode = str(REPEAT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_REPEAT_EXECUTION_MODES: + raise ValueError( + f"REPEAT_EXECUTION_MODE must be one of {SUPPORTED_REPEAT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_percent_execution_mode() -> str: + mode = str(PERCENT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_EXECUTION_MODES: + raise ValueError( + f"PERCENT_EXECUTION_MODE must be one of {SUPPORTED_PERCENT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def split_repeat_indices_to_run() -> list[int]: + if using_fixed_phase_mode(): + return phase_execution_indices_to_run() + + split_indices = all_split_repeat_indices() + if current_split_execution_mode() == "auto": + return split_indices + + if not SELECTED_SPLIT_INDICES: + raise ValueError( + "SELECTED_SPLIT_INDICES must be non-empty when SPLIT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = split_indices[-1] + for raw_index in SELECTED_SPLIT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_SPLIT_INDICES entries must be integer split indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_SPLIT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_SPLIT_INDICES contains duplicate split index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def subset_repeat_indices_to_run() -> list[int]: + repeat_indices = list(range(1, max_subset_repeat_index() + 1)) + if current_repeat_execution_mode() == "auto": + return repeat_indices + + if not SELECTED_REPEAT_INDICES: + raise ValueError( + "SELECTED_REPEAT_INDICES must be non-empty when REPEAT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = repeat_indices[-1] + for raw_index in SELECTED_REPEAT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_REPEAT_INDICES entries must be integer repeat indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_REPEAT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_REPEAT_INDICES contains duplicate repeat index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def percent_specs_to_run() -> list[PercentRepeatSpec]: + all_specs = percent_specs() + if current_percent_execution_mode() == "auto": + return all_specs + + if not SELECTED_DATASET_PERCENTS: + raise ValueError( + "SELECTED_DATASET_PERCENTS must be non-empty when PERCENT_EXECUTION_MODE='manual'." + ) + + all_percent_ints = {spec.percent_int for spec in all_specs} + selected: list[PercentRepeatSpec] = [] + seen: set[int] = set() + for raw_percent in SELECTED_DATASET_PERCENTS: + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "SELECTED_DATASET_PERCENTS entries must be integer percentages in the range [1, 100]." + ) + if raw_percent not in all_percent_ints: + raise ValueError( + f"SELECTED_DATASET_PERCENTS entry {raw_percent} is not defined in " + f"DATASET_PERCENT_REPEAT_COUNTS. Available: {sorted(all_percent_ints)}." + ) + if raw_percent in seen: + raise ValueError(f"SELECTED_DATASET_PERCENTS contains duplicate percent {raw_percent}.") + seen.add(raw_percent) + spec_map = {spec.percent_int: spec for spec in all_specs} + for raw_percent in SELECTED_DATASET_PERCENTS: + selected.append(spec_map[raw_percent]) + selected.sort(key=lambda s: s.percent_int) + return selected + + +def validate_repeated_holdout_settings() -> None: + if using_fixed_phase_mode(): + if phase_count() != 10: + raise ValueError( + f"fixed phase mode requires NUM_PHASES=10, got {phase_count()}." + ) + phase_fold_indices(1) + if current_dataset_name() != "BUSI_with_classes": + raise ValueError( + "fixed phase mode currently supports DATASET_NAME='BUSI_with_classes' only." + ) + if current_busi_with_classes_split_policy() != "stratified": + raise ValueError( + "fixed phase mode requires BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"fixed phase mode requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + current_phase_execution_mode() + else: + if int(NUM_STRATIFIED_SPLIT_REPEATS) <= 0: + raise ValueError("NUM_STRATIFIED_SPLIT_REPEATS must be a positive integer.") + current_split_execution_mode() + current_percent_sampling_mode() + current_repeat_execution_mode() + current_percent_execution_mode() + split_repeat_indices_to_run() + subset_repeat_indices_to_run() + selected_percent_specs = percent_specs_to_run() + if using_fixed_phase_mode(): + if len(selected_percent_specs) != 1 or int(selected_percent_specs[0].percent_int) != 100: + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "If PERCENT_EXECUTION_MODE='manual', set SELECTED_DATASET_PERCENTS=[100]." + ) + + +def repeated_holdout_split_policy() -> str | None: + if base.current_dataset_name() == "BUSI_with_classes": + return base.current_busi_with_classes_split_policy() + return None + + +def active_specs_for_subset_repeat(subset_repeat_index: int) -> list[PercentRepeatSpec]: + return [ + spec + for spec in percent_specs() + if subset_repeat_index <= int(spec.repeat_count) + ] + + +def percent_specs() -> list[PercentRepeatSpec]: + global PERCENT_SPECS_CACHE + if PERCENT_SPECS_CACHE is not None: + return list(PERCENT_SPECS_CACHE) + specs: list[PercentRepeatSpec] = [] + if not DATASET_PERCENT_REPEAT_COUNTS: + raise ValueError("DATASET_PERCENT_REPEAT_COUNTS must contain at least one percentage entry.") + for raw_percent, raw_repeat_count in DATASET_PERCENT_REPEAT_COUNTS.items(): + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "DATASET_PERCENT_REPEAT_COUNTS keys must be integer percentages in the range [1, 100]." + ) + if raw_percent <= 0 or raw_percent > 100: + raise ValueError(f"Invalid dataset percent {raw_percent}; expected an integer in [1, 100].") + if isinstance(raw_repeat_count, bool) or int(raw_repeat_count) <= 0: + raise ValueError( + f"Invalid repeat count for percent {raw_percent}: {raw_repeat_count!r}. Expected a positive integer." + ) + repeat_count = int(raw_repeat_count) + if using_fixed_phase_mode() and raw_percent == 100 and repeat_count != 1: + raise ValueError( + "fixed phase mode requires DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got repeat_count={repeat_count} for percent 100." + ) + if raw_percent == 100 and repeat_count > 1: + print( + f"[Repeated Holdout] Percent 100 was configured with repeat_count={repeat_count}. " + "Collapsing to one effective repeat per split." + ) + repeat_count = 1 + fraction = float(raw_percent) / 100.0 + specs.append(PercentRepeatSpec(percent_int=raw_percent, fraction=fraction, repeat_count=repeat_count)) + specs.sort(key=lambda item: item.percent_int) + if using_fixed_phase_mode(): + if len(specs) != 1 or int(specs[0].percent_int) != 100: + configured = {spec.percent_int: spec.repeat_count for spec in specs} + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "Set DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got {configured}." + ) + PERCENT_SPECS_CACHE = list(specs) + return list(PERCENT_SPECS_CACHE) + + +def fold_experiment_summary(model_config: base.RuntimeModelConfig) -> None: + if using_fixed_phase_mode(): + base.banner("RUNNER FOLDS | FIXED STRATIFIED 10-PHASE 8/1/1") + else: + base.banner("RUNNER FOLDS | REPEATED STRATIFIED HOLDOUT") + print(f"Experiment name : {FOLDS_EXPERIMENT_NAME}") + print(f"Resume folds : {RESUME_FOLDS}") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Dataset name : {base.current_dataset_name()}") + print(f"Split type : {base.SPLIT_TYPE}") + print(f"Split generation mode : {current_split_generation_mode()}") + print(f"Generation display : {split_generation_display_name()}") + if using_fixed_phase_mode(): + print(f"Phase count : {phase_count()}") + print(f"Phase val offset : {phase_val_offset()}") + print(f"Phase execution mode : {current_phase_execution_mode()}") + print("Train percent mode : 100% of phase-train only") + if current_phase_execution_mode() == "manual": + print(f"Selected phases : {phase_execution_indices_to_run()}") + else: + print(f"Split repeats : {NUM_STRATIFIED_SPLIT_REPEATS}") + print(f"Split execution mode : {current_split_execution_mode()}") + if current_split_execution_mode() == "manual": + print(f"Selected split indices: {split_repeat_indices_to_run()}") + print(f"Sampling mode : {current_percent_sampling_mode()}") + print(f"Repeat execution mode : {current_repeat_execution_mode()}") + if current_repeat_execution_mode() == "manual": + print(f"Selected repeat idxs : {subset_repeat_indices_to_run()}") + print(f"Percent execution mode: {current_percent_execution_mode()}") + if current_percent_execution_mode() == "manual": + print(f"Selected percents : {[s.percent_int for s in percent_specs_to_run()]}") + print(f"Strategies : {base.STRATEGIES}") + print( + "Percent repeats : " + + ", ".join(f"{spec.percent_int}% x{spec.repeat_count}" for spec in percent_specs()) + ) + print(f"Execution mode : {base.EXECUTION_MODE}") + print(f"Run smoke test : {base.RUN_SMOKE_TEST}") + print(f"Test iter control : {base.TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {base.TEST_ITERATION_T}") + print(f"Run overfit test : {base.RUN_OVERFIT_TEST}") + print(f"Run Optuna : {base.RUN_OPTUNA}") + print(f"Load existing studies : {base.LOAD_EXISTING_STUDIES}") + print(f"Repo backup enabled : {ASYNC_REPO_BACKUP_AFTER_PHASE}") + if ASYNC_REPO_BACKUP_AFTER_PHASE: + print(f"Repo backup target : hf://{HF_REPO_TYPE}/{HF_REPO_ID or ''}") + print(f"Repo backup cadence : every {HF_BACKUP_EVERY_N_PHASES} phases") + print(f"Initial backup on run : {HF_BACKUP_ON_START}") + print(f"Phase timing summary : {_phase_timing_summary_path()}") + print(f"Backbone : {model_config.backbone_display_name()}") + + +def config_snapshot(model_config: base.RuntimeModelConfig) -> dict[str, Any]: + base_config = { + name: _jsonify(getattr(base, name)) + for name in sorted(dir(base)) + if name.isupper() and not name.startswith("_") + and name not in RUNTIME_ONLY_CONFIG_KEYS + } + return { + "folds_runner": { + "SPLIT_GENERATION_MODE": current_split_generation_mode(), + "NUM_STRATIFIED_SPLIT_REPEATS": int(NUM_STRATIFIED_SPLIT_REPEATS), + "NUM_PHASES": int(NUM_PHASES), + "PHASE_VAL_OFFSET": int(PHASE_VAL_OFFSET), + "DATASET_PERCENT_REPEAT_COUNTS": _jsonify(DATASET_PERCENT_REPEAT_COUNTS), + "PERCENT_SAMPLING_MODE": current_percent_sampling_mode(), + "FOLDS_EXPERIMENT_NAME": str(FOLDS_EXPERIMENT_NAME), + "RESUME_FOLDS": bool(RESUME_FOLDS), + "REPEATED_HOLDOUT_ROOT": str(REPEATED_HOLDOUT_ROOT), + "EXPERIMENT_ROOT": str(EXPERIMENT_ROOT), + "EXPERIMENT_DB_PATH": str(EXPERIMENT_DB_PATH), + }, + "runner": base_config, + "model_config": model_config.to_payload(), + } + + +def portable_config_snapshot_for_fingerprint(snapshot: dict[str, Any]) -> dict[str, Any]: + portable = json.loads(json.dumps(snapshot, sort_keys=True)) + folds_runner = portable.get("folds_runner") + if isinstance(folds_runner, dict): + for key in PORTABLE_FINGERPRINT_FOLD_KEYS: + folds_runner.pop(key, None) + return portable + + +def config_fingerprint(snapshot: dict[str, Any]) -> str: + return stable_hash(json.dumps(portable_config_snapshot_for_fingerprint(snapshot), sort_keys=True)) + + +def dataset_fingerprint(sample_records: list[dict[str, str]]) -> str: + payload = { + "dataset_name": base.current_dataset_name(), + "records": [ + { + "filename": record["filename"], + "image_rel_path": record["image_rel_path"], + "mask_rel_path": record["mask_rel_path"], + "class_label": record.get("class_label"), + } + for record in sample_records + ], + } + return stable_hash(json.dumps(payload, sort_keys=True)) + + +def cycle_dirname(split_repeat_index: int) -> str: + return f"{primary_unit_name()}_{split_repeat_index:03d}" + + +def split_manifest_path(split_repeat_index: int) -> Path: + return SPLIT_MANIFESTS_DIR / f"{cycle_dirname(split_repeat_index)}.json" + + +def subset_manifest_path(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return SUBSET_MANIFESTS_DIR / ( + f"{cycle_dirname(split_repeat_index)}_pct_{percent_int:03d}_repeat_{subset_repeat_index:02d}.json" + ) + + +def repeat_root(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return ( + EXPERIMENT_ROOT + / cycle_dirname(split_repeat_index) + / f"pct_{percent_int:03d}" + / f"repeat_{subset_repeat_index:02d}" + ) + + +def run_dir_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "final") + + +def overfit_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "overfit_test") + + +def strategy_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir) + + +def fold_study_paths_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> tuple[Path, Path, Path]: + strategy_root = strategy_root_for(strategy, ctx, model_config) + return strategy_root, ensure_dir(strategy_root / "study"), ensure_dir(strategy_root / "trials") + + +def select_sample_records() -> tuple[list[dict[str, str]], Path]: + dataset_name = base.current_dataset_name() + images_dir, annotations_dir = base.current_dataset_dirs() + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {base.DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + + matched, missing_masks, missing_images = base.validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + sample_records = base.build_sample_records( + matched, + images_subdir=images_dir.relative_to(dataset_root).as_posix(), + annotations_subdir=annotations_dir.relative_to(dataset_root).as_posix(), + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = base.current_pipeline_check_path() + if pipeline_check_path is not None: + base.validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + return sample_records, dataset_root + + +def record_filenames(records: list[dict[str, str]]) -> list[str]: + return [str(record["filename"]) for record in records] + + +def duplicate_filenames(records: list[dict[str, str]]) -> list[str]: + counts = Counter(record_filenames(records)) + return sorted(name for name, count in counts.items() if count > 1) + + +def format_filename_preview(filenames: list[str], *, limit: int = 5) -> str: + preview = filenames[:limit] + suffix = "" if len(filenames) <= limit else f" ... (+{len(filenames) - limit} more)" + return f"{preview}{suffix}" + + +def overlap_preview(leaks: dict[str, list[str]], *, limit: int = 5) -> str: + if not leaks: + return "[]" + key = sorted(leaks.keys())[0] + return f"{key}: {format_filename_preview(leaks[key], limit=limit)}" + + +def validate_disjoint_record_sets( + record_sets: dict[str, list[dict[str, str]]], + *, + context: str, + expected_filenames: set[str] | None = None, +) -> None: + split_filenames: dict[str, list[str]] = {} + for split_name, records in record_sets.items(): + duplicates = duplicate_filenames(records) + if duplicates: + raise RuntimeError( + f"Duplicate filenames detected inside {context} {split_name}: " + f"{format_filename_preview(duplicates)}" + ) + split_filenames[split_name] = record_filenames(records) + + leaks = base.check_data_leakage(split_filenames) + if leaks: + raise RuntimeError( + f"Data leakage detected for {context}: {overlap_preview(leaks)}" + ) + + if expected_filenames is not None: + actual_filenames = set().union(*(set(values) for values in split_filenames.values())) + missing = sorted(expected_filenames - actual_filenames) + extra = sorted(actual_filenames - expected_filenames) + if missing or extra: + details: list[str] = [] + if missing: + details.append(f"missing={format_filename_preview(missing)}") + if extra: + details.append(f"extra={format_filename_preview(extra)}") + raise RuntimeError( + f"{context} does not match the expected dataset membership: {'; '.join(details)}" + ) + + +def validate_fixed_phase_dataset_requirements(sample_records: list[dict[str, str]]) -> None: + class_distribution = base.compute_class_distribution(sample_records) + if class_distribution is None: + raise RuntimeError( + "fixed phase mode requires class-aware records with class_label metadata." + ) + insufficient = { + label: int(count) + for label, count in class_distribution.items() + if int(count) < phase_count() + } + if insufficient: + raise RuntimeError( + "fixed phase mode requires enough samples to place every class in every phase. " + f"Need >= {phase_count()} samples per class, got {insufficient}." + ) + + +def build_fixed_stratified_phase_folds( + sample_records: list[dict[str, str]], + *, + seed: int, +) -> dict[int, list[dict[str, str]]]: + folds: dict[int, list[dict[str, str]]] = {index: [] for index in phase_indices()} + grouped = base.group_records_by_class(sample_records) + for class_label in sorted(grouped.keys()): + records = base.deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"phase_partition::{phase_count()}::{class_label}", + ) + for record_index, record in enumerate(records): + fold_index = (record_index % phase_count()) + 1 + folds[fold_index].append(dict(record)) + + for fold_index in phase_indices(): + folds[fold_index] = base.deterministic_shuffle_records( + folds[fold_index], + seed=seed, + tag=f"phase_partition::{phase_count()}::fold::{fold_index:03d}", + ) + return folds + + +def validate_fixed_phase_folds( + phase_folds: dict[int, list[dict[str, str]]], + *, + sample_records: list[dict[str, str]], +) -> None: + if sorted(phase_folds.keys()) != phase_indices(): + raise RuntimeError( + f"Expected fixed phase folds for indices {phase_indices()}, got {sorted(phase_folds.keys())}." + ) + validate_disjoint_record_sets( + {f"fold_{fold_index:03d}": records for fold_index, records in sorted(phase_folds.items())}, + context="fixed phase fold partition", + expected_filenames={record["filename"] for record in sample_records}, + ) + + +def build_phase_base_split( + phase_folds: dict[int, list[dict[str, str]]], + *, + phase_index: int, + seed: int, +) -> dict[str, list[dict[str, str]]]: + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + train_records: list[dict[str, str]] = [] + for fold_index in phase_indices(): + if fold_index in {val_fold_index, test_fold_index}: + continue + train_records.extend(dict(record) for record in phase_folds[fold_index]) + return { + "train": base.deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"phase::{phase_index:03d}::train", + ), + "val": base.deterministic_shuffle_records( + phase_folds[val_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::val", + ), + "test": base.deterministic_shuffle_records( + phase_folds[test_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::test", + ), + } + + +def build_base_split_for_repeat(sample_records: list[dict[str, str]], split_seed: int) -> dict[str, list[dict[str, str]]]: + dataset_name = base.current_dataset_name() + if dataset_name == "BUSI_with_classes": + split_policy = repeated_holdout_split_policy() + if split_policy != "stratified": + raise ValueError( + "RUNNER_FOLDS.py requires BUSI_with_classes to use BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + return base.build_stratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + return base.build_unstratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + + +def validate_base_split( + base_splits: dict[str, list[dict[str, str]]], + *, + split_repeat_index: int, + expected_filenames: set[str] | None = None, +) -> None: + context = f"{primary_unit_name()}={split_repeat_index:03d}" + validate_disjoint_record_sets( + base_splits, + context=context, + expected_filenames=expected_filenames, + ) + + +def validate_phase_coverage( + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]], + *, + sample_records: list[dict[str, str]], +) -> None: + expected_phase_indices = phase_indices() + if sorted(phase_splits_by_index.keys()) != expected_phase_indices: + raise RuntimeError( + f"Expected materialized phases {expected_phase_indices}, got {sorted(phase_splits_by_index.keys())}." + ) + + expected_filenames = {record["filename"] for record in sample_records} + train_counts: Counter[str] = Counter() + val_counts: Counter[str] = Counter() + test_counts: Counter[str] = Counter() + + for phase_index, phase_splits in sorted(phase_splits_by_index.items()): + validate_disjoint_record_sets( + phase_splits, + context=f"phase={phase_index:03d}", + expected_filenames=expected_filenames, + ) + train_counts.update(record_filenames(phase_splits["train"])) + val_counts.update(record_filenames(phase_splits["val"])) + test_counts.update(record_filenames(phase_splits["test"])) + + expected_counts = { + "train": phase_count() - 2, + "val": 1, + "test": 1, + } + counters_by_name = { + "train": train_counts, + "val": val_counts, + "test": test_counts, + } + for split_name, expected_count in expected_counts.items(): + offending = sorted( + filename + for filename in expected_filenames + if int(counters_by_name[split_name].get(filename, 0)) != expected_count + ) + if offending: + raise RuntimeError( + f"Invalid global phase coverage for {split_name}: expected each filename to appear " + f"{expected_count} time(s), offenders={format_filename_preview(offending)}" + ) + + +def build_subset_for_repeat( + train_records: list[dict[str, str]], + *, + percent_fraction: float, + subset_seed: int, +) -> list[dict[str, str]]: + if percent_fraction >= 1.0: + return [dict(record) for record in train_records] + subsets = base.build_nested_train_subsets( + train_records, + [percent_fraction], + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + return subsets[base.percent_label(percent_fraction)] + + +def build_incremental_subset_chain( + train_records: list[dict[str, str]], + *, + active_specs: list[PercentRepeatSpec], + subset_seed: int, +) -> dict[str, list[dict[str, str]]]: + fractions = [spec.fraction for spec in active_specs] + return base.build_nested_train_subsets( + train_records, + fractions, + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + + +def iter_manifested_contexts( + split_repeat_indices: list[int] | None = None, + subset_repeat_indices: list[int] | None = None, +) -> Iterator[FoldRunContext]: + selected_indices = all_split_repeat_indices() if split_repeat_indices is None else list(split_repeat_indices) + selected_subset_repeats = ( + subset_repeat_indices_to_run() if subset_repeat_indices is None else list(subset_repeat_indices) + ) + selected_subset_repeat_set = set(selected_subset_repeats) + for split_repeat_index in selected_indices: + for spec in percent_specs_to_run(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + if subset_repeat_index not in selected_subset_repeat_set: + continue + yield load_context(split_repeat_index, spec.percent_int, subset_repeat_index) + + +def validate_subset_records( + train_records: list[dict[str, str]], + subset_records: list[dict[str, str]], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + train_filenames = {record["filename"] for record in train_records} + subset_filenames = [record["filename"] for record in subset_records] + cycle_context = f"{primary_unit_name()}={split_repeat_index:03d}" + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames found in subset {cycle_context}, " + f"percent={percent_int}, repeat={subset_repeat_index}." + ) + outside_train = sorted(set(subset_filenames) - train_filenames) + if outside_train: + raise RuntimeError( + f"Subset contains filenames outside the base train split for " + f"{cycle_context}, percent={percent_int}, repeat={subset_repeat_index}: {outside_train[:5]}" + ) + + +"""============================================================================= +SQLITE LEDGER +============================================================================= +""" + + +def require_ledger() -> sqlite3.Connection: + if LEDGER_CONN is None: + raise RuntimeError("Ledger is not initialized.") + return LEDGER_CONN + + +def ledger_execute(sql: str, params: tuple[Any, ...] = ()) -> sqlite3.Cursor: + conn = require_ledger() + cursor = conn.execute(sql, params) + conn.commit() + return cursor + + +def setup_ledger(path: Path) -> sqlite3.Connection: + ensure_dir(path.parent) + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=FULL") + conn.execute( + """ + CREATE TABLE IF NOT EXISTS experiment_meta ( + experiment_name TEXT PRIMARY KEY, + config_fingerprint TEXT NOT NULL, + config_json TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + base_seed INTEGER NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS split_manifests ( + split_repeat_index INTEGER PRIMARY KEY, + split_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_count INTEGER NOT NULL, + val_count INTEGER NOT NULL, + test_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS subset_manifests ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_subset_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS run_status ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + dataset_fraction REAL NOT NULL, + split_seed INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + split_manifest_path TEXT NOT NULL, + subset_manifest_path TEXT NOT NULL, + run_dir TEXT NOT NULL, + status TEXT NOT NULL, + stage TEXT NOT NULL, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at TEXT, + updated_at TEXT NOT NULL, + heartbeat_at TEXT, + completed_at TEXT, + last_epoch INTEGER, + latest_checkpoint_path TEXT, + best_checkpoint_path TEXT, + evaluation_path TEXT, + best_metric_name TEXT, + best_metric_value REAL, + elapsed_seconds REAL, + error_text TEXT, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS final_metrics ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + metric_name TEXT NOT NULL, + mean REAL NOT NULL, + std REAL, + run_dir TEXT NOT NULL, + evaluation_path TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy, metric_name) + ) + """ + ) + conn.commit() + return conn + + +def fetch_one(sql: str, params: tuple[Any, ...]) -> sqlite3.Row | None: + return require_ledger().execute(sql, params).fetchone() + + +def load_run_status(key: RunKey) -> sqlite3.Row | None: + return fetch_one( + """ + SELECT * + FROM run_status + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + + +def upsert_experiment_meta( + *, + snapshot: dict[str, Any], + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO experiment_meta ( + experiment_name, + config_fingerprint, + config_json, + dataset_fingerprint, + base_seed, + created_at + ) VALUES (?, ?, ?, ?, ?, ?) + """, + ( + FOLDS_EXPERIMENT_NAME, + config_hash, + json.dumps(snapshot, sort_keys=True), + data_hash, + int(base.SEED), + now_utc_iso(), + ), + ) + + +def existing_experiment_meta() -> sqlite3.Row | None: + return fetch_one( + "SELECT * FROM experiment_meta WHERE experiment_name = ?", + (FOLDS_EXPERIMENT_NAME,), + ) + + +def ledger_row_count(table_name: str) -> int: + row = require_ledger().execute(f"SELECT COUNT(*) AS count FROM {table_name}").fetchone() + return int(row["count"]) if row is not None else 0 + + +def upsert_split_manifest_row( + *, + split_repeat_index: int, + split_seed: int, + manifest_path: Path, + train_count: int, + val_count: int, + test_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO split_manifests ( + split_repeat_index, + split_seed, + manifest_path, + train_count, + val_count, + test_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + split_seed, + str(manifest_path.resolve()), + train_count, + val_count, + test_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_subset_manifest_row( + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, + subset_seed: int, + manifest_path: Path, + subset_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO subset_manifests ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + subset_seed, + manifest_path, + train_subset_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + percent_int, + subset_repeat_index, + subset_seed, + str(manifest_path.resolve()), + subset_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_run_plan_row( + *, + key: RunKey, + dataset_fraction: float, + split_seed: int, + subset_seed: int, + split_manifest: Path, + subset_manifest: Path, + run_dir: Path, +) -> None: + existing = load_run_status(key) + if existing is not None: + return + ledger_execute( + """ + INSERT INTO run_status ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + dataset_fraction, + split_seed, + subset_seed, + split_manifest_path, + subset_manifest_path, + run_dir, + status, + stage, + attempt_count, + updated_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + dataset_fraction, + split_seed, + subset_seed, + str(split_manifest.resolve()), + str(subset_manifest.resolve()), + str(run_dir.resolve()), + "planned", + "manifested", + 0, + now_utc_iso(), + ), + ) + + +def mark_stale_running_as_interrupted() -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE status = 'running' + """, + (now_utc_iso(),), + ) + + +def mark_run_running(key: RunKey, *, stage: str) -> None: + row = load_run_status(key) + attempt_count = 1 if row is None else int(row["attempt_count"]) + 1 + started_at = row["started_at"] if row is not None else None + if not started_at: + started_at = now_utc_iso() + ledger_execute( + """ + UPDATE run_status + SET status = 'running', + stage = ?, + attempt_count = ?, + started_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + attempt_count, + started_at, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_stage(key: RunKey, *, stage: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET stage = ?, + status = 'running', + updated_at = ?, + heartbeat_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def update_run_checkpoint_progress( + key: RunKey, + *, + checkpoint_path: Path, + epoch: int, + best_metric_name: str, + best_metric_value: float, + elapsed_seconds: float, +) -> None: + column_name = "best_checkpoint_path" if checkpoint_path.name == "best.pt" else "latest_checkpoint_path" + sql = f""" + UPDATE run_status + SET {column_name} = ?, + last_epoch = ?, + best_metric_name = ?, + best_metric_value = ?, + elapsed_seconds = ?, + status = 'running', + stage = 'training', + heartbeat_at = ?, + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """ + ledger_execute( + sql, + ( + str(checkpoint_path.resolve()), + int(epoch), + str(best_metric_name), + float(best_metric_value), + float(elapsed_seconds), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_failed(key: RunKey, error_text: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'failed', + updated_at = ?, + error_text = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + error_text, + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_interrupted(key: RunKey) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def ingest_evaluation_into_db(key: RunKey, evaluation_path: Path, run_dir: Path) -> None: + payload = base.load_json(evaluation_path) + metrics = payload.get("metrics", {}) + ledger_execute( + """ + DELETE FROM final_metrics + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + conn = require_ledger() + for metric_name, metric_payload in metrics.items(): + conn.execute( + """ + INSERT INTO final_metrics ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + metric_name, + mean, + std, + run_dir, + evaluation_path + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + str(metric_name), + float(metric_payload.get("mean", 0.0)), + float(metric_payload.get("std")) if metric_payload.get("std") is not None else None, + str(run_dir.resolve()), + str(evaluation_path.resolve()), + ), + ) + conn.commit() + best_metric_name = str(payload.get("best_metric_name", "")) + best_metric_value = None + if best_metric_name and best_metric_name in metrics: + best_metric_value = float(metrics[best_metric_name]["mean"]) + ledger_execute( + """ + UPDATE run_status + SET status = 'completed', + stage = 'done', + evaluation_path = ?, + best_metric_name = COALESCE(?, best_metric_name), + best_metric_value = COALESCE(?, best_metric_value), + completed_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + str(evaluation_path.resolve()), + best_metric_name or None, + best_metric_value, + now_utc_iso(), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def completed_run_rows() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + ORDER BY split_repeat_index, dataset_percent, subset_repeat_index, strategy + """ + ).fetchall() + ) + + +def metric_rows_for_completed_runs() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT + rs.split_repeat_index, + rs.dataset_percent, + rs.subset_repeat_index, + rs.strategy, + rs.run_dir, + rs.split_manifest_path, + rs.subset_manifest_path, + rs.evaluation_path, + fm.metric_name, + fm.mean AS metric_mean, + fm.std AS metric_std + FROM final_metrics fm + JOIN run_status rs + ON rs.split_repeat_index = fm.split_repeat_index + AND rs.dataset_percent = fm.dataset_percent + AND rs.subset_repeat_index = fm.subset_repeat_index + AND rs.strategy = fm.strategy + WHERE rs.status = 'completed' + ORDER BY rs.split_repeat_index, rs.dataset_percent, rs.subset_repeat_index, rs.strategy, fm.metric_name + """ + ).fetchall() + ) + + +def export_stats() -> None: + ensure_dir(EXPORTS_DIR) + rows = metric_rows_for_completed_runs() + split_manifest_cache: dict[str, dict[str, Any]] = {} + + def split_manifest_payload(path_text: str) -> dict[str, Any]: + cached = split_manifest_cache.get(path_text) + if cached is None: + cached = base.load_json(Path(path_text)) + split_manifest_cache[path_text] = cached + return cached + + raw_rows_by_run: dict[tuple[int, int, int, int], dict[str, Any]] = {} + for row in rows: + key = ( + int(row["split_repeat_index"]), + int(row["dataset_percent"]), + int(row["subset_repeat_index"]), + int(row["strategy"]), + ) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + raw_row = raw_rows_by_run.setdefault( + key, + { + "split_repeat_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": row["run_dir"], + "split_manifest_path": row["split_manifest_path"], + "subset_manifest_path": row["subset_manifest_path"], + "evaluation_path": row["evaluation_path"], + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + }, + ) + if manifest_payload.get("phase_index") is not None: + raw_row["phase_index"] = int(manifest_payload["phase_index"]) + raw_row["phase_val_fold_index"] = int(manifest_payload["phase_val_fold_index"]) + raw_row["phase_test_fold_index"] = int(manifest_payload["phase_test_fold_index"]) + raw_row[f"{row['metric_name']}_mean"] = float(row["metric_mean"]) + raw_row[f"{row['metric_name']}_std"] = ( + float(row["metric_std"]) if row["metric_std"] is not None else None + ) + + raw_rows = list(raw_rows_by_run.values()) + raw_rows.sort( + key=lambda item: ( + item["split_repeat_index"], + item["dataset_percent"], + item["subset_repeat_index"], + item["strategy"], + ) + ) + + if raw_rows: + raw_fieldnames = sorted({key for row in raw_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=raw_fieldnames) + writer.writeheader() + writer.writerows(raw_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "raw_run_metrics.csv") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", raw_rows) + else: + atomic_write_text(EXPORTS_DIR / "raw_run_metrics.csv", "") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", []) + + grouped: dict[tuple[int, int], dict[str, Any]] = {} + for row in rows: + group_key = (int(row["dataset_percent"]), int(row["strategy"])) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + bucket = grouped.setdefault( + group_key, + { + "dataset_percent": int(row["dataset_percent"]), + "strategy": int(row["strategy"]), + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + "_phase_indices": set(), + "_metric_values": {}, + }, + ) + if manifest_payload.get("phase_index") is not None: + bucket["_phase_indices"].add(int(manifest_payload["phase_index"])) + bucket["_metric_values"].setdefault(str(row["metric_name"]), []).append( + { + "mean": float(row["metric_mean"]), + "std": float(row["metric_std"]) if row["metric_std"] is not None else None, + } + ) + + aggregated_rows: list[dict[str, Any]] = [] + for (_percent_int, _strategy), bucket in sorted(grouped.items()): + row = { + "dataset_percent": bucket["dataset_percent"], + "strategy": bucket["strategy"], + "split_generation_mode": bucket["split_generation_mode"], + } + metric_values: dict[str, list[dict[str, float | None]]] = bucket["_metric_values"] + row["n_runs"] = max((len(values) for values in metric_values.values()), default=0) + if bucket["_phase_indices"]: + phase_indices = sorted(int(value) for value in bucket["_phase_indices"]) + row["completed_phase_count"] = len(phase_indices) + row["completed_phases"] = ",".join(str(value) for value in phase_indices) + for metric_name, values in sorted(metric_values.items()): + means = [value["mean"] for value in values] + stds = [value["std"] for value in values if value["std"] is not None] + row[f"{metric_name}_mean"] = float(base.np.mean(means)) if means else None + row[f"{metric_name}_std"] = float(base.np.std(means)) if means else None + row[f"{metric_name}_within_run_std_mean"] = float(base.np.mean(stds)) if stds else None + aggregated_rows.append(row) + + if aggregated_rows: + aggregated_fieldnames = sorted({key for row in aggregated_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=aggregated_fieldnames) + writer.writeheader() + writer.writerows(aggregated_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", aggregated_rows) + else: + atomic_write_text(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv", "") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", []) + + +"""============================================================================= +BASE MODULE PATCHES +============================================================================= +""" + + +def patched_save_json(path: str | Path, payload: Any) -> None: + atomic_save_json(path, payload) + + +def _context_matches_percent(ctx: FoldRunContext | None, percent: float) -> bool: + if ctx is None: + return False + return abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12 + + +def patched_percent_root(percent: float) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return ensure_dir(CURRENT_FOLD_CONTEXT.repeat_root) + return ORIGINAL_PERCENT_ROOT(percent) + + +def patched_strategy_root_for_percent( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return strategy_root_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STRATEGY_ROOT_FOR_PERCENT(strategy, percent, model_config) + + +def patched_final_root_for_strategy( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return run_dir_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_FINAL_ROOT_FOR_STRATEGY(strategy, percent, model_config) + + +def patched_study_paths_for( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return fold_study_paths_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STUDY_PATHS_FOR(strategy, percent, model_config) + + +def patched_save_checkpoint( + path: Path, + *, + run_type: str, + model: base.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + scaler: Any, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": base._unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + base.validate_checkpoint_payload( + Path(path), + payload, + required_keys=base.checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + atomic_torch_save(path, payload) + base.write_checkpoint_manifest(path, payload) + + if run_type != "final" or CURRENT_FOLD_CONTEXT is None: + return + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(run_config["strategy"]), + ) + update_run_checkpoint_progress( + run_key, + checkpoint_path=Path(path), + epoch=int(epoch), + best_metric_name=str(best_metric_name), + best_metric_value=float(best_metric_value), + elapsed_seconds=float(elapsed_seconds), + ) + + +def patched_run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: base.DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, dict[str, float]]: + if CURRENT_FOLD_CONTEXT is not None and LEDGER_CONN is not None: + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(strategy), + ) + mark_run_stage(run_key, stage="evaluating") + return ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +def install_base_patches() -> None: + base.save_json = patched_save_json + base.percent_root = patched_percent_root + base.strategy_root_for_percent = patched_strategy_root_for_percent + base.final_root_for_strategy = patched_final_root_for_strategy + base.study_paths_for = patched_study_paths_for + base.save_checkpoint = patched_save_checkpoint + base.run_evaluation_for_run = patched_run_evaluation_for_run + base.RESUME_IDENTITY_KEYS = BASE_RESUME_IDENTITY_KEYS + ( + "folds_experiment_name", + "split_repeat_index", + "subset_repeat_index", + "split_seed", + "subset_seed", + "base_split_manifest_path", + "subset_manifest_path", + ) + if RESUME_FOLDS and base.RUN_OPTUNA: + base.LOAD_EXISTING_STUDIES = True + + +@contextmanager +def activate_context(ctx: FoldRunContext) -> Iterator[None]: + global CURRENT_FOLD_CONTEXT + previous = CURRENT_FOLD_CONTEXT + CURRENT_FOLD_CONTEXT = ctx + try: + yield + finally: + CURRENT_FOLD_CONTEXT = previous + + +"""============================================================================= +EXPERIMENT PLAN MATERIALIZATION +============================================================================= +""" + + +def create_split_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + base_splits: dict[str, list[dict[str, str]]], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "base_splits": { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + }, + "counts": {split_name: len(records) for split_name, records in base_splits.items()}, + "class_distributions": { + split_name: base.compute_class_distribution(records) + for split_name, records in base_splits.items() + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + payload["phase_partition_seed"] = int(split_seed) + return payload + + +def create_subset_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + percent_int: int, + percent_fraction: float, + subset_repeat_index: int, + subset_seed: int, + split_manifest: Path, + base_splits: dict[str, list[dict[str, str]]], + subset_records: list[dict[str, str]], + subset_sampling_source: str, + sampling_chain_dataset_percents: list[int], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "dataset_percent": percent_int, + "dataset_fraction": percent_fraction, + "subset_repeat_index": subset_repeat_index, + "subset_seed": subset_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "subset_sampling_source": subset_sampling_source, + "sampling_chain_dataset_percents": [int(value) for value in sampling_chain_dataset_percents], + "parent_split_manifest_path": str(split_manifest.resolve()), + "train_records": [dict(record) for record in subset_records], + "val_records": [dict(record) for record in base_splits["val"]], + "test_records": [dict(record) for record in base_splits["test"]], + "base_train_records": [dict(record) for record in base_splits["train"]], + "counts": { + "base_train": len(base_splits["train"]), + "train_subset": len(subset_records), + "val": len(base_splits["val"]), + "test": len(base_splits["test"]), + }, + "class_distributions": { + "base_train": base.compute_class_distribution(base_splits["train"]), + "train_subset": base.compute_class_distribution(subset_records), + "val": base.compute_class_distribution(base_splits["val"]), + "test": base.compute_class_distribution(base_splits["test"]), + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + return payload + + +def validate_materialized_phase_manifests(*, sample_records: list[dict[str, str]]) -> None: + if not using_fixed_phase_mode(): + return + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + for phase_index in phase_indices(): + manifest_path = split_manifest_path(phase_index) + if not manifest_path.exists(): + raise RuntimeError(f"Missing phase manifest for phase={phase_index:03d}: {manifest_path}") + payload = base.load_json(manifest_path) + if str(payload.get("split_generation_mode", "")).strip().lower() != "fixed_stratified_phases_8_1_1": + raise RuntimeError( + f"Expected fixed phase split_generation_mode in {manifest_path}, got " + f"{payload.get('split_generation_mode')!r}." + ) + if int(payload.get("phase_index", -1)) != phase_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_index={payload.get('phase_index')!r}, " + f"expected {phase_index}." + ) + if int(payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_count={payload.get('phase_count')!r}, " + f"expected {phase_count()}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_val_fold_index=" + f"{payload.get('phase_val_fold_index')!r}, expected {val_fold_index}." + ) + if int(payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_test_fold_index=" + f"{payload.get('phase_test_fold_index')!r}, expected {test_fold_index}." + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + + +def validate_materialized_subset_manifests() -> None: + if not using_fixed_phase_mode(): + return + for phase_index in phase_indices(): + split_payload = base.load_json(split_manifest_path(phase_index)) + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + manifest_path = subset_manifest_path(phase_index, spec.percent_int, subset_repeat_index) + if not manifest_path.exists(): + raise RuntimeError( + f"Missing subset manifest for phase={phase_index:03d}, " + f"percent={spec.percent_int}, repeat={subset_repeat_index}: {manifest_path}" + ) + subset_payload = base.load_json(manifest_path) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=phase_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + +def validate_loaded_context_payloads( + split_payload: dict[str, Any], + subset_payload: dict[str, Any], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + base_splits = { + split_name: [dict(record) for record in split_payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_base_split(base_splits, split_repeat_index=split_repeat_index) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + validate_disjoint_record_sets( + { + "train_subset": train_records, + "val": val_records, + "test": test_records, + }, + context=( + f"{primary_unit_name()}={split_repeat_index:03d}, " + f"percent={percent_int}, repeat={subset_repeat_index}" + ), + ) + validate_subset_records( + base_splits["train"], + train_records, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + if set(record_filenames(base_train_records)) != set(record_filenames(base_splits["train"])): + raise RuntimeError( + f"Subset manifest base train records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(val_records)) != set(record_filenames(base_splits["val"])): + raise RuntimeError( + f"Subset manifest validation records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(test_records)) != set(record_filenames(base_splits["test"])): + raise RuntimeError( + f"Subset manifest test records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if using_fixed_phase_mode(): + phase_index = int(split_payload.get("phase_index", split_repeat_index)) + if int(split_payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{split_payload.get('phase_count')!r}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(split_payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{split_payload.get('phase_val_fold_index')!r}." + ) + if int(split_payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{split_payload.get('phase_test_fold_index')!r}." + ) + if int(subset_payload.get("phase_index", phase_index)) != phase_index: + raise RuntimeError( + f"Subset manifest phase_index={subset_payload.get('phase_index')!r} does not match " + f"phase={phase_index:03d}." + ) + if int(subset_payload.get("phase_count", phase_count())) != phase_count(): + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{subset_payload.get('phase_count')!r}." + ) + if int(subset_payload.get("phase_val_fold_index", val_fold_index)) != val_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{subset_payload.get('phase_val_fold_index')!r}." + ) + if int(subset_payload.get("phase_test_fold_index", test_fold_index)) != test_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{subset_payload.get('phase_test_fold_index')!r}." + ) + + +def materialize_experiment_plan( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + ensure_dir(EXPERIMENT_ROOT) + ensure_dir(SPLIT_MANIFESTS_DIR) + ensure_dir(SUBSET_MANIFESTS_DIR) + ensure_dir(EXPORTS_DIR) + + snapshot = config_snapshot(model_config) + config_hash = config_fingerprint(snapshot) + data_hash = dataset_fingerprint(sample_records) + sampling_mode = current_percent_sampling_mode() + upsert_experiment_meta(snapshot=snapshot, config_hash=config_hash, data_hash=data_hash) + expected_filenames = {record["filename"] for record in sample_records} + partition_seed_value = partition_seed() + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + fixed_phase_folds: dict[int, list[dict[str, str]]] | None = None + if using_fixed_phase_mode(): + validate_fixed_phase_dataset_requirements(sample_records) + fixed_phase_folds = build_fixed_stratified_phase_folds( + sample_records, + seed=partition_seed_value, + ) + validate_fixed_phase_folds(fixed_phase_folds, sample_records=sample_records) + + for split_repeat_index in all_split_repeat_indices(): + phase_index = None + phase_val_fold_index = None + phase_test_fold_index = None + if using_fixed_phase_mode(): + if fixed_phase_folds is None: + raise RuntimeError("Fixed phase folds were not initialized.") + split_seed = partition_seed_value + phase_index = split_repeat_index + phase_val_fold_index, phase_test_fold_index = phase_fold_indices(phase_index) + base_splits = build_phase_base_split( + fixed_phase_folds, + phase_index=phase_index, + seed=split_seed, + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + } + else: + split_seed = fold_seed(f"split::{split_repeat_index}") + base_splits = build_base_split_for_repeat(sample_records, split_seed) + validate_base_split( + base_splits, + split_repeat_index=split_repeat_index, + expected_filenames=expected_filenames, + ) + + split_manifest = split_manifest_path(split_repeat_index) + split_payload = create_split_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + base_splits=base_splits, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(split_manifest, split_payload) + upsert_split_manifest_row( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + manifest_path=split_manifest, + train_count=len(base_splits["train"]), + val_count=len(base_splits["val"]), + test_count=len(base_splits["test"]), + config_hash=config_hash, + data_hash=data_hash, + ) + + if sampling_mode == "independent": + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + subset_seed = fold_seed( + f"subset::{split_repeat_index}::{spec.percent_int}::{subset_repeat_index}" + ) + subset_records = build_subset_for_repeat( + base_splits["train"], + percent_fraction=spec.fraction, + subset_seed=subset_seed, + ) + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="independent", + sampling_chain_dataset_percents=[spec.percent_int], + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + continue + + max_subset_repeat_index = max(spec.repeat_count for spec in percent_specs()) + for subset_repeat_index in range(1, max_subset_repeat_index + 1): + active_specs = active_specs_for_subset_repeat(subset_repeat_index) + if not active_specs: + continue + subset_seed = fold_seed(f"subset::{split_repeat_index}::repeat::{subset_repeat_index}") + subset_chain = build_incremental_subset_chain( + base_splits["train"], + active_specs=active_specs, + subset_seed=subset_seed, + ) + chain_dataset_percents = [spec.percent_int for spec in active_specs] + for spec in active_specs: + subset_records = subset_chain[base.percent_label(spec.fraction)] + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="incremental_chain", + sampling_chain_dataset_percents=chain_dataset_percents, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + + if using_fixed_phase_mode(): + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +def validate_or_create_experiment( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + meta = existing_experiment_meta() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not RESUME_FOLDS and not ignore_resume_folds_gate: + if meta is not None: + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + if meta is None: + if ledger_row_count("split_manifests") > 0 or ledger_row_count("run_status") > 0: + raise RuntimeError( + f"Experiment DB {EXPERIMENT_DB_PATH} contains run state but is missing experiment metadata." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + current_snapshot = config_snapshot(model_config) + current_hash = config_fingerprint(current_snapshot) + current_data_hash = dataset_fingerprint(sample_records) + stored_config_hash = str(meta["config_fingerprint"]) + stored_portable_hash = "" + try: + stored_config_json = json.loads(str(meta["config_json"])) + stored_portable_hash = config_fingerprint(stored_config_json) + except Exception as exc: + print(f"[Resume] Could not recompute portable config fingerprint from stored metadata: {exc}") + if stored_config_hash != current_hash and stored_portable_hash != current_hash: + raise RuntimeError( + f"Existing experiment config fingerprint does not match current configuration for {EXPERIMENT_ROOT}." + ) + if stored_config_hash != current_hash and stored_portable_hash == current_hash: + print( + "[Resume] Accepted existing experiment metadata with a portable config fingerprint match " + "(machine-specific paths/runtime resume flag changed)." + ) + if str(meta["dataset_fingerprint"]) != current_data_hash: + raise RuntimeError( + f"Existing experiment dataset fingerprint does not match current dataset contents for {EXPERIMENT_ROOT}." + ) + if using_fixed_phase_mode(): + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +"""============================================================================= +RUNTIME BUNDLE CONSTRUCTION +============================================================================= +""" + + +def load_context(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> FoldRunContext: + split_payload = base.load_json(split_manifest_path(split_repeat_index)) + subset_payload = base.load_json(subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index)) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + return FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=percent_int, + percent_fraction=float(subset_payload["dataset_fraction"]), + split_seed=int(split_payload["split_seed"]), + subset_seed=int(subset_payload["subset_seed"]), + repeat_root=repeat_root(split_repeat_index, percent_int, subset_repeat_index), + split_manifest_path=split_manifest_path(split_repeat_index), + subset_manifest_path=subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index), + ) + + +def build_fold_data_bundle(ctx: FoldRunContext) -> base.DataBundle: + split_payload = base.load_json(ctx.split_manifest_path) + subset_payload = base.load_json(ctx.subset_manifest_path) + dataset_root = Path(base.current_dataset_dirs()[0]).parent.resolve() + phase_index = ( + int(split_payload.get("phase_index", ctx.split_repeat_index)) + if str(split_payload.get("split_generation_mode", "")).strip().lower() == "fixed_stratified_phases_8_1_1" + else None + ) + cycle_token = f"phase{phase_index:03d}" if phase_index is not None else f"split{ctx.split_repeat_index:03d}" + normalization_cache_path = ( + ctx.repeat_root + / ( + f"norm_stats_{base.normalization_cache_tag()}_{base.SPLIT_TYPE}_{ctx.percent_int:03d}pct_" + f"{cycle_token}_repeat{ctx.subset_repeat_index:02d}.json" + ) + ) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + + base_train_class_distribution = base.compute_class_distribution(base_train_records) + train_class_distribution = base.compute_class_distribution(train_records) + val_class_distribution = base.compute_class_distribution(val_records) + test_class_distribution = base.compute_class_distribution(test_records) + + base.print_loaded_class_distribution( + split_type=base.SPLIT_TYPE, + train_subset_key=str(ctx.percent_int), + base_train_records=base_train_records, + train_records=train_records, + val_records=val_records, + test_records=test_records, + ) + + global_mean, global_std, normalization_source = base.compute_busi_statistics( + dataset_root=dataset_root, + sample_records=train_records, + cache_path=normalization_cache_path, + ) + + payload = { + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "dataset_splits_path": str(ctx.split_manifest_path.resolve()), + "dataset_root": str(dataset_root), + "split_source": ( + "fixed_phase_manifest" + if phase_index is not None + else "repeated_holdout_manifest" + ), + "split_generation_mode": str(split_payload.get("split_generation_mode", current_split_generation_mode())), + "split_type": base.SPLIT_TYPE, + "percent_sampling_mode": str( + subset_payload.get("percent_sampling_mode", split_payload.get("percent_sampling_mode", "independent")) + ), + "dataset_percent": ctx.percent_fraction, + "train_subset_key": str(ctx.percent_int), + "train_subset_variant": int(ctx.subset_repeat_index), + "train_subset_source": str(subset_payload.get("subset_sampling_source", "repeated_holdout_repeat")), + "selected_split_manifest_path": str(ctx.subset_manifest_path.resolve()), + "sampling_chain_dataset_percents": [ + int(value) for value in subset_payload.get("sampling_chain_dataset_percents", [ctx.percent_int]) + ], + "base_train_count": len(base_train_records), + "train_count": len(train_records), + "val_count": len(val_records), + "test_count": len(test_records), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(normalization_cache_path.resolve()), + "normalization_source": normalization_source, + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "split_seed": ctx.split_seed, + "subset_seed": ctx.subset_seed, + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "folds_experiment_root": str(EXPERIMENT_ROOT.resolve()), + } + if phase_index is not None: + payload["phase_index"] = phase_index + payload["phase_count"] = int(split_payload["phase_count"]) + payload["phase_val_fold_index"] = int(split_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(split_payload["phase_test_fold_index"]) + + base.print_split_summary(payload) + base.print_normalization_summary(payload) + + train_split_name = ( + f"train {base.SPLIT_TYPE} {ctx.percent_int}% phase{phase_index:03d}" + if phase_index is not None + else f"train {base.SPLIT_TYPE} {ctx.percent_int}% split{ctx.split_repeat_index:03d}" + ) + val_split_name = ( + f"val {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"val {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + test_split_name = ( + f"test {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"test {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + loader_prefix = ( + f"{base.SPLIT_TYPE}:{ctx.percent_int}:phase{phase_index:03d}" + if phase_index is not None + else f"{base.SPLIT_TYPE}:{ctx.percent_int}:split{ctx.split_repeat_index:03d}" + ) + + train_ds = base.BUSIDataset( + train_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=True, + split_name=train_split_name, + ) + val_ds = base.BUSIDataset( + val_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=val_split_name, + ) + test_ds = base.BUSIDataset( + test_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=test_split_name, + ) + bundle = base.DataBundle( + percent=ctx.percent_fraction, + split_payload=payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=base.make_loader( + train_ds, + shuffle=True, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:train", + ), + val_loader=base.make_loader( + val_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:val", + ), + test_loader=base.make_loader( + test_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:test", + ), + ) + base.print_preload_summary(bundle) + return bundle + + +def release_bundle(bundle: base.DataBundle | None) -> None: + if bundle is None: + return + del bundle + gc.collect() + base.run_cuda_cleanup(context="bundle release") + + +def checkpoint_candidates(run_dir: Path) -> list[Path]: + return [ + run_dir / "checkpoints" / "latest.pt", + run_dir / "checkpoints" / "best.pt", + ] + + +def resolve_resume_checkpoint(run_dir: Path) -> Path | None: + for candidate in checkpoint_candidates(run_dir): + if candidate.exists(): + return candidate + return None + + +"""============================================================================= +RUN EXECUTION +============================================================================= +""" + + +def strategy_requires_strategy2_checkpoint(strategy: int) -> bool: + return ( + base.EXECUTION_MODE == "train_eval" and strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 + ) + + +def fold_param_metadata(ctx: FoldRunContext) -> dict[str, Any]: + subset_payload = base.load_json(ctx.subset_manifest_path) + payload = { + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "split_repeat_index": int(ctx.split_repeat_index), + "subset_repeat_index": int(ctx.subset_repeat_index), + "split_seed": int(ctx.split_seed), + "subset_seed": int(ctx.subset_seed), + "split_generation_mode": str(subset_payload.get("split_generation_mode", current_split_generation_mode())), + "percent_sampling_mode": str(subset_payload.get("percent_sampling_mode", current_percent_sampling_mode())), + "subset_sampling_source": str(subset_payload.get("subset_sampling_source", "independent")), + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + } + if subset_payload.get("phase_index") is not None: + payload["phase_index"] = int(subset_payload["phase_index"]) + payload["phase_count"] = int(subset_payload["phase_count"]) + payload["phase_val_fold_index"] = int(subset_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(subset_payload["phase_test_fold_index"]) + return payload + + +def finalize_run_from_artifacts(key: RunKey, run_dir: Path) -> bool: + evaluation_path = run_dir / "evaluation.json" + if not evaluation_path.exists(): + return False + ingest_evaluation_into_db(key, evaluation_path, run_dir) + export_stats() + return True + + +def execute_final_run( + *, + strategy: int, + ctx: FoldRunContext, + bundle: base.DataBundle, + model_config: base.RuntimeModelConfig, +) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None: + raise RuntimeError(f"Run plan row is missing for {run_key}.") + if str(row["status"]) == "completed": + return + if str(row["status"]) == "failed": + print( + f"[{split_generation_display_name()}] Skipping failed run " + f"{run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload)}." + ) + return + + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + + strategy2_checkpoint_path: str | Path | None = None + run_name = run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload) + with activate_context(ctx): + banner_prefix = "PHASE RUN" if using_fixed_phase_mode() else "REPEATED HOLDOUT RUN" + base.banner(f"{banner_prefix} | {run_name}") + if strategy_requires_strategy2_checkpoint(strategy): + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + + if base.EXECUTION_MODE == "eval_only": + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + summary_path = run_dir / "summary.json" + if summary_path.exists() and not (run_dir / "evaluation.json").exists(): + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + if base.RUN_SMOKE_TEST: + base.maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + params = base.resolve_job_params( + strategy, + ctx.percent_fraction, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = {**params, **fold_param_metadata(ctx)} + + resume_checkpoint_path = None + if str(row["status"]) in {"interrupted", "running"}: + resume_checkpoint_path = resolve_resume_checkpoint(run_dir) + + mark_run_running(run_key, stage="training") + base.run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=run_dir, + params=params, + max_epochs=base.strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + + +def reconcile_existing_artifacts(ctx: FoldRunContext, strategy: int, model_config: base.RuntimeModelConfig) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + return + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + if (run_dir / "summary.json").exists(): + mark_run_interrupted(run_key) + mark_run_stage(run_key, stage="evaluating") + return + if resolve_resume_checkpoint(run_dir) is not None: + mark_run_interrupted(run_key) + + +def maybe_reset_study_artifacts(model_config: base.RuntimeModelConfig) -> None: + if not base.RESET_ALL_STUDIES_EACH_RUN or not base.RUN_OPTUNA: + return + if RESUME_FOLDS: + print( + f"[{split_generation_display_name()}] RESET_ALL_STUDIES_EACH_RUN ignored because RESUME_FOLDS=True." + ) + return + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + with activate_context(ctx): + for strategy in base.STRATEGIES: + base.reset_study_artifacts(strategy, ctx.percent_fraction, model_config=model_config) + + +def run_overfit_mode(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE OVERFIT TEST MODE") + else: + base.banner("REPEATED HOLDOUT OVERFIT TEST MODE") + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + base.run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=overfit_root_for(strategy, ctx, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_eval_only_without_ledger(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EVAL ONLY | LEDGER BYPASSED") + else: + base.banner("REPEATED HOLDOUT EVAL ONLY | LEDGER BYPASSED") + print("[Eval Only] Skipping experiment ledger and evaluating directly from manifests and checkpoints.") + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + run_name = run_identity_label( + strategy=strategy, + percent=ctx.percent_fraction, + split_payload=bundle.split_payload, + ) + base.banner(f"EVAL ONLY | {run_name}") + base.run_evaluation_for_run( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir_for(strategy, ctx, model_config), + strategy2_checkpoint_path=None, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_pass_for_statuses( + statuses: set[str], + *, + model_config: base.RuntimeModelConfig, +) -> None: + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + pending_strategies = [] + for strategy in base.STRATEGIES: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is not None and str(row["status"]) in statuses: + pending_strategies.append(int(strategy)) + if not pending_strategies: + continue + + bundle = build_fold_data_bundle(ctx) + phase_had_error = False + try: + for strategy in pending_strategies: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + continue + try: + execute_final_run( + strategy=strategy, + ctx=ctx, + bundle=bundle, + model_config=model_config, + ) + except KeyboardInterrupt: + phase_had_error = True + mark_run_interrupted(run_key) + raise + except Exception: + phase_had_error = True + error_text = traceback.format_exc() + mark_run_failed(run_key, error_text) + print(error_text) + finally: + if using_fixed_phase_mode(): + write_phase_timing_summary_after_phase(ctx.split_repeat_index) + if not phase_had_error: + threading.Thread( + target=run_repo_backup_after_phase, + args=(ctx.split_repeat_index,), + daemon=True, + ).start() + release_bundle(bundle) + + +"""============================================================================= +MAIN +============================================================================= +""" + + +def run_repeated_holdout_main() -> int: + global LEDGER_CONN + + validate_repeated_holdout_settings() + if not str(FOLDS_EXPERIMENT_NAME).strip(): + raise ValueError("FOLDS_EXPERIMENT_NAME must be non-empty.") + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"RUNNER_FOLDS.py currently requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + + install_base_patches() + base.SAVE_LATEST_EVERY_EPOCH = True + + base.set_global_seed(base.SEED) + model_config = base.current_model_config() + fold_experiment_summary(model_config) + + if str(base.EXECUTION_MODE).strip().lower() == "eval_only": + select_sample_records() + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + return run_eval_only_without_ledger(model_config) + + sample_records, _dataset_root = select_sample_records() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not ignore_resume_folds_gate and not RESUME_FOLDS and EXPERIMENT_ROOT.exists(): + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + if ( + not ignore_resume_folds_gate + and RESUME_FOLDS + and not EXPERIMENT_DB_PATH.exists() + and EXPERIMENT_ROOT.exists() + and any(EXPERIMENT_ROOT.iterdir()) + ): + raise RuntimeError( + f"Experiment root {EXPERIMENT_ROOT} already exists without a valid SQLite ledger at {EXPERIMENT_DB_PATH}. " + "Refusing to attach to ambiguous state." + ) + + LEDGER_CONN = setup_ledger(EXPERIMENT_DB_PATH) + try: + validate_or_create_experiment(sample_records=sample_records, model_config=model_config) + mark_stale_running_as_interrupted() + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + for strategy in base.STRATEGIES: + reconcile_existing_artifacts(ctx, int(strategy), model_config) + + export_stats() + maybe_reset_study_artifacts(model_config) + + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + + run_pass_for_statuses({"interrupted", "running"}, model_config=model_config) + run_pass_for_statuses({"planned"}, model_config=model_config) + export_stats() + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EXECUTION COMPLETE") + else: + base.banner("REPEATED HOLDOUT COMPLETE") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Raw metrics export : {EXPORTS_DIR / 'raw_run_metrics.csv'}") + print(f"Aggregate export : {EXPORTS_DIR / 'aggregated_metrics_by_percent_strategy.csv'}") + return 0 + finally: + if LEDGER_CONN is not None: + LEDGER_CONN.close() + LEDGER_CONN = None + +def run_single_run_main() -> int: + global DATASET_PERCENTS + banner("MLR ALL STRATEGIES BAYES RUNNER") + DATASET_PERCENTS = normalize_dataset_percents(DATASET_PERCENTS) + set_global_seed(SEED) + model_config = current_model_config() + dataset_name = current_dataset_name() + images_dir, annotations_dir = current_dataset_dirs() + if EXECUTION_MODE not in {"train_eval", "eval_only"}: + raise ValueError(f"EXECUTION_MODE must be 'train_eval' or 'eval_only', got {EXECUTION_MODE!r}") + if SPLIT_TYPE not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"SPLIT_TYPE must be one of {SUPPORTED_SPLIT_TYPES}, got {SPLIT_TYPE!r}") + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + ensure_specific_checkpoint_scope("EVAL_CHECKPOINT_MODE", EVAL_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("STRATEGY2_CHECKPOINT_MODE", STRATEGY2_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("TRAIN_RESUME_MODE", TRAIN_RESUME_MODE) + + print_environment_summary(model_config) + split_registry, split_source = load_or_create_dataset_splits( + images_dir=images_dir, + annotations_dir=annotations_dir, + split_json_path=current_dataset_splits_json_path(), + train_fractions=DATASET_PERCENTS, + seed=SEED, + ) + + bundles: dict[float, DataBundle] = {} + for percent in DATASET_PERCENTS: + bundles[percent] = build_data_bundle(percent, split_registry, split_source) + + if RUN_OVERFIT_TEST: + run_configured_overfit_tests(bundles, model_config=model_config) + banner("OVERFIT TESTS COMPLETE") + return 0 + + if RESET_ALL_STUDIES_EACH_RUN: + if RUN_OPTUNA: + banner("RESETTING OPTUNA STUDIES") + for strategy in STRATEGIES: + for percent in DATASET_PERCENTS: + reset_study_artifacts(strategy, percent, model_config=model_config) + else: + print("[Optuna Reset] Skipped because RUN_OPTUNA=False.") + + try: + for percent in DATASET_PERCENTS: + banner(f"PERCENT STAGE | {percent_text(percent)}") + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and EXECUTION_MODE == "train_eval" and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + + if EXECUTION_MODE == "train_eval": + maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = resolve_job_params( + strategy, + percent, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + banner( + f"FINAL RETRAIN | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_final_training( + strategy, + bundle, + params, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + else: + banner( + f"EVAL ONLY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_evaluation_for_run( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=final_root_for_strategy(strategy, percent, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + except Exception: + banner("RUN FAILED") + traceback.print_exc() + return 1 + + banner("ALL DONE") + return 0 + + +def main() -> int: + validate_hf_backup_settings() + run_initial_hf_backup() + if EXPERIMENT_MODE not in SUPPORTED_EXPERIMENT_MODES: + raise ValueError( + f"EXPERIMENT_MODE must be one of {SUPPORTED_EXPERIMENT_MODES}, got {EXPERIMENT_MODE!r}" + ) + if EXPERIMENT_MODE == "repeated_holdout": + return run_repeated_holdout_main() + return run_single_run_main() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ablations/abl_ab2_tmax2.py b/ablations/abl_ab2_tmax2.py new file mode 100644 index 0000000000000000000000000000000000000000..07c7908bfa24efbb7bb4c65177f2bfcc7d72dbc8 --- /dev/null +++ b/ablations/abl_ab2_tmax2.py @@ -0,0 +1,13598 @@ +from __future__ import annotations +import csv +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from datetime import datetime, timezone +import gc +import hashlib +import importlib +import inspect +import json +import math +import os +import random +import shutil +import sqlite3 +import subprocess +import sys +import tarfile +import tempfile +import threading +import time +import traceback +import weakref +from collections import Counter +from contextlib import contextmanager, nullcontext +from pathlib import Path +from typing import Any, Iterator, Literal + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import segmentation_models_pytorch as smp +os.environ.setdefault("NNPACK_DISABLE", "1") +import torch +torch.backends.nnpack.enabled = False +import torch.nn as nn +import torch.nn.functional as F +from optuna.storages import RDBStorage +from PIL import Image as PILImage +from scipy import ndimage +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +"""============================================================================= +EDIT ME +============================================================================= +""" + +PROJECT_DIR = Path(__file__).resolve().parent.parent # ABLATION: repo root (this copy lives in ablations/) +TRANSUNET_REPO_DIR = PROJECT_DIR / "TransUNet" +TRANSUNET_VIT_NAME = "R50-ViT-B_16" +TRANSUNET_N_SKIP = 3 +TRANSUNET_PRETRAINED_PATH = PROJECT_DIR / "model" / "vit_checkpoint" / "imagenet21k" / "R50+ViT-B_16.npz" + +RUNS_ROOT = PROJECT_DIR / "runs" +HARD_CODED_PARAM_DIR = PROJECT_DIR +MODEL_NAME = "Segformer_B0_revamped_nt_2" + +EXPERIMENT_MODE = "repeated_holdout" # "single_run" or "repeated_holdout" +SUPPORTED_EXPERIMENT_MODES = ("single_run", "repeated_holdout") +SPLIT_GENERATION_MODE = "fixed_stratified_phases_8_1_1" # "repeated_holdout" or "fixed_stratified_phases_8_1_1" +SUPPORTED_SPLIT_GENERATION_MODES = ("repeated_holdout", "fixed_stratified_phases_8_1_1") +NUM_STRATIFIED_SPLIT_REPEATS = 5 +NUM_PHASES = 10 +PHASE_VAL_OFFSET = 1 +DATASET_PERCENT_REPEAT_COUNTS: dict[int, int] = { + # 5: 4, + # 15: 3, + # 30: 3, + # 50: 2, + 100: 1, +} +PERCENT_SAMPLING_MODE = "incremental" # "independent" or "incremental" +SUPPORTED_PERCENT_SAMPLING_MODES = ("independent", "incremental") +PERCENT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PERCENT_EXECUTION_MODES = ("auto", "manual") +SELECTED_DATASET_PERCENTS: list[int] = [100] +SPLIT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_SPLIT_EXECUTION_MODES = ("auto", "manual") +SELECTED_SPLIT_INDICES: list[int] = [1] + +PHASE_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PHASE_EXECUTION_MODES = ("auto", "manual") +SELECTED_PHASES: list[int] = [1] # used only when PHASE_EXECUTION_MODE="manual" + +REPEAT_EXECUTION_MODE = "auto" # "auto" or "manual" +SUPPORTED_REPEAT_EXECUTION_MODES = ("auto", "manual") +SELECTED_REPEAT_INDICES: list[int] = [1] + +FOLDS_EXPERIMENT_NAME = "stratified_holdout_v1" +RESUME_FOLDS = False +ASYNC_REPO_BACKUP_AFTER_PHASE = False +# Hugging Face dataset repo to mirror the project into. Set via env so nothing is +# hardcoded: export HF_REPO_ID="your-username/ADVAI24JUN-backup" and HF_TOKEN=... +HF_REPO_ID = os.environ.get("HF_REPO_ID", "") +HF_REPO_TYPE = "dataset" +# Only upload after every Nth phase (boundary), so we don't hammer HF every phase. +HF_BACKUP_EVERY_N_PHASES = 1 +HF_BACKUP_MAX_RETRIES = 5 +# Run one synchronous backup BEFORE training starts: it creates the repo and uploads +# the current project state, proving the whole backup pipeline works before we commit +# hours of compute. Phase backups later refresh this same repo. +HF_BACKUP_ON_START = False +# Glob patterns excluded from the upload (matched against repo-relative paths). +HF_IGNORE_PATTERNS = ( + "**/.git/**", + "**/__pycache__/**", + "**/.ipynb_checkpoints/**", + "**/.cache/**", + "**/.venv/**", + "*.pyc", + ".DS_Store", +) + +DATASET_NAME = "BUSI_with_classes" # "BUSI" or "BUSI_with_classes" +SUPPORTED_DATASET_NAMES = ("BUSI", "BUSI_with_classes") +DATA_ROOT = PROJECT_DIR / DATASET_NAME +BUSI_WITH_CLASSES_SPLIT_POLICY = "stratified" # "balanced_train" or "stratified" +SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES = ("balanced_train", "stratified") + +SUPPORTED_STRATEGIES: tuple[int, ...] = (2,3) +STRATEGIES = [2,3] +DATASET_PERCENTS = [] #ignored in the folding [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1.0] #, 0.5, 1.0] #, 0.5, 1.0] +SPLIT_TYPE = "80_10_10" +SUPPORTED_SPLIT_TYPES = ("80_10_10", "70_10_20") +DATASET_SPLITS_JSON = PROJECT_DIR / "dataset_splits.json" +DATASET_SPLITS_VERSION = 1 +TRAIN_SUBSET_VARIANT = 1 # 0 uses the persisted subset; >0 deterministically resamples only the train subset from the frozen base train split. +NUM_TRIALS = 30 +STUDY_DIRECTION = "maximize" +BEST_CHECKPOINT_METRICS = { + 2: "val_iou", + 3: "val_refine_score", +} +OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR = "best_observed_objective" +OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR = "best_observed_objective_name" +SUPPORTED_CHECKPOINT_METRICS = { + "val_loss", + "val_dice", + "val_iou", + "val_biou", + "val_refine_score", + "val_decoder_dice", + "val_decoder_iou", + "val_decoder_biou", + "val_dice_gain", + "val_iou_gain", + "val_biou_gain", + "val_actor_loss", + "val_critic_loss", + "val_ce_loss", + "val_dice_loss", + "val_reward", + "val_entropy", +} + +SEED = 42 +IMG_SIZE = 128 +# d = 0 -> auto (floor(0.02 * diag)); any positive int overrides. +# Recommended: 0 (auto) -> resolves to ~4 px for IMG_SIZE=128. +BOUNDARY_IOU_D: int = 0 +BATCH_SIZE = 16 # Recommended to prevent OOM +NUM_WORKERS = 128 # Recommended with RAM-preloaded datasets to avoid worker RAM duplication. +USE_PIN_MEMORY = True +USE_PERSISTENT_WORKERS = True +PRELOAD_TO_RAM = True + +SMP_ENCODER_NAME = "mit_b0" +SMP_ENCODER_WEIGHTS = "imagenet" +SMP_ENCODER_DEPTH = 5 +SMP_ENCODER_PROJ_DIM = 192 +SMP_DECODER_TYPE = "Segformer" +BACKBONE_FAMILY = "smp" # "smp" or "custom_vgg" +ENABLE_CUSTOM_VGG_BACKBONE = False +VGG_FEATURE_SCALES = 4 +VGG_FEATURE_DILATION = 1 + +USE_IMAGENET_NORM = True +REPLACE_BN_WITH_GN = True +GN_NUM_GROUPS = 8 +NUM_ACTIONS = 2 + +STRATEGY_1_MAX_EPOCHS = 100 +STRATEGY_2_MAX_EPOCHS = 100 +STRATEGY_3_MAX_EPOCHS = 120 +STRATEGY_4_MAX_EPOCHS = 100 +STRATEGY_5_MAX_EPOCHS = 100 +VALIDATE_EVERY_N_EPOCHS = 1 +CHECKPOINT_EVERY_N_EPOCHS = 0 +SAVE_LATEST_EVERY_EPOCH = True +SAVE_HISTORY_INCREMENTALLY = False +EARLY_STOPPING_PATIENCE = 0 +VERBOSE_EPOCH_LOG = False + +DEFAULT_HEAD_LR = 1e-4 +DEFAULT_ENCODER_LR = 1e-5 +DEFAULT_WEIGHT_DECAY = 1e-4 +DEFAULT_TMAX = 5 +TEST_ITERATION_CONTROL = False # If True, validation/evaluation/inference uses TEST_ITERATION_T instead of full tmax. +TEST_ITERATION_T = 1 # Applied only when TEST_ITERATION_CONTROL=True. Clamped to [1, tmax]. +DEFAULT_GAMMA = 0.95 +DEFAULT_CRITIC_LOSS_WEIGHT = 0.5 +DEFAULT_ENTROPY_ALPHA_INIT = 0.2 +DEFAULT_ENTROPY_TARGET_RATIO = 0.25 +DEFAULT_ENTROPY_LR = 3e-4 +DEFAULT_CE_WEIGHT = 0.5 +DEFAULT_DICE_WEIGHT = 0.5 +DEFAULT_DROPOUT_P = 0.2 +DEFAULT_GRAD_CLIP_NORM = 6.0 +DEFAULT_MASK_UPDATE_STEP = 0.1 +DEFAULT_FOREGROUND_REWARD_WEIGHT = 0.0 +DEFAULT_RECALL_REWARD_WEIGHT = 1.0 +DEFAULT_DICE_REWARD_WEIGHT = 0.35 +DEFAULT_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_PRIOR_REWARD_WEIGHT = 0.01 +DEFAULT_DECODER_GAIN_REWARD_WEIGHT = 0.5 +DEFAULT_REWARD_SCALE = 1.0 +DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION = True +DEFAULT_STRATEGY3_VARIANT = "lite" +DEFAULT_STRATEGY3_NUM_ACTIONS = 3 +DEFAULT_REFINE_DELTA_SMALL = 0.03 +DEFAULT_REFINE_DELTA_LARGE = 0.08 +DEFAULT_STRATEGY3_AUX_CE_WEIGHT = 0.40 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH = 25 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS = 15 +DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION = 0.10 +DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE = 30 +DEFAULT_STRATEGY3_PROBE_MODE = "rolling_random" +DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM = 0.25 +DEFAULT_STRATEGY3_RL_LOSS_SCALE = 10.0 +DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED = True +DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES = 8 +DEFAULT_STRATEGY3_MC_DROPOUT_P = 0.2 +DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_READ = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE = True +DEFAULT_STRATEGY3_DELTA_MAX = 0.10 +DEFAULT_STRATEGY3_SAM_ATTENTION_GRID = 64 +DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT = 1.0 +DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE = False +DEFAULT_BIOU_REWARD_WEIGHT = 1.0 +DEFAULT_IOU_REWARD_WEIGHT = 1.0 +DEFAULT_KEEP_CORRECT_REWARD_WEIGHT = 0.05 +DEFAULT_STRATEGY3_A3C_ENTROPY_COEFF = 0.0 +DEFAULT_STRATEGY3_ENTROPY_TARGET_RATIO = 0.20 +DEFAULT_STRATEGY3_ENTROPY_ALPHA_INIT = 0.005 +DEFAULT_STRATEGY3_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_EARLY_STOPPING_MONITOR = "auto" +DEFAULT_EARLY_STOPPING_MODE = "auto" +DEFAULT_EARLY_STOPPING_MIN_DELTA = 0.0 +DEFAULT_EARLY_STOPPING_START_EPOCH = 30 +DEFAULT_EXPLORATION_EPS = 0.1 +EXPLORATION_EPS_EPOCHS = 20 +ATTENTION_MAX_TOKENS = 1024 +ATTENTION_MIN_POOL_SIZE = 16 + +_STRATEGY3_MC_DROPOUT_WARNED = False +_STRATEGY3_SAM_GRID_WARNED: set[int] = set() +_STRATEGY3_MC_CACHE_SCHEMA_VERSION = 1 +_STRATEGY3_MC_FILE_SHA256_CACHE: dict[str, str] = {} + +SCHEDULER_FACTOR = 0.5 +SCHEDULER_PATIENCE = 5 +SCHEDULER_THRESHOLD = 1e-3 +SCHEDULER_MIN_LR = 1e-5 + +HEAD_LR_RANGE = (1e-5, 3e-3) +ENCODER_LR_RANGE = (1e-6, 3e-3) +WEIGHT_DECAY_RANGE = (1e-6, 1e-2) +TMAX_RANGE = (3, 10) +ENTROPY_LR_RANGE = (1e-5, 1e-3) +DROPOUT_P_RANGE = (0.0, 0.5) + +USE_TRIAL_PRUNING = True +TRIAL_PRUNER_WARMUP_STEPS = 80 +TRIAL_PRUNER_PATIENCE_STEPS = 40 +LOAD_EXISTING_STUDIES = False +SKIP_EXISTING_FINALS = False +RUN_OPTUNA = False +RESET_ALL_STUDIES_EACH_RUN = False +USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF = False + +EXECUTION_MODE = "train_eval" # "train_eval" or "eval_only" +EVAL_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +EVAL_SPECIFIC_CHECKPOINT = "" +STRATEGY2_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = { + # Non-phase mode — keyed by dataset percent (float): + # 0.1: "runs/EfficientNet_Strategy2_New/pct_10/strategy_2/final/checkpoints/epoch_0089.pt", + # 0.5: "Strategy2_Checkpoints/strat2_50_best.pt", + # 1.0: "runs/EfficientNet_Strategy2_New/pct_100/strategy_2/final/checkpoints/best.pt", + # Phase mode — keyed by phase index (int): + 1: "/content/UNET_REVAMP/best_strat2.pt", + 2: "/content/UNET_REVAMP/best_strat2_2.pt", + 3: "/content/UNET_REVAMP/best_strat2_3.pt", +} +STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 = True +TRAIN_RESUME_MODE = "off" # "off", "latest", "best", or "specific" +TRAIN_RESUME_SPECIFIC_CHECKPOINT = "" +OPTUNA_HEARTBEAT_INTERVAL = 60 +OPTUNA_HEARTBEAT_GRACE_PERIOD = 180 + +USE_AMP = True +AMP_DTYPE = "bfloat16" # "auto", "bfloat16", or "float16" +USE_CHANNELS_LAST = True +USE_TORCH_COMPILE = True +STEPWISE_BACKWARD = True +ALLOW_TF32 = True + +RUN_SMOKE_TEST = False +SMOKE_TEST_SAMPLE_INDEX = 0 +RUN_OVERFIT_TEST = False +OVERFIT_N_BATCHES = 2 +OVERFIT_N_EPOCHS = 100 +OVERFIT_HEAD_LR = 1e-3 +OVERFIT_ENCODER_LR = 1e-4 +OVERFIT_PRINT_EVERY = 5 +WRITE_EPOCH_DIAGNOSTIC = True +EPOCH_DIAGNOSTIC_TRAIN_BATCHES = 2 +EPOCH_DIAGNOSTIC_VAL_BATCHES = 2 +CONTROLLED_MASK_THRESHOLD = 0.50 + +REQUIRED_HPARAM_KEYS = ("head_lr", "encoder_lr", "weight_decay", "dropout_p", "tmax", "entropy_lr") + +_TRANSUNET_REQUIRED_NPZ_KEYS: tuple[str, ...] = ( + "embedding/kernel", + "embedding/bias", + "Transformer/encoder_norm/scale", + "Transformer/encoder_norm/bias", + "Transformer/posembed_input/pos_embedding", + "conv_root/kernel", + "gn_root/scale", + "gn_root/bias", + "Transformer/encoderblock_0/MultiHeadDotProductAttention_1/query/kernel", +) +_TRANSUNET_ENCODER_ALIASES: set[str] = {"vitb16r50", "r50vitb16"} +_TRANSUNET_VISION_TRANSFORMER: Any | None = None +_TRANSUNET_CONFIGS: dict[str, Any] | None = None +_TEST_ITERATION_NOTICE_CACHE: set[tuple[str, int, int]] = set() + +"""============================================================================= +IF OPTUNA IS OFF --> USE ME +============================================================================= +""" + +# Key format: ":" +# Each value is a JSON filename in HARD_CODED_PARAM_DIR containing the required hyperparameters. +MANUAL_HPARAMS_IF_OPTUNA_OFF: dict[str, str] = { + "2:100": "param_segformer/best_params_strat2.json", + "3:100": "param_segformer/best_params_strat3.json", +} + +# ===================== ABLATION HARNESS OVERRIDE ===================== +# Auto-generated. Outputs go to a separate MODEL_NAME subtree; strategy 3 +# only; the frozen strategy-2 base is reused from the original run tree. +MODEL_NAME = "Segformer_B0_AB2_tmax2" +STRATEGIES = [3] +STRATEGY2_CHECKPOINT_MODE = "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = {1: str(PROJECT_DIR / "best.pt")} +MANUAL_HPARAMS_IF_OPTUNA_OFF = {**MANUAL_HPARAMS_IF_OPTUNA_OFF, "3:100": "param_segformer/abl_ab2_tmax2.json"} +# ===================================================================== + +"""============================================================================= +RUNTIME SETUP +============================================================================= +""" + +torch.set_float32_matmul_precision("high") +if torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32 + torch.backends.cudnn.allow_tf32 = ALLOW_TF32 +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = True + +def select_runtime_device() -> tuple[torch.device, str]: + if torch.cuda.is_available(): + return torch.device("cuda"), "cuda" + + mps_backend = getattr(torch.backends, "mps", None) + if mps_backend is not None and mps_backend.is_available(): + try: + _probe = torch.zeros(1, device="mps") + del _probe + return torch.device("mps"), "mps" + except Exception as exc: + print(f"[Device] MPS detected but failed to initialize ({exc}). Falling back to CPU.") + + return torch.device("cpu"), "cpu" + +DEVICE, DEVICE_FALLBACK_SOURCE = select_runtime_device() +CURRENT_JOB_PARAMS: dict[str, Any] = {} + +@dataclass(frozen=True) +class RuntimeModelConfig: + backbone_family: str + smp_encoder_name: str + smp_encoder_weights: str | None + smp_encoder_depth: int + smp_encoder_proj_dim: int + smp_decoder_type: str + vgg_feature_scales: int + vgg_feature_dilation: int + + @classmethod + def from_globals(cls) -> RuntimeModelConfig: + return cls( + backbone_family=str(BACKBONE_FAMILY).strip().lower(), + smp_encoder_name=str(SMP_ENCODER_NAME), + smp_encoder_weights=SMP_ENCODER_WEIGHTS, + smp_encoder_depth=int(SMP_ENCODER_DEPTH), + smp_encoder_proj_dim=int(SMP_ENCODER_PROJ_DIM), + smp_decoder_type=str(SMP_DECODER_TYPE), + vgg_feature_scales=int(VGG_FEATURE_SCALES), + vgg_feature_dilation=int(VGG_FEATURE_DILATION), + ) + + @classmethod + def from_payload(cls, payload: dict[str, Any] | None) -> RuntimeModelConfig: + payload = payload or {} + return cls( + backbone_family=str(payload.get("backbone_family", "smp")).strip().lower(), + smp_encoder_name=str(payload.get("smp_encoder_name", SMP_ENCODER_NAME)), + smp_encoder_weights=payload.get("smp_encoder_weights", SMP_ENCODER_WEIGHTS), + smp_encoder_depth=int(payload.get("smp_encoder_depth", SMP_ENCODER_DEPTH)), + smp_encoder_proj_dim=int(payload.get("smp_encoder_proj_dim", SMP_ENCODER_PROJ_DIM)), + smp_decoder_type=str(payload.get("smp_decoder_type", SMP_DECODER_TYPE)), + vgg_feature_scales=int(payload.get("vgg_feature_scales", VGG_FEATURE_SCALES)), + vgg_feature_dilation=int(payload.get("vgg_feature_dilation", VGG_FEATURE_DILATION)), + ) + + def validate(self) -> RuntimeModelConfig: + if self.backbone_family not in {"smp", "custom_vgg"}: + raise ValueError(f"BACKBONE_FAMILY must be 'smp' or 'custom_vgg', got {self.backbone_family!r}") + if self.vgg_feature_scales not in {3, 4}: + raise ValueError(f"VGG_FEATURE_SCALES must be 3 or 4, got {self.vgg_feature_scales}") + if self.vgg_feature_dilation < 1: + raise ValueError(f"VGG_FEATURE_DILATION must be >= 1, got {self.vgg_feature_dilation}") + if self.smp_encoder_depth < 1: + raise ValueError(f"SMP_ENCODER_DEPTH must be >= 1, got {self.smp_encoder_depth}") + if self.smp_encoder_proj_dim < 0: + raise ValueError(f"SMP_ENCODER_PROJ_DIM must be >= 0, got {self.smp_encoder_proj_dim}") + if _normalized_model_token(self.smp_decoder_type) == "transunet": + if _normalized_model_token(self.smp_encoder_name) not in _TRANSUNET_ENCODER_ALIASES: + print( + "[RuntimeModelConfig] Warning: SMP_DECODER_TYPE='TransUNet' is wired for " + "SMP_ENCODER_NAME='ViTB16R50' (or 'R50ViTB16'). " + f"Received {self.smp_encoder_name!r}." + ) + if IMG_SIZE % 16 != 0: + raise ValueError( + f"TransUNet requires IMG_SIZE divisible by 16, got IMG_SIZE={IMG_SIZE}." + ) + return self + + def to_payload(self) -> dict[str, Any]: + return { + "backbone_family": self.backbone_family, + "smp_encoder_name": self.smp_encoder_name, + "smp_encoder_weights": self.smp_encoder_weights, + "smp_encoder_depth": self.smp_encoder_depth, + "smp_encoder_proj_dim": self.smp_encoder_proj_dim, + "smp_decoder_type": self.smp_decoder_type, + "vgg_feature_scales": self.vgg_feature_scales, + "vgg_feature_dilation": self.vgg_feature_dilation, + } + + def backbone_tag(self) -> str: + return self.backbone_family + + def backbone_display_name(self) -> str: + if self.backbone_family == "custom_vgg": + return f"Custom VGG (scales={self.vgg_feature_scales}, dilation={self.vgg_feature_dilation})" + return f"SMP {self.smp_encoder_name}" + +def current_model_config() -> RuntimeModelConfig: + return RuntimeModelConfig.from_globals().validate() + +"""============================================================================= +UTILITIES +============================================================================= +""" + +def _normalized_model_token(value: str | None) -> str: + return "".join(ch for ch in str(value or "") if ch.isalnum()).lower() + + +def _is_transunet_selection( + model_config: RuntimeModelConfig | None = None, + *, + encoder_name: str | None = None, + decoder_type: str | None = None, +) -> bool: + if model_config is not None: + encoder_name = model_config.smp_encoder_name + decoder_type = model_config.smp_decoder_type + enc = _normalized_model_token(encoder_name) + dec = _normalized_model_token(decoder_type) + return dec == "transunet" and enc in _TRANSUNET_ENCODER_ALIASES + + +def _resolve_test_iteration_tmax(tmax: int, *, context: str) -> int: + effective_tmax = max(int(tmax), 1) + if not TEST_ITERATION_CONTROL: + return effective_tmax + + requested_t = int(TEST_ITERATION_T) + if requested_t < 1: + raise ValueError( + f"TEST_ITERATION_T must be >= 1 when TEST_ITERATION_CONTROL=True, got {requested_t}." + ) + + effective_tmax = min(effective_tmax, requested_t) + cache_key = (context, int(tmax), effective_tmax) + if cache_key not in _TEST_ITERATION_NOTICE_CACHE: + if requested_t > int(tmax): + print( + f"[Test Iteration Control] {context}: TEST_ITERATION_T={requested_t} exceeds tmax={int(tmax)}; " + f"using t={effective_tmax}." + ) + else: + print( + f"[Test Iteration Control] {context}: overriding test rollout steps " + f"from tmax={int(tmax)} to t={effective_tmax}." + ) + _TEST_ITERATION_NOTICE_CACHE.add(cache_key) + return effective_tmax + +def banner(title: str) -> None: + line = "=" * 80 + print(f"\n{line}\n{title}\n{line}") + +def section(title: str) -> None: + print(f"\n{'-' * 80}\n{title}\n{'-' * 80}") + +def ensure_dir(path: str | Path) -> Path: + path = Path(path).expanduser().resolve() + path.mkdir(parents=True, exist_ok=True) + return path + +def save_json(path: str | Path, payload: Any) -> None: + path = Path(path) + ensure_dir(path.parent) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + +def load_json(path: str | Path) -> Any: + with Path(path).open("r", encoding="utf-8") as f: + return json.load(f) + +def _format_history_log_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key == "lr" or key.endswith("_lr"): + return f"{value:.6e}" + return json.dumps(value) + return json.dumps(value) + +def format_history_log_row(row: dict[str, Any]) -> str: + return ", ".join(f"{key}={_format_history_log_value(key, value)}" for key, value in row.items()) + +def _format_epoch_metric(value: Any, *, scientific: bool = False) -> str: + if value is None: + return "null" + if isinstance(value, (float, int, np.floating, np.integer)): + value = float(value) + return f"{value:.6e}" if scientific else f"{value:.4f}" + return str(value) + +def format_concise_epoch_log( + row: dict[str, Any], + *, + best_metric_name: str, + best_metric_value: float, +) -> str: + fields: list[tuple[str, Any, bool]] = [ + ("train_loss", row.get("train_loss"), False), + ("train_iou", row.get("train_iou"), False), + ("train_entropy", row.get("train_entropy"), False), + ("val_loss", row.get("val_loss"), False), + ("val_iou", row.get("val_iou"), False), + ("val_dice", row.get("val_dice"), False), + ("val_iou_gain", row.get("val_iou_gain"), False), + ("val_biou_gain", row.get("val_biou_gain"), False), + ("head_lr", row.get("lr"), True), + ("encoder_lr", row.get("encoder_lr"), True), + (best_metric_name, best_metric_value, False), + ("study_best", row.get("study_best_objective"), False), + ] + parts = [ + f"{name}={_format_epoch_metric(value, scientific=scientific)}" + for name, value, scientific in fields + if value is not None + ] + early_monitor_name = row.get("early_stopping_monitor_name") + if early_monitor_name: + parts.append(f"es_monitor={early_monitor_name}") + if row.get("early_stopping_monitor_value") is not None: + parts.append(f"es_value={_format_epoch_metric(row.get('early_stopping_monitor_value'))}") + if row.get("early_stopping_best_value") is not None: + parts.append(f"es_best={_format_epoch_metric(row.get('early_stopping_best_value'))}") + if row.get("early_stopping_wait") is not None and row.get("early_stopping_patience") is not None: + parts.append( + f"es_wait={int(row.get('early_stopping_wait'))}/{int(row.get('early_stopping_patience'))}" + ) + if row.get("early_stopping_active") is not None: + parts.append(f"es_active={bool(row.get('early_stopping_active'))}") + if row.get("strategy3_freeze_active") is not None: + parts.append(f"s3_frozen={bool(row.get('strategy3_freeze_active'))}") + if row.get("study_best_trial") is not None: + parts.append(f"study_best_trial={int(row.get('study_best_trial'))}") + return ", ".join(parts) + +def _optuna_direction_is_maximize(direction: Any) -> bool: + direction_name = str(getattr(direction, "name", direction)).lower() + return direction_name.endswith("maximize") + +def _optuna_value_is_better( + candidate: float | None, + current: float | None, + *, + direction: Any, +) -> bool: + if candidate is None: + return False + if current is None: + return True + return float(candidate) > float(current) if _optuna_direction_is_maximize(direction) else float(candidate) < float(current) + +def _optuna_trial_state_name(trial: Any) -> str: + state = getattr(trial, "state", None) + return str(getattr(state, "name", state)).upper() + +def _optuna_trial_user_attr_float(trial: Any, attr_name: str) -> float | None: + user_attrs = getattr(trial, "user_attrs", None) + if not isinstance(user_attrs, dict): + return None + value = user_attrs.get(attr_name) + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + +def _optuna_trial_best_intermediate_value( + trial: Any, + *, + direction: Any, +) -> float | None: + best_value: float | None = None + for value in getattr(trial, "intermediate_values", {}).values(): + if value is None: + continue + candidate_value = float(value) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_value = candidate_value + return best_value + +def _optuna_trial_best_observed_value( + trial: Any, + *, + direction: Any, + current_best_value: float | None = None, +) -> float | None: + if current_best_value is not None: + return float(current_best_value) + + best_observed = _optuna_trial_user_attr_float(trial, OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR) + if best_observed is not None: + return best_observed + + state_name = _optuna_trial_state_name(trial) + if state_name == "COMPLETE": + value = getattr(trial, "value", None) + return None if value is None else float(value) + + best_intermediate = _optuna_trial_best_intermediate_value(trial, direction=direction) + if best_intermediate is not None: + return best_intermediate + + value = getattr(trial, "value", None) + return None if value is None else float(value) + +def _current_optuna_study_best_candidate( + study: optuna.study.Study, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> tuple[Any | None, float | None]: + direction = getattr(study, "direction", STUDY_DIRECTION) + best_trial: Any | None = None + best_value: float | None = None + + for study_trial in getattr(study, "trials", []): + if _optuna_trial_state_name(study_trial) not in {"COMPLETE", "PRUNED"}: + continue + candidate_value = _optuna_trial_best_observed_value(study_trial, direction=direction) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_trial = study_trial + best_value = candidate_value + + if current_trial is not None: + live_trial_best = _optuna_trial_best_observed_value( + current_trial, + direction=direction, + current_best_value=current_best_value, + ) + if _optuna_value_is_better(live_trial_best, best_value, direction=direction): + best_trial = current_trial + best_value = live_trial_best + + return best_trial, best_value + +def _current_optuna_study_best_snapshot( + trial: optuna.trial.Trial | None, + *, + current_best_value: float | None = None, +) -> tuple[float | None, int | None]: + if trial is None: + return None, None + study = getattr(trial, "study", None) + if study is None: + return None, None + + best_trial, best_value = _current_optuna_study_best_candidate( + study, + current_trial=trial, + current_best_value=current_best_value, + ) + best_trial_number = None if best_trial is None else int(getattr(best_trial, "number", -1)) + return best_value, best_trial_number + +def set_global_seed(seed: int = 42) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def stable_int_from_text(text: str) -> int: + value = 0 + for byte in text.encode("utf-8"): + value = (value * 131 + byte) % (2 ** 31 - 1) + return value + +def seed_worker(worker_id: int) -> None: + del worker_id + worker_seed = torch.initial_seed() % (2 ** 32) + random.seed(worker_seed) + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + +def make_seeded_generator(seed: int, tag: str) -> torch.Generator: + generator = torch.Generator() + generator.manual_seed(seed + stable_int_from_text(tag)) + return generator + +def cuda_memory_snapshot() -> str: + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return "allocated=0.00 GB, reserved=0.00 GB, peak=0.00 GB" + allocated = torch.cuda.memory_allocated(device=DEVICE) / (1024 ** 3) + reserved = torch.cuda.memory_reserved(device=DEVICE) / (1024 ** 3) + peak = torch.cuda.max_memory_allocated(device=DEVICE) / (1024 ** 3) + return f"allocated={allocated:.2f} GB, reserved={reserved:.2f} GB, peak={peak:.2f} GB" + +def run_cuda_cleanup(context: str | None = None) -> None: + gc.collect() + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return + try: + torch.cuda.synchronize(device=DEVICE) + except Exception: + pass + try: + torch.cuda.empty_cache() + except Exception: + pass + try: + torch.cuda.ipc_collect() + except Exception: + pass + if context is not None: + print(f"[CUDA Cleanup] {context}: {cuda_memory_snapshot()}") + try: + torch.cuda.reset_peak_memory_stats(device=DEVICE) + except Exception: + pass + +def prune_directory_except(root: Path, keep_file_names: set[str]) -> None: + if not root.exists(): + return + keep_paths = {root / name for name in keep_file_names} + for path in sorted((p for p in root.rglob("*") if p.is_file()), reverse=True): + if path not in keep_paths: + path.unlink() + for path in sorted((p for p in root.rglob("*") if p.is_dir()), reverse=True): + if path != root: + try: + path.rmdir() + except OSError: + pass + +def prune_optuna_trial_dir(trial_dir: Path) -> None: + if trial_dir.exists(): + shutil.rmtree(trial_dir, ignore_errors=True) + +def prune_optuna_study_dir(study_root: Path) -> None: + prune_directory_except(study_root, {"best_params.json", "summary.json", "study.sqlite3"}) + +def to_device(batch: Any, device: torch.device) -> Any: + if torch.is_tensor(batch): + return batch.to(device, non_blocking=True) + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, list): + return [to_device(v, device) for v in batch] + if isinstance(batch, tuple): + return tuple(to_device(v, device) for v in batch) + return batch + +def _normalized_decimal_text(value: Decimal) -> str: + normalized = value.normalize() + text = format(normalized, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text or "0" + +def _fraction_decimal(value: Any, *, field_name: str) -> Decimal: + if isinstance(value, bool): + raise TypeError(f"{field_name} must be a real number in (0, 1], got boolean {value!r}.") + try: + decimal_value = Decimal(str(value).strip()) + except (InvalidOperation, ValueError) as exc: + raise ValueError(f"{field_name} must be a real number in (0, 1], got {value!r}.") from exc + if not decimal_value.is_finite(): + raise ValueError(f"{field_name} must be finite, got {value!r}.") + if decimal_value <= 0 or decimal_value > 1: + raise ValueError(f"{field_name} must be in the interval (0, 1], got {value!r}.") + return decimal_value + +def _percent_decimal(value: Any, *, field_name: str = "dataset percent") -> Decimal: + return _fraction_decimal(value, field_name=field_name) * Decimal("100") + +def normalize_dataset_percents(values: list[float] | tuple[float, ...]) -> list[float]: + if not values: + raise ValueError("DATASET_PERCENTS must contain at least one fraction in (0, 1].") + normalized: dict[str, float] = {} + for value in values: + fraction = _fraction_decimal(value, field_name="DATASET_PERCENTS entry") + normalized[_normalized_decimal_text(fraction)] = float(fraction) + return [normalized[key] for key in sorted(normalized, key=Decimal)] + +def percent_label(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)).replace(".", "p") + +def percent_display(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)) + +def percent_text(percent: float) -> str: + return f"{percent_display(percent)}%" + + +def run_identity_parts( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> list[str]: + parts: list[str] = [] + payload = split_payload or {} + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase={int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split={int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split={split_type}") + + if subset_repeat_index is not None: + parts.append(f"repeat={int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant={int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy={int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct={percent_text(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial={int(trial_number):03d}") + + return parts + + +def run_identity_label( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + parts = run_identity_parts( + strategy=strategy, + percent=percent, + trial_number=trial_number, + split_payload=split_payload, + ) + return " | ".join(parts) if parts else "run" + + +def run_identity_slug( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + payload = split_payload or {} + parts: list[str] = [] + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase_{int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split_{int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split_{str(split_type)}") + + if subset_repeat_index is not None: + parts.append(f"repeat_{int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant_{int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy_{int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct_{percent_label(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial_{int(trial_number):03d}") + + return "__".join(parts) if parts else "run" + + +def current_dataset_name() -> str: + dataset_name = str(DATASET_NAME).strip() + if dataset_name not in SUPPORTED_DATASET_NAMES: + raise ValueError(f"DATASET_NAME must be one of {SUPPORTED_DATASET_NAMES}, got {dataset_name!r}") + return dataset_name + +def current_busi_with_classes_split_policy() -> str: + split_policy = str(BUSI_WITH_CLASSES_SPLIT_POLICY).strip().lower() + if split_policy not in SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES: + raise ValueError( + f"BUSI_WITH_CLASSES_SPLIT_POLICY must be one of {SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES}, " + f"got {split_policy!r}" + ) + return split_policy + +def current_dataset_splits_json_path() -> Path: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATASET_SPLITS_JSON + return PROJECT_DIR / f"dataset_splits_{dataset_name.lower()}_{current_busi_with_classes_split_policy()}.json" + +def current_dataset_dirs() -> tuple[Path, Path]: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATA_ROOT / "images", DATA_ROOT / "annotations" + return DATA_ROOT / "all_images", DATA_ROOT / "all_masks" + +def current_pipeline_check_path() -> Path | None: + if current_dataset_name() != "BUSI_with_classes": + return None + return DATA_ROOT / "pipeline_check.json" + +def normalization_cache_tag() -> str: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return "BUSI" + return f"{dataset_name}_{current_busi_with_classes_split_policy()}" + +def resolve_amp_dtype(key: str) -> torch.dtype: + key = key.lower().strip() + if key == "auto": + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + return torch.float16 + if key in {"float16", "fp16", "half"}: + return torch.float16 + if key in {"bfloat16", "bf16"}: + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + print("[AMP] bfloat16 requested but unsupported here. Falling back to float16.") + return torch.float16 + raise ValueError(f"Unsupported AMP_DTYPE: {key}") + +def amp_autocast_enabled(device: torch.device) -> bool: + return USE_AMP and device.type in {"cuda", "mps"} + +def autocast_ctx(enabled: bool, device: torch.device, amp_dtype: torch.dtype): + if not enabled: + return nullcontext() + return torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=True) + +def make_grad_scaler(enabled: bool, amp_dtype: torch.dtype, device: torch.device): + if not enabled or device.type != "cuda" or amp_dtype == torch.bfloat16: + return None + try: + return torch.amp.GradScaler("cuda", enabled=True, init_scale=8192.0) + except Exception: + return torch.cuda.amp.GradScaler(enabled=True, init_scale=8192.0) + +def format_seconds(seconds: float) -> str: + seconds = int(seconds) + h, rem = divmod(seconds, 3600) + m, s = divmod(rem, 60) + return f"{h:02d}:{m:02d}:{s:02d}" + +def tensor_bytes(t: torch.Tensor) -> int: + return t.numel() * t.element_size() + +def bytes_to_gb(num_bytes: int) -> float: + return num_bytes / (1024 ** 3) + +def set_current_job_params(payload: dict[str, Any] | None = None) -> None: + CURRENT_JOB_PARAMS.clear() + if payload: + CURRENT_JOB_PARAMS.update(dict(payload)) + +def _job_param(name: str, default: Any) -> Any: + return CURRENT_JOB_PARAMS.get(name, default) + +def _alpha_log_floor() -> float: + return math.log(max(float(_job_param("min_alpha", math.exp(-5.0))), 1e-6)) + +def _keep_action_index(action_count: int) -> int: + action_count = max(int(action_count), 1) + if action_count >= 3: + return action_count // 2 + return action_count - 1 + +def _strategy3_variant() -> str: + raw = str(_job_param("strategy3_variant", DEFAULT_STRATEGY3_VARIANT)).strip().lower() + return raw or DEFAULT_STRATEGY3_VARIANT + +def _strategy3_annealed_weight( + base_weight: float, + *, + current_epoch: int, + anneal_start_epoch: int = 1, + anneal_epochs: int, +) -> float: + base_weight = float(base_weight) + if base_weight <= 0.0: + return 0.0 + anneal_start_epoch = max(int(anneal_start_epoch), 1) + anneal_epochs = max(int(anneal_epochs), 0) + if current_epoch < anneal_start_epoch: + return 0.0 + if anneal_epochs <= 0: + return base_weight + progress = min( + max((float(current_epoch) - float(anneal_start_epoch)) / float(anneal_epochs), 0.0), + 1.0, + ) + floor_fraction = float( + _job_param( + "strategy3_aux_ce_floor_fraction", + DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + ) + ) + floor_fraction = min(max(floor_fraction, 0.0), 1.0) + fraction = max(1.0 - progress, floor_fraction) + return base_weight * fraction + +def _strategy3_annealed_aux_ce_weight(current_epoch: int) -> float: + return _strategy3_annealed_weight( + float(_job_param("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT)), + current_epoch=int(current_epoch), + anneal_start_epoch=int( + _job_param( + "strategy3_aux_ce_anneal_start_epoch", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + ) + ), + anneal_epochs=int( + _job_param( + "strategy3_aux_ce_anneal_epochs", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + ) + ), + ) + +def _strategy3_exploration_eps(current_epoch: int) -> float: + base_eps = max(float(_job_param("strategy3_exploration_eps", DEFAULT_EXPLORATION_EPS)), 0.0) + decay_epochs = max(int(_job_param("strategy3_exploration_eps_epochs", EXPLORATION_EPS_EPOCHS)), 0) + if base_eps <= 0.0: + return 0.0 + if decay_epochs <= 0: + return base_eps + progress = min(max((float(current_epoch) - 1.0) / float(decay_epochs), 0.0), 1.0) + return base_eps * (1.0 - progress) + +def _bootstrap_value_target(model: nn.Module, value_next: torch.Tensor) -> torch.Tensor: + neighborhood_value = getattr(model, "neighborhood_value", None) + if callable(neighborhood_value): + return neighborhood_value(value_next) + return value_next + +def _strategy3_delta_max() -> float: + return float(_job_param("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX)) + +def _strategy3_policy_delta(policy_raw: torch.Tensor) -> torch.Tensor: + return torch.tanh(policy_raw.float()) * _strategy3_delta_max() + +def _strategy3_apply_delta(seg: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: + seg_f = seg.float() + delta_f = delta.float() + return (seg_f + delta_f).clamp(0.0, 1.0).to(dtype=seg.dtype) + +def _strategy3_advantage_normalize_enabled() -> bool: + return bool(_job_param("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE)) + +def _normalize_strategy3_advantage_map(advantage_map: torch.Tensor) -> torch.Tensor: + if not _strategy3_advantage_normalize_enabled(): + return advantage_map + if advantage_map.ndim < 4: + mean = advantage_map.mean() + std = advantage_map.std(unbiased=False) + return (advantage_map - mean) / (std + 1e-6) + mean = advantage_map.mean(dim=(2, 3), keepdim=True) + std = advantage_map.std(dim=(2, 3), unbiased=False, keepdim=True) + return (advantage_map - mean) / (std + 1e-6) + +def _strategy3_actor_advantage( + reward_map: torch.Tensor, + value_t: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + advantage_map = reward_map + float(gamma) * value_next.detach() - value_t.detach() + return _normalize_strategy3_advantage_map(advantage_map) + +def _strategy3_critic_target( + reward_map: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + return reward_map.detach() + float(gamma) * value_next.detach() + +def _strategy3_delta_distribution(delta_map: torch.Tensor) -> dict[str, float]: + delta_f = delta_map.detach().float() + abs_delta = delta_f.abs() + return { + "mean_delta": float(delta_f.mean().item()), + "mean_abs_delta": float(abs_delta.mean().item()), + "positive_pct": float((delta_f > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((delta_f < -1e-6).float().mean().item() * 100.0), + "near_zero_pct": float((abs_delta <= 1e-6).float().mean().item() * 100.0), + "max_abs_delta": float(abs_delta.max().item()), + } + +def _bernoulli_predictive_entropy(prob: torch.Tensor) -> torch.Tensor: + prob_f = prob.float().clamp(1e-6, 1.0 - 1e-6) + return -(prob_f * torch.log(prob_f) + (1.0 - prob_f) * torch.log1p(-prob_f)) + +def _iter_strategy3_dropout_modules(model: nn.Module) -> Iterator[nn.Module]: + for module in _unwrap_compiled(model).modules(): + if isinstance(module, (nn.Dropout, nn.Dropout2d)): + yield module + +@contextmanager +def _strategy3_mc_dropout_scope(model: nn.Module) -> Iterator[None]: + global _STRATEGY3_MC_DROPOUT_WARNED + + requested_p = float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)) + if requested_p <= 0.0 and not _STRATEGY3_MC_DROPOUT_WARNED: + print("[Strategy3] MC-dropout requested with non-positive dropout p; variance maps may collapse to zero.") + _STRATEGY3_MC_DROPOUT_WARNED = True + + saved_states: list[tuple[nn.Module, bool, float | None]] = [] + for module in _iter_strategy3_dropout_modules(model): + saved_states.append((module, bool(module.training), getattr(module, "p", None))) + module.train(True) + if hasattr(module, "p") and requested_p > 0.0: + module.p = requested_p + try: + yield + finally: + for module, was_training, saved_p in saved_states: + module.train(was_training) + if saved_p is not None and hasattr(module, "p"): + module.p = saved_p + +def _strategy3_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_fn: Any, +) -> tuple[torch.Tensor, torch.Tensor]: + if not bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)): + with torch.no_grad(): + zeros = torch.zeros_like(decoder_prob.float()) + pred_entropy = _bernoulli_predictive_entropy(decoder_prob) + return zeros, pred_entropy + + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + with torch.no_grad(): + mc_probs: list[torch.Tensor] = [] + with _strategy3_mc_dropout_scope(model): + for _ in range(samples): + logits = sample_fn() + mc_probs.append(torch.sigmoid(logits).float()) + stacked = torch.stack(mc_probs, dim=0) + mean_prob = stacked.mean(dim=0) + variance = stacked.var(dim=0, unbiased=False) + pred_entropy = _bernoulli_predictive_entropy(mean_prob) + return variance.to(dtype=decoder_prob.dtype), pred_entropy.to(dtype=decoder_prob.dtype) + +def _strategy3_mc_config_hash() -> tuple[bool, int, float]: + enabled = bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)) + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + dropout_p = round(float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)), 8) + return enabled, samples, dropout_p + +def _strategy3_mc_disk_cache_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED)) + +def _strategy3_mc_disk_cache_read_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ)) + +def _strategy3_mc_disk_cache_write_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE)) + +def _strategy3_normalize_sample_ids( + sample_ids: list[str] | tuple[str, ...] | None, + *, + batch_size: int, +) -> list[str] | None: + if sample_ids is None: + return None + normalized = [str(item) for item in sample_ids] + if len(normalized) != int(batch_size): + raise ValueError( + f"Strategy 3 eval MC cache expected {batch_size} sample_ids, got {len(normalized)}." + ) + return normalized + +def _strategy3_ensure_mc_cache_state(model: nn.Module) -> nn.Module: + raw_model = getattr(model, "_orig_mod", model) + if not hasattr(raw_model, "_strategy3_mc_cache"): + raw_model._strategy3_mc_cache = {} + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint"): + raw_model._strategy3_mc_cache_fingerprint = "" + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint_sources"): + raw_model._strategy3_mc_cache_fingerprint_sources = {} + if not hasattr(raw_model, "_strategy3_strategy2_checkpoint_path"): + raw_model._strategy3_strategy2_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_eval_checkpoint_path"): + raw_model._strategy3_eval_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_mc_cache_stats"): + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return raw_model + +def _strategy3_reset_mc_cache_stats(model: nn.Module) -> None: + raw_model = _strategy3_ensure_mc_cache_state(model) + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + +def _strategy3_get_mc_cache_stats(model: nn.Module) -> dict[str, int]: + raw_model = _strategy3_ensure_mc_cache_state(model) + stats = raw_model._strategy3_mc_cache_stats + return { + "ram_hits": int(stats.get("ram_hits", 0)), + "disk_hits": int(stats.get("disk_hits", 0)), + "misses": int(stats.get("misses", 0)), + "writes": int(stats.get("writes", 0)), + } + +def _strategy3_checkpoint_sha256(path: str | Path | None) -> str | None: + if not path: + return None + resolved = str(Path(path).expanduser().resolve()) + cached = _STRATEGY3_MC_FILE_SHA256_CACHE.get(resolved) + if cached is not None: + return cached + checkpoint_path = Path(resolved) + if not checkpoint_path.is_file(): + return None + digest = hashlib.sha256() + with checkpoint_path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + checksum = digest.hexdigest() + _STRATEGY3_MC_FILE_SHA256_CACHE[resolved] = checksum + return checksum + +def _strategy3_decoder_state_hash(model: nn.Module) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + modules: list[tuple[str, nn.Module]] = [] + if isinstance(raw_model, PixelDRLMG_WithDecoder): + modules = [ + ("smp_model.encoder", raw_model.smp_model.encoder), + ("smp_model.decoder", raw_model.smp_model.decoder), + ("smp_model.segmentation_head", raw_model.smp_model.segmentation_head), + ] + elif isinstance(raw_model, PixelDRLMG_VGGWithDecoder): + modules = [ + ("encoder", raw_model.encoder), + ("segmentation_head", raw_model.segmentation_head), + ] + else: + return stable_hash(raw_model.__class__.__name__) + + digest = hashlib.sha256() + for prefix, module in modules: + for name, tensor in sorted(module.state_dict().items()): + tensor_cpu = tensor.detach().cpu().contiguous() + digest.update(prefix.encode("utf-8")) + digest.update(b"\0") + digest.update(name.encode("utf-8")) + digest.update(b"\0") + digest.update(str(tensor_cpu.dtype).encode("utf-8")) + digest.update(b"\0") + digest.update(json.dumps(list(tensor_cpu.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(tensor_cpu.numpy().tobytes()) + return digest.hexdigest() + +def _strategy3_resolve_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> tuple[str, dict[str, Any]]: + raw_model = _strategy3_ensure_mc_cache_state(model) + strategy2_path = ( + str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if strategy2_checkpoint_path + else getattr(raw_model, "_strategy3_strategy2_checkpoint_path", None) + ) + eval_path = ( + str(Path(eval_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path + else getattr(raw_model, "_strategy3_eval_checkpoint_path", None) + ) + decoder_hash = _strategy3_decoder_state_hash(raw_model) + sources: dict[str, Any] = {"decoder_state_hash": decoder_hash} + if strategy2_path: + sources["strategy2_checkpoint"] = strategy2_path + strategy2_sha = _strategy3_checkpoint_sha256(strategy2_path) + if strategy2_sha is not None: + sources["strategy2_sha256"] = strategy2_sha + if eval_path: + sources["eval_checkpoint"] = eval_path + eval_sha = _strategy3_checkpoint_sha256(eval_path) + if eval_sha is not None: + sources["eval_checkpoint_sha256"] = eval_sha + return decoder_hash, sources + +def _strategy3_bump_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + if strategy2_checkpoint_path is not None: + raw_model._strategy3_strategy2_checkpoint_path = str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path is not None: + raw_model._strategy3_eval_checkpoint_path = str(Path(eval_checkpoint_path).expanduser().resolve()) + fingerprint, sources = _strategy3_resolve_mc_cache_fingerprint( + raw_model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + eval_checkpoint_path=eval_checkpoint_path, + ) + fingerprint_changed = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) != str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint = str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint_sources = dict(sources) + if fingerprint_changed: + clear_cache = getattr(raw_model, "clear_strategy3_mc_cache", None) + if callable(clear_cache): + clear_cache() + else: + raw_model._strategy3_mc_cache.clear() + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return str(fingerprint) + +def _strategy3_mc_disk_cache_root(run_dir: Path | None) -> Path | None: + if run_dir is None or not _strategy3_mc_disk_cache_enabled(): + return None + return Path(run_dir) / "mc_cache" + +def _strategy3_mc_disk_entry_path( + root: Path, + fingerprint: str, + split: str, + sample_id: str, +) -> Path: + safe_sample_id = str(sample_id).replace(os.sep, "__").replace("/", "__") + return Path(root) / str(fingerprint)[:16] / str(split) / f"{safe_sample_id}.pt" + +def _strategy3_load_mc_maps_from_disk( + path: Path, + *, + sample_id: str, + fingerprint: str, + mc_config_hash: tuple[bool, int, float], + split: str, +) -> tuple[torch.Tensor, torch.Tensor] | None: + if not path.is_file(): + return None + try: + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + payload = torch.load(path, map_location="cpu", weights_only=False) + except Exception: + return None + + if not isinstance(payload, dict): + return None + if int(payload.get("schema_version", -1)) != int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION): + return None + if str(payload.get("sample_id", "")) != str(sample_id): + return None + if str(payload.get("split", "")) != str(split): + return None + if str(payload.get("fingerprint", "")) != str(fingerprint): + return None + if tuple(payload.get("mc_config_hash", ())) != tuple(mc_config_hash): + return None + if int(payload.get("img_size", -1)) != int(IMG_SIZE): + return None + + variance = payload.get("mc_variance") + pred_entropy = payload.get("pred_entropy") + if not (torch.is_tensor(variance) and torch.is_tensor(pred_entropy)): + return None + if variance.ndim != 4 or pred_entropy.ndim != 4: + return None + return ( + variance.detach().to(device="cpu", dtype=torch.float32).contiguous(), + pred_entropy.detach().to(device="cpu", dtype=torch.float32).contiguous(), + ) + +def _strategy3_save_mc_maps_to_disk(path: Path, payload: dict[str, Any]) -> None: + atomic_torch_save(path, payload) + +def _strategy3_write_mc_cache_manifest( + run_dir: Path | None, + *, + fingerprint: str, + fingerprint_sources: dict[str, Any], + mc_config_hash: tuple[bool, int, float], + split: str, + split_write_count: int, +) -> None: + if run_dir is None: + return + manifest_path = Path(run_dir) / "mc_cache" / "manifest.json" + existing: dict[str, Any] = {} + if manifest_path.exists(): + try: + loaded = load_json(manifest_path) + except Exception: + loaded = {} + if isinstance(loaded, dict): + existing = loaded + existing_fingerprint = str(existing.get("fingerprint", "")) + existing_config = tuple(existing.get("mc_config_hash", ())) + if existing_fingerprint != str(fingerprint) or existing_config != tuple(mc_config_hash): + existing = {} + splits = dict(existing.get("splits", {})) if isinstance(existing.get("splits", {}), dict) else {} + splits[str(split)] = int(splits.get(str(split), 0)) + int(max(split_write_count, 0)) + payload = { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "fingerprint": str(fingerprint), + "fingerprint_sources": dict(fingerprint_sources), + "mc_config_hash": list(mc_config_hash), + "img_size": int(IMG_SIZE), + "dataset_name": current_dataset_name(), + "splits": splits, + } + atomic_save_json(manifest_path, payload) + +def _strategy3_mc_disk_mode( + *, + split: str | None, + run_dir: Path | None, +) -> tuple[bool, bool, Path | None, str | None]: + normalized_split = str(split).strip().lower() if split is not None else None + if normalized_split not in (None, "train", "val", "test"): + raise ValueError(f"Unsupported Strategy 3 MC cache split {split!r}.") + if normalized_split == "train": + return False, False, None, normalized_split + root = _strategy3_mc_disk_cache_root(run_dir) + can_use_disk = normalized_split in {"val", "test"} and root is not None + return ( + bool(can_use_disk and _strategy3_mc_disk_cache_read_enabled()), + bool(can_use_disk and _strategy3_mc_disk_cache_write_enabled()), + root, + normalized_split, + ) + +def _strategy3_prepare_cached_sample_pair( + sample_variance: torch.Tensor, + sample_entropy: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + return ( + sample_variance.detach().to(device="cpu", dtype=torch.float32).clone(), + sample_entropy.detach().to(device="cpu", dtype=torch.float32).clone(), + ) + +def _strategy3_cached_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_ids: list[str] | tuple[str, ...] | None, + compute_sample_maps: Any, + mc_cache_split: str | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + normalized_sample_ids = _strategy3_normalize_sample_ids(sample_ids, batch_size=int(decoder_prob.shape[0])) + if normalized_sample_ids is None: + raise ValueError("Strategy 3 eval MC cache requires non-empty sample_ids.") + + raw_model = _strategy3_ensure_mc_cache_state(model) + fingerprint = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) or _strategy3_bump_mc_cache_fingerprint(raw_model) + cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = raw_model._strategy3_mc_cache + stats = raw_model._strategy3_mc_cache_stats + mc_hash = _strategy3_mc_config_hash() + disk_read_enabled, disk_write_enabled, disk_root, normalized_split = _strategy3_mc_disk_mode( + split=mc_cache_split, + run_dir=mc_cache_run_dir, + ) + manifest_write_count = 0 + variance_samples: list[torch.Tensor] = [] + entropy_samples: list[torch.Tensor] = [] + + for sample_index, sample_id in enumerate(normalized_sample_ids): + cache_key = (fingerprint, sample_id, mc_hash) + cached_pair = cache.get(cache_key) + if cached_pair is not None: + stats["ram_hits"] = int(stats.get("ram_hits", 0)) + 1 + else: + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_read_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + cached_pair = _strategy3_load_mc_maps_from_disk( + disk_path, + sample_id=sample_id, + fingerprint=fingerprint, + mc_config_hash=mc_hash, + split=normalized_split, + ) + if cached_pair is not None: + cache[cache_key] = _strategy3_prepare_cached_sample_pair(*cached_pair) + stats["disk_hits"] = int(stats.get("disk_hits", 0)) + 1 + else: + sample_variance, sample_entropy = compute_sample_maps(sample_index) + cached_pair = _strategy3_prepare_cached_sample_pair(sample_variance, sample_entropy) + cache[cache_key] = cached_pair + stats["misses"] = int(stats.get("misses", 0)) + 1 + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_write_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + entry_exists = disk_path.exists() + _strategy3_save_mc_maps_to_disk( + disk_path, + { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "sample_id": str(sample_id), + "split": str(normalized_split), + "fingerprint": str(fingerprint), + "mc_config_hash": tuple(mc_hash), + "img_size": int(IMG_SIZE), + "dtype": "float32", + "created_at": datetime.now(timezone.utc).isoformat(), + "mc_variance": cached_pair[0], + "pred_entropy": cached_pair[1], + }, + ) + stats["writes"] = int(stats.get("writes", 0)) + 1 + if not entry_exists: + manifest_write_count += 1 + + cached_variance, cached_entropy = cached_pair + variance_samples.append(cached_variance.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + entropy_samples.append(cached_entropy.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + + if manifest_write_count > 0 and normalized_split is not None: + _strategy3_write_mc_cache_manifest( + mc_cache_run_dir, + fingerprint=fingerprint, + fingerprint_sources=dict(getattr(raw_model, "_strategy3_mc_cache_fingerprint_sources", {})), + mc_config_hash=mc_hash, + split=normalized_split, + split_write_count=manifest_write_count, + ) + + return torch.cat(variance_samples, dim=0), torch.cat(entropy_samples, dim=0) + +def _require_supported_strategy(strategy: int) -> int: + strategy = int(strategy) + if strategy not in SUPPORTED_STRATEGIES: + raise ValueError( + f"Unsupported strategy {strategy}. Supported strategies are {list(SUPPORTED_STRATEGIES)}." + ) + return strategy + +def _resolve_checkpoint_metric_name(metric_name: Any, *, strategy: int) -> str: + if not isinstance(metric_name, str) or not metric_name.strip(): + raise KeyError( + f"No best-checkpoint metric configured for strategy {strategy}. " + f"Set BEST_CHECKPOINT_METRICS[{strategy}] or best_checkpoint_metric_name to a non-empty metric name." + ) + metric_name = metric_name.strip() + if metric_name not in SUPPORTED_CHECKPOINT_METRICS: + raise KeyError( + f"Unsupported best-checkpoint metric {metric_name!r} for strategy {strategy}. " + f"Supported metrics: {sorted(SUPPORTED_CHECKPOINT_METRICS)}." + ) + return metric_name + +def _strategy_selection_metric_name(strategy: int) -> str: + strategy = _require_supported_strategy(strategy) + metric_name = _job_param( + f"strategy{strategy}_best_checkpoint_metric_name", + _job_param("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS.get(strategy)), + ) + return _resolve_checkpoint_metric_name(metric_name, strategy=strategy) + +def _strategy_selection_metric_value(strategy: int, metrics: dict[str, Any]) -> float: + metric_name = _strategy_selection_metric_name(strategy) + value = metrics.get(metric_name) + if value is None: + raise KeyError( + f"Configured best-checkpoint metric {metric_name!r} for strategy {strategy} " + f"is missing from metrics payload keys={sorted(metrics.keys())}." + ) + return float(value) + +def _early_stopping_monitor_name(strategy: int) -> str: + raw = str(_job_param("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR)).strip() + if not raw or raw.lower() == "auto": + return _strategy_selection_metric_name(strategy) + return raw + +def _early_stopping_mode(strategy: int, monitor_name: str | None = None) -> str: + raw = str(_job_param("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE)).strip().lower() + if raw in {"min", "max"}: + return raw + if raw != "auto": + raise ValueError(f"Unsupported early_stopping_mode={raw!r}. Expected 'auto', 'min', or 'max'.") + monitor_name = monitor_name or _early_stopping_monitor_name(strategy) + lowered = monitor_name.lower() + if "loss" in lowered or lowered.startswith("hd") or lowered.endswith("error"): + return "min" + return "max" + +def _early_stopping_min_delta() -> float: + return max(float(_job_param("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA)), 0.0) + +def _early_stopping_start_epoch() -> int: + return max(int(_job_param("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH)), 1) + +def _early_stopping_patience() -> int: + return max(int(_job_param("early_stopping_patience", EARLY_STOPPING_PATIENCE)), 0) + +def _early_stopping_monitor_value( + metrics: dict[str, Any], + *, + strategy: int, + monitor_name: str, +) -> float | None: + value = metrics.get(monitor_name) + if value is None and monitor_name == _strategy_selection_metric_name(strategy): + value = _strategy_selection_metric_value(strategy, metrics) + if value is None: + return None + return float(value) + +def _early_stopping_improved( + current_value: float, + best_value: float | None, + *, + mode: str, + min_delta: float, +) -> bool: + if best_value is None: + return True + if mode == "min": + return current_value < (best_value - min_delta) + if mode == "max": + return current_value > (best_value + min_delta) + raise ValueError(f"Unsupported early stopping comparison mode: {mode!r}") + +def _strategy3_requested_bootstrap_freeze() -> bool: + return bool( + _job_param( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + ) + +def _module_freeze_state(module: nn.Module | None) -> str: + if not isinstance(module, nn.Module): + return "n/a" + requires_grad_flags = [bool(param.requires_grad) for param in module.parameters()] + if not requires_grad_flags: + return "n/a" + if all(not flag for flag in requires_grad_flags): + return "frozen" + if all(requires_grad_flags): + return "trainable" + return "mixed" + +def _strategy3_bootstrap_freeze_status(model: nn.Module) -> dict[str, Any]: + raw = _raw_decoder_rl_model(model) + status = { + "bootstrap_loaded": False, + "freeze_requested": False, + "freeze_active": False, + "encoder_state": "n/a", + "decoder_state": "n/a", + "segmentation_head_state": "n/a", + } + if raw is None: + return status + + status["bootstrap_loaded"] = bool(getattr(raw, "strategy2_bootstrap_loaded", False)) + status["freeze_requested"] = bool(getattr(raw, "freeze_bootstrapped_segmentation", False)) + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + status["encoder_state"] = _module_freeze_state(getattr(smp_model, "encoder", None)) + status["decoder_state"] = _module_freeze_state(getattr(smp_model, "decoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(smp_model, "segmentation_head", None)) + else: + status["encoder_state"] = _module_freeze_state(getattr(raw, "encoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(raw, "segmentation_head", None)) + + relevant_states = [ + state + for state in ( + status["encoder_state"], + status["decoder_state"], + status["segmentation_head_state"], + ) + if state != "n/a" + ] + status["freeze_active"] = bool( + status["bootstrap_loaded"] and relevant_states and all(state == "frozen" for state in relevant_states) + ) + return status + +def _strategy3_decoder_is_frozen(model: nn.Module) -> bool: + return bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]) + +def _strategy3_loss_weights( + model: nn.Module, + *, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + decoder_ce_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(ce_weight) + decoder_dice_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(dice_weight) + return { + "decoder_ce": float(_job_param("strategy3_decoder_ce_weight", decoder_ce_default)), + "decoder_dice": float(_job_param("strategy3_decoder_dice_weight", decoder_dice_default)), + } + +def _strategy3_keep_frozen_modules_in_eval(model: nn.Module) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]): + return + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + module_names = ("encoder", "decoder", "segmentation_head") + module_root = smp_model + else: + module_names = ("encoder", "segmentation_head") + module_root = raw + for module_name in module_names: + module = getattr(module_root, module_name, None) + if isinstance(module, nn.Module): + module.eval() + +def _strategy3_apply_rollout_step( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + return apply_actions( + seg, + actions, + num_actions=int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)), + ).to(dtype=seg.dtype) + +def _refinement_deltas( + *, + action_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + small = float(_job_param("refine_delta_small", DEFAULT_REFINE_DELTA_SMALL)) + large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + if action_count == 3: + values = (-large, 0.0, small) + elif action_count == 4: + values = (-large, -small, 0.0, small) + elif action_count == 5: + values = (-large, -small, 0.0, small, large) + else: + raise ValueError( + f"Unsupported Strategy 3 action count {action_count}. " + "Expected one of {3, 4, 5}." + ) + return torch.tensor(values, device=device, dtype=dtype) + +def threshold_binary_mask(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).to(dtype=mask.dtype) + +def threshold_binary_long(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).long() + +"""============================================================================= +BUSI SPLIT + NORMALIZATION +============================================================================= +""" + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) +IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) + +def validate_image_mask_consistency(images_dir: Path, annotations_dir: Path): + image_files = {f for f in os.listdir(images_dir) if not f.startswith(".") and f.lower().endswith(".png")} + mask_files = {f for f in os.listdir(annotations_dir) if not f.startswith(".") and f.lower().endswith(".png")} + matched = sorted(image_files & mask_files) + missing_masks = sorted(image_files - mask_files) + missing_images = sorted(mask_files - image_files) + return matched, missing_masks, missing_images + +def parse_busi_with_classes_label(filename: str) -> str: + upper_name = str(filename).upper() + if upper_name.endswith("_B.PNG"): + return "benign" + if upper_name.endswith("_M.PNG"): + return "malignant" + raise ValueError( + f"BUSI_with_classes filename must end with '_B.png' or '_M.png', got {filename!r}" + ) + +def _candidate_report_dicts(payload: dict[str, Any]) -> list[dict[str, Any]]: + candidates = [payload] + for key in ("counts", "summary", "dataset", "report", "metadata"): + value = payload.get(key) + if isinstance(value, dict): + candidates.append(value) + return candidates + +def _extract_report_int(payload: dict[str, Any], keys: tuple[str, ...]) -> int | None: + for candidate in _candidate_report_dicts(payload): + for key in keys: + value = candidate.get(key) + if isinstance(value, bool): + continue + if isinstance(value, (int, np.integer)): + return int(value) + if isinstance(value, float) and float(value).is_integer(): + return int(value) + return None + +def _extract_report_filenames(payload: dict[str, Any]) -> set[str] | None: + for candidate in _candidate_report_dicts(payload): + filenames = candidate.get("filenames") + if isinstance(filenames, list) and all(isinstance(item, str) for item in filenames): + return set(filenames) + + pairs = candidate.get("pairs") + if isinstance(pairs, list): + extracted = {item["filename"] for item in pairs if isinstance(item, dict) and isinstance(item.get("filename"), str)} + if extracted: + return extracted + return None + +def validate_busi_with_classes_pipeline_report(report_path: Path, sample_records: list[dict[str, str]]) -> None: + if not report_path.exists(): + return + + payload = load_json(report_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict payload in {report_path}, found {type(payload).__name__}.") + + benign_count = sum(1 for record in sample_records if record.get("class_label") == "benign") + malignant_count = sum(1 for record in sample_records if record.get("class_label") == "malignant") + expected_counts = { + "total_pairs": len(sample_records), + "benign": benign_count, + "malignant": malignant_count, + } + report_counts = { + "total_pairs": _extract_report_int(payload, ("total_pairs", "pair_count", "num_pairs", "total")), + "benign": _extract_report_int(payload, ("benign", "benign_count", "num_benign")), + "malignant": _extract_report_int(payload, ("malignant", "malignant_count", "num_malignant")), + } + for key, expected_value in expected_counts.items(): + report_value = report_counts[key] + if report_value is not None and report_value != expected_value: + raise RuntimeError( + f"pipeline_check mismatch for {key}: discovered={expected_value}, report={report_value} ({report_path})" + ) + + report_filenames = _extract_report_filenames(payload) + if report_filenames is not None: + discovered_filenames = {record["filename"] for record in sample_records} + if report_filenames != discovered_filenames: + missing_from_report = sorted(discovered_filenames - report_filenames)[:10] + extra_in_report = sorted(report_filenames - discovered_filenames)[:10] + raise RuntimeError( + f"pipeline_check filenames mismatch for {report_path}: " + f"missing_from_report={missing_from_report}, extra_in_report={extra_in_report}" + ) + + print(f"[Pipeline Check] Validated BUSI_with_classes metadata from {report_path}") + +def check_data_leakage(splits: dict[str, list[str]]) -> dict[str, list[str]]: + leaks: dict[str, list[str]] = {} + split_names = list(splits.keys()) + for i, lhs in enumerate(split_names): + for rhs in split_names[i + 1 :]: + overlap = sorted(set(splits[lhs]) & set(splits[rhs])) + if overlap: + leaks[f"{lhs} ∩ {rhs}"] = overlap + return leaks + +def _project_relative_path(path: Path) -> str: + resolved = Path(path).resolve() + try: + return str(resolved.relative_to(PROJECT_DIR.resolve())) + except ValueError: + return str(resolved) + +def resolve_dataset_root_from_registry(split_registry: dict[str, Any]) -> Path: + dataset_root = Path(split_registry["dataset_root"]) + if dataset_root.is_absolute(): + return dataset_root + return (PROJECT_DIR / dataset_root).resolve() + +def make_sample_record( + filename: str, + images_subdir: str, + annotations_subdir: str, + *, + class_label: str | None = None, +) -> dict[str, str]: + record = { + "filename": filename, + "image_rel_path": str(Path(images_subdir) / filename), + "mask_rel_path": str(Path(annotations_subdir) / filename), + } + if class_label is not None: + record["class_label"] = class_label + return record + +def build_sample_records( + filenames: list[str], + *, + images_subdir: str, + annotations_subdir: str, + dataset_name: str, +) -> list[dict[str, str]]: + records = [] + for filename in sorted(filenames): + class_label = parse_busi_with_classes_label(filename) if dataset_name == "BUSI_with_classes" else None + records.append( + make_sample_record( + filename, + images_subdir, + annotations_subdir, + class_label=class_label, + ) + ) + return records + +def split_ratios_for_type(split_type: str) -> tuple[float, float]: + if split_type == "80_10_10": + return 0.80, 0.10 + if split_type == "70_10_20": + return 0.70, 0.10 + raise ValueError(f"Unsupported split_type: {split_type}") + +def deterministic_shuffle_records(records: list[dict[str, str]], *, seed: int, tag: str) -> list[dict[str, str]]: + rng = random.Random(seed + stable_int_from_text(tag)) + shuffled = [dict(record) for record in records] + rng.shuffle(shuffled) + return shuffled + +def train_subset_variant_suffix(variant: int | None = None) -> str: + variant_value = int(TRAIN_SUBSET_VARIANT if variant is None else variant) + return "" if variant_value <= 0 else f"_variant{variant_value:02d}" + +def group_records_by_class(sample_records: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + grouped: dict[str, list[dict[str, str]]] = {} + for record in sample_records: + class_label = record.get("class_label") + if class_label is None: + raise RuntimeError("Expected class_label in sample record for class-aware splitting.") + grouped.setdefault(class_label, []).append(dict(record)) + return grouped + +def allocate_counts_by_ratio(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + total_available = sum(available_counts.values()) + if total_available <= 0: + return allocation + + exact = {label: total_size * available_counts[label] / total_available for label in available_counts} + for label in available_counts: + allocation[label] = min(available_counts[label], int(math.floor(exact[label]))) + + remaining = min(total_size, total_available) - sum(allocation.values()) + order = sorted( + available_counts.keys(), + key=lambda label: (exact[label] - math.floor(exact[label]), available_counts[label], label), + reverse=True, + ) + while remaining > 0: + progressed = False + for label in order: + if allocation[label] < available_counts[label]: + allocation[label] += 1 + remaining -= 1 + progressed = True + if remaining == 0: + break + if not progressed: + break + return allocation + +def allocate_balanced_counts(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + labels = sorted(available_counts.keys()) + half = total_size // 2 + for label in labels: + allocation[label] = min(available_counts[label], half) + + remaining = min(total_size, sum(available_counts.values())) - sum(allocation.values()) + while remaining > 0: + candidates = [label for label in labels if allocation[label] < available_counts[label]] + if not candidates: + break + best_label = max( + candidates, + key=lambda label: ( + available_counts[label] - allocation[label], + 1 if label == "benign" else 0, + label, + ), + ) + allocation[best_label] += 1 + remaining -= 1 + return allocation + +def build_unstratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + records = deterministic_shuffle_records(sample_records, seed=seed, tag=f"base::{split_type}") + + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + return { + "train": records[:train_end], + "val": records[train_end:val_end], + "test": records[val_end:], + } + +def build_stratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + grouped = group_records_by_class(sample_records) + splits = {"train": [], "val": [], "test": []} + + for class_label in sorted(grouped.keys()): + records = deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"base::{split_type}::{class_label}", + ) + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + splits["train"].extend(records[:train_end]) + splits["val"].extend(records[train_end:val_end]) + splits["test"].extend(records[val_end:]) + + for split_name in splits: + splits[split_name] = deterministic_shuffle_records( + splits[split_name], + seed=seed, + tag=f"base::{split_type}::{split_name}", + ) + return splits + +def build_balanced_train_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + test_ratio = 1.0 - train_ratio - val_ratio + grouped = group_records_by_class(sample_records) + if sorted(grouped.keys()) != ["benign", "malignant"]: + raise RuntimeError( + f"balanced_train split policy expects benign/malignant classes, found {sorted(grouped.keys())}" + ) + + shuffled = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"base::{split_type}::balanced_train::{class_label}", + ) + for class_label, records in grouped.items() + } + + nominal_train_size = int(len(sample_records) * train_ratio) + per_class_train = min( + nominal_train_size // 2, + *(len(records) for records in shuffled.values()), + ) + + train_records: list[dict[str, str]] = [] + remaining_by_class: dict[str, list[dict[str, str]]] = {} + for class_label in sorted(shuffled.keys()): + records = shuffled[class_label] + train_records.extend(records[:per_class_train]) + remaining_by_class[class_label] = records[per_class_train:] + + remainder_val_fraction = val_ratio / max(val_ratio + test_ratio, 1e-8) + val_records: list[dict[str, str]] = [] + test_records: list[dict[str, str]] = [] + for class_label in sorted(remaining_by_class.keys()): + records = remaining_by_class[class_label] + val_count = int(len(records) * remainder_val_fraction) + val_records.extend(records[:val_count]) + test_records.extend(records[val_count:]) + + return { + "train": deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::train", + ), + "val": deterministic_shuffle_records( + val_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::val", + ), + "test": deterministic_shuffle_records( + test_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::test", + ), + } + +def build_nested_train_subsets( + train_records: list[dict[str, str]], + train_fractions: list[float], + *, + split_type: str, + seed: int, + split_policy: str | None = None, + subset_variant: int = 0, +) -> dict[str, list[dict[str, str]]]: + if not train_records: + return {} + + variant_tag = "" if int(subset_variant) <= 0 else f"::variant::{int(subset_variant)}" + ordered_records = deterministic_shuffle_records(train_records, seed=seed, tag=f"subset::{split_type}{variant_tag}") + use_class_labels = any("class_label" in record for record in train_records) + if not use_class_labels: + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + subsets[subset_key] = [dict(record) for record in ordered_records[:subset_size]] + return subsets + + grouped = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{class_label}{variant_tag}", + ) + for class_label, records in group_records_by_class(train_records).items() + } + available_counts = {class_label: len(records) for class_label, records in grouped.items()} + + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + if split_policy == "balanced_train": + class_counts = allocate_balanced_counts(subset_size, available_counts) + else: + class_counts = allocate_counts_by_ratio(subset_size, available_counts) + + subset_records: list[dict[str, str]] = [] + for class_label in sorted(grouped.keys()): + subset_records.extend([dict(record) for record in grouped[class_label][: class_counts[class_label]]]) + subsets[subset_key] = deterministic_shuffle_records( + subset_records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{subset_key}{variant_tag}", + ) + return subsets + +def train_fraction_from_subset_key(subset_key: str) -> float: + subset_text = str(subset_key).strip().lower() + if not subset_text: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") + try: + percent = Decimal(subset_text.replace("p", ".")) + except InvalidOperation as exc: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") from exc + if not percent.is_finite() or percent <= 0 or percent > 100: + raise RuntimeError(f"Train subset key {subset_key!r} must represent a percentage in the range (0, 100].") + return float(percent / Decimal("100")) + +def validate_persisted_split_no_leakage(split_type: str, split_entry: dict[str, Any], *, source: str) -> None: + base_splits = split_entry["base_splits"] + base_filenames: dict[str, list[str]] = {} + for split_name, records in base_splits.items(): + filenames = [record["filename"] for record in records] + if len(filenames) != len(set(filenames)): + raise RuntimeError(f"Duplicate filenames detected inside {split_name} for split_type={split_type}.") + base_filenames[split_name] = filenames + + leaks = check_data_leakage(base_filenames) + if leaks: + raise RuntimeError(f"Data leakage detected for split_type={split_type}: {list(leaks.keys())}") + + base_train = set(base_filenames["train"]) + previous_subset: set[str] = set() + for subset_key in sorted(split_entry["train_subsets"].keys(), key=train_fraction_from_subset_key): + subset_filenames = [record["filename"] for record in split_entry["train_subsets"][subset_key]] + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames detected inside train subset {subset_key} for split_type={split_type}." + ) + subset_set = set(subset_filenames) + missing = sorted(subset_set - base_train) + if missing: + raise RuntimeError( + f"Train subset {subset_key} contains files outside the base train split for split_type={split_type}." + ) + if previous_subset and not previous_subset.issubset(subset_set): + raise RuntimeError( + f"Train subsets are not nested for split_type={split_type}." + ) + previous_subset = subset_set + + print(f"[Split Check] No data leakage detected for split_type={split_type} ({source}).") + +def repair_persisted_train_subsets( + split_registry: dict[str, Any], + requested_train_fractions: list[float], + *, + split_json_path: Path, + seed: int, +) -> bool: + split_entries = split_registry.get("split_types", {}) + requested_fractions = normalize_dataset_percents(requested_train_fractions) + combined_fractions = {float(value) for value in split_registry.get("train_fractions", [])} + combined_fractions.update(requested_fractions) + dataset_name = str(split_registry.get("dataset_name", "BUSI")) + split_policy = split_registry.get("split_policy") if dataset_name == "BUSI_with_classes" else None + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + for subset_key in train_subsets.keys(): + combined_fractions.add(train_fraction_from_subset_key(subset_key)) + + combined_fractions_list = normalize_dataset_percents(list(combined_fractions)) + requested_keys = {percent_label(fraction) for fraction in requested_fractions} + registry_seed = int(split_registry.get("seed", seed)) + repaired = False + + if split_registry.get("train_fractions") != combined_fractions_list: + split_registry["train_fractions"] = combined_fractions_list + repaired = True + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + missing_requested_keys = sorted(requested_keys - set(train_subsets.keys()), key=train_fraction_from_subset_key) + if not missing_requested_keys: + continue + + split_entry["train_subsets"] = build_nested_train_subsets( + split_entry["base_splits"]["train"], + combined_fractions_list, + split_type=split_type, + seed=registry_seed, + split_policy=split_policy, + ) + print( + f"[Splits] Rebuilt missing train subsets {missing_requested_keys} " + f"for split_type={split_type} in {split_json_path}" + ) + repaired = True + + if repaired: + save_json(split_json_path, split_registry) + print(f"[Splits] Updated persisted dataset splits at {split_json_path}") + return repaired + +def load_or_create_dataset_splits( + images_dir: Path, + annotations_dir: Path, + split_json_path: Path, + train_fractions: list[float], + seed: int, +) -> tuple[dict[str, Any], str]: + train_fractions = normalize_dataset_percents(train_fractions) + images_dir = Path(images_dir).resolve() + annotations_dir = Path(annotations_dir).resolve() + split_json_path = Path(split_json_path).resolve() + dataset_name = current_dataset_name() + split_policy = current_busi_with_classes_split_policy() if dataset_name == "BUSI_with_classes" else None + if split_json_path.exists(): + split_registry = load_json(split_json_path) + if split_registry.get("version") != DATASET_SPLITS_VERSION: + raise RuntimeError( + f"Unsupported dataset_splits.json version in {split_json_path}. " + f"Expected version={DATASET_SPLITS_VERSION}." + ) + persisted_dataset_name = str(split_registry.get("dataset_name", "BUSI")) + if persisted_dataset_name != dataset_name: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets dataset_name={persisted_dataset_name!r}, " + f"but current DATASET_NAME={dataset_name!r}." + ) + persisted_split_policy = split_registry.get("split_policy") + if dataset_name == "BUSI_with_classes" and persisted_split_policy != split_policy: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets split_policy={persisted_split_policy!r}, " + f"but current BUSI_WITH_CLASSES_SPLIT_POLICY={split_policy!r}." + ) + split_entries = split_registry.get("split_types") + if not isinstance(split_entries, dict): + raise RuntimeError(f"Invalid split_types payload in {split_json_path}.") + for split_type in SUPPORTED_SPLIT_TYPES: + if split_type not in split_entries: + raise RuntimeError( + f"dataset_splits.json is missing split_type={split_type}. Delete it to regenerate cleanly." + ) + repaired = repair_persisted_train_subsets( + split_registry, + train_fractions, + split_json_path=split_json_path, + seed=seed, + ) + source = "repaired" if repaired else "loaded" + if dataset_name == "BUSI_with_classes": + sample_records = build_sample_records( + validate_image_mask_consistency(images_dir, annotations_dir)[0], + images_subdir=split_registry["images_subdir"], + annotations_subdir=split_registry["annotations_subdir"], + dataset_name=dataset_name, + ) + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + for split_type in SUPPORTED_SPLIT_TYPES: + validate_persisted_split_no_leakage(split_type, split_entries[split_type], source=source) + if repaired: + print(f"[Splits] Loaded and repaired persisted dataset splits from {split_json_path}") + else: + print(f"[Splits] Loaded persisted dataset splits from {split_json_path}") + return split_registry, source + + matched, missing_masks, missing_images = validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + images_subdir = images_dir.relative_to(dataset_root).as_posix() + annotations_subdir = annotations_dir.relative_to(dataset_root).as_posix() + sample_records = build_sample_records( + matched, + images_subdir=images_subdir, + annotations_subdir=annotations_subdir, + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + split_registry = { + "version": DATASET_SPLITS_VERSION, + "dataset_name": dataset_name, + "split_policy": split_policy, + "dataset_root": _project_relative_path(dataset_root), + "images_subdir": images_subdir, + "annotations_subdir": annotations_subdir, + "seed": seed, + "train_fractions": list(train_fractions), + "split_types": {}, + } + + for split_type in SUPPORTED_SPLIT_TYPES: + if dataset_name == "BUSI_with_classes": + if split_policy == "balanced_train": + base_splits = build_balanced_train_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_stratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_unstratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + train_subsets = build_nested_train_subsets( + base_splits["train"], + train_fractions, + split_type=split_type, + seed=seed, + split_policy=split_policy, + ) + split_entry = { + "split_type": split_type, + "base_splits": base_splits, + "train_subsets": train_subsets, + } + validate_persisted_split_no_leakage(split_type, split_entry, source="created") + split_registry["split_types"][split_type] = split_entry + + save_json(split_json_path, split_registry) + print(f"[Splits] Created persisted dataset splits at {split_json_path}") + return split_registry, "created" + +def select_persisted_split( + split_registry: dict[str, Any], + split_type: str, + train_fraction: float, +) -> dict[str, Any]: + if split_type not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"Unsupported split_type: {split_type}") + + split_entries = split_registry.get("split_types", {}) + if split_type not in split_entries: + raise KeyError( + f"Requested split_type={split_type} is not available in dataset_splits.json. " + "Delete the JSON file to regenerate it with the new configuration." + ) + + subset_key = percent_label(train_fraction) + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + if subset_key not in train_subsets: + raise KeyError( + f"Requested train fraction={train_fraction} (key={subset_key}) is not available in dataset_splits.json." + ) + + return { + "dataset_root": resolve_dataset_root_from_registry(split_registry), + "split_type": split_type, + "train_fraction": float(train_fraction), + "train_subset_key": subset_key, + "train_subset_variant": 0, + "train_subset_source": "persisted", + "base_train_records": split_entry["base_splits"]["train"], + "train_records": train_subsets[subset_key], + "val_records": split_entry["base_splits"]["val"], + "test_records": split_entry["base_splits"]["test"], + } + +def apply_train_subset_variant( + selected_split: dict[str, Any], + split_registry: dict[str, Any], + *, + subset_variant: int, +) -> dict[str, Any]: + variant = int(subset_variant) + if variant <= 0 or float(selected_split["train_fraction"]) >= 1.0: + return selected_split + + split_policy = split_registry.get("split_policy") if current_dataset_name() == "BUSI_with_classes" else None + variant_subsets = build_nested_train_subsets( + selected_split["base_train_records"], + [float(selected_split["train_fraction"])], + split_type=str(selected_split["split_type"]), + seed=int(split_registry.get("seed", SEED)), + split_policy=split_policy, + subset_variant=variant, + ) + subset_key = str(selected_split["train_subset_key"]) + updated_split = dict(selected_split) + updated_split["train_records"] = variant_subsets[subset_key] + updated_split["train_subset_variant"] = variant + updated_split["train_subset_source"] = "variant_override" + return updated_split + +def export_selected_split_manifest( + pct_root: Path, + *, + percent: float, + split_source: str, + selected_split: dict[str, Any], +) -> Path: + variant_suffix = train_subset_variant_suffix(int(selected_split.get("train_subset_variant", 0))) + manifest_path = pct_root / ( + f"selected_split_{selected_split['split_type']}_{percent_label(percent)}pct{variant_suffix}.json" + ) + payload = { + "dataset_name": current_dataset_name(), + "dataset_root": str(Path(selected_split["dataset_root"]).resolve()), + "dataset_percent": float(percent), + "dataset_percent_label": percent_label(percent), + "split_source": split_source, + "split_type": str(selected_split["split_type"]), + "train_fraction": float(selected_split["train_fraction"]), + "train_subset_key": str(selected_split["train_subset_key"]), + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(manifest_path.resolve()), + "base_train_records": [dict(record) for record in selected_split["base_train_records"]], + "train_records": [dict(record) for record in selected_split["train_records"]], + "val_records": [dict(record) for record in selected_split["val_records"]], + "test_records": [dict(record) for record in selected_split["test_records"]], + } + save_json(manifest_path, payload) + return manifest_path + +def compute_busi_statistics( + dataset_root: Path, + sample_records: list[dict[str, str]], + cache_path: Path, +) -> tuple[float, float, str]: + filenames = [record["filename"] for record in sample_records] + if cache_path.exists(): + stats = load_json(cache_path) + if stats.get("filenames") == filenames: + print(f"[Normalization] Loaded cached normalization stats from {cache_path}") + return float(stats["global_mean"]), float(stats["global_std"]), "loaded_from_cache" + + total_sum = np.float64(0.0) + total_sq_sum = np.float64(0.0) + total_pixels = 0 + + for record in tqdm(sample_records, desc="Computing BUSI train mean/std", leave=False): + image_path = dataset_root / record["image_rel_path"] + img = np.array(PILImage.open(image_path)).astype(np.float64) + total_sum += img.sum() + total_sq_sum += (img ** 2).sum() + total_pixels += img.size + + global_mean = float(total_sum / total_pixels) + global_std = float(np.sqrt(total_sq_sum / total_pixels - global_mean ** 2)) + if global_std < 1e-6: + global_std = 1.0 + + save_json( + cache_path, + { + "global_mean": global_mean, + "global_std": global_std, + "total_pixels": int(total_pixels), + "num_images": len(sample_records), + "filenames": filenames, + }, + ) + print(f"[Normalization] Computed and saved normalization stats to {cache_path}") + return global_mean, global_std, "computed_fresh" + +def compute_class_distribution(sample_records: list[dict[str, str]]) -> dict[str, int] | None: + if not sample_records or not any("class_label" in record for record in sample_records): + return None + return { + "benign": sum(1 for record in sample_records if record.get("class_label") == "benign"), + "malignant": sum(1 for record in sample_records if record.get("class_label") == "malignant"), + } + +def format_class_distribution(class_distribution: dict[str, int] | None) -> str: + if class_distribution is None: + return "unavailable" + benign = int(class_distribution.get("benign", 0)) + malignant = int(class_distribution.get("malignant", 0)) + total = benign + malignant + return f"benign={benign}, malignant={malignant}, total={total}" + +def print_loaded_class_distribution( + *, + split_type: str, + train_subset_key: str, + base_train_records: list[dict[str, str]], + train_records: list[dict[str, str]], + val_records: list[dict[str, str]], + test_records: list[dict[str, str]], +) -> None: + if not any("class_label" in record for record in train_records): + return + section(f"Loaded Class Distribution | {split_type} | {train_subset_key}%") + print(f"Base train classes : {format_class_distribution(compute_class_distribution(base_train_records))}") + print(f"Train subset classes : {format_class_distribution(compute_class_distribution(train_records))}") + print(f"Validation classes : {format_class_distribution(compute_class_distribution(val_records))}") + print(f"Test classes : {format_class_distribution(compute_class_distribution(test_records))}") + +def print_split_summary(payload: dict[str, Any]) -> None: + unit_name = "Phase" if payload.get("phase_index") is not None else "Split" + section(f"{unit_name} Summary | {payload['split_type']} | {payload['train_subset_key']}%") + print(f"Dataset name : {payload['dataset_name']}") + if payload.get("dataset_split_policy") is not None: + print(f"Dataset split policy : {payload['dataset_split_policy']}") + print(f"Dataset splits JSON : {payload['dataset_splits_path']}") + print(f"Split source : {payload['split_source']}") + print(f"Split type used : {payload['split_type']}") + if payload.get("split_generation_mode") is not None: + print(f"Split generation mode : {payload['split_generation_mode']}") + if payload.get("phase_index") is not None: + print(f"Phase index : {payload['phase_index']}") + print(f"Phase val/test folds : val={payload['phase_val_fold_index']}, test={payload['phase_test_fold_index']}") + if payload.get("percent_sampling_mode") is not None: + print(f"Percent sampling mode : {payload['percent_sampling_mode']}") + print(f"Train fraction : {payload['train_subset_key']}% of frozen base train") + print(f"Train subset variant : {payload.get('train_subset_variant', 0)}") + print(f"Train subset source : {payload.get('train_subset_source', 'persisted')}") + if payload.get("sampling_chain_dataset_percents") is not None: + print(f"Sampling chain percents: {payload['sampling_chain_dataset_percents']}") + print(f"Base train samples : {payload['base_train_count']}") + print(f"Train subset samples : {payload['train_count']}") + print(f"Validation samples : {payload['val_count']}") + print(f"Test samples : {payload['test_count']}") + if payload.get("base_train_class_distribution") is not None: + print(f"Base train classes : {format_class_distribution(payload['base_train_class_distribution'])}") + print(f"Train subset classes : {format_class_distribution(payload['train_class_distribution'])}") + print(f"Validation classes : {format_class_distribution(payload['val_class_distribution'])}") + print(f"Test classes : {format_class_distribution(payload['test_class_distribution'])}") + print(f"Validation/Test frozen : {payload['val_test_frozen']}") + print(f"Leakage check : {payload['leakage_check']}") + +def print_normalization_summary(payload: dict[str, Any]) -> None: + mode = "ImageNet mean/std" if USE_IMAGENET_NORM else "Dataset train mean/std" + print(f"Dataset name : {payload['dataset_name']}") + print(f"Normalization mode : {mode}") + print(f"Stats cache path : {payload['normalization_cache_path']}") + print(f"Stats source : {payload['normalization_source']}") + print(f"Split type used : {payload['split_type']}") + variant_suffix = train_subset_variant_suffix(int(payload.get("train_subset_variant", 0))) + print( + f"Stats computed from : {payload['train_count']} train samples " + f"({payload['train_subset_key']}%{variant_suffix})" + ) +# ============================================================================= +# IMAGE PREPARATION + DATASETS +# ============================================================================= + +def _to_three_channels(image: np.ndarray) -> np.ndarray: + if image.ndim == 2: + image = image[..., None] + if image.shape[2] == 1: + image = np.repeat(image, 3, axis=2) + elif image.shape[2] > 3: + image = image[..., :3] + return image + +def _prepare_image(raw: np.ndarray, global_mean: float, global_std: float) -> np.ndarray: + img = raw.astype(np.float32) + img = _to_three_channels(img) + if IMG_SIZE > 0 and (img.shape[0] != IMG_SIZE or img.shape[1] != IMG_SIZE): + img = np.array( + PILImage.fromarray(img.astype(np.uint8)).resize((IMG_SIZE, IMG_SIZE), PILImage.BILINEAR) + ).astype(np.float32) + if USE_IMAGENET_NORM: + if img.max() > 1.0: + img = img / 255.0 + img = (img - IMAGENET_MEAN) / IMAGENET_STD + else: + img = (img - global_mean) / global_std + return np.transpose(img, (2, 0, 1)).copy() + +def _prepare_mask(raw: np.ndarray) -> np.ndarray: + mask = raw.astype(np.uint8) + if mask.ndim == 3: + mask = mask[..., 0] + if IMG_SIZE > 0 and (mask.shape[0] != IMG_SIZE or mask.shape[1] != IMG_SIZE): + pil_mask = PILImage.fromarray(mask) + if pil_mask.mode != "L": + pil_mask = pil_mask.convert("L") + mask = np.array(pil_mask.resize((IMG_SIZE, IMG_SIZE), PILImage.NEAREST)) + return ((mask > 0).astype(np.float32))[None, ...].copy() + +def print_imagenet_normalization_status() -> bool: + uses_imagenet_norm = bool(USE_IMAGENET_NORM) + if uses_imagenet_norm: + print("✅🖼️ ImageNet normalization is ACTIVE in `_prepare_image`.") + else: + print("⚠️🧪 ImageNet normalization is NOT active in `_prepare_image`.") + print("⚠️📊 Using dataset global mean/std normalization instead.") + if SMP_ENCODER_WEIGHTS == "imagenet" and not uses_imagenet_norm: + print("⚠️🚨 Encoder weights are set to ImageNet, but ImageNet normalization is disabled.") + return uses_imagenet_norm + +def _gaussian_kernel1d( + sigma: float, + *, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + if sigma <= 0: + return torch.ones(1, device=device, dtype=dtype) + radius = max(int(math.ceil(3.0 * sigma)), 1) + coords = torch.arange(-radius, radius + 1, device=device, dtype=dtype) + kernel = torch.exp(-(coords.square()) / max(2.0 * sigma * sigma, 1e-6)) + return kernel / kernel.sum().clamp_min(1e-12) + +def _smooth_displacement_field(field: torch.Tensor, sigma: float) -> torch.Tensor: + kernel = _gaussian_kernel1d(sigma, device=field.device, dtype=field.dtype) + if kernel.numel() == 1: + return field + radius = kernel.numel() // 2 + kernel_y = kernel.view(1, 1, -1, 1) + kernel_x = kernel.view(1, 1, 1, -1) + field = F.conv2d(field, kernel_y, padding=(radius, 0)) + field = F.conv2d(field, kernel_x, padding=(0, radius)) + return field + +def _apply_elastic_deformation( + image: torch.Tensor, + mask: torch.Tensor, + *, + alpha: float = 8.0, + sigma: float = 4.0, +) -> tuple[torch.Tensor, torch.Tensor]: + _, h, w = image.shape + if h < 2 or w < 2: + return image.contiguous(), mask.contiguous() + + device = image.device + dtype = image.dtype + dx = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + dy = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + + yy, xx = torch.meshgrid( + torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype), + torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype), + indexing="ij", + ) + grid = torch.stack((xx, yy), dim=-1).unsqueeze(0) + grid[..., 0] = grid[..., 0] + dx.squeeze(0).squeeze(0) * (2.0 / max(w - 1, 1)) + grid[..., 1] = grid[..., 1] + dy.squeeze(0).squeeze(0) * (2.0 / max(h - 1, 1)) + grid = grid.clamp(-1.25, 1.25) + + image_out = F.grid_sample( + image.unsqueeze(0), + grid, + mode="bilinear", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + mask_out = F.grid_sample( + mask.unsqueeze(0), + grid, + mode="nearest", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + return image_out.contiguous(), mask_out.clamp(0.0, 1.0).contiguous() + +def _apply_minimal_train_aug(image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(2,)) + mask = torch.flip(mask, dims=(2,)) + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(1,)) + mask = torch.flip(mask, dims=(1,)) + if torch.rand(1).item() < 0.5: + k = 1 if torch.rand(1).item() < 0.5 else 3 + image = torch.rot90(image, k=k, dims=(1, 2)) + mask = torch.rot90(mask, k=k, dims=(1, 2)) + elastic_aug_prob = float(_job_param("elastic_aug_prob", 0.0)) + if elastic_aug_prob > 0 and torch.rand(1).item() < elastic_aug_prob: + image, mask = _apply_elastic_deformation(image, mask) + return image.contiguous(), mask.contiguous() + +class BUSIDataset(Dataset): + def __init__( + self, + sample_records: list[dict[str, str]], + dataset_root: Path, + global_mean: float, + global_std: float, + *, + preload: bool, + augment: bool, + split_name: str, + ) -> None: + super().__init__() + self.sample_records = [dict(record) for record in sample_records] + self.dataset_root = Path(dataset_root) + self.global_mean = float(global_mean) + self.global_std = float(global_std) + self.preload = preload + self.augment = augment + self.split_name = split_name + self._images: list[torch.Tensor] = [] + self._masks: list[torch.Tensor] = [] + self._raw_cache_bytes = 0 + + if not self.preload: + raise ValueError("PRELOAD_TO_RAM is mandatory in this RunPod runner.") + self._preload_to_ram() + + def _preload_to_ram(self) -> None: + desc = f"Preloading {self.split_name} ({len(self.sample_records)} samples) to RAM" + for record in tqdm(self.sample_records, desc=desc, leave=False): + raw_img = np.array(PILImage.open(self.dataset_root / record["image_rel_path"])) + raw_mask = np.array(PILImage.open(self.dataset_root / record["mask_rel_path"])) + if raw_img.shape[:2] != raw_mask.shape[:2]: + raise RuntimeError( + f"Image/mask spatial size mismatch for {record['filename']}: " + f"image={raw_img.shape[:2]}, mask={raw_mask.shape[:2]}" + ) + image = torch.from_numpy(_prepare_image(raw_img, self.global_mean, self.global_std)) + mask = torch.from_numpy(_prepare_mask(raw_mask)) + self._raw_cache_bytes += tensor_bytes(image) + tensor_bytes(mask) + self._images.append(image) + self._masks.append(mask) + + def __len__(self) -> int: + return len(self.sample_records) + + def __getitem__(self, index: int) -> dict[str, Any]: + image = self._images[index].clone() + mask = self._masks[index].clone() + if self.augment: + image, mask = _apply_minimal_train_aug(image, mask) + return { + "image": image, + "mask": mask, + "sample_id": Path(self.sample_records[index]["filename"]).stem, + "dataset": current_dataset_name(), + } + + @property + def cache_bytes(self) -> int: + return self._raw_cache_bytes + +class CUDAPrefetcher: + def __init__(self, loader: DataLoader, device: torch.device) -> None: + self.loader = loader + self.device = device + self._use_cuda = device.type == "cuda" + self._iter = None + self._stream = None + self._next_batch = None + + def __len__(self) -> int: + return len(self.loader) + + def __iter__(self): + self._iter = iter(self.loader) + self._stream = torch.cuda.Stream(device=self.device) if self._use_cuda else None + self._next_batch = None + self._preload() + return self + + def close(self) -> None: + self._next_batch = None + self._iter = None + self._stream = None + + def _preload(self) -> None: + if self._iter is None: + self._next_batch = None + return + try: + self._next_batch = next(self._iter) + except StopIteration: + self._next_batch = None + return + if self._use_cuda: + assert self._stream is not None + with torch.cuda.stream(self._stream): + self._next_batch = to_device(self._next_batch, self.device) + else: + self._next_batch = to_device(self._next_batch, self.device) + + def __next__(self): + if self._next_batch is None: + self.close() + raise StopIteration + if self._use_cuda: + assert self._stream is not None + torch.cuda.current_stream(self.device).wait_stream(self._stream) + batch = self._next_batch + self._preload() + if self._next_batch is None: + self._iter = None + self._stream = None + return batch + +class DataBundle: + def __init__( + self, + *, + percent: float, + split_payload: dict[str, Any], + train_ds: BUSIDataset, + val_ds: BUSIDataset, + test_ds: BUSIDataset, + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + ) -> None: + self.percent = percent + self.split_payload = split_payload + self.train_ds = train_ds + self.val_ds = val_ds + self.test_ds = test_ds + self.train_loader = train_loader + self.val_loader = val_loader + self.test_loader = test_loader + + @property + def global_mean(self) -> float: + return float(self.split_payload["global_mean"]) + + @property + def global_std(self) -> float: + return float(self.split_payload["global_std"]) + + @property + def total_cache_bytes(self) -> int: + return self.train_ds.cache_bytes + self.val_ds.cache_bytes + self.test_ds.cache_bytes + +def make_loader(dataset: Dataset, shuffle: bool, *, loader_tag: str) -> DataLoader: + num_workers = NUM_WORKERS + persistent_workers = USE_PERSISTENT_WORKERS and num_workers > 0 + pin_memory = USE_PIN_MEMORY and DEVICE.type == "cuda" + generator = make_seeded_generator(SEED, loader_tag) + return DataLoader( + dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, + persistent_workers=persistent_workers, + worker_init_fn=seed_worker, + generator=generator, + ) + +def build_data_bundle(percent: float, split_registry: dict[str, Any], split_source: str) -> DataBundle: + pct_label = percent_label(percent) + pct_text = percent_text(percent) + selected_split = select_persisted_split(split_registry, SPLIT_TYPE, percent) + selected_split = apply_train_subset_variant( + selected_split, + split_registry, + subset_variant=TRAIN_SUBSET_VARIANT, + ) + pct_root = ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{pct_label}") + split_manifest_path = export_selected_split_manifest( + pct_root, + percent=percent, + split_source=split_source, + selected_split=selected_split, + ) + stats_cache_path = pct_root / ( + f"norm_stats_{normalization_cache_tag()}_{SPLIT_TYPE}_{pct_label}pct" + f"{train_subset_variant_suffix(int(selected_split.get('train_subset_variant', 0)))}.json" + ) + base_train_class_distribution = compute_class_distribution(selected_split["base_train_records"]) + train_class_distribution = compute_class_distribution(selected_split["train_records"]) + val_class_distribution = compute_class_distribution(selected_split["val_records"]) + test_class_distribution = compute_class_distribution(selected_split["test_records"]) + print_loaded_class_distribution( + split_type=selected_split["split_type"], + train_subset_key=selected_split["train_subset_key"], + base_train_records=selected_split["base_train_records"], + train_records=selected_split["train_records"], + val_records=selected_split["val_records"], + test_records=selected_split["test_records"], + ) + dataset_root = Path(selected_split["dataset_root"]).resolve() + global_mean, global_std, normalization_source = compute_busi_statistics( + dataset_root=dataset_root, + sample_records=selected_split["train_records"], + cache_path=stats_cache_path, + ) + + split_payload = { + "dataset_name": current_dataset_name(), + "dataset_split_policy": split_registry.get("split_policy"), + "dataset_splits_path": str(current_dataset_splits_json_path().resolve()), + "dataset_root": str(dataset_root), + "split_source": split_source, + "split_type": SPLIT_TYPE, + "dataset_percent": percent, + "train_subset_key": selected_split["train_subset_key"], + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(split_manifest_path.resolve()), + "base_train_count": len(selected_split["base_train_records"]), + "train_count": len(selected_split["train_records"]), + "val_count": len(selected_split["val_records"]), + "test_count": len(selected_split["test_records"]), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(stats_cache_path.resolve()), + "normalization_source": normalization_source, + } + + print_split_summary(split_payload) + print_normalization_summary(split_payload) + + train_ds = BUSIDataset( + selected_split["train_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=True, + split_name=f"train {SPLIT_TYPE} {pct_text}", + ) + val_ds = BUSIDataset( + selected_split["val_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"val {SPLIT_TYPE}", + ) + test_ds = BUSIDataset( + selected_split["test_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"test {SPLIT_TYPE}", + ) + + bundle = DataBundle( + percent=percent, + split_payload=split_payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=make_loader(train_ds, shuffle=True, loader_tag=f"{SPLIT_TYPE}:{pct_label}:train"), + val_loader=make_loader(val_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:val"), + test_loader=make_loader(test_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:test"), + ) + print_preload_summary(bundle) + return bundle + +def print_preload_summary(bundle: DataBundle) -> None: + section( + f"RAM Preload Summary | {bundle.split_payload['split_type']} | {int(bundle.percent * 100)}%" + ) + print(f"Train samples : {len(bundle.train_ds)}") + print(f"Val samples : {len(bundle.val_ds)}") + print(f"Test samples : {len(bundle.test_ds)}") + print(f"Train batches : {len(bundle.train_loader)}") + print(f"Val batches : {len(bundle.val_loader)}") + print(f"Test batches : {len(bundle.test_loader)}") + print(f"Global mean : {bundle.global_mean:.6f}") + print(f"Global std : {bundle.global_std:.6f}") + first = bundle.train_ds[0] + print(f"Sample image shape : {tuple(first['image'].shape)}") + print(f"Sample mask shape : {tuple(first['mask'].shape)}") + print(f"Sample image dtype : {first['image'].dtype}") + print(f"Sample mask dtype : {first['mask'].dtype}") + print(f"Estimated RAM preload : {bytes_to_gb(bundle.total_cache_bytes):.3f} GB") + +"""============================================================================= +MODEL DEFINITIONS +============================================================================= +""" + +def strategy_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + strategy = _require_supported_strategy(strategy) + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + if strategy == 2: + return "Strategy 2: Custom VGG + Segmentation Head (Supervised)" + if strategy == 3: + return "Strategy 3 Lite: Custom VGG + Segmentation Head + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + + if strategy == 2: + return f"Strategy 2: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) supervised" + if strategy == 3: + return f"Strategy 3 Lite: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + +def _apply_omega_conv(omega_conv: nn.Conv2d, value_next: torch.Tensor) -> torch.Tensor: + weight = omega_conv.weight + value_next = value_next.to(device=weight.device, dtype=weight.dtype) + return omega_conv(value_next) + +def _conv3x3(in_ch: int, out_ch: int, dilation: int = 1) -> nn.Conv2d: + return nn.Conv2d( + in_ch, + out_ch, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=True, + ) + +class _ConvBlock(nn.Module): + def __init__( + self, + in_ch: int, + out_ch: int, + dilation: int = 1, + *, + num_groups: int = 0, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.conv = _conv3x3(in_ch, out_ch, dilation=dilation) + self.norm = _group_norm(out_ch, num_groups=num_groups) if num_groups > 0 else nn.Identity() + self.act = nn.ReLU(inplace=True) + self.drop = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.act(self.norm(self.conv(x)))) + +def _group_norm(num_channels: int, *, num_groups: int = GN_NUM_GROUPS) -> nn.GroupNorm: + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + return nn.GroupNorm(groups, num_channels) + + +class _MultiScaleRefineBranch(nn.Module): + """Processes raw encoder features at each scale independently, then fuses + them into a single feature map. This gives the refinement head access to + multi-resolution spatial cues (edges at low levels, semantics at high + levels) that the 1x1 projection squashes away.""" + + def __init__( + self, + encoder_channels: list[int] | tuple[int, ...], + out_channels: int, + per_scale_channels: int = 32, + ) -> None: + super().__init__() + self._valid_indices: list[int] = [i for i, c in enumerate(encoder_channels) if c > 0] + self.scale_convs = nn.ModuleList() + for i in self._valid_indices: + self.scale_convs.append(nn.Sequential( + nn.Conv2d(encoder_channels[i], per_scale_channels, kernel_size=1, bias=False), + _group_norm(per_scale_channels), + nn.ReLU(inplace=True), + )) + total_ch = per_scale_channels * len(self._valid_indices) + self.fuse = nn.Sequential( + nn.Conv2d(total_ch, out_channels, kernel_size=3, padding=1, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2, dilation=2, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + ) + self._init_small() + + def _init_small(self) -> None: + """Small-magnitude init so the branch starts as a near-zero residual.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + m.weight.data.mul_(0.1) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + parts: list[torch.Tensor] = [] + for idx, conv in zip(self._valid_indices, self.scale_convs): + out = conv(encoder_features[idx]) + if out.shape[-2] != h or out.shape[-1] != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + parts.append(out) + return self.fuse(torch.cat(parts, dim=1)) + + +class SelfAttentionModule(nn.Module): + def __init__(self, channels: int) -> None: + super().__init__() + mid = max(channels // 8, 1) + self.query = nn.Conv2d(channels, mid, 1) + self.key = nn.Conv2d(channels, mid, 1) + self.value = nn.Conv2d(channels, channels, 1) + self.gamma = nn.Parameter(torch.tensor([0.1], dtype=torch.float32)) + + def forward(self, f: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + b, c, h, w = f.shape + pooled = f + target_grid = max(int(_job_param("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID)), 1) + if target_grid < max(h, w): + stride_h = max(1, math.ceil(h / target_grid)) + stride_w = max(1, math.ceil(w / target_grid)) + pooled = F.avg_pool2d(f, kernel_size=(stride_h, stride_w), stride=(stride_h, stride_w)) + if target_grid >= 64 and target_grid not in _STRATEGY3_SAM_GRID_WARNED: + print( + "[Strategy3] Self-attention grid " + f"{target_grid}x{target_grid} requested; this implies a much heavier attention matrix " + "(for example 64x64 -> 4096 tokens). Lower strategy3_sam_attention_grid if this is too slow." + ) + _STRATEGY3_SAM_GRID_WARNED.add(target_grid) + + ph, pw = pooled.shape[-2:] + q = self.query(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + k = self.key(pooled).view(b, -1, ph * pw) + v = self.value(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + attn = torch.softmax(q @ k / (q.shape[-1] ** 0.5), dim=-1) + out = (attn @ v).permute(0, 2, 1).view(b, c, ph, pw) + if ph != h or pw != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + return f + self.gamma * out, attn + + def forward_features(self, f: torch.Tensor) -> torch.Tensor: + out, _ = self.forward(f) + return out + +class DilatedPolicyHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.classifier = nn.Conv2d(64, NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + bias = torch.full((NUM_ACTIONS,), -2.0, dtype=torch.float32) + keep_index = NUM_ACTIONS // 2 if NUM_ACTIONS >= 3 else NUM_ACTIONS - 1 + bias[keep_index] = 2.0 + with torch.no_grad(): + self.classifier.bias.copy_(bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class DilatedValueHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.readout = nn.Conv2d(64, 1, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.body(x) + return self.readout(features) + +def replace_bn_with_gn(model: nn.Module, num_groups: int = 8) -> nn.Module: + for name, module in model.named_children(): + if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)): + num_channels = module.num_features + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + setattr(model, name, nn.GroupNorm(groups, num_channels, eps=module.eps, affine=module.affine)) + else: + replace_bn_with_gn(module, num_groups=num_groups) + return model + +def _ensure_transunet_repo_on_path() -> None: + repo_dir = TRANSUNET_REPO_DIR.resolve() + if not repo_dir.is_dir(): + raise FileNotFoundError( + f"TransUNet repo not found at {repo_dir}. Expected the official repo in " + f"{TRANSUNET_REPO_DIR}." + ) + repo_str = str(repo_dir) + if repo_str not in sys.path: + sys.path.insert(0, repo_str) + + +def _load_transunet_components() -> tuple[Any, dict[str, Any]]: + global _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + if _TRANSUNET_VISION_TRANSFORMER is None or _TRANSUNET_CONFIGS is None: + _ensure_transunet_repo_on_path() + try: + vit_module = importlib.import_module("networks.vit_seg_modeling") + except Exception as exc: + raise RuntimeError( + "Unable to import the official TransUNet modules. Ensure the TransUNet repo is present " + "and dependencies such as ml_collections, scipy, and torch are installed." + ) from exc + _TRANSUNET_VISION_TRANSFORMER = getattr(vit_module, "VisionTransformer") + _TRANSUNET_CONFIGS = getattr(vit_module, "CONFIGS") + return _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + + +def _transunet_tensor_changed(before: torch.Tensor, after: torch.Tensor) -> bool: + return not torch.equal(before, after) + + +def _load_and_verify_transunet_checkpoint( + vit_model: nn.Module, + *, + pretrained_path: Path, + img_size: int, + n_skip: int, +) -> dict[str, Any]: + checkpoint_path = Path(pretrained_path).expanduser().resolve() + if not checkpoint_path.is_file(): + raise FileNotFoundError( + f"TransUNet checkpoint not found at {checkpoint_path}. " + f"Expected ImageNet weights at {TRANSUNET_PRETRAINED_PATH.resolve()}." + ) + + weights = np.load(checkpoint_path, allow_pickle=False) + try: + missing_keys = [key for key in _TRANSUNET_REQUIRED_NPZ_KEYS if key not in weights] + if missing_keys: + raise RuntimeError( + f"TransUNet checkpoint {checkpoint_path} is missing required arrays: {missing_keys}" + ) + + position_embeddings = vit_model.transformer.embeddings.position_embeddings + root_conv = vit_model.transformer.embeddings.hybrid_model.root.conv.weight + block0_query = vit_model.transformer.encoder.layer[0].attn.query.weight + + pos_before = position_embeddings.detach().cpu().clone() + root_before = root_conv.detach().cpu().clone() + query_before = block0_query.detach().cpu().clone() + + posemb_source_shape = tuple(weights["Transformer/posembed_input/pos_embedding"].shape) + posemb_target_shape = tuple(position_embeddings.shape) + array_count = len(getattr(weights, "files", [])) + file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024) + + vit_model.load_from(weights=weights) + + pos_after = position_embeddings.detach().cpu() + root_after = root_conv.detach().cpu() + query_after = block0_query.detach().cpu() + + updated = { + "PosEmbed updated": _transunet_tensor_changed(pos_before, pos_after), + "ResNet root conv updated": _transunet_tensor_changed(root_before, root_after), + "ViT block-0 query updated": _transunet_tensor_changed(query_before, query_after), + } + + section("TransUNet Checkpoint Verification") + print("[TransUNet] OK Loaded R50+ViT-B/16 ImageNet checkpoint") + print(f"[TransUNet] File : {checkpoint_path} ({file_size_mb:.1f} MB)") + print(f"[TransUNet] NPZ arrays : {array_count}") + print( + "[TransUNet] PosEmbed shape : " + f"src {posemb_source_shape} -> tgt {posemb_target_shape}" + f"{' (interpolated)' if posemb_source_shape != posemb_target_shape else ''}" + ) + for label, status in updated.items(): + print(f"[TransUNet] {label:<22}: {status}") + print( + "[TransUNet] " + f"img_size={img_size}, patches.grid=({img_size // 16}, {img_size // 16}), " + f"n_skip={n_skip}, n_classes=1" + ) + + failed = [label for label, status in updated.items() if not status] + if failed: + raise RuntimeError( + "TransUNet checkpoint load verification failed. The following tensors were unchanged after " + f"load_from(...): {failed}. Training was stopped to avoid using a randomly initialized model." + ) + + return { + "checkpoint_path": str(checkpoint_path), + "array_count": array_count, + "file_size_mb": file_size_mb, + "posemb_source_shape": posemb_source_shape, + "posemb_target_shape": posemb_target_shape, + "updated": updated, + } + finally: + close_fn = getattr(weights, "close", None) + if callable(close_fn): + close_fn() + + +class _TransUNetEncoder(nn.Module): + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.transformer = transformer + self.out_channels = (3, 64, 256, 512, 768) + self._vit_token_cache: torch.Tensor | None = None + self._decoder_skip_cache: list[torch.Tensor] | None = None + + def _clear_cache(self) -> None: + self._vit_token_cache = None + self._decoder_skip_cache = None + + def decoder_inputs(self) -> tuple[torch.Tensor, list[torch.Tensor]]: + if self._vit_token_cache is None or self._decoder_skip_cache is None: + raise RuntimeError( + "TransUNet decoder was called before the encoder cache was populated. " + "Call the encoder first in the current forward pass." + ) + return self._vit_token_cache, self._decoder_skip_cache + + def forward(self, x: torch.Tensor) -> list[torch.Tensor]: + self._clear_cache() + if x.shape[1] == 1: + model_input = x.repeat(1, 3, 1, 1) + elif x.shape[1] == 3: + model_input = x + else: + raise ValueError(f"TransUNet expects 1 or 3 input channels, got {x.shape[1]}.") + + embedding_output, hybrid_features = self.transformer.embeddings(model_input) + hidden_states, _ = self.transformer.encoder(embedding_output) + if hybrid_features is None or len(hybrid_features) < 3: + raise RuntimeError( + "TransUNet hybrid ResNet features were not produced as expected." + ) + + deepest_skip, mid_skip, shallow_skip = hybrid_features[:3] + batch_size, n_patch, hidden_dim = hidden_states.shape + side = math.isqrt(n_patch) + if side * side != n_patch: + raise RuntimeError( + f"TransUNet token grid is not square: n_patch={n_patch}." + ) + vit_out = hidden_states.permute(0, 2, 1).contiguous().view(batch_size, hidden_dim, side, side) + + self._vit_token_cache = hidden_states + self._decoder_skip_cache = [deepest_skip, mid_skip, shallow_skip] + return [model_input, shallow_skip, mid_skip, deepest_skip, vit_out] + + +class _TransUNetDecoder(nn.Module): + def __init__(self, decoder_core: nn.Module, encoder: _TransUNetEncoder) -> None: + super().__init__() + self.decoder_core = decoder_core + self._encoder_ref = weakref.ref(encoder) + + def _encoder(self) -> _TransUNetEncoder: + encoder = self._encoder_ref() + if encoder is None: + raise RuntimeError("TransUNet encoder reference is no longer available.") + return encoder + + def forward(self, *features: torch.Tensor) -> torch.Tensor: + del features + hidden_states, skip_features = self._encoder().decoder_inputs() + return self.decoder_core(hidden_states, features=skip_features) + + +class TransUNetSMPAdapter(nn.Module): + def __init__(self, *, img_size: int, pretrained_path: Path) -> None: + super().__init__() + if img_size % 16 != 0: + raise ValueError(f"TransUNet requires img_size divisible by 16, got {img_size}.") + + vision_transformer_cls, configs = _load_transunet_components() + if TRANSUNET_VIT_NAME not in configs: + raise KeyError( + f"TransUNet config {TRANSUNET_VIT_NAME!r} not found in the official repo." + ) + + config_vit = copy.deepcopy(configs[TRANSUNET_VIT_NAME]) + config_vit.n_classes = 1 + config_vit.n_skip = TRANSUNET_N_SKIP + config_vit.classifier = "seg" + config_vit.patches.grid = (img_size // 16, img_size // 16) + + vit_model = vision_transformer_cls(config_vit, img_size=img_size, num_classes=1) + self.checkpoint_summary = _load_and_verify_transunet_checkpoint( + vit_model, + pretrained_path=pretrained_path, + img_size=img_size, + n_skip=TRANSUNET_N_SKIP, + ) + self.encoder = _TransUNetEncoder(vit_model.transformer) + self.decoder = _TransUNetDecoder(vit_model.decoder, self.encoder) + self.segmentation_head = vit_model.segmentation_head + self.classification_head = None + self.transunet_config = config_vit + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.encoder(x) + decoder_output = run_smp_decoder(self.decoder, encoder_features) + logits = self.segmentation_head(decoder_output) + return logits + +class HalfVGG16DilatedExtractor(nn.Module): + def __init__(self, *, dilation: int = 1, num_scales: int = 3) -> None: + super().__init__() + self.num_scales = num_scales + deep_dropout = 0.1 + + self.conv1_1 = _ConvBlock(3, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv1_2 = _ConvBlock(32, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv2_1 = _ConvBlock(32, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv2_2 = _ConvBlock(64, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv3_1 = _ConvBlock(64, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_2 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_3 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.conv4_1 = _ConvBlock(128, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_2 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_3 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + @property + def out_channels(self) -> int: + return (32 + 64 + 128) if self.num_scales == 3 else (32 + 64 + 128 + 256) + + @property + def pyramid_channels(self) -> list[int]: + return [32, 64, 128] if self.num_scales == 3 else [32, 64, 128, 256] + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + x = self.conv1_1(x) + src1 = self.conv1_2(x) + x = self.pool(src1) + + x = self.conv2_1(x) + src2 = self.conv2_2(x) + x = self.pool(src2) + + x = self.conv3_1(x) + x = self.conv3_2(x) + src3 = self.conv3_3(x) + + if self.num_scales == 3: + return [src1, src2, src3] + + x = self.pool(src3) + x = self.conv4_1(x) + x = self.conv4_2(x) + src4 = self.conv4_3(x) + return [src1, src2, src3, src4] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + pyramid = self.forward_pyramid(x) + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + +class CustomVGGEncoderWrapper(nn.Module): + def __init__(self, *, num_scales: int, dilation: int) -> None: + super().__init__() + self.encoder = HalfVGG16DilatedExtractor(dilation=dilation, num_scales=num_scales) + self.projection = None + + @property + def out_channels(self) -> int: + return self.encoder.out_channels + + @property + def pyramid_channels(self) -> list[int]: + return self.encoder.pyramid_channels + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + return self.encoder.forward_pyramid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.encoder(x) + +class SMPEncoderWrapper(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + depth: int, + in_channels: int, + proj_dim: int, + ) -> None: + super().__init__() + self.encoder = smp.encoders.get_encoder( + encoder_name, + in_channels=in_channels, + depth=depth, + weights=encoder_weights, + ) + if REPLACE_BN_WITH_GN: + replace_bn_with_gn(self.encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + self._out_channels = proj_dim + else: + self.projection = None + self._out_channels = raw_channels + + @property + def out_channels(self) -> int: + return self._out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + features = self.encoder(x) + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + +class VGGDecoderBlock(nn.Module): + def __init__(self, *, in_channels: int, skip_channels: int, out_channels: int) -> None: + super().__init__() + self.block = nn.Sequential( + _ConvBlock(in_channels + skip_channels, out_channels, num_groups=GN_NUM_GROUPS), + _ConvBlock(out_channels, out_channels, num_groups=GN_NUM_GROUPS), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = F.interpolate(x, size=skip.shape[-2:], mode="bilinear", align_corners=False) + return self.block(torch.cat([x, skip], dim=1)) + +class VGGSegmentationHead(nn.Module): + def __init__(self, *, pyramid_channels: list[int], dropout_p: float) -> None: + super().__init__() + if len(pyramid_channels) not in {3, 4}: + raise ValueError(f"Expected 3 or 4 VGG pyramid channels, got {pyramid_channels}") + + self.dropout = nn.Dropout2d(p=dropout_p) + self.num_scales = len(pyramid_channels) + + deepest = pyramid_channels[-1] + self.bridge = _ConvBlock(deepest, deepest, num_groups=GN_NUM_GROUPS) + if self.num_scales == 4: + self.up3 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[2], out_channels=128) + self.up2 = VGGDecoderBlock(in_channels=128, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + else: + self.up2 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + self.out_conv = nn.Conv2d(32, 1, kernel_size=1) + + def forward(self, pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + features = list(pyramid) + x = self.bridge(self.dropout(features[-1])) + if self.num_scales == 4: + x = self.up3(x, features[2]) + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + else: + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + return self.out_conv(x) + +class PixelDRLMG_SMP(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = SMPEncoderWrapper( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + depth=encoder_depth, + in_channels=3, + proj_dim=proj_dim, + ) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + features = self.extractor(x) + return self.sam(features) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class PixelDRLMG_VGG(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.extractor(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class SupervisedSMPModel(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int = 5, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + self.dropout = nn.Dropout2d(p=dropout_p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.smp_encoder(x) + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + decoder_output = self.dropout(decoder_output) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + +class SupervisedVGGModel(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + +class RefinementPolicyHead(nn.Module): + A3C_NUM_ACTIONS = 1 + + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 256, dilation=1, num_groups=GN_NUM_GROUPS), + _ConvBlock(256, 128, dilation=2, num_groups=GN_NUM_GROUPS), + _ConvBlock(128, 64, dilation=3, num_groups=GN_NUM_GROUPS), + ) + self.classifier = nn.Conv2d(64, self.A3C_NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + if self.classifier.bias is not None: + nn.init.zeros_(self.classifier.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class PixelDRLMG_WithDecoder(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.smp_encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + ch = proj_dim + else: + self.projection = None + ch = raw_channels + + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.multi_scale_refine = _MultiScaleRefineBranch( + encoder_channels=list(self.smp_encoder.out_channels), + out_channels=ch, + per_scale_channels=32, + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + self.multi_scale_refine.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "decoder", "segmentation_head"): + module = getattr(self.smp_model, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_from_features( + self, + features: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + + def _encoder_concat(self, x: torch.Tensor) -> torch.Tensor: + return self._concat_from_features(self.smp_encoder(x), output_size=x.shape[-2:]) + + def forward_decoder_from_features( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + ) -> torch.Tensor: + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.smp_model(x) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, Any]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + encoder_features = self.smp_encoder(x) + decoder_logits = self.forward_decoder_from_features(encoder_features) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_features=[feat[sample_index:sample_index + 1] for feat in encoder_features]: self.forward_decoder_from_features(sample_features), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.forward_decoder_from_features(encoder_features), + ) + return { + "base_features": self._concat_from_features(encoder_features, output_size=x.shape[-2:]), + "encoder_features": list(encoder_features), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + if encoder_features is not None: + ms_feat = self.multi_scale_refine(encoder_features, output_size=fused.shape[-2:]) + fused = fused + ms_feat + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + concat_feat = self._encoder_concat(x) + return self.sam(concat_feat) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +class PixelDRLMG_VGGWithDecoder(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + ch = self.encoder.out_channels + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "segmentation_head"): + module = getattr(self, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_pyramid( + self, + pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, torch.Tensor]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + pyramid = self.encoder.forward_pyramid(x) + decoder_logits = self.segmentation_head(pyramid) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_pyramid=[feat[sample_index:sample_index + 1] for feat in pyramid]: self.segmentation_head(sample_pyramid), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.segmentation_head(pyramid), + ) + return { + "base_features": self._concat_pyramid(pyramid, output_size=x.shape[-2:]), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + del encoder_features + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.encoder(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +def run_smp_decoder(decoder: nn.Module, encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + signature = inspect.signature(decoder.forward) + parameters = list(signature.parameters.values()) + if any(param.kind == inspect.Parameter.VAR_POSITIONAL for param in parameters): + return decoder(*encoder_features) + if len(parameters) == 1: + return decoder(encoder_features) + return decoder(*encoder_features) + +def checkpoint_run_config_payload(payload: dict[str, Any]) -> dict[str, Any]: + return payload.get("run_config") or payload.get("config") or {} + +def _raw_decoder_rl_model( + model: nn.Module, +) -> PixelDRLMG_WithDecoder | PixelDRLMG_VGGWithDecoder | None: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return raw + return None + +def _uses_refinement_runtime(model: nn.Module, *, strategy: int | None = None) -> bool: + raw = _raw_decoder_rl_model(model) + if raw is None: + return False + if strategy is not None and strategy != 3: + return False + return bool(getattr(raw, "use_refinement", False)) + +def _policy_action_count_from_state_dict(state_dict: dict[str, Any]) -> int | None: + for key in ( + "policy_head.classifier.weight", + "policy_head.classifier.bias", + "policy_head.net.4.weight", + "policy_head.net.4.bias", + ): + tensor = state_dict.get(key) + if torch.is_tensor(tensor): + return int(tensor.shape[0]) + return None + +def _model_policy_action_count(model: nn.Module) -> int | None: + raw = _unwrap_compiled(model) + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d): + return int(classifier.out_channels) + return None + +def _set_model_policy_action_count(model: nn.Module, action_count: int) -> bool: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) == 1: + return False + policy_head = getattr(raw, "policy_head", None) + classifier = getattr(policy_head, "classifier", None) + if not isinstance(classifier, nn.Conv2d): + return False + if int(classifier.out_channels) == int(action_count): + return False + + new_classifier = nn.Conv2d( + classifier.in_channels, + int(action_count), + kernel_size=classifier.kernel_size, + stride=classifier.stride, + padding=classifier.padding, + dilation=classifier.dilation, + groups=classifier.groups, + bias=classifier.bias is not None, + padding_mode=classifier.padding_mode, + ).to(device=classifier.weight.device, dtype=classifier.weight.dtype) + nn.init.xavier_uniform_(new_classifier.weight) + if new_classifier.bias is not None: + nn.init.zeros_(new_classifier.bias) + policy_head.classifier = new_classifier + return True + +def _configure_policy_head_compatibility( + model: nn.Module, + state_dict: dict[str, Any], + *, + source: str, +) -> int | None: + action_count = _policy_action_count_from_state_dict(state_dict) + if action_count is None: + return None + if _set_model_policy_action_count(model, action_count): + print(f"[Policy Compatibility] source={source} num_actions={action_count}") + return action_count + +def _strategy3_checkpoint_layout_info( + state_dict: dict[str, Any], + run_config: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = run_config or {} + strategy = run_config.get("strategy") + has_legacy_policy_head = any(key.startswith("policy_head.net.") for key in state_dict) + has_new_policy_body = any(key.startswith("policy_head.body.") for key in state_dict) + has_new_policy_classifier = any(key.startswith("policy_head.classifier.") for key in state_dict) + has_refinement_adapter = any(key.startswith("refinement_adapter.") for key in state_dict) + is_strategy3_decoder_checkpoint = bool( + strategy == 3 + or has_legacy_policy_head + or has_new_policy_body + or has_new_policy_classifier + or has_refinement_adapter + ) + use_refinement = bool( + has_refinement_adapter or ((has_new_policy_body or has_new_policy_classifier) and not has_legacy_policy_head) + ) + return { + "strategy": strategy, + "is_strategy3_decoder_checkpoint": is_strategy3_decoder_checkpoint, + "has_legacy_policy_head": has_legacy_policy_head, + "has_new_policy_head": bool(has_new_policy_body or has_new_policy_classifier), + "has_refinement_adapter": has_refinement_adapter, + "requires_policy_remap": has_legacy_policy_head, + "policy_action_count": _policy_action_count_from_state_dict(state_dict), + "use_refinement": use_refinement, + "compatibility_mode": "refinement" if use_refinement else "legacy", + } + +def inspect_strategy3_checkpoint_compatibility(path: str | Path) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + layout = _strategy3_checkpoint_layout_info(payload.get("model_state_dict", {}), checkpoint_run_config_payload(payload)) + layout["path"] = str(checkpoint_path) + return layout + +def _configure_strategy3_model_compatibility( + model: nn.Module, + layout: dict[str, Any], + *, + source: str, +) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not layout.get("is_strategy3_decoder_checkpoint"): + return + raw.set_refinement_mode(bool(layout["use_refinement"])) + if not bool(layout["use_refinement"]): + raw.refinement_adapter.eval() + if hasattr(raw, "multi_scale_refine"): + raw.multi_scale_refine.eval() + print( + "[Strategy3 Compatibility] " + f"source={source} mode={layout['compatibility_mode']} " + f"legacy_policy_head={layout['has_legacy_policy_head']} " + f"refinement_adapter={layout['has_refinement_adapter']}" + ) + +def _ensure_strategy3_refinement_adapter_compatible( + model: nn.Module, + state_dict: dict[str, Any], + *, + checkpoint_path: str | Path, +) -> None: + raw_model = _unwrap_compiled(model) + if not isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return + target_state = raw_model.state_dict() + mismatched: list[str] = [] + for key, value in state_dict.items(): + if not key.startswith(("refinement_adapter.", "policy_head.classifier.")): + continue + target_value = target_state.get(key) + if target_value is None: + continue + if tuple(target_value.shape) != tuple(value.shape): + mismatched.append( + f"{key}: checkpoint={tuple(value.shape)} model={tuple(target_value.shape)}" + ) + if mismatched: + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected after the continuous Strategy 3 redesign. " + f"Checkpoint={Path(checkpoint_path).resolve()} mismatches={mismatched[:4]}. " + "Resume/eval from legacy S3 checkpoints is not supported; retrain Strategy 3 from the Strategy 2 bootstrap checkpoint." + ) + +def _configure_model_from_checkpoint_path( + model: nn.Module, + checkpoint_path: str | Path, +) -> dict[str, Any]: + checkpoint_path = Path(checkpoint_path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state_dict = payload.get("model_state_dict", {}) + _configure_policy_head_compatibility(model, state_dict, source=str(checkpoint_path)) + layout = _strategy3_checkpoint_layout_info(state_dict, checkpoint_run_config_payload(payload)) + if layout.get("is_strategy3_decoder_checkpoint") and layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected: the checkpoint uses a discrete multi-action " + f"policy head with out_channels={layout.get('policy_action_count')}. " + "The current Strategy 3 implementation requires a continuous 1-channel delta head." + ) + _configure_strategy3_model_compatibility(model, layout, source=str(checkpoint_path)) + layout["path"] = str(checkpoint_path) + return layout + +def _remap_legacy_policy_head_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]: + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + if key.startswith("policy_head.net."): + suffix = key[len("policy_head.net."):] + layer_idx, dot, rest = suffix.partition(".") + if dot: + if layer_idx in {"0", "1", "2", "3"}: + remapped[f"policy_head.body.{layer_idx}.{rest}"] = value + continue + if layer_idx == "4": + remapped[f"policy_head.classifier.{rest}"] = value + continue + remapped[key] = value + return remapped + +def _load_strategy2_checkpoint_payload( + path: str | Path, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False) + saved_config = checkpoint_run_config_payload(ckpt) + if saved_config: + saved_model_config = RuntimeModelConfig.from_payload(saved_config).validate() + if saved_model_config.backbone_family != model_config.backbone_family: + raise ValueError( + f"Strategy 2 checkpoint backbone family mismatch: requested {model_config.backbone_family!r}, " + f"checkpoint has {saved_model_config.backbone_family!r} at {checkpoint_path}." + ) + return ckpt + +def _preview_state_keys(keys: list[str], *, limit: int = 8) -> str: + if not keys: + return "none" + preview = ", ".join(keys[:limit]) + if len(keys) > limit: + preview += ", ..." + return preview + +def _strict_load_strategy2_submodule( + target_module: nn.Module, + *, + checkpoint_state_dict: dict[str, Any], + checkpoint_prefix: str, + checkpoint_path: str | Path, + target_name: str, +) -> None: + extracted = { + key[len(checkpoint_prefix):]: value + for key, value in checkpoint_state_dict.items() + if key.startswith(checkpoint_prefix) + } + if not extracted: + raise RuntimeError( + f"Strategy 2 bootstrap failed for {target_name}: no checkpoint keys found with prefix " + f"{checkpoint_prefix!r} in {Path(checkpoint_path).resolve()}." + ) + + target_state = target_module.state_dict() + missing = sorted(set(target_state.keys()) - set(extracted.keys())) + unexpected = sorted(set(extracted.keys()) - set(target_state.keys())) + if missing or unexpected: + section(f"Strategy 2 Bootstrap Mismatch | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + print(f"Missing keys ({len(missing)}) : {_preview_state_keys(missing)}") + print(f"Unexpected keys ({len(unexpected)}): {_preview_state_keys(unexpected)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap failed for {target_name} from {Path(checkpoint_path).resolve()}. " + f"Missing keys={len(missing)}, unexpected keys={len(unexpected)}." + ) + + try: + load_result = target_module.load_state_dict(extracted, strict=True) + except Exception as exc: + section(f"Strategy 2 Bootstrap Strict Load Failure | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap load failed for {target_name} from " + f"{Path(checkpoint_path).resolve()}: {exc}" + ) from exc + + post_missing = list(getattr(load_result, "missing_keys", [])) + post_unexpected = list(getattr(load_result, "unexpected_keys", [])) + if post_missing or post_unexpected: + raise RuntimeError( + f"Strict Strategy 2 bootstrap reported residual mismatches for {target_name}: " + f"missing={post_missing}, unexpected={post_unexpected}" + ) + + section(f"Strategy 2 Bootstrap OK | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Loaded tensors : {len(extracted)}") + print("Strict load : passed") + +def _use_channels_last_for_run(model_config: RuntimeModelConfig | None = None) -> bool: + model_config = (model_config or current_model_config()).validate() + if not USE_CHANNELS_LAST: + return False + if DEVICE.type != "cuda": + return False + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + if ( + model_config.backbone_family == "smp" + and "efficientnet" in model_config.smp_encoder_name.lower() + and USE_AMP + and amp_dtype in {torch.float16, torch.bfloat16} + ): + print("[MemoryFormat] Disabling channels_last for EfficientNet + AMP stability.") + return False + return True + +def build_model( + strategy: int, + dropout_p: float, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[nn.Module, str, bool]: + strategy = _require_supported_strategy(strategy) + model_config = model_config.validate() + if model_config.backbone_family == "custom_vgg": + if not bool(ENABLE_CUSTOM_VGG_BACKBONE): + raise RuntimeError( + "The legacy custom VGG backbone is feature-flagged off. " + "Set ENABLE_CUSTOM_VGG_BACKBONE=True to opt into the unused VGG code path." + ) + if strategy == 2: + model = SupervisedVGGModel( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_VGGWithDecoder( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.encoder, + checkpoint_state_dict=s2_state, + checkpoint_prefix="encoder.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.encoder", + ) + _strict_load_strategy2_submodule( + model.segmentation_head, + checkpoint_state_dict=s2_state, + checkpoint_prefix="segmentation_head.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.segmentation_head", + ) + if freeze_bootstrapped_segmentation: + model.encoder.requires_grad_(False) + model.segmentation_head.requires_grad_(False) + if hasattr(model.encoder, "projection") and model.encoder.projection is not None: + model.encoder.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + else: + if strategy == 2: + model = SupervisedSMPModel( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_WithDecoder( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + proj_dim=model_config.smp_encoder_proj_dim, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.smp_model, + checkpoint_state_dict=s2_state, + checkpoint_prefix="smp_model.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.smp_model", + ) + if freeze_bootstrapped_segmentation: + model.smp_model.encoder.requires_grad_(False) + model.smp_model.decoder.requires_grad_(False) + if hasattr(model.smp_model, "segmentation_head"): + model.smp_model.segmentation_head.requires_grad_(False) + if hasattr(model, "projection") and model.projection is not None: + model.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + + use_channels_last_now = _use_channels_last_for_run(model_config) + if strategy == 3: + classifier = getattr(getattr(_unwrap_compiled(model), "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) != 1: + raise RuntimeError("Strategy 3 expects a continuous 1-channel policy head.") + _strategy3_bump_mc_cache_fingerprint( + model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + model = model.to(DEVICE) + if use_channels_last_now: + model = model.to(memory_format=torch.channels_last) + + compiled = False + if USE_TORCH_COMPILE and hasattr(torch, "compile"): + try: + model = torch.compile(model, mode="max-autotune") + compiled = True + except Exception as exc: + print(f"[Compile] torch.compile skipped: {exc}") + return model, strategy_name(strategy, model_config), compiled + +def _unwrap_compiled(model: nn.Module) -> nn.Module: + return getattr(model, "_orig_mod", model) + +def count_parameters(module: nn.Module | None, *, only_trainable: bool = False) -> int: + if module is None: + return 0 + if only_trainable: + return sum(p.numel() for p in module.parameters() if p.requires_grad) + return sum(p.numel() for p in module.parameters()) + +def print_model_parameter_summary( + *, + model: nn.Module, + description: str, + strategy: int, + model_config: RuntimeModelConfig, + dropout_p: float, + amp_dtype: torch.dtype, + compiled: bool, +) -> None: + raw = _unwrap_compiled(model) + total_params = count_parameters(raw) + trainable_params = count_parameters(raw, only_trainable=True) + frozen_params = total_params - trainable_params + bn_count = sum(1 for m in raw.modules() if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d))) + gn_count = sum(1 for m in raw.modules() if isinstance(m, nn.GroupNorm)) + + section(f"Model Parameter Summary | {description}") + print(f"Strategy : {strategy}") + print(f"Model : {description}") + print(f"Dropout p : {dropout_p:.4f}") + print(f"Total params : {total_params:,}") + print(f"Trainable params : {trainable_params:,}") + print(f"Frozen params : {frozen_params:,}") + print(f"BN layers : {bn_count}") + print(f"GN layers : {gn_count}") + print(f"channels_last : {_use_channels_last_for_run(model_config)}") + print(f"AMP dtype : {amp_dtype}") + print(f"torch.compile : {compiled}") + print(f"Backbone family : {model_config.backbone_family}") + if strategy == 3: + print(f"S3 variant : {_strategy3_variant()}") + freeze_status = _strategy3_bootstrap_freeze_status(model) + print(f"S3 bootstrap loaded : {freeze_status['bootstrap_loaded']}") + print(f"S3 freeze requested : {freeze_status['freeze_requested']}") + print(f"S3 frozen now : {freeze_status['freeze_active']}") + print(f"S3 encoder state : {freeze_status['encoder_state']}") + if freeze_status["decoder_state"] != "n/a": + print(f"S3 decoder state : {freeze_status['decoder_state']}") + if freeze_status["segmentation_head_state"] != "n/a": + print(f"S3 seg head state : {freeze_status['segmentation_head_state']}") + + block_counts: dict[str, int] = {} + if strategy == 2: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + block_counts["dropout"] = count_parameters(getattr(raw, "dropout", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + elif strategy == 3: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + block_counts["sam"] = count_parameters(getattr(raw, "sam", None)) + block_counts["policy_head"] = count_parameters(getattr(raw, "policy_head", None)) + block_counts["value_head"] = count_parameters(getattr(raw, "value_head", None)) + + for name, value in block_counts.items(): + print(f"{name:22s}: {value:,}") + +"""============================================================================= +METRICS + CHECKPOINTS +============================================================================= +""" + +_EPS = 1e-4 + +def _as_bool(mask: np.ndarray) -> np.ndarray: + return (mask[0] if mask.ndim == 3 else mask).astype(bool) + +def _tp_fp_fn(pred: np.ndarray, target: np.ndarray): + p, t = _as_bool(pred), _as_bool(target) + tp = float((p & t).sum()) + fp = float((p & ~t).sum()) + fn = float((~p & t).sum()) + return tp, fp, fn, float(t.sum()), float(p.sum()) + +def dice_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (2 * tp + _EPS) / (t + p + _EPS) + +def ppv_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, fp, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fp + _EPS) + +def sensitivity_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, fn, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fn + _EPS) + +def iou_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (tp + _EPS) / (t + p - tp + _EPS) + +def _boundary_1px(mask: np.ndarray) -> np.ndarray: + m = _as_bool(mask) + if not m.any(): + return m + return m ^ ndimage.binary_erosion(m, iterations=1, border_value=0) + +def boundary_iou_contour_score(pred: np.ndarray, target: np.ndarray) -> float: + pb, tb = _boundary_1px(pred), _boundary_1px(target) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _biou_d(img_hw: tuple[int, int]) -> int: + """Resolve the Boundary IoU dilation width d for a given image size.""" + d = int(BOUNDARY_IOU_D) + if d > 0: + return d + height, width = img_hw + return max(1, int(round(0.02 * math.hypot(height, width)))) + +def _boundary_band(mask: np.ndarray, d: int) -> np.ndarray: + """Return the d-pixel inner boundary band used by paper-standard BIoU.""" + m = _as_bool(mask) + if not m.any(): + return m + eroded = ndimage.binary_erosion(m, iterations=max(int(d), 1), border_value=0) + return m & ~eroded + +def boundary_iou_score(pred: np.ndarray, target: np.ndarray) -> float: + """Boundary IoU from Cheng et al. CVPR 2021 using a d-pixel inner band.""" + height, width = _as_bool(target).shape + d = _biou_d((height, width)) + pb = _boundary_band(pred, d) + tb = _boundary_band(target, d) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _surf_dist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _as_bool(a), _as_bool(b) + if not a.any() and not b.any(): + return np.array([0.0], dtype=np.float32) + if not a.any() or not b.any(): + return np.array([np.inf], dtype=np.float32) + ba, bb = _boundary_1px(a), _boundary_1px(b) + return ndimage.distance_transform_edt(~bb)[ba].astype(np.float32) + +def hd95_score(pred: np.ndarray, target: np.ndarray) -> float: + distances = np.concatenate([_surf_dist(pred, target), _surf_dist(target, pred)]) + if np.isinf(distances).any(): + height, width = _as_bool(target).shape + return float(math.hypot(height, width)) + return float(np.percentile(distances, 95)) + +def compute_all_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + return { + "dice": dice_score(pred, target), + "ppv": ppv_score(pred, target), + "sen": sensitivity_score(pred, target), + "iou": iou_score(pred, target), + "biou": boundary_iou_score(pred, target), + "biou_contour": boundary_iou_contour_score(pred, target), + "hd95": hd95_score(pred, target), + } + +def checkpoint_manifest_path(path: Path) -> Path: + path = Path(path) + return path.with_name(f"{path.name}.meta.json") + +def checkpoint_history_path(run_dir: Path, run_type: str) -> Path: + if run_type == "overfit": + return Path(run_dir) / "overfit_history.json" + return Path(run_dir) / "history.json" + +def checkpoint_state_presence(payload: dict[str, Any]) -> dict[str, bool]: + tracked = [ + "model_state_dict", + "optimizer_state_dict", + "scheduler_state_dict", + "scaler_state_dict", + "log_alpha", + "alpha_optimizer_state_dict", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + "history", + "resume_source", + ] + return {name: name in payload for name in tracked} + +def write_checkpoint_manifest( + path: Path, + payload: dict[str, Any], + *, + extra: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = checkpoint_run_config_payload(payload) + manifest = { + "checkpoint_path": str(Path(path).resolve()), + "run_type": payload.get("run_type", "unknown"), + "epoch": int(payload.get("epoch", 0)), + "strategy": run_config.get("strategy"), + "dataset_percent": run_config.get("dataset_percent"), + "backbone_family": run_config.get("backbone_family", "smp"), + "saved_keys": sorted(payload.keys()), + "state_presence": checkpoint_state_presence(payload), + } + if "resume_source" in payload: + manifest["resume_source"] = payload["resume_source"] + if extra: + manifest.update(extra) + save_json(checkpoint_manifest_path(path), manifest) + return manifest + +def checkpoint_required_keys( + *, + optimizer: torch.optim.Optimizer | None, + scheduler: CosineAnnealingLR | None, + scaler: Any | None, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + require_run_metadata: bool, +) -> list[str]: + keys = ["epoch", "model_state_dict"] + if require_run_metadata: + keys.extend( + [ + "run_type", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + ] + ) + if optimizer is not None: + keys.append("optimizer_state_dict") + if scheduler is not None: + keys.append("scheduler_state_dict") + if scaler is not None: + keys.append("scaler_state_dict") + if log_alpha is not None: + keys.append("log_alpha") + if alpha_optimizer is not None: + keys.append("alpha_optimizer_state_dict") + return keys + +def validate_checkpoint_payload( + path: Path, + payload: dict[str, Any], + *, + required_keys: list[str], + expected_run_type: str | None = None, +) -> None: + missing = [name for name in required_keys if name not in payload] + if missing: + raise KeyError(f"Checkpoint {path} is missing required keys: {missing}") + if expected_run_type is not None and payload.get("run_type") != expected_run_type: + raise ValueError( + f"Checkpoint {path} run_type mismatch: expected {expected_run_type!r}, " + f"got {payload.get('run_type')!r}." + ) + +def save_checkpoint( + path: Path, + *, + run_type: str, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: ReduceLROnPlateau | None, + scaler: Any | None, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": _unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + validate_checkpoint_payload( + path, + payload, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + torch.save(payload, path) + write_checkpoint_manifest(path, payload) + +def load_checkpoint( + path: Path, + *, + model: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: ReduceLROnPlateau | None = None, + scaler: Any | None = None, + device: torch.device, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + expected_run_type: str | None = None, + require_run_metadata: bool = False, +) -> dict[str, Any]: + ckpt = torch.load(path, map_location=device, weights_only=False) + validate_checkpoint_payload( + path, + ckpt, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=require_run_metadata, + ), + expected_run_type=expected_run_type, + ) + + raw_model = _unwrap_compiled(model) + state_dict = ckpt["model_state_dict"] + load_strict = True + compat_layout: dict[str, Any] | None = None + _configure_policy_head_compatibility(model, state_dict, source=str(path)) + if any(key.startswith("policy_head.net.") for key in state_dict): + state_dict = _remap_legacy_policy_head_state_dict(state_dict) + if isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + compat_layout = _strategy3_checkpoint_layout_info(ckpt["model_state_dict"], checkpoint_run_config_payload(ckpt)) + if compat_layout["is_strategy3_decoder_checkpoint"]: + if compat_layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected during restore. " + f"Checkpoint={path} policy_head_out_channels={compat_layout.get('policy_action_count')}. " + "Resume/eval from pre-redesign Strategy 3 checkpoints is not supported." + ) + _configure_strategy3_model_compatibility(model, compat_layout, source=str(path)) + _ensure_strategy3_refinement_adapter_compatible(model, state_dict, checkpoint_path=path) + load_strict = bool(compat_layout["use_refinement"]) + + incompatible = raw_model.load_state_dict(state_dict, strict=load_strict) + if hasattr(raw_model, "clear_strategy3_mc_cache"): + _strategy3_bump_mc_cache_fingerprint(raw_model, eval_checkpoint_path=path) + if not load_strict: + missing_keys = [key for key in incompatible.missing_keys if not key.startswith(("refinement_adapter.", "multi_scale_refine."))] + unexpected_keys = list(incompatible.unexpected_keys) + if missing_keys or unexpected_keys: + print( + "[Checkpoint Restore] Non-strict legacy Strategy 3 load " + f"missing={missing_keys} unexpected={unexpected_keys}" + ) + + if optimizer is not None and "optimizer_state_dict" in ckpt: + try: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + except ValueError: + if compat_layout is None or compat_layout.get("compatibility_mode") != "legacy": + raise + print( + f"[Checkpoint Restore] Skipping optimizer state for legacy Strategy 3 checkpoint at {path} " + "because the parameter layout differs from the refinement-capable model." + ) + if scheduler is not None and "scheduler_state_dict" in ckpt: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler is not None and "scaler_state_dict" in ckpt: + scaler.load_state_dict(ckpt["scaler_state_dict"]) + if log_alpha is not None and "log_alpha" in ckpt: + with torch.no_grad(): + log_alpha.fill_(float(ckpt["log_alpha"])) + if alpha_optimizer is not None and "alpha_optimizer_state_dict" in ckpt: + alpha_optimizer.load_state_dict(ckpt["alpha_optimizer_state_dict"]) + restored = checkpoint_state_presence(ckpt) + restore_info = { + "restored_keys": restored, + "restored_at_epoch": int(ckpt.get("epoch", 0)), + "expected_run_type": expected_run_type, + } + write_checkpoint_manifest(path, ckpt, extra={"last_restore": restore_info}) + print( + f"[Checkpoint Restore] path={path} epoch={ckpt.get('epoch')} " + f"run_type={ckpt.get('run_type', 'unknown')} " + f"backbone={checkpoint_run_config_payload(ckpt).get('backbone_family', 'unknown')}" + ) + return ckpt + +"""============================================================================= +TRAINING + VALIDATION +============================================================================= +""" + +def _policy_log_probs_and_entropy(policy_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs = F.log_softmax(logits, dim=1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=1).mean() + return log_probs, entropy + +def _log_prob_for_actions(log_probs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor: + return log_probs.gather(1, actions.unsqueeze(1)) + +def sample_actions( + policy_logits: torch.Tensor, + stochastic: bool, + exploration_eps: float = 0.0, + keep_action_index: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs, entropy = _policy_log_probs_and_entropy(policy_logits) + if stochastic: + uniform = torch.rand_like(logits) + gumbel = -torch.log(-torch.log(uniform + 1e-8) + 1e-8) + actions = (logits + gumbel).argmax(dim=1) + if exploration_eps > 0: + keep_index = _keep_action_index(logits.shape[1]) if keep_action_index is None else int(keep_action_index) + keep_actions = torch.full_like(actions, keep_index) + random_mask = torch.rand(actions.shape, device=actions.device) < exploration_eps + actions = torch.where(random_mask, keep_actions, actions) + else: + actions = logits.argmax(dim=1) + log_prob = _log_prob_for_actions(log_probs, actions) + return actions, log_prob, entropy + +def apply_actions( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + num_actions = int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)) + if num_actions >= 3: + deltas = _refinement_deltas(action_count=num_actions, device=seg.device, dtype=seg.dtype) + delta = deltas[actions.long()].unsqueeze(1) + return (seg + delta).clamp_(0.0, 1.0) + action_map = actions.unsqueeze(1) + return seg * (action_map == 1).to(dtype=seg.dtype) + +def _soft_dice_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + denom = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) + return (2.0 * inter + 1e-6) / (denom + 1e-6) + +def _soft_iou_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - inter + return (inter + 1e-6) / (union + 1e-6) + +def _soft_recall_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + true_positive = (pred * target).sum(dim=(1, 2, 3)) + positives = target.sum(dim=(1, 2, 3)) + return (true_positive + 1e-6) / (positives + 1e-6) + +def _soft_boundary(mask: torch.Tensor) -> torch.Tensor: + return (mask - F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1)).abs() + +def _differentiable_boundary(mask: torch.Tensor, kernel_size: int = 3) -> torch.Tensor: + padding = kernel_size // 2 + mask_f = mask.float().clamp(0.0, 1.0) + eroded = 1.0 - F.max_pool2d(1.0 - mask_f, kernel_size, stride=1, padding=padding) + return (mask_f - eroded).clamp(0.0, 1.0) + +def _soft_iou_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + pred_f = pred.float().clamp(0.0, 1.0) + target_f = target.float().clamp(0.0, 1.0) + inter = (pred_f * target_f).sum(dim=(2, 3), keepdim=True) + union = (pred_f + target_f - pred_f * target_f).sum(dim=(2, 3), keepdim=True) + return inter / (union + 1e-6) + +def differentiable_biou_loss( + pred: torch.Tensor, + target: torch.Tensor, + kernel_size: int | None = None, + *, + reduction: str = "mean", +) -> torch.Tensor: + if kernel_size is None: + height, width = int(pred.shape[-2]), int(pred.shape[-1]) + d = _biou_d((height, width)) + kernel_size = 2 * d + 1 + kernel_size = max(int(kernel_size), 1) + if kernel_size % 2 == 0: + kernel_size += 1 + pred_boundary = _differentiable_boundary(pred, kernel_size=kernel_size) + target_boundary = _differentiable_boundary(target, kernel_size=kernel_size) + inter = (pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + union = (pred_boundary + target_boundary - pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + loss = 1.0 - (inter + 1e-6) / (union + 1e-6) + if reduction == "none": + return loss + if reduction == "mean": + return loss.mean() + raise ValueError(f"Unsupported differentiable_biou_loss reduction {reduction!r}.") + +def _strategy3_expected_next_mask( + policy_logits: torch.Tensor, + base_seg: torch.Tensor, + *, + action_count: int, +) -> tuple[torch.Tensor, torch.Tensor]: + logits_f = policy_logits.float() + base_seg_f = base_seg.float() + probs = F.softmax(logits_f, dim=1) + deltas = _refinement_deltas(action_count=action_count, device=logits_f.device, dtype=logits_f.dtype) + expected_delta = (probs * deltas.view(1, -1, 1, 1)).sum(dim=1, keepdim=True) + predicted_next = (base_seg_f + expected_delta).clamp(1e-4, 1.0 - 1e-4) + return predicted_next, deltas + +def _strategy3_action_targets( + seg_mask: torch.Tensor, + gt_mask: torch.Tensor, + deltas: torch.Tensor, +) -> torch.Tensor: + target_delta = gt_mask.float() - seg_mask.float() + return (target_delta - deltas.view(1, -1, 1, 1)).abs().argmin(dim=1) + +def compute_refinement_reward( + seg: torch.Tensor, + seg_next: torch.Tensor, + gt_mask: torch.Tensor, + *, + return_details: bool = False, +) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]: + seg_f = seg.float() + seg_next_f = seg_next.float() + gt_f = gt_mask.float().clamp(0.0, 1.0) + + r1_weight = float(_job_param("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT)) + biou_reward_weight = float(_job_param("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT)) + + target_dir = 2.0 * gt_f - 1.0 + progress = target_dir * (seg_next_f - seg_f) + room = gt_f * (1.0 - seg_f) + (1.0 - gt_f) * seg_f + r1 = r1_weight * progress * room + + biou_before = 1.0 - differentiable_biou_loss(seg_f, gt_f, reduction="none") + biou_next = 1.0 - differentiable_biou_loss(seg_next_f, gt_f, reduction="none") + biou_delta = biou_next - biou_before + r3 = biou_reward_weight * biou_delta.expand_as(seg_next_f) + + reward = (r1 + r3).clamp(-3.0, 3.0) + if not return_details: + return reward + return reward, { + "biou_before": biou_before, + "biou_next": biou_next, + "biou_delta": biou_delta, + } + +def compute_strategy1_aux_segmentation_loss( + policy_logits: torch.Tensor, + gt_mask: torch.Tensor, + *, + ce_weight: float, + dice_weight: float, + seg_mask: torch.Tensor | None = None, +) -> tuple[torch.Tensor, float, float]: + logits_f = policy_logits.float() + gt_mask_f = gt_mask.float() + num_actions = int(logits_f.shape[1]) + + if num_actions >= 3: + base_seg = seg_mask.float() if seg_mask is not None else torch.full_like(gt_mask_f, 0.5) + predicted_next, deltas = _strategy3_expected_next_mask( + policy_logits, + base_seg, + action_count=num_actions, + ) + action_targets = _strategy3_action_targets(base_seg, gt_mask_f, deltas) + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + boundary_dice_w = float(_job_param("strategy3_aux_boundary_dice_weight", 0.0)) + + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) + delta_large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + hard_margin = delta_large * 1.5 + with torch.no_grad(): + dist_to_thresh = (base_seg - threshold).abs() + hard_mask = (dist_to_thresh < hard_margin).squeeze(1) + + if ce_weight > 0: + action_ce_mix = float(_job_param("aux_action_ce_mix", 0.75)) + action_ce_mix = min(max(action_ce_mix, 0.0), 1.0) + + if hard_mask.any(): + logits_hw = policy_logits.float().permute(0, 2, 3, 1) + targets_hw = action_targets + action_ce = F.cross_entropy(logits_hw[hard_mask], targets_hw[hard_mask]) + else: + action_ce = F.cross_entropy(policy_logits.float(), action_targets) + + predicted_next_logits = torch.logit(predicted_next) + if hard_mask.any(): + gt_hard = gt_mask_f.squeeze(1)[hard_mask] + pred_hard = predicted_next_logits.squeeze(1)[hard_mask] + bce = F.binary_cross_entropy_with_logits(pred_hard, gt_hard) + else: + bce = F.binary_cross_entropy_with_logits(predicted_next_logits, gt_mask_f) + + ce_term = action_ce_mix * action_ce + (1.0 - action_ce_mix) * bce + aux_loss = aux_loss + ce_weight * ce_term + ce_loss_value = float(ce_term.detach().item()) + + if dice_weight > 0: + inter = (predicted_next * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (predicted_next.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + if boundary_dice_w > 0: + bd_loss = differentiable_biou_loss(predicted_next, gt_mask_f) + aux_loss = aux_loss + boundary_dice_w * bd_loss + + return aux_loss, ce_loss_value, dice_loss_value + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + + if ce_weight > 0: + if num_actions >= 3: + if seg_mask is None: + seg_mask = torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(seg_mask).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + ce_loss_value = float(ce_loss.detach().item()) + + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + inter = (probs_fg * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + return aux_loss, ce_loss_value, dice_loss_value + +def make_optimizer( + model: nn.Module, + strategy: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + rl_lr: float | None = None, +): + raw = _unwrap_compiled(model) + encoder_params = [] + decoder_params = [] + rl_params = [] + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + if ( + name.startswith("extractor.encoder.") + or name.startswith("encoder.") + or name.startswith("smp_model.encoder.") + or name.startswith("smp_encoder.") + ): + encoder_params.append(param) + elif "decoder" in name or "segmentation_head" in name: + decoder_params.append(param) + else: + rl_params.append(param) + + decoder_lr = float(_job_param("decoder_lr", head_lr)) + rl_group_lr = float(_job_param("rl_lr", rl_lr if rl_lr is not None else head_lr)) + + param_groups: list[dict[str, Any]] = [] + + if encoder_params: + param_groups.append({"params": encoder_params, "lr": encoder_lr}) + + if decoder_params: + param_groups.append({"params": decoder_params, "lr": decoder_lr}) + + if rl_params: + param_groups.append({"params": rl_params, "lr": rl_group_lr}) + + try: + optimizer = AdamW(param_groups, weight_decay=weight_decay, fused=DEVICE.type == "cuda") + except Exception: + optimizer = AdamW(param_groups, weight_decay=weight_decay) + + return optimizer + +def infer_segmentation_mask( + model: nn.Module, + image: torch.Tensor, + tmax: int, + *, + strategy: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> torch.Tensor: + model.eval() + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + return threshold_binary_mask(torch.sigmoid(logits)).float() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="infer_segmentation_mask") + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta) + return threshold_binary_mask(seg.float()).float() + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + policy_logits = model.forward_policy_only(x_t) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + return seg.float() + +def train_step( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor, + alpha_optimizer: torch.optim.Optimizer, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + initial_mask: torch.Tensor | None = None, + decoder_loss_extra: torch.Tensor | None = None, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + if initial_mask is not None: + seg = initial_mask.to(device=image.device, dtype=image.dtype) + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + alpha = log_alpha.exp() + + total_actor = 0.0 + total_critic = 0.0 + total_loss = 0.0 + total_reward = 0.0 + total_entropy = 0.0 + total_ce_loss = 0.0 + total_dice_loss = 0.0 + accum_tensor = None + alpha_loss_accum = torch.tensor(0.0, device=image.device, dtype=torch.float32) + aux_fused = False + + optimizer.zero_grad(set_to_none=True) + + for _ in range(tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=True) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + + neighborhood_next = _bootstrap_value_target(model, value_next) + target = reward + gamma * neighborhood_next + advantage = target - value_t + critic_loss = F.smooth_l1_loss(value_t, target) + actor_loss = -(log_prob * advantage.detach()).mean() + actor_loss = actor_loss - alpha.detach() * entropy + step_loss = (actor_loss + critic_loss_weight * critic_loss) / float(tmax) + alpha_loss_accum = alpha_loss_accum + (log_alpha * (entropy.detach() - target_entropy)) / float(tmax) + + if not aux_fused and initial_mask is None and (ce_weight > 0 or dice_weight > 0): + aux_loss, ce_loss_value, dice_loss_value = compute_strategy1_aux_segmentation_loss( + policy_logits, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + if ce_weight > 0: + total_ce_loss = ce_loss_value + if dice_weight > 0: + total_dice_loss = dice_loss_value + step_loss = step_loss + aux_loss + aux_fused = True + + total_actor += float(actor_loss.detach().item()) + total_critic += float(critic_loss.detach().item()) + total_loss += float(step_loss.detach().item()) + total_reward += float(reward.detach().mean().item()) + total_entropy += float(entropy.detach().item()) + + if stepwise_backward: + if scaler is not None: + scaler.scale(step_loss).backward() + else: + step_loss.backward() + else: + accum_tensor = step_loss if accum_tensor is None else accum_tensor + step_loss + + seg = seg_next.detach() + + if not stepwise_backward and accum_tensor is not None: + if scaler is not None: + scaler.scale(accum_tensor).backward() + else: + accum_tensor.backward() + + if not aux_fused and (ce_weight > 0 or dice_weight > 0): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + logits_f = policy_aux.float() + aux_loss = torch.zeros(1, device=image.device, dtype=torch.float32) + if ce_weight > 0: + num_actions = int(logits_f.shape[1]) + if num_actions >= 3: + init_seg = initial_mask if initial_mask is not None else torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(init_seg).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + total_ce_loss = float(ce_loss.detach().item()) + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + gt_f = gt_mask.float() + inter = (probs_fg * gt_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + total_dice_loss = float(dice_loss.detach().item()) + if decoder_loss_extra is not None: + aux_loss = aux_loss + decoder_loss_extra + if scaler is not None: + scaler.scale(aux_loss).backward() + else: + aux_loss.backward() + total_loss += float(aux_loss.detach().item()) + elif decoder_loss_extra is not None: + if scaler is not None: + scaler.scale(decoder_loss_extra).backward() + else: + decoder_loss_extra.backward() + total_loss += float(decoder_loss_extra.detach().item()) + + if scaler is not None: + scaler.unscale_(optimizer) + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + alpha_optimizer.zero_grad(set_to_none=True) + alpha_loss_accum.backward() + alpha_optimizer.step() + with torch.no_grad(): + log_alpha.clamp_(min=_alpha_log_floor(), max=5.0) + + return { + "loss": total_loss, + "actor_loss": total_actor / tmax, + "critic_loss": total_critic / tmax, + "mean_reward": total_reward / tmax, + "entropy": total_entropy / tmax, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": seg.detach(), + } + +def train_step_strategy3( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + current_epoch: int, + max_epochs: int, +) -> dict[str, Any]: + if not _uses_refinement_runtime(model, strategy=3): + raise RuntimeError( + "Legacy non-refinement Strategy 3 training is not supported after the continuous Strategy 3 redesign." + ) + + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + annealed_aux_ce_weight = _strategy3_annealed_aux_ce_weight(current_epoch) + del stepwise_backward, max_epochs, log_alpha, alpha_optimizer, target_entropy + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context(image, mc_mode="train") + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"] + base_features = refinement_context["base_features"] + encoder_features = refinement_context.get("encoder_features") + mc_variance = refinement_context["mc_variance"] + pred_entropy = refinement_context["pred_entropy"] + seg = decoder_prob.detach().to(device=image.device, dtype=image.dtype) + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + + decoder_loss = torch.zeros((), device=image.device, dtype=torch.float32) + decoder_ce_loss_value = 0.0 + decoder_dice_loss_value = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + decoder_ce = F.binary_cross_entropy_with_logits(dl_f, gt_f) + decoder_loss = decoder_loss + loss_weights["decoder_ce"] * decoder_ce + decoder_ce_loss_value = float(decoder_ce.detach().item()) + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + decoder_dice = 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + decoder_loss = decoder_loss + loss_weights["decoder_dice"] * decoder_dice + decoder_dice_loss_value = float(decoder_dice.detach().item()) + + optimizer.zero_grad(set_to_none=True) + + a3c_grad_clip = float(_job_param("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM)) + + refinement_base_features = base_features + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = mc_variance.detach() + detached_pred_entropy = pred_entropy.detach() + detached_encoder_features = [f.detach() for f in encoder_features] if encoder_features is not None else None + + step_action_hists: list[dict[str, float]] = [] + step_mask_deltas: list[float] = [] + step_reward_means: list[float] = [] + step_reward_pos_pcts: list[float] = [] + step_reward_zero_pcts: list[float] = [] + step_biou_deltas: list[float] = [] + advantage_maps: list[torch.Tensor] = [] + critic_targets: list[torch.Tensor] = [] + value_maps: list[torch.Tensor] = [] + + actor_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + critic_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + total_actor = 0.0 + total_critic = 0.0 + total_reward = 0.0 + total_ce_loss = decoder_ce_loss_value + total_dice_loss = decoder_dice_loss_value + effective_steps = max(int(tmax), 1) + final_refined_seg_for_aux: torch.Tensor | None = None + + for _ in range(effective_steps): + seg_before = seg.detach() + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg_before, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg_before.dtype) + seg_next = _strategy3_apply_delta(seg_before, delta) + reward_map, reward_details = compute_refinement_reward( + seg_before, + seg_next, + gt_mask.float(), + return_details=True, + ) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next.detach(), + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + value_next = model.value_from_state(state_next).detach() + + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + step_actor = -actor_advantage.mean() + step_critic = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + actor_loss_tensor = actor_loss_tensor + step_actor / float(effective_steps) + critic_loss_tensor = critic_loss_tensor + step_critic / float(effective_steps) + total_actor += float(step_actor.detach().item()) + total_critic += float(step_critic.detach().item()) + total_reward += float(reward_map.detach().mean().item()) + + step_action_hists.append(_strategy3_delta_distribution(delta)) + step_mask_deltas.append(float(delta.detach().abs().mean().item())) + step_reward_means.append(float(reward_map.detach().mean().item())) + step_reward_pos_pcts.append(float((reward_map.detach() > 0).float().mean().item() * 100.0)) + step_reward_zero_pcts.append(float((reward_map.detach().abs() < 1e-8).float().mean().item() * 100.0)) + step_biou_deltas.append(float(reward_details["biou_delta"].detach().mean().item())) + advantage_maps.append(actor_advantage.detach()) + critic_targets.append(critic_target.detach()) + value_maps.append(value_t.detach()) + final_refined_seg_for_aux = seg_next + seg = seg_next.detach() + + aux_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + aux_ce_loss_value = 0.0 + aux_dice_loss_value = 0.0 + if annealed_aux_ce_weight > 0.0 and final_refined_seg_for_aux is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refined_prob = final_refined_seg_for_aux.float().clamp(1e-6, 1.0 - 1e-6) + gt_f = gt_mask.float() + supervised_aux = torch.zeros((), device=image.device, dtype=torch.float32) + if ce_weight > 0: + refined_logits = torch.logit(refined_prob) + aux_ce = F.binary_cross_entropy_with_logits(refined_logits, gt_f) + supervised_aux = supervised_aux + float(ce_weight) * aux_ce + aux_ce_loss_value = float(aux_ce.detach().item()) + if dice_weight > 0: + inter = (refined_prob * gt_f).sum() + aux_dice = 1.0 - (2.0 * inter + 1e-6) / (refined_prob.sum() + gt_f.sum() + 1e-6) + supervised_aux = supervised_aux + float(dice_weight) * aux_dice + aux_dice_loss_value = float(aux_dice.detach().item()) + aux_loss_tensor = float(annealed_aux_ce_weight) * supervised_aux + total_ce_loss += aux_ce_loss_value + total_dice_loss += aux_dice_loss_value + + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + rl_loss = rl_loss_scale * (actor_loss_tensor + critic_loss_weight * critic_loss_tensor) + total_loss_tensor = decoder_loss + rl_loss + aux_loss_tensor + + if scaler is not None: + scaler.scale(total_loss_tensor).backward() + scaler.unscale_(optimizer) + else: + total_loss_tensor.backward() + + effective_grad_clip = a3c_grad_clip if a3c_grad_clip > 0 else grad_clip_norm + total_grad_norm = ( + float(torch.nn.utils.clip_grad_norm_(model.parameters(), effective_grad_clip).item()) + if effective_grad_clip > 0 + else 0.0 + ) + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + adv_means = [float(a.mean().item()) for a in advantage_maps] + adv_stds = [ + float(a.std(unbiased=False).item()) + for a in advantage_maps + if a.numel() > 1 + ] + value_pred_errors = [ + float((target - value).abs().mean().item()) + for target, value in zip(critic_targets, value_maps) + ] + mean_value_pred = float(torch.stack([value.mean() for value in value_maps]).mean().item()) if value_maps else 0.0 + avg_action_dist: dict[str, float] = {} + if step_action_hists: + all_keys = set() + for h in step_action_hists: + all_keys.update(h.keys()) + for k in sorted(all_keys): + avg_action_dist[k] = float(np.mean([h.get(k, 0.0) for h in step_action_hists])) + + return { + "loss": float(total_loss_tensor.detach().item()), + "actor_loss": total_actor / float(effective_steps), + "critic_loss": total_critic / float(effective_steps), + "mean_reward": total_reward / float(effective_steps), + "entropy": 0.0, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": effective_grad_clip, + "final_mask": threshold_binary_mask(seg.detach().float()).float(), + "effective_steps": effective_steps, + "action_distribution": avg_action_dist, + "mask_delta_mean": float(np.mean(step_mask_deltas)) if step_mask_deltas else 0.0, + "reward_per_step": step_reward_means, + "reward_pos_pct_per_step": step_reward_pos_pcts, + "reward_zeros_pct": float(np.mean(step_reward_zero_pcts)) if step_reward_zero_pcts else 0.0, + "biou_delta_mean": float(np.mean(step_biou_deltas)) if step_biou_deltas else 0.0, + "advantage_mean": float(np.mean(adv_means)), + "advantage_std": float(np.nanmean(adv_stds)) if adv_stds else 0.0, + "value_pred_error_mean": float(np.mean(value_pred_errors)), + "mean_value_pred": mean_value_pred, + "rl_loss_scale_used": float(rl_loss_scale), + "annealed_aux_ce_weight": float(annealed_aux_ce_weight), + "alpha": 0.0, + } + +def train_step_supervised( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + grad_clip_norm: float, + ce_weight: float = 0.5, + dice_weight: float = 0.5, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + optimizer.zero_grad(set_to_none=True) + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + logits_f = logits.float() + gt_f = gt_mask.float() + loss = torch.zeros(1, device=image.device, dtype=torch.float32) + ce_loss_val = 0.0 + dice_loss_val = 0.0 + if ce_weight > 0: + bce = F.binary_cross_entropy_with_logits(logits_f, gt_f, reduction="mean") + loss = loss + ce_weight * bce + ce_loss_val = float(bce.detach().item()) + if dice_weight > 0: + pred_f = torch.sigmoid(logits_f) + inter = (pred_f * gt_f).sum() + dice_l = 1.0 - (2.0 * inter + 1e-6) / (pred_f.sum() + gt_f.sum() + 1e-6) + loss = loss + dice_weight * dice_l + dice_loss_val = float(dice_l.detach().item()) + + if scaler is not None: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + final_mask = threshold_binary_mask(torch.sigmoid(logits_f)).float().detach() + return { + "loss": float(loss.detach().item()), + "actor_loss": 0.0, + "critic_loss": 0.0, + "mean_reward": 0.0, + "entropy": 0.0, + "ce_loss": ce_loss_val, + "dice_loss": dice_loss_val, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": final_mask, + } + +@torch.inference_mode() +def validate( + model: nn.Module, + loader: DataLoader, + *, + run_dir: Path | None, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + gamma: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + strategy = _require_supported_strategy(strategy) + model.eval() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="validate") if strategy != 2 else max(int(tmax), 1) + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + losses: list[float] = [] + dice_scores: list[float] = [] + iou_scores: list[float] = [] + biou_scores: list[float] = [] + entropies: list[float] = [] + rewards: list[float] = [] + actor_losses: list[float] = [] + critic_losses: list[float] = [] + ce_losses: list[float] = [] + dice_losses: list[float] = [] + decoder_dice_scores: list[float] = [] + decoder_iou_scores: list[float] = [] + decoder_biou_scores: list[float] = [] + val_binary_flips_total: list[float] = [] + val_binary_flips_correct: list[float] = [] + val_binary_flips_wrong: list[float] = [] + prefetcher = CUDAPrefetcher(loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(loader), desc="Validating", leave=False): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred_prob = torch.sigmoid(logits).float() + pred = threshold_binary_mask(pred_prob).float() + bce = F.binary_cross_entropy_with_logits(logits.float(), gt_mask.float(), reduction="mean") if ce_weight > 0 else torch.tensor(0.0, device=image.device) + inter_prob = (pred_prob * gt_mask.float()).sum() + dice_loss = 1.0 - (2.0 * inter_prob + 1e-6) / (pred_prob.sum() + gt_mask.sum() + 1e-6) if dice_weight > 0 else torch.tensor(0.0, device=image.device) + losses.append(float((ce_weight * bce + dice_weight * dice_loss).item())) + ce_losses.append(float(bce.item()) if ce_weight > 0 else 0.0) + dice_losses.append(float(dice_loss.item()) if dice_weight > 0 else 0.0) + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"].float() + seg = decoder_prob.float() + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += loss_weights["decoder_ce"] * batch_ce + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += loss_weights["decoder_dice"] * batch_dice + decoder_pred = threshold_binary_mask(decoder_prob.float()).float() + decoder_inter = (decoder_pred * gt_mask.float()).sum(dim=(1, 2, 3)) + decoder_pred_sum = decoder_pred.sum(dim=(1, 2, 3)) + decoder_gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + decoder_dice = (2.0 * decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum + _EPS) + decoder_iou = (decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum - decoder_inter + _EPS) + decoder_dice_scores.extend(decoder_dice.cpu().tolist()) + decoder_iou_scores.extend(decoder_iou.cpu().tolist()) + else: + fg_count = gt_mask.sum().clamp(min=1.0) + bg_count = (gt_mask == 0).sum().clamp(min=1.0) + pos_weight = bg_count / fg_count + _weight_map = torch.where(gt_mask == 1, pos_weight, torch.ones_like(gt_mask)) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + decoder_logits = model.forward_decoder(image) + seg = threshold_binary_mask(torch.sigmoid(decoder_logits)).float() + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if ce_weight > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += ce_weight * batch_ce + if dice_weight > 0: + dm_prob = torch.sigmoid(dl_f) + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += dice_weight * batch_dice + else: + seg = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + + batch_actor = 0.0 + batch_critic = 0.0 + batch_reward = 0.0 + batch_entropy = 0.0 + batch_loss = decoder_loss + decoder_ce_base = batch_ce + decoder_dice_base = batch_dice + aux_ce_total = 0.0 + aux_dice_total = 0.0 + effective_steps = effective_tmax + + if strategy == 3 and refinement_runtime: + refinement_base_features = refinement_context["base_features"] + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = refinement_context["mc_variance"].detach() + detached_pred_entropy = refinement_context["pred_entropy"].detach() + _enc_feats = refinement_context.get("encoder_features") + detached_enc_feats = [f.detach() for f in _enc_feats] if _enc_feats is not None else None + effective_steps = effective_tmax + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + value_next = model.value_from_state(state_next).detach() + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + actor_loss = -actor_advantage.mean() + critic_loss = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward_map.mean().item()) + batch_loss += float((actor_loss + critic_loss_weight * critic_loss).item()) + seg = seg_next + + batch_ce = decoder_ce_base + batch_dice = decoder_dice_base + batch_loss = decoder_loss + rl_loss_scale * ((batch_loss - decoder_loss) / float(max(effective_steps, 1))) + pred = threshold_binary_mask(seg.float()).float() + # Track binary mask flips between decoder and RL-refined prediction + flipped = (decoder_pred != pred) + gt_binary = (gt_mask.float() > 0.5) + correct_flips = flipped & ((pred > 0.5) == gt_binary) + wrong_flips = flipped & ((pred > 0.5) != gt_binary) + total_px = max(pred.numel(), 1) + val_binary_flips_total.append(float(flipped.float().sum().item() / total_px * 100.0)) + val_binary_flips_correct.append(float(correct_flips.float().sum().item() / total_px * 100.0)) + val_binary_flips_wrong.append(float(wrong_flips.float().sum().item() / total_px * 100.0)) + else: + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=False) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = ((seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2)) + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + target = reward + gamma * _bootstrap_value_target(model, value_next) + advantage = target - value_t + actor_loss = -(log_prob * advantage.detach()).mean() + critic_loss = F.smooth_l1_loss(value_t, target) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward.mean().item()) + batch_entropy += float(entropy.item()) + batch_loss += float(((actor_loss + critic_loss_weight * critic_loss) / float(max(effective_tmax, 1))).item()) + seg = seg_next + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + aux_loss, aux_ce, aux_dice = compute_strategy1_aux_segmentation_loss( + policy_aux, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + batch_loss += float(aux_loss.item()) + batch_ce = aux_ce + batch_dice = aux_dice + pred = infer_segmentation_mask( + model, + image, + effective_tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ).float() + + ce_losses.append(batch_ce) + dice_losses.append(batch_dice) + actor_losses.append(batch_actor / float(max(effective_steps, 1))) + critic_losses.append(batch_critic / float(max(effective_steps, 1))) + rewards.append(batch_reward / float(max(effective_steps, 1))) + entropies.append(batch_entropy / float(max(effective_steps, 1))) + losses.append(batch_loss) + + inter = (pred * gt_mask.float()).sum(dim=(1, 2, 3)) + pred_sum = pred.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + dice_scores.extend(dice.cpu().tolist()) + iou_scores.extend(iou.cpu().tolist()) + pred_np = pred.detach().cpu().numpy() + gt_np = gt_mask.float().detach().cpu().numpy() + for idx in range(pred_np.shape[0]): + biou_scores.append(boundary_iou_score(pred_np[idx], gt_np[idx])) + if strategy == 3 and decoder_dice_scores: + decoder_pred_np = decoder_pred.detach().cpu().numpy() + for idx in range(decoder_pred_np.shape[0]): + decoder_biou_scores.append(boundary_iou_score(decoder_pred_np[idx], gt_np[idx])) + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + val_decoder_dice = float(np.mean(decoder_dice_scores)) if decoder_dice_scores else None + val_decoder_iou = float(np.mean(decoder_iou_scores)) if decoder_iou_scores else None + val_decoder_biou = float(np.mean(decoder_biou_scores)) if decoder_biou_scores else None + val_dice = float(np.mean(dice_scores)) if dice_scores else 0.0 + val_iou = float(np.mean(iou_scores)) if iou_scores else 0.0 + val_biou = float(np.mean(biou_scores)) if biou_scores else 0.0 + val_refine_score = 0.5 * (val_iou + val_biou) + + return { + "val_loss": float(np.mean(losses)) if losses else 0.0, + "val_dice": val_dice, + "val_iou": val_iou, + "val_biou": val_biou, + "val_refine_score": val_refine_score, + "val_decoder_dice": val_decoder_dice, + "val_decoder_iou": val_decoder_iou, + "val_decoder_biou": val_decoder_biou, + "val_dice_gain": None if val_decoder_dice is None else val_dice - val_decoder_dice, + "val_iou_gain": None if val_decoder_iou is None else val_iou - val_decoder_iou, + "val_biou_gain": None if val_decoder_biou is None else val_biou - val_decoder_biou, + "val_actor_loss": float(np.mean(actor_losses)) if actor_losses else 0.0, + "val_critic_loss": float(np.mean(critic_losses)) if critic_losses else 0.0, + "val_ce_loss": float(np.mean(ce_losses)) if ce_losses else 0.0, + "val_dice_loss": float(np.mean(dice_losses)) if dice_losses else 0.0, + "val_reward": float(np.mean(rewards)) if rewards else 0.0, + "val_entropy": float(np.mean(entropies)) if entropies else 0.0, + "val_binary_flips_total_pct": float(np.mean(val_binary_flips_total)) if val_binary_flips_total else None, + "val_binary_flips_correct_pct": float(np.mean(val_binary_flips_correct)) if val_binary_flips_correct else None, + "val_binary_flips_wrong_pct": float(np.mean(val_binary_flips_wrong)) if val_binary_flips_wrong else None, + } + +def _save_training_plots(history: list[dict[str, Any]], plots_dir: Path) -> None: + if len(history) < 1: + return + ensure_dir(plots_dir) + epochs = [row["epoch"] for row in history] + plot_specs = [ + ("loss.png", "Loss", [("train_loss", "Train"), ("val_loss", "Val")]), + ("dice.png", "Dice", [("train_dice", "Train"), ("val_dice", "Val")]), + ("iou.png", "IoU", [("train_iou", "Train"), ("val_iou", "Val")]), + ("reward.png", "Reward", [("train_mean_reward", "Train"), ("val_reward", "Val")]), + ("ce_loss.png", "CE Loss", [("train_ce_loss", "Train")]), + ("dice_loss.png", "Dice Loss", [("train_dice_loss", "Train")]), + ("lr.png", "Learning Rate", [("lr", "Head LR"), ("encoder_lr", "Encoder LR")]), + ] + for file_name, title, curves in plot_specs: + fig, ax = plt.subplots(figsize=(8, 4)) + has_data = False + for key, label in curves: + values = [(row["epoch"], row[key]) for row in history if key in row] + if not values: + continue + xs, ys = zip(*values) + ax.plot(xs, ys, label=label, linewidth=1.2) + has_data = True + if has_data: + ax.set_title(title) + ax.set_xlabel("Epoch") + ax.set_ylabel(title) + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(plots_dir / file_name, dpi=110) + plt.close(fig) + +RESUME_IDENTITY_KEYS = ( + "strategy", + "dataset_percent", + "dataset_name", + "dataset_split_policy", + "split_type", + "train_subset_key", + "train_subset_variant", + "best_checkpoint_metric_name", + "backbone_family", + "smp_encoder_name", + "smp_encoder_weights", + "smp_encoder_depth", + "smp_encoder_proj_dim", + "smp_decoder_type", + "vgg_feature_scales", + "vgg_feature_dilation", + "head_lr", + "encoder_lr", + "weight_decay", + "dropout_p", + "tmax", + "entropy_lr", +) + +PORTABLE_RESUME_PATH_KEYS = frozenset( + { + "base_split_manifest_path", + "subset_manifest_path", + } +) + +def _path_parts(value: Any) -> tuple[str, ...]: + if value is None: + return () + return tuple(part for part in Path(str(value)).parts if part not in {"", os.sep}) + +def _portable_path_token(value: Any) -> str: + if value in (None, ""): + return "" + path = Path(str(value)).expanduser() + roots: list[tuple[str, Path]] = [] + experiment_root = globals().get("EXPERIMENT_ROOT") + if experiment_root is not None: + roots.append(("EXPERIMENT_ROOT", Path(experiment_root))) + roots.append(("PROJECT_DIR", PROJECT_DIR)) + for label, root in roots: + try: + rel = path.resolve().relative_to(root.resolve()) + return f"{label}:{rel.as_posix()}" + except (OSError, ValueError): + continue + parts = _path_parts(value) + for marker in ("runs", "repeated_holdout", "manifests", "checkpoints"): + if marker in parts: + return "/".join(parts[parts.index(marker):]) + return "/".join(parts) + +def _resume_path_values_match(current: Any, saved: Any) -> tuple[bool, str]: + current_text = str(current or "") + saved_text = str(saved or "") + if current_text == saved_text: + return True, "exact" + + current_token = _portable_path_token(current_text) + saved_token = _portable_path_token(saved_text) + if current_token and current_token == saved_token: + return True, "portable-token" + + current_parts = _path_parts(current_text) + saved_parts = _path_parts(saved_text) + max_suffix = min(len(current_parts), len(saved_parts)) + for length in range(max_suffix, 2, -1): + if current_parts[-length:] == saved_parts[-length:]: + return True, f"suffix:{length}" + return False, f"current_token={current_token!r}, checkpoint_token={saved_token!r}" + +def _resume_value_matches(current: Any, saved: Any) -> bool: + if isinstance(current, (int, float)) and isinstance(saved, (int, float)) and not isinstance(current, bool): + return math.isclose(float(current), float(saved), rel_tol=1e-9, abs_tol=1e-12) + return current == saved + +def validate_resume_checkpoint_identity( + current_run_config: dict[str, Any], + saved_run_config: dict[str, Any], + *, + checkpoint_path: Path, +) -> None: + mismatches: list[str] = [] + for key in RESUME_IDENTITY_KEYS: + if key not in current_run_config or key not in saved_run_config: + mismatches.append(f"{key}: current={current_run_config.get(key)!r}, checkpoint={saved_run_config.get(key)!r}") + continue + if key in PORTABLE_RESUME_PATH_KEYS: + matches, reason = _resume_path_values_match(current_run_config[key], saved_run_config[key]) + if matches: + if str(current_run_config[key]) != str(saved_run_config[key]): + print( + f"[Resume] Accepted portable path match for {key}: " + f"current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}, reason={reason}." + ) + continue + mismatches.append( + f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r} ({reason})" + ) + continue + if not _resume_value_matches(current_run_config[key], saved_run_config[key]): + mismatches.append(f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}") + + current_s2 = current_run_config.get("strategy2_checkpoint_path") + saved_s2 = saved_run_config.get("strategy2_checkpoint_path") + if current_s2 or saved_s2: + if str(current_s2 or "") != str(saved_s2 or ""): + # Warn but do not abort: when resuming, the model weights are fully restored + # from the resume checkpoint (not re-loaded from the strategy2 checkpoint), + # so a path change (e.g. file moved/renamed) does not affect correctness. + print( + f"[WARN] strategy2_checkpoint_path changed since checkpoint was saved " + f"(current={current_s2!r}, checkpoint={saved_s2!r}). " + f"Resuming anyway — model state comes from the resume checkpoint." + ) + + if mismatches: + raise ValueError( + f"Resume checkpoint identity mismatch for {checkpoint_path}:\n" + "\n".join(f" - {line}" for line in mismatches) + ) + +def load_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> list[dict[str, Any]]: + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + epoch_metrics = checkpoint_payload.get("epoch_metrics") + history: list[dict[str, Any]] = [] + checkpoint_history = checkpoint_payload.get("history") + if isinstance(checkpoint_history, list): + history = [dict(row) for row in checkpoint_history if isinstance(row, dict)] + history = [row for row in history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, list): + raise RuntimeError(f"Expected list history at {history_path}, found {type(payload).__name__}.") + file_history = [dict(row) for row in payload if isinstance(row, dict)] + file_history = [row for row in file_history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if len(file_history) >= len(history): + history = file_history + if not history and isinstance(epoch_metrics, dict): + history = [dict(epoch_metrics)] + elif history and isinstance(epoch_metrics, dict): + if int(history[-1].get("epoch", 0)) < checkpoint_epoch: + history.append(dict(epoch_metrics)) + return history + +def train_model( + *, + run_type: str, + model_config: RuntimeModelConfig, + run_config: dict[str, Any], + model: nn.Module, + description: str, + strategy: int, + run_dir: Path, + bundle: DataBundle, + max_epochs: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + tmax: int, + entropy_lr: float, + entropy_alpha_init: float, + entropy_target_ratio: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, + dropout_p: float, + resume_checkpoint_path: Path | None = None, + trial: optuna.trial.Trial | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=description, + strategy=strategy, + model_config=model_config, + dropout_p=dropout_p, + amp_dtype=amp_dtype, + compiled=hasattr(model, "_orig_mod"), + ) + + strategy3_freeze_status = _strategy3_bootstrap_freeze_status(model) if strategy == 3 else None + strategy3_frozen_decoder = strategy == 3 and _strategy3_decoder_is_frozen(model) + decoder_head_lr = float(_job_param("decoder_lr", 0.0 if strategy3_frozen_decoder else head_lr * 0.1)) + encoder_group_lr = 0.0 if strategy3_frozen_decoder else encoder_lr + rl_group_lr = float(_job_param("rl_lr", head_lr)) + optimizer = make_optimizer( + model, + strategy, + head_lr=decoder_head_lr, + encoder_lr=encoder_group_lr, + weight_decay=weight_decay, + rl_lr=rl_group_lr, + ) + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_epochs, + eta_min=1e-6, # floor + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + + del entropy_alpha_init, entropy_lr, entropy_target_ratio + target_entropy = 0.0 + log_alpha = None + alpha_optimizer = None + + save_artifacts = run_type == "final" + save_history_incrementally = bool(run_config.get("save_history_incrementally", SAVE_HISTORY_INCREMENTALLY)) + write_epoch_diagnostic = bool(run_config.get("write_epoch_diagnostic", WRITE_EPOCH_DIAGNOSTIC)) + ckpt_dir = ensure_dir(run_dir / "checkpoints") if save_artifacts else None + plots_dir = ensure_dir(run_dir / "plots") if save_artifacts else None + history_path = checkpoint_history_path(run_dir, run_type) + diagnostic_path = diagnostic_path_for_run(run_dir) if run_type == "final" and write_epoch_diagnostic else None + history: list[dict[str, Any]] = [] + selection_metric_name = _strategy_selection_metric_name(strategy) + early_stopping_monitor_name = _early_stopping_monitor_name(strategy) + early_stopping_mode = _early_stopping_mode(strategy, early_stopping_monitor_name) + early_stopping_min_delta = _early_stopping_min_delta() + early_stopping_start_epoch = _early_stopping_start_epoch() + early_stopping_patience = _early_stopping_patience() + epoch_probe_mode = str(run_config.get("epoch_probe_mode", "fixed")).strip().lower() + best_model_metric = -float("inf") + patience_counter = 0 + best_early_stopping_metric: float | None = None + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + diagnostic_payload: dict[str, Any] | None = None + train_probe_batches: list[dict[str, Any]] = [] + val_probe_batches: list[dict[str, Any]] = [] + run_label = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ) + if diagnostic_path is not None: + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=0, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=0, + split_tag="val", + ) + else: + train_probe_batches = _fixed_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + ) + val_probe_batches = _fixed_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + ) + diagnostic_payload = empty_epoch_diagnostic_payload( + run_type=run_type, + run_config=run_config, + bundle=bundle, + train_probe_batches=train_probe_batches, + val_probe_batches=val_probe_batches, + ) + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + expected_run_type=run_type, + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + selection_metric_name = str(checkpoint_payload.get("best_metric_name", selection_metric_name)) + best_model_metric = float(checkpoint_payload["best_metric_value"]) + patience_counter = int(checkpoint_payload.get("patience_counter", 0)) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", run_type), + } + epoch_metrics = checkpoint_payload.get("epoch_metrics") + if isinstance(epoch_metrics, dict) and epoch_metrics.get("early_stopping_best_value") is not None: + best_early_stopping_metric = float(epoch_metrics["early_stopping_best_value"]) + if diagnostic_path is not None and diagnostic_payload is not None: + diagnostic_payload = load_epoch_diagnostic_for_resume( + diagnostic_path, + checkpoint_payload, + diagnostic_payload, + ) + print( + f"[Resume] {run_label} | {run_type} continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{max_epochs}." + ) + prev_params = _snapshot_params(model) if diagnostic_path is not None else None + start_time = time.time() + validate_interval = max(int(VALIDATE_EVERY_N_EPOCHS), 1) + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + + for epoch in range(start_epoch, max_epochs + 1): + epoch_losses: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_reward: list[float] = [] + epoch_entropy: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_loss: list[float] = [] + epoch_grad: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_effective_steps: list[float] = [] + epoch_mask_deltas: list[float] = [] + epoch_advantage_means: list[float] = [] + epoch_advantage_stds: list[float] = [] + epoch_value_pred_errors: list[float] = [] + epoch_reward_zero_pcts: list[float] = [] + epoch_biou_deltas: list[float] = [] + epoch_mean_value_preds: list[float] = [] + epoch_annealed_aux_ce_weights: list[float] = [] + epoch_rl_loss_scales: list[float] = [] + epoch_reinforce_losses: list[float] = [] + epoch_entropy_losses: list[float] = [] + epoch_entropy_bonuses_used: list[float] = [] + epoch_alphas: list[float] = [] + epoch_action_dists: list[dict[str, float]] = [] + + prefetcher = CUDAPrefetcher(bundle.train_loader, DEVICE) + progress = tqdm(prefetcher, total=len(bundle.train_loader), desc=f"Epoch {epoch}/{max_epochs}", leave=False) + for batch in progress: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=tmax, + critic_loss_weight=critic_loss_weight, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=ce_weight, + dice_weight=dice_weight, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=max_epochs, + ) + epoch_losses.append(metrics["loss"]) + epoch_actor.append(metrics["actor_loss"]) + epoch_critic.append(metrics["critic_loss"]) + epoch_reward.append(metrics["mean_reward"]) + epoch_entropy.append(metrics["entropy"]) + epoch_ce.append(metrics["ce_loss"]) + epoch_dice_loss.append(metrics["dice_loss"]) + epoch_grad.append(metrics["grad_norm"]) + if "effective_steps" in metrics: + epoch_effective_steps.append(float(metrics["effective_steps"])) + if "mask_delta_mean" in metrics: + epoch_mask_deltas.append(metrics["mask_delta_mean"]) + if "advantage_mean" in metrics: + epoch_advantage_means.append(metrics["advantage_mean"]) + if "advantage_std" in metrics: + epoch_advantage_stds.append(metrics["advantage_std"]) + if "value_pred_error_mean" in metrics: + epoch_value_pred_errors.append(metrics["value_pred_error_mean"]) + if "reward_zeros_pct" in metrics: + epoch_reward_zero_pcts.append(float(metrics["reward_zeros_pct"])) + if "biou_delta_mean" in metrics: + epoch_biou_deltas.append(float(metrics["biou_delta_mean"])) + if "mean_value_pred" in metrics: + epoch_mean_value_preds.append(float(metrics["mean_value_pred"])) + if "annealed_aux_ce_weight" in metrics: + epoch_annealed_aux_ce_weights.append(float(metrics["annealed_aux_ce_weight"])) + if "rl_loss_scale_used" in metrics: + epoch_rl_loss_scales.append(float(metrics["rl_loss_scale_used"])) + if "reinforce_loss" in metrics: + epoch_reinforce_losses.append(float(metrics["reinforce_loss"])) + if "entropy_loss" in metrics: + epoch_entropy_losses.append(float(metrics["entropy_loss"])) + if "entropy_bonus_used" in metrics: + epoch_entropy_bonuses_used.append(float(metrics["entropy_bonus_used"])) + if "alpha" in metrics: + epoch_alphas.append(metrics["alpha"]) + if "action_distribution" in metrics and metrics["action_distribution"]: + epoch_action_dists.append(metrics["action_distribution"]) + + pred_mask = metrics["final_mask"] + gt_mask = batch["mask"].float() + inter = (pred_mask * gt_mask).sum(dim=(1, 2, 3)) + pred_sum = pred_mask.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + epoch_dices.extend(dice.detach().cpu().tolist()) + epoch_ious.extend(iou.detach().cpu().tolist()) + + head_lr_now = float(optimizer.param_groups[-1]["lr"]) + enc_lr_now = float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else head_lr_now + progress.set_postfix(loss=f"{metrics['loss']:.4f}", iou=f"{np.mean(epoch_ious):.4f}", lr=f"{head_lr_now:.2e}") + + should_validate = epoch % validate_interval == 0 or epoch == max_epochs + val_metrics: dict[str, float | None] = { + "val_loss": None, + "val_dice": None, + "val_iou": None, + "val_biou": None, + "val_decoder_dice": None, + "val_decoder_iou": None, + "val_decoder_biou": None, + "val_dice_gain": None, + "val_iou_gain": None, + "val_biou_gain": None, + "val_reward": None, + "val_entropy": None, + } + if should_validate: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: running validation on {len(bundle.val_loader)} batches..." + ) + validated_metrics = validate( + model, + bundle.val_loader, + run_dir=run_dir, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + gamma=DEFAULT_GAMMA, + critic_loss_weight=critic_loss_weight, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + val_metrics.update(validated_metrics) + scheduler.step() # always step up the scheduler + grad_stats: dict[str, Any] = {} + param_stats: dict[str, Any] = {} + if diagnostic_path is not None: + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_epoch_action_dist: dict[str, float] = {} + if epoch_action_dists: + all_act_keys = set() + for d in epoch_action_dists: + all_act_keys.update(d.keys()) + for k in sorted(all_act_keys): + avg_epoch_action_dist[k] = float(np.mean([d.get(k, 0.0) for d in epoch_action_dists])) + + row = { + "epoch": epoch, + "train_loss": float(np.mean(epoch_losses)) if epoch_losses else 0.0, + "train_actor_loss": float(np.mean(epoch_actor)) if epoch_actor else 0.0, + "train_critic_loss": float(np.mean(epoch_critic)) if epoch_critic else 0.0, + "train_mean_reward": float(np.mean(epoch_reward)) if epoch_reward else 0.0, + "train_entropy": float(np.mean(epoch_entropy)) if epoch_entropy else 0.0, + "train_ce_loss": float(np.mean(epoch_ce)) if epoch_ce else 0.0, + "train_dice_loss": float(np.mean(epoch_dice_loss)) if epoch_dice_loss else 0.0, + "train_dice": float(np.mean(epoch_dices)) if epoch_dices else 0.0, + "train_iou": float(np.mean(epoch_ious)) if epoch_ious else 0.0, + "grad_norm": float(np.mean(epoch_grad)) if epoch_grad else 0.0, + "lr": float(optimizer.param_groups[-1]["lr"]), + "encoder_lr": float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else float(optimizer.param_groups[-1]["lr"]), + "alpha": float(log_alpha.exp().detach().item()) if log_alpha is not None else None, + "train_effective_steps": float(np.mean(epoch_effective_steps)) if epoch_effective_steps else 0.0, + "train_mask_delta_mean": float(np.mean(epoch_mask_deltas)) if epoch_mask_deltas else 0.0, + "train_advantage_mean": float(np.mean(epoch_advantage_means)) if epoch_advantage_means else 0.0, + "train_advantage_std": _nanmean_or_default(epoch_advantage_stds, 0.0), + "train_value_pred_error": float(np.mean(epoch_value_pred_errors)) if epoch_value_pred_errors else 0.0, + "train_reward_zeros_pct": float(np.mean(epoch_reward_zero_pcts)) if epoch_reward_zero_pcts else 0.0, + "train_biou_delta_mean": float(np.mean(epoch_biou_deltas)) if epoch_biou_deltas else 0.0, + "train_mean_value_pred": float(np.mean(epoch_mean_value_preds)) if epoch_mean_value_preds else 0.0, + "train_annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "train_rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "train_reinforce_loss": float(np.mean(epoch_reinforce_losses)) if epoch_reinforce_losses else 0.0, + "train_entropy_loss": float(np.mean(epoch_entropy_losses)) if epoch_entropy_losses else 0.0, + "train_entropy_bonus_used": float(np.mean(epoch_entropy_bonuses_used)) if epoch_entropy_bonuses_used else 0.0, + "train_alpha": float(np.mean(epoch_alphas)) if epoch_alphas else 0.0, + "train_action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + "validated_this_epoch": should_validate, + **val_metrics, + } + if strategy == 3: + if row["train_advantage_std"] < 0.005: + low_advantage_std_streak += 1 + else: + low_advantage_std_streak = 0 + if abs(row["train_mean_value_pred"]) < 0.001: + low_mean_value_pred_streak += 1 + else: + low_mean_value_pred_streak = 0 + else: + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + if strategy3_freeze_status is not None: + row["strategy3_bootstrap_loaded"] = bool(strategy3_freeze_status["bootstrap_loaded"]) + row["strategy3_freeze_requested"] = bool(strategy3_freeze_status["freeze_requested"]) + row["strategy3_freeze_active"] = bool(strategy3_freeze_status["freeze_active"]) + row["strategy3_encoder_state"] = str(strategy3_freeze_status["encoder_state"]) + row["strategy3_decoder_state"] = str(strategy3_freeze_status["decoder_state"]) + row["strategy3_segmentation_head_state"] = str(strategy3_freeze_status["segmentation_head_state"]) + history.append(row) + + improved = False + early_stopping_improved_now = False + if should_validate: + selected_metric_value = _strategy_selection_metric_value(strategy, val_metrics) + row["selection_metric_name"] = selection_metric_name + row["selection_metric_value"] = selected_metric_value + improved = selected_metric_value > best_model_metric + if improved: + best_model_metric = selected_metric_value + if trial is not None: + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR, float(best_model_metric)) + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR, selection_metric_name) + early_stopping_monitor_value = _early_stopping_monitor_value( + row, + strategy=strategy, + monitor_name=early_stopping_monitor_name, + ) + early_stopping_active = epoch >= early_stopping_start_epoch + if early_stopping_active and early_stopping_monitor_value is not None: + early_stopping_improved_now = _early_stopping_improved( + early_stopping_monitor_value, + best_early_stopping_metric, + mode=early_stopping_mode, + min_delta=early_stopping_min_delta, + ) + if early_stopping_improved_now: + best_early_stopping_metric = early_stopping_monitor_value + patience_counter = 0 + else: + patience_counter += 1 + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = early_stopping_monitor_value + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = early_stopping_active + row["early_stopping_improved"] = early_stopping_improved_now + row["early_stopping_wait"] = int(patience_counter) + if improved and save_artifacts and ckpt_dir is not None: + save_checkpoint( + ckpt_dir / "best.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + else: + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = None + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = False + row["early_stopping_improved"] = False + row["early_stopping_wait"] = int(patience_counter) + + if save_artifacts and save_history_incrementally: + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + save_json(history_path, history) + + if diagnostic_path is not None and diagnostic_payload is not None: + epoch_alerts = _numerical_health_check(row, prefix=f"epoch[{epoch}]:") + if low_advantage_std_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] advantage_std collapsed - RL gradient near zero") + if low_mean_value_pred_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] critic degenerate - mean value prediction stuck near zero") + if int(grad_stats.get("n_nan", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected NaN gradients") + if int(grad_stats.get("n_inf", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected Inf gradients") + + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="val", + ) + + train_probe = _evaluate_probe_batches( + model, + train_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="train", + mc_cache_run_dir=run_dir, + ) + val_probe = _evaluate_probe_batches( + model, + val_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + epoch_alerts.extend(train_probe.get("alerts", [])) + epoch_alerts.extend(val_probe.get("alerts", [])) + + diagnostic_payload["epochs"].append( + { + "epoch": epoch, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "is_new_best": bool(improved), + "best_metric_name": selection_metric_name, + "best_metric_value_so_far": float(best_model_metric), + "patience_counter": int(patience_counter), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value_so_far": best_early_stopping_metric, + "history_row": dict(row), + "train_batch_summary": { + "loss": _summary_stats(epoch_losses), + "actor_loss": _summary_stats(epoch_actor), + "critic_loss": _summary_stats(epoch_critic), + "reward": _summary_stats(epoch_reward), + "entropy": _summary_stats(epoch_entropy), + "ce_loss": _summary_stats(epoch_ce), + "dice_loss": _summary_stats(epoch_dice_loss), + "grad_norm": _summary_stats(epoch_grad), + "dice": _summary_stats(epoch_dices), + "iou": _summary_stats(epoch_ious), + "effective_steps": _summary_stats(epoch_effective_steps), + "mask_delta": _summary_stats(epoch_mask_deltas), + "advantage_mean": _summary_stats(epoch_advantage_means), + "advantage_std": _summary_stats(epoch_advantage_stds), + "value_pred_error": _summary_stats(epoch_value_pred_errors), + "reward_zeros_pct": _summary_stats(epoch_reward_zero_pcts), + "biou_delta_mean": _summary_stats(epoch_biou_deltas), + "mean_value_pred": _summary_stats(epoch_mean_value_preds), + "annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "reinforce_loss": _summary_stats(epoch_reinforce_losses), + "entropy_loss": _summary_stats(epoch_entropy_losses), + "entropy_bonus_used": _summary_stats(epoch_entropy_bonuses_used), + "alpha": _summary_stats(epoch_alphas), + "action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + }, + "optimizer": { + "param_groups": _optimizer_diagnostics(optimizer), + "scheduler_last_lr": [float(value) for value in scheduler.get_last_lr()], + "target_entropy": float(target_entropy) if log_alpha is not None else 0.0, + }, + "grad_diagnostics": grad_stats, + "param_diagnostics": param_stats, + "probe_batches": { + "mode": epoch_probe_mode, + "train": _probe_batch_id_lists(train_probe_batches), + "val": _probe_batch_id_lists(val_probe_batches), + }, + "probes": { + "train_fixed": train_probe, + "val_fixed": val_probe, + }, + "probe_epoch_summary": { + "train_fixed": _format_probe_deterioration("train", train_probe, tmax), + "val_fixed": _format_probe_deterioration("val", val_probe, tmax), + }, + "alerts": epoch_alerts, + } + ) + save_json(diagnostic_path, diagnostic_payload) + + if save_artifacts and ckpt_dir is not None and SAVE_LATEST_EVERY_EPOCH and run_type != "trial": + save_checkpoint( + ckpt_dir / "latest.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + if save_artifacts and ckpt_dir is not None and CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0 and run_type != "trial": + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + + if trial is not None and should_validate: + reported_metric = row.get("selection_metric_value") + if reported_metric is None: + reported_metric = _strategy_selection_metric_value(strategy, val_metrics) + if reported_metric is None: + reported_metric = float(val_metrics["val_iou"]) + reported_metric = float(reported_metric) + trial.report(reported_metric, step=epoch) + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot( + trial, + current_best_value=best_model_metric, + ) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + if USE_TRIAL_PRUNING and epoch >= TRIAL_PRUNER_WARMUP_STEPS and trial.should_prune(): + raise optuna.TrialPruned( + f"Trial pruned at epoch {epoch} with " + f"{selection_metric_name}={reported_metric:.4f}" + ) + + if trial is not None and row.get("study_best_objective") is None: + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot(trial) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + + if VERBOSE_EPOCH_LOG: + tqdm.write(f"[{run_label}] Epoch {epoch}/{max_epochs}") + tqdm.write(json.dumps(row, indent=2)) + else: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: " + f"{format_concise_epoch_log(row, best_metric_name=selection_metric_name, best_metric_value=best_model_metric)}" + ) + + if should_validate and early_stopping_patience > 0 and patience_counter >= early_stopping_patience: + print( + f"Early stopping triggered at epoch {epoch}: " + f"monitor={early_stopping_monitor_name} mode={early_stopping_mode} " + f"best={best_early_stopping_metric} current={row.get('early_stopping_monitor_value')} " + f"min_delta={early_stopping_min_delta:.6g} wait={patience_counter}/{early_stopping_patience}." + ) + break + + elapsed = elapsed_before_resume + (time.time() - start_time) + if save_artifacts: + save_json(history_path, history) + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + summary = { + "best_model_metric_name": selection_metric_name, + "best_model_metric": float(best_model_metric), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value": best_early_stopping_metric, + "best_val_iou": max((float(r["val_iou"]) for r in history if r.get("val_iou") is not None), default=0.0), + "best_val_dice": max((float(r["val_dice"]) for r in history if r.get("val_dice") is not None), default=0.0), + "best_val_biou": max((float(r["val_biou"]) for r in history if r.get("val_biou") is not None), default=0.0), + "best_val_iou_gain": max((float(r["val_iou_gain"]) for r in history if r.get("val_iou_gain") is not None), default=0.0), + "best_val_biou_gain": max((float(r["val_biou_gain"]) for r in history if r.get("val_biou_gain") is not None), default=0.0), + "final_epoch": int(history[-1]["epoch"]) if history else int(start_epoch - 1), + "elapsed_seconds": elapsed, + "seconds_per_epoch": elapsed / max(len(history), 1), + "device_used": str(DEVICE), + "strategy": strategy, + "run_type": run_type, + "resumed": resume_source is not None, + } + if strategy3_freeze_status is not None: + summary.update( + { + "strategy3_bootstrap_loaded": bool(strategy3_freeze_status["bootstrap_loaded"]), + "strategy3_freeze_requested": bool(strategy3_freeze_status["freeze_requested"]), + "strategy3_freeze_active": bool(strategy3_freeze_status["freeze_active"]), + "strategy3_encoder_state": str(strategy3_freeze_status["encoder_state"]), + "strategy3_decoder_state": str(strategy3_freeze_status["decoder_state"]), + "strategy3_segmentation_head_state": str(strategy3_freeze_status["segmentation_head_state"]), + } + ) + if resume_source is not None: + summary["resume_source"] = resume_source + if save_artifacts: + save_json(run_dir / "summary.json", summary) + return summary, history + +"""============================================================================= +EVALUATION + SMOKE TEST +============================================================================= +""" + +def _save_rgb_panel(image_chw: np.ndarray, pred_hw: np.ndarray, gt_hw: np.ndarray, output_path: Path, title: str) -> None: + img = image_chw.transpose(1, 2, 0) + img = (img - img.min()) / (img.max() - img.min() + 1e-8) + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(img) + axes[0].set_title("Input") + axes[1].imshow(pred_hw, cmap="gray", vmin=0, vmax=1) + axes[1].set_title("Prediction") + axes[2].imshow(gt_hw, cmap="gray", vmin=0, vmax=1) + axes[2].set_title("Ground Truth") + for ax in axes: + ax.axis("off") + fig.suptitle(title) + fig.tight_layout() + fig.savefig(output_path, dpi=120) + plt.close(fig) + + +def _synchronize_device_for_timing(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _write_evaluation_timing_csv( + path: Path, + *, + timing_summary: dict[str, Any], +) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "scope", + "strategy", + "tmax", + "device", + "num_batches", + "num_samples", + "total_inference_ms", + "avg_batch_inference_ms", + "std_batch_inference_ms", + "avg_sample_inference_ms", + "std_sample_inference_ms", + "mean_per_image_inference_ms", + "std_per_image_inference_ms", + "mean_per_image_inference_seconds", + "std_per_image_inference_seconds", + ], + ) + writer.writeheader() + writer.writerow( + { + "scope": str(timing_summary["scope"]), + "strategy": int(timing_summary["strategy"]), + "tmax": int(timing_summary["tmax"]), + "device": str(timing_summary["device"]), + "num_batches": int(timing_summary["num_batches"]), + "num_samples": int(timing_summary["num_samples"]), + "total_inference_ms": f"{float(timing_summary['total_inference_ms']):.6f}", + "avg_batch_inference_ms": f"{float(timing_summary['avg_batch_inference_ms']):.6f}", + "std_batch_inference_ms": f"{float(timing_summary['std_batch_inference_ms']):.6f}", + "avg_sample_inference_ms": f"{float(timing_summary['avg_sample_inference_ms']):.6f}", + "std_sample_inference_ms": f"{float(timing_summary['std_sample_inference_ms']):.6f}", + "mean_per_image_inference_ms": f"{float(timing_summary['mean_per_image_inference_ms']):.6f}", + "std_per_image_inference_ms": f"{float(timing_summary['std_per_image_inference_ms']):.6f}", + "mean_per_image_inference_seconds": f"{float(timing_summary['mean_per_image_inference_seconds']):.9f}", + "std_per_image_inference_seconds": f"{float(timing_summary['std_per_image_inference_seconds']):.9f}", + } + ) + + +def evaluate_model( + *, + model: nn.Module, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + strategy: int, + tmax: int, + best_metric_name: str, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + pred_dir = ensure_dir(run_dir / "predictions") + pred_255_dir = ensure_dir(run_dir / "predictions_255") + + model.eval() + per_metric = {k: [] for k in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95")} + per_sample: list[dict[str, Any]] = [] + inference_total_ms = 0.0 + inference_batch_count = 0 + inference_sample_count = 0 + inference_batch_times_ms: list[float] = [] + inference_sample_times_ms: list[float] = [] + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + + with torch.inference_mode(): + prefetcher = CUDAPrefetcher(bundle.test_loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(bundle.test_loader), desc="Evaluating", leave=False): + image = batch["image"] + gt = batch["mask"] + sample_ids = [str(item) for item in batch["sample_id"]] + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + _synchronize_device_for_timing(DEVICE) + inference_start = time.perf_counter() + pred = infer_segmentation_mask( + model, + image, + tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="test", + mc_cache_run_dir=run_dir, + ).float() + _synchronize_device_for_timing(DEVICE) + inference_elapsed_ms = (time.perf_counter() - inference_start) * 1000.0 + inference_total_ms += inference_elapsed_ms + inference_batch_count += 1 + batch_size = int(pred.shape[0]) + inference_sample_count += batch_size + inference_batch_times_ms.append(float(inference_elapsed_ms)) + per_image_inference_ms = float(inference_elapsed_ms) / float(max(batch_size, 1)) + inference_sample_times_ms.extend([per_image_inference_ms] * batch_size) + pred_np = pred.cpu().numpy().astype(np.uint8) + gt_np = gt.cpu().numpy().astype(np.uint8) + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + for key, value in metrics.items(): + per_metric.setdefault(key, []).append(value) + per_sample.append( + { + "sample_id": sample_ids[idx], + **metrics, + "inference_time_ms": per_image_inference_ms, + "inference_time_seconds": per_image_inference_ms / 1000.0, + } + ) + mask_2d = pred_np[idx].squeeze() + PILImage.fromarray(mask_2d).save(pred_dir / f"{sample_ids[idx]}.png") + PILImage.fromarray((mask_2d * 255).astype(np.uint8)).save(pred_255_dir / f"{sample_ids[idx]}.png") + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate: dict[str, dict[str, float]] = {} + for key, values in per_metric.items(): + values_np = np.array(values, dtype=np.float32) + aggregate[key] = {"mean": float(values_np.mean()), "std": float(values_np.std())} + batch_times_np = np.array(inference_batch_times_ms, dtype=np.float64) + sample_times_np = np.array(inference_sample_times_ms, dtype=np.float64) + timing_summary = { + "scope": "test_set_evaluation", + "strategy": int(strategy), + "tmax": int(tmax), + "device": str(DEVICE), + "num_batches": int(inference_batch_count), + "num_samples": int(inference_sample_count), + "total_inference_ms": float(inference_total_ms), + "avg_batch_inference_ms": float(batch_times_np.mean()) if batch_times_np.size > 0 else 0.0, + "std_batch_inference_ms": float(batch_times_np.std()) if batch_times_np.size > 0 else 0.0, + "avg_sample_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_sample_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_seconds": float(sample_times_np.mean() / 1000.0) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_seconds": float(sample_times_np.std() / 1000.0) if sample_times_np.size > 0 else 0.0, + } + + save_json( + run_dir / "evaluation.json", + { + "strategy": strategy, + "best_metric_name": str(best_metric_name), + "metrics": aggregate, + "per_sample": per_sample, + "timing": timing_summary, + }, + ) + + df_all = pd.DataFrame(per_sample) + avg_row = {} + for column in df_all.columns: + avg_row[column] = df_all[column].mean() if pd.api.types.is_numeric_dtype(df_all[column]) else "AVERAGE" + df_samples = pd.concat([df_all, pd.DataFrame([avg_row])], ignore_index=True) + df_summary = pd.DataFrame(aggregate).T + df_summary.index.name = "metric" + df_low_iou = df_all[df_all["iou"] < 0.01] + history_path = run_dir / "history.json" + df_history = pd.DataFrame(load_json(history_path)) if history_path.exists() else None + + xlsx_path = run_dir / "evaluation_results.xlsx" + with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer: + df_samples.to_excel(writer, sheet_name="Per Sample", index=False) + df_summary.to_excel(writer, sheet_name="Summary") + if not df_low_iou.empty: + df_low_iou.to_excel(writer, sheet_name="Low IoU Samples", index=False) + if df_history is not None: + df_history.to_excel(writer, sheet_name="Training History", index=False) + + csv_rows = [{"sample_id": row["sample_id"]} for row in df_low_iou.to_dict(orient="records")] + save_json(run_dir / "evaluation_summary.json", {"mean_iou": aggregate["iou"]["mean"], "mean_dice": aggregate["dice"]["mean"]}) + pd.DataFrame(csv_rows).to_csv(run_dir / "low_iou_samples.csv", index=False) + _write_evaluation_timing_csv( + run_dir / "timing.csv", + timing_summary=timing_summary, + ) + return aggregate, per_sample + +def percent_root(percent: float) -> Path: + return ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}") + +def strategy_dir_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + return f"strategy_{strategy}_custom_vgg" + return f"strategy_{strategy}" + +def strategy_root_for_percent( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(percent_root(percent) / strategy_dir_name(strategy, model_config)) + +def final_root_for_strategy( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(strategy_root_for_percent(strategy, percent, model_config) / "final") + +def ensure_specific_checkpoint_scope(selector_name: str, selector_mode: str) -> None: + if selector_mode != "specific": + return + # Allow STRATEGY2 checkpoints with dict mapping for dynamic per-percent selection + if "strategy2" in selector_name.lower() and isinstance(STRATEGY2_SPECIFIC_CHECKPOINT, dict): + return + if len(STRATEGIES) != 1 or len(DATASET_PERCENTS) != 1: + raise ValueError( + f"{selector_name}=specific is only supported when exactly one strategy and one dataset percent are selected. " + f"Got STRATEGIES={STRATEGIES} and DATASET_PERCENTS={DATASET_PERCENTS}." + ) + +def resolve_checkpoint_path( + *, + run_dir: Path, + selector_mode: str, + specific_checkpoint: str | Path | dict, + purpose: str, +) -> Path: + run_dir = Path(run_dir) + if selector_mode == "latest": + checkpoint_path = run_dir / "checkpoints" / "latest.pt" + elif selector_mode == "best": + checkpoint_path = run_dir / "checkpoints" / "best.pt" + elif selector_mode == "specific": + ensure_specific_checkpoint_scope(purpose, selector_mode) + if not specific_checkpoint: + raise ValueError(f"{purpose}=specific requires a non-empty specific checkpoint path.") + checkpoint_path = Path(specific_checkpoint).expanduser().resolve() + else: + raise ValueError(f"Unsupported checkpoint selector mode '{selector_mode}' for {purpose}.") + + if not checkpoint_path.exists(): + raise FileNotFoundError(f"Checkpoint for {purpose} not found: {checkpoint_path}") + return checkpoint_path + +def resolve_train_resume_checkpoint_path(run_dir: Path) -> Path | None: + if TRAIN_RESUME_MODE == "off": + return None + return resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=TRAIN_RESUME_MODE, + specific_checkpoint=TRAIN_RESUME_SPECIFIC_CHECKPOINT, + purpose="train_resume_checkpoint", + ) + +def resolve_strategy2_checkpoint_path( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + if strategy != 3: + raise ValueError(f"Strategy 2 dependency checkpoint requested for unsupported strategy {strategy}.") + + specific_checkpoint = STRATEGY2_SPECIFIC_CHECKPOINT + if isinstance(specific_checkpoint, dict): + ctx = globals().get("CURRENT_FOLD_CONTEXT") + _phase_mode_fn = globals().get("using_fixed_phase_mode") + in_phase_mode = callable(_phase_mode_fn) and _phase_mode_fn() + if in_phase_mode and ctx is not None: + # Phase mode: key by phase index (split_repeat_index) + specific_checkpoint = specific_checkpoint.get(ctx.split_repeat_index, "") + else: + # Non-phase mode: key by dataset percent (float) + specific_checkpoint = specific_checkpoint.get(percent, "") + + checkpoint_path = resolve_checkpoint_path( + run_dir=final_root_for_strategy(2, percent, model_config), + selector_mode=STRATEGY2_CHECKPOINT_MODE, + specific_checkpoint=specific_checkpoint, + purpose="strategy2_checkpoint", + ) + + # Print which checkpoint is being used + checkpoint_label = run_identity_label(strategy=strategy, percent=percent) + ctx = globals().get("CURRENT_FOLD_CONTEXT") + if ctx is not None and abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12: + checkpoint_label = run_identity_label( + strategy=strategy, + percent=percent, + split_payload={ + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "dataset_percent": ctx.percent_fraction, + }, + ) + print(f"[Strategy 2 Checkpoint] {checkpoint_label} | Loading: {checkpoint_path}") + + return checkpoint_path + +def load_required_hparams(payload: dict[str, Any], *, source: str, strategy: int, percent: float) -> dict[str, Any]: + missing_keys = [name for name in REQUIRED_HPARAM_KEYS if name not in payload] + if missing_keys: + raise KeyError( + f"Incomplete hyperparameters for strategy={strategy}, percent={percent_text(percent)} from {source}. " + f"Missing keys: {missing_keys}. Required keys: {REQUIRED_HPARAM_KEYS}." + ) + return dict(payload) + +def load_saved_best_params_if_optuna_off( + strategy: int, + percent: float, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + _, study_root, _ = study_paths_for(strategy, percent, model_config) + best_params_path = study_root / "best_params.json" + if not best_params_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=True, but no saved best params were found " + f"for strategy={strategy}, percent={percent_text(percent)} at {best_params_path}." + ) + params = load_json(best_params_path) + params = load_required_hparams( + params, + source=str(best_params_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using saved best parameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {best_params_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def load_manual_hparams_if_optuna_off(strategy: int, percent: float) -> dict[str, Any]: + key = manual_hparams_key(strategy, percent) + if key not in MANUAL_HPARAMS_IF_OPTUNA_OFF: + raise KeyError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but no manual hyperparameter " + f"JSON filename was found for strategy={strategy}, percent={percent_text(percent)} under key '{key}'. " + f"Required keys: {REQUIRED_HPARAM_KEYS}." + ) + manual_filename = MANUAL_HPARAMS_IF_OPTUNA_OFF[key] + manual_path = (HARD_CODED_PARAM_DIR / manual_filename).resolve() + if not manual_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but the manual hyperparameter " + f"JSON file for strategy={strategy}, percent={percent_text(percent)} was not found at {manual_path}. " + f"Configured key='{key}', filename='{manual_filename}'." + ) + params = load_json(manual_path) + params = load_required_hparams( + params, + source=str(manual_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using manual hyperparameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {manual_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def resolve_job_params( + strategy: int, + percent: float, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if RUN_OPTUNA: + banner( + f"OPTUNA STUDY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return run_study( + strategy, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + if USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + return load_saved_best_params_if_optuna_off(strategy, percent, model_config=model_config) + + return load_manual_hparams_if_optuna_off(strategy, percent) + +def read_run_config_for_eval(run_dir: Path, checkpoint_path: Path) -> dict[str, Any]: + run_config_path = Path(run_dir) / "run_config.json" + if run_config_path.exists(): + return load_json(run_config_path) + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + return checkpoint_run_config_payload(ckpt) + +def run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + checkpoint_path = resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=EVAL_CHECKPOINT_MODE, + specific_checkpoint=EVAL_SPECIFIC_CHECKPOINT, + purpose="evaluation_checkpoint", + ) + effective_run_dir = Path(run_dir) + if not (effective_run_dir / "run_config.json").exists() and checkpoint_path.parent.name == "checkpoints": + effective_run_dir = checkpoint_path.parent.parent + print( + f"[Evaluation] {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)} " + f"| checkpoint={checkpoint_path}" + ) + runtime_config = read_run_config_for_eval(effective_run_dir, checkpoint_path) + set_current_job_params(runtime_config) + model_config = RuntimeModelConfig.from_payload(runtime_config).validate() + if strategy == 3 and runtime_config.get("strategy2_checkpoint_path"): + strategy2_checkpoint_path = runtime_config["strategy2_checkpoint_path"] + elif strategy == 3: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + dropout_p = float(runtime_config.get("dropout_p", DEFAULT_DROPOUT_P)) + tmax = int(runtime_config.get("tmax", DEFAULT_TMAX)) + eval_model, _description, _compiled = build_model( + strategy, + dropout_p, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + checkpoint_payload = load_checkpoint( + checkpoint_path, + model=eval_model, + device=DEVICE, + ) + best_metric_name = str( + checkpoint_payload.get("best_metric_name") + or runtime_config.get("best_checkpoint_metric_name") + or _strategy_selection_metric_name(strategy) + ) + aggregate, per_sample = evaluate_model( + model=eval_model, + model_config=model_config, + bundle=bundle, + run_dir=effective_run_dir, + strategy=strategy, + tmax=tmax, + best_metric_name=best_metric_name, + ) + evaluation_json_path = effective_run_dir / "evaluation.json" + evaluation_payload = load_json(evaluation_json_path) + evaluation_payload["checkpoint_mode"] = EVAL_CHECKPOINT_MODE + evaluation_payload["checkpoint_path"] = str(checkpoint_path) + evaluation_payload["best_metric_name"] = best_metric_name + if checkpoint_payload.get("best_metric_value") is not None: + evaluation_payload["best_metric_value"] = float(checkpoint_payload["best_metric_value"]) + save_json(evaluation_json_path, evaluation_payload) + del eval_model + run_cuda_cleanup( + context=f"evaluation {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return aggregate, per_sample + +def run_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + smoke_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + banner( + f"PRE-TRAINING SMOKE TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + smoke_root = ensure_dir(smoke_root) + if RUN_OPTUNA: + set_current_job_params() + elif USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + set_current_job_params( + load_saved_best_params_if_optuna_off(strategy, bundle.percent, model_config=model_config) + ) + else: + set_current_job_params(load_manual_hparams_if_optuna_off(strategy, bundle.percent)) + sample = bundle.test_ds[SMOKE_TEST_SAMPLE_INDEX] + image = sample["image"].unsqueeze(0).to(DEVICE) + raw_image = sample["image"].numpy() + raw_gt = sample["mask"].squeeze(0).numpy() + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + print_model_parameter_summary( + model=model, + description=f"{description} | Smoke Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + pred = infer_segmentation_mask( + model, + image, + DEFAULT_TMAX, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=_use_channels_last_for_run(model_config), + sample_ids=[str(sample["sample_id"])], + mc_cache_split="test", + mc_cache_run_dir=smoke_root, + ).float() + pred_np = pred[0, 0].detach().cpu().numpy() + panel_path = smoke_root / "smoke_panel.png" + raw_mask_path = smoke_root / "smoke_prediction.png" + _save_rgb_panel( + raw_image, + pred_np, + raw_gt, + panel_path, + f"Smoke Test | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}", + ) + PILImage.fromarray((pred_np * 255).astype(np.uint8)).save(raw_mask_path) + del model + run_cuda_cleanup( + context=f"smoke {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + print( + f"[Smoke Test] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} passed. " + f"Saved panel to {panel_path.name} and mask to {raw_mask_path.name}." + ) + +"""============================================================================= +OVERFIT TEST +============================================================================= +""" + +OVERFIT_HISTORY_KEYS = ( + "dice", + "iou", + "loss", + "reward", + "actor_loss", + "critic_loss", + "ce_loss", + "dice_loss", + "entropy", + "grad_norm", + "action_dist", + "reward_pos_pct", + "pred_fg_pct", + "gt_fg_pct", +) + +def empty_overfit_history() -> dict[str, list[Any]]: + return {key: [] for key in OVERFIT_HISTORY_KEYS} + +def load_overfit_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> dict[str, list[Any]]: + history = empty_overfit_history() + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict history at {history_path}, found {type(payload).__name__}.") + for key in OVERFIT_HISTORY_KEYS: + values = payload.get(key, []) + if isinstance(values, list): + history[key] = list(values[:checkpoint_epoch]) + return history + + epoch_metrics = checkpoint_payload.get("epoch_metrics", {}) + if isinstance(epoch_metrics, dict): + for key in OVERFIT_HISTORY_KEYS: + if key in epoch_metrics: + history[key].append(epoch_metrics[key]) + return history + +def _grad_diagnostics(model: nn.Module) -> dict[str, Any]: + raw = _unwrap_compiled(model) + groups: dict[str, list[float]] = {} + total_sq = 0.0 + n_nan = 0 + n_inf = 0 + n_zero = 0 + n_total_params = 0 + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + n_total_params += 1 + if param.grad is None: + n_zero += 1 + continue + grad_norm = float(param.grad.data.norm(2).item()) + if math.isnan(grad_norm): + n_nan += 1 + continue + if math.isinf(grad_norm): + n_inf += 1 + continue + total_sq += grad_norm ** 2 + group_name = name.split(".", 1)[0] + groups.setdefault(group_name, []).append(grad_norm) + + group_stats: dict[str, dict[str, float | int]] = {} + for group_name, norms in groups.items(): + group_stats[group_name] = { + "min": min(norms), + "max": max(norms), + "mean": sum(norms) / len(norms), + "count": len(norms), + } + return { + "global_norm": total_sq ** 0.5, + "groups": group_stats, + "n_nan": n_nan, + "n_inf": n_inf, + "n_zero_grad": n_zero, + "n_total": n_total_params, + } + +def _param_diagnostics(model: nn.Module, prev_params: dict[str, torch.Tensor] | None = None) -> dict[str, dict[str, float]]: + raw = _unwrap_compiled(model) + info: dict[str, dict[str, list[float]]] = {} + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + param_norm = float(param.data.norm(2).item()) + group_name = name.split(".", 1)[0] + entry = info.setdefault(group_name, {"norms": [], "update_ratios": []}) + entry["norms"].append(param_norm) + if prev_params is not None and name in prev_params: + delta = float((param.data - prev_params[name]).norm(2).item()) + entry["update_ratios"].append(delta / max(param_norm, 1e-12)) + + summary: dict[str, dict[str, float]] = {} + for group_name, values in info.items(): + norms = values["norms"] + ratios = values["update_ratios"] + summary[group_name] = { + "p_min": min(norms), + "p_max": max(norms), + "p_mean": sum(norms) / len(norms), + } + if ratios: + summary[group_name]["ur_min"] = min(ratios) + summary[group_name]["ur_max"] = max(ratios) + summary[group_name]["ur_mean"] = sum(ratios) / len(ratios) + return summary + +def _snapshot_params(model: nn.Module) -> dict[str, torch.Tensor]: + raw = _unwrap_compiled(model) + return { + name: param.data.detach().clone() + for name, param in raw.named_parameters() + if param.requires_grad + } + +def _action_distribution( + model: nn.Module, + image: torch.Tensor, + seg: torch.Tensor, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + *, + strategy: int | None = None, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[list[dict[str, float]], torch.Tensor]: + distributions: list[dict[str, float]] = [] + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_action_distribution") if strategy != 2 else max(int(tmax), 1) + refinement_context: dict[str, torch.Tensor] | None = None + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = seg.float() + for _step in range(effective_tmax): + if refinement_context is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _ = model.forward_from_state(state) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta).to(dtype=seg.dtype) + distributions.append(_strategy3_delta_distribution(delta)) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + total_pixels = max(actions.numel(), 1) + step_dist: dict[str, float] = {} + action_count = int(policy_logits.shape[1]) + for action_idx in range(action_count): + step_dist[str(action_idx)] = float((actions == action_idx).sum().item()) / total_pixels * 100.0 + distributions.append(step_dist) + if refinement_context is not None: + return distributions, threshold_binary_mask(seg.float()).float() + return distributions, seg + +def _numerical_health_check(outputs_dict: dict[str, Any], prefix: str = "") -> list[str]: + alerts: list[str] = [] + for name, value in outputs_dict.items(): + if value is None: + continue + if isinstance(value, (int, float)): + if math.isnan(value): + alerts.append(f"{prefix}{name} = NaN") + elif math.isinf(value): + alerts.append(f"{prefix}{name} = Inf") + elif name == "train_reward_zeros_pct" and float(value) > 98.0: + alerts.append(f"{prefix}reward is degenerate (>98% zero-reward pixels)") + continue + if torch.is_tensor(value): + if torch.isnan(value).any(): + alerts.append(f"{prefix}{name} contains NaN") + if torch.isinf(value).any(): + alerts.append(f"{prefix}{name} contains Inf") + return alerts + +def _batch_binary_metrics(pred: torch.Tensor, gt: torch.Tensor) -> tuple[list[float], list[float]]: + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt.detach().cpu().numpy().astype(np.uint8) + dices: list[float] = [] + ious: list[float] = [] + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + dices.append(float(metrics["dice"])) + ious.append(float(metrics["iou"])) + return dices, ious + +def diagnostic_path_for_run(run_dir: Path) -> Path: + return Path(run_dir) / "diagnostic.json" + +def _summary_stats(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "mean": None, "std": None, "min": None, "max": None} + arr = np.asarray(values, dtype=np.float64) + return { + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std()), + "min": float(arr.min()), + "max": float(arr.max()), + } + +def _nanmean_or_default(values: list[float], default: float = 0.0) -> float: + if not values: + return float(default) + arr = np.asarray(values, dtype=np.float64) + if np.isnan(arr).all(): + return float(default) + return float(np.nanmean(arr)) + +def _tensor_stats(tensor: torch.Tensor | None) -> dict[str, Any] | None: + if tensor is None: + return None + data = tensor.detach().float() + flat = data.reshape(-1) + if flat.numel() == 0: + return {"shape": list(data.shape), "dtype": str(tensor.dtype), "numel": 0} + return { + "shape": list(data.shape), + "dtype": str(tensor.dtype), + "numel": int(flat.numel()), + "mean": float(flat.mean().item()), + "std": float(flat.std(unbiased=False).item()), + "min": float(flat.min().item()), + "max": float(flat.max().item()), + } + +def _action_histogram(actions: torch.Tensor, action_count: int) -> dict[str, float]: + total_pixels = max(actions.numel(), 1) + return { + str(action_idx): float((actions == action_idx).sum().item()) / total_pixels * 100.0 + for action_idx in range(action_count) + } + +def _jsonable_action_distribution(distributions: list[dict[int, float]] | list[dict[str, float]]) -> list[dict[str, float]]: + jsonable: list[dict[str, float]] = [] + for step_dist in distributions: + jsonable.append({str(key): float(value) for key, value in step_dist.items()}) + return jsonable + +def _average_action_distributions( + distributions_per_batch: list[list[dict[str, float]]], + steps: int, +) -> list[dict[str, float]]: + averaged: list[dict[str, float]] = [] + if not distributions_per_batch: + return averaged + for step_idx in range(steps): + action_keys = sorted( + { + str(action_idx) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) + for action_idx in batch_dist[step_idx].keys() + } + ) + if not action_keys: + continue + step_summary: dict[str, float] = {} + for action_idx in action_keys: + values = [ + float(batch_dist[step_idx][action_idx]) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) and action_idx in batch_dist[step_idx] + ] + step_summary[str(action_idx)] = float(np.mean(values)) if values else 0.0 + averaged.append(step_summary) + return averaged + +def _trajectory_degradation_summary(step_trace: list[dict[str, Any]]) -> dict[str, Any]: + if not step_trace: + return {} + ious = [float(step.get("iou_mean", 0.0)) for step in step_trace] + dices = [float(step.get("dice_mean", 0.0)) for step in step_trace] + ts = [int(step.get("t", idx)) for idx, step in enumerate(step_trace)] + init_iou = ious[0] + init_dice = dices[0] + best_iou = max(ious) + best_dice = max(dices) + best_iou_t = ts[ious.index(best_iou)] + best_dice_t = ts[dices.index(best_dice)] + first_worse_than_initial_iou_t = next((ts[idx] for idx, value in enumerate(ious[1:], start=1) if value < init_iou - 1e-6), None) + first_worse_than_prev_iou_t = next((ts[idx] for idx in range(1, len(ious)) if ious[idx] < ious[idx - 1] - 1e-6), None) + largest_iou_drop = max(best_iou - value for value in ious) + largest_iou_drop_t = ts[max(range(len(ious)), key=lambda idx: best_iou - ious[idx])] + return { + "steps_recorded": len(step_trace) - 1, + "best_iou_t": best_iou_t, + "best_iou": best_iou, + "best_dice_t": best_dice_t, + "best_dice": best_dice, + "final_t": ts[-1], + "final_iou": ious[-1], + "final_dice": dices[-1], + "delta_final_vs_init_iou": ious[-1] - init_iou, + "delta_final_vs_init_dice": dices[-1] - init_dice, + "delta_final_vs_best_iou": ious[-1] - best_iou, + "delta_final_vs_best_dice": dices[-1] - best_dice, + "first_worse_than_initial_iou_t": first_worse_than_initial_iou_t, + "first_worse_than_prev_iou_t": first_worse_than_prev_iou_t, + "largest_iou_drop_from_best": largest_iou_drop, + "largest_iou_drop_t": largest_iou_drop_t, + } + +def _average_rollout_traces(traces_per_batch: list[list[dict[str, Any]]]) -> list[dict[str, Any]]: + averaged: list[dict[str, Any]] = [] + if not traces_per_batch: + return averaged + max_steps = max(len(trace) for trace in traces_per_batch) + for step_idx in range(max_steps): + present = [trace[step_idx] for trace in traces_per_batch if step_idx < len(trace)] + if not present: + continue + reward_pos_values = [float(step["reward_pos_pct"]) for step in present if step.get("reward_pos_pct") is not None] + value_scores = [float(step["value_score"]) for step in present if step.get("value_score") is not None] + averaged.append( + { + "t": int(np.mean([float(step.get("t", step_idx)) for step in present])), + "dice_mean": float(np.mean([float(step.get("dice_mean", 0.0)) for step in present])), + "iou_mean": float(np.mean([float(step.get("iou_mean", 0.0)) for step in present])), + "pred_fg_pct": float(np.mean([float(step.get("pred_fg_pct", 0.0)) for step in present])), + "reward_pos_pct": float(np.mean(reward_pos_values)) if reward_pos_values else None, + "value_score": float(np.mean(value_scores)) if value_scores else None, + } + ) + return averaged + +def _rollout_probe_trace( + model: nn.Module, + image: torch.Tensor, + gt_mask: torch.Tensor, + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_rollout_probe_trace") if strategy != 2 else max(int(tmax), 1) + rollout_trace: list[dict[str, Any]] = [] + batch_action_dist: list[dict[str, float]] = [] + reward_pos_pct = 0.0 + init_fg_pct = 0.0 + first_action_dist: dict[str, float] | None = None + first_policy_stats: dict[str, Any] | None = None + first_value_stats: dict[str, Any] | None = None + decoder_prob_stats: dict[str, Any] | None = None + first_entropy: float | None = None + selected_t = 0 + + def record_step( + *, + t: int, + seg_tensor: torch.Tensor, + seg_prev: torch.Tensor | None = None, + delta_map: torch.Tensor | None = None, + value_score: float | None = None, + action_distribution: dict[str, float] | None = None, + reward_pos: float | None = None, + reward_map_tensor: torch.Tensor | None = None, + step_entropy: float | None = None, + policy_stats: dict[str, Any] | None = None, + ) -> None: + pred_t = threshold_binary_mask(seg_tensor.float()).float() + dice_vals, iou_vals = _batch_binary_metrics(pred_t, gt_mask.float()) + step_data: dict[str, Any] = { + "t": int(t), + "dice_mean": float(np.mean(dice_vals)) if dice_vals else 0.0, + "iou_mean": float(np.mean(iou_vals)) if iou_vals else 0.0, + "pred_fg_pct": float(pred_t.sum().item()) / max(pred_t.numel(), 1) * 100.0, + "reward_pos_pct": None if reward_pos is None else float(reward_pos), + "value_score": None if value_score is None else float(value_score), + "action_distribution": action_distribution, + "seg_soft_stats": _tensor_stats(seg_tensor), + "entropy": step_entropy, + "policy_logit_stats": policy_stats, + } + if seg_prev is not None: + delta = seg_tensor.float() - seg_prev.float() + abs_delta = delta.abs() + # Binary mask flip tracking: how many pixels actually change in the thresholded output + binary_prev = threshold_binary_mask(seg_prev.float()).float() + binary_curr = threshold_binary_mask(seg_tensor.float()).float() + binary_flipped = (binary_prev != binary_curr) + flipped_to_fg = binary_flipped & (binary_curr > 0.5) + flipped_to_bg = binary_flipped & (binary_curr < 0.5) + gt_binary_local = (gt_mask.float() > 0.5) + correct_flips = binary_flipped & ((binary_curr > 0.5) == gt_binary_local) + wrong_flips = binary_flipped & ((binary_curr > 0.5) != gt_binary_local) + total_px = max(binary_prev.numel(), 1) + step_data["mask_delta"] = { + "mean_abs_change": float(abs_delta.mean().item()), + "max_change": float(abs_delta.max().item()), + "pct_pixels_changed": float((abs_delta > 1e-6).float().mean().item() * 100.0), + "fg_gained_pct": float((delta > 1e-6).float().mean().item() * 100.0), + "fg_lost_pct": float((delta < -1e-6).float().mean().item() * 100.0), + } + step_data["binary_mask_flips"] = { + "total_flipped_pct": float(binary_flipped.float().sum().item() / total_px * 100.0), + "flipped_to_fg_pct": float(flipped_to_fg.float().sum().item() / total_px * 100.0), + "flipped_to_bg_pct": float(flipped_to_bg.float().sum().item() / total_px * 100.0), + "correct_flips_pct": float(correct_flips.float().sum().item() / total_px * 100.0), + "wrong_flips_pct": float(wrong_flips.float().sum().item() / total_px * 100.0), + "flip_accuracy": float(correct_flips.float().sum().item() / max(binary_flipped.float().sum().item(), 1.0) * 100.0), + } + if reward_map_tensor is not None: + step_data["reward_stats"] = { + "mean": float(reward_map_tensor.mean().item()), + "std": float(reward_map_tensor.std().item()), + "min": float(reward_map_tensor.min().item()), + "max": float(reward_map_tensor.max().item()), + "pct_positive": float((reward_map_tensor > 0).float().mean().item() * 100.0), + "pct_negative": float((reward_map_tensor < 0).float().mean().item() * 100.0), + "pct_zero": float((reward_map_tensor.abs() < 1e-8).float().mean().item() * 100.0), + } + if delta_map is not None and seg_prev is not None: + gt_f = gt_mask.float() + ref_pred = threshold_binary_mask(seg_prev.float()).float() + gt_fg = (gt_f > 0.5).squeeze(1) + gt_bg = ~gt_fg + pred_fg = (ref_pred > 0.5).squeeze(1) + tp_mask = pred_fg & gt_fg + tn_mask = (~pred_fg) & gt_bg + fp_mask = pred_fg & gt_bg + fn_mask = (~pred_fg) & gt_fg + delta_squeezed = delta_map.squeeze(1).detach().float() + action_breakdown: dict[str, dict[str, float]] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + if pixel_mask.any(): + class_delta = delta_squeezed[pixel_mask] + action_breakdown[label] = { + "mean_delta": float(class_delta.mean().item()), + "mean_abs_delta": float(class_delta.abs().mean().item()), + "positive_pct": float((class_delta > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((class_delta < -1e-6).float().mean().item() * 100.0), + } + else: + action_breakdown[label] = { + "mean_delta": 0.0, + "mean_abs_delta": 0.0, + "positive_pct": 0.0, + "negative_pct": 0.0, + } + step_data["action_on_class"] = action_breakdown + if reward_map_tensor is not None: + reward_squeezed = reward_map_tensor.squeeze(1) if reward_map_tensor.ndim == 4 else reward_map_tensor + per_class_reward: dict[str, float] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + per_class_reward[label] = float(reward_squeezed[pixel_mask].mean().item()) if pixel_mask.any() else 0.0 + step_data["per_action_reward"] = per_class_reward + rollout_trace.append(step_data) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + record_step(t=0, seg_tensor=torch.sigmoid(logits)) + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": _trajectory_degradation_summary(rollout_trace), + "selected_t": selected_t, + "effective_tmax": effective_tmax, + } + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + decoder_prob_stats = _tensor_stats(refinement_context["decoder_prob"]) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + selected_t = 0 + + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, value_t = model.forward_from_state(state_t) + current_score = float(value_t.detach().mean().item()) + delta = _strategy3_policy_delta(policy_logits).to(dtype=seg.dtype) + + action_dist = _strategy3_delta_distribution(delta) + batch_action_dist.append(action_dist) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_value_stats = _tensor_stats(value_t) + first_entropy = 0.0 + record_step(t=0, seg_tensor=seg, value_score=current_score, step_entropy=first_entropy, policy_stats=first_policy_stats) + + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + step_entropy_val = 0.0 + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + seg_prev=seg, + delta_map=delta, + action_distribution=action_dist, + reward_pos=step_reward_pos, + reward_map_tensor=reward_map, + step_entropy=step_entropy_val, + policy_stats=_tensor_stats(policy_logits), + ) + seg = seg_next + selected_t = step_idx + 1 + + pred = threshold_binary_mask(seg.float()).float() + decoder_pred = threshold_binary_mask(refinement_context["decoder_prob"].float()).float() + decoder_dice_vals, decoder_iou_vals = _batch_binary_metrics(decoder_pred, gt_mask.float()) + decoder_baseline = { + "dice": float(np.mean(decoder_dice_vals)) if decoder_dice_vals else 0.0, + "iou": float(np.mean(decoder_iou_vals)) if decoder_iou_vals else 0.0, + "fg_pct": float(decoder_pred.sum().item()) / max(decoder_pred.numel(), 1) * 100.0, + } + final_dice_vals, final_iou_vals = _batch_binary_metrics(pred, gt_mask.float()) + rl_vs_decoder = { + "decoder_dice": decoder_baseline["dice"], + "decoder_iou": decoder_baseline["iou"], + "final_dice": float(np.mean(final_dice_vals)) if final_dice_vals else 0.0, + "final_iou": float(np.mean(final_iou_vals)) if final_iou_vals else 0.0, + "dice_gain": (float(np.mean(final_dice_vals)) if final_dice_vals else 0.0) - decoder_baseline["dice"], + "iou_gain": (float(np.mean(final_iou_vals)) if final_iou_vals else 0.0) - decoder_baseline["iou"], + } + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = selected_t + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": selected_t, + "effective_tmax": effective_tmax, + "decoder_baseline": decoder_baseline, + "rl_vs_decoder": rl_vs_decoder, + } + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + record_step(t=0, seg_tensor=seg) + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, entropy = sample_actions(policy_logits, stochastic=False) + action_dist = _action_histogram(actions, int(policy_logits.shape[1])) + batch_action_dist.append({str(key): float(value) for key, value in action_dist.items()}) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_entropy = float(entropy.detach().item()) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + reward_map = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + action_distribution=action_dist, + reward_pos=step_reward_pos, + ) + seg = seg_next + pred = seg.float() + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = len(rollout_trace) - 1 + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": len(rollout_trace) - 1, + "effective_tmax": effective_tmax, + } + +def _format_probe_deterioration(label: str, probe_payload: dict[str, Any], tmax: int) -> str: + effective_tmax = int(probe_payload.get("effective_tmax", tmax)) + degradation = probe_payload.get("aggregate", {}).get("degradation", {}) + if not degradation: + return f"{label}: no degradation trace" + first_worse = degradation.get("first_worse_than_initial_iou_t") + first_step_drop = degradation.get("first_worse_than_prev_iou_t") + best_t = degradation.get("best_iou_t") + final_t = degradation.get("final_t") + delta_best = degradation.get("delta_final_vs_best_iou") + worst_t = degradation.get("largest_iou_drop_t") + worst_drop = degradation.get("largest_iou_drop_from_best") + return ( + f"{label}: first_worse={first_worse}/{effective_tmax} " + f"first_drop={first_step_drop}/{effective_tmax} " + f"best={best_t}/{effective_tmax} final={final_t}/{effective_tmax} " + f"final-best_iou={float(delta_best):+.4f} " + f"worst={worst_t}/{effective_tmax} drop={float(worst_drop):+.4f}" + ) + +def _optimizer_diagnostics(optimizer: torch.optim.Optimizer) -> list[dict[str, Any]]: + groups: list[dict[str, Any]] = [] + for group_idx, group in enumerate(optimizer.param_groups): + num_tensors = len(group.get("params", [])) + num_elements = int(sum(param.numel() for param in group.get("params", []))) + groups.append( + { + "index": group_idx, + "lr": float(group.get("lr", 0.0)), + "weight_decay": float(group.get("weight_decay", 0.0)), + "num_tensors": num_tensors, + "num_elements": num_elements, + } + ) + return groups + +def _probe_batches_from_indices( + dataset: BUSIDataset, + *, + indices: list[int], + device: torch.device, +) -> list[dict[str, Any]]: + batches: list[dict[str, Any]] = [] + for start in range(0, len(indices), BATCH_SIZE): + batch_indices = indices[start:start + BATCH_SIZE] + if not batch_indices: + continue + images = torch.stack([dataset._images[idx].clone() for idx in batch_indices], dim=0) + masks = torch.stack([dataset._masks[idx].clone() for idx in batch_indices], dim=0) + sample_ids = [Path(dataset.sample_records[idx]["filename"]).stem for idx in batch_indices] + batches.append( + to_device( + { + "image": images, + "mask": masks, + "sample_id": sample_ids, + "dataset": current_dataset_name(), + }, + device, + ) + ) + return batches + +def _fixed_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + return _probe_batches_from_indices( + dataset, + indices=list(range(max_samples)), + device=device, + ) + +def _rolling_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, + epoch: int, + split_tag: str, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + if max_samples <= 0: + return [] + if max_samples >= len(dataset): + indices = list(range(len(dataset))) + else: + rng = random.Random(SEED + stable_int_from_text(f"probe:{split_tag}:epoch:{int(epoch)}")) + indices = rng.sample(range(len(dataset)), k=max_samples) + return _probe_batches_from_indices(dataset, indices=indices, device=device) + +def _probe_batch_id_lists(fixed_batches: list[dict[str, Any]]) -> list[list[str]]: + return [list(batch.get("sample_id", [])) for batch in fixed_batches] + +def _reference_eval_payloads(project_dir: Path, percent: float) -> dict[str, Any]: + refs: dict[str, Any] = {} + pct = percent_label(percent) + strat2_dir = project_dir / "strat2_history" + strat3_dir = project_dir / "strat3_history_best" + strat2_candidates = [ + strat2_dir / f"evaluation_strat2_pc{pct}.json", + strat2_dir / f"evaluation_strat2_pct{pct}.json", + ] + strat3_candidate = strat3_dir / f"evaluation_{pct} (1)" + for candidate in strat2_candidates: + if candidate.exists(): + refs["strategy2_reference"] = load_json(candidate) + break + if strat3_candidate.exists(): + refs["strategy3_best_reference"] = load_json(strat3_candidate) + return refs + +def empty_epoch_diagnostic_payload( + *, + run_type: str, + run_config: dict[str, Any], + bundle: DataBundle, + train_probe_batches: list[dict[str, Any]], + val_probe_batches: list[dict[str, Any]], +) -> dict[str, Any]: + payload = { + "diagnostic_version": 1, + "run_type": run_type, + "strategy": int(run_config["strategy"]), + "dataset_percent": float(bundle.percent), + "run_config": run_config, + "probe_setup": { + "mode": str(run_config.get("epoch_probe_mode", "fixed")), + "train_probe_batches": _probe_batch_id_lists(train_probe_batches), + "val_probe_batches": _probe_batch_id_lists(val_probe_batches), + "train_probe_batch_count": len(train_probe_batches), + "val_probe_batch_count": len(val_probe_batches), + "tmax": int(run_config.get("tmax", DEFAULT_TMAX)), + }, + "epochs": [], + } + payload.update(_reference_eval_payloads(PROJECT_DIR, bundle.percent)) + return payload + +def load_epoch_diagnostic_for_resume( + path: Path, + checkpoint_payload: dict[str, Any] | None, + default_payload: dict[str, Any], +) -> dict[str, Any]: + payload = dict(default_payload) + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) if checkpoint_payload is not None else 0 + if path.exists(): + loaded = load_json(path) + if isinstance(loaded, dict): + payload.update({k: v for k, v in loaded.items() if k != "epochs"}) + epochs = loaded.get("epochs", []) + if isinstance(epochs, list): + payload["epochs"] = [dict(row) for row in epochs if isinstance(row, dict) and int(row.get("epoch", 0)) <= checkpoint_epoch] + if "epochs" not in payload: + payload["epochs"] = [] + return payload + +def _evaluate_probe_batches( + model: nn.Module, + fixed_batches: list[dict[str, Any]], + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + if not fixed_batches: + return {"n_batches": 0, "batch_details": [], "aggregate": {}, "alerts": []} + + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_evaluate_probe_batches") if strategy != 2 else max(int(tmax), 1) + was_training = model.training + batch_details: list[dict[str, Any]] = [] + alerts: list[str] = [] + action_distributions: list[list[dict[str, float]]] = [] + rollout_traces: list[list[dict[str, Any]]] = [] + metric_lists: dict[str, list[float]] = { + key: [] + for key in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95") + } + reward_pos_values: list[float] = [] + pred_fg_values: list[float] = [] + gt_fg_values: list[float] = [] + init_fg_values: list[float] = [] + decoder_dices: list[float] = [] + decoder_ious: list[float] = [] + iou_gains: list[float] = [] + dice_gains: list[float] = [] + + model.eval() + try: + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + with torch.inference_mode(): + for batch_index, batch in enumerate(fixed_batches): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + + rollout_probe = _rollout_probe_trace( + model, + image, + gt_mask, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + pred = rollout_probe["final_pred"].float() + batch_action_dist = rollout_probe["action_distribution"] + reward_pos_pct = float(rollout_probe["reward_pos_pct"]) + init_fg_pct = float(rollout_probe["init_fg_pct"]) + first_action_dist = rollout_probe["first_action_distribution"] + first_policy_stats = rollout_probe["first_policy_stats"] + first_value_stats = rollout_probe["first_value_stats"] + decoder_prob_stats = rollout_probe["decoder_prob_stats"] + first_entropy = rollout_probe["first_entropy"] + rollout_trace = rollout_probe["rollout_trace"] + rollout_summary = rollout_probe["rollout_summary"] + + if batch_action_dist: + action_distributions.append(batch_action_dist) + if rollout_trace: + rollout_traces.append(rollout_trace) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + reward_pos_values.append(reward_pos_pct) + pred_fg_values.append(pred_fg_pct) + gt_fg_values.append(gt_fg_pct) + init_fg_values.append(init_fg_pct) + rl_vs_dec = rollout_probe.get("rl_vs_decoder") + if rl_vs_dec: + decoder_dices.append(rl_vs_dec["decoder_dice"]) + decoder_ious.append(rl_vs_dec["decoder_iou"]) + iou_gains.append(rl_vs_dec["iou_gain"]) + dice_gains.append(rl_vs_dec["dice_gain"]) + + batch_alerts = _numerical_health_check( + { + "pred": pred, + "gt_mask": gt_mask, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "reward_pos_pct": reward_pos_pct, + "first_entropy": first_entropy if first_entropy is not None else 0.0, + }, + prefix=f"probe[{batch_index}]:", + ) + alerts.extend(batch_alerts) + + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt_mask.detach().cpu().numpy().astype(np.uint8) + per_sample: list[dict[str, Any]] = [] + for sample_index in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[sample_index], gt_np[sample_index]) + per_sample.append( + { + "sample_id": sample_ids[sample_index] if sample_index < len(sample_ids) else f"sample_{sample_index}", + **{key: float(value) for key, value in metrics.items()}, + } + ) + for key, value in metrics.items(): + metric_lists.setdefault(key, []).append(float(value)) + + batch_details.append( + { + "batch_index": batch_index, + "sample_ids": sample_ids, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "init_fg_pct": init_fg_pct, + "reward_pos_pct": reward_pos_pct, + "action_distribution": _jsonable_action_distribution(batch_action_dist), + "first_action_distribution": first_action_dist, + "first_entropy": first_entropy, + "decoder_prob_stats": decoder_prob_stats, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "pred_stats": _tensor_stats(pred), + "rollout_trace": rollout_trace, + "rollout_summary": rollout_summary, + "decoder_baseline": rollout_probe.get("decoder_baseline"), + "rl_vs_decoder": rollout_probe.get("rl_vs_decoder"), + "alerts": batch_alerts, + "per_sample": per_sample, + } + ) + finally: + model.train(was_training) + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate_trace = _average_rollout_traces(rollout_traces) + rl_vs_decoder_aggregate: dict[str, Any] = {} + if decoder_dices: + rl_vs_decoder_aggregate = { + "decoder_dice": _summary_stats(decoder_dices), + "decoder_iou": _summary_stats(decoder_ious), + "iou_gain": _summary_stats(iou_gains), + "dice_gain": _summary_stats(dice_gains), + } + return { + "n_batches": len(fixed_batches), + "batch_details": batch_details, + "aggregate": { + "metrics": {key: _summary_stats(values) for key, values in metric_lists.items()}, + "reward_pos_pct": _summary_stats(reward_pos_values), + "pred_fg_pct": _summary_stats(pred_fg_values), + "gt_fg_pct": _summary_stats(gt_fg_values), + "init_fg_pct": _summary_stats(init_fg_values), + "action_distribution": _average_action_distributions(action_distributions, effective_tmax), + "rollout_trace": aggregate_trace, + "degradation": _trajectory_degradation_summary(aggregate_trace), + "rl_vs_decoder": rl_vs_decoder_aggregate, + }, + "alerts": alerts, + "effective_tmax": effective_tmax, + } + +def run_overfit_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + overfit_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + overfit_root = ensure_dir(overfit_root) + ckpt_dir = ensure_dir(overfit_root / "checkpoints") + history_path = checkpoint_history_path(overfit_root, "overfit") + run_config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": "overfit", + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "max_epochs": OVERFIT_N_EPOCHS, + "head_lr": OVERFIT_HEAD_LR, + "encoder_lr": OVERFIT_ENCODER_LR, + "weight_decay": DEFAULT_WEIGHT_DECAY, + "dropout_p": DEFAULT_DROPOUT_P, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "gamma": DEFAULT_GAMMA, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + if strategy == 3: + run_config.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(run_config["strategy3_freeze_bootstrapped_segmentation"]) + ) + run_config.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + run_config.setdefault("decoder_lr", 0.0 if bootstrap_freeze else OVERFIT_HEAD_LR * 0.1) + run_config.setdefault("rl_lr", OVERFIT_HEAD_LR) + run_config.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + run_config.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + run_config.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + run_config.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + run_config.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + run_config.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + run_config.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + run_config.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + run_config.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + run_config.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + run_config.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + run_config.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + run_config.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + run_config.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + run_config.setdefault("elastic_aug_prob", 0.3) + run_config.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + run_config.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + run_config.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + run_config.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + run_config.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + run_config.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + run_config.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + run_config.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + run_config.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + run_config.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + run_config.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + run_config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + run_config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + save_json(overfit_root / "run_config.json", run_config) + set_current_job_params(run_config) + + banner( + f"OVERFIT TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + effective_test_tmax = _resolve_test_iteration_tmax(DEFAULT_TMAX, context="run_overfit_test") if strategy != 2 else max(int(DEFAULT_TMAX), 1) + print( + f"[Overfit] Fixed batches={OVERFIT_N_BATCHES}, epochs={OVERFIT_N_EPOCHS}, " + f"head_lr={OVERFIT_HEAD_LR:.2e}, encoder_lr={OVERFIT_ENCODER_LR:.2e}" + ) + + fixed_batches: list[dict[str, Any]] = [] + for batch_index, batch in enumerate(bundle.train_loader): + fixed_batches.append(to_device(batch, DEVICE)) + if batch_index + 1 >= OVERFIT_N_BATCHES: + break + if not fixed_batches: + raise RuntimeError("Overfit test could not collect any training batches.") + if len(fixed_batches) < OVERFIT_N_BATCHES: + print(f"[Overfit] Warning: only {len(fixed_batches)} train batch(es) available.") + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(overfit_root) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=f"{description} | Overfit Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + + optimizer = make_optimizer( + model, + strategy, + head_lr=OVERFIT_HEAD_LR, + encoder_lr=OVERFIT_ENCODER_LR, + weight_decay=DEFAULT_WEIGHT_DECAY, + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + log_alpha: torch.Tensor | None = None + alpha_optimizer: Adam | None = None + target_entropy = 0.0 + + history = empty_overfit_history() + prev_loss: float | None = None + best_dice = -1.0 + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + expected_run_type="overfit", + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + best_dice = float(checkpoint_payload["best_metric_value"]) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_overfit_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", "overfit"), + } + print( + f"[Resume] overfit run continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{OVERFIT_N_EPOCHS}." + ) + if history["loss"]: + prev_loss = float(history["loss"][-1]) + prev_params = _snapshot_params(model) + start_time = time.time() + + for epoch in range(start_epoch, OVERFIT_N_EPOCHS + 1): + full_dump = epoch <= 5 or epoch % max(OVERFIT_PRINT_EVERY, 1) == 0 or epoch == OVERFIT_N_EPOCHS + epoch_losses: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_rewards: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_losses: list[float] = [] + epoch_entropy: list[float] = [] + epoch_grad_norms: list[float] = [] + epoch_action_dist: list[list[dict[str, float]]] = [] + epoch_reward_pos_pct: list[float] = [] + epoch_pred_fg_pct: list[float] = [] + epoch_gt_fg_pct: list[float] = [] + epoch_alerts: list[str] = [] + + for batch in fixed_batches: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=DEFAULT_TMAX, + critic_loss_weight=DEFAULT_CRITIC_LOSS_WEIGHT, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=OVERFIT_N_EPOCHS, + ) + + epoch_losses.append(float(metrics["loss"])) + epoch_rewards.append(float(metrics["mean_reward"])) + epoch_actor.append(float(metrics["actor_loss"])) + epoch_critic.append(float(metrics["critic_loss"])) + epoch_ce.append(float(metrics["ce_loss"])) + epoch_dice_losses.append(float(metrics["dice_loss"])) + epoch_entropy.append(float(metrics["entropy"])) + epoch_grad_norms.append(float(metrics["grad_norm"])) + epoch_alerts.extend( + _numerical_health_check( + { + "loss": metrics["loss"], + "actor_loss": metrics["actor_loss"], + "critic_loss": metrics["critic_loss"], + "reward": metrics["mean_reward"], + "entropy": metrics["entropy"], + "grad_norm": metrics["grad_norm"], + "ce_loss": metrics["ce_loss"], + "dice_loss": metrics["dice_loss"], + }, + prefix="train:", + ) + ) + + model.eval() + with torch.inference_mode(): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + )["decoder_prob"].float() + else: + init_mask = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + action_dist, pred = _action_distribution( + model, + image, + init_mask, + effective_test_tmax, + use_amp, + amp_dtype, + strategy=strategy, + sample_ids=sample_ids, + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + epoch_action_dist.append(action_dist) + + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + soft_init_mask = refinement_context["decoder_prob"].float() + state_t = model.forward_refinement_state( + refinement_context["base_features"], + soft_init_mask, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, _ = model.forward_from_state(state_t) + first_delta = _strategy3_policy_delta(policy_logits).to(dtype=soft_init_mask.dtype) + first_seg = _strategy3_apply_delta(soft_init_mask, first_delta) + reward_map = compute_refinement_reward( + soft_init_mask, first_seg, gt_mask.float(), + ) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * init_mask + policy_logits = model.forward_policy_only(masked) + first_actions, _, _ = sample_actions(policy_logits, stochastic=False) + first_seg = apply_actions(init_mask, first_actions, num_actions=policy_logits.shape[1]) + reward_map = (init_mask - gt_mask).pow(2) - (first_seg - gt_mask).pow(2) + epoch_reward_pos_pct.append(float((reward_map > 0).float().mean().item() * 100.0)) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + epoch_pred_fg_pct.append(pred_fg_pct) + epoch_gt_fg_pct.append(gt_fg_pct) + dice_values, iou_values = _batch_binary_metrics(pred.float(), gt_mask.float()) + epoch_dices.extend(dice_values) + epoch_ious.extend(iou_values) + epoch_alerts.extend( + _numerical_health_check( + {"pred": pred, "gt_mask": gt_mask}, + prefix="eval:", + ) + ) + model.train() + + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_loss = float(np.mean(epoch_losses)) if epoch_losses else 0.0 + avg_dice = float(np.mean(epoch_dices)) if epoch_dices else 0.0 + avg_iou = float(np.mean(epoch_ious)) if epoch_ious else 0.0 + avg_reward = float(np.mean(epoch_rewards)) if epoch_rewards else 0.0 + avg_actor = float(np.mean(epoch_actor)) if epoch_actor else 0.0 + avg_critic = float(np.mean(epoch_critic)) if epoch_critic else 0.0 + avg_ce = float(np.mean(epoch_ce)) if epoch_ce else 0.0 + avg_dice_loss = float(np.mean(epoch_dice_losses)) if epoch_dice_losses else 0.0 + avg_entropy = float(np.mean(epoch_entropy)) if epoch_entropy else 0.0 + avg_grad_norm = float(np.mean(epoch_grad_norms)) if epoch_grad_norms else 0.0 + avg_reward_pos = float(np.mean(epoch_reward_pos_pct)) if epoch_reward_pos_pct else 0.0 + avg_pred_fg = float(np.mean(epoch_pred_fg_pct)) if epoch_pred_fg_pct else 0.0 + avg_gt_fg = float(np.mean(epoch_gt_fg_pct)) if epoch_gt_fg_pct else 0.0 + + avg_action_dist = _average_action_distributions(epoch_action_dist, effective_test_tmax) + + history["dice"].append(avg_dice) + history["iou"].append(avg_iou) + history["loss"].append(avg_loss) + history["reward"].append(avg_reward) + history["actor_loss"].append(avg_actor) + history["critic_loss"].append(avg_critic) + history["ce_loss"].append(avg_ce) + history["dice_loss"].append(avg_dice_loss) + history["entropy"].append(avg_entropy) + history["grad_norm"].append(avg_grad_norm) + history["action_dist"].append(avg_action_dist) + history["reward_pos_pct"].append(avg_reward_pos) + history["pred_fg_pct"].append(avg_pred_fg) + history["gt_fg_pct"].append(avg_gt_fg) + save_json(history_path, history) + + loss_delta = avg_loss - prev_loss if prev_loss is not None else 0.0 + prev_loss = avg_loss + if epoch_alerts: + print(f"[Overfit][Epoch {epoch}] Numerical alerts: {' | '.join(epoch_alerts)}") + + if full_dump: + current_alpha = float(log_alpha.exp().detach().item()) if log_alpha is not None else 0.0 + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} (delta={loss_delta:+.6f}) " + f"dice={avg_dice:.4f} iou={avg_iou:.4f} reward={avg_reward:+.6f} " + f"entropy={avg_entropy:.6f} alpha={current_alpha:.4f}" + ) + print( + f"[Overfit][Epoch {epoch:03d}] ce={avg_ce:.6f} dice_l={avg_dice_loss:.6f} " + f"grad_norm={avg_grad_norm:.6f} global_grad={grad_stats['global_norm']:.6f}" + ) + if avg_action_dist: + first = avg_action_dist[0] + last = avg_action_dist[-1] + print( + f"[Overfit][Epoch {epoch:03d}] action step0={first} step_last={last} " + f"reward_pos={avg_reward_pos:.2f}%" + ) + print( + f"[Overfit][Epoch {epoch:03d}] pred_fg={avg_pred_fg:.2f}% gt_fg={avg_gt_fg:.2f}% " + f"param_groups={list(param_stats.keys())}" + ) + else: + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} dice={avg_dice:.4f} " + f"iou={avg_iou:.4f} reward={avg_reward:+.6f}" + ) + + row = { + "epoch": epoch, + "dice": avg_dice, + "iou": avg_iou, + "loss": avg_loss, + "reward": avg_reward, + "actor_loss": avg_actor, + "critic_loss": avg_critic, + "ce_loss": avg_ce, + "dice_loss": avg_dice_loss, + "entropy": avg_entropy, + "grad_norm": avg_grad_norm, + "action_dist": avg_action_dist, + "reward_pos_pct": avg_reward_pos, + "pred_fg_pct": avg_pred_fg, + "gt_fg_pct": avg_gt_fg, + } + if avg_dice > best_dice: + best_dice = avg_dice + save_checkpoint( + ckpt_dir / "best.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + if SAVE_LATEST_EVERY_EPOCH: + save_checkpoint( + ckpt_dir / "latest.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + if CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0: + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + peak_dice = max(history["dice"]) if history["dice"] else 0.0 + final_dice = history["dice"][-1] if history["dice"] else 0.0 + summary = { + "run_type": "overfit", + "strategy": strategy, + "peak_dice": peak_dice, + "final_dice": final_dice, + "description": description, + "resumed": resume_source is not None, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "final_epoch": max(len(history["dice"]), start_epoch - 1), + } + if resume_source is not None: + summary["resume_source"] = resume_source + save_json(overfit_root / "summary.json", summary) + print( + f"[Overfit] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} | " + f"peak_dice={peak_dice:.4f}, final_dice={final_dice:.4f}" + ) + + del model + run_cuda_cleanup( + context=f"overfit {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + return {**summary, "history": history} + +def run_configured_overfit_tests( + bundles: dict[float, DataBundle], + *, + model_config: RuntimeModelConfig, +) -> None: + banner("OVERFIT TEST MODE") + for percent in DATASET_PERCENTS: + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=strategy_root_for_percent(strategy, percent, model_config) / "overfit_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + +"""============================================================================= +OPTUNA + ORCHESTRATION +============================================================================= +""" + +def strategy_epochs(strategy: int) -> int: + strategy = _require_supported_strategy(strategy) + if strategy == 2: + return STRATEGY_2_MAX_EPOCHS + if strategy == 3: + return STRATEGY_3_MAX_EPOCHS + raise ValueError(f"Unsupported strategy for epoch selection: {strategy}") + +def suggest_hyperparameters(trial: optuna.trial.Trial, strategy: int) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if strategy == 3: + rl_lr = trial.suggest_float("rl_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + return { + "head_lr": rl_lr, + "encoder_lr": ENCODER_LR_RANGE[0], + "decoder_lr": 0.0, + "strategy3_decoder_ce_weight": 0.0, + "strategy3_decoder_dice_weight": 0.0, + "strategy3_freeze_bootstrapped_segmentation": True, + "strategy3_variant": DEFAULT_STRATEGY3_VARIANT, + "rl_lr": rl_lr, + "weight_decay": trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True), + "dropout_p": trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]), + "tmax": trial.suggest_int("tmax", TMAX_RANGE[0], TMAX_RANGE[1]), + "smp_encoder_proj_dim": trial.suggest_categorical("smp_encoder_proj_dim", [64, 128, 192, 256]), + "critic_loss_weight": trial.suggest_float("critic_loss_weight", 0.10, 1.50), + "strategy3_mc_dropout_enabled": True, + "strategy3_mc_dropout_samples": trial.suggest_categorical("strategy3_mc_dropout_samples", [4, 8, 12]), + "strategy3_mc_dropout_p": trial.suggest_float("strategy3_mc_dropout_p", 0.05, 0.35), + "strategy3_delta_max": trial.suggest_float("strategy3_delta_max", 0.03, 0.20), + "strategy3_sam_attention_grid": trial.suggest_categorical("strategy3_sam_attention_grid", [16, 32, 64]), + "strategy3_r1_progress_weight": trial.suggest_float("strategy3_r1_progress_weight", 0.25, 2.0), + "biou_reward_weight": trial.suggest_float("biou_reward_weight", 0.0, 2.0), + "strategy3_advantage_normalize": trial.suggest_categorical("strategy3_advantage_normalize", [False, True]), + "strategy3_rl_grad_clip_norm": trial.suggest_float("strategy3_rl_grad_clip_norm", 0.5, 4.0), + "strategy3_rl_loss_scale": trial.suggest_float("strategy3_rl_loss_scale", 2.0, 50.0, log=True), + "strategy3_aux_ce_weight": DEFAULT_STRATEGY3_AUX_CE_WEIGHT, + "strategy3_aux_ce_anneal_start_epoch": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + "strategy3_aux_ce_anneal_epochs": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + "strategy3_aux_ce_floor_fraction": DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + "threshold": trial.suggest_float("threshold", 0.35, 0.65), + "elastic_aug_prob": trial.suggest_float("elastic_aug_prob", 0.0, 0.5), + "epoch_probe_mode": DEFAULT_STRATEGY3_PROBE_MODE, + "early_stopping_patience": DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[3], + } + + head_lr = trial.suggest_float("head_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + encoder_lr = trial.suggest_float("encoder_lr", ENCODER_LR_RANGE[0], min(ENCODER_LR_RANGE[1], head_lr), log=True) + weight_decay = trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True) + dropout_p = trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]) + params = { + "head_lr": head_lr, + "encoder_lr": encoder_lr, + "weight_decay": weight_decay, + "dropout_p": dropout_p, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[2], + } + return params + +def _format_hparam_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key in {"head_lr", "encoder_lr", "entropy_lr"}: + return f"{value:.3e}" + return f"{value:.6g}" + return str(value) + +def log_optuna_trial_start( + *, + study_name: str, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + trial_dir: Path, + params: dict[str, Any], + max_epochs: int, +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + lines = [ + "", + "-" * 80, + f"OPTUNA TRIAL START | {run_name}", + "-" * 80, + f"Study name : {study_name}", + f"Run dir : {trial_dir}", + f"Max epochs : {max_epochs}", + f"Objective metric : {_strategy_selection_metric_name(strategy)}", + ] + for key in sorted(params): + lines.append(f"{key:22s}: {_format_hparam_value(key, params[key])}") + tqdm.write("\n".join(lines)) + +def log_optuna_trial_result( + *, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + metric_value: float, + aggregate: dict[str, dict[str, float]], +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + tqdm.write( + f"[{run_name}] completed: {_strategy_selection_metric_name(strategy)}={metric_value:.4f}, " + f"best_test_iou={aggregate['iou']['mean']:.4f}" + ) + +def study_paths_for( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + pct_root = RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}" + strategy_root = pct_root / strategy_dir_name(strategy, model_config) + study_root = strategy_root / "study" + trials_root = strategy_root / "trials" + return strategy_root, study_root, trials_root + +def manual_hparams_key(strategy: int, percent: float) -> str: + return f"{strategy}:{percent_label(percent)}" + +def reset_study_artifacts(strategy: int, percent: float, *, model_config: RuntimeModelConfig) -> None: + strategy_root, study_root, trials_root = study_paths_for(strategy, percent, model_config) + removed_any = False + for path in (study_root, trials_root): + if path.exists(): + shutil.rmtree(path) + removed_any = True + if removed_any: + print( + f"[Optuna Reset] Removed cached study artifacts for strategy={strategy}, " + f"percent={percent_text(percent)} under {strategy_root}." + ) + else: + print( + f"[Optuna Reset] No existing study artifacts found for strategy={strategy}, " + f"percent={percent_text(percent)}." + ) + +class _PlateauPruner(optuna.pruners.BasePruner): + """Prune a trial whose metric has plateaued (no improvement to its + own personal best within a patience window). + + Behaviour: + - During the first *n_warmup_steps* epochs: never prune. + - After warmup, track the trial's own best metric and the epoch + at which it was achieved. + - If *patience_steps* epochs pass without the trial beating its + own best, the trial is pruned (it has stagnated). + """ + + def __init__( + self, + n_warmup_steps: int = 80, + patience_steps: int = 40, + ) -> None: + self._n_warmup_steps = n_warmup_steps + self._patience_steps = patience_steps + + def prune( + self, + study: "optuna.study.Study", + trial: "optuna.trial.FrozenTrial", + ) -> bool: + step = trial.last_step + if step is None or step < self._n_warmup_steps: + return False + + post_warmup = { + s: v for s, v in trial.intermediate_values.items() + if s >= self._n_warmup_steps + } + if not post_warmup: + return False + + best_step = max(post_warmup, key=post_warmup.get) + epochs_since_improvement = step - best_step + return epochs_since_improvement >= self._patience_steps + + +def pruner_for_run() -> optuna.pruners.BasePruner: + if USE_TRIAL_PRUNING: + return _PlateauPruner( + n_warmup_steps=TRIAL_PRUNER_WARMUP_STEPS, + patience_steps=TRIAL_PRUNER_PATIENCE_STEPS, + ) + return optuna.pruners.NopPruner() + +def run_single_job( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + params: dict[str, Any], + max_epochs: int, + trial: optuna.trial.Trial | None, + strategy2_checkpoint_path: str | Path | None = None, + resume_checkpoint_path: Path | None = None, + retrying_from_trial_number: int | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, dict[str, float]]]: + strategy = _require_supported_strategy(strategy) + params = dict(params) + params.setdefault("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS[strategy]) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + if strategy == 3: + params.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(params["strategy3_freeze_bootstrapped_segmentation"]) + ) + params.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + params.setdefault("decoder_lr", 0.0 if bootstrap_freeze else params["head_lr"] * 0.1) + params.setdefault("rl_lr", params["head_lr"]) + params.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + params.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + params.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + params.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + params.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + params.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + params.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + params.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + params.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + params.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + params.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + params.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + params.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + params.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + params.setdefault("elastic_aug_prob", 0.3) + params.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + params.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + params.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + params.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + params.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + params.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + params.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + params.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + params.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + params.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + params.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + set_current_job_params(params) + if "smp_encoder_proj_dim" in params and int(params["smp_encoder_proj_dim"]) != model_config.smp_encoder_proj_dim: + model_config = RuntimeModelConfig.from_payload( + {**model_config.to_payload(), "smp_encoder_proj_dim": int(params["smp_encoder_proj_dim"])} + ).validate() + entropy_target_ratio = float(_job_param("entropy_target_ratio", 0.35)) + entropy_alpha_init = float(_job_param("entropy_alpha_init", 0.12)) + critic_loss_weight = float(_job_param("critic_loss_weight", DEFAULT_CRITIC_LOSS_WEIGHT)) + ensure_dir(run_dir) + run_type = "trial" if trial is not None else "final" + config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": run_type, + "run_name": run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ), + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "train_subset_variant": bundle.split_payload.get("train_subset_variant", 0), + "train_subset_source": bundle.split_payload.get("train_subset_source", "persisted"), + "selected_split_manifest_path": bundle.split_payload.get("selected_split_manifest_path"), + "normalization_cache_path": bundle.split_payload["normalization_cache_path"], + "best_checkpoint_metric_name": _strategy_selection_metric_name(strategy), + "best_checkpoint_metrics": {str(key): value for key, value in BEST_CHECKPOINT_METRICS.items()}, + "save_history_incrementally": bool(SAVE_HISTORY_INCREMENTALLY), + "write_epoch_diagnostic": bool(WRITE_EPOCH_DIAGNOSTIC), + "head_lr": params["head_lr"], + "encoder_lr": params["encoder_lr"], + "weight_decay": params["weight_decay"], + "dropout_p": params["dropout_p"], + "tmax": params["tmax"], + "entropy_lr": params["entropy_lr"], + "max_epochs": max_epochs, + "gamma": DEFAULT_GAMMA, + "critic_loss_weight": critic_loss_weight, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "scheduler_factor": SCHEDULER_FACTOR, + "scheduler_patience": SCHEDULER_PATIENCE, + "scheduler_threshold": SCHEDULER_THRESHOLD, + "scheduler_min_lr": SCHEDULER_MIN_LR, + "execution_mode": EXECUTION_MODE, + "evaluation_checkpoint_mode": EVAL_CHECKPOINT_MODE, + "strategy2_checkpoint_mode": STRATEGY2_CHECKPOINT_MODE, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + config.update({key: value for key, value in params.items() if key not in config}) + config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + if resume_checkpoint_path is not None: + config["resume_checkpoint_path"] = str(Path(resume_checkpoint_path)) + if retrying_from_trial_number is not None: + config["retrying_from_trial_number"] = int(retrying_from_trial_number) + save_json(run_dir / "run_config.json", config) + + model: nn.Module | None = None + try: + model, description, _compiled = build_model( + strategy, + params["dropout_p"], + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + summary, history = train_model( + run_type=run_type, + model_config=model_config, + run_config=config, + model=model, + description=description, + strategy=strategy, + run_dir=run_dir, + bundle=bundle, + max_epochs=max_epochs, + head_lr=params["head_lr"], + encoder_lr=params["encoder_lr"], + weight_decay=params["weight_decay"], + tmax=params["tmax"], + entropy_lr=params["entropy_lr"], + entropy_alpha_init=entropy_alpha_init, + entropy_target_ratio=entropy_target_ratio, + critic_loss_weight=critic_loss_weight, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + dropout_p=params["dropout_p"], + resume_checkpoint_path=resume_checkpoint_path, + trial=trial, + ) + + if trial is not None: + save_json( + run_dir / "summary.json", + { + "params": params, + "best_iou": float(summary["best_val_iou"]), + "best_model_metric_name": str(summary["best_model_metric_name"]), + "best_model_metric": float(summary["best_model_metric"]), + "resumed": bool(resume_checkpoint_path is not None), + "retrying_from_trial_number": retrying_from_trial_number, + }, + ) + return summary, history, {} + + del model + model = None + run_cuda_cleanup() + + aggregate, _per_sample = run_evaluation_for_run( + strategy=strategy, + percent=bundle.percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + return summary, history, aggregate + finally: + if model is not None: + del model + model = None + run_cuda_cleanup() + +def _save_best_params_so_far( + study: optuna.study.Study, + study_root: Path, + strategy: int, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> None: + best, _best_value = _current_optuna_study_best_candidate( + study, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if best is None: + return + params = dict(best.params) + if strategy == 2: + params.setdefault("tmax", DEFAULT_TMAX) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", params) + +def run_study( + strategy: int, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + strategy_root, study_root, trials_root = study_paths_for(strategy, bundle.percent, model_config) + ensure_dir(strategy_root.parent) + strategy_root = ensure_dir(strategy_root) + study_root = ensure_dir(strategy_root / "study") + trials_root = ensure_dir(strategy_root / "trials") + storage_path = study_root / "study.sqlite3" + storage = RDBStorage( + url=f"sqlite:///{storage_path.resolve()}", + heartbeat_interval=OPTUNA_HEARTBEAT_INTERVAL, + grace_period=OPTUNA_HEARTBEAT_GRACE_PERIOD, + ) + sampler = optuna.samplers.TPESampler(seed=SEED) + study_name = ( + f"{MODEL_NAME}_{model_config.backbone_tag()}_{run_identity_slug(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + study = optuna.create_study( + study_name=study_name, + direction=STUDY_DIRECTION, + sampler=sampler, + pruner=pruner_for_run(), + storage=storage, + load_if_exists=LOAD_EXISTING_STUDIES, + ) + existing_trials = [trial for trial in study.trials if trial.state.is_finished()] + if existing_trials: + print( + f"[Optuna Study] Loaded existing study '{study_name}' with " + f"{len(existing_trials)} existing finished trial(s). Running {NUM_TRIALS} new trial(s)." + ) + else: + print(f"[Optuna Study] Starting new study '{study_name}' with {NUM_TRIALS} trial(s).") + + def objective(trial: optuna.trial.Trial) -> float: + trial_dir = ensure_dir(trials_root / f"trial_{trial.number:03d}") + params = suggest_hyperparameters(trial, strategy) + log_optuna_trial_start( + study_name=study_name, + strategy=strategy, + bundle=bundle, + trial=trial, + trial_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + ) + summary: dict[str, Any] | None = None + _history: list[dict[str, Any]] | None = None + aggregate: dict[str, dict[str, float]] | None = None + completed_successfully = False + pruned_by_optuna = False + run_cuda_cleanup() + try: + summary, _history, aggregate = run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + trial=trial, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + metric_value = float(summary["best_model_metric"]) + completed_successfully = True + tqdm.write( + f"[{run_identity_label(strategy=strategy, percent=bundle.percent, trial_number=trial.number, split_payload=bundle.split_payload)}] " + f"completed: {summary['best_model_metric_name']}={metric_value:.4f}" + ) + return metric_value + except optuna.TrialPruned: + pruned_by_optuna = True + raise + finally: + current_trial = trial if (completed_successfully or pruned_by_optuna) else None + current_best_value = None + if summary is not None and summary.get("best_model_metric") is not None: + current_best_value = float(summary["best_model_metric"]) + _save_best_params_so_far( + study, + study_root, + strategy, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if aggregate is not None: + del aggregate + aggregate = None + if _history is not None: + del _history + _history = None + if summary is not None: + del summary + summary = None + prune_optuna_trial_dir(trial_dir) + run_cuda_cleanup(context=f"trial {trial.number:03d} boundary") + + study.optimize(objective, n_trials=NUM_TRIALS, show_progress_bar=True) + + best_trial, best_observed_value = _current_optuna_study_best_candidate(study) + if best_trial is None or best_observed_value is None: + raise RuntimeError( + f"Study '{study_name}' has no trials with recorded best-observed values, so best params cannot be resolved. " + f"Finished trials={len([trial for trial in study.trials if trial.state.is_finished()])}, " + f"configured cap={NUM_TRIALS}." + ) + + best_params = dict(best_trial.params) + if strategy == 2: + best_params.setdefault("tmax", DEFAULT_TMAX) + best_params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", best_params) + optuna_best_value: float | None + try: + optuna_best_value = float(study.best_value) + except Exception: + optuna_best_value = None + save_json( + study_root / "summary.json", + { + "best_params": best_params, + "optimized_param_names": sorted(best_params.keys()), + "best_metric_name": _strategy_selection_metric_name(strategy), + "best_metric_value": float(best_observed_value), + "best_observed_value": float(best_observed_value), + "best_trial_number": int(getattr(best_trial, "number", -1)), + "optuna_best_value": optuna_best_value, + "best_iou": float(best_observed_value) if _strategy_selection_metric_name(strategy) == "val_iou" else None, + "finished_trials": len([trial for trial in study.trials if trial.state.is_finished()]), + "completed_trials": len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + "target_trials": int(NUM_TRIALS), + "ran_trials": int(NUM_TRIALS), + }, + ) + prune_optuna_study_dir(study_root) + if trials_root.exists(): + shutil.rmtree(trials_root, ignore_errors=True) + return best_params + +def run_final_training( + strategy: int, + bundle: DataBundle, + params: dict[str, Any], + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + strategy = _require_supported_strategy(strategy) + final_root = final_root_for_strategy(strategy, bundle.percent, model_config) + if SKIP_EXISTING_FINALS and (final_root / "summary.json").exists(): + print(f"Skipping existing final run: {final_root}") + return + save_json(final_root / "best_params.json", params) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(final_root) + run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=final_root, + params=params, + max_epochs=strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + +def print_environment_summary(model_config: RuntimeModelConfig) -> None: + banner("RUNTIME SUMMARY") + images_dir, annotations_dir = current_dataset_dirs() + print(f"Project dir : {PROJECT_DIR}") + print(f"Data root : {DATA_ROOT}") + print(f"Runs root : {RUNS_ROOT}") + print(f"Dataset name : {current_dataset_name()}") + if current_dataset_name() == "BUSI_with_classes": + print(f"Dataset split policy : {current_busi_with_classes_split_policy()}") + print(f"Images dir : {images_dir}") + print(f"Masks dir : {annotations_dir}") + print(f"Dataset splits json : {current_dataset_splits_json_path()}") + print(f"Split type : {SPLIT_TYPE}") + print(f"Experiment mode : {EXPERIMENT_MODE}") + print(f"Device : {DEVICE}") + print(f"Device source : {DEVICE_FALLBACK_SOURCE}") + print(f"Model name : {MODEL_NAME}") + print(f"Seed : {SEED}") + print(f"PyTorch version : {torch.__version__}") + print(f"Batch size : {BATCH_SIZE}") + print(f"Use AMP : {USE_AMP}") + print(f"Num workers : {NUM_WORKERS}") + print(f"Pin memory : {USE_PIN_MEMORY}") + print(f"CuDNN deterministic : {torch.backends.cudnn.deterministic}") + print(f"CuDNN benchmark : {torch.backends.cudnn.benchmark}") + + if DEVICE.type == "cuda": + props = torch.cuda.get_device_properties(0) + print(f"GPU : {torch.cuda.get_device_name(0)}") + print(f"GPU VRAM : {props.total_memory / (1024 ** 3):.2f} GB") + print(f"AMP dtype : {resolve_amp_dtype(AMP_DTYPE)}") + print(f"Trial pruning : {USE_TRIAL_PRUNING}") + print(f"Backbone family : {model_config.backbone_family}") + if model_config.backbone_family == "custom_vgg": + print(f"VGG feature scales : {model_config.vgg_feature_scales}") + print(f"VGG feature dilation : {model_config.vgg_feature_dilation}") + else: + print(f"SMP encoder : {model_config.smp_encoder_name}") + print(f"SMP encoder depth : {model_config.smp_encoder_depth}") + print(f"SMP encoder proj dim : {model_config.smp_encoder_proj_dim}") + print(f"SMP decoder : {model_config.smp_decoder_type}") + print(f"Strategies : {STRATEGIES}") + print(f"Dataset percents : {[percent_text(value) for value in DATASET_PERCENTS]}") + print(f"Best metrics : {BEST_CHECKPOINT_METRICS}") + print(f"History incremental : {SAVE_HISTORY_INCREMENTALLY}") + print(f"Write diagnostics : {WRITE_EPOCH_DIAGNOSTIC}") + print_imagenet_normalization_status() + print(f"Trials per study : {NUM_TRIALS}") + print(f"Execution mode : {EXECUTION_MODE}") + print(f"Run Optuna : {RUN_OPTUNA}") + print(f"Use saved best params : {USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF}") + print(f"Reset studies/run : {RESET_ALL_STUDIES_EACH_RUN}") + print(f"Load existing studies : {LOAD_EXISTING_STUDIES}") + print(f"Eval ckpt selector : {EVAL_CHECKPOINT_MODE}") + print(f"S2 ckpt selector : {STRATEGY2_CHECKPOINT_MODE}") + print(f"S3 bootstrap from S2 : {STRATEGY3_BOOTSTRAP_FROM_STRATEGY2}") + print(f"S3 freeze default : {DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION}") + print(f"Train resume mode : {TRAIN_RESUME_MODE}") + print(f"Verbose epoch log : {VERBOSE_EPOCH_LOG}") + print(f"Validate every epochs : {VALIDATE_EVERY_N_EPOCHS}") + print(f"Smoke test enabled : {RUN_SMOKE_TEST}") + print(f"Test iter control : {TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {TEST_ITERATION_T}") + print(f"Overfit test enabled : {RUN_OVERFIT_TEST}") + print(f"Overfit batches : {OVERFIT_N_BATCHES}") + print(f"Overfit epochs : {OVERFIT_N_EPOCHS}") + +def maybe_run_strategy_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + if not RUN_SMOKE_TEST: + return + if EXECUTION_MODE == "eval_only": + return + run_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + smoke_root=strategy_root_for_percent(strategy, bundle.percent, model_config) / "smoke_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +"""============================================================================= +REPEATED HOLDOUT INTEGRATION +============================================================================= +""" + +base = sys.modules[__name__] + +REPEATED_HOLDOUT_ROOT = base.RUNS_ROOT / base.MODEL_NAME / "repeated_holdout" +EXPERIMENT_ROOT = REPEATED_HOLDOUT_ROOT / FOLDS_EXPERIMENT_NAME +EXPERIMENT_DB_PATH = EXPERIMENT_ROOT / "experiment_state.sqlite3" +SPLIT_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "splits" +SUBSET_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "subsets" +EXPORTS_DIR = EXPERIMENT_ROOT / "exports" +"""============================================================================= +RUNTIME STATE +============================================================================= +""" + + +@dataclass(frozen=True) +class PercentRepeatSpec: + percent_int: int + fraction: float + repeat_count: int + + +@dataclass(frozen=True) +class FoldRunContext: + split_repeat_index: int + subset_repeat_index: int + percent_int: int + percent_fraction: float + split_seed: int + subset_seed: int + repeat_root: Path + split_manifest_path: Path + subset_manifest_path: Path + + +@dataclass(frozen=True) +class RunKey: + split_repeat_index: int + dataset_percent: int + subset_repeat_index: int + strategy: int + + +CURRENT_FOLD_CONTEXT: FoldRunContext | None = None +LEDGER_CONN: sqlite3.Connection | None = None +PERCENT_SPECS_CACHE: list[PercentRepeatSpec] | None = None + +ORIGINAL_SAVE_JSON = base.save_json +ORIGINAL_PERCENT_ROOT = base.percent_root +ORIGINAL_STRATEGY_ROOT_FOR_PERCENT = base.strategy_root_for_percent +ORIGINAL_FINAL_ROOT_FOR_STRATEGY = base.final_root_for_strategy +ORIGINAL_STUDY_PATHS_FOR = base.study_paths_for +ORIGINAL_SAVE_CHECKPOINT = base.save_checkpoint +ORIGINAL_RUN_EVALUATION_FOR_RUN = base.run_evaluation_for_run + +BASE_RESUME_IDENTITY_KEYS = tuple(base.RESUME_IDENTITY_KEYS) +RUNTIME_ONLY_CONFIG_KEYS = frozenset( + { + "PERCENT_EXECUTION_MODE", + "SELECTED_DATASET_PERCENTS", + "SPLIT_EXECUTION_MODE", + "SELECTED_SPLIT_INDICES", + "PHASE_EXECUTION_MODE", + "SELECTED_PHASES", + "REPEAT_EXECUTION_MODE", + "SELECTED_REPEAT_INDICES", + } +) +PORTABLE_FINGERPRINT_FOLD_KEYS = frozenset( + { + "RESUME_FOLDS", + "REPEATED_HOLDOUT_ROOT", + "EXPERIMENT_ROOT", + "EXPERIMENT_DB_PATH", + } +) + + +"""============================================================================= +UTILITIES +============================================================================= +""" + + +def now_utc_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _jsonify(value: Any) -> Any: + if isinstance(value, Path): + return str(value) + if isinstance(value, dict): + return {str(key): _jsonify(val) for key, val in sorted(value.items(), key=lambda item: str(item[0]))} + if isinstance(value, (list, tuple)): + return [_jsonify(item) for item in value] + if isinstance(value, set): + return [_jsonify(item) for item in sorted(value, key=str)] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return repr(value) + + +def _summary_mean_std(values: list[float]) -> dict[str, float]: + arr = np.array(values, dtype=np.float64) + return { + "mean": float(arr.mean()) if arr.size > 0 else 0.0, + "std": float(arr.std()) if arr.size > 0 else 0.0, + } + + +def _phase_timing_summary_path() -> Path: + return EXPERIMENT_ROOT / "phase_timing_summary.json" + + +def _completed_run_rows_for_phase(phase_index: int) -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + AND split_repeat_index = ? + ORDER BY dataset_percent, subset_repeat_index, strategy + """, + (int(phase_index),), + ).fetchall() + ) + + +def _run_training_elapsed_seconds(row: sqlite3.Row) -> float | None: + run_dir = Path(str(row["run_dir"])) + summary_path = run_dir / "summary.json" + if summary_path.exists(): + try: + summary = base.load_json(summary_path) + if summary.get("elapsed_seconds") is not None: + return float(summary["elapsed_seconds"]) + except Exception as exc: + print(f"[Timing] Could not read {summary_path}: {exc}") + if row["elapsed_seconds"] is not None: + return float(row["elapsed_seconds"]) + return None + + +def write_phase_timing_summary_after_phase(phase_index: int) -> None: + if LEDGER_CONN is None: + return + rows = _completed_run_rows_for_phase(phase_index) + if not rows: + return + + run_entries: list[dict[str, Any]] = [] + phase_values: list[float] = [] + for row in rows: + elapsed = _run_training_elapsed_seconds(row) + entry = { + "phase_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": str(row["run_dir"]), + "training_elapsed_seconds": elapsed, + } + run_entries.append(entry) + if elapsed is not None: + phase_values.append(float(elapsed)) + + phase_stats = _summary_mean_std(phase_values) + existing_payload: dict[str, Any] = {} + summary_path = _phase_timing_summary_path() + if summary_path.exists(): + try: + existing_payload = base.load_json(summary_path) + except Exception as exc: + print(f"[Timing] Could not read existing phase timing summary {summary_path}: {exc}") + + phases_by_index: dict[int, dict[str, Any]] = {} + for phase_payload in existing_payload.get("phases", []): + if isinstance(phase_payload, dict) and phase_payload.get("phase_index") is not None: + phases_by_index[int(phase_payload["phase_index"])] = dict(phase_payload) + phases_by_index[int(phase_index)] = { + "phase_index": int(phase_index), + "completed_strategy_count": len(run_entries), + "completed_strategies": [int(entry["strategy"]) for entry in run_entries], + "runs": run_entries, + "training_elapsed_seconds_mean": phase_stats["mean"], + "training_elapsed_seconds_std": phase_stats["std"], + "updated_at": now_utc_iso(), + } + + phases = [phases_by_index[index] for index in sorted(phases_by_index)] + global_phase_means = [ + float(phase["training_elapsed_seconds_mean"]) + for phase in phases + if phase.get("training_elapsed_seconds_mean") is not None + ] + global_stats = _summary_mean_std(global_phase_means) + payload = { + "scope": "fixed_phase_training_time", + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": len(phases), + "training_elapsed_seconds_mean_across_phases": global_stats["mean"], + "training_elapsed_seconds_std_across_phases": global_stats["std"], + "phases": phases, + "updated_at": now_utc_iso(), + } + atomic_save_json(summary_path, payload) + print( + f"[Timing] Phase {phase_index:03d} training time summary updated -> {summary_path} " + f"(mean={phase_stats['mean']:.2f}s, std={phase_stats['std']:.2f}s)." + ) + + +def validate_hf_backup_settings() -> None: + """Fail fast at startup if backups are enabled but HF env vars are missing. + + Refuses to run rather than discovering hours into training (at the first + backup) that nothing can be uploaded. Disable by setting + ASYNC_REPO_BACKUP_AFTER_PHASE = False if you intentionally want no backups. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + missing = [] + if not repo_id: + missing.append("HF_REPO_ID (target repo, e.g. 'your-username/ADVAI24JUN-backup')") + if not token: + missing.append("HF_TOKEN (Hugging Face write token)") + if missing: + raise RuntimeError( + "Hugging Face backup is enabled (ASYNC_REPO_BACKUP_AFTER_PHASE = True) " + "but required environment variables are not set:\n - " + + "\n - ".join(missing) + + "\n\nSet them before running, e.g.:\n" + " export HF_REPO_ID='your-username/ADVAI24JUN-backup'\n" + " export HF_TOKEN='hf_xxxxxxxxxxxxxxxxxxxxx'\n" + "Or set ASYNC_REPO_BACKUP_AFTER_PHASE = False to run without backups." + ) + + +def _hf_backup_due(phase_index: int) -> bool: + """True only on every HF_BACKUP_EVERY_N_PHASES-th phase (0-indexed boundary).""" + n = max(1, int(HF_BACKUP_EVERY_N_PHASES)) + return (phase_index + 1) % n == 0 + + +def _hf_upload_project(*, label: str) -> bool: + """Create the HF dataset repo if needed and mirror PROJECT_DIR into it. + + Shared by the initial pre-training backup and the per-phase backups. + `upload_large_folder` is resumable and content-addressed: unchanged files are + skipped and an interrupted upload (e.g. a 503) can be safely re-run, so the repo + always converges to the latest project state. Retries with backoff to ride out + transient HF outages. Returns True on a verified successful upload. + """ + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + if not repo_id: + print(f"[Backup] Skipping HF backup ({label}): HF_REPO_ID is not set (export HF_REPO_ID=user/repo).") + return False + if not token: + print(f"[Backup] Skipping HF backup ({label}): HF_TOKEN env var is not set.") + return False + + try: + from huggingface_hub import HfApi + except Exception: + print(f"[Backup] Skipping HF backup ({label}): huggingface_hub not installed (pip install huggingface_hub).") + return False + + api = HfApi(token=token) + try: + api.create_repo(repo_id=repo_id, repo_type=HF_REPO_TYPE, private=True, exist_ok=True) + except Exception as exc: + print(f"[Backup] Could not ensure HF repo {repo_id} exists: {exc}") + + last_exc: Exception | None = None + for attempt in range(1, HF_BACKUP_MAX_RETRIES + 1): + try: + print( + f"[Backup] {label}: uploading project to " + f"hf://{HF_REPO_TYPE}/{repo_id} (attempt {attempt}/{HF_BACKUP_MAX_RETRIES})..." + ) + api.upload_large_folder( + repo_id=repo_id, + repo_type=HF_REPO_TYPE, + folder_path=str(PROJECT_DIR.resolve()), + ignore_patterns=list(HF_IGNORE_PATTERNS), + print_report=True, + ) + print(f"[Backup] {label}: HF backup complete -> {repo_id}.") + return True + except Exception as exc: + last_exc = exc + wait = min(60, 5 * attempt) + print(f"[Backup] {label}: HF upload attempt {attempt} failed: {exc}. Retrying in {wait}s...") + time.sleep(wait) + print(f"[Backup] {label}: HF backup FAILED after {HF_BACKUP_MAX_RETRIES} attempts: {last_exc}") + return False + + +def run_initial_hf_backup() -> None: + """Fresh backup BEFORE any training begins. + + Creates the repo and uploads the current project state synchronously, so the + entire backup pipeline (repo creation, token, upload) is proven before we commit + hours of compute. Later phase backups refresh this same repo. Runs in the + foreground on purpose -- if the first backup cannot complete, we want to know now. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE or not HF_BACKUP_ON_START: + return + print("[Backup] Running initial pre-training backup (this proves the backup pipeline before training)...") + _hf_upload_project(label="Initial backup") + + +def run_repo_backup_after_phase(phase_index: int) -> None: + """Refresh the Hugging Face dataset repo after a training phase. + + Fires only on every HF_BACKUP_EVERY_N_PHASES-th phase so we don't hammer HF. + Runs in a background thread at the call site. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + if not _hf_backup_due(phase_index): + print( + f"[Backup] Phase {phase_index:03d}: skipping HF backup " + f"(uploads every {HF_BACKUP_EVERY_N_PHASES} phases)." + ) + return + _hf_upload_project(label=f"Phase {phase_index:03d}") + + +def atomic_write_text(path: str | Path, text: str) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_write_bytes(path: str | Path, payload: bytes) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_save_json(path: str | Path, payload: Any) -> None: + atomic_write_text(path, json.dumps(payload, indent=2, sort_keys=True)) + + +def atomic_torch_save(path: str | Path, payload: Any) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + torch.save(payload, handle) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def stable_hash(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def stable_int(text: str) -> int: + return base.stable_int_from_text(text) + + +def fold_seed(tag: str) -> int: + return int(base.SEED) + stable_int(tag) + + +def current_split_generation_mode() -> str: + mode = str(SPLIT_GENERATION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_GENERATION_MODES: + raise ValueError( + f"SPLIT_GENERATION_MODE must be one of {SUPPORTED_SPLIT_GENERATION_MODES}, got {mode!r}" + ) + return mode + + +def using_fixed_phase_mode() -> bool: + return current_split_generation_mode() == "fixed_stratified_phases_8_1_1" + + +def primary_unit_name(*, plural: bool = False) -> str: + if using_fixed_phase_mode(): + return "phases" if plural else "phase" + return "splits" if plural else "split" + + +def current_phase_execution_mode() -> str: + mode = str(PHASE_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PHASE_EXECUTION_MODES: + raise ValueError( + f"PHASE_EXECUTION_MODE must be one of {SUPPORTED_PHASE_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def phase_count() -> int: + if isinstance(NUM_PHASES, bool) or int(NUM_PHASES) <= 0: + raise ValueError("NUM_PHASES must be a positive integer.") + return int(NUM_PHASES) + + +def phase_val_offset() -> int: + if isinstance(PHASE_VAL_OFFSET, bool): + raise TypeError("PHASE_VAL_OFFSET must be an integer.") + return int(PHASE_VAL_OFFSET) + + +def phase_indices() -> list[int]: + return list(range(1, phase_count() + 1)) + + +def phase_execution_indices_to_run() -> list[int]: + indices = phase_indices() + if current_phase_execution_mode() == "auto": + return indices + + if not SELECTED_PHASES: + raise ValueError("SELECTED_PHASES must be non-empty when PHASE_EXECUTION_MODE='manual'.") + + selected: list[int] = [] + seen: set[int] = set() + max_index = indices[-1] + for raw_index in SELECTED_PHASES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + f"SELECTED_PHASES entries must be integer phase indices in the range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_PHASES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_PHASES contains duplicate phase index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def phase_fold_indices(phase_index: int) -> tuple[int, int]: + count = phase_count() + if phase_index < 1 or phase_index > count: + raise ValueError(f"Phase index must be in [1, {count}], got {phase_index}.") + val_offset = phase_val_offset() + if val_offset <= 0 or val_offset >= count: + raise ValueError( + f"PHASE_VAL_OFFSET must be in [1, {count - 1}] for {count} phases, got {val_offset}." + ) + test_fold_index = phase_index + val_fold_index = ((phase_index - 1 + val_offset) % count) + 1 + return val_fold_index, test_fold_index + + +def partition_seed() -> int: + if using_fixed_phase_mode(): + return fold_seed(f"phase_partition::{phase_count()}") + return int(base.SEED) + + +def split_generation_display_name() -> str: + if using_fixed_phase_mode(): + return "fixed stratified 10-phase 8/1/1" + return "repeated stratified holdout" + + +def cycle_index_label(index: int) -> str: + return f"{primary_unit_name()}_{int(index):03d}" + + +def cycle_identity_label(index: int) -> str: + return f"{primary_unit_name()}={int(index):03d}" + + +def all_split_repeat_indices() -> list[int]: + if using_fixed_phase_mode(): + return phase_indices() + return list(range(1, int(NUM_STRATIFIED_SPLIT_REPEATS) + 1)) + + +def max_subset_repeat_index() -> int: + return max(int(spec.repeat_count) for spec in percent_specs()) + + +def current_percent_sampling_mode() -> str: + mode = str(PERCENT_SAMPLING_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_SAMPLING_MODES: + raise ValueError( + f"PERCENT_SAMPLING_MODE must be one of {SUPPORTED_PERCENT_SAMPLING_MODES}, got {mode!r}" + ) + return mode + + +def current_split_execution_mode() -> str: + mode = str(SPLIT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_EXECUTION_MODES: + raise ValueError( + f"SPLIT_EXECUTION_MODE must be one of {SUPPORTED_SPLIT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_repeat_execution_mode() -> str: + mode = str(REPEAT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_REPEAT_EXECUTION_MODES: + raise ValueError( + f"REPEAT_EXECUTION_MODE must be one of {SUPPORTED_REPEAT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_percent_execution_mode() -> str: + mode = str(PERCENT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_EXECUTION_MODES: + raise ValueError( + f"PERCENT_EXECUTION_MODE must be one of {SUPPORTED_PERCENT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def split_repeat_indices_to_run() -> list[int]: + if using_fixed_phase_mode(): + return phase_execution_indices_to_run() + + split_indices = all_split_repeat_indices() + if current_split_execution_mode() == "auto": + return split_indices + + if not SELECTED_SPLIT_INDICES: + raise ValueError( + "SELECTED_SPLIT_INDICES must be non-empty when SPLIT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = split_indices[-1] + for raw_index in SELECTED_SPLIT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_SPLIT_INDICES entries must be integer split indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_SPLIT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_SPLIT_INDICES contains duplicate split index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def subset_repeat_indices_to_run() -> list[int]: + repeat_indices = list(range(1, max_subset_repeat_index() + 1)) + if current_repeat_execution_mode() == "auto": + return repeat_indices + + if not SELECTED_REPEAT_INDICES: + raise ValueError( + "SELECTED_REPEAT_INDICES must be non-empty when REPEAT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = repeat_indices[-1] + for raw_index in SELECTED_REPEAT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_REPEAT_INDICES entries must be integer repeat indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_REPEAT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_REPEAT_INDICES contains duplicate repeat index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def percent_specs_to_run() -> list[PercentRepeatSpec]: + all_specs = percent_specs() + if current_percent_execution_mode() == "auto": + return all_specs + + if not SELECTED_DATASET_PERCENTS: + raise ValueError( + "SELECTED_DATASET_PERCENTS must be non-empty when PERCENT_EXECUTION_MODE='manual'." + ) + + all_percent_ints = {spec.percent_int for spec in all_specs} + selected: list[PercentRepeatSpec] = [] + seen: set[int] = set() + for raw_percent in SELECTED_DATASET_PERCENTS: + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "SELECTED_DATASET_PERCENTS entries must be integer percentages in the range [1, 100]." + ) + if raw_percent not in all_percent_ints: + raise ValueError( + f"SELECTED_DATASET_PERCENTS entry {raw_percent} is not defined in " + f"DATASET_PERCENT_REPEAT_COUNTS. Available: {sorted(all_percent_ints)}." + ) + if raw_percent in seen: + raise ValueError(f"SELECTED_DATASET_PERCENTS contains duplicate percent {raw_percent}.") + seen.add(raw_percent) + spec_map = {spec.percent_int: spec for spec in all_specs} + for raw_percent in SELECTED_DATASET_PERCENTS: + selected.append(spec_map[raw_percent]) + selected.sort(key=lambda s: s.percent_int) + return selected + + +def validate_repeated_holdout_settings() -> None: + if using_fixed_phase_mode(): + if phase_count() != 10: + raise ValueError( + f"fixed phase mode requires NUM_PHASES=10, got {phase_count()}." + ) + phase_fold_indices(1) + if current_dataset_name() != "BUSI_with_classes": + raise ValueError( + "fixed phase mode currently supports DATASET_NAME='BUSI_with_classes' only." + ) + if current_busi_with_classes_split_policy() != "stratified": + raise ValueError( + "fixed phase mode requires BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"fixed phase mode requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + current_phase_execution_mode() + else: + if int(NUM_STRATIFIED_SPLIT_REPEATS) <= 0: + raise ValueError("NUM_STRATIFIED_SPLIT_REPEATS must be a positive integer.") + current_split_execution_mode() + current_percent_sampling_mode() + current_repeat_execution_mode() + current_percent_execution_mode() + split_repeat_indices_to_run() + subset_repeat_indices_to_run() + selected_percent_specs = percent_specs_to_run() + if using_fixed_phase_mode(): + if len(selected_percent_specs) != 1 or int(selected_percent_specs[0].percent_int) != 100: + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "If PERCENT_EXECUTION_MODE='manual', set SELECTED_DATASET_PERCENTS=[100]." + ) + + +def repeated_holdout_split_policy() -> str | None: + if base.current_dataset_name() == "BUSI_with_classes": + return base.current_busi_with_classes_split_policy() + return None + + +def active_specs_for_subset_repeat(subset_repeat_index: int) -> list[PercentRepeatSpec]: + return [ + spec + for spec in percent_specs() + if subset_repeat_index <= int(spec.repeat_count) + ] + + +def percent_specs() -> list[PercentRepeatSpec]: + global PERCENT_SPECS_CACHE + if PERCENT_SPECS_CACHE is not None: + return list(PERCENT_SPECS_CACHE) + specs: list[PercentRepeatSpec] = [] + if not DATASET_PERCENT_REPEAT_COUNTS: + raise ValueError("DATASET_PERCENT_REPEAT_COUNTS must contain at least one percentage entry.") + for raw_percent, raw_repeat_count in DATASET_PERCENT_REPEAT_COUNTS.items(): + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "DATASET_PERCENT_REPEAT_COUNTS keys must be integer percentages in the range [1, 100]." + ) + if raw_percent <= 0 or raw_percent > 100: + raise ValueError(f"Invalid dataset percent {raw_percent}; expected an integer in [1, 100].") + if isinstance(raw_repeat_count, bool) or int(raw_repeat_count) <= 0: + raise ValueError( + f"Invalid repeat count for percent {raw_percent}: {raw_repeat_count!r}. Expected a positive integer." + ) + repeat_count = int(raw_repeat_count) + if using_fixed_phase_mode() and raw_percent == 100 and repeat_count != 1: + raise ValueError( + "fixed phase mode requires DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got repeat_count={repeat_count} for percent 100." + ) + if raw_percent == 100 and repeat_count > 1: + print( + f"[Repeated Holdout] Percent 100 was configured with repeat_count={repeat_count}. " + "Collapsing to one effective repeat per split." + ) + repeat_count = 1 + fraction = float(raw_percent) / 100.0 + specs.append(PercentRepeatSpec(percent_int=raw_percent, fraction=fraction, repeat_count=repeat_count)) + specs.sort(key=lambda item: item.percent_int) + if using_fixed_phase_mode(): + if len(specs) != 1 or int(specs[0].percent_int) != 100: + configured = {spec.percent_int: spec.repeat_count for spec in specs} + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "Set DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got {configured}." + ) + PERCENT_SPECS_CACHE = list(specs) + return list(PERCENT_SPECS_CACHE) + + +def fold_experiment_summary(model_config: base.RuntimeModelConfig) -> None: + if using_fixed_phase_mode(): + base.banner("RUNNER FOLDS | FIXED STRATIFIED 10-PHASE 8/1/1") + else: + base.banner("RUNNER FOLDS | REPEATED STRATIFIED HOLDOUT") + print(f"Experiment name : {FOLDS_EXPERIMENT_NAME}") + print(f"Resume folds : {RESUME_FOLDS}") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Dataset name : {base.current_dataset_name()}") + print(f"Split type : {base.SPLIT_TYPE}") + print(f"Split generation mode : {current_split_generation_mode()}") + print(f"Generation display : {split_generation_display_name()}") + if using_fixed_phase_mode(): + print(f"Phase count : {phase_count()}") + print(f"Phase val offset : {phase_val_offset()}") + print(f"Phase execution mode : {current_phase_execution_mode()}") + print("Train percent mode : 100% of phase-train only") + if current_phase_execution_mode() == "manual": + print(f"Selected phases : {phase_execution_indices_to_run()}") + else: + print(f"Split repeats : {NUM_STRATIFIED_SPLIT_REPEATS}") + print(f"Split execution mode : {current_split_execution_mode()}") + if current_split_execution_mode() == "manual": + print(f"Selected split indices: {split_repeat_indices_to_run()}") + print(f"Sampling mode : {current_percent_sampling_mode()}") + print(f"Repeat execution mode : {current_repeat_execution_mode()}") + if current_repeat_execution_mode() == "manual": + print(f"Selected repeat idxs : {subset_repeat_indices_to_run()}") + print(f"Percent execution mode: {current_percent_execution_mode()}") + if current_percent_execution_mode() == "manual": + print(f"Selected percents : {[s.percent_int for s in percent_specs_to_run()]}") + print(f"Strategies : {base.STRATEGIES}") + print( + "Percent repeats : " + + ", ".join(f"{spec.percent_int}% x{spec.repeat_count}" for spec in percent_specs()) + ) + print(f"Execution mode : {base.EXECUTION_MODE}") + print(f"Run smoke test : {base.RUN_SMOKE_TEST}") + print(f"Test iter control : {base.TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {base.TEST_ITERATION_T}") + print(f"Run overfit test : {base.RUN_OVERFIT_TEST}") + print(f"Run Optuna : {base.RUN_OPTUNA}") + print(f"Load existing studies : {base.LOAD_EXISTING_STUDIES}") + print(f"Repo backup enabled : {ASYNC_REPO_BACKUP_AFTER_PHASE}") + if ASYNC_REPO_BACKUP_AFTER_PHASE: + print(f"Repo backup target : hf://{HF_REPO_TYPE}/{HF_REPO_ID or ''}") + print(f"Repo backup cadence : every {HF_BACKUP_EVERY_N_PHASES} phases") + print(f"Initial backup on run : {HF_BACKUP_ON_START}") + print(f"Phase timing summary : {_phase_timing_summary_path()}") + print(f"Backbone : {model_config.backbone_display_name()}") + + +def config_snapshot(model_config: base.RuntimeModelConfig) -> dict[str, Any]: + base_config = { + name: _jsonify(getattr(base, name)) + for name in sorted(dir(base)) + if name.isupper() and not name.startswith("_") + and name not in RUNTIME_ONLY_CONFIG_KEYS + } + return { + "folds_runner": { + "SPLIT_GENERATION_MODE": current_split_generation_mode(), + "NUM_STRATIFIED_SPLIT_REPEATS": int(NUM_STRATIFIED_SPLIT_REPEATS), + "NUM_PHASES": int(NUM_PHASES), + "PHASE_VAL_OFFSET": int(PHASE_VAL_OFFSET), + "DATASET_PERCENT_REPEAT_COUNTS": _jsonify(DATASET_PERCENT_REPEAT_COUNTS), + "PERCENT_SAMPLING_MODE": current_percent_sampling_mode(), + "FOLDS_EXPERIMENT_NAME": str(FOLDS_EXPERIMENT_NAME), + "RESUME_FOLDS": bool(RESUME_FOLDS), + "REPEATED_HOLDOUT_ROOT": str(REPEATED_HOLDOUT_ROOT), + "EXPERIMENT_ROOT": str(EXPERIMENT_ROOT), + "EXPERIMENT_DB_PATH": str(EXPERIMENT_DB_PATH), + }, + "runner": base_config, + "model_config": model_config.to_payload(), + } + + +def portable_config_snapshot_for_fingerprint(snapshot: dict[str, Any]) -> dict[str, Any]: + portable = json.loads(json.dumps(snapshot, sort_keys=True)) + folds_runner = portable.get("folds_runner") + if isinstance(folds_runner, dict): + for key in PORTABLE_FINGERPRINT_FOLD_KEYS: + folds_runner.pop(key, None) + return portable + + +def config_fingerprint(snapshot: dict[str, Any]) -> str: + return stable_hash(json.dumps(portable_config_snapshot_for_fingerprint(snapshot), sort_keys=True)) + + +def dataset_fingerprint(sample_records: list[dict[str, str]]) -> str: + payload = { + "dataset_name": base.current_dataset_name(), + "records": [ + { + "filename": record["filename"], + "image_rel_path": record["image_rel_path"], + "mask_rel_path": record["mask_rel_path"], + "class_label": record.get("class_label"), + } + for record in sample_records + ], + } + return stable_hash(json.dumps(payload, sort_keys=True)) + + +def cycle_dirname(split_repeat_index: int) -> str: + return f"{primary_unit_name()}_{split_repeat_index:03d}" + + +def split_manifest_path(split_repeat_index: int) -> Path: + return SPLIT_MANIFESTS_DIR / f"{cycle_dirname(split_repeat_index)}.json" + + +def subset_manifest_path(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return SUBSET_MANIFESTS_DIR / ( + f"{cycle_dirname(split_repeat_index)}_pct_{percent_int:03d}_repeat_{subset_repeat_index:02d}.json" + ) + + +def repeat_root(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return ( + EXPERIMENT_ROOT + / cycle_dirname(split_repeat_index) + / f"pct_{percent_int:03d}" + / f"repeat_{subset_repeat_index:02d}" + ) + + +def run_dir_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "final") + + +def overfit_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "overfit_test") + + +def strategy_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir) + + +def fold_study_paths_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> tuple[Path, Path, Path]: + strategy_root = strategy_root_for(strategy, ctx, model_config) + return strategy_root, ensure_dir(strategy_root / "study"), ensure_dir(strategy_root / "trials") + + +def select_sample_records() -> tuple[list[dict[str, str]], Path]: + dataset_name = base.current_dataset_name() + images_dir, annotations_dir = base.current_dataset_dirs() + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {base.DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + + matched, missing_masks, missing_images = base.validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + sample_records = base.build_sample_records( + matched, + images_subdir=images_dir.relative_to(dataset_root).as_posix(), + annotations_subdir=annotations_dir.relative_to(dataset_root).as_posix(), + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = base.current_pipeline_check_path() + if pipeline_check_path is not None: + base.validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + return sample_records, dataset_root + + +def record_filenames(records: list[dict[str, str]]) -> list[str]: + return [str(record["filename"]) for record in records] + + +def duplicate_filenames(records: list[dict[str, str]]) -> list[str]: + counts = Counter(record_filenames(records)) + return sorted(name for name, count in counts.items() if count > 1) + + +def format_filename_preview(filenames: list[str], *, limit: int = 5) -> str: + preview = filenames[:limit] + suffix = "" if len(filenames) <= limit else f" ... (+{len(filenames) - limit} more)" + return f"{preview}{suffix}" + + +def overlap_preview(leaks: dict[str, list[str]], *, limit: int = 5) -> str: + if not leaks: + return "[]" + key = sorted(leaks.keys())[0] + return f"{key}: {format_filename_preview(leaks[key], limit=limit)}" + + +def validate_disjoint_record_sets( + record_sets: dict[str, list[dict[str, str]]], + *, + context: str, + expected_filenames: set[str] | None = None, +) -> None: + split_filenames: dict[str, list[str]] = {} + for split_name, records in record_sets.items(): + duplicates = duplicate_filenames(records) + if duplicates: + raise RuntimeError( + f"Duplicate filenames detected inside {context} {split_name}: " + f"{format_filename_preview(duplicates)}" + ) + split_filenames[split_name] = record_filenames(records) + + leaks = base.check_data_leakage(split_filenames) + if leaks: + raise RuntimeError( + f"Data leakage detected for {context}: {overlap_preview(leaks)}" + ) + + if expected_filenames is not None: + actual_filenames = set().union(*(set(values) for values in split_filenames.values())) + missing = sorted(expected_filenames - actual_filenames) + extra = sorted(actual_filenames - expected_filenames) + if missing or extra: + details: list[str] = [] + if missing: + details.append(f"missing={format_filename_preview(missing)}") + if extra: + details.append(f"extra={format_filename_preview(extra)}") + raise RuntimeError( + f"{context} does not match the expected dataset membership: {'; '.join(details)}" + ) + + +def validate_fixed_phase_dataset_requirements(sample_records: list[dict[str, str]]) -> None: + class_distribution = base.compute_class_distribution(sample_records) + if class_distribution is None: + raise RuntimeError( + "fixed phase mode requires class-aware records with class_label metadata." + ) + insufficient = { + label: int(count) + for label, count in class_distribution.items() + if int(count) < phase_count() + } + if insufficient: + raise RuntimeError( + "fixed phase mode requires enough samples to place every class in every phase. " + f"Need >= {phase_count()} samples per class, got {insufficient}." + ) + + +def build_fixed_stratified_phase_folds( + sample_records: list[dict[str, str]], + *, + seed: int, +) -> dict[int, list[dict[str, str]]]: + folds: dict[int, list[dict[str, str]]] = {index: [] for index in phase_indices()} + grouped = base.group_records_by_class(sample_records) + for class_label in sorted(grouped.keys()): + records = base.deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"phase_partition::{phase_count()}::{class_label}", + ) + for record_index, record in enumerate(records): + fold_index = (record_index % phase_count()) + 1 + folds[fold_index].append(dict(record)) + + for fold_index in phase_indices(): + folds[fold_index] = base.deterministic_shuffle_records( + folds[fold_index], + seed=seed, + tag=f"phase_partition::{phase_count()}::fold::{fold_index:03d}", + ) + return folds + + +def validate_fixed_phase_folds( + phase_folds: dict[int, list[dict[str, str]]], + *, + sample_records: list[dict[str, str]], +) -> None: + if sorted(phase_folds.keys()) != phase_indices(): + raise RuntimeError( + f"Expected fixed phase folds for indices {phase_indices()}, got {sorted(phase_folds.keys())}." + ) + validate_disjoint_record_sets( + {f"fold_{fold_index:03d}": records for fold_index, records in sorted(phase_folds.items())}, + context="fixed phase fold partition", + expected_filenames={record["filename"] for record in sample_records}, + ) + + +def build_phase_base_split( + phase_folds: dict[int, list[dict[str, str]]], + *, + phase_index: int, + seed: int, +) -> dict[str, list[dict[str, str]]]: + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + train_records: list[dict[str, str]] = [] + for fold_index in phase_indices(): + if fold_index in {val_fold_index, test_fold_index}: + continue + train_records.extend(dict(record) for record in phase_folds[fold_index]) + return { + "train": base.deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"phase::{phase_index:03d}::train", + ), + "val": base.deterministic_shuffle_records( + phase_folds[val_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::val", + ), + "test": base.deterministic_shuffle_records( + phase_folds[test_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::test", + ), + } + + +def build_base_split_for_repeat(sample_records: list[dict[str, str]], split_seed: int) -> dict[str, list[dict[str, str]]]: + dataset_name = base.current_dataset_name() + if dataset_name == "BUSI_with_classes": + split_policy = repeated_holdout_split_policy() + if split_policy != "stratified": + raise ValueError( + "RUNNER_FOLDS.py requires BUSI_with_classes to use BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + return base.build_stratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + return base.build_unstratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + + +def validate_base_split( + base_splits: dict[str, list[dict[str, str]]], + *, + split_repeat_index: int, + expected_filenames: set[str] | None = None, +) -> None: + context = f"{primary_unit_name()}={split_repeat_index:03d}" + validate_disjoint_record_sets( + base_splits, + context=context, + expected_filenames=expected_filenames, + ) + + +def validate_phase_coverage( + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]], + *, + sample_records: list[dict[str, str]], +) -> None: + expected_phase_indices = phase_indices() + if sorted(phase_splits_by_index.keys()) != expected_phase_indices: + raise RuntimeError( + f"Expected materialized phases {expected_phase_indices}, got {sorted(phase_splits_by_index.keys())}." + ) + + expected_filenames = {record["filename"] for record in sample_records} + train_counts: Counter[str] = Counter() + val_counts: Counter[str] = Counter() + test_counts: Counter[str] = Counter() + + for phase_index, phase_splits in sorted(phase_splits_by_index.items()): + validate_disjoint_record_sets( + phase_splits, + context=f"phase={phase_index:03d}", + expected_filenames=expected_filenames, + ) + train_counts.update(record_filenames(phase_splits["train"])) + val_counts.update(record_filenames(phase_splits["val"])) + test_counts.update(record_filenames(phase_splits["test"])) + + expected_counts = { + "train": phase_count() - 2, + "val": 1, + "test": 1, + } + counters_by_name = { + "train": train_counts, + "val": val_counts, + "test": test_counts, + } + for split_name, expected_count in expected_counts.items(): + offending = sorted( + filename + for filename in expected_filenames + if int(counters_by_name[split_name].get(filename, 0)) != expected_count + ) + if offending: + raise RuntimeError( + f"Invalid global phase coverage for {split_name}: expected each filename to appear " + f"{expected_count} time(s), offenders={format_filename_preview(offending)}" + ) + + +def build_subset_for_repeat( + train_records: list[dict[str, str]], + *, + percent_fraction: float, + subset_seed: int, +) -> list[dict[str, str]]: + if percent_fraction >= 1.0: + return [dict(record) for record in train_records] + subsets = base.build_nested_train_subsets( + train_records, + [percent_fraction], + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + return subsets[base.percent_label(percent_fraction)] + + +def build_incremental_subset_chain( + train_records: list[dict[str, str]], + *, + active_specs: list[PercentRepeatSpec], + subset_seed: int, +) -> dict[str, list[dict[str, str]]]: + fractions = [spec.fraction for spec in active_specs] + return base.build_nested_train_subsets( + train_records, + fractions, + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + + +def iter_manifested_contexts( + split_repeat_indices: list[int] | None = None, + subset_repeat_indices: list[int] | None = None, +) -> Iterator[FoldRunContext]: + selected_indices = all_split_repeat_indices() if split_repeat_indices is None else list(split_repeat_indices) + selected_subset_repeats = ( + subset_repeat_indices_to_run() if subset_repeat_indices is None else list(subset_repeat_indices) + ) + selected_subset_repeat_set = set(selected_subset_repeats) + for split_repeat_index in selected_indices: + for spec in percent_specs_to_run(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + if subset_repeat_index not in selected_subset_repeat_set: + continue + yield load_context(split_repeat_index, spec.percent_int, subset_repeat_index) + + +def validate_subset_records( + train_records: list[dict[str, str]], + subset_records: list[dict[str, str]], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + train_filenames = {record["filename"] for record in train_records} + subset_filenames = [record["filename"] for record in subset_records] + cycle_context = f"{primary_unit_name()}={split_repeat_index:03d}" + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames found in subset {cycle_context}, " + f"percent={percent_int}, repeat={subset_repeat_index}." + ) + outside_train = sorted(set(subset_filenames) - train_filenames) + if outside_train: + raise RuntimeError( + f"Subset contains filenames outside the base train split for " + f"{cycle_context}, percent={percent_int}, repeat={subset_repeat_index}: {outside_train[:5]}" + ) + + +"""============================================================================= +SQLITE LEDGER +============================================================================= +""" + + +def require_ledger() -> sqlite3.Connection: + if LEDGER_CONN is None: + raise RuntimeError("Ledger is not initialized.") + return LEDGER_CONN + + +def ledger_execute(sql: str, params: tuple[Any, ...] = ()) -> sqlite3.Cursor: + conn = require_ledger() + cursor = conn.execute(sql, params) + conn.commit() + return cursor + + +def setup_ledger(path: Path) -> sqlite3.Connection: + ensure_dir(path.parent) + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=FULL") + conn.execute( + """ + CREATE TABLE IF NOT EXISTS experiment_meta ( + experiment_name TEXT PRIMARY KEY, + config_fingerprint TEXT NOT NULL, + config_json TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + base_seed INTEGER NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS split_manifests ( + split_repeat_index INTEGER PRIMARY KEY, + split_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_count INTEGER NOT NULL, + val_count INTEGER NOT NULL, + test_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS subset_manifests ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_subset_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS run_status ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + dataset_fraction REAL NOT NULL, + split_seed INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + split_manifest_path TEXT NOT NULL, + subset_manifest_path TEXT NOT NULL, + run_dir TEXT NOT NULL, + status TEXT NOT NULL, + stage TEXT NOT NULL, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at TEXT, + updated_at TEXT NOT NULL, + heartbeat_at TEXT, + completed_at TEXT, + last_epoch INTEGER, + latest_checkpoint_path TEXT, + best_checkpoint_path TEXT, + evaluation_path TEXT, + best_metric_name TEXT, + best_metric_value REAL, + elapsed_seconds REAL, + error_text TEXT, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS final_metrics ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + metric_name TEXT NOT NULL, + mean REAL NOT NULL, + std REAL, + run_dir TEXT NOT NULL, + evaluation_path TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy, metric_name) + ) + """ + ) + conn.commit() + return conn + + +def fetch_one(sql: str, params: tuple[Any, ...]) -> sqlite3.Row | None: + return require_ledger().execute(sql, params).fetchone() + + +def load_run_status(key: RunKey) -> sqlite3.Row | None: + return fetch_one( + """ + SELECT * + FROM run_status + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + + +def upsert_experiment_meta( + *, + snapshot: dict[str, Any], + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO experiment_meta ( + experiment_name, + config_fingerprint, + config_json, + dataset_fingerprint, + base_seed, + created_at + ) VALUES (?, ?, ?, ?, ?, ?) + """, + ( + FOLDS_EXPERIMENT_NAME, + config_hash, + json.dumps(snapshot, sort_keys=True), + data_hash, + int(base.SEED), + now_utc_iso(), + ), + ) + + +def existing_experiment_meta() -> sqlite3.Row | None: + return fetch_one( + "SELECT * FROM experiment_meta WHERE experiment_name = ?", + (FOLDS_EXPERIMENT_NAME,), + ) + + +def ledger_row_count(table_name: str) -> int: + row = require_ledger().execute(f"SELECT COUNT(*) AS count FROM {table_name}").fetchone() + return int(row["count"]) if row is not None else 0 + + +def upsert_split_manifest_row( + *, + split_repeat_index: int, + split_seed: int, + manifest_path: Path, + train_count: int, + val_count: int, + test_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO split_manifests ( + split_repeat_index, + split_seed, + manifest_path, + train_count, + val_count, + test_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + split_seed, + str(manifest_path.resolve()), + train_count, + val_count, + test_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_subset_manifest_row( + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, + subset_seed: int, + manifest_path: Path, + subset_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO subset_manifests ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + subset_seed, + manifest_path, + train_subset_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + percent_int, + subset_repeat_index, + subset_seed, + str(manifest_path.resolve()), + subset_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_run_plan_row( + *, + key: RunKey, + dataset_fraction: float, + split_seed: int, + subset_seed: int, + split_manifest: Path, + subset_manifest: Path, + run_dir: Path, +) -> None: + existing = load_run_status(key) + if existing is not None: + return + ledger_execute( + """ + INSERT INTO run_status ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + dataset_fraction, + split_seed, + subset_seed, + split_manifest_path, + subset_manifest_path, + run_dir, + status, + stage, + attempt_count, + updated_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + dataset_fraction, + split_seed, + subset_seed, + str(split_manifest.resolve()), + str(subset_manifest.resolve()), + str(run_dir.resolve()), + "planned", + "manifested", + 0, + now_utc_iso(), + ), + ) + + +def mark_stale_running_as_interrupted() -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE status = 'running' + """, + (now_utc_iso(),), + ) + + +def mark_run_running(key: RunKey, *, stage: str) -> None: + row = load_run_status(key) + attempt_count = 1 if row is None else int(row["attempt_count"]) + 1 + started_at = row["started_at"] if row is not None else None + if not started_at: + started_at = now_utc_iso() + ledger_execute( + """ + UPDATE run_status + SET status = 'running', + stage = ?, + attempt_count = ?, + started_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + attempt_count, + started_at, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_stage(key: RunKey, *, stage: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET stage = ?, + status = 'running', + updated_at = ?, + heartbeat_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def update_run_checkpoint_progress( + key: RunKey, + *, + checkpoint_path: Path, + epoch: int, + best_metric_name: str, + best_metric_value: float, + elapsed_seconds: float, +) -> None: + column_name = "best_checkpoint_path" if checkpoint_path.name == "best.pt" else "latest_checkpoint_path" + sql = f""" + UPDATE run_status + SET {column_name} = ?, + last_epoch = ?, + best_metric_name = ?, + best_metric_value = ?, + elapsed_seconds = ?, + status = 'running', + stage = 'training', + heartbeat_at = ?, + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """ + ledger_execute( + sql, + ( + str(checkpoint_path.resolve()), + int(epoch), + str(best_metric_name), + float(best_metric_value), + float(elapsed_seconds), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_failed(key: RunKey, error_text: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'failed', + updated_at = ?, + error_text = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + error_text, + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_interrupted(key: RunKey) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def ingest_evaluation_into_db(key: RunKey, evaluation_path: Path, run_dir: Path) -> None: + payload = base.load_json(evaluation_path) + metrics = payload.get("metrics", {}) + ledger_execute( + """ + DELETE FROM final_metrics + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + conn = require_ledger() + for metric_name, metric_payload in metrics.items(): + conn.execute( + """ + INSERT INTO final_metrics ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + metric_name, + mean, + std, + run_dir, + evaluation_path + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + str(metric_name), + float(metric_payload.get("mean", 0.0)), + float(metric_payload.get("std")) if metric_payload.get("std") is not None else None, + str(run_dir.resolve()), + str(evaluation_path.resolve()), + ), + ) + conn.commit() + best_metric_name = str(payload.get("best_metric_name", "")) + best_metric_value = None + if best_metric_name and best_metric_name in metrics: + best_metric_value = float(metrics[best_metric_name]["mean"]) + ledger_execute( + """ + UPDATE run_status + SET status = 'completed', + stage = 'done', + evaluation_path = ?, + best_metric_name = COALESCE(?, best_metric_name), + best_metric_value = COALESCE(?, best_metric_value), + completed_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + str(evaluation_path.resolve()), + best_metric_name or None, + best_metric_value, + now_utc_iso(), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def completed_run_rows() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + ORDER BY split_repeat_index, dataset_percent, subset_repeat_index, strategy + """ + ).fetchall() + ) + + +def metric_rows_for_completed_runs() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT + rs.split_repeat_index, + rs.dataset_percent, + rs.subset_repeat_index, + rs.strategy, + rs.run_dir, + rs.split_manifest_path, + rs.subset_manifest_path, + rs.evaluation_path, + fm.metric_name, + fm.mean AS metric_mean, + fm.std AS metric_std + FROM final_metrics fm + JOIN run_status rs + ON rs.split_repeat_index = fm.split_repeat_index + AND rs.dataset_percent = fm.dataset_percent + AND rs.subset_repeat_index = fm.subset_repeat_index + AND rs.strategy = fm.strategy + WHERE rs.status = 'completed' + ORDER BY rs.split_repeat_index, rs.dataset_percent, rs.subset_repeat_index, rs.strategy, fm.metric_name + """ + ).fetchall() + ) + + +def export_stats() -> None: + ensure_dir(EXPORTS_DIR) + rows = metric_rows_for_completed_runs() + split_manifest_cache: dict[str, dict[str, Any]] = {} + + def split_manifest_payload(path_text: str) -> dict[str, Any]: + cached = split_manifest_cache.get(path_text) + if cached is None: + cached = base.load_json(Path(path_text)) + split_manifest_cache[path_text] = cached + return cached + + raw_rows_by_run: dict[tuple[int, int, int, int], dict[str, Any]] = {} + for row in rows: + key = ( + int(row["split_repeat_index"]), + int(row["dataset_percent"]), + int(row["subset_repeat_index"]), + int(row["strategy"]), + ) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + raw_row = raw_rows_by_run.setdefault( + key, + { + "split_repeat_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": row["run_dir"], + "split_manifest_path": row["split_manifest_path"], + "subset_manifest_path": row["subset_manifest_path"], + "evaluation_path": row["evaluation_path"], + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + }, + ) + if manifest_payload.get("phase_index") is not None: + raw_row["phase_index"] = int(manifest_payload["phase_index"]) + raw_row["phase_val_fold_index"] = int(manifest_payload["phase_val_fold_index"]) + raw_row["phase_test_fold_index"] = int(manifest_payload["phase_test_fold_index"]) + raw_row[f"{row['metric_name']}_mean"] = float(row["metric_mean"]) + raw_row[f"{row['metric_name']}_std"] = ( + float(row["metric_std"]) if row["metric_std"] is not None else None + ) + + raw_rows = list(raw_rows_by_run.values()) + raw_rows.sort( + key=lambda item: ( + item["split_repeat_index"], + item["dataset_percent"], + item["subset_repeat_index"], + item["strategy"], + ) + ) + + if raw_rows: + raw_fieldnames = sorted({key for row in raw_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=raw_fieldnames) + writer.writeheader() + writer.writerows(raw_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "raw_run_metrics.csv") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", raw_rows) + else: + atomic_write_text(EXPORTS_DIR / "raw_run_metrics.csv", "") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", []) + + grouped: dict[tuple[int, int], dict[str, Any]] = {} + for row in rows: + group_key = (int(row["dataset_percent"]), int(row["strategy"])) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + bucket = grouped.setdefault( + group_key, + { + "dataset_percent": int(row["dataset_percent"]), + "strategy": int(row["strategy"]), + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + "_phase_indices": set(), + "_metric_values": {}, + }, + ) + if manifest_payload.get("phase_index") is not None: + bucket["_phase_indices"].add(int(manifest_payload["phase_index"])) + bucket["_metric_values"].setdefault(str(row["metric_name"]), []).append( + { + "mean": float(row["metric_mean"]), + "std": float(row["metric_std"]) if row["metric_std"] is not None else None, + } + ) + + aggregated_rows: list[dict[str, Any]] = [] + for (_percent_int, _strategy), bucket in sorted(grouped.items()): + row = { + "dataset_percent": bucket["dataset_percent"], + "strategy": bucket["strategy"], + "split_generation_mode": bucket["split_generation_mode"], + } + metric_values: dict[str, list[dict[str, float | None]]] = bucket["_metric_values"] + row["n_runs"] = max((len(values) for values in metric_values.values()), default=0) + if bucket["_phase_indices"]: + phase_indices = sorted(int(value) for value in bucket["_phase_indices"]) + row["completed_phase_count"] = len(phase_indices) + row["completed_phases"] = ",".join(str(value) for value in phase_indices) + for metric_name, values in sorted(metric_values.items()): + means = [value["mean"] for value in values] + stds = [value["std"] for value in values if value["std"] is not None] + row[f"{metric_name}_mean"] = float(base.np.mean(means)) if means else None + row[f"{metric_name}_std"] = float(base.np.std(means)) if means else None + row[f"{metric_name}_within_run_std_mean"] = float(base.np.mean(stds)) if stds else None + aggregated_rows.append(row) + + if aggregated_rows: + aggregated_fieldnames = sorted({key for row in aggregated_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=aggregated_fieldnames) + writer.writeheader() + writer.writerows(aggregated_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", aggregated_rows) + else: + atomic_write_text(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv", "") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", []) + + +"""============================================================================= +BASE MODULE PATCHES +============================================================================= +""" + + +def patched_save_json(path: str | Path, payload: Any) -> None: + atomic_save_json(path, payload) + + +def _context_matches_percent(ctx: FoldRunContext | None, percent: float) -> bool: + if ctx is None: + return False + return abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12 + + +def patched_percent_root(percent: float) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return ensure_dir(CURRENT_FOLD_CONTEXT.repeat_root) + return ORIGINAL_PERCENT_ROOT(percent) + + +def patched_strategy_root_for_percent( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return strategy_root_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STRATEGY_ROOT_FOR_PERCENT(strategy, percent, model_config) + + +def patched_final_root_for_strategy( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return run_dir_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_FINAL_ROOT_FOR_STRATEGY(strategy, percent, model_config) + + +def patched_study_paths_for( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return fold_study_paths_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STUDY_PATHS_FOR(strategy, percent, model_config) + + +def patched_save_checkpoint( + path: Path, + *, + run_type: str, + model: base.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + scaler: Any, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": base._unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + base.validate_checkpoint_payload( + Path(path), + payload, + required_keys=base.checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + atomic_torch_save(path, payload) + base.write_checkpoint_manifest(path, payload) + + if run_type != "final" or CURRENT_FOLD_CONTEXT is None: + return + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(run_config["strategy"]), + ) + update_run_checkpoint_progress( + run_key, + checkpoint_path=Path(path), + epoch=int(epoch), + best_metric_name=str(best_metric_name), + best_metric_value=float(best_metric_value), + elapsed_seconds=float(elapsed_seconds), + ) + + +def patched_run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: base.DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, dict[str, float]]: + if CURRENT_FOLD_CONTEXT is not None and LEDGER_CONN is not None: + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(strategy), + ) + mark_run_stage(run_key, stage="evaluating") + return ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +def install_base_patches() -> None: + base.save_json = patched_save_json + base.percent_root = patched_percent_root + base.strategy_root_for_percent = patched_strategy_root_for_percent + base.final_root_for_strategy = patched_final_root_for_strategy + base.study_paths_for = patched_study_paths_for + base.save_checkpoint = patched_save_checkpoint + base.run_evaluation_for_run = patched_run_evaluation_for_run + base.RESUME_IDENTITY_KEYS = BASE_RESUME_IDENTITY_KEYS + ( + "folds_experiment_name", + "split_repeat_index", + "subset_repeat_index", + "split_seed", + "subset_seed", + "base_split_manifest_path", + "subset_manifest_path", + ) + if RESUME_FOLDS and base.RUN_OPTUNA: + base.LOAD_EXISTING_STUDIES = True + + +@contextmanager +def activate_context(ctx: FoldRunContext) -> Iterator[None]: + global CURRENT_FOLD_CONTEXT + previous = CURRENT_FOLD_CONTEXT + CURRENT_FOLD_CONTEXT = ctx + try: + yield + finally: + CURRENT_FOLD_CONTEXT = previous + + +"""============================================================================= +EXPERIMENT PLAN MATERIALIZATION +============================================================================= +""" + + +def create_split_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + base_splits: dict[str, list[dict[str, str]]], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "base_splits": { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + }, + "counts": {split_name: len(records) for split_name, records in base_splits.items()}, + "class_distributions": { + split_name: base.compute_class_distribution(records) + for split_name, records in base_splits.items() + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + payload["phase_partition_seed"] = int(split_seed) + return payload + + +def create_subset_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + percent_int: int, + percent_fraction: float, + subset_repeat_index: int, + subset_seed: int, + split_manifest: Path, + base_splits: dict[str, list[dict[str, str]]], + subset_records: list[dict[str, str]], + subset_sampling_source: str, + sampling_chain_dataset_percents: list[int], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "dataset_percent": percent_int, + "dataset_fraction": percent_fraction, + "subset_repeat_index": subset_repeat_index, + "subset_seed": subset_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "subset_sampling_source": subset_sampling_source, + "sampling_chain_dataset_percents": [int(value) for value in sampling_chain_dataset_percents], + "parent_split_manifest_path": str(split_manifest.resolve()), + "train_records": [dict(record) for record in subset_records], + "val_records": [dict(record) for record in base_splits["val"]], + "test_records": [dict(record) for record in base_splits["test"]], + "base_train_records": [dict(record) for record in base_splits["train"]], + "counts": { + "base_train": len(base_splits["train"]), + "train_subset": len(subset_records), + "val": len(base_splits["val"]), + "test": len(base_splits["test"]), + }, + "class_distributions": { + "base_train": base.compute_class_distribution(base_splits["train"]), + "train_subset": base.compute_class_distribution(subset_records), + "val": base.compute_class_distribution(base_splits["val"]), + "test": base.compute_class_distribution(base_splits["test"]), + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + return payload + + +def validate_materialized_phase_manifests(*, sample_records: list[dict[str, str]]) -> None: + if not using_fixed_phase_mode(): + return + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + for phase_index in phase_indices(): + manifest_path = split_manifest_path(phase_index) + if not manifest_path.exists(): + raise RuntimeError(f"Missing phase manifest for phase={phase_index:03d}: {manifest_path}") + payload = base.load_json(manifest_path) + if str(payload.get("split_generation_mode", "")).strip().lower() != "fixed_stratified_phases_8_1_1": + raise RuntimeError( + f"Expected fixed phase split_generation_mode in {manifest_path}, got " + f"{payload.get('split_generation_mode')!r}." + ) + if int(payload.get("phase_index", -1)) != phase_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_index={payload.get('phase_index')!r}, " + f"expected {phase_index}." + ) + if int(payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_count={payload.get('phase_count')!r}, " + f"expected {phase_count()}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_val_fold_index=" + f"{payload.get('phase_val_fold_index')!r}, expected {val_fold_index}." + ) + if int(payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_test_fold_index=" + f"{payload.get('phase_test_fold_index')!r}, expected {test_fold_index}." + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + + +def validate_materialized_subset_manifests() -> None: + if not using_fixed_phase_mode(): + return + for phase_index in phase_indices(): + split_payload = base.load_json(split_manifest_path(phase_index)) + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + manifest_path = subset_manifest_path(phase_index, spec.percent_int, subset_repeat_index) + if not manifest_path.exists(): + raise RuntimeError( + f"Missing subset manifest for phase={phase_index:03d}, " + f"percent={spec.percent_int}, repeat={subset_repeat_index}: {manifest_path}" + ) + subset_payload = base.load_json(manifest_path) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=phase_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + +def validate_loaded_context_payloads( + split_payload: dict[str, Any], + subset_payload: dict[str, Any], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + base_splits = { + split_name: [dict(record) for record in split_payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_base_split(base_splits, split_repeat_index=split_repeat_index) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + validate_disjoint_record_sets( + { + "train_subset": train_records, + "val": val_records, + "test": test_records, + }, + context=( + f"{primary_unit_name()}={split_repeat_index:03d}, " + f"percent={percent_int}, repeat={subset_repeat_index}" + ), + ) + validate_subset_records( + base_splits["train"], + train_records, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + if set(record_filenames(base_train_records)) != set(record_filenames(base_splits["train"])): + raise RuntimeError( + f"Subset manifest base train records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(val_records)) != set(record_filenames(base_splits["val"])): + raise RuntimeError( + f"Subset manifest validation records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(test_records)) != set(record_filenames(base_splits["test"])): + raise RuntimeError( + f"Subset manifest test records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if using_fixed_phase_mode(): + phase_index = int(split_payload.get("phase_index", split_repeat_index)) + if int(split_payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{split_payload.get('phase_count')!r}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(split_payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{split_payload.get('phase_val_fold_index')!r}." + ) + if int(split_payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{split_payload.get('phase_test_fold_index')!r}." + ) + if int(subset_payload.get("phase_index", phase_index)) != phase_index: + raise RuntimeError( + f"Subset manifest phase_index={subset_payload.get('phase_index')!r} does not match " + f"phase={phase_index:03d}." + ) + if int(subset_payload.get("phase_count", phase_count())) != phase_count(): + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{subset_payload.get('phase_count')!r}." + ) + if int(subset_payload.get("phase_val_fold_index", val_fold_index)) != val_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{subset_payload.get('phase_val_fold_index')!r}." + ) + if int(subset_payload.get("phase_test_fold_index", test_fold_index)) != test_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{subset_payload.get('phase_test_fold_index')!r}." + ) + + +def materialize_experiment_plan( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + ensure_dir(EXPERIMENT_ROOT) + ensure_dir(SPLIT_MANIFESTS_DIR) + ensure_dir(SUBSET_MANIFESTS_DIR) + ensure_dir(EXPORTS_DIR) + + snapshot = config_snapshot(model_config) + config_hash = config_fingerprint(snapshot) + data_hash = dataset_fingerprint(sample_records) + sampling_mode = current_percent_sampling_mode() + upsert_experiment_meta(snapshot=snapshot, config_hash=config_hash, data_hash=data_hash) + expected_filenames = {record["filename"] for record in sample_records} + partition_seed_value = partition_seed() + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + fixed_phase_folds: dict[int, list[dict[str, str]]] | None = None + if using_fixed_phase_mode(): + validate_fixed_phase_dataset_requirements(sample_records) + fixed_phase_folds = build_fixed_stratified_phase_folds( + sample_records, + seed=partition_seed_value, + ) + validate_fixed_phase_folds(fixed_phase_folds, sample_records=sample_records) + + for split_repeat_index in all_split_repeat_indices(): + phase_index = None + phase_val_fold_index = None + phase_test_fold_index = None + if using_fixed_phase_mode(): + if fixed_phase_folds is None: + raise RuntimeError("Fixed phase folds were not initialized.") + split_seed = partition_seed_value + phase_index = split_repeat_index + phase_val_fold_index, phase_test_fold_index = phase_fold_indices(phase_index) + base_splits = build_phase_base_split( + fixed_phase_folds, + phase_index=phase_index, + seed=split_seed, + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + } + else: + split_seed = fold_seed(f"split::{split_repeat_index}") + base_splits = build_base_split_for_repeat(sample_records, split_seed) + validate_base_split( + base_splits, + split_repeat_index=split_repeat_index, + expected_filenames=expected_filenames, + ) + + split_manifest = split_manifest_path(split_repeat_index) + split_payload = create_split_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + base_splits=base_splits, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(split_manifest, split_payload) + upsert_split_manifest_row( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + manifest_path=split_manifest, + train_count=len(base_splits["train"]), + val_count=len(base_splits["val"]), + test_count=len(base_splits["test"]), + config_hash=config_hash, + data_hash=data_hash, + ) + + if sampling_mode == "independent": + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + subset_seed = fold_seed( + f"subset::{split_repeat_index}::{spec.percent_int}::{subset_repeat_index}" + ) + subset_records = build_subset_for_repeat( + base_splits["train"], + percent_fraction=spec.fraction, + subset_seed=subset_seed, + ) + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="independent", + sampling_chain_dataset_percents=[spec.percent_int], + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + continue + + max_subset_repeat_index = max(spec.repeat_count for spec in percent_specs()) + for subset_repeat_index in range(1, max_subset_repeat_index + 1): + active_specs = active_specs_for_subset_repeat(subset_repeat_index) + if not active_specs: + continue + subset_seed = fold_seed(f"subset::{split_repeat_index}::repeat::{subset_repeat_index}") + subset_chain = build_incremental_subset_chain( + base_splits["train"], + active_specs=active_specs, + subset_seed=subset_seed, + ) + chain_dataset_percents = [spec.percent_int for spec in active_specs] + for spec in active_specs: + subset_records = subset_chain[base.percent_label(spec.fraction)] + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="incremental_chain", + sampling_chain_dataset_percents=chain_dataset_percents, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + + if using_fixed_phase_mode(): + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +def validate_or_create_experiment( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + meta = existing_experiment_meta() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not RESUME_FOLDS and not ignore_resume_folds_gate: + if meta is not None: + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + if meta is None: + if ledger_row_count("split_manifests") > 0 or ledger_row_count("run_status") > 0: + raise RuntimeError( + f"Experiment DB {EXPERIMENT_DB_PATH} contains run state but is missing experiment metadata." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + current_snapshot = config_snapshot(model_config) + current_hash = config_fingerprint(current_snapshot) + current_data_hash = dataset_fingerprint(sample_records) + stored_config_hash = str(meta["config_fingerprint"]) + stored_portable_hash = "" + try: + stored_config_json = json.loads(str(meta["config_json"])) + stored_portable_hash = config_fingerprint(stored_config_json) + except Exception as exc: + print(f"[Resume] Could not recompute portable config fingerprint from stored metadata: {exc}") + if stored_config_hash != current_hash and stored_portable_hash != current_hash: + raise RuntimeError( + f"Existing experiment config fingerprint does not match current configuration for {EXPERIMENT_ROOT}." + ) + if stored_config_hash != current_hash and stored_portable_hash == current_hash: + print( + "[Resume] Accepted existing experiment metadata with a portable config fingerprint match " + "(machine-specific paths/runtime resume flag changed)." + ) + if str(meta["dataset_fingerprint"]) != current_data_hash: + raise RuntimeError( + f"Existing experiment dataset fingerprint does not match current dataset contents for {EXPERIMENT_ROOT}." + ) + if using_fixed_phase_mode(): + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +"""============================================================================= +RUNTIME BUNDLE CONSTRUCTION +============================================================================= +""" + + +def load_context(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> FoldRunContext: + split_payload = base.load_json(split_manifest_path(split_repeat_index)) + subset_payload = base.load_json(subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index)) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + return FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=percent_int, + percent_fraction=float(subset_payload["dataset_fraction"]), + split_seed=int(split_payload["split_seed"]), + subset_seed=int(subset_payload["subset_seed"]), + repeat_root=repeat_root(split_repeat_index, percent_int, subset_repeat_index), + split_manifest_path=split_manifest_path(split_repeat_index), + subset_manifest_path=subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index), + ) + + +def build_fold_data_bundle(ctx: FoldRunContext) -> base.DataBundle: + split_payload = base.load_json(ctx.split_manifest_path) + subset_payload = base.load_json(ctx.subset_manifest_path) + dataset_root = Path(base.current_dataset_dirs()[0]).parent.resolve() + phase_index = ( + int(split_payload.get("phase_index", ctx.split_repeat_index)) + if str(split_payload.get("split_generation_mode", "")).strip().lower() == "fixed_stratified_phases_8_1_1" + else None + ) + cycle_token = f"phase{phase_index:03d}" if phase_index is not None else f"split{ctx.split_repeat_index:03d}" + normalization_cache_path = ( + ctx.repeat_root + / ( + f"norm_stats_{base.normalization_cache_tag()}_{base.SPLIT_TYPE}_{ctx.percent_int:03d}pct_" + f"{cycle_token}_repeat{ctx.subset_repeat_index:02d}.json" + ) + ) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + + base_train_class_distribution = base.compute_class_distribution(base_train_records) + train_class_distribution = base.compute_class_distribution(train_records) + val_class_distribution = base.compute_class_distribution(val_records) + test_class_distribution = base.compute_class_distribution(test_records) + + base.print_loaded_class_distribution( + split_type=base.SPLIT_TYPE, + train_subset_key=str(ctx.percent_int), + base_train_records=base_train_records, + train_records=train_records, + val_records=val_records, + test_records=test_records, + ) + + global_mean, global_std, normalization_source = base.compute_busi_statistics( + dataset_root=dataset_root, + sample_records=train_records, + cache_path=normalization_cache_path, + ) + + payload = { + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "dataset_splits_path": str(ctx.split_manifest_path.resolve()), + "dataset_root": str(dataset_root), + "split_source": ( + "fixed_phase_manifest" + if phase_index is not None + else "repeated_holdout_manifest" + ), + "split_generation_mode": str(split_payload.get("split_generation_mode", current_split_generation_mode())), + "split_type": base.SPLIT_TYPE, + "percent_sampling_mode": str( + subset_payload.get("percent_sampling_mode", split_payload.get("percent_sampling_mode", "independent")) + ), + "dataset_percent": ctx.percent_fraction, + "train_subset_key": str(ctx.percent_int), + "train_subset_variant": int(ctx.subset_repeat_index), + "train_subset_source": str(subset_payload.get("subset_sampling_source", "repeated_holdout_repeat")), + "selected_split_manifest_path": str(ctx.subset_manifest_path.resolve()), + "sampling_chain_dataset_percents": [ + int(value) for value in subset_payload.get("sampling_chain_dataset_percents", [ctx.percent_int]) + ], + "base_train_count": len(base_train_records), + "train_count": len(train_records), + "val_count": len(val_records), + "test_count": len(test_records), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(normalization_cache_path.resolve()), + "normalization_source": normalization_source, + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "split_seed": ctx.split_seed, + "subset_seed": ctx.subset_seed, + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "folds_experiment_root": str(EXPERIMENT_ROOT.resolve()), + } + if phase_index is not None: + payload["phase_index"] = phase_index + payload["phase_count"] = int(split_payload["phase_count"]) + payload["phase_val_fold_index"] = int(split_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(split_payload["phase_test_fold_index"]) + + base.print_split_summary(payload) + base.print_normalization_summary(payload) + + train_split_name = ( + f"train {base.SPLIT_TYPE} {ctx.percent_int}% phase{phase_index:03d}" + if phase_index is not None + else f"train {base.SPLIT_TYPE} {ctx.percent_int}% split{ctx.split_repeat_index:03d}" + ) + val_split_name = ( + f"val {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"val {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + test_split_name = ( + f"test {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"test {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + loader_prefix = ( + f"{base.SPLIT_TYPE}:{ctx.percent_int}:phase{phase_index:03d}" + if phase_index is not None + else f"{base.SPLIT_TYPE}:{ctx.percent_int}:split{ctx.split_repeat_index:03d}" + ) + + train_ds = base.BUSIDataset( + train_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=True, + split_name=train_split_name, + ) + val_ds = base.BUSIDataset( + val_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=val_split_name, + ) + test_ds = base.BUSIDataset( + test_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=test_split_name, + ) + bundle = base.DataBundle( + percent=ctx.percent_fraction, + split_payload=payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=base.make_loader( + train_ds, + shuffle=True, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:train", + ), + val_loader=base.make_loader( + val_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:val", + ), + test_loader=base.make_loader( + test_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:test", + ), + ) + base.print_preload_summary(bundle) + return bundle + + +def release_bundle(bundle: base.DataBundle | None) -> None: + if bundle is None: + return + del bundle + gc.collect() + base.run_cuda_cleanup(context="bundle release") + + +def checkpoint_candidates(run_dir: Path) -> list[Path]: + return [ + run_dir / "checkpoints" / "latest.pt", + run_dir / "checkpoints" / "best.pt", + ] + + +def resolve_resume_checkpoint(run_dir: Path) -> Path | None: + for candidate in checkpoint_candidates(run_dir): + if candidate.exists(): + return candidate + return None + + +"""============================================================================= +RUN EXECUTION +============================================================================= +""" + + +def strategy_requires_strategy2_checkpoint(strategy: int) -> bool: + return ( + base.EXECUTION_MODE == "train_eval" and strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 + ) + + +def fold_param_metadata(ctx: FoldRunContext) -> dict[str, Any]: + subset_payload = base.load_json(ctx.subset_manifest_path) + payload = { + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "split_repeat_index": int(ctx.split_repeat_index), + "subset_repeat_index": int(ctx.subset_repeat_index), + "split_seed": int(ctx.split_seed), + "subset_seed": int(ctx.subset_seed), + "split_generation_mode": str(subset_payload.get("split_generation_mode", current_split_generation_mode())), + "percent_sampling_mode": str(subset_payload.get("percent_sampling_mode", current_percent_sampling_mode())), + "subset_sampling_source": str(subset_payload.get("subset_sampling_source", "independent")), + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + } + if subset_payload.get("phase_index") is not None: + payload["phase_index"] = int(subset_payload["phase_index"]) + payload["phase_count"] = int(subset_payload["phase_count"]) + payload["phase_val_fold_index"] = int(subset_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(subset_payload["phase_test_fold_index"]) + return payload + + +def finalize_run_from_artifacts(key: RunKey, run_dir: Path) -> bool: + evaluation_path = run_dir / "evaluation.json" + if not evaluation_path.exists(): + return False + ingest_evaluation_into_db(key, evaluation_path, run_dir) + export_stats() + return True + + +def execute_final_run( + *, + strategy: int, + ctx: FoldRunContext, + bundle: base.DataBundle, + model_config: base.RuntimeModelConfig, +) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None: + raise RuntimeError(f"Run plan row is missing for {run_key}.") + if str(row["status"]) == "completed": + return + if str(row["status"]) == "failed": + print( + f"[{split_generation_display_name()}] Skipping failed run " + f"{run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload)}." + ) + return + + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + + strategy2_checkpoint_path: str | Path | None = None + run_name = run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload) + with activate_context(ctx): + banner_prefix = "PHASE RUN" if using_fixed_phase_mode() else "REPEATED HOLDOUT RUN" + base.banner(f"{banner_prefix} | {run_name}") + if strategy_requires_strategy2_checkpoint(strategy): + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + + if base.EXECUTION_MODE == "eval_only": + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + summary_path = run_dir / "summary.json" + if summary_path.exists() and not (run_dir / "evaluation.json").exists(): + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + if base.RUN_SMOKE_TEST: + base.maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + params = base.resolve_job_params( + strategy, + ctx.percent_fraction, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = {**params, **fold_param_metadata(ctx)} + + resume_checkpoint_path = None + if str(row["status"]) in {"interrupted", "running"}: + resume_checkpoint_path = resolve_resume_checkpoint(run_dir) + + mark_run_running(run_key, stage="training") + base.run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=run_dir, + params=params, + max_epochs=base.strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + + +def reconcile_existing_artifacts(ctx: FoldRunContext, strategy: int, model_config: base.RuntimeModelConfig) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + return + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + if (run_dir / "summary.json").exists(): + mark_run_interrupted(run_key) + mark_run_stage(run_key, stage="evaluating") + return + if resolve_resume_checkpoint(run_dir) is not None: + mark_run_interrupted(run_key) + + +def maybe_reset_study_artifacts(model_config: base.RuntimeModelConfig) -> None: + if not base.RESET_ALL_STUDIES_EACH_RUN or not base.RUN_OPTUNA: + return + if RESUME_FOLDS: + print( + f"[{split_generation_display_name()}] RESET_ALL_STUDIES_EACH_RUN ignored because RESUME_FOLDS=True." + ) + return + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + with activate_context(ctx): + for strategy in base.STRATEGIES: + base.reset_study_artifacts(strategy, ctx.percent_fraction, model_config=model_config) + + +def run_overfit_mode(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE OVERFIT TEST MODE") + else: + base.banner("REPEATED HOLDOUT OVERFIT TEST MODE") + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + base.run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=overfit_root_for(strategy, ctx, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_eval_only_without_ledger(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EVAL ONLY | LEDGER BYPASSED") + else: + base.banner("REPEATED HOLDOUT EVAL ONLY | LEDGER BYPASSED") + print("[Eval Only] Skipping experiment ledger and evaluating directly from manifests and checkpoints.") + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + run_name = run_identity_label( + strategy=strategy, + percent=ctx.percent_fraction, + split_payload=bundle.split_payload, + ) + base.banner(f"EVAL ONLY | {run_name}") + base.run_evaluation_for_run( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir_for(strategy, ctx, model_config), + strategy2_checkpoint_path=None, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_pass_for_statuses( + statuses: set[str], + *, + model_config: base.RuntimeModelConfig, +) -> None: + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + pending_strategies = [] + for strategy in base.STRATEGIES: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is not None and str(row["status"]) in statuses: + pending_strategies.append(int(strategy)) + if not pending_strategies: + continue + + bundle = build_fold_data_bundle(ctx) + phase_had_error = False + try: + for strategy in pending_strategies: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + continue + try: + execute_final_run( + strategy=strategy, + ctx=ctx, + bundle=bundle, + model_config=model_config, + ) + except KeyboardInterrupt: + phase_had_error = True + mark_run_interrupted(run_key) + raise + except Exception: + phase_had_error = True + error_text = traceback.format_exc() + mark_run_failed(run_key, error_text) + print(error_text) + finally: + if using_fixed_phase_mode(): + write_phase_timing_summary_after_phase(ctx.split_repeat_index) + if not phase_had_error: + threading.Thread( + target=run_repo_backup_after_phase, + args=(ctx.split_repeat_index,), + daemon=True, + ).start() + release_bundle(bundle) + + +"""============================================================================= +MAIN +============================================================================= +""" + + +def run_repeated_holdout_main() -> int: + global LEDGER_CONN + + validate_repeated_holdout_settings() + if not str(FOLDS_EXPERIMENT_NAME).strip(): + raise ValueError("FOLDS_EXPERIMENT_NAME must be non-empty.") + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"RUNNER_FOLDS.py currently requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + + install_base_patches() + base.SAVE_LATEST_EVERY_EPOCH = True + + base.set_global_seed(base.SEED) + model_config = base.current_model_config() + fold_experiment_summary(model_config) + + if str(base.EXECUTION_MODE).strip().lower() == "eval_only": + select_sample_records() + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + return run_eval_only_without_ledger(model_config) + + sample_records, _dataset_root = select_sample_records() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not ignore_resume_folds_gate and not RESUME_FOLDS and EXPERIMENT_ROOT.exists(): + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + if ( + not ignore_resume_folds_gate + and RESUME_FOLDS + and not EXPERIMENT_DB_PATH.exists() + and EXPERIMENT_ROOT.exists() + and any(EXPERIMENT_ROOT.iterdir()) + ): + raise RuntimeError( + f"Experiment root {EXPERIMENT_ROOT} already exists without a valid SQLite ledger at {EXPERIMENT_DB_PATH}. " + "Refusing to attach to ambiguous state." + ) + + LEDGER_CONN = setup_ledger(EXPERIMENT_DB_PATH) + try: + validate_or_create_experiment(sample_records=sample_records, model_config=model_config) + mark_stale_running_as_interrupted() + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + for strategy in base.STRATEGIES: + reconcile_existing_artifacts(ctx, int(strategy), model_config) + + export_stats() + maybe_reset_study_artifacts(model_config) + + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + + run_pass_for_statuses({"interrupted", "running"}, model_config=model_config) + run_pass_for_statuses({"planned"}, model_config=model_config) + export_stats() + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EXECUTION COMPLETE") + else: + base.banner("REPEATED HOLDOUT COMPLETE") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Raw metrics export : {EXPORTS_DIR / 'raw_run_metrics.csv'}") + print(f"Aggregate export : {EXPORTS_DIR / 'aggregated_metrics_by_percent_strategy.csv'}") + return 0 + finally: + if LEDGER_CONN is not None: + LEDGER_CONN.close() + LEDGER_CONN = None + +def run_single_run_main() -> int: + global DATASET_PERCENTS + banner("MLR ALL STRATEGIES BAYES RUNNER") + DATASET_PERCENTS = normalize_dataset_percents(DATASET_PERCENTS) + set_global_seed(SEED) + model_config = current_model_config() + dataset_name = current_dataset_name() + images_dir, annotations_dir = current_dataset_dirs() + if EXECUTION_MODE not in {"train_eval", "eval_only"}: + raise ValueError(f"EXECUTION_MODE must be 'train_eval' or 'eval_only', got {EXECUTION_MODE!r}") + if SPLIT_TYPE not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"SPLIT_TYPE must be one of {SUPPORTED_SPLIT_TYPES}, got {SPLIT_TYPE!r}") + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + ensure_specific_checkpoint_scope("EVAL_CHECKPOINT_MODE", EVAL_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("STRATEGY2_CHECKPOINT_MODE", STRATEGY2_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("TRAIN_RESUME_MODE", TRAIN_RESUME_MODE) + + print_environment_summary(model_config) + split_registry, split_source = load_or_create_dataset_splits( + images_dir=images_dir, + annotations_dir=annotations_dir, + split_json_path=current_dataset_splits_json_path(), + train_fractions=DATASET_PERCENTS, + seed=SEED, + ) + + bundles: dict[float, DataBundle] = {} + for percent in DATASET_PERCENTS: + bundles[percent] = build_data_bundle(percent, split_registry, split_source) + + if RUN_OVERFIT_TEST: + run_configured_overfit_tests(bundles, model_config=model_config) + banner("OVERFIT TESTS COMPLETE") + return 0 + + if RESET_ALL_STUDIES_EACH_RUN: + if RUN_OPTUNA: + banner("RESETTING OPTUNA STUDIES") + for strategy in STRATEGIES: + for percent in DATASET_PERCENTS: + reset_study_artifacts(strategy, percent, model_config=model_config) + else: + print("[Optuna Reset] Skipped because RUN_OPTUNA=False.") + + try: + for percent in DATASET_PERCENTS: + banner(f"PERCENT STAGE | {percent_text(percent)}") + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and EXECUTION_MODE == "train_eval" and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + + if EXECUTION_MODE == "train_eval": + maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = resolve_job_params( + strategy, + percent, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + banner( + f"FINAL RETRAIN | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_final_training( + strategy, + bundle, + params, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + else: + banner( + f"EVAL ONLY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_evaluation_for_run( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=final_root_for_strategy(strategy, percent, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + except Exception: + banner("RUN FAILED") + traceback.print_exc() + return 1 + + banner("ALL DONE") + return 0 + + +def main() -> int: + validate_hf_backup_settings() + run_initial_hf_backup() + if EXPERIMENT_MODE not in SUPPORTED_EXPERIMENT_MODES: + raise ValueError( + f"EXPERIMENT_MODE must be one of {SUPPORTED_EXPERIMENT_MODES}, got {EXPERIMENT_MODE!r}" + ) + if EXPERIMENT_MODE == "repeated_holdout": + return run_repeated_holdout_main() + return run_single_run_main() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ablations/abl_ab3_r3_only.py b/ablations/abl_ab3_r3_only.py new file mode 100644 index 0000000000000000000000000000000000000000..5790ed5015fa8f38df6fd6247901f0cd83ac9fc5 --- /dev/null +++ b/ablations/abl_ab3_r3_only.py @@ -0,0 +1,13598 @@ +from __future__ import annotations +import csv +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from datetime import datetime, timezone +import gc +import hashlib +import importlib +import inspect +import json +import math +import os +import random +import shutil +import sqlite3 +import subprocess +import sys +import tarfile +import tempfile +import threading +import time +import traceback +import weakref +from collections import Counter +from contextlib import contextmanager, nullcontext +from pathlib import Path +from typing import Any, Iterator, Literal + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import segmentation_models_pytorch as smp +os.environ.setdefault("NNPACK_DISABLE", "1") +import torch +torch.backends.nnpack.enabled = False +import torch.nn as nn +import torch.nn.functional as F +from optuna.storages import RDBStorage +from PIL import Image as PILImage +from scipy import ndimage +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +"""============================================================================= +EDIT ME +============================================================================= +""" + +PROJECT_DIR = Path(__file__).resolve().parent.parent # ABLATION: repo root (this copy lives in ablations/) +TRANSUNET_REPO_DIR = PROJECT_DIR / "TransUNet" +TRANSUNET_VIT_NAME = "R50-ViT-B_16" +TRANSUNET_N_SKIP = 3 +TRANSUNET_PRETRAINED_PATH = PROJECT_DIR / "model" / "vit_checkpoint" / "imagenet21k" / "R50+ViT-B_16.npz" + +RUNS_ROOT = PROJECT_DIR / "runs" +HARD_CODED_PARAM_DIR = PROJECT_DIR +MODEL_NAME = "Segformer_B0_revamped_nt_2" + +EXPERIMENT_MODE = "repeated_holdout" # "single_run" or "repeated_holdout" +SUPPORTED_EXPERIMENT_MODES = ("single_run", "repeated_holdout") +SPLIT_GENERATION_MODE = "fixed_stratified_phases_8_1_1" # "repeated_holdout" or "fixed_stratified_phases_8_1_1" +SUPPORTED_SPLIT_GENERATION_MODES = ("repeated_holdout", "fixed_stratified_phases_8_1_1") +NUM_STRATIFIED_SPLIT_REPEATS = 5 +NUM_PHASES = 10 +PHASE_VAL_OFFSET = 1 +DATASET_PERCENT_REPEAT_COUNTS: dict[int, int] = { + # 5: 4, + # 15: 3, + # 30: 3, + # 50: 2, + 100: 1, +} +PERCENT_SAMPLING_MODE = "incremental" # "independent" or "incremental" +SUPPORTED_PERCENT_SAMPLING_MODES = ("independent", "incremental") +PERCENT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PERCENT_EXECUTION_MODES = ("auto", "manual") +SELECTED_DATASET_PERCENTS: list[int] = [100] +SPLIT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_SPLIT_EXECUTION_MODES = ("auto", "manual") +SELECTED_SPLIT_INDICES: list[int] = [1] + +PHASE_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PHASE_EXECUTION_MODES = ("auto", "manual") +SELECTED_PHASES: list[int] = [1] # used only when PHASE_EXECUTION_MODE="manual" + +REPEAT_EXECUTION_MODE = "auto" # "auto" or "manual" +SUPPORTED_REPEAT_EXECUTION_MODES = ("auto", "manual") +SELECTED_REPEAT_INDICES: list[int] = [1] + +FOLDS_EXPERIMENT_NAME = "stratified_holdout_v1" +RESUME_FOLDS = False +ASYNC_REPO_BACKUP_AFTER_PHASE = False +# Hugging Face dataset repo to mirror the project into. Set via env so nothing is +# hardcoded: export HF_REPO_ID="your-username/ADVAI24JUN-backup" and HF_TOKEN=... +HF_REPO_ID = os.environ.get("HF_REPO_ID", "") +HF_REPO_TYPE = "dataset" +# Only upload after every Nth phase (boundary), so we don't hammer HF every phase. +HF_BACKUP_EVERY_N_PHASES = 1 +HF_BACKUP_MAX_RETRIES = 5 +# Run one synchronous backup BEFORE training starts: it creates the repo and uploads +# the current project state, proving the whole backup pipeline works before we commit +# hours of compute. Phase backups later refresh this same repo. +HF_BACKUP_ON_START = False +# Glob patterns excluded from the upload (matched against repo-relative paths). +HF_IGNORE_PATTERNS = ( + "**/.git/**", + "**/__pycache__/**", + "**/.ipynb_checkpoints/**", + "**/.cache/**", + "**/.venv/**", + "*.pyc", + ".DS_Store", +) + +DATASET_NAME = "BUSI_with_classes" # "BUSI" or "BUSI_with_classes" +SUPPORTED_DATASET_NAMES = ("BUSI", "BUSI_with_classes") +DATA_ROOT = PROJECT_DIR / DATASET_NAME +BUSI_WITH_CLASSES_SPLIT_POLICY = "stratified" # "balanced_train" or "stratified" +SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES = ("balanced_train", "stratified") + +SUPPORTED_STRATEGIES: tuple[int, ...] = (2,3) +STRATEGIES = [2,3] +DATASET_PERCENTS = [] #ignored in the folding [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1.0] #, 0.5, 1.0] #, 0.5, 1.0] +SPLIT_TYPE = "80_10_10" +SUPPORTED_SPLIT_TYPES = ("80_10_10", "70_10_20") +DATASET_SPLITS_JSON = PROJECT_DIR / "dataset_splits.json" +DATASET_SPLITS_VERSION = 1 +TRAIN_SUBSET_VARIANT = 1 # 0 uses the persisted subset; >0 deterministically resamples only the train subset from the frozen base train split. +NUM_TRIALS = 30 +STUDY_DIRECTION = "maximize" +BEST_CHECKPOINT_METRICS = { + 2: "val_iou", + 3: "val_refine_score", +} +OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR = "best_observed_objective" +OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR = "best_observed_objective_name" +SUPPORTED_CHECKPOINT_METRICS = { + "val_loss", + "val_dice", + "val_iou", + "val_biou", + "val_refine_score", + "val_decoder_dice", + "val_decoder_iou", + "val_decoder_biou", + "val_dice_gain", + "val_iou_gain", + "val_biou_gain", + "val_actor_loss", + "val_critic_loss", + "val_ce_loss", + "val_dice_loss", + "val_reward", + "val_entropy", +} + +SEED = 42 +IMG_SIZE = 128 +# d = 0 -> auto (floor(0.02 * diag)); any positive int overrides. +# Recommended: 0 (auto) -> resolves to ~4 px for IMG_SIZE=128. +BOUNDARY_IOU_D: int = 0 +BATCH_SIZE = 16 # Recommended to prevent OOM +NUM_WORKERS = 128 # Recommended with RAM-preloaded datasets to avoid worker RAM duplication. +USE_PIN_MEMORY = True +USE_PERSISTENT_WORKERS = True +PRELOAD_TO_RAM = True + +SMP_ENCODER_NAME = "efficientnet-b0" +SMP_ENCODER_WEIGHTS = "imagenet" +SMP_ENCODER_DEPTH = 5 +SMP_ENCODER_PROJ_DIM = 256 +SMP_DECODER_TYPE = "Unet" +BACKBONE_FAMILY = "smp" # "smp" or "custom_vgg" +ENABLE_CUSTOM_VGG_BACKBONE = False +VGG_FEATURE_SCALES = 4 +VGG_FEATURE_DILATION = 1 + +USE_IMAGENET_NORM = True +REPLACE_BN_WITH_GN = True +GN_NUM_GROUPS = 8 +NUM_ACTIONS = 2 + +STRATEGY_1_MAX_EPOCHS = 100 +STRATEGY_2_MAX_EPOCHS = 100 +STRATEGY_3_MAX_EPOCHS = 120 +STRATEGY_4_MAX_EPOCHS = 100 +STRATEGY_5_MAX_EPOCHS = 100 +VALIDATE_EVERY_N_EPOCHS = 1 +CHECKPOINT_EVERY_N_EPOCHS = 0 +SAVE_LATEST_EVERY_EPOCH = True +SAVE_HISTORY_INCREMENTALLY = False +EARLY_STOPPING_PATIENCE = 0 +VERBOSE_EPOCH_LOG = False + +DEFAULT_HEAD_LR = 1e-4 +DEFAULT_ENCODER_LR = 1e-5 +DEFAULT_WEIGHT_DECAY = 1e-4 +DEFAULT_TMAX = 5 +TEST_ITERATION_CONTROL = False # If True, validation/evaluation/inference uses TEST_ITERATION_T instead of full tmax. +TEST_ITERATION_T = 1 # Applied only when TEST_ITERATION_CONTROL=True. Clamped to [1, tmax]. +DEFAULT_GAMMA = 0.95 +DEFAULT_CRITIC_LOSS_WEIGHT = 0.5 +DEFAULT_ENTROPY_ALPHA_INIT = 0.2 +DEFAULT_ENTROPY_TARGET_RATIO = 0.25 +DEFAULT_ENTROPY_LR = 3e-4 +DEFAULT_CE_WEIGHT = 0.5 +DEFAULT_DICE_WEIGHT = 0.5 +DEFAULT_DROPOUT_P = 0.2 +DEFAULT_GRAD_CLIP_NORM = 6.0 +DEFAULT_MASK_UPDATE_STEP = 0.1 +DEFAULT_FOREGROUND_REWARD_WEIGHT = 0.0 +DEFAULT_RECALL_REWARD_WEIGHT = 1.0 +DEFAULT_DICE_REWARD_WEIGHT = 0.35 +DEFAULT_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_PRIOR_REWARD_WEIGHT = 0.01 +DEFAULT_DECODER_GAIN_REWARD_WEIGHT = 0.5 +DEFAULT_REWARD_SCALE = 1.0 +DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION = True +DEFAULT_STRATEGY3_VARIANT = "lite" +DEFAULT_STRATEGY3_NUM_ACTIONS = 3 +DEFAULT_REFINE_DELTA_SMALL = 0.03 +DEFAULT_REFINE_DELTA_LARGE = 0.08 +DEFAULT_STRATEGY3_AUX_CE_WEIGHT = 0.40 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH = 25 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS = 15 +DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION = 0.10 +DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE = 30 +DEFAULT_STRATEGY3_PROBE_MODE = "rolling_random" +DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM = 0.25 +DEFAULT_STRATEGY3_RL_LOSS_SCALE = 10.0 +DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED = True +DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES = 8 +DEFAULT_STRATEGY3_MC_DROPOUT_P = 0.2 +DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_READ = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE = True +DEFAULT_STRATEGY3_DELTA_MAX = 0.10 +DEFAULT_STRATEGY3_SAM_ATTENTION_GRID = 64 +DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT = 1.0 +DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE = False +DEFAULT_BIOU_REWARD_WEIGHT = 1.0 +DEFAULT_IOU_REWARD_WEIGHT = 1.0 +DEFAULT_KEEP_CORRECT_REWARD_WEIGHT = 0.05 +DEFAULT_STRATEGY3_A3C_ENTROPY_COEFF = 0.0 +DEFAULT_STRATEGY3_ENTROPY_TARGET_RATIO = 0.20 +DEFAULT_STRATEGY3_ENTROPY_ALPHA_INIT = 0.005 +DEFAULT_STRATEGY3_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_EARLY_STOPPING_MONITOR = "auto" +DEFAULT_EARLY_STOPPING_MODE = "auto" +DEFAULT_EARLY_STOPPING_MIN_DELTA = 0.0 +DEFAULT_EARLY_STOPPING_START_EPOCH = 30 +DEFAULT_EXPLORATION_EPS = 0.1 +EXPLORATION_EPS_EPOCHS = 20 +ATTENTION_MAX_TOKENS = 1024 +ATTENTION_MIN_POOL_SIZE = 16 + +_STRATEGY3_MC_DROPOUT_WARNED = False +_STRATEGY3_SAM_GRID_WARNED: set[int] = set() +_STRATEGY3_MC_CACHE_SCHEMA_VERSION = 1 +_STRATEGY3_MC_FILE_SHA256_CACHE: dict[str, str] = {} + +SCHEDULER_FACTOR = 0.5 +SCHEDULER_PATIENCE = 5 +SCHEDULER_THRESHOLD = 1e-3 +SCHEDULER_MIN_LR = 1e-5 + +HEAD_LR_RANGE = (1e-5, 3e-3) +ENCODER_LR_RANGE = (1e-6, 3e-3) +WEIGHT_DECAY_RANGE = (1e-6, 1e-2) +TMAX_RANGE = (3, 10) +ENTROPY_LR_RANGE = (1e-5, 1e-3) +DROPOUT_P_RANGE = (0.0, 0.5) + +USE_TRIAL_PRUNING = True +TRIAL_PRUNER_WARMUP_STEPS = 80 +TRIAL_PRUNER_PATIENCE_STEPS = 40 +LOAD_EXISTING_STUDIES = False +SKIP_EXISTING_FINALS = False +RUN_OPTUNA = False +RESET_ALL_STUDIES_EACH_RUN = False +USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF = False + +EXECUTION_MODE = "train_eval" # "train_eval" or "eval_only" +EVAL_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +EVAL_SPECIFIC_CHECKPOINT = "" +STRATEGY2_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = { + # Non-phase mode — keyed by dataset percent (float): + # 0.1: "runs/EfficientNet_Strategy2_New/pct_10/strategy_2/final/checkpoints/epoch_0089.pt", + # 0.5: "Strategy2_Checkpoints/strat2_50_best.pt", + # 1.0: "runs/EfficientNet_Strategy2_New/pct_100/strategy_2/final/checkpoints/best.pt", + # Phase mode — keyed by phase index (int): + 1: "/content/UNET_REVAMP/best_strat2.pt", + 2: "/content/UNET_REVAMP/best_strat2_2.pt", + 3: "/content/UNET_REVAMP/best_strat2_3.pt", +} +STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 = True +TRAIN_RESUME_MODE = "off" # "off", "latest", "best", or "specific" +TRAIN_RESUME_SPECIFIC_CHECKPOINT = "" +OPTUNA_HEARTBEAT_INTERVAL = 60 +OPTUNA_HEARTBEAT_GRACE_PERIOD = 180 + +USE_AMP = True +AMP_DTYPE = "bfloat16" # "auto", "bfloat16", or "float16" +USE_CHANNELS_LAST = True +USE_TORCH_COMPILE = True +STEPWISE_BACKWARD = True +ALLOW_TF32 = True + +RUN_SMOKE_TEST = False +SMOKE_TEST_SAMPLE_INDEX = 0 +RUN_OVERFIT_TEST = False +OVERFIT_N_BATCHES = 2 +OVERFIT_N_EPOCHS = 100 +OVERFIT_HEAD_LR = 1e-3 +OVERFIT_ENCODER_LR = 1e-4 +OVERFIT_PRINT_EVERY = 5 +WRITE_EPOCH_DIAGNOSTIC = True +EPOCH_DIAGNOSTIC_TRAIN_BATCHES = 2 +EPOCH_DIAGNOSTIC_VAL_BATCHES = 2 +CONTROLLED_MASK_THRESHOLD = 0.50 + +REQUIRED_HPARAM_KEYS = ("head_lr", "encoder_lr", "weight_decay", "dropout_p", "tmax", "entropy_lr") + +_TRANSUNET_REQUIRED_NPZ_KEYS: tuple[str, ...] = ( + "embedding/kernel", + "embedding/bias", + "Transformer/encoder_norm/scale", + "Transformer/encoder_norm/bias", + "Transformer/posembed_input/pos_embedding", + "conv_root/kernel", + "gn_root/scale", + "gn_root/bias", + "Transformer/encoderblock_0/MultiHeadDotProductAttention_1/query/kernel", +) +_TRANSUNET_ENCODER_ALIASES: set[str] = {"vitb16r50", "r50vitb16"} +_TRANSUNET_VISION_TRANSFORMER: Any | None = None +_TRANSUNET_CONFIGS: dict[str, Any] | None = None +_TEST_ITERATION_NOTICE_CACHE: set[tuple[str, int, int]] = set() + +"""============================================================================= +IF OPTUNA IS OFF --> USE ME +============================================================================= +""" + +# Key format: ":" +# Each value is a JSON filename in HARD_CODED_PARAM_DIR containing the required hyperparameters. +MANUAL_HPARAMS_IF_OPTUNA_OFF: dict[str, str] = { + "2:100": "param_segformer/best_params_strat2.json", + "3:100": "param_segformer/best_params_strat3.json", +} + +# ===================== ABLATION HARNESS OVERRIDE ===================== +# Auto-generated. Outputs go to a separate MODEL_NAME subtree; strategy 3 +# only; the frozen strategy-2 base is reused from the original run tree. +MODEL_NAME = "Unet_B0_AB3_r3_only" +STRATEGIES = [3] +STRATEGY2_CHECKPOINT_MODE = "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = {1: str(PROJECT_DIR / "best_unet.pt")} +MANUAL_HPARAMS_IF_OPTUNA_OFF = {**MANUAL_HPARAMS_IF_OPTUNA_OFF, "3:100": "param_unet/abl_ab3_r3_only.json"} +# ===================================================================== + +"""============================================================================= +RUNTIME SETUP +============================================================================= +""" + +torch.set_float32_matmul_precision("high") +if torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32 + torch.backends.cudnn.allow_tf32 = ALLOW_TF32 +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = True + +def select_runtime_device() -> tuple[torch.device, str]: + if torch.cuda.is_available(): + return torch.device("cuda"), "cuda" + + mps_backend = getattr(torch.backends, "mps", None) + if mps_backend is not None and mps_backend.is_available(): + try: + _probe = torch.zeros(1, device="mps") + del _probe + return torch.device("mps"), "mps" + except Exception as exc: + print(f"[Device] MPS detected but failed to initialize ({exc}). Falling back to CPU.") + + return torch.device("cpu"), "cpu" + +DEVICE, DEVICE_FALLBACK_SOURCE = select_runtime_device() +CURRENT_JOB_PARAMS: dict[str, Any] = {} + +@dataclass(frozen=True) +class RuntimeModelConfig: + backbone_family: str + smp_encoder_name: str + smp_encoder_weights: str | None + smp_encoder_depth: int + smp_encoder_proj_dim: int + smp_decoder_type: str + vgg_feature_scales: int + vgg_feature_dilation: int + + @classmethod + def from_globals(cls) -> RuntimeModelConfig: + return cls( + backbone_family=str(BACKBONE_FAMILY).strip().lower(), + smp_encoder_name=str(SMP_ENCODER_NAME), + smp_encoder_weights=SMP_ENCODER_WEIGHTS, + smp_encoder_depth=int(SMP_ENCODER_DEPTH), + smp_encoder_proj_dim=int(SMP_ENCODER_PROJ_DIM), + smp_decoder_type=str(SMP_DECODER_TYPE), + vgg_feature_scales=int(VGG_FEATURE_SCALES), + vgg_feature_dilation=int(VGG_FEATURE_DILATION), + ) + + @classmethod + def from_payload(cls, payload: dict[str, Any] | None) -> RuntimeModelConfig: + payload = payload or {} + return cls( + backbone_family=str(payload.get("backbone_family", "smp")).strip().lower(), + smp_encoder_name=str(payload.get("smp_encoder_name", SMP_ENCODER_NAME)), + smp_encoder_weights=payload.get("smp_encoder_weights", SMP_ENCODER_WEIGHTS), + smp_encoder_depth=int(payload.get("smp_encoder_depth", SMP_ENCODER_DEPTH)), + smp_encoder_proj_dim=int(payload.get("smp_encoder_proj_dim", SMP_ENCODER_PROJ_DIM)), + smp_decoder_type=str(payload.get("smp_decoder_type", SMP_DECODER_TYPE)), + vgg_feature_scales=int(payload.get("vgg_feature_scales", VGG_FEATURE_SCALES)), + vgg_feature_dilation=int(payload.get("vgg_feature_dilation", VGG_FEATURE_DILATION)), + ) + + def validate(self) -> RuntimeModelConfig: + if self.backbone_family not in {"smp", "custom_vgg"}: + raise ValueError(f"BACKBONE_FAMILY must be 'smp' or 'custom_vgg', got {self.backbone_family!r}") + if self.vgg_feature_scales not in {3, 4}: + raise ValueError(f"VGG_FEATURE_SCALES must be 3 or 4, got {self.vgg_feature_scales}") + if self.vgg_feature_dilation < 1: + raise ValueError(f"VGG_FEATURE_DILATION must be >= 1, got {self.vgg_feature_dilation}") + if self.smp_encoder_depth < 1: + raise ValueError(f"SMP_ENCODER_DEPTH must be >= 1, got {self.smp_encoder_depth}") + if self.smp_encoder_proj_dim < 0: + raise ValueError(f"SMP_ENCODER_PROJ_DIM must be >= 0, got {self.smp_encoder_proj_dim}") + if _normalized_model_token(self.smp_decoder_type) == "transunet": + if _normalized_model_token(self.smp_encoder_name) not in _TRANSUNET_ENCODER_ALIASES: + print( + "[RuntimeModelConfig] Warning: SMP_DECODER_TYPE='TransUNet' is wired for " + "SMP_ENCODER_NAME='ViTB16R50' (or 'R50ViTB16'). " + f"Received {self.smp_encoder_name!r}." + ) + if IMG_SIZE % 16 != 0: + raise ValueError( + f"TransUNet requires IMG_SIZE divisible by 16, got IMG_SIZE={IMG_SIZE}." + ) + return self + + def to_payload(self) -> dict[str, Any]: + return { + "backbone_family": self.backbone_family, + "smp_encoder_name": self.smp_encoder_name, + "smp_encoder_weights": self.smp_encoder_weights, + "smp_encoder_depth": self.smp_encoder_depth, + "smp_encoder_proj_dim": self.smp_encoder_proj_dim, + "smp_decoder_type": self.smp_decoder_type, + "vgg_feature_scales": self.vgg_feature_scales, + "vgg_feature_dilation": self.vgg_feature_dilation, + } + + def backbone_tag(self) -> str: + return self.backbone_family + + def backbone_display_name(self) -> str: + if self.backbone_family == "custom_vgg": + return f"Custom VGG (scales={self.vgg_feature_scales}, dilation={self.vgg_feature_dilation})" + return f"SMP {self.smp_encoder_name}" + +def current_model_config() -> RuntimeModelConfig: + return RuntimeModelConfig.from_globals().validate() + +"""============================================================================= +UTILITIES +============================================================================= +""" + +def _normalized_model_token(value: str | None) -> str: + return "".join(ch for ch in str(value or "") if ch.isalnum()).lower() + + +def _is_transunet_selection( + model_config: RuntimeModelConfig | None = None, + *, + encoder_name: str | None = None, + decoder_type: str | None = None, +) -> bool: + if model_config is not None: + encoder_name = model_config.smp_encoder_name + decoder_type = model_config.smp_decoder_type + enc = _normalized_model_token(encoder_name) + dec = _normalized_model_token(decoder_type) + return dec == "transunet" and enc in _TRANSUNET_ENCODER_ALIASES + + +def _resolve_test_iteration_tmax(tmax: int, *, context: str) -> int: + effective_tmax = max(int(tmax), 1) + if not TEST_ITERATION_CONTROL: + return effective_tmax + + requested_t = int(TEST_ITERATION_T) + if requested_t < 1: + raise ValueError( + f"TEST_ITERATION_T must be >= 1 when TEST_ITERATION_CONTROL=True, got {requested_t}." + ) + + effective_tmax = min(effective_tmax, requested_t) + cache_key = (context, int(tmax), effective_tmax) + if cache_key not in _TEST_ITERATION_NOTICE_CACHE: + if requested_t > int(tmax): + print( + f"[Test Iteration Control] {context}: TEST_ITERATION_T={requested_t} exceeds tmax={int(tmax)}; " + f"using t={effective_tmax}." + ) + else: + print( + f"[Test Iteration Control] {context}: overriding test rollout steps " + f"from tmax={int(tmax)} to t={effective_tmax}." + ) + _TEST_ITERATION_NOTICE_CACHE.add(cache_key) + return effective_tmax + +def banner(title: str) -> None: + line = "=" * 80 + print(f"\n{line}\n{title}\n{line}") + +def section(title: str) -> None: + print(f"\n{'-' * 80}\n{title}\n{'-' * 80}") + +def ensure_dir(path: str | Path) -> Path: + path = Path(path).expanduser().resolve() + path.mkdir(parents=True, exist_ok=True) + return path + +def save_json(path: str | Path, payload: Any) -> None: + path = Path(path) + ensure_dir(path.parent) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + +def load_json(path: str | Path) -> Any: + with Path(path).open("r", encoding="utf-8") as f: + return json.load(f) + +def _format_history_log_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key == "lr" or key.endswith("_lr"): + return f"{value:.6e}" + return json.dumps(value) + return json.dumps(value) + +def format_history_log_row(row: dict[str, Any]) -> str: + return ", ".join(f"{key}={_format_history_log_value(key, value)}" for key, value in row.items()) + +def _format_epoch_metric(value: Any, *, scientific: bool = False) -> str: + if value is None: + return "null" + if isinstance(value, (float, int, np.floating, np.integer)): + value = float(value) + return f"{value:.6e}" if scientific else f"{value:.4f}" + return str(value) + +def format_concise_epoch_log( + row: dict[str, Any], + *, + best_metric_name: str, + best_metric_value: float, +) -> str: + fields: list[tuple[str, Any, bool]] = [ + ("train_loss", row.get("train_loss"), False), + ("train_iou", row.get("train_iou"), False), + ("train_entropy", row.get("train_entropy"), False), + ("val_loss", row.get("val_loss"), False), + ("val_iou", row.get("val_iou"), False), + ("val_dice", row.get("val_dice"), False), + ("val_iou_gain", row.get("val_iou_gain"), False), + ("val_biou_gain", row.get("val_biou_gain"), False), + ("head_lr", row.get("lr"), True), + ("encoder_lr", row.get("encoder_lr"), True), + (best_metric_name, best_metric_value, False), + ("study_best", row.get("study_best_objective"), False), + ] + parts = [ + f"{name}={_format_epoch_metric(value, scientific=scientific)}" + for name, value, scientific in fields + if value is not None + ] + early_monitor_name = row.get("early_stopping_monitor_name") + if early_monitor_name: + parts.append(f"es_monitor={early_monitor_name}") + if row.get("early_stopping_monitor_value") is not None: + parts.append(f"es_value={_format_epoch_metric(row.get('early_stopping_monitor_value'))}") + if row.get("early_stopping_best_value") is not None: + parts.append(f"es_best={_format_epoch_metric(row.get('early_stopping_best_value'))}") + if row.get("early_stopping_wait") is not None and row.get("early_stopping_patience") is not None: + parts.append( + f"es_wait={int(row.get('early_stopping_wait'))}/{int(row.get('early_stopping_patience'))}" + ) + if row.get("early_stopping_active") is not None: + parts.append(f"es_active={bool(row.get('early_stopping_active'))}") + if row.get("strategy3_freeze_active") is not None: + parts.append(f"s3_frozen={bool(row.get('strategy3_freeze_active'))}") + if row.get("study_best_trial") is not None: + parts.append(f"study_best_trial={int(row.get('study_best_trial'))}") + return ", ".join(parts) + +def _optuna_direction_is_maximize(direction: Any) -> bool: + direction_name = str(getattr(direction, "name", direction)).lower() + return direction_name.endswith("maximize") + +def _optuna_value_is_better( + candidate: float | None, + current: float | None, + *, + direction: Any, +) -> bool: + if candidate is None: + return False + if current is None: + return True + return float(candidate) > float(current) if _optuna_direction_is_maximize(direction) else float(candidate) < float(current) + +def _optuna_trial_state_name(trial: Any) -> str: + state = getattr(trial, "state", None) + return str(getattr(state, "name", state)).upper() + +def _optuna_trial_user_attr_float(trial: Any, attr_name: str) -> float | None: + user_attrs = getattr(trial, "user_attrs", None) + if not isinstance(user_attrs, dict): + return None + value = user_attrs.get(attr_name) + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + +def _optuna_trial_best_intermediate_value( + trial: Any, + *, + direction: Any, +) -> float | None: + best_value: float | None = None + for value in getattr(trial, "intermediate_values", {}).values(): + if value is None: + continue + candidate_value = float(value) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_value = candidate_value + return best_value + +def _optuna_trial_best_observed_value( + trial: Any, + *, + direction: Any, + current_best_value: float | None = None, +) -> float | None: + if current_best_value is not None: + return float(current_best_value) + + best_observed = _optuna_trial_user_attr_float(trial, OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR) + if best_observed is not None: + return best_observed + + state_name = _optuna_trial_state_name(trial) + if state_name == "COMPLETE": + value = getattr(trial, "value", None) + return None if value is None else float(value) + + best_intermediate = _optuna_trial_best_intermediate_value(trial, direction=direction) + if best_intermediate is not None: + return best_intermediate + + value = getattr(trial, "value", None) + return None if value is None else float(value) + +def _current_optuna_study_best_candidate( + study: optuna.study.Study, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> tuple[Any | None, float | None]: + direction = getattr(study, "direction", STUDY_DIRECTION) + best_trial: Any | None = None + best_value: float | None = None + + for study_trial in getattr(study, "trials", []): + if _optuna_trial_state_name(study_trial) not in {"COMPLETE", "PRUNED"}: + continue + candidate_value = _optuna_trial_best_observed_value(study_trial, direction=direction) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_trial = study_trial + best_value = candidate_value + + if current_trial is not None: + live_trial_best = _optuna_trial_best_observed_value( + current_trial, + direction=direction, + current_best_value=current_best_value, + ) + if _optuna_value_is_better(live_trial_best, best_value, direction=direction): + best_trial = current_trial + best_value = live_trial_best + + return best_trial, best_value + +def _current_optuna_study_best_snapshot( + trial: optuna.trial.Trial | None, + *, + current_best_value: float | None = None, +) -> tuple[float | None, int | None]: + if trial is None: + return None, None + study = getattr(trial, "study", None) + if study is None: + return None, None + + best_trial, best_value = _current_optuna_study_best_candidate( + study, + current_trial=trial, + current_best_value=current_best_value, + ) + best_trial_number = None if best_trial is None else int(getattr(best_trial, "number", -1)) + return best_value, best_trial_number + +def set_global_seed(seed: int = 42) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def stable_int_from_text(text: str) -> int: + value = 0 + for byte in text.encode("utf-8"): + value = (value * 131 + byte) % (2 ** 31 - 1) + return value + +def seed_worker(worker_id: int) -> None: + del worker_id + worker_seed = torch.initial_seed() % (2 ** 32) + random.seed(worker_seed) + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + +def make_seeded_generator(seed: int, tag: str) -> torch.Generator: + generator = torch.Generator() + generator.manual_seed(seed + stable_int_from_text(tag)) + return generator + +def cuda_memory_snapshot() -> str: + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return "allocated=0.00 GB, reserved=0.00 GB, peak=0.00 GB" + allocated = torch.cuda.memory_allocated(device=DEVICE) / (1024 ** 3) + reserved = torch.cuda.memory_reserved(device=DEVICE) / (1024 ** 3) + peak = torch.cuda.max_memory_allocated(device=DEVICE) / (1024 ** 3) + return f"allocated={allocated:.2f} GB, reserved={reserved:.2f} GB, peak={peak:.2f} GB" + +def run_cuda_cleanup(context: str | None = None) -> None: + gc.collect() + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return + try: + torch.cuda.synchronize(device=DEVICE) + except Exception: + pass + try: + torch.cuda.empty_cache() + except Exception: + pass + try: + torch.cuda.ipc_collect() + except Exception: + pass + if context is not None: + print(f"[CUDA Cleanup] {context}: {cuda_memory_snapshot()}") + try: + torch.cuda.reset_peak_memory_stats(device=DEVICE) + except Exception: + pass + +def prune_directory_except(root: Path, keep_file_names: set[str]) -> None: + if not root.exists(): + return + keep_paths = {root / name for name in keep_file_names} + for path in sorted((p for p in root.rglob("*") if p.is_file()), reverse=True): + if path not in keep_paths: + path.unlink() + for path in sorted((p for p in root.rglob("*") if p.is_dir()), reverse=True): + if path != root: + try: + path.rmdir() + except OSError: + pass + +def prune_optuna_trial_dir(trial_dir: Path) -> None: + if trial_dir.exists(): + shutil.rmtree(trial_dir, ignore_errors=True) + +def prune_optuna_study_dir(study_root: Path) -> None: + prune_directory_except(study_root, {"best_params.json", "summary.json", "study.sqlite3"}) + +def to_device(batch: Any, device: torch.device) -> Any: + if torch.is_tensor(batch): + return batch.to(device, non_blocking=True) + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, list): + return [to_device(v, device) for v in batch] + if isinstance(batch, tuple): + return tuple(to_device(v, device) for v in batch) + return batch + +def _normalized_decimal_text(value: Decimal) -> str: + normalized = value.normalize() + text = format(normalized, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text or "0" + +def _fraction_decimal(value: Any, *, field_name: str) -> Decimal: + if isinstance(value, bool): + raise TypeError(f"{field_name} must be a real number in (0, 1], got boolean {value!r}.") + try: + decimal_value = Decimal(str(value).strip()) + except (InvalidOperation, ValueError) as exc: + raise ValueError(f"{field_name} must be a real number in (0, 1], got {value!r}.") from exc + if not decimal_value.is_finite(): + raise ValueError(f"{field_name} must be finite, got {value!r}.") + if decimal_value <= 0 or decimal_value > 1: + raise ValueError(f"{field_name} must be in the interval (0, 1], got {value!r}.") + return decimal_value + +def _percent_decimal(value: Any, *, field_name: str = "dataset percent") -> Decimal: + return _fraction_decimal(value, field_name=field_name) * Decimal("100") + +def normalize_dataset_percents(values: list[float] | tuple[float, ...]) -> list[float]: + if not values: + raise ValueError("DATASET_PERCENTS must contain at least one fraction in (0, 1].") + normalized: dict[str, float] = {} + for value in values: + fraction = _fraction_decimal(value, field_name="DATASET_PERCENTS entry") + normalized[_normalized_decimal_text(fraction)] = float(fraction) + return [normalized[key] for key in sorted(normalized, key=Decimal)] + +def percent_label(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)).replace(".", "p") + +def percent_display(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)) + +def percent_text(percent: float) -> str: + return f"{percent_display(percent)}%" + + +def run_identity_parts( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> list[str]: + parts: list[str] = [] + payload = split_payload or {} + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase={int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split={int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split={split_type}") + + if subset_repeat_index is not None: + parts.append(f"repeat={int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant={int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy={int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct={percent_text(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial={int(trial_number):03d}") + + return parts + + +def run_identity_label( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + parts = run_identity_parts( + strategy=strategy, + percent=percent, + trial_number=trial_number, + split_payload=split_payload, + ) + return " | ".join(parts) if parts else "run" + + +def run_identity_slug( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + payload = split_payload or {} + parts: list[str] = [] + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase_{int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split_{int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split_{str(split_type)}") + + if subset_repeat_index is not None: + parts.append(f"repeat_{int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant_{int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy_{int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct_{percent_label(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial_{int(trial_number):03d}") + + return "__".join(parts) if parts else "run" + + +def current_dataset_name() -> str: + dataset_name = str(DATASET_NAME).strip() + if dataset_name not in SUPPORTED_DATASET_NAMES: + raise ValueError(f"DATASET_NAME must be one of {SUPPORTED_DATASET_NAMES}, got {dataset_name!r}") + return dataset_name + +def current_busi_with_classes_split_policy() -> str: + split_policy = str(BUSI_WITH_CLASSES_SPLIT_POLICY).strip().lower() + if split_policy not in SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES: + raise ValueError( + f"BUSI_WITH_CLASSES_SPLIT_POLICY must be one of {SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES}, " + f"got {split_policy!r}" + ) + return split_policy + +def current_dataset_splits_json_path() -> Path: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATASET_SPLITS_JSON + return PROJECT_DIR / f"dataset_splits_{dataset_name.lower()}_{current_busi_with_classes_split_policy()}.json" + +def current_dataset_dirs() -> tuple[Path, Path]: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATA_ROOT / "images", DATA_ROOT / "annotations" + return DATA_ROOT / "all_images", DATA_ROOT / "all_masks" + +def current_pipeline_check_path() -> Path | None: + if current_dataset_name() != "BUSI_with_classes": + return None + return DATA_ROOT / "pipeline_check.json" + +def normalization_cache_tag() -> str: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return "BUSI" + return f"{dataset_name}_{current_busi_with_classes_split_policy()}" + +def resolve_amp_dtype(key: str) -> torch.dtype: + key = key.lower().strip() + if key == "auto": + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + return torch.float16 + if key in {"float16", "fp16", "half"}: + return torch.float16 + if key in {"bfloat16", "bf16"}: + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + print("[AMP] bfloat16 requested but unsupported here. Falling back to float16.") + return torch.float16 + raise ValueError(f"Unsupported AMP_DTYPE: {key}") + +def amp_autocast_enabled(device: torch.device) -> bool: + return USE_AMP and device.type in {"cuda", "mps"} + +def autocast_ctx(enabled: bool, device: torch.device, amp_dtype: torch.dtype): + if not enabled: + return nullcontext() + return torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=True) + +def make_grad_scaler(enabled: bool, amp_dtype: torch.dtype, device: torch.device): + if not enabled or device.type != "cuda" or amp_dtype == torch.bfloat16: + return None + try: + return torch.amp.GradScaler("cuda", enabled=True, init_scale=8192.0) + except Exception: + return torch.cuda.amp.GradScaler(enabled=True, init_scale=8192.0) + +def format_seconds(seconds: float) -> str: + seconds = int(seconds) + h, rem = divmod(seconds, 3600) + m, s = divmod(rem, 60) + return f"{h:02d}:{m:02d}:{s:02d}" + +def tensor_bytes(t: torch.Tensor) -> int: + return t.numel() * t.element_size() + +def bytes_to_gb(num_bytes: int) -> float: + return num_bytes / (1024 ** 3) + +def set_current_job_params(payload: dict[str, Any] | None = None) -> None: + CURRENT_JOB_PARAMS.clear() + if payload: + CURRENT_JOB_PARAMS.update(dict(payload)) + +def _job_param(name: str, default: Any) -> Any: + return CURRENT_JOB_PARAMS.get(name, default) + +def _alpha_log_floor() -> float: + return math.log(max(float(_job_param("min_alpha", math.exp(-5.0))), 1e-6)) + +def _keep_action_index(action_count: int) -> int: + action_count = max(int(action_count), 1) + if action_count >= 3: + return action_count // 2 + return action_count - 1 + +def _strategy3_variant() -> str: + raw = str(_job_param("strategy3_variant", DEFAULT_STRATEGY3_VARIANT)).strip().lower() + return raw or DEFAULT_STRATEGY3_VARIANT + +def _strategy3_annealed_weight( + base_weight: float, + *, + current_epoch: int, + anneal_start_epoch: int = 1, + anneal_epochs: int, +) -> float: + base_weight = float(base_weight) + if base_weight <= 0.0: + return 0.0 + anneal_start_epoch = max(int(anneal_start_epoch), 1) + anneal_epochs = max(int(anneal_epochs), 0) + if current_epoch < anneal_start_epoch: + return 0.0 + if anneal_epochs <= 0: + return base_weight + progress = min( + max((float(current_epoch) - float(anneal_start_epoch)) / float(anneal_epochs), 0.0), + 1.0, + ) + floor_fraction = float( + _job_param( + "strategy3_aux_ce_floor_fraction", + DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + ) + ) + floor_fraction = min(max(floor_fraction, 0.0), 1.0) + fraction = max(1.0 - progress, floor_fraction) + return base_weight * fraction + +def _strategy3_annealed_aux_ce_weight(current_epoch: int) -> float: + return _strategy3_annealed_weight( + float(_job_param("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT)), + current_epoch=int(current_epoch), + anneal_start_epoch=int( + _job_param( + "strategy3_aux_ce_anneal_start_epoch", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + ) + ), + anneal_epochs=int( + _job_param( + "strategy3_aux_ce_anneal_epochs", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + ) + ), + ) + +def _strategy3_exploration_eps(current_epoch: int) -> float: + base_eps = max(float(_job_param("strategy3_exploration_eps", DEFAULT_EXPLORATION_EPS)), 0.0) + decay_epochs = max(int(_job_param("strategy3_exploration_eps_epochs", EXPLORATION_EPS_EPOCHS)), 0) + if base_eps <= 0.0: + return 0.0 + if decay_epochs <= 0: + return base_eps + progress = min(max((float(current_epoch) - 1.0) / float(decay_epochs), 0.0), 1.0) + return base_eps * (1.0 - progress) + +def _bootstrap_value_target(model: nn.Module, value_next: torch.Tensor) -> torch.Tensor: + neighborhood_value = getattr(model, "neighborhood_value", None) + if callable(neighborhood_value): + return neighborhood_value(value_next) + return value_next + +def _strategy3_delta_max() -> float: + return float(_job_param("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX)) + +def _strategy3_policy_delta(policy_raw: torch.Tensor) -> torch.Tensor: + return torch.tanh(policy_raw.float()) * _strategy3_delta_max() + +def _strategy3_apply_delta(seg: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: + seg_f = seg.float() + delta_f = delta.float() + return (seg_f + delta_f).clamp(0.0, 1.0).to(dtype=seg.dtype) + +def _strategy3_advantage_normalize_enabled() -> bool: + return bool(_job_param("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE)) + +def _normalize_strategy3_advantage_map(advantage_map: torch.Tensor) -> torch.Tensor: + if not _strategy3_advantage_normalize_enabled(): + return advantage_map + if advantage_map.ndim < 4: + mean = advantage_map.mean() + std = advantage_map.std(unbiased=False) + return (advantage_map - mean) / (std + 1e-6) + mean = advantage_map.mean(dim=(2, 3), keepdim=True) + std = advantage_map.std(dim=(2, 3), unbiased=False, keepdim=True) + return (advantage_map - mean) / (std + 1e-6) + +def _strategy3_actor_advantage( + reward_map: torch.Tensor, + value_t: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + advantage_map = reward_map + float(gamma) * value_next.detach() - value_t.detach() + return _normalize_strategy3_advantage_map(advantage_map) + +def _strategy3_critic_target( + reward_map: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + return reward_map.detach() + float(gamma) * value_next.detach() + +def _strategy3_delta_distribution(delta_map: torch.Tensor) -> dict[str, float]: + delta_f = delta_map.detach().float() + abs_delta = delta_f.abs() + return { + "mean_delta": float(delta_f.mean().item()), + "mean_abs_delta": float(abs_delta.mean().item()), + "positive_pct": float((delta_f > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((delta_f < -1e-6).float().mean().item() * 100.0), + "near_zero_pct": float((abs_delta <= 1e-6).float().mean().item() * 100.0), + "max_abs_delta": float(abs_delta.max().item()), + } + +def _bernoulli_predictive_entropy(prob: torch.Tensor) -> torch.Tensor: + prob_f = prob.float().clamp(1e-6, 1.0 - 1e-6) + return -(prob_f * torch.log(prob_f) + (1.0 - prob_f) * torch.log1p(-prob_f)) + +def _iter_strategy3_dropout_modules(model: nn.Module) -> Iterator[nn.Module]: + for module in _unwrap_compiled(model).modules(): + if isinstance(module, (nn.Dropout, nn.Dropout2d)): + yield module + +@contextmanager +def _strategy3_mc_dropout_scope(model: nn.Module) -> Iterator[None]: + global _STRATEGY3_MC_DROPOUT_WARNED + + requested_p = float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)) + if requested_p <= 0.0 and not _STRATEGY3_MC_DROPOUT_WARNED: + print("[Strategy3] MC-dropout requested with non-positive dropout p; variance maps may collapse to zero.") + _STRATEGY3_MC_DROPOUT_WARNED = True + + saved_states: list[tuple[nn.Module, bool, float | None]] = [] + for module in _iter_strategy3_dropout_modules(model): + saved_states.append((module, bool(module.training), getattr(module, "p", None))) + module.train(True) + if hasattr(module, "p") and requested_p > 0.0: + module.p = requested_p + try: + yield + finally: + for module, was_training, saved_p in saved_states: + module.train(was_training) + if saved_p is not None and hasattr(module, "p"): + module.p = saved_p + +def _strategy3_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_fn: Any, +) -> tuple[torch.Tensor, torch.Tensor]: + if not bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)): + with torch.no_grad(): + zeros = torch.zeros_like(decoder_prob.float()) + pred_entropy = _bernoulli_predictive_entropy(decoder_prob) + return zeros, pred_entropy + + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + with torch.no_grad(): + mc_probs: list[torch.Tensor] = [] + with _strategy3_mc_dropout_scope(model): + for _ in range(samples): + logits = sample_fn() + mc_probs.append(torch.sigmoid(logits).float()) + stacked = torch.stack(mc_probs, dim=0) + mean_prob = stacked.mean(dim=0) + variance = stacked.var(dim=0, unbiased=False) + pred_entropy = _bernoulli_predictive_entropy(mean_prob) + return variance.to(dtype=decoder_prob.dtype), pred_entropy.to(dtype=decoder_prob.dtype) + +def _strategy3_mc_config_hash() -> tuple[bool, int, float]: + enabled = bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)) + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + dropout_p = round(float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)), 8) + return enabled, samples, dropout_p + +def _strategy3_mc_disk_cache_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED)) + +def _strategy3_mc_disk_cache_read_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ)) + +def _strategy3_mc_disk_cache_write_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE)) + +def _strategy3_normalize_sample_ids( + sample_ids: list[str] | tuple[str, ...] | None, + *, + batch_size: int, +) -> list[str] | None: + if sample_ids is None: + return None + normalized = [str(item) for item in sample_ids] + if len(normalized) != int(batch_size): + raise ValueError( + f"Strategy 3 eval MC cache expected {batch_size} sample_ids, got {len(normalized)}." + ) + return normalized + +def _strategy3_ensure_mc_cache_state(model: nn.Module) -> nn.Module: + raw_model = getattr(model, "_orig_mod", model) + if not hasattr(raw_model, "_strategy3_mc_cache"): + raw_model._strategy3_mc_cache = {} + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint"): + raw_model._strategy3_mc_cache_fingerprint = "" + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint_sources"): + raw_model._strategy3_mc_cache_fingerprint_sources = {} + if not hasattr(raw_model, "_strategy3_strategy2_checkpoint_path"): + raw_model._strategy3_strategy2_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_eval_checkpoint_path"): + raw_model._strategy3_eval_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_mc_cache_stats"): + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return raw_model + +def _strategy3_reset_mc_cache_stats(model: nn.Module) -> None: + raw_model = _strategy3_ensure_mc_cache_state(model) + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + +def _strategy3_get_mc_cache_stats(model: nn.Module) -> dict[str, int]: + raw_model = _strategy3_ensure_mc_cache_state(model) + stats = raw_model._strategy3_mc_cache_stats + return { + "ram_hits": int(stats.get("ram_hits", 0)), + "disk_hits": int(stats.get("disk_hits", 0)), + "misses": int(stats.get("misses", 0)), + "writes": int(stats.get("writes", 0)), + } + +def _strategy3_checkpoint_sha256(path: str | Path | None) -> str | None: + if not path: + return None + resolved = str(Path(path).expanduser().resolve()) + cached = _STRATEGY3_MC_FILE_SHA256_CACHE.get(resolved) + if cached is not None: + return cached + checkpoint_path = Path(resolved) + if not checkpoint_path.is_file(): + return None + digest = hashlib.sha256() + with checkpoint_path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + checksum = digest.hexdigest() + _STRATEGY3_MC_FILE_SHA256_CACHE[resolved] = checksum + return checksum + +def _strategy3_decoder_state_hash(model: nn.Module) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + modules: list[tuple[str, nn.Module]] = [] + if isinstance(raw_model, PixelDRLMG_WithDecoder): + modules = [ + ("smp_model.encoder", raw_model.smp_model.encoder), + ("smp_model.decoder", raw_model.smp_model.decoder), + ("smp_model.segmentation_head", raw_model.smp_model.segmentation_head), + ] + elif isinstance(raw_model, PixelDRLMG_VGGWithDecoder): + modules = [ + ("encoder", raw_model.encoder), + ("segmentation_head", raw_model.segmentation_head), + ] + else: + return stable_hash(raw_model.__class__.__name__) + + digest = hashlib.sha256() + for prefix, module in modules: + for name, tensor in sorted(module.state_dict().items()): + tensor_cpu = tensor.detach().cpu().contiguous() + digest.update(prefix.encode("utf-8")) + digest.update(b"\0") + digest.update(name.encode("utf-8")) + digest.update(b"\0") + digest.update(str(tensor_cpu.dtype).encode("utf-8")) + digest.update(b"\0") + digest.update(json.dumps(list(tensor_cpu.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(tensor_cpu.numpy().tobytes()) + return digest.hexdigest() + +def _strategy3_resolve_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> tuple[str, dict[str, Any]]: + raw_model = _strategy3_ensure_mc_cache_state(model) + strategy2_path = ( + str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if strategy2_checkpoint_path + else getattr(raw_model, "_strategy3_strategy2_checkpoint_path", None) + ) + eval_path = ( + str(Path(eval_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path + else getattr(raw_model, "_strategy3_eval_checkpoint_path", None) + ) + decoder_hash = _strategy3_decoder_state_hash(raw_model) + sources: dict[str, Any] = {"decoder_state_hash": decoder_hash} + if strategy2_path: + sources["strategy2_checkpoint"] = strategy2_path + strategy2_sha = _strategy3_checkpoint_sha256(strategy2_path) + if strategy2_sha is not None: + sources["strategy2_sha256"] = strategy2_sha + if eval_path: + sources["eval_checkpoint"] = eval_path + eval_sha = _strategy3_checkpoint_sha256(eval_path) + if eval_sha is not None: + sources["eval_checkpoint_sha256"] = eval_sha + return decoder_hash, sources + +def _strategy3_bump_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + if strategy2_checkpoint_path is not None: + raw_model._strategy3_strategy2_checkpoint_path = str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path is not None: + raw_model._strategy3_eval_checkpoint_path = str(Path(eval_checkpoint_path).expanduser().resolve()) + fingerprint, sources = _strategy3_resolve_mc_cache_fingerprint( + raw_model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + eval_checkpoint_path=eval_checkpoint_path, + ) + fingerprint_changed = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) != str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint = str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint_sources = dict(sources) + if fingerprint_changed: + clear_cache = getattr(raw_model, "clear_strategy3_mc_cache", None) + if callable(clear_cache): + clear_cache() + else: + raw_model._strategy3_mc_cache.clear() + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return str(fingerprint) + +def _strategy3_mc_disk_cache_root(run_dir: Path | None) -> Path | None: + if run_dir is None or not _strategy3_mc_disk_cache_enabled(): + return None + return Path(run_dir) / "mc_cache" + +def _strategy3_mc_disk_entry_path( + root: Path, + fingerprint: str, + split: str, + sample_id: str, +) -> Path: + safe_sample_id = str(sample_id).replace(os.sep, "__").replace("/", "__") + return Path(root) / str(fingerprint)[:16] / str(split) / f"{safe_sample_id}.pt" + +def _strategy3_load_mc_maps_from_disk( + path: Path, + *, + sample_id: str, + fingerprint: str, + mc_config_hash: tuple[bool, int, float], + split: str, +) -> tuple[torch.Tensor, torch.Tensor] | None: + if not path.is_file(): + return None + try: + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + payload = torch.load(path, map_location="cpu", weights_only=False) + except Exception: + return None + + if not isinstance(payload, dict): + return None + if int(payload.get("schema_version", -1)) != int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION): + return None + if str(payload.get("sample_id", "")) != str(sample_id): + return None + if str(payload.get("split", "")) != str(split): + return None + if str(payload.get("fingerprint", "")) != str(fingerprint): + return None + if tuple(payload.get("mc_config_hash", ())) != tuple(mc_config_hash): + return None + if int(payload.get("img_size", -1)) != int(IMG_SIZE): + return None + + variance = payload.get("mc_variance") + pred_entropy = payload.get("pred_entropy") + if not (torch.is_tensor(variance) and torch.is_tensor(pred_entropy)): + return None + if variance.ndim != 4 or pred_entropy.ndim != 4: + return None + return ( + variance.detach().to(device="cpu", dtype=torch.float32).contiguous(), + pred_entropy.detach().to(device="cpu", dtype=torch.float32).contiguous(), + ) + +def _strategy3_save_mc_maps_to_disk(path: Path, payload: dict[str, Any]) -> None: + atomic_torch_save(path, payload) + +def _strategy3_write_mc_cache_manifest( + run_dir: Path | None, + *, + fingerprint: str, + fingerprint_sources: dict[str, Any], + mc_config_hash: tuple[bool, int, float], + split: str, + split_write_count: int, +) -> None: + if run_dir is None: + return + manifest_path = Path(run_dir) / "mc_cache" / "manifest.json" + existing: dict[str, Any] = {} + if manifest_path.exists(): + try: + loaded = load_json(manifest_path) + except Exception: + loaded = {} + if isinstance(loaded, dict): + existing = loaded + existing_fingerprint = str(existing.get("fingerprint", "")) + existing_config = tuple(existing.get("mc_config_hash", ())) + if existing_fingerprint != str(fingerprint) or existing_config != tuple(mc_config_hash): + existing = {} + splits = dict(existing.get("splits", {})) if isinstance(existing.get("splits", {}), dict) else {} + splits[str(split)] = int(splits.get(str(split), 0)) + int(max(split_write_count, 0)) + payload = { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "fingerprint": str(fingerprint), + "fingerprint_sources": dict(fingerprint_sources), + "mc_config_hash": list(mc_config_hash), + "img_size": int(IMG_SIZE), + "dataset_name": current_dataset_name(), + "splits": splits, + } + atomic_save_json(manifest_path, payload) + +def _strategy3_mc_disk_mode( + *, + split: str | None, + run_dir: Path | None, +) -> tuple[bool, bool, Path | None, str | None]: + normalized_split = str(split).strip().lower() if split is not None else None + if normalized_split not in (None, "train", "val", "test"): + raise ValueError(f"Unsupported Strategy 3 MC cache split {split!r}.") + if normalized_split == "train": + return False, False, None, normalized_split + root = _strategy3_mc_disk_cache_root(run_dir) + can_use_disk = normalized_split in {"val", "test"} and root is not None + return ( + bool(can_use_disk and _strategy3_mc_disk_cache_read_enabled()), + bool(can_use_disk and _strategy3_mc_disk_cache_write_enabled()), + root, + normalized_split, + ) + +def _strategy3_prepare_cached_sample_pair( + sample_variance: torch.Tensor, + sample_entropy: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + return ( + sample_variance.detach().to(device="cpu", dtype=torch.float32).clone(), + sample_entropy.detach().to(device="cpu", dtype=torch.float32).clone(), + ) + +def _strategy3_cached_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_ids: list[str] | tuple[str, ...] | None, + compute_sample_maps: Any, + mc_cache_split: str | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + normalized_sample_ids = _strategy3_normalize_sample_ids(sample_ids, batch_size=int(decoder_prob.shape[0])) + if normalized_sample_ids is None: + raise ValueError("Strategy 3 eval MC cache requires non-empty sample_ids.") + + raw_model = _strategy3_ensure_mc_cache_state(model) + fingerprint = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) or _strategy3_bump_mc_cache_fingerprint(raw_model) + cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = raw_model._strategy3_mc_cache + stats = raw_model._strategy3_mc_cache_stats + mc_hash = _strategy3_mc_config_hash() + disk_read_enabled, disk_write_enabled, disk_root, normalized_split = _strategy3_mc_disk_mode( + split=mc_cache_split, + run_dir=mc_cache_run_dir, + ) + manifest_write_count = 0 + variance_samples: list[torch.Tensor] = [] + entropy_samples: list[torch.Tensor] = [] + + for sample_index, sample_id in enumerate(normalized_sample_ids): + cache_key = (fingerprint, sample_id, mc_hash) + cached_pair = cache.get(cache_key) + if cached_pair is not None: + stats["ram_hits"] = int(stats.get("ram_hits", 0)) + 1 + else: + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_read_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + cached_pair = _strategy3_load_mc_maps_from_disk( + disk_path, + sample_id=sample_id, + fingerprint=fingerprint, + mc_config_hash=mc_hash, + split=normalized_split, + ) + if cached_pair is not None: + cache[cache_key] = _strategy3_prepare_cached_sample_pair(*cached_pair) + stats["disk_hits"] = int(stats.get("disk_hits", 0)) + 1 + else: + sample_variance, sample_entropy = compute_sample_maps(sample_index) + cached_pair = _strategy3_prepare_cached_sample_pair(sample_variance, sample_entropy) + cache[cache_key] = cached_pair + stats["misses"] = int(stats.get("misses", 0)) + 1 + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_write_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + entry_exists = disk_path.exists() + _strategy3_save_mc_maps_to_disk( + disk_path, + { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "sample_id": str(sample_id), + "split": str(normalized_split), + "fingerprint": str(fingerprint), + "mc_config_hash": tuple(mc_hash), + "img_size": int(IMG_SIZE), + "dtype": "float32", + "created_at": datetime.now(timezone.utc).isoformat(), + "mc_variance": cached_pair[0], + "pred_entropy": cached_pair[1], + }, + ) + stats["writes"] = int(stats.get("writes", 0)) + 1 + if not entry_exists: + manifest_write_count += 1 + + cached_variance, cached_entropy = cached_pair + variance_samples.append(cached_variance.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + entropy_samples.append(cached_entropy.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + + if manifest_write_count > 0 and normalized_split is not None: + _strategy3_write_mc_cache_manifest( + mc_cache_run_dir, + fingerprint=fingerprint, + fingerprint_sources=dict(getattr(raw_model, "_strategy3_mc_cache_fingerprint_sources", {})), + mc_config_hash=mc_hash, + split=normalized_split, + split_write_count=manifest_write_count, + ) + + return torch.cat(variance_samples, dim=0), torch.cat(entropy_samples, dim=0) + +def _require_supported_strategy(strategy: int) -> int: + strategy = int(strategy) + if strategy not in SUPPORTED_STRATEGIES: + raise ValueError( + f"Unsupported strategy {strategy}. Supported strategies are {list(SUPPORTED_STRATEGIES)}." + ) + return strategy + +def _resolve_checkpoint_metric_name(metric_name: Any, *, strategy: int) -> str: + if not isinstance(metric_name, str) or not metric_name.strip(): + raise KeyError( + f"No best-checkpoint metric configured for strategy {strategy}. " + f"Set BEST_CHECKPOINT_METRICS[{strategy}] or best_checkpoint_metric_name to a non-empty metric name." + ) + metric_name = metric_name.strip() + if metric_name not in SUPPORTED_CHECKPOINT_METRICS: + raise KeyError( + f"Unsupported best-checkpoint metric {metric_name!r} for strategy {strategy}. " + f"Supported metrics: {sorted(SUPPORTED_CHECKPOINT_METRICS)}." + ) + return metric_name + +def _strategy_selection_metric_name(strategy: int) -> str: + strategy = _require_supported_strategy(strategy) + metric_name = _job_param( + f"strategy{strategy}_best_checkpoint_metric_name", + _job_param("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS.get(strategy)), + ) + return _resolve_checkpoint_metric_name(metric_name, strategy=strategy) + +def _strategy_selection_metric_value(strategy: int, metrics: dict[str, Any]) -> float: + metric_name = _strategy_selection_metric_name(strategy) + value = metrics.get(metric_name) + if value is None: + raise KeyError( + f"Configured best-checkpoint metric {metric_name!r} for strategy {strategy} " + f"is missing from metrics payload keys={sorted(metrics.keys())}." + ) + return float(value) + +def _early_stopping_monitor_name(strategy: int) -> str: + raw = str(_job_param("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR)).strip() + if not raw or raw.lower() == "auto": + return _strategy_selection_metric_name(strategy) + return raw + +def _early_stopping_mode(strategy: int, monitor_name: str | None = None) -> str: + raw = str(_job_param("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE)).strip().lower() + if raw in {"min", "max"}: + return raw + if raw != "auto": + raise ValueError(f"Unsupported early_stopping_mode={raw!r}. Expected 'auto', 'min', or 'max'.") + monitor_name = monitor_name or _early_stopping_monitor_name(strategy) + lowered = monitor_name.lower() + if "loss" in lowered or lowered.startswith("hd") or lowered.endswith("error"): + return "min" + return "max" + +def _early_stopping_min_delta() -> float: + return max(float(_job_param("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA)), 0.0) + +def _early_stopping_start_epoch() -> int: + return max(int(_job_param("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH)), 1) + +def _early_stopping_patience() -> int: + return max(int(_job_param("early_stopping_patience", EARLY_STOPPING_PATIENCE)), 0) + +def _early_stopping_monitor_value( + metrics: dict[str, Any], + *, + strategy: int, + monitor_name: str, +) -> float | None: + value = metrics.get(monitor_name) + if value is None and monitor_name == _strategy_selection_metric_name(strategy): + value = _strategy_selection_metric_value(strategy, metrics) + if value is None: + return None + return float(value) + +def _early_stopping_improved( + current_value: float, + best_value: float | None, + *, + mode: str, + min_delta: float, +) -> bool: + if best_value is None: + return True + if mode == "min": + return current_value < (best_value - min_delta) + if mode == "max": + return current_value > (best_value + min_delta) + raise ValueError(f"Unsupported early stopping comparison mode: {mode!r}") + +def _strategy3_requested_bootstrap_freeze() -> bool: + return bool( + _job_param( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + ) + +def _module_freeze_state(module: nn.Module | None) -> str: + if not isinstance(module, nn.Module): + return "n/a" + requires_grad_flags = [bool(param.requires_grad) for param in module.parameters()] + if not requires_grad_flags: + return "n/a" + if all(not flag for flag in requires_grad_flags): + return "frozen" + if all(requires_grad_flags): + return "trainable" + return "mixed" + +def _strategy3_bootstrap_freeze_status(model: nn.Module) -> dict[str, Any]: + raw = _raw_decoder_rl_model(model) + status = { + "bootstrap_loaded": False, + "freeze_requested": False, + "freeze_active": False, + "encoder_state": "n/a", + "decoder_state": "n/a", + "segmentation_head_state": "n/a", + } + if raw is None: + return status + + status["bootstrap_loaded"] = bool(getattr(raw, "strategy2_bootstrap_loaded", False)) + status["freeze_requested"] = bool(getattr(raw, "freeze_bootstrapped_segmentation", False)) + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + status["encoder_state"] = _module_freeze_state(getattr(smp_model, "encoder", None)) + status["decoder_state"] = _module_freeze_state(getattr(smp_model, "decoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(smp_model, "segmentation_head", None)) + else: + status["encoder_state"] = _module_freeze_state(getattr(raw, "encoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(raw, "segmentation_head", None)) + + relevant_states = [ + state + for state in ( + status["encoder_state"], + status["decoder_state"], + status["segmentation_head_state"], + ) + if state != "n/a" + ] + status["freeze_active"] = bool( + status["bootstrap_loaded"] and relevant_states and all(state == "frozen" for state in relevant_states) + ) + return status + +def _strategy3_decoder_is_frozen(model: nn.Module) -> bool: + return bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]) + +def _strategy3_loss_weights( + model: nn.Module, + *, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + decoder_ce_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(ce_weight) + decoder_dice_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(dice_weight) + return { + "decoder_ce": float(_job_param("strategy3_decoder_ce_weight", decoder_ce_default)), + "decoder_dice": float(_job_param("strategy3_decoder_dice_weight", decoder_dice_default)), + } + +def _strategy3_keep_frozen_modules_in_eval(model: nn.Module) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]): + return + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + module_names = ("encoder", "decoder", "segmentation_head") + module_root = smp_model + else: + module_names = ("encoder", "segmentation_head") + module_root = raw + for module_name in module_names: + module = getattr(module_root, module_name, None) + if isinstance(module, nn.Module): + module.eval() + +def _strategy3_apply_rollout_step( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + return apply_actions( + seg, + actions, + num_actions=int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)), + ).to(dtype=seg.dtype) + +def _refinement_deltas( + *, + action_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + small = float(_job_param("refine_delta_small", DEFAULT_REFINE_DELTA_SMALL)) + large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + if action_count == 3: + values = (-large, 0.0, small) + elif action_count == 4: + values = (-large, -small, 0.0, small) + elif action_count == 5: + values = (-large, -small, 0.0, small, large) + else: + raise ValueError( + f"Unsupported Strategy 3 action count {action_count}. " + "Expected one of {3, 4, 5}." + ) + return torch.tensor(values, device=device, dtype=dtype) + +def threshold_binary_mask(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).to(dtype=mask.dtype) + +def threshold_binary_long(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).long() + +"""============================================================================= +BUSI SPLIT + NORMALIZATION +============================================================================= +""" + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) +IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) + +def validate_image_mask_consistency(images_dir: Path, annotations_dir: Path): + image_files = {f for f in os.listdir(images_dir) if not f.startswith(".") and f.lower().endswith(".png")} + mask_files = {f for f in os.listdir(annotations_dir) if not f.startswith(".") and f.lower().endswith(".png")} + matched = sorted(image_files & mask_files) + missing_masks = sorted(image_files - mask_files) + missing_images = sorted(mask_files - image_files) + return matched, missing_masks, missing_images + +def parse_busi_with_classes_label(filename: str) -> str: + upper_name = str(filename).upper() + if upper_name.endswith("_B.PNG"): + return "benign" + if upper_name.endswith("_M.PNG"): + return "malignant" + raise ValueError( + f"BUSI_with_classes filename must end with '_B.png' or '_M.png', got {filename!r}" + ) + +def _candidate_report_dicts(payload: dict[str, Any]) -> list[dict[str, Any]]: + candidates = [payload] + for key in ("counts", "summary", "dataset", "report", "metadata"): + value = payload.get(key) + if isinstance(value, dict): + candidates.append(value) + return candidates + +def _extract_report_int(payload: dict[str, Any], keys: tuple[str, ...]) -> int | None: + for candidate in _candidate_report_dicts(payload): + for key in keys: + value = candidate.get(key) + if isinstance(value, bool): + continue + if isinstance(value, (int, np.integer)): + return int(value) + if isinstance(value, float) and float(value).is_integer(): + return int(value) + return None + +def _extract_report_filenames(payload: dict[str, Any]) -> set[str] | None: + for candidate in _candidate_report_dicts(payload): + filenames = candidate.get("filenames") + if isinstance(filenames, list) and all(isinstance(item, str) for item in filenames): + return set(filenames) + + pairs = candidate.get("pairs") + if isinstance(pairs, list): + extracted = {item["filename"] for item in pairs if isinstance(item, dict) and isinstance(item.get("filename"), str)} + if extracted: + return extracted + return None + +def validate_busi_with_classes_pipeline_report(report_path: Path, sample_records: list[dict[str, str]]) -> None: + if not report_path.exists(): + return + + payload = load_json(report_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict payload in {report_path}, found {type(payload).__name__}.") + + benign_count = sum(1 for record in sample_records if record.get("class_label") == "benign") + malignant_count = sum(1 for record in sample_records if record.get("class_label") == "malignant") + expected_counts = { + "total_pairs": len(sample_records), + "benign": benign_count, + "malignant": malignant_count, + } + report_counts = { + "total_pairs": _extract_report_int(payload, ("total_pairs", "pair_count", "num_pairs", "total")), + "benign": _extract_report_int(payload, ("benign", "benign_count", "num_benign")), + "malignant": _extract_report_int(payload, ("malignant", "malignant_count", "num_malignant")), + } + for key, expected_value in expected_counts.items(): + report_value = report_counts[key] + if report_value is not None and report_value != expected_value: + raise RuntimeError( + f"pipeline_check mismatch for {key}: discovered={expected_value}, report={report_value} ({report_path})" + ) + + report_filenames = _extract_report_filenames(payload) + if report_filenames is not None: + discovered_filenames = {record["filename"] for record in sample_records} + if report_filenames != discovered_filenames: + missing_from_report = sorted(discovered_filenames - report_filenames)[:10] + extra_in_report = sorted(report_filenames - discovered_filenames)[:10] + raise RuntimeError( + f"pipeline_check filenames mismatch for {report_path}: " + f"missing_from_report={missing_from_report}, extra_in_report={extra_in_report}" + ) + + print(f"[Pipeline Check] Validated BUSI_with_classes metadata from {report_path}") + +def check_data_leakage(splits: dict[str, list[str]]) -> dict[str, list[str]]: + leaks: dict[str, list[str]] = {} + split_names = list(splits.keys()) + for i, lhs in enumerate(split_names): + for rhs in split_names[i + 1 :]: + overlap = sorted(set(splits[lhs]) & set(splits[rhs])) + if overlap: + leaks[f"{lhs} ∩ {rhs}"] = overlap + return leaks + +def _project_relative_path(path: Path) -> str: + resolved = Path(path).resolve() + try: + return str(resolved.relative_to(PROJECT_DIR.resolve())) + except ValueError: + return str(resolved) + +def resolve_dataset_root_from_registry(split_registry: dict[str, Any]) -> Path: + dataset_root = Path(split_registry["dataset_root"]) + if dataset_root.is_absolute(): + return dataset_root + return (PROJECT_DIR / dataset_root).resolve() + +def make_sample_record( + filename: str, + images_subdir: str, + annotations_subdir: str, + *, + class_label: str | None = None, +) -> dict[str, str]: + record = { + "filename": filename, + "image_rel_path": str(Path(images_subdir) / filename), + "mask_rel_path": str(Path(annotations_subdir) / filename), + } + if class_label is not None: + record["class_label"] = class_label + return record + +def build_sample_records( + filenames: list[str], + *, + images_subdir: str, + annotations_subdir: str, + dataset_name: str, +) -> list[dict[str, str]]: + records = [] + for filename in sorted(filenames): + class_label = parse_busi_with_classes_label(filename) if dataset_name == "BUSI_with_classes" else None + records.append( + make_sample_record( + filename, + images_subdir, + annotations_subdir, + class_label=class_label, + ) + ) + return records + +def split_ratios_for_type(split_type: str) -> tuple[float, float]: + if split_type == "80_10_10": + return 0.80, 0.10 + if split_type == "70_10_20": + return 0.70, 0.10 + raise ValueError(f"Unsupported split_type: {split_type}") + +def deterministic_shuffle_records(records: list[dict[str, str]], *, seed: int, tag: str) -> list[dict[str, str]]: + rng = random.Random(seed + stable_int_from_text(tag)) + shuffled = [dict(record) for record in records] + rng.shuffle(shuffled) + return shuffled + +def train_subset_variant_suffix(variant: int | None = None) -> str: + variant_value = int(TRAIN_SUBSET_VARIANT if variant is None else variant) + return "" if variant_value <= 0 else f"_variant{variant_value:02d}" + +def group_records_by_class(sample_records: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + grouped: dict[str, list[dict[str, str]]] = {} + for record in sample_records: + class_label = record.get("class_label") + if class_label is None: + raise RuntimeError("Expected class_label in sample record for class-aware splitting.") + grouped.setdefault(class_label, []).append(dict(record)) + return grouped + +def allocate_counts_by_ratio(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + total_available = sum(available_counts.values()) + if total_available <= 0: + return allocation + + exact = {label: total_size * available_counts[label] / total_available for label in available_counts} + for label in available_counts: + allocation[label] = min(available_counts[label], int(math.floor(exact[label]))) + + remaining = min(total_size, total_available) - sum(allocation.values()) + order = sorted( + available_counts.keys(), + key=lambda label: (exact[label] - math.floor(exact[label]), available_counts[label], label), + reverse=True, + ) + while remaining > 0: + progressed = False + for label in order: + if allocation[label] < available_counts[label]: + allocation[label] += 1 + remaining -= 1 + progressed = True + if remaining == 0: + break + if not progressed: + break + return allocation + +def allocate_balanced_counts(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + labels = sorted(available_counts.keys()) + half = total_size // 2 + for label in labels: + allocation[label] = min(available_counts[label], half) + + remaining = min(total_size, sum(available_counts.values())) - sum(allocation.values()) + while remaining > 0: + candidates = [label for label in labels if allocation[label] < available_counts[label]] + if not candidates: + break + best_label = max( + candidates, + key=lambda label: ( + available_counts[label] - allocation[label], + 1 if label == "benign" else 0, + label, + ), + ) + allocation[best_label] += 1 + remaining -= 1 + return allocation + +def build_unstratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + records = deterministic_shuffle_records(sample_records, seed=seed, tag=f"base::{split_type}") + + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + return { + "train": records[:train_end], + "val": records[train_end:val_end], + "test": records[val_end:], + } + +def build_stratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + grouped = group_records_by_class(sample_records) + splits = {"train": [], "val": [], "test": []} + + for class_label in sorted(grouped.keys()): + records = deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"base::{split_type}::{class_label}", + ) + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + splits["train"].extend(records[:train_end]) + splits["val"].extend(records[train_end:val_end]) + splits["test"].extend(records[val_end:]) + + for split_name in splits: + splits[split_name] = deterministic_shuffle_records( + splits[split_name], + seed=seed, + tag=f"base::{split_type}::{split_name}", + ) + return splits + +def build_balanced_train_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + test_ratio = 1.0 - train_ratio - val_ratio + grouped = group_records_by_class(sample_records) + if sorted(grouped.keys()) != ["benign", "malignant"]: + raise RuntimeError( + f"balanced_train split policy expects benign/malignant classes, found {sorted(grouped.keys())}" + ) + + shuffled = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"base::{split_type}::balanced_train::{class_label}", + ) + for class_label, records in grouped.items() + } + + nominal_train_size = int(len(sample_records) * train_ratio) + per_class_train = min( + nominal_train_size // 2, + *(len(records) for records in shuffled.values()), + ) + + train_records: list[dict[str, str]] = [] + remaining_by_class: dict[str, list[dict[str, str]]] = {} + for class_label in sorted(shuffled.keys()): + records = shuffled[class_label] + train_records.extend(records[:per_class_train]) + remaining_by_class[class_label] = records[per_class_train:] + + remainder_val_fraction = val_ratio / max(val_ratio + test_ratio, 1e-8) + val_records: list[dict[str, str]] = [] + test_records: list[dict[str, str]] = [] + for class_label in sorted(remaining_by_class.keys()): + records = remaining_by_class[class_label] + val_count = int(len(records) * remainder_val_fraction) + val_records.extend(records[:val_count]) + test_records.extend(records[val_count:]) + + return { + "train": deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::train", + ), + "val": deterministic_shuffle_records( + val_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::val", + ), + "test": deterministic_shuffle_records( + test_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::test", + ), + } + +def build_nested_train_subsets( + train_records: list[dict[str, str]], + train_fractions: list[float], + *, + split_type: str, + seed: int, + split_policy: str | None = None, + subset_variant: int = 0, +) -> dict[str, list[dict[str, str]]]: + if not train_records: + return {} + + variant_tag = "" if int(subset_variant) <= 0 else f"::variant::{int(subset_variant)}" + ordered_records = deterministic_shuffle_records(train_records, seed=seed, tag=f"subset::{split_type}{variant_tag}") + use_class_labels = any("class_label" in record for record in train_records) + if not use_class_labels: + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + subsets[subset_key] = [dict(record) for record in ordered_records[:subset_size]] + return subsets + + grouped = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{class_label}{variant_tag}", + ) + for class_label, records in group_records_by_class(train_records).items() + } + available_counts = {class_label: len(records) for class_label, records in grouped.items()} + + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + if split_policy == "balanced_train": + class_counts = allocate_balanced_counts(subset_size, available_counts) + else: + class_counts = allocate_counts_by_ratio(subset_size, available_counts) + + subset_records: list[dict[str, str]] = [] + for class_label in sorted(grouped.keys()): + subset_records.extend([dict(record) for record in grouped[class_label][: class_counts[class_label]]]) + subsets[subset_key] = deterministic_shuffle_records( + subset_records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{subset_key}{variant_tag}", + ) + return subsets + +def train_fraction_from_subset_key(subset_key: str) -> float: + subset_text = str(subset_key).strip().lower() + if not subset_text: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") + try: + percent = Decimal(subset_text.replace("p", ".")) + except InvalidOperation as exc: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") from exc + if not percent.is_finite() or percent <= 0 or percent > 100: + raise RuntimeError(f"Train subset key {subset_key!r} must represent a percentage in the range (0, 100].") + return float(percent / Decimal("100")) + +def validate_persisted_split_no_leakage(split_type: str, split_entry: dict[str, Any], *, source: str) -> None: + base_splits = split_entry["base_splits"] + base_filenames: dict[str, list[str]] = {} + for split_name, records in base_splits.items(): + filenames = [record["filename"] for record in records] + if len(filenames) != len(set(filenames)): + raise RuntimeError(f"Duplicate filenames detected inside {split_name} for split_type={split_type}.") + base_filenames[split_name] = filenames + + leaks = check_data_leakage(base_filenames) + if leaks: + raise RuntimeError(f"Data leakage detected for split_type={split_type}: {list(leaks.keys())}") + + base_train = set(base_filenames["train"]) + previous_subset: set[str] = set() + for subset_key in sorted(split_entry["train_subsets"].keys(), key=train_fraction_from_subset_key): + subset_filenames = [record["filename"] for record in split_entry["train_subsets"][subset_key]] + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames detected inside train subset {subset_key} for split_type={split_type}." + ) + subset_set = set(subset_filenames) + missing = sorted(subset_set - base_train) + if missing: + raise RuntimeError( + f"Train subset {subset_key} contains files outside the base train split for split_type={split_type}." + ) + if previous_subset and not previous_subset.issubset(subset_set): + raise RuntimeError( + f"Train subsets are not nested for split_type={split_type}." + ) + previous_subset = subset_set + + print(f"[Split Check] No data leakage detected for split_type={split_type} ({source}).") + +def repair_persisted_train_subsets( + split_registry: dict[str, Any], + requested_train_fractions: list[float], + *, + split_json_path: Path, + seed: int, +) -> bool: + split_entries = split_registry.get("split_types", {}) + requested_fractions = normalize_dataset_percents(requested_train_fractions) + combined_fractions = {float(value) for value in split_registry.get("train_fractions", [])} + combined_fractions.update(requested_fractions) + dataset_name = str(split_registry.get("dataset_name", "BUSI")) + split_policy = split_registry.get("split_policy") if dataset_name == "BUSI_with_classes" else None + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + for subset_key in train_subsets.keys(): + combined_fractions.add(train_fraction_from_subset_key(subset_key)) + + combined_fractions_list = normalize_dataset_percents(list(combined_fractions)) + requested_keys = {percent_label(fraction) for fraction in requested_fractions} + registry_seed = int(split_registry.get("seed", seed)) + repaired = False + + if split_registry.get("train_fractions") != combined_fractions_list: + split_registry["train_fractions"] = combined_fractions_list + repaired = True + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + missing_requested_keys = sorted(requested_keys - set(train_subsets.keys()), key=train_fraction_from_subset_key) + if not missing_requested_keys: + continue + + split_entry["train_subsets"] = build_nested_train_subsets( + split_entry["base_splits"]["train"], + combined_fractions_list, + split_type=split_type, + seed=registry_seed, + split_policy=split_policy, + ) + print( + f"[Splits] Rebuilt missing train subsets {missing_requested_keys} " + f"for split_type={split_type} in {split_json_path}" + ) + repaired = True + + if repaired: + save_json(split_json_path, split_registry) + print(f"[Splits] Updated persisted dataset splits at {split_json_path}") + return repaired + +def load_or_create_dataset_splits( + images_dir: Path, + annotations_dir: Path, + split_json_path: Path, + train_fractions: list[float], + seed: int, +) -> tuple[dict[str, Any], str]: + train_fractions = normalize_dataset_percents(train_fractions) + images_dir = Path(images_dir).resolve() + annotations_dir = Path(annotations_dir).resolve() + split_json_path = Path(split_json_path).resolve() + dataset_name = current_dataset_name() + split_policy = current_busi_with_classes_split_policy() if dataset_name == "BUSI_with_classes" else None + if split_json_path.exists(): + split_registry = load_json(split_json_path) + if split_registry.get("version") != DATASET_SPLITS_VERSION: + raise RuntimeError( + f"Unsupported dataset_splits.json version in {split_json_path}. " + f"Expected version={DATASET_SPLITS_VERSION}." + ) + persisted_dataset_name = str(split_registry.get("dataset_name", "BUSI")) + if persisted_dataset_name != dataset_name: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets dataset_name={persisted_dataset_name!r}, " + f"but current DATASET_NAME={dataset_name!r}." + ) + persisted_split_policy = split_registry.get("split_policy") + if dataset_name == "BUSI_with_classes" and persisted_split_policy != split_policy: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets split_policy={persisted_split_policy!r}, " + f"but current BUSI_WITH_CLASSES_SPLIT_POLICY={split_policy!r}." + ) + split_entries = split_registry.get("split_types") + if not isinstance(split_entries, dict): + raise RuntimeError(f"Invalid split_types payload in {split_json_path}.") + for split_type in SUPPORTED_SPLIT_TYPES: + if split_type not in split_entries: + raise RuntimeError( + f"dataset_splits.json is missing split_type={split_type}. Delete it to regenerate cleanly." + ) + repaired = repair_persisted_train_subsets( + split_registry, + train_fractions, + split_json_path=split_json_path, + seed=seed, + ) + source = "repaired" if repaired else "loaded" + if dataset_name == "BUSI_with_classes": + sample_records = build_sample_records( + validate_image_mask_consistency(images_dir, annotations_dir)[0], + images_subdir=split_registry["images_subdir"], + annotations_subdir=split_registry["annotations_subdir"], + dataset_name=dataset_name, + ) + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + for split_type in SUPPORTED_SPLIT_TYPES: + validate_persisted_split_no_leakage(split_type, split_entries[split_type], source=source) + if repaired: + print(f"[Splits] Loaded and repaired persisted dataset splits from {split_json_path}") + else: + print(f"[Splits] Loaded persisted dataset splits from {split_json_path}") + return split_registry, source + + matched, missing_masks, missing_images = validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + images_subdir = images_dir.relative_to(dataset_root).as_posix() + annotations_subdir = annotations_dir.relative_to(dataset_root).as_posix() + sample_records = build_sample_records( + matched, + images_subdir=images_subdir, + annotations_subdir=annotations_subdir, + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + split_registry = { + "version": DATASET_SPLITS_VERSION, + "dataset_name": dataset_name, + "split_policy": split_policy, + "dataset_root": _project_relative_path(dataset_root), + "images_subdir": images_subdir, + "annotations_subdir": annotations_subdir, + "seed": seed, + "train_fractions": list(train_fractions), + "split_types": {}, + } + + for split_type in SUPPORTED_SPLIT_TYPES: + if dataset_name == "BUSI_with_classes": + if split_policy == "balanced_train": + base_splits = build_balanced_train_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_stratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_unstratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + train_subsets = build_nested_train_subsets( + base_splits["train"], + train_fractions, + split_type=split_type, + seed=seed, + split_policy=split_policy, + ) + split_entry = { + "split_type": split_type, + "base_splits": base_splits, + "train_subsets": train_subsets, + } + validate_persisted_split_no_leakage(split_type, split_entry, source="created") + split_registry["split_types"][split_type] = split_entry + + save_json(split_json_path, split_registry) + print(f"[Splits] Created persisted dataset splits at {split_json_path}") + return split_registry, "created" + +def select_persisted_split( + split_registry: dict[str, Any], + split_type: str, + train_fraction: float, +) -> dict[str, Any]: + if split_type not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"Unsupported split_type: {split_type}") + + split_entries = split_registry.get("split_types", {}) + if split_type not in split_entries: + raise KeyError( + f"Requested split_type={split_type} is not available in dataset_splits.json. " + "Delete the JSON file to regenerate it with the new configuration." + ) + + subset_key = percent_label(train_fraction) + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + if subset_key not in train_subsets: + raise KeyError( + f"Requested train fraction={train_fraction} (key={subset_key}) is not available in dataset_splits.json." + ) + + return { + "dataset_root": resolve_dataset_root_from_registry(split_registry), + "split_type": split_type, + "train_fraction": float(train_fraction), + "train_subset_key": subset_key, + "train_subset_variant": 0, + "train_subset_source": "persisted", + "base_train_records": split_entry["base_splits"]["train"], + "train_records": train_subsets[subset_key], + "val_records": split_entry["base_splits"]["val"], + "test_records": split_entry["base_splits"]["test"], + } + +def apply_train_subset_variant( + selected_split: dict[str, Any], + split_registry: dict[str, Any], + *, + subset_variant: int, +) -> dict[str, Any]: + variant = int(subset_variant) + if variant <= 0 or float(selected_split["train_fraction"]) >= 1.0: + return selected_split + + split_policy = split_registry.get("split_policy") if current_dataset_name() == "BUSI_with_classes" else None + variant_subsets = build_nested_train_subsets( + selected_split["base_train_records"], + [float(selected_split["train_fraction"])], + split_type=str(selected_split["split_type"]), + seed=int(split_registry.get("seed", SEED)), + split_policy=split_policy, + subset_variant=variant, + ) + subset_key = str(selected_split["train_subset_key"]) + updated_split = dict(selected_split) + updated_split["train_records"] = variant_subsets[subset_key] + updated_split["train_subset_variant"] = variant + updated_split["train_subset_source"] = "variant_override" + return updated_split + +def export_selected_split_manifest( + pct_root: Path, + *, + percent: float, + split_source: str, + selected_split: dict[str, Any], +) -> Path: + variant_suffix = train_subset_variant_suffix(int(selected_split.get("train_subset_variant", 0))) + manifest_path = pct_root / ( + f"selected_split_{selected_split['split_type']}_{percent_label(percent)}pct{variant_suffix}.json" + ) + payload = { + "dataset_name": current_dataset_name(), + "dataset_root": str(Path(selected_split["dataset_root"]).resolve()), + "dataset_percent": float(percent), + "dataset_percent_label": percent_label(percent), + "split_source": split_source, + "split_type": str(selected_split["split_type"]), + "train_fraction": float(selected_split["train_fraction"]), + "train_subset_key": str(selected_split["train_subset_key"]), + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(manifest_path.resolve()), + "base_train_records": [dict(record) for record in selected_split["base_train_records"]], + "train_records": [dict(record) for record in selected_split["train_records"]], + "val_records": [dict(record) for record in selected_split["val_records"]], + "test_records": [dict(record) for record in selected_split["test_records"]], + } + save_json(manifest_path, payload) + return manifest_path + +def compute_busi_statistics( + dataset_root: Path, + sample_records: list[dict[str, str]], + cache_path: Path, +) -> tuple[float, float, str]: + filenames = [record["filename"] for record in sample_records] + if cache_path.exists(): + stats = load_json(cache_path) + if stats.get("filenames") == filenames: + print(f"[Normalization] Loaded cached normalization stats from {cache_path}") + return float(stats["global_mean"]), float(stats["global_std"]), "loaded_from_cache" + + total_sum = np.float64(0.0) + total_sq_sum = np.float64(0.0) + total_pixels = 0 + + for record in tqdm(sample_records, desc="Computing BUSI train mean/std", leave=False): + image_path = dataset_root / record["image_rel_path"] + img = np.array(PILImage.open(image_path)).astype(np.float64) + total_sum += img.sum() + total_sq_sum += (img ** 2).sum() + total_pixels += img.size + + global_mean = float(total_sum / total_pixels) + global_std = float(np.sqrt(total_sq_sum / total_pixels - global_mean ** 2)) + if global_std < 1e-6: + global_std = 1.0 + + save_json( + cache_path, + { + "global_mean": global_mean, + "global_std": global_std, + "total_pixels": int(total_pixels), + "num_images": len(sample_records), + "filenames": filenames, + }, + ) + print(f"[Normalization] Computed and saved normalization stats to {cache_path}") + return global_mean, global_std, "computed_fresh" + +def compute_class_distribution(sample_records: list[dict[str, str]]) -> dict[str, int] | None: + if not sample_records or not any("class_label" in record for record in sample_records): + return None + return { + "benign": sum(1 for record in sample_records if record.get("class_label") == "benign"), + "malignant": sum(1 for record in sample_records if record.get("class_label") == "malignant"), + } + +def format_class_distribution(class_distribution: dict[str, int] | None) -> str: + if class_distribution is None: + return "unavailable" + benign = int(class_distribution.get("benign", 0)) + malignant = int(class_distribution.get("malignant", 0)) + total = benign + malignant + return f"benign={benign}, malignant={malignant}, total={total}" + +def print_loaded_class_distribution( + *, + split_type: str, + train_subset_key: str, + base_train_records: list[dict[str, str]], + train_records: list[dict[str, str]], + val_records: list[dict[str, str]], + test_records: list[dict[str, str]], +) -> None: + if not any("class_label" in record for record in train_records): + return + section(f"Loaded Class Distribution | {split_type} | {train_subset_key}%") + print(f"Base train classes : {format_class_distribution(compute_class_distribution(base_train_records))}") + print(f"Train subset classes : {format_class_distribution(compute_class_distribution(train_records))}") + print(f"Validation classes : {format_class_distribution(compute_class_distribution(val_records))}") + print(f"Test classes : {format_class_distribution(compute_class_distribution(test_records))}") + +def print_split_summary(payload: dict[str, Any]) -> None: + unit_name = "Phase" if payload.get("phase_index") is not None else "Split" + section(f"{unit_name} Summary | {payload['split_type']} | {payload['train_subset_key']}%") + print(f"Dataset name : {payload['dataset_name']}") + if payload.get("dataset_split_policy") is not None: + print(f"Dataset split policy : {payload['dataset_split_policy']}") + print(f"Dataset splits JSON : {payload['dataset_splits_path']}") + print(f"Split source : {payload['split_source']}") + print(f"Split type used : {payload['split_type']}") + if payload.get("split_generation_mode") is not None: + print(f"Split generation mode : {payload['split_generation_mode']}") + if payload.get("phase_index") is not None: + print(f"Phase index : {payload['phase_index']}") + print(f"Phase val/test folds : val={payload['phase_val_fold_index']}, test={payload['phase_test_fold_index']}") + if payload.get("percent_sampling_mode") is not None: + print(f"Percent sampling mode : {payload['percent_sampling_mode']}") + print(f"Train fraction : {payload['train_subset_key']}% of frozen base train") + print(f"Train subset variant : {payload.get('train_subset_variant', 0)}") + print(f"Train subset source : {payload.get('train_subset_source', 'persisted')}") + if payload.get("sampling_chain_dataset_percents") is not None: + print(f"Sampling chain percents: {payload['sampling_chain_dataset_percents']}") + print(f"Base train samples : {payload['base_train_count']}") + print(f"Train subset samples : {payload['train_count']}") + print(f"Validation samples : {payload['val_count']}") + print(f"Test samples : {payload['test_count']}") + if payload.get("base_train_class_distribution") is not None: + print(f"Base train classes : {format_class_distribution(payload['base_train_class_distribution'])}") + print(f"Train subset classes : {format_class_distribution(payload['train_class_distribution'])}") + print(f"Validation classes : {format_class_distribution(payload['val_class_distribution'])}") + print(f"Test classes : {format_class_distribution(payload['test_class_distribution'])}") + print(f"Validation/Test frozen : {payload['val_test_frozen']}") + print(f"Leakage check : {payload['leakage_check']}") + +def print_normalization_summary(payload: dict[str, Any]) -> None: + mode = "ImageNet mean/std" if USE_IMAGENET_NORM else "Dataset train mean/std" + print(f"Dataset name : {payload['dataset_name']}") + print(f"Normalization mode : {mode}") + print(f"Stats cache path : {payload['normalization_cache_path']}") + print(f"Stats source : {payload['normalization_source']}") + print(f"Split type used : {payload['split_type']}") + variant_suffix = train_subset_variant_suffix(int(payload.get("train_subset_variant", 0))) + print( + f"Stats computed from : {payload['train_count']} train samples " + f"({payload['train_subset_key']}%{variant_suffix})" + ) +# ============================================================================= +# IMAGE PREPARATION + DATASETS +# ============================================================================= + +def _to_three_channels(image: np.ndarray) -> np.ndarray: + if image.ndim == 2: + image = image[..., None] + if image.shape[2] == 1: + image = np.repeat(image, 3, axis=2) + elif image.shape[2] > 3: + image = image[..., :3] + return image + +def _prepare_image(raw: np.ndarray, global_mean: float, global_std: float) -> np.ndarray: + img = raw.astype(np.float32) + img = _to_three_channels(img) + if IMG_SIZE > 0 and (img.shape[0] != IMG_SIZE or img.shape[1] != IMG_SIZE): + img = np.array( + PILImage.fromarray(img.astype(np.uint8)).resize((IMG_SIZE, IMG_SIZE), PILImage.BILINEAR) + ).astype(np.float32) + if USE_IMAGENET_NORM: + if img.max() > 1.0: + img = img / 255.0 + img = (img - IMAGENET_MEAN) / IMAGENET_STD + else: + img = (img - global_mean) / global_std + return np.transpose(img, (2, 0, 1)).copy() + +def _prepare_mask(raw: np.ndarray) -> np.ndarray: + mask = raw.astype(np.uint8) + if mask.ndim == 3: + mask = mask[..., 0] + if IMG_SIZE > 0 and (mask.shape[0] != IMG_SIZE or mask.shape[1] != IMG_SIZE): + pil_mask = PILImage.fromarray(mask) + if pil_mask.mode != "L": + pil_mask = pil_mask.convert("L") + mask = np.array(pil_mask.resize((IMG_SIZE, IMG_SIZE), PILImage.NEAREST)) + return ((mask > 0).astype(np.float32))[None, ...].copy() + +def print_imagenet_normalization_status() -> bool: + uses_imagenet_norm = bool(USE_IMAGENET_NORM) + if uses_imagenet_norm: + print("✅🖼️ ImageNet normalization is ACTIVE in `_prepare_image`.") + else: + print("⚠️🧪 ImageNet normalization is NOT active in `_prepare_image`.") + print("⚠️📊 Using dataset global mean/std normalization instead.") + if SMP_ENCODER_WEIGHTS == "imagenet" and not uses_imagenet_norm: + print("⚠️🚨 Encoder weights are set to ImageNet, but ImageNet normalization is disabled.") + return uses_imagenet_norm + +def _gaussian_kernel1d( + sigma: float, + *, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + if sigma <= 0: + return torch.ones(1, device=device, dtype=dtype) + radius = max(int(math.ceil(3.0 * sigma)), 1) + coords = torch.arange(-radius, radius + 1, device=device, dtype=dtype) + kernel = torch.exp(-(coords.square()) / max(2.0 * sigma * sigma, 1e-6)) + return kernel / kernel.sum().clamp_min(1e-12) + +def _smooth_displacement_field(field: torch.Tensor, sigma: float) -> torch.Tensor: + kernel = _gaussian_kernel1d(sigma, device=field.device, dtype=field.dtype) + if kernel.numel() == 1: + return field + radius = kernel.numel() // 2 + kernel_y = kernel.view(1, 1, -1, 1) + kernel_x = kernel.view(1, 1, 1, -1) + field = F.conv2d(field, kernel_y, padding=(radius, 0)) + field = F.conv2d(field, kernel_x, padding=(0, radius)) + return field + +def _apply_elastic_deformation( + image: torch.Tensor, + mask: torch.Tensor, + *, + alpha: float = 8.0, + sigma: float = 4.0, +) -> tuple[torch.Tensor, torch.Tensor]: + _, h, w = image.shape + if h < 2 or w < 2: + return image.contiguous(), mask.contiguous() + + device = image.device + dtype = image.dtype + dx = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + dy = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + + yy, xx = torch.meshgrid( + torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype), + torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype), + indexing="ij", + ) + grid = torch.stack((xx, yy), dim=-1).unsqueeze(0) + grid[..., 0] = grid[..., 0] + dx.squeeze(0).squeeze(0) * (2.0 / max(w - 1, 1)) + grid[..., 1] = grid[..., 1] + dy.squeeze(0).squeeze(0) * (2.0 / max(h - 1, 1)) + grid = grid.clamp(-1.25, 1.25) + + image_out = F.grid_sample( + image.unsqueeze(0), + grid, + mode="bilinear", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + mask_out = F.grid_sample( + mask.unsqueeze(0), + grid, + mode="nearest", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + return image_out.contiguous(), mask_out.clamp(0.0, 1.0).contiguous() + +def _apply_minimal_train_aug(image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(2,)) + mask = torch.flip(mask, dims=(2,)) + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(1,)) + mask = torch.flip(mask, dims=(1,)) + if torch.rand(1).item() < 0.5: + k = 1 if torch.rand(1).item() < 0.5 else 3 + image = torch.rot90(image, k=k, dims=(1, 2)) + mask = torch.rot90(mask, k=k, dims=(1, 2)) + elastic_aug_prob = float(_job_param("elastic_aug_prob", 0.0)) + if elastic_aug_prob > 0 and torch.rand(1).item() < elastic_aug_prob: + image, mask = _apply_elastic_deformation(image, mask) + return image.contiguous(), mask.contiguous() + +class BUSIDataset(Dataset): + def __init__( + self, + sample_records: list[dict[str, str]], + dataset_root: Path, + global_mean: float, + global_std: float, + *, + preload: bool, + augment: bool, + split_name: str, + ) -> None: + super().__init__() + self.sample_records = [dict(record) for record in sample_records] + self.dataset_root = Path(dataset_root) + self.global_mean = float(global_mean) + self.global_std = float(global_std) + self.preload = preload + self.augment = augment + self.split_name = split_name + self._images: list[torch.Tensor] = [] + self._masks: list[torch.Tensor] = [] + self._raw_cache_bytes = 0 + + if not self.preload: + raise ValueError("PRELOAD_TO_RAM is mandatory in this RunPod runner.") + self._preload_to_ram() + + def _preload_to_ram(self) -> None: + desc = f"Preloading {self.split_name} ({len(self.sample_records)} samples) to RAM" + for record in tqdm(self.sample_records, desc=desc, leave=False): + raw_img = np.array(PILImage.open(self.dataset_root / record["image_rel_path"])) + raw_mask = np.array(PILImage.open(self.dataset_root / record["mask_rel_path"])) + if raw_img.shape[:2] != raw_mask.shape[:2]: + raise RuntimeError( + f"Image/mask spatial size mismatch for {record['filename']}: " + f"image={raw_img.shape[:2]}, mask={raw_mask.shape[:2]}" + ) + image = torch.from_numpy(_prepare_image(raw_img, self.global_mean, self.global_std)) + mask = torch.from_numpy(_prepare_mask(raw_mask)) + self._raw_cache_bytes += tensor_bytes(image) + tensor_bytes(mask) + self._images.append(image) + self._masks.append(mask) + + def __len__(self) -> int: + return len(self.sample_records) + + def __getitem__(self, index: int) -> dict[str, Any]: + image = self._images[index].clone() + mask = self._masks[index].clone() + if self.augment: + image, mask = _apply_minimal_train_aug(image, mask) + return { + "image": image, + "mask": mask, + "sample_id": Path(self.sample_records[index]["filename"]).stem, + "dataset": current_dataset_name(), + } + + @property + def cache_bytes(self) -> int: + return self._raw_cache_bytes + +class CUDAPrefetcher: + def __init__(self, loader: DataLoader, device: torch.device) -> None: + self.loader = loader + self.device = device + self._use_cuda = device.type == "cuda" + self._iter = None + self._stream = None + self._next_batch = None + + def __len__(self) -> int: + return len(self.loader) + + def __iter__(self): + self._iter = iter(self.loader) + self._stream = torch.cuda.Stream(device=self.device) if self._use_cuda else None + self._next_batch = None + self._preload() + return self + + def close(self) -> None: + self._next_batch = None + self._iter = None + self._stream = None + + def _preload(self) -> None: + if self._iter is None: + self._next_batch = None + return + try: + self._next_batch = next(self._iter) + except StopIteration: + self._next_batch = None + return + if self._use_cuda: + assert self._stream is not None + with torch.cuda.stream(self._stream): + self._next_batch = to_device(self._next_batch, self.device) + else: + self._next_batch = to_device(self._next_batch, self.device) + + def __next__(self): + if self._next_batch is None: + self.close() + raise StopIteration + if self._use_cuda: + assert self._stream is not None + torch.cuda.current_stream(self.device).wait_stream(self._stream) + batch = self._next_batch + self._preload() + if self._next_batch is None: + self._iter = None + self._stream = None + return batch + +class DataBundle: + def __init__( + self, + *, + percent: float, + split_payload: dict[str, Any], + train_ds: BUSIDataset, + val_ds: BUSIDataset, + test_ds: BUSIDataset, + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + ) -> None: + self.percent = percent + self.split_payload = split_payload + self.train_ds = train_ds + self.val_ds = val_ds + self.test_ds = test_ds + self.train_loader = train_loader + self.val_loader = val_loader + self.test_loader = test_loader + + @property + def global_mean(self) -> float: + return float(self.split_payload["global_mean"]) + + @property + def global_std(self) -> float: + return float(self.split_payload["global_std"]) + + @property + def total_cache_bytes(self) -> int: + return self.train_ds.cache_bytes + self.val_ds.cache_bytes + self.test_ds.cache_bytes + +def make_loader(dataset: Dataset, shuffle: bool, *, loader_tag: str) -> DataLoader: + num_workers = NUM_WORKERS + persistent_workers = USE_PERSISTENT_WORKERS and num_workers > 0 + pin_memory = USE_PIN_MEMORY and DEVICE.type == "cuda" + generator = make_seeded_generator(SEED, loader_tag) + return DataLoader( + dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, + persistent_workers=persistent_workers, + worker_init_fn=seed_worker, + generator=generator, + ) + +def build_data_bundle(percent: float, split_registry: dict[str, Any], split_source: str) -> DataBundle: + pct_label = percent_label(percent) + pct_text = percent_text(percent) + selected_split = select_persisted_split(split_registry, SPLIT_TYPE, percent) + selected_split = apply_train_subset_variant( + selected_split, + split_registry, + subset_variant=TRAIN_SUBSET_VARIANT, + ) + pct_root = ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{pct_label}") + split_manifest_path = export_selected_split_manifest( + pct_root, + percent=percent, + split_source=split_source, + selected_split=selected_split, + ) + stats_cache_path = pct_root / ( + f"norm_stats_{normalization_cache_tag()}_{SPLIT_TYPE}_{pct_label}pct" + f"{train_subset_variant_suffix(int(selected_split.get('train_subset_variant', 0)))}.json" + ) + base_train_class_distribution = compute_class_distribution(selected_split["base_train_records"]) + train_class_distribution = compute_class_distribution(selected_split["train_records"]) + val_class_distribution = compute_class_distribution(selected_split["val_records"]) + test_class_distribution = compute_class_distribution(selected_split["test_records"]) + print_loaded_class_distribution( + split_type=selected_split["split_type"], + train_subset_key=selected_split["train_subset_key"], + base_train_records=selected_split["base_train_records"], + train_records=selected_split["train_records"], + val_records=selected_split["val_records"], + test_records=selected_split["test_records"], + ) + dataset_root = Path(selected_split["dataset_root"]).resolve() + global_mean, global_std, normalization_source = compute_busi_statistics( + dataset_root=dataset_root, + sample_records=selected_split["train_records"], + cache_path=stats_cache_path, + ) + + split_payload = { + "dataset_name": current_dataset_name(), + "dataset_split_policy": split_registry.get("split_policy"), + "dataset_splits_path": str(current_dataset_splits_json_path().resolve()), + "dataset_root": str(dataset_root), + "split_source": split_source, + "split_type": SPLIT_TYPE, + "dataset_percent": percent, + "train_subset_key": selected_split["train_subset_key"], + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(split_manifest_path.resolve()), + "base_train_count": len(selected_split["base_train_records"]), + "train_count": len(selected_split["train_records"]), + "val_count": len(selected_split["val_records"]), + "test_count": len(selected_split["test_records"]), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(stats_cache_path.resolve()), + "normalization_source": normalization_source, + } + + print_split_summary(split_payload) + print_normalization_summary(split_payload) + + train_ds = BUSIDataset( + selected_split["train_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=True, + split_name=f"train {SPLIT_TYPE} {pct_text}", + ) + val_ds = BUSIDataset( + selected_split["val_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"val {SPLIT_TYPE}", + ) + test_ds = BUSIDataset( + selected_split["test_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"test {SPLIT_TYPE}", + ) + + bundle = DataBundle( + percent=percent, + split_payload=split_payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=make_loader(train_ds, shuffle=True, loader_tag=f"{SPLIT_TYPE}:{pct_label}:train"), + val_loader=make_loader(val_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:val"), + test_loader=make_loader(test_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:test"), + ) + print_preload_summary(bundle) + return bundle + +def print_preload_summary(bundle: DataBundle) -> None: + section( + f"RAM Preload Summary | {bundle.split_payload['split_type']} | {int(bundle.percent * 100)}%" + ) + print(f"Train samples : {len(bundle.train_ds)}") + print(f"Val samples : {len(bundle.val_ds)}") + print(f"Test samples : {len(bundle.test_ds)}") + print(f"Train batches : {len(bundle.train_loader)}") + print(f"Val batches : {len(bundle.val_loader)}") + print(f"Test batches : {len(bundle.test_loader)}") + print(f"Global mean : {bundle.global_mean:.6f}") + print(f"Global std : {bundle.global_std:.6f}") + first = bundle.train_ds[0] + print(f"Sample image shape : {tuple(first['image'].shape)}") + print(f"Sample mask shape : {tuple(first['mask'].shape)}") + print(f"Sample image dtype : {first['image'].dtype}") + print(f"Sample mask dtype : {first['mask'].dtype}") + print(f"Estimated RAM preload : {bytes_to_gb(bundle.total_cache_bytes):.3f} GB") + +"""============================================================================= +MODEL DEFINITIONS +============================================================================= +""" + +def strategy_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + strategy = _require_supported_strategy(strategy) + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + if strategy == 2: + return "Strategy 2: Custom VGG + Segmentation Head (Supervised)" + if strategy == 3: + return "Strategy 3 Lite: Custom VGG + Segmentation Head + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + + if strategy == 2: + return f"Strategy 2: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) supervised" + if strategy == 3: + return f"Strategy 3 Lite: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + +def _apply_omega_conv(omega_conv: nn.Conv2d, value_next: torch.Tensor) -> torch.Tensor: + weight = omega_conv.weight + value_next = value_next.to(device=weight.device, dtype=weight.dtype) + return omega_conv(value_next) + +def _conv3x3(in_ch: int, out_ch: int, dilation: int = 1) -> nn.Conv2d: + return nn.Conv2d( + in_ch, + out_ch, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=True, + ) + +class _ConvBlock(nn.Module): + def __init__( + self, + in_ch: int, + out_ch: int, + dilation: int = 1, + *, + num_groups: int = 0, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.conv = _conv3x3(in_ch, out_ch, dilation=dilation) + self.norm = _group_norm(out_ch, num_groups=num_groups) if num_groups > 0 else nn.Identity() + self.act = nn.ReLU(inplace=True) + self.drop = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.act(self.norm(self.conv(x)))) + +def _group_norm(num_channels: int, *, num_groups: int = GN_NUM_GROUPS) -> nn.GroupNorm: + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + return nn.GroupNorm(groups, num_channels) + + +class _MultiScaleRefineBranch(nn.Module): + """Processes raw encoder features at each scale independently, then fuses + them into a single feature map. This gives the refinement head access to + multi-resolution spatial cues (edges at low levels, semantics at high + levels) that the 1x1 projection squashes away.""" + + def __init__( + self, + encoder_channels: list[int] | tuple[int, ...], + out_channels: int, + per_scale_channels: int = 32, + ) -> None: + super().__init__() + self._valid_indices: list[int] = [i for i, c in enumerate(encoder_channels) if c > 0] + self.scale_convs = nn.ModuleList() + for i in self._valid_indices: + self.scale_convs.append(nn.Sequential( + nn.Conv2d(encoder_channels[i], per_scale_channels, kernel_size=1, bias=False), + _group_norm(per_scale_channels), + nn.ReLU(inplace=True), + )) + total_ch = per_scale_channels * len(self._valid_indices) + self.fuse = nn.Sequential( + nn.Conv2d(total_ch, out_channels, kernel_size=3, padding=1, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2, dilation=2, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + ) + self._init_small() + + def _init_small(self) -> None: + """Small-magnitude init so the branch starts as a near-zero residual.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + m.weight.data.mul_(0.1) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + parts: list[torch.Tensor] = [] + for idx, conv in zip(self._valid_indices, self.scale_convs): + out = conv(encoder_features[idx]) + if out.shape[-2] != h or out.shape[-1] != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + parts.append(out) + return self.fuse(torch.cat(parts, dim=1)) + + +class SelfAttentionModule(nn.Module): + def __init__(self, channels: int) -> None: + super().__init__() + mid = max(channels // 8, 1) + self.query = nn.Conv2d(channels, mid, 1) + self.key = nn.Conv2d(channels, mid, 1) + self.value = nn.Conv2d(channels, channels, 1) + self.gamma = nn.Parameter(torch.tensor([0.1], dtype=torch.float32)) + + def forward(self, f: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + b, c, h, w = f.shape + pooled = f + target_grid = max(int(_job_param("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID)), 1) + if target_grid < max(h, w): + stride_h = max(1, math.ceil(h / target_grid)) + stride_w = max(1, math.ceil(w / target_grid)) + pooled = F.avg_pool2d(f, kernel_size=(stride_h, stride_w), stride=(stride_h, stride_w)) + if target_grid >= 64 and target_grid not in _STRATEGY3_SAM_GRID_WARNED: + print( + "[Strategy3] Self-attention grid " + f"{target_grid}x{target_grid} requested; this implies a much heavier attention matrix " + "(for example 64x64 -> 4096 tokens). Lower strategy3_sam_attention_grid if this is too slow." + ) + _STRATEGY3_SAM_GRID_WARNED.add(target_grid) + + ph, pw = pooled.shape[-2:] + q = self.query(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + k = self.key(pooled).view(b, -1, ph * pw) + v = self.value(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + attn = torch.softmax(q @ k / (q.shape[-1] ** 0.5), dim=-1) + out = (attn @ v).permute(0, 2, 1).view(b, c, ph, pw) + if ph != h or pw != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + return f + self.gamma * out, attn + + def forward_features(self, f: torch.Tensor) -> torch.Tensor: + out, _ = self.forward(f) + return out + +class DilatedPolicyHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.classifier = nn.Conv2d(64, NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + bias = torch.full((NUM_ACTIONS,), -2.0, dtype=torch.float32) + keep_index = NUM_ACTIONS // 2 if NUM_ACTIONS >= 3 else NUM_ACTIONS - 1 + bias[keep_index] = 2.0 + with torch.no_grad(): + self.classifier.bias.copy_(bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class DilatedValueHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.readout = nn.Conv2d(64, 1, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.body(x) + return self.readout(features) + +def replace_bn_with_gn(model: nn.Module, num_groups: int = 8) -> nn.Module: + for name, module in model.named_children(): + if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)): + num_channels = module.num_features + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + setattr(model, name, nn.GroupNorm(groups, num_channels, eps=module.eps, affine=module.affine)) + else: + replace_bn_with_gn(module, num_groups=num_groups) + return model + +def _ensure_transunet_repo_on_path() -> None: + repo_dir = TRANSUNET_REPO_DIR.resolve() + if not repo_dir.is_dir(): + raise FileNotFoundError( + f"TransUNet repo not found at {repo_dir}. Expected the official repo in " + f"{TRANSUNET_REPO_DIR}." + ) + repo_str = str(repo_dir) + if repo_str not in sys.path: + sys.path.insert(0, repo_str) + + +def _load_transunet_components() -> tuple[Any, dict[str, Any]]: + global _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + if _TRANSUNET_VISION_TRANSFORMER is None or _TRANSUNET_CONFIGS is None: + _ensure_transunet_repo_on_path() + try: + vit_module = importlib.import_module("networks.vit_seg_modeling") + except Exception as exc: + raise RuntimeError( + "Unable to import the official TransUNet modules. Ensure the TransUNet repo is present " + "and dependencies such as ml_collections, scipy, and torch are installed." + ) from exc + _TRANSUNET_VISION_TRANSFORMER = getattr(vit_module, "VisionTransformer") + _TRANSUNET_CONFIGS = getattr(vit_module, "CONFIGS") + return _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + + +def _transunet_tensor_changed(before: torch.Tensor, after: torch.Tensor) -> bool: + return not torch.equal(before, after) + + +def _load_and_verify_transunet_checkpoint( + vit_model: nn.Module, + *, + pretrained_path: Path, + img_size: int, + n_skip: int, +) -> dict[str, Any]: + checkpoint_path = Path(pretrained_path).expanduser().resolve() + if not checkpoint_path.is_file(): + raise FileNotFoundError( + f"TransUNet checkpoint not found at {checkpoint_path}. " + f"Expected ImageNet weights at {TRANSUNET_PRETRAINED_PATH.resolve()}." + ) + + weights = np.load(checkpoint_path, allow_pickle=False) + try: + missing_keys = [key for key in _TRANSUNET_REQUIRED_NPZ_KEYS if key not in weights] + if missing_keys: + raise RuntimeError( + f"TransUNet checkpoint {checkpoint_path} is missing required arrays: {missing_keys}" + ) + + position_embeddings = vit_model.transformer.embeddings.position_embeddings + root_conv = vit_model.transformer.embeddings.hybrid_model.root.conv.weight + block0_query = vit_model.transformer.encoder.layer[0].attn.query.weight + + pos_before = position_embeddings.detach().cpu().clone() + root_before = root_conv.detach().cpu().clone() + query_before = block0_query.detach().cpu().clone() + + posemb_source_shape = tuple(weights["Transformer/posembed_input/pos_embedding"].shape) + posemb_target_shape = tuple(position_embeddings.shape) + array_count = len(getattr(weights, "files", [])) + file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024) + + vit_model.load_from(weights=weights) + + pos_after = position_embeddings.detach().cpu() + root_after = root_conv.detach().cpu() + query_after = block0_query.detach().cpu() + + updated = { + "PosEmbed updated": _transunet_tensor_changed(pos_before, pos_after), + "ResNet root conv updated": _transunet_tensor_changed(root_before, root_after), + "ViT block-0 query updated": _transunet_tensor_changed(query_before, query_after), + } + + section("TransUNet Checkpoint Verification") + print("[TransUNet] OK Loaded R50+ViT-B/16 ImageNet checkpoint") + print(f"[TransUNet] File : {checkpoint_path} ({file_size_mb:.1f} MB)") + print(f"[TransUNet] NPZ arrays : {array_count}") + print( + "[TransUNet] PosEmbed shape : " + f"src {posemb_source_shape} -> tgt {posemb_target_shape}" + f"{' (interpolated)' if posemb_source_shape != posemb_target_shape else ''}" + ) + for label, status in updated.items(): + print(f"[TransUNet] {label:<22}: {status}") + print( + "[TransUNet] " + f"img_size={img_size}, patches.grid=({img_size // 16}, {img_size // 16}), " + f"n_skip={n_skip}, n_classes=1" + ) + + failed = [label for label, status in updated.items() if not status] + if failed: + raise RuntimeError( + "TransUNet checkpoint load verification failed. The following tensors were unchanged after " + f"load_from(...): {failed}. Training was stopped to avoid using a randomly initialized model." + ) + + return { + "checkpoint_path": str(checkpoint_path), + "array_count": array_count, + "file_size_mb": file_size_mb, + "posemb_source_shape": posemb_source_shape, + "posemb_target_shape": posemb_target_shape, + "updated": updated, + } + finally: + close_fn = getattr(weights, "close", None) + if callable(close_fn): + close_fn() + + +class _TransUNetEncoder(nn.Module): + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.transformer = transformer + self.out_channels = (3, 64, 256, 512, 768) + self._vit_token_cache: torch.Tensor | None = None + self._decoder_skip_cache: list[torch.Tensor] | None = None + + def _clear_cache(self) -> None: + self._vit_token_cache = None + self._decoder_skip_cache = None + + def decoder_inputs(self) -> tuple[torch.Tensor, list[torch.Tensor]]: + if self._vit_token_cache is None or self._decoder_skip_cache is None: + raise RuntimeError( + "TransUNet decoder was called before the encoder cache was populated. " + "Call the encoder first in the current forward pass." + ) + return self._vit_token_cache, self._decoder_skip_cache + + def forward(self, x: torch.Tensor) -> list[torch.Tensor]: + self._clear_cache() + if x.shape[1] == 1: + model_input = x.repeat(1, 3, 1, 1) + elif x.shape[1] == 3: + model_input = x + else: + raise ValueError(f"TransUNet expects 1 or 3 input channels, got {x.shape[1]}.") + + embedding_output, hybrid_features = self.transformer.embeddings(model_input) + hidden_states, _ = self.transformer.encoder(embedding_output) + if hybrid_features is None or len(hybrid_features) < 3: + raise RuntimeError( + "TransUNet hybrid ResNet features were not produced as expected." + ) + + deepest_skip, mid_skip, shallow_skip = hybrid_features[:3] + batch_size, n_patch, hidden_dim = hidden_states.shape + side = math.isqrt(n_patch) + if side * side != n_patch: + raise RuntimeError( + f"TransUNet token grid is not square: n_patch={n_patch}." + ) + vit_out = hidden_states.permute(0, 2, 1).contiguous().view(batch_size, hidden_dim, side, side) + + self._vit_token_cache = hidden_states + self._decoder_skip_cache = [deepest_skip, mid_skip, shallow_skip] + return [model_input, shallow_skip, mid_skip, deepest_skip, vit_out] + + +class _TransUNetDecoder(nn.Module): + def __init__(self, decoder_core: nn.Module, encoder: _TransUNetEncoder) -> None: + super().__init__() + self.decoder_core = decoder_core + self._encoder_ref = weakref.ref(encoder) + + def _encoder(self) -> _TransUNetEncoder: + encoder = self._encoder_ref() + if encoder is None: + raise RuntimeError("TransUNet encoder reference is no longer available.") + return encoder + + def forward(self, *features: torch.Tensor) -> torch.Tensor: + del features + hidden_states, skip_features = self._encoder().decoder_inputs() + return self.decoder_core(hidden_states, features=skip_features) + + +class TransUNetSMPAdapter(nn.Module): + def __init__(self, *, img_size: int, pretrained_path: Path) -> None: + super().__init__() + if img_size % 16 != 0: + raise ValueError(f"TransUNet requires img_size divisible by 16, got {img_size}.") + + vision_transformer_cls, configs = _load_transunet_components() + if TRANSUNET_VIT_NAME not in configs: + raise KeyError( + f"TransUNet config {TRANSUNET_VIT_NAME!r} not found in the official repo." + ) + + config_vit = copy.deepcopy(configs[TRANSUNET_VIT_NAME]) + config_vit.n_classes = 1 + config_vit.n_skip = TRANSUNET_N_SKIP + config_vit.classifier = "seg" + config_vit.patches.grid = (img_size // 16, img_size // 16) + + vit_model = vision_transformer_cls(config_vit, img_size=img_size, num_classes=1) + self.checkpoint_summary = _load_and_verify_transunet_checkpoint( + vit_model, + pretrained_path=pretrained_path, + img_size=img_size, + n_skip=TRANSUNET_N_SKIP, + ) + self.encoder = _TransUNetEncoder(vit_model.transformer) + self.decoder = _TransUNetDecoder(vit_model.decoder, self.encoder) + self.segmentation_head = vit_model.segmentation_head + self.classification_head = None + self.transunet_config = config_vit + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.encoder(x) + decoder_output = run_smp_decoder(self.decoder, encoder_features) + logits = self.segmentation_head(decoder_output) + return logits + +class HalfVGG16DilatedExtractor(nn.Module): + def __init__(self, *, dilation: int = 1, num_scales: int = 3) -> None: + super().__init__() + self.num_scales = num_scales + deep_dropout = 0.1 + + self.conv1_1 = _ConvBlock(3, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv1_2 = _ConvBlock(32, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv2_1 = _ConvBlock(32, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv2_2 = _ConvBlock(64, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv3_1 = _ConvBlock(64, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_2 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_3 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.conv4_1 = _ConvBlock(128, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_2 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_3 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + @property + def out_channels(self) -> int: + return (32 + 64 + 128) if self.num_scales == 3 else (32 + 64 + 128 + 256) + + @property + def pyramid_channels(self) -> list[int]: + return [32, 64, 128] if self.num_scales == 3 else [32, 64, 128, 256] + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + x = self.conv1_1(x) + src1 = self.conv1_2(x) + x = self.pool(src1) + + x = self.conv2_1(x) + src2 = self.conv2_2(x) + x = self.pool(src2) + + x = self.conv3_1(x) + x = self.conv3_2(x) + src3 = self.conv3_3(x) + + if self.num_scales == 3: + return [src1, src2, src3] + + x = self.pool(src3) + x = self.conv4_1(x) + x = self.conv4_2(x) + src4 = self.conv4_3(x) + return [src1, src2, src3, src4] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + pyramid = self.forward_pyramid(x) + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + +class CustomVGGEncoderWrapper(nn.Module): + def __init__(self, *, num_scales: int, dilation: int) -> None: + super().__init__() + self.encoder = HalfVGG16DilatedExtractor(dilation=dilation, num_scales=num_scales) + self.projection = None + + @property + def out_channels(self) -> int: + return self.encoder.out_channels + + @property + def pyramid_channels(self) -> list[int]: + return self.encoder.pyramid_channels + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + return self.encoder.forward_pyramid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.encoder(x) + +class SMPEncoderWrapper(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + depth: int, + in_channels: int, + proj_dim: int, + ) -> None: + super().__init__() + self.encoder = smp.encoders.get_encoder( + encoder_name, + in_channels=in_channels, + depth=depth, + weights=encoder_weights, + ) + if REPLACE_BN_WITH_GN: + replace_bn_with_gn(self.encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + self._out_channels = proj_dim + else: + self.projection = None + self._out_channels = raw_channels + + @property + def out_channels(self) -> int: + return self._out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + features = self.encoder(x) + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + +class VGGDecoderBlock(nn.Module): + def __init__(self, *, in_channels: int, skip_channels: int, out_channels: int) -> None: + super().__init__() + self.block = nn.Sequential( + _ConvBlock(in_channels + skip_channels, out_channels, num_groups=GN_NUM_GROUPS), + _ConvBlock(out_channels, out_channels, num_groups=GN_NUM_GROUPS), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = F.interpolate(x, size=skip.shape[-2:], mode="bilinear", align_corners=False) + return self.block(torch.cat([x, skip], dim=1)) + +class VGGSegmentationHead(nn.Module): + def __init__(self, *, pyramid_channels: list[int], dropout_p: float) -> None: + super().__init__() + if len(pyramid_channels) not in {3, 4}: + raise ValueError(f"Expected 3 or 4 VGG pyramid channels, got {pyramid_channels}") + + self.dropout = nn.Dropout2d(p=dropout_p) + self.num_scales = len(pyramid_channels) + + deepest = pyramid_channels[-1] + self.bridge = _ConvBlock(deepest, deepest, num_groups=GN_NUM_GROUPS) + if self.num_scales == 4: + self.up3 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[2], out_channels=128) + self.up2 = VGGDecoderBlock(in_channels=128, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + else: + self.up2 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + self.out_conv = nn.Conv2d(32, 1, kernel_size=1) + + def forward(self, pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + features = list(pyramid) + x = self.bridge(self.dropout(features[-1])) + if self.num_scales == 4: + x = self.up3(x, features[2]) + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + else: + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + return self.out_conv(x) + +class PixelDRLMG_SMP(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = SMPEncoderWrapper( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + depth=encoder_depth, + in_channels=3, + proj_dim=proj_dim, + ) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + features = self.extractor(x) + return self.sam(features) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class PixelDRLMG_VGG(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.extractor(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class SupervisedSMPModel(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int = 5, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + self.dropout = nn.Dropout2d(p=dropout_p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.smp_encoder(x) + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + decoder_output = self.dropout(decoder_output) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + +class SupervisedVGGModel(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + +class RefinementPolicyHead(nn.Module): + A3C_NUM_ACTIONS = 1 + + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 256, dilation=1, num_groups=GN_NUM_GROUPS), + _ConvBlock(256, 128, dilation=2, num_groups=GN_NUM_GROUPS), + _ConvBlock(128, 64, dilation=3, num_groups=GN_NUM_GROUPS), + ) + self.classifier = nn.Conv2d(64, self.A3C_NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + if self.classifier.bias is not None: + nn.init.zeros_(self.classifier.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class PixelDRLMG_WithDecoder(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.smp_encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + ch = proj_dim + else: + self.projection = None + ch = raw_channels + + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.multi_scale_refine = _MultiScaleRefineBranch( + encoder_channels=list(self.smp_encoder.out_channels), + out_channels=ch, + per_scale_channels=32, + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + self.multi_scale_refine.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "decoder", "segmentation_head"): + module = getattr(self.smp_model, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_from_features( + self, + features: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + + def _encoder_concat(self, x: torch.Tensor) -> torch.Tensor: + return self._concat_from_features(self.smp_encoder(x), output_size=x.shape[-2:]) + + def forward_decoder_from_features( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + ) -> torch.Tensor: + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.smp_model(x) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, Any]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + encoder_features = self.smp_encoder(x) + decoder_logits = self.forward_decoder_from_features(encoder_features) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_features=[feat[sample_index:sample_index + 1] for feat in encoder_features]: self.forward_decoder_from_features(sample_features), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.forward_decoder_from_features(encoder_features), + ) + return { + "base_features": self._concat_from_features(encoder_features, output_size=x.shape[-2:]), + "encoder_features": list(encoder_features), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + if encoder_features is not None: + ms_feat = self.multi_scale_refine(encoder_features, output_size=fused.shape[-2:]) + fused = fused + ms_feat + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + concat_feat = self._encoder_concat(x) + return self.sam(concat_feat) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +class PixelDRLMG_VGGWithDecoder(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + ch = self.encoder.out_channels + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "segmentation_head"): + module = getattr(self, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_pyramid( + self, + pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, torch.Tensor]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + pyramid = self.encoder.forward_pyramid(x) + decoder_logits = self.segmentation_head(pyramid) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_pyramid=[feat[sample_index:sample_index + 1] for feat in pyramid]: self.segmentation_head(sample_pyramid), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.segmentation_head(pyramid), + ) + return { + "base_features": self._concat_pyramid(pyramid, output_size=x.shape[-2:]), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + del encoder_features + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.encoder(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +def run_smp_decoder(decoder: nn.Module, encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + signature = inspect.signature(decoder.forward) + parameters = list(signature.parameters.values()) + if any(param.kind == inspect.Parameter.VAR_POSITIONAL for param in parameters): + return decoder(*encoder_features) + if len(parameters) == 1: + return decoder(encoder_features) + return decoder(*encoder_features) + +def checkpoint_run_config_payload(payload: dict[str, Any]) -> dict[str, Any]: + return payload.get("run_config") or payload.get("config") or {} + +def _raw_decoder_rl_model( + model: nn.Module, +) -> PixelDRLMG_WithDecoder | PixelDRLMG_VGGWithDecoder | None: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return raw + return None + +def _uses_refinement_runtime(model: nn.Module, *, strategy: int | None = None) -> bool: + raw = _raw_decoder_rl_model(model) + if raw is None: + return False + if strategy is not None and strategy != 3: + return False + return bool(getattr(raw, "use_refinement", False)) + +def _policy_action_count_from_state_dict(state_dict: dict[str, Any]) -> int | None: + for key in ( + "policy_head.classifier.weight", + "policy_head.classifier.bias", + "policy_head.net.4.weight", + "policy_head.net.4.bias", + ): + tensor = state_dict.get(key) + if torch.is_tensor(tensor): + return int(tensor.shape[0]) + return None + +def _model_policy_action_count(model: nn.Module) -> int | None: + raw = _unwrap_compiled(model) + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d): + return int(classifier.out_channels) + return None + +def _set_model_policy_action_count(model: nn.Module, action_count: int) -> bool: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) == 1: + return False + policy_head = getattr(raw, "policy_head", None) + classifier = getattr(policy_head, "classifier", None) + if not isinstance(classifier, nn.Conv2d): + return False + if int(classifier.out_channels) == int(action_count): + return False + + new_classifier = nn.Conv2d( + classifier.in_channels, + int(action_count), + kernel_size=classifier.kernel_size, + stride=classifier.stride, + padding=classifier.padding, + dilation=classifier.dilation, + groups=classifier.groups, + bias=classifier.bias is not None, + padding_mode=classifier.padding_mode, + ).to(device=classifier.weight.device, dtype=classifier.weight.dtype) + nn.init.xavier_uniform_(new_classifier.weight) + if new_classifier.bias is not None: + nn.init.zeros_(new_classifier.bias) + policy_head.classifier = new_classifier + return True + +def _configure_policy_head_compatibility( + model: nn.Module, + state_dict: dict[str, Any], + *, + source: str, +) -> int | None: + action_count = _policy_action_count_from_state_dict(state_dict) + if action_count is None: + return None + if _set_model_policy_action_count(model, action_count): + print(f"[Policy Compatibility] source={source} num_actions={action_count}") + return action_count + +def _strategy3_checkpoint_layout_info( + state_dict: dict[str, Any], + run_config: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = run_config or {} + strategy = run_config.get("strategy") + has_legacy_policy_head = any(key.startswith("policy_head.net.") for key in state_dict) + has_new_policy_body = any(key.startswith("policy_head.body.") for key in state_dict) + has_new_policy_classifier = any(key.startswith("policy_head.classifier.") for key in state_dict) + has_refinement_adapter = any(key.startswith("refinement_adapter.") for key in state_dict) + is_strategy3_decoder_checkpoint = bool( + strategy == 3 + or has_legacy_policy_head + or has_new_policy_body + or has_new_policy_classifier + or has_refinement_adapter + ) + use_refinement = bool( + has_refinement_adapter or ((has_new_policy_body or has_new_policy_classifier) and not has_legacy_policy_head) + ) + return { + "strategy": strategy, + "is_strategy3_decoder_checkpoint": is_strategy3_decoder_checkpoint, + "has_legacy_policy_head": has_legacy_policy_head, + "has_new_policy_head": bool(has_new_policy_body or has_new_policy_classifier), + "has_refinement_adapter": has_refinement_adapter, + "requires_policy_remap": has_legacy_policy_head, + "policy_action_count": _policy_action_count_from_state_dict(state_dict), + "use_refinement": use_refinement, + "compatibility_mode": "refinement" if use_refinement else "legacy", + } + +def inspect_strategy3_checkpoint_compatibility(path: str | Path) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + layout = _strategy3_checkpoint_layout_info(payload.get("model_state_dict", {}), checkpoint_run_config_payload(payload)) + layout["path"] = str(checkpoint_path) + return layout + +def _configure_strategy3_model_compatibility( + model: nn.Module, + layout: dict[str, Any], + *, + source: str, +) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not layout.get("is_strategy3_decoder_checkpoint"): + return + raw.set_refinement_mode(bool(layout["use_refinement"])) + if not bool(layout["use_refinement"]): + raw.refinement_adapter.eval() + if hasattr(raw, "multi_scale_refine"): + raw.multi_scale_refine.eval() + print( + "[Strategy3 Compatibility] " + f"source={source} mode={layout['compatibility_mode']} " + f"legacy_policy_head={layout['has_legacy_policy_head']} " + f"refinement_adapter={layout['has_refinement_adapter']}" + ) + +def _ensure_strategy3_refinement_adapter_compatible( + model: nn.Module, + state_dict: dict[str, Any], + *, + checkpoint_path: str | Path, +) -> None: + raw_model = _unwrap_compiled(model) + if not isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return + target_state = raw_model.state_dict() + mismatched: list[str] = [] + for key, value in state_dict.items(): + if not key.startswith(("refinement_adapter.", "policy_head.classifier.")): + continue + target_value = target_state.get(key) + if target_value is None: + continue + if tuple(target_value.shape) != tuple(value.shape): + mismatched.append( + f"{key}: checkpoint={tuple(value.shape)} model={tuple(target_value.shape)}" + ) + if mismatched: + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected after the continuous Strategy 3 redesign. " + f"Checkpoint={Path(checkpoint_path).resolve()} mismatches={mismatched[:4]}. " + "Resume/eval from legacy S3 checkpoints is not supported; retrain Strategy 3 from the Strategy 2 bootstrap checkpoint." + ) + +def _configure_model_from_checkpoint_path( + model: nn.Module, + checkpoint_path: str | Path, +) -> dict[str, Any]: + checkpoint_path = Path(checkpoint_path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state_dict = payload.get("model_state_dict", {}) + _configure_policy_head_compatibility(model, state_dict, source=str(checkpoint_path)) + layout = _strategy3_checkpoint_layout_info(state_dict, checkpoint_run_config_payload(payload)) + if layout.get("is_strategy3_decoder_checkpoint") and layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected: the checkpoint uses a discrete multi-action " + f"policy head with out_channels={layout.get('policy_action_count')}. " + "The current Strategy 3 implementation requires a continuous 1-channel delta head." + ) + _configure_strategy3_model_compatibility(model, layout, source=str(checkpoint_path)) + layout["path"] = str(checkpoint_path) + return layout + +def _remap_legacy_policy_head_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]: + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + if key.startswith("policy_head.net."): + suffix = key[len("policy_head.net."):] + layer_idx, dot, rest = suffix.partition(".") + if dot: + if layer_idx in {"0", "1", "2", "3"}: + remapped[f"policy_head.body.{layer_idx}.{rest}"] = value + continue + if layer_idx == "4": + remapped[f"policy_head.classifier.{rest}"] = value + continue + remapped[key] = value + return remapped + +def _load_strategy2_checkpoint_payload( + path: str | Path, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False) + saved_config = checkpoint_run_config_payload(ckpt) + if saved_config: + saved_model_config = RuntimeModelConfig.from_payload(saved_config).validate() + if saved_model_config.backbone_family != model_config.backbone_family: + raise ValueError( + f"Strategy 2 checkpoint backbone family mismatch: requested {model_config.backbone_family!r}, " + f"checkpoint has {saved_model_config.backbone_family!r} at {checkpoint_path}." + ) + return ckpt + +def _preview_state_keys(keys: list[str], *, limit: int = 8) -> str: + if not keys: + return "none" + preview = ", ".join(keys[:limit]) + if len(keys) > limit: + preview += ", ..." + return preview + +def _strict_load_strategy2_submodule( + target_module: nn.Module, + *, + checkpoint_state_dict: dict[str, Any], + checkpoint_prefix: str, + checkpoint_path: str | Path, + target_name: str, +) -> None: + extracted = { + key[len(checkpoint_prefix):]: value + for key, value in checkpoint_state_dict.items() + if key.startswith(checkpoint_prefix) + } + if not extracted: + raise RuntimeError( + f"Strategy 2 bootstrap failed for {target_name}: no checkpoint keys found with prefix " + f"{checkpoint_prefix!r} in {Path(checkpoint_path).resolve()}." + ) + + target_state = target_module.state_dict() + missing = sorted(set(target_state.keys()) - set(extracted.keys())) + unexpected = sorted(set(extracted.keys()) - set(target_state.keys())) + if missing or unexpected: + section(f"Strategy 2 Bootstrap Mismatch | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + print(f"Missing keys ({len(missing)}) : {_preview_state_keys(missing)}") + print(f"Unexpected keys ({len(unexpected)}): {_preview_state_keys(unexpected)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap failed for {target_name} from {Path(checkpoint_path).resolve()}. " + f"Missing keys={len(missing)}, unexpected keys={len(unexpected)}." + ) + + try: + load_result = target_module.load_state_dict(extracted, strict=True) + except Exception as exc: + section(f"Strategy 2 Bootstrap Strict Load Failure | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap load failed for {target_name} from " + f"{Path(checkpoint_path).resolve()}: {exc}" + ) from exc + + post_missing = list(getattr(load_result, "missing_keys", [])) + post_unexpected = list(getattr(load_result, "unexpected_keys", [])) + if post_missing or post_unexpected: + raise RuntimeError( + f"Strict Strategy 2 bootstrap reported residual mismatches for {target_name}: " + f"missing={post_missing}, unexpected={post_unexpected}" + ) + + section(f"Strategy 2 Bootstrap OK | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Loaded tensors : {len(extracted)}") + print("Strict load : passed") + +def _use_channels_last_for_run(model_config: RuntimeModelConfig | None = None) -> bool: + model_config = (model_config or current_model_config()).validate() + if not USE_CHANNELS_LAST: + return False + if DEVICE.type != "cuda": + return False + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + if ( + model_config.backbone_family == "smp" + and "efficientnet" in model_config.smp_encoder_name.lower() + and USE_AMP + and amp_dtype in {torch.float16, torch.bfloat16} + ): + print("[MemoryFormat] Disabling channels_last for EfficientNet + AMP stability.") + return False + return True + +def build_model( + strategy: int, + dropout_p: float, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[nn.Module, str, bool]: + strategy = _require_supported_strategy(strategy) + model_config = model_config.validate() + if model_config.backbone_family == "custom_vgg": + if not bool(ENABLE_CUSTOM_VGG_BACKBONE): + raise RuntimeError( + "The legacy custom VGG backbone is feature-flagged off. " + "Set ENABLE_CUSTOM_VGG_BACKBONE=True to opt into the unused VGG code path." + ) + if strategy == 2: + model = SupervisedVGGModel( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_VGGWithDecoder( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.encoder, + checkpoint_state_dict=s2_state, + checkpoint_prefix="encoder.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.encoder", + ) + _strict_load_strategy2_submodule( + model.segmentation_head, + checkpoint_state_dict=s2_state, + checkpoint_prefix="segmentation_head.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.segmentation_head", + ) + if freeze_bootstrapped_segmentation: + model.encoder.requires_grad_(False) + model.segmentation_head.requires_grad_(False) + if hasattr(model.encoder, "projection") and model.encoder.projection is not None: + model.encoder.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + else: + if strategy == 2: + model = SupervisedSMPModel( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_WithDecoder( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + proj_dim=model_config.smp_encoder_proj_dim, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.smp_model, + checkpoint_state_dict=s2_state, + checkpoint_prefix="smp_model.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.smp_model", + ) + if freeze_bootstrapped_segmentation: + model.smp_model.encoder.requires_grad_(False) + model.smp_model.decoder.requires_grad_(False) + if hasattr(model.smp_model, "segmentation_head"): + model.smp_model.segmentation_head.requires_grad_(False) + if hasattr(model, "projection") and model.projection is not None: + model.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + + use_channels_last_now = _use_channels_last_for_run(model_config) + if strategy == 3: + classifier = getattr(getattr(_unwrap_compiled(model), "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) != 1: + raise RuntimeError("Strategy 3 expects a continuous 1-channel policy head.") + _strategy3_bump_mc_cache_fingerprint( + model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + model = model.to(DEVICE) + if use_channels_last_now: + model = model.to(memory_format=torch.channels_last) + + compiled = False + if USE_TORCH_COMPILE and hasattr(torch, "compile"): + try: + model = torch.compile(model, mode="max-autotune") + compiled = True + except Exception as exc: + print(f"[Compile] torch.compile skipped: {exc}") + return model, strategy_name(strategy, model_config), compiled + +def _unwrap_compiled(model: nn.Module) -> nn.Module: + return getattr(model, "_orig_mod", model) + +def count_parameters(module: nn.Module | None, *, only_trainable: bool = False) -> int: + if module is None: + return 0 + if only_trainable: + return sum(p.numel() for p in module.parameters() if p.requires_grad) + return sum(p.numel() for p in module.parameters()) + +def print_model_parameter_summary( + *, + model: nn.Module, + description: str, + strategy: int, + model_config: RuntimeModelConfig, + dropout_p: float, + amp_dtype: torch.dtype, + compiled: bool, +) -> None: + raw = _unwrap_compiled(model) + total_params = count_parameters(raw) + trainable_params = count_parameters(raw, only_trainable=True) + frozen_params = total_params - trainable_params + bn_count = sum(1 for m in raw.modules() if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d))) + gn_count = sum(1 for m in raw.modules() if isinstance(m, nn.GroupNorm)) + + section(f"Model Parameter Summary | {description}") + print(f"Strategy : {strategy}") + print(f"Model : {description}") + print(f"Dropout p : {dropout_p:.4f}") + print(f"Total params : {total_params:,}") + print(f"Trainable params : {trainable_params:,}") + print(f"Frozen params : {frozen_params:,}") + print(f"BN layers : {bn_count}") + print(f"GN layers : {gn_count}") + print(f"channels_last : {_use_channels_last_for_run(model_config)}") + print(f"AMP dtype : {amp_dtype}") + print(f"torch.compile : {compiled}") + print(f"Backbone family : {model_config.backbone_family}") + if strategy == 3: + print(f"S3 variant : {_strategy3_variant()}") + freeze_status = _strategy3_bootstrap_freeze_status(model) + print(f"S3 bootstrap loaded : {freeze_status['bootstrap_loaded']}") + print(f"S3 freeze requested : {freeze_status['freeze_requested']}") + print(f"S3 frozen now : {freeze_status['freeze_active']}") + print(f"S3 encoder state : {freeze_status['encoder_state']}") + if freeze_status["decoder_state"] != "n/a": + print(f"S3 decoder state : {freeze_status['decoder_state']}") + if freeze_status["segmentation_head_state"] != "n/a": + print(f"S3 seg head state : {freeze_status['segmentation_head_state']}") + + block_counts: dict[str, int] = {} + if strategy == 2: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + block_counts["dropout"] = count_parameters(getattr(raw, "dropout", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + elif strategy == 3: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + block_counts["sam"] = count_parameters(getattr(raw, "sam", None)) + block_counts["policy_head"] = count_parameters(getattr(raw, "policy_head", None)) + block_counts["value_head"] = count_parameters(getattr(raw, "value_head", None)) + + for name, value in block_counts.items(): + print(f"{name:22s}: {value:,}") + +"""============================================================================= +METRICS + CHECKPOINTS +============================================================================= +""" + +_EPS = 1e-4 + +def _as_bool(mask: np.ndarray) -> np.ndarray: + return (mask[0] if mask.ndim == 3 else mask).astype(bool) + +def _tp_fp_fn(pred: np.ndarray, target: np.ndarray): + p, t = _as_bool(pred), _as_bool(target) + tp = float((p & t).sum()) + fp = float((p & ~t).sum()) + fn = float((~p & t).sum()) + return tp, fp, fn, float(t.sum()), float(p.sum()) + +def dice_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (2 * tp + _EPS) / (t + p + _EPS) + +def ppv_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, fp, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fp + _EPS) + +def sensitivity_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, fn, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fn + _EPS) + +def iou_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (tp + _EPS) / (t + p - tp + _EPS) + +def _boundary_1px(mask: np.ndarray) -> np.ndarray: + m = _as_bool(mask) + if not m.any(): + return m + return m ^ ndimage.binary_erosion(m, iterations=1, border_value=0) + +def boundary_iou_contour_score(pred: np.ndarray, target: np.ndarray) -> float: + pb, tb = _boundary_1px(pred), _boundary_1px(target) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _biou_d(img_hw: tuple[int, int]) -> int: + """Resolve the Boundary IoU dilation width d for a given image size.""" + d = int(BOUNDARY_IOU_D) + if d > 0: + return d + height, width = img_hw + return max(1, int(round(0.02 * math.hypot(height, width)))) + +def _boundary_band(mask: np.ndarray, d: int) -> np.ndarray: + """Return the d-pixel inner boundary band used by paper-standard BIoU.""" + m = _as_bool(mask) + if not m.any(): + return m + eroded = ndimage.binary_erosion(m, iterations=max(int(d), 1), border_value=0) + return m & ~eroded + +def boundary_iou_score(pred: np.ndarray, target: np.ndarray) -> float: + """Boundary IoU from Cheng et al. CVPR 2021 using a d-pixel inner band.""" + height, width = _as_bool(target).shape + d = _biou_d((height, width)) + pb = _boundary_band(pred, d) + tb = _boundary_band(target, d) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _surf_dist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _as_bool(a), _as_bool(b) + if not a.any() and not b.any(): + return np.array([0.0], dtype=np.float32) + if not a.any() or not b.any(): + return np.array([np.inf], dtype=np.float32) + ba, bb = _boundary_1px(a), _boundary_1px(b) + return ndimage.distance_transform_edt(~bb)[ba].astype(np.float32) + +def hd95_score(pred: np.ndarray, target: np.ndarray) -> float: + distances = np.concatenate([_surf_dist(pred, target), _surf_dist(target, pred)]) + if np.isinf(distances).any(): + height, width = _as_bool(target).shape + return float(math.hypot(height, width)) + return float(np.percentile(distances, 95)) + +def compute_all_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + return { + "dice": dice_score(pred, target), + "ppv": ppv_score(pred, target), + "sen": sensitivity_score(pred, target), + "iou": iou_score(pred, target), + "biou": boundary_iou_score(pred, target), + "biou_contour": boundary_iou_contour_score(pred, target), + "hd95": hd95_score(pred, target), + } + +def checkpoint_manifest_path(path: Path) -> Path: + path = Path(path) + return path.with_name(f"{path.name}.meta.json") + +def checkpoint_history_path(run_dir: Path, run_type: str) -> Path: + if run_type == "overfit": + return Path(run_dir) / "overfit_history.json" + return Path(run_dir) / "history.json" + +def checkpoint_state_presence(payload: dict[str, Any]) -> dict[str, bool]: + tracked = [ + "model_state_dict", + "optimizer_state_dict", + "scheduler_state_dict", + "scaler_state_dict", + "log_alpha", + "alpha_optimizer_state_dict", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + "history", + "resume_source", + ] + return {name: name in payload for name in tracked} + +def write_checkpoint_manifest( + path: Path, + payload: dict[str, Any], + *, + extra: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = checkpoint_run_config_payload(payload) + manifest = { + "checkpoint_path": str(Path(path).resolve()), + "run_type": payload.get("run_type", "unknown"), + "epoch": int(payload.get("epoch", 0)), + "strategy": run_config.get("strategy"), + "dataset_percent": run_config.get("dataset_percent"), + "backbone_family": run_config.get("backbone_family", "smp"), + "saved_keys": sorted(payload.keys()), + "state_presence": checkpoint_state_presence(payload), + } + if "resume_source" in payload: + manifest["resume_source"] = payload["resume_source"] + if extra: + manifest.update(extra) + save_json(checkpoint_manifest_path(path), manifest) + return manifest + +def checkpoint_required_keys( + *, + optimizer: torch.optim.Optimizer | None, + scheduler: CosineAnnealingLR | None, + scaler: Any | None, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + require_run_metadata: bool, +) -> list[str]: + keys = ["epoch", "model_state_dict"] + if require_run_metadata: + keys.extend( + [ + "run_type", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + ] + ) + if optimizer is not None: + keys.append("optimizer_state_dict") + if scheduler is not None: + keys.append("scheduler_state_dict") + if scaler is not None: + keys.append("scaler_state_dict") + if log_alpha is not None: + keys.append("log_alpha") + if alpha_optimizer is not None: + keys.append("alpha_optimizer_state_dict") + return keys + +def validate_checkpoint_payload( + path: Path, + payload: dict[str, Any], + *, + required_keys: list[str], + expected_run_type: str | None = None, +) -> None: + missing = [name for name in required_keys if name not in payload] + if missing: + raise KeyError(f"Checkpoint {path} is missing required keys: {missing}") + if expected_run_type is not None and payload.get("run_type") != expected_run_type: + raise ValueError( + f"Checkpoint {path} run_type mismatch: expected {expected_run_type!r}, " + f"got {payload.get('run_type')!r}." + ) + +def save_checkpoint( + path: Path, + *, + run_type: str, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: ReduceLROnPlateau | None, + scaler: Any | None, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": _unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + validate_checkpoint_payload( + path, + payload, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + torch.save(payload, path) + write_checkpoint_manifest(path, payload) + +def load_checkpoint( + path: Path, + *, + model: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: ReduceLROnPlateau | None = None, + scaler: Any | None = None, + device: torch.device, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + expected_run_type: str | None = None, + require_run_metadata: bool = False, +) -> dict[str, Any]: + ckpt = torch.load(path, map_location=device, weights_only=False) + validate_checkpoint_payload( + path, + ckpt, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=require_run_metadata, + ), + expected_run_type=expected_run_type, + ) + + raw_model = _unwrap_compiled(model) + state_dict = ckpt["model_state_dict"] + load_strict = True + compat_layout: dict[str, Any] | None = None + _configure_policy_head_compatibility(model, state_dict, source=str(path)) + if any(key.startswith("policy_head.net.") for key in state_dict): + state_dict = _remap_legacy_policy_head_state_dict(state_dict) + if isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + compat_layout = _strategy3_checkpoint_layout_info(ckpt["model_state_dict"], checkpoint_run_config_payload(ckpt)) + if compat_layout["is_strategy3_decoder_checkpoint"]: + if compat_layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected during restore. " + f"Checkpoint={path} policy_head_out_channels={compat_layout.get('policy_action_count')}. " + "Resume/eval from pre-redesign Strategy 3 checkpoints is not supported." + ) + _configure_strategy3_model_compatibility(model, compat_layout, source=str(path)) + _ensure_strategy3_refinement_adapter_compatible(model, state_dict, checkpoint_path=path) + load_strict = bool(compat_layout["use_refinement"]) + + incompatible = raw_model.load_state_dict(state_dict, strict=load_strict) + if hasattr(raw_model, "clear_strategy3_mc_cache"): + _strategy3_bump_mc_cache_fingerprint(raw_model, eval_checkpoint_path=path) + if not load_strict: + missing_keys = [key for key in incompatible.missing_keys if not key.startswith(("refinement_adapter.", "multi_scale_refine."))] + unexpected_keys = list(incompatible.unexpected_keys) + if missing_keys or unexpected_keys: + print( + "[Checkpoint Restore] Non-strict legacy Strategy 3 load " + f"missing={missing_keys} unexpected={unexpected_keys}" + ) + + if optimizer is not None and "optimizer_state_dict" in ckpt: + try: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + except ValueError: + if compat_layout is None or compat_layout.get("compatibility_mode") != "legacy": + raise + print( + f"[Checkpoint Restore] Skipping optimizer state for legacy Strategy 3 checkpoint at {path} " + "because the parameter layout differs from the refinement-capable model." + ) + if scheduler is not None and "scheduler_state_dict" in ckpt: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler is not None and "scaler_state_dict" in ckpt: + scaler.load_state_dict(ckpt["scaler_state_dict"]) + if log_alpha is not None and "log_alpha" in ckpt: + with torch.no_grad(): + log_alpha.fill_(float(ckpt["log_alpha"])) + if alpha_optimizer is not None and "alpha_optimizer_state_dict" in ckpt: + alpha_optimizer.load_state_dict(ckpt["alpha_optimizer_state_dict"]) + restored = checkpoint_state_presence(ckpt) + restore_info = { + "restored_keys": restored, + "restored_at_epoch": int(ckpt.get("epoch", 0)), + "expected_run_type": expected_run_type, + } + write_checkpoint_manifest(path, ckpt, extra={"last_restore": restore_info}) + print( + f"[Checkpoint Restore] path={path} epoch={ckpt.get('epoch')} " + f"run_type={ckpt.get('run_type', 'unknown')} " + f"backbone={checkpoint_run_config_payload(ckpt).get('backbone_family', 'unknown')}" + ) + return ckpt + +"""============================================================================= +TRAINING + VALIDATION +============================================================================= +""" + +def _policy_log_probs_and_entropy(policy_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs = F.log_softmax(logits, dim=1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=1).mean() + return log_probs, entropy + +def _log_prob_for_actions(log_probs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor: + return log_probs.gather(1, actions.unsqueeze(1)) + +def sample_actions( + policy_logits: torch.Tensor, + stochastic: bool, + exploration_eps: float = 0.0, + keep_action_index: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs, entropy = _policy_log_probs_and_entropy(policy_logits) + if stochastic: + uniform = torch.rand_like(logits) + gumbel = -torch.log(-torch.log(uniform + 1e-8) + 1e-8) + actions = (logits + gumbel).argmax(dim=1) + if exploration_eps > 0: + keep_index = _keep_action_index(logits.shape[1]) if keep_action_index is None else int(keep_action_index) + keep_actions = torch.full_like(actions, keep_index) + random_mask = torch.rand(actions.shape, device=actions.device) < exploration_eps + actions = torch.where(random_mask, keep_actions, actions) + else: + actions = logits.argmax(dim=1) + log_prob = _log_prob_for_actions(log_probs, actions) + return actions, log_prob, entropy + +def apply_actions( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + num_actions = int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)) + if num_actions >= 3: + deltas = _refinement_deltas(action_count=num_actions, device=seg.device, dtype=seg.dtype) + delta = deltas[actions.long()].unsqueeze(1) + return (seg + delta).clamp_(0.0, 1.0) + action_map = actions.unsqueeze(1) + return seg * (action_map == 1).to(dtype=seg.dtype) + +def _soft_dice_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + denom = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) + return (2.0 * inter + 1e-6) / (denom + 1e-6) + +def _soft_iou_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - inter + return (inter + 1e-6) / (union + 1e-6) + +def _soft_recall_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + true_positive = (pred * target).sum(dim=(1, 2, 3)) + positives = target.sum(dim=(1, 2, 3)) + return (true_positive + 1e-6) / (positives + 1e-6) + +def _soft_boundary(mask: torch.Tensor) -> torch.Tensor: + return (mask - F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1)).abs() + +def _differentiable_boundary(mask: torch.Tensor, kernel_size: int = 3) -> torch.Tensor: + padding = kernel_size // 2 + mask_f = mask.float().clamp(0.0, 1.0) + eroded = 1.0 - F.max_pool2d(1.0 - mask_f, kernel_size, stride=1, padding=padding) + return (mask_f - eroded).clamp(0.0, 1.0) + +def _soft_iou_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + pred_f = pred.float().clamp(0.0, 1.0) + target_f = target.float().clamp(0.0, 1.0) + inter = (pred_f * target_f).sum(dim=(2, 3), keepdim=True) + union = (pred_f + target_f - pred_f * target_f).sum(dim=(2, 3), keepdim=True) + return inter / (union + 1e-6) + +def differentiable_biou_loss( + pred: torch.Tensor, + target: torch.Tensor, + kernel_size: int | None = None, + *, + reduction: str = "mean", +) -> torch.Tensor: + if kernel_size is None: + height, width = int(pred.shape[-2]), int(pred.shape[-1]) + d = _biou_d((height, width)) + kernel_size = 2 * d + 1 + kernel_size = max(int(kernel_size), 1) + if kernel_size % 2 == 0: + kernel_size += 1 + pred_boundary = _differentiable_boundary(pred, kernel_size=kernel_size) + target_boundary = _differentiable_boundary(target, kernel_size=kernel_size) + inter = (pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + union = (pred_boundary + target_boundary - pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + loss = 1.0 - (inter + 1e-6) / (union + 1e-6) + if reduction == "none": + return loss + if reduction == "mean": + return loss.mean() + raise ValueError(f"Unsupported differentiable_biou_loss reduction {reduction!r}.") + +def _strategy3_expected_next_mask( + policy_logits: torch.Tensor, + base_seg: torch.Tensor, + *, + action_count: int, +) -> tuple[torch.Tensor, torch.Tensor]: + logits_f = policy_logits.float() + base_seg_f = base_seg.float() + probs = F.softmax(logits_f, dim=1) + deltas = _refinement_deltas(action_count=action_count, device=logits_f.device, dtype=logits_f.dtype) + expected_delta = (probs * deltas.view(1, -1, 1, 1)).sum(dim=1, keepdim=True) + predicted_next = (base_seg_f + expected_delta).clamp(1e-4, 1.0 - 1e-4) + return predicted_next, deltas + +def _strategy3_action_targets( + seg_mask: torch.Tensor, + gt_mask: torch.Tensor, + deltas: torch.Tensor, +) -> torch.Tensor: + target_delta = gt_mask.float() - seg_mask.float() + return (target_delta - deltas.view(1, -1, 1, 1)).abs().argmin(dim=1) + +def compute_refinement_reward( + seg: torch.Tensor, + seg_next: torch.Tensor, + gt_mask: torch.Tensor, + *, + return_details: bool = False, +) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]: + seg_f = seg.float() + seg_next_f = seg_next.float() + gt_f = gt_mask.float().clamp(0.0, 1.0) + + r1_weight = float(_job_param("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT)) + biou_reward_weight = float(_job_param("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT)) + + target_dir = 2.0 * gt_f - 1.0 + progress = target_dir * (seg_next_f - seg_f) + room = gt_f * (1.0 - seg_f) + (1.0 - gt_f) * seg_f + r1 = r1_weight * progress * room + + biou_before = 1.0 - differentiable_biou_loss(seg_f, gt_f, reduction="none") + biou_next = 1.0 - differentiable_biou_loss(seg_next_f, gt_f, reduction="none") + biou_delta = biou_next - biou_before + r3 = biou_reward_weight * biou_delta.expand_as(seg_next_f) + + reward = (r1 + r3).clamp(-3.0, 3.0) + if not return_details: + return reward + return reward, { + "biou_before": biou_before, + "biou_next": biou_next, + "biou_delta": biou_delta, + } + +def compute_strategy1_aux_segmentation_loss( + policy_logits: torch.Tensor, + gt_mask: torch.Tensor, + *, + ce_weight: float, + dice_weight: float, + seg_mask: torch.Tensor | None = None, +) -> tuple[torch.Tensor, float, float]: + logits_f = policy_logits.float() + gt_mask_f = gt_mask.float() + num_actions = int(logits_f.shape[1]) + + if num_actions >= 3: + base_seg = seg_mask.float() if seg_mask is not None else torch.full_like(gt_mask_f, 0.5) + predicted_next, deltas = _strategy3_expected_next_mask( + policy_logits, + base_seg, + action_count=num_actions, + ) + action_targets = _strategy3_action_targets(base_seg, gt_mask_f, deltas) + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + boundary_dice_w = float(_job_param("strategy3_aux_boundary_dice_weight", 0.0)) + + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) + delta_large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + hard_margin = delta_large * 1.5 + with torch.no_grad(): + dist_to_thresh = (base_seg - threshold).abs() + hard_mask = (dist_to_thresh < hard_margin).squeeze(1) + + if ce_weight > 0: + action_ce_mix = float(_job_param("aux_action_ce_mix", 0.75)) + action_ce_mix = min(max(action_ce_mix, 0.0), 1.0) + + if hard_mask.any(): + logits_hw = policy_logits.float().permute(0, 2, 3, 1) + targets_hw = action_targets + action_ce = F.cross_entropy(logits_hw[hard_mask], targets_hw[hard_mask]) + else: + action_ce = F.cross_entropy(policy_logits.float(), action_targets) + + predicted_next_logits = torch.logit(predicted_next) + if hard_mask.any(): + gt_hard = gt_mask_f.squeeze(1)[hard_mask] + pred_hard = predicted_next_logits.squeeze(1)[hard_mask] + bce = F.binary_cross_entropy_with_logits(pred_hard, gt_hard) + else: + bce = F.binary_cross_entropy_with_logits(predicted_next_logits, gt_mask_f) + + ce_term = action_ce_mix * action_ce + (1.0 - action_ce_mix) * bce + aux_loss = aux_loss + ce_weight * ce_term + ce_loss_value = float(ce_term.detach().item()) + + if dice_weight > 0: + inter = (predicted_next * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (predicted_next.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + if boundary_dice_w > 0: + bd_loss = differentiable_biou_loss(predicted_next, gt_mask_f) + aux_loss = aux_loss + boundary_dice_w * bd_loss + + return aux_loss, ce_loss_value, dice_loss_value + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + + if ce_weight > 0: + if num_actions >= 3: + if seg_mask is None: + seg_mask = torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(seg_mask).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + ce_loss_value = float(ce_loss.detach().item()) + + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + inter = (probs_fg * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + return aux_loss, ce_loss_value, dice_loss_value + +def make_optimizer( + model: nn.Module, + strategy: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + rl_lr: float | None = None, +): + raw = _unwrap_compiled(model) + encoder_params = [] + decoder_params = [] + rl_params = [] + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + if ( + name.startswith("extractor.encoder.") + or name.startswith("encoder.") + or name.startswith("smp_model.encoder.") + or name.startswith("smp_encoder.") + ): + encoder_params.append(param) + elif "decoder" in name or "segmentation_head" in name: + decoder_params.append(param) + else: + rl_params.append(param) + + decoder_lr = float(_job_param("decoder_lr", head_lr)) + rl_group_lr = float(_job_param("rl_lr", rl_lr if rl_lr is not None else head_lr)) + + param_groups: list[dict[str, Any]] = [] + + if encoder_params: + param_groups.append({"params": encoder_params, "lr": encoder_lr}) + + if decoder_params: + param_groups.append({"params": decoder_params, "lr": decoder_lr}) + + if rl_params: + param_groups.append({"params": rl_params, "lr": rl_group_lr}) + + try: + optimizer = AdamW(param_groups, weight_decay=weight_decay, fused=DEVICE.type == "cuda") + except Exception: + optimizer = AdamW(param_groups, weight_decay=weight_decay) + + return optimizer + +def infer_segmentation_mask( + model: nn.Module, + image: torch.Tensor, + tmax: int, + *, + strategy: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> torch.Tensor: + model.eval() + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + return threshold_binary_mask(torch.sigmoid(logits)).float() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="infer_segmentation_mask") + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta) + return threshold_binary_mask(seg.float()).float() + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + policy_logits = model.forward_policy_only(x_t) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + return seg.float() + +def train_step( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor, + alpha_optimizer: torch.optim.Optimizer, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + initial_mask: torch.Tensor | None = None, + decoder_loss_extra: torch.Tensor | None = None, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + if initial_mask is not None: + seg = initial_mask.to(device=image.device, dtype=image.dtype) + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + alpha = log_alpha.exp() + + total_actor = 0.0 + total_critic = 0.0 + total_loss = 0.0 + total_reward = 0.0 + total_entropy = 0.0 + total_ce_loss = 0.0 + total_dice_loss = 0.0 + accum_tensor = None + alpha_loss_accum = torch.tensor(0.0, device=image.device, dtype=torch.float32) + aux_fused = False + + optimizer.zero_grad(set_to_none=True) + + for _ in range(tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=True) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + + neighborhood_next = _bootstrap_value_target(model, value_next) + target = reward + gamma * neighborhood_next + advantage = target - value_t + critic_loss = F.smooth_l1_loss(value_t, target) + actor_loss = -(log_prob * advantage.detach()).mean() + actor_loss = actor_loss - alpha.detach() * entropy + step_loss = (actor_loss + critic_loss_weight * critic_loss) / float(tmax) + alpha_loss_accum = alpha_loss_accum + (log_alpha * (entropy.detach() - target_entropy)) / float(tmax) + + if not aux_fused and initial_mask is None and (ce_weight > 0 or dice_weight > 0): + aux_loss, ce_loss_value, dice_loss_value = compute_strategy1_aux_segmentation_loss( + policy_logits, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + if ce_weight > 0: + total_ce_loss = ce_loss_value + if dice_weight > 0: + total_dice_loss = dice_loss_value + step_loss = step_loss + aux_loss + aux_fused = True + + total_actor += float(actor_loss.detach().item()) + total_critic += float(critic_loss.detach().item()) + total_loss += float(step_loss.detach().item()) + total_reward += float(reward.detach().mean().item()) + total_entropy += float(entropy.detach().item()) + + if stepwise_backward: + if scaler is not None: + scaler.scale(step_loss).backward() + else: + step_loss.backward() + else: + accum_tensor = step_loss if accum_tensor is None else accum_tensor + step_loss + + seg = seg_next.detach() + + if not stepwise_backward and accum_tensor is not None: + if scaler is not None: + scaler.scale(accum_tensor).backward() + else: + accum_tensor.backward() + + if not aux_fused and (ce_weight > 0 or dice_weight > 0): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + logits_f = policy_aux.float() + aux_loss = torch.zeros(1, device=image.device, dtype=torch.float32) + if ce_weight > 0: + num_actions = int(logits_f.shape[1]) + if num_actions >= 3: + init_seg = initial_mask if initial_mask is not None else torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(init_seg).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + total_ce_loss = float(ce_loss.detach().item()) + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + gt_f = gt_mask.float() + inter = (probs_fg * gt_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + total_dice_loss = float(dice_loss.detach().item()) + if decoder_loss_extra is not None: + aux_loss = aux_loss + decoder_loss_extra + if scaler is not None: + scaler.scale(aux_loss).backward() + else: + aux_loss.backward() + total_loss += float(aux_loss.detach().item()) + elif decoder_loss_extra is not None: + if scaler is not None: + scaler.scale(decoder_loss_extra).backward() + else: + decoder_loss_extra.backward() + total_loss += float(decoder_loss_extra.detach().item()) + + if scaler is not None: + scaler.unscale_(optimizer) + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + alpha_optimizer.zero_grad(set_to_none=True) + alpha_loss_accum.backward() + alpha_optimizer.step() + with torch.no_grad(): + log_alpha.clamp_(min=_alpha_log_floor(), max=5.0) + + return { + "loss": total_loss, + "actor_loss": total_actor / tmax, + "critic_loss": total_critic / tmax, + "mean_reward": total_reward / tmax, + "entropy": total_entropy / tmax, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": seg.detach(), + } + +def train_step_strategy3( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + current_epoch: int, + max_epochs: int, +) -> dict[str, Any]: + if not _uses_refinement_runtime(model, strategy=3): + raise RuntimeError( + "Legacy non-refinement Strategy 3 training is not supported after the continuous Strategy 3 redesign." + ) + + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + annealed_aux_ce_weight = _strategy3_annealed_aux_ce_weight(current_epoch) + del stepwise_backward, max_epochs, log_alpha, alpha_optimizer, target_entropy + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context(image, mc_mode="train") + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"] + base_features = refinement_context["base_features"] + encoder_features = refinement_context.get("encoder_features") + mc_variance = refinement_context["mc_variance"] + pred_entropy = refinement_context["pred_entropy"] + seg = decoder_prob.detach().to(device=image.device, dtype=image.dtype) + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + + decoder_loss = torch.zeros((), device=image.device, dtype=torch.float32) + decoder_ce_loss_value = 0.0 + decoder_dice_loss_value = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + decoder_ce = F.binary_cross_entropy_with_logits(dl_f, gt_f) + decoder_loss = decoder_loss + loss_weights["decoder_ce"] * decoder_ce + decoder_ce_loss_value = float(decoder_ce.detach().item()) + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + decoder_dice = 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + decoder_loss = decoder_loss + loss_weights["decoder_dice"] * decoder_dice + decoder_dice_loss_value = float(decoder_dice.detach().item()) + + optimizer.zero_grad(set_to_none=True) + + a3c_grad_clip = float(_job_param("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM)) + + refinement_base_features = base_features + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = mc_variance.detach() + detached_pred_entropy = pred_entropy.detach() + detached_encoder_features = [f.detach() for f in encoder_features] if encoder_features is not None else None + + step_action_hists: list[dict[str, float]] = [] + step_mask_deltas: list[float] = [] + step_reward_means: list[float] = [] + step_reward_pos_pcts: list[float] = [] + step_reward_zero_pcts: list[float] = [] + step_biou_deltas: list[float] = [] + advantage_maps: list[torch.Tensor] = [] + critic_targets: list[torch.Tensor] = [] + value_maps: list[torch.Tensor] = [] + + actor_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + critic_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + total_actor = 0.0 + total_critic = 0.0 + total_reward = 0.0 + total_ce_loss = decoder_ce_loss_value + total_dice_loss = decoder_dice_loss_value + effective_steps = max(int(tmax), 1) + final_refined_seg_for_aux: torch.Tensor | None = None + + for _ in range(effective_steps): + seg_before = seg.detach() + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg_before, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg_before.dtype) + seg_next = _strategy3_apply_delta(seg_before, delta) + reward_map, reward_details = compute_refinement_reward( + seg_before, + seg_next, + gt_mask.float(), + return_details=True, + ) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next.detach(), + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + value_next = model.value_from_state(state_next).detach() + + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + step_actor = -actor_advantage.mean() + step_critic = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + actor_loss_tensor = actor_loss_tensor + step_actor / float(effective_steps) + critic_loss_tensor = critic_loss_tensor + step_critic / float(effective_steps) + total_actor += float(step_actor.detach().item()) + total_critic += float(step_critic.detach().item()) + total_reward += float(reward_map.detach().mean().item()) + + step_action_hists.append(_strategy3_delta_distribution(delta)) + step_mask_deltas.append(float(delta.detach().abs().mean().item())) + step_reward_means.append(float(reward_map.detach().mean().item())) + step_reward_pos_pcts.append(float((reward_map.detach() > 0).float().mean().item() * 100.0)) + step_reward_zero_pcts.append(float((reward_map.detach().abs() < 1e-8).float().mean().item() * 100.0)) + step_biou_deltas.append(float(reward_details["biou_delta"].detach().mean().item())) + advantage_maps.append(actor_advantage.detach()) + critic_targets.append(critic_target.detach()) + value_maps.append(value_t.detach()) + final_refined_seg_for_aux = seg_next + seg = seg_next.detach() + + aux_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + aux_ce_loss_value = 0.0 + aux_dice_loss_value = 0.0 + if annealed_aux_ce_weight > 0.0 and final_refined_seg_for_aux is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refined_prob = final_refined_seg_for_aux.float().clamp(1e-6, 1.0 - 1e-6) + gt_f = gt_mask.float() + supervised_aux = torch.zeros((), device=image.device, dtype=torch.float32) + if ce_weight > 0: + refined_logits = torch.logit(refined_prob) + aux_ce = F.binary_cross_entropy_with_logits(refined_logits, gt_f) + supervised_aux = supervised_aux + float(ce_weight) * aux_ce + aux_ce_loss_value = float(aux_ce.detach().item()) + if dice_weight > 0: + inter = (refined_prob * gt_f).sum() + aux_dice = 1.0 - (2.0 * inter + 1e-6) / (refined_prob.sum() + gt_f.sum() + 1e-6) + supervised_aux = supervised_aux + float(dice_weight) * aux_dice + aux_dice_loss_value = float(aux_dice.detach().item()) + aux_loss_tensor = float(annealed_aux_ce_weight) * supervised_aux + total_ce_loss += aux_ce_loss_value + total_dice_loss += aux_dice_loss_value + + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + rl_loss = rl_loss_scale * (actor_loss_tensor + critic_loss_weight * critic_loss_tensor) + total_loss_tensor = decoder_loss + rl_loss + aux_loss_tensor + + if scaler is not None: + scaler.scale(total_loss_tensor).backward() + scaler.unscale_(optimizer) + else: + total_loss_tensor.backward() + + effective_grad_clip = a3c_grad_clip if a3c_grad_clip > 0 else grad_clip_norm + total_grad_norm = ( + float(torch.nn.utils.clip_grad_norm_(model.parameters(), effective_grad_clip).item()) + if effective_grad_clip > 0 + else 0.0 + ) + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + adv_means = [float(a.mean().item()) for a in advantage_maps] + adv_stds = [ + float(a.std(unbiased=False).item()) + for a in advantage_maps + if a.numel() > 1 + ] + value_pred_errors = [ + float((target - value).abs().mean().item()) + for target, value in zip(critic_targets, value_maps) + ] + mean_value_pred = float(torch.stack([value.mean() for value in value_maps]).mean().item()) if value_maps else 0.0 + avg_action_dist: dict[str, float] = {} + if step_action_hists: + all_keys = set() + for h in step_action_hists: + all_keys.update(h.keys()) + for k in sorted(all_keys): + avg_action_dist[k] = float(np.mean([h.get(k, 0.0) for h in step_action_hists])) + + return { + "loss": float(total_loss_tensor.detach().item()), + "actor_loss": total_actor / float(effective_steps), + "critic_loss": total_critic / float(effective_steps), + "mean_reward": total_reward / float(effective_steps), + "entropy": 0.0, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": effective_grad_clip, + "final_mask": threshold_binary_mask(seg.detach().float()).float(), + "effective_steps": effective_steps, + "action_distribution": avg_action_dist, + "mask_delta_mean": float(np.mean(step_mask_deltas)) if step_mask_deltas else 0.0, + "reward_per_step": step_reward_means, + "reward_pos_pct_per_step": step_reward_pos_pcts, + "reward_zeros_pct": float(np.mean(step_reward_zero_pcts)) if step_reward_zero_pcts else 0.0, + "biou_delta_mean": float(np.mean(step_biou_deltas)) if step_biou_deltas else 0.0, + "advantage_mean": float(np.mean(adv_means)), + "advantage_std": float(np.nanmean(adv_stds)) if adv_stds else 0.0, + "value_pred_error_mean": float(np.mean(value_pred_errors)), + "mean_value_pred": mean_value_pred, + "rl_loss_scale_used": float(rl_loss_scale), + "annealed_aux_ce_weight": float(annealed_aux_ce_weight), + "alpha": 0.0, + } + +def train_step_supervised( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + grad_clip_norm: float, + ce_weight: float = 0.5, + dice_weight: float = 0.5, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + optimizer.zero_grad(set_to_none=True) + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + logits_f = logits.float() + gt_f = gt_mask.float() + loss = torch.zeros(1, device=image.device, dtype=torch.float32) + ce_loss_val = 0.0 + dice_loss_val = 0.0 + if ce_weight > 0: + bce = F.binary_cross_entropy_with_logits(logits_f, gt_f, reduction="mean") + loss = loss + ce_weight * bce + ce_loss_val = float(bce.detach().item()) + if dice_weight > 0: + pred_f = torch.sigmoid(logits_f) + inter = (pred_f * gt_f).sum() + dice_l = 1.0 - (2.0 * inter + 1e-6) / (pred_f.sum() + gt_f.sum() + 1e-6) + loss = loss + dice_weight * dice_l + dice_loss_val = float(dice_l.detach().item()) + + if scaler is not None: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + final_mask = threshold_binary_mask(torch.sigmoid(logits_f)).float().detach() + return { + "loss": float(loss.detach().item()), + "actor_loss": 0.0, + "critic_loss": 0.0, + "mean_reward": 0.0, + "entropy": 0.0, + "ce_loss": ce_loss_val, + "dice_loss": dice_loss_val, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": final_mask, + } + +@torch.inference_mode() +def validate( + model: nn.Module, + loader: DataLoader, + *, + run_dir: Path | None, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + gamma: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + strategy = _require_supported_strategy(strategy) + model.eval() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="validate") if strategy != 2 else max(int(tmax), 1) + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + losses: list[float] = [] + dice_scores: list[float] = [] + iou_scores: list[float] = [] + biou_scores: list[float] = [] + entropies: list[float] = [] + rewards: list[float] = [] + actor_losses: list[float] = [] + critic_losses: list[float] = [] + ce_losses: list[float] = [] + dice_losses: list[float] = [] + decoder_dice_scores: list[float] = [] + decoder_iou_scores: list[float] = [] + decoder_biou_scores: list[float] = [] + val_binary_flips_total: list[float] = [] + val_binary_flips_correct: list[float] = [] + val_binary_flips_wrong: list[float] = [] + prefetcher = CUDAPrefetcher(loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(loader), desc="Validating", leave=False): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred_prob = torch.sigmoid(logits).float() + pred = threshold_binary_mask(pred_prob).float() + bce = F.binary_cross_entropy_with_logits(logits.float(), gt_mask.float(), reduction="mean") if ce_weight > 0 else torch.tensor(0.0, device=image.device) + inter_prob = (pred_prob * gt_mask.float()).sum() + dice_loss = 1.0 - (2.0 * inter_prob + 1e-6) / (pred_prob.sum() + gt_mask.sum() + 1e-6) if dice_weight > 0 else torch.tensor(0.0, device=image.device) + losses.append(float((ce_weight * bce + dice_weight * dice_loss).item())) + ce_losses.append(float(bce.item()) if ce_weight > 0 else 0.0) + dice_losses.append(float(dice_loss.item()) if dice_weight > 0 else 0.0) + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"].float() + seg = decoder_prob.float() + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += loss_weights["decoder_ce"] * batch_ce + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += loss_weights["decoder_dice"] * batch_dice + decoder_pred = threshold_binary_mask(decoder_prob.float()).float() + decoder_inter = (decoder_pred * gt_mask.float()).sum(dim=(1, 2, 3)) + decoder_pred_sum = decoder_pred.sum(dim=(1, 2, 3)) + decoder_gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + decoder_dice = (2.0 * decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum + _EPS) + decoder_iou = (decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum - decoder_inter + _EPS) + decoder_dice_scores.extend(decoder_dice.cpu().tolist()) + decoder_iou_scores.extend(decoder_iou.cpu().tolist()) + else: + fg_count = gt_mask.sum().clamp(min=1.0) + bg_count = (gt_mask == 0).sum().clamp(min=1.0) + pos_weight = bg_count / fg_count + _weight_map = torch.where(gt_mask == 1, pos_weight, torch.ones_like(gt_mask)) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + decoder_logits = model.forward_decoder(image) + seg = threshold_binary_mask(torch.sigmoid(decoder_logits)).float() + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if ce_weight > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += ce_weight * batch_ce + if dice_weight > 0: + dm_prob = torch.sigmoid(dl_f) + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += dice_weight * batch_dice + else: + seg = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + + batch_actor = 0.0 + batch_critic = 0.0 + batch_reward = 0.0 + batch_entropy = 0.0 + batch_loss = decoder_loss + decoder_ce_base = batch_ce + decoder_dice_base = batch_dice + aux_ce_total = 0.0 + aux_dice_total = 0.0 + effective_steps = effective_tmax + + if strategy == 3 and refinement_runtime: + refinement_base_features = refinement_context["base_features"] + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = refinement_context["mc_variance"].detach() + detached_pred_entropy = refinement_context["pred_entropy"].detach() + _enc_feats = refinement_context.get("encoder_features") + detached_enc_feats = [f.detach() for f in _enc_feats] if _enc_feats is not None else None + effective_steps = effective_tmax + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + value_next = model.value_from_state(state_next).detach() + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + actor_loss = -actor_advantage.mean() + critic_loss = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward_map.mean().item()) + batch_loss += float((actor_loss + critic_loss_weight * critic_loss).item()) + seg = seg_next + + batch_ce = decoder_ce_base + batch_dice = decoder_dice_base + batch_loss = decoder_loss + rl_loss_scale * ((batch_loss - decoder_loss) / float(max(effective_steps, 1))) + pred = threshold_binary_mask(seg.float()).float() + # Track binary mask flips between decoder and RL-refined prediction + flipped = (decoder_pred != pred) + gt_binary = (gt_mask.float() > 0.5) + correct_flips = flipped & ((pred > 0.5) == gt_binary) + wrong_flips = flipped & ((pred > 0.5) != gt_binary) + total_px = max(pred.numel(), 1) + val_binary_flips_total.append(float(flipped.float().sum().item() / total_px * 100.0)) + val_binary_flips_correct.append(float(correct_flips.float().sum().item() / total_px * 100.0)) + val_binary_flips_wrong.append(float(wrong_flips.float().sum().item() / total_px * 100.0)) + else: + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=False) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = ((seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2)) + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + target = reward + gamma * _bootstrap_value_target(model, value_next) + advantage = target - value_t + actor_loss = -(log_prob * advantage.detach()).mean() + critic_loss = F.smooth_l1_loss(value_t, target) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward.mean().item()) + batch_entropy += float(entropy.item()) + batch_loss += float(((actor_loss + critic_loss_weight * critic_loss) / float(max(effective_tmax, 1))).item()) + seg = seg_next + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + aux_loss, aux_ce, aux_dice = compute_strategy1_aux_segmentation_loss( + policy_aux, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + batch_loss += float(aux_loss.item()) + batch_ce = aux_ce + batch_dice = aux_dice + pred = infer_segmentation_mask( + model, + image, + effective_tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ).float() + + ce_losses.append(batch_ce) + dice_losses.append(batch_dice) + actor_losses.append(batch_actor / float(max(effective_steps, 1))) + critic_losses.append(batch_critic / float(max(effective_steps, 1))) + rewards.append(batch_reward / float(max(effective_steps, 1))) + entropies.append(batch_entropy / float(max(effective_steps, 1))) + losses.append(batch_loss) + + inter = (pred * gt_mask.float()).sum(dim=(1, 2, 3)) + pred_sum = pred.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + dice_scores.extend(dice.cpu().tolist()) + iou_scores.extend(iou.cpu().tolist()) + pred_np = pred.detach().cpu().numpy() + gt_np = gt_mask.float().detach().cpu().numpy() + for idx in range(pred_np.shape[0]): + biou_scores.append(boundary_iou_score(pred_np[idx], gt_np[idx])) + if strategy == 3 and decoder_dice_scores: + decoder_pred_np = decoder_pred.detach().cpu().numpy() + for idx in range(decoder_pred_np.shape[0]): + decoder_biou_scores.append(boundary_iou_score(decoder_pred_np[idx], gt_np[idx])) + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + val_decoder_dice = float(np.mean(decoder_dice_scores)) if decoder_dice_scores else None + val_decoder_iou = float(np.mean(decoder_iou_scores)) if decoder_iou_scores else None + val_decoder_biou = float(np.mean(decoder_biou_scores)) if decoder_biou_scores else None + val_dice = float(np.mean(dice_scores)) if dice_scores else 0.0 + val_iou = float(np.mean(iou_scores)) if iou_scores else 0.0 + val_biou = float(np.mean(biou_scores)) if biou_scores else 0.0 + val_refine_score = 0.5 * (val_iou + val_biou) + + return { + "val_loss": float(np.mean(losses)) if losses else 0.0, + "val_dice": val_dice, + "val_iou": val_iou, + "val_biou": val_biou, + "val_refine_score": val_refine_score, + "val_decoder_dice": val_decoder_dice, + "val_decoder_iou": val_decoder_iou, + "val_decoder_biou": val_decoder_biou, + "val_dice_gain": None if val_decoder_dice is None else val_dice - val_decoder_dice, + "val_iou_gain": None if val_decoder_iou is None else val_iou - val_decoder_iou, + "val_biou_gain": None if val_decoder_biou is None else val_biou - val_decoder_biou, + "val_actor_loss": float(np.mean(actor_losses)) if actor_losses else 0.0, + "val_critic_loss": float(np.mean(critic_losses)) if critic_losses else 0.0, + "val_ce_loss": float(np.mean(ce_losses)) if ce_losses else 0.0, + "val_dice_loss": float(np.mean(dice_losses)) if dice_losses else 0.0, + "val_reward": float(np.mean(rewards)) if rewards else 0.0, + "val_entropy": float(np.mean(entropies)) if entropies else 0.0, + "val_binary_flips_total_pct": float(np.mean(val_binary_flips_total)) if val_binary_flips_total else None, + "val_binary_flips_correct_pct": float(np.mean(val_binary_flips_correct)) if val_binary_flips_correct else None, + "val_binary_flips_wrong_pct": float(np.mean(val_binary_flips_wrong)) if val_binary_flips_wrong else None, + } + +def _save_training_plots(history: list[dict[str, Any]], plots_dir: Path) -> None: + if len(history) < 1: + return + ensure_dir(plots_dir) + epochs = [row["epoch"] for row in history] + plot_specs = [ + ("loss.png", "Loss", [("train_loss", "Train"), ("val_loss", "Val")]), + ("dice.png", "Dice", [("train_dice", "Train"), ("val_dice", "Val")]), + ("iou.png", "IoU", [("train_iou", "Train"), ("val_iou", "Val")]), + ("reward.png", "Reward", [("train_mean_reward", "Train"), ("val_reward", "Val")]), + ("ce_loss.png", "CE Loss", [("train_ce_loss", "Train")]), + ("dice_loss.png", "Dice Loss", [("train_dice_loss", "Train")]), + ("lr.png", "Learning Rate", [("lr", "Head LR"), ("encoder_lr", "Encoder LR")]), + ] + for file_name, title, curves in plot_specs: + fig, ax = plt.subplots(figsize=(8, 4)) + has_data = False + for key, label in curves: + values = [(row["epoch"], row[key]) for row in history if key in row] + if not values: + continue + xs, ys = zip(*values) + ax.plot(xs, ys, label=label, linewidth=1.2) + has_data = True + if has_data: + ax.set_title(title) + ax.set_xlabel("Epoch") + ax.set_ylabel(title) + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(plots_dir / file_name, dpi=110) + plt.close(fig) + +RESUME_IDENTITY_KEYS = ( + "strategy", + "dataset_percent", + "dataset_name", + "dataset_split_policy", + "split_type", + "train_subset_key", + "train_subset_variant", + "best_checkpoint_metric_name", + "backbone_family", + "smp_encoder_name", + "smp_encoder_weights", + "smp_encoder_depth", + "smp_encoder_proj_dim", + "smp_decoder_type", + "vgg_feature_scales", + "vgg_feature_dilation", + "head_lr", + "encoder_lr", + "weight_decay", + "dropout_p", + "tmax", + "entropy_lr", +) + +PORTABLE_RESUME_PATH_KEYS = frozenset( + { + "base_split_manifest_path", + "subset_manifest_path", + } +) + +def _path_parts(value: Any) -> tuple[str, ...]: + if value is None: + return () + return tuple(part for part in Path(str(value)).parts if part not in {"", os.sep}) + +def _portable_path_token(value: Any) -> str: + if value in (None, ""): + return "" + path = Path(str(value)).expanduser() + roots: list[tuple[str, Path]] = [] + experiment_root = globals().get("EXPERIMENT_ROOT") + if experiment_root is not None: + roots.append(("EXPERIMENT_ROOT", Path(experiment_root))) + roots.append(("PROJECT_DIR", PROJECT_DIR)) + for label, root in roots: + try: + rel = path.resolve().relative_to(root.resolve()) + return f"{label}:{rel.as_posix()}" + except (OSError, ValueError): + continue + parts = _path_parts(value) + for marker in ("runs", "repeated_holdout", "manifests", "checkpoints"): + if marker in parts: + return "/".join(parts[parts.index(marker):]) + return "/".join(parts) + +def _resume_path_values_match(current: Any, saved: Any) -> tuple[bool, str]: + current_text = str(current or "") + saved_text = str(saved or "") + if current_text == saved_text: + return True, "exact" + + current_token = _portable_path_token(current_text) + saved_token = _portable_path_token(saved_text) + if current_token and current_token == saved_token: + return True, "portable-token" + + current_parts = _path_parts(current_text) + saved_parts = _path_parts(saved_text) + max_suffix = min(len(current_parts), len(saved_parts)) + for length in range(max_suffix, 2, -1): + if current_parts[-length:] == saved_parts[-length:]: + return True, f"suffix:{length}" + return False, f"current_token={current_token!r}, checkpoint_token={saved_token!r}" + +def _resume_value_matches(current: Any, saved: Any) -> bool: + if isinstance(current, (int, float)) and isinstance(saved, (int, float)) and not isinstance(current, bool): + return math.isclose(float(current), float(saved), rel_tol=1e-9, abs_tol=1e-12) + return current == saved + +def validate_resume_checkpoint_identity( + current_run_config: dict[str, Any], + saved_run_config: dict[str, Any], + *, + checkpoint_path: Path, +) -> None: + mismatches: list[str] = [] + for key in RESUME_IDENTITY_KEYS: + if key not in current_run_config or key not in saved_run_config: + mismatches.append(f"{key}: current={current_run_config.get(key)!r}, checkpoint={saved_run_config.get(key)!r}") + continue + if key in PORTABLE_RESUME_PATH_KEYS: + matches, reason = _resume_path_values_match(current_run_config[key], saved_run_config[key]) + if matches: + if str(current_run_config[key]) != str(saved_run_config[key]): + print( + f"[Resume] Accepted portable path match for {key}: " + f"current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}, reason={reason}." + ) + continue + mismatches.append( + f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r} ({reason})" + ) + continue + if not _resume_value_matches(current_run_config[key], saved_run_config[key]): + mismatches.append(f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}") + + current_s2 = current_run_config.get("strategy2_checkpoint_path") + saved_s2 = saved_run_config.get("strategy2_checkpoint_path") + if current_s2 or saved_s2: + if str(current_s2 or "") != str(saved_s2 or ""): + # Warn but do not abort: when resuming, the model weights are fully restored + # from the resume checkpoint (not re-loaded from the strategy2 checkpoint), + # so a path change (e.g. file moved/renamed) does not affect correctness. + print( + f"[WARN] strategy2_checkpoint_path changed since checkpoint was saved " + f"(current={current_s2!r}, checkpoint={saved_s2!r}). " + f"Resuming anyway — model state comes from the resume checkpoint." + ) + + if mismatches: + raise ValueError( + f"Resume checkpoint identity mismatch for {checkpoint_path}:\n" + "\n".join(f" - {line}" for line in mismatches) + ) + +def load_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> list[dict[str, Any]]: + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + epoch_metrics = checkpoint_payload.get("epoch_metrics") + history: list[dict[str, Any]] = [] + checkpoint_history = checkpoint_payload.get("history") + if isinstance(checkpoint_history, list): + history = [dict(row) for row in checkpoint_history if isinstance(row, dict)] + history = [row for row in history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, list): + raise RuntimeError(f"Expected list history at {history_path}, found {type(payload).__name__}.") + file_history = [dict(row) for row in payload if isinstance(row, dict)] + file_history = [row for row in file_history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if len(file_history) >= len(history): + history = file_history + if not history and isinstance(epoch_metrics, dict): + history = [dict(epoch_metrics)] + elif history and isinstance(epoch_metrics, dict): + if int(history[-1].get("epoch", 0)) < checkpoint_epoch: + history.append(dict(epoch_metrics)) + return history + +def train_model( + *, + run_type: str, + model_config: RuntimeModelConfig, + run_config: dict[str, Any], + model: nn.Module, + description: str, + strategy: int, + run_dir: Path, + bundle: DataBundle, + max_epochs: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + tmax: int, + entropy_lr: float, + entropy_alpha_init: float, + entropy_target_ratio: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, + dropout_p: float, + resume_checkpoint_path: Path | None = None, + trial: optuna.trial.Trial | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=description, + strategy=strategy, + model_config=model_config, + dropout_p=dropout_p, + amp_dtype=amp_dtype, + compiled=hasattr(model, "_orig_mod"), + ) + + strategy3_freeze_status = _strategy3_bootstrap_freeze_status(model) if strategy == 3 else None + strategy3_frozen_decoder = strategy == 3 and _strategy3_decoder_is_frozen(model) + decoder_head_lr = float(_job_param("decoder_lr", 0.0 if strategy3_frozen_decoder else head_lr * 0.1)) + encoder_group_lr = 0.0 if strategy3_frozen_decoder else encoder_lr + rl_group_lr = float(_job_param("rl_lr", head_lr)) + optimizer = make_optimizer( + model, + strategy, + head_lr=decoder_head_lr, + encoder_lr=encoder_group_lr, + weight_decay=weight_decay, + rl_lr=rl_group_lr, + ) + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_epochs, + eta_min=1e-6, # floor + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + + del entropy_alpha_init, entropy_lr, entropy_target_ratio + target_entropy = 0.0 + log_alpha = None + alpha_optimizer = None + + save_artifacts = run_type == "final" + save_history_incrementally = bool(run_config.get("save_history_incrementally", SAVE_HISTORY_INCREMENTALLY)) + write_epoch_diagnostic = bool(run_config.get("write_epoch_diagnostic", WRITE_EPOCH_DIAGNOSTIC)) + ckpt_dir = ensure_dir(run_dir / "checkpoints") if save_artifacts else None + plots_dir = ensure_dir(run_dir / "plots") if save_artifacts else None + history_path = checkpoint_history_path(run_dir, run_type) + diagnostic_path = diagnostic_path_for_run(run_dir) if run_type == "final" and write_epoch_diagnostic else None + history: list[dict[str, Any]] = [] + selection_metric_name = _strategy_selection_metric_name(strategy) + early_stopping_monitor_name = _early_stopping_monitor_name(strategy) + early_stopping_mode = _early_stopping_mode(strategy, early_stopping_monitor_name) + early_stopping_min_delta = _early_stopping_min_delta() + early_stopping_start_epoch = _early_stopping_start_epoch() + early_stopping_patience = _early_stopping_patience() + epoch_probe_mode = str(run_config.get("epoch_probe_mode", "fixed")).strip().lower() + best_model_metric = -float("inf") + patience_counter = 0 + best_early_stopping_metric: float | None = None + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + diagnostic_payload: dict[str, Any] | None = None + train_probe_batches: list[dict[str, Any]] = [] + val_probe_batches: list[dict[str, Any]] = [] + run_label = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ) + if diagnostic_path is not None: + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=0, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=0, + split_tag="val", + ) + else: + train_probe_batches = _fixed_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + ) + val_probe_batches = _fixed_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + ) + diagnostic_payload = empty_epoch_diagnostic_payload( + run_type=run_type, + run_config=run_config, + bundle=bundle, + train_probe_batches=train_probe_batches, + val_probe_batches=val_probe_batches, + ) + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + expected_run_type=run_type, + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + selection_metric_name = str(checkpoint_payload.get("best_metric_name", selection_metric_name)) + best_model_metric = float(checkpoint_payload["best_metric_value"]) + patience_counter = int(checkpoint_payload.get("patience_counter", 0)) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", run_type), + } + epoch_metrics = checkpoint_payload.get("epoch_metrics") + if isinstance(epoch_metrics, dict) and epoch_metrics.get("early_stopping_best_value") is not None: + best_early_stopping_metric = float(epoch_metrics["early_stopping_best_value"]) + if diagnostic_path is not None and diagnostic_payload is not None: + diagnostic_payload = load_epoch_diagnostic_for_resume( + diagnostic_path, + checkpoint_payload, + diagnostic_payload, + ) + print( + f"[Resume] {run_label} | {run_type} continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{max_epochs}." + ) + prev_params = _snapshot_params(model) if diagnostic_path is not None else None + start_time = time.time() + validate_interval = max(int(VALIDATE_EVERY_N_EPOCHS), 1) + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + + for epoch in range(start_epoch, max_epochs + 1): + epoch_losses: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_reward: list[float] = [] + epoch_entropy: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_loss: list[float] = [] + epoch_grad: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_effective_steps: list[float] = [] + epoch_mask_deltas: list[float] = [] + epoch_advantage_means: list[float] = [] + epoch_advantage_stds: list[float] = [] + epoch_value_pred_errors: list[float] = [] + epoch_reward_zero_pcts: list[float] = [] + epoch_biou_deltas: list[float] = [] + epoch_mean_value_preds: list[float] = [] + epoch_annealed_aux_ce_weights: list[float] = [] + epoch_rl_loss_scales: list[float] = [] + epoch_reinforce_losses: list[float] = [] + epoch_entropy_losses: list[float] = [] + epoch_entropy_bonuses_used: list[float] = [] + epoch_alphas: list[float] = [] + epoch_action_dists: list[dict[str, float]] = [] + + prefetcher = CUDAPrefetcher(bundle.train_loader, DEVICE) + progress = tqdm(prefetcher, total=len(bundle.train_loader), desc=f"Epoch {epoch}/{max_epochs}", leave=False) + for batch in progress: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=tmax, + critic_loss_weight=critic_loss_weight, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=ce_weight, + dice_weight=dice_weight, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=max_epochs, + ) + epoch_losses.append(metrics["loss"]) + epoch_actor.append(metrics["actor_loss"]) + epoch_critic.append(metrics["critic_loss"]) + epoch_reward.append(metrics["mean_reward"]) + epoch_entropy.append(metrics["entropy"]) + epoch_ce.append(metrics["ce_loss"]) + epoch_dice_loss.append(metrics["dice_loss"]) + epoch_grad.append(metrics["grad_norm"]) + if "effective_steps" in metrics: + epoch_effective_steps.append(float(metrics["effective_steps"])) + if "mask_delta_mean" in metrics: + epoch_mask_deltas.append(metrics["mask_delta_mean"]) + if "advantage_mean" in metrics: + epoch_advantage_means.append(metrics["advantage_mean"]) + if "advantage_std" in metrics: + epoch_advantage_stds.append(metrics["advantage_std"]) + if "value_pred_error_mean" in metrics: + epoch_value_pred_errors.append(metrics["value_pred_error_mean"]) + if "reward_zeros_pct" in metrics: + epoch_reward_zero_pcts.append(float(metrics["reward_zeros_pct"])) + if "biou_delta_mean" in metrics: + epoch_biou_deltas.append(float(metrics["biou_delta_mean"])) + if "mean_value_pred" in metrics: + epoch_mean_value_preds.append(float(metrics["mean_value_pred"])) + if "annealed_aux_ce_weight" in metrics: + epoch_annealed_aux_ce_weights.append(float(metrics["annealed_aux_ce_weight"])) + if "rl_loss_scale_used" in metrics: + epoch_rl_loss_scales.append(float(metrics["rl_loss_scale_used"])) + if "reinforce_loss" in metrics: + epoch_reinforce_losses.append(float(metrics["reinforce_loss"])) + if "entropy_loss" in metrics: + epoch_entropy_losses.append(float(metrics["entropy_loss"])) + if "entropy_bonus_used" in metrics: + epoch_entropy_bonuses_used.append(float(metrics["entropy_bonus_used"])) + if "alpha" in metrics: + epoch_alphas.append(metrics["alpha"]) + if "action_distribution" in metrics and metrics["action_distribution"]: + epoch_action_dists.append(metrics["action_distribution"]) + + pred_mask = metrics["final_mask"] + gt_mask = batch["mask"].float() + inter = (pred_mask * gt_mask).sum(dim=(1, 2, 3)) + pred_sum = pred_mask.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + epoch_dices.extend(dice.detach().cpu().tolist()) + epoch_ious.extend(iou.detach().cpu().tolist()) + + head_lr_now = float(optimizer.param_groups[-1]["lr"]) + enc_lr_now = float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else head_lr_now + progress.set_postfix(loss=f"{metrics['loss']:.4f}", iou=f"{np.mean(epoch_ious):.4f}", lr=f"{head_lr_now:.2e}") + + should_validate = epoch % validate_interval == 0 or epoch == max_epochs + val_metrics: dict[str, float | None] = { + "val_loss": None, + "val_dice": None, + "val_iou": None, + "val_biou": None, + "val_decoder_dice": None, + "val_decoder_iou": None, + "val_decoder_biou": None, + "val_dice_gain": None, + "val_iou_gain": None, + "val_biou_gain": None, + "val_reward": None, + "val_entropy": None, + } + if should_validate: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: running validation on {len(bundle.val_loader)} batches..." + ) + validated_metrics = validate( + model, + bundle.val_loader, + run_dir=run_dir, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + gamma=DEFAULT_GAMMA, + critic_loss_weight=critic_loss_weight, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + val_metrics.update(validated_metrics) + scheduler.step() # always step up the scheduler + grad_stats: dict[str, Any] = {} + param_stats: dict[str, Any] = {} + if diagnostic_path is not None: + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_epoch_action_dist: dict[str, float] = {} + if epoch_action_dists: + all_act_keys = set() + for d in epoch_action_dists: + all_act_keys.update(d.keys()) + for k in sorted(all_act_keys): + avg_epoch_action_dist[k] = float(np.mean([d.get(k, 0.0) for d in epoch_action_dists])) + + row = { + "epoch": epoch, + "train_loss": float(np.mean(epoch_losses)) if epoch_losses else 0.0, + "train_actor_loss": float(np.mean(epoch_actor)) if epoch_actor else 0.0, + "train_critic_loss": float(np.mean(epoch_critic)) if epoch_critic else 0.0, + "train_mean_reward": float(np.mean(epoch_reward)) if epoch_reward else 0.0, + "train_entropy": float(np.mean(epoch_entropy)) if epoch_entropy else 0.0, + "train_ce_loss": float(np.mean(epoch_ce)) if epoch_ce else 0.0, + "train_dice_loss": float(np.mean(epoch_dice_loss)) if epoch_dice_loss else 0.0, + "train_dice": float(np.mean(epoch_dices)) if epoch_dices else 0.0, + "train_iou": float(np.mean(epoch_ious)) if epoch_ious else 0.0, + "grad_norm": float(np.mean(epoch_grad)) if epoch_grad else 0.0, + "lr": float(optimizer.param_groups[-1]["lr"]), + "encoder_lr": float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else float(optimizer.param_groups[-1]["lr"]), + "alpha": float(log_alpha.exp().detach().item()) if log_alpha is not None else None, + "train_effective_steps": float(np.mean(epoch_effective_steps)) if epoch_effective_steps else 0.0, + "train_mask_delta_mean": float(np.mean(epoch_mask_deltas)) if epoch_mask_deltas else 0.0, + "train_advantage_mean": float(np.mean(epoch_advantage_means)) if epoch_advantage_means else 0.0, + "train_advantage_std": _nanmean_or_default(epoch_advantage_stds, 0.0), + "train_value_pred_error": float(np.mean(epoch_value_pred_errors)) if epoch_value_pred_errors else 0.0, + "train_reward_zeros_pct": float(np.mean(epoch_reward_zero_pcts)) if epoch_reward_zero_pcts else 0.0, + "train_biou_delta_mean": float(np.mean(epoch_biou_deltas)) if epoch_biou_deltas else 0.0, + "train_mean_value_pred": float(np.mean(epoch_mean_value_preds)) if epoch_mean_value_preds else 0.0, + "train_annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "train_rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "train_reinforce_loss": float(np.mean(epoch_reinforce_losses)) if epoch_reinforce_losses else 0.0, + "train_entropy_loss": float(np.mean(epoch_entropy_losses)) if epoch_entropy_losses else 0.0, + "train_entropy_bonus_used": float(np.mean(epoch_entropy_bonuses_used)) if epoch_entropy_bonuses_used else 0.0, + "train_alpha": float(np.mean(epoch_alphas)) if epoch_alphas else 0.0, + "train_action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + "validated_this_epoch": should_validate, + **val_metrics, + } + if strategy == 3: + if row["train_advantage_std"] < 0.005: + low_advantage_std_streak += 1 + else: + low_advantage_std_streak = 0 + if abs(row["train_mean_value_pred"]) < 0.001: + low_mean_value_pred_streak += 1 + else: + low_mean_value_pred_streak = 0 + else: + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + if strategy3_freeze_status is not None: + row["strategy3_bootstrap_loaded"] = bool(strategy3_freeze_status["bootstrap_loaded"]) + row["strategy3_freeze_requested"] = bool(strategy3_freeze_status["freeze_requested"]) + row["strategy3_freeze_active"] = bool(strategy3_freeze_status["freeze_active"]) + row["strategy3_encoder_state"] = str(strategy3_freeze_status["encoder_state"]) + row["strategy3_decoder_state"] = str(strategy3_freeze_status["decoder_state"]) + row["strategy3_segmentation_head_state"] = str(strategy3_freeze_status["segmentation_head_state"]) + history.append(row) + + improved = False + early_stopping_improved_now = False + if should_validate: + selected_metric_value = _strategy_selection_metric_value(strategy, val_metrics) + row["selection_metric_name"] = selection_metric_name + row["selection_metric_value"] = selected_metric_value + improved = selected_metric_value > best_model_metric + if improved: + best_model_metric = selected_metric_value + if trial is not None: + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR, float(best_model_metric)) + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR, selection_metric_name) + early_stopping_monitor_value = _early_stopping_monitor_value( + row, + strategy=strategy, + monitor_name=early_stopping_monitor_name, + ) + early_stopping_active = epoch >= early_stopping_start_epoch + if early_stopping_active and early_stopping_monitor_value is not None: + early_stopping_improved_now = _early_stopping_improved( + early_stopping_monitor_value, + best_early_stopping_metric, + mode=early_stopping_mode, + min_delta=early_stopping_min_delta, + ) + if early_stopping_improved_now: + best_early_stopping_metric = early_stopping_monitor_value + patience_counter = 0 + else: + patience_counter += 1 + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = early_stopping_monitor_value + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = early_stopping_active + row["early_stopping_improved"] = early_stopping_improved_now + row["early_stopping_wait"] = int(patience_counter) + if improved and save_artifacts and ckpt_dir is not None: + save_checkpoint( + ckpt_dir / "best.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + else: + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = None + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = False + row["early_stopping_improved"] = False + row["early_stopping_wait"] = int(patience_counter) + + if save_artifacts and save_history_incrementally: + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + save_json(history_path, history) + + if diagnostic_path is not None and diagnostic_payload is not None: + epoch_alerts = _numerical_health_check(row, prefix=f"epoch[{epoch}]:") + if low_advantage_std_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] advantage_std collapsed - RL gradient near zero") + if low_mean_value_pred_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] critic degenerate - mean value prediction stuck near zero") + if int(grad_stats.get("n_nan", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected NaN gradients") + if int(grad_stats.get("n_inf", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected Inf gradients") + + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="val", + ) + + train_probe = _evaluate_probe_batches( + model, + train_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="train", + mc_cache_run_dir=run_dir, + ) + val_probe = _evaluate_probe_batches( + model, + val_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + epoch_alerts.extend(train_probe.get("alerts", [])) + epoch_alerts.extend(val_probe.get("alerts", [])) + + diagnostic_payload["epochs"].append( + { + "epoch": epoch, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "is_new_best": bool(improved), + "best_metric_name": selection_metric_name, + "best_metric_value_so_far": float(best_model_metric), + "patience_counter": int(patience_counter), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value_so_far": best_early_stopping_metric, + "history_row": dict(row), + "train_batch_summary": { + "loss": _summary_stats(epoch_losses), + "actor_loss": _summary_stats(epoch_actor), + "critic_loss": _summary_stats(epoch_critic), + "reward": _summary_stats(epoch_reward), + "entropy": _summary_stats(epoch_entropy), + "ce_loss": _summary_stats(epoch_ce), + "dice_loss": _summary_stats(epoch_dice_loss), + "grad_norm": _summary_stats(epoch_grad), + "dice": _summary_stats(epoch_dices), + "iou": _summary_stats(epoch_ious), + "effective_steps": _summary_stats(epoch_effective_steps), + "mask_delta": _summary_stats(epoch_mask_deltas), + "advantage_mean": _summary_stats(epoch_advantage_means), + "advantage_std": _summary_stats(epoch_advantage_stds), + "value_pred_error": _summary_stats(epoch_value_pred_errors), + "reward_zeros_pct": _summary_stats(epoch_reward_zero_pcts), + "biou_delta_mean": _summary_stats(epoch_biou_deltas), + "mean_value_pred": _summary_stats(epoch_mean_value_preds), + "annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "reinforce_loss": _summary_stats(epoch_reinforce_losses), + "entropy_loss": _summary_stats(epoch_entropy_losses), + "entropy_bonus_used": _summary_stats(epoch_entropy_bonuses_used), + "alpha": _summary_stats(epoch_alphas), + "action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + }, + "optimizer": { + "param_groups": _optimizer_diagnostics(optimizer), + "scheduler_last_lr": [float(value) for value in scheduler.get_last_lr()], + "target_entropy": float(target_entropy) if log_alpha is not None else 0.0, + }, + "grad_diagnostics": grad_stats, + "param_diagnostics": param_stats, + "probe_batches": { + "mode": epoch_probe_mode, + "train": _probe_batch_id_lists(train_probe_batches), + "val": _probe_batch_id_lists(val_probe_batches), + }, + "probes": { + "train_fixed": train_probe, + "val_fixed": val_probe, + }, + "probe_epoch_summary": { + "train_fixed": _format_probe_deterioration("train", train_probe, tmax), + "val_fixed": _format_probe_deterioration("val", val_probe, tmax), + }, + "alerts": epoch_alerts, + } + ) + save_json(diagnostic_path, diagnostic_payload) + + if save_artifacts and ckpt_dir is not None and SAVE_LATEST_EVERY_EPOCH and run_type != "trial": + save_checkpoint( + ckpt_dir / "latest.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + if save_artifacts and ckpt_dir is not None and CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0 and run_type != "trial": + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + + if trial is not None and should_validate: + reported_metric = row.get("selection_metric_value") + if reported_metric is None: + reported_metric = _strategy_selection_metric_value(strategy, val_metrics) + if reported_metric is None: + reported_metric = float(val_metrics["val_iou"]) + reported_metric = float(reported_metric) + trial.report(reported_metric, step=epoch) + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot( + trial, + current_best_value=best_model_metric, + ) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + if USE_TRIAL_PRUNING and epoch >= TRIAL_PRUNER_WARMUP_STEPS and trial.should_prune(): + raise optuna.TrialPruned( + f"Trial pruned at epoch {epoch} with " + f"{selection_metric_name}={reported_metric:.4f}" + ) + + if trial is not None and row.get("study_best_objective") is None: + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot(trial) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + + if VERBOSE_EPOCH_LOG: + tqdm.write(f"[{run_label}] Epoch {epoch}/{max_epochs}") + tqdm.write(json.dumps(row, indent=2)) + else: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: " + f"{format_concise_epoch_log(row, best_metric_name=selection_metric_name, best_metric_value=best_model_metric)}" + ) + + if should_validate and early_stopping_patience > 0 and patience_counter >= early_stopping_patience: + print( + f"Early stopping triggered at epoch {epoch}: " + f"monitor={early_stopping_monitor_name} mode={early_stopping_mode} " + f"best={best_early_stopping_metric} current={row.get('early_stopping_monitor_value')} " + f"min_delta={early_stopping_min_delta:.6g} wait={patience_counter}/{early_stopping_patience}." + ) + break + + elapsed = elapsed_before_resume + (time.time() - start_time) + if save_artifacts: + save_json(history_path, history) + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + summary = { + "best_model_metric_name": selection_metric_name, + "best_model_metric": float(best_model_metric), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value": best_early_stopping_metric, + "best_val_iou": max((float(r["val_iou"]) for r in history if r.get("val_iou") is not None), default=0.0), + "best_val_dice": max((float(r["val_dice"]) for r in history if r.get("val_dice") is not None), default=0.0), + "best_val_biou": max((float(r["val_biou"]) for r in history if r.get("val_biou") is not None), default=0.0), + "best_val_iou_gain": max((float(r["val_iou_gain"]) for r in history if r.get("val_iou_gain") is not None), default=0.0), + "best_val_biou_gain": max((float(r["val_biou_gain"]) for r in history if r.get("val_biou_gain") is not None), default=0.0), + "final_epoch": int(history[-1]["epoch"]) if history else int(start_epoch - 1), + "elapsed_seconds": elapsed, + "seconds_per_epoch": elapsed / max(len(history), 1), + "device_used": str(DEVICE), + "strategy": strategy, + "run_type": run_type, + "resumed": resume_source is not None, + } + if strategy3_freeze_status is not None: + summary.update( + { + "strategy3_bootstrap_loaded": bool(strategy3_freeze_status["bootstrap_loaded"]), + "strategy3_freeze_requested": bool(strategy3_freeze_status["freeze_requested"]), + "strategy3_freeze_active": bool(strategy3_freeze_status["freeze_active"]), + "strategy3_encoder_state": str(strategy3_freeze_status["encoder_state"]), + "strategy3_decoder_state": str(strategy3_freeze_status["decoder_state"]), + "strategy3_segmentation_head_state": str(strategy3_freeze_status["segmentation_head_state"]), + } + ) + if resume_source is not None: + summary["resume_source"] = resume_source + if save_artifacts: + save_json(run_dir / "summary.json", summary) + return summary, history + +"""============================================================================= +EVALUATION + SMOKE TEST +============================================================================= +""" + +def _save_rgb_panel(image_chw: np.ndarray, pred_hw: np.ndarray, gt_hw: np.ndarray, output_path: Path, title: str) -> None: + img = image_chw.transpose(1, 2, 0) + img = (img - img.min()) / (img.max() - img.min() + 1e-8) + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(img) + axes[0].set_title("Input") + axes[1].imshow(pred_hw, cmap="gray", vmin=0, vmax=1) + axes[1].set_title("Prediction") + axes[2].imshow(gt_hw, cmap="gray", vmin=0, vmax=1) + axes[2].set_title("Ground Truth") + for ax in axes: + ax.axis("off") + fig.suptitle(title) + fig.tight_layout() + fig.savefig(output_path, dpi=120) + plt.close(fig) + + +def _synchronize_device_for_timing(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _write_evaluation_timing_csv( + path: Path, + *, + timing_summary: dict[str, Any], +) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "scope", + "strategy", + "tmax", + "device", + "num_batches", + "num_samples", + "total_inference_ms", + "avg_batch_inference_ms", + "std_batch_inference_ms", + "avg_sample_inference_ms", + "std_sample_inference_ms", + "mean_per_image_inference_ms", + "std_per_image_inference_ms", + "mean_per_image_inference_seconds", + "std_per_image_inference_seconds", + ], + ) + writer.writeheader() + writer.writerow( + { + "scope": str(timing_summary["scope"]), + "strategy": int(timing_summary["strategy"]), + "tmax": int(timing_summary["tmax"]), + "device": str(timing_summary["device"]), + "num_batches": int(timing_summary["num_batches"]), + "num_samples": int(timing_summary["num_samples"]), + "total_inference_ms": f"{float(timing_summary['total_inference_ms']):.6f}", + "avg_batch_inference_ms": f"{float(timing_summary['avg_batch_inference_ms']):.6f}", + "std_batch_inference_ms": f"{float(timing_summary['std_batch_inference_ms']):.6f}", + "avg_sample_inference_ms": f"{float(timing_summary['avg_sample_inference_ms']):.6f}", + "std_sample_inference_ms": f"{float(timing_summary['std_sample_inference_ms']):.6f}", + "mean_per_image_inference_ms": f"{float(timing_summary['mean_per_image_inference_ms']):.6f}", + "std_per_image_inference_ms": f"{float(timing_summary['std_per_image_inference_ms']):.6f}", + "mean_per_image_inference_seconds": f"{float(timing_summary['mean_per_image_inference_seconds']):.9f}", + "std_per_image_inference_seconds": f"{float(timing_summary['std_per_image_inference_seconds']):.9f}", + } + ) + + +def evaluate_model( + *, + model: nn.Module, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + strategy: int, + tmax: int, + best_metric_name: str, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + pred_dir = ensure_dir(run_dir / "predictions") + pred_255_dir = ensure_dir(run_dir / "predictions_255") + + model.eval() + per_metric = {k: [] for k in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95")} + per_sample: list[dict[str, Any]] = [] + inference_total_ms = 0.0 + inference_batch_count = 0 + inference_sample_count = 0 + inference_batch_times_ms: list[float] = [] + inference_sample_times_ms: list[float] = [] + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + + with torch.inference_mode(): + prefetcher = CUDAPrefetcher(bundle.test_loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(bundle.test_loader), desc="Evaluating", leave=False): + image = batch["image"] + gt = batch["mask"] + sample_ids = [str(item) for item in batch["sample_id"]] + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + _synchronize_device_for_timing(DEVICE) + inference_start = time.perf_counter() + pred = infer_segmentation_mask( + model, + image, + tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="test", + mc_cache_run_dir=run_dir, + ).float() + _synchronize_device_for_timing(DEVICE) + inference_elapsed_ms = (time.perf_counter() - inference_start) * 1000.0 + inference_total_ms += inference_elapsed_ms + inference_batch_count += 1 + batch_size = int(pred.shape[0]) + inference_sample_count += batch_size + inference_batch_times_ms.append(float(inference_elapsed_ms)) + per_image_inference_ms = float(inference_elapsed_ms) / float(max(batch_size, 1)) + inference_sample_times_ms.extend([per_image_inference_ms] * batch_size) + pred_np = pred.cpu().numpy().astype(np.uint8) + gt_np = gt.cpu().numpy().astype(np.uint8) + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + for key, value in metrics.items(): + per_metric.setdefault(key, []).append(value) + per_sample.append( + { + "sample_id": sample_ids[idx], + **metrics, + "inference_time_ms": per_image_inference_ms, + "inference_time_seconds": per_image_inference_ms / 1000.0, + } + ) + mask_2d = pred_np[idx].squeeze() + PILImage.fromarray(mask_2d).save(pred_dir / f"{sample_ids[idx]}.png") + PILImage.fromarray((mask_2d * 255).astype(np.uint8)).save(pred_255_dir / f"{sample_ids[idx]}.png") + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate: dict[str, dict[str, float]] = {} + for key, values in per_metric.items(): + values_np = np.array(values, dtype=np.float32) + aggregate[key] = {"mean": float(values_np.mean()), "std": float(values_np.std())} + batch_times_np = np.array(inference_batch_times_ms, dtype=np.float64) + sample_times_np = np.array(inference_sample_times_ms, dtype=np.float64) + timing_summary = { + "scope": "test_set_evaluation", + "strategy": int(strategy), + "tmax": int(tmax), + "device": str(DEVICE), + "num_batches": int(inference_batch_count), + "num_samples": int(inference_sample_count), + "total_inference_ms": float(inference_total_ms), + "avg_batch_inference_ms": float(batch_times_np.mean()) if batch_times_np.size > 0 else 0.0, + "std_batch_inference_ms": float(batch_times_np.std()) if batch_times_np.size > 0 else 0.0, + "avg_sample_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_sample_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_seconds": float(sample_times_np.mean() / 1000.0) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_seconds": float(sample_times_np.std() / 1000.0) if sample_times_np.size > 0 else 0.0, + } + + save_json( + run_dir / "evaluation.json", + { + "strategy": strategy, + "best_metric_name": str(best_metric_name), + "metrics": aggregate, + "per_sample": per_sample, + "timing": timing_summary, + }, + ) + + df_all = pd.DataFrame(per_sample) + avg_row = {} + for column in df_all.columns: + avg_row[column] = df_all[column].mean() if pd.api.types.is_numeric_dtype(df_all[column]) else "AVERAGE" + df_samples = pd.concat([df_all, pd.DataFrame([avg_row])], ignore_index=True) + df_summary = pd.DataFrame(aggregate).T + df_summary.index.name = "metric" + df_low_iou = df_all[df_all["iou"] < 0.01] + history_path = run_dir / "history.json" + df_history = pd.DataFrame(load_json(history_path)) if history_path.exists() else None + + xlsx_path = run_dir / "evaluation_results.xlsx" + with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer: + df_samples.to_excel(writer, sheet_name="Per Sample", index=False) + df_summary.to_excel(writer, sheet_name="Summary") + if not df_low_iou.empty: + df_low_iou.to_excel(writer, sheet_name="Low IoU Samples", index=False) + if df_history is not None: + df_history.to_excel(writer, sheet_name="Training History", index=False) + + csv_rows = [{"sample_id": row["sample_id"]} for row in df_low_iou.to_dict(orient="records")] + save_json(run_dir / "evaluation_summary.json", {"mean_iou": aggregate["iou"]["mean"], "mean_dice": aggregate["dice"]["mean"]}) + pd.DataFrame(csv_rows).to_csv(run_dir / "low_iou_samples.csv", index=False) + _write_evaluation_timing_csv( + run_dir / "timing.csv", + timing_summary=timing_summary, + ) + return aggregate, per_sample + +def percent_root(percent: float) -> Path: + return ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}") + +def strategy_dir_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + return f"strategy_{strategy}_custom_vgg" + return f"strategy_{strategy}" + +def strategy_root_for_percent( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(percent_root(percent) / strategy_dir_name(strategy, model_config)) + +def final_root_for_strategy( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(strategy_root_for_percent(strategy, percent, model_config) / "final") + +def ensure_specific_checkpoint_scope(selector_name: str, selector_mode: str) -> None: + if selector_mode != "specific": + return + # Allow STRATEGY2 checkpoints with dict mapping for dynamic per-percent selection + if "strategy2" in selector_name.lower() and isinstance(STRATEGY2_SPECIFIC_CHECKPOINT, dict): + return + if len(STRATEGIES) != 1 or len(DATASET_PERCENTS) != 1: + raise ValueError( + f"{selector_name}=specific is only supported when exactly one strategy and one dataset percent are selected. " + f"Got STRATEGIES={STRATEGIES} and DATASET_PERCENTS={DATASET_PERCENTS}." + ) + +def resolve_checkpoint_path( + *, + run_dir: Path, + selector_mode: str, + specific_checkpoint: str | Path | dict, + purpose: str, +) -> Path: + run_dir = Path(run_dir) + if selector_mode == "latest": + checkpoint_path = run_dir / "checkpoints" / "latest.pt" + elif selector_mode == "best": + checkpoint_path = run_dir / "checkpoints" / "best.pt" + elif selector_mode == "specific": + ensure_specific_checkpoint_scope(purpose, selector_mode) + if not specific_checkpoint: + raise ValueError(f"{purpose}=specific requires a non-empty specific checkpoint path.") + checkpoint_path = Path(specific_checkpoint).expanduser().resolve() + else: + raise ValueError(f"Unsupported checkpoint selector mode '{selector_mode}' for {purpose}.") + + if not checkpoint_path.exists(): + raise FileNotFoundError(f"Checkpoint for {purpose} not found: {checkpoint_path}") + return checkpoint_path + +def resolve_train_resume_checkpoint_path(run_dir: Path) -> Path | None: + if TRAIN_RESUME_MODE == "off": + return None + return resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=TRAIN_RESUME_MODE, + specific_checkpoint=TRAIN_RESUME_SPECIFIC_CHECKPOINT, + purpose="train_resume_checkpoint", + ) + +def resolve_strategy2_checkpoint_path( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + if strategy != 3: + raise ValueError(f"Strategy 2 dependency checkpoint requested for unsupported strategy {strategy}.") + + specific_checkpoint = STRATEGY2_SPECIFIC_CHECKPOINT + if isinstance(specific_checkpoint, dict): + ctx = globals().get("CURRENT_FOLD_CONTEXT") + _phase_mode_fn = globals().get("using_fixed_phase_mode") + in_phase_mode = callable(_phase_mode_fn) and _phase_mode_fn() + if in_phase_mode and ctx is not None: + # Phase mode: key by phase index (split_repeat_index) + specific_checkpoint = specific_checkpoint.get(ctx.split_repeat_index, "") + else: + # Non-phase mode: key by dataset percent (float) + specific_checkpoint = specific_checkpoint.get(percent, "") + + checkpoint_path = resolve_checkpoint_path( + run_dir=final_root_for_strategy(2, percent, model_config), + selector_mode=STRATEGY2_CHECKPOINT_MODE, + specific_checkpoint=specific_checkpoint, + purpose="strategy2_checkpoint", + ) + + # Print which checkpoint is being used + checkpoint_label = run_identity_label(strategy=strategy, percent=percent) + ctx = globals().get("CURRENT_FOLD_CONTEXT") + if ctx is not None and abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12: + checkpoint_label = run_identity_label( + strategy=strategy, + percent=percent, + split_payload={ + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "dataset_percent": ctx.percent_fraction, + }, + ) + print(f"[Strategy 2 Checkpoint] {checkpoint_label} | Loading: {checkpoint_path}") + + return checkpoint_path + +def load_required_hparams(payload: dict[str, Any], *, source: str, strategy: int, percent: float) -> dict[str, Any]: + missing_keys = [name for name in REQUIRED_HPARAM_KEYS if name not in payload] + if missing_keys: + raise KeyError( + f"Incomplete hyperparameters for strategy={strategy}, percent={percent_text(percent)} from {source}. " + f"Missing keys: {missing_keys}. Required keys: {REQUIRED_HPARAM_KEYS}." + ) + return dict(payload) + +def load_saved_best_params_if_optuna_off( + strategy: int, + percent: float, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + _, study_root, _ = study_paths_for(strategy, percent, model_config) + best_params_path = study_root / "best_params.json" + if not best_params_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=True, but no saved best params were found " + f"for strategy={strategy}, percent={percent_text(percent)} at {best_params_path}." + ) + params = load_json(best_params_path) + params = load_required_hparams( + params, + source=str(best_params_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using saved best parameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {best_params_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def load_manual_hparams_if_optuna_off(strategy: int, percent: float) -> dict[str, Any]: + key = manual_hparams_key(strategy, percent) + if key not in MANUAL_HPARAMS_IF_OPTUNA_OFF: + raise KeyError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but no manual hyperparameter " + f"JSON filename was found for strategy={strategy}, percent={percent_text(percent)} under key '{key}'. " + f"Required keys: {REQUIRED_HPARAM_KEYS}." + ) + manual_filename = MANUAL_HPARAMS_IF_OPTUNA_OFF[key] + manual_path = (HARD_CODED_PARAM_DIR / manual_filename).resolve() + if not manual_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but the manual hyperparameter " + f"JSON file for strategy={strategy}, percent={percent_text(percent)} was not found at {manual_path}. " + f"Configured key='{key}', filename='{manual_filename}'." + ) + params = load_json(manual_path) + params = load_required_hparams( + params, + source=str(manual_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using manual hyperparameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {manual_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def resolve_job_params( + strategy: int, + percent: float, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if RUN_OPTUNA: + banner( + f"OPTUNA STUDY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return run_study( + strategy, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + if USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + return load_saved_best_params_if_optuna_off(strategy, percent, model_config=model_config) + + return load_manual_hparams_if_optuna_off(strategy, percent) + +def read_run_config_for_eval(run_dir: Path, checkpoint_path: Path) -> dict[str, Any]: + run_config_path = Path(run_dir) / "run_config.json" + if run_config_path.exists(): + return load_json(run_config_path) + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + return checkpoint_run_config_payload(ckpt) + +def run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + checkpoint_path = resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=EVAL_CHECKPOINT_MODE, + specific_checkpoint=EVAL_SPECIFIC_CHECKPOINT, + purpose="evaluation_checkpoint", + ) + effective_run_dir = Path(run_dir) + if not (effective_run_dir / "run_config.json").exists() and checkpoint_path.parent.name == "checkpoints": + effective_run_dir = checkpoint_path.parent.parent + print( + f"[Evaluation] {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)} " + f"| checkpoint={checkpoint_path}" + ) + runtime_config = read_run_config_for_eval(effective_run_dir, checkpoint_path) + set_current_job_params(runtime_config) + model_config = RuntimeModelConfig.from_payload(runtime_config).validate() + if strategy == 3 and runtime_config.get("strategy2_checkpoint_path"): + strategy2_checkpoint_path = runtime_config["strategy2_checkpoint_path"] + elif strategy == 3: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + dropout_p = float(runtime_config.get("dropout_p", DEFAULT_DROPOUT_P)) + tmax = int(runtime_config.get("tmax", DEFAULT_TMAX)) + eval_model, _description, _compiled = build_model( + strategy, + dropout_p, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + checkpoint_payload = load_checkpoint( + checkpoint_path, + model=eval_model, + device=DEVICE, + ) + best_metric_name = str( + checkpoint_payload.get("best_metric_name") + or runtime_config.get("best_checkpoint_metric_name") + or _strategy_selection_metric_name(strategy) + ) + aggregate, per_sample = evaluate_model( + model=eval_model, + model_config=model_config, + bundle=bundle, + run_dir=effective_run_dir, + strategy=strategy, + tmax=tmax, + best_metric_name=best_metric_name, + ) + evaluation_json_path = effective_run_dir / "evaluation.json" + evaluation_payload = load_json(evaluation_json_path) + evaluation_payload["checkpoint_mode"] = EVAL_CHECKPOINT_MODE + evaluation_payload["checkpoint_path"] = str(checkpoint_path) + evaluation_payload["best_metric_name"] = best_metric_name + if checkpoint_payload.get("best_metric_value") is not None: + evaluation_payload["best_metric_value"] = float(checkpoint_payload["best_metric_value"]) + save_json(evaluation_json_path, evaluation_payload) + del eval_model + run_cuda_cleanup( + context=f"evaluation {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return aggregate, per_sample + +def run_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + smoke_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + banner( + f"PRE-TRAINING SMOKE TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + smoke_root = ensure_dir(smoke_root) + if RUN_OPTUNA: + set_current_job_params() + elif USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + set_current_job_params( + load_saved_best_params_if_optuna_off(strategy, bundle.percent, model_config=model_config) + ) + else: + set_current_job_params(load_manual_hparams_if_optuna_off(strategy, bundle.percent)) + sample = bundle.test_ds[SMOKE_TEST_SAMPLE_INDEX] + image = sample["image"].unsqueeze(0).to(DEVICE) + raw_image = sample["image"].numpy() + raw_gt = sample["mask"].squeeze(0).numpy() + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + print_model_parameter_summary( + model=model, + description=f"{description} | Smoke Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + pred = infer_segmentation_mask( + model, + image, + DEFAULT_TMAX, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=_use_channels_last_for_run(model_config), + sample_ids=[str(sample["sample_id"])], + mc_cache_split="test", + mc_cache_run_dir=smoke_root, + ).float() + pred_np = pred[0, 0].detach().cpu().numpy() + panel_path = smoke_root / "smoke_panel.png" + raw_mask_path = smoke_root / "smoke_prediction.png" + _save_rgb_panel( + raw_image, + pred_np, + raw_gt, + panel_path, + f"Smoke Test | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}", + ) + PILImage.fromarray((pred_np * 255).astype(np.uint8)).save(raw_mask_path) + del model + run_cuda_cleanup( + context=f"smoke {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + print( + f"[Smoke Test] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} passed. " + f"Saved panel to {panel_path.name} and mask to {raw_mask_path.name}." + ) + +"""============================================================================= +OVERFIT TEST +============================================================================= +""" + +OVERFIT_HISTORY_KEYS = ( + "dice", + "iou", + "loss", + "reward", + "actor_loss", + "critic_loss", + "ce_loss", + "dice_loss", + "entropy", + "grad_norm", + "action_dist", + "reward_pos_pct", + "pred_fg_pct", + "gt_fg_pct", +) + +def empty_overfit_history() -> dict[str, list[Any]]: + return {key: [] for key in OVERFIT_HISTORY_KEYS} + +def load_overfit_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> dict[str, list[Any]]: + history = empty_overfit_history() + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict history at {history_path}, found {type(payload).__name__}.") + for key in OVERFIT_HISTORY_KEYS: + values = payload.get(key, []) + if isinstance(values, list): + history[key] = list(values[:checkpoint_epoch]) + return history + + epoch_metrics = checkpoint_payload.get("epoch_metrics", {}) + if isinstance(epoch_metrics, dict): + for key in OVERFIT_HISTORY_KEYS: + if key in epoch_metrics: + history[key].append(epoch_metrics[key]) + return history + +def _grad_diagnostics(model: nn.Module) -> dict[str, Any]: + raw = _unwrap_compiled(model) + groups: dict[str, list[float]] = {} + total_sq = 0.0 + n_nan = 0 + n_inf = 0 + n_zero = 0 + n_total_params = 0 + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + n_total_params += 1 + if param.grad is None: + n_zero += 1 + continue + grad_norm = float(param.grad.data.norm(2).item()) + if math.isnan(grad_norm): + n_nan += 1 + continue + if math.isinf(grad_norm): + n_inf += 1 + continue + total_sq += grad_norm ** 2 + group_name = name.split(".", 1)[0] + groups.setdefault(group_name, []).append(grad_norm) + + group_stats: dict[str, dict[str, float | int]] = {} + for group_name, norms in groups.items(): + group_stats[group_name] = { + "min": min(norms), + "max": max(norms), + "mean": sum(norms) / len(norms), + "count": len(norms), + } + return { + "global_norm": total_sq ** 0.5, + "groups": group_stats, + "n_nan": n_nan, + "n_inf": n_inf, + "n_zero_grad": n_zero, + "n_total": n_total_params, + } + +def _param_diagnostics(model: nn.Module, prev_params: dict[str, torch.Tensor] | None = None) -> dict[str, dict[str, float]]: + raw = _unwrap_compiled(model) + info: dict[str, dict[str, list[float]]] = {} + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + param_norm = float(param.data.norm(2).item()) + group_name = name.split(".", 1)[0] + entry = info.setdefault(group_name, {"norms": [], "update_ratios": []}) + entry["norms"].append(param_norm) + if prev_params is not None and name in prev_params: + delta = float((param.data - prev_params[name]).norm(2).item()) + entry["update_ratios"].append(delta / max(param_norm, 1e-12)) + + summary: dict[str, dict[str, float]] = {} + for group_name, values in info.items(): + norms = values["norms"] + ratios = values["update_ratios"] + summary[group_name] = { + "p_min": min(norms), + "p_max": max(norms), + "p_mean": sum(norms) / len(norms), + } + if ratios: + summary[group_name]["ur_min"] = min(ratios) + summary[group_name]["ur_max"] = max(ratios) + summary[group_name]["ur_mean"] = sum(ratios) / len(ratios) + return summary + +def _snapshot_params(model: nn.Module) -> dict[str, torch.Tensor]: + raw = _unwrap_compiled(model) + return { + name: param.data.detach().clone() + for name, param in raw.named_parameters() + if param.requires_grad + } + +def _action_distribution( + model: nn.Module, + image: torch.Tensor, + seg: torch.Tensor, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + *, + strategy: int | None = None, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[list[dict[str, float]], torch.Tensor]: + distributions: list[dict[str, float]] = [] + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_action_distribution") if strategy != 2 else max(int(tmax), 1) + refinement_context: dict[str, torch.Tensor] | None = None + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = seg.float() + for _step in range(effective_tmax): + if refinement_context is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _ = model.forward_from_state(state) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta).to(dtype=seg.dtype) + distributions.append(_strategy3_delta_distribution(delta)) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + total_pixels = max(actions.numel(), 1) + step_dist: dict[str, float] = {} + action_count = int(policy_logits.shape[1]) + for action_idx in range(action_count): + step_dist[str(action_idx)] = float((actions == action_idx).sum().item()) / total_pixels * 100.0 + distributions.append(step_dist) + if refinement_context is not None: + return distributions, threshold_binary_mask(seg.float()).float() + return distributions, seg + +def _numerical_health_check(outputs_dict: dict[str, Any], prefix: str = "") -> list[str]: + alerts: list[str] = [] + for name, value in outputs_dict.items(): + if value is None: + continue + if isinstance(value, (int, float)): + if math.isnan(value): + alerts.append(f"{prefix}{name} = NaN") + elif math.isinf(value): + alerts.append(f"{prefix}{name} = Inf") + elif name == "train_reward_zeros_pct" and float(value) > 98.0: + alerts.append(f"{prefix}reward is degenerate (>98% zero-reward pixels)") + continue + if torch.is_tensor(value): + if torch.isnan(value).any(): + alerts.append(f"{prefix}{name} contains NaN") + if torch.isinf(value).any(): + alerts.append(f"{prefix}{name} contains Inf") + return alerts + +def _batch_binary_metrics(pred: torch.Tensor, gt: torch.Tensor) -> tuple[list[float], list[float]]: + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt.detach().cpu().numpy().astype(np.uint8) + dices: list[float] = [] + ious: list[float] = [] + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + dices.append(float(metrics["dice"])) + ious.append(float(metrics["iou"])) + return dices, ious + +def diagnostic_path_for_run(run_dir: Path) -> Path: + return Path(run_dir) / "diagnostic.json" + +def _summary_stats(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "mean": None, "std": None, "min": None, "max": None} + arr = np.asarray(values, dtype=np.float64) + return { + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std()), + "min": float(arr.min()), + "max": float(arr.max()), + } + +def _nanmean_or_default(values: list[float], default: float = 0.0) -> float: + if not values: + return float(default) + arr = np.asarray(values, dtype=np.float64) + if np.isnan(arr).all(): + return float(default) + return float(np.nanmean(arr)) + +def _tensor_stats(tensor: torch.Tensor | None) -> dict[str, Any] | None: + if tensor is None: + return None + data = tensor.detach().float() + flat = data.reshape(-1) + if flat.numel() == 0: + return {"shape": list(data.shape), "dtype": str(tensor.dtype), "numel": 0} + return { + "shape": list(data.shape), + "dtype": str(tensor.dtype), + "numel": int(flat.numel()), + "mean": float(flat.mean().item()), + "std": float(flat.std(unbiased=False).item()), + "min": float(flat.min().item()), + "max": float(flat.max().item()), + } + +def _action_histogram(actions: torch.Tensor, action_count: int) -> dict[str, float]: + total_pixels = max(actions.numel(), 1) + return { + str(action_idx): float((actions == action_idx).sum().item()) / total_pixels * 100.0 + for action_idx in range(action_count) + } + +def _jsonable_action_distribution(distributions: list[dict[int, float]] | list[dict[str, float]]) -> list[dict[str, float]]: + jsonable: list[dict[str, float]] = [] + for step_dist in distributions: + jsonable.append({str(key): float(value) for key, value in step_dist.items()}) + return jsonable + +def _average_action_distributions( + distributions_per_batch: list[list[dict[str, float]]], + steps: int, +) -> list[dict[str, float]]: + averaged: list[dict[str, float]] = [] + if not distributions_per_batch: + return averaged + for step_idx in range(steps): + action_keys = sorted( + { + str(action_idx) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) + for action_idx in batch_dist[step_idx].keys() + } + ) + if not action_keys: + continue + step_summary: dict[str, float] = {} + for action_idx in action_keys: + values = [ + float(batch_dist[step_idx][action_idx]) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) and action_idx in batch_dist[step_idx] + ] + step_summary[str(action_idx)] = float(np.mean(values)) if values else 0.0 + averaged.append(step_summary) + return averaged + +def _trajectory_degradation_summary(step_trace: list[dict[str, Any]]) -> dict[str, Any]: + if not step_trace: + return {} + ious = [float(step.get("iou_mean", 0.0)) for step in step_trace] + dices = [float(step.get("dice_mean", 0.0)) for step in step_trace] + ts = [int(step.get("t", idx)) for idx, step in enumerate(step_trace)] + init_iou = ious[0] + init_dice = dices[0] + best_iou = max(ious) + best_dice = max(dices) + best_iou_t = ts[ious.index(best_iou)] + best_dice_t = ts[dices.index(best_dice)] + first_worse_than_initial_iou_t = next((ts[idx] for idx, value in enumerate(ious[1:], start=1) if value < init_iou - 1e-6), None) + first_worse_than_prev_iou_t = next((ts[idx] for idx in range(1, len(ious)) if ious[idx] < ious[idx - 1] - 1e-6), None) + largest_iou_drop = max(best_iou - value for value in ious) + largest_iou_drop_t = ts[max(range(len(ious)), key=lambda idx: best_iou - ious[idx])] + return { + "steps_recorded": len(step_trace) - 1, + "best_iou_t": best_iou_t, + "best_iou": best_iou, + "best_dice_t": best_dice_t, + "best_dice": best_dice, + "final_t": ts[-1], + "final_iou": ious[-1], + "final_dice": dices[-1], + "delta_final_vs_init_iou": ious[-1] - init_iou, + "delta_final_vs_init_dice": dices[-1] - init_dice, + "delta_final_vs_best_iou": ious[-1] - best_iou, + "delta_final_vs_best_dice": dices[-1] - best_dice, + "first_worse_than_initial_iou_t": first_worse_than_initial_iou_t, + "first_worse_than_prev_iou_t": first_worse_than_prev_iou_t, + "largest_iou_drop_from_best": largest_iou_drop, + "largest_iou_drop_t": largest_iou_drop_t, + } + +def _average_rollout_traces(traces_per_batch: list[list[dict[str, Any]]]) -> list[dict[str, Any]]: + averaged: list[dict[str, Any]] = [] + if not traces_per_batch: + return averaged + max_steps = max(len(trace) for trace in traces_per_batch) + for step_idx in range(max_steps): + present = [trace[step_idx] for trace in traces_per_batch if step_idx < len(trace)] + if not present: + continue + reward_pos_values = [float(step["reward_pos_pct"]) for step in present if step.get("reward_pos_pct") is not None] + value_scores = [float(step["value_score"]) for step in present if step.get("value_score") is not None] + averaged.append( + { + "t": int(np.mean([float(step.get("t", step_idx)) for step in present])), + "dice_mean": float(np.mean([float(step.get("dice_mean", 0.0)) for step in present])), + "iou_mean": float(np.mean([float(step.get("iou_mean", 0.0)) for step in present])), + "pred_fg_pct": float(np.mean([float(step.get("pred_fg_pct", 0.0)) for step in present])), + "reward_pos_pct": float(np.mean(reward_pos_values)) if reward_pos_values else None, + "value_score": float(np.mean(value_scores)) if value_scores else None, + } + ) + return averaged + +def _rollout_probe_trace( + model: nn.Module, + image: torch.Tensor, + gt_mask: torch.Tensor, + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_rollout_probe_trace") if strategy != 2 else max(int(tmax), 1) + rollout_trace: list[dict[str, Any]] = [] + batch_action_dist: list[dict[str, float]] = [] + reward_pos_pct = 0.0 + init_fg_pct = 0.0 + first_action_dist: dict[str, float] | None = None + first_policy_stats: dict[str, Any] | None = None + first_value_stats: dict[str, Any] | None = None + decoder_prob_stats: dict[str, Any] | None = None + first_entropy: float | None = None + selected_t = 0 + + def record_step( + *, + t: int, + seg_tensor: torch.Tensor, + seg_prev: torch.Tensor | None = None, + delta_map: torch.Tensor | None = None, + value_score: float | None = None, + action_distribution: dict[str, float] | None = None, + reward_pos: float | None = None, + reward_map_tensor: torch.Tensor | None = None, + step_entropy: float | None = None, + policy_stats: dict[str, Any] | None = None, + ) -> None: + pred_t = threshold_binary_mask(seg_tensor.float()).float() + dice_vals, iou_vals = _batch_binary_metrics(pred_t, gt_mask.float()) + step_data: dict[str, Any] = { + "t": int(t), + "dice_mean": float(np.mean(dice_vals)) if dice_vals else 0.0, + "iou_mean": float(np.mean(iou_vals)) if iou_vals else 0.0, + "pred_fg_pct": float(pred_t.sum().item()) / max(pred_t.numel(), 1) * 100.0, + "reward_pos_pct": None if reward_pos is None else float(reward_pos), + "value_score": None if value_score is None else float(value_score), + "action_distribution": action_distribution, + "seg_soft_stats": _tensor_stats(seg_tensor), + "entropy": step_entropy, + "policy_logit_stats": policy_stats, + } + if seg_prev is not None: + delta = seg_tensor.float() - seg_prev.float() + abs_delta = delta.abs() + # Binary mask flip tracking: how many pixels actually change in the thresholded output + binary_prev = threshold_binary_mask(seg_prev.float()).float() + binary_curr = threshold_binary_mask(seg_tensor.float()).float() + binary_flipped = (binary_prev != binary_curr) + flipped_to_fg = binary_flipped & (binary_curr > 0.5) + flipped_to_bg = binary_flipped & (binary_curr < 0.5) + gt_binary_local = (gt_mask.float() > 0.5) + correct_flips = binary_flipped & ((binary_curr > 0.5) == gt_binary_local) + wrong_flips = binary_flipped & ((binary_curr > 0.5) != gt_binary_local) + total_px = max(binary_prev.numel(), 1) + step_data["mask_delta"] = { + "mean_abs_change": float(abs_delta.mean().item()), + "max_change": float(abs_delta.max().item()), + "pct_pixels_changed": float((abs_delta > 1e-6).float().mean().item() * 100.0), + "fg_gained_pct": float((delta > 1e-6).float().mean().item() * 100.0), + "fg_lost_pct": float((delta < -1e-6).float().mean().item() * 100.0), + } + step_data["binary_mask_flips"] = { + "total_flipped_pct": float(binary_flipped.float().sum().item() / total_px * 100.0), + "flipped_to_fg_pct": float(flipped_to_fg.float().sum().item() / total_px * 100.0), + "flipped_to_bg_pct": float(flipped_to_bg.float().sum().item() / total_px * 100.0), + "correct_flips_pct": float(correct_flips.float().sum().item() / total_px * 100.0), + "wrong_flips_pct": float(wrong_flips.float().sum().item() / total_px * 100.0), + "flip_accuracy": float(correct_flips.float().sum().item() / max(binary_flipped.float().sum().item(), 1.0) * 100.0), + } + if reward_map_tensor is not None: + step_data["reward_stats"] = { + "mean": float(reward_map_tensor.mean().item()), + "std": float(reward_map_tensor.std().item()), + "min": float(reward_map_tensor.min().item()), + "max": float(reward_map_tensor.max().item()), + "pct_positive": float((reward_map_tensor > 0).float().mean().item() * 100.0), + "pct_negative": float((reward_map_tensor < 0).float().mean().item() * 100.0), + "pct_zero": float((reward_map_tensor.abs() < 1e-8).float().mean().item() * 100.0), + } + if delta_map is not None and seg_prev is not None: + gt_f = gt_mask.float() + ref_pred = threshold_binary_mask(seg_prev.float()).float() + gt_fg = (gt_f > 0.5).squeeze(1) + gt_bg = ~gt_fg + pred_fg = (ref_pred > 0.5).squeeze(1) + tp_mask = pred_fg & gt_fg + tn_mask = (~pred_fg) & gt_bg + fp_mask = pred_fg & gt_bg + fn_mask = (~pred_fg) & gt_fg + delta_squeezed = delta_map.squeeze(1).detach().float() + action_breakdown: dict[str, dict[str, float]] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + if pixel_mask.any(): + class_delta = delta_squeezed[pixel_mask] + action_breakdown[label] = { + "mean_delta": float(class_delta.mean().item()), + "mean_abs_delta": float(class_delta.abs().mean().item()), + "positive_pct": float((class_delta > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((class_delta < -1e-6).float().mean().item() * 100.0), + } + else: + action_breakdown[label] = { + "mean_delta": 0.0, + "mean_abs_delta": 0.0, + "positive_pct": 0.0, + "negative_pct": 0.0, + } + step_data["action_on_class"] = action_breakdown + if reward_map_tensor is not None: + reward_squeezed = reward_map_tensor.squeeze(1) if reward_map_tensor.ndim == 4 else reward_map_tensor + per_class_reward: dict[str, float] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + per_class_reward[label] = float(reward_squeezed[pixel_mask].mean().item()) if pixel_mask.any() else 0.0 + step_data["per_action_reward"] = per_class_reward + rollout_trace.append(step_data) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + record_step(t=0, seg_tensor=torch.sigmoid(logits)) + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": _trajectory_degradation_summary(rollout_trace), + "selected_t": selected_t, + "effective_tmax": effective_tmax, + } + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + decoder_prob_stats = _tensor_stats(refinement_context["decoder_prob"]) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + selected_t = 0 + + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, value_t = model.forward_from_state(state_t) + current_score = float(value_t.detach().mean().item()) + delta = _strategy3_policy_delta(policy_logits).to(dtype=seg.dtype) + + action_dist = _strategy3_delta_distribution(delta) + batch_action_dist.append(action_dist) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_value_stats = _tensor_stats(value_t) + first_entropy = 0.0 + record_step(t=0, seg_tensor=seg, value_score=current_score, step_entropy=first_entropy, policy_stats=first_policy_stats) + + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + step_entropy_val = 0.0 + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + seg_prev=seg, + delta_map=delta, + action_distribution=action_dist, + reward_pos=step_reward_pos, + reward_map_tensor=reward_map, + step_entropy=step_entropy_val, + policy_stats=_tensor_stats(policy_logits), + ) + seg = seg_next + selected_t = step_idx + 1 + + pred = threshold_binary_mask(seg.float()).float() + decoder_pred = threshold_binary_mask(refinement_context["decoder_prob"].float()).float() + decoder_dice_vals, decoder_iou_vals = _batch_binary_metrics(decoder_pred, gt_mask.float()) + decoder_baseline = { + "dice": float(np.mean(decoder_dice_vals)) if decoder_dice_vals else 0.0, + "iou": float(np.mean(decoder_iou_vals)) if decoder_iou_vals else 0.0, + "fg_pct": float(decoder_pred.sum().item()) / max(decoder_pred.numel(), 1) * 100.0, + } + final_dice_vals, final_iou_vals = _batch_binary_metrics(pred, gt_mask.float()) + rl_vs_decoder = { + "decoder_dice": decoder_baseline["dice"], + "decoder_iou": decoder_baseline["iou"], + "final_dice": float(np.mean(final_dice_vals)) if final_dice_vals else 0.0, + "final_iou": float(np.mean(final_iou_vals)) if final_iou_vals else 0.0, + "dice_gain": (float(np.mean(final_dice_vals)) if final_dice_vals else 0.0) - decoder_baseline["dice"], + "iou_gain": (float(np.mean(final_iou_vals)) if final_iou_vals else 0.0) - decoder_baseline["iou"], + } + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = selected_t + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": selected_t, + "effective_tmax": effective_tmax, + "decoder_baseline": decoder_baseline, + "rl_vs_decoder": rl_vs_decoder, + } + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + record_step(t=0, seg_tensor=seg) + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, entropy = sample_actions(policy_logits, stochastic=False) + action_dist = _action_histogram(actions, int(policy_logits.shape[1])) + batch_action_dist.append({str(key): float(value) for key, value in action_dist.items()}) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_entropy = float(entropy.detach().item()) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + reward_map = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + action_distribution=action_dist, + reward_pos=step_reward_pos, + ) + seg = seg_next + pred = seg.float() + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = len(rollout_trace) - 1 + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": len(rollout_trace) - 1, + "effective_tmax": effective_tmax, + } + +def _format_probe_deterioration(label: str, probe_payload: dict[str, Any], tmax: int) -> str: + effective_tmax = int(probe_payload.get("effective_tmax", tmax)) + degradation = probe_payload.get("aggregate", {}).get("degradation", {}) + if not degradation: + return f"{label}: no degradation trace" + first_worse = degradation.get("first_worse_than_initial_iou_t") + first_step_drop = degradation.get("first_worse_than_prev_iou_t") + best_t = degradation.get("best_iou_t") + final_t = degradation.get("final_t") + delta_best = degradation.get("delta_final_vs_best_iou") + worst_t = degradation.get("largest_iou_drop_t") + worst_drop = degradation.get("largest_iou_drop_from_best") + return ( + f"{label}: first_worse={first_worse}/{effective_tmax} " + f"first_drop={first_step_drop}/{effective_tmax} " + f"best={best_t}/{effective_tmax} final={final_t}/{effective_tmax} " + f"final-best_iou={float(delta_best):+.4f} " + f"worst={worst_t}/{effective_tmax} drop={float(worst_drop):+.4f}" + ) + +def _optimizer_diagnostics(optimizer: torch.optim.Optimizer) -> list[dict[str, Any]]: + groups: list[dict[str, Any]] = [] + for group_idx, group in enumerate(optimizer.param_groups): + num_tensors = len(group.get("params", [])) + num_elements = int(sum(param.numel() for param in group.get("params", []))) + groups.append( + { + "index": group_idx, + "lr": float(group.get("lr", 0.0)), + "weight_decay": float(group.get("weight_decay", 0.0)), + "num_tensors": num_tensors, + "num_elements": num_elements, + } + ) + return groups + +def _probe_batches_from_indices( + dataset: BUSIDataset, + *, + indices: list[int], + device: torch.device, +) -> list[dict[str, Any]]: + batches: list[dict[str, Any]] = [] + for start in range(0, len(indices), BATCH_SIZE): + batch_indices = indices[start:start + BATCH_SIZE] + if not batch_indices: + continue + images = torch.stack([dataset._images[idx].clone() for idx in batch_indices], dim=0) + masks = torch.stack([dataset._masks[idx].clone() for idx in batch_indices], dim=0) + sample_ids = [Path(dataset.sample_records[idx]["filename"]).stem for idx in batch_indices] + batches.append( + to_device( + { + "image": images, + "mask": masks, + "sample_id": sample_ids, + "dataset": current_dataset_name(), + }, + device, + ) + ) + return batches + +def _fixed_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + return _probe_batches_from_indices( + dataset, + indices=list(range(max_samples)), + device=device, + ) + +def _rolling_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, + epoch: int, + split_tag: str, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + if max_samples <= 0: + return [] + if max_samples >= len(dataset): + indices = list(range(len(dataset))) + else: + rng = random.Random(SEED + stable_int_from_text(f"probe:{split_tag}:epoch:{int(epoch)}")) + indices = rng.sample(range(len(dataset)), k=max_samples) + return _probe_batches_from_indices(dataset, indices=indices, device=device) + +def _probe_batch_id_lists(fixed_batches: list[dict[str, Any]]) -> list[list[str]]: + return [list(batch.get("sample_id", [])) for batch in fixed_batches] + +def _reference_eval_payloads(project_dir: Path, percent: float) -> dict[str, Any]: + refs: dict[str, Any] = {} + pct = percent_label(percent) + strat2_dir = project_dir / "strat2_history" + strat3_dir = project_dir / "strat3_history_best" + strat2_candidates = [ + strat2_dir / f"evaluation_strat2_pc{pct}.json", + strat2_dir / f"evaluation_strat2_pct{pct}.json", + ] + strat3_candidate = strat3_dir / f"evaluation_{pct} (1)" + for candidate in strat2_candidates: + if candidate.exists(): + refs["strategy2_reference"] = load_json(candidate) + break + if strat3_candidate.exists(): + refs["strategy3_best_reference"] = load_json(strat3_candidate) + return refs + +def empty_epoch_diagnostic_payload( + *, + run_type: str, + run_config: dict[str, Any], + bundle: DataBundle, + train_probe_batches: list[dict[str, Any]], + val_probe_batches: list[dict[str, Any]], +) -> dict[str, Any]: + payload = { + "diagnostic_version": 1, + "run_type": run_type, + "strategy": int(run_config["strategy"]), + "dataset_percent": float(bundle.percent), + "run_config": run_config, + "probe_setup": { + "mode": str(run_config.get("epoch_probe_mode", "fixed")), + "train_probe_batches": _probe_batch_id_lists(train_probe_batches), + "val_probe_batches": _probe_batch_id_lists(val_probe_batches), + "train_probe_batch_count": len(train_probe_batches), + "val_probe_batch_count": len(val_probe_batches), + "tmax": int(run_config.get("tmax", DEFAULT_TMAX)), + }, + "epochs": [], + } + payload.update(_reference_eval_payloads(PROJECT_DIR, bundle.percent)) + return payload + +def load_epoch_diagnostic_for_resume( + path: Path, + checkpoint_payload: dict[str, Any] | None, + default_payload: dict[str, Any], +) -> dict[str, Any]: + payload = dict(default_payload) + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) if checkpoint_payload is not None else 0 + if path.exists(): + loaded = load_json(path) + if isinstance(loaded, dict): + payload.update({k: v for k, v in loaded.items() if k != "epochs"}) + epochs = loaded.get("epochs", []) + if isinstance(epochs, list): + payload["epochs"] = [dict(row) for row in epochs if isinstance(row, dict) and int(row.get("epoch", 0)) <= checkpoint_epoch] + if "epochs" not in payload: + payload["epochs"] = [] + return payload + +def _evaluate_probe_batches( + model: nn.Module, + fixed_batches: list[dict[str, Any]], + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + if not fixed_batches: + return {"n_batches": 0, "batch_details": [], "aggregate": {}, "alerts": []} + + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_evaluate_probe_batches") if strategy != 2 else max(int(tmax), 1) + was_training = model.training + batch_details: list[dict[str, Any]] = [] + alerts: list[str] = [] + action_distributions: list[list[dict[str, float]]] = [] + rollout_traces: list[list[dict[str, Any]]] = [] + metric_lists: dict[str, list[float]] = { + key: [] + for key in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95") + } + reward_pos_values: list[float] = [] + pred_fg_values: list[float] = [] + gt_fg_values: list[float] = [] + init_fg_values: list[float] = [] + decoder_dices: list[float] = [] + decoder_ious: list[float] = [] + iou_gains: list[float] = [] + dice_gains: list[float] = [] + + model.eval() + try: + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + with torch.inference_mode(): + for batch_index, batch in enumerate(fixed_batches): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + + rollout_probe = _rollout_probe_trace( + model, + image, + gt_mask, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + pred = rollout_probe["final_pred"].float() + batch_action_dist = rollout_probe["action_distribution"] + reward_pos_pct = float(rollout_probe["reward_pos_pct"]) + init_fg_pct = float(rollout_probe["init_fg_pct"]) + first_action_dist = rollout_probe["first_action_distribution"] + first_policy_stats = rollout_probe["first_policy_stats"] + first_value_stats = rollout_probe["first_value_stats"] + decoder_prob_stats = rollout_probe["decoder_prob_stats"] + first_entropy = rollout_probe["first_entropy"] + rollout_trace = rollout_probe["rollout_trace"] + rollout_summary = rollout_probe["rollout_summary"] + + if batch_action_dist: + action_distributions.append(batch_action_dist) + if rollout_trace: + rollout_traces.append(rollout_trace) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + reward_pos_values.append(reward_pos_pct) + pred_fg_values.append(pred_fg_pct) + gt_fg_values.append(gt_fg_pct) + init_fg_values.append(init_fg_pct) + rl_vs_dec = rollout_probe.get("rl_vs_decoder") + if rl_vs_dec: + decoder_dices.append(rl_vs_dec["decoder_dice"]) + decoder_ious.append(rl_vs_dec["decoder_iou"]) + iou_gains.append(rl_vs_dec["iou_gain"]) + dice_gains.append(rl_vs_dec["dice_gain"]) + + batch_alerts = _numerical_health_check( + { + "pred": pred, + "gt_mask": gt_mask, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "reward_pos_pct": reward_pos_pct, + "first_entropy": first_entropy if first_entropy is not None else 0.0, + }, + prefix=f"probe[{batch_index}]:", + ) + alerts.extend(batch_alerts) + + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt_mask.detach().cpu().numpy().astype(np.uint8) + per_sample: list[dict[str, Any]] = [] + for sample_index in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[sample_index], gt_np[sample_index]) + per_sample.append( + { + "sample_id": sample_ids[sample_index] if sample_index < len(sample_ids) else f"sample_{sample_index}", + **{key: float(value) for key, value in metrics.items()}, + } + ) + for key, value in metrics.items(): + metric_lists.setdefault(key, []).append(float(value)) + + batch_details.append( + { + "batch_index": batch_index, + "sample_ids": sample_ids, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "init_fg_pct": init_fg_pct, + "reward_pos_pct": reward_pos_pct, + "action_distribution": _jsonable_action_distribution(batch_action_dist), + "first_action_distribution": first_action_dist, + "first_entropy": first_entropy, + "decoder_prob_stats": decoder_prob_stats, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "pred_stats": _tensor_stats(pred), + "rollout_trace": rollout_trace, + "rollout_summary": rollout_summary, + "decoder_baseline": rollout_probe.get("decoder_baseline"), + "rl_vs_decoder": rollout_probe.get("rl_vs_decoder"), + "alerts": batch_alerts, + "per_sample": per_sample, + } + ) + finally: + model.train(was_training) + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate_trace = _average_rollout_traces(rollout_traces) + rl_vs_decoder_aggregate: dict[str, Any] = {} + if decoder_dices: + rl_vs_decoder_aggregate = { + "decoder_dice": _summary_stats(decoder_dices), + "decoder_iou": _summary_stats(decoder_ious), + "iou_gain": _summary_stats(iou_gains), + "dice_gain": _summary_stats(dice_gains), + } + return { + "n_batches": len(fixed_batches), + "batch_details": batch_details, + "aggregate": { + "metrics": {key: _summary_stats(values) for key, values in metric_lists.items()}, + "reward_pos_pct": _summary_stats(reward_pos_values), + "pred_fg_pct": _summary_stats(pred_fg_values), + "gt_fg_pct": _summary_stats(gt_fg_values), + "init_fg_pct": _summary_stats(init_fg_values), + "action_distribution": _average_action_distributions(action_distributions, effective_tmax), + "rollout_trace": aggregate_trace, + "degradation": _trajectory_degradation_summary(aggregate_trace), + "rl_vs_decoder": rl_vs_decoder_aggregate, + }, + "alerts": alerts, + "effective_tmax": effective_tmax, + } + +def run_overfit_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + overfit_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + overfit_root = ensure_dir(overfit_root) + ckpt_dir = ensure_dir(overfit_root / "checkpoints") + history_path = checkpoint_history_path(overfit_root, "overfit") + run_config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": "overfit", + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "max_epochs": OVERFIT_N_EPOCHS, + "head_lr": OVERFIT_HEAD_LR, + "encoder_lr": OVERFIT_ENCODER_LR, + "weight_decay": DEFAULT_WEIGHT_DECAY, + "dropout_p": DEFAULT_DROPOUT_P, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "gamma": DEFAULT_GAMMA, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + if strategy == 3: + run_config.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(run_config["strategy3_freeze_bootstrapped_segmentation"]) + ) + run_config.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + run_config.setdefault("decoder_lr", 0.0 if bootstrap_freeze else OVERFIT_HEAD_LR * 0.1) + run_config.setdefault("rl_lr", OVERFIT_HEAD_LR) + run_config.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + run_config.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + run_config.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + run_config.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + run_config.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + run_config.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + run_config.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + run_config.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + run_config.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + run_config.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + run_config.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + run_config.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + run_config.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + run_config.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + run_config.setdefault("elastic_aug_prob", 0.3) + run_config.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + run_config.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + run_config.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + run_config.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + run_config.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + run_config.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + run_config.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + run_config.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + run_config.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + run_config.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + run_config.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + run_config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + run_config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + save_json(overfit_root / "run_config.json", run_config) + set_current_job_params(run_config) + + banner( + f"OVERFIT TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + effective_test_tmax = _resolve_test_iteration_tmax(DEFAULT_TMAX, context="run_overfit_test") if strategy != 2 else max(int(DEFAULT_TMAX), 1) + print( + f"[Overfit] Fixed batches={OVERFIT_N_BATCHES}, epochs={OVERFIT_N_EPOCHS}, " + f"head_lr={OVERFIT_HEAD_LR:.2e}, encoder_lr={OVERFIT_ENCODER_LR:.2e}" + ) + + fixed_batches: list[dict[str, Any]] = [] + for batch_index, batch in enumerate(bundle.train_loader): + fixed_batches.append(to_device(batch, DEVICE)) + if batch_index + 1 >= OVERFIT_N_BATCHES: + break + if not fixed_batches: + raise RuntimeError("Overfit test could not collect any training batches.") + if len(fixed_batches) < OVERFIT_N_BATCHES: + print(f"[Overfit] Warning: only {len(fixed_batches)} train batch(es) available.") + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(overfit_root) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=f"{description} | Overfit Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + + optimizer = make_optimizer( + model, + strategy, + head_lr=OVERFIT_HEAD_LR, + encoder_lr=OVERFIT_ENCODER_LR, + weight_decay=DEFAULT_WEIGHT_DECAY, + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + log_alpha: torch.Tensor | None = None + alpha_optimizer: Adam | None = None + target_entropy = 0.0 + + history = empty_overfit_history() + prev_loss: float | None = None + best_dice = -1.0 + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + expected_run_type="overfit", + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + best_dice = float(checkpoint_payload["best_metric_value"]) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_overfit_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", "overfit"), + } + print( + f"[Resume] overfit run continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{OVERFIT_N_EPOCHS}." + ) + if history["loss"]: + prev_loss = float(history["loss"][-1]) + prev_params = _snapshot_params(model) + start_time = time.time() + + for epoch in range(start_epoch, OVERFIT_N_EPOCHS + 1): + full_dump = epoch <= 5 or epoch % max(OVERFIT_PRINT_EVERY, 1) == 0 or epoch == OVERFIT_N_EPOCHS + epoch_losses: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_rewards: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_losses: list[float] = [] + epoch_entropy: list[float] = [] + epoch_grad_norms: list[float] = [] + epoch_action_dist: list[list[dict[str, float]]] = [] + epoch_reward_pos_pct: list[float] = [] + epoch_pred_fg_pct: list[float] = [] + epoch_gt_fg_pct: list[float] = [] + epoch_alerts: list[str] = [] + + for batch in fixed_batches: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=DEFAULT_TMAX, + critic_loss_weight=DEFAULT_CRITIC_LOSS_WEIGHT, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=OVERFIT_N_EPOCHS, + ) + + epoch_losses.append(float(metrics["loss"])) + epoch_rewards.append(float(metrics["mean_reward"])) + epoch_actor.append(float(metrics["actor_loss"])) + epoch_critic.append(float(metrics["critic_loss"])) + epoch_ce.append(float(metrics["ce_loss"])) + epoch_dice_losses.append(float(metrics["dice_loss"])) + epoch_entropy.append(float(metrics["entropy"])) + epoch_grad_norms.append(float(metrics["grad_norm"])) + epoch_alerts.extend( + _numerical_health_check( + { + "loss": metrics["loss"], + "actor_loss": metrics["actor_loss"], + "critic_loss": metrics["critic_loss"], + "reward": metrics["mean_reward"], + "entropy": metrics["entropy"], + "grad_norm": metrics["grad_norm"], + "ce_loss": metrics["ce_loss"], + "dice_loss": metrics["dice_loss"], + }, + prefix="train:", + ) + ) + + model.eval() + with torch.inference_mode(): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + )["decoder_prob"].float() + else: + init_mask = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + action_dist, pred = _action_distribution( + model, + image, + init_mask, + effective_test_tmax, + use_amp, + amp_dtype, + strategy=strategy, + sample_ids=sample_ids, + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + epoch_action_dist.append(action_dist) + + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + soft_init_mask = refinement_context["decoder_prob"].float() + state_t = model.forward_refinement_state( + refinement_context["base_features"], + soft_init_mask, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, _ = model.forward_from_state(state_t) + first_delta = _strategy3_policy_delta(policy_logits).to(dtype=soft_init_mask.dtype) + first_seg = _strategy3_apply_delta(soft_init_mask, first_delta) + reward_map = compute_refinement_reward( + soft_init_mask, first_seg, gt_mask.float(), + ) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * init_mask + policy_logits = model.forward_policy_only(masked) + first_actions, _, _ = sample_actions(policy_logits, stochastic=False) + first_seg = apply_actions(init_mask, first_actions, num_actions=policy_logits.shape[1]) + reward_map = (init_mask - gt_mask).pow(2) - (first_seg - gt_mask).pow(2) + epoch_reward_pos_pct.append(float((reward_map > 0).float().mean().item() * 100.0)) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + epoch_pred_fg_pct.append(pred_fg_pct) + epoch_gt_fg_pct.append(gt_fg_pct) + dice_values, iou_values = _batch_binary_metrics(pred.float(), gt_mask.float()) + epoch_dices.extend(dice_values) + epoch_ious.extend(iou_values) + epoch_alerts.extend( + _numerical_health_check( + {"pred": pred, "gt_mask": gt_mask}, + prefix="eval:", + ) + ) + model.train() + + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_loss = float(np.mean(epoch_losses)) if epoch_losses else 0.0 + avg_dice = float(np.mean(epoch_dices)) if epoch_dices else 0.0 + avg_iou = float(np.mean(epoch_ious)) if epoch_ious else 0.0 + avg_reward = float(np.mean(epoch_rewards)) if epoch_rewards else 0.0 + avg_actor = float(np.mean(epoch_actor)) if epoch_actor else 0.0 + avg_critic = float(np.mean(epoch_critic)) if epoch_critic else 0.0 + avg_ce = float(np.mean(epoch_ce)) if epoch_ce else 0.0 + avg_dice_loss = float(np.mean(epoch_dice_losses)) if epoch_dice_losses else 0.0 + avg_entropy = float(np.mean(epoch_entropy)) if epoch_entropy else 0.0 + avg_grad_norm = float(np.mean(epoch_grad_norms)) if epoch_grad_norms else 0.0 + avg_reward_pos = float(np.mean(epoch_reward_pos_pct)) if epoch_reward_pos_pct else 0.0 + avg_pred_fg = float(np.mean(epoch_pred_fg_pct)) if epoch_pred_fg_pct else 0.0 + avg_gt_fg = float(np.mean(epoch_gt_fg_pct)) if epoch_gt_fg_pct else 0.0 + + avg_action_dist = _average_action_distributions(epoch_action_dist, effective_test_tmax) + + history["dice"].append(avg_dice) + history["iou"].append(avg_iou) + history["loss"].append(avg_loss) + history["reward"].append(avg_reward) + history["actor_loss"].append(avg_actor) + history["critic_loss"].append(avg_critic) + history["ce_loss"].append(avg_ce) + history["dice_loss"].append(avg_dice_loss) + history["entropy"].append(avg_entropy) + history["grad_norm"].append(avg_grad_norm) + history["action_dist"].append(avg_action_dist) + history["reward_pos_pct"].append(avg_reward_pos) + history["pred_fg_pct"].append(avg_pred_fg) + history["gt_fg_pct"].append(avg_gt_fg) + save_json(history_path, history) + + loss_delta = avg_loss - prev_loss if prev_loss is not None else 0.0 + prev_loss = avg_loss + if epoch_alerts: + print(f"[Overfit][Epoch {epoch}] Numerical alerts: {' | '.join(epoch_alerts)}") + + if full_dump: + current_alpha = float(log_alpha.exp().detach().item()) if log_alpha is not None else 0.0 + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} (delta={loss_delta:+.6f}) " + f"dice={avg_dice:.4f} iou={avg_iou:.4f} reward={avg_reward:+.6f} " + f"entropy={avg_entropy:.6f} alpha={current_alpha:.4f}" + ) + print( + f"[Overfit][Epoch {epoch:03d}] ce={avg_ce:.6f} dice_l={avg_dice_loss:.6f} " + f"grad_norm={avg_grad_norm:.6f} global_grad={grad_stats['global_norm']:.6f}" + ) + if avg_action_dist: + first = avg_action_dist[0] + last = avg_action_dist[-1] + print( + f"[Overfit][Epoch {epoch:03d}] action step0={first} step_last={last} " + f"reward_pos={avg_reward_pos:.2f}%" + ) + print( + f"[Overfit][Epoch {epoch:03d}] pred_fg={avg_pred_fg:.2f}% gt_fg={avg_gt_fg:.2f}% " + f"param_groups={list(param_stats.keys())}" + ) + else: + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} dice={avg_dice:.4f} " + f"iou={avg_iou:.4f} reward={avg_reward:+.6f}" + ) + + row = { + "epoch": epoch, + "dice": avg_dice, + "iou": avg_iou, + "loss": avg_loss, + "reward": avg_reward, + "actor_loss": avg_actor, + "critic_loss": avg_critic, + "ce_loss": avg_ce, + "dice_loss": avg_dice_loss, + "entropy": avg_entropy, + "grad_norm": avg_grad_norm, + "action_dist": avg_action_dist, + "reward_pos_pct": avg_reward_pos, + "pred_fg_pct": avg_pred_fg, + "gt_fg_pct": avg_gt_fg, + } + if avg_dice > best_dice: + best_dice = avg_dice + save_checkpoint( + ckpt_dir / "best.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + if SAVE_LATEST_EVERY_EPOCH: + save_checkpoint( + ckpt_dir / "latest.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + if CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0: + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + peak_dice = max(history["dice"]) if history["dice"] else 0.0 + final_dice = history["dice"][-1] if history["dice"] else 0.0 + summary = { + "run_type": "overfit", + "strategy": strategy, + "peak_dice": peak_dice, + "final_dice": final_dice, + "description": description, + "resumed": resume_source is not None, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "final_epoch": max(len(history["dice"]), start_epoch - 1), + } + if resume_source is not None: + summary["resume_source"] = resume_source + save_json(overfit_root / "summary.json", summary) + print( + f"[Overfit] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} | " + f"peak_dice={peak_dice:.4f}, final_dice={final_dice:.4f}" + ) + + del model + run_cuda_cleanup( + context=f"overfit {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + return {**summary, "history": history} + +def run_configured_overfit_tests( + bundles: dict[float, DataBundle], + *, + model_config: RuntimeModelConfig, +) -> None: + banner("OVERFIT TEST MODE") + for percent in DATASET_PERCENTS: + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=strategy_root_for_percent(strategy, percent, model_config) / "overfit_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + +"""============================================================================= +OPTUNA + ORCHESTRATION +============================================================================= +""" + +def strategy_epochs(strategy: int) -> int: + strategy = _require_supported_strategy(strategy) + if strategy == 2: + return STRATEGY_2_MAX_EPOCHS + if strategy == 3: + return STRATEGY_3_MAX_EPOCHS + raise ValueError(f"Unsupported strategy for epoch selection: {strategy}") + +def suggest_hyperparameters(trial: optuna.trial.Trial, strategy: int) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if strategy == 3: + rl_lr = trial.suggest_float("rl_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + return { + "head_lr": rl_lr, + "encoder_lr": ENCODER_LR_RANGE[0], + "decoder_lr": 0.0, + "strategy3_decoder_ce_weight": 0.0, + "strategy3_decoder_dice_weight": 0.0, + "strategy3_freeze_bootstrapped_segmentation": True, + "strategy3_variant": DEFAULT_STRATEGY3_VARIANT, + "rl_lr": rl_lr, + "weight_decay": trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True), + "dropout_p": trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]), + "tmax": trial.suggest_int("tmax", TMAX_RANGE[0], TMAX_RANGE[1]), + "smp_encoder_proj_dim": trial.suggest_categorical("smp_encoder_proj_dim", [64, 128, 192, 256]), + "critic_loss_weight": trial.suggest_float("critic_loss_weight", 0.10, 1.50), + "strategy3_mc_dropout_enabled": True, + "strategy3_mc_dropout_samples": trial.suggest_categorical("strategy3_mc_dropout_samples", [4, 8, 12]), + "strategy3_mc_dropout_p": trial.suggest_float("strategy3_mc_dropout_p", 0.05, 0.35), + "strategy3_delta_max": trial.suggest_float("strategy3_delta_max", 0.03, 0.20), + "strategy3_sam_attention_grid": trial.suggest_categorical("strategy3_sam_attention_grid", [16, 32, 64]), + "strategy3_r1_progress_weight": trial.suggest_float("strategy3_r1_progress_weight", 0.25, 2.0), + "biou_reward_weight": trial.suggest_float("biou_reward_weight", 0.0, 2.0), + "strategy3_advantage_normalize": trial.suggest_categorical("strategy3_advantage_normalize", [False, True]), + "strategy3_rl_grad_clip_norm": trial.suggest_float("strategy3_rl_grad_clip_norm", 0.5, 4.0), + "strategy3_rl_loss_scale": trial.suggest_float("strategy3_rl_loss_scale", 2.0, 50.0, log=True), + "strategy3_aux_ce_weight": DEFAULT_STRATEGY3_AUX_CE_WEIGHT, + "strategy3_aux_ce_anneal_start_epoch": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + "strategy3_aux_ce_anneal_epochs": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + "strategy3_aux_ce_floor_fraction": DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + "threshold": trial.suggest_float("threshold", 0.35, 0.65), + "elastic_aug_prob": trial.suggest_float("elastic_aug_prob", 0.0, 0.5), + "epoch_probe_mode": DEFAULT_STRATEGY3_PROBE_MODE, + "early_stopping_patience": DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[3], + } + + head_lr = trial.suggest_float("head_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + encoder_lr = trial.suggest_float("encoder_lr", ENCODER_LR_RANGE[0], min(ENCODER_LR_RANGE[1], head_lr), log=True) + weight_decay = trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True) + dropout_p = trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]) + params = { + "head_lr": head_lr, + "encoder_lr": encoder_lr, + "weight_decay": weight_decay, + "dropout_p": dropout_p, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[2], + } + return params + +def _format_hparam_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key in {"head_lr", "encoder_lr", "entropy_lr"}: + return f"{value:.3e}" + return f"{value:.6g}" + return str(value) + +def log_optuna_trial_start( + *, + study_name: str, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + trial_dir: Path, + params: dict[str, Any], + max_epochs: int, +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + lines = [ + "", + "-" * 80, + f"OPTUNA TRIAL START | {run_name}", + "-" * 80, + f"Study name : {study_name}", + f"Run dir : {trial_dir}", + f"Max epochs : {max_epochs}", + f"Objective metric : {_strategy_selection_metric_name(strategy)}", + ] + for key in sorted(params): + lines.append(f"{key:22s}: {_format_hparam_value(key, params[key])}") + tqdm.write("\n".join(lines)) + +def log_optuna_trial_result( + *, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + metric_value: float, + aggregate: dict[str, dict[str, float]], +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + tqdm.write( + f"[{run_name}] completed: {_strategy_selection_metric_name(strategy)}={metric_value:.4f}, " + f"best_test_iou={aggregate['iou']['mean']:.4f}" + ) + +def study_paths_for( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + pct_root = RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}" + strategy_root = pct_root / strategy_dir_name(strategy, model_config) + study_root = strategy_root / "study" + trials_root = strategy_root / "trials" + return strategy_root, study_root, trials_root + +def manual_hparams_key(strategy: int, percent: float) -> str: + return f"{strategy}:{percent_label(percent)}" + +def reset_study_artifacts(strategy: int, percent: float, *, model_config: RuntimeModelConfig) -> None: + strategy_root, study_root, trials_root = study_paths_for(strategy, percent, model_config) + removed_any = False + for path in (study_root, trials_root): + if path.exists(): + shutil.rmtree(path) + removed_any = True + if removed_any: + print( + f"[Optuna Reset] Removed cached study artifacts for strategy={strategy}, " + f"percent={percent_text(percent)} under {strategy_root}." + ) + else: + print( + f"[Optuna Reset] No existing study artifacts found for strategy={strategy}, " + f"percent={percent_text(percent)}." + ) + +class _PlateauPruner(optuna.pruners.BasePruner): + """Prune a trial whose metric has plateaued (no improvement to its + own personal best within a patience window). + + Behaviour: + - During the first *n_warmup_steps* epochs: never prune. + - After warmup, track the trial's own best metric and the epoch + at which it was achieved. + - If *patience_steps* epochs pass without the trial beating its + own best, the trial is pruned (it has stagnated). + """ + + def __init__( + self, + n_warmup_steps: int = 80, + patience_steps: int = 40, + ) -> None: + self._n_warmup_steps = n_warmup_steps + self._patience_steps = patience_steps + + def prune( + self, + study: "optuna.study.Study", + trial: "optuna.trial.FrozenTrial", + ) -> bool: + step = trial.last_step + if step is None or step < self._n_warmup_steps: + return False + + post_warmup = { + s: v for s, v in trial.intermediate_values.items() + if s >= self._n_warmup_steps + } + if not post_warmup: + return False + + best_step = max(post_warmup, key=post_warmup.get) + epochs_since_improvement = step - best_step + return epochs_since_improvement >= self._patience_steps + + +def pruner_for_run() -> optuna.pruners.BasePruner: + if USE_TRIAL_PRUNING: + return _PlateauPruner( + n_warmup_steps=TRIAL_PRUNER_WARMUP_STEPS, + patience_steps=TRIAL_PRUNER_PATIENCE_STEPS, + ) + return optuna.pruners.NopPruner() + +def run_single_job( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + params: dict[str, Any], + max_epochs: int, + trial: optuna.trial.Trial | None, + strategy2_checkpoint_path: str | Path | None = None, + resume_checkpoint_path: Path | None = None, + retrying_from_trial_number: int | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, dict[str, float]]]: + strategy = _require_supported_strategy(strategy) + params = dict(params) + params.setdefault("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS[strategy]) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + if strategy == 3: + params.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(params["strategy3_freeze_bootstrapped_segmentation"]) + ) + params.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + params.setdefault("decoder_lr", 0.0 if bootstrap_freeze else params["head_lr"] * 0.1) + params.setdefault("rl_lr", params["head_lr"]) + params.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + params.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + params.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + params.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + params.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + params.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + params.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + params.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + params.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + params.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + params.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + params.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + params.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + params.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + params.setdefault("elastic_aug_prob", 0.3) + params.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + params.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + params.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + params.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + params.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + params.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + params.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + params.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + params.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + params.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + params.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + set_current_job_params(params) + if "smp_encoder_proj_dim" in params and int(params["smp_encoder_proj_dim"]) != model_config.smp_encoder_proj_dim: + model_config = RuntimeModelConfig.from_payload( + {**model_config.to_payload(), "smp_encoder_proj_dim": int(params["smp_encoder_proj_dim"])} + ).validate() + entropy_target_ratio = float(_job_param("entropy_target_ratio", 0.35)) + entropy_alpha_init = float(_job_param("entropy_alpha_init", 0.12)) + critic_loss_weight = float(_job_param("critic_loss_weight", DEFAULT_CRITIC_LOSS_WEIGHT)) + ensure_dir(run_dir) + run_type = "trial" if trial is not None else "final" + config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": run_type, + "run_name": run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ), + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "train_subset_variant": bundle.split_payload.get("train_subset_variant", 0), + "train_subset_source": bundle.split_payload.get("train_subset_source", "persisted"), + "selected_split_manifest_path": bundle.split_payload.get("selected_split_manifest_path"), + "normalization_cache_path": bundle.split_payload["normalization_cache_path"], + "best_checkpoint_metric_name": _strategy_selection_metric_name(strategy), + "best_checkpoint_metrics": {str(key): value for key, value in BEST_CHECKPOINT_METRICS.items()}, + "save_history_incrementally": bool(SAVE_HISTORY_INCREMENTALLY), + "write_epoch_diagnostic": bool(WRITE_EPOCH_DIAGNOSTIC), + "head_lr": params["head_lr"], + "encoder_lr": params["encoder_lr"], + "weight_decay": params["weight_decay"], + "dropout_p": params["dropout_p"], + "tmax": params["tmax"], + "entropy_lr": params["entropy_lr"], + "max_epochs": max_epochs, + "gamma": DEFAULT_GAMMA, + "critic_loss_weight": critic_loss_weight, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "scheduler_factor": SCHEDULER_FACTOR, + "scheduler_patience": SCHEDULER_PATIENCE, + "scheduler_threshold": SCHEDULER_THRESHOLD, + "scheduler_min_lr": SCHEDULER_MIN_LR, + "execution_mode": EXECUTION_MODE, + "evaluation_checkpoint_mode": EVAL_CHECKPOINT_MODE, + "strategy2_checkpoint_mode": STRATEGY2_CHECKPOINT_MODE, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + config.update({key: value for key, value in params.items() if key not in config}) + config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + if resume_checkpoint_path is not None: + config["resume_checkpoint_path"] = str(Path(resume_checkpoint_path)) + if retrying_from_trial_number is not None: + config["retrying_from_trial_number"] = int(retrying_from_trial_number) + save_json(run_dir / "run_config.json", config) + + model: nn.Module | None = None + try: + model, description, _compiled = build_model( + strategy, + params["dropout_p"], + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + summary, history = train_model( + run_type=run_type, + model_config=model_config, + run_config=config, + model=model, + description=description, + strategy=strategy, + run_dir=run_dir, + bundle=bundle, + max_epochs=max_epochs, + head_lr=params["head_lr"], + encoder_lr=params["encoder_lr"], + weight_decay=params["weight_decay"], + tmax=params["tmax"], + entropy_lr=params["entropy_lr"], + entropy_alpha_init=entropy_alpha_init, + entropy_target_ratio=entropy_target_ratio, + critic_loss_weight=critic_loss_weight, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + dropout_p=params["dropout_p"], + resume_checkpoint_path=resume_checkpoint_path, + trial=trial, + ) + + if trial is not None: + save_json( + run_dir / "summary.json", + { + "params": params, + "best_iou": float(summary["best_val_iou"]), + "best_model_metric_name": str(summary["best_model_metric_name"]), + "best_model_metric": float(summary["best_model_metric"]), + "resumed": bool(resume_checkpoint_path is not None), + "retrying_from_trial_number": retrying_from_trial_number, + }, + ) + return summary, history, {} + + del model + model = None + run_cuda_cleanup() + + aggregate, _per_sample = run_evaluation_for_run( + strategy=strategy, + percent=bundle.percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + return summary, history, aggregate + finally: + if model is not None: + del model + model = None + run_cuda_cleanup() + +def _save_best_params_so_far( + study: optuna.study.Study, + study_root: Path, + strategy: int, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> None: + best, _best_value = _current_optuna_study_best_candidate( + study, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if best is None: + return + params = dict(best.params) + if strategy == 2: + params.setdefault("tmax", DEFAULT_TMAX) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", params) + +def run_study( + strategy: int, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + strategy_root, study_root, trials_root = study_paths_for(strategy, bundle.percent, model_config) + ensure_dir(strategy_root.parent) + strategy_root = ensure_dir(strategy_root) + study_root = ensure_dir(strategy_root / "study") + trials_root = ensure_dir(strategy_root / "trials") + storage_path = study_root / "study.sqlite3" + storage = RDBStorage( + url=f"sqlite:///{storage_path.resolve()}", + heartbeat_interval=OPTUNA_HEARTBEAT_INTERVAL, + grace_period=OPTUNA_HEARTBEAT_GRACE_PERIOD, + ) + sampler = optuna.samplers.TPESampler(seed=SEED) + study_name = ( + f"{MODEL_NAME}_{model_config.backbone_tag()}_{run_identity_slug(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + study = optuna.create_study( + study_name=study_name, + direction=STUDY_DIRECTION, + sampler=sampler, + pruner=pruner_for_run(), + storage=storage, + load_if_exists=LOAD_EXISTING_STUDIES, + ) + existing_trials = [trial for trial in study.trials if trial.state.is_finished()] + if existing_trials: + print( + f"[Optuna Study] Loaded existing study '{study_name}' with " + f"{len(existing_trials)} existing finished trial(s). Running {NUM_TRIALS} new trial(s)." + ) + else: + print(f"[Optuna Study] Starting new study '{study_name}' with {NUM_TRIALS} trial(s).") + + def objective(trial: optuna.trial.Trial) -> float: + trial_dir = ensure_dir(trials_root / f"trial_{trial.number:03d}") + params = suggest_hyperparameters(trial, strategy) + log_optuna_trial_start( + study_name=study_name, + strategy=strategy, + bundle=bundle, + trial=trial, + trial_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + ) + summary: dict[str, Any] | None = None + _history: list[dict[str, Any]] | None = None + aggregate: dict[str, dict[str, float]] | None = None + completed_successfully = False + pruned_by_optuna = False + run_cuda_cleanup() + try: + summary, _history, aggregate = run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + trial=trial, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + metric_value = float(summary["best_model_metric"]) + completed_successfully = True + tqdm.write( + f"[{run_identity_label(strategy=strategy, percent=bundle.percent, trial_number=trial.number, split_payload=bundle.split_payload)}] " + f"completed: {summary['best_model_metric_name']}={metric_value:.4f}" + ) + return metric_value + except optuna.TrialPruned: + pruned_by_optuna = True + raise + finally: + current_trial = trial if (completed_successfully or pruned_by_optuna) else None + current_best_value = None + if summary is not None and summary.get("best_model_metric") is not None: + current_best_value = float(summary["best_model_metric"]) + _save_best_params_so_far( + study, + study_root, + strategy, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if aggregate is not None: + del aggregate + aggregate = None + if _history is not None: + del _history + _history = None + if summary is not None: + del summary + summary = None + prune_optuna_trial_dir(trial_dir) + run_cuda_cleanup(context=f"trial {trial.number:03d} boundary") + + study.optimize(objective, n_trials=NUM_TRIALS, show_progress_bar=True) + + best_trial, best_observed_value = _current_optuna_study_best_candidate(study) + if best_trial is None or best_observed_value is None: + raise RuntimeError( + f"Study '{study_name}' has no trials with recorded best-observed values, so best params cannot be resolved. " + f"Finished trials={len([trial for trial in study.trials if trial.state.is_finished()])}, " + f"configured cap={NUM_TRIALS}." + ) + + best_params = dict(best_trial.params) + if strategy == 2: + best_params.setdefault("tmax", DEFAULT_TMAX) + best_params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", best_params) + optuna_best_value: float | None + try: + optuna_best_value = float(study.best_value) + except Exception: + optuna_best_value = None + save_json( + study_root / "summary.json", + { + "best_params": best_params, + "optimized_param_names": sorted(best_params.keys()), + "best_metric_name": _strategy_selection_metric_name(strategy), + "best_metric_value": float(best_observed_value), + "best_observed_value": float(best_observed_value), + "best_trial_number": int(getattr(best_trial, "number", -1)), + "optuna_best_value": optuna_best_value, + "best_iou": float(best_observed_value) if _strategy_selection_metric_name(strategy) == "val_iou" else None, + "finished_trials": len([trial for trial in study.trials if trial.state.is_finished()]), + "completed_trials": len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + "target_trials": int(NUM_TRIALS), + "ran_trials": int(NUM_TRIALS), + }, + ) + prune_optuna_study_dir(study_root) + if trials_root.exists(): + shutil.rmtree(trials_root, ignore_errors=True) + return best_params + +def run_final_training( + strategy: int, + bundle: DataBundle, + params: dict[str, Any], + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + strategy = _require_supported_strategy(strategy) + final_root = final_root_for_strategy(strategy, bundle.percent, model_config) + if SKIP_EXISTING_FINALS and (final_root / "summary.json").exists(): + print(f"Skipping existing final run: {final_root}") + return + save_json(final_root / "best_params.json", params) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(final_root) + run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=final_root, + params=params, + max_epochs=strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + +def print_environment_summary(model_config: RuntimeModelConfig) -> None: + banner("RUNTIME SUMMARY") + images_dir, annotations_dir = current_dataset_dirs() + print(f"Project dir : {PROJECT_DIR}") + print(f"Data root : {DATA_ROOT}") + print(f"Runs root : {RUNS_ROOT}") + print(f"Dataset name : {current_dataset_name()}") + if current_dataset_name() == "BUSI_with_classes": + print(f"Dataset split policy : {current_busi_with_classes_split_policy()}") + print(f"Images dir : {images_dir}") + print(f"Masks dir : {annotations_dir}") + print(f"Dataset splits json : {current_dataset_splits_json_path()}") + print(f"Split type : {SPLIT_TYPE}") + print(f"Experiment mode : {EXPERIMENT_MODE}") + print(f"Device : {DEVICE}") + print(f"Device source : {DEVICE_FALLBACK_SOURCE}") + print(f"Model name : {MODEL_NAME}") + print(f"Seed : {SEED}") + print(f"PyTorch version : {torch.__version__}") + print(f"Batch size : {BATCH_SIZE}") + print(f"Use AMP : {USE_AMP}") + print(f"Num workers : {NUM_WORKERS}") + print(f"Pin memory : {USE_PIN_MEMORY}") + print(f"CuDNN deterministic : {torch.backends.cudnn.deterministic}") + print(f"CuDNN benchmark : {torch.backends.cudnn.benchmark}") + + if DEVICE.type == "cuda": + props = torch.cuda.get_device_properties(0) + print(f"GPU : {torch.cuda.get_device_name(0)}") + print(f"GPU VRAM : {props.total_memory / (1024 ** 3):.2f} GB") + print(f"AMP dtype : {resolve_amp_dtype(AMP_DTYPE)}") + print(f"Trial pruning : {USE_TRIAL_PRUNING}") + print(f"Backbone family : {model_config.backbone_family}") + if model_config.backbone_family == "custom_vgg": + print(f"VGG feature scales : {model_config.vgg_feature_scales}") + print(f"VGG feature dilation : {model_config.vgg_feature_dilation}") + else: + print(f"SMP encoder : {model_config.smp_encoder_name}") + print(f"SMP encoder depth : {model_config.smp_encoder_depth}") + print(f"SMP encoder proj dim : {model_config.smp_encoder_proj_dim}") + print(f"SMP decoder : {model_config.smp_decoder_type}") + print(f"Strategies : {STRATEGIES}") + print(f"Dataset percents : {[percent_text(value) for value in DATASET_PERCENTS]}") + print(f"Best metrics : {BEST_CHECKPOINT_METRICS}") + print(f"History incremental : {SAVE_HISTORY_INCREMENTALLY}") + print(f"Write diagnostics : {WRITE_EPOCH_DIAGNOSTIC}") + print_imagenet_normalization_status() + print(f"Trials per study : {NUM_TRIALS}") + print(f"Execution mode : {EXECUTION_MODE}") + print(f"Run Optuna : {RUN_OPTUNA}") + print(f"Use saved best params : {USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF}") + print(f"Reset studies/run : {RESET_ALL_STUDIES_EACH_RUN}") + print(f"Load existing studies : {LOAD_EXISTING_STUDIES}") + print(f"Eval ckpt selector : {EVAL_CHECKPOINT_MODE}") + print(f"S2 ckpt selector : {STRATEGY2_CHECKPOINT_MODE}") + print(f"S3 bootstrap from S2 : {STRATEGY3_BOOTSTRAP_FROM_STRATEGY2}") + print(f"S3 freeze default : {DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION}") + print(f"Train resume mode : {TRAIN_RESUME_MODE}") + print(f"Verbose epoch log : {VERBOSE_EPOCH_LOG}") + print(f"Validate every epochs : {VALIDATE_EVERY_N_EPOCHS}") + print(f"Smoke test enabled : {RUN_SMOKE_TEST}") + print(f"Test iter control : {TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {TEST_ITERATION_T}") + print(f"Overfit test enabled : {RUN_OVERFIT_TEST}") + print(f"Overfit batches : {OVERFIT_N_BATCHES}") + print(f"Overfit epochs : {OVERFIT_N_EPOCHS}") + +def maybe_run_strategy_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + if not RUN_SMOKE_TEST: + return + if EXECUTION_MODE == "eval_only": + return + run_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + smoke_root=strategy_root_for_percent(strategy, bundle.percent, model_config) / "smoke_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +"""============================================================================= +REPEATED HOLDOUT INTEGRATION +============================================================================= +""" + +base = sys.modules[__name__] + +REPEATED_HOLDOUT_ROOT = base.RUNS_ROOT / base.MODEL_NAME / "repeated_holdout" +EXPERIMENT_ROOT = REPEATED_HOLDOUT_ROOT / FOLDS_EXPERIMENT_NAME +EXPERIMENT_DB_PATH = EXPERIMENT_ROOT / "experiment_state.sqlite3" +SPLIT_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "splits" +SUBSET_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "subsets" +EXPORTS_DIR = EXPERIMENT_ROOT / "exports" +"""============================================================================= +RUNTIME STATE +============================================================================= +""" + + +@dataclass(frozen=True) +class PercentRepeatSpec: + percent_int: int + fraction: float + repeat_count: int + + +@dataclass(frozen=True) +class FoldRunContext: + split_repeat_index: int + subset_repeat_index: int + percent_int: int + percent_fraction: float + split_seed: int + subset_seed: int + repeat_root: Path + split_manifest_path: Path + subset_manifest_path: Path + + +@dataclass(frozen=True) +class RunKey: + split_repeat_index: int + dataset_percent: int + subset_repeat_index: int + strategy: int + + +CURRENT_FOLD_CONTEXT: FoldRunContext | None = None +LEDGER_CONN: sqlite3.Connection | None = None +PERCENT_SPECS_CACHE: list[PercentRepeatSpec] | None = None + +ORIGINAL_SAVE_JSON = base.save_json +ORIGINAL_PERCENT_ROOT = base.percent_root +ORIGINAL_STRATEGY_ROOT_FOR_PERCENT = base.strategy_root_for_percent +ORIGINAL_FINAL_ROOT_FOR_STRATEGY = base.final_root_for_strategy +ORIGINAL_STUDY_PATHS_FOR = base.study_paths_for +ORIGINAL_SAVE_CHECKPOINT = base.save_checkpoint +ORIGINAL_RUN_EVALUATION_FOR_RUN = base.run_evaluation_for_run + +BASE_RESUME_IDENTITY_KEYS = tuple(base.RESUME_IDENTITY_KEYS) +RUNTIME_ONLY_CONFIG_KEYS = frozenset( + { + "PERCENT_EXECUTION_MODE", + "SELECTED_DATASET_PERCENTS", + "SPLIT_EXECUTION_MODE", + "SELECTED_SPLIT_INDICES", + "PHASE_EXECUTION_MODE", + "SELECTED_PHASES", + "REPEAT_EXECUTION_MODE", + "SELECTED_REPEAT_INDICES", + } +) +PORTABLE_FINGERPRINT_FOLD_KEYS = frozenset( + { + "RESUME_FOLDS", + "REPEATED_HOLDOUT_ROOT", + "EXPERIMENT_ROOT", + "EXPERIMENT_DB_PATH", + } +) + + +"""============================================================================= +UTILITIES +============================================================================= +""" + + +def now_utc_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _jsonify(value: Any) -> Any: + if isinstance(value, Path): + return str(value) + if isinstance(value, dict): + return {str(key): _jsonify(val) for key, val in sorted(value.items(), key=lambda item: str(item[0]))} + if isinstance(value, (list, tuple)): + return [_jsonify(item) for item in value] + if isinstance(value, set): + return [_jsonify(item) for item in sorted(value, key=str)] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return repr(value) + + +def _summary_mean_std(values: list[float]) -> dict[str, float]: + arr = np.array(values, dtype=np.float64) + return { + "mean": float(arr.mean()) if arr.size > 0 else 0.0, + "std": float(arr.std()) if arr.size > 0 else 0.0, + } + + +def _phase_timing_summary_path() -> Path: + return EXPERIMENT_ROOT / "phase_timing_summary.json" + + +def _completed_run_rows_for_phase(phase_index: int) -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + AND split_repeat_index = ? + ORDER BY dataset_percent, subset_repeat_index, strategy + """, + (int(phase_index),), + ).fetchall() + ) + + +def _run_training_elapsed_seconds(row: sqlite3.Row) -> float | None: + run_dir = Path(str(row["run_dir"])) + summary_path = run_dir / "summary.json" + if summary_path.exists(): + try: + summary = base.load_json(summary_path) + if summary.get("elapsed_seconds") is not None: + return float(summary["elapsed_seconds"]) + except Exception as exc: + print(f"[Timing] Could not read {summary_path}: {exc}") + if row["elapsed_seconds"] is not None: + return float(row["elapsed_seconds"]) + return None + + +def write_phase_timing_summary_after_phase(phase_index: int) -> None: + if LEDGER_CONN is None: + return + rows = _completed_run_rows_for_phase(phase_index) + if not rows: + return + + run_entries: list[dict[str, Any]] = [] + phase_values: list[float] = [] + for row in rows: + elapsed = _run_training_elapsed_seconds(row) + entry = { + "phase_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": str(row["run_dir"]), + "training_elapsed_seconds": elapsed, + } + run_entries.append(entry) + if elapsed is not None: + phase_values.append(float(elapsed)) + + phase_stats = _summary_mean_std(phase_values) + existing_payload: dict[str, Any] = {} + summary_path = _phase_timing_summary_path() + if summary_path.exists(): + try: + existing_payload = base.load_json(summary_path) + except Exception as exc: + print(f"[Timing] Could not read existing phase timing summary {summary_path}: {exc}") + + phases_by_index: dict[int, dict[str, Any]] = {} + for phase_payload in existing_payload.get("phases", []): + if isinstance(phase_payload, dict) and phase_payload.get("phase_index") is not None: + phases_by_index[int(phase_payload["phase_index"])] = dict(phase_payload) + phases_by_index[int(phase_index)] = { + "phase_index": int(phase_index), + "completed_strategy_count": len(run_entries), + "completed_strategies": [int(entry["strategy"]) for entry in run_entries], + "runs": run_entries, + "training_elapsed_seconds_mean": phase_stats["mean"], + "training_elapsed_seconds_std": phase_stats["std"], + "updated_at": now_utc_iso(), + } + + phases = [phases_by_index[index] for index in sorted(phases_by_index)] + global_phase_means = [ + float(phase["training_elapsed_seconds_mean"]) + for phase in phases + if phase.get("training_elapsed_seconds_mean") is not None + ] + global_stats = _summary_mean_std(global_phase_means) + payload = { + "scope": "fixed_phase_training_time", + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": len(phases), + "training_elapsed_seconds_mean_across_phases": global_stats["mean"], + "training_elapsed_seconds_std_across_phases": global_stats["std"], + "phases": phases, + "updated_at": now_utc_iso(), + } + atomic_save_json(summary_path, payload) + print( + f"[Timing] Phase {phase_index:03d} training time summary updated -> {summary_path} " + f"(mean={phase_stats['mean']:.2f}s, std={phase_stats['std']:.2f}s)." + ) + + +def validate_hf_backup_settings() -> None: + """Fail fast at startup if backups are enabled but HF env vars are missing. + + Refuses to run rather than discovering hours into training (at the first + backup) that nothing can be uploaded. Disable by setting + ASYNC_REPO_BACKUP_AFTER_PHASE = False if you intentionally want no backups. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + missing = [] + if not repo_id: + missing.append("HF_REPO_ID (target repo, e.g. 'your-username/ADVAI24JUN-backup')") + if not token: + missing.append("HF_TOKEN (Hugging Face write token)") + if missing: + raise RuntimeError( + "Hugging Face backup is enabled (ASYNC_REPO_BACKUP_AFTER_PHASE = True) " + "but required environment variables are not set:\n - " + + "\n - ".join(missing) + + "\n\nSet them before running, e.g.:\n" + " export HF_REPO_ID='your-username/ADVAI24JUN-backup'\n" + " export HF_TOKEN='hf_xxxxxxxxxxxxxxxxxxxxx'\n" + "Or set ASYNC_REPO_BACKUP_AFTER_PHASE = False to run without backups." + ) + + +def _hf_backup_due(phase_index: int) -> bool: + """True only on every HF_BACKUP_EVERY_N_PHASES-th phase (0-indexed boundary).""" + n = max(1, int(HF_BACKUP_EVERY_N_PHASES)) + return (phase_index + 1) % n == 0 + + +def _hf_upload_project(*, label: str) -> bool: + """Create the HF dataset repo if needed and mirror PROJECT_DIR into it. + + Shared by the initial pre-training backup and the per-phase backups. + `upload_large_folder` is resumable and content-addressed: unchanged files are + skipped and an interrupted upload (e.g. a 503) can be safely re-run, so the repo + always converges to the latest project state. Retries with backoff to ride out + transient HF outages. Returns True on a verified successful upload. + """ + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + if not repo_id: + print(f"[Backup] Skipping HF backup ({label}): HF_REPO_ID is not set (export HF_REPO_ID=user/repo).") + return False + if not token: + print(f"[Backup] Skipping HF backup ({label}): HF_TOKEN env var is not set.") + return False + + try: + from huggingface_hub import HfApi + except Exception: + print(f"[Backup] Skipping HF backup ({label}): huggingface_hub not installed (pip install huggingface_hub).") + return False + + api = HfApi(token=token) + try: + api.create_repo(repo_id=repo_id, repo_type=HF_REPO_TYPE, private=True, exist_ok=True) + except Exception as exc: + print(f"[Backup] Could not ensure HF repo {repo_id} exists: {exc}") + + last_exc: Exception | None = None + for attempt in range(1, HF_BACKUP_MAX_RETRIES + 1): + try: + print( + f"[Backup] {label}: uploading project to " + f"hf://{HF_REPO_TYPE}/{repo_id} (attempt {attempt}/{HF_BACKUP_MAX_RETRIES})..." + ) + api.upload_large_folder( + repo_id=repo_id, + repo_type=HF_REPO_TYPE, + folder_path=str(PROJECT_DIR.resolve()), + ignore_patterns=list(HF_IGNORE_PATTERNS), + print_report=True, + ) + print(f"[Backup] {label}: HF backup complete -> {repo_id}.") + return True + except Exception as exc: + last_exc = exc + wait = min(60, 5 * attempt) + print(f"[Backup] {label}: HF upload attempt {attempt} failed: {exc}. Retrying in {wait}s...") + time.sleep(wait) + print(f"[Backup] {label}: HF backup FAILED after {HF_BACKUP_MAX_RETRIES} attempts: {last_exc}") + return False + + +def run_initial_hf_backup() -> None: + """Fresh backup BEFORE any training begins. + + Creates the repo and uploads the current project state synchronously, so the + entire backup pipeline (repo creation, token, upload) is proven before we commit + hours of compute. Later phase backups refresh this same repo. Runs in the + foreground on purpose -- if the first backup cannot complete, we want to know now. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE or not HF_BACKUP_ON_START: + return + print("[Backup] Running initial pre-training backup (this proves the backup pipeline before training)...") + _hf_upload_project(label="Initial backup") + + +def run_repo_backup_after_phase(phase_index: int) -> None: + """Refresh the Hugging Face dataset repo after a training phase. + + Fires only on every HF_BACKUP_EVERY_N_PHASES-th phase so we don't hammer HF. + Runs in a background thread at the call site. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + if not _hf_backup_due(phase_index): + print( + f"[Backup] Phase {phase_index:03d}: skipping HF backup " + f"(uploads every {HF_BACKUP_EVERY_N_PHASES} phases)." + ) + return + _hf_upload_project(label=f"Phase {phase_index:03d}") + + +def atomic_write_text(path: str | Path, text: str) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_write_bytes(path: str | Path, payload: bytes) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_save_json(path: str | Path, payload: Any) -> None: + atomic_write_text(path, json.dumps(payload, indent=2, sort_keys=True)) + + +def atomic_torch_save(path: str | Path, payload: Any) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + torch.save(payload, handle) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def stable_hash(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def stable_int(text: str) -> int: + return base.stable_int_from_text(text) + + +def fold_seed(tag: str) -> int: + return int(base.SEED) + stable_int(tag) + + +def current_split_generation_mode() -> str: + mode = str(SPLIT_GENERATION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_GENERATION_MODES: + raise ValueError( + f"SPLIT_GENERATION_MODE must be one of {SUPPORTED_SPLIT_GENERATION_MODES}, got {mode!r}" + ) + return mode + + +def using_fixed_phase_mode() -> bool: + return current_split_generation_mode() == "fixed_stratified_phases_8_1_1" + + +def primary_unit_name(*, plural: bool = False) -> str: + if using_fixed_phase_mode(): + return "phases" if plural else "phase" + return "splits" if plural else "split" + + +def current_phase_execution_mode() -> str: + mode = str(PHASE_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PHASE_EXECUTION_MODES: + raise ValueError( + f"PHASE_EXECUTION_MODE must be one of {SUPPORTED_PHASE_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def phase_count() -> int: + if isinstance(NUM_PHASES, bool) or int(NUM_PHASES) <= 0: + raise ValueError("NUM_PHASES must be a positive integer.") + return int(NUM_PHASES) + + +def phase_val_offset() -> int: + if isinstance(PHASE_VAL_OFFSET, bool): + raise TypeError("PHASE_VAL_OFFSET must be an integer.") + return int(PHASE_VAL_OFFSET) + + +def phase_indices() -> list[int]: + return list(range(1, phase_count() + 1)) + + +def phase_execution_indices_to_run() -> list[int]: + indices = phase_indices() + if current_phase_execution_mode() == "auto": + return indices + + if not SELECTED_PHASES: + raise ValueError("SELECTED_PHASES must be non-empty when PHASE_EXECUTION_MODE='manual'.") + + selected: list[int] = [] + seen: set[int] = set() + max_index = indices[-1] + for raw_index in SELECTED_PHASES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + f"SELECTED_PHASES entries must be integer phase indices in the range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_PHASES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_PHASES contains duplicate phase index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def phase_fold_indices(phase_index: int) -> tuple[int, int]: + count = phase_count() + if phase_index < 1 or phase_index > count: + raise ValueError(f"Phase index must be in [1, {count}], got {phase_index}.") + val_offset = phase_val_offset() + if val_offset <= 0 or val_offset >= count: + raise ValueError( + f"PHASE_VAL_OFFSET must be in [1, {count - 1}] for {count} phases, got {val_offset}." + ) + test_fold_index = phase_index + val_fold_index = ((phase_index - 1 + val_offset) % count) + 1 + return val_fold_index, test_fold_index + + +def partition_seed() -> int: + if using_fixed_phase_mode(): + return fold_seed(f"phase_partition::{phase_count()}") + return int(base.SEED) + + +def split_generation_display_name() -> str: + if using_fixed_phase_mode(): + return "fixed stratified 10-phase 8/1/1" + return "repeated stratified holdout" + + +def cycle_index_label(index: int) -> str: + return f"{primary_unit_name()}_{int(index):03d}" + + +def cycle_identity_label(index: int) -> str: + return f"{primary_unit_name()}={int(index):03d}" + + +def all_split_repeat_indices() -> list[int]: + if using_fixed_phase_mode(): + return phase_indices() + return list(range(1, int(NUM_STRATIFIED_SPLIT_REPEATS) + 1)) + + +def max_subset_repeat_index() -> int: + return max(int(spec.repeat_count) for spec in percent_specs()) + + +def current_percent_sampling_mode() -> str: + mode = str(PERCENT_SAMPLING_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_SAMPLING_MODES: + raise ValueError( + f"PERCENT_SAMPLING_MODE must be one of {SUPPORTED_PERCENT_SAMPLING_MODES}, got {mode!r}" + ) + return mode + + +def current_split_execution_mode() -> str: + mode = str(SPLIT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_EXECUTION_MODES: + raise ValueError( + f"SPLIT_EXECUTION_MODE must be one of {SUPPORTED_SPLIT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_repeat_execution_mode() -> str: + mode = str(REPEAT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_REPEAT_EXECUTION_MODES: + raise ValueError( + f"REPEAT_EXECUTION_MODE must be one of {SUPPORTED_REPEAT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_percent_execution_mode() -> str: + mode = str(PERCENT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_EXECUTION_MODES: + raise ValueError( + f"PERCENT_EXECUTION_MODE must be one of {SUPPORTED_PERCENT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def split_repeat_indices_to_run() -> list[int]: + if using_fixed_phase_mode(): + return phase_execution_indices_to_run() + + split_indices = all_split_repeat_indices() + if current_split_execution_mode() == "auto": + return split_indices + + if not SELECTED_SPLIT_INDICES: + raise ValueError( + "SELECTED_SPLIT_INDICES must be non-empty when SPLIT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = split_indices[-1] + for raw_index in SELECTED_SPLIT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_SPLIT_INDICES entries must be integer split indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_SPLIT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_SPLIT_INDICES contains duplicate split index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def subset_repeat_indices_to_run() -> list[int]: + repeat_indices = list(range(1, max_subset_repeat_index() + 1)) + if current_repeat_execution_mode() == "auto": + return repeat_indices + + if not SELECTED_REPEAT_INDICES: + raise ValueError( + "SELECTED_REPEAT_INDICES must be non-empty when REPEAT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = repeat_indices[-1] + for raw_index in SELECTED_REPEAT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_REPEAT_INDICES entries must be integer repeat indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_REPEAT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_REPEAT_INDICES contains duplicate repeat index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def percent_specs_to_run() -> list[PercentRepeatSpec]: + all_specs = percent_specs() + if current_percent_execution_mode() == "auto": + return all_specs + + if not SELECTED_DATASET_PERCENTS: + raise ValueError( + "SELECTED_DATASET_PERCENTS must be non-empty when PERCENT_EXECUTION_MODE='manual'." + ) + + all_percent_ints = {spec.percent_int for spec in all_specs} + selected: list[PercentRepeatSpec] = [] + seen: set[int] = set() + for raw_percent in SELECTED_DATASET_PERCENTS: + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "SELECTED_DATASET_PERCENTS entries must be integer percentages in the range [1, 100]." + ) + if raw_percent not in all_percent_ints: + raise ValueError( + f"SELECTED_DATASET_PERCENTS entry {raw_percent} is not defined in " + f"DATASET_PERCENT_REPEAT_COUNTS. Available: {sorted(all_percent_ints)}." + ) + if raw_percent in seen: + raise ValueError(f"SELECTED_DATASET_PERCENTS contains duplicate percent {raw_percent}.") + seen.add(raw_percent) + spec_map = {spec.percent_int: spec for spec in all_specs} + for raw_percent in SELECTED_DATASET_PERCENTS: + selected.append(spec_map[raw_percent]) + selected.sort(key=lambda s: s.percent_int) + return selected + + +def validate_repeated_holdout_settings() -> None: + if using_fixed_phase_mode(): + if phase_count() != 10: + raise ValueError( + f"fixed phase mode requires NUM_PHASES=10, got {phase_count()}." + ) + phase_fold_indices(1) + if current_dataset_name() != "BUSI_with_classes": + raise ValueError( + "fixed phase mode currently supports DATASET_NAME='BUSI_with_classes' only." + ) + if current_busi_with_classes_split_policy() != "stratified": + raise ValueError( + "fixed phase mode requires BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"fixed phase mode requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + current_phase_execution_mode() + else: + if int(NUM_STRATIFIED_SPLIT_REPEATS) <= 0: + raise ValueError("NUM_STRATIFIED_SPLIT_REPEATS must be a positive integer.") + current_split_execution_mode() + current_percent_sampling_mode() + current_repeat_execution_mode() + current_percent_execution_mode() + split_repeat_indices_to_run() + subset_repeat_indices_to_run() + selected_percent_specs = percent_specs_to_run() + if using_fixed_phase_mode(): + if len(selected_percent_specs) != 1 or int(selected_percent_specs[0].percent_int) != 100: + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "If PERCENT_EXECUTION_MODE='manual', set SELECTED_DATASET_PERCENTS=[100]." + ) + + +def repeated_holdout_split_policy() -> str | None: + if base.current_dataset_name() == "BUSI_with_classes": + return base.current_busi_with_classes_split_policy() + return None + + +def active_specs_for_subset_repeat(subset_repeat_index: int) -> list[PercentRepeatSpec]: + return [ + spec + for spec in percent_specs() + if subset_repeat_index <= int(spec.repeat_count) + ] + + +def percent_specs() -> list[PercentRepeatSpec]: + global PERCENT_SPECS_CACHE + if PERCENT_SPECS_CACHE is not None: + return list(PERCENT_SPECS_CACHE) + specs: list[PercentRepeatSpec] = [] + if not DATASET_PERCENT_REPEAT_COUNTS: + raise ValueError("DATASET_PERCENT_REPEAT_COUNTS must contain at least one percentage entry.") + for raw_percent, raw_repeat_count in DATASET_PERCENT_REPEAT_COUNTS.items(): + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "DATASET_PERCENT_REPEAT_COUNTS keys must be integer percentages in the range [1, 100]." + ) + if raw_percent <= 0 or raw_percent > 100: + raise ValueError(f"Invalid dataset percent {raw_percent}; expected an integer in [1, 100].") + if isinstance(raw_repeat_count, bool) or int(raw_repeat_count) <= 0: + raise ValueError( + f"Invalid repeat count for percent {raw_percent}: {raw_repeat_count!r}. Expected a positive integer." + ) + repeat_count = int(raw_repeat_count) + if using_fixed_phase_mode() and raw_percent == 100 and repeat_count != 1: + raise ValueError( + "fixed phase mode requires DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got repeat_count={repeat_count} for percent 100." + ) + if raw_percent == 100 and repeat_count > 1: + print( + f"[Repeated Holdout] Percent 100 was configured with repeat_count={repeat_count}. " + "Collapsing to one effective repeat per split." + ) + repeat_count = 1 + fraction = float(raw_percent) / 100.0 + specs.append(PercentRepeatSpec(percent_int=raw_percent, fraction=fraction, repeat_count=repeat_count)) + specs.sort(key=lambda item: item.percent_int) + if using_fixed_phase_mode(): + if len(specs) != 1 or int(specs[0].percent_int) != 100: + configured = {spec.percent_int: spec.repeat_count for spec in specs} + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "Set DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got {configured}." + ) + PERCENT_SPECS_CACHE = list(specs) + return list(PERCENT_SPECS_CACHE) + + +def fold_experiment_summary(model_config: base.RuntimeModelConfig) -> None: + if using_fixed_phase_mode(): + base.banner("RUNNER FOLDS | FIXED STRATIFIED 10-PHASE 8/1/1") + else: + base.banner("RUNNER FOLDS | REPEATED STRATIFIED HOLDOUT") + print(f"Experiment name : {FOLDS_EXPERIMENT_NAME}") + print(f"Resume folds : {RESUME_FOLDS}") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Dataset name : {base.current_dataset_name()}") + print(f"Split type : {base.SPLIT_TYPE}") + print(f"Split generation mode : {current_split_generation_mode()}") + print(f"Generation display : {split_generation_display_name()}") + if using_fixed_phase_mode(): + print(f"Phase count : {phase_count()}") + print(f"Phase val offset : {phase_val_offset()}") + print(f"Phase execution mode : {current_phase_execution_mode()}") + print("Train percent mode : 100% of phase-train only") + if current_phase_execution_mode() == "manual": + print(f"Selected phases : {phase_execution_indices_to_run()}") + else: + print(f"Split repeats : {NUM_STRATIFIED_SPLIT_REPEATS}") + print(f"Split execution mode : {current_split_execution_mode()}") + if current_split_execution_mode() == "manual": + print(f"Selected split indices: {split_repeat_indices_to_run()}") + print(f"Sampling mode : {current_percent_sampling_mode()}") + print(f"Repeat execution mode : {current_repeat_execution_mode()}") + if current_repeat_execution_mode() == "manual": + print(f"Selected repeat idxs : {subset_repeat_indices_to_run()}") + print(f"Percent execution mode: {current_percent_execution_mode()}") + if current_percent_execution_mode() == "manual": + print(f"Selected percents : {[s.percent_int for s in percent_specs_to_run()]}") + print(f"Strategies : {base.STRATEGIES}") + print( + "Percent repeats : " + + ", ".join(f"{spec.percent_int}% x{spec.repeat_count}" for spec in percent_specs()) + ) + print(f"Execution mode : {base.EXECUTION_MODE}") + print(f"Run smoke test : {base.RUN_SMOKE_TEST}") + print(f"Test iter control : {base.TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {base.TEST_ITERATION_T}") + print(f"Run overfit test : {base.RUN_OVERFIT_TEST}") + print(f"Run Optuna : {base.RUN_OPTUNA}") + print(f"Load existing studies : {base.LOAD_EXISTING_STUDIES}") + print(f"Repo backup enabled : {ASYNC_REPO_BACKUP_AFTER_PHASE}") + if ASYNC_REPO_BACKUP_AFTER_PHASE: + print(f"Repo backup target : hf://{HF_REPO_TYPE}/{HF_REPO_ID or ''}") + print(f"Repo backup cadence : every {HF_BACKUP_EVERY_N_PHASES} phases") + print(f"Initial backup on run : {HF_BACKUP_ON_START}") + print(f"Phase timing summary : {_phase_timing_summary_path()}") + print(f"Backbone : {model_config.backbone_display_name()}") + + +def config_snapshot(model_config: base.RuntimeModelConfig) -> dict[str, Any]: + base_config = { + name: _jsonify(getattr(base, name)) + for name in sorted(dir(base)) + if name.isupper() and not name.startswith("_") + and name not in RUNTIME_ONLY_CONFIG_KEYS + } + return { + "folds_runner": { + "SPLIT_GENERATION_MODE": current_split_generation_mode(), + "NUM_STRATIFIED_SPLIT_REPEATS": int(NUM_STRATIFIED_SPLIT_REPEATS), + "NUM_PHASES": int(NUM_PHASES), + "PHASE_VAL_OFFSET": int(PHASE_VAL_OFFSET), + "DATASET_PERCENT_REPEAT_COUNTS": _jsonify(DATASET_PERCENT_REPEAT_COUNTS), + "PERCENT_SAMPLING_MODE": current_percent_sampling_mode(), + "FOLDS_EXPERIMENT_NAME": str(FOLDS_EXPERIMENT_NAME), + "RESUME_FOLDS": bool(RESUME_FOLDS), + "REPEATED_HOLDOUT_ROOT": str(REPEATED_HOLDOUT_ROOT), + "EXPERIMENT_ROOT": str(EXPERIMENT_ROOT), + "EXPERIMENT_DB_PATH": str(EXPERIMENT_DB_PATH), + }, + "runner": base_config, + "model_config": model_config.to_payload(), + } + + +def portable_config_snapshot_for_fingerprint(snapshot: dict[str, Any]) -> dict[str, Any]: + portable = json.loads(json.dumps(snapshot, sort_keys=True)) + folds_runner = portable.get("folds_runner") + if isinstance(folds_runner, dict): + for key in PORTABLE_FINGERPRINT_FOLD_KEYS: + folds_runner.pop(key, None) + return portable + + +def config_fingerprint(snapshot: dict[str, Any]) -> str: + return stable_hash(json.dumps(portable_config_snapshot_for_fingerprint(snapshot), sort_keys=True)) + + +def dataset_fingerprint(sample_records: list[dict[str, str]]) -> str: + payload = { + "dataset_name": base.current_dataset_name(), + "records": [ + { + "filename": record["filename"], + "image_rel_path": record["image_rel_path"], + "mask_rel_path": record["mask_rel_path"], + "class_label": record.get("class_label"), + } + for record in sample_records + ], + } + return stable_hash(json.dumps(payload, sort_keys=True)) + + +def cycle_dirname(split_repeat_index: int) -> str: + return f"{primary_unit_name()}_{split_repeat_index:03d}" + + +def split_manifest_path(split_repeat_index: int) -> Path: + return SPLIT_MANIFESTS_DIR / f"{cycle_dirname(split_repeat_index)}.json" + + +def subset_manifest_path(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return SUBSET_MANIFESTS_DIR / ( + f"{cycle_dirname(split_repeat_index)}_pct_{percent_int:03d}_repeat_{subset_repeat_index:02d}.json" + ) + + +def repeat_root(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return ( + EXPERIMENT_ROOT + / cycle_dirname(split_repeat_index) + / f"pct_{percent_int:03d}" + / f"repeat_{subset_repeat_index:02d}" + ) + + +def run_dir_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "final") + + +def overfit_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "overfit_test") + + +def strategy_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir) + + +def fold_study_paths_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> tuple[Path, Path, Path]: + strategy_root = strategy_root_for(strategy, ctx, model_config) + return strategy_root, ensure_dir(strategy_root / "study"), ensure_dir(strategy_root / "trials") + + +def select_sample_records() -> tuple[list[dict[str, str]], Path]: + dataset_name = base.current_dataset_name() + images_dir, annotations_dir = base.current_dataset_dirs() + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {base.DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + + matched, missing_masks, missing_images = base.validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + sample_records = base.build_sample_records( + matched, + images_subdir=images_dir.relative_to(dataset_root).as_posix(), + annotations_subdir=annotations_dir.relative_to(dataset_root).as_posix(), + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = base.current_pipeline_check_path() + if pipeline_check_path is not None: + base.validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + return sample_records, dataset_root + + +def record_filenames(records: list[dict[str, str]]) -> list[str]: + return [str(record["filename"]) for record in records] + + +def duplicate_filenames(records: list[dict[str, str]]) -> list[str]: + counts = Counter(record_filenames(records)) + return sorted(name for name, count in counts.items() if count > 1) + + +def format_filename_preview(filenames: list[str], *, limit: int = 5) -> str: + preview = filenames[:limit] + suffix = "" if len(filenames) <= limit else f" ... (+{len(filenames) - limit} more)" + return f"{preview}{suffix}" + + +def overlap_preview(leaks: dict[str, list[str]], *, limit: int = 5) -> str: + if not leaks: + return "[]" + key = sorted(leaks.keys())[0] + return f"{key}: {format_filename_preview(leaks[key], limit=limit)}" + + +def validate_disjoint_record_sets( + record_sets: dict[str, list[dict[str, str]]], + *, + context: str, + expected_filenames: set[str] | None = None, +) -> None: + split_filenames: dict[str, list[str]] = {} + for split_name, records in record_sets.items(): + duplicates = duplicate_filenames(records) + if duplicates: + raise RuntimeError( + f"Duplicate filenames detected inside {context} {split_name}: " + f"{format_filename_preview(duplicates)}" + ) + split_filenames[split_name] = record_filenames(records) + + leaks = base.check_data_leakage(split_filenames) + if leaks: + raise RuntimeError( + f"Data leakage detected for {context}: {overlap_preview(leaks)}" + ) + + if expected_filenames is not None: + actual_filenames = set().union(*(set(values) for values in split_filenames.values())) + missing = sorted(expected_filenames - actual_filenames) + extra = sorted(actual_filenames - expected_filenames) + if missing or extra: + details: list[str] = [] + if missing: + details.append(f"missing={format_filename_preview(missing)}") + if extra: + details.append(f"extra={format_filename_preview(extra)}") + raise RuntimeError( + f"{context} does not match the expected dataset membership: {'; '.join(details)}" + ) + + +def validate_fixed_phase_dataset_requirements(sample_records: list[dict[str, str]]) -> None: + class_distribution = base.compute_class_distribution(sample_records) + if class_distribution is None: + raise RuntimeError( + "fixed phase mode requires class-aware records with class_label metadata." + ) + insufficient = { + label: int(count) + for label, count in class_distribution.items() + if int(count) < phase_count() + } + if insufficient: + raise RuntimeError( + "fixed phase mode requires enough samples to place every class in every phase. " + f"Need >= {phase_count()} samples per class, got {insufficient}." + ) + + +def build_fixed_stratified_phase_folds( + sample_records: list[dict[str, str]], + *, + seed: int, +) -> dict[int, list[dict[str, str]]]: + folds: dict[int, list[dict[str, str]]] = {index: [] for index in phase_indices()} + grouped = base.group_records_by_class(sample_records) + for class_label in sorted(grouped.keys()): + records = base.deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"phase_partition::{phase_count()}::{class_label}", + ) + for record_index, record in enumerate(records): + fold_index = (record_index % phase_count()) + 1 + folds[fold_index].append(dict(record)) + + for fold_index in phase_indices(): + folds[fold_index] = base.deterministic_shuffle_records( + folds[fold_index], + seed=seed, + tag=f"phase_partition::{phase_count()}::fold::{fold_index:03d}", + ) + return folds + + +def validate_fixed_phase_folds( + phase_folds: dict[int, list[dict[str, str]]], + *, + sample_records: list[dict[str, str]], +) -> None: + if sorted(phase_folds.keys()) != phase_indices(): + raise RuntimeError( + f"Expected fixed phase folds for indices {phase_indices()}, got {sorted(phase_folds.keys())}." + ) + validate_disjoint_record_sets( + {f"fold_{fold_index:03d}": records for fold_index, records in sorted(phase_folds.items())}, + context="fixed phase fold partition", + expected_filenames={record["filename"] for record in sample_records}, + ) + + +def build_phase_base_split( + phase_folds: dict[int, list[dict[str, str]]], + *, + phase_index: int, + seed: int, +) -> dict[str, list[dict[str, str]]]: + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + train_records: list[dict[str, str]] = [] + for fold_index in phase_indices(): + if fold_index in {val_fold_index, test_fold_index}: + continue + train_records.extend(dict(record) for record in phase_folds[fold_index]) + return { + "train": base.deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"phase::{phase_index:03d}::train", + ), + "val": base.deterministic_shuffle_records( + phase_folds[val_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::val", + ), + "test": base.deterministic_shuffle_records( + phase_folds[test_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::test", + ), + } + + +def build_base_split_for_repeat(sample_records: list[dict[str, str]], split_seed: int) -> dict[str, list[dict[str, str]]]: + dataset_name = base.current_dataset_name() + if dataset_name == "BUSI_with_classes": + split_policy = repeated_holdout_split_policy() + if split_policy != "stratified": + raise ValueError( + "RUNNER_FOLDS.py requires BUSI_with_classes to use BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + return base.build_stratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + return base.build_unstratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + + +def validate_base_split( + base_splits: dict[str, list[dict[str, str]]], + *, + split_repeat_index: int, + expected_filenames: set[str] | None = None, +) -> None: + context = f"{primary_unit_name()}={split_repeat_index:03d}" + validate_disjoint_record_sets( + base_splits, + context=context, + expected_filenames=expected_filenames, + ) + + +def validate_phase_coverage( + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]], + *, + sample_records: list[dict[str, str]], +) -> None: + expected_phase_indices = phase_indices() + if sorted(phase_splits_by_index.keys()) != expected_phase_indices: + raise RuntimeError( + f"Expected materialized phases {expected_phase_indices}, got {sorted(phase_splits_by_index.keys())}." + ) + + expected_filenames = {record["filename"] for record in sample_records} + train_counts: Counter[str] = Counter() + val_counts: Counter[str] = Counter() + test_counts: Counter[str] = Counter() + + for phase_index, phase_splits in sorted(phase_splits_by_index.items()): + validate_disjoint_record_sets( + phase_splits, + context=f"phase={phase_index:03d}", + expected_filenames=expected_filenames, + ) + train_counts.update(record_filenames(phase_splits["train"])) + val_counts.update(record_filenames(phase_splits["val"])) + test_counts.update(record_filenames(phase_splits["test"])) + + expected_counts = { + "train": phase_count() - 2, + "val": 1, + "test": 1, + } + counters_by_name = { + "train": train_counts, + "val": val_counts, + "test": test_counts, + } + for split_name, expected_count in expected_counts.items(): + offending = sorted( + filename + for filename in expected_filenames + if int(counters_by_name[split_name].get(filename, 0)) != expected_count + ) + if offending: + raise RuntimeError( + f"Invalid global phase coverage for {split_name}: expected each filename to appear " + f"{expected_count} time(s), offenders={format_filename_preview(offending)}" + ) + + +def build_subset_for_repeat( + train_records: list[dict[str, str]], + *, + percent_fraction: float, + subset_seed: int, +) -> list[dict[str, str]]: + if percent_fraction >= 1.0: + return [dict(record) for record in train_records] + subsets = base.build_nested_train_subsets( + train_records, + [percent_fraction], + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + return subsets[base.percent_label(percent_fraction)] + + +def build_incremental_subset_chain( + train_records: list[dict[str, str]], + *, + active_specs: list[PercentRepeatSpec], + subset_seed: int, +) -> dict[str, list[dict[str, str]]]: + fractions = [spec.fraction for spec in active_specs] + return base.build_nested_train_subsets( + train_records, + fractions, + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + + +def iter_manifested_contexts( + split_repeat_indices: list[int] | None = None, + subset_repeat_indices: list[int] | None = None, +) -> Iterator[FoldRunContext]: + selected_indices = all_split_repeat_indices() if split_repeat_indices is None else list(split_repeat_indices) + selected_subset_repeats = ( + subset_repeat_indices_to_run() if subset_repeat_indices is None else list(subset_repeat_indices) + ) + selected_subset_repeat_set = set(selected_subset_repeats) + for split_repeat_index in selected_indices: + for spec in percent_specs_to_run(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + if subset_repeat_index not in selected_subset_repeat_set: + continue + yield load_context(split_repeat_index, spec.percent_int, subset_repeat_index) + + +def validate_subset_records( + train_records: list[dict[str, str]], + subset_records: list[dict[str, str]], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + train_filenames = {record["filename"] for record in train_records} + subset_filenames = [record["filename"] for record in subset_records] + cycle_context = f"{primary_unit_name()}={split_repeat_index:03d}" + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames found in subset {cycle_context}, " + f"percent={percent_int}, repeat={subset_repeat_index}." + ) + outside_train = sorted(set(subset_filenames) - train_filenames) + if outside_train: + raise RuntimeError( + f"Subset contains filenames outside the base train split for " + f"{cycle_context}, percent={percent_int}, repeat={subset_repeat_index}: {outside_train[:5]}" + ) + + +"""============================================================================= +SQLITE LEDGER +============================================================================= +""" + + +def require_ledger() -> sqlite3.Connection: + if LEDGER_CONN is None: + raise RuntimeError("Ledger is not initialized.") + return LEDGER_CONN + + +def ledger_execute(sql: str, params: tuple[Any, ...] = ()) -> sqlite3.Cursor: + conn = require_ledger() + cursor = conn.execute(sql, params) + conn.commit() + return cursor + + +def setup_ledger(path: Path) -> sqlite3.Connection: + ensure_dir(path.parent) + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=FULL") + conn.execute( + """ + CREATE TABLE IF NOT EXISTS experiment_meta ( + experiment_name TEXT PRIMARY KEY, + config_fingerprint TEXT NOT NULL, + config_json TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + base_seed INTEGER NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS split_manifests ( + split_repeat_index INTEGER PRIMARY KEY, + split_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_count INTEGER NOT NULL, + val_count INTEGER NOT NULL, + test_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS subset_manifests ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_subset_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS run_status ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + dataset_fraction REAL NOT NULL, + split_seed INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + split_manifest_path TEXT NOT NULL, + subset_manifest_path TEXT NOT NULL, + run_dir TEXT NOT NULL, + status TEXT NOT NULL, + stage TEXT NOT NULL, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at TEXT, + updated_at TEXT NOT NULL, + heartbeat_at TEXT, + completed_at TEXT, + last_epoch INTEGER, + latest_checkpoint_path TEXT, + best_checkpoint_path TEXT, + evaluation_path TEXT, + best_metric_name TEXT, + best_metric_value REAL, + elapsed_seconds REAL, + error_text TEXT, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS final_metrics ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + metric_name TEXT NOT NULL, + mean REAL NOT NULL, + std REAL, + run_dir TEXT NOT NULL, + evaluation_path TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy, metric_name) + ) + """ + ) + conn.commit() + return conn + + +def fetch_one(sql: str, params: tuple[Any, ...]) -> sqlite3.Row | None: + return require_ledger().execute(sql, params).fetchone() + + +def load_run_status(key: RunKey) -> sqlite3.Row | None: + return fetch_one( + """ + SELECT * + FROM run_status + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + + +def upsert_experiment_meta( + *, + snapshot: dict[str, Any], + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO experiment_meta ( + experiment_name, + config_fingerprint, + config_json, + dataset_fingerprint, + base_seed, + created_at + ) VALUES (?, ?, ?, ?, ?, ?) + """, + ( + FOLDS_EXPERIMENT_NAME, + config_hash, + json.dumps(snapshot, sort_keys=True), + data_hash, + int(base.SEED), + now_utc_iso(), + ), + ) + + +def existing_experiment_meta() -> sqlite3.Row | None: + return fetch_one( + "SELECT * FROM experiment_meta WHERE experiment_name = ?", + (FOLDS_EXPERIMENT_NAME,), + ) + + +def ledger_row_count(table_name: str) -> int: + row = require_ledger().execute(f"SELECT COUNT(*) AS count FROM {table_name}").fetchone() + return int(row["count"]) if row is not None else 0 + + +def upsert_split_manifest_row( + *, + split_repeat_index: int, + split_seed: int, + manifest_path: Path, + train_count: int, + val_count: int, + test_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO split_manifests ( + split_repeat_index, + split_seed, + manifest_path, + train_count, + val_count, + test_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + split_seed, + str(manifest_path.resolve()), + train_count, + val_count, + test_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_subset_manifest_row( + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, + subset_seed: int, + manifest_path: Path, + subset_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO subset_manifests ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + subset_seed, + manifest_path, + train_subset_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + percent_int, + subset_repeat_index, + subset_seed, + str(manifest_path.resolve()), + subset_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_run_plan_row( + *, + key: RunKey, + dataset_fraction: float, + split_seed: int, + subset_seed: int, + split_manifest: Path, + subset_manifest: Path, + run_dir: Path, +) -> None: + existing = load_run_status(key) + if existing is not None: + return + ledger_execute( + """ + INSERT INTO run_status ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + dataset_fraction, + split_seed, + subset_seed, + split_manifest_path, + subset_manifest_path, + run_dir, + status, + stage, + attempt_count, + updated_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + dataset_fraction, + split_seed, + subset_seed, + str(split_manifest.resolve()), + str(subset_manifest.resolve()), + str(run_dir.resolve()), + "planned", + "manifested", + 0, + now_utc_iso(), + ), + ) + + +def mark_stale_running_as_interrupted() -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE status = 'running' + """, + (now_utc_iso(),), + ) + + +def mark_run_running(key: RunKey, *, stage: str) -> None: + row = load_run_status(key) + attempt_count = 1 if row is None else int(row["attempt_count"]) + 1 + started_at = row["started_at"] if row is not None else None + if not started_at: + started_at = now_utc_iso() + ledger_execute( + """ + UPDATE run_status + SET status = 'running', + stage = ?, + attempt_count = ?, + started_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + attempt_count, + started_at, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_stage(key: RunKey, *, stage: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET stage = ?, + status = 'running', + updated_at = ?, + heartbeat_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def update_run_checkpoint_progress( + key: RunKey, + *, + checkpoint_path: Path, + epoch: int, + best_metric_name: str, + best_metric_value: float, + elapsed_seconds: float, +) -> None: + column_name = "best_checkpoint_path" if checkpoint_path.name == "best.pt" else "latest_checkpoint_path" + sql = f""" + UPDATE run_status + SET {column_name} = ?, + last_epoch = ?, + best_metric_name = ?, + best_metric_value = ?, + elapsed_seconds = ?, + status = 'running', + stage = 'training', + heartbeat_at = ?, + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """ + ledger_execute( + sql, + ( + str(checkpoint_path.resolve()), + int(epoch), + str(best_metric_name), + float(best_metric_value), + float(elapsed_seconds), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_failed(key: RunKey, error_text: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'failed', + updated_at = ?, + error_text = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + error_text, + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_interrupted(key: RunKey) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def ingest_evaluation_into_db(key: RunKey, evaluation_path: Path, run_dir: Path) -> None: + payload = base.load_json(evaluation_path) + metrics = payload.get("metrics", {}) + ledger_execute( + """ + DELETE FROM final_metrics + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + conn = require_ledger() + for metric_name, metric_payload in metrics.items(): + conn.execute( + """ + INSERT INTO final_metrics ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + metric_name, + mean, + std, + run_dir, + evaluation_path + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + str(metric_name), + float(metric_payload.get("mean", 0.0)), + float(metric_payload.get("std")) if metric_payload.get("std") is not None else None, + str(run_dir.resolve()), + str(evaluation_path.resolve()), + ), + ) + conn.commit() + best_metric_name = str(payload.get("best_metric_name", "")) + best_metric_value = None + if best_metric_name and best_metric_name in metrics: + best_metric_value = float(metrics[best_metric_name]["mean"]) + ledger_execute( + """ + UPDATE run_status + SET status = 'completed', + stage = 'done', + evaluation_path = ?, + best_metric_name = COALESCE(?, best_metric_name), + best_metric_value = COALESCE(?, best_metric_value), + completed_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + str(evaluation_path.resolve()), + best_metric_name or None, + best_metric_value, + now_utc_iso(), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def completed_run_rows() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + ORDER BY split_repeat_index, dataset_percent, subset_repeat_index, strategy + """ + ).fetchall() + ) + + +def metric_rows_for_completed_runs() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT + rs.split_repeat_index, + rs.dataset_percent, + rs.subset_repeat_index, + rs.strategy, + rs.run_dir, + rs.split_manifest_path, + rs.subset_manifest_path, + rs.evaluation_path, + fm.metric_name, + fm.mean AS metric_mean, + fm.std AS metric_std + FROM final_metrics fm + JOIN run_status rs + ON rs.split_repeat_index = fm.split_repeat_index + AND rs.dataset_percent = fm.dataset_percent + AND rs.subset_repeat_index = fm.subset_repeat_index + AND rs.strategy = fm.strategy + WHERE rs.status = 'completed' + ORDER BY rs.split_repeat_index, rs.dataset_percent, rs.subset_repeat_index, rs.strategy, fm.metric_name + """ + ).fetchall() + ) + + +def export_stats() -> None: + ensure_dir(EXPORTS_DIR) + rows = metric_rows_for_completed_runs() + split_manifest_cache: dict[str, dict[str, Any]] = {} + + def split_manifest_payload(path_text: str) -> dict[str, Any]: + cached = split_manifest_cache.get(path_text) + if cached is None: + cached = base.load_json(Path(path_text)) + split_manifest_cache[path_text] = cached + return cached + + raw_rows_by_run: dict[tuple[int, int, int, int], dict[str, Any]] = {} + for row in rows: + key = ( + int(row["split_repeat_index"]), + int(row["dataset_percent"]), + int(row["subset_repeat_index"]), + int(row["strategy"]), + ) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + raw_row = raw_rows_by_run.setdefault( + key, + { + "split_repeat_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": row["run_dir"], + "split_manifest_path": row["split_manifest_path"], + "subset_manifest_path": row["subset_manifest_path"], + "evaluation_path": row["evaluation_path"], + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + }, + ) + if manifest_payload.get("phase_index") is not None: + raw_row["phase_index"] = int(manifest_payload["phase_index"]) + raw_row["phase_val_fold_index"] = int(manifest_payload["phase_val_fold_index"]) + raw_row["phase_test_fold_index"] = int(manifest_payload["phase_test_fold_index"]) + raw_row[f"{row['metric_name']}_mean"] = float(row["metric_mean"]) + raw_row[f"{row['metric_name']}_std"] = ( + float(row["metric_std"]) if row["metric_std"] is not None else None + ) + + raw_rows = list(raw_rows_by_run.values()) + raw_rows.sort( + key=lambda item: ( + item["split_repeat_index"], + item["dataset_percent"], + item["subset_repeat_index"], + item["strategy"], + ) + ) + + if raw_rows: + raw_fieldnames = sorted({key for row in raw_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=raw_fieldnames) + writer.writeheader() + writer.writerows(raw_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "raw_run_metrics.csv") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", raw_rows) + else: + atomic_write_text(EXPORTS_DIR / "raw_run_metrics.csv", "") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", []) + + grouped: dict[tuple[int, int], dict[str, Any]] = {} + for row in rows: + group_key = (int(row["dataset_percent"]), int(row["strategy"])) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + bucket = grouped.setdefault( + group_key, + { + "dataset_percent": int(row["dataset_percent"]), + "strategy": int(row["strategy"]), + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + "_phase_indices": set(), + "_metric_values": {}, + }, + ) + if manifest_payload.get("phase_index") is not None: + bucket["_phase_indices"].add(int(manifest_payload["phase_index"])) + bucket["_metric_values"].setdefault(str(row["metric_name"]), []).append( + { + "mean": float(row["metric_mean"]), + "std": float(row["metric_std"]) if row["metric_std"] is not None else None, + } + ) + + aggregated_rows: list[dict[str, Any]] = [] + for (_percent_int, _strategy), bucket in sorted(grouped.items()): + row = { + "dataset_percent": bucket["dataset_percent"], + "strategy": bucket["strategy"], + "split_generation_mode": bucket["split_generation_mode"], + } + metric_values: dict[str, list[dict[str, float | None]]] = bucket["_metric_values"] + row["n_runs"] = max((len(values) for values in metric_values.values()), default=0) + if bucket["_phase_indices"]: + phase_indices = sorted(int(value) for value in bucket["_phase_indices"]) + row["completed_phase_count"] = len(phase_indices) + row["completed_phases"] = ",".join(str(value) for value in phase_indices) + for metric_name, values in sorted(metric_values.items()): + means = [value["mean"] for value in values] + stds = [value["std"] for value in values if value["std"] is not None] + row[f"{metric_name}_mean"] = float(base.np.mean(means)) if means else None + row[f"{metric_name}_std"] = float(base.np.std(means)) if means else None + row[f"{metric_name}_within_run_std_mean"] = float(base.np.mean(stds)) if stds else None + aggregated_rows.append(row) + + if aggregated_rows: + aggregated_fieldnames = sorted({key for row in aggregated_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=aggregated_fieldnames) + writer.writeheader() + writer.writerows(aggregated_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", aggregated_rows) + else: + atomic_write_text(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv", "") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", []) + + +"""============================================================================= +BASE MODULE PATCHES +============================================================================= +""" + + +def patched_save_json(path: str | Path, payload: Any) -> None: + atomic_save_json(path, payload) + + +def _context_matches_percent(ctx: FoldRunContext | None, percent: float) -> bool: + if ctx is None: + return False + return abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12 + + +def patched_percent_root(percent: float) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return ensure_dir(CURRENT_FOLD_CONTEXT.repeat_root) + return ORIGINAL_PERCENT_ROOT(percent) + + +def patched_strategy_root_for_percent( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return strategy_root_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STRATEGY_ROOT_FOR_PERCENT(strategy, percent, model_config) + + +def patched_final_root_for_strategy( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return run_dir_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_FINAL_ROOT_FOR_STRATEGY(strategy, percent, model_config) + + +def patched_study_paths_for( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return fold_study_paths_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STUDY_PATHS_FOR(strategy, percent, model_config) + + +def patched_save_checkpoint( + path: Path, + *, + run_type: str, + model: base.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + scaler: Any, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": base._unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + base.validate_checkpoint_payload( + Path(path), + payload, + required_keys=base.checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + atomic_torch_save(path, payload) + base.write_checkpoint_manifest(path, payload) + + if run_type != "final" or CURRENT_FOLD_CONTEXT is None: + return + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(run_config["strategy"]), + ) + update_run_checkpoint_progress( + run_key, + checkpoint_path=Path(path), + epoch=int(epoch), + best_metric_name=str(best_metric_name), + best_metric_value=float(best_metric_value), + elapsed_seconds=float(elapsed_seconds), + ) + + +def patched_run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: base.DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, dict[str, float]]: + if CURRENT_FOLD_CONTEXT is not None and LEDGER_CONN is not None: + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(strategy), + ) + mark_run_stage(run_key, stage="evaluating") + return ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +def install_base_patches() -> None: + base.save_json = patched_save_json + base.percent_root = patched_percent_root + base.strategy_root_for_percent = patched_strategy_root_for_percent + base.final_root_for_strategy = patched_final_root_for_strategy + base.study_paths_for = patched_study_paths_for + base.save_checkpoint = patched_save_checkpoint + base.run_evaluation_for_run = patched_run_evaluation_for_run + base.RESUME_IDENTITY_KEYS = BASE_RESUME_IDENTITY_KEYS + ( + "folds_experiment_name", + "split_repeat_index", + "subset_repeat_index", + "split_seed", + "subset_seed", + "base_split_manifest_path", + "subset_manifest_path", + ) + if RESUME_FOLDS and base.RUN_OPTUNA: + base.LOAD_EXISTING_STUDIES = True + + +@contextmanager +def activate_context(ctx: FoldRunContext) -> Iterator[None]: + global CURRENT_FOLD_CONTEXT + previous = CURRENT_FOLD_CONTEXT + CURRENT_FOLD_CONTEXT = ctx + try: + yield + finally: + CURRENT_FOLD_CONTEXT = previous + + +"""============================================================================= +EXPERIMENT PLAN MATERIALIZATION +============================================================================= +""" + + +def create_split_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + base_splits: dict[str, list[dict[str, str]]], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "base_splits": { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + }, + "counts": {split_name: len(records) for split_name, records in base_splits.items()}, + "class_distributions": { + split_name: base.compute_class_distribution(records) + for split_name, records in base_splits.items() + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + payload["phase_partition_seed"] = int(split_seed) + return payload + + +def create_subset_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + percent_int: int, + percent_fraction: float, + subset_repeat_index: int, + subset_seed: int, + split_manifest: Path, + base_splits: dict[str, list[dict[str, str]]], + subset_records: list[dict[str, str]], + subset_sampling_source: str, + sampling_chain_dataset_percents: list[int], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "dataset_percent": percent_int, + "dataset_fraction": percent_fraction, + "subset_repeat_index": subset_repeat_index, + "subset_seed": subset_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "subset_sampling_source": subset_sampling_source, + "sampling_chain_dataset_percents": [int(value) for value in sampling_chain_dataset_percents], + "parent_split_manifest_path": str(split_manifest.resolve()), + "train_records": [dict(record) for record in subset_records], + "val_records": [dict(record) for record in base_splits["val"]], + "test_records": [dict(record) for record in base_splits["test"]], + "base_train_records": [dict(record) for record in base_splits["train"]], + "counts": { + "base_train": len(base_splits["train"]), + "train_subset": len(subset_records), + "val": len(base_splits["val"]), + "test": len(base_splits["test"]), + }, + "class_distributions": { + "base_train": base.compute_class_distribution(base_splits["train"]), + "train_subset": base.compute_class_distribution(subset_records), + "val": base.compute_class_distribution(base_splits["val"]), + "test": base.compute_class_distribution(base_splits["test"]), + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + return payload + + +def validate_materialized_phase_manifests(*, sample_records: list[dict[str, str]]) -> None: + if not using_fixed_phase_mode(): + return + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + for phase_index in phase_indices(): + manifest_path = split_manifest_path(phase_index) + if not manifest_path.exists(): + raise RuntimeError(f"Missing phase manifest for phase={phase_index:03d}: {manifest_path}") + payload = base.load_json(manifest_path) + if str(payload.get("split_generation_mode", "")).strip().lower() != "fixed_stratified_phases_8_1_1": + raise RuntimeError( + f"Expected fixed phase split_generation_mode in {manifest_path}, got " + f"{payload.get('split_generation_mode')!r}." + ) + if int(payload.get("phase_index", -1)) != phase_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_index={payload.get('phase_index')!r}, " + f"expected {phase_index}." + ) + if int(payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_count={payload.get('phase_count')!r}, " + f"expected {phase_count()}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_val_fold_index=" + f"{payload.get('phase_val_fold_index')!r}, expected {val_fold_index}." + ) + if int(payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_test_fold_index=" + f"{payload.get('phase_test_fold_index')!r}, expected {test_fold_index}." + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + + +def validate_materialized_subset_manifests() -> None: + if not using_fixed_phase_mode(): + return + for phase_index in phase_indices(): + split_payload = base.load_json(split_manifest_path(phase_index)) + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + manifest_path = subset_manifest_path(phase_index, spec.percent_int, subset_repeat_index) + if not manifest_path.exists(): + raise RuntimeError( + f"Missing subset manifest for phase={phase_index:03d}, " + f"percent={spec.percent_int}, repeat={subset_repeat_index}: {manifest_path}" + ) + subset_payload = base.load_json(manifest_path) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=phase_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + +def validate_loaded_context_payloads( + split_payload: dict[str, Any], + subset_payload: dict[str, Any], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + base_splits = { + split_name: [dict(record) for record in split_payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_base_split(base_splits, split_repeat_index=split_repeat_index) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + validate_disjoint_record_sets( + { + "train_subset": train_records, + "val": val_records, + "test": test_records, + }, + context=( + f"{primary_unit_name()}={split_repeat_index:03d}, " + f"percent={percent_int}, repeat={subset_repeat_index}" + ), + ) + validate_subset_records( + base_splits["train"], + train_records, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + if set(record_filenames(base_train_records)) != set(record_filenames(base_splits["train"])): + raise RuntimeError( + f"Subset manifest base train records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(val_records)) != set(record_filenames(base_splits["val"])): + raise RuntimeError( + f"Subset manifest validation records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(test_records)) != set(record_filenames(base_splits["test"])): + raise RuntimeError( + f"Subset manifest test records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if using_fixed_phase_mode(): + phase_index = int(split_payload.get("phase_index", split_repeat_index)) + if int(split_payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{split_payload.get('phase_count')!r}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(split_payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{split_payload.get('phase_val_fold_index')!r}." + ) + if int(split_payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{split_payload.get('phase_test_fold_index')!r}." + ) + if int(subset_payload.get("phase_index", phase_index)) != phase_index: + raise RuntimeError( + f"Subset manifest phase_index={subset_payload.get('phase_index')!r} does not match " + f"phase={phase_index:03d}." + ) + if int(subset_payload.get("phase_count", phase_count())) != phase_count(): + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{subset_payload.get('phase_count')!r}." + ) + if int(subset_payload.get("phase_val_fold_index", val_fold_index)) != val_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{subset_payload.get('phase_val_fold_index')!r}." + ) + if int(subset_payload.get("phase_test_fold_index", test_fold_index)) != test_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{subset_payload.get('phase_test_fold_index')!r}." + ) + + +def materialize_experiment_plan( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + ensure_dir(EXPERIMENT_ROOT) + ensure_dir(SPLIT_MANIFESTS_DIR) + ensure_dir(SUBSET_MANIFESTS_DIR) + ensure_dir(EXPORTS_DIR) + + snapshot = config_snapshot(model_config) + config_hash = config_fingerprint(snapshot) + data_hash = dataset_fingerprint(sample_records) + sampling_mode = current_percent_sampling_mode() + upsert_experiment_meta(snapshot=snapshot, config_hash=config_hash, data_hash=data_hash) + expected_filenames = {record["filename"] for record in sample_records} + partition_seed_value = partition_seed() + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + fixed_phase_folds: dict[int, list[dict[str, str]]] | None = None + if using_fixed_phase_mode(): + validate_fixed_phase_dataset_requirements(sample_records) + fixed_phase_folds = build_fixed_stratified_phase_folds( + sample_records, + seed=partition_seed_value, + ) + validate_fixed_phase_folds(fixed_phase_folds, sample_records=sample_records) + + for split_repeat_index in all_split_repeat_indices(): + phase_index = None + phase_val_fold_index = None + phase_test_fold_index = None + if using_fixed_phase_mode(): + if fixed_phase_folds is None: + raise RuntimeError("Fixed phase folds were not initialized.") + split_seed = partition_seed_value + phase_index = split_repeat_index + phase_val_fold_index, phase_test_fold_index = phase_fold_indices(phase_index) + base_splits = build_phase_base_split( + fixed_phase_folds, + phase_index=phase_index, + seed=split_seed, + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + } + else: + split_seed = fold_seed(f"split::{split_repeat_index}") + base_splits = build_base_split_for_repeat(sample_records, split_seed) + validate_base_split( + base_splits, + split_repeat_index=split_repeat_index, + expected_filenames=expected_filenames, + ) + + split_manifest = split_manifest_path(split_repeat_index) + split_payload = create_split_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + base_splits=base_splits, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(split_manifest, split_payload) + upsert_split_manifest_row( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + manifest_path=split_manifest, + train_count=len(base_splits["train"]), + val_count=len(base_splits["val"]), + test_count=len(base_splits["test"]), + config_hash=config_hash, + data_hash=data_hash, + ) + + if sampling_mode == "independent": + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + subset_seed = fold_seed( + f"subset::{split_repeat_index}::{spec.percent_int}::{subset_repeat_index}" + ) + subset_records = build_subset_for_repeat( + base_splits["train"], + percent_fraction=spec.fraction, + subset_seed=subset_seed, + ) + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="independent", + sampling_chain_dataset_percents=[spec.percent_int], + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + continue + + max_subset_repeat_index = max(spec.repeat_count for spec in percent_specs()) + for subset_repeat_index in range(1, max_subset_repeat_index + 1): + active_specs = active_specs_for_subset_repeat(subset_repeat_index) + if not active_specs: + continue + subset_seed = fold_seed(f"subset::{split_repeat_index}::repeat::{subset_repeat_index}") + subset_chain = build_incremental_subset_chain( + base_splits["train"], + active_specs=active_specs, + subset_seed=subset_seed, + ) + chain_dataset_percents = [spec.percent_int for spec in active_specs] + for spec in active_specs: + subset_records = subset_chain[base.percent_label(spec.fraction)] + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="incremental_chain", + sampling_chain_dataset_percents=chain_dataset_percents, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + + if using_fixed_phase_mode(): + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +def validate_or_create_experiment( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + meta = existing_experiment_meta() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not RESUME_FOLDS and not ignore_resume_folds_gate: + if meta is not None: + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + if meta is None: + if ledger_row_count("split_manifests") > 0 or ledger_row_count("run_status") > 0: + raise RuntimeError( + f"Experiment DB {EXPERIMENT_DB_PATH} contains run state but is missing experiment metadata." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + current_snapshot = config_snapshot(model_config) + current_hash = config_fingerprint(current_snapshot) + current_data_hash = dataset_fingerprint(sample_records) + stored_config_hash = str(meta["config_fingerprint"]) + stored_portable_hash = "" + try: + stored_config_json = json.loads(str(meta["config_json"])) + stored_portable_hash = config_fingerprint(stored_config_json) + except Exception as exc: + print(f"[Resume] Could not recompute portable config fingerprint from stored metadata: {exc}") + if stored_config_hash != current_hash and stored_portable_hash != current_hash: + raise RuntimeError( + f"Existing experiment config fingerprint does not match current configuration for {EXPERIMENT_ROOT}." + ) + if stored_config_hash != current_hash and stored_portable_hash == current_hash: + print( + "[Resume] Accepted existing experiment metadata with a portable config fingerprint match " + "(machine-specific paths/runtime resume flag changed)." + ) + if str(meta["dataset_fingerprint"]) != current_data_hash: + raise RuntimeError( + f"Existing experiment dataset fingerprint does not match current dataset contents for {EXPERIMENT_ROOT}." + ) + if using_fixed_phase_mode(): + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +"""============================================================================= +RUNTIME BUNDLE CONSTRUCTION +============================================================================= +""" + + +def load_context(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> FoldRunContext: + split_payload = base.load_json(split_manifest_path(split_repeat_index)) + subset_payload = base.load_json(subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index)) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + return FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=percent_int, + percent_fraction=float(subset_payload["dataset_fraction"]), + split_seed=int(split_payload["split_seed"]), + subset_seed=int(subset_payload["subset_seed"]), + repeat_root=repeat_root(split_repeat_index, percent_int, subset_repeat_index), + split_manifest_path=split_manifest_path(split_repeat_index), + subset_manifest_path=subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index), + ) + + +def build_fold_data_bundle(ctx: FoldRunContext) -> base.DataBundle: + split_payload = base.load_json(ctx.split_manifest_path) + subset_payload = base.load_json(ctx.subset_manifest_path) + dataset_root = Path(base.current_dataset_dirs()[0]).parent.resolve() + phase_index = ( + int(split_payload.get("phase_index", ctx.split_repeat_index)) + if str(split_payload.get("split_generation_mode", "")).strip().lower() == "fixed_stratified_phases_8_1_1" + else None + ) + cycle_token = f"phase{phase_index:03d}" if phase_index is not None else f"split{ctx.split_repeat_index:03d}" + normalization_cache_path = ( + ctx.repeat_root + / ( + f"norm_stats_{base.normalization_cache_tag()}_{base.SPLIT_TYPE}_{ctx.percent_int:03d}pct_" + f"{cycle_token}_repeat{ctx.subset_repeat_index:02d}.json" + ) + ) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + + base_train_class_distribution = base.compute_class_distribution(base_train_records) + train_class_distribution = base.compute_class_distribution(train_records) + val_class_distribution = base.compute_class_distribution(val_records) + test_class_distribution = base.compute_class_distribution(test_records) + + base.print_loaded_class_distribution( + split_type=base.SPLIT_TYPE, + train_subset_key=str(ctx.percent_int), + base_train_records=base_train_records, + train_records=train_records, + val_records=val_records, + test_records=test_records, + ) + + global_mean, global_std, normalization_source = base.compute_busi_statistics( + dataset_root=dataset_root, + sample_records=train_records, + cache_path=normalization_cache_path, + ) + + payload = { + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "dataset_splits_path": str(ctx.split_manifest_path.resolve()), + "dataset_root": str(dataset_root), + "split_source": ( + "fixed_phase_manifest" + if phase_index is not None + else "repeated_holdout_manifest" + ), + "split_generation_mode": str(split_payload.get("split_generation_mode", current_split_generation_mode())), + "split_type": base.SPLIT_TYPE, + "percent_sampling_mode": str( + subset_payload.get("percent_sampling_mode", split_payload.get("percent_sampling_mode", "independent")) + ), + "dataset_percent": ctx.percent_fraction, + "train_subset_key": str(ctx.percent_int), + "train_subset_variant": int(ctx.subset_repeat_index), + "train_subset_source": str(subset_payload.get("subset_sampling_source", "repeated_holdout_repeat")), + "selected_split_manifest_path": str(ctx.subset_manifest_path.resolve()), + "sampling_chain_dataset_percents": [ + int(value) for value in subset_payload.get("sampling_chain_dataset_percents", [ctx.percent_int]) + ], + "base_train_count": len(base_train_records), + "train_count": len(train_records), + "val_count": len(val_records), + "test_count": len(test_records), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(normalization_cache_path.resolve()), + "normalization_source": normalization_source, + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "split_seed": ctx.split_seed, + "subset_seed": ctx.subset_seed, + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "folds_experiment_root": str(EXPERIMENT_ROOT.resolve()), + } + if phase_index is not None: + payload["phase_index"] = phase_index + payload["phase_count"] = int(split_payload["phase_count"]) + payload["phase_val_fold_index"] = int(split_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(split_payload["phase_test_fold_index"]) + + base.print_split_summary(payload) + base.print_normalization_summary(payload) + + train_split_name = ( + f"train {base.SPLIT_TYPE} {ctx.percent_int}% phase{phase_index:03d}" + if phase_index is not None + else f"train {base.SPLIT_TYPE} {ctx.percent_int}% split{ctx.split_repeat_index:03d}" + ) + val_split_name = ( + f"val {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"val {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + test_split_name = ( + f"test {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"test {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + loader_prefix = ( + f"{base.SPLIT_TYPE}:{ctx.percent_int}:phase{phase_index:03d}" + if phase_index is not None + else f"{base.SPLIT_TYPE}:{ctx.percent_int}:split{ctx.split_repeat_index:03d}" + ) + + train_ds = base.BUSIDataset( + train_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=True, + split_name=train_split_name, + ) + val_ds = base.BUSIDataset( + val_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=val_split_name, + ) + test_ds = base.BUSIDataset( + test_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=test_split_name, + ) + bundle = base.DataBundle( + percent=ctx.percent_fraction, + split_payload=payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=base.make_loader( + train_ds, + shuffle=True, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:train", + ), + val_loader=base.make_loader( + val_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:val", + ), + test_loader=base.make_loader( + test_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:test", + ), + ) + base.print_preload_summary(bundle) + return bundle + + +def release_bundle(bundle: base.DataBundle | None) -> None: + if bundle is None: + return + del bundle + gc.collect() + base.run_cuda_cleanup(context="bundle release") + + +def checkpoint_candidates(run_dir: Path) -> list[Path]: + return [ + run_dir / "checkpoints" / "latest.pt", + run_dir / "checkpoints" / "best.pt", + ] + + +def resolve_resume_checkpoint(run_dir: Path) -> Path | None: + for candidate in checkpoint_candidates(run_dir): + if candidate.exists(): + return candidate + return None + + +"""============================================================================= +RUN EXECUTION +============================================================================= +""" + + +def strategy_requires_strategy2_checkpoint(strategy: int) -> bool: + return ( + base.EXECUTION_MODE == "train_eval" and strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 + ) + + +def fold_param_metadata(ctx: FoldRunContext) -> dict[str, Any]: + subset_payload = base.load_json(ctx.subset_manifest_path) + payload = { + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "split_repeat_index": int(ctx.split_repeat_index), + "subset_repeat_index": int(ctx.subset_repeat_index), + "split_seed": int(ctx.split_seed), + "subset_seed": int(ctx.subset_seed), + "split_generation_mode": str(subset_payload.get("split_generation_mode", current_split_generation_mode())), + "percent_sampling_mode": str(subset_payload.get("percent_sampling_mode", current_percent_sampling_mode())), + "subset_sampling_source": str(subset_payload.get("subset_sampling_source", "independent")), + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + } + if subset_payload.get("phase_index") is not None: + payload["phase_index"] = int(subset_payload["phase_index"]) + payload["phase_count"] = int(subset_payload["phase_count"]) + payload["phase_val_fold_index"] = int(subset_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(subset_payload["phase_test_fold_index"]) + return payload + + +def finalize_run_from_artifacts(key: RunKey, run_dir: Path) -> bool: + evaluation_path = run_dir / "evaluation.json" + if not evaluation_path.exists(): + return False + ingest_evaluation_into_db(key, evaluation_path, run_dir) + export_stats() + return True + + +def execute_final_run( + *, + strategy: int, + ctx: FoldRunContext, + bundle: base.DataBundle, + model_config: base.RuntimeModelConfig, +) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None: + raise RuntimeError(f"Run plan row is missing for {run_key}.") + if str(row["status"]) == "completed": + return + if str(row["status"]) == "failed": + print( + f"[{split_generation_display_name()}] Skipping failed run " + f"{run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload)}." + ) + return + + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + + strategy2_checkpoint_path: str | Path | None = None + run_name = run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload) + with activate_context(ctx): + banner_prefix = "PHASE RUN" if using_fixed_phase_mode() else "REPEATED HOLDOUT RUN" + base.banner(f"{banner_prefix} | {run_name}") + if strategy_requires_strategy2_checkpoint(strategy): + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + + if base.EXECUTION_MODE == "eval_only": + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + summary_path = run_dir / "summary.json" + if summary_path.exists() and not (run_dir / "evaluation.json").exists(): + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + if base.RUN_SMOKE_TEST: + base.maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + params = base.resolve_job_params( + strategy, + ctx.percent_fraction, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = {**params, **fold_param_metadata(ctx)} + + resume_checkpoint_path = None + if str(row["status"]) in {"interrupted", "running"}: + resume_checkpoint_path = resolve_resume_checkpoint(run_dir) + + mark_run_running(run_key, stage="training") + base.run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=run_dir, + params=params, + max_epochs=base.strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + + +def reconcile_existing_artifacts(ctx: FoldRunContext, strategy: int, model_config: base.RuntimeModelConfig) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + return + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + if (run_dir / "summary.json").exists(): + mark_run_interrupted(run_key) + mark_run_stage(run_key, stage="evaluating") + return + if resolve_resume_checkpoint(run_dir) is not None: + mark_run_interrupted(run_key) + + +def maybe_reset_study_artifacts(model_config: base.RuntimeModelConfig) -> None: + if not base.RESET_ALL_STUDIES_EACH_RUN or not base.RUN_OPTUNA: + return + if RESUME_FOLDS: + print( + f"[{split_generation_display_name()}] RESET_ALL_STUDIES_EACH_RUN ignored because RESUME_FOLDS=True." + ) + return + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + with activate_context(ctx): + for strategy in base.STRATEGIES: + base.reset_study_artifacts(strategy, ctx.percent_fraction, model_config=model_config) + + +def run_overfit_mode(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE OVERFIT TEST MODE") + else: + base.banner("REPEATED HOLDOUT OVERFIT TEST MODE") + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + base.run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=overfit_root_for(strategy, ctx, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_eval_only_without_ledger(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EVAL ONLY | LEDGER BYPASSED") + else: + base.banner("REPEATED HOLDOUT EVAL ONLY | LEDGER BYPASSED") + print("[Eval Only] Skipping experiment ledger and evaluating directly from manifests and checkpoints.") + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + run_name = run_identity_label( + strategy=strategy, + percent=ctx.percent_fraction, + split_payload=bundle.split_payload, + ) + base.banner(f"EVAL ONLY | {run_name}") + base.run_evaluation_for_run( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir_for(strategy, ctx, model_config), + strategy2_checkpoint_path=None, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_pass_for_statuses( + statuses: set[str], + *, + model_config: base.RuntimeModelConfig, +) -> None: + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + pending_strategies = [] + for strategy in base.STRATEGIES: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is not None and str(row["status"]) in statuses: + pending_strategies.append(int(strategy)) + if not pending_strategies: + continue + + bundle = build_fold_data_bundle(ctx) + phase_had_error = False + try: + for strategy in pending_strategies: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + continue + try: + execute_final_run( + strategy=strategy, + ctx=ctx, + bundle=bundle, + model_config=model_config, + ) + except KeyboardInterrupt: + phase_had_error = True + mark_run_interrupted(run_key) + raise + except Exception: + phase_had_error = True + error_text = traceback.format_exc() + mark_run_failed(run_key, error_text) + print(error_text) + finally: + if using_fixed_phase_mode(): + write_phase_timing_summary_after_phase(ctx.split_repeat_index) + if not phase_had_error: + threading.Thread( + target=run_repo_backup_after_phase, + args=(ctx.split_repeat_index,), + daemon=True, + ).start() + release_bundle(bundle) + + +"""============================================================================= +MAIN +============================================================================= +""" + + +def run_repeated_holdout_main() -> int: + global LEDGER_CONN + + validate_repeated_holdout_settings() + if not str(FOLDS_EXPERIMENT_NAME).strip(): + raise ValueError("FOLDS_EXPERIMENT_NAME must be non-empty.") + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"RUNNER_FOLDS.py currently requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + + install_base_patches() + base.SAVE_LATEST_EVERY_EPOCH = True + + base.set_global_seed(base.SEED) + model_config = base.current_model_config() + fold_experiment_summary(model_config) + + if str(base.EXECUTION_MODE).strip().lower() == "eval_only": + select_sample_records() + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + return run_eval_only_without_ledger(model_config) + + sample_records, _dataset_root = select_sample_records() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not ignore_resume_folds_gate and not RESUME_FOLDS and EXPERIMENT_ROOT.exists(): + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + if ( + not ignore_resume_folds_gate + and RESUME_FOLDS + and not EXPERIMENT_DB_PATH.exists() + and EXPERIMENT_ROOT.exists() + and any(EXPERIMENT_ROOT.iterdir()) + ): + raise RuntimeError( + f"Experiment root {EXPERIMENT_ROOT} already exists without a valid SQLite ledger at {EXPERIMENT_DB_PATH}. " + "Refusing to attach to ambiguous state." + ) + + LEDGER_CONN = setup_ledger(EXPERIMENT_DB_PATH) + try: + validate_or_create_experiment(sample_records=sample_records, model_config=model_config) + mark_stale_running_as_interrupted() + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + for strategy in base.STRATEGIES: + reconcile_existing_artifacts(ctx, int(strategy), model_config) + + export_stats() + maybe_reset_study_artifacts(model_config) + + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + + run_pass_for_statuses({"interrupted", "running"}, model_config=model_config) + run_pass_for_statuses({"planned"}, model_config=model_config) + export_stats() + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EXECUTION COMPLETE") + else: + base.banner("REPEATED HOLDOUT COMPLETE") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Raw metrics export : {EXPORTS_DIR / 'raw_run_metrics.csv'}") + print(f"Aggregate export : {EXPORTS_DIR / 'aggregated_metrics_by_percent_strategy.csv'}") + return 0 + finally: + if LEDGER_CONN is not None: + LEDGER_CONN.close() + LEDGER_CONN = None + +def run_single_run_main() -> int: + global DATASET_PERCENTS + banner("MLR ALL STRATEGIES BAYES RUNNER") + DATASET_PERCENTS = normalize_dataset_percents(DATASET_PERCENTS) + set_global_seed(SEED) + model_config = current_model_config() + dataset_name = current_dataset_name() + images_dir, annotations_dir = current_dataset_dirs() + if EXECUTION_MODE not in {"train_eval", "eval_only"}: + raise ValueError(f"EXECUTION_MODE must be 'train_eval' or 'eval_only', got {EXECUTION_MODE!r}") + if SPLIT_TYPE not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"SPLIT_TYPE must be one of {SUPPORTED_SPLIT_TYPES}, got {SPLIT_TYPE!r}") + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + ensure_specific_checkpoint_scope("EVAL_CHECKPOINT_MODE", EVAL_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("STRATEGY2_CHECKPOINT_MODE", STRATEGY2_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("TRAIN_RESUME_MODE", TRAIN_RESUME_MODE) + + print_environment_summary(model_config) + split_registry, split_source = load_or_create_dataset_splits( + images_dir=images_dir, + annotations_dir=annotations_dir, + split_json_path=current_dataset_splits_json_path(), + train_fractions=DATASET_PERCENTS, + seed=SEED, + ) + + bundles: dict[float, DataBundle] = {} + for percent in DATASET_PERCENTS: + bundles[percent] = build_data_bundle(percent, split_registry, split_source) + + if RUN_OVERFIT_TEST: + run_configured_overfit_tests(bundles, model_config=model_config) + banner("OVERFIT TESTS COMPLETE") + return 0 + + if RESET_ALL_STUDIES_EACH_RUN: + if RUN_OPTUNA: + banner("RESETTING OPTUNA STUDIES") + for strategy in STRATEGIES: + for percent in DATASET_PERCENTS: + reset_study_artifacts(strategy, percent, model_config=model_config) + else: + print("[Optuna Reset] Skipped because RUN_OPTUNA=False.") + + try: + for percent in DATASET_PERCENTS: + banner(f"PERCENT STAGE | {percent_text(percent)}") + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and EXECUTION_MODE == "train_eval" and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + + if EXECUTION_MODE == "train_eval": + maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = resolve_job_params( + strategy, + percent, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + banner( + f"FINAL RETRAIN | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_final_training( + strategy, + bundle, + params, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + else: + banner( + f"EVAL ONLY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_evaluation_for_run( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=final_root_for_strategy(strategy, percent, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + except Exception: + banner("RUN FAILED") + traceback.print_exc() + return 1 + + banner("ALL DONE") + return 0 + + +def main() -> int: + validate_hf_backup_settings() + run_initial_hf_backup() + if EXPERIMENT_MODE not in SUPPORTED_EXPERIMENT_MODES: + raise ValueError( + f"EXPERIMENT_MODE must be one of {SUPPORTED_EXPERIMENT_MODES}, got {EXPERIMENT_MODE!r}" + ) + if EXPERIMENT_MODE == "repeated_holdout": + return run_repeated_holdout_main() + return run_single_run_main() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ablations/abl_ab7_no_sam.py b/ablations/abl_ab7_no_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..8e0db9ceb7bbd79687b171c46fc51db9ddeec133 --- /dev/null +++ b/ablations/abl_ab7_no_sam.py @@ -0,0 +1,13598 @@ +from __future__ import annotations +import csv +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from datetime import datetime, timezone +import gc +import hashlib +import importlib +import inspect +import json +import math +import os +import random +import shutil +import sqlite3 +import subprocess +import sys +import tarfile +import tempfile +import threading +import time +import traceback +import weakref +from collections import Counter +from contextlib import contextmanager, nullcontext +from pathlib import Path +from typing import Any, Iterator, Literal + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import segmentation_models_pytorch as smp +os.environ.setdefault("NNPACK_DISABLE", "1") +import torch +torch.backends.nnpack.enabled = False +import torch.nn as nn +import torch.nn.functional as F +from optuna.storages import RDBStorage +from PIL import Image as PILImage +from scipy import ndimage +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +"""============================================================================= +EDIT ME +============================================================================= +""" + +PROJECT_DIR = Path(__file__).resolve().parent.parent # ABLATION: repo root (this copy lives in ablations/) +TRANSUNET_REPO_DIR = PROJECT_DIR / "TransUNet" +TRANSUNET_VIT_NAME = "R50-ViT-B_16" +TRANSUNET_N_SKIP = 3 +TRANSUNET_PRETRAINED_PATH = PROJECT_DIR / "model" / "vit_checkpoint" / "imagenet21k" / "R50+ViT-B_16.npz" + +RUNS_ROOT = PROJECT_DIR / "runs" +HARD_CODED_PARAM_DIR = PROJECT_DIR +MODEL_NAME = "Segformer_B0_revamped_nt_2" + +EXPERIMENT_MODE = "repeated_holdout" # "single_run" or "repeated_holdout" +SUPPORTED_EXPERIMENT_MODES = ("single_run", "repeated_holdout") +SPLIT_GENERATION_MODE = "fixed_stratified_phases_8_1_1" # "repeated_holdout" or "fixed_stratified_phases_8_1_1" +SUPPORTED_SPLIT_GENERATION_MODES = ("repeated_holdout", "fixed_stratified_phases_8_1_1") +NUM_STRATIFIED_SPLIT_REPEATS = 5 +NUM_PHASES = 10 +PHASE_VAL_OFFSET = 1 +DATASET_PERCENT_REPEAT_COUNTS: dict[int, int] = { + # 5: 4, + # 15: 3, + # 30: 3, + # 50: 2, + 100: 1, +} +PERCENT_SAMPLING_MODE = "incremental" # "independent" or "incremental" +SUPPORTED_PERCENT_SAMPLING_MODES = ("independent", "incremental") +PERCENT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PERCENT_EXECUTION_MODES = ("auto", "manual") +SELECTED_DATASET_PERCENTS: list[int] = [100] +SPLIT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_SPLIT_EXECUTION_MODES = ("auto", "manual") +SELECTED_SPLIT_INDICES: list[int] = [1] + +PHASE_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PHASE_EXECUTION_MODES = ("auto", "manual") +SELECTED_PHASES: list[int] = [1] # used only when PHASE_EXECUTION_MODE="manual" + +REPEAT_EXECUTION_MODE = "auto" # "auto" or "manual" +SUPPORTED_REPEAT_EXECUTION_MODES = ("auto", "manual") +SELECTED_REPEAT_INDICES: list[int] = [1] + +FOLDS_EXPERIMENT_NAME = "stratified_holdout_v1" +RESUME_FOLDS = False +ASYNC_REPO_BACKUP_AFTER_PHASE = False +# Hugging Face dataset repo to mirror the project into. Set via env so nothing is +# hardcoded: export HF_REPO_ID="your-username/ADVAI24JUN-backup" and HF_TOKEN=... +HF_REPO_ID = os.environ.get("HF_REPO_ID", "") +HF_REPO_TYPE = "dataset" +# Only upload after every Nth phase (boundary), so we don't hammer HF every phase. +HF_BACKUP_EVERY_N_PHASES = 1 +HF_BACKUP_MAX_RETRIES = 5 +# Run one synchronous backup BEFORE training starts: it creates the repo and uploads +# the current project state, proving the whole backup pipeline works before we commit +# hours of compute. Phase backups later refresh this same repo. +HF_BACKUP_ON_START = False +# Glob patterns excluded from the upload (matched against repo-relative paths). +HF_IGNORE_PATTERNS = ( + "**/.git/**", + "**/__pycache__/**", + "**/.ipynb_checkpoints/**", + "**/.cache/**", + "**/.venv/**", + "*.pyc", + ".DS_Store", +) + +DATASET_NAME = "BUSI_with_classes" # "BUSI" or "BUSI_with_classes" +SUPPORTED_DATASET_NAMES = ("BUSI", "BUSI_with_classes") +DATA_ROOT = PROJECT_DIR / DATASET_NAME +BUSI_WITH_CLASSES_SPLIT_POLICY = "stratified" # "balanced_train" or "stratified" +SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES = ("balanced_train", "stratified") + +SUPPORTED_STRATEGIES: tuple[int, ...] = (2,3) +STRATEGIES = [2,3] +DATASET_PERCENTS = [] #ignored in the folding [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1.0] #, 0.5, 1.0] #, 0.5, 1.0] +SPLIT_TYPE = "80_10_10" +SUPPORTED_SPLIT_TYPES = ("80_10_10", "70_10_20") +DATASET_SPLITS_JSON = PROJECT_DIR / "dataset_splits.json" +DATASET_SPLITS_VERSION = 1 +TRAIN_SUBSET_VARIANT = 1 # 0 uses the persisted subset; >0 deterministically resamples only the train subset from the frozen base train split. +NUM_TRIALS = 30 +STUDY_DIRECTION = "maximize" +BEST_CHECKPOINT_METRICS = { + 2: "val_iou", + 3: "val_refine_score", +} +OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR = "best_observed_objective" +OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR = "best_observed_objective_name" +SUPPORTED_CHECKPOINT_METRICS = { + "val_loss", + "val_dice", + "val_iou", + "val_biou", + "val_refine_score", + "val_decoder_dice", + "val_decoder_iou", + "val_decoder_biou", + "val_dice_gain", + "val_iou_gain", + "val_biou_gain", + "val_actor_loss", + "val_critic_loss", + "val_ce_loss", + "val_dice_loss", + "val_reward", + "val_entropy", +} + +SEED = 42 +IMG_SIZE = 128 +# d = 0 -> auto (floor(0.02 * diag)); any positive int overrides. +# Recommended: 0 (auto) -> resolves to ~4 px for IMG_SIZE=128. +BOUNDARY_IOU_D: int = 0 +BATCH_SIZE = 16 # Recommended to prevent OOM +NUM_WORKERS = 128 # Recommended with RAM-preloaded datasets to avoid worker RAM duplication. +USE_PIN_MEMORY = True +USE_PERSISTENT_WORKERS = True +PRELOAD_TO_RAM = True + +SMP_ENCODER_NAME = "mit_b0" +SMP_ENCODER_WEIGHTS = "imagenet" +SMP_ENCODER_DEPTH = 5 +SMP_ENCODER_PROJ_DIM = 192 +SMP_DECODER_TYPE = "Segformer" +BACKBONE_FAMILY = "smp" # "smp" or "custom_vgg" +ENABLE_CUSTOM_VGG_BACKBONE = False +VGG_FEATURE_SCALES = 4 +VGG_FEATURE_DILATION = 1 + +USE_IMAGENET_NORM = True +REPLACE_BN_WITH_GN = True +GN_NUM_GROUPS = 8 +NUM_ACTIONS = 2 + +STRATEGY_1_MAX_EPOCHS = 100 +STRATEGY_2_MAX_EPOCHS = 100 +STRATEGY_3_MAX_EPOCHS = 120 +STRATEGY_4_MAX_EPOCHS = 100 +STRATEGY_5_MAX_EPOCHS = 100 +VALIDATE_EVERY_N_EPOCHS = 1 +CHECKPOINT_EVERY_N_EPOCHS = 0 +SAVE_LATEST_EVERY_EPOCH = True +SAVE_HISTORY_INCREMENTALLY = False +EARLY_STOPPING_PATIENCE = 0 +VERBOSE_EPOCH_LOG = False + +DEFAULT_HEAD_LR = 1e-4 +DEFAULT_ENCODER_LR = 1e-5 +DEFAULT_WEIGHT_DECAY = 1e-4 +DEFAULT_TMAX = 5 +TEST_ITERATION_CONTROL = False # If True, validation/evaluation/inference uses TEST_ITERATION_T instead of full tmax. +TEST_ITERATION_T = 1 # Applied only when TEST_ITERATION_CONTROL=True. Clamped to [1, tmax]. +DEFAULT_GAMMA = 0.95 +DEFAULT_CRITIC_LOSS_WEIGHT = 0.5 +DEFAULT_ENTROPY_ALPHA_INIT = 0.2 +DEFAULT_ENTROPY_TARGET_RATIO = 0.25 +DEFAULT_ENTROPY_LR = 3e-4 +DEFAULT_CE_WEIGHT = 0.5 +DEFAULT_DICE_WEIGHT = 0.5 +DEFAULT_DROPOUT_P = 0.2 +DEFAULT_GRAD_CLIP_NORM = 6.0 +DEFAULT_MASK_UPDATE_STEP = 0.1 +DEFAULT_FOREGROUND_REWARD_WEIGHT = 0.0 +DEFAULT_RECALL_REWARD_WEIGHT = 1.0 +DEFAULT_DICE_REWARD_WEIGHT = 0.35 +DEFAULT_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_PRIOR_REWARD_WEIGHT = 0.01 +DEFAULT_DECODER_GAIN_REWARD_WEIGHT = 0.5 +DEFAULT_REWARD_SCALE = 1.0 +DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION = True +DEFAULT_STRATEGY3_VARIANT = "lite" +DEFAULT_STRATEGY3_NUM_ACTIONS = 3 +DEFAULT_REFINE_DELTA_SMALL = 0.03 +DEFAULT_REFINE_DELTA_LARGE = 0.08 +DEFAULT_STRATEGY3_AUX_CE_WEIGHT = 0.40 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH = 25 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS = 15 +DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION = 0.10 +DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE = 30 +DEFAULT_STRATEGY3_PROBE_MODE = "rolling_random" +DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM = 0.25 +DEFAULT_STRATEGY3_RL_LOSS_SCALE = 10.0 +DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED = True +DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES = 8 +DEFAULT_STRATEGY3_MC_DROPOUT_P = 0.2 +DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_READ = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE = True +DEFAULT_STRATEGY3_DELTA_MAX = 0.10 +DEFAULT_STRATEGY3_SAM_ATTENTION_GRID = 64 +DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT = 1.0 +DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE = False +DEFAULT_BIOU_REWARD_WEIGHT = 1.0 +DEFAULT_IOU_REWARD_WEIGHT = 1.0 +DEFAULT_KEEP_CORRECT_REWARD_WEIGHT = 0.05 +DEFAULT_STRATEGY3_A3C_ENTROPY_COEFF = 0.0 +DEFAULT_STRATEGY3_ENTROPY_TARGET_RATIO = 0.20 +DEFAULT_STRATEGY3_ENTROPY_ALPHA_INIT = 0.005 +DEFAULT_STRATEGY3_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_EARLY_STOPPING_MONITOR = "auto" +DEFAULT_EARLY_STOPPING_MODE = "auto" +DEFAULT_EARLY_STOPPING_MIN_DELTA = 0.0 +DEFAULT_EARLY_STOPPING_START_EPOCH = 30 +DEFAULT_EXPLORATION_EPS = 0.1 +EXPLORATION_EPS_EPOCHS = 20 +ATTENTION_MAX_TOKENS = 1024 +ATTENTION_MIN_POOL_SIZE = 16 + +_STRATEGY3_MC_DROPOUT_WARNED = False +_STRATEGY3_SAM_GRID_WARNED: set[int] = set() +_STRATEGY3_MC_CACHE_SCHEMA_VERSION = 1 +_STRATEGY3_MC_FILE_SHA256_CACHE: dict[str, str] = {} + +SCHEDULER_FACTOR = 0.5 +SCHEDULER_PATIENCE = 5 +SCHEDULER_THRESHOLD = 1e-3 +SCHEDULER_MIN_LR = 1e-5 + +HEAD_LR_RANGE = (1e-5, 3e-3) +ENCODER_LR_RANGE = (1e-6, 3e-3) +WEIGHT_DECAY_RANGE = (1e-6, 1e-2) +TMAX_RANGE = (3, 10) +ENTROPY_LR_RANGE = (1e-5, 1e-3) +DROPOUT_P_RANGE = (0.0, 0.5) + +USE_TRIAL_PRUNING = True +TRIAL_PRUNER_WARMUP_STEPS = 80 +TRIAL_PRUNER_PATIENCE_STEPS = 40 +LOAD_EXISTING_STUDIES = False +SKIP_EXISTING_FINALS = False +RUN_OPTUNA = False +RESET_ALL_STUDIES_EACH_RUN = False +USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF = False + +EXECUTION_MODE = "train_eval" # "train_eval" or "eval_only" +EVAL_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +EVAL_SPECIFIC_CHECKPOINT = "" +STRATEGY2_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = { + # Non-phase mode — keyed by dataset percent (float): + # 0.1: "runs/EfficientNet_Strategy2_New/pct_10/strategy_2/final/checkpoints/epoch_0089.pt", + # 0.5: "Strategy2_Checkpoints/strat2_50_best.pt", + # 1.0: "runs/EfficientNet_Strategy2_New/pct_100/strategy_2/final/checkpoints/best.pt", + # Phase mode — keyed by phase index (int): + 1: "/content/UNET_REVAMP/best_strat2.pt", + 2: "/content/UNET_REVAMP/best_strat2_2.pt", + 3: "/content/UNET_REVAMP/best_strat2_3.pt", +} +STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 = True +TRAIN_RESUME_MODE = "off" # "off", "latest", "best", or "specific" +TRAIN_RESUME_SPECIFIC_CHECKPOINT = "" +OPTUNA_HEARTBEAT_INTERVAL = 60 +OPTUNA_HEARTBEAT_GRACE_PERIOD = 180 + +USE_AMP = True +AMP_DTYPE = "bfloat16" # "auto", "bfloat16", or "float16" +USE_CHANNELS_LAST = True +USE_TORCH_COMPILE = True +STEPWISE_BACKWARD = True +ALLOW_TF32 = True + +RUN_SMOKE_TEST = False +SMOKE_TEST_SAMPLE_INDEX = 0 +RUN_OVERFIT_TEST = False +OVERFIT_N_BATCHES = 2 +OVERFIT_N_EPOCHS = 100 +OVERFIT_HEAD_LR = 1e-3 +OVERFIT_ENCODER_LR = 1e-4 +OVERFIT_PRINT_EVERY = 5 +WRITE_EPOCH_DIAGNOSTIC = True +EPOCH_DIAGNOSTIC_TRAIN_BATCHES = 2 +EPOCH_DIAGNOSTIC_VAL_BATCHES = 2 +CONTROLLED_MASK_THRESHOLD = 0.50 + +REQUIRED_HPARAM_KEYS = ("head_lr", "encoder_lr", "weight_decay", "dropout_p", "tmax", "entropy_lr") + +_TRANSUNET_REQUIRED_NPZ_KEYS: tuple[str, ...] = ( + "embedding/kernel", + "embedding/bias", + "Transformer/encoder_norm/scale", + "Transformer/encoder_norm/bias", + "Transformer/posembed_input/pos_embedding", + "conv_root/kernel", + "gn_root/scale", + "gn_root/bias", + "Transformer/encoderblock_0/MultiHeadDotProductAttention_1/query/kernel", +) +_TRANSUNET_ENCODER_ALIASES: set[str] = {"vitb16r50", "r50vitb16"} +_TRANSUNET_VISION_TRANSFORMER: Any | None = None +_TRANSUNET_CONFIGS: dict[str, Any] | None = None +_TEST_ITERATION_NOTICE_CACHE: set[tuple[str, int, int]] = set() + +"""============================================================================= +IF OPTUNA IS OFF --> USE ME +============================================================================= +""" + +# Key format: ":" +# Each value is a JSON filename in HARD_CODED_PARAM_DIR containing the required hyperparameters. +MANUAL_HPARAMS_IF_OPTUNA_OFF: dict[str, str] = { + "2:100": "param_segformer/best_params_strat2.json", + "3:100": "param_segformer/best_params_strat3.json", +} + +# ===================== ABLATION HARNESS OVERRIDE ===================== +# Auto-generated. Outputs go to a separate MODEL_NAME subtree; strategy 3 +# only; the frozen strategy-2 base is reused from the original run tree. +MODEL_NAME = "Segformer_B0_AB7_no_sam" +STRATEGIES = [3] +STRATEGY2_CHECKPOINT_MODE = "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = {1: str(PROJECT_DIR / "best.pt")} +MANUAL_HPARAMS_IF_OPTUNA_OFF = {**MANUAL_HPARAMS_IF_OPTUNA_OFF, "3:100": "param_segformer/best_params_strat3.json"} +# ===================================================================== + +"""============================================================================= +RUNTIME SETUP +============================================================================= +""" + +torch.set_float32_matmul_precision("high") +if torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32 + torch.backends.cudnn.allow_tf32 = ALLOW_TF32 +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = True + +def select_runtime_device() -> tuple[torch.device, str]: + if torch.cuda.is_available(): + return torch.device("cuda"), "cuda" + + mps_backend = getattr(torch.backends, "mps", None) + if mps_backend is not None and mps_backend.is_available(): + try: + _probe = torch.zeros(1, device="mps") + del _probe + return torch.device("mps"), "mps" + except Exception as exc: + print(f"[Device] MPS detected but failed to initialize ({exc}). Falling back to CPU.") + + return torch.device("cpu"), "cpu" + +DEVICE, DEVICE_FALLBACK_SOURCE = select_runtime_device() +CURRENT_JOB_PARAMS: dict[str, Any] = {} + +@dataclass(frozen=True) +class RuntimeModelConfig: + backbone_family: str + smp_encoder_name: str + smp_encoder_weights: str | None + smp_encoder_depth: int + smp_encoder_proj_dim: int + smp_decoder_type: str + vgg_feature_scales: int + vgg_feature_dilation: int + + @classmethod + def from_globals(cls) -> RuntimeModelConfig: + return cls( + backbone_family=str(BACKBONE_FAMILY).strip().lower(), + smp_encoder_name=str(SMP_ENCODER_NAME), + smp_encoder_weights=SMP_ENCODER_WEIGHTS, + smp_encoder_depth=int(SMP_ENCODER_DEPTH), + smp_encoder_proj_dim=int(SMP_ENCODER_PROJ_DIM), + smp_decoder_type=str(SMP_DECODER_TYPE), + vgg_feature_scales=int(VGG_FEATURE_SCALES), + vgg_feature_dilation=int(VGG_FEATURE_DILATION), + ) + + @classmethod + def from_payload(cls, payload: dict[str, Any] | None) -> RuntimeModelConfig: + payload = payload or {} + return cls( + backbone_family=str(payload.get("backbone_family", "smp")).strip().lower(), + smp_encoder_name=str(payload.get("smp_encoder_name", SMP_ENCODER_NAME)), + smp_encoder_weights=payload.get("smp_encoder_weights", SMP_ENCODER_WEIGHTS), + smp_encoder_depth=int(payload.get("smp_encoder_depth", SMP_ENCODER_DEPTH)), + smp_encoder_proj_dim=int(payload.get("smp_encoder_proj_dim", SMP_ENCODER_PROJ_DIM)), + smp_decoder_type=str(payload.get("smp_decoder_type", SMP_DECODER_TYPE)), + vgg_feature_scales=int(payload.get("vgg_feature_scales", VGG_FEATURE_SCALES)), + vgg_feature_dilation=int(payload.get("vgg_feature_dilation", VGG_FEATURE_DILATION)), + ) + + def validate(self) -> RuntimeModelConfig: + if self.backbone_family not in {"smp", "custom_vgg"}: + raise ValueError(f"BACKBONE_FAMILY must be 'smp' or 'custom_vgg', got {self.backbone_family!r}") + if self.vgg_feature_scales not in {3, 4}: + raise ValueError(f"VGG_FEATURE_SCALES must be 3 or 4, got {self.vgg_feature_scales}") + if self.vgg_feature_dilation < 1: + raise ValueError(f"VGG_FEATURE_DILATION must be >= 1, got {self.vgg_feature_dilation}") + if self.smp_encoder_depth < 1: + raise ValueError(f"SMP_ENCODER_DEPTH must be >= 1, got {self.smp_encoder_depth}") + if self.smp_encoder_proj_dim < 0: + raise ValueError(f"SMP_ENCODER_PROJ_DIM must be >= 0, got {self.smp_encoder_proj_dim}") + if _normalized_model_token(self.smp_decoder_type) == "transunet": + if _normalized_model_token(self.smp_encoder_name) not in _TRANSUNET_ENCODER_ALIASES: + print( + "[RuntimeModelConfig] Warning: SMP_DECODER_TYPE='TransUNet' is wired for " + "SMP_ENCODER_NAME='ViTB16R50' (or 'R50ViTB16'). " + f"Received {self.smp_encoder_name!r}." + ) + if IMG_SIZE % 16 != 0: + raise ValueError( + f"TransUNet requires IMG_SIZE divisible by 16, got IMG_SIZE={IMG_SIZE}." + ) + return self + + def to_payload(self) -> dict[str, Any]: + return { + "backbone_family": self.backbone_family, + "smp_encoder_name": self.smp_encoder_name, + "smp_encoder_weights": self.smp_encoder_weights, + "smp_encoder_depth": self.smp_encoder_depth, + "smp_encoder_proj_dim": self.smp_encoder_proj_dim, + "smp_decoder_type": self.smp_decoder_type, + "vgg_feature_scales": self.vgg_feature_scales, + "vgg_feature_dilation": self.vgg_feature_dilation, + } + + def backbone_tag(self) -> str: + return self.backbone_family + + def backbone_display_name(self) -> str: + if self.backbone_family == "custom_vgg": + return f"Custom VGG (scales={self.vgg_feature_scales}, dilation={self.vgg_feature_dilation})" + return f"SMP {self.smp_encoder_name}" + +def current_model_config() -> RuntimeModelConfig: + return RuntimeModelConfig.from_globals().validate() + +"""============================================================================= +UTILITIES +============================================================================= +""" + +def _normalized_model_token(value: str | None) -> str: + return "".join(ch for ch in str(value or "") if ch.isalnum()).lower() + + +def _is_transunet_selection( + model_config: RuntimeModelConfig | None = None, + *, + encoder_name: str | None = None, + decoder_type: str | None = None, +) -> bool: + if model_config is not None: + encoder_name = model_config.smp_encoder_name + decoder_type = model_config.smp_decoder_type + enc = _normalized_model_token(encoder_name) + dec = _normalized_model_token(decoder_type) + return dec == "transunet" and enc in _TRANSUNET_ENCODER_ALIASES + + +def _resolve_test_iteration_tmax(tmax: int, *, context: str) -> int: + effective_tmax = max(int(tmax), 1) + if not TEST_ITERATION_CONTROL: + return effective_tmax + + requested_t = int(TEST_ITERATION_T) + if requested_t < 1: + raise ValueError( + f"TEST_ITERATION_T must be >= 1 when TEST_ITERATION_CONTROL=True, got {requested_t}." + ) + + effective_tmax = min(effective_tmax, requested_t) + cache_key = (context, int(tmax), effective_tmax) + if cache_key not in _TEST_ITERATION_NOTICE_CACHE: + if requested_t > int(tmax): + print( + f"[Test Iteration Control] {context}: TEST_ITERATION_T={requested_t} exceeds tmax={int(tmax)}; " + f"using t={effective_tmax}." + ) + else: + print( + f"[Test Iteration Control] {context}: overriding test rollout steps " + f"from tmax={int(tmax)} to t={effective_tmax}." + ) + _TEST_ITERATION_NOTICE_CACHE.add(cache_key) + return effective_tmax + +def banner(title: str) -> None: + line = "=" * 80 + print(f"\n{line}\n{title}\n{line}") + +def section(title: str) -> None: + print(f"\n{'-' * 80}\n{title}\n{'-' * 80}") + +def ensure_dir(path: str | Path) -> Path: + path = Path(path).expanduser().resolve() + path.mkdir(parents=True, exist_ok=True) + return path + +def save_json(path: str | Path, payload: Any) -> None: + path = Path(path) + ensure_dir(path.parent) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + +def load_json(path: str | Path) -> Any: + with Path(path).open("r", encoding="utf-8") as f: + return json.load(f) + +def _format_history_log_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key == "lr" or key.endswith("_lr"): + return f"{value:.6e}" + return json.dumps(value) + return json.dumps(value) + +def format_history_log_row(row: dict[str, Any]) -> str: + return ", ".join(f"{key}={_format_history_log_value(key, value)}" for key, value in row.items()) + +def _format_epoch_metric(value: Any, *, scientific: bool = False) -> str: + if value is None: + return "null" + if isinstance(value, (float, int, np.floating, np.integer)): + value = float(value) + return f"{value:.6e}" if scientific else f"{value:.4f}" + return str(value) + +def format_concise_epoch_log( + row: dict[str, Any], + *, + best_metric_name: str, + best_metric_value: float, +) -> str: + fields: list[tuple[str, Any, bool]] = [ + ("train_loss", row.get("train_loss"), False), + ("train_iou", row.get("train_iou"), False), + ("train_entropy", row.get("train_entropy"), False), + ("val_loss", row.get("val_loss"), False), + ("val_iou", row.get("val_iou"), False), + ("val_dice", row.get("val_dice"), False), + ("val_iou_gain", row.get("val_iou_gain"), False), + ("val_biou_gain", row.get("val_biou_gain"), False), + ("head_lr", row.get("lr"), True), + ("encoder_lr", row.get("encoder_lr"), True), + (best_metric_name, best_metric_value, False), + ("study_best", row.get("study_best_objective"), False), + ] + parts = [ + f"{name}={_format_epoch_metric(value, scientific=scientific)}" + for name, value, scientific in fields + if value is not None + ] + early_monitor_name = row.get("early_stopping_monitor_name") + if early_monitor_name: + parts.append(f"es_monitor={early_monitor_name}") + if row.get("early_stopping_monitor_value") is not None: + parts.append(f"es_value={_format_epoch_metric(row.get('early_stopping_monitor_value'))}") + if row.get("early_stopping_best_value") is not None: + parts.append(f"es_best={_format_epoch_metric(row.get('early_stopping_best_value'))}") + if row.get("early_stopping_wait") is not None and row.get("early_stopping_patience") is not None: + parts.append( + f"es_wait={int(row.get('early_stopping_wait'))}/{int(row.get('early_stopping_patience'))}" + ) + if row.get("early_stopping_active") is not None: + parts.append(f"es_active={bool(row.get('early_stopping_active'))}") + if row.get("strategy3_freeze_active") is not None: + parts.append(f"s3_frozen={bool(row.get('strategy3_freeze_active'))}") + if row.get("study_best_trial") is not None: + parts.append(f"study_best_trial={int(row.get('study_best_trial'))}") + return ", ".join(parts) + +def _optuna_direction_is_maximize(direction: Any) -> bool: + direction_name = str(getattr(direction, "name", direction)).lower() + return direction_name.endswith("maximize") + +def _optuna_value_is_better( + candidate: float | None, + current: float | None, + *, + direction: Any, +) -> bool: + if candidate is None: + return False + if current is None: + return True + return float(candidate) > float(current) if _optuna_direction_is_maximize(direction) else float(candidate) < float(current) + +def _optuna_trial_state_name(trial: Any) -> str: + state = getattr(trial, "state", None) + return str(getattr(state, "name", state)).upper() + +def _optuna_trial_user_attr_float(trial: Any, attr_name: str) -> float | None: + user_attrs = getattr(trial, "user_attrs", None) + if not isinstance(user_attrs, dict): + return None + value = user_attrs.get(attr_name) + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + +def _optuna_trial_best_intermediate_value( + trial: Any, + *, + direction: Any, +) -> float | None: + best_value: float | None = None + for value in getattr(trial, "intermediate_values", {}).values(): + if value is None: + continue + candidate_value = float(value) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_value = candidate_value + return best_value + +def _optuna_trial_best_observed_value( + trial: Any, + *, + direction: Any, + current_best_value: float | None = None, +) -> float | None: + if current_best_value is not None: + return float(current_best_value) + + best_observed = _optuna_trial_user_attr_float(trial, OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR) + if best_observed is not None: + return best_observed + + state_name = _optuna_trial_state_name(trial) + if state_name == "COMPLETE": + value = getattr(trial, "value", None) + return None if value is None else float(value) + + best_intermediate = _optuna_trial_best_intermediate_value(trial, direction=direction) + if best_intermediate is not None: + return best_intermediate + + value = getattr(trial, "value", None) + return None if value is None else float(value) + +def _current_optuna_study_best_candidate( + study: optuna.study.Study, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> tuple[Any | None, float | None]: + direction = getattr(study, "direction", STUDY_DIRECTION) + best_trial: Any | None = None + best_value: float | None = None + + for study_trial in getattr(study, "trials", []): + if _optuna_trial_state_name(study_trial) not in {"COMPLETE", "PRUNED"}: + continue + candidate_value = _optuna_trial_best_observed_value(study_trial, direction=direction) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_trial = study_trial + best_value = candidate_value + + if current_trial is not None: + live_trial_best = _optuna_trial_best_observed_value( + current_trial, + direction=direction, + current_best_value=current_best_value, + ) + if _optuna_value_is_better(live_trial_best, best_value, direction=direction): + best_trial = current_trial + best_value = live_trial_best + + return best_trial, best_value + +def _current_optuna_study_best_snapshot( + trial: optuna.trial.Trial | None, + *, + current_best_value: float | None = None, +) -> tuple[float | None, int | None]: + if trial is None: + return None, None + study = getattr(trial, "study", None) + if study is None: + return None, None + + best_trial, best_value = _current_optuna_study_best_candidate( + study, + current_trial=trial, + current_best_value=current_best_value, + ) + best_trial_number = None if best_trial is None else int(getattr(best_trial, "number", -1)) + return best_value, best_trial_number + +def set_global_seed(seed: int = 42) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def stable_int_from_text(text: str) -> int: + value = 0 + for byte in text.encode("utf-8"): + value = (value * 131 + byte) % (2 ** 31 - 1) + return value + +def seed_worker(worker_id: int) -> None: + del worker_id + worker_seed = torch.initial_seed() % (2 ** 32) + random.seed(worker_seed) + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + +def make_seeded_generator(seed: int, tag: str) -> torch.Generator: + generator = torch.Generator() + generator.manual_seed(seed + stable_int_from_text(tag)) + return generator + +def cuda_memory_snapshot() -> str: + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return "allocated=0.00 GB, reserved=0.00 GB, peak=0.00 GB" + allocated = torch.cuda.memory_allocated(device=DEVICE) / (1024 ** 3) + reserved = torch.cuda.memory_reserved(device=DEVICE) / (1024 ** 3) + peak = torch.cuda.max_memory_allocated(device=DEVICE) / (1024 ** 3) + return f"allocated={allocated:.2f} GB, reserved={reserved:.2f} GB, peak={peak:.2f} GB" + +def run_cuda_cleanup(context: str | None = None) -> None: + gc.collect() + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return + try: + torch.cuda.synchronize(device=DEVICE) + except Exception: + pass + try: + torch.cuda.empty_cache() + except Exception: + pass + try: + torch.cuda.ipc_collect() + except Exception: + pass + if context is not None: + print(f"[CUDA Cleanup] {context}: {cuda_memory_snapshot()}") + try: + torch.cuda.reset_peak_memory_stats(device=DEVICE) + except Exception: + pass + +def prune_directory_except(root: Path, keep_file_names: set[str]) -> None: + if not root.exists(): + return + keep_paths = {root / name for name in keep_file_names} + for path in sorted((p for p in root.rglob("*") if p.is_file()), reverse=True): + if path not in keep_paths: + path.unlink() + for path in sorted((p for p in root.rglob("*") if p.is_dir()), reverse=True): + if path != root: + try: + path.rmdir() + except OSError: + pass + +def prune_optuna_trial_dir(trial_dir: Path) -> None: + if trial_dir.exists(): + shutil.rmtree(trial_dir, ignore_errors=True) + +def prune_optuna_study_dir(study_root: Path) -> None: + prune_directory_except(study_root, {"best_params.json", "summary.json", "study.sqlite3"}) + +def to_device(batch: Any, device: torch.device) -> Any: + if torch.is_tensor(batch): + return batch.to(device, non_blocking=True) + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, list): + return [to_device(v, device) for v in batch] + if isinstance(batch, tuple): + return tuple(to_device(v, device) for v in batch) + return batch + +def _normalized_decimal_text(value: Decimal) -> str: + normalized = value.normalize() + text = format(normalized, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text or "0" + +def _fraction_decimal(value: Any, *, field_name: str) -> Decimal: + if isinstance(value, bool): + raise TypeError(f"{field_name} must be a real number in (0, 1], got boolean {value!r}.") + try: + decimal_value = Decimal(str(value).strip()) + except (InvalidOperation, ValueError) as exc: + raise ValueError(f"{field_name} must be a real number in (0, 1], got {value!r}.") from exc + if not decimal_value.is_finite(): + raise ValueError(f"{field_name} must be finite, got {value!r}.") + if decimal_value <= 0 or decimal_value > 1: + raise ValueError(f"{field_name} must be in the interval (0, 1], got {value!r}.") + return decimal_value + +def _percent_decimal(value: Any, *, field_name: str = "dataset percent") -> Decimal: + return _fraction_decimal(value, field_name=field_name) * Decimal("100") + +def normalize_dataset_percents(values: list[float] | tuple[float, ...]) -> list[float]: + if not values: + raise ValueError("DATASET_PERCENTS must contain at least one fraction in (0, 1].") + normalized: dict[str, float] = {} + for value in values: + fraction = _fraction_decimal(value, field_name="DATASET_PERCENTS entry") + normalized[_normalized_decimal_text(fraction)] = float(fraction) + return [normalized[key] for key in sorted(normalized, key=Decimal)] + +def percent_label(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)).replace(".", "p") + +def percent_display(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)) + +def percent_text(percent: float) -> str: + return f"{percent_display(percent)}%" + + +def run_identity_parts( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> list[str]: + parts: list[str] = [] + payload = split_payload or {} + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase={int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split={int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split={split_type}") + + if subset_repeat_index is not None: + parts.append(f"repeat={int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant={int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy={int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct={percent_text(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial={int(trial_number):03d}") + + return parts + + +def run_identity_label( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + parts = run_identity_parts( + strategy=strategy, + percent=percent, + trial_number=trial_number, + split_payload=split_payload, + ) + return " | ".join(parts) if parts else "run" + + +def run_identity_slug( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + payload = split_payload or {} + parts: list[str] = [] + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase_{int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split_{int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split_{str(split_type)}") + + if subset_repeat_index is not None: + parts.append(f"repeat_{int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant_{int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy_{int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct_{percent_label(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial_{int(trial_number):03d}") + + return "__".join(parts) if parts else "run" + + +def current_dataset_name() -> str: + dataset_name = str(DATASET_NAME).strip() + if dataset_name not in SUPPORTED_DATASET_NAMES: + raise ValueError(f"DATASET_NAME must be one of {SUPPORTED_DATASET_NAMES}, got {dataset_name!r}") + return dataset_name + +def current_busi_with_classes_split_policy() -> str: + split_policy = str(BUSI_WITH_CLASSES_SPLIT_POLICY).strip().lower() + if split_policy not in SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES: + raise ValueError( + f"BUSI_WITH_CLASSES_SPLIT_POLICY must be one of {SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES}, " + f"got {split_policy!r}" + ) + return split_policy + +def current_dataset_splits_json_path() -> Path: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATASET_SPLITS_JSON + return PROJECT_DIR / f"dataset_splits_{dataset_name.lower()}_{current_busi_with_classes_split_policy()}.json" + +def current_dataset_dirs() -> tuple[Path, Path]: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATA_ROOT / "images", DATA_ROOT / "annotations" + return DATA_ROOT / "all_images", DATA_ROOT / "all_masks" + +def current_pipeline_check_path() -> Path | None: + if current_dataset_name() != "BUSI_with_classes": + return None + return DATA_ROOT / "pipeline_check.json" + +def normalization_cache_tag() -> str: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return "BUSI" + return f"{dataset_name}_{current_busi_with_classes_split_policy()}" + +def resolve_amp_dtype(key: str) -> torch.dtype: + key = key.lower().strip() + if key == "auto": + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + return torch.float16 + if key in {"float16", "fp16", "half"}: + return torch.float16 + if key in {"bfloat16", "bf16"}: + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + print("[AMP] bfloat16 requested but unsupported here. Falling back to float16.") + return torch.float16 + raise ValueError(f"Unsupported AMP_DTYPE: {key}") + +def amp_autocast_enabled(device: torch.device) -> bool: + return USE_AMP and device.type in {"cuda", "mps"} + +def autocast_ctx(enabled: bool, device: torch.device, amp_dtype: torch.dtype): + if not enabled: + return nullcontext() + return torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=True) + +def make_grad_scaler(enabled: bool, amp_dtype: torch.dtype, device: torch.device): + if not enabled or device.type != "cuda" or amp_dtype == torch.bfloat16: + return None + try: + return torch.amp.GradScaler("cuda", enabled=True, init_scale=8192.0) + except Exception: + return torch.cuda.amp.GradScaler(enabled=True, init_scale=8192.0) + +def format_seconds(seconds: float) -> str: + seconds = int(seconds) + h, rem = divmod(seconds, 3600) + m, s = divmod(rem, 60) + return f"{h:02d}:{m:02d}:{s:02d}" + +def tensor_bytes(t: torch.Tensor) -> int: + return t.numel() * t.element_size() + +def bytes_to_gb(num_bytes: int) -> float: + return num_bytes / (1024 ** 3) + +def set_current_job_params(payload: dict[str, Any] | None = None) -> None: + CURRENT_JOB_PARAMS.clear() + if payload: + CURRENT_JOB_PARAMS.update(dict(payload)) + +def _job_param(name: str, default: Any) -> Any: + return CURRENT_JOB_PARAMS.get(name, default) + +def _alpha_log_floor() -> float: + return math.log(max(float(_job_param("min_alpha", math.exp(-5.0))), 1e-6)) + +def _keep_action_index(action_count: int) -> int: + action_count = max(int(action_count), 1) + if action_count >= 3: + return action_count // 2 + return action_count - 1 + +def _strategy3_variant() -> str: + raw = str(_job_param("strategy3_variant", DEFAULT_STRATEGY3_VARIANT)).strip().lower() + return raw or DEFAULT_STRATEGY3_VARIANT + +def _strategy3_annealed_weight( + base_weight: float, + *, + current_epoch: int, + anneal_start_epoch: int = 1, + anneal_epochs: int, +) -> float: + base_weight = float(base_weight) + if base_weight <= 0.0: + return 0.0 + anneal_start_epoch = max(int(anneal_start_epoch), 1) + anneal_epochs = max(int(anneal_epochs), 0) + if current_epoch < anneal_start_epoch: + return 0.0 + if anneal_epochs <= 0: + return base_weight + progress = min( + max((float(current_epoch) - float(anneal_start_epoch)) / float(anneal_epochs), 0.0), + 1.0, + ) + floor_fraction = float( + _job_param( + "strategy3_aux_ce_floor_fraction", + DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + ) + ) + floor_fraction = min(max(floor_fraction, 0.0), 1.0) + fraction = max(1.0 - progress, floor_fraction) + return base_weight * fraction + +def _strategy3_annealed_aux_ce_weight(current_epoch: int) -> float: + return _strategy3_annealed_weight( + float(_job_param("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT)), + current_epoch=int(current_epoch), + anneal_start_epoch=int( + _job_param( + "strategy3_aux_ce_anneal_start_epoch", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + ) + ), + anneal_epochs=int( + _job_param( + "strategy3_aux_ce_anneal_epochs", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + ) + ), + ) + +def _strategy3_exploration_eps(current_epoch: int) -> float: + base_eps = max(float(_job_param("strategy3_exploration_eps", DEFAULT_EXPLORATION_EPS)), 0.0) + decay_epochs = max(int(_job_param("strategy3_exploration_eps_epochs", EXPLORATION_EPS_EPOCHS)), 0) + if base_eps <= 0.0: + return 0.0 + if decay_epochs <= 0: + return base_eps + progress = min(max((float(current_epoch) - 1.0) / float(decay_epochs), 0.0), 1.0) + return base_eps * (1.0 - progress) + +def _bootstrap_value_target(model: nn.Module, value_next: torch.Tensor) -> torch.Tensor: + neighborhood_value = getattr(model, "neighborhood_value", None) + if callable(neighborhood_value): + return neighborhood_value(value_next) + return value_next + +def _strategy3_delta_max() -> float: + return float(_job_param("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX)) + +def _strategy3_policy_delta(policy_raw: torch.Tensor) -> torch.Tensor: + return torch.tanh(policy_raw.float()) * _strategy3_delta_max() + +def _strategy3_apply_delta(seg: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: + seg_f = seg.float() + delta_f = delta.float() + return (seg_f + delta_f).clamp(0.0, 1.0).to(dtype=seg.dtype) + +def _strategy3_advantage_normalize_enabled() -> bool: + return bool(_job_param("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE)) + +def _normalize_strategy3_advantage_map(advantage_map: torch.Tensor) -> torch.Tensor: + if not _strategy3_advantage_normalize_enabled(): + return advantage_map + if advantage_map.ndim < 4: + mean = advantage_map.mean() + std = advantage_map.std(unbiased=False) + return (advantage_map - mean) / (std + 1e-6) + mean = advantage_map.mean(dim=(2, 3), keepdim=True) + std = advantage_map.std(dim=(2, 3), unbiased=False, keepdim=True) + return (advantage_map - mean) / (std + 1e-6) + +def _strategy3_actor_advantage( + reward_map: torch.Tensor, + value_t: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + advantage_map = reward_map + float(gamma) * value_next.detach() - value_t.detach() + return _normalize_strategy3_advantage_map(advantage_map) + +def _strategy3_critic_target( + reward_map: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + return reward_map.detach() + float(gamma) * value_next.detach() + +def _strategy3_delta_distribution(delta_map: torch.Tensor) -> dict[str, float]: + delta_f = delta_map.detach().float() + abs_delta = delta_f.abs() + return { + "mean_delta": float(delta_f.mean().item()), + "mean_abs_delta": float(abs_delta.mean().item()), + "positive_pct": float((delta_f > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((delta_f < -1e-6).float().mean().item() * 100.0), + "near_zero_pct": float((abs_delta <= 1e-6).float().mean().item() * 100.0), + "max_abs_delta": float(abs_delta.max().item()), + } + +def _bernoulli_predictive_entropy(prob: torch.Tensor) -> torch.Tensor: + prob_f = prob.float().clamp(1e-6, 1.0 - 1e-6) + return -(prob_f * torch.log(prob_f) + (1.0 - prob_f) * torch.log1p(-prob_f)) + +def _iter_strategy3_dropout_modules(model: nn.Module) -> Iterator[nn.Module]: + for module in _unwrap_compiled(model).modules(): + if isinstance(module, (nn.Dropout, nn.Dropout2d)): + yield module + +@contextmanager +def _strategy3_mc_dropout_scope(model: nn.Module) -> Iterator[None]: + global _STRATEGY3_MC_DROPOUT_WARNED + + requested_p = float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)) + if requested_p <= 0.0 and not _STRATEGY3_MC_DROPOUT_WARNED: + print("[Strategy3] MC-dropout requested with non-positive dropout p; variance maps may collapse to zero.") + _STRATEGY3_MC_DROPOUT_WARNED = True + + saved_states: list[tuple[nn.Module, bool, float | None]] = [] + for module in _iter_strategy3_dropout_modules(model): + saved_states.append((module, bool(module.training), getattr(module, "p", None))) + module.train(True) + if hasattr(module, "p") and requested_p > 0.0: + module.p = requested_p + try: + yield + finally: + for module, was_training, saved_p in saved_states: + module.train(was_training) + if saved_p is not None and hasattr(module, "p"): + module.p = saved_p + +def _strategy3_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_fn: Any, +) -> tuple[torch.Tensor, torch.Tensor]: + if not bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)): + with torch.no_grad(): + zeros = torch.zeros_like(decoder_prob.float()) + pred_entropy = _bernoulli_predictive_entropy(decoder_prob) + return zeros, pred_entropy + + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + with torch.no_grad(): + mc_probs: list[torch.Tensor] = [] + with _strategy3_mc_dropout_scope(model): + for _ in range(samples): + logits = sample_fn() + mc_probs.append(torch.sigmoid(logits).float()) + stacked = torch.stack(mc_probs, dim=0) + mean_prob = stacked.mean(dim=0) + variance = stacked.var(dim=0, unbiased=False) + pred_entropy = _bernoulli_predictive_entropy(mean_prob) + return variance.to(dtype=decoder_prob.dtype), pred_entropy.to(dtype=decoder_prob.dtype) + +def _strategy3_mc_config_hash() -> tuple[bool, int, float]: + enabled = bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)) + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + dropout_p = round(float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)), 8) + return enabled, samples, dropout_p + +def _strategy3_mc_disk_cache_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED)) + +def _strategy3_mc_disk_cache_read_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ)) + +def _strategy3_mc_disk_cache_write_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE)) + +def _strategy3_normalize_sample_ids( + sample_ids: list[str] | tuple[str, ...] | None, + *, + batch_size: int, +) -> list[str] | None: + if sample_ids is None: + return None + normalized = [str(item) for item in sample_ids] + if len(normalized) != int(batch_size): + raise ValueError( + f"Strategy 3 eval MC cache expected {batch_size} sample_ids, got {len(normalized)}." + ) + return normalized + +def _strategy3_ensure_mc_cache_state(model: nn.Module) -> nn.Module: + raw_model = getattr(model, "_orig_mod", model) + if not hasattr(raw_model, "_strategy3_mc_cache"): + raw_model._strategy3_mc_cache = {} + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint"): + raw_model._strategy3_mc_cache_fingerprint = "" + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint_sources"): + raw_model._strategy3_mc_cache_fingerprint_sources = {} + if not hasattr(raw_model, "_strategy3_strategy2_checkpoint_path"): + raw_model._strategy3_strategy2_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_eval_checkpoint_path"): + raw_model._strategy3_eval_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_mc_cache_stats"): + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return raw_model + +def _strategy3_reset_mc_cache_stats(model: nn.Module) -> None: + raw_model = _strategy3_ensure_mc_cache_state(model) + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + +def _strategy3_get_mc_cache_stats(model: nn.Module) -> dict[str, int]: + raw_model = _strategy3_ensure_mc_cache_state(model) + stats = raw_model._strategy3_mc_cache_stats + return { + "ram_hits": int(stats.get("ram_hits", 0)), + "disk_hits": int(stats.get("disk_hits", 0)), + "misses": int(stats.get("misses", 0)), + "writes": int(stats.get("writes", 0)), + } + +def _strategy3_checkpoint_sha256(path: str | Path | None) -> str | None: + if not path: + return None + resolved = str(Path(path).expanduser().resolve()) + cached = _STRATEGY3_MC_FILE_SHA256_CACHE.get(resolved) + if cached is not None: + return cached + checkpoint_path = Path(resolved) + if not checkpoint_path.is_file(): + return None + digest = hashlib.sha256() + with checkpoint_path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + checksum = digest.hexdigest() + _STRATEGY3_MC_FILE_SHA256_CACHE[resolved] = checksum + return checksum + +def _strategy3_decoder_state_hash(model: nn.Module) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + modules: list[tuple[str, nn.Module]] = [] + if isinstance(raw_model, PixelDRLMG_WithDecoder): + modules = [ + ("smp_model.encoder", raw_model.smp_model.encoder), + ("smp_model.decoder", raw_model.smp_model.decoder), + ("smp_model.segmentation_head", raw_model.smp_model.segmentation_head), + ] + elif isinstance(raw_model, PixelDRLMG_VGGWithDecoder): + modules = [ + ("encoder", raw_model.encoder), + ("segmentation_head", raw_model.segmentation_head), + ] + else: + return stable_hash(raw_model.__class__.__name__) + + digest = hashlib.sha256() + for prefix, module in modules: + for name, tensor in sorted(module.state_dict().items()): + tensor_cpu = tensor.detach().cpu().contiguous() + digest.update(prefix.encode("utf-8")) + digest.update(b"\0") + digest.update(name.encode("utf-8")) + digest.update(b"\0") + digest.update(str(tensor_cpu.dtype).encode("utf-8")) + digest.update(b"\0") + digest.update(json.dumps(list(tensor_cpu.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(tensor_cpu.numpy().tobytes()) + return digest.hexdigest() + +def _strategy3_resolve_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> tuple[str, dict[str, Any]]: + raw_model = _strategy3_ensure_mc_cache_state(model) + strategy2_path = ( + str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if strategy2_checkpoint_path + else getattr(raw_model, "_strategy3_strategy2_checkpoint_path", None) + ) + eval_path = ( + str(Path(eval_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path + else getattr(raw_model, "_strategy3_eval_checkpoint_path", None) + ) + decoder_hash = _strategy3_decoder_state_hash(raw_model) + sources: dict[str, Any] = {"decoder_state_hash": decoder_hash} + if strategy2_path: + sources["strategy2_checkpoint"] = strategy2_path + strategy2_sha = _strategy3_checkpoint_sha256(strategy2_path) + if strategy2_sha is not None: + sources["strategy2_sha256"] = strategy2_sha + if eval_path: + sources["eval_checkpoint"] = eval_path + eval_sha = _strategy3_checkpoint_sha256(eval_path) + if eval_sha is not None: + sources["eval_checkpoint_sha256"] = eval_sha + return decoder_hash, sources + +def _strategy3_bump_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + if strategy2_checkpoint_path is not None: + raw_model._strategy3_strategy2_checkpoint_path = str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path is not None: + raw_model._strategy3_eval_checkpoint_path = str(Path(eval_checkpoint_path).expanduser().resolve()) + fingerprint, sources = _strategy3_resolve_mc_cache_fingerprint( + raw_model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + eval_checkpoint_path=eval_checkpoint_path, + ) + fingerprint_changed = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) != str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint = str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint_sources = dict(sources) + if fingerprint_changed: + clear_cache = getattr(raw_model, "clear_strategy3_mc_cache", None) + if callable(clear_cache): + clear_cache() + else: + raw_model._strategy3_mc_cache.clear() + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return str(fingerprint) + +def _strategy3_mc_disk_cache_root(run_dir: Path | None) -> Path | None: + if run_dir is None or not _strategy3_mc_disk_cache_enabled(): + return None + return Path(run_dir) / "mc_cache" + +def _strategy3_mc_disk_entry_path( + root: Path, + fingerprint: str, + split: str, + sample_id: str, +) -> Path: + safe_sample_id = str(sample_id).replace(os.sep, "__").replace("/", "__") + return Path(root) / str(fingerprint)[:16] / str(split) / f"{safe_sample_id}.pt" + +def _strategy3_load_mc_maps_from_disk( + path: Path, + *, + sample_id: str, + fingerprint: str, + mc_config_hash: tuple[bool, int, float], + split: str, +) -> tuple[torch.Tensor, torch.Tensor] | None: + if not path.is_file(): + return None + try: + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + payload = torch.load(path, map_location="cpu", weights_only=False) + except Exception: + return None + + if not isinstance(payload, dict): + return None + if int(payload.get("schema_version", -1)) != int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION): + return None + if str(payload.get("sample_id", "")) != str(sample_id): + return None + if str(payload.get("split", "")) != str(split): + return None + if str(payload.get("fingerprint", "")) != str(fingerprint): + return None + if tuple(payload.get("mc_config_hash", ())) != tuple(mc_config_hash): + return None + if int(payload.get("img_size", -1)) != int(IMG_SIZE): + return None + + variance = payload.get("mc_variance") + pred_entropy = payload.get("pred_entropy") + if not (torch.is_tensor(variance) and torch.is_tensor(pred_entropy)): + return None + if variance.ndim != 4 or pred_entropy.ndim != 4: + return None + return ( + variance.detach().to(device="cpu", dtype=torch.float32).contiguous(), + pred_entropy.detach().to(device="cpu", dtype=torch.float32).contiguous(), + ) + +def _strategy3_save_mc_maps_to_disk(path: Path, payload: dict[str, Any]) -> None: + atomic_torch_save(path, payload) + +def _strategy3_write_mc_cache_manifest( + run_dir: Path | None, + *, + fingerprint: str, + fingerprint_sources: dict[str, Any], + mc_config_hash: tuple[bool, int, float], + split: str, + split_write_count: int, +) -> None: + if run_dir is None: + return + manifest_path = Path(run_dir) / "mc_cache" / "manifest.json" + existing: dict[str, Any] = {} + if manifest_path.exists(): + try: + loaded = load_json(manifest_path) + except Exception: + loaded = {} + if isinstance(loaded, dict): + existing = loaded + existing_fingerprint = str(existing.get("fingerprint", "")) + existing_config = tuple(existing.get("mc_config_hash", ())) + if existing_fingerprint != str(fingerprint) or existing_config != tuple(mc_config_hash): + existing = {} + splits = dict(existing.get("splits", {})) if isinstance(existing.get("splits", {}), dict) else {} + splits[str(split)] = int(splits.get(str(split), 0)) + int(max(split_write_count, 0)) + payload = { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "fingerprint": str(fingerprint), + "fingerprint_sources": dict(fingerprint_sources), + "mc_config_hash": list(mc_config_hash), + "img_size": int(IMG_SIZE), + "dataset_name": current_dataset_name(), + "splits": splits, + } + atomic_save_json(manifest_path, payload) + +def _strategy3_mc_disk_mode( + *, + split: str | None, + run_dir: Path | None, +) -> tuple[bool, bool, Path | None, str | None]: + normalized_split = str(split).strip().lower() if split is not None else None + if normalized_split not in (None, "train", "val", "test"): + raise ValueError(f"Unsupported Strategy 3 MC cache split {split!r}.") + if normalized_split == "train": + return False, False, None, normalized_split + root = _strategy3_mc_disk_cache_root(run_dir) + can_use_disk = normalized_split in {"val", "test"} and root is not None + return ( + bool(can_use_disk and _strategy3_mc_disk_cache_read_enabled()), + bool(can_use_disk and _strategy3_mc_disk_cache_write_enabled()), + root, + normalized_split, + ) + +def _strategy3_prepare_cached_sample_pair( + sample_variance: torch.Tensor, + sample_entropy: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + return ( + sample_variance.detach().to(device="cpu", dtype=torch.float32).clone(), + sample_entropy.detach().to(device="cpu", dtype=torch.float32).clone(), + ) + +def _strategy3_cached_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_ids: list[str] | tuple[str, ...] | None, + compute_sample_maps: Any, + mc_cache_split: str | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + normalized_sample_ids = _strategy3_normalize_sample_ids(sample_ids, batch_size=int(decoder_prob.shape[0])) + if normalized_sample_ids is None: + raise ValueError("Strategy 3 eval MC cache requires non-empty sample_ids.") + + raw_model = _strategy3_ensure_mc_cache_state(model) + fingerprint = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) or _strategy3_bump_mc_cache_fingerprint(raw_model) + cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = raw_model._strategy3_mc_cache + stats = raw_model._strategy3_mc_cache_stats + mc_hash = _strategy3_mc_config_hash() + disk_read_enabled, disk_write_enabled, disk_root, normalized_split = _strategy3_mc_disk_mode( + split=mc_cache_split, + run_dir=mc_cache_run_dir, + ) + manifest_write_count = 0 + variance_samples: list[torch.Tensor] = [] + entropy_samples: list[torch.Tensor] = [] + + for sample_index, sample_id in enumerate(normalized_sample_ids): + cache_key = (fingerprint, sample_id, mc_hash) + cached_pair = cache.get(cache_key) + if cached_pair is not None: + stats["ram_hits"] = int(stats.get("ram_hits", 0)) + 1 + else: + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_read_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + cached_pair = _strategy3_load_mc_maps_from_disk( + disk_path, + sample_id=sample_id, + fingerprint=fingerprint, + mc_config_hash=mc_hash, + split=normalized_split, + ) + if cached_pair is not None: + cache[cache_key] = _strategy3_prepare_cached_sample_pair(*cached_pair) + stats["disk_hits"] = int(stats.get("disk_hits", 0)) + 1 + else: + sample_variance, sample_entropy = compute_sample_maps(sample_index) + cached_pair = _strategy3_prepare_cached_sample_pair(sample_variance, sample_entropy) + cache[cache_key] = cached_pair + stats["misses"] = int(stats.get("misses", 0)) + 1 + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_write_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + entry_exists = disk_path.exists() + _strategy3_save_mc_maps_to_disk( + disk_path, + { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "sample_id": str(sample_id), + "split": str(normalized_split), + "fingerprint": str(fingerprint), + "mc_config_hash": tuple(mc_hash), + "img_size": int(IMG_SIZE), + "dtype": "float32", + "created_at": datetime.now(timezone.utc).isoformat(), + "mc_variance": cached_pair[0], + "pred_entropy": cached_pair[1], + }, + ) + stats["writes"] = int(stats.get("writes", 0)) + 1 + if not entry_exists: + manifest_write_count += 1 + + cached_variance, cached_entropy = cached_pair + variance_samples.append(cached_variance.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + entropy_samples.append(cached_entropy.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + + if manifest_write_count > 0 and normalized_split is not None: + _strategy3_write_mc_cache_manifest( + mc_cache_run_dir, + fingerprint=fingerprint, + fingerprint_sources=dict(getattr(raw_model, "_strategy3_mc_cache_fingerprint_sources", {})), + mc_config_hash=mc_hash, + split=normalized_split, + split_write_count=manifest_write_count, + ) + + return torch.cat(variance_samples, dim=0), torch.cat(entropy_samples, dim=0) + +def _require_supported_strategy(strategy: int) -> int: + strategy = int(strategy) + if strategy not in SUPPORTED_STRATEGIES: + raise ValueError( + f"Unsupported strategy {strategy}. Supported strategies are {list(SUPPORTED_STRATEGIES)}." + ) + return strategy + +def _resolve_checkpoint_metric_name(metric_name: Any, *, strategy: int) -> str: + if not isinstance(metric_name, str) or not metric_name.strip(): + raise KeyError( + f"No best-checkpoint metric configured for strategy {strategy}. " + f"Set BEST_CHECKPOINT_METRICS[{strategy}] or best_checkpoint_metric_name to a non-empty metric name." + ) + metric_name = metric_name.strip() + if metric_name not in SUPPORTED_CHECKPOINT_METRICS: + raise KeyError( + f"Unsupported best-checkpoint metric {metric_name!r} for strategy {strategy}. " + f"Supported metrics: {sorted(SUPPORTED_CHECKPOINT_METRICS)}." + ) + return metric_name + +def _strategy_selection_metric_name(strategy: int) -> str: + strategy = _require_supported_strategy(strategy) + metric_name = _job_param( + f"strategy{strategy}_best_checkpoint_metric_name", + _job_param("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS.get(strategy)), + ) + return _resolve_checkpoint_metric_name(metric_name, strategy=strategy) + +def _strategy_selection_metric_value(strategy: int, metrics: dict[str, Any]) -> float: + metric_name = _strategy_selection_metric_name(strategy) + value = metrics.get(metric_name) + if value is None: + raise KeyError( + f"Configured best-checkpoint metric {metric_name!r} for strategy {strategy} " + f"is missing from metrics payload keys={sorted(metrics.keys())}." + ) + return float(value) + +def _early_stopping_monitor_name(strategy: int) -> str: + raw = str(_job_param("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR)).strip() + if not raw or raw.lower() == "auto": + return _strategy_selection_metric_name(strategy) + return raw + +def _early_stopping_mode(strategy: int, monitor_name: str | None = None) -> str: + raw = str(_job_param("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE)).strip().lower() + if raw in {"min", "max"}: + return raw + if raw != "auto": + raise ValueError(f"Unsupported early_stopping_mode={raw!r}. Expected 'auto', 'min', or 'max'.") + monitor_name = monitor_name or _early_stopping_monitor_name(strategy) + lowered = monitor_name.lower() + if "loss" in lowered or lowered.startswith("hd") or lowered.endswith("error"): + return "min" + return "max" + +def _early_stopping_min_delta() -> float: + return max(float(_job_param("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA)), 0.0) + +def _early_stopping_start_epoch() -> int: + return max(int(_job_param("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH)), 1) + +def _early_stopping_patience() -> int: + return max(int(_job_param("early_stopping_patience", EARLY_STOPPING_PATIENCE)), 0) + +def _early_stopping_monitor_value( + metrics: dict[str, Any], + *, + strategy: int, + monitor_name: str, +) -> float | None: + value = metrics.get(monitor_name) + if value is None and monitor_name == _strategy_selection_metric_name(strategy): + value = _strategy_selection_metric_value(strategy, metrics) + if value is None: + return None + return float(value) + +def _early_stopping_improved( + current_value: float, + best_value: float | None, + *, + mode: str, + min_delta: float, +) -> bool: + if best_value is None: + return True + if mode == "min": + return current_value < (best_value - min_delta) + if mode == "max": + return current_value > (best_value + min_delta) + raise ValueError(f"Unsupported early stopping comparison mode: {mode!r}") + +def _strategy3_requested_bootstrap_freeze() -> bool: + return bool( + _job_param( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + ) + +def _module_freeze_state(module: nn.Module | None) -> str: + if not isinstance(module, nn.Module): + return "n/a" + requires_grad_flags = [bool(param.requires_grad) for param in module.parameters()] + if not requires_grad_flags: + return "n/a" + if all(not flag for flag in requires_grad_flags): + return "frozen" + if all(requires_grad_flags): + return "trainable" + return "mixed" + +def _strategy3_bootstrap_freeze_status(model: nn.Module) -> dict[str, Any]: + raw = _raw_decoder_rl_model(model) + status = { + "bootstrap_loaded": False, + "freeze_requested": False, + "freeze_active": False, + "encoder_state": "n/a", + "decoder_state": "n/a", + "segmentation_head_state": "n/a", + } + if raw is None: + return status + + status["bootstrap_loaded"] = bool(getattr(raw, "strategy2_bootstrap_loaded", False)) + status["freeze_requested"] = bool(getattr(raw, "freeze_bootstrapped_segmentation", False)) + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + status["encoder_state"] = _module_freeze_state(getattr(smp_model, "encoder", None)) + status["decoder_state"] = _module_freeze_state(getattr(smp_model, "decoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(smp_model, "segmentation_head", None)) + else: + status["encoder_state"] = _module_freeze_state(getattr(raw, "encoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(raw, "segmentation_head", None)) + + relevant_states = [ + state + for state in ( + status["encoder_state"], + status["decoder_state"], + status["segmentation_head_state"], + ) + if state != "n/a" + ] + status["freeze_active"] = bool( + status["bootstrap_loaded"] and relevant_states and all(state == "frozen" for state in relevant_states) + ) + return status + +def _strategy3_decoder_is_frozen(model: nn.Module) -> bool: + return bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]) + +def _strategy3_loss_weights( + model: nn.Module, + *, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + decoder_ce_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(ce_weight) + decoder_dice_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(dice_weight) + return { + "decoder_ce": float(_job_param("strategy3_decoder_ce_weight", decoder_ce_default)), + "decoder_dice": float(_job_param("strategy3_decoder_dice_weight", decoder_dice_default)), + } + +def _strategy3_keep_frozen_modules_in_eval(model: nn.Module) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]): + return + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + module_names = ("encoder", "decoder", "segmentation_head") + module_root = smp_model + else: + module_names = ("encoder", "segmentation_head") + module_root = raw + for module_name in module_names: + module = getattr(module_root, module_name, None) + if isinstance(module, nn.Module): + module.eval() + +def _strategy3_apply_rollout_step( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + return apply_actions( + seg, + actions, + num_actions=int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)), + ).to(dtype=seg.dtype) + +def _refinement_deltas( + *, + action_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + small = float(_job_param("refine_delta_small", DEFAULT_REFINE_DELTA_SMALL)) + large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + if action_count == 3: + values = (-large, 0.0, small) + elif action_count == 4: + values = (-large, -small, 0.0, small) + elif action_count == 5: + values = (-large, -small, 0.0, small, large) + else: + raise ValueError( + f"Unsupported Strategy 3 action count {action_count}. " + "Expected one of {3, 4, 5}." + ) + return torch.tensor(values, device=device, dtype=dtype) + +def threshold_binary_mask(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).to(dtype=mask.dtype) + +def threshold_binary_long(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).long() + +"""============================================================================= +BUSI SPLIT + NORMALIZATION +============================================================================= +""" + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) +IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) + +def validate_image_mask_consistency(images_dir: Path, annotations_dir: Path): + image_files = {f for f in os.listdir(images_dir) if not f.startswith(".") and f.lower().endswith(".png")} + mask_files = {f for f in os.listdir(annotations_dir) if not f.startswith(".") and f.lower().endswith(".png")} + matched = sorted(image_files & mask_files) + missing_masks = sorted(image_files - mask_files) + missing_images = sorted(mask_files - image_files) + return matched, missing_masks, missing_images + +def parse_busi_with_classes_label(filename: str) -> str: + upper_name = str(filename).upper() + if upper_name.endswith("_B.PNG"): + return "benign" + if upper_name.endswith("_M.PNG"): + return "malignant" + raise ValueError( + f"BUSI_with_classes filename must end with '_B.png' or '_M.png', got {filename!r}" + ) + +def _candidate_report_dicts(payload: dict[str, Any]) -> list[dict[str, Any]]: + candidates = [payload] + for key in ("counts", "summary", "dataset", "report", "metadata"): + value = payload.get(key) + if isinstance(value, dict): + candidates.append(value) + return candidates + +def _extract_report_int(payload: dict[str, Any], keys: tuple[str, ...]) -> int | None: + for candidate in _candidate_report_dicts(payload): + for key in keys: + value = candidate.get(key) + if isinstance(value, bool): + continue + if isinstance(value, (int, np.integer)): + return int(value) + if isinstance(value, float) and float(value).is_integer(): + return int(value) + return None + +def _extract_report_filenames(payload: dict[str, Any]) -> set[str] | None: + for candidate in _candidate_report_dicts(payload): + filenames = candidate.get("filenames") + if isinstance(filenames, list) and all(isinstance(item, str) for item in filenames): + return set(filenames) + + pairs = candidate.get("pairs") + if isinstance(pairs, list): + extracted = {item["filename"] for item in pairs if isinstance(item, dict) and isinstance(item.get("filename"), str)} + if extracted: + return extracted + return None + +def validate_busi_with_classes_pipeline_report(report_path: Path, sample_records: list[dict[str, str]]) -> None: + if not report_path.exists(): + return + + payload = load_json(report_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict payload in {report_path}, found {type(payload).__name__}.") + + benign_count = sum(1 for record in sample_records if record.get("class_label") == "benign") + malignant_count = sum(1 for record in sample_records if record.get("class_label") == "malignant") + expected_counts = { + "total_pairs": len(sample_records), + "benign": benign_count, + "malignant": malignant_count, + } + report_counts = { + "total_pairs": _extract_report_int(payload, ("total_pairs", "pair_count", "num_pairs", "total")), + "benign": _extract_report_int(payload, ("benign", "benign_count", "num_benign")), + "malignant": _extract_report_int(payload, ("malignant", "malignant_count", "num_malignant")), + } + for key, expected_value in expected_counts.items(): + report_value = report_counts[key] + if report_value is not None and report_value != expected_value: + raise RuntimeError( + f"pipeline_check mismatch for {key}: discovered={expected_value}, report={report_value} ({report_path})" + ) + + report_filenames = _extract_report_filenames(payload) + if report_filenames is not None: + discovered_filenames = {record["filename"] for record in sample_records} + if report_filenames != discovered_filenames: + missing_from_report = sorted(discovered_filenames - report_filenames)[:10] + extra_in_report = sorted(report_filenames - discovered_filenames)[:10] + raise RuntimeError( + f"pipeline_check filenames mismatch for {report_path}: " + f"missing_from_report={missing_from_report}, extra_in_report={extra_in_report}" + ) + + print(f"[Pipeline Check] Validated BUSI_with_classes metadata from {report_path}") + +def check_data_leakage(splits: dict[str, list[str]]) -> dict[str, list[str]]: + leaks: dict[str, list[str]] = {} + split_names = list(splits.keys()) + for i, lhs in enumerate(split_names): + for rhs in split_names[i + 1 :]: + overlap = sorted(set(splits[lhs]) & set(splits[rhs])) + if overlap: + leaks[f"{lhs} ∩ {rhs}"] = overlap + return leaks + +def _project_relative_path(path: Path) -> str: + resolved = Path(path).resolve() + try: + return str(resolved.relative_to(PROJECT_DIR.resolve())) + except ValueError: + return str(resolved) + +def resolve_dataset_root_from_registry(split_registry: dict[str, Any]) -> Path: + dataset_root = Path(split_registry["dataset_root"]) + if dataset_root.is_absolute(): + return dataset_root + return (PROJECT_DIR / dataset_root).resolve() + +def make_sample_record( + filename: str, + images_subdir: str, + annotations_subdir: str, + *, + class_label: str | None = None, +) -> dict[str, str]: + record = { + "filename": filename, + "image_rel_path": str(Path(images_subdir) / filename), + "mask_rel_path": str(Path(annotations_subdir) / filename), + } + if class_label is not None: + record["class_label"] = class_label + return record + +def build_sample_records( + filenames: list[str], + *, + images_subdir: str, + annotations_subdir: str, + dataset_name: str, +) -> list[dict[str, str]]: + records = [] + for filename in sorted(filenames): + class_label = parse_busi_with_classes_label(filename) if dataset_name == "BUSI_with_classes" else None + records.append( + make_sample_record( + filename, + images_subdir, + annotations_subdir, + class_label=class_label, + ) + ) + return records + +def split_ratios_for_type(split_type: str) -> tuple[float, float]: + if split_type == "80_10_10": + return 0.80, 0.10 + if split_type == "70_10_20": + return 0.70, 0.10 + raise ValueError(f"Unsupported split_type: {split_type}") + +def deterministic_shuffle_records(records: list[dict[str, str]], *, seed: int, tag: str) -> list[dict[str, str]]: + rng = random.Random(seed + stable_int_from_text(tag)) + shuffled = [dict(record) for record in records] + rng.shuffle(shuffled) + return shuffled + +def train_subset_variant_suffix(variant: int | None = None) -> str: + variant_value = int(TRAIN_SUBSET_VARIANT if variant is None else variant) + return "" if variant_value <= 0 else f"_variant{variant_value:02d}" + +def group_records_by_class(sample_records: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + grouped: dict[str, list[dict[str, str]]] = {} + for record in sample_records: + class_label = record.get("class_label") + if class_label is None: + raise RuntimeError("Expected class_label in sample record for class-aware splitting.") + grouped.setdefault(class_label, []).append(dict(record)) + return grouped + +def allocate_counts_by_ratio(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + total_available = sum(available_counts.values()) + if total_available <= 0: + return allocation + + exact = {label: total_size * available_counts[label] / total_available for label in available_counts} + for label in available_counts: + allocation[label] = min(available_counts[label], int(math.floor(exact[label]))) + + remaining = min(total_size, total_available) - sum(allocation.values()) + order = sorted( + available_counts.keys(), + key=lambda label: (exact[label] - math.floor(exact[label]), available_counts[label], label), + reverse=True, + ) + while remaining > 0: + progressed = False + for label in order: + if allocation[label] < available_counts[label]: + allocation[label] += 1 + remaining -= 1 + progressed = True + if remaining == 0: + break + if not progressed: + break + return allocation + +def allocate_balanced_counts(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + labels = sorted(available_counts.keys()) + half = total_size // 2 + for label in labels: + allocation[label] = min(available_counts[label], half) + + remaining = min(total_size, sum(available_counts.values())) - sum(allocation.values()) + while remaining > 0: + candidates = [label for label in labels if allocation[label] < available_counts[label]] + if not candidates: + break + best_label = max( + candidates, + key=lambda label: ( + available_counts[label] - allocation[label], + 1 if label == "benign" else 0, + label, + ), + ) + allocation[best_label] += 1 + remaining -= 1 + return allocation + +def build_unstratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + records = deterministic_shuffle_records(sample_records, seed=seed, tag=f"base::{split_type}") + + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + return { + "train": records[:train_end], + "val": records[train_end:val_end], + "test": records[val_end:], + } + +def build_stratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + grouped = group_records_by_class(sample_records) + splits = {"train": [], "val": [], "test": []} + + for class_label in sorted(grouped.keys()): + records = deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"base::{split_type}::{class_label}", + ) + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + splits["train"].extend(records[:train_end]) + splits["val"].extend(records[train_end:val_end]) + splits["test"].extend(records[val_end:]) + + for split_name in splits: + splits[split_name] = deterministic_shuffle_records( + splits[split_name], + seed=seed, + tag=f"base::{split_type}::{split_name}", + ) + return splits + +def build_balanced_train_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + test_ratio = 1.0 - train_ratio - val_ratio + grouped = group_records_by_class(sample_records) + if sorted(grouped.keys()) != ["benign", "malignant"]: + raise RuntimeError( + f"balanced_train split policy expects benign/malignant classes, found {sorted(grouped.keys())}" + ) + + shuffled = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"base::{split_type}::balanced_train::{class_label}", + ) + for class_label, records in grouped.items() + } + + nominal_train_size = int(len(sample_records) * train_ratio) + per_class_train = min( + nominal_train_size // 2, + *(len(records) for records in shuffled.values()), + ) + + train_records: list[dict[str, str]] = [] + remaining_by_class: dict[str, list[dict[str, str]]] = {} + for class_label in sorted(shuffled.keys()): + records = shuffled[class_label] + train_records.extend(records[:per_class_train]) + remaining_by_class[class_label] = records[per_class_train:] + + remainder_val_fraction = val_ratio / max(val_ratio + test_ratio, 1e-8) + val_records: list[dict[str, str]] = [] + test_records: list[dict[str, str]] = [] + for class_label in sorted(remaining_by_class.keys()): + records = remaining_by_class[class_label] + val_count = int(len(records) * remainder_val_fraction) + val_records.extend(records[:val_count]) + test_records.extend(records[val_count:]) + + return { + "train": deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::train", + ), + "val": deterministic_shuffle_records( + val_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::val", + ), + "test": deterministic_shuffle_records( + test_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::test", + ), + } + +def build_nested_train_subsets( + train_records: list[dict[str, str]], + train_fractions: list[float], + *, + split_type: str, + seed: int, + split_policy: str | None = None, + subset_variant: int = 0, +) -> dict[str, list[dict[str, str]]]: + if not train_records: + return {} + + variant_tag = "" if int(subset_variant) <= 0 else f"::variant::{int(subset_variant)}" + ordered_records = deterministic_shuffle_records(train_records, seed=seed, tag=f"subset::{split_type}{variant_tag}") + use_class_labels = any("class_label" in record for record in train_records) + if not use_class_labels: + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + subsets[subset_key] = [dict(record) for record in ordered_records[:subset_size]] + return subsets + + grouped = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{class_label}{variant_tag}", + ) + for class_label, records in group_records_by_class(train_records).items() + } + available_counts = {class_label: len(records) for class_label, records in grouped.items()} + + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + if split_policy == "balanced_train": + class_counts = allocate_balanced_counts(subset_size, available_counts) + else: + class_counts = allocate_counts_by_ratio(subset_size, available_counts) + + subset_records: list[dict[str, str]] = [] + for class_label in sorted(grouped.keys()): + subset_records.extend([dict(record) for record in grouped[class_label][: class_counts[class_label]]]) + subsets[subset_key] = deterministic_shuffle_records( + subset_records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{subset_key}{variant_tag}", + ) + return subsets + +def train_fraction_from_subset_key(subset_key: str) -> float: + subset_text = str(subset_key).strip().lower() + if not subset_text: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") + try: + percent = Decimal(subset_text.replace("p", ".")) + except InvalidOperation as exc: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") from exc + if not percent.is_finite() or percent <= 0 or percent > 100: + raise RuntimeError(f"Train subset key {subset_key!r} must represent a percentage in the range (0, 100].") + return float(percent / Decimal("100")) + +def validate_persisted_split_no_leakage(split_type: str, split_entry: dict[str, Any], *, source: str) -> None: + base_splits = split_entry["base_splits"] + base_filenames: dict[str, list[str]] = {} + for split_name, records in base_splits.items(): + filenames = [record["filename"] for record in records] + if len(filenames) != len(set(filenames)): + raise RuntimeError(f"Duplicate filenames detected inside {split_name} for split_type={split_type}.") + base_filenames[split_name] = filenames + + leaks = check_data_leakage(base_filenames) + if leaks: + raise RuntimeError(f"Data leakage detected for split_type={split_type}: {list(leaks.keys())}") + + base_train = set(base_filenames["train"]) + previous_subset: set[str] = set() + for subset_key in sorted(split_entry["train_subsets"].keys(), key=train_fraction_from_subset_key): + subset_filenames = [record["filename"] for record in split_entry["train_subsets"][subset_key]] + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames detected inside train subset {subset_key} for split_type={split_type}." + ) + subset_set = set(subset_filenames) + missing = sorted(subset_set - base_train) + if missing: + raise RuntimeError( + f"Train subset {subset_key} contains files outside the base train split for split_type={split_type}." + ) + if previous_subset and not previous_subset.issubset(subset_set): + raise RuntimeError( + f"Train subsets are not nested for split_type={split_type}." + ) + previous_subset = subset_set + + print(f"[Split Check] No data leakage detected for split_type={split_type} ({source}).") + +def repair_persisted_train_subsets( + split_registry: dict[str, Any], + requested_train_fractions: list[float], + *, + split_json_path: Path, + seed: int, +) -> bool: + split_entries = split_registry.get("split_types", {}) + requested_fractions = normalize_dataset_percents(requested_train_fractions) + combined_fractions = {float(value) for value in split_registry.get("train_fractions", [])} + combined_fractions.update(requested_fractions) + dataset_name = str(split_registry.get("dataset_name", "BUSI")) + split_policy = split_registry.get("split_policy") if dataset_name == "BUSI_with_classes" else None + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + for subset_key in train_subsets.keys(): + combined_fractions.add(train_fraction_from_subset_key(subset_key)) + + combined_fractions_list = normalize_dataset_percents(list(combined_fractions)) + requested_keys = {percent_label(fraction) for fraction in requested_fractions} + registry_seed = int(split_registry.get("seed", seed)) + repaired = False + + if split_registry.get("train_fractions") != combined_fractions_list: + split_registry["train_fractions"] = combined_fractions_list + repaired = True + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + missing_requested_keys = sorted(requested_keys - set(train_subsets.keys()), key=train_fraction_from_subset_key) + if not missing_requested_keys: + continue + + split_entry["train_subsets"] = build_nested_train_subsets( + split_entry["base_splits"]["train"], + combined_fractions_list, + split_type=split_type, + seed=registry_seed, + split_policy=split_policy, + ) + print( + f"[Splits] Rebuilt missing train subsets {missing_requested_keys} " + f"for split_type={split_type} in {split_json_path}" + ) + repaired = True + + if repaired: + save_json(split_json_path, split_registry) + print(f"[Splits] Updated persisted dataset splits at {split_json_path}") + return repaired + +def load_or_create_dataset_splits( + images_dir: Path, + annotations_dir: Path, + split_json_path: Path, + train_fractions: list[float], + seed: int, +) -> tuple[dict[str, Any], str]: + train_fractions = normalize_dataset_percents(train_fractions) + images_dir = Path(images_dir).resolve() + annotations_dir = Path(annotations_dir).resolve() + split_json_path = Path(split_json_path).resolve() + dataset_name = current_dataset_name() + split_policy = current_busi_with_classes_split_policy() if dataset_name == "BUSI_with_classes" else None + if split_json_path.exists(): + split_registry = load_json(split_json_path) + if split_registry.get("version") != DATASET_SPLITS_VERSION: + raise RuntimeError( + f"Unsupported dataset_splits.json version in {split_json_path}. " + f"Expected version={DATASET_SPLITS_VERSION}." + ) + persisted_dataset_name = str(split_registry.get("dataset_name", "BUSI")) + if persisted_dataset_name != dataset_name: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets dataset_name={persisted_dataset_name!r}, " + f"but current DATASET_NAME={dataset_name!r}." + ) + persisted_split_policy = split_registry.get("split_policy") + if dataset_name == "BUSI_with_classes" and persisted_split_policy != split_policy: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets split_policy={persisted_split_policy!r}, " + f"but current BUSI_WITH_CLASSES_SPLIT_POLICY={split_policy!r}." + ) + split_entries = split_registry.get("split_types") + if not isinstance(split_entries, dict): + raise RuntimeError(f"Invalid split_types payload in {split_json_path}.") + for split_type in SUPPORTED_SPLIT_TYPES: + if split_type not in split_entries: + raise RuntimeError( + f"dataset_splits.json is missing split_type={split_type}. Delete it to regenerate cleanly." + ) + repaired = repair_persisted_train_subsets( + split_registry, + train_fractions, + split_json_path=split_json_path, + seed=seed, + ) + source = "repaired" if repaired else "loaded" + if dataset_name == "BUSI_with_classes": + sample_records = build_sample_records( + validate_image_mask_consistency(images_dir, annotations_dir)[0], + images_subdir=split_registry["images_subdir"], + annotations_subdir=split_registry["annotations_subdir"], + dataset_name=dataset_name, + ) + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + for split_type in SUPPORTED_SPLIT_TYPES: + validate_persisted_split_no_leakage(split_type, split_entries[split_type], source=source) + if repaired: + print(f"[Splits] Loaded and repaired persisted dataset splits from {split_json_path}") + else: + print(f"[Splits] Loaded persisted dataset splits from {split_json_path}") + return split_registry, source + + matched, missing_masks, missing_images = validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + images_subdir = images_dir.relative_to(dataset_root).as_posix() + annotations_subdir = annotations_dir.relative_to(dataset_root).as_posix() + sample_records = build_sample_records( + matched, + images_subdir=images_subdir, + annotations_subdir=annotations_subdir, + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + split_registry = { + "version": DATASET_SPLITS_VERSION, + "dataset_name": dataset_name, + "split_policy": split_policy, + "dataset_root": _project_relative_path(dataset_root), + "images_subdir": images_subdir, + "annotations_subdir": annotations_subdir, + "seed": seed, + "train_fractions": list(train_fractions), + "split_types": {}, + } + + for split_type in SUPPORTED_SPLIT_TYPES: + if dataset_name == "BUSI_with_classes": + if split_policy == "balanced_train": + base_splits = build_balanced_train_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_stratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_unstratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + train_subsets = build_nested_train_subsets( + base_splits["train"], + train_fractions, + split_type=split_type, + seed=seed, + split_policy=split_policy, + ) + split_entry = { + "split_type": split_type, + "base_splits": base_splits, + "train_subsets": train_subsets, + } + validate_persisted_split_no_leakage(split_type, split_entry, source="created") + split_registry["split_types"][split_type] = split_entry + + save_json(split_json_path, split_registry) + print(f"[Splits] Created persisted dataset splits at {split_json_path}") + return split_registry, "created" + +def select_persisted_split( + split_registry: dict[str, Any], + split_type: str, + train_fraction: float, +) -> dict[str, Any]: + if split_type not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"Unsupported split_type: {split_type}") + + split_entries = split_registry.get("split_types", {}) + if split_type not in split_entries: + raise KeyError( + f"Requested split_type={split_type} is not available in dataset_splits.json. " + "Delete the JSON file to regenerate it with the new configuration." + ) + + subset_key = percent_label(train_fraction) + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + if subset_key not in train_subsets: + raise KeyError( + f"Requested train fraction={train_fraction} (key={subset_key}) is not available in dataset_splits.json." + ) + + return { + "dataset_root": resolve_dataset_root_from_registry(split_registry), + "split_type": split_type, + "train_fraction": float(train_fraction), + "train_subset_key": subset_key, + "train_subset_variant": 0, + "train_subset_source": "persisted", + "base_train_records": split_entry["base_splits"]["train"], + "train_records": train_subsets[subset_key], + "val_records": split_entry["base_splits"]["val"], + "test_records": split_entry["base_splits"]["test"], + } + +def apply_train_subset_variant( + selected_split: dict[str, Any], + split_registry: dict[str, Any], + *, + subset_variant: int, +) -> dict[str, Any]: + variant = int(subset_variant) + if variant <= 0 or float(selected_split["train_fraction"]) >= 1.0: + return selected_split + + split_policy = split_registry.get("split_policy") if current_dataset_name() == "BUSI_with_classes" else None + variant_subsets = build_nested_train_subsets( + selected_split["base_train_records"], + [float(selected_split["train_fraction"])], + split_type=str(selected_split["split_type"]), + seed=int(split_registry.get("seed", SEED)), + split_policy=split_policy, + subset_variant=variant, + ) + subset_key = str(selected_split["train_subset_key"]) + updated_split = dict(selected_split) + updated_split["train_records"] = variant_subsets[subset_key] + updated_split["train_subset_variant"] = variant + updated_split["train_subset_source"] = "variant_override" + return updated_split + +def export_selected_split_manifest( + pct_root: Path, + *, + percent: float, + split_source: str, + selected_split: dict[str, Any], +) -> Path: + variant_suffix = train_subset_variant_suffix(int(selected_split.get("train_subset_variant", 0))) + manifest_path = pct_root / ( + f"selected_split_{selected_split['split_type']}_{percent_label(percent)}pct{variant_suffix}.json" + ) + payload = { + "dataset_name": current_dataset_name(), + "dataset_root": str(Path(selected_split["dataset_root"]).resolve()), + "dataset_percent": float(percent), + "dataset_percent_label": percent_label(percent), + "split_source": split_source, + "split_type": str(selected_split["split_type"]), + "train_fraction": float(selected_split["train_fraction"]), + "train_subset_key": str(selected_split["train_subset_key"]), + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(manifest_path.resolve()), + "base_train_records": [dict(record) for record in selected_split["base_train_records"]], + "train_records": [dict(record) for record in selected_split["train_records"]], + "val_records": [dict(record) for record in selected_split["val_records"]], + "test_records": [dict(record) for record in selected_split["test_records"]], + } + save_json(manifest_path, payload) + return manifest_path + +def compute_busi_statistics( + dataset_root: Path, + sample_records: list[dict[str, str]], + cache_path: Path, +) -> tuple[float, float, str]: + filenames = [record["filename"] for record in sample_records] + if cache_path.exists(): + stats = load_json(cache_path) + if stats.get("filenames") == filenames: + print(f"[Normalization] Loaded cached normalization stats from {cache_path}") + return float(stats["global_mean"]), float(stats["global_std"]), "loaded_from_cache" + + total_sum = np.float64(0.0) + total_sq_sum = np.float64(0.0) + total_pixels = 0 + + for record in tqdm(sample_records, desc="Computing BUSI train mean/std", leave=False): + image_path = dataset_root / record["image_rel_path"] + img = np.array(PILImage.open(image_path)).astype(np.float64) + total_sum += img.sum() + total_sq_sum += (img ** 2).sum() + total_pixels += img.size + + global_mean = float(total_sum / total_pixels) + global_std = float(np.sqrt(total_sq_sum / total_pixels - global_mean ** 2)) + if global_std < 1e-6: + global_std = 1.0 + + save_json( + cache_path, + { + "global_mean": global_mean, + "global_std": global_std, + "total_pixels": int(total_pixels), + "num_images": len(sample_records), + "filenames": filenames, + }, + ) + print(f"[Normalization] Computed and saved normalization stats to {cache_path}") + return global_mean, global_std, "computed_fresh" + +def compute_class_distribution(sample_records: list[dict[str, str]]) -> dict[str, int] | None: + if not sample_records or not any("class_label" in record for record in sample_records): + return None + return { + "benign": sum(1 for record in sample_records if record.get("class_label") == "benign"), + "malignant": sum(1 for record in sample_records if record.get("class_label") == "malignant"), + } + +def format_class_distribution(class_distribution: dict[str, int] | None) -> str: + if class_distribution is None: + return "unavailable" + benign = int(class_distribution.get("benign", 0)) + malignant = int(class_distribution.get("malignant", 0)) + total = benign + malignant + return f"benign={benign}, malignant={malignant}, total={total}" + +def print_loaded_class_distribution( + *, + split_type: str, + train_subset_key: str, + base_train_records: list[dict[str, str]], + train_records: list[dict[str, str]], + val_records: list[dict[str, str]], + test_records: list[dict[str, str]], +) -> None: + if not any("class_label" in record for record in train_records): + return + section(f"Loaded Class Distribution | {split_type} | {train_subset_key}%") + print(f"Base train classes : {format_class_distribution(compute_class_distribution(base_train_records))}") + print(f"Train subset classes : {format_class_distribution(compute_class_distribution(train_records))}") + print(f"Validation classes : {format_class_distribution(compute_class_distribution(val_records))}") + print(f"Test classes : {format_class_distribution(compute_class_distribution(test_records))}") + +def print_split_summary(payload: dict[str, Any]) -> None: + unit_name = "Phase" if payload.get("phase_index") is not None else "Split" + section(f"{unit_name} Summary | {payload['split_type']} | {payload['train_subset_key']}%") + print(f"Dataset name : {payload['dataset_name']}") + if payload.get("dataset_split_policy") is not None: + print(f"Dataset split policy : {payload['dataset_split_policy']}") + print(f"Dataset splits JSON : {payload['dataset_splits_path']}") + print(f"Split source : {payload['split_source']}") + print(f"Split type used : {payload['split_type']}") + if payload.get("split_generation_mode") is not None: + print(f"Split generation mode : {payload['split_generation_mode']}") + if payload.get("phase_index") is not None: + print(f"Phase index : {payload['phase_index']}") + print(f"Phase val/test folds : val={payload['phase_val_fold_index']}, test={payload['phase_test_fold_index']}") + if payload.get("percent_sampling_mode") is not None: + print(f"Percent sampling mode : {payload['percent_sampling_mode']}") + print(f"Train fraction : {payload['train_subset_key']}% of frozen base train") + print(f"Train subset variant : {payload.get('train_subset_variant', 0)}") + print(f"Train subset source : {payload.get('train_subset_source', 'persisted')}") + if payload.get("sampling_chain_dataset_percents") is not None: + print(f"Sampling chain percents: {payload['sampling_chain_dataset_percents']}") + print(f"Base train samples : {payload['base_train_count']}") + print(f"Train subset samples : {payload['train_count']}") + print(f"Validation samples : {payload['val_count']}") + print(f"Test samples : {payload['test_count']}") + if payload.get("base_train_class_distribution") is not None: + print(f"Base train classes : {format_class_distribution(payload['base_train_class_distribution'])}") + print(f"Train subset classes : {format_class_distribution(payload['train_class_distribution'])}") + print(f"Validation classes : {format_class_distribution(payload['val_class_distribution'])}") + print(f"Test classes : {format_class_distribution(payload['test_class_distribution'])}") + print(f"Validation/Test frozen : {payload['val_test_frozen']}") + print(f"Leakage check : {payload['leakage_check']}") + +def print_normalization_summary(payload: dict[str, Any]) -> None: + mode = "ImageNet mean/std" if USE_IMAGENET_NORM else "Dataset train mean/std" + print(f"Dataset name : {payload['dataset_name']}") + print(f"Normalization mode : {mode}") + print(f"Stats cache path : {payload['normalization_cache_path']}") + print(f"Stats source : {payload['normalization_source']}") + print(f"Split type used : {payload['split_type']}") + variant_suffix = train_subset_variant_suffix(int(payload.get("train_subset_variant", 0))) + print( + f"Stats computed from : {payload['train_count']} train samples " + f"({payload['train_subset_key']}%{variant_suffix})" + ) +# ============================================================================= +# IMAGE PREPARATION + DATASETS +# ============================================================================= + +def _to_three_channels(image: np.ndarray) -> np.ndarray: + if image.ndim == 2: + image = image[..., None] + if image.shape[2] == 1: + image = np.repeat(image, 3, axis=2) + elif image.shape[2] > 3: + image = image[..., :3] + return image + +def _prepare_image(raw: np.ndarray, global_mean: float, global_std: float) -> np.ndarray: + img = raw.astype(np.float32) + img = _to_three_channels(img) + if IMG_SIZE > 0 and (img.shape[0] != IMG_SIZE or img.shape[1] != IMG_SIZE): + img = np.array( + PILImage.fromarray(img.astype(np.uint8)).resize((IMG_SIZE, IMG_SIZE), PILImage.BILINEAR) + ).astype(np.float32) + if USE_IMAGENET_NORM: + if img.max() > 1.0: + img = img / 255.0 + img = (img - IMAGENET_MEAN) / IMAGENET_STD + else: + img = (img - global_mean) / global_std + return np.transpose(img, (2, 0, 1)).copy() + +def _prepare_mask(raw: np.ndarray) -> np.ndarray: + mask = raw.astype(np.uint8) + if mask.ndim == 3: + mask = mask[..., 0] + if IMG_SIZE > 0 and (mask.shape[0] != IMG_SIZE or mask.shape[1] != IMG_SIZE): + pil_mask = PILImage.fromarray(mask) + if pil_mask.mode != "L": + pil_mask = pil_mask.convert("L") + mask = np.array(pil_mask.resize((IMG_SIZE, IMG_SIZE), PILImage.NEAREST)) + return ((mask > 0).astype(np.float32))[None, ...].copy() + +def print_imagenet_normalization_status() -> bool: + uses_imagenet_norm = bool(USE_IMAGENET_NORM) + if uses_imagenet_norm: + print("✅🖼️ ImageNet normalization is ACTIVE in `_prepare_image`.") + else: + print("⚠️🧪 ImageNet normalization is NOT active in `_prepare_image`.") + print("⚠️📊 Using dataset global mean/std normalization instead.") + if SMP_ENCODER_WEIGHTS == "imagenet" and not uses_imagenet_norm: + print("⚠️🚨 Encoder weights are set to ImageNet, but ImageNet normalization is disabled.") + return uses_imagenet_norm + +def _gaussian_kernel1d( + sigma: float, + *, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + if sigma <= 0: + return torch.ones(1, device=device, dtype=dtype) + radius = max(int(math.ceil(3.0 * sigma)), 1) + coords = torch.arange(-radius, radius + 1, device=device, dtype=dtype) + kernel = torch.exp(-(coords.square()) / max(2.0 * sigma * sigma, 1e-6)) + return kernel / kernel.sum().clamp_min(1e-12) + +def _smooth_displacement_field(field: torch.Tensor, sigma: float) -> torch.Tensor: + kernel = _gaussian_kernel1d(sigma, device=field.device, dtype=field.dtype) + if kernel.numel() == 1: + return field + radius = kernel.numel() // 2 + kernel_y = kernel.view(1, 1, -1, 1) + kernel_x = kernel.view(1, 1, 1, -1) + field = F.conv2d(field, kernel_y, padding=(radius, 0)) + field = F.conv2d(field, kernel_x, padding=(0, radius)) + return field + +def _apply_elastic_deformation( + image: torch.Tensor, + mask: torch.Tensor, + *, + alpha: float = 8.0, + sigma: float = 4.0, +) -> tuple[torch.Tensor, torch.Tensor]: + _, h, w = image.shape + if h < 2 or w < 2: + return image.contiguous(), mask.contiguous() + + device = image.device + dtype = image.dtype + dx = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + dy = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + + yy, xx = torch.meshgrid( + torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype), + torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype), + indexing="ij", + ) + grid = torch.stack((xx, yy), dim=-1).unsqueeze(0) + grid[..., 0] = grid[..., 0] + dx.squeeze(0).squeeze(0) * (2.0 / max(w - 1, 1)) + grid[..., 1] = grid[..., 1] + dy.squeeze(0).squeeze(0) * (2.0 / max(h - 1, 1)) + grid = grid.clamp(-1.25, 1.25) + + image_out = F.grid_sample( + image.unsqueeze(0), + grid, + mode="bilinear", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + mask_out = F.grid_sample( + mask.unsqueeze(0), + grid, + mode="nearest", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + return image_out.contiguous(), mask_out.clamp(0.0, 1.0).contiguous() + +def _apply_minimal_train_aug(image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(2,)) + mask = torch.flip(mask, dims=(2,)) + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(1,)) + mask = torch.flip(mask, dims=(1,)) + if torch.rand(1).item() < 0.5: + k = 1 if torch.rand(1).item() < 0.5 else 3 + image = torch.rot90(image, k=k, dims=(1, 2)) + mask = torch.rot90(mask, k=k, dims=(1, 2)) + elastic_aug_prob = float(_job_param("elastic_aug_prob", 0.0)) + if elastic_aug_prob > 0 and torch.rand(1).item() < elastic_aug_prob: + image, mask = _apply_elastic_deformation(image, mask) + return image.contiguous(), mask.contiguous() + +class BUSIDataset(Dataset): + def __init__( + self, + sample_records: list[dict[str, str]], + dataset_root: Path, + global_mean: float, + global_std: float, + *, + preload: bool, + augment: bool, + split_name: str, + ) -> None: + super().__init__() + self.sample_records = [dict(record) for record in sample_records] + self.dataset_root = Path(dataset_root) + self.global_mean = float(global_mean) + self.global_std = float(global_std) + self.preload = preload + self.augment = augment + self.split_name = split_name + self._images: list[torch.Tensor] = [] + self._masks: list[torch.Tensor] = [] + self._raw_cache_bytes = 0 + + if not self.preload: + raise ValueError("PRELOAD_TO_RAM is mandatory in this RunPod runner.") + self._preload_to_ram() + + def _preload_to_ram(self) -> None: + desc = f"Preloading {self.split_name} ({len(self.sample_records)} samples) to RAM" + for record in tqdm(self.sample_records, desc=desc, leave=False): + raw_img = np.array(PILImage.open(self.dataset_root / record["image_rel_path"])) + raw_mask = np.array(PILImage.open(self.dataset_root / record["mask_rel_path"])) + if raw_img.shape[:2] != raw_mask.shape[:2]: + raise RuntimeError( + f"Image/mask spatial size mismatch for {record['filename']}: " + f"image={raw_img.shape[:2]}, mask={raw_mask.shape[:2]}" + ) + image = torch.from_numpy(_prepare_image(raw_img, self.global_mean, self.global_std)) + mask = torch.from_numpy(_prepare_mask(raw_mask)) + self._raw_cache_bytes += tensor_bytes(image) + tensor_bytes(mask) + self._images.append(image) + self._masks.append(mask) + + def __len__(self) -> int: + return len(self.sample_records) + + def __getitem__(self, index: int) -> dict[str, Any]: + image = self._images[index].clone() + mask = self._masks[index].clone() + if self.augment: + image, mask = _apply_minimal_train_aug(image, mask) + return { + "image": image, + "mask": mask, + "sample_id": Path(self.sample_records[index]["filename"]).stem, + "dataset": current_dataset_name(), + } + + @property + def cache_bytes(self) -> int: + return self._raw_cache_bytes + +class CUDAPrefetcher: + def __init__(self, loader: DataLoader, device: torch.device) -> None: + self.loader = loader + self.device = device + self._use_cuda = device.type == "cuda" + self._iter = None + self._stream = None + self._next_batch = None + + def __len__(self) -> int: + return len(self.loader) + + def __iter__(self): + self._iter = iter(self.loader) + self._stream = torch.cuda.Stream(device=self.device) if self._use_cuda else None + self._next_batch = None + self._preload() + return self + + def close(self) -> None: + self._next_batch = None + self._iter = None + self._stream = None + + def _preload(self) -> None: + if self._iter is None: + self._next_batch = None + return + try: + self._next_batch = next(self._iter) + except StopIteration: + self._next_batch = None + return + if self._use_cuda: + assert self._stream is not None + with torch.cuda.stream(self._stream): + self._next_batch = to_device(self._next_batch, self.device) + else: + self._next_batch = to_device(self._next_batch, self.device) + + def __next__(self): + if self._next_batch is None: + self.close() + raise StopIteration + if self._use_cuda: + assert self._stream is not None + torch.cuda.current_stream(self.device).wait_stream(self._stream) + batch = self._next_batch + self._preload() + if self._next_batch is None: + self._iter = None + self._stream = None + return batch + +class DataBundle: + def __init__( + self, + *, + percent: float, + split_payload: dict[str, Any], + train_ds: BUSIDataset, + val_ds: BUSIDataset, + test_ds: BUSIDataset, + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + ) -> None: + self.percent = percent + self.split_payload = split_payload + self.train_ds = train_ds + self.val_ds = val_ds + self.test_ds = test_ds + self.train_loader = train_loader + self.val_loader = val_loader + self.test_loader = test_loader + + @property + def global_mean(self) -> float: + return float(self.split_payload["global_mean"]) + + @property + def global_std(self) -> float: + return float(self.split_payload["global_std"]) + + @property + def total_cache_bytes(self) -> int: + return self.train_ds.cache_bytes + self.val_ds.cache_bytes + self.test_ds.cache_bytes + +def make_loader(dataset: Dataset, shuffle: bool, *, loader_tag: str) -> DataLoader: + num_workers = NUM_WORKERS + persistent_workers = USE_PERSISTENT_WORKERS and num_workers > 0 + pin_memory = USE_PIN_MEMORY and DEVICE.type == "cuda" + generator = make_seeded_generator(SEED, loader_tag) + return DataLoader( + dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, + persistent_workers=persistent_workers, + worker_init_fn=seed_worker, + generator=generator, + ) + +def build_data_bundle(percent: float, split_registry: dict[str, Any], split_source: str) -> DataBundle: + pct_label = percent_label(percent) + pct_text = percent_text(percent) + selected_split = select_persisted_split(split_registry, SPLIT_TYPE, percent) + selected_split = apply_train_subset_variant( + selected_split, + split_registry, + subset_variant=TRAIN_SUBSET_VARIANT, + ) + pct_root = ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{pct_label}") + split_manifest_path = export_selected_split_manifest( + pct_root, + percent=percent, + split_source=split_source, + selected_split=selected_split, + ) + stats_cache_path = pct_root / ( + f"norm_stats_{normalization_cache_tag()}_{SPLIT_TYPE}_{pct_label}pct" + f"{train_subset_variant_suffix(int(selected_split.get('train_subset_variant', 0)))}.json" + ) + base_train_class_distribution = compute_class_distribution(selected_split["base_train_records"]) + train_class_distribution = compute_class_distribution(selected_split["train_records"]) + val_class_distribution = compute_class_distribution(selected_split["val_records"]) + test_class_distribution = compute_class_distribution(selected_split["test_records"]) + print_loaded_class_distribution( + split_type=selected_split["split_type"], + train_subset_key=selected_split["train_subset_key"], + base_train_records=selected_split["base_train_records"], + train_records=selected_split["train_records"], + val_records=selected_split["val_records"], + test_records=selected_split["test_records"], + ) + dataset_root = Path(selected_split["dataset_root"]).resolve() + global_mean, global_std, normalization_source = compute_busi_statistics( + dataset_root=dataset_root, + sample_records=selected_split["train_records"], + cache_path=stats_cache_path, + ) + + split_payload = { + "dataset_name": current_dataset_name(), + "dataset_split_policy": split_registry.get("split_policy"), + "dataset_splits_path": str(current_dataset_splits_json_path().resolve()), + "dataset_root": str(dataset_root), + "split_source": split_source, + "split_type": SPLIT_TYPE, + "dataset_percent": percent, + "train_subset_key": selected_split["train_subset_key"], + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(split_manifest_path.resolve()), + "base_train_count": len(selected_split["base_train_records"]), + "train_count": len(selected_split["train_records"]), + "val_count": len(selected_split["val_records"]), + "test_count": len(selected_split["test_records"]), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(stats_cache_path.resolve()), + "normalization_source": normalization_source, + } + + print_split_summary(split_payload) + print_normalization_summary(split_payload) + + train_ds = BUSIDataset( + selected_split["train_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=True, + split_name=f"train {SPLIT_TYPE} {pct_text}", + ) + val_ds = BUSIDataset( + selected_split["val_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"val {SPLIT_TYPE}", + ) + test_ds = BUSIDataset( + selected_split["test_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"test {SPLIT_TYPE}", + ) + + bundle = DataBundle( + percent=percent, + split_payload=split_payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=make_loader(train_ds, shuffle=True, loader_tag=f"{SPLIT_TYPE}:{pct_label}:train"), + val_loader=make_loader(val_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:val"), + test_loader=make_loader(test_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:test"), + ) + print_preload_summary(bundle) + return bundle + +def print_preload_summary(bundle: DataBundle) -> None: + section( + f"RAM Preload Summary | {bundle.split_payload['split_type']} | {int(bundle.percent * 100)}%" + ) + print(f"Train samples : {len(bundle.train_ds)}") + print(f"Val samples : {len(bundle.val_ds)}") + print(f"Test samples : {len(bundle.test_ds)}") + print(f"Train batches : {len(bundle.train_loader)}") + print(f"Val batches : {len(bundle.val_loader)}") + print(f"Test batches : {len(bundle.test_loader)}") + print(f"Global mean : {bundle.global_mean:.6f}") + print(f"Global std : {bundle.global_std:.6f}") + first = bundle.train_ds[0] + print(f"Sample image shape : {tuple(first['image'].shape)}") + print(f"Sample mask shape : {tuple(first['mask'].shape)}") + print(f"Sample image dtype : {first['image'].dtype}") + print(f"Sample mask dtype : {first['mask'].dtype}") + print(f"Estimated RAM preload : {bytes_to_gb(bundle.total_cache_bytes):.3f} GB") + +"""============================================================================= +MODEL DEFINITIONS +============================================================================= +""" + +def strategy_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + strategy = _require_supported_strategy(strategy) + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + if strategy == 2: + return "Strategy 2: Custom VGG + Segmentation Head (Supervised)" + if strategy == 3: + return "Strategy 3 Lite: Custom VGG + Segmentation Head + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + + if strategy == 2: + return f"Strategy 2: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) supervised" + if strategy == 3: + return f"Strategy 3 Lite: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + +def _apply_omega_conv(omega_conv: nn.Conv2d, value_next: torch.Tensor) -> torch.Tensor: + weight = omega_conv.weight + value_next = value_next.to(device=weight.device, dtype=weight.dtype) + return omega_conv(value_next) + +def _conv3x3(in_ch: int, out_ch: int, dilation: int = 1) -> nn.Conv2d: + return nn.Conv2d( + in_ch, + out_ch, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=True, + ) + +class _ConvBlock(nn.Module): + def __init__( + self, + in_ch: int, + out_ch: int, + dilation: int = 1, + *, + num_groups: int = 0, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.conv = _conv3x3(in_ch, out_ch, dilation=dilation) + self.norm = _group_norm(out_ch, num_groups=num_groups) if num_groups > 0 else nn.Identity() + self.act = nn.ReLU(inplace=True) + self.drop = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.act(self.norm(self.conv(x)))) + +def _group_norm(num_channels: int, *, num_groups: int = GN_NUM_GROUPS) -> nn.GroupNorm: + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + return nn.GroupNorm(groups, num_channels) + + +class _MultiScaleRefineBranch(nn.Module): + """Processes raw encoder features at each scale independently, then fuses + them into a single feature map. This gives the refinement head access to + multi-resolution spatial cues (edges at low levels, semantics at high + levels) that the 1x1 projection squashes away.""" + + def __init__( + self, + encoder_channels: list[int] | tuple[int, ...], + out_channels: int, + per_scale_channels: int = 32, + ) -> None: + super().__init__() + self._valid_indices: list[int] = [i for i, c in enumerate(encoder_channels) if c > 0] + self.scale_convs = nn.ModuleList() + for i in self._valid_indices: + self.scale_convs.append(nn.Sequential( + nn.Conv2d(encoder_channels[i], per_scale_channels, kernel_size=1, bias=False), + _group_norm(per_scale_channels), + nn.ReLU(inplace=True), + )) + total_ch = per_scale_channels * len(self._valid_indices) + self.fuse = nn.Sequential( + nn.Conv2d(total_ch, out_channels, kernel_size=3, padding=1, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2, dilation=2, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + ) + self._init_small() + + def _init_small(self) -> None: + """Small-magnitude init so the branch starts as a near-zero residual.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + m.weight.data.mul_(0.1) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + parts: list[torch.Tensor] = [] + for idx, conv in zip(self._valid_indices, self.scale_convs): + out = conv(encoder_features[idx]) + if out.shape[-2] != h or out.shape[-1] != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + parts.append(out) + return self.fuse(torch.cat(parts, dim=1)) + + +class SelfAttentionModule(nn.Module): + def __init__(self, channels: int) -> None: + super().__init__() + mid = max(channels // 8, 1) + self.query = nn.Conv2d(channels, mid, 1) + self.key = nn.Conv2d(channels, mid, 1) + self.value = nn.Conv2d(channels, channels, 1) + self.gamma = nn.Parameter(torch.tensor([0.1], dtype=torch.float32)) + + def forward(self, f: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + b, c, h, w = f.shape + pooled = f + target_grid = max(int(_job_param("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID)), 1) + if target_grid < max(h, w): + stride_h = max(1, math.ceil(h / target_grid)) + stride_w = max(1, math.ceil(w / target_grid)) + pooled = F.avg_pool2d(f, kernel_size=(stride_h, stride_w), stride=(stride_h, stride_w)) + if target_grid >= 64 and target_grid not in _STRATEGY3_SAM_GRID_WARNED: + print( + "[Strategy3] Self-attention grid " + f"{target_grid}x{target_grid} requested; this implies a much heavier attention matrix " + "(for example 64x64 -> 4096 tokens). Lower strategy3_sam_attention_grid if this is too slow." + ) + _STRATEGY3_SAM_GRID_WARNED.add(target_grid) + + ph, pw = pooled.shape[-2:] + q = self.query(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + k = self.key(pooled).view(b, -1, ph * pw) + v = self.value(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + attn = torch.softmax(q @ k / (q.shape[-1] ** 0.5), dim=-1) + out = (attn @ v).permute(0, 2, 1).view(b, c, ph, pw) + if ph != h or pw != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + return f + self.gamma * out, attn + + def forward_features(self, f: torch.Tensor) -> torch.Tensor: + out, _ = self.forward(f) + return out + +class DilatedPolicyHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.classifier = nn.Conv2d(64, NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + bias = torch.full((NUM_ACTIONS,), -2.0, dtype=torch.float32) + keep_index = NUM_ACTIONS // 2 if NUM_ACTIONS >= 3 else NUM_ACTIONS - 1 + bias[keep_index] = 2.0 + with torch.no_grad(): + self.classifier.bias.copy_(bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class DilatedValueHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.readout = nn.Conv2d(64, 1, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.body(x) + return self.readout(features) + +def replace_bn_with_gn(model: nn.Module, num_groups: int = 8) -> nn.Module: + for name, module in model.named_children(): + if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)): + num_channels = module.num_features + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + setattr(model, name, nn.GroupNorm(groups, num_channels, eps=module.eps, affine=module.affine)) + else: + replace_bn_with_gn(module, num_groups=num_groups) + return model + +def _ensure_transunet_repo_on_path() -> None: + repo_dir = TRANSUNET_REPO_DIR.resolve() + if not repo_dir.is_dir(): + raise FileNotFoundError( + f"TransUNet repo not found at {repo_dir}. Expected the official repo in " + f"{TRANSUNET_REPO_DIR}." + ) + repo_str = str(repo_dir) + if repo_str not in sys.path: + sys.path.insert(0, repo_str) + + +def _load_transunet_components() -> tuple[Any, dict[str, Any]]: + global _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + if _TRANSUNET_VISION_TRANSFORMER is None or _TRANSUNET_CONFIGS is None: + _ensure_transunet_repo_on_path() + try: + vit_module = importlib.import_module("networks.vit_seg_modeling") + except Exception as exc: + raise RuntimeError( + "Unable to import the official TransUNet modules. Ensure the TransUNet repo is present " + "and dependencies such as ml_collections, scipy, and torch are installed." + ) from exc + _TRANSUNET_VISION_TRANSFORMER = getattr(vit_module, "VisionTransformer") + _TRANSUNET_CONFIGS = getattr(vit_module, "CONFIGS") + return _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + + +def _transunet_tensor_changed(before: torch.Tensor, after: torch.Tensor) -> bool: + return not torch.equal(before, after) + + +def _load_and_verify_transunet_checkpoint( + vit_model: nn.Module, + *, + pretrained_path: Path, + img_size: int, + n_skip: int, +) -> dict[str, Any]: + checkpoint_path = Path(pretrained_path).expanduser().resolve() + if not checkpoint_path.is_file(): + raise FileNotFoundError( + f"TransUNet checkpoint not found at {checkpoint_path}. " + f"Expected ImageNet weights at {TRANSUNET_PRETRAINED_PATH.resolve()}." + ) + + weights = np.load(checkpoint_path, allow_pickle=False) + try: + missing_keys = [key for key in _TRANSUNET_REQUIRED_NPZ_KEYS if key not in weights] + if missing_keys: + raise RuntimeError( + f"TransUNet checkpoint {checkpoint_path} is missing required arrays: {missing_keys}" + ) + + position_embeddings = vit_model.transformer.embeddings.position_embeddings + root_conv = vit_model.transformer.embeddings.hybrid_model.root.conv.weight + block0_query = vit_model.transformer.encoder.layer[0].attn.query.weight + + pos_before = position_embeddings.detach().cpu().clone() + root_before = root_conv.detach().cpu().clone() + query_before = block0_query.detach().cpu().clone() + + posemb_source_shape = tuple(weights["Transformer/posembed_input/pos_embedding"].shape) + posemb_target_shape = tuple(position_embeddings.shape) + array_count = len(getattr(weights, "files", [])) + file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024) + + vit_model.load_from(weights=weights) + + pos_after = position_embeddings.detach().cpu() + root_after = root_conv.detach().cpu() + query_after = block0_query.detach().cpu() + + updated = { + "PosEmbed updated": _transunet_tensor_changed(pos_before, pos_after), + "ResNet root conv updated": _transunet_tensor_changed(root_before, root_after), + "ViT block-0 query updated": _transunet_tensor_changed(query_before, query_after), + } + + section("TransUNet Checkpoint Verification") + print("[TransUNet] OK Loaded R50+ViT-B/16 ImageNet checkpoint") + print(f"[TransUNet] File : {checkpoint_path} ({file_size_mb:.1f} MB)") + print(f"[TransUNet] NPZ arrays : {array_count}") + print( + "[TransUNet] PosEmbed shape : " + f"src {posemb_source_shape} -> tgt {posemb_target_shape}" + f"{' (interpolated)' if posemb_source_shape != posemb_target_shape else ''}" + ) + for label, status in updated.items(): + print(f"[TransUNet] {label:<22}: {status}") + print( + "[TransUNet] " + f"img_size={img_size}, patches.grid=({img_size // 16}, {img_size // 16}), " + f"n_skip={n_skip}, n_classes=1" + ) + + failed = [label for label, status in updated.items() if not status] + if failed: + raise RuntimeError( + "TransUNet checkpoint load verification failed. The following tensors were unchanged after " + f"load_from(...): {failed}. Training was stopped to avoid using a randomly initialized model." + ) + + return { + "checkpoint_path": str(checkpoint_path), + "array_count": array_count, + "file_size_mb": file_size_mb, + "posemb_source_shape": posemb_source_shape, + "posemb_target_shape": posemb_target_shape, + "updated": updated, + } + finally: + close_fn = getattr(weights, "close", None) + if callable(close_fn): + close_fn() + + +class _TransUNetEncoder(nn.Module): + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.transformer = transformer + self.out_channels = (3, 64, 256, 512, 768) + self._vit_token_cache: torch.Tensor | None = None + self._decoder_skip_cache: list[torch.Tensor] | None = None + + def _clear_cache(self) -> None: + self._vit_token_cache = None + self._decoder_skip_cache = None + + def decoder_inputs(self) -> tuple[torch.Tensor, list[torch.Tensor]]: + if self._vit_token_cache is None or self._decoder_skip_cache is None: + raise RuntimeError( + "TransUNet decoder was called before the encoder cache was populated. " + "Call the encoder first in the current forward pass." + ) + return self._vit_token_cache, self._decoder_skip_cache + + def forward(self, x: torch.Tensor) -> list[torch.Tensor]: + self._clear_cache() + if x.shape[1] == 1: + model_input = x.repeat(1, 3, 1, 1) + elif x.shape[1] == 3: + model_input = x + else: + raise ValueError(f"TransUNet expects 1 or 3 input channels, got {x.shape[1]}.") + + embedding_output, hybrid_features = self.transformer.embeddings(model_input) + hidden_states, _ = self.transformer.encoder(embedding_output) + if hybrid_features is None or len(hybrid_features) < 3: + raise RuntimeError( + "TransUNet hybrid ResNet features were not produced as expected." + ) + + deepest_skip, mid_skip, shallow_skip = hybrid_features[:3] + batch_size, n_patch, hidden_dim = hidden_states.shape + side = math.isqrt(n_patch) + if side * side != n_patch: + raise RuntimeError( + f"TransUNet token grid is not square: n_patch={n_patch}." + ) + vit_out = hidden_states.permute(0, 2, 1).contiguous().view(batch_size, hidden_dim, side, side) + + self._vit_token_cache = hidden_states + self._decoder_skip_cache = [deepest_skip, mid_skip, shallow_skip] + return [model_input, shallow_skip, mid_skip, deepest_skip, vit_out] + + +class _TransUNetDecoder(nn.Module): + def __init__(self, decoder_core: nn.Module, encoder: _TransUNetEncoder) -> None: + super().__init__() + self.decoder_core = decoder_core + self._encoder_ref = weakref.ref(encoder) + + def _encoder(self) -> _TransUNetEncoder: + encoder = self._encoder_ref() + if encoder is None: + raise RuntimeError("TransUNet encoder reference is no longer available.") + return encoder + + def forward(self, *features: torch.Tensor) -> torch.Tensor: + del features + hidden_states, skip_features = self._encoder().decoder_inputs() + return self.decoder_core(hidden_states, features=skip_features) + + +class TransUNetSMPAdapter(nn.Module): + def __init__(self, *, img_size: int, pretrained_path: Path) -> None: + super().__init__() + if img_size % 16 != 0: + raise ValueError(f"TransUNet requires img_size divisible by 16, got {img_size}.") + + vision_transformer_cls, configs = _load_transunet_components() + if TRANSUNET_VIT_NAME not in configs: + raise KeyError( + f"TransUNet config {TRANSUNET_VIT_NAME!r} not found in the official repo." + ) + + config_vit = copy.deepcopy(configs[TRANSUNET_VIT_NAME]) + config_vit.n_classes = 1 + config_vit.n_skip = TRANSUNET_N_SKIP + config_vit.classifier = "seg" + config_vit.patches.grid = (img_size // 16, img_size // 16) + + vit_model = vision_transformer_cls(config_vit, img_size=img_size, num_classes=1) + self.checkpoint_summary = _load_and_verify_transunet_checkpoint( + vit_model, + pretrained_path=pretrained_path, + img_size=img_size, + n_skip=TRANSUNET_N_SKIP, + ) + self.encoder = _TransUNetEncoder(vit_model.transformer) + self.decoder = _TransUNetDecoder(vit_model.decoder, self.encoder) + self.segmentation_head = vit_model.segmentation_head + self.classification_head = None + self.transunet_config = config_vit + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.encoder(x) + decoder_output = run_smp_decoder(self.decoder, encoder_features) + logits = self.segmentation_head(decoder_output) + return logits + +class HalfVGG16DilatedExtractor(nn.Module): + def __init__(self, *, dilation: int = 1, num_scales: int = 3) -> None: + super().__init__() + self.num_scales = num_scales + deep_dropout = 0.1 + + self.conv1_1 = _ConvBlock(3, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv1_2 = _ConvBlock(32, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv2_1 = _ConvBlock(32, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv2_2 = _ConvBlock(64, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv3_1 = _ConvBlock(64, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_2 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_3 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.conv4_1 = _ConvBlock(128, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_2 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_3 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + @property + def out_channels(self) -> int: + return (32 + 64 + 128) if self.num_scales == 3 else (32 + 64 + 128 + 256) + + @property + def pyramid_channels(self) -> list[int]: + return [32, 64, 128] if self.num_scales == 3 else [32, 64, 128, 256] + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + x = self.conv1_1(x) + src1 = self.conv1_2(x) + x = self.pool(src1) + + x = self.conv2_1(x) + src2 = self.conv2_2(x) + x = self.pool(src2) + + x = self.conv3_1(x) + x = self.conv3_2(x) + src3 = self.conv3_3(x) + + if self.num_scales == 3: + return [src1, src2, src3] + + x = self.pool(src3) + x = self.conv4_1(x) + x = self.conv4_2(x) + src4 = self.conv4_3(x) + return [src1, src2, src3, src4] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + pyramid = self.forward_pyramid(x) + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + +class CustomVGGEncoderWrapper(nn.Module): + def __init__(self, *, num_scales: int, dilation: int) -> None: + super().__init__() + self.encoder = HalfVGG16DilatedExtractor(dilation=dilation, num_scales=num_scales) + self.projection = None + + @property + def out_channels(self) -> int: + return self.encoder.out_channels + + @property + def pyramid_channels(self) -> list[int]: + return self.encoder.pyramid_channels + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + return self.encoder.forward_pyramid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.encoder(x) + +class SMPEncoderWrapper(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + depth: int, + in_channels: int, + proj_dim: int, + ) -> None: + super().__init__() + self.encoder = smp.encoders.get_encoder( + encoder_name, + in_channels=in_channels, + depth=depth, + weights=encoder_weights, + ) + if REPLACE_BN_WITH_GN: + replace_bn_with_gn(self.encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + self._out_channels = proj_dim + else: + self.projection = None + self._out_channels = raw_channels + + @property + def out_channels(self) -> int: + return self._out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + features = self.encoder(x) + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + +class VGGDecoderBlock(nn.Module): + def __init__(self, *, in_channels: int, skip_channels: int, out_channels: int) -> None: + super().__init__() + self.block = nn.Sequential( + _ConvBlock(in_channels + skip_channels, out_channels, num_groups=GN_NUM_GROUPS), + _ConvBlock(out_channels, out_channels, num_groups=GN_NUM_GROUPS), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = F.interpolate(x, size=skip.shape[-2:], mode="bilinear", align_corners=False) + return self.block(torch.cat([x, skip], dim=1)) + +class VGGSegmentationHead(nn.Module): + def __init__(self, *, pyramid_channels: list[int], dropout_p: float) -> None: + super().__init__() + if len(pyramid_channels) not in {3, 4}: + raise ValueError(f"Expected 3 or 4 VGG pyramid channels, got {pyramid_channels}") + + self.dropout = nn.Dropout2d(p=dropout_p) + self.num_scales = len(pyramid_channels) + + deepest = pyramid_channels[-1] + self.bridge = _ConvBlock(deepest, deepest, num_groups=GN_NUM_GROUPS) + if self.num_scales == 4: + self.up3 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[2], out_channels=128) + self.up2 = VGGDecoderBlock(in_channels=128, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + else: + self.up2 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + self.out_conv = nn.Conv2d(32, 1, kernel_size=1) + + def forward(self, pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + features = list(pyramid) + x = self.bridge(self.dropout(features[-1])) + if self.num_scales == 4: + x = self.up3(x, features[2]) + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + else: + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + return self.out_conv(x) + +class PixelDRLMG_SMP(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = SMPEncoderWrapper( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + depth=encoder_depth, + in_channels=3, + proj_dim=proj_dim, + ) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + features = self.extractor(x) + return self.sam(features) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class PixelDRLMG_VGG(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.extractor(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class SupervisedSMPModel(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int = 5, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + self.dropout = nn.Dropout2d(p=dropout_p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.smp_encoder(x) + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + decoder_output = self.dropout(decoder_output) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + +class SupervisedVGGModel(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + +class RefinementPolicyHead(nn.Module): + A3C_NUM_ACTIONS = 1 + + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 256, dilation=1, num_groups=GN_NUM_GROUPS), + _ConvBlock(256, 128, dilation=2, num_groups=GN_NUM_GROUPS), + _ConvBlock(128, 64, dilation=3, num_groups=GN_NUM_GROUPS), + ) + self.classifier = nn.Conv2d(64, self.A3C_NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + if self.classifier.bias is not None: + nn.init.zeros_(self.classifier.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class PixelDRLMG_WithDecoder(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.smp_encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + ch = proj_dim + else: + self.projection = None + ch = raw_channels + + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.multi_scale_refine = _MultiScaleRefineBranch( + encoder_channels=list(self.smp_encoder.out_channels), + out_channels=ch, + per_scale_channels=32, + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + self.multi_scale_refine.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "decoder", "segmentation_head"): + module = getattr(self.smp_model, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_from_features( + self, + features: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + + def _encoder_concat(self, x: torch.Tensor) -> torch.Tensor: + return self._concat_from_features(self.smp_encoder(x), output_size=x.shape[-2:]) + + def forward_decoder_from_features( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + ) -> torch.Tensor: + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.smp_model(x) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, Any]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + encoder_features = self.smp_encoder(x) + decoder_logits = self.forward_decoder_from_features(encoder_features) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_features=[feat[sample_index:sample_index + 1] for feat in encoder_features]: self.forward_decoder_from_features(sample_features), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.forward_decoder_from_features(encoder_features), + ) + return { + "base_features": self._concat_from_features(encoder_features, output_size=x.shape[-2:]), + "encoder_features": list(encoder_features), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + if encoder_features is not None: + ms_feat = self.multi_scale_refine(encoder_features, output_size=fused.shape[-2:]) + fused = fused + ms_feat + return fused # ABLATION: SAM disabled + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + concat_feat = self._encoder_concat(x) + return self.sam(concat_feat) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +class PixelDRLMG_VGGWithDecoder(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + ch = self.encoder.out_channels + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "segmentation_head"): + module = getattr(self, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_pyramid( + self, + pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, torch.Tensor]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + pyramid = self.encoder.forward_pyramid(x) + decoder_logits = self.segmentation_head(pyramid) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_pyramid=[feat[sample_index:sample_index + 1] for feat in pyramid]: self.segmentation_head(sample_pyramid), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.segmentation_head(pyramid), + ) + return { + "base_features": self._concat_pyramid(pyramid, output_size=x.shape[-2:]), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + del encoder_features + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.encoder(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +def run_smp_decoder(decoder: nn.Module, encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + signature = inspect.signature(decoder.forward) + parameters = list(signature.parameters.values()) + if any(param.kind == inspect.Parameter.VAR_POSITIONAL for param in parameters): + return decoder(*encoder_features) + if len(parameters) == 1: + return decoder(encoder_features) + return decoder(*encoder_features) + +def checkpoint_run_config_payload(payload: dict[str, Any]) -> dict[str, Any]: + return payload.get("run_config") or payload.get("config") or {} + +def _raw_decoder_rl_model( + model: nn.Module, +) -> PixelDRLMG_WithDecoder | PixelDRLMG_VGGWithDecoder | None: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return raw + return None + +def _uses_refinement_runtime(model: nn.Module, *, strategy: int | None = None) -> bool: + raw = _raw_decoder_rl_model(model) + if raw is None: + return False + if strategy is not None and strategy != 3: + return False + return bool(getattr(raw, "use_refinement", False)) + +def _policy_action_count_from_state_dict(state_dict: dict[str, Any]) -> int | None: + for key in ( + "policy_head.classifier.weight", + "policy_head.classifier.bias", + "policy_head.net.4.weight", + "policy_head.net.4.bias", + ): + tensor = state_dict.get(key) + if torch.is_tensor(tensor): + return int(tensor.shape[0]) + return None + +def _model_policy_action_count(model: nn.Module) -> int | None: + raw = _unwrap_compiled(model) + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d): + return int(classifier.out_channels) + return None + +def _set_model_policy_action_count(model: nn.Module, action_count: int) -> bool: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) == 1: + return False + policy_head = getattr(raw, "policy_head", None) + classifier = getattr(policy_head, "classifier", None) + if not isinstance(classifier, nn.Conv2d): + return False + if int(classifier.out_channels) == int(action_count): + return False + + new_classifier = nn.Conv2d( + classifier.in_channels, + int(action_count), + kernel_size=classifier.kernel_size, + stride=classifier.stride, + padding=classifier.padding, + dilation=classifier.dilation, + groups=classifier.groups, + bias=classifier.bias is not None, + padding_mode=classifier.padding_mode, + ).to(device=classifier.weight.device, dtype=classifier.weight.dtype) + nn.init.xavier_uniform_(new_classifier.weight) + if new_classifier.bias is not None: + nn.init.zeros_(new_classifier.bias) + policy_head.classifier = new_classifier + return True + +def _configure_policy_head_compatibility( + model: nn.Module, + state_dict: dict[str, Any], + *, + source: str, +) -> int | None: + action_count = _policy_action_count_from_state_dict(state_dict) + if action_count is None: + return None + if _set_model_policy_action_count(model, action_count): + print(f"[Policy Compatibility] source={source} num_actions={action_count}") + return action_count + +def _strategy3_checkpoint_layout_info( + state_dict: dict[str, Any], + run_config: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = run_config or {} + strategy = run_config.get("strategy") + has_legacy_policy_head = any(key.startswith("policy_head.net.") for key in state_dict) + has_new_policy_body = any(key.startswith("policy_head.body.") for key in state_dict) + has_new_policy_classifier = any(key.startswith("policy_head.classifier.") for key in state_dict) + has_refinement_adapter = any(key.startswith("refinement_adapter.") for key in state_dict) + is_strategy3_decoder_checkpoint = bool( + strategy == 3 + or has_legacy_policy_head + or has_new_policy_body + or has_new_policy_classifier + or has_refinement_adapter + ) + use_refinement = bool( + has_refinement_adapter or ((has_new_policy_body or has_new_policy_classifier) and not has_legacy_policy_head) + ) + return { + "strategy": strategy, + "is_strategy3_decoder_checkpoint": is_strategy3_decoder_checkpoint, + "has_legacy_policy_head": has_legacy_policy_head, + "has_new_policy_head": bool(has_new_policy_body or has_new_policy_classifier), + "has_refinement_adapter": has_refinement_adapter, + "requires_policy_remap": has_legacy_policy_head, + "policy_action_count": _policy_action_count_from_state_dict(state_dict), + "use_refinement": use_refinement, + "compatibility_mode": "refinement" if use_refinement else "legacy", + } + +def inspect_strategy3_checkpoint_compatibility(path: str | Path) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + layout = _strategy3_checkpoint_layout_info(payload.get("model_state_dict", {}), checkpoint_run_config_payload(payload)) + layout["path"] = str(checkpoint_path) + return layout + +def _configure_strategy3_model_compatibility( + model: nn.Module, + layout: dict[str, Any], + *, + source: str, +) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not layout.get("is_strategy3_decoder_checkpoint"): + return + raw.set_refinement_mode(bool(layout["use_refinement"])) + if not bool(layout["use_refinement"]): + raw.refinement_adapter.eval() + if hasattr(raw, "multi_scale_refine"): + raw.multi_scale_refine.eval() + print( + "[Strategy3 Compatibility] " + f"source={source} mode={layout['compatibility_mode']} " + f"legacy_policy_head={layout['has_legacy_policy_head']} " + f"refinement_adapter={layout['has_refinement_adapter']}" + ) + +def _ensure_strategy3_refinement_adapter_compatible( + model: nn.Module, + state_dict: dict[str, Any], + *, + checkpoint_path: str | Path, +) -> None: + raw_model = _unwrap_compiled(model) + if not isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return + target_state = raw_model.state_dict() + mismatched: list[str] = [] + for key, value in state_dict.items(): + if not key.startswith(("refinement_adapter.", "policy_head.classifier.")): + continue + target_value = target_state.get(key) + if target_value is None: + continue + if tuple(target_value.shape) != tuple(value.shape): + mismatched.append( + f"{key}: checkpoint={tuple(value.shape)} model={tuple(target_value.shape)}" + ) + if mismatched: + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected after the continuous Strategy 3 redesign. " + f"Checkpoint={Path(checkpoint_path).resolve()} mismatches={mismatched[:4]}. " + "Resume/eval from legacy S3 checkpoints is not supported; retrain Strategy 3 from the Strategy 2 bootstrap checkpoint." + ) + +def _configure_model_from_checkpoint_path( + model: nn.Module, + checkpoint_path: str | Path, +) -> dict[str, Any]: + checkpoint_path = Path(checkpoint_path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state_dict = payload.get("model_state_dict", {}) + _configure_policy_head_compatibility(model, state_dict, source=str(checkpoint_path)) + layout = _strategy3_checkpoint_layout_info(state_dict, checkpoint_run_config_payload(payload)) + if layout.get("is_strategy3_decoder_checkpoint") and layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected: the checkpoint uses a discrete multi-action " + f"policy head with out_channels={layout.get('policy_action_count')}. " + "The current Strategy 3 implementation requires a continuous 1-channel delta head." + ) + _configure_strategy3_model_compatibility(model, layout, source=str(checkpoint_path)) + layout["path"] = str(checkpoint_path) + return layout + +def _remap_legacy_policy_head_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]: + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + if key.startswith("policy_head.net."): + suffix = key[len("policy_head.net."):] + layer_idx, dot, rest = suffix.partition(".") + if dot: + if layer_idx in {"0", "1", "2", "3"}: + remapped[f"policy_head.body.{layer_idx}.{rest}"] = value + continue + if layer_idx == "4": + remapped[f"policy_head.classifier.{rest}"] = value + continue + remapped[key] = value + return remapped + +def _load_strategy2_checkpoint_payload( + path: str | Path, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False) + saved_config = checkpoint_run_config_payload(ckpt) + if saved_config: + saved_model_config = RuntimeModelConfig.from_payload(saved_config).validate() + if saved_model_config.backbone_family != model_config.backbone_family: + raise ValueError( + f"Strategy 2 checkpoint backbone family mismatch: requested {model_config.backbone_family!r}, " + f"checkpoint has {saved_model_config.backbone_family!r} at {checkpoint_path}." + ) + return ckpt + +def _preview_state_keys(keys: list[str], *, limit: int = 8) -> str: + if not keys: + return "none" + preview = ", ".join(keys[:limit]) + if len(keys) > limit: + preview += ", ..." + return preview + +def _strict_load_strategy2_submodule( + target_module: nn.Module, + *, + checkpoint_state_dict: dict[str, Any], + checkpoint_prefix: str, + checkpoint_path: str | Path, + target_name: str, +) -> None: + extracted = { + key[len(checkpoint_prefix):]: value + for key, value in checkpoint_state_dict.items() + if key.startswith(checkpoint_prefix) + } + if not extracted: + raise RuntimeError( + f"Strategy 2 bootstrap failed for {target_name}: no checkpoint keys found with prefix " + f"{checkpoint_prefix!r} in {Path(checkpoint_path).resolve()}." + ) + + target_state = target_module.state_dict() + missing = sorted(set(target_state.keys()) - set(extracted.keys())) + unexpected = sorted(set(extracted.keys()) - set(target_state.keys())) + if missing or unexpected: + section(f"Strategy 2 Bootstrap Mismatch | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + print(f"Missing keys ({len(missing)}) : {_preview_state_keys(missing)}") + print(f"Unexpected keys ({len(unexpected)}): {_preview_state_keys(unexpected)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap failed for {target_name} from {Path(checkpoint_path).resolve()}. " + f"Missing keys={len(missing)}, unexpected keys={len(unexpected)}." + ) + + try: + load_result = target_module.load_state_dict(extracted, strict=True) + except Exception as exc: + section(f"Strategy 2 Bootstrap Strict Load Failure | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap load failed for {target_name} from " + f"{Path(checkpoint_path).resolve()}: {exc}" + ) from exc + + post_missing = list(getattr(load_result, "missing_keys", [])) + post_unexpected = list(getattr(load_result, "unexpected_keys", [])) + if post_missing or post_unexpected: + raise RuntimeError( + f"Strict Strategy 2 bootstrap reported residual mismatches for {target_name}: " + f"missing={post_missing}, unexpected={post_unexpected}" + ) + + section(f"Strategy 2 Bootstrap OK | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Loaded tensors : {len(extracted)}") + print("Strict load : passed") + +def _use_channels_last_for_run(model_config: RuntimeModelConfig | None = None) -> bool: + model_config = (model_config or current_model_config()).validate() + if not USE_CHANNELS_LAST: + return False + if DEVICE.type != "cuda": + return False + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + if ( + model_config.backbone_family == "smp" + and "efficientnet" in model_config.smp_encoder_name.lower() + and USE_AMP + and amp_dtype in {torch.float16, torch.bfloat16} + ): + print("[MemoryFormat] Disabling channels_last for EfficientNet + AMP stability.") + return False + return True + +def build_model( + strategy: int, + dropout_p: float, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[nn.Module, str, bool]: + strategy = _require_supported_strategy(strategy) + model_config = model_config.validate() + if model_config.backbone_family == "custom_vgg": + if not bool(ENABLE_CUSTOM_VGG_BACKBONE): + raise RuntimeError( + "The legacy custom VGG backbone is feature-flagged off. " + "Set ENABLE_CUSTOM_VGG_BACKBONE=True to opt into the unused VGG code path." + ) + if strategy == 2: + model = SupervisedVGGModel( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_VGGWithDecoder( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.encoder, + checkpoint_state_dict=s2_state, + checkpoint_prefix="encoder.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.encoder", + ) + _strict_load_strategy2_submodule( + model.segmentation_head, + checkpoint_state_dict=s2_state, + checkpoint_prefix="segmentation_head.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.segmentation_head", + ) + if freeze_bootstrapped_segmentation: + model.encoder.requires_grad_(False) + model.segmentation_head.requires_grad_(False) + if hasattr(model.encoder, "projection") and model.encoder.projection is not None: + model.encoder.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + else: + if strategy == 2: + model = SupervisedSMPModel( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_WithDecoder( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + proj_dim=model_config.smp_encoder_proj_dim, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.smp_model, + checkpoint_state_dict=s2_state, + checkpoint_prefix="smp_model.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.smp_model", + ) + if freeze_bootstrapped_segmentation: + model.smp_model.encoder.requires_grad_(False) + model.smp_model.decoder.requires_grad_(False) + if hasattr(model.smp_model, "segmentation_head"): + model.smp_model.segmentation_head.requires_grad_(False) + if hasattr(model, "projection") and model.projection is not None: + model.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + + use_channels_last_now = _use_channels_last_for_run(model_config) + if strategy == 3: + classifier = getattr(getattr(_unwrap_compiled(model), "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) != 1: + raise RuntimeError("Strategy 3 expects a continuous 1-channel policy head.") + _strategy3_bump_mc_cache_fingerprint( + model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + model = model.to(DEVICE) + if use_channels_last_now: + model = model.to(memory_format=torch.channels_last) + + compiled = False + if USE_TORCH_COMPILE and hasattr(torch, "compile"): + try: + model = torch.compile(model, mode="max-autotune") + compiled = True + except Exception as exc: + print(f"[Compile] torch.compile skipped: {exc}") + return model, strategy_name(strategy, model_config), compiled + +def _unwrap_compiled(model: nn.Module) -> nn.Module: + return getattr(model, "_orig_mod", model) + +def count_parameters(module: nn.Module | None, *, only_trainable: bool = False) -> int: + if module is None: + return 0 + if only_trainable: + return sum(p.numel() for p in module.parameters() if p.requires_grad) + return sum(p.numel() for p in module.parameters()) + +def print_model_parameter_summary( + *, + model: nn.Module, + description: str, + strategy: int, + model_config: RuntimeModelConfig, + dropout_p: float, + amp_dtype: torch.dtype, + compiled: bool, +) -> None: + raw = _unwrap_compiled(model) + total_params = count_parameters(raw) + trainable_params = count_parameters(raw, only_trainable=True) + frozen_params = total_params - trainable_params + bn_count = sum(1 for m in raw.modules() if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d))) + gn_count = sum(1 for m in raw.modules() if isinstance(m, nn.GroupNorm)) + + section(f"Model Parameter Summary | {description}") + print(f"Strategy : {strategy}") + print(f"Model : {description}") + print(f"Dropout p : {dropout_p:.4f}") + print(f"Total params : {total_params:,}") + print(f"Trainable params : {trainable_params:,}") + print(f"Frozen params : {frozen_params:,}") + print(f"BN layers : {bn_count}") + print(f"GN layers : {gn_count}") + print(f"channels_last : {_use_channels_last_for_run(model_config)}") + print(f"AMP dtype : {amp_dtype}") + print(f"torch.compile : {compiled}") + print(f"Backbone family : {model_config.backbone_family}") + if strategy == 3: + print(f"S3 variant : {_strategy3_variant()}") + freeze_status = _strategy3_bootstrap_freeze_status(model) + print(f"S3 bootstrap loaded : {freeze_status['bootstrap_loaded']}") + print(f"S3 freeze requested : {freeze_status['freeze_requested']}") + print(f"S3 frozen now : {freeze_status['freeze_active']}") + print(f"S3 encoder state : {freeze_status['encoder_state']}") + if freeze_status["decoder_state"] != "n/a": + print(f"S3 decoder state : {freeze_status['decoder_state']}") + if freeze_status["segmentation_head_state"] != "n/a": + print(f"S3 seg head state : {freeze_status['segmentation_head_state']}") + + block_counts: dict[str, int] = {} + if strategy == 2: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + block_counts["dropout"] = count_parameters(getattr(raw, "dropout", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + elif strategy == 3: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + block_counts["sam"] = count_parameters(getattr(raw, "sam", None)) + block_counts["policy_head"] = count_parameters(getattr(raw, "policy_head", None)) + block_counts["value_head"] = count_parameters(getattr(raw, "value_head", None)) + + for name, value in block_counts.items(): + print(f"{name:22s}: {value:,}") + +"""============================================================================= +METRICS + CHECKPOINTS +============================================================================= +""" + +_EPS = 1e-4 + +def _as_bool(mask: np.ndarray) -> np.ndarray: + return (mask[0] if mask.ndim == 3 else mask).astype(bool) + +def _tp_fp_fn(pred: np.ndarray, target: np.ndarray): + p, t = _as_bool(pred), _as_bool(target) + tp = float((p & t).sum()) + fp = float((p & ~t).sum()) + fn = float((~p & t).sum()) + return tp, fp, fn, float(t.sum()), float(p.sum()) + +def dice_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (2 * tp + _EPS) / (t + p + _EPS) + +def ppv_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, fp, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fp + _EPS) + +def sensitivity_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, fn, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fn + _EPS) + +def iou_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (tp + _EPS) / (t + p - tp + _EPS) + +def _boundary_1px(mask: np.ndarray) -> np.ndarray: + m = _as_bool(mask) + if not m.any(): + return m + return m ^ ndimage.binary_erosion(m, iterations=1, border_value=0) + +def boundary_iou_contour_score(pred: np.ndarray, target: np.ndarray) -> float: + pb, tb = _boundary_1px(pred), _boundary_1px(target) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _biou_d(img_hw: tuple[int, int]) -> int: + """Resolve the Boundary IoU dilation width d for a given image size.""" + d = int(BOUNDARY_IOU_D) + if d > 0: + return d + height, width = img_hw + return max(1, int(round(0.02 * math.hypot(height, width)))) + +def _boundary_band(mask: np.ndarray, d: int) -> np.ndarray: + """Return the d-pixel inner boundary band used by paper-standard BIoU.""" + m = _as_bool(mask) + if not m.any(): + return m + eroded = ndimage.binary_erosion(m, iterations=max(int(d), 1), border_value=0) + return m & ~eroded + +def boundary_iou_score(pred: np.ndarray, target: np.ndarray) -> float: + """Boundary IoU from Cheng et al. CVPR 2021 using a d-pixel inner band.""" + height, width = _as_bool(target).shape + d = _biou_d((height, width)) + pb = _boundary_band(pred, d) + tb = _boundary_band(target, d) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _surf_dist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _as_bool(a), _as_bool(b) + if not a.any() and not b.any(): + return np.array([0.0], dtype=np.float32) + if not a.any() or not b.any(): + return np.array([np.inf], dtype=np.float32) + ba, bb = _boundary_1px(a), _boundary_1px(b) + return ndimage.distance_transform_edt(~bb)[ba].astype(np.float32) + +def hd95_score(pred: np.ndarray, target: np.ndarray) -> float: + distances = np.concatenate([_surf_dist(pred, target), _surf_dist(target, pred)]) + if np.isinf(distances).any(): + height, width = _as_bool(target).shape + return float(math.hypot(height, width)) + return float(np.percentile(distances, 95)) + +def compute_all_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + return { + "dice": dice_score(pred, target), + "ppv": ppv_score(pred, target), + "sen": sensitivity_score(pred, target), + "iou": iou_score(pred, target), + "biou": boundary_iou_score(pred, target), + "biou_contour": boundary_iou_contour_score(pred, target), + "hd95": hd95_score(pred, target), + } + +def checkpoint_manifest_path(path: Path) -> Path: + path = Path(path) + return path.with_name(f"{path.name}.meta.json") + +def checkpoint_history_path(run_dir: Path, run_type: str) -> Path: + if run_type == "overfit": + return Path(run_dir) / "overfit_history.json" + return Path(run_dir) / "history.json" + +def checkpoint_state_presence(payload: dict[str, Any]) -> dict[str, bool]: + tracked = [ + "model_state_dict", + "optimizer_state_dict", + "scheduler_state_dict", + "scaler_state_dict", + "log_alpha", + "alpha_optimizer_state_dict", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + "history", + "resume_source", + ] + return {name: name in payload for name in tracked} + +def write_checkpoint_manifest( + path: Path, + payload: dict[str, Any], + *, + extra: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = checkpoint_run_config_payload(payload) + manifest = { + "checkpoint_path": str(Path(path).resolve()), + "run_type": payload.get("run_type", "unknown"), + "epoch": int(payload.get("epoch", 0)), + "strategy": run_config.get("strategy"), + "dataset_percent": run_config.get("dataset_percent"), + "backbone_family": run_config.get("backbone_family", "smp"), + "saved_keys": sorted(payload.keys()), + "state_presence": checkpoint_state_presence(payload), + } + if "resume_source" in payload: + manifest["resume_source"] = payload["resume_source"] + if extra: + manifest.update(extra) + save_json(checkpoint_manifest_path(path), manifest) + return manifest + +def checkpoint_required_keys( + *, + optimizer: torch.optim.Optimizer | None, + scheduler: CosineAnnealingLR | None, + scaler: Any | None, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + require_run_metadata: bool, +) -> list[str]: + keys = ["epoch", "model_state_dict"] + if require_run_metadata: + keys.extend( + [ + "run_type", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + ] + ) + if optimizer is not None: + keys.append("optimizer_state_dict") + if scheduler is not None: + keys.append("scheduler_state_dict") + if scaler is not None: + keys.append("scaler_state_dict") + if log_alpha is not None: + keys.append("log_alpha") + if alpha_optimizer is not None: + keys.append("alpha_optimizer_state_dict") + return keys + +def validate_checkpoint_payload( + path: Path, + payload: dict[str, Any], + *, + required_keys: list[str], + expected_run_type: str | None = None, +) -> None: + missing = [name for name in required_keys if name not in payload] + if missing: + raise KeyError(f"Checkpoint {path} is missing required keys: {missing}") + if expected_run_type is not None and payload.get("run_type") != expected_run_type: + raise ValueError( + f"Checkpoint {path} run_type mismatch: expected {expected_run_type!r}, " + f"got {payload.get('run_type')!r}." + ) + +def save_checkpoint( + path: Path, + *, + run_type: str, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: ReduceLROnPlateau | None, + scaler: Any | None, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": _unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + validate_checkpoint_payload( + path, + payload, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + torch.save(payload, path) + write_checkpoint_manifest(path, payload) + +def load_checkpoint( + path: Path, + *, + model: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: ReduceLROnPlateau | None = None, + scaler: Any | None = None, + device: torch.device, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + expected_run_type: str | None = None, + require_run_metadata: bool = False, +) -> dict[str, Any]: + ckpt = torch.load(path, map_location=device, weights_only=False) + validate_checkpoint_payload( + path, + ckpt, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=require_run_metadata, + ), + expected_run_type=expected_run_type, + ) + + raw_model = _unwrap_compiled(model) + state_dict = ckpt["model_state_dict"] + load_strict = True + compat_layout: dict[str, Any] | None = None + _configure_policy_head_compatibility(model, state_dict, source=str(path)) + if any(key.startswith("policy_head.net.") for key in state_dict): + state_dict = _remap_legacy_policy_head_state_dict(state_dict) + if isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + compat_layout = _strategy3_checkpoint_layout_info(ckpt["model_state_dict"], checkpoint_run_config_payload(ckpt)) + if compat_layout["is_strategy3_decoder_checkpoint"]: + if compat_layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected during restore. " + f"Checkpoint={path} policy_head_out_channels={compat_layout.get('policy_action_count')}. " + "Resume/eval from pre-redesign Strategy 3 checkpoints is not supported." + ) + _configure_strategy3_model_compatibility(model, compat_layout, source=str(path)) + _ensure_strategy3_refinement_adapter_compatible(model, state_dict, checkpoint_path=path) + load_strict = bool(compat_layout["use_refinement"]) + + incompatible = raw_model.load_state_dict(state_dict, strict=load_strict) + if hasattr(raw_model, "clear_strategy3_mc_cache"): + _strategy3_bump_mc_cache_fingerprint(raw_model, eval_checkpoint_path=path) + if not load_strict: + missing_keys = [key for key in incompatible.missing_keys if not key.startswith(("refinement_adapter.", "multi_scale_refine."))] + unexpected_keys = list(incompatible.unexpected_keys) + if missing_keys or unexpected_keys: + print( + "[Checkpoint Restore] Non-strict legacy Strategy 3 load " + f"missing={missing_keys} unexpected={unexpected_keys}" + ) + + if optimizer is not None and "optimizer_state_dict" in ckpt: + try: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + except ValueError: + if compat_layout is None or compat_layout.get("compatibility_mode") != "legacy": + raise + print( + f"[Checkpoint Restore] Skipping optimizer state for legacy Strategy 3 checkpoint at {path} " + "because the parameter layout differs from the refinement-capable model." + ) + if scheduler is not None and "scheduler_state_dict" in ckpt: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler is not None and "scaler_state_dict" in ckpt: + scaler.load_state_dict(ckpt["scaler_state_dict"]) + if log_alpha is not None and "log_alpha" in ckpt: + with torch.no_grad(): + log_alpha.fill_(float(ckpt["log_alpha"])) + if alpha_optimizer is not None and "alpha_optimizer_state_dict" in ckpt: + alpha_optimizer.load_state_dict(ckpt["alpha_optimizer_state_dict"]) + restored = checkpoint_state_presence(ckpt) + restore_info = { + "restored_keys": restored, + "restored_at_epoch": int(ckpt.get("epoch", 0)), + "expected_run_type": expected_run_type, + } + write_checkpoint_manifest(path, ckpt, extra={"last_restore": restore_info}) + print( + f"[Checkpoint Restore] path={path} epoch={ckpt.get('epoch')} " + f"run_type={ckpt.get('run_type', 'unknown')} " + f"backbone={checkpoint_run_config_payload(ckpt).get('backbone_family', 'unknown')}" + ) + return ckpt + +"""============================================================================= +TRAINING + VALIDATION +============================================================================= +""" + +def _policy_log_probs_and_entropy(policy_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs = F.log_softmax(logits, dim=1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=1).mean() + return log_probs, entropy + +def _log_prob_for_actions(log_probs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor: + return log_probs.gather(1, actions.unsqueeze(1)) + +def sample_actions( + policy_logits: torch.Tensor, + stochastic: bool, + exploration_eps: float = 0.0, + keep_action_index: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs, entropy = _policy_log_probs_and_entropy(policy_logits) + if stochastic: + uniform = torch.rand_like(logits) + gumbel = -torch.log(-torch.log(uniform + 1e-8) + 1e-8) + actions = (logits + gumbel).argmax(dim=1) + if exploration_eps > 0: + keep_index = _keep_action_index(logits.shape[1]) if keep_action_index is None else int(keep_action_index) + keep_actions = torch.full_like(actions, keep_index) + random_mask = torch.rand(actions.shape, device=actions.device) < exploration_eps + actions = torch.where(random_mask, keep_actions, actions) + else: + actions = logits.argmax(dim=1) + log_prob = _log_prob_for_actions(log_probs, actions) + return actions, log_prob, entropy + +def apply_actions( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + num_actions = int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)) + if num_actions >= 3: + deltas = _refinement_deltas(action_count=num_actions, device=seg.device, dtype=seg.dtype) + delta = deltas[actions.long()].unsqueeze(1) + return (seg + delta).clamp_(0.0, 1.0) + action_map = actions.unsqueeze(1) + return seg * (action_map == 1).to(dtype=seg.dtype) + +def _soft_dice_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + denom = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) + return (2.0 * inter + 1e-6) / (denom + 1e-6) + +def _soft_iou_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - inter + return (inter + 1e-6) / (union + 1e-6) + +def _soft_recall_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + true_positive = (pred * target).sum(dim=(1, 2, 3)) + positives = target.sum(dim=(1, 2, 3)) + return (true_positive + 1e-6) / (positives + 1e-6) + +def _soft_boundary(mask: torch.Tensor) -> torch.Tensor: + return (mask - F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1)).abs() + +def _differentiable_boundary(mask: torch.Tensor, kernel_size: int = 3) -> torch.Tensor: + padding = kernel_size // 2 + mask_f = mask.float().clamp(0.0, 1.0) + eroded = 1.0 - F.max_pool2d(1.0 - mask_f, kernel_size, stride=1, padding=padding) + return (mask_f - eroded).clamp(0.0, 1.0) + +def _soft_iou_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + pred_f = pred.float().clamp(0.0, 1.0) + target_f = target.float().clamp(0.0, 1.0) + inter = (pred_f * target_f).sum(dim=(2, 3), keepdim=True) + union = (pred_f + target_f - pred_f * target_f).sum(dim=(2, 3), keepdim=True) + return inter / (union + 1e-6) + +def differentiable_biou_loss( + pred: torch.Tensor, + target: torch.Tensor, + kernel_size: int | None = None, + *, + reduction: str = "mean", +) -> torch.Tensor: + if kernel_size is None: + height, width = int(pred.shape[-2]), int(pred.shape[-1]) + d = _biou_d((height, width)) + kernel_size = 2 * d + 1 + kernel_size = max(int(kernel_size), 1) + if kernel_size % 2 == 0: + kernel_size += 1 + pred_boundary = _differentiable_boundary(pred, kernel_size=kernel_size) + target_boundary = _differentiable_boundary(target, kernel_size=kernel_size) + inter = (pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + union = (pred_boundary + target_boundary - pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + loss = 1.0 - (inter + 1e-6) / (union + 1e-6) + if reduction == "none": + return loss + if reduction == "mean": + return loss.mean() + raise ValueError(f"Unsupported differentiable_biou_loss reduction {reduction!r}.") + +def _strategy3_expected_next_mask( + policy_logits: torch.Tensor, + base_seg: torch.Tensor, + *, + action_count: int, +) -> tuple[torch.Tensor, torch.Tensor]: + logits_f = policy_logits.float() + base_seg_f = base_seg.float() + probs = F.softmax(logits_f, dim=1) + deltas = _refinement_deltas(action_count=action_count, device=logits_f.device, dtype=logits_f.dtype) + expected_delta = (probs * deltas.view(1, -1, 1, 1)).sum(dim=1, keepdim=True) + predicted_next = (base_seg_f + expected_delta).clamp(1e-4, 1.0 - 1e-4) + return predicted_next, deltas + +def _strategy3_action_targets( + seg_mask: torch.Tensor, + gt_mask: torch.Tensor, + deltas: torch.Tensor, +) -> torch.Tensor: + target_delta = gt_mask.float() - seg_mask.float() + return (target_delta - deltas.view(1, -1, 1, 1)).abs().argmin(dim=1) + +def compute_refinement_reward( + seg: torch.Tensor, + seg_next: torch.Tensor, + gt_mask: torch.Tensor, + *, + return_details: bool = False, +) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]: + seg_f = seg.float() + seg_next_f = seg_next.float() + gt_f = gt_mask.float().clamp(0.0, 1.0) + + r1_weight = float(_job_param("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT)) + biou_reward_weight = float(_job_param("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT)) + + target_dir = 2.0 * gt_f - 1.0 + progress = target_dir * (seg_next_f - seg_f) + room = gt_f * (1.0 - seg_f) + (1.0 - gt_f) * seg_f + r1 = r1_weight * progress * room + + biou_before = 1.0 - differentiable_biou_loss(seg_f, gt_f, reduction="none") + biou_next = 1.0 - differentiable_biou_loss(seg_next_f, gt_f, reduction="none") + biou_delta = biou_next - biou_before + r3 = biou_reward_weight * biou_delta.expand_as(seg_next_f) + + reward = (r1 + r3).clamp(-3.0, 3.0) + if not return_details: + return reward + return reward, { + "biou_before": biou_before, + "biou_next": biou_next, + "biou_delta": biou_delta, + } + +def compute_strategy1_aux_segmentation_loss( + policy_logits: torch.Tensor, + gt_mask: torch.Tensor, + *, + ce_weight: float, + dice_weight: float, + seg_mask: torch.Tensor | None = None, +) -> tuple[torch.Tensor, float, float]: + logits_f = policy_logits.float() + gt_mask_f = gt_mask.float() + num_actions = int(logits_f.shape[1]) + + if num_actions >= 3: + base_seg = seg_mask.float() if seg_mask is not None else torch.full_like(gt_mask_f, 0.5) + predicted_next, deltas = _strategy3_expected_next_mask( + policy_logits, + base_seg, + action_count=num_actions, + ) + action_targets = _strategy3_action_targets(base_seg, gt_mask_f, deltas) + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + boundary_dice_w = float(_job_param("strategy3_aux_boundary_dice_weight", 0.0)) + + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) + delta_large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + hard_margin = delta_large * 1.5 + with torch.no_grad(): + dist_to_thresh = (base_seg - threshold).abs() + hard_mask = (dist_to_thresh < hard_margin).squeeze(1) + + if ce_weight > 0: + action_ce_mix = float(_job_param("aux_action_ce_mix", 0.75)) + action_ce_mix = min(max(action_ce_mix, 0.0), 1.0) + + if hard_mask.any(): + logits_hw = policy_logits.float().permute(0, 2, 3, 1) + targets_hw = action_targets + action_ce = F.cross_entropy(logits_hw[hard_mask], targets_hw[hard_mask]) + else: + action_ce = F.cross_entropy(policy_logits.float(), action_targets) + + predicted_next_logits = torch.logit(predicted_next) + if hard_mask.any(): + gt_hard = gt_mask_f.squeeze(1)[hard_mask] + pred_hard = predicted_next_logits.squeeze(1)[hard_mask] + bce = F.binary_cross_entropy_with_logits(pred_hard, gt_hard) + else: + bce = F.binary_cross_entropy_with_logits(predicted_next_logits, gt_mask_f) + + ce_term = action_ce_mix * action_ce + (1.0 - action_ce_mix) * bce + aux_loss = aux_loss + ce_weight * ce_term + ce_loss_value = float(ce_term.detach().item()) + + if dice_weight > 0: + inter = (predicted_next * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (predicted_next.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + if boundary_dice_w > 0: + bd_loss = differentiable_biou_loss(predicted_next, gt_mask_f) + aux_loss = aux_loss + boundary_dice_w * bd_loss + + return aux_loss, ce_loss_value, dice_loss_value + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + + if ce_weight > 0: + if num_actions >= 3: + if seg_mask is None: + seg_mask = torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(seg_mask).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + ce_loss_value = float(ce_loss.detach().item()) + + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + inter = (probs_fg * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + return aux_loss, ce_loss_value, dice_loss_value + +def make_optimizer( + model: nn.Module, + strategy: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + rl_lr: float | None = None, +): + raw = _unwrap_compiled(model) + encoder_params = [] + decoder_params = [] + rl_params = [] + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + if ( + name.startswith("extractor.encoder.") + or name.startswith("encoder.") + or name.startswith("smp_model.encoder.") + or name.startswith("smp_encoder.") + ): + encoder_params.append(param) + elif "decoder" in name or "segmentation_head" in name: + decoder_params.append(param) + else: + rl_params.append(param) + + decoder_lr = float(_job_param("decoder_lr", head_lr)) + rl_group_lr = float(_job_param("rl_lr", rl_lr if rl_lr is not None else head_lr)) + + param_groups: list[dict[str, Any]] = [] + + if encoder_params: + param_groups.append({"params": encoder_params, "lr": encoder_lr}) + + if decoder_params: + param_groups.append({"params": decoder_params, "lr": decoder_lr}) + + if rl_params: + param_groups.append({"params": rl_params, "lr": rl_group_lr}) + + try: + optimizer = AdamW(param_groups, weight_decay=weight_decay, fused=DEVICE.type == "cuda") + except Exception: + optimizer = AdamW(param_groups, weight_decay=weight_decay) + + return optimizer + +def infer_segmentation_mask( + model: nn.Module, + image: torch.Tensor, + tmax: int, + *, + strategy: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> torch.Tensor: + model.eval() + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + return threshold_binary_mask(torch.sigmoid(logits)).float() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="infer_segmentation_mask") + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta) + return threshold_binary_mask(seg.float()).float() + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + policy_logits = model.forward_policy_only(x_t) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + return seg.float() + +def train_step( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor, + alpha_optimizer: torch.optim.Optimizer, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + initial_mask: torch.Tensor | None = None, + decoder_loss_extra: torch.Tensor | None = None, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + if initial_mask is not None: + seg = initial_mask.to(device=image.device, dtype=image.dtype) + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + alpha = log_alpha.exp() + + total_actor = 0.0 + total_critic = 0.0 + total_loss = 0.0 + total_reward = 0.0 + total_entropy = 0.0 + total_ce_loss = 0.0 + total_dice_loss = 0.0 + accum_tensor = None + alpha_loss_accum = torch.tensor(0.0, device=image.device, dtype=torch.float32) + aux_fused = False + + optimizer.zero_grad(set_to_none=True) + + for _ in range(tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=True) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + + neighborhood_next = _bootstrap_value_target(model, value_next) + target = reward + gamma * neighborhood_next + advantage = target - value_t + critic_loss = F.smooth_l1_loss(value_t, target) + actor_loss = -(log_prob * advantage.detach()).mean() + actor_loss = actor_loss - alpha.detach() * entropy + step_loss = (actor_loss + critic_loss_weight * critic_loss) / float(tmax) + alpha_loss_accum = alpha_loss_accum + (log_alpha * (entropy.detach() - target_entropy)) / float(tmax) + + if not aux_fused and initial_mask is None and (ce_weight > 0 or dice_weight > 0): + aux_loss, ce_loss_value, dice_loss_value = compute_strategy1_aux_segmentation_loss( + policy_logits, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + if ce_weight > 0: + total_ce_loss = ce_loss_value + if dice_weight > 0: + total_dice_loss = dice_loss_value + step_loss = step_loss + aux_loss + aux_fused = True + + total_actor += float(actor_loss.detach().item()) + total_critic += float(critic_loss.detach().item()) + total_loss += float(step_loss.detach().item()) + total_reward += float(reward.detach().mean().item()) + total_entropy += float(entropy.detach().item()) + + if stepwise_backward: + if scaler is not None: + scaler.scale(step_loss).backward() + else: + step_loss.backward() + else: + accum_tensor = step_loss if accum_tensor is None else accum_tensor + step_loss + + seg = seg_next.detach() + + if not stepwise_backward and accum_tensor is not None: + if scaler is not None: + scaler.scale(accum_tensor).backward() + else: + accum_tensor.backward() + + if not aux_fused and (ce_weight > 0 or dice_weight > 0): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + logits_f = policy_aux.float() + aux_loss = torch.zeros(1, device=image.device, dtype=torch.float32) + if ce_weight > 0: + num_actions = int(logits_f.shape[1]) + if num_actions >= 3: + init_seg = initial_mask if initial_mask is not None else torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(init_seg).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + total_ce_loss = float(ce_loss.detach().item()) + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + gt_f = gt_mask.float() + inter = (probs_fg * gt_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + total_dice_loss = float(dice_loss.detach().item()) + if decoder_loss_extra is not None: + aux_loss = aux_loss + decoder_loss_extra + if scaler is not None: + scaler.scale(aux_loss).backward() + else: + aux_loss.backward() + total_loss += float(aux_loss.detach().item()) + elif decoder_loss_extra is not None: + if scaler is not None: + scaler.scale(decoder_loss_extra).backward() + else: + decoder_loss_extra.backward() + total_loss += float(decoder_loss_extra.detach().item()) + + if scaler is not None: + scaler.unscale_(optimizer) + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + alpha_optimizer.zero_grad(set_to_none=True) + alpha_loss_accum.backward() + alpha_optimizer.step() + with torch.no_grad(): + log_alpha.clamp_(min=_alpha_log_floor(), max=5.0) + + return { + "loss": total_loss, + "actor_loss": total_actor / tmax, + "critic_loss": total_critic / tmax, + "mean_reward": total_reward / tmax, + "entropy": total_entropy / tmax, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": seg.detach(), + } + +def train_step_strategy3( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + current_epoch: int, + max_epochs: int, +) -> dict[str, Any]: + if not _uses_refinement_runtime(model, strategy=3): + raise RuntimeError( + "Legacy non-refinement Strategy 3 training is not supported after the continuous Strategy 3 redesign." + ) + + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + annealed_aux_ce_weight = _strategy3_annealed_aux_ce_weight(current_epoch) + del stepwise_backward, max_epochs, log_alpha, alpha_optimizer, target_entropy + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context(image, mc_mode="train") + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"] + base_features = refinement_context["base_features"] + encoder_features = refinement_context.get("encoder_features") + mc_variance = refinement_context["mc_variance"] + pred_entropy = refinement_context["pred_entropy"] + seg = decoder_prob.detach().to(device=image.device, dtype=image.dtype) + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + + decoder_loss = torch.zeros((), device=image.device, dtype=torch.float32) + decoder_ce_loss_value = 0.0 + decoder_dice_loss_value = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + decoder_ce = F.binary_cross_entropy_with_logits(dl_f, gt_f) + decoder_loss = decoder_loss + loss_weights["decoder_ce"] * decoder_ce + decoder_ce_loss_value = float(decoder_ce.detach().item()) + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + decoder_dice = 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + decoder_loss = decoder_loss + loss_weights["decoder_dice"] * decoder_dice + decoder_dice_loss_value = float(decoder_dice.detach().item()) + + optimizer.zero_grad(set_to_none=True) + + a3c_grad_clip = float(_job_param("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM)) + + refinement_base_features = base_features + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = mc_variance.detach() + detached_pred_entropy = pred_entropy.detach() + detached_encoder_features = [f.detach() for f in encoder_features] if encoder_features is not None else None + + step_action_hists: list[dict[str, float]] = [] + step_mask_deltas: list[float] = [] + step_reward_means: list[float] = [] + step_reward_pos_pcts: list[float] = [] + step_reward_zero_pcts: list[float] = [] + step_biou_deltas: list[float] = [] + advantage_maps: list[torch.Tensor] = [] + critic_targets: list[torch.Tensor] = [] + value_maps: list[torch.Tensor] = [] + + actor_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + critic_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + total_actor = 0.0 + total_critic = 0.0 + total_reward = 0.0 + total_ce_loss = decoder_ce_loss_value + total_dice_loss = decoder_dice_loss_value + effective_steps = max(int(tmax), 1) + final_refined_seg_for_aux: torch.Tensor | None = None + + for _ in range(effective_steps): + seg_before = seg.detach() + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg_before, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg_before.dtype) + seg_next = _strategy3_apply_delta(seg_before, delta) + reward_map, reward_details = compute_refinement_reward( + seg_before, + seg_next, + gt_mask.float(), + return_details=True, + ) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next.detach(), + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + value_next = model.value_from_state(state_next).detach() + + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + step_actor = -actor_advantage.mean() + step_critic = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + actor_loss_tensor = actor_loss_tensor + step_actor / float(effective_steps) + critic_loss_tensor = critic_loss_tensor + step_critic / float(effective_steps) + total_actor += float(step_actor.detach().item()) + total_critic += float(step_critic.detach().item()) + total_reward += float(reward_map.detach().mean().item()) + + step_action_hists.append(_strategy3_delta_distribution(delta)) + step_mask_deltas.append(float(delta.detach().abs().mean().item())) + step_reward_means.append(float(reward_map.detach().mean().item())) + step_reward_pos_pcts.append(float((reward_map.detach() > 0).float().mean().item() * 100.0)) + step_reward_zero_pcts.append(float((reward_map.detach().abs() < 1e-8).float().mean().item() * 100.0)) + step_biou_deltas.append(float(reward_details["biou_delta"].detach().mean().item())) + advantage_maps.append(actor_advantage.detach()) + critic_targets.append(critic_target.detach()) + value_maps.append(value_t.detach()) + final_refined_seg_for_aux = seg_next + seg = seg_next.detach() + + aux_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + aux_ce_loss_value = 0.0 + aux_dice_loss_value = 0.0 + if annealed_aux_ce_weight > 0.0 and final_refined_seg_for_aux is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refined_prob = final_refined_seg_for_aux.float().clamp(1e-6, 1.0 - 1e-6) + gt_f = gt_mask.float() + supervised_aux = torch.zeros((), device=image.device, dtype=torch.float32) + if ce_weight > 0: + refined_logits = torch.logit(refined_prob) + aux_ce = F.binary_cross_entropy_with_logits(refined_logits, gt_f) + supervised_aux = supervised_aux + float(ce_weight) * aux_ce + aux_ce_loss_value = float(aux_ce.detach().item()) + if dice_weight > 0: + inter = (refined_prob * gt_f).sum() + aux_dice = 1.0 - (2.0 * inter + 1e-6) / (refined_prob.sum() + gt_f.sum() + 1e-6) + supervised_aux = supervised_aux + float(dice_weight) * aux_dice + aux_dice_loss_value = float(aux_dice.detach().item()) + aux_loss_tensor = float(annealed_aux_ce_weight) * supervised_aux + total_ce_loss += aux_ce_loss_value + total_dice_loss += aux_dice_loss_value + + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + rl_loss = rl_loss_scale * (actor_loss_tensor + critic_loss_weight * critic_loss_tensor) + total_loss_tensor = decoder_loss + rl_loss + aux_loss_tensor + + if scaler is not None: + scaler.scale(total_loss_tensor).backward() + scaler.unscale_(optimizer) + else: + total_loss_tensor.backward() + + effective_grad_clip = a3c_grad_clip if a3c_grad_clip > 0 else grad_clip_norm + total_grad_norm = ( + float(torch.nn.utils.clip_grad_norm_(model.parameters(), effective_grad_clip).item()) + if effective_grad_clip > 0 + else 0.0 + ) + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + adv_means = [float(a.mean().item()) for a in advantage_maps] + adv_stds = [ + float(a.std(unbiased=False).item()) + for a in advantage_maps + if a.numel() > 1 + ] + value_pred_errors = [ + float((target - value).abs().mean().item()) + for target, value in zip(critic_targets, value_maps) + ] + mean_value_pred = float(torch.stack([value.mean() for value in value_maps]).mean().item()) if value_maps else 0.0 + avg_action_dist: dict[str, float] = {} + if step_action_hists: + all_keys = set() + for h in step_action_hists: + all_keys.update(h.keys()) + for k in sorted(all_keys): + avg_action_dist[k] = float(np.mean([h.get(k, 0.0) for h in step_action_hists])) + + return { + "loss": float(total_loss_tensor.detach().item()), + "actor_loss": total_actor / float(effective_steps), + "critic_loss": total_critic / float(effective_steps), + "mean_reward": total_reward / float(effective_steps), + "entropy": 0.0, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": effective_grad_clip, + "final_mask": threshold_binary_mask(seg.detach().float()).float(), + "effective_steps": effective_steps, + "action_distribution": avg_action_dist, + "mask_delta_mean": float(np.mean(step_mask_deltas)) if step_mask_deltas else 0.0, + "reward_per_step": step_reward_means, + "reward_pos_pct_per_step": step_reward_pos_pcts, + "reward_zeros_pct": float(np.mean(step_reward_zero_pcts)) if step_reward_zero_pcts else 0.0, + "biou_delta_mean": float(np.mean(step_biou_deltas)) if step_biou_deltas else 0.0, + "advantage_mean": float(np.mean(adv_means)), + "advantage_std": float(np.nanmean(adv_stds)) if adv_stds else 0.0, + "value_pred_error_mean": float(np.mean(value_pred_errors)), + "mean_value_pred": mean_value_pred, + "rl_loss_scale_used": float(rl_loss_scale), + "annealed_aux_ce_weight": float(annealed_aux_ce_weight), + "alpha": 0.0, + } + +def train_step_supervised( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + grad_clip_norm: float, + ce_weight: float = 0.5, + dice_weight: float = 0.5, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + optimizer.zero_grad(set_to_none=True) + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + logits_f = logits.float() + gt_f = gt_mask.float() + loss = torch.zeros(1, device=image.device, dtype=torch.float32) + ce_loss_val = 0.0 + dice_loss_val = 0.0 + if ce_weight > 0: + bce = F.binary_cross_entropy_with_logits(logits_f, gt_f, reduction="mean") + loss = loss + ce_weight * bce + ce_loss_val = float(bce.detach().item()) + if dice_weight > 0: + pred_f = torch.sigmoid(logits_f) + inter = (pred_f * gt_f).sum() + dice_l = 1.0 - (2.0 * inter + 1e-6) / (pred_f.sum() + gt_f.sum() + 1e-6) + loss = loss + dice_weight * dice_l + dice_loss_val = float(dice_l.detach().item()) + + if scaler is not None: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + final_mask = threshold_binary_mask(torch.sigmoid(logits_f)).float().detach() + return { + "loss": float(loss.detach().item()), + "actor_loss": 0.0, + "critic_loss": 0.0, + "mean_reward": 0.0, + "entropy": 0.0, + "ce_loss": ce_loss_val, + "dice_loss": dice_loss_val, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": final_mask, + } + +@torch.inference_mode() +def validate( + model: nn.Module, + loader: DataLoader, + *, + run_dir: Path | None, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + gamma: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + strategy = _require_supported_strategy(strategy) + model.eval() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="validate") if strategy != 2 else max(int(tmax), 1) + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + losses: list[float] = [] + dice_scores: list[float] = [] + iou_scores: list[float] = [] + biou_scores: list[float] = [] + entropies: list[float] = [] + rewards: list[float] = [] + actor_losses: list[float] = [] + critic_losses: list[float] = [] + ce_losses: list[float] = [] + dice_losses: list[float] = [] + decoder_dice_scores: list[float] = [] + decoder_iou_scores: list[float] = [] + decoder_biou_scores: list[float] = [] + val_binary_flips_total: list[float] = [] + val_binary_flips_correct: list[float] = [] + val_binary_flips_wrong: list[float] = [] + prefetcher = CUDAPrefetcher(loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(loader), desc="Validating", leave=False): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred_prob = torch.sigmoid(logits).float() + pred = threshold_binary_mask(pred_prob).float() + bce = F.binary_cross_entropy_with_logits(logits.float(), gt_mask.float(), reduction="mean") if ce_weight > 0 else torch.tensor(0.0, device=image.device) + inter_prob = (pred_prob * gt_mask.float()).sum() + dice_loss = 1.0 - (2.0 * inter_prob + 1e-6) / (pred_prob.sum() + gt_mask.sum() + 1e-6) if dice_weight > 0 else torch.tensor(0.0, device=image.device) + losses.append(float((ce_weight * bce + dice_weight * dice_loss).item())) + ce_losses.append(float(bce.item()) if ce_weight > 0 else 0.0) + dice_losses.append(float(dice_loss.item()) if dice_weight > 0 else 0.0) + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"].float() + seg = decoder_prob.float() + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += loss_weights["decoder_ce"] * batch_ce + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += loss_weights["decoder_dice"] * batch_dice + decoder_pred = threshold_binary_mask(decoder_prob.float()).float() + decoder_inter = (decoder_pred * gt_mask.float()).sum(dim=(1, 2, 3)) + decoder_pred_sum = decoder_pred.sum(dim=(1, 2, 3)) + decoder_gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + decoder_dice = (2.0 * decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum + _EPS) + decoder_iou = (decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum - decoder_inter + _EPS) + decoder_dice_scores.extend(decoder_dice.cpu().tolist()) + decoder_iou_scores.extend(decoder_iou.cpu().tolist()) + else: + fg_count = gt_mask.sum().clamp(min=1.0) + bg_count = (gt_mask == 0).sum().clamp(min=1.0) + pos_weight = bg_count / fg_count + _weight_map = torch.where(gt_mask == 1, pos_weight, torch.ones_like(gt_mask)) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + decoder_logits = model.forward_decoder(image) + seg = threshold_binary_mask(torch.sigmoid(decoder_logits)).float() + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if ce_weight > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += ce_weight * batch_ce + if dice_weight > 0: + dm_prob = torch.sigmoid(dl_f) + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += dice_weight * batch_dice + else: + seg = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + + batch_actor = 0.0 + batch_critic = 0.0 + batch_reward = 0.0 + batch_entropy = 0.0 + batch_loss = decoder_loss + decoder_ce_base = batch_ce + decoder_dice_base = batch_dice + aux_ce_total = 0.0 + aux_dice_total = 0.0 + effective_steps = effective_tmax + + if strategy == 3 and refinement_runtime: + refinement_base_features = refinement_context["base_features"] + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = refinement_context["mc_variance"].detach() + detached_pred_entropy = refinement_context["pred_entropy"].detach() + _enc_feats = refinement_context.get("encoder_features") + detached_enc_feats = [f.detach() for f in _enc_feats] if _enc_feats is not None else None + effective_steps = effective_tmax + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + value_next = model.value_from_state(state_next).detach() + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + actor_loss = -actor_advantage.mean() + critic_loss = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward_map.mean().item()) + batch_loss += float((actor_loss + critic_loss_weight * critic_loss).item()) + seg = seg_next + + batch_ce = decoder_ce_base + batch_dice = decoder_dice_base + batch_loss = decoder_loss + rl_loss_scale * ((batch_loss - decoder_loss) / float(max(effective_steps, 1))) + pred = threshold_binary_mask(seg.float()).float() + # Track binary mask flips between decoder and RL-refined prediction + flipped = (decoder_pred != pred) + gt_binary = (gt_mask.float() > 0.5) + correct_flips = flipped & ((pred > 0.5) == gt_binary) + wrong_flips = flipped & ((pred > 0.5) != gt_binary) + total_px = max(pred.numel(), 1) + val_binary_flips_total.append(float(flipped.float().sum().item() / total_px * 100.0)) + val_binary_flips_correct.append(float(correct_flips.float().sum().item() / total_px * 100.0)) + val_binary_flips_wrong.append(float(wrong_flips.float().sum().item() / total_px * 100.0)) + else: + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=False) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = ((seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2)) + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + target = reward + gamma * _bootstrap_value_target(model, value_next) + advantage = target - value_t + actor_loss = -(log_prob * advantage.detach()).mean() + critic_loss = F.smooth_l1_loss(value_t, target) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward.mean().item()) + batch_entropy += float(entropy.item()) + batch_loss += float(((actor_loss + critic_loss_weight * critic_loss) / float(max(effective_tmax, 1))).item()) + seg = seg_next + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + aux_loss, aux_ce, aux_dice = compute_strategy1_aux_segmentation_loss( + policy_aux, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + batch_loss += float(aux_loss.item()) + batch_ce = aux_ce + batch_dice = aux_dice + pred = infer_segmentation_mask( + model, + image, + effective_tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ).float() + + ce_losses.append(batch_ce) + dice_losses.append(batch_dice) + actor_losses.append(batch_actor / float(max(effective_steps, 1))) + critic_losses.append(batch_critic / float(max(effective_steps, 1))) + rewards.append(batch_reward / float(max(effective_steps, 1))) + entropies.append(batch_entropy / float(max(effective_steps, 1))) + losses.append(batch_loss) + + inter = (pred * gt_mask.float()).sum(dim=(1, 2, 3)) + pred_sum = pred.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + dice_scores.extend(dice.cpu().tolist()) + iou_scores.extend(iou.cpu().tolist()) + pred_np = pred.detach().cpu().numpy() + gt_np = gt_mask.float().detach().cpu().numpy() + for idx in range(pred_np.shape[0]): + biou_scores.append(boundary_iou_score(pred_np[idx], gt_np[idx])) + if strategy == 3 and decoder_dice_scores: + decoder_pred_np = decoder_pred.detach().cpu().numpy() + for idx in range(decoder_pred_np.shape[0]): + decoder_biou_scores.append(boundary_iou_score(decoder_pred_np[idx], gt_np[idx])) + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + val_decoder_dice = float(np.mean(decoder_dice_scores)) if decoder_dice_scores else None + val_decoder_iou = float(np.mean(decoder_iou_scores)) if decoder_iou_scores else None + val_decoder_biou = float(np.mean(decoder_biou_scores)) if decoder_biou_scores else None + val_dice = float(np.mean(dice_scores)) if dice_scores else 0.0 + val_iou = float(np.mean(iou_scores)) if iou_scores else 0.0 + val_biou = float(np.mean(biou_scores)) if biou_scores else 0.0 + val_refine_score = 0.5 * (val_iou + val_biou) + + return { + "val_loss": float(np.mean(losses)) if losses else 0.0, + "val_dice": val_dice, + "val_iou": val_iou, + "val_biou": val_biou, + "val_refine_score": val_refine_score, + "val_decoder_dice": val_decoder_dice, + "val_decoder_iou": val_decoder_iou, + "val_decoder_biou": val_decoder_biou, + "val_dice_gain": None if val_decoder_dice is None else val_dice - val_decoder_dice, + "val_iou_gain": None if val_decoder_iou is None else val_iou - val_decoder_iou, + "val_biou_gain": None if val_decoder_biou is None else val_biou - val_decoder_biou, + "val_actor_loss": float(np.mean(actor_losses)) if actor_losses else 0.0, + "val_critic_loss": float(np.mean(critic_losses)) if critic_losses else 0.0, + "val_ce_loss": float(np.mean(ce_losses)) if ce_losses else 0.0, + "val_dice_loss": float(np.mean(dice_losses)) if dice_losses else 0.0, + "val_reward": float(np.mean(rewards)) if rewards else 0.0, + "val_entropy": float(np.mean(entropies)) if entropies else 0.0, + "val_binary_flips_total_pct": float(np.mean(val_binary_flips_total)) if val_binary_flips_total else None, + "val_binary_flips_correct_pct": float(np.mean(val_binary_flips_correct)) if val_binary_flips_correct else None, + "val_binary_flips_wrong_pct": float(np.mean(val_binary_flips_wrong)) if val_binary_flips_wrong else None, + } + +def _save_training_plots(history: list[dict[str, Any]], plots_dir: Path) -> None: + if len(history) < 1: + return + ensure_dir(plots_dir) + epochs = [row["epoch"] for row in history] + plot_specs = [ + ("loss.png", "Loss", [("train_loss", "Train"), ("val_loss", "Val")]), + ("dice.png", "Dice", [("train_dice", "Train"), ("val_dice", "Val")]), + ("iou.png", "IoU", [("train_iou", "Train"), ("val_iou", "Val")]), + ("reward.png", "Reward", [("train_mean_reward", "Train"), ("val_reward", "Val")]), + ("ce_loss.png", "CE Loss", [("train_ce_loss", "Train")]), + ("dice_loss.png", "Dice Loss", [("train_dice_loss", "Train")]), + ("lr.png", "Learning Rate", [("lr", "Head LR"), ("encoder_lr", "Encoder LR")]), + ] + for file_name, title, curves in plot_specs: + fig, ax = plt.subplots(figsize=(8, 4)) + has_data = False + for key, label in curves: + values = [(row["epoch"], row[key]) for row in history if key in row] + if not values: + continue + xs, ys = zip(*values) + ax.plot(xs, ys, label=label, linewidth=1.2) + has_data = True + if has_data: + ax.set_title(title) + ax.set_xlabel("Epoch") + ax.set_ylabel(title) + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(plots_dir / file_name, dpi=110) + plt.close(fig) + +RESUME_IDENTITY_KEYS = ( + "strategy", + "dataset_percent", + "dataset_name", + "dataset_split_policy", + "split_type", + "train_subset_key", + "train_subset_variant", + "best_checkpoint_metric_name", + "backbone_family", + "smp_encoder_name", + "smp_encoder_weights", + "smp_encoder_depth", + "smp_encoder_proj_dim", + "smp_decoder_type", + "vgg_feature_scales", + "vgg_feature_dilation", + "head_lr", + "encoder_lr", + "weight_decay", + "dropout_p", + "tmax", + "entropy_lr", +) + +PORTABLE_RESUME_PATH_KEYS = frozenset( + { + "base_split_manifest_path", + "subset_manifest_path", + } +) + +def _path_parts(value: Any) -> tuple[str, ...]: + if value is None: + return () + return tuple(part for part in Path(str(value)).parts if part not in {"", os.sep}) + +def _portable_path_token(value: Any) -> str: + if value in (None, ""): + return "" + path = Path(str(value)).expanduser() + roots: list[tuple[str, Path]] = [] + experiment_root = globals().get("EXPERIMENT_ROOT") + if experiment_root is not None: + roots.append(("EXPERIMENT_ROOT", Path(experiment_root))) + roots.append(("PROJECT_DIR", PROJECT_DIR)) + for label, root in roots: + try: + rel = path.resolve().relative_to(root.resolve()) + return f"{label}:{rel.as_posix()}" + except (OSError, ValueError): + continue + parts = _path_parts(value) + for marker in ("runs", "repeated_holdout", "manifests", "checkpoints"): + if marker in parts: + return "/".join(parts[parts.index(marker):]) + return "/".join(parts) + +def _resume_path_values_match(current: Any, saved: Any) -> tuple[bool, str]: + current_text = str(current or "") + saved_text = str(saved or "") + if current_text == saved_text: + return True, "exact" + + current_token = _portable_path_token(current_text) + saved_token = _portable_path_token(saved_text) + if current_token and current_token == saved_token: + return True, "portable-token" + + current_parts = _path_parts(current_text) + saved_parts = _path_parts(saved_text) + max_suffix = min(len(current_parts), len(saved_parts)) + for length in range(max_suffix, 2, -1): + if current_parts[-length:] == saved_parts[-length:]: + return True, f"suffix:{length}" + return False, f"current_token={current_token!r}, checkpoint_token={saved_token!r}" + +def _resume_value_matches(current: Any, saved: Any) -> bool: + if isinstance(current, (int, float)) and isinstance(saved, (int, float)) and not isinstance(current, bool): + return math.isclose(float(current), float(saved), rel_tol=1e-9, abs_tol=1e-12) + return current == saved + +def validate_resume_checkpoint_identity( + current_run_config: dict[str, Any], + saved_run_config: dict[str, Any], + *, + checkpoint_path: Path, +) -> None: + mismatches: list[str] = [] + for key in RESUME_IDENTITY_KEYS: + if key not in current_run_config or key not in saved_run_config: + mismatches.append(f"{key}: current={current_run_config.get(key)!r}, checkpoint={saved_run_config.get(key)!r}") + continue + if key in PORTABLE_RESUME_PATH_KEYS: + matches, reason = _resume_path_values_match(current_run_config[key], saved_run_config[key]) + if matches: + if str(current_run_config[key]) != str(saved_run_config[key]): + print( + f"[Resume] Accepted portable path match for {key}: " + f"current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}, reason={reason}." + ) + continue + mismatches.append( + f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r} ({reason})" + ) + continue + if not _resume_value_matches(current_run_config[key], saved_run_config[key]): + mismatches.append(f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}") + + current_s2 = current_run_config.get("strategy2_checkpoint_path") + saved_s2 = saved_run_config.get("strategy2_checkpoint_path") + if current_s2 or saved_s2: + if str(current_s2 or "") != str(saved_s2 or ""): + # Warn but do not abort: when resuming, the model weights are fully restored + # from the resume checkpoint (not re-loaded from the strategy2 checkpoint), + # so a path change (e.g. file moved/renamed) does not affect correctness. + print( + f"[WARN] strategy2_checkpoint_path changed since checkpoint was saved " + f"(current={current_s2!r}, checkpoint={saved_s2!r}). " + f"Resuming anyway — model state comes from the resume checkpoint." + ) + + if mismatches: + raise ValueError( + f"Resume checkpoint identity mismatch for {checkpoint_path}:\n" + "\n".join(f" - {line}" for line in mismatches) + ) + +def load_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> list[dict[str, Any]]: + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + epoch_metrics = checkpoint_payload.get("epoch_metrics") + history: list[dict[str, Any]] = [] + checkpoint_history = checkpoint_payload.get("history") + if isinstance(checkpoint_history, list): + history = [dict(row) for row in checkpoint_history if isinstance(row, dict)] + history = [row for row in history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, list): + raise RuntimeError(f"Expected list history at {history_path}, found {type(payload).__name__}.") + file_history = [dict(row) for row in payload if isinstance(row, dict)] + file_history = [row for row in file_history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if len(file_history) >= len(history): + history = file_history + if not history and isinstance(epoch_metrics, dict): + history = [dict(epoch_metrics)] + elif history and isinstance(epoch_metrics, dict): + if int(history[-1].get("epoch", 0)) < checkpoint_epoch: + history.append(dict(epoch_metrics)) + return history + +def train_model( + *, + run_type: str, + model_config: RuntimeModelConfig, + run_config: dict[str, Any], + model: nn.Module, + description: str, + strategy: int, + run_dir: Path, + bundle: DataBundle, + max_epochs: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + tmax: int, + entropy_lr: float, + entropy_alpha_init: float, + entropy_target_ratio: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, + dropout_p: float, + resume_checkpoint_path: Path | None = None, + trial: optuna.trial.Trial | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=description, + strategy=strategy, + model_config=model_config, + dropout_p=dropout_p, + amp_dtype=amp_dtype, + compiled=hasattr(model, "_orig_mod"), + ) + + strategy3_freeze_status = _strategy3_bootstrap_freeze_status(model) if strategy == 3 else None + strategy3_frozen_decoder = strategy == 3 and _strategy3_decoder_is_frozen(model) + decoder_head_lr = float(_job_param("decoder_lr", 0.0 if strategy3_frozen_decoder else head_lr * 0.1)) + encoder_group_lr = 0.0 if strategy3_frozen_decoder else encoder_lr + rl_group_lr = float(_job_param("rl_lr", head_lr)) + optimizer = make_optimizer( + model, + strategy, + head_lr=decoder_head_lr, + encoder_lr=encoder_group_lr, + weight_decay=weight_decay, + rl_lr=rl_group_lr, + ) + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_epochs, + eta_min=1e-6, # floor + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + + del entropy_alpha_init, entropy_lr, entropy_target_ratio + target_entropy = 0.0 + log_alpha = None + alpha_optimizer = None + + save_artifacts = run_type == "final" + save_history_incrementally = bool(run_config.get("save_history_incrementally", SAVE_HISTORY_INCREMENTALLY)) + write_epoch_diagnostic = bool(run_config.get("write_epoch_diagnostic", WRITE_EPOCH_DIAGNOSTIC)) + ckpt_dir = ensure_dir(run_dir / "checkpoints") if save_artifacts else None + plots_dir = ensure_dir(run_dir / "plots") if save_artifacts else None + history_path = checkpoint_history_path(run_dir, run_type) + diagnostic_path = diagnostic_path_for_run(run_dir) if run_type == "final" and write_epoch_diagnostic else None + history: list[dict[str, Any]] = [] + selection_metric_name = _strategy_selection_metric_name(strategy) + early_stopping_monitor_name = _early_stopping_monitor_name(strategy) + early_stopping_mode = _early_stopping_mode(strategy, early_stopping_monitor_name) + early_stopping_min_delta = _early_stopping_min_delta() + early_stopping_start_epoch = _early_stopping_start_epoch() + early_stopping_patience = _early_stopping_patience() + epoch_probe_mode = str(run_config.get("epoch_probe_mode", "fixed")).strip().lower() + best_model_metric = -float("inf") + patience_counter = 0 + best_early_stopping_metric: float | None = None + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + diagnostic_payload: dict[str, Any] | None = None + train_probe_batches: list[dict[str, Any]] = [] + val_probe_batches: list[dict[str, Any]] = [] + run_label = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ) + if diagnostic_path is not None: + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=0, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=0, + split_tag="val", + ) + else: + train_probe_batches = _fixed_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + ) + val_probe_batches = _fixed_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + ) + diagnostic_payload = empty_epoch_diagnostic_payload( + run_type=run_type, + run_config=run_config, + bundle=bundle, + train_probe_batches=train_probe_batches, + val_probe_batches=val_probe_batches, + ) + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + expected_run_type=run_type, + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + selection_metric_name = str(checkpoint_payload.get("best_metric_name", selection_metric_name)) + best_model_metric = float(checkpoint_payload["best_metric_value"]) + patience_counter = int(checkpoint_payload.get("patience_counter", 0)) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", run_type), + } + epoch_metrics = checkpoint_payload.get("epoch_metrics") + if isinstance(epoch_metrics, dict) and epoch_metrics.get("early_stopping_best_value") is not None: + best_early_stopping_metric = float(epoch_metrics["early_stopping_best_value"]) + if diagnostic_path is not None and diagnostic_payload is not None: + diagnostic_payload = load_epoch_diagnostic_for_resume( + diagnostic_path, + checkpoint_payload, + diagnostic_payload, + ) + print( + f"[Resume] {run_label} | {run_type} continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{max_epochs}." + ) + prev_params = _snapshot_params(model) if diagnostic_path is not None else None + start_time = time.time() + validate_interval = max(int(VALIDATE_EVERY_N_EPOCHS), 1) + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + + for epoch in range(start_epoch, max_epochs + 1): + epoch_losses: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_reward: list[float] = [] + epoch_entropy: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_loss: list[float] = [] + epoch_grad: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_effective_steps: list[float] = [] + epoch_mask_deltas: list[float] = [] + epoch_advantage_means: list[float] = [] + epoch_advantage_stds: list[float] = [] + epoch_value_pred_errors: list[float] = [] + epoch_reward_zero_pcts: list[float] = [] + epoch_biou_deltas: list[float] = [] + epoch_mean_value_preds: list[float] = [] + epoch_annealed_aux_ce_weights: list[float] = [] + epoch_rl_loss_scales: list[float] = [] + epoch_reinforce_losses: list[float] = [] + epoch_entropy_losses: list[float] = [] + epoch_entropy_bonuses_used: list[float] = [] + epoch_alphas: list[float] = [] + epoch_action_dists: list[dict[str, float]] = [] + + prefetcher = CUDAPrefetcher(bundle.train_loader, DEVICE) + progress = tqdm(prefetcher, total=len(bundle.train_loader), desc=f"Epoch {epoch}/{max_epochs}", leave=False) + for batch in progress: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=tmax, + critic_loss_weight=critic_loss_weight, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=ce_weight, + dice_weight=dice_weight, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=max_epochs, + ) + epoch_losses.append(metrics["loss"]) + epoch_actor.append(metrics["actor_loss"]) + epoch_critic.append(metrics["critic_loss"]) + epoch_reward.append(metrics["mean_reward"]) + epoch_entropy.append(metrics["entropy"]) + epoch_ce.append(metrics["ce_loss"]) + epoch_dice_loss.append(metrics["dice_loss"]) + epoch_grad.append(metrics["grad_norm"]) + if "effective_steps" in metrics: + epoch_effective_steps.append(float(metrics["effective_steps"])) + if "mask_delta_mean" in metrics: + epoch_mask_deltas.append(metrics["mask_delta_mean"]) + if "advantage_mean" in metrics: + epoch_advantage_means.append(metrics["advantage_mean"]) + if "advantage_std" in metrics: + epoch_advantage_stds.append(metrics["advantage_std"]) + if "value_pred_error_mean" in metrics: + epoch_value_pred_errors.append(metrics["value_pred_error_mean"]) + if "reward_zeros_pct" in metrics: + epoch_reward_zero_pcts.append(float(metrics["reward_zeros_pct"])) + if "biou_delta_mean" in metrics: + epoch_biou_deltas.append(float(metrics["biou_delta_mean"])) + if "mean_value_pred" in metrics: + epoch_mean_value_preds.append(float(metrics["mean_value_pred"])) + if "annealed_aux_ce_weight" in metrics: + epoch_annealed_aux_ce_weights.append(float(metrics["annealed_aux_ce_weight"])) + if "rl_loss_scale_used" in metrics: + epoch_rl_loss_scales.append(float(metrics["rl_loss_scale_used"])) + if "reinforce_loss" in metrics: + epoch_reinforce_losses.append(float(metrics["reinforce_loss"])) + if "entropy_loss" in metrics: + epoch_entropy_losses.append(float(metrics["entropy_loss"])) + if "entropy_bonus_used" in metrics: + epoch_entropy_bonuses_used.append(float(metrics["entropy_bonus_used"])) + if "alpha" in metrics: + epoch_alphas.append(metrics["alpha"]) + if "action_distribution" in metrics and metrics["action_distribution"]: + epoch_action_dists.append(metrics["action_distribution"]) + + pred_mask = metrics["final_mask"] + gt_mask = batch["mask"].float() + inter = (pred_mask * gt_mask).sum(dim=(1, 2, 3)) + pred_sum = pred_mask.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + epoch_dices.extend(dice.detach().cpu().tolist()) + epoch_ious.extend(iou.detach().cpu().tolist()) + + head_lr_now = float(optimizer.param_groups[-1]["lr"]) + enc_lr_now = float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else head_lr_now + progress.set_postfix(loss=f"{metrics['loss']:.4f}", iou=f"{np.mean(epoch_ious):.4f}", lr=f"{head_lr_now:.2e}") + + should_validate = epoch % validate_interval == 0 or epoch == max_epochs + val_metrics: dict[str, float | None] = { + "val_loss": None, + "val_dice": None, + "val_iou": None, + "val_biou": None, + "val_decoder_dice": None, + "val_decoder_iou": None, + "val_decoder_biou": None, + "val_dice_gain": None, + "val_iou_gain": None, + "val_biou_gain": None, + "val_reward": None, + "val_entropy": None, + } + if should_validate: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: running validation on {len(bundle.val_loader)} batches..." + ) + validated_metrics = validate( + model, + bundle.val_loader, + run_dir=run_dir, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + gamma=DEFAULT_GAMMA, + critic_loss_weight=critic_loss_weight, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + val_metrics.update(validated_metrics) + scheduler.step() # always step up the scheduler + grad_stats: dict[str, Any] = {} + param_stats: dict[str, Any] = {} + if diagnostic_path is not None: + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_epoch_action_dist: dict[str, float] = {} + if epoch_action_dists: + all_act_keys = set() + for d in epoch_action_dists: + all_act_keys.update(d.keys()) + for k in sorted(all_act_keys): + avg_epoch_action_dist[k] = float(np.mean([d.get(k, 0.0) for d in epoch_action_dists])) + + row = { + "epoch": epoch, + "train_loss": float(np.mean(epoch_losses)) if epoch_losses else 0.0, + "train_actor_loss": float(np.mean(epoch_actor)) if epoch_actor else 0.0, + "train_critic_loss": float(np.mean(epoch_critic)) if epoch_critic else 0.0, + "train_mean_reward": float(np.mean(epoch_reward)) if epoch_reward else 0.0, + "train_entropy": float(np.mean(epoch_entropy)) if epoch_entropy else 0.0, + "train_ce_loss": float(np.mean(epoch_ce)) if epoch_ce else 0.0, + "train_dice_loss": float(np.mean(epoch_dice_loss)) if epoch_dice_loss else 0.0, + "train_dice": float(np.mean(epoch_dices)) if epoch_dices else 0.0, + "train_iou": float(np.mean(epoch_ious)) if epoch_ious else 0.0, + "grad_norm": float(np.mean(epoch_grad)) if epoch_grad else 0.0, + "lr": float(optimizer.param_groups[-1]["lr"]), + "encoder_lr": float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else float(optimizer.param_groups[-1]["lr"]), + "alpha": float(log_alpha.exp().detach().item()) if log_alpha is not None else None, + "train_effective_steps": float(np.mean(epoch_effective_steps)) if epoch_effective_steps else 0.0, + "train_mask_delta_mean": float(np.mean(epoch_mask_deltas)) if epoch_mask_deltas else 0.0, + "train_advantage_mean": float(np.mean(epoch_advantage_means)) if epoch_advantage_means else 0.0, + "train_advantage_std": _nanmean_or_default(epoch_advantage_stds, 0.0), + "train_value_pred_error": float(np.mean(epoch_value_pred_errors)) if epoch_value_pred_errors else 0.0, + "train_reward_zeros_pct": float(np.mean(epoch_reward_zero_pcts)) if epoch_reward_zero_pcts else 0.0, + "train_biou_delta_mean": float(np.mean(epoch_biou_deltas)) if epoch_biou_deltas else 0.0, + "train_mean_value_pred": float(np.mean(epoch_mean_value_preds)) if epoch_mean_value_preds else 0.0, + "train_annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "train_rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "train_reinforce_loss": float(np.mean(epoch_reinforce_losses)) if epoch_reinforce_losses else 0.0, + "train_entropy_loss": float(np.mean(epoch_entropy_losses)) if epoch_entropy_losses else 0.0, + "train_entropy_bonus_used": float(np.mean(epoch_entropy_bonuses_used)) if epoch_entropy_bonuses_used else 0.0, + "train_alpha": float(np.mean(epoch_alphas)) if epoch_alphas else 0.0, + "train_action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + "validated_this_epoch": should_validate, + **val_metrics, + } + if strategy == 3: + if row["train_advantage_std"] < 0.005: + low_advantage_std_streak += 1 + else: + low_advantage_std_streak = 0 + if abs(row["train_mean_value_pred"]) < 0.001: + low_mean_value_pred_streak += 1 + else: + low_mean_value_pred_streak = 0 + else: + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + if strategy3_freeze_status is not None: + row["strategy3_bootstrap_loaded"] = bool(strategy3_freeze_status["bootstrap_loaded"]) + row["strategy3_freeze_requested"] = bool(strategy3_freeze_status["freeze_requested"]) + row["strategy3_freeze_active"] = bool(strategy3_freeze_status["freeze_active"]) + row["strategy3_encoder_state"] = str(strategy3_freeze_status["encoder_state"]) + row["strategy3_decoder_state"] = str(strategy3_freeze_status["decoder_state"]) + row["strategy3_segmentation_head_state"] = str(strategy3_freeze_status["segmentation_head_state"]) + history.append(row) + + improved = False + early_stopping_improved_now = False + if should_validate: + selected_metric_value = _strategy_selection_metric_value(strategy, val_metrics) + row["selection_metric_name"] = selection_metric_name + row["selection_metric_value"] = selected_metric_value + improved = selected_metric_value > best_model_metric + if improved: + best_model_metric = selected_metric_value + if trial is not None: + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR, float(best_model_metric)) + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR, selection_metric_name) + early_stopping_monitor_value = _early_stopping_monitor_value( + row, + strategy=strategy, + monitor_name=early_stopping_monitor_name, + ) + early_stopping_active = epoch >= early_stopping_start_epoch + if early_stopping_active and early_stopping_monitor_value is not None: + early_stopping_improved_now = _early_stopping_improved( + early_stopping_monitor_value, + best_early_stopping_metric, + mode=early_stopping_mode, + min_delta=early_stopping_min_delta, + ) + if early_stopping_improved_now: + best_early_stopping_metric = early_stopping_monitor_value + patience_counter = 0 + else: + patience_counter += 1 + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = early_stopping_monitor_value + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = early_stopping_active + row["early_stopping_improved"] = early_stopping_improved_now + row["early_stopping_wait"] = int(patience_counter) + if improved and save_artifacts and ckpt_dir is not None: + save_checkpoint( + ckpt_dir / "best.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + else: + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = None + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = False + row["early_stopping_improved"] = False + row["early_stopping_wait"] = int(patience_counter) + + if save_artifacts and save_history_incrementally: + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + save_json(history_path, history) + + if diagnostic_path is not None and diagnostic_payload is not None: + epoch_alerts = _numerical_health_check(row, prefix=f"epoch[{epoch}]:") + if low_advantage_std_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] advantage_std collapsed - RL gradient near zero") + if low_mean_value_pred_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] critic degenerate - mean value prediction stuck near zero") + if int(grad_stats.get("n_nan", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected NaN gradients") + if int(grad_stats.get("n_inf", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected Inf gradients") + + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="val", + ) + + train_probe = _evaluate_probe_batches( + model, + train_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="train", + mc_cache_run_dir=run_dir, + ) + val_probe = _evaluate_probe_batches( + model, + val_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + epoch_alerts.extend(train_probe.get("alerts", [])) + epoch_alerts.extend(val_probe.get("alerts", [])) + + diagnostic_payload["epochs"].append( + { + "epoch": epoch, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "is_new_best": bool(improved), + "best_metric_name": selection_metric_name, + "best_metric_value_so_far": float(best_model_metric), + "patience_counter": int(patience_counter), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value_so_far": best_early_stopping_metric, + "history_row": dict(row), + "train_batch_summary": { + "loss": _summary_stats(epoch_losses), + "actor_loss": _summary_stats(epoch_actor), + "critic_loss": _summary_stats(epoch_critic), + "reward": _summary_stats(epoch_reward), + "entropy": _summary_stats(epoch_entropy), + "ce_loss": _summary_stats(epoch_ce), + "dice_loss": _summary_stats(epoch_dice_loss), + "grad_norm": _summary_stats(epoch_grad), + "dice": _summary_stats(epoch_dices), + "iou": _summary_stats(epoch_ious), + "effective_steps": _summary_stats(epoch_effective_steps), + "mask_delta": _summary_stats(epoch_mask_deltas), + "advantage_mean": _summary_stats(epoch_advantage_means), + "advantage_std": _summary_stats(epoch_advantage_stds), + "value_pred_error": _summary_stats(epoch_value_pred_errors), + "reward_zeros_pct": _summary_stats(epoch_reward_zero_pcts), + "biou_delta_mean": _summary_stats(epoch_biou_deltas), + "mean_value_pred": _summary_stats(epoch_mean_value_preds), + "annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "reinforce_loss": _summary_stats(epoch_reinforce_losses), + "entropy_loss": _summary_stats(epoch_entropy_losses), + "entropy_bonus_used": _summary_stats(epoch_entropy_bonuses_used), + "alpha": _summary_stats(epoch_alphas), + "action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + }, + "optimizer": { + "param_groups": _optimizer_diagnostics(optimizer), + "scheduler_last_lr": [float(value) for value in scheduler.get_last_lr()], + "target_entropy": float(target_entropy) if log_alpha is not None else 0.0, + }, + "grad_diagnostics": grad_stats, + "param_diagnostics": param_stats, + "probe_batches": { + "mode": epoch_probe_mode, + "train": _probe_batch_id_lists(train_probe_batches), + "val": _probe_batch_id_lists(val_probe_batches), + }, + "probes": { + "train_fixed": train_probe, + "val_fixed": val_probe, + }, + "probe_epoch_summary": { + "train_fixed": _format_probe_deterioration("train", train_probe, tmax), + "val_fixed": _format_probe_deterioration("val", val_probe, tmax), + }, + "alerts": epoch_alerts, + } + ) + save_json(diagnostic_path, diagnostic_payload) + + if save_artifacts and ckpt_dir is not None and SAVE_LATEST_EVERY_EPOCH and run_type != "trial": + save_checkpoint( + ckpt_dir / "latest.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + if save_artifacts and ckpt_dir is not None and CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0 and run_type != "trial": + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + + if trial is not None and should_validate: + reported_metric = row.get("selection_metric_value") + if reported_metric is None: + reported_metric = _strategy_selection_metric_value(strategy, val_metrics) + if reported_metric is None: + reported_metric = float(val_metrics["val_iou"]) + reported_metric = float(reported_metric) + trial.report(reported_metric, step=epoch) + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot( + trial, + current_best_value=best_model_metric, + ) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + if USE_TRIAL_PRUNING and epoch >= TRIAL_PRUNER_WARMUP_STEPS and trial.should_prune(): + raise optuna.TrialPruned( + f"Trial pruned at epoch {epoch} with " + f"{selection_metric_name}={reported_metric:.4f}" + ) + + if trial is not None and row.get("study_best_objective") is None: + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot(trial) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + + if VERBOSE_EPOCH_LOG: + tqdm.write(f"[{run_label}] Epoch {epoch}/{max_epochs}") + tqdm.write(json.dumps(row, indent=2)) + else: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: " + f"{format_concise_epoch_log(row, best_metric_name=selection_metric_name, best_metric_value=best_model_metric)}" + ) + + if should_validate and early_stopping_patience > 0 and patience_counter >= early_stopping_patience: + print( + f"Early stopping triggered at epoch {epoch}: " + f"monitor={early_stopping_monitor_name} mode={early_stopping_mode} " + f"best={best_early_stopping_metric} current={row.get('early_stopping_monitor_value')} " + f"min_delta={early_stopping_min_delta:.6g} wait={patience_counter}/{early_stopping_patience}." + ) + break + + elapsed = elapsed_before_resume + (time.time() - start_time) + if save_artifacts: + save_json(history_path, history) + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + summary = { + "best_model_metric_name": selection_metric_name, + "best_model_metric": float(best_model_metric), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value": best_early_stopping_metric, + "best_val_iou": max((float(r["val_iou"]) for r in history if r.get("val_iou") is not None), default=0.0), + "best_val_dice": max((float(r["val_dice"]) for r in history if r.get("val_dice") is not None), default=0.0), + "best_val_biou": max((float(r["val_biou"]) for r in history if r.get("val_biou") is not None), default=0.0), + "best_val_iou_gain": max((float(r["val_iou_gain"]) for r in history if r.get("val_iou_gain") is not None), default=0.0), + "best_val_biou_gain": max((float(r["val_biou_gain"]) for r in history if r.get("val_biou_gain") is not None), default=0.0), + "final_epoch": int(history[-1]["epoch"]) if history else int(start_epoch - 1), + "elapsed_seconds": elapsed, + "seconds_per_epoch": elapsed / max(len(history), 1), + "device_used": str(DEVICE), + "strategy": strategy, + "run_type": run_type, + "resumed": resume_source is not None, + } + if strategy3_freeze_status is not None: + summary.update( + { + "strategy3_bootstrap_loaded": bool(strategy3_freeze_status["bootstrap_loaded"]), + "strategy3_freeze_requested": bool(strategy3_freeze_status["freeze_requested"]), + "strategy3_freeze_active": bool(strategy3_freeze_status["freeze_active"]), + "strategy3_encoder_state": str(strategy3_freeze_status["encoder_state"]), + "strategy3_decoder_state": str(strategy3_freeze_status["decoder_state"]), + "strategy3_segmentation_head_state": str(strategy3_freeze_status["segmentation_head_state"]), + } + ) + if resume_source is not None: + summary["resume_source"] = resume_source + if save_artifacts: + save_json(run_dir / "summary.json", summary) + return summary, history + +"""============================================================================= +EVALUATION + SMOKE TEST +============================================================================= +""" + +def _save_rgb_panel(image_chw: np.ndarray, pred_hw: np.ndarray, gt_hw: np.ndarray, output_path: Path, title: str) -> None: + img = image_chw.transpose(1, 2, 0) + img = (img - img.min()) / (img.max() - img.min() + 1e-8) + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(img) + axes[0].set_title("Input") + axes[1].imshow(pred_hw, cmap="gray", vmin=0, vmax=1) + axes[1].set_title("Prediction") + axes[2].imshow(gt_hw, cmap="gray", vmin=0, vmax=1) + axes[2].set_title("Ground Truth") + for ax in axes: + ax.axis("off") + fig.suptitle(title) + fig.tight_layout() + fig.savefig(output_path, dpi=120) + plt.close(fig) + + +def _synchronize_device_for_timing(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _write_evaluation_timing_csv( + path: Path, + *, + timing_summary: dict[str, Any], +) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "scope", + "strategy", + "tmax", + "device", + "num_batches", + "num_samples", + "total_inference_ms", + "avg_batch_inference_ms", + "std_batch_inference_ms", + "avg_sample_inference_ms", + "std_sample_inference_ms", + "mean_per_image_inference_ms", + "std_per_image_inference_ms", + "mean_per_image_inference_seconds", + "std_per_image_inference_seconds", + ], + ) + writer.writeheader() + writer.writerow( + { + "scope": str(timing_summary["scope"]), + "strategy": int(timing_summary["strategy"]), + "tmax": int(timing_summary["tmax"]), + "device": str(timing_summary["device"]), + "num_batches": int(timing_summary["num_batches"]), + "num_samples": int(timing_summary["num_samples"]), + "total_inference_ms": f"{float(timing_summary['total_inference_ms']):.6f}", + "avg_batch_inference_ms": f"{float(timing_summary['avg_batch_inference_ms']):.6f}", + "std_batch_inference_ms": f"{float(timing_summary['std_batch_inference_ms']):.6f}", + "avg_sample_inference_ms": f"{float(timing_summary['avg_sample_inference_ms']):.6f}", + "std_sample_inference_ms": f"{float(timing_summary['std_sample_inference_ms']):.6f}", + "mean_per_image_inference_ms": f"{float(timing_summary['mean_per_image_inference_ms']):.6f}", + "std_per_image_inference_ms": f"{float(timing_summary['std_per_image_inference_ms']):.6f}", + "mean_per_image_inference_seconds": f"{float(timing_summary['mean_per_image_inference_seconds']):.9f}", + "std_per_image_inference_seconds": f"{float(timing_summary['std_per_image_inference_seconds']):.9f}", + } + ) + + +def evaluate_model( + *, + model: nn.Module, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + strategy: int, + tmax: int, + best_metric_name: str, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + pred_dir = ensure_dir(run_dir / "predictions") + pred_255_dir = ensure_dir(run_dir / "predictions_255") + + model.eval() + per_metric = {k: [] for k in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95")} + per_sample: list[dict[str, Any]] = [] + inference_total_ms = 0.0 + inference_batch_count = 0 + inference_sample_count = 0 + inference_batch_times_ms: list[float] = [] + inference_sample_times_ms: list[float] = [] + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + + with torch.inference_mode(): + prefetcher = CUDAPrefetcher(bundle.test_loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(bundle.test_loader), desc="Evaluating", leave=False): + image = batch["image"] + gt = batch["mask"] + sample_ids = [str(item) for item in batch["sample_id"]] + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + _synchronize_device_for_timing(DEVICE) + inference_start = time.perf_counter() + pred = infer_segmentation_mask( + model, + image, + tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="test", + mc_cache_run_dir=run_dir, + ).float() + _synchronize_device_for_timing(DEVICE) + inference_elapsed_ms = (time.perf_counter() - inference_start) * 1000.0 + inference_total_ms += inference_elapsed_ms + inference_batch_count += 1 + batch_size = int(pred.shape[0]) + inference_sample_count += batch_size + inference_batch_times_ms.append(float(inference_elapsed_ms)) + per_image_inference_ms = float(inference_elapsed_ms) / float(max(batch_size, 1)) + inference_sample_times_ms.extend([per_image_inference_ms] * batch_size) + pred_np = pred.cpu().numpy().astype(np.uint8) + gt_np = gt.cpu().numpy().astype(np.uint8) + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + for key, value in metrics.items(): + per_metric.setdefault(key, []).append(value) + per_sample.append( + { + "sample_id": sample_ids[idx], + **metrics, + "inference_time_ms": per_image_inference_ms, + "inference_time_seconds": per_image_inference_ms / 1000.0, + } + ) + mask_2d = pred_np[idx].squeeze() + PILImage.fromarray(mask_2d).save(pred_dir / f"{sample_ids[idx]}.png") + PILImage.fromarray((mask_2d * 255).astype(np.uint8)).save(pred_255_dir / f"{sample_ids[idx]}.png") + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate: dict[str, dict[str, float]] = {} + for key, values in per_metric.items(): + values_np = np.array(values, dtype=np.float32) + aggregate[key] = {"mean": float(values_np.mean()), "std": float(values_np.std())} + batch_times_np = np.array(inference_batch_times_ms, dtype=np.float64) + sample_times_np = np.array(inference_sample_times_ms, dtype=np.float64) + timing_summary = { + "scope": "test_set_evaluation", + "strategy": int(strategy), + "tmax": int(tmax), + "device": str(DEVICE), + "num_batches": int(inference_batch_count), + "num_samples": int(inference_sample_count), + "total_inference_ms": float(inference_total_ms), + "avg_batch_inference_ms": float(batch_times_np.mean()) if batch_times_np.size > 0 else 0.0, + "std_batch_inference_ms": float(batch_times_np.std()) if batch_times_np.size > 0 else 0.0, + "avg_sample_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_sample_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_seconds": float(sample_times_np.mean() / 1000.0) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_seconds": float(sample_times_np.std() / 1000.0) if sample_times_np.size > 0 else 0.0, + } + + save_json( + run_dir / "evaluation.json", + { + "strategy": strategy, + "best_metric_name": str(best_metric_name), + "metrics": aggregate, + "per_sample": per_sample, + "timing": timing_summary, + }, + ) + + df_all = pd.DataFrame(per_sample) + avg_row = {} + for column in df_all.columns: + avg_row[column] = df_all[column].mean() if pd.api.types.is_numeric_dtype(df_all[column]) else "AVERAGE" + df_samples = pd.concat([df_all, pd.DataFrame([avg_row])], ignore_index=True) + df_summary = pd.DataFrame(aggregate).T + df_summary.index.name = "metric" + df_low_iou = df_all[df_all["iou"] < 0.01] + history_path = run_dir / "history.json" + df_history = pd.DataFrame(load_json(history_path)) if history_path.exists() else None + + xlsx_path = run_dir / "evaluation_results.xlsx" + with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer: + df_samples.to_excel(writer, sheet_name="Per Sample", index=False) + df_summary.to_excel(writer, sheet_name="Summary") + if not df_low_iou.empty: + df_low_iou.to_excel(writer, sheet_name="Low IoU Samples", index=False) + if df_history is not None: + df_history.to_excel(writer, sheet_name="Training History", index=False) + + csv_rows = [{"sample_id": row["sample_id"]} for row in df_low_iou.to_dict(orient="records")] + save_json(run_dir / "evaluation_summary.json", {"mean_iou": aggregate["iou"]["mean"], "mean_dice": aggregate["dice"]["mean"]}) + pd.DataFrame(csv_rows).to_csv(run_dir / "low_iou_samples.csv", index=False) + _write_evaluation_timing_csv( + run_dir / "timing.csv", + timing_summary=timing_summary, + ) + return aggregate, per_sample + +def percent_root(percent: float) -> Path: + return ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}") + +def strategy_dir_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + return f"strategy_{strategy}_custom_vgg" + return f"strategy_{strategy}" + +def strategy_root_for_percent( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(percent_root(percent) / strategy_dir_name(strategy, model_config)) + +def final_root_for_strategy( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(strategy_root_for_percent(strategy, percent, model_config) / "final") + +def ensure_specific_checkpoint_scope(selector_name: str, selector_mode: str) -> None: + if selector_mode != "specific": + return + # Allow STRATEGY2 checkpoints with dict mapping for dynamic per-percent selection + if "strategy2" in selector_name.lower() and isinstance(STRATEGY2_SPECIFIC_CHECKPOINT, dict): + return + if len(STRATEGIES) != 1 or len(DATASET_PERCENTS) != 1: + raise ValueError( + f"{selector_name}=specific is only supported when exactly one strategy and one dataset percent are selected. " + f"Got STRATEGIES={STRATEGIES} and DATASET_PERCENTS={DATASET_PERCENTS}." + ) + +def resolve_checkpoint_path( + *, + run_dir: Path, + selector_mode: str, + specific_checkpoint: str | Path | dict, + purpose: str, +) -> Path: + run_dir = Path(run_dir) + if selector_mode == "latest": + checkpoint_path = run_dir / "checkpoints" / "latest.pt" + elif selector_mode == "best": + checkpoint_path = run_dir / "checkpoints" / "best.pt" + elif selector_mode == "specific": + ensure_specific_checkpoint_scope(purpose, selector_mode) + if not specific_checkpoint: + raise ValueError(f"{purpose}=specific requires a non-empty specific checkpoint path.") + checkpoint_path = Path(specific_checkpoint).expanduser().resolve() + else: + raise ValueError(f"Unsupported checkpoint selector mode '{selector_mode}' for {purpose}.") + + if not checkpoint_path.exists(): + raise FileNotFoundError(f"Checkpoint for {purpose} not found: {checkpoint_path}") + return checkpoint_path + +def resolve_train_resume_checkpoint_path(run_dir: Path) -> Path | None: + if TRAIN_RESUME_MODE == "off": + return None + return resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=TRAIN_RESUME_MODE, + specific_checkpoint=TRAIN_RESUME_SPECIFIC_CHECKPOINT, + purpose="train_resume_checkpoint", + ) + +def resolve_strategy2_checkpoint_path( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + if strategy != 3: + raise ValueError(f"Strategy 2 dependency checkpoint requested for unsupported strategy {strategy}.") + + specific_checkpoint = STRATEGY2_SPECIFIC_CHECKPOINT + if isinstance(specific_checkpoint, dict): + ctx = globals().get("CURRENT_FOLD_CONTEXT") + _phase_mode_fn = globals().get("using_fixed_phase_mode") + in_phase_mode = callable(_phase_mode_fn) and _phase_mode_fn() + if in_phase_mode and ctx is not None: + # Phase mode: key by phase index (split_repeat_index) + specific_checkpoint = specific_checkpoint.get(ctx.split_repeat_index, "") + else: + # Non-phase mode: key by dataset percent (float) + specific_checkpoint = specific_checkpoint.get(percent, "") + + checkpoint_path = resolve_checkpoint_path( + run_dir=final_root_for_strategy(2, percent, model_config), + selector_mode=STRATEGY2_CHECKPOINT_MODE, + specific_checkpoint=specific_checkpoint, + purpose="strategy2_checkpoint", + ) + + # Print which checkpoint is being used + checkpoint_label = run_identity_label(strategy=strategy, percent=percent) + ctx = globals().get("CURRENT_FOLD_CONTEXT") + if ctx is not None and abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12: + checkpoint_label = run_identity_label( + strategy=strategy, + percent=percent, + split_payload={ + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "dataset_percent": ctx.percent_fraction, + }, + ) + print(f"[Strategy 2 Checkpoint] {checkpoint_label} | Loading: {checkpoint_path}") + + return checkpoint_path + +def load_required_hparams(payload: dict[str, Any], *, source: str, strategy: int, percent: float) -> dict[str, Any]: + missing_keys = [name for name in REQUIRED_HPARAM_KEYS if name not in payload] + if missing_keys: + raise KeyError( + f"Incomplete hyperparameters for strategy={strategy}, percent={percent_text(percent)} from {source}. " + f"Missing keys: {missing_keys}. Required keys: {REQUIRED_HPARAM_KEYS}." + ) + return dict(payload) + +def load_saved_best_params_if_optuna_off( + strategy: int, + percent: float, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + _, study_root, _ = study_paths_for(strategy, percent, model_config) + best_params_path = study_root / "best_params.json" + if not best_params_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=True, but no saved best params were found " + f"for strategy={strategy}, percent={percent_text(percent)} at {best_params_path}." + ) + params = load_json(best_params_path) + params = load_required_hparams( + params, + source=str(best_params_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using saved best parameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {best_params_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def load_manual_hparams_if_optuna_off(strategy: int, percent: float) -> dict[str, Any]: + key = manual_hparams_key(strategy, percent) + if key not in MANUAL_HPARAMS_IF_OPTUNA_OFF: + raise KeyError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but no manual hyperparameter " + f"JSON filename was found for strategy={strategy}, percent={percent_text(percent)} under key '{key}'. " + f"Required keys: {REQUIRED_HPARAM_KEYS}." + ) + manual_filename = MANUAL_HPARAMS_IF_OPTUNA_OFF[key] + manual_path = (HARD_CODED_PARAM_DIR / manual_filename).resolve() + if not manual_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but the manual hyperparameter " + f"JSON file for strategy={strategy}, percent={percent_text(percent)} was not found at {manual_path}. " + f"Configured key='{key}', filename='{manual_filename}'." + ) + params = load_json(manual_path) + params = load_required_hparams( + params, + source=str(manual_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using manual hyperparameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {manual_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def resolve_job_params( + strategy: int, + percent: float, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if RUN_OPTUNA: + banner( + f"OPTUNA STUDY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return run_study( + strategy, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + if USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + return load_saved_best_params_if_optuna_off(strategy, percent, model_config=model_config) + + return load_manual_hparams_if_optuna_off(strategy, percent) + +def read_run_config_for_eval(run_dir: Path, checkpoint_path: Path) -> dict[str, Any]: + run_config_path = Path(run_dir) / "run_config.json" + if run_config_path.exists(): + return load_json(run_config_path) + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + return checkpoint_run_config_payload(ckpt) + +def run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + checkpoint_path = resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=EVAL_CHECKPOINT_MODE, + specific_checkpoint=EVAL_SPECIFIC_CHECKPOINT, + purpose="evaluation_checkpoint", + ) + effective_run_dir = Path(run_dir) + if not (effective_run_dir / "run_config.json").exists() and checkpoint_path.parent.name == "checkpoints": + effective_run_dir = checkpoint_path.parent.parent + print( + f"[Evaluation] {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)} " + f"| checkpoint={checkpoint_path}" + ) + runtime_config = read_run_config_for_eval(effective_run_dir, checkpoint_path) + set_current_job_params(runtime_config) + model_config = RuntimeModelConfig.from_payload(runtime_config).validate() + if strategy == 3 and runtime_config.get("strategy2_checkpoint_path"): + strategy2_checkpoint_path = runtime_config["strategy2_checkpoint_path"] + elif strategy == 3: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + dropout_p = float(runtime_config.get("dropout_p", DEFAULT_DROPOUT_P)) + tmax = int(runtime_config.get("tmax", DEFAULT_TMAX)) + eval_model, _description, _compiled = build_model( + strategy, + dropout_p, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + checkpoint_payload = load_checkpoint( + checkpoint_path, + model=eval_model, + device=DEVICE, + ) + best_metric_name = str( + checkpoint_payload.get("best_metric_name") + or runtime_config.get("best_checkpoint_metric_name") + or _strategy_selection_metric_name(strategy) + ) + aggregate, per_sample = evaluate_model( + model=eval_model, + model_config=model_config, + bundle=bundle, + run_dir=effective_run_dir, + strategy=strategy, + tmax=tmax, + best_metric_name=best_metric_name, + ) + evaluation_json_path = effective_run_dir / "evaluation.json" + evaluation_payload = load_json(evaluation_json_path) + evaluation_payload["checkpoint_mode"] = EVAL_CHECKPOINT_MODE + evaluation_payload["checkpoint_path"] = str(checkpoint_path) + evaluation_payload["best_metric_name"] = best_metric_name + if checkpoint_payload.get("best_metric_value") is not None: + evaluation_payload["best_metric_value"] = float(checkpoint_payload["best_metric_value"]) + save_json(evaluation_json_path, evaluation_payload) + del eval_model + run_cuda_cleanup( + context=f"evaluation {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return aggregate, per_sample + +def run_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + smoke_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + banner( + f"PRE-TRAINING SMOKE TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + smoke_root = ensure_dir(smoke_root) + if RUN_OPTUNA: + set_current_job_params() + elif USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + set_current_job_params( + load_saved_best_params_if_optuna_off(strategy, bundle.percent, model_config=model_config) + ) + else: + set_current_job_params(load_manual_hparams_if_optuna_off(strategy, bundle.percent)) + sample = bundle.test_ds[SMOKE_TEST_SAMPLE_INDEX] + image = sample["image"].unsqueeze(0).to(DEVICE) + raw_image = sample["image"].numpy() + raw_gt = sample["mask"].squeeze(0).numpy() + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + print_model_parameter_summary( + model=model, + description=f"{description} | Smoke Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + pred = infer_segmentation_mask( + model, + image, + DEFAULT_TMAX, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=_use_channels_last_for_run(model_config), + sample_ids=[str(sample["sample_id"])], + mc_cache_split="test", + mc_cache_run_dir=smoke_root, + ).float() + pred_np = pred[0, 0].detach().cpu().numpy() + panel_path = smoke_root / "smoke_panel.png" + raw_mask_path = smoke_root / "smoke_prediction.png" + _save_rgb_panel( + raw_image, + pred_np, + raw_gt, + panel_path, + f"Smoke Test | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}", + ) + PILImage.fromarray((pred_np * 255).astype(np.uint8)).save(raw_mask_path) + del model + run_cuda_cleanup( + context=f"smoke {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + print( + f"[Smoke Test] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} passed. " + f"Saved panel to {panel_path.name} and mask to {raw_mask_path.name}." + ) + +"""============================================================================= +OVERFIT TEST +============================================================================= +""" + +OVERFIT_HISTORY_KEYS = ( + "dice", + "iou", + "loss", + "reward", + "actor_loss", + "critic_loss", + "ce_loss", + "dice_loss", + "entropy", + "grad_norm", + "action_dist", + "reward_pos_pct", + "pred_fg_pct", + "gt_fg_pct", +) + +def empty_overfit_history() -> dict[str, list[Any]]: + return {key: [] for key in OVERFIT_HISTORY_KEYS} + +def load_overfit_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> dict[str, list[Any]]: + history = empty_overfit_history() + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict history at {history_path}, found {type(payload).__name__}.") + for key in OVERFIT_HISTORY_KEYS: + values = payload.get(key, []) + if isinstance(values, list): + history[key] = list(values[:checkpoint_epoch]) + return history + + epoch_metrics = checkpoint_payload.get("epoch_metrics", {}) + if isinstance(epoch_metrics, dict): + for key in OVERFIT_HISTORY_KEYS: + if key in epoch_metrics: + history[key].append(epoch_metrics[key]) + return history + +def _grad_diagnostics(model: nn.Module) -> dict[str, Any]: + raw = _unwrap_compiled(model) + groups: dict[str, list[float]] = {} + total_sq = 0.0 + n_nan = 0 + n_inf = 0 + n_zero = 0 + n_total_params = 0 + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + n_total_params += 1 + if param.grad is None: + n_zero += 1 + continue + grad_norm = float(param.grad.data.norm(2).item()) + if math.isnan(grad_norm): + n_nan += 1 + continue + if math.isinf(grad_norm): + n_inf += 1 + continue + total_sq += grad_norm ** 2 + group_name = name.split(".", 1)[0] + groups.setdefault(group_name, []).append(grad_norm) + + group_stats: dict[str, dict[str, float | int]] = {} + for group_name, norms in groups.items(): + group_stats[group_name] = { + "min": min(norms), + "max": max(norms), + "mean": sum(norms) / len(norms), + "count": len(norms), + } + return { + "global_norm": total_sq ** 0.5, + "groups": group_stats, + "n_nan": n_nan, + "n_inf": n_inf, + "n_zero_grad": n_zero, + "n_total": n_total_params, + } + +def _param_diagnostics(model: nn.Module, prev_params: dict[str, torch.Tensor] | None = None) -> dict[str, dict[str, float]]: + raw = _unwrap_compiled(model) + info: dict[str, dict[str, list[float]]] = {} + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + param_norm = float(param.data.norm(2).item()) + group_name = name.split(".", 1)[0] + entry = info.setdefault(group_name, {"norms": [], "update_ratios": []}) + entry["norms"].append(param_norm) + if prev_params is not None and name in prev_params: + delta = float((param.data - prev_params[name]).norm(2).item()) + entry["update_ratios"].append(delta / max(param_norm, 1e-12)) + + summary: dict[str, dict[str, float]] = {} + for group_name, values in info.items(): + norms = values["norms"] + ratios = values["update_ratios"] + summary[group_name] = { + "p_min": min(norms), + "p_max": max(norms), + "p_mean": sum(norms) / len(norms), + } + if ratios: + summary[group_name]["ur_min"] = min(ratios) + summary[group_name]["ur_max"] = max(ratios) + summary[group_name]["ur_mean"] = sum(ratios) / len(ratios) + return summary + +def _snapshot_params(model: nn.Module) -> dict[str, torch.Tensor]: + raw = _unwrap_compiled(model) + return { + name: param.data.detach().clone() + for name, param in raw.named_parameters() + if param.requires_grad + } + +def _action_distribution( + model: nn.Module, + image: torch.Tensor, + seg: torch.Tensor, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + *, + strategy: int | None = None, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[list[dict[str, float]], torch.Tensor]: + distributions: list[dict[str, float]] = [] + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_action_distribution") if strategy != 2 else max(int(tmax), 1) + refinement_context: dict[str, torch.Tensor] | None = None + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = seg.float() + for _step in range(effective_tmax): + if refinement_context is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _ = model.forward_from_state(state) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta).to(dtype=seg.dtype) + distributions.append(_strategy3_delta_distribution(delta)) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + total_pixels = max(actions.numel(), 1) + step_dist: dict[str, float] = {} + action_count = int(policy_logits.shape[1]) + for action_idx in range(action_count): + step_dist[str(action_idx)] = float((actions == action_idx).sum().item()) / total_pixels * 100.0 + distributions.append(step_dist) + if refinement_context is not None: + return distributions, threshold_binary_mask(seg.float()).float() + return distributions, seg + +def _numerical_health_check(outputs_dict: dict[str, Any], prefix: str = "") -> list[str]: + alerts: list[str] = [] + for name, value in outputs_dict.items(): + if value is None: + continue + if isinstance(value, (int, float)): + if math.isnan(value): + alerts.append(f"{prefix}{name} = NaN") + elif math.isinf(value): + alerts.append(f"{prefix}{name} = Inf") + elif name == "train_reward_zeros_pct" and float(value) > 98.0: + alerts.append(f"{prefix}reward is degenerate (>98% zero-reward pixels)") + continue + if torch.is_tensor(value): + if torch.isnan(value).any(): + alerts.append(f"{prefix}{name} contains NaN") + if torch.isinf(value).any(): + alerts.append(f"{prefix}{name} contains Inf") + return alerts + +def _batch_binary_metrics(pred: torch.Tensor, gt: torch.Tensor) -> tuple[list[float], list[float]]: + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt.detach().cpu().numpy().astype(np.uint8) + dices: list[float] = [] + ious: list[float] = [] + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + dices.append(float(metrics["dice"])) + ious.append(float(metrics["iou"])) + return dices, ious + +def diagnostic_path_for_run(run_dir: Path) -> Path: + return Path(run_dir) / "diagnostic.json" + +def _summary_stats(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "mean": None, "std": None, "min": None, "max": None} + arr = np.asarray(values, dtype=np.float64) + return { + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std()), + "min": float(arr.min()), + "max": float(arr.max()), + } + +def _nanmean_or_default(values: list[float], default: float = 0.0) -> float: + if not values: + return float(default) + arr = np.asarray(values, dtype=np.float64) + if np.isnan(arr).all(): + return float(default) + return float(np.nanmean(arr)) + +def _tensor_stats(tensor: torch.Tensor | None) -> dict[str, Any] | None: + if tensor is None: + return None + data = tensor.detach().float() + flat = data.reshape(-1) + if flat.numel() == 0: + return {"shape": list(data.shape), "dtype": str(tensor.dtype), "numel": 0} + return { + "shape": list(data.shape), + "dtype": str(tensor.dtype), + "numel": int(flat.numel()), + "mean": float(flat.mean().item()), + "std": float(flat.std(unbiased=False).item()), + "min": float(flat.min().item()), + "max": float(flat.max().item()), + } + +def _action_histogram(actions: torch.Tensor, action_count: int) -> dict[str, float]: + total_pixels = max(actions.numel(), 1) + return { + str(action_idx): float((actions == action_idx).sum().item()) / total_pixels * 100.0 + for action_idx in range(action_count) + } + +def _jsonable_action_distribution(distributions: list[dict[int, float]] | list[dict[str, float]]) -> list[dict[str, float]]: + jsonable: list[dict[str, float]] = [] + for step_dist in distributions: + jsonable.append({str(key): float(value) for key, value in step_dist.items()}) + return jsonable + +def _average_action_distributions( + distributions_per_batch: list[list[dict[str, float]]], + steps: int, +) -> list[dict[str, float]]: + averaged: list[dict[str, float]] = [] + if not distributions_per_batch: + return averaged + for step_idx in range(steps): + action_keys = sorted( + { + str(action_idx) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) + for action_idx in batch_dist[step_idx].keys() + } + ) + if not action_keys: + continue + step_summary: dict[str, float] = {} + for action_idx in action_keys: + values = [ + float(batch_dist[step_idx][action_idx]) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) and action_idx in batch_dist[step_idx] + ] + step_summary[str(action_idx)] = float(np.mean(values)) if values else 0.0 + averaged.append(step_summary) + return averaged + +def _trajectory_degradation_summary(step_trace: list[dict[str, Any]]) -> dict[str, Any]: + if not step_trace: + return {} + ious = [float(step.get("iou_mean", 0.0)) for step in step_trace] + dices = [float(step.get("dice_mean", 0.0)) for step in step_trace] + ts = [int(step.get("t", idx)) for idx, step in enumerate(step_trace)] + init_iou = ious[0] + init_dice = dices[0] + best_iou = max(ious) + best_dice = max(dices) + best_iou_t = ts[ious.index(best_iou)] + best_dice_t = ts[dices.index(best_dice)] + first_worse_than_initial_iou_t = next((ts[idx] for idx, value in enumerate(ious[1:], start=1) if value < init_iou - 1e-6), None) + first_worse_than_prev_iou_t = next((ts[idx] for idx in range(1, len(ious)) if ious[idx] < ious[idx - 1] - 1e-6), None) + largest_iou_drop = max(best_iou - value for value in ious) + largest_iou_drop_t = ts[max(range(len(ious)), key=lambda idx: best_iou - ious[idx])] + return { + "steps_recorded": len(step_trace) - 1, + "best_iou_t": best_iou_t, + "best_iou": best_iou, + "best_dice_t": best_dice_t, + "best_dice": best_dice, + "final_t": ts[-1], + "final_iou": ious[-1], + "final_dice": dices[-1], + "delta_final_vs_init_iou": ious[-1] - init_iou, + "delta_final_vs_init_dice": dices[-1] - init_dice, + "delta_final_vs_best_iou": ious[-1] - best_iou, + "delta_final_vs_best_dice": dices[-1] - best_dice, + "first_worse_than_initial_iou_t": first_worse_than_initial_iou_t, + "first_worse_than_prev_iou_t": first_worse_than_prev_iou_t, + "largest_iou_drop_from_best": largest_iou_drop, + "largest_iou_drop_t": largest_iou_drop_t, + } + +def _average_rollout_traces(traces_per_batch: list[list[dict[str, Any]]]) -> list[dict[str, Any]]: + averaged: list[dict[str, Any]] = [] + if not traces_per_batch: + return averaged + max_steps = max(len(trace) for trace in traces_per_batch) + for step_idx in range(max_steps): + present = [trace[step_idx] for trace in traces_per_batch if step_idx < len(trace)] + if not present: + continue + reward_pos_values = [float(step["reward_pos_pct"]) for step in present if step.get("reward_pos_pct") is not None] + value_scores = [float(step["value_score"]) for step in present if step.get("value_score") is not None] + averaged.append( + { + "t": int(np.mean([float(step.get("t", step_idx)) for step in present])), + "dice_mean": float(np.mean([float(step.get("dice_mean", 0.0)) for step in present])), + "iou_mean": float(np.mean([float(step.get("iou_mean", 0.0)) for step in present])), + "pred_fg_pct": float(np.mean([float(step.get("pred_fg_pct", 0.0)) for step in present])), + "reward_pos_pct": float(np.mean(reward_pos_values)) if reward_pos_values else None, + "value_score": float(np.mean(value_scores)) if value_scores else None, + } + ) + return averaged + +def _rollout_probe_trace( + model: nn.Module, + image: torch.Tensor, + gt_mask: torch.Tensor, + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_rollout_probe_trace") if strategy != 2 else max(int(tmax), 1) + rollout_trace: list[dict[str, Any]] = [] + batch_action_dist: list[dict[str, float]] = [] + reward_pos_pct = 0.0 + init_fg_pct = 0.0 + first_action_dist: dict[str, float] | None = None + first_policy_stats: dict[str, Any] | None = None + first_value_stats: dict[str, Any] | None = None + decoder_prob_stats: dict[str, Any] | None = None + first_entropy: float | None = None + selected_t = 0 + + def record_step( + *, + t: int, + seg_tensor: torch.Tensor, + seg_prev: torch.Tensor | None = None, + delta_map: torch.Tensor | None = None, + value_score: float | None = None, + action_distribution: dict[str, float] | None = None, + reward_pos: float | None = None, + reward_map_tensor: torch.Tensor | None = None, + step_entropy: float | None = None, + policy_stats: dict[str, Any] | None = None, + ) -> None: + pred_t = threshold_binary_mask(seg_tensor.float()).float() + dice_vals, iou_vals = _batch_binary_metrics(pred_t, gt_mask.float()) + step_data: dict[str, Any] = { + "t": int(t), + "dice_mean": float(np.mean(dice_vals)) if dice_vals else 0.0, + "iou_mean": float(np.mean(iou_vals)) if iou_vals else 0.0, + "pred_fg_pct": float(pred_t.sum().item()) / max(pred_t.numel(), 1) * 100.0, + "reward_pos_pct": None if reward_pos is None else float(reward_pos), + "value_score": None if value_score is None else float(value_score), + "action_distribution": action_distribution, + "seg_soft_stats": _tensor_stats(seg_tensor), + "entropy": step_entropy, + "policy_logit_stats": policy_stats, + } + if seg_prev is not None: + delta = seg_tensor.float() - seg_prev.float() + abs_delta = delta.abs() + # Binary mask flip tracking: how many pixels actually change in the thresholded output + binary_prev = threshold_binary_mask(seg_prev.float()).float() + binary_curr = threshold_binary_mask(seg_tensor.float()).float() + binary_flipped = (binary_prev != binary_curr) + flipped_to_fg = binary_flipped & (binary_curr > 0.5) + flipped_to_bg = binary_flipped & (binary_curr < 0.5) + gt_binary_local = (gt_mask.float() > 0.5) + correct_flips = binary_flipped & ((binary_curr > 0.5) == gt_binary_local) + wrong_flips = binary_flipped & ((binary_curr > 0.5) != gt_binary_local) + total_px = max(binary_prev.numel(), 1) + step_data["mask_delta"] = { + "mean_abs_change": float(abs_delta.mean().item()), + "max_change": float(abs_delta.max().item()), + "pct_pixels_changed": float((abs_delta > 1e-6).float().mean().item() * 100.0), + "fg_gained_pct": float((delta > 1e-6).float().mean().item() * 100.0), + "fg_lost_pct": float((delta < -1e-6).float().mean().item() * 100.0), + } + step_data["binary_mask_flips"] = { + "total_flipped_pct": float(binary_flipped.float().sum().item() / total_px * 100.0), + "flipped_to_fg_pct": float(flipped_to_fg.float().sum().item() / total_px * 100.0), + "flipped_to_bg_pct": float(flipped_to_bg.float().sum().item() / total_px * 100.0), + "correct_flips_pct": float(correct_flips.float().sum().item() / total_px * 100.0), + "wrong_flips_pct": float(wrong_flips.float().sum().item() / total_px * 100.0), + "flip_accuracy": float(correct_flips.float().sum().item() / max(binary_flipped.float().sum().item(), 1.0) * 100.0), + } + if reward_map_tensor is not None: + step_data["reward_stats"] = { + "mean": float(reward_map_tensor.mean().item()), + "std": float(reward_map_tensor.std().item()), + "min": float(reward_map_tensor.min().item()), + "max": float(reward_map_tensor.max().item()), + "pct_positive": float((reward_map_tensor > 0).float().mean().item() * 100.0), + "pct_negative": float((reward_map_tensor < 0).float().mean().item() * 100.0), + "pct_zero": float((reward_map_tensor.abs() < 1e-8).float().mean().item() * 100.0), + } + if delta_map is not None and seg_prev is not None: + gt_f = gt_mask.float() + ref_pred = threshold_binary_mask(seg_prev.float()).float() + gt_fg = (gt_f > 0.5).squeeze(1) + gt_bg = ~gt_fg + pred_fg = (ref_pred > 0.5).squeeze(1) + tp_mask = pred_fg & gt_fg + tn_mask = (~pred_fg) & gt_bg + fp_mask = pred_fg & gt_bg + fn_mask = (~pred_fg) & gt_fg + delta_squeezed = delta_map.squeeze(1).detach().float() + action_breakdown: dict[str, dict[str, float]] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + if pixel_mask.any(): + class_delta = delta_squeezed[pixel_mask] + action_breakdown[label] = { + "mean_delta": float(class_delta.mean().item()), + "mean_abs_delta": float(class_delta.abs().mean().item()), + "positive_pct": float((class_delta > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((class_delta < -1e-6).float().mean().item() * 100.0), + } + else: + action_breakdown[label] = { + "mean_delta": 0.0, + "mean_abs_delta": 0.0, + "positive_pct": 0.0, + "negative_pct": 0.0, + } + step_data["action_on_class"] = action_breakdown + if reward_map_tensor is not None: + reward_squeezed = reward_map_tensor.squeeze(1) if reward_map_tensor.ndim == 4 else reward_map_tensor + per_class_reward: dict[str, float] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + per_class_reward[label] = float(reward_squeezed[pixel_mask].mean().item()) if pixel_mask.any() else 0.0 + step_data["per_action_reward"] = per_class_reward + rollout_trace.append(step_data) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + record_step(t=0, seg_tensor=torch.sigmoid(logits)) + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": _trajectory_degradation_summary(rollout_trace), + "selected_t": selected_t, + "effective_tmax": effective_tmax, + } + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + decoder_prob_stats = _tensor_stats(refinement_context["decoder_prob"]) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + selected_t = 0 + + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, value_t = model.forward_from_state(state_t) + current_score = float(value_t.detach().mean().item()) + delta = _strategy3_policy_delta(policy_logits).to(dtype=seg.dtype) + + action_dist = _strategy3_delta_distribution(delta) + batch_action_dist.append(action_dist) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_value_stats = _tensor_stats(value_t) + first_entropy = 0.0 + record_step(t=0, seg_tensor=seg, value_score=current_score, step_entropy=first_entropy, policy_stats=first_policy_stats) + + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + step_entropy_val = 0.0 + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + seg_prev=seg, + delta_map=delta, + action_distribution=action_dist, + reward_pos=step_reward_pos, + reward_map_tensor=reward_map, + step_entropy=step_entropy_val, + policy_stats=_tensor_stats(policy_logits), + ) + seg = seg_next + selected_t = step_idx + 1 + + pred = threshold_binary_mask(seg.float()).float() + decoder_pred = threshold_binary_mask(refinement_context["decoder_prob"].float()).float() + decoder_dice_vals, decoder_iou_vals = _batch_binary_metrics(decoder_pred, gt_mask.float()) + decoder_baseline = { + "dice": float(np.mean(decoder_dice_vals)) if decoder_dice_vals else 0.0, + "iou": float(np.mean(decoder_iou_vals)) if decoder_iou_vals else 0.0, + "fg_pct": float(decoder_pred.sum().item()) / max(decoder_pred.numel(), 1) * 100.0, + } + final_dice_vals, final_iou_vals = _batch_binary_metrics(pred, gt_mask.float()) + rl_vs_decoder = { + "decoder_dice": decoder_baseline["dice"], + "decoder_iou": decoder_baseline["iou"], + "final_dice": float(np.mean(final_dice_vals)) if final_dice_vals else 0.0, + "final_iou": float(np.mean(final_iou_vals)) if final_iou_vals else 0.0, + "dice_gain": (float(np.mean(final_dice_vals)) if final_dice_vals else 0.0) - decoder_baseline["dice"], + "iou_gain": (float(np.mean(final_iou_vals)) if final_iou_vals else 0.0) - decoder_baseline["iou"], + } + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = selected_t + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": selected_t, + "effective_tmax": effective_tmax, + "decoder_baseline": decoder_baseline, + "rl_vs_decoder": rl_vs_decoder, + } + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + record_step(t=0, seg_tensor=seg) + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, entropy = sample_actions(policy_logits, stochastic=False) + action_dist = _action_histogram(actions, int(policy_logits.shape[1])) + batch_action_dist.append({str(key): float(value) for key, value in action_dist.items()}) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_entropy = float(entropy.detach().item()) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + reward_map = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + action_distribution=action_dist, + reward_pos=step_reward_pos, + ) + seg = seg_next + pred = seg.float() + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = len(rollout_trace) - 1 + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": len(rollout_trace) - 1, + "effective_tmax": effective_tmax, + } + +def _format_probe_deterioration(label: str, probe_payload: dict[str, Any], tmax: int) -> str: + effective_tmax = int(probe_payload.get("effective_tmax", tmax)) + degradation = probe_payload.get("aggregate", {}).get("degradation", {}) + if not degradation: + return f"{label}: no degradation trace" + first_worse = degradation.get("first_worse_than_initial_iou_t") + first_step_drop = degradation.get("first_worse_than_prev_iou_t") + best_t = degradation.get("best_iou_t") + final_t = degradation.get("final_t") + delta_best = degradation.get("delta_final_vs_best_iou") + worst_t = degradation.get("largest_iou_drop_t") + worst_drop = degradation.get("largest_iou_drop_from_best") + return ( + f"{label}: first_worse={first_worse}/{effective_tmax} " + f"first_drop={first_step_drop}/{effective_tmax} " + f"best={best_t}/{effective_tmax} final={final_t}/{effective_tmax} " + f"final-best_iou={float(delta_best):+.4f} " + f"worst={worst_t}/{effective_tmax} drop={float(worst_drop):+.4f}" + ) + +def _optimizer_diagnostics(optimizer: torch.optim.Optimizer) -> list[dict[str, Any]]: + groups: list[dict[str, Any]] = [] + for group_idx, group in enumerate(optimizer.param_groups): + num_tensors = len(group.get("params", [])) + num_elements = int(sum(param.numel() for param in group.get("params", []))) + groups.append( + { + "index": group_idx, + "lr": float(group.get("lr", 0.0)), + "weight_decay": float(group.get("weight_decay", 0.0)), + "num_tensors": num_tensors, + "num_elements": num_elements, + } + ) + return groups + +def _probe_batches_from_indices( + dataset: BUSIDataset, + *, + indices: list[int], + device: torch.device, +) -> list[dict[str, Any]]: + batches: list[dict[str, Any]] = [] + for start in range(0, len(indices), BATCH_SIZE): + batch_indices = indices[start:start + BATCH_SIZE] + if not batch_indices: + continue + images = torch.stack([dataset._images[idx].clone() for idx in batch_indices], dim=0) + masks = torch.stack([dataset._masks[idx].clone() for idx in batch_indices], dim=0) + sample_ids = [Path(dataset.sample_records[idx]["filename"]).stem for idx in batch_indices] + batches.append( + to_device( + { + "image": images, + "mask": masks, + "sample_id": sample_ids, + "dataset": current_dataset_name(), + }, + device, + ) + ) + return batches + +def _fixed_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + return _probe_batches_from_indices( + dataset, + indices=list(range(max_samples)), + device=device, + ) + +def _rolling_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, + epoch: int, + split_tag: str, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + if max_samples <= 0: + return [] + if max_samples >= len(dataset): + indices = list(range(len(dataset))) + else: + rng = random.Random(SEED + stable_int_from_text(f"probe:{split_tag}:epoch:{int(epoch)}")) + indices = rng.sample(range(len(dataset)), k=max_samples) + return _probe_batches_from_indices(dataset, indices=indices, device=device) + +def _probe_batch_id_lists(fixed_batches: list[dict[str, Any]]) -> list[list[str]]: + return [list(batch.get("sample_id", [])) for batch in fixed_batches] + +def _reference_eval_payloads(project_dir: Path, percent: float) -> dict[str, Any]: + refs: dict[str, Any] = {} + pct = percent_label(percent) + strat2_dir = project_dir / "strat2_history" + strat3_dir = project_dir / "strat3_history_best" + strat2_candidates = [ + strat2_dir / f"evaluation_strat2_pc{pct}.json", + strat2_dir / f"evaluation_strat2_pct{pct}.json", + ] + strat3_candidate = strat3_dir / f"evaluation_{pct} (1)" + for candidate in strat2_candidates: + if candidate.exists(): + refs["strategy2_reference"] = load_json(candidate) + break + if strat3_candidate.exists(): + refs["strategy3_best_reference"] = load_json(strat3_candidate) + return refs + +def empty_epoch_diagnostic_payload( + *, + run_type: str, + run_config: dict[str, Any], + bundle: DataBundle, + train_probe_batches: list[dict[str, Any]], + val_probe_batches: list[dict[str, Any]], +) -> dict[str, Any]: + payload = { + "diagnostic_version": 1, + "run_type": run_type, + "strategy": int(run_config["strategy"]), + "dataset_percent": float(bundle.percent), + "run_config": run_config, + "probe_setup": { + "mode": str(run_config.get("epoch_probe_mode", "fixed")), + "train_probe_batches": _probe_batch_id_lists(train_probe_batches), + "val_probe_batches": _probe_batch_id_lists(val_probe_batches), + "train_probe_batch_count": len(train_probe_batches), + "val_probe_batch_count": len(val_probe_batches), + "tmax": int(run_config.get("tmax", DEFAULT_TMAX)), + }, + "epochs": [], + } + payload.update(_reference_eval_payloads(PROJECT_DIR, bundle.percent)) + return payload + +def load_epoch_diagnostic_for_resume( + path: Path, + checkpoint_payload: dict[str, Any] | None, + default_payload: dict[str, Any], +) -> dict[str, Any]: + payload = dict(default_payload) + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) if checkpoint_payload is not None else 0 + if path.exists(): + loaded = load_json(path) + if isinstance(loaded, dict): + payload.update({k: v for k, v in loaded.items() if k != "epochs"}) + epochs = loaded.get("epochs", []) + if isinstance(epochs, list): + payload["epochs"] = [dict(row) for row in epochs if isinstance(row, dict) and int(row.get("epoch", 0)) <= checkpoint_epoch] + if "epochs" not in payload: + payload["epochs"] = [] + return payload + +def _evaluate_probe_batches( + model: nn.Module, + fixed_batches: list[dict[str, Any]], + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + if not fixed_batches: + return {"n_batches": 0, "batch_details": [], "aggregate": {}, "alerts": []} + + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_evaluate_probe_batches") if strategy != 2 else max(int(tmax), 1) + was_training = model.training + batch_details: list[dict[str, Any]] = [] + alerts: list[str] = [] + action_distributions: list[list[dict[str, float]]] = [] + rollout_traces: list[list[dict[str, Any]]] = [] + metric_lists: dict[str, list[float]] = { + key: [] + for key in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95") + } + reward_pos_values: list[float] = [] + pred_fg_values: list[float] = [] + gt_fg_values: list[float] = [] + init_fg_values: list[float] = [] + decoder_dices: list[float] = [] + decoder_ious: list[float] = [] + iou_gains: list[float] = [] + dice_gains: list[float] = [] + + model.eval() + try: + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + with torch.inference_mode(): + for batch_index, batch in enumerate(fixed_batches): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + + rollout_probe = _rollout_probe_trace( + model, + image, + gt_mask, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + pred = rollout_probe["final_pred"].float() + batch_action_dist = rollout_probe["action_distribution"] + reward_pos_pct = float(rollout_probe["reward_pos_pct"]) + init_fg_pct = float(rollout_probe["init_fg_pct"]) + first_action_dist = rollout_probe["first_action_distribution"] + first_policy_stats = rollout_probe["first_policy_stats"] + first_value_stats = rollout_probe["first_value_stats"] + decoder_prob_stats = rollout_probe["decoder_prob_stats"] + first_entropy = rollout_probe["first_entropy"] + rollout_trace = rollout_probe["rollout_trace"] + rollout_summary = rollout_probe["rollout_summary"] + + if batch_action_dist: + action_distributions.append(batch_action_dist) + if rollout_trace: + rollout_traces.append(rollout_trace) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + reward_pos_values.append(reward_pos_pct) + pred_fg_values.append(pred_fg_pct) + gt_fg_values.append(gt_fg_pct) + init_fg_values.append(init_fg_pct) + rl_vs_dec = rollout_probe.get("rl_vs_decoder") + if rl_vs_dec: + decoder_dices.append(rl_vs_dec["decoder_dice"]) + decoder_ious.append(rl_vs_dec["decoder_iou"]) + iou_gains.append(rl_vs_dec["iou_gain"]) + dice_gains.append(rl_vs_dec["dice_gain"]) + + batch_alerts = _numerical_health_check( + { + "pred": pred, + "gt_mask": gt_mask, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "reward_pos_pct": reward_pos_pct, + "first_entropy": first_entropy if first_entropy is not None else 0.0, + }, + prefix=f"probe[{batch_index}]:", + ) + alerts.extend(batch_alerts) + + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt_mask.detach().cpu().numpy().astype(np.uint8) + per_sample: list[dict[str, Any]] = [] + for sample_index in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[sample_index], gt_np[sample_index]) + per_sample.append( + { + "sample_id": sample_ids[sample_index] if sample_index < len(sample_ids) else f"sample_{sample_index}", + **{key: float(value) for key, value in metrics.items()}, + } + ) + for key, value in metrics.items(): + metric_lists.setdefault(key, []).append(float(value)) + + batch_details.append( + { + "batch_index": batch_index, + "sample_ids": sample_ids, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "init_fg_pct": init_fg_pct, + "reward_pos_pct": reward_pos_pct, + "action_distribution": _jsonable_action_distribution(batch_action_dist), + "first_action_distribution": first_action_dist, + "first_entropy": first_entropy, + "decoder_prob_stats": decoder_prob_stats, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "pred_stats": _tensor_stats(pred), + "rollout_trace": rollout_trace, + "rollout_summary": rollout_summary, + "decoder_baseline": rollout_probe.get("decoder_baseline"), + "rl_vs_decoder": rollout_probe.get("rl_vs_decoder"), + "alerts": batch_alerts, + "per_sample": per_sample, + } + ) + finally: + model.train(was_training) + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate_trace = _average_rollout_traces(rollout_traces) + rl_vs_decoder_aggregate: dict[str, Any] = {} + if decoder_dices: + rl_vs_decoder_aggregate = { + "decoder_dice": _summary_stats(decoder_dices), + "decoder_iou": _summary_stats(decoder_ious), + "iou_gain": _summary_stats(iou_gains), + "dice_gain": _summary_stats(dice_gains), + } + return { + "n_batches": len(fixed_batches), + "batch_details": batch_details, + "aggregate": { + "metrics": {key: _summary_stats(values) for key, values in metric_lists.items()}, + "reward_pos_pct": _summary_stats(reward_pos_values), + "pred_fg_pct": _summary_stats(pred_fg_values), + "gt_fg_pct": _summary_stats(gt_fg_values), + "init_fg_pct": _summary_stats(init_fg_values), + "action_distribution": _average_action_distributions(action_distributions, effective_tmax), + "rollout_trace": aggregate_trace, + "degradation": _trajectory_degradation_summary(aggregate_trace), + "rl_vs_decoder": rl_vs_decoder_aggregate, + }, + "alerts": alerts, + "effective_tmax": effective_tmax, + } + +def run_overfit_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + overfit_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + overfit_root = ensure_dir(overfit_root) + ckpt_dir = ensure_dir(overfit_root / "checkpoints") + history_path = checkpoint_history_path(overfit_root, "overfit") + run_config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": "overfit", + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "max_epochs": OVERFIT_N_EPOCHS, + "head_lr": OVERFIT_HEAD_LR, + "encoder_lr": OVERFIT_ENCODER_LR, + "weight_decay": DEFAULT_WEIGHT_DECAY, + "dropout_p": DEFAULT_DROPOUT_P, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "gamma": DEFAULT_GAMMA, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + if strategy == 3: + run_config.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(run_config["strategy3_freeze_bootstrapped_segmentation"]) + ) + run_config.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + run_config.setdefault("decoder_lr", 0.0 if bootstrap_freeze else OVERFIT_HEAD_LR * 0.1) + run_config.setdefault("rl_lr", OVERFIT_HEAD_LR) + run_config.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + run_config.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + run_config.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + run_config.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + run_config.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + run_config.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + run_config.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + run_config.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + run_config.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + run_config.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + run_config.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + run_config.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + run_config.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + run_config.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + run_config.setdefault("elastic_aug_prob", 0.3) + run_config.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + run_config.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + run_config.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + run_config.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + run_config.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + run_config.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + run_config.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + run_config.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + run_config.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + run_config.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + run_config.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + run_config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + run_config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + save_json(overfit_root / "run_config.json", run_config) + set_current_job_params(run_config) + + banner( + f"OVERFIT TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + effective_test_tmax = _resolve_test_iteration_tmax(DEFAULT_TMAX, context="run_overfit_test") if strategy != 2 else max(int(DEFAULT_TMAX), 1) + print( + f"[Overfit] Fixed batches={OVERFIT_N_BATCHES}, epochs={OVERFIT_N_EPOCHS}, " + f"head_lr={OVERFIT_HEAD_LR:.2e}, encoder_lr={OVERFIT_ENCODER_LR:.2e}" + ) + + fixed_batches: list[dict[str, Any]] = [] + for batch_index, batch in enumerate(bundle.train_loader): + fixed_batches.append(to_device(batch, DEVICE)) + if batch_index + 1 >= OVERFIT_N_BATCHES: + break + if not fixed_batches: + raise RuntimeError("Overfit test could not collect any training batches.") + if len(fixed_batches) < OVERFIT_N_BATCHES: + print(f"[Overfit] Warning: only {len(fixed_batches)} train batch(es) available.") + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(overfit_root) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=f"{description} | Overfit Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + + optimizer = make_optimizer( + model, + strategy, + head_lr=OVERFIT_HEAD_LR, + encoder_lr=OVERFIT_ENCODER_LR, + weight_decay=DEFAULT_WEIGHT_DECAY, + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + log_alpha: torch.Tensor | None = None + alpha_optimizer: Adam | None = None + target_entropy = 0.0 + + history = empty_overfit_history() + prev_loss: float | None = None + best_dice = -1.0 + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + expected_run_type="overfit", + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + best_dice = float(checkpoint_payload["best_metric_value"]) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_overfit_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", "overfit"), + } + print( + f"[Resume] overfit run continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{OVERFIT_N_EPOCHS}." + ) + if history["loss"]: + prev_loss = float(history["loss"][-1]) + prev_params = _snapshot_params(model) + start_time = time.time() + + for epoch in range(start_epoch, OVERFIT_N_EPOCHS + 1): + full_dump = epoch <= 5 or epoch % max(OVERFIT_PRINT_EVERY, 1) == 0 or epoch == OVERFIT_N_EPOCHS + epoch_losses: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_rewards: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_losses: list[float] = [] + epoch_entropy: list[float] = [] + epoch_grad_norms: list[float] = [] + epoch_action_dist: list[list[dict[str, float]]] = [] + epoch_reward_pos_pct: list[float] = [] + epoch_pred_fg_pct: list[float] = [] + epoch_gt_fg_pct: list[float] = [] + epoch_alerts: list[str] = [] + + for batch in fixed_batches: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=DEFAULT_TMAX, + critic_loss_weight=DEFAULT_CRITIC_LOSS_WEIGHT, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=OVERFIT_N_EPOCHS, + ) + + epoch_losses.append(float(metrics["loss"])) + epoch_rewards.append(float(metrics["mean_reward"])) + epoch_actor.append(float(metrics["actor_loss"])) + epoch_critic.append(float(metrics["critic_loss"])) + epoch_ce.append(float(metrics["ce_loss"])) + epoch_dice_losses.append(float(metrics["dice_loss"])) + epoch_entropy.append(float(metrics["entropy"])) + epoch_grad_norms.append(float(metrics["grad_norm"])) + epoch_alerts.extend( + _numerical_health_check( + { + "loss": metrics["loss"], + "actor_loss": metrics["actor_loss"], + "critic_loss": metrics["critic_loss"], + "reward": metrics["mean_reward"], + "entropy": metrics["entropy"], + "grad_norm": metrics["grad_norm"], + "ce_loss": metrics["ce_loss"], + "dice_loss": metrics["dice_loss"], + }, + prefix="train:", + ) + ) + + model.eval() + with torch.inference_mode(): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + )["decoder_prob"].float() + else: + init_mask = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + action_dist, pred = _action_distribution( + model, + image, + init_mask, + effective_test_tmax, + use_amp, + amp_dtype, + strategy=strategy, + sample_ids=sample_ids, + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + epoch_action_dist.append(action_dist) + + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + soft_init_mask = refinement_context["decoder_prob"].float() + state_t = model.forward_refinement_state( + refinement_context["base_features"], + soft_init_mask, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, _ = model.forward_from_state(state_t) + first_delta = _strategy3_policy_delta(policy_logits).to(dtype=soft_init_mask.dtype) + first_seg = _strategy3_apply_delta(soft_init_mask, first_delta) + reward_map = compute_refinement_reward( + soft_init_mask, first_seg, gt_mask.float(), + ) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * init_mask + policy_logits = model.forward_policy_only(masked) + first_actions, _, _ = sample_actions(policy_logits, stochastic=False) + first_seg = apply_actions(init_mask, first_actions, num_actions=policy_logits.shape[1]) + reward_map = (init_mask - gt_mask).pow(2) - (first_seg - gt_mask).pow(2) + epoch_reward_pos_pct.append(float((reward_map > 0).float().mean().item() * 100.0)) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + epoch_pred_fg_pct.append(pred_fg_pct) + epoch_gt_fg_pct.append(gt_fg_pct) + dice_values, iou_values = _batch_binary_metrics(pred.float(), gt_mask.float()) + epoch_dices.extend(dice_values) + epoch_ious.extend(iou_values) + epoch_alerts.extend( + _numerical_health_check( + {"pred": pred, "gt_mask": gt_mask}, + prefix="eval:", + ) + ) + model.train() + + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_loss = float(np.mean(epoch_losses)) if epoch_losses else 0.0 + avg_dice = float(np.mean(epoch_dices)) if epoch_dices else 0.0 + avg_iou = float(np.mean(epoch_ious)) if epoch_ious else 0.0 + avg_reward = float(np.mean(epoch_rewards)) if epoch_rewards else 0.0 + avg_actor = float(np.mean(epoch_actor)) if epoch_actor else 0.0 + avg_critic = float(np.mean(epoch_critic)) if epoch_critic else 0.0 + avg_ce = float(np.mean(epoch_ce)) if epoch_ce else 0.0 + avg_dice_loss = float(np.mean(epoch_dice_losses)) if epoch_dice_losses else 0.0 + avg_entropy = float(np.mean(epoch_entropy)) if epoch_entropy else 0.0 + avg_grad_norm = float(np.mean(epoch_grad_norms)) if epoch_grad_norms else 0.0 + avg_reward_pos = float(np.mean(epoch_reward_pos_pct)) if epoch_reward_pos_pct else 0.0 + avg_pred_fg = float(np.mean(epoch_pred_fg_pct)) if epoch_pred_fg_pct else 0.0 + avg_gt_fg = float(np.mean(epoch_gt_fg_pct)) if epoch_gt_fg_pct else 0.0 + + avg_action_dist = _average_action_distributions(epoch_action_dist, effective_test_tmax) + + history["dice"].append(avg_dice) + history["iou"].append(avg_iou) + history["loss"].append(avg_loss) + history["reward"].append(avg_reward) + history["actor_loss"].append(avg_actor) + history["critic_loss"].append(avg_critic) + history["ce_loss"].append(avg_ce) + history["dice_loss"].append(avg_dice_loss) + history["entropy"].append(avg_entropy) + history["grad_norm"].append(avg_grad_norm) + history["action_dist"].append(avg_action_dist) + history["reward_pos_pct"].append(avg_reward_pos) + history["pred_fg_pct"].append(avg_pred_fg) + history["gt_fg_pct"].append(avg_gt_fg) + save_json(history_path, history) + + loss_delta = avg_loss - prev_loss if prev_loss is not None else 0.0 + prev_loss = avg_loss + if epoch_alerts: + print(f"[Overfit][Epoch {epoch}] Numerical alerts: {' | '.join(epoch_alerts)}") + + if full_dump: + current_alpha = float(log_alpha.exp().detach().item()) if log_alpha is not None else 0.0 + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} (delta={loss_delta:+.6f}) " + f"dice={avg_dice:.4f} iou={avg_iou:.4f} reward={avg_reward:+.6f} " + f"entropy={avg_entropy:.6f} alpha={current_alpha:.4f}" + ) + print( + f"[Overfit][Epoch {epoch:03d}] ce={avg_ce:.6f} dice_l={avg_dice_loss:.6f} " + f"grad_norm={avg_grad_norm:.6f} global_grad={grad_stats['global_norm']:.6f}" + ) + if avg_action_dist: + first = avg_action_dist[0] + last = avg_action_dist[-1] + print( + f"[Overfit][Epoch {epoch:03d}] action step0={first} step_last={last} " + f"reward_pos={avg_reward_pos:.2f}%" + ) + print( + f"[Overfit][Epoch {epoch:03d}] pred_fg={avg_pred_fg:.2f}% gt_fg={avg_gt_fg:.2f}% " + f"param_groups={list(param_stats.keys())}" + ) + else: + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} dice={avg_dice:.4f} " + f"iou={avg_iou:.4f} reward={avg_reward:+.6f}" + ) + + row = { + "epoch": epoch, + "dice": avg_dice, + "iou": avg_iou, + "loss": avg_loss, + "reward": avg_reward, + "actor_loss": avg_actor, + "critic_loss": avg_critic, + "ce_loss": avg_ce, + "dice_loss": avg_dice_loss, + "entropy": avg_entropy, + "grad_norm": avg_grad_norm, + "action_dist": avg_action_dist, + "reward_pos_pct": avg_reward_pos, + "pred_fg_pct": avg_pred_fg, + "gt_fg_pct": avg_gt_fg, + } + if avg_dice > best_dice: + best_dice = avg_dice + save_checkpoint( + ckpt_dir / "best.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + if SAVE_LATEST_EVERY_EPOCH: + save_checkpoint( + ckpt_dir / "latest.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + if CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0: + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + peak_dice = max(history["dice"]) if history["dice"] else 0.0 + final_dice = history["dice"][-1] if history["dice"] else 0.0 + summary = { + "run_type": "overfit", + "strategy": strategy, + "peak_dice": peak_dice, + "final_dice": final_dice, + "description": description, + "resumed": resume_source is not None, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "final_epoch": max(len(history["dice"]), start_epoch - 1), + } + if resume_source is not None: + summary["resume_source"] = resume_source + save_json(overfit_root / "summary.json", summary) + print( + f"[Overfit] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} | " + f"peak_dice={peak_dice:.4f}, final_dice={final_dice:.4f}" + ) + + del model + run_cuda_cleanup( + context=f"overfit {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + return {**summary, "history": history} + +def run_configured_overfit_tests( + bundles: dict[float, DataBundle], + *, + model_config: RuntimeModelConfig, +) -> None: + banner("OVERFIT TEST MODE") + for percent in DATASET_PERCENTS: + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=strategy_root_for_percent(strategy, percent, model_config) / "overfit_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + +"""============================================================================= +OPTUNA + ORCHESTRATION +============================================================================= +""" + +def strategy_epochs(strategy: int) -> int: + strategy = _require_supported_strategy(strategy) + if strategy == 2: + return STRATEGY_2_MAX_EPOCHS + if strategy == 3: + return STRATEGY_3_MAX_EPOCHS + raise ValueError(f"Unsupported strategy for epoch selection: {strategy}") + +def suggest_hyperparameters(trial: optuna.trial.Trial, strategy: int) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if strategy == 3: + rl_lr = trial.suggest_float("rl_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + return { + "head_lr": rl_lr, + "encoder_lr": ENCODER_LR_RANGE[0], + "decoder_lr": 0.0, + "strategy3_decoder_ce_weight": 0.0, + "strategy3_decoder_dice_weight": 0.0, + "strategy3_freeze_bootstrapped_segmentation": True, + "strategy3_variant": DEFAULT_STRATEGY3_VARIANT, + "rl_lr": rl_lr, + "weight_decay": trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True), + "dropout_p": trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]), + "tmax": trial.suggest_int("tmax", TMAX_RANGE[0], TMAX_RANGE[1]), + "smp_encoder_proj_dim": trial.suggest_categorical("smp_encoder_proj_dim", [64, 128, 192, 256]), + "critic_loss_weight": trial.suggest_float("critic_loss_weight", 0.10, 1.50), + "strategy3_mc_dropout_enabled": True, + "strategy3_mc_dropout_samples": trial.suggest_categorical("strategy3_mc_dropout_samples", [4, 8, 12]), + "strategy3_mc_dropout_p": trial.suggest_float("strategy3_mc_dropout_p", 0.05, 0.35), + "strategy3_delta_max": trial.suggest_float("strategy3_delta_max", 0.03, 0.20), + "strategy3_sam_attention_grid": trial.suggest_categorical("strategy3_sam_attention_grid", [16, 32, 64]), + "strategy3_r1_progress_weight": trial.suggest_float("strategy3_r1_progress_weight", 0.25, 2.0), + "biou_reward_weight": trial.suggest_float("biou_reward_weight", 0.0, 2.0), + "strategy3_advantage_normalize": trial.suggest_categorical("strategy3_advantage_normalize", [False, True]), + "strategy3_rl_grad_clip_norm": trial.suggest_float("strategy3_rl_grad_clip_norm", 0.5, 4.0), + "strategy3_rl_loss_scale": trial.suggest_float("strategy3_rl_loss_scale", 2.0, 50.0, log=True), + "strategy3_aux_ce_weight": DEFAULT_STRATEGY3_AUX_CE_WEIGHT, + "strategy3_aux_ce_anneal_start_epoch": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + "strategy3_aux_ce_anneal_epochs": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + "strategy3_aux_ce_floor_fraction": DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + "threshold": trial.suggest_float("threshold", 0.35, 0.65), + "elastic_aug_prob": trial.suggest_float("elastic_aug_prob", 0.0, 0.5), + "epoch_probe_mode": DEFAULT_STRATEGY3_PROBE_MODE, + "early_stopping_patience": DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[3], + } + + head_lr = trial.suggest_float("head_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + encoder_lr = trial.suggest_float("encoder_lr", ENCODER_LR_RANGE[0], min(ENCODER_LR_RANGE[1], head_lr), log=True) + weight_decay = trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True) + dropout_p = trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]) + params = { + "head_lr": head_lr, + "encoder_lr": encoder_lr, + "weight_decay": weight_decay, + "dropout_p": dropout_p, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[2], + } + return params + +def _format_hparam_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key in {"head_lr", "encoder_lr", "entropy_lr"}: + return f"{value:.3e}" + return f"{value:.6g}" + return str(value) + +def log_optuna_trial_start( + *, + study_name: str, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + trial_dir: Path, + params: dict[str, Any], + max_epochs: int, +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + lines = [ + "", + "-" * 80, + f"OPTUNA TRIAL START | {run_name}", + "-" * 80, + f"Study name : {study_name}", + f"Run dir : {trial_dir}", + f"Max epochs : {max_epochs}", + f"Objective metric : {_strategy_selection_metric_name(strategy)}", + ] + for key in sorted(params): + lines.append(f"{key:22s}: {_format_hparam_value(key, params[key])}") + tqdm.write("\n".join(lines)) + +def log_optuna_trial_result( + *, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + metric_value: float, + aggregate: dict[str, dict[str, float]], +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + tqdm.write( + f"[{run_name}] completed: {_strategy_selection_metric_name(strategy)}={metric_value:.4f}, " + f"best_test_iou={aggregate['iou']['mean']:.4f}" + ) + +def study_paths_for( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + pct_root = RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}" + strategy_root = pct_root / strategy_dir_name(strategy, model_config) + study_root = strategy_root / "study" + trials_root = strategy_root / "trials" + return strategy_root, study_root, trials_root + +def manual_hparams_key(strategy: int, percent: float) -> str: + return f"{strategy}:{percent_label(percent)}" + +def reset_study_artifacts(strategy: int, percent: float, *, model_config: RuntimeModelConfig) -> None: + strategy_root, study_root, trials_root = study_paths_for(strategy, percent, model_config) + removed_any = False + for path in (study_root, trials_root): + if path.exists(): + shutil.rmtree(path) + removed_any = True + if removed_any: + print( + f"[Optuna Reset] Removed cached study artifacts for strategy={strategy}, " + f"percent={percent_text(percent)} under {strategy_root}." + ) + else: + print( + f"[Optuna Reset] No existing study artifacts found for strategy={strategy}, " + f"percent={percent_text(percent)}." + ) + +class _PlateauPruner(optuna.pruners.BasePruner): + """Prune a trial whose metric has plateaued (no improvement to its + own personal best within a patience window). + + Behaviour: + - During the first *n_warmup_steps* epochs: never prune. + - After warmup, track the trial's own best metric and the epoch + at which it was achieved. + - If *patience_steps* epochs pass without the trial beating its + own best, the trial is pruned (it has stagnated). + """ + + def __init__( + self, + n_warmup_steps: int = 80, + patience_steps: int = 40, + ) -> None: + self._n_warmup_steps = n_warmup_steps + self._patience_steps = patience_steps + + def prune( + self, + study: "optuna.study.Study", + trial: "optuna.trial.FrozenTrial", + ) -> bool: + step = trial.last_step + if step is None or step < self._n_warmup_steps: + return False + + post_warmup = { + s: v for s, v in trial.intermediate_values.items() + if s >= self._n_warmup_steps + } + if not post_warmup: + return False + + best_step = max(post_warmup, key=post_warmup.get) + epochs_since_improvement = step - best_step + return epochs_since_improvement >= self._patience_steps + + +def pruner_for_run() -> optuna.pruners.BasePruner: + if USE_TRIAL_PRUNING: + return _PlateauPruner( + n_warmup_steps=TRIAL_PRUNER_WARMUP_STEPS, + patience_steps=TRIAL_PRUNER_PATIENCE_STEPS, + ) + return optuna.pruners.NopPruner() + +def run_single_job( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + params: dict[str, Any], + max_epochs: int, + trial: optuna.trial.Trial | None, + strategy2_checkpoint_path: str | Path | None = None, + resume_checkpoint_path: Path | None = None, + retrying_from_trial_number: int | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, dict[str, float]]]: + strategy = _require_supported_strategy(strategy) + params = dict(params) + params.setdefault("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS[strategy]) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + if strategy == 3: + params.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(params["strategy3_freeze_bootstrapped_segmentation"]) + ) + params.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + params.setdefault("decoder_lr", 0.0 if bootstrap_freeze else params["head_lr"] * 0.1) + params.setdefault("rl_lr", params["head_lr"]) + params.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + params.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + params.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + params.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + params.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + params.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + params.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + params.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + params.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + params.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + params.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + params.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + params.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + params.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + params.setdefault("elastic_aug_prob", 0.3) + params.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + params.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + params.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + params.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + params.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + params.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + params.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + params.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + params.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + params.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + params.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + set_current_job_params(params) + if "smp_encoder_proj_dim" in params and int(params["smp_encoder_proj_dim"]) != model_config.smp_encoder_proj_dim: + model_config = RuntimeModelConfig.from_payload( + {**model_config.to_payload(), "smp_encoder_proj_dim": int(params["smp_encoder_proj_dim"])} + ).validate() + entropy_target_ratio = float(_job_param("entropy_target_ratio", 0.35)) + entropy_alpha_init = float(_job_param("entropy_alpha_init", 0.12)) + critic_loss_weight = float(_job_param("critic_loss_weight", DEFAULT_CRITIC_LOSS_WEIGHT)) + ensure_dir(run_dir) + run_type = "trial" if trial is not None else "final" + config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": run_type, + "run_name": run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ), + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "train_subset_variant": bundle.split_payload.get("train_subset_variant", 0), + "train_subset_source": bundle.split_payload.get("train_subset_source", "persisted"), + "selected_split_manifest_path": bundle.split_payload.get("selected_split_manifest_path"), + "normalization_cache_path": bundle.split_payload["normalization_cache_path"], + "best_checkpoint_metric_name": _strategy_selection_metric_name(strategy), + "best_checkpoint_metrics": {str(key): value for key, value in BEST_CHECKPOINT_METRICS.items()}, + "save_history_incrementally": bool(SAVE_HISTORY_INCREMENTALLY), + "write_epoch_diagnostic": bool(WRITE_EPOCH_DIAGNOSTIC), + "head_lr": params["head_lr"], + "encoder_lr": params["encoder_lr"], + "weight_decay": params["weight_decay"], + "dropout_p": params["dropout_p"], + "tmax": params["tmax"], + "entropy_lr": params["entropy_lr"], + "max_epochs": max_epochs, + "gamma": DEFAULT_GAMMA, + "critic_loss_weight": critic_loss_weight, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "scheduler_factor": SCHEDULER_FACTOR, + "scheduler_patience": SCHEDULER_PATIENCE, + "scheduler_threshold": SCHEDULER_THRESHOLD, + "scheduler_min_lr": SCHEDULER_MIN_LR, + "execution_mode": EXECUTION_MODE, + "evaluation_checkpoint_mode": EVAL_CHECKPOINT_MODE, + "strategy2_checkpoint_mode": STRATEGY2_CHECKPOINT_MODE, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + config.update({key: value for key, value in params.items() if key not in config}) + config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + if resume_checkpoint_path is not None: + config["resume_checkpoint_path"] = str(Path(resume_checkpoint_path)) + if retrying_from_trial_number is not None: + config["retrying_from_trial_number"] = int(retrying_from_trial_number) + save_json(run_dir / "run_config.json", config) + + model: nn.Module | None = None + try: + model, description, _compiled = build_model( + strategy, + params["dropout_p"], + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + summary, history = train_model( + run_type=run_type, + model_config=model_config, + run_config=config, + model=model, + description=description, + strategy=strategy, + run_dir=run_dir, + bundle=bundle, + max_epochs=max_epochs, + head_lr=params["head_lr"], + encoder_lr=params["encoder_lr"], + weight_decay=params["weight_decay"], + tmax=params["tmax"], + entropy_lr=params["entropy_lr"], + entropy_alpha_init=entropy_alpha_init, + entropy_target_ratio=entropy_target_ratio, + critic_loss_weight=critic_loss_weight, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + dropout_p=params["dropout_p"], + resume_checkpoint_path=resume_checkpoint_path, + trial=trial, + ) + + if trial is not None: + save_json( + run_dir / "summary.json", + { + "params": params, + "best_iou": float(summary["best_val_iou"]), + "best_model_metric_name": str(summary["best_model_metric_name"]), + "best_model_metric": float(summary["best_model_metric"]), + "resumed": bool(resume_checkpoint_path is not None), + "retrying_from_trial_number": retrying_from_trial_number, + }, + ) + return summary, history, {} + + del model + model = None + run_cuda_cleanup() + + aggregate, _per_sample = run_evaluation_for_run( + strategy=strategy, + percent=bundle.percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + return summary, history, aggregate + finally: + if model is not None: + del model + model = None + run_cuda_cleanup() + +def _save_best_params_so_far( + study: optuna.study.Study, + study_root: Path, + strategy: int, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> None: + best, _best_value = _current_optuna_study_best_candidate( + study, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if best is None: + return + params = dict(best.params) + if strategy == 2: + params.setdefault("tmax", DEFAULT_TMAX) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", params) + +def run_study( + strategy: int, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + strategy_root, study_root, trials_root = study_paths_for(strategy, bundle.percent, model_config) + ensure_dir(strategy_root.parent) + strategy_root = ensure_dir(strategy_root) + study_root = ensure_dir(strategy_root / "study") + trials_root = ensure_dir(strategy_root / "trials") + storage_path = study_root / "study.sqlite3" + storage = RDBStorage( + url=f"sqlite:///{storage_path.resolve()}", + heartbeat_interval=OPTUNA_HEARTBEAT_INTERVAL, + grace_period=OPTUNA_HEARTBEAT_GRACE_PERIOD, + ) + sampler = optuna.samplers.TPESampler(seed=SEED) + study_name = ( + f"{MODEL_NAME}_{model_config.backbone_tag()}_{run_identity_slug(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + study = optuna.create_study( + study_name=study_name, + direction=STUDY_DIRECTION, + sampler=sampler, + pruner=pruner_for_run(), + storage=storage, + load_if_exists=LOAD_EXISTING_STUDIES, + ) + existing_trials = [trial for trial in study.trials if trial.state.is_finished()] + if existing_trials: + print( + f"[Optuna Study] Loaded existing study '{study_name}' with " + f"{len(existing_trials)} existing finished trial(s). Running {NUM_TRIALS} new trial(s)." + ) + else: + print(f"[Optuna Study] Starting new study '{study_name}' with {NUM_TRIALS} trial(s).") + + def objective(trial: optuna.trial.Trial) -> float: + trial_dir = ensure_dir(trials_root / f"trial_{trial.number:03d}") + params = suggest_hyperparameters(trial, strategy) + log_optuna_trial_start( + study_name=study_name, + strategy=strategy, + bundle=bundle, + trial=trial, + trial_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + ) + summary: dict[str, Any] | None = None + _history: list[dict[str, Any]] | None = None + aggregate: dict[str, dict[str, float]] | None = None + completed_successfully = False + pruned_by_optuna = False + run_cuda_cleanup() + try: + summary, _history, aggregate = run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + trial=trial, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + metric_value = float(summary["best_model_metric"]) + completed_successfully = True + tqdm.write( + f"[{run_identity_label(strategy=strategy, percent=bundle.percent, trial_number=trial.number, split_payload=bundle.split_payload)}] " + f"completed: {summary['best_model_metric_name']}={metric_value:.4f}" + ) + return metric_value + except optuna.TrialPruned: + pruned_by_optuna = True + raise + finally: + current_trial = trial if (completed_successfully or pruned_by_optuna) else None + current_best_value = None + if summary is not None and summary.get("best_model_metric") is not None: + current_best_value = float(summary["best_model_metric"]) + _save_best_params_so_far( + study, + study_root, + strategy, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if aggregate is not None: + del aggregate + aggregate = None + if _history is not None: + del _history + _history = None + if summary is not None: + del summary + summary = None + prune_optuna_trial_dir(trial_dir) + run_cuda_cleanup(context=f"trial {trial.number:03d} boundary") + + study.optimize(objective, n_trials=NUM_TRIALS, show_progress_bar=True) + + best_trial, best_observed_value = _current_optuna_study_best_candidate(study) + if best_trial is None or best_observed_value is None: + raise RuntimeError( + f"Study '{study_name}' has no trials with recorded best-observed values, so best params cannot be resolved. " + f"Finished trials={len([trial for trial in study.trials if trial.state.is_finished()])}, " + f"configured cap={NUM_TRIALS}." + ) + + best_params = dict(best_trial.params) + if strategy == 2: + best_params.setdefault("tmax", DEFAULT_TMAX) + best_params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", best_params) + optuna_best_value: float | None + try: + optuna_best_value = float(study.best_value) + except Exception: + optuna_best_value = None + save_json( + study_root / "summary.json", + { + "best_params": best_params, + "optimized_param_names": sorted(best_params.keys()), + "best_metric_name": _strategy_selection_metric_name(strategy), + "best_metric_value": float(best_observed_value), + "best_observed_value": float(best_observed_value), + "best_trial_number": int(getattr(best_trial, "number", -1)), + "optuna_best_value": optuna_best_value, + "best_iou": float(best_observed_value) if _strategy_selection_metric_name(strategy) == "val_iou" else None, + "finished_trials": len([trial for trial in study.trials if trial.state.is_finished()]), + "completed_trials": len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + "target_trials": int(NUM_TRIALS), + "ran_trials": int(NUM_TRIALS), + }, + ) + prune_optuna_study_dir(study_root) + if trials_root.exists(): + shutil.rmtree(trials_root, ignore_errors=True) + return best_params + +def run_final_training( + strategy: int, + bundle: DataBundle, + params: dict[str, Any], + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + strategy = _require_supported_strategy(strategy) + final_root = final_root_for_strategy(strategy, bundle.percent, model_config) + if SKIP_EXISTING_FINALS and (final_root / "summary.json").exists(): + print(f"Skipping existing final run: {final_root}") + return + save_json(final_root / "best_params.json", params) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(final_root) + run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=final_root, + params=params, + max_epochs=strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + +def print_environment_summary(model_config: RuntimeModelConfig) -> None: + banner("RUNTIME SUMMARY") + images_dir, annotations_dir = current_dataset_dirs() + print(f"Project dir : {PROJECT_DIR}") + print(f"Data root : {DATA_ROOT}") + print(f"Runs root : {RUNS_ROOT}") + print(f"Dataset name : {current_dataset_name()}") + if current_dataset_name() == "BUSI_with_classes": + print(f"Dataset split policy : {current_busi_with_classes_split_policy()}") + print(f"Images dir : {images_dir}") + print(f"Masks dir : {annotations_dir}") + print(f"Dataset splits json : {current_dataset_splits_json_path()}") + print(f"Split type : {SPLIT_TYPE}") + print(f"Experiment mode : {EXPERIMENT_MODE}") + print(f"Device : {DEVICE}") + print(f"Device source : {DEVICE_FALLBACK_SOURCE}") + print(f"Model name : {MODEL_NAME}") + print(f"Seed : {SEED}") + print(f"PyTorch version : {torch.__version__}") + print(f"Batch size : {BATCH_SIZE}") + print(f"Use AMP : {USE_AMP}") + print(f"Num workers : {NUM_WORKERS}") + print(f"Pin memory : {USE_PIN_MEMORY}") + print(f"CuDNN deterministic : {torch.backends.cudnn.deterministic}") + print(f"CuDNN benchmark : {torch.backends.cudnn.benchmark}") + + if DEVICE.type == "cuda": + props = torch.cuda.get_device_properties(0) + print(f"GPU : {torch.cuda.get_device_name(0)}") + print(f"GPU VRAM : {props.total_memory / (1024 ** 3):.2f} GB") + print(f"AMP dtype : {resolve_amp_dtype(AMP_DTYPE)}") + print(f"Trial pruning : {USE_TRIAL_PRUNING}") + print(f"Backbone family : {model_config.backbone_family}") + if model_config.backbone_family == "custom_vgg": + print(f"VGG feature scales : {model_config.vgg_feature_scales}") + print(f"VGG feature dilation : {model_config.vgg_feature_dilation}") + else: + print(f"SMP encoder : {model_config.smp_encoder_name}") + print(f"SMP encoder depth : {model_config.smp_encoder_depth}") + print(f"SMP encoder proj dim : {model_config.smp_encoder_proj_dim}") + print(f"SMP decoder : {model_config.smp_decoder_type}") + print(f"Strategies : {STRATEGIES}") + print(f"Dataset percents : {[percent_text(value) for value in DATASET_PERCENTS]}") + print(f"Best metrics : {BEST_CHECKPOINT_METRICS}") + print(f"History incremental : {SAVE_HISTORY_INCREMENTALLY}") + print(f"Write diagnostics : {WRITE_EPOCH_DIAGNOSTIC}") + print_imagenet_normalization_status() + print(f"Trials per study : {NUM_TRIALS}") + print(f"Execution mode : {EXECUTION_MODE}") + print(f"Run Optuna : {RUN_OPTUNA}") + print(f"Use saved best params : {USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF}") + print(f"Reset studies/run : {RESET_ALL_STUDIES_EACH_RUN}") + print(f"Load existing studies : {LOAD_EXISTING_STUDIES}") + print(f"Eval ckpt selector : {EVAL_CHECKPOINT_MODE}") + print(f"S2 ckpt selector : {STRATEGY2_CHECKPOINT_MODE}") + print(f"S3 bootstrap from S2 : {STRATEGY3_BOOTSTRAP_FROM_STRATEGY2}") + print(f"S3 freeze default : {DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION}") + print(f"Train resume mode : {TRAIN_RESUME_MODE}") + print(f"Verbose epoch log : {VERBOSE_EPOCH_LOG}") + print(f"Validate every epochs : {VALIDATE_EVERY_N_EPOCHS}") + print(f"Smoke test enabled : {RUN_SMOKE_TEST}") + print(f"Test iter control : {TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {TEST_ITERATION_T}") + print(f"Overfit test enabled : {RUN_OVERFIT_TEST}") + print(f"Overfit batches : {OVERFIT_N_BATCHES}") + print(f"Overfit epochs : {OVERFIT_N_EPOCHS}") + +def maybe_run_strategy_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + if not RUN_SMOKE_TEST: + return + if EXECUTION_MODE == "eval_only": + return + run_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + smoke_root=strategy_root_for_percent(strategy, bundle.percent, model_config) / "smoke_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +"""============================================================================= +REPEATED HOLDOUT INTEGRATION +============================================================================= +""" + +base = sys.modules[__name__] + +REPEATED_HOLDOUT_ROOT = base.RUNS_ROOT / base.MODEL_NAME / "repeated_holdout" +EXPERIMENT_ROOT = REPEATED_HOLDOUT_ROOT / FOLDS_EXPERIMENT_NAME +EXPERIMENT_DB_PATH = EXPERIMENT_ROOT / "experiment_state.sqlite3" +SPLIT_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "splits" +SUBSET_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "subsets" +EXPORTS_DIR = EXPERIMENT_ROOT / "exports" +"""============================================================================= +RUNTIME STATE +============================================================================= +""" + + +@dataclass(frozen=True) +class PercentRepeatSpec: + percent_int: int + fraction: float + repeat_count: int + + +@dataclass(frozen=True) +class FoldRunContext: + split_repeat_index: int + subset_repeat_index: int + percent_int: int + percent_fraction: float + split_seed: int + subset_seed: int + repeat_root: Path + split_manifest_path: Path + subset_manifest_path: Path + + +@dataclass(frozen=True) +class RunKey: + split_repeat_index: int + dataset_percent: int + subset_repeat_index: int + strategy: int + + +CURRENT_FOLD_CONTEXT: FoldRunContext | None = None +LEDGER_CONN: sqlite3.Connection | None = None +PERCENT_SPECS_CACHE: list[PercentRepeatSpec] | None = None + +ORIGINAL_SAVE_JSON = base.save_json +ORIGINAL_PERCENT_ROOT = base.percent_root +ORIGINAL_STRATEGY_ROOT_FOR_PERCENT = base.strategy_root_for_percent +ORIGINAL_FINAL_ROOT_FOR_STRATEGY = base.final_root_for_strategy +ORIGINAL_STUDY_PATHS_FOR = base.study_paths_for +ORIGINAL_SAVE_CHECKPOINT = base.save_checkpoint +ORIGINAL_RUN_EVALUATION_FOR_RUN = base.run_evaluation_for_run + +BASE_RESUME_IDENTITY_KEYS = tuple(base.RESUME_IDENTITY_KEYS) +RUNTIME_ONLY_CONFIG_KEYS = frozenset( + { + "PERCENT_EXECUTION_MODE", + "SELECTED_DATASET_PERCENTS", + "SPLIT_EXECUTION_MODE", + "SELECTED_SPLIT_INDICES", + "PHASE_EXECUTION_MODE", + "SELECTED_PHASES", + "REPEAT_EXECUTION_MODE", + "SELECTED_REPEAT_INDICES", + } +) +PORTABLE_FINGERPRINT_FOLD_KEYS = frozenset( + { + "RESUME_FOLDS", + "REPEATED_HOLDOUT_ROOT", + "EXPERIMENT_ROOT", + "EXPERIMENT_DB_PATH", + } +) + + +"""============================================================================= +UTILITIES +============================================================================= +""" + + +def now_utc_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _jsonify(value: Any) -> Any: + if isinstance(value, Path): + return str(value) + if isinstance(value, dict): + return {str(key): _jsonify(val) for key, val in sorted(value.items(), key=lambda item: str(item[0]))} + if isinstance(value, (list, tuple)): + return [_jsonify(item) for item in value] + if isinstance(value, set): + return [_jsonify(item) for item in sorted(value, key=str)] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return repr(value) + + +def _summary_mean_std(values: list[float]) -> dict[str, float]: + arr = np.array(values, dtype=np.float64) + return { + "mean": float(arr.mean()) if arr.size > 0 else 0.0, + "std": float(arr.std()) if arr.size > 0 else 0.0, + } + + +def _phase_timing_summary_path() -> Path: + return EXPERIMENT_ROOT / "phase_timing_summary.json" + + +def _completed_run_rows_for_phase(phase_index: int) -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + AND split_repeat_index = ? + ORDER BY dataset_percent, subset_repeat_index, strategy + """, + (int(phase_index),), + ).fetchall() + ) + + +def _run_training_elapsed_seconds(row: sqlite3.Row) -> float | None: + run_dir = Path(str(row["run_dir"])) + summary_path = run_dir / "summary.json" + if summary_path.exists(): + try: + summary = base.load_json(summary_path) + if summary.get("elapsed_seconds") is not None: + return float(summary["elapsed_seconds"]) + except Exception as exc: + print(f"[Timing] Could not read {summary_path}: {exc}") + if row["elapsed_seconds"] is not None: + return float(row["elapsed_seconds"]) + return None + + +def write_phase_timing_summary_after_phase(phase_index: int) -> None: + if LEDGER_CONN is None: + return + rows = _completed_run_rows_for_phase(phase_index) + if not rows: + return + + run_entries: list[dict[str, Any]] = [] + phase_values: list[float] = [] + for row in rows: + elapsed = _run_training_elapsed_seconds(row) + entry = { + "phase_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": str(row["run_dir"]), + "training_elapsed_seconds": elapsed, + } + run_entries.append(entry) + if elapsed is not None: + phase_values.append(float(elapsed)) + + phase_stats = _summary_mean_std(phase_values) + existing_payload: dict[str, Any] = {} + summary_path = _phase_timing_summary_path() + if summary_path.exists(): + try: + existing_payload = base.load_json(summary_path) + except Exception as exc: + print(f"[Timing] Could not read existing phase timing summary {summary_path}: {exc}") + + phases_by_index: dict[int, dict[str, Any]] = {} + for phase_payload in existing_payload.get("phases", []): + if isinstance(phase_payload, dict) and phase_payload.get("phase_index") is not None: + phases_by_index[int(phase_payload["phase_index"])] = dict(phase_payload) + phases_by_index[int(phase_index)] = { + "phase_index": int(phase_index), + "completed_strategy_count": len(run_entries), + "completed_strategies": [int(entry["strategy"]) for entry in run_entries], + "runs": run_entries, + "training_elapsed_seconds_mean": phase_stats["mean"], + "training_elapsed_seconds_std": phase_stats["std"], + "updated_at": now_utc_iso(), + } + + phases = [phases_by_index[index] for index in sorted(phases_by_index)] + global_phase_means = [ + float(phase["training_elapsed_seconds_mean"]) + for phase in phases + if phase.get("training_elapsed_seconds_mean") is not None + ] + global_stats = _summary_mean_std(global_phase_means) + payload = { + "scope": "fixed_phase_training_time", + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": len(phases), + "training_elapsed_seconds_mean_across_phases": global_stats["mean"], + "training_elapsed_seconds_std_across_phases": global_stats["std"], + "phases": phases, + "updated_at": now_utc_iso(), + } + atomic_save_json(summary_path, payload) + print( + f"[Timing] Phase {phase_index:03d} training time summary updated -> {summary_path} " + f"(mean={phase_stats['mean']:.2f}s, std={phase_stats['std']:.2f}s)." + ) + + +def validate_hf_backup_settings() -> None: + """Fail fast at startup if backups are enabled but HF env vars are missing. + + Refuses to run rather than discovering hours into training (at the first + backup) that nothing can be uploaded. Disable by setting + ASYNC_REPO_BACKUP_AFTER_PHASE = False if you intentionally want no backups. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + missing = [] + if not repo_id: + missing.append("HF_REPO_ID (target repo, e.g. 'your-username/ADVAI24JUN-backup')") + if not token: + missing.append("HF_TOKEN (Hugging Face write token)") + if missing: + raise RuntimeError( + "Hugging Face backup is enabled (ASYNC_REPO_BACKUP_AFTER_PHASE = True) " + "but required environment variables are not set:\n - " + + "\n - ".join(missing) + + "\n\nSet them before running, e.g.:\n" + " export HF_REPO_ID='your-username/ADVAI24JUN-backup'\n" + " export HF_TOKEN='hf_xxxxxxxxxxxxxxxxxxxxx'\n" + "Or set ASYNC_REPO_BACKUP_AFTER_PHASE = False to run without backups." + ) + + +def _hf_backup_due(phase_index: int) -> bool: + """True only on every HF_BACKUP_EVERY_N_PHASES-th phase (0-indexed boundary).""" + n = max(1, int(HF_BACKUP_EVERY_N_PHASES)) + return (phase_index + 1) % n == 0 + + +def _hf_upload_project(*, label: str) -> bool: + """Create the HF dataset repo if needed and mirror PROJECT_DIR into it. + + Shared by the initial pre-training backup and the per-phase backups. + `upload_large_folder` is resumable and content-addressed: unchanged files are + skipped and an interrupted upload (e.g. a 503) can be safely re-run, so the repo + always converges to the latest project state. Retries with backoff to ride out + transient HF outages. Returns True on a verified successful upload. + """ + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + if not repo_id: + print(f"[Backup] Skipping HF backup ({label}): HF_REPO_ID is not set (export HF_REPO_ID=user/repo).") + return False + if not token: + print(f"[Backup] Skipping HF backup ({label}): HF_TOKEN env var is not set.") + return False + + try: + from huggingface_hub import HfApi + except Exception: + print(f"[Backup] Skipping HF backup ({label}): huggingface_hub not installed (pip install huggingface_hub).") + return False + + api = HfApi(token=token) + try: + api.create_repo(repo_id=repo_id, repo_type=HF_REPO_TYPE, private=True, exist_ok=True) + except Exception as exc: + print(f"[Backup] Could not ensure HF repo {repo_id} exists: {exc}") + + last_exc: Exception | None = None + for attempt in range(1, HF_BACKUP_MAX_RETRIES + 1): + try: + print( + f"[Backup] {label}: uploading project to " + f"hf://{HF_REPO_TYPE}/{repo_id} (attempt {attempt}/{HF_BACKUP_MAX_RETRIES})..." + ) + api.upload_large_folder( + repo_id=repo_id, + repo_type=HF_REPO_TYPE, + folder_path=str(PROJECT_DIR.resolve()), + ignore_patterns=list(HF_IGNORE_PATTERNS), + print_report=True, + ) + print(f"[Backup] {label}: HF backup complete -> {repo_id}.") + return True + except Exception as exc: + last_exc = exc + wait = min(60, 5 * attempt) + print(f"[Backup] {label}: HF upload attempt {attempt} failed: {exc}. Retrying in {wait}s...") + time.sleep(wait) + print(f"[Backup] {label}: HF backup FAILED after {HF_BACKUP_MAX_RETRIES} attempts: {last_exc}") + return False + + +def run_initial_hf_backup() -> None: + """Fresh backup BEFORE any training begins. + + Creates the repo and uploads the current project state synchronously, so the + entire backup pipeline (repo creation, token, upload) is proven before we commit + hours of compute. Later phase backups refresh this same repo. Runs in the + foreground on purpose -- if the first backup cannot complete, we want to know now. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE or not HF_BACKUP_ON_START: + return + print("[Backup] Running initial pre-training backup (this proves the backup pipeline before training)...") + _hf_upload_project(label="Initial backup") + + +def run_repo_backup_after_phase(phase_index: int) -> None: + """Refresh the Hugging Face dataset repo after a training phase. + + Fires only on every HF_BACKUP_EVERY_N_PHASES-th phase so we don't hammer HF. + Runs in a background thread at the call site. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + if not _hf_backup_due(phase_index): + print( + f"[Backup] Phase {phase_index:03d}: skipping HF backup " + f"(uploads every {HF_BACKUP_EVERY_N_PHASES} phases)." + ) + return + _hf_upload_project(label=f"Phase {phase_index:03d}") + + +def atomic_write_text(path: str | Path, text: str) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_write_bytes(path: str | Path, payload: bytes) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_save_json(path: str | Path, payload: Any) -> None: + atomic_write_text(path, json.dumps(payload, indent=2, sort_keys=True)) + + +def atomic_torch_save(path: str | Path, payload: Any) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + torch.save(payload, handle) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def stable_hash(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def stable_int(text: str) -> int: + return base.stable_int_from_text(text) + + +def fold_seed(tag: str) -> int: + return int(base.SEED) + stable_int(tag) + + +def current_split_generation_mode() -> str: + mode = str(SPLIT_GENERATION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_GENERATION_MODES: + raise ValueError( + f"SPLIT_GENERATION_MODE must be one of {SUPPORTED_SPLIT_GENERATION_MODES}, got {mode!r}" + ) + return mode + + +def using_fixed_phase_mode() -> bool: + return current_split_generation_mode() == "fixed_stratified_phases_8_1_1" + + +def primary_unit_name(*, plural: bool = False) -> str: + if using_fixed_phase_mode(): + return "phases" if plural else "phase" + return "splits" if plural else "split" + + +def current_phase_execution_mode() -> str: + mode = str(PHASE_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PHASE_EXECUTION_MODES: + raise ValueError( + f"PHASE_EXECUTION_MODE must be one of {SUPPORTED_PHASE_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def phase_count() -> int: + if isinstance(NUM_PHASES, bool) or int(NUM_PHASES) <= 0: + raise ValueError("NUM_PHASES must be a positive integer.") + return int(NUM_PHASES) + + +def phase_val_offset() -> int: + if isinstance(PHASE_VAL_OFFSET, bool): + raise TypeError("PHASE_VAL_OFFSET must be an integer.") + return int(PHASE_VAL_OFFSET) + + +def phase_indices() -> list[int]: + return list(range(1, phase_count() + 1)) + + +def phase_execution_indices_to_run() -> list[int]: + indices = phase_indices() + if current_phase_execution_mode() == "auto": + return indices + + if not SELECTED_PHASES: + raise ValueError("SELECTED_PHASES must be non-empty when PHASE_EXECUTION_MODE='manual'.") + + selected: list[int] = [] + seen: set[int] = set() + max_index = indices[-1] + for raw_index in SELECTED_PHASES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + f"SELECTED_PHASES entries must be integer phase indices in the range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_PHASES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_PHASES contains duplicate phase index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def phase_fold_indices(phase_index: int) -> tuple[int, int]: + count = phase_count() + if phase_index < 1 or phase_index > count: + raise ValueError(f"Phase index must be in [1, {count}], got {phase_index}.") + val_offset = phase_val_offset() + if val_offset <= 0 or val_offset >= count: + raise ValueError( + f"PHASE_VAL_OFFSET must be in [1, {count - 1}] for {count} phases, got {val_offset}." + ) + test_fold_index = phase_index + val_fold_index = ((phase_index - 1 + val_offset) % count) + 1 + return val_fold_index, test_fold_index + + +def partition_seed() -> int: + if using_fixed_phase_mode(): + return fold_seed(f"phase_partition::{phase_count()}") + return int(base.SEED) + + +def split_generation_display_name() -> str: + if using_fixed_phase_mode(): + return "fixed stratified 10-phase 8/1/1" + return "repeated stratified holdout" + + +def cycle_index_label(index: int) -> str: + return f"{primary_unit_name()}_{int(index):03d}" + + +def cycle_identity_label(index: int) -> str: + return f"{primary_unit_name()}={int(index):03d}" + + +def all_split_repeat_indices() -> list[int]: + if using_fixed_phase_mode(): + return phase_indices() + return list(range(1, int(NUM_STRATIFIED_SPLIT_REPEATS) + 1)) + + +def max_subset_repeat_index() -> int: + return max(int(spec.repeat_count) for spec in percent_specs()) + + +def current_percent_sampling_mode() -> str: + mode = str(PERCENT_SAMPLING_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_SAMPLING_MODES: + raise ValueError( + f"PERCENT_SAMPLING_MODE must be one of {SUPPORTED_PERCENT_SAMPLING_MODES}, got {mode!r}" + ) + return mode + + +def current_split_execution_mode() -> str: + mode = str(SPLIT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_EXECUTION_MODES: + raise ValueError( + f"SPLIT_EXECUTION_MODE must be one of {SUPPORTED_SPLIT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_repeat_execution_mode() -> str: + mode = str(REPEAT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_REPEAT_EXECUTION_MODES: + raise ValueError( + f"REPEAT_EXECUTION_MODE must be one of {SUPPORTED_REPEAT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_percent_execution_mode() -> str: + mode = str(PERCENT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_EXECUTION_MODES: + raise ValueError( + f"PERCENT_EXECUTION_MODE must be one of {SUPPORTED_PERCENT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def split_repeat_indices_to_run() -> list[int]: + if using_fixed_phase_mode(): + return phase_execution_indices_to_run() + + split_indices = all_split_repeat_indices() + if current_split_execution_mode() == "auto": + return split_indices + + if not SELECTED_SPLIT_INDICES: + raise ValueError( + "SELECTED_SPLIT_INDICES must be non-empty when SPLIT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = split_indices[-1] + for raw_index in SELECTED_SPLIT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_SPLIT_INDICES entries must be integer split indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_SPLIT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_SPLIT_INDICES contains duplicate split index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def subset_repeat_indices_to_run() -> list[int]: + repeat_indices = list(range(1, max_subset_repeat_index() + 1)) + if current_repeat_execution_mode() == "auto": + return repeat_indices + + if not SELECTED_REPEAT_INDICES: + raise ValueError( + "SELECTED_REPEAT_INDICES must be non-empty when REPEAT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = repeat_indices[-1] + for raw_index in SELECTED_REPEAT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_REPEAT_INDICES entries must be integer repeat indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_REPEAT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_REPEAT_INDICES contains duplicate repeat index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def percent_specs_to_run() -> list[PercentRepeatSpec]: + all_specs = percent_specs() + if current_percent_execution_mode() == "auto": + return all_specs + + if not SELECTED_DATASET_PERCENTS: + raise ValueError( + "SELECTED_DATASET_PERCENTS must be non-empty when PERCENT_EXECUTION_MODE='manual'." + ) + + all_percent_ints = {spec.percent_int for spec in all_specs} + selected: list[PercentRepeatSpec] = [] + seen: set[int] = set() + for raw_percent in SELECTED_DATASET_PERCENTS: + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "SELECTED_DATASET_PERCENTS entries must be integer percentages in the range [1, 100]." + ) + if raw_percent not in all_percent_ints: + raise ValueError( + f"SELECTED_DATASET_PERCENTS entry {raw_percent} is not defined in " + f"DATASET_PERCENT_REPEAT_COUNTS. Available: {sorted(all_percent_ints)}." + ) + if raw_percent in seen: + raise ValueError(f"SELECTED_DATASET_PERCENTS contains duplicate percent {raw_percent}.") + seen.add(raw_percent) + spec_map = {spec.percent_int: spec for spec in all_specs} + for raw_percent in SELECTED_DATASET_PERCENTS: + selected.append(spec_map[raw_percent]) + selected.sort(key=lambda s: s.percent_int) + return selected + + +def validate_repeated_holdout_settings() -> None: + if using_fixed_phase_mode(): + if phase_count() != 10: + raise ValueError( + f"fixed phase mode requires NUM_PHASES=10, got {phase_count()}." + ) + phase_fold_indices(1) + if current_dataset_name() != "BUSI_with_classes": + raise ValueError( + "fixed phase mode currently supports DATASET_NAME='BUSI_with_classes' only." + ) + if current_busi_with_classes_split_policy() != "stratified": + raise ValueError( + "fixed phase mode requires BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"fixed phase mode requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + current_phase_execution_mode() + else: + if int(NUM_STRATIFIED_SPLIT_REPEATS) <= 0: + raise ValueError("NUM_STRATIFIED_SPLIT_REPEATS must be a positive integer.") + current_split_execution_mode() + current_percent_sampling_mode() + current_repeat_execution_mode() + current_percent_execution_mode() + split_repeat_indices_to_run() + subset_repeat_indices_to_run() + selected_percent_specs = percent_specs_to_run() + if using_fixed_phase_mode(): + if len(selected_percent_specs) != 1 or int(selected_percent_specs[0].percent_int) != 100: + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "If PERCENT_EXECUTION_MODE='manual', set SELECTED_DATASET_PERCENTS=[100]." + ) + + +def repeated_holdout_split_policy() -> str | None: + if base.current_dataset_name() == "BUSI_with_classes": + return base.current_busi_with_classes_split_policy() + return None + + +def active_specs_for_subset_repeat(subset_repeat_index: int) -> list[PercentRepeatSpec]: + return [ + spec + for spec in percent_specs() + if subset_repeat_index <= int(spec.repeat_count) + ] + + +def percent_specs() -> list[PercentRepeatSpec]: + global PERCENT_SPECS_CACHE + if PERCENT_SPECS_CACHE is not None: + return list(PERCENT_SPECS_CACHE) + specs: list[PercentRepeatSpec] = [] + if not DATASET_PERCENT_REPEAT_COUNTS: + raise ValueError("DATASET_PERCENT_REPEAT_COUNTS must contain at least one percentage entry.") + for raw_percent, raw_repeat_count in DATASET_PERCENT_REPEAT_COUNTS.items(): + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "DATASET_PERCENT_REPEAT_COUNTS keys must be integer percentages in the range [1, 100]." + ) + if raw_percent <= 0 or raw_percent > 100: + raise ValueError(f"Invalid dataset percent {raw_percent}; expected an integer in [1, 100].") + if isinstance(raw_repeat_count, bool) or int(raw_repeat_count) <= 0: + raise ValueError( + f"Invalid repeat count for percent {raw_percent}: {raw_repeat_count!r}. Expected a positive integer." + ) + repeat_count = int(raw_repeat_count) + if using_fixed_phase_mode() and raw_percent == 100 and repeat_count != 1: + raise ValueError( + "fixed phase mode requires DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got repeat_count={repeat_count} for percent 100." + ) + if raw_percent == 100 and repeat_count > 1: + print( + f"[Repeated Holdout] Percent 100 was configured with repeat_count={repeat_count}. " + "Collapsing to one effective repeat per split." + ) + repeat_count = 1 + fraction = float(raw_percent) / 100.0 + specs.append(PercentRepeatSpec(percent_int=raw_percent, fraction=fraction, repeat_count=repeat_count)) + specs.sort(key=lambda item: item.percent_int) + if using_fixed_phase_mode(): + if len(specs) != 1 or int(specs[0].percent_int) != 100: + configured = {spec.percent_int: spec.repeat_count for spec in specs} + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "Set DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got {configured}." + ) + PERCENT_SPECS_CACHE = list(specs) + return list(PERCENT_SPECS_CACHE) + + +def fold_experiment_summary(model_config: base.RuntimeModelConfig) -> None: + if using_fixed_phase_mode(): + base.banner("RUNNER FOLDS | FIXED STRATIFIED 10-PHASE 8/1/1") + else: + base.banner("RUNNER FOLDS | REPEATED STRATIFIED HOLDOUT") + print(f"Experiment name : {FOLDS_EXPERIMENT_NAME}") + print(f"Resume folds : {RESUME_FOLDS}") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Dataset name : {base.current_dataset_name()}") + print(f"Split type : {base.SPLIT_TYPE}") + print(f"Split generation mode : {current_split_generation_mode()}") + print(f"Generation display : {split_generation_display_name()}") + if using_fixed_phase_mode(): + print(f"Phase count : {phase_count()}") + print(f"Phase val offset : {phase_val_offset()}") + print(f"Phase execution mode : {current_phase_execution_mode()}") + print("Train percent mode : 100% of phase-train only") + if current_phase_execution_mode() == "manual": + print(f"Selected phases : {phase_execution_indices_to_run()}") + else: + print(f"Split repeats : {NUM_STRATIFIED_SPLIT_REPEATS}") + print(f"Split execution mode : {current_split_execution_mode()}") + if current_split_execution_mode() == "manual": + print(f"Selected split indices: {split_repeat_indices_to_run()}") + print(f"Sampling mode : {current_percent_sampling_mode()}") + print(f"Repeat execution mode : {current_repeat_execution_mode()}") + if current_repeat_execution_mode() == "manual": + print(f"Selected repeat idxs : {subset_repeat_indices_to_run()}") + print(f"Percent execution mode: {current_percent_execution_mode()}") + if current_percent_execution_mode() == "manual": + print(f"Selected percents : {[s.percent_int for s in percent_specs_to_run()]}") + print(f"Strategies : {base.STRATEGIES}") + print( + "Percent repeats : " + + ", ".join(f"{spec.percent_int}% x{spec.repeat_count}" for spec in percent_specs()) + ) + print(f"Execution mode : {base.EXECUTION_MODE}") + print(f"Run smoke test : {base.RUN_SMOKE_TEST}") + print(f"Test iter control : {base.TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {base.TEST_ITERATION_T}") + print(f"Run overfit test : {base.RUN_OVERFIT_TEST}") + print(f"Run Optuna : {base.RUN_OPTUNA}") + print(f"Load existing studies : {base.LOAD_EXISTING_STUDIES}") + print(f"Repo backup enabled : {ASYNC_REPO_BACKUP_AFTER_PHASE}") + if ASYNC_REPO_BACKUP_AFTER_PHASE: + print(f"Repo backup target : hf://{HF_REPO_TYPE}/{HF_REPO_ID or ''}") + print(f"Repo backup cadence : every {HF_BACKUP_EVERY_N_PHASES} phases") + print(f"Initial backup on run : {HF_BACKUP_ON_START}") + print(f"Phase timing summary : {_phase_timing_summary_path()}") + print(f"Backbone : {model_config.backbone_display_name()}") + + +def config_snapshot(model_config: base.RuntimeModelConfig) -> dict[str, Any]: + base_config = { + name: _jsonify(getattr(base, name)) + for name in sorted(dir(base)) + if name.isupper() and not name.startswith("_") + and name not in RUNTIME_ONLY_CONFIG_KEYS + } + return { + "folds_runner": { + "SPLIT_GENERATION_MODE": current_split_generation_mode(), + "NUM_STRATIFIED_SPLIT_REPEATS": int(NUM_STRATIFIED_SPLIT_REPEATS), + "NUM_PHASES": int(NUM_PHASES), + "PHASE_VAL_OFFSET": int(PHASE_VAL_OFFSET), + "DATASET_PERCENT_REPEAT_COUNTS": _jsonify(DATASET_PERCENT_REPEAT_COUNTS), + "PERCENT_SAMPLING_MODE": current_percent_sampling_mode(), + "FOLDS_EXPERIMENT_NAME": str(FOLDS_EXPERIMENT_NAME), + "RESUME_FOLDS": bool(RESUME_FOLDS), + "REPEATED_HOLDOUT_ROOT": str(REPEATED_HOLDOUT_ROOT), + "EXPERIMENT_ROOT": str(EXPERIMENT_ROOT), + "EXPERIMENT_DB_PATH": str(EXPERIMENT_DB_PATH), + }, + "runner": base_config, + "model_config": model_config.to_payload(), + } + + +def portable_config_snapshot_for_fingerprint(snapshot: dict[str, Any]) -> dict[str, Any]: + portable = json.loads(json.dumps(snapshot, sort_keys=True)) + folds_runner = portable.get("folds_runner") + if isinstance(folds_runner, dict): + for key in PORTABLE_FINGERPRINT_FOLD_KEYS: + folds_runner.pop(key, None) + return portable + + +def config_fingerprint(snapshot: dict[str, Any]) -> str: + return stable_hash(json.dumps(portable_config_snapshot_for_fingerprint(snapshot), sort_keys=True)) + + +def dataset_fingerprint(sample_records: list[dict[str, str]]) -> str: + payload = { + "dataset_name": base.current_dataset_name(), + "records": [ + { + "filename": record["filename"], + "image_rel_path": record["image_rel_path"], + "mask_rel_path": record["mask_rel_path"], + "class_label": record.get("class_label"), + } + for record in sample_records + ], + } + return stable_hash(json.dumps(payload, sort_keys=True)) + + +def cycle_dirname(split_repeat_index: int) -> str: + return f"{primary_unit_name()}_{split_repeat_index:03d}" + + +def split_manifest_path(split_repeat_index: int) -> Path: + return SPLIT_MANIFESTS_DIR / f"{cycle_dirname(split_repeat_index)}.json" + + +def subset_manifest_path(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return SUBSET_MANIFESTS_DIR / ( + f"{cycle_dirname(split_repeat_index)}_pct_{percent_int:03d}_repeat_{subset_repeat_index:02d}.json" + ) + + +def repeat_root(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return ( + EXPERIMENT_ROOT + / cycle_dirname(split_repeat_index) + / f"pct_{percent_int:03d}" + / f"repeat_{subset_repeat_index:02d}" + ) + + +def run_dir_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "final") + + +def overfit_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "overfit_test") + + +def strategy_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir) + + +def fold_study_paths_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> tuple[Path, Path, Path]: + strategy_root = strategy_root_for(strategy, ctx, model_config) + return strategy_root, ensure_dir(strategy_root / "study"), ensure_dir(strategy_root / "trials") + + +def select_sample_records() -> tuple[list[dict[str, str]], Path]: + dataset_name = base.current_dataset_name() + images_dir, annotations_dir = base.current_dataset_dirs() + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {base.DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + + matched, missing_masks, missing_images = base.validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + sample_records = base.build_sample_records( + matched, + images_subdir=images_dir.relative_to(dataset_root).as_posix(), + annotations_subdir=annotations_dir.relative_to(dataset_root).as_posix(), + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = base.current_pipeline_check_path() + if pipeline_check_path is not None: + base.validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + return sample_records, dataset_root + + +def record_filenames(records: list[dict[str, str]]) -> list[str]: + return [str(record["filename"]) for record in records] + + +def duplicate_filenames(records: list[dict[str, str]]) -> list[str]: + counts = Counter(record_filenames(records)) + return sorted(name for name, count in counts.items() if count > 1) + + +def format_filename_preview(filenames: list[str], *, limit: int = 5) -> str: + preview = filenames[:limit] + suffix = "" if len(filenames) <= limit else f" ... (+{len(filenames) - limit} more)" + return f"{preview}{suffix}" + + +def overlap_preview(leaks: dict[str, list[str]], *, limit: int = 5) -> str: + if not leaks: + return "[]" + key = sorted(leaks.keys())[0] + return f"{key}: {format_filename_preview(leaks[key], limit=limit)}" + + +def validate_disjoint_record_sets( + record_sets: dict[str, list[dict[str, str]]], + *, + context: str, + expected_filenames: set[str] | None = None, +) -> None: + split_filenames: dict[str, list[str]] = {} + for split_name, records in record_sets.items(): + duplicates = duplicate_filenames(records) + if duplicates: + raise RuntimeError( + f"Duplicate filenames detected inside {context} {split_name}: " + f"{format_filename_preview(duplicates)}" + ) + split_filenames[split_name] = record_filenames(records) + + leaks = base.check_data_leakage(split_filenames) + if leaks: + raise RuntimeError( + f"Data leakage detected for {context}: {overlap_preview(leaks)}" + ) + + if expected_filenames is not None: + actual_filenames = set().union(*(set(values) for values in split_filenames.values())) + missing = sorted(expected_filenames - actual_filenames) + extra = sorted(actual_filenames - expected_filenames) + if missing or extra: + details: list[str] = [] + if missing: + details.append(f"missing={format_filename_preview(missing)}") + if extra: + details.append(f"extra={format_filename_preview(extra)}") + raise RuntimeError( + f"{context} does not match the expected dataset membership: {'; '.join(details)}" + ) + + +def validate_fixed_phase_dataset_requirements(sample_records: list[dict[str, str]]) -> None: + class_distribution = base.compute_class_distribution(sample_records) + if class_distribution is None: + raise RuntimeError( + "fixed phase mode requires class-aware records with class_label metadata." + ) + insufficient = { + label: int(count) + for label, count in class_distribution.items() + if int(count) < phase_count() + } + if insufficient: + raise RuntimeError( + "fixed phase mode requires enough samples to place every class in every phase. " + f"Need >= {phase_count()} samples per class, got {insufficient}." + ) + + +def build_fixed_stratified_phase_folds( + sample_records: list[dict[str, str]], + *, + seed: int, +) -> dict[int, list[dict[str, str]]]: + folds: dict[int, list[dict[str, str]]] = {index: [] for index in phase_indices()} + grouped = base.group_records_by_class(sample_records) + for class_label in sorted(grouped.keys()): + records = base.deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"phase_partition::{phase_count()}::{class_label}", + ) + for record_index, record in enumerate(records): + fold_index = (record_index % phase_count()) + 1 + folds[fold_index].append(dict(record)) + + for fold_index in phase_indices(): + folds[fold_index] = base.deterministic_shuffle_records( + folds[fold_index], + seed=seed, + tag=f"phase_partition::{phase_count()}::fold::{fold_index:03d}", + ) + return folds + + +def validate_fixed_phase_folds( + phase_folds: dict[int, list[dict[str, str]]], + *, + sample_records: list[dict[str, str]], +) -> None: + if sorted(phase_folds.keys()) != phase_indices(): + raise RuntimeError( + f"Expected fixed phase folds for indices {phase_indices()}, got {sorted(phase_folds.keys())}." + ) + validate_disjoint_record_sets( + {f"fold_{fold_index:03d}": records for fold_index, records in sorted(phase_folds.items())}, + context="fixed phase fold partition", + expected_filenames={record["filename"] for record in sample_records}, + ) + + +def build_phase_base_split( + phase_folds: dict[int, list[dict[str, str]]], + *, + phase_index: int, + seed: int, +) -> dict[str, list[dict[str, str]]]: + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + train_records: list[dict[str, str]] = [] + for fold_index in phase_indices(): + if fold_index in {val_fold_index, test_fold_index}: + continue + train_records.extend(dict(record) for record in phase_folds[fold_index]) + return { + "train": base.deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"phase::{phase_index:03d}::train", + ), + "val": base.deterministic_shuffle_records( + phase_folds[val_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::val", + ), + "test": base.deterministic_shuffle_records( + phase_folds[test_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::test", + ), + } + + +def build_base_split_for_repeat(sample_records: list[dict[str, str]], split_seed: int) -> dict[str, list[dict[str, str]]]: + dataset_name = base.current_dataset_name() + if dataset_name == "BUSI_with_classes": + split_policy = repeated_holdout_split_policy() + if split_policy != "stratified": + raise ValueError( + "RUNNER_FOLDS.py requires BUSI_with_classes to use BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + return base.build_stratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + return base.build_unstratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + + +def validate_base_split( + base_splits: dict[str, list[dict[str, str]]], + *, + split_repeat_index: int, + expected_filenames: set[str] | None = None, +) -> None: + context = f"{primary_unit_name()}={split_repeat_index:03d}" + validate_disjoint_record_sets( + base_splits, + context=context, + expected_filenames=expected_filenames, + ) + + +def validate_phase_coverage( + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]], + *, + sample_records: list[dict[str, str]], +) -> None: + expected_phase_indices = phase_indices() + if sorted(phase_splits_by_index.keys()) != expected_phase_indices: + raise RuntimeError( + f"Expected materialized phases {expected_phase_indices}, got {sorted(phase_splits_by_index.keys())}." + ) + + expected_filenames = {record["filename"] for record in sample_records} + train_counts: Counter[str] = Counter() + val_counts: Counter[str] = Counter() + test_counts: Counter[str] = Counter() + + for phase_index, phase_splits in sorted(phase_splits_by_index.items()): + validate_disjoint_record_sets( + phase_splits, + context=f"phase={phase_index:03d}", + expected_filenames=expected_filenames, + ) + train_counts.update(record_filenames(phase_splits["train"])) + val_counts.update(record_filenames(phase_splits["val"])) + test_counts.update(record_filenames(phase_splits["test"])) + + expected_counts = { + "train": phase_count() - 2, + "val": 1, + "test": 1, + } + counters_by_name = { + "train": train_counts, + "val": val_counts, + "test": test_counts, + } + for split_name, expected_count in expected_counts.items(): + offending = sorted( + filename + for filename in expected_filenames + if int(counters_by_name[split_name].get(filename, 0)) != expected_count + ) + if offending: + raise RuntimeError( + f"Invalid global phase coverage for {split_name}: expected each filename to appear " + f"{expected_count} time(s), offenders={format_filename_preview(offending)}" + ) + + +def build_subset_for_repeat( + train_records: list[dict[str, str]], + *, + percent_fraction: float, + subset_seed: int, +) -> list[dict[str, str]]: + if percent_fraction >= 1.0: + return [dict(record) for record in train_records] + subsets = base.build_nested_train_subsets( + train_records, + [percent_fraction], + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + return subsets[base.percent_label(percent_fraction)] + + +def build_incremental_subset_chain( + train_records: list[dict[str, str]], + *, + active_specs: list[PercentRepeatSpec], + subset_seed: int, +) -> dict[str, list[dict[str, str]]]: + fractions = [spec.fraction for spec in active_specs] + return base.build_nested_train_subsets( + train_records, + fractions, + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + + +def iter_manifested_contexts( + split_repeat_indices: list[int] | None = None, + subset_repeat_indices: list[int] | None = None, +) -> Iterator[FoldRunContext]: + selected_indices = all_split_repeat_indices() if split_repeat_indices is None else list(split_repeat_indices) + selected_subset_repeats = ( + subset_repeat_indices_to_run() if subset_repeat_indices is None else list(subset_repeat_indices) + ) + selected_subset_repeat_set = set(selected_subset_repeats) + for split_repeat_index in selected_indices: + for spec in percent_specs_to_run(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + if subset_repeat_index not in selected_subset_repeat_set: + continue + yield load_context(split_repeat_index, spec.percent_int, subset_repeat_index) + + +def validate_subset_records( + train_records: list[dict[str, str]], + subset_records: list[dict[str, str]], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + train_filenames = {record["filename"] for record in train_records} + subset_filenames = [record["filename"] for record in subset_records] + cycle_context = f"{primary_unit_name()}={split_repeat_index:03d}" + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames found in subset {cycle_context}, " + f"percent={percent_int}, repeat={subset_repeat_index}." + ) + outside_train = sorted(set(subset_filenames) - train_filenames) + if outside_train: + raise RuntimeError( + f"Subset contains filenames outside the base train split for " + f"{cycle_context}, percent={percent_int}, repeat={subset_repeat_index}: {outside_train[:5]}" + ) + + +"""============================================================================= +SQLITE LEDGER +============================================================================= +""" + + +def require_ledger() -> sqlite3.Connection: + if LEDGER_CONN is None: + raise RuntimeError("Ledger is not initialized.") + return LEDGER_CONN + + +def ledger_execute(sql: str, params: tuple[Any, ...] = ()) -> sqlite3.Cursor: + conn = require_ledger() + cursor = conn.execute(sql, params) + conn.commit() + return cursor + + +def setup_ledger(path: Path) -> sqlite3.Connection: + ensure_dir(path.parent) + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=FULL") + conn.execute( + """ + CREATE TABLE IF NOT EXISTS experiment_meta ( + experiment_name TEXT PRIMARY KEY, + config_fingerprint TEXT NOT NULL, + config_json TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + base_seed INTEGER NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS split_manifests ( + split_repeat_index INTEGER PRIMARY KEY, + split_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_count INTEGER NOT NULL, + val_count INTEGER NOT NULL, + test_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS subset_manifests ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_subset_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS run_status ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + dataset_fraction REAL NOT NULL, + split_seed INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + split_manifest_path TEXT NOT NULL, + subset_manifest_path TEXT NOT NULL, + run_dir TEXT NOT NULL, + status TEXT NOT NULL, + stage TEXT NOT NULL, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at TEXT, + updated_at TEXT NOT NULL, + heartbeat_at TEXT, + completed_at TEXT, + last_epoch INTEGER, + latest_checkpoint_path TEXT, + best_checkpoint_path TEXT, + evaluation_path TEXT, + best_metric_name TEXT, + best_metric_value REAL, + elapsed_seconds REAL, + error_text TEXT, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS final_metrics ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + metric_name TEXT NOT NULL, + mean REAL NOT NULL, + std REAL, + run_dir TEXT NOT NULL, + evaluation_path TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy, metric_name) + ) + """ + ) + conn.commit() + return conn + + +def fetch_one(sql: str, params: tuple[Any, ...]) -> sqlite3.Row | None: + return require_ledger().execute(sql, params).fetchone() + + +def load_run_status(key: RunKey) -> sqlite3.Row | None: + return fetch_one( + """ + SELECT * + FROM run_status + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + + +def upsert_experiment_meta( + *, + snapshot: dict[str, Any], + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO experiment_meta ( + experiment_name, + config_fingerprint, + config_json, + dataset_fingerprint, + base_seed, + created_at + ) VALUES (?, ?, ?, ?, ?, ?) + """, + ( + FOLDS_EXPERIMENT_NAME, + config_hash, + json.dumps(snapshot, sort_keys=True), + data_hash, + int(base.SEED), + now_utc_iso(), + ), + ) + + +def existing_experiment_meta() -> sqlite3.Row | None: + return fetch_one( + "SELECT * FROM experiment_meta WHERE experiment_name = ?", + (FOLDS_EXPERIMENT_NAME,), + ) + + +def ledger_row_count(table_name: str) -> int: + row = require_ledger().execute(f"SELECT COUNT(*) AS count FROM {table_name}").fetchone() + return int(row["count"]) if row is not None else 0 + + +def upsert_split_manifest_row( + *, + split_repeat_index: int, + split_seed: int, + manifest_path: Path, + train_count: int, + val_count: int, + test_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO split_manifests ( + split_repeat_index, + split_seed, + manifest_path, + train_count, + val_count, + test_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + split_seed, + str(manifest_path.resolve()), + train_count, + val_count, + test_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_subset_manifest_row( + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, + subset_seed: int, + manifest_path: Path, + subset_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO subset_manifests ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + subset_seed, + manifest_path, + train_subset_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + percent_int, + subset_repeat_index, + subset_seed, + str(manifest_path.resolve()), + subset_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_run_plan_row( + *, + key: RunKey, + dataset_fraction: float, + split_seed: int, + subset_seed: int, + split_manifest: Path, + subset_manifest: Path, + run_dir: Path, +) -> None: + existing = load_run_status(key) + if existing is not None: + return + ledger_execute( + """ + INSERT INTO run_status ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + dataset_fraction, + split_seed, + subset_seed, + split_manifest_path, + subset_manifest_path, + run_dir, + status, + stage, + attempt_count, + updated_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + dataset_fraction, + split_seed, + subset_seed, + str(split_manifest.resolve()), + str(subset_manifest.resolve()), + str(run_dir.resolve()), + "planned", + "manifested", + 0, + now_utc_iso(), + ), + ) + + +def mark_stale_running_as_interrupted() -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE status = 'running' + """, + (now_utc_iso(),), + ) + + +def mark_run_running(key: RunKey, *, stage: str) -> None: + row = load_run_status(key) + attempt_count = 1 if row is None else int(row["attempt_count"]) + 1 + started_at = row["started_at"] if row is not None else None + if not started_at: + started_at = now_utc_iso() + ledger_execute( + """ + UPDATE run_status + SET status = 'running', + stage = ?, + attempt_count = ?, + started_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + attempt_count, + started_at, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_stage(key: RunKey, *, stage: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET stage = ?, + status = 'running', + updated_at = ?, + heartbeat_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def update_run_checkpoint_progress( + key: RunKey, + *, + checkpoint_path: Path, + epoch: int, + best_metric_name: str, + best_metric_value: float, + elapsed_seconds: float, +) -> None: + column_name = "best_checkpoint_path" if checkpoint_path.name == "best.pt" else "latest_checkpoint_path" + sql = f""" + UPDATE run_status + SET {column_name} = ?, + last_epoch = ?, + best_metric_name = ?, + best_metric_value = ?, + elapsed_seconds = ?, + status = 'running', + stage = 'training', + heartbeat_at = ?, + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """ + ledger_execute( + sql, + ( + str(checkpoint_path.resolve()), + int(epoch), + str(best_metric_name), + float(best_metric_value), + float(elapsed_seconds), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_failed(key: RunKey, error_text: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'failed', + updated_at = ?, + error_text = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + error_text, + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_interrupted(key: RunKey) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def ingest_evaluation_into_db(key: RunKey, evaluation_path: Path, run_dir: Path) -> None: + payload = base.load_json(evaluation_path) + metrics = payload.get("metrics", {}) + ledger_execute( + """ + DELETE FROM final_metrics + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + conn = require_ledger() + for metric_name, metric_payload in metrics.items(): + conn.execute( + """ + INSERT INTO final_metrics ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + metric_name, + mean, + std, + run_dir, + evaluation_path + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + str(metric_name), + float(metric_payload.get("mean", 0.0)), + float(metric_payload.get("std")) if metric_payload.get("std") is not None else None, + str(run_dir.resolve()), + str(evaluation_path.resolve()), + ), + ) + conn.commit() + best_metric_name = str(payload.get("best_metric_name", "")) + best_metric_value = None + if best_metric_name and best_metric_name in metrics: + best_metric_value = float(metrics[best_metric_name]["mean"]) + ledger_execute( + """ + UPDATE run_status + SET status = 'completed', + stage = 'done', + evaluation_path = ?, + best_metric_name = COALESCE(?, best_metric_name), + best_metric_value = COALESCE(?, best_metric_value), + completed_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + str(evaluation_path.resolve()), + best_metric_name or None, + best_metric_value, + now_utc_iso(), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def completed_run_rows() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + ORDER BY split_repeat_index, dataset_percent, subset_repeat_index, strategy + """ + ).fetchall() + ) + + +def metric_rows_for_completed_runs() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT + rs.split_repeat_index, + rs.dataset_percent, + rs.subset_repeat_index, + rs.strategy, + rs.run_dir, + rs.split_manifest_path, + rs.subset_manifest_path, + rs.evaluation_path, + fm.metric_name, + fm.mean AS metric_mean, + fm.std AS metric_std + FROM final_metrics fm + JOIN run_status rs + ON rs.split_repeat_index = fm.split_repeat_index + AND rs.dataset_percent = fm.dataset_percent + AND rs.subset_repeat_index = fm.subset_repeat_index + AND rs.strategy = fm.strategy + WHERE rs.status = 'completed' + ORDER BY rs.split_repeat_index, rs.dataset_percent, rs.subset_repeat_index, rs.strategy, fm.metric_name + """ + ).fetchall() + ) + + +def export_stats() -> None: + ensure_dir(EXPORTS_DIR) + rows = metric_rows_for_completed_runs() + split_manifest_cache: dict[str, dict[str, Any]] = {} + + def split_manifest_payload(path_text: str) -> dict[str, Any]: + cached = split_manifest_cache.get(path_text) + if cached is None: + cached = base.load_json(Path(path_text)) + split_manifest_cache[path_text] = cached + return cached + + raw_rows_by_run: dict[tuple[int, int, int, int], dict[str, Any]] = {} + for row in rows: + key = ( + int(row["split_repeat_index"]), + int(row["dataset_percent"]), + int(row["subset_repeat_index"]), + int(row["strategy"]), + ) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + raw_row = raw_rows_by_run.setdefault( + key, + { + "split_repeat_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": row["run_dir"], + "split_manifest_path": row["split_manifest_path"], + "subset_manifest_path": row["subset_manifest_path"], + "evaluation_path": row["evaluation_path"], + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + }, + ) + if manifest_payload.get("phase_index") is not None: + raw_row["phase_index"] = int(manifest_payload["phase_index"]) + raw_row["phase_val_fold_index"] = int(manifest_payload["phase_val_fold_index"]) + raw_row["phase_test_fold_index"] = int(manifest_payload["phase_test_fold_index"]) + raw_row[f"{row['metric_name']}_mean"] = float(row["metric_mean"]) + raw_row[f"{row['metric_name']}_std"] = ( + float(row["metric_std"]) if row["metric_std"] is not None else None + ) + + raw_rows = list(raw_rows_by_run.values()) + raw_rows.sort( + key=lambda item: ( + item["split_repeat_index"], + item["dataset_percent"], + item["subset_repeat_index"], + item["strategy"], + ) + ) + + if raw_rows: + raw_fieldnames = sorted({key for row in raw_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=raw_fieldnames) + writer.writeheader() + writer.writerows(raw_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "raw_run_metrics.csv") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", raw_rows) + else: + atomic_write_text(EXPORTS_DIR / "raw_run_metrics.csv", "") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", []) + + grouped: dict[tuple[int, int], dict[str, Any]] = {} + for row in rows: + group_key = (int(row["dataset_percent"]), int(row["strategy"])) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + bucket = grouped.setdefault( + group_key, + { + "dataset_percent": int(row["dataset_percent"]), + "strategy": int(row["strategy"]), + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + "_phase_indices": set(), + "_metric_values": {}, + }, + ) + if manifest_payload.get("phase_index") is not None: + bucket["_phase_indices"].add(int(manifest_payload["phase_index"])) + bucket["_metric_values"].setdefault(str(row["metric_name"]), []).append( + { + "mean": float(row["metric_mean"]), + "std": float(row["metric_std"]) if row["metric_std"] is not None else None, + } + ) + + aggregated_rows: list[dict[str, Any]] = [] + for (_percent_int, _strategy), bucket in sorted(grouped.items()): + row = { + "dataset_percent": bucket["dataset_percent"], + "strategy": bucket["strategy"], + "split_generation_mode": bucket["split_generation_mode"], + } + metric_values: dict[str, list[dict[str, float | None]]] = bucket["_metric_values"] + row["n_runs"] = max((len(values) for values in metric_values.values()), default=0) + if bucket["_phase_indices"]: + phase_indices = sorted(int(value) for value in bucket["_phase_indices"]) + row["completed_phase_count"] = len(phase_indices) + row["completed_phases"] = ",".join(str(value) for value in phase_indices) + for metric_name, values in sorted(metric_values.items()): + means = [value["mean"] for value in values] + stds = [value["std"] for value in values if value["std"] is not None] + row[f"{metric_name}_mean"] = float(base.np.mean(means)) if means else None + row[f"{metric_name}_std"] = float(base.np.std(means)) if means else None + row[f"{metric_name}_within_run_std_mean"] = float(base.np.mean(stds)) if stds else None + aggregated_rows.append(row) + + if aggregated_rows: + aggregated_fieldnames = sorted({key for row in aggregated_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=aggregated_fieldnames) + writer.writeheader() + writer.writerows(aggregated_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", aggregated_rows) + else: + atomic_write_text(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv", "") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", []) + + +"""============================================================================= +BASE MODULE PATCHES +============================================================================= +""" + + +def patched_save_json(path: str | Path, payload: Any) -> None: + atomic_save_json(path, payload) + + +def _context_matches_percent(ctx: FoldRunContext | None, percent: float) -> bool: + if ctx is None: + return False + return abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12 + + +def patched_percent_root(percent: float) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return ensure_dir(CURRENT_FOLD_CONTEXT.repeat_root) + return ORIGINAL_PERCENT_ROOT(percent) + + +def patched_strategy_root_for_percent( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return strategy_root_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STRATEGY_ROOT_FOR_PERCENT(strategy, percent, model_config) + + +def patched_final_root_for_strategy( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return run_dir_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_FINAL_ROOT_FOR_STRATEGY(strategy, percent, model_config) + + +def patched_study_paths_for( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return fold_study_paths_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STUDY_PATHS_FOR(strategy, percent, model_config) + + +def patched_save_checkpoint( + path: Path, + *, + run_type: str, + model: base.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + scaler: Any, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": base._unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + base.validate_checkpoint_payload( + Path(path), + payload, + required_keys=base.checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + atomic_torch_save(path, payload) + base.write_checkpoint_manifest(path, payload) + + if run_type != "final" or CURRENT_FOLD_CONTEXT is None: + return + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(run_config["strategy"]), + ) + update_run_checkpoint_progress( + run_key, + checkpoint_path=Path(path), + epoch=int(epoch), + best_metric_name=str(best_metric_name), + best_metric_value=float(best_metric_value), + elapsed_seconds=float(elapsed_seconds), + ) + + +def patched_run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: base.DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, dict[str, float]]: + if CURRENT_FOLD_CONTEXT is not None and LEDGER_CONN is not None: + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(strategy), + ) + mark_run_stage(run_key, stage="evaluating") + return ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +def install_base_patches() -> None: + base.save_json = patched_save_json + base.percent_root = patched_percent_root + base.strategy_root_for_percent = patched_strategy_root_for_percent + base.final_root_for_strategy = patched_final_root_for_strategy + base.study_paths_for = patched_study_paths_for + base.save_checkpoint = patched_save_checkpoint + base.run_evaluation_for_run = patched_run_evaluation_for_run + base.RESUME_IDENTITY_KEYS = BASE_RESUME_IDENTITY_KEYS + ( + "folds_experiment_name", + "split_repeat_index", + "subset_repeat_index", + "split_seed", + "subset_seed", + "base_split_manifest_path", + "subset_manifest_path", + ) + if RESUME_FOLDS and base.RUN_OPTUNA: + base.LOAD_EXISTING_STUDIES = True + + +@contextmanager +def activate_context(ctx: FoldRunContext) -> Iterator[None]: + global CURRENT_FOLD_CONTEXT + previous = CURRENT_FOLD_CONTEXT + CURRENT_FOLD_CONTEXT = ctx + try: + yield + finally: + CURRENT_FOLD_CONTEXT = previous + + +"""============================================================================= +EXPERIMENT PLAN MATERIALIZATION +============================================================================= +""" + + +def create_split_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + base_splits: dict[str, list[dict[str, str]]], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "base_splits": { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + }, + "counts": {split_name: len(records) for split_name, records in base_splits.items()}, + "class_distributions": { + split_name: base.compute_class_distribution(records) + for split_name, records in base_splits.items() + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + payload["phase_partition_seed"] = int(split_seed) + return payload + + +def create_subset_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + percent_int: int, + percent_fraction: float, + subset_repeat_index: int, + subset_seed: int, + split_manifest: Path, + base_splits: dict[str, list[dict[str, str]]], + subset_records: list[dict[str, str]], + subset_sampling_source: str, + sampling_chain_dataset_percents: list[int], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "dataset_percent": percent_int, + "dataset_fraction": percent_fraction, + "subset_repeat_index": subset_repeat_index, + "subset_seed": subset_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "subset_sampling_source": subset_sampling_source, + "sampling_chain_dataset_percents": [int(value) for value in sampling_chain_dataset_percents], + "parent_split_manifest_path": str(split_manifest.resolve()), + "train_records": [dict(record) for record in subset_records], + "val_records": [dict(record) for record in base_splits["val"]], + "test_records": [dict(record) for record in base_splits["test"]], + "base_train_records": [dict(record) for record in base_splits["train"]], + "counts": { + "base_train": len(base_splits["train"]), + "train_subset": len(subset_records), + "val": len(base_splits["val"]), + "test": len(base_splits["test"]), + }, + "class_distributions": { + "base_train": base.compute_class_distribution(base_splits["train"]), + "train_subset": base.compute_class_distribution(subset_records), + "val": base.compute_class_distribution(base_splits["val"]), + "test": base.compute_class_distribution(base_splits["test"]), + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + return payload + + +def validate_materialized_phase_manifests(*, sample_records: list[dict[str, str]]) -> None: + if not using_fixed_phase_mode(): + return + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + for phase_index in phase_indices(): + manifest_path = split_manifest_path(phase_index) + if not manifest_path.exists(): + raise RuntimeError(f"Missing phase manifest for phase={phase_index:03d}: {manifest_path}") + payload = base.load_json(manifest_path) + if str(payload.get("split_generation_mode", "")).strip().lower() != "fixed_stratified_phases_8_1_1": + raise RuntimeError( + f"Expected fixed phase split_generation_mode in {manifest_path}, got " + f"{payload.get('split_generation_mode')!r}." + ) + if int(payload.get("phase_index", -1)) != phase_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_index={payload.get('phase_index')!r}, " + f"expected {phase_index}." + ) + if int(payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_count={payload.get('phase_count')!r}, " + f"expected {phase_count()}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_val_fold_index=" + f"{payload.get('phase_val_fold_index')!r}, expected {val_fold_index}." + ) + if int(payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_test_fold_index=" + f"{payload.get('phase_test_fold_index')!r}, expected {test_fold_index}." + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + + +def validate_materialized_subset_manifests() -> None: + if not using_fixed_phase_mode(): + return + for phase_index in phase_indices(): + split_payload = base.load_json(split_manifest_path(phase_index)) + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + manifest_path = subset_manifest_path(phase_index, spec.percent_int, subset_repeat_index) + if not manifest_path.exists(): + raise RuntimeError( + f"Missing subset manifest for phase={phase_index:03d}, " + f"percent={spec.percent_int}, repeat={subset_repeat_index}: {manifest_path}" + ) + subset_payload = base.load_json(manifest_path) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=phase_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + +def validate_loaded_context_payloads( + split_payload: dict[str, Any], + subset_payload: dict[str, Any], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + base_splits = { + split_name: [dict(record) for record in split_payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_base_split(base_splits, split_repeat_index=split_repeat_index) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + validate_disjoint_record_sets( + { + "train_subset": train_records, + "val": val_records, + "test": test_records, + }, + context=( + f"{primary_unit_name()}={split_repeat_index:03d}, " + f"percent={percent_int}, repeat={subset_repeat_index}" + ), + ) + validate_subset_records( + base_splits["train"], + train_records, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + if set(record_filenames(base_train_records)) != set(record_filenames(base_splits["train"])): + raise RuntimeError( + f"Subset manifest base train records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(val_records)) != set(record_filenames(base_splits["val"])): + raise RuntimeError( + f"Subset manifest validation records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(test_records)) != set(record_filenames(base_splits["test"])): + raise RuntimeError( + f"Subset manifest test records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if using_fixed_phase_mode(): + phase_index = int(split_payload.get("phase_index", split_repeat_index)) + if int(split_payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{split_payload.get('phase_count')!r}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(split_payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{split_payload.get('phase_val_fold_index')!r}." + ) + if int(split_payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{split_payload.get('phase_test_fold_index')!r}." + ) + if int(subset_payload.get("phase_index", phase_index)) != phase_index: + raise RuntimeError( + f"Subset manifest phase_index={subset_payload.get('phase_index')!r} does not match " + f"phase={phase_index:03d}." + ) + if int(subset_payload.get("phase_count", phase_count())) != phase_count(): + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{subset_payload.get('phase_count')!r}." + ) + if int(subset_payload.get("phase_val_fold_index", val_fold_index)) != val_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{subset_payload.get('phase_val_fold_index')!r}." + ) + if int(subset_payload.get("phase_test_fold_index", test_fold_index)) != test_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{subset_payload.get('phase_test_fold_index')!r}." + ) + + +def materialize_experiment_plan( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + ensure_dir(EXPERIMENT_ROOT) + ensure_dir(SPLIT_MANIFESTS_DIR) + ensure_dir(SUBSET_MANIFESTS_DIR) + ensure_dir(EXPORTS_DIR) + + snapshot = config_snapshot(model_config) + config_hash = config_fingerprint(snapshot) + data_hash = dataset_fingerprint(sample_records) + sampling_mode = current_percent_sampling_mode() + upsert_experiment_meta(snapshot=snapshot, config_hash=config_hash, data_hash=data_hash) + expected_filenames = {record["filename"] for record in sample_records} + partition_seed_value = partition_seed() + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + fixed_phase_folds: dict[int, list[dict[str, str]]] | None = None + if using_fixed_phase_mode(): + validate_fixed_phase_dataset_requirements(sample_records) + fixed_phase_folds = build_fixed_stratified_phase_folds( + sample_records, + seed=partition_seed_value, + ) + validate_fixed_phase_folds(fixed_phase_folds, sample_records=sample_records) + + for split_repeat_index in all_split_repeat_indices(): + phase_index = None + phase_val_fold_index = None + phase_test_fold_index = None + if using_fixed_phase_mode(): + if fixed_phase_folds is None: + raise RuntimeError("Fixed phase folds were not initialized.") + split_seed = partition_seed_value + phase_index = split_repeat_index + phase_val_fold_index, phase_test_fold_index = phase_fold_indices(phase_index) + base_splits = build_phase_base_split( + fixed_phase_folds, + phase_index=phase_index, + seed=split_seed, + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + } + else: + split_seed = fold_seed(f"split::{split_repeat_index}") + base_splits = build_base_split_for_repeat(sample_records, split_seed) + validate_base_split( + base_splits, + split_repeat_index=split_repeat_index, + expected_filenames=expected_filenames, + ) + + split_manifest = split_manifest_path(split_repeat_index) + split_payload = create_split_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + base_splits=base_splits, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(split_manifest, split_payload) + upsert_split_manifest_row( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + manifest_path=split_manifest, + train_count=len(base_splits["train"]), + val_count=len(base_splits["val"]), + test_count=len(base_splits["test"]), + config_hash=config_hash, + data_hash=data_hash, + ) + + if sampling_mode == "independent": + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + subset_seed = fold_seed( + f"subset::{split_repeat_index}::{spec.percent_int}::{subset_repeat_index}" + ) + subset_records = build_subset_for_repeat( + base_splits["train"], + percent_fraction=spec.fraction, + subset_seed=subset_seed, + ) + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="independent", + sampling_chain_dataset_percents=[spec.percent_int], + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + continue + + max_subset_repeat_index = max(spec.repeat_count for spec in percent_specs()) + for subset_repeat_index in range(1, max_subset_repeat_index + 1): + active_specs = active_specs_for_subset_repeat(subset_repeat_index) + if not active_specs: + continue + subset_seed = fold_seed(f"subset::{split_repeat_index}::repeat::{subset_repeat_index}") + subset_chain = build_incremental_subset_chain( + base_splits["train"], + active_specs=active_specs, + subset_seed=subset_seed, + ) + chain_dataset_percents = [spec.percent_int for spec in active_specs] + for spec in active_specs: + subset_records = subset_chain[base.percent_label(spec.fraction)] + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="incremental_chain", + sampling_chain_dataset_percents=chain_dataset_percents, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + + if using_fixed_phase_mode(): + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +def validate_or_create_experiment( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + meta = existing_experiment_meta() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not RESUME_FOLDS and not ignore_resume_folds_gate: + if meta is not None: + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + if meta is None: + if ledger_row_count("split_manifests") > 0 or ledger_row_count("run_status") > 0: + raise RuntimeError( + f"Experiment DB {EXPERIMENT_DB_PATH} contains run state but is missing experiment metadata." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + current_snapshot = config_snapshot(model_config) + current_hash = config_fingerprint(current_snapshot) + current_data_hash = dataset_fingerprint(sample_records) + stored_config_hash = str(meta["config_fingerprint"]) + stored_portable_hash = "" + try: + stored_config_json = json.loads(str(meta["config_json"])) + stored_portable_hash = config_fingerprint(stored_config_json) + except Exception as exc: + print(f"[Resume] Could not recompute portable config fingerprint from stored metadata: {exc}") + if stored_config_hash != current_hash and stored_portable_hash != current_hash: + raise RuntimeError( + f"Existing experiment config fingerprint does not match current configuration for {EXPERIMENT_ROOT}." + ) + if stored_config_hash != current_hash and stored_portable_hash == current_hash: + print( + "[Resume] Accepted existing experiment metadata with a portable config fingerprint match " + "(machine-specific paths/runtime resume flag changed)." + ) + if str(meta["dataset_fingerprint"]) != current_data_hash: + raise RuntimeError( + f"Existing experiment dataset fingerprint does not match current dataset contents for {EXPERIMENT_ROOT}." + ) + if using_fixed_phase_mode(): + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +"""============================================================================= +RUNTIME BUNDLE CONSTRUCTION +============================================================================= +""" + + +def load_context(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> FoldRunContext: + split_payload = base.load_json(split_manifest_path(split_repeat_index)) + subset_payload = base.load_json(subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index)) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + return FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=percent_int, + percent_fraction=float(subset_payload["dataset_fraction"]), + split_seed=int(split_payload["split_seed"]), + subset_seed=int(subset_payload["subset_seed"]), + repeat_root=repeat_root(split_repeat_index, percent_int, subset_repeat_index), + split_manifest_path=split_manifest_path(split_repeat_index), + subset_manifest_path=subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index), + ) + + +def build_fold_data_bundle(ctx: FoldRunContext) -> base.DataBundle: + split_payload = base.load_json(ctx.split_manifest_path) + subset_payload = base.load_json(ctx.subset_manifest_path) + dataset_root = Path(base.current_dataset_dirs()[0]).parent.resolve() + phase_index = ( + int(split_payload.get("phase_index", ctx.split_repeat_index)) + if str(split_payload.get("split_generation_mode", "")).strip().lower() == "fixed_stratified_phases_8_1_1" + else None + ) + cycle_token = f"phase{phase_index:03d}" if phase_index is not None else f"split{ctx.split_repeat_index:03d}" + normalization_cache_path = ( + ctx.repeat_root + / ( + f"norm_stats_{base.normalization_cache_tag()}_{base.SPLIT_TYPE}_{ctx.percent_int:03d}pct_" + f"{cycle_token}_repeat{ctx.subset_repeat_index:02d}.json" + ) + ) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + + base_train_class_distribution = base.compute_class_distribution(base_train_records) + train_class_distribution = base.compute_class_distribution(train_records) + val_class_distribution = base.compute_class_distribution(val_records) + test_class_distribution = base.compute_class_distribution(test_records) + + base.print_loaded_class_distribution( + split_type=base.SPLIT_TYPE, + train_subset_key=str(ctx.percent_int), + base_train_records=base_train_records, + train_records=train_records, + val_records=val_records, + test_records=test_records, + ) + + global_mean, global_std, normalization_source = base.compute_busi_statistics( + dataset_root=dataset_root, + sample_records=train_records, + cache_path=normalization_cache_path, + ) + + payload = { + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "dataset_splits_path": str(ctx.split_manifest_path.resolve()), + "dataset_root": str(dataset_root), + "split_source": ( + "fixed_phase_manifest" + if phase_index is not None + else "repeated_holdout_manifest" + ), + "split_generation_mode": str(split_payload.get("split_generation_mode", current_split_generation_mode())), + "split_type": base.SPLIT_TYPE, + "percent_sampling_mode": str( + subset_payload.get("percent_sampling_mode", split_payload.get("percent_sampling_mode", "independent")) + ), + "dataset_percent": ctx.percent_fraction, + "train_subset_key": str(ctx.percent_int), + "train_subset_variant": int(ctx.subset_repeat_index), + "train_subset_source": str(subset_payload.get("subset_sampling_source", "repeated_holdout_repeat")), + "selected_split_manifest_path": str(ctx.subset_manifest_path.resolve()), + "sampling_chain_dataset_percents": [ + int(value) for value in subset_payload.get("sampling_chain_dataset_percents", [ctx.percent_int]) + ], + "base_train_count": len(base_train_records), + "train_count": len(train_records), + "val_count": len(val_records), + "test_count": len(test_records), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(normalization_cache_path.resolve()), + "normalization_source": normalization_source, + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "split_seed": ctx.split_seed, + "subset_seed": ctx.subset_seed, + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "folds_experiment_root": str(EXPERIMENT_ROOT.resolve()), + } + if phase_index is not None: + payload["phase_index"] = phase_index + payload["phase_count"] = int(split_payload["phase_count"]) + payload["phase_val_fold_index"] = int(split_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(split_payload["phase_test_fold_index"]) + + base.print_split_summary(payload) + base.print_normalization_summary(payload) + + train_split_name = ( + f"train {base.SPLIT_TYPE} {ctx.percent_int}% phase{phase_index:03d}" + if phase_index is not None + else f"train {base.SPLIT_TYPE} {ctx.percent_int}% split{ctx.split_repeat_index:03d}" + ) + val_split_name = ( + f"val {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"val {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + test_split_name = ( + f"test {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"test {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + loader_prefix = ( + f"{base.SPLIT_TYPE}:{ctx.percent_int}:phase{phase_index:03d}" + if phase_index is not None + else f"{base.SPLIT_TYPE}:{ctx.percent_int}:split{ctx.split_repeat_index:03d}" + ) + + train_ds = base.BUSIDataset( + train_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=True, + split_name=train_split_name, + ) + val_ds = base.BUSIDataset( + val_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=val_split_name, + ) + test_ds = base.BUSIDataset( + test_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=test_split_name, + ) + bundle = base.DataBundle( + percent=ctx.percent_fraction, + split_payload=payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=base.make_loader( + train_ds, + shuffle=True, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:train", + ), + val_loader=base.make_loader( + val_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:val", + ), + test_loader=base.make_loader( + test_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:test", + ), + ) + base.print_preload_summary(bundle) + return bundle + + +def release_bundle(bundle: base.DataBundle | None) -> None: + if bundle is None: + return + del bundle + gc.collect() + base.run_cuda_cleanup(context="bundle release") + + +def checkpoint_candidates(run_dir: Path) -> list[Path]: + return [ + run_dir / "checkpoints" / "latest.pt", + run_dir / "checkpoints" / "best.pt", + ] + + +def resolve_resume_checkpoint(run_dir: Path) -> Path | None: + for candidate in checkpoint_candidates(run_dir): + if candidate.exists(): + return candidate + return None + + +"""============================================================================= +RUN EXECUTION +============================================================================= +""" + + +def strategy_requires_strategy2_checkpoint(strategy: int) -> bool: + return ( + base.EXECUTION_MODE == "train_eval" and strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 + ) + + +def fold_param_metadata(ctx: FoldRunContext) -> dict[str, Any]: + subset_payload = base.load_json(ctx.subset_manifest_path) + payload = { + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "split_repeat_index": int(ctx.split_repeat_index), + "subset_repeat_index": int(ctx.subset_repeat_index), + "split_seed": int(ctx.split_seed), + "subset_seed": int(ctx.subset_seed), + "split_generation_mode": str(subset_payload.get("split_generation_mode", current_split_generation_mode())), + "percent_sampling_mode": str(subset_payload.get("percent_sampling_mode", current_percent_sampling_mode())), + "subset_sampling_source": str(subset_payload.get("subset_sampling_source", "independent")), + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + } + if subset_payload.get("phase_index") is not None: + payload["phase_index"] = int(subset_payload["phase_index"]) + payload["phase_count"] = int(subset_payload["phase_count"]) + payload["phase_val_fold_index"] = int(subset_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(subset_payload["phase_test_fold_index"]) + return payload + + +def finalize_run_from_artifacts(key: RunKey, run_dir: Path) -> bool: + evaluation_path = run_dir / "evaluation.json" + if not evaluation_path.exists(): + return False + ingest_evaluation_into_db(key, evaluation_path, run_dir) + export_stats() + return True + + +def execute_final_run( + *, + strategy: int, + ctx: FoldRunContext, + bundle: base.DataBundle, + model_config: base.RuntimeModelConfig, +) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None: + raise RuntimeError(f"Run plan row is missing for {run_key}.") + if str(row["status"]) == "completed": + return + if str(row["status"]) == "failed": + print( + f"[{split_generation_display_name()}] Skipping failed run " + f"{run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload)}." + ) + return + + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + + strategy2_checkpoint_path: str | Path | None = None + run_name = run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload) + with activate_context(ctx): + banner_prefix = "PHASE RUN" if using_fixed_phase_mode() else "REPEATED HOLDOUT RUN" + base.banner(f"{banner_prefix} | {run_name}") + if strategy_requires_strategy2_checkpoint(strategy): + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + + if base.EXECUTION_MODE == "eval_only": + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + summary_path = run_dir / "summary.json" + if summary_path.exists() and not (run_dir / "evaluation.json").exists(): + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + if base.RUN_SMOKE_TEST: + base.maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + params = base.resolve_job_params( + strategy, + ctx.percent_fraction, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = {**params, **fold_param_metadata(ctx)} + + resume_checkpoint_path = None + if str(row["status"]) in {"interrupted", "running"}: + resume_checkpoint_path = resolve_resume_checkpoint(run_dir) + + mark_run_running(run_key, stage="training") + base.run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=run_dir, + params=params, + max_epochs=base.strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + + +def reconcile_existing_artifacts(ctx: FoldRunContext, strategy: int, model_config: base.RuntimeModelConfig) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + return + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + if (run_dir / "summary.json").exists(): + mark_run_interrupted(run_key) + mark_run_stage(run_key, stage="evaluating") + return + if resolve_resume_checkpoint(run_dir) is not None: + mark_run_interrupted(run_key) + + +def maybe_reset_study_artifacts(model_config: base.RuntimeModelConfig) -> None: + if not base.RESET_ALL_STUDIES_EACH_RUN or not base.RUN_OPTUNA: + return + if RESUME_FOLDS: + print( + f"[{split_generation_display_name()}] RESET_ALL_STUDIES_EACH_RUN ignored because RESUME_FOLDS=True." + ) + return + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + with activate_context(ctx): + for strategy in base.STRATEGIES: + base.reset_study_artifacts(strategy, ctx.percent_fraction, model_config=model_config) + + +def run_overfit_mode(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE OVERFIT TEST MODE") + else: + base.banner("REPEATED HOLDOUT OVERFIT TEST MODE") + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + base.run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=overfit_root_for(strategy, ctx, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_eval_only_without_ledger(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EVAL ONLY | LEDGER BYPASSED") + else: + base.banner("REPEATED HOLDOUT EVAL ONLY | LEDGER BYPASSED") + print("[Eval Only] Skipping experiment ledger and evaluating directly from manifests and checkpoints.") + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + run_name = run_identity_label( + strategy=strategy, + percent=ctx.percent_fraction, + split_payload=bundle.split_payload, + ) + base.banner(f"EVAL ONLY | {run_name}") + base.run_evaluation_for_run( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir_for(strategy, ctx, model_config), + strategy2_checkpoint_path=None, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_pass_for_statuses( + statuses: set[str], + *, + model_config: base.RuntimeModelConfig, +) -> None: + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + pending_strategies = [] + for strategy in base.STRATEGIES: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is not None and str(row["status"]) in statuses: + pending_strategies.append(int(strategy)) + if not pending_strategies: + continue + + bundle = build_fold_data_bundle(ctx) + phase_had_error = False + try: + for strategy in pending_strategies: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + continue + try: + execute_final_run( + strategy=strategy, + ctx=ctx, + bundle=bundle, + model_config=model_config, + ) + except KeyboardInterrupt: + phase_had_error = True + mark_run_interrupted(run_key) + raise + except Exception: + phase_had_error = True + error_text = traceback.format_exc() + mark_run_failed(run_key, error_text) + print(error_text) + finally: + if using_fixed_phase_mode(): + write_phase_timing_summary_after_phase(ctx.split_repeat_index) + if not phase_had_error: + threading.Thread( + target=run_repo_backup_after_phase, + args=(ctx.split_repeat_index,), + daemon=True, + ).start() + release_bundle(bundle) + + +"""============================================================================= +MAIN +============================================================================= +""" + + +def run_repeated_holdout_main() -> int: + global LEDGER_CONN + + validate_repeated_holdout_settings() + if not str(FOLDS_EXPERIMENT_NAME).strip(): + raise ValueError("FOLDS_EXPERIMENT_NAME must be non-empty.") + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"RUNNER_FOLDS.py currently requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + + install_base_patches() + base.SAVE_LATEST_EVERY_EPOCH = True + + base.set_global_seed(base.SEED) + model_config = base.current_model_config() + fold_experiment_summary(model_config) + + if str(base.EXECUTION_MODE).strip().lower() == "eval_only": + select_sample_records() + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + return run_eval_only_without_ledger(model_config) + + sample_records, _dataset_root = select_sample_records() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not ignore_resume_folds_gate and not RESUME_FOLDS and EXPERIMENT_ROOT.exists(): + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + if ( + not ignore_resume_folds_gate + and RESUME_FOLDS + and not EXPERIMENT_DB_PATH.exists() + and EXPERIMENT_ROOT.exists() + and any(EXPERIMENT_ROOT.iterdir()) + ): + raise RuntimeError( + f"Experiment root {EXPERIMENT_ROOT} already exists without a valid SQLite ledger at {EXPERIMENT_DB_PATH}. " + "Refusing to attach to ambiguous state." + ) + + LEDGER_CONN = setup_ledger(EXPERIMENT_DB_PATH) + try: + validate_or_create_experiment(sample_records=sample_records, model_config=model_config) + mark_stale_running_as_interrupted() + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + for strategy in base.STRATEGIES: + reconcile_existing_artifacts(ctx, int(strategy), model_config) + + export_stats() + maybe_reset_study_artifacts(model_config) + + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + + run_pass_for_statuses({"interrupted", "running"}, model_config=model_config) + run_pass_for_statuses({"planned"}, model_config=model_config) + export_stats() + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EXECUTION COMPLETE") + else: + base.banner("REPEATED HOLDOUT COMPLETE") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Raw metrics export : {EXPORTS_DIR / 'raw_run_metrics.csv'}") + print(f"Aggregate export : {EXPORTS_DIR / 'aggregated_metrics_by_percent_strategy.csv'}") + return 0 + finally: + if LEDGER_CONN is not None: + LEDGER_CONN.close() + LEDGER_CONN = None + +def run_single_run_main() -> int: + global DATASET_PERCENTS + banner("MLR ALL STRATEGIES BAYES RUNNER") + DATASET_PERCENTS = normalize_dataset_percents(DATASET_PERCENTS) + set_global_seed(SEED) + model_config = current_model_config() + dataset_name = current_dataset_name() + images_dir, annotations_dir = current_dataset_dirs() + if EXECUTION_MODE not in {"train_eval", "eval_only"}: + raise ValueError(f"EXECUTION_MODE must be 'train_eval' or 'eval_only', got {EXECUTION_MODE!r}") + if SPLIT_TYPE not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"SPLIT_TYPE must be one of {SUPPORTED_SPLIT_TYPES}, got {SPLIT_TYPE!r}") + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + ensure_specific_checkpoint_scope("EVAL_CHECKPOINT_MODE", EVAL_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("STRATEGY2_CHECKPOINT_MODE", STRATEGY2_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("TRAIN_RESUME_MODE", TRAIN_RESUME_MODE) + + print_environment_summary(model_config) + split_registry, split_source = load_or_create_dataset_splits( + images_dir=images_dir, + annotations_dir=annotations_dir, + split_json_path=current_dataset_splits_json_path(), + train_fractions=DATASET_PERCENTS, + seed=SEED, + ) + + bundles: dict[float, DataBundle] = {} + for percent in DATASET_PERCENTS: + bundles[percent] = build_data_bundle(percent, split_registry, split_source) + + if RUN_OVERFIT_TEST: + run_configured_overfit_tests(bundles, model_config=model_config) + banner("OVERFIT TESTS COMPLETE") + return 0 + + if RESET_ALL_STUDIES_EACH_RUN: + if RUN_OPTUNA: + banner("RESETTING OPTUNA STUDIES") + for strategy in STRATEGIES: + for percent in DATASET_PERCENTS: + reset_study_artifacts(strategy, percent, model_config=model_config) + else: + print("[Optuna Reset] Skipped because RUN_OPTUNA=False.") + + try: + for percent in DATASET_PERCENTS: + banner(f"PERCENT STAGE | {percent_text(percent)}") + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and EXECUTION_MODE == "train_eval" and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + + if EXECUTION_MODE == "train_eval": + maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = resolve_job_params( + strategy, + percent, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + banner( + f"FINAL RETRAIN | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_final_training( + strategy, + bundle, + params, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + else: + banner( + f"EVAL ONLY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_evaluation_for_run( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=final_root_for_strategy(strategy, percent, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + except Exception: + banner("RUN FAILED") + traceback.print_exc() + return 1 + + banner("ALL DONE") + return 0 + + +def main() -> int: + validate_hf_backup_settings() + run_initial_hf_backup() + if EXPERIMENT_MODE not in SUPPORTED_EXPERIMENT_MODES: + raise ValueError( + f"EXPERIMENT_MODE must be one of {SUPPORTED_EXPERIMENT_MODES}, got {EXPERIMENT_MODE!r}" + ) + if EXPERIMENT_MODE == "repeated_holdout": + return run_repeated_holdout_main() + return run_single_run_main() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/ablations/abl_ab8_no_boundary.py b/ablations/abl_ab8_no_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..e5356473e9444eed5cf8d195873d67fa89d3112c --- /dev/null +++ b/ablations/abl_ab8_no_boundary.py @@ -0,0 +1,13598 @@ +from __future__ import annotations +import csv +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from datetime import datetime, timezone +import gc +import hashlib +import importlib +import inspect +import json +import math +import os +import random +import shutil +import sqlite3 +import subprocess +import sys +import tarfile +import tempfile +import threading +import time +import traceback +import weakref +from collections import Counter +from contextlib import contextmanager, nullcontext +from pathlib import Path +from typing import Any, Iterator, Literal + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import segmentation_models_pytorch as smp +os.environ.setdefault("NNPACK_DISABLE", "1") +import torch +torch.backends.nnpack.enabled = False +import torch.nn as nn +import torch.nn.functional as F +from optuna.storages import RDBStorage +from PIL import Image as PILImage +from scipy import ndimage +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader, Dataset +from tqdm.auto import tqdm + +"""============================================================================= +EDIT ME +============================================================================= +""" + +PROJECT_DIR = Path(__file__).resolve().parent.parent # ABLATION: repo root (this copy lives in ablations/) +TRANSUNET_REPO_DIR = PROJECT_DIR / "TransUNet" +TRANSUNET_VIT_NAME = "R50-ViT-B_16" +TRANSUNET_N_SKIP = 3 +TRANSUNET_PRETRAINED_PATH = PROJECT_DIR / "model" / "vit_checkpoint" / "imagenet21k" / "R50+ViT-B_16.npz" + +RUNS_ROOT = PROJECT_DIR / "runs" +HARD_CODED_PARAM_DIR = PROJECT_DIR +MODEL_NAME = "Segformer_B0_revamped_nt_2" + +EXPERIMENT_MODE = "repeated_holdout" # "single_run" or "repeated_holdout" +SUPPORTED_EXPERIMENT_MODES = ("single_run", "repeated_holdout") +SPLIT_GENERATION_MODE = "fixed_stratified_phases_8_1_1" # "repeated_holdout" or "fixed_stratified_phases_8_1_1" +SUPPORTED_SPLIT_GENERATION_MODES = ("repeated_holdout", "fixed_stratified_phases_8_1_1") +NUM_STRATIFIED_SPLIT_REPEATS = 5 +NUM_PHASES = 10 +PHASE_VAL_OFFSET = 1 +DATASET_PERCENT_REPEAT_COUNTS: dict[int, int] = { + # 5: 4, + # 15: 3, + # 30: 3, + # 50: 2, + 100: 1, +} +PERCENT_SAMPLING_MODE = "incremental" # "independent" or "incremental" +SUPPORTED_PERCENT_SAMPLING_MODES = ("independent", "incremental") +PERCENT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PERCENT_EXECUTION_MODES = ("auto", "manual") +SELECTED_DATASET_PERCENTS: list[int] = [100] +SPLIT_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_SPLIT_EXECUTION_MODES = ("auto", "manual") +SELECTED_SPLIT_INDICES: list[int] = [1] + +PHASE_EXECUTION_MODE = "manual" # "auto" or "manual" +SUPPORTED_PHASE_EXECUTION_MODES = ("auto", "manual") +SELECTED_PHASES: list[int] = [1] # used only when PHASE_EXECUTION_MODE="manual" + +REPEAT_EXECUTION_MODE = "auto" # "auto" or "manual" +SUPPORTED_REPEAT_EXECUTION_MODES = ("auto", "manual") +SELECTED_REPEAT_INDICES: list[int] = [1] + +FOLDS_EXPERIMENT_NAME = "stratified_holdout_v1" +RESUME_FOLDS = False +ASYNC_REPO_BACKUP_AFTER_PHASE = False +# Hugging Face dataset repo to mirror the project into. Set via env so nothing is +# hardcoded: export HF_REPO_ID="your-username/ADVAI24JUN-backup" and HF_TOKEN=... +HF_REPO_ID = os.environ.get("HF_REPO_ID", "") +HF_REPO_TYPE = "dataset" +# Only upload after every Nth phase (boundary), so we don't hammer HF every phase. +HF_BACKUP_EVERY_N_PHASES = 1 +HF_BACKUP_MAX_RETRIES = 5 +# Run one synchronous backup BEFORE training starts: it creates the repo and uploads +# the current project state, proving the whole backup pipeline works before we commit +# hours of compute. Phase backups later refresh this same repo. +HF_BACKUP_ON_START = False +# Glob patterns excluded from the upload (matched against repo-relative paths). +HF_IGNORE_PATTERNS = ( + "**/.git/**", + "**/__pycache__/**", + "**/.ipynb_checkpoints/**", + "**/.cache/**", + "**/.venv/**", + "*.pyc", + ".DS_Store", +) + +DATASET_NAME = "BUSI_with_classes" # "BUSI" or "BUSI_with_classes" +SUPPORTED_DATASET_NAMES = ("BUSI", "BUSI_with_classes") +DATA_ROOT = PROJECT_DIR / DATASET_NAME +BUSI_WITH_CLASSES_SPLIT_POLICY = "stratified" # "balanced_train" or "stratified" +SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES = ("balanced_train", "stratified") + +SUPPORTED_STRATEGIES: tuple[int, ...] = (2,3) +STRATEGIES = [2,3] +DATASET_PERCENTS = [] #ignored in the folding [0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1.0] #, 0.5, 1.0] #, 0.5, 1.0] +SPLIT_TYPE = "80_10_10" +SUPPORTED_SPLIT_TYPES = ("80_10_10", "70_10_20") +DATASET_SPLITS_JSON = PROJECT_DIR / "dataset_splits.json" +DATASET_SPLITS_VERSION = 1 +TRAIN_SUBSET_VARIANT = 1 # 0 uses the persisted subset; >0 deterministically resamples only the train subset from the frozen base train split. +NUM_TRIALS = 30 +STUDY_DIRECTION = "maximize" +BEST_CHECKPOINT_METRICS = { + 2: "val_iou", + 3: "val_refine_score", +} +OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR = "best_observed_objective" +OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR = "best_observed_objective_name" +SUPPORTED_CHECKPOINT_METRICS = { + "val_loss", + "val_dice", + "val_iou", + "val_biou", + "val_refine_score", + "val_decoder_dice", + "val_decoder_iou", + "val_decoder_biou", + "val_dice_gain", + "val_iou_gain", + "val_biou_gain", + "val_actor_loss", + "val_critic_loss", + "val_ce_loss", + "val_dice_loss", + "val_reward", + "val_entropy", +} + +SEED = 42 +IMG_SIZE = 128 +# d = 0 -> auto (floor(0.02 * diag)); any positive int overrides. +# Recommended: 0 (auto) -> resolves to ~4 px for IMG_SIZE=128. +BOUNDARY_IOU_D: int = 0 +BATCH_SIZE = 16 # Recommended to prevent OOM +NUM_WORKERS = 128 # Recommended with RAM-preloaded datasets to avoid worker RAM duplication. +USE_PIN_MEMORY = True +USE_PERSISTENT_WORKERS = True +PRELOAD_TO_RAM = True + +SMP_ENCODER_NAME = "mit_b0" +SMP_ENCODER_WEIGHTS = "imagenet" +SMP_ENCODER_DEPTH = 5 +SMP_ENCODER_PROJ_DIM = 192 +SMP_DECODER_TYPE = "Segformer" +BACKBONE_FAMILY = "smp" # "smp" or "custom_vgg" +ENABLE_CUSTOM_VGG_BACKBONE = False +VGG_FEATURE_SCALES = 4 +VGG_FEATURE_DILATION = 1 + +USE_IMAGENET_NORM = True +REPLACE_BN_WITH_GN = True +GN_NUM_GROUPS = 8 +NUM_ACTIONS = 2 + +STRATEGY_1_MAX_EPOCHS = 100 +STRATEGY_2_MAX_EPOCHS = 100 +STRATEGY_3_MAX_EPOCHS = 120 +STRATEGY_4_MAX_EPOCHS = 100 +STRATEGY_5_MAX_EPOCHS = 100 +VALIDATE_EVERY_N_EPOCHS = 1 +CHECKPOINT_EVERY_N_EPOCHS = 0 +SAVE_LATEST_EVERY_EPOCH = True +SAVE_HISTORY_INCREMENTALLY = False +EARLY_STOPPING_PATIENCE = 0 +VERBOSE_EPOCH_LOG = False + +DEFAULT_HEAD_LR = 1e-4 +DEFAULT_ENCODER_LR = 1e-5 +DEFAULT_WEIGHT_DECAY = 1e-4 +DEFAULT_TMAX = 5 +TEST_ITERATION_CONTROL = False # If True, validation/evaluation/inference uses TEST_ITERATION_T instead of full tmax. +TEST_ITERATION_T = 1 # Applied only when TEST_ITERATION_CONTROL=True. Clamped to [1, tmax]. +DEFAULT_GAMMA = 0.95 +DEFAULT_CRITIC_LOSS_WEIGHT = 0.5 +DEFAULT_ENTROPY_ALPHA_INIT = 0.2 +DEFAULT_ENTROPY_TARGET_RATIO = 0.25 +DEFAULT_ENTROPY_LR = 3e-4 +DEFAULT_CE_WEIGHT = 0.5 +DEFAULT_DICE_WEIGHT = 0.5 +DEFAULT_DROPOUT_P = 0.2 +DEFAULT_GRAD_CLIP_NORM = 6.0 +DEFAULT_MASK_UPDATE_STEP = 0.1 +DEFAULT_FOREGROUND_REWARD_WEIGHT = 0.0 +DEFAULT_RECALL_REWARD_WEIGHT = 1.0 +DEFAULT_DICE_REWARD_WEIGHT = 0.35 +DEFAULT_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_PRIOR_REWARD_WEIGHT = 0.01 +DEFAULT_DECODER_GAIN_REWARD_WEIGHT = 0.5 +DEFAULT_REWARD_SCALE = 1.0 +DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION = True +DEFAULT_STRATEGY3_VARIANT = "lite" +DEFAULT_STRATEGY3_NUM_ACTIONS = 3 +DEFAULT_REFINE_DELTA_SMALL = 0.03 +DEFAULT_REFINE_DELTA_LARGE = 0.08 +DEFAULT_STRATEGY3_AUX_CE_WEIGHT = 0.40 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH = 25 +DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS = 15 +DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION = 0.10 +DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE = 30 +DEFAULT_STRATEGY3_PROBE_MODE = "rolling_random" +DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM = 0.25 +DEFAULT_STRATEGY3_RL_LOSS_SCALE = 10.0 +DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED = True +DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES = 8 +DEFAULT_STRATEGY3_MC_DROPOUT_P = 0.2 +DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_READ = True +DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE = True +DEFAULT_STRATEGY3_DELTA_MAX = 0.10 +DEFAULT_STRATEGY3_SAM_ATTENTION_GRID = 64 +DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT = 1.0 +DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE = False +DEFAULT_BIOU_REWARD_WEIGHT = 1.0 +DEFAULT_IOU_REWARD_WEIGHT = 1.0 +DEFAULT_KEEP_CORRECT_REWARD_WEIGHT = 0.05 +DEFAULT_STRATEGY3_A3C_ENTROPY_COEFF = 0.0 +DEFAULT_STRATEGY3_ENTROPY_TARGET_RATIO = 0.20 +DEFAULT_STRATEGY3_ENTROPY_ALPHA_INIT = 0.005 +DEFAULT_STRATEGY3_BOUNDARY_REWARD_WEIGHT = 0.5 +DEFAULT_EARLY_STOPPING_MONITOR = "auto" +DEFAULT_EARLY_STOPPING_MODE = "auto" +DEFAULT_EARLY_STOPPING_MIN_DELTA = 0.0 +DEFAULT_EARLY_STOPPING_START_EPOCH = 30 +DEFAULT_EXPLORATION_EPS = 0.1 +EXPLORATION_EPS_EPOCHS = 20 +ATTENTION_MAX_TOKENS = 1024 +ATTENTION_MIN_POOL_SIZE = 16 + +_STRATEGY3_MC_DROPOUT_WARNED = False +_STRATEGY3_SAM_GRID_WARNED: set[int] = set() +_STRATEGY3_MC_CACHE_SCHEMA_VERSION = 1 +_STRATEGY3_MC_FILE_SHA256_CACHE: dict[str, str] = {} + +SCHEDULER_FACTOR = 0.5 +SCHEDULER_PATIENCE = 5 +SCHEDULER_THRESHOLD = 1e-3 +SCHEDULER_MIN_LR = 1e-5 + +HEAD_LR_RANGE = (1e-5, 3e-3) +ENCODER_LR_RANGE = (1e-6, 3e-3) +WEIGHT_DECAY_RANGE = (1e-6, 1e-2) +TMAX_RANGE = (3, 10) +ENTROPY_LR_RANGE = (1e-5, 1e-3) +DROPOUT_P_RANGE = (0.0, 0.5) + +USE_TRIAL_PRUNING = True +TRIAL_PRUNER_WARMUP_STEPS = 80 +TRIAL_PRUNER_PATIENCE_STEPS = 40 +LOAD_EXISTING_STUDIES = False +SKIP_EXISTING_FINALS = False +RUN_OPTUNA = False +RESET_ALL_STUDIES_EACH_RUN = False +USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF = False + +EXECUTION_MODE = "train_eval" # "train_eval" or "eval_only" +EVAL_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +EVAL_SPECIFIC_CHECKPOINT = "" +STRATEGY2_CHECKPOINT_MODE = "best" # "latest", "best", or "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = { + # Non-phase mode — keyed by dataset percent (float): + # 0.1: "runs/EfficientNet_Strategy2_New/pct_10/strategy_2/final/checkpoints/epoch_0089.pt", + # 0.5: "Strategy2_Checkpoints/strat2_50_best.pt", + # 1.0: "runs/EfficientNet_Strategy2_New/pct_100/strategy_2/final/checkpoints/best.pt", + # Phase mode — keyed by phase index (int): + 1: "/content/UNET_REVAMP/best_strat2.pt", + 2: "/content/UNET_REVAMP/best_strat2_2.pt", + 3: "/content/UNET_REVAMP/best_strat2_3.pt", +} +STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 = True +TRAIN_RESUME_MODE = "off" # "off", "latest", "best", or "specific" +TRAIN_RESUME_SPECIFIC_CHECKPOINT = "" +OPTUNA_HEARTBEAT_INTERVAL = 60 +OPTUNA_HEARTBEAT_GRACE_PERIOD = 180 + +USE_AMP = True +AMP_DTYPE = "bfloat16" # "auto", "bfloat16", or "float16" +USE_CHANNELS_LAST = True +USE_TORCH_COMPILE = True +STEPWISE_BACKWARD = True +ALLOW_TF32 = True + +RUN_SMOKE_TEST = False +SMOKE_TEST_SAMPLE_INDEX = 0 +RUN_OVERFIT_TEST = False +OVERFIT_N_BATCHES = 2 +OVERFIT_N_EPOCHS = 100 +OVERFIT_HEAD_LR = 1e-3 +OVERFIT_ENCODER_LR = 1e-4 +OVERFIT_PRINT_EVERY = 5 +WRITE_EPOCH_DIAGNOSTIC = True +EPOCH_DIAGNOSTIC_TRAIN_BATCHES = 2 +EPOCH_DIAGNOSTIC_VAL_BATCHES = 2 +CONTROLLED_MASK_THRESHOLD = 0.50 + +REQUIRED_HPARAM_KEYS = ("head_lr", "encoder_lr", "weight_decay", "dropout_p", "tmax", "entropy_lr") + +_TRANSUNET_REQUIRED_NPZ_KEYS: tuple[str, ...] = ( + "embedding/kernel", + "embedding/bias", + "Transformer/encoder_norm/scale", + "Transformer/encoder_norm/bias", + "Transformer/posembed_input/pos_embedding", + "conv_root/kernel", + "gn_root/scale", + "gn_root/bias", + "Transformer/encoderblock_0/MultiHeadDotProductAttention_1/query/kernel", +) +_TRANSUNET_ENCODER_ALIASES: set[str] = {"vitb16r50", "r50vitb16"} +_TRANSUNET_VISION_TRANSFORMER: Any | None = None +_TRANSUNET_CONFIGS: dict[str, Any] | None = None +_TEST_ITERATION_NOTICE_CACHE: set[tuple[str, int, int]] = set() + +"""============================================================================= +IF OPTUNA IS OFF --> USE ME +============================================================================= +""" + +# Key format: ":" +# Each value is a JSON filename in HARD_CODED_PARAM_DIR containing the required hyperparameters. +MANUAL_HPARAMS_IF_OPTUNA_OFF: dict[str, str] = { + "2:100": "param_segformer/best_params_strat2.json", + "3:100": "param_segformer/best_params_strat3.json", +} + +# ===================== ABLATION HARNESS OVERRIDE ===================== +# Auto-generated. Outputs go to a separate MODEL_NAME subtree; strategy 3 +# only; the frozen strategy-2 base is reused from the original run tree. +MODEL_NAME = "Segformer_B0_AB8_no_boundary" +STRATEGIES = [3] +STRATEGY2_CHECKPOINT_MODE = "specific" +STRATEGY2_SPECIFIC_CHECKPOINT = {1: str(PROJECT_DIR / "best.pt")} +MANUAL_HPARAMS_IF_OPTUNA_OFF = {**MANUAL_HPARAMS_IF_OPTUNA_OFF, "3:100": "param_segformer/best_params_strat3.json"} +# ===================================================================== + +"""============================================================================= +RUNTIME SETUP +============================================================================= +""" + +torch.set_float32_matmul_precision("high") +if torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = ALLOW_TF32 + torch.backends.cudnn.allow_tf32 = ALLOW_TF32 +torch.backends.cudnn.deterministic = False +torch.backends.cudnn.benchmark = True + +def select_runtime_device() -> tuple[torch.device, str]: + if torch.cuda.is_available(): + return torch.device("cuda"), "cuda" + + mps_backend = getattr(torch.backends, "mps", None) + if mps_backend is not None and mps_backend.is_available(): + try: + _probe = torch.zeros(1, device="mps") + del _probe + return torch.device("mps"), "mps" + except Exception as exc: + print(f"[Device] MPS detected but failed to initialize ({exc}). Falling back to CPU.") + + return torch.device("cpu"), "cpu" + +DEVICE, DEVICE_FALLBACK_SOURCE = select_runtime_device() +CURRENT_JOB_PARAMS: dict[str, Any] = {} + +@dataclass(frozen=True) +class RuntimeModelConfig: + backbone_family: str + smp_encoder_name: str + smp_encoder_weights: str | None + smp_encoder_depth: int + smp_encoder_proj_dim: int + smp_decoder_type: str + vgg_feature_scales: int + vgg_feature_dilation: int + + @classmethod + def from_globals(cls) -> RuntimeModelConfig: + return cls( + backbone_family=str(BACKBONE_FAMILY).strip().lower(), + smp_encoder_name=str(SMP_ENCODER_NAME), + smp_encoder_weights=SMP_ENCODER_WEIGHTS, + smp_encoder_depth=int(SMP_ENCODER_DEPTH), + smp_encoder_proj_dim=int(SMP_ENCODER_PROJ_DIM), + smp_decoder_type=str(SMP_DECODER_TYPE), + vgg_feature_scales=int(VGG_FEATURE_SCALES), + vgg_feature_dilation=int(VGG_FEATURE_DILATION), + ) + + @classmethod + def from_payload(cls, payload: dict[str, Any] | None) -> RuntimeModelConfig: + payload = payload or {} + return cls( + backbone_family=str(payload.get("backbone_family", "smp")).strip().lower(), + smp_encoder_name=str(payload.get("smp_encoder_name", SMP_ENCODER_NAME)), + smp_encoder_weights=payload.get("smp_encoder_weights", SMP_ENCODER_WEIGHTS), + smp_encoder_depth=int(payload.get("smp_encoder_depth", SMP_ENCODER_DEPTH)), + smp_encoder_proj_dim=int(payload.get("smp_encoder_proj_dim", SMP_ENCODER_PROJ_DIM)), + smp_decoder_type=str(payload.get("smp_decoder_type", SMP_DECODER_TYPE)), + vgg_feature_scales=int(payload.get("vgg_feature_scales", VGG_FEATURE_SCALES)), + vgg_feature_dilation=int(payload.get("vgg_feature_dilation", VGG_FEATURE_DILATION)), + ) + + def validate(self) -> RuntimeModelConfig: + if self.backbone_family not in {"smp", "custom_vgg"}: + raise ValueError(f"BACKBONE_FAMILY must be 'smp' or 'custom_vgg', got {self.backbone_family!r}") + if self.vgg_feature_scales not in {3, 4}: + raise ValueError(f"VGG_FEATURE_SCALES must be 3 or 4, got {self.vgg_feature_scales}") + if self.vgg_feature_dilation < 1: + raise ValueError(f"VGG_FEATURE_DILATION must be >= 1, got {self.vgg_feature_dilation}") + if self.smp_encoder_depth < 1: + raise ValueError(f"SMP_ENCODER_DEPTH must be >= 1, got {self.smp_encoder_depth}") + if self.smp_encoder_proj_dim < 0: + raise ValueError(f"SMP_ENCODER_PROJ_DIM must be >= 0, got {self.smp_encoder_proj_dim}") + if _normalized_model_token(self.smp_decoder_type) == "transunet": + if _normalized_model_token(self.smp_encoder_name) not in _TRANSUNET_ENCODER_ALIASES: + print( + "[RuntimeModelConfig] Warning: SMP_DECODER_TYPE='TransUNet' is wired for " + "SMP_ENCODER_NAME='ViTB16R50' (or 'R50ViTB16'). " + f"Received {self.smp_encoder_name!r}." + ) + if IMG_SIZE % 16 != 0: + raise ValueError( + f"TransUNet requires IMG_SIZE divisible by 16, got IMG_SIZE={IMG_SIZE}." + ) + return self + + def to_payload(self) -> dict[str, Any]: + return { + "backbone_family": self.backbone_family, + "smp_encoder_name": self.smp_encoder_name, + "smp_encoder_weights": self.smp_encoder_weights, + "smp_encoder_depth": self.smp_encoder_depth, + "smp_encoder_proj_dim": self.smp_encoder_proj_dim, + "smp_decoder_type": self.smp_decoder_type, + "vgg_feature_scales": self.vgg_feature_scales, + "vgg_feature_dilation": self.vgg_feature_dilation, + } + + def backbone_tag(self) -> str: + return self.backbone_family + + def backbone_display_name(self) -> str: + if self.backbone_family == "custom_vgg": + return f"Custom VGG (scales={self.vgg_feature_scales}, dilation={self.vgg_feature_dilation})" + return f"SMP {self.smp_encoder_name}" + +def current_model_config() -> RuntimeModelConfig: + return RuntimeModelConfig.from_globals().validate() + +"""============================================================================= +UTILITIES +============================================================================= +""" + +def _normalized_model_token(value: str | None) -> str: + return "".join(ch for ch in str(value or "") if ch.isalnum()).lower() + + +def _is_transunet_selection( + model_config: RuntimeModelConfig | None = None, + *, + encoder_name: str | None = None, + decoder_type: str | None = None, +) -> bool: + if model_config is not None: + encoder_name = model_config.smp_encoder_name + decoder_type = model_config.smp_decoder_type + enc = _normalized_model_token(encoder_name) + dec = _normalized_model_token(decoder_type) + return dec == "transunet" and enc in _TRANSUNET_ENCODER_ALIASES + + +def _resolve_test_iteration_tmax(tmax: int, *, context: str) -> int: + effective_tmax = max(int(tmax), 1) + if not TEST_ITERATION_CONTROL: + return effective_tmax + + requested_t = int(TEST_ITERATION_T) + if requested_t < 1: + raise ValueError( + f"TEST_ITERATION_T must be >= 1 when TEST_ITERATION_CONTROL=True, got {requested_t}." + ) + + effective_tmax = min(effective_tmax, requested_t) + cache_key = (context, int(tmax), effective_tmax) + if cache_key not in _TEST_ITERATION_NOTICE_CACHE: + if requested_t > int(tmax): + print( + f"[Test Iteration Control] {context}: TEST_ITERATION_T={requested_t} exceeds tmax={int(tmax)}; " + f"using t={effective_tmax}." + ) + else: + print( + f"[Test Iteration Control] {context}: overriding test rollout steps " + f"from tmax={int(tmax)} to t={effective_tmax}." + ) + _TEST_ITERATION_NOTICE_CACHE.add(cache_key) + return effective_tmax + +def banner(title: str) -> None: + line = "=" * 80 + print(f"\n{line}\n{title}\n{line}") + +def section(title: str) -> None: + print(f"\n{'-' * 80}\n{title}\n{'-' * 80}") + +def ensure_dir(path: str | Path) -> Path: + path = Path(path).expanduser().resolve() + path.mkdir(parents=True, exist_ok=True) + return path + +def save_json(path: str | Path, payload: Any) -> None: + path = Path(path) + ensure_dir(path.parent) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + +def load_json(path: str | Path) -> Any: + with Path(path).open("r", encoding="utf-8") as f: + return json.load(f) + +def _format_history_log_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key == "lr" or key.endswith("_lr"): + return f"{value:.6e}" + return json.dumps(value) + return json.dumps(value) + +def format_history_log_row(row: dict[str, Any]) -> str: + return ", ".join(f"{key}={_format_history_log_value(key, value)}" for key, value in row.items()) + +def _format_epoch_metric(value: Any, *, scientific: bool = False) -> str: + if value is None: + return "null" + if isinstance(value, (float, int, np.floating, np.integer)): + value = float(value) + return f"{value:.6e}" if scientific else f"{value:.4f}" + return str(value) + +def format_concise_epoch_log( + row: dict[str, Any], + *, + best_metric_name: str, + best_metric_value: float, +) -> str: + fields: list[tuple[str, Any, bool]] = [ + ("train_loss", row.get("train_loss"), False), + ("train_iou", row.get("train_iou"), False), + ("train_entropy", row.get("train_entropy"), False), + ("val_loss", row.get("val_loss"), False), + ("val_iou", row.get("val_iou"), False), + ("val_dice", row.get("val_dice"), False), + ("val_iou_gain", row.get("val_iou_gain"), False), + ("val_biou_gain", row.get("val_biou_gain"), False), + ("head_lr", row.get("lr"), True), + ("encoder_lr", row.get("encoder_lr"), True), + (best_metric_name, best_metric_value, False), + ("study_best", row.get("study_best_objective"), False), + ] + parts = [ + f"{name}={_format_epoch_metric(value, scientific=scientific)}" + for name, value, scientific in fields + if value is not None + ] + early_monitor_name = row.get("early_stopping_monitor_name") + if early_monitor_name: + parts.append(f"es_monitor={early_monitor_name}") + if row.get("early_stopping_monitor_value") is not None: + parts.append(f"es_value={_format_epoch_metric(row.get('early_stopping_monitor_value'))}") + if row.get("early_stopping_best_value") is not None: + parts.append(f"es_best={_format_epoch_metric(row.get('early_stopping_best_value'))}") + if row.get("early_stopping_wait") is not None and row.get("early_stopping_patience") is not None: + parts.append( + f"es_wait={int(row.get('early_stopping_wait'))}/{int(row.get('early_stopping_patience'))}" + ) + if row.get("early_stopping_active") is not None: + parts.append(f"es_active={bool(row.get('early_stopping_active'))}") + if row.get("strategy3_freeze_active") is not None: + parts.append(f"s3_frozen={bool(row.get('strategy3_freeze_active'))}") + if row.get("study_best_trial") is not None: + parts.append(f"study_best_trial={int(row.get('study_best_trial'))}") + return ", ".join(parts) + +def _optuna_direction_is_maximize(direction: Any) -> bool: + direction_name = str(getattr(direction, "name", direction)).lower() + return direction_name.endswith("maximize") + +def _optuna_value_is_better( + candidate: float | None, + current: float | None, + *, + direction: Any, +) -> bool: + if candidate is None: + return False + if current is None: + return True + return float(candidate) > float(current) if _optuna_direction_is_maximize(direction) else float(candidate) < float(current) + +def _optuna_trial_state_name(trial: Any) -> str: + state = getattr(trial, "state", None) + return str(getattr(state, "name", state)).upper() + +def _optuna_trial_user_attr_float(trial: Any, attr_name: str) -> float | None: + user_attrs = getattr(trial, "user_attrs", None) + if not isinstance(user_attrs, dict): + return None + value = user_attrs.get(attr_name) + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + +def _optuna_trial_best_intermediate_value( + trial: Any, + *, + direction: Any, +) -> float | None: + best_value: float | None = None + for value in getattr(trial, "intermediate_values", {}).values(): + if value is None: + continue + candidate_value = float(value) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_value = candidate_value + return best_value + +def _optuna_trial_best_observed_value( + trial: Any, + *, + direction: Any, + current_best_value: float | None = None, +) -> float | None: + if current_best_value is not None: + return float(current_best_value) + + best_observed = _optuna_trial_user_attr_float(trial, OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR) + if best_observed is not None: + return best_observed + + state_name = _optuna_trial_state_name(trial) + if state_name == "COMPLETE": + value = getattr(trial, "value", None) + return None if value is None else float(value) + + best_intermediate = _optuna_trial_best_intermediate_value(trial, direction=direction) + if best_intermediate is not None: + return best_intermediate + + value = getattr(trial, "value", None) + return None if value is None else float(value) + +def _current_optuna_study_best_candidate( + study: optuna.study.Study, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> tuple[Any | None, float | None]: + direction = getattr(study, "direction", STUDY_DIRECTION) + best_trial: Any | None = None + best_value: float | None = None + + for study_trial in getattr(study, "trials", []): + if _optuna_trial_state_name(study_trial) not in {"COMPLETE", "PRUNED"}: + continue + candidate_value = _optuna_trial_best_observed_value(study_trial, direction=direction) + if _optuna_value_is_better(candidate_value, best_value, direction=direction): + best_trial = study_trial + best_value = candidate_value + + if current_trial is not None: + live_trial_best = _optuna_trial_best_observed_value( + current_trial, + direction=direction, + current_best_value=current_best_value, + ) + if _optuna_value_is_better(live_trial_best, best_value, direction=direction): + best_trial = current_trial + best_value = live_trial_best + + return best_trial, best_value + +def _current_optuna_study_best_snapshot( + trial: optuna.trial.Trial | None, + *, + current_best_value: float | None = None, +) -> tuple[float | None, int | None]: + if trial is None: + return None, None + study = getattr(trial, "study", None) + if study is None: + return None, None + + best_trial, best_value = _current_optuna_study_best_candidate( + study, + current_trial=trial, + current_best_value=current_best_value, + ) + best_trial_number = None if best_trial is None else int(getattr(best_trial, "number", -1)) + return best_value, best_trial_number + +def set_global_seed(seed: int = 42) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def stable_int_from_text(text: str) -> int: + value = 0 + for byte in text.encode("utf-8"): + value = (value * 131 + byte) % (2 ** 31 - 1) + return value + +def seed_worker(worker_id: int) -> None: + del worker_id + worker_seed = torch.initial_seed() % (2 ** 32) + random.seed(worker_seed) + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + +def make_seeded_generator(seed: int, tag: str) -> torch.Generator: + generator = torch.Generator() + generator.manual_seed(seed + stable_int_from_text(tag)) + return generator + +def cuda_memory_snapshot() -> str: + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return "allocated=0.00 GB, reserved=0.00 GB, peak=0.00 GB" + allocated = torch.cuda.memory_allocated(device=DEVICE) / (1024 ** 3) + reserved = torch.cuda.memory_reserved(device=DEVICE) / (1024 ** 3) + peak = torch.cuda.max_memory_allocated(device=DEVICE) / (1024 ** 3) + return f"allocated={allocated:.2f} GB, reserved={reserved:.2f} GB, peak={peak:.2f} GB" + +def run_cuda_cleanup(context: str | None = None) -> None: + gc.collect() + if DEVICE.type != "cuda" or not torch.cuda.is_available(): + return + try: + torch.cuda.synchronize(device=DEVICE) + except Exception: + pass + try: + torch.cuda.empty_cache() + except Exception: + pass + try: + torch.cuda.ipc_collect() + except Exception: + pass + if context is not None: + print(f"[CUDA Cleanup] {context}: {cuda_memory_snapshot()}") + try: + torch.cuda.reset_peak_memory_stats(device=DEVICE) + except Exception: + pass + +def prune_directory_except(root: Path, keep_file_names: set[str]) -> None: + if not root.exists(): + return + keep_paths = {root / name for name in keep_file_names} + for path in sorted((p for p in root.rglob("*") if p.is_file()), reverse=True): + if path not in keep_paths: + path.unlink() + for path in sorted((p for p in root.rglob("*") if p.is_dir()), reverse=True): + if path != root: + try: + path.rmdir() + except OSError: + pass + +def prune_optuna_trial_dir(trial_dir: Path) -> None: + if trial_dir.exists(): + shutil.rmtree(trial_dir, ignore_errors=True) + +def prune_optuna_study_dir(study_root: Path) -> None: + prune_directory_except(study_root, {"best_params.json", "summary.json", "study.sqlite3"}) + +def to_device(batch: Any, device: torch.device) -> Any: + if torch.is_tensor(batch): + return batch.to(device, non_blocking=True) + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, list): + return [to_device(v, device) for v in batch] + if isinstance(batch, tuple): + return tuple(to_device(v, device) for v in batch) + return batch + +def _normalized_decimal_text(value: Decimal) -> str: + normalized = value.normalize() + text = format(normalized, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text or "0" + +def _fraction_decimal(value: Any, *, field_name: str) -> Decimal: + if isinstance(value, bool): + raise TypeError(f"{field_name} must be a real number in (0, 1], got boolean {value!r}.") + try: + decimal_value = Decimal(str(value).strip()) + except (InvalidOperation, ValueError) as exc: + raise ValueError(f"{field_name} must be a real number in (0, 1], got {value!r}.") from exc + if not decimal_value.is_finite(): + raise ValueError(f"{field_name} must be finite, got {value!r}.") + if decimal_value <= 0 or decimal_value > 1: + raise ValueError(f"{field_name} must be in the interval (0, 1], got {value!r}.") + return decimal_value + +def _percent_decimal(value: Any, *, field_name: str = "dataset percent") -> Decimal: + return _fraction_decimal(value, field_name=field_name) * Decimal("100") + +def normalize_dataset_percents(values: list[float] | tuple[float, ...]) -> list[float]: + if not values: + raise ValueError("DATASET_PERCENTS must contain at least one fraction in (0, 1].") + normalized: dict[str, float] = {} + for value in values: + fraction = _fraction_decimal(value, field_name="DATASET_PERCENTS entry") + normalized[_normalized_decimal_text(fraction)] = float(fraction) + return [normalized[key] for key in sorted(normalized, key=Decimal)] + +def percent_label(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)).replace(".", "p") + +def percent_display(percent: float) -> str: + return _normalized_decimal_text(_percent_decimal(percent)) + +def percent_text(percent: float) -> str: + return f"{percent_display(percent)}%" + + +def run_identity_parts( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> list[str]: + parts: list[str] = [] + payload = split_payload or {} + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase={int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split={int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split={split_type}") + + if subset_repeat_index is not None: + parts.append(f"repeat={int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant={int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy={int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct={percent_text(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial={int(trial_number):03d}") + + return parts + + +def run_identity_label( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + parts = run_identity_parts( + strategy=strategy, + percent=percent, + trial_number=trial_number, + split_payload=split_payload, + ) + return " | ".join(parts) if parts else "run" + + +def run_identity_slug( + *, + strategy: int | None = None, + percent: float | None = None, + trial_number: int | None = None, + split_payload: dict[str, Any] | None = None, +) -> str: + payload = split_payload or {} + parts: list[str] = [] + split_generation_mode = str(payload.get("split_generation_mode", "")).strip().lower() + phase_index = payload.get("phase_index") + split_repeat_index = payload.get("split_repeat_index") + subset_repeat_index = payload.get("subset_repeat_index") + split_type = payload.get("split_type") + train_subset_variant = payload.get("train_subset_variant") + + if phase_index is not None or split_generation_mode == "fixed_stratified_phases_8_1_1": + effective_phase_index = phase_index if phase_index is not None else split_repeat_index + if effective_phase_index is not None: + parts.append(f"phase_{int(effective_phase_index):03d}") + elif split_repeat_index is not None: + parts.append(f"split_{int(split_repeat_index):03d}") + elif split_type is not None: + parts.append(f"split_{str(split_type)}") + + if subset_repeat_index is not None: + parts.append(f"repeat_{int(subset_repeat_index):02d}") + elif train_subset_variant is not None and int(train_subset_variant) > 0: + parts.append(f"variant_{int(train_subset_variant):02d}") + + if strategy is not None: + parts.append(f"strategy_{int(strategy)}") + + effective_percent = percent + if effective_percent is None and payload.get("dataset_percent") is not None: + effective_percent = float(payload["dataset_percent"]) + if effective_percent is not None: + parts.append(f"pct_{percent_label(float(effective_percent))}") + + if trial_number is not None: + parts.append(f"trial_{int(trial_number):03d}") + + return "__".join(parts) if parts else "run" + + +def current_dataset_name() -> str: + dataset_name = str(DATASET_NAME).strip() + if dataset_name not in SUPPORTED_DATASET_NAMES: + raise ValueError(f"DATASET_NAME must be one of {SUPPORTED_DATASET_NAMES}, got {dataset_name!r}") + return dataset_name + +def current_busi_with_classes_split_policy() -> str: + split_policy = str(BUSI_WITH_CLASSES_SPLIT_POLICY).strip().lower() + if split_policy not in SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES: + raise ValueError( + f"BUSI_WITH_CLASSES_SPLIT_POLICY must be one of {SUPPORTED_BUSI_WITH_CLASSES_SPLIT_POLICIES}, " + f"got {split_policy!r}" + ) + return split_policy + +def current_dataset_splits_json_path() -> Path: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATASET_SPLITS_JSON + return PROJECT_DIR / f"dataset_splits_{dataset_name.lower()}_{current_busi_with_classes_split_policy()}.json" + +def current_dataset_dirs() -> tuple[Path, Path]: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return DATA_ROOT / "images", DATA_ROOT / "annotations" + return DATA_ROOT / "all_images", DATA_ROOT / "all_masks" + +def current_pipeline_check_path() -> Path | None: + if current_dataset_name() != "BUSI_with_classes": + return None + return DATA_ROOT / "pipeline_check.json" + +def normalization_cache_tag() -> str: + dataset_name = current_dataset_name() + if dataset_name == "BUSI": + return "BUSI" + return f"{dataset_name}_{current_busi_with_classes_split_policy()}" + +def resolve_amp_dtype(key: str) -> torch.dtype: + key = key.lower().strip() + if key == "auto": + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + return torch.float16 + if key in {"float16", "fp16", "half"}: + return torch.float16 + if key in {"bfloat16", "bf16"}: + if DEVICE.type == "cuda": + try: + if torch.cuda.is_bf16_supported(): + return torch.bfloat16 + except Exception: + major, _minor = torch.cuda.get_device_capability() + if major >= 8: + return torch.bfloat16 + print("[AMP] bfloat16 requested but unsupported here. Falling back to float16.") + return torch.float16 + raise ValueError(f"Unsupported AMP_DTYPE: {key}") + +def amp_autocast_enabled(device: torch.device) -> bool: + return USE_AMP and device.type in {"cuda", "mps"} + +def autocast_ctx(enabled: bool, device: torch.device, amp_dtype: torch.dtype): + if not enabled: + return nullcontext() + return torch.autocast(device_type=device.type, dtype=amp_dtype, enabled=True) + +def make_grad_scaler(enabled: bool, amp_dtype: torch.dtype, device: torch.device): + if not enabled or device.type != "cuda" or amp_dtype == torch.bfloat16: + return None + try: + return torch.amp.GradScaler("cuda", enabled=True, init_scale=8192.0) + except Exception: + return torch.cuda.amp.GradScaler(enabled=True, init_scale=8192.0) + +def format_seconds(seconds: float) -> str: + seconds = int(seconds) + h, rem = divmod(seconds, 3600) + m, s = divmod(rem, 60) + return f"{h:02d}:{m:02d}:{s:02d}" + +def tensor_bytes(t: torch.Tensor) -> int: + return t.numel() * t.element_size() + +def bytes_to_gb(num_bytes: int) -> float: + return num_bytes / (1024 ** 3) + +def set_current_job_params(payload: dict[str, Any] | None = None) -> None: + CURRENT_JOB_PARAMS.clear() + if payload: + CURRENT_JOB_PARAMS.update(dict(payload)) + +def _job_param(name: str, default: Any) -> Any: + return CURRENT_JOB_PARAMS.get(name, default) + +def _alpha_log_floor() -> float: + return math.log(max(float(_job_param("min_alpha", math.exp(-5.0))), 1e-6)) + +def _keep_action_index(action_count: int) -> int: + action_count = max(int(action_count), 1) + if action_count >= 3: + return action_count // 2 + return action_count - 1 + +def _strategy3_variant() -> str: + raw = str(_job_param("strategy3_variant", DEFAULT_STRATEGY3_VARIANT)).strip().lower() + return raw or DEFAULT_STRATEGY3_VARIANT + +def _strategy3_annealed_weight( + base_weight: float, + *, + current_epoch: int, + anneal_start_epoch: int = 1, + anneal_epochs: int, +) -> float: + base_weight = float(base_weight) + if base_weight <= 0.0: + return 0.0 + anneal_start_epoch = max(int(anneal_start_epoch), 1) + anneal_epochs = max(int(anneal_epochs), 0) + if current_epoch < anneal_start_epoch: + return 0.0 + if anneal_epochs <= 0: + return base_weight + progress = min( + max((float(current_epoch) - float(anneal_start_epoch)) / float(anneal_epochs), 0.0), + 1.0, + ) + floor_fraction = float( + _job_param( + "strategy3_aux_ce_floor_fraction", + DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + ) + ) + floor_fraction = min(max(floor_fraction, 0.0), 1.0) + fraction = max(1.0 - progress, floor_fraction) + return base_weight * fraction + +def _strategy3_annealed_aux_ce_weight(current_epoch: int) -> float: + return _strategy3_annealed_weight( + float(_job_param("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT)), + current_epoch=int(current_epoch), + anneal_start_epoch=int( + _job_param( + "strategy3_aux_ce_anneal_start_epoch", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + ) + ), + anneal_epochs=int( + _job_param( + "strategy3_aux_ce_anneal_epochs", + DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + ) + ), + ) + +def _strategy3_exploration_eps(current_epoch: int) -> float: + base_eps = max(float(_job_param("strategy3_exploration_eps", DEFAULT_EXPLORATION_EPS)), 0.0) + decay_epochs = max(int(_job_param("strategy3_exploration_eps_epochs", EXPLORATION_EPS_EPOCHS)), 0) + if base_eps <= 0.0: + return 0.0 + if decay_epochs <= 0: + return base_eps + progress = min(max((float(current_epoch) - 1.0) / float(decay_epochs), 0.0), 1.0) + return base_eps * (1.0 - progress) + +def _bootstrap_value_target(model: nn.Module, value_next: torch.Tensor) -> torch.Tensor: + neighborhood_value = getattr(model, "neighborhood_value", None) + if callable(neighborhood_value): + return neighborhood_value(value_next) + return value_next + +def _strategy3_delta_max() -> float: + return float(_job_param("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX)) + +def _strategy3_policy_delta(policy_raw: torch.Tensor) -> torch.Tensor: + return torch.tanh(policy_raw.float()) * _strategy3_delta_max() + +def _strategy3_apply_delta(seg: torch.Tensor, delta: torch.Tensor) -> torch.Tensor: + seg_f = seg.float() + delta_f = delta.float() + return (seg_f + delta_f).clamp(0.0, 1.0).to(dtype=seg.dtype) + +def _strategy3_advantage_normalize_enabled() -> bool: + return bool(_job_param("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE)) + +def _normalize_strategy3_advantage_map(advantage_map: torch.Tensor) -> torch.Tensor: + if not _strategy3_advantage_normalize_enabled(): + return advantage_map + if advantage_map.ndim < 4: + mean = advantage_map.mean() + std = advantage_map.std(unbiased=False) + return (advantage_map - mean) / (std + 1e-6) + mean = advantage_map.mean(dim=(2, 3), keepdim=True) + std = advantage_map.std(dim=(2, 3), unbiased=False, keepdim=True) + return (advantage_map - mean) / (std + 1e-6) + +def _strategy3_actor_advantage( + reward_map: torch.Tensor, + value_t: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + advantage_map = reward_map + float(gamma) * value_next.detach() - value_t.detach() + return _normalize_strategy3_advantage_map(advantage_map) + +def _strategy3_critic_target( + reward_map: torch.Tensor, + value_next: torch.Tensor, + *, + gamma: float, +) -> torch.Tensor: + return reward_map.detach() + float(gamma) * value_next.detach() + +def _strategy3_delta_distribution(delta_map: torch.Tensor) -> dict[str, float]: + delta_f = delta_map.detach().float() + abs_delta = delta_f.abs() + return { + "mean_delta": float(delta_f.mean().item()), + "mean_abs_delta": float(abs_delta.mean().item()), + "positive_pct": float((delta_f > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((delta_f < -1e-6).float().mean().item() * 100.0), + "near_zero_pct": float((abs_delta <= 1e-6).float().mean().item() * 100.0), + "max_abs_delta": float(abs_delta.max().item()), + } + +def _bernoulli_predictive_entropy(prob: torch.Tensor) -> torch.Tensor: + prob_f = prob.float().clamp(1e-6, 1.0 - 1e-6) + return -(prob_f * torch.log(prob_f) + (1.0 - prob_f) * torch.log1p(-prob_f)) + +def _iter_strategy3_dropout_modules(model: nn.Module) -> Iterator[nn.Module]: + for module in _unwrap_compiled(model).modules(): + if isinstance(module, (nn.Dropout, nn.Dropout2d)): + yield module + +@contextmanager +def _strategy3_mc_dropout_scope(model: nn.Module) -> Iterator[None]: + global _STRATEGY3_MC_DROPOUT_WARNED + + requested_p = float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)) + if requested_p <= 0.0 and not _STRATEGY3_MC_DROPOUT_WARNED: + print("[Strategy3] MC-dropout requested with non-positive dropout p; variance maps may collapse to zero.") + _STRATEGY3_MC_DROPOUT_WARNED = True + + saved_states: list[tuple[nn.Module, bool, float | None]] = [] + for module in _iter_strategy3_dropout_modules(model): + saved_states.append((module, bool(module.training), getattr(module, "p", None))) + module.train(True) + if hasattr(module, "p") and requested_p > 0.0: + module.p = requested_p + try: + yield + finally: + for module, was_training, saved_p in saved_states: + module.train(was_training) + if saved_p is not None and hasattr(module, "p"): + module.p = saved_p + +def _strategy3_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_fn: Any, +) -> tuple[torch.Tensor, torch.Tensor]: + if not bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)): + with torch.no_grad(): + zeros = torch.zeros_like(decoder_prob.float()) + pred_entropy = _bernoulli_predictive_entropy(decoder_prob) + return zeros, pred_entropy + + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + with torch.no_grad(): + mc_probs: list[torch.Tensor] = [] + with _strategy3_mc_dropout_scope(model): + for _ in range(samples): + logits = sample_fn() + mc_probs.append(torch.sigmoid(logits).float()) + stacked = torch.stack(mc_probs, dim=0) + mean_prob = stacked.mean(dim=0) + variance = stacked.var(dim=0, unbiased=False) + pred_entropy = _bernoulli_predictive_entropy(mean_prob) + return variance.to(dtype=decoder_prob.dtype), pred_entropy.to(dtype=decoder_prob.dtype) + +def _strategy3_mc_config_hash() -> tuple[bool, int, float]: + enabled = bool(_job_param("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED)) + samples = max(int(_job_param("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES)), 1) + dropout_p = round(float(_job_param("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P)), 8) + return enabled, samples, dropout_p + +def _strategy3_mc_disk_cache_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED)) + +def _strategy3_mc_disk_cache_read_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ)) + +def _strategy3_mc_disk_cache_write_enabled() -> bool: + return bool(_job_param("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE)) + +def _strategy3_normalize_sample_ids( + sample_ids: list[str] | tuple[str, ...] | None, + *, + batch_size: int, +) -> list[str] | None: + if sample_ids is None: + return None + normalized = [str(item) for item in sample_ids] + if len(normalized) != int(batch_size): + raise ValueError( + f"Strategy 3 eval MC cache expected {batch_size} sample_ids, got {len(normalized)}." + ) + return normalized + +def _strategy3_ensure_mc_cache_state(model: nn.Module) -> nn.Module: + raw_model = getattr(model, "_orig_mod", model) + if not hasattr(raw_model, "_strategy3_mc_cache"): + raw_model._strategy3_mc_cache = {} + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint"): + raw_model._strategy3_mc_cache_fingerprint = "" + if not hasattr(raw_model, "_strategy3_mc_cache_fingerprint_sources"): + raw_model._strategy3_mc_cache_fingerprint_sources = {} + if not hasattr(raw_model, "_strategy3_strategy2_checkpoint_path"): + raw_model._strategy3_strategy2_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_eval_checkpoint_path"): + raw_model._strategy3_eval_checkpoint_path = None + if not hasattr(raw_model, "_strategy3_mc_cache_stats"): + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return raw_model + +def _strategy3_reset_mc_cache_stats(model: nn.Module) -> None: + raw_model = _strategy3_ensure_mc_cache_state(model) + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + +def _strategy3_get_mc_cache_stats(model: nn.Module) -> dict[str, int]: + raw_model = _strategy3_ensure_mc_cache_state(model) + stats = raw_model._strategy3_mc_cache_stats + return { + "ram_hits": int(stats.get("ram_hits", 0)), + "disk_hits": int(stats.get("disk_hits", 0)), + "misses": int(stats.get("misses", 0)), + "writes": int(stats.get("writes", 0)), + } + +def _strategy3_checkpoint_sha256(path: str | Path | None) -> str | None: + if not path: + return None + resolved = str(Path(path).expanduser().resolve()) + cached = _STRATEGY3_MC_FILE_SHA256_CACHE.get(resolved) + if cached is not None: + return cached + checkpoint_path = Path(resolved) + if not checkpoint_path.is_file(): + return None + digest = hashlib.sha256() + with checkpoint_path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + checksum = digest.hexdigest() + _STRATEGY3_MC_FILE_SHA256_CACHE[resolved] = checksum + return checksum + +def _strategy3_decoder_state_hash(model: nn.Module) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + modules: list[tuple[str, nn.Module]] = [] + if isinstance(raw_model, PixelDRLMG_WithDecoder): + modules = [ + ("smp_model.encoder", raw_model.smp_model.encoder), + ("smp_model.decoder", raw_model.smp_model.decoder), + ("smp_model.segmentation_head", raw_model.smp_model.segmentation_head), + ] + elif isinstance(raw_model, PixelDRLMG_VGGWithDecoder): + modules = [ + ("encoder", raw_model.encoder), + ("segmentation_head", raw_model.segmentation_head), + ] + else: + return stable_hash(raw_model.__class__.__name__) + + digest = hashlib.sha256() + for prefix, module in modules: + for name, tensor in sorted(module.state_dict().items()): + tensor_cpu = tensor.detach().cpu().contiguous() + digest.update(prefix.encode("utf-8")) + digest.update(b"\0") + digest.update(name.encode("utf-8")) + digest.update(b"\0") + digest.update(str(tensor_cpu.dtype).encode("utf-8")) + digest.update(b"\0") + digest.update(json.dumps(list(tensor_cpu.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(tensor_cpu.numpy().tobytes()) + return digest.hexdigest() + +def _strategy3_resolve_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> tuple[str, dict[str, Any]]: + raw_model = _strategy3_ensure_mc_cache_state(model) + strategy2_path = ( + str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if strategy2_checkpoint_path + else getattr(raw_model, "_strategy3_strategy2_checkpoint_path", None) + ) + eval_path = ( + str(Path(eval_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path + else getattr(raw_model, "_strategy3_eval_checkpoint_path", None) + ) + decoder_hash = _strategy3_decoder_state_hash(raw_model) + sources: dict[str, Any] = {"decoder_state_hash": decoder_hash} + if strategy2_path: + sources["strategy2_checkpoint"] = strategy2_path + strategy2_sha = _strategy3_checkpoint_sha256(strategy2_path) + if strategy2_sha is not None: + sources["strategy2_sha256"] = strategy2_sha + if eval_path: + sources["eval_checkpoint"] = eval_path + eval_sha = _strategy3_checkpoint_sha256(eval_path) + if eval_sha is not None: + sources["eval_checkpoint_sha256"] = eval_sha + return decoder_hash, sources + +def _strategy3_bump_mc_cache_fingerprint( + model: nn.Module, + *, + strategy2_checkpoint_path: str | Path | None = None, + eval_checkpoint_path: str | Path | None = None, +) -> str: + raw_model = _strategy3_ensure_mc_cache_state(model) + if strategy2_checkpoint_path is not None: + raw_model._strategy3_strategy2_checkpoint_path = str(Path(strategy2_checkpoint_path).expanduser().resolve()) + if eval_checkpoint_path is not None: + raw_model._strategy3_eval_checkpoint_path = str(Path(eval_checkpoint_path).expanduser().resolve()) + fingerprint, sources = _strategy3_resolve_mc_cache_fingerprint( + raw_model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + eval_checkpoint_path=eval_checkpoint_path, + ) + fingerprint_changed = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) != str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint = str(fingerprint) + raw_model._strategy3_mc_cache_fingerprint_sources = dict(sources) + if fingerprint_changed: + clear_cache = getattr(raw_model, "clear_strategy3_mc_cache", None) + if callable(clear_cache): + clear_cache() + else: + raw_model._strategy3_mc_cache.clear() + raw_model._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + return str(fingerprint) + +def _strategy3_mc_disk_cache_root(run_dir: Path | None) -> Path | None: + if run_dir is None or not _strategy3_mc_disk_cache_enabled(): + return None + return Path(run_dir) / "mc_cache" + +def _strategy3_mc_disk_entry_path( + root: Path, + fingerprint: str, + split: str, + sample_id: str, +) -> Path: + safe_sample_id = str(sample_id).replace(os.sep, "__").replace("/", "__") + return Path(root) / str(fingerprint)[:16] / str(split) / f"{safe_sample_id}.pt" + +def _strategy3_load_mc_maps_from_disk( + path: Path, + *, + sample_id: str, + fingerprint: str, + mc_config_hash: tuple[bool, int, float], + split: str, +) -> tuple[torch.Tensor, torch.Tensor] | None: + if not path.is_file(): + return None + try: + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except TypeError: + payload = torch.load(path, map_location="cpu", weights_only=False) + except Exception: + return None + + if not isinstance(payload, dict): + return None + if int(payload.get("schema_version", -1)) != int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION): + return None + if str(payload.get("sample_id", "")) != str(sample_id): + return None + if str(payload.get("split", "")) != str(split): + return None + if str(payload.get("fingerprint", "")) != str(fingerprint): + return None + if tuple(payload.get("mc_config_hash", ())) != tuple(mc_config_hash): + return None + if int(payload.get("img_size", -1)) != int(IMG_SIZE): + return None + + variance = payload.get("mc_variance") + pred_entropy = payload.get("pred_entropy") + if not (torch.is_tensor(variance) and torch.is_tensor(pred_entropy)): + return None + if variance.ndim != 4 or pred_entropy.ndim != 4: + return None + return ( + variance.detach().to(device="cpu", dtype=torch.float32).contiguous(), + pred_entropy.detach().to(device="cpu", dtype=torch.float32).contiguous(), + ) + +def _strategy3_save_mc_maps_to_disk(path: Path, payload: dict[str, Any]) -> None: + atomic_torch_save(path, payload) + +def _strategy3_write_mc_cache_manifest( + run_dir: Path | None, + *, + fingerprint: str, + fingerprint_sources: dict[str, Any], + mc_config_hash: tuple[bool, int, float], + split: str, + split_write_count: int, +) -> None: + if run_dir is None: + return + manifest_path = Path(run_dir) / "mc_cache" / "manifest.json" + existing: dict[str, Any] = {} + if manifest_path.exists(): + try: + loaded = load_json(manifest_path) + except Exception: + loaded = {} + if isinstance(loaded, dict): + existing = loaded + existing_fingerprint = str(existing.get("fingerprint", "")) + existing_config = tuple(existing.get("mc_config_hash", ())) + if existing_fingerprint != str(fingerprint) or existing_config != tuple(mc_config_hash): + existing = {} + splits = dict(existing.get("splits", {})) if isinstance(existing.get("splits", {}), dict) else {} + splits[str(split)] = int(splits.get(str(split), 0)) + int(max(split_write_count, 0)) + payload = { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "fingerprint": str(fingerprint), + "fingerprint_sources": dict(fingerprint_sources), + "mc_config_hash": list(mc_config_hash), + "img_size": int(IMG_SIZE), + "dataset_name": current_dataset_name(), + "splits": splits, + } + atomic_save_json(manifest_path, payload) + +def _strategy3_mc_disk_mode( + *, + split: str | None, + run_dir: Path | None, +) -> tuple[bool, bool, Path | None, str | None]: + normalized_split = str(split).strip().lower() if split is not None else None + if normalized_split not in (None, "train", "val", "test"): + raise ValueError(f"Unsupported Strategy 3 MC cache split {split!r}.") + if normalized_split == "train": + return False, False, None, normalized_split + root = _strategy3_mc_disk_cache_root(run_dir) + can_use_disk = normalized_split in {"val", "test"} and root is not None + return ( + bool(can_use_disk and _strategy3_mc_disk_cache_read_enabled()), + bool(can_use_disk and _strategy3_mc_disk_cache_write_enabled()), + root, + normalized_split, + ) + +def _strategy3_prepare_cached_sample_pair( + sample_variance: torch.Tensor, + sample_entropy: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + return ( + sample_variance.detach().to(device="cpu", dtype=torch.float32).clone(), + sample_entropy.detach().to(device="cpu", dtype=torch.float32).clone(), + ) + +def _strategy3_cached_mc_uncertainty_maps( + model: nn.Module, + *, + decoder_prob: torch.Tensor, + sample_ids: list[str] | tuple[str, ...] | None, + compute_sample_maps: Any, + mc_cache_split: str | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + normalized_sample_ids = _strategy3_normalize_sample_ids(sample_ids, batch_size=int(decoder_prob.shape[0])) + if normalized_sample_ids is None: + raise ValueError("Strategy 3 eval MC cache requires non-empty sample_ids.") + + raw_model = _strategy3_ensure_mc_cache_state(model) + fingerprint = str(getattr(raw_model, "_strategy3_mc_cache_fingerprint", "")) or _strategy3_bump_mc_cache_fingerprint(raw_model) + cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = raw_model._strategy3_mc_cache + stats = raw_model._strategy3_mc_cache_stats + mc_hash = _strategy3_mc_config_hash() + disk_read_enabled, disk_write_enabled, disk_root, normalized_split = _strategy3_mc_disk_mode( + split=mc_cache_split, + run_dir=mc_cache_run_dir, + ) + manifest_write_count = 0 + variance_samples: list[torch.Tensor] = [] + entropy_samples: list[torch.Tensor] = [] + + for sample_index, sample_id in enumerate(normalized_sample_ids): + cache_key = (fingerprint, sample_id, mc_hash) + cached_pair = cache.get(cache_key) + if cached_pair is not None: + stats["ram_hits"] = int(stats.get("ram_hits", 0)) + 1 + else: + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_read_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + cached_pair = _strategy3_load_mc_maps_from_disk( + disk_path, + sample_id=sample_id, + fingerprint=fingerprint, + mc_config_hash=mc_hash, + split=normalized_split, + ) + if cached_pair is not None: + cache[cache_key] = _strategy3_prepare_cached_sample_pair(*cached_pair) + stats["disk_hits"] = int(stats.get("disk_hits", 0)) + 1 + else: + sample_variance, sample_entropy = compute_sample_maps(sample_index) + cached_pair = _strategy3_prepare_cached_sample_pair(sample_variance, sample_entropy) + cache[cache_key] = cached_pair + stats["misses"] = int(stats.get("misses", 0)) + 1 + disk_path = ( + _strategy3_mc_disk_entry_path(disk_root, fingerprint, normalized_split, sample_id) + if disk_write_enabled and disk_root is not None and normalized_split is not None + else None + ) + if disk_path is not None: + entry_exists = disk_path.exists() + _strategy3_save_mc_maps_to_disk( + disk_path, + { + "schema_version": int(_STRATEGY3_MC_CACHE_SCHEMA_VERSION), + "sample_id": str(sample_id), + "split": str(normalized_split), + "fingerprint": str(fingerprint), + "mc_config_hash": tuple(mc_hash), + "img_size": int(IMG_SIZE), + "dtype": "float32", + "created_at": datetime.now(timezone.utc).isoformat(), + "mc_variance": cached_pair[0], + "pred_entropy": cached_pair[1], + }, + ) + stats["writes"] = int(stats.get("writes", 0)) + 1 + if not entry_exists: + manifest_write_count += 1 + + cached_variance, cached_entropy = cached_pair + variance_samples.append(cached_variance.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + entropy_samples.append(cached_entropy.to(device=decoder_prob.device, dtype=decoder_prob.dtype)) + + if manifest_write_count > 0 and normalized_split is not None: + _strategy3_write_mc_cache_manifest( + mc_cache_run_dir, + fingerprint=fingerprint, + fingerprint_sources=dict(getattr(raw_model, "_strategy3_mc_cache_fingerprint_sources", {})), + mc_config_hash=mc_hash, + split=normalized_split, + split_write_count=manifest_write_count, + ) + + return torch.cat(variance_samples, dim=0), torch.cat(entropy_samples, dim=0) + +def _require_supported_strategy(strategy: int) -> int: + strategy = int(strategy) + if strategy not in SUPPORTED_STRATEGIES: + raise ValueError( + f"Unsupported strategy {strategy}. Supported strategies are {list(SUPPORTED_STRATEGIES)}." + ) + return strategy + +def _resolve_checkpoint_metric_name(metric_name: Any, *, strategy: int) -> str: + if not isinstance(metric_name, str) or not metric_name.strip(): + raise KeyError( + f"No best-checkpoint metric configured for strategy {strategy}. " + f"Set BEST_CHECKPOINT_METRICS[{strategy}] or best_checkpoint_metric_name to a non-empty metric name." + ) + metric_name = metric_name.strip() + if metric_name not in SUPPORTED_CHECKPOINT_METRICS: + raise KeyError( + f"Unsupported best-checkpoint metric {metric_name!r} for strategy {strategy}. " + f"Supported metrics: {sorted(SUPPORTED_CHECKPOINT_METRICS)}." + ) + return metric_name + +def _strategy_selection_metric_name(strategy: int) -> str: + strategy = _require_supported_strategy(strategy) + metric_name = _job_param( + f"strategy{strategy}_best_checkpoint_metric_name", + _job_param("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS.get(strategy)), + ) + return _resolve_checkpoint_metric_name(metric_name, strategy=strategy) + +def _strategy_selection_metric_value(strategy: int, metrics: dict[str, Any]) -> float: + metric_name = _strategy_selection_metric_name(strategy) + value = metrics.get(metric_name) + if value is None: + raise KeyError( + f"Configured best-checkpoint metric {metric_name!r} for strategy {strategy} " + f"is missing from metrics payload keys={sorted(metrics.keys())}." + ) + return float(value) + +def _early_stopping_monitor_name(strategy: int) -> str: + raw = str(_job_param("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR)).strip() + if not raw or raw.lower() == "auto": + return _strategy_selection_metric_name(strategy) + return raw + +def _early_stopping_mode(strategy: int, monitor_name: str | None = None) -> str: + raw = str(_job_param("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE)).strip().lower() + if raw in {"min", "max"}: + return raw + if raw != "auto": + raise ValueError(f"Unsupported early_stopping_mode={raw!r}. Expected 'auto', 'min', or 'max'.") + monitor_name = monitor_name or _early_stopping_monitor_name(strategy) + lowered = monitor_name.lower() + if "loss" in lowered or lowered.startswith("hd") or lowered.endswith("error"): + return "min" + return "max" + +def _early_stopping_min_delta() -> float: + return max(float(_job_param("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA)), 0.0) + +def _early_stopping_start_epoch() -> int: + return max(int(_job_param("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH)), 1) + +def _early_stopping_patience() -> int: + return max(int(_job_param("early_stopping_patience", EARLY_STOPPING_PATIENCE)), 0) + +def _early_stopping_monitor_value( + metrics: dict[str, Any], + *, + strategy: int, + monitor_name: str, +) -> float | None: + value = metrics.get(monitor_name) + if value is None and monitor_name == _strategy_selection_metric_name(strategy): + value = _strategy_selection_metric_value(strategy, metrics) + if value is None: + return None + return float(value) + +def _early_stopping_improved( + current_value: float, + best_value: float | None, + *, + mode: str, + min_delta: float, +) -> bool: + if best_value is None: + return True + if mode == "min": + return current_value < (best_value - min_delta) + if mode == "max": + return current_value > (best_value + min_delta) + raise ValueError(f"Unsupported early stopping comparison mode: {mode!r}") + +def _strategy3_requested_bootstrap_freeze() -> bool: + return bool( + _job_param( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + ) + +def _module_freeze_state(module: nn.Module | None) -> str: + if not isinstance(module, nn.Module): + return "n/a" + requires_grad_flags = [bool(param.requires_grad) for param in module.parameters()] + if not requires_grad_flags: + return "n/a" + if all(not flag for flag in requires_grad_flags): + return "frozen" + if all(requires_grad_flags): + return "trainable" + return "mixed" + +def _strategy3_bootstrap_freeze_status(model: nn.Module) -> dict[str, Any]: + raw = _raw_decoder_rl_model(model) + status = { + "bootstrap_loaded": False, + "freeze_requested": False, + "freeze_active": False, + "encoder_state": "n/a", + "decoder_state": "n/a", + "segmentation_head_state": "n/a", + } + if raw is None: + return status + + status["bootstrap_loaded"] = bool(getattr(raw, "strategy2_bootstrap_loaded", False)) + status["freeze_requested"] = bool(getattr(raw, "freeze_bootstrapped_segmentation", False)) + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + status["encoder_state"] = _module_freeze_state(getattr(smp_model, "encoder", None)) + status["decoder_state"] = _module_freeze_state(getattr(smp_model, "decoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(smp_model, "segmentation_head", None)) + else: + status["encoder_state"] = _module_freeze_state(getattr(raw, "encoder", None)) + status["segmentation_head_state"] = _module_freeze_state(getattr(raw, "segmentation_head", None)) + + relevant_states = [ + state + for state in ( + status["encoder_state"], + status["decoder_state"], + status["segmentation_head_state"], + ) + if state != "n/a" + ] + status["freeze_active"] = bool( + status["bootstrap_loaded"] and relevant_states and all(state == "frozen" for state in relevant_states) + ) + return status + +def _strategy3_decoder_is_frozen(model: nn.Module) -> bool: + return bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]) + +def _strategy3_loss_weights( + model: nn.Module, + *, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + decoder_ce_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(ce_weight) + decoder_dice_default = 0.0 if _strategy3_decoder_is_frozen(model) else float(dice_weight) + return { + "decoder_ce": float(_job_param("strategy3_decoder_ce_weight", decoder_ce_default)), + "decoder_dice": float(_job_param("strategy3_decoder_dice_weight", decoder_dice_default)), + } + +def _strategy3_keep_frozen_modules_in_eval(model: nn.Module) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not bool(_strategy3_bootstrap_freeze_status(model)["freeze_active"]): + return + smp_model = getattr(raw, "smp_model", None) + if smp_model is not None: + module_names = ("encoder", "decoder", "segmentation_head") + module_root = smp_model + else: + module_names = ("encoder", "segmentation_head") + module_root = raw + for module_name in module_names: + module = getattr(module_root, module_name, None) + if isinstance(module, nn.Module): + module.eval() + +def _strategy3_apply_rollout_step( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + return apply_actions( + seg, + actions, + num_actions=int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)), + ).to(dtype=seg.dtype) + +def _refinement_deltas( + *, + action_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + small = float(_job_param("refine_delta_small", DEFAULT_REFINE_DELTA_SMALL)) + large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + if action_count == 3: + values = (-large, 0.0, small) + elif action_count == 4: + values = (-large, -small, 0.0, small) + elif action_count == 5: + values = (-large, -small, 0.0, small, large) + else: + raise ValueError( + f"Unsupported Strategy 3 action count {action_count}. " + "Expected one of {3, 4, 5}." + ) + return torch.tensor(values, device=device, dtype=dtype) + +def threshold_binary_mask(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).to(dtype=mask.dtype) + +def threshold_binary_long(mask: torch.Tensor, threshold: float | None = None) -> torch.Tensor: + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) if threshold is None else float(threshold) + return (mask > threshold).long() + +"""============================================================================= +BUSI SPLIT + NORMALIZATION +============================================================================= +""" + +IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) +IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) + +def validate_image_mask_consistency(images_dir: Path, annotations_dir: Path): + image_files = {f for f in os.listdir(images_dir) if not f.startswith(".") and f.lower().endswith(".png")} + mask_files = {f for f in os.listdir(annotations_dir) if not f.startswith(".") and f.lower().endswith(".png")} + matched = sorted(image_files & mask_files) + missing_masks = sorted(image_files - mask_files) + missing_images = sorted(mask_files - image_files) + return matched, missing_masks, missing_images + +def parse_busi_with_classes_label(filename: str) -> str: + upper_name = str(filename).upper() + if upper_name.endswith("_B.PNG"): + return "benign" + if upper_name.endswith("_M.PNG"): + return "malignant" + raise ValueError( + f"BUSI_with_classes filename must end with '_B.png' or '_M.png', got {filename!r}" + ) + +def _candidate_report_dicts(payload: dict[str, Any]) -> list[dict[str, Any]]: + candidates = [payload] + for key in ("counts", "summary", "dataset", "report", "metadata"): + value = payload.get(key) + if isinstance(value, dict): + candidates.append(value) + return candidates + +def _extract_report_int(payload: dict[str, Any], keys: tuple[str, ...]) -> int | None: + for candidate in _candidate_report_dicts(payload): + for key in keys: + value = candidate.get(key) + if isinstance(value, bool): + continue + if isinstance(value, (int, np.integer)): + return int(value) + if isinstance(value, float) and float(value).is_integer(): + return int(value) + return None + +def _extract_report_filenames(payload: dict[str, Any]) -> set[str] | None: + for candidate in _candidate_report_dicts(payload): + filenames = candidate.get("filenames") + if isinstance(filenames, list) and all(isinstance(item, str) for item in filenames): + return set(filenames) + + pairs = candidate.get("pairs") + if isinstance(pairs, list): + extracted = {item["filename"] for item in pairs if isinstance(item, dict) and isinstance(item.get("filename"), str)} + if extracted: + return extracted + return None + +def validate_busi_with_classes_pipeline_report(report_path: Path, sample_records: list[dict[str, str]]) -> None: + if not report_path.exists(): + return + + payload = load_json(report_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict payload in {report_path}, found {type(payload).__name__}.") + + benign_count = sum(1 for record in sample_records if record.get("class_label") == "benign") + malignant_count = sum(1 for record in sample_records if record.get("class_label") == "malignant") + expected_counts = { + "total_pairs": len(sample_records), + "benign": benign_count, + "malignant": malignant_count, + } + report_counts = { + "total_pairs": _extract_report_int(payload, ("total_pairs", "pair_count", "num_pairs", "total")), + "benign": _extract_report_int(payload, ("benign", "benign_count", "num_benign")), + "malignant": _extract_report_int(payload, ("malignant", "malignant_count", "num_malignant")), + } + for key, expected_value in expected_counts.items(): + report_value = report_counts[key] + if report_value is not None and report_value != expected_value: + raise RuntimeError( + f"pipeline_check mismatch for {key}: discovered={expected_value}, report={report_value} ({report_path})" + ) + + report_filenames = _extract_report_filenames(payload) + if report_filenames is not None: + discovered_filenames = {record["filename"] for record in sample_records} + if report_filenames != discovered_filenames: + missing_from_report = sorted(discovered_filenames - report_filenames)[:10] + extra_in_report = sorted(report_filenames - discovered_filenames)[:10] + raise RuntimeError( + f"pipeline_check filenames mismatch for {report_path}: " + f"missing_from_report={missing_from_report}, extra_in_report={extra_in_report}" + ) + + print(f"[Pipeline Check] Validated BUSI_with_classes metadata from {report_path}") + +def check_data_leakage(splits: dict[str, list[str]]) -> dict[str, list[str]]: + leaks: dict[str, list[str]] = {} + split_names = list(splits.keys()) + for i, lhs in enumerate(split_names): + for rhs in split_names[i + 1 :]: + overlap = sorted(set(splits[lhs]) & set(splits[rhs])) + if overlap: + leaks[f"{lhs} ∩ {rhs}"] = overlap + return leaks + +def _project_relative_path(path: Path) -> str: + resolved = Path(path).resolve() + try: + return str(resolved.relative_to(PROJECT_DIR.resolve())) + except ValueError: + return str(resolved) + +def resolve_dataset_root_from_registry(split_registry: dict[str, Any]) -> Path: + dataset_root = Path(split_registry["dataset_root"]) + if dataset_root.is_absolute(): + return dataset_root + return (PROJECT_DIR / dataset_root).resolve() + +def make_sample_record( + filename: str, + images_subdir: str, + annotations_subdir: str, + *, + class_label: str | None = None, +) -> dict[str, str]: + record = { + "filename": filename, + "image_rel_path": str(Path(images_subdir) / filename), + "mask_rel_path": str(Path(annotations_subdir) / filename), + } + if class_label is not None: + record["class_label"] = class_label + return record + +def build_sample_records( + filenames: list[str], + *, + images_subdir: str, + annotations_subdir: str, + dataset_name: str, +) -> list[dict[str, str]]: + records = [] + for filename in sorted(filenames): + class_label = parse_busi_with_classes_label(filename) if dataset_name == "BUSI_with_classes" else None + records.append( + make_sample_record( + filename, + images_subdir, + annotations_subdir, + class_label=class_label, + ) + ) + return records + +def split_ratios_for_type(split_type: str) -> tuple[float, float]: + if split_type == "80_10_10": + return 0.80, 0.10 + if split_type == "70_10_20": + return 0.70, 0.10 + raise ValueError(f"Unsupported split_type: {split_type}") + +def deterministic_shuffle_records(records: list[dict[str, str]], *, seed: int, tag: str) -> list[dict[str, str]]: + rng = random.Random(seed + stable_int_from_text(tag)) + shuffled = [dict(record) for record in records] + rng.shuffle(shuffled) + return shuffled + +def train_subset_variant_suffix(variant: int | None = None) -> str: + variant_value = int(TRAIN_SUBSET_VARIANT if variant is None else variant) + return "" if variant_value <= 0 else f"_variant{variant_value:02d}" + +def group_records_by_class(sample_records: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + grouped: dict[str, list[dict[str, str]]] = {} + for record in sample_records: + class_label = record.get("class_label") + if class_label is None: + raise RuntimeError("Expected class_label in sample record for class-aware splitting.") + grouped.setdefault(class_label, []).append(dict(record)) + return grouped + +def allocate_counts_by_ratio(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + total_available = sum(available_counts.values()) + if total_available <= 0: + return allocation + + exact = {label: total_size * available_counts[label] / total_available for label in available_counts} + for label in available_counts: + allocation[label] = min(available_counts[label], int(math.floor(exact[label]))) + + remaining = min(total_size, total_available) - sum(allocation.values()) + order = sorted( + available_counts.keys(), + key=lambda label: (exact[label] - math.floor(exact[label]), available_counts[label], label), + reverse=True, + ) + while remaining > 0: + progressed = False + for label in order: + if allocation[label] < available_counts[label]: + allocation[label] += 1 + remaining -= 1 + progressed = True + if remaining == 0: + break + if not progressed: + break + return allocation + +def allocate_balanced_counts(total_size: int, available_counts: dict[str, int]) -> dict[str, int]: + allocation = {label: 0 for label in available_counts} + if total_size <= 0 or not available_counts: + return allocation + + labels = sorted(available_counts.keys()) + half = total_size // 2 + for label in labels: + allocation[label] = min(available_counts[label], half) + + remaining = min(total_size, sum(available_counts.values())) - sum(allocation.values()) + while remaining > 0: + candidates = [label for label in labels if allocation[label] < available_counts[label]] + if not candidates: + break + best_label = max( + candidates, + key=lambda label: ( + available_counts[label] - allocation[label], + 1 if label == "benign" else 0, + label, + ), + ) + allocation[best_label] += 1 + remaining -= 1 + return allocation + +def build_unstratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + records = deterministic_shuffle_records(sample_records, seed=seed, tag=f"base::{split_type}") + + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + return { + "train": records[:train_end], + "val": records[train_end:val_end], + "test": records[val_end:], + } + +def build_stratified_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + grouped = group_records_by_class(sample_records) + splits = {"train": [], "val": [], "test": []} + + for class_label in sorted(grouped.keys()): + records = deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"base::{split_type}::{class_label}", + ) + total_samples = len(records) + train_end = int(total_samples * train_ratio) + val_end = int(total_samples * (train_ratio + val_ratio)) + splits["train"].extend(records[:train_end]) + splits["val"].extend(records[train_end:val_end]) + splits["test"].extend(records[val_end:]) + + for split_name in splits: + splits[split_name] = deterministic_shuffle_records( + splits[split_name], + seed=seed, + tag=f"base::{split_type}::{split_name}", + ) + return splits + +def build_balanced_train_base_split( + sample_records: list[dict[str, str]], + *, + split_type: str, + seed: int, +) -> dict[str, list[dict[str, str]]]: + train_ratio, val_ratio = split_ratios_for_type(split_type) + test_ratio = 1.0 - train_ratio - val_ratio + grouped = group_records_by_class(sample_records) + if sorted(grouped.keys()) != ["benign", "malignant"]: + raise RuntimeError( + f"balanced_train split policy expects benign/malignant classes, found {sorted(grouped.keys())}" + ) + + shuffled = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"base::{split_type}::balanced_train::{class_label}", + ) + for class_label, records in grouped.items() + } + + nominal_train_size = int(len(sample_records) * train_ratio) + per_class_train = min( + nominal_train_size // 2, + *(len(records) for records in shuffled.values()), + ) + + train_records: list[dict[str, str]] = [] + remaining_by_class: dict[str, list[dict[str, str]]] = {} + for class_label in sorted(shuffled.keys()): + records = shuffled[class_label] + train_records.extend(records[:per_class_train]) + remaining_by_class[class_label] = records[per_class_train:] + + remainder_val_fraction = val_ratio / max(val_ratio + test_ratio, 1e-8) + val_records: list[dict[str, str]] = [] + test_records: list[dict[str, str]] = [] + for class_label in sorted(remaining_by_class.keys()): + records = remaining_by_class[class_label] + val_count = int(len(records) * remainder_val_fraction) + val_records.extend(records[:val_count]) + test_records.extend(records[val_count:]) + + return { + "train": deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::train", + ), + "val": deterministic_shuffle_records( + val_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::val", + ), + "test": deterministic_shuffle_records( + test_records, + seed=seed, + tag=f"base::{split_type}::balanced_train::test", + ), + } + +def build_nested_train_subsets( + train_records: list[dict[str, str]], + train_fractions: list[float], + *, + split_type: str, + seed: int, + split_policy: str | None = None, + subset_variant: int = 0, +) -> dict[str, list[dict[str, str]]]: + if not train_records: + return {} + + variant_tag = "" if int(subset_variant) <= 0 else f"::variant::{int(subset_variant)}" + ordered_records = deterministic_shuffle_records(train_records, seed=seed, tag=f"subset::{split_type}{variant_tag}") + use_class_labels = any("class_label" in record for record in train_records) + if not use_class_labels: + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + subsets[subset_key] = [dict(record) for record in ordered_records[:subset_size]] + return subsets + + grouped = { + class_label: deterministic_shuffle_records( + records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{class_label}{variant_tag}", + ) + for class_label, records in group_records_by_class(train_records).items() + } + available_counts = {class_label: len(records) for class_label, records in grouped.items()} + + subsets: dict[str, list[dict[str, str]]] = {} + for fraction in normalize_dataset_percents(train_fractions): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError(f"Invalid training fraction: {fraction}") + subset_key = percent_label(fraction) + subset_size = len(ordered_records) if fraction >= 1.0 else max(1, int(len(ordered_records) * fraction)) + if split_policy == "balanced_train": + class_counts = allocate_balanced_counts(subset_size, available_counts) + else: + class_counts = allocate_counts_by_ratio(subset_size, available_counts) + + subset_records: list[dict[str, str]] = [] + for class_label in sorted(grouped.keys()): + subset_records.extend([dict(record) for record in grouped[class_label][: class_counts[class_label]]]) + subsets[subset_key] = deterministic_shuffle_records( + subset_records, + seed=seed, + tag=f"subset::{split_type}::{split_policy or 'stratified'}::{subset_key}{variant_tag}", + ) + return subsets + +def train_fraction_from_subset_key(subset_key: str) -> float: + subset_text = str(subset_key).strip().lower() + if not subset_text: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") + try: + percent = Decimal(subset_text.replace("p", ".")) + except InvalidOperation as exc: + raise RuntimeError(f"Invalid train subset key {subset_key!r} in dataset_splits.json.") from exc + if not percent.is_finite() or percent <= 0 or percent > 100: + raise RuntimeError(f"Train subset key {subset_key!r} must represent a percentage in the range (0, 100].") + return float(percent / Decimal("100")) + +def validate_persisted_split_no_leakage(split_type: str, split_entry: dict[str, Any], *, source: str) -> None: + base_splits = split_entry["base_splits"] + base_filenames: dict[str, list[str]] = {} + for split_name, records in base_splits.items(): + filenames = [record["filename"] for record in records] + if len(filenames) != len(set(filenames)): + raise RuntimeError(f"Duplicate filenames detected inside {split_name} for split_type={split_type}.") + base_filenames[split_name] = filenames + + leaks = check_data_leakage(base_filenames) + if leaks: + raise RuntimeError(f"Data leakage detected for split_type={split_type}: {list(leaks.keys())}") + + base_train = set(base_filenames["train"]) + previous_subset: set[str] = set() + for subset_key in sorted(split_entry["train_subsets"].keys(), key=train_fraction_from_subset_key): + subset_filenames = [record["filename"] for record in split_entry["train_subsets"][subset_key]] + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames detected inside train subset {subset_key} for split_type={split_type}." + ) + subset_set = set(subset_filenames) + missing = sorted(subset_set - base_train) + if missing: + raise RuntimeError( + f"Train subset {subset_key} contains files outside the base train split for split_type={split_type}." + ) + if previous_subset and not previous_subset.issubset(subset_set): + raise RuntimeError( + f"Train subsets are not nested for split_type={split_type}." + ) + previous_subset = subset_set + + print(f"[Split Check] No data leakage detected for split_type={split_type} ({source}).") + +def repair_persisted_train_subsets( + split_registry: dict[str, Any], + requested_train_fractions: list[float], + *, + split_json_path: Path, + seed: int, +) -> bool: + split_entries = split_registry.get("split_types", {}) + requested_fractions = normalize_dataset_percents(requested_train_fractions) + combined_fractions = {float(value) for value in split_registry.get("train_fractions", [])} + combined_fractions.update(requested_fractions) + dataset_name = str(split_registry.get("dataset_name", "BUSI")) + split_policy = split_registry.get("split_policy") if dataset_name == "BUSI_with_classes" else None + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + for subset_key in train_subsets.keys(): + combined_fractions.add(train_fraction_from_subset_key(subset_key)) + + combined_fractions_list = normalize_dataset_percents(list(combined_fractions)) + requested_keys = {percent_label(fraction) for fraction in requested_fractions} + registry_seed = int(split_registry.get("seed", seed)) + repaired = False + + if split_registry.get("train_fractions") != combined_fractions_list: + split_registry["train_fractions"] = combined_fractions_list + repaired = True + + for split_type in SUPPORTED_SPLIT_TYPES: + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + missing_requested_keys = sorted(requested_keys - set(train_subsets.keys()), key=train_fraction_from_subset_key) + if not missing_requested_keys: + continue + + split_entry["train_subsets"] = build_nested_train_subsets( + split_entry["base_splits"]["train"], + combined_fractions_list, + split_type=split_type, + seed=registry_seed, + split_policy=split_policy, + ) + print( + f"[Splits] Rebuilt missing train subsets {missing_requested_keys} " + f"for split_type={split_type} in {split_json_path}" + ) + repaired = True + + if repaired: + save_json(split_json_path, split_registry) + print(f"[Splits] Updated persisted dataset splits at {split_json_path}") + return repaired + +def load_or_create_dataset_splits( + images_dir: Path, + annotations_dir: Path, + split_json_path: Path, + train_fractions: list[float], + seed: int, +) -> tuple[dict[str, Any], str]: + train_fractions = normalize_dataset_percents(train_fractions) + images_dir = Path(images_dir).resolve() + annotations_dir = Path(annotations_dir).resolve() + split_json_path = Path(split_json_path).resolve() + dataset_name = current_dataset_name() + split_policy = current_busi_with_classes_split_policy() if dataset_name == "BUSI_with_classes" else None + if split_json_path.exists(): + split_registry = load_json(split_json_path) + if split_registry.get("version") != DATASET_SPLITS_VERSION: + raise RuntimeError( + f"Unsupported dataset_splits.json version in {split_json_path}. " + f"Expected version={DATASET_SPLITS_VERSION}." + ) + persisted_dataset_name = str(split_registry.get("dataset_name", "BUSI")) + if persisted_dataset_name != dataset_name: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets dataset_name={persisted_dataset_name!r}, " + f"but current DATASET_NAME={dataset_name!r}." + ) + persisted_split_policy = split_registry.get("split_policy") + if dataset_name == "BUSI_with_classes" and persisted_split_policy != split_policy: + raise RuntimeError( + f"dataset_splits.json at {split_json_path} targets split_policy={persisted_split_policy!r}, " + f"but current BUSI_WITH_CLASSES_SPLIT_POLICY={split_policy!r}." + ) + split_entries = split_registry.get("split_types") + if not isinstance(split_entries, dict): + raise RuntimeError(f"Invalid split_types payload in {split_json_path}.") + for split_type in SUPPORTED_SPLIT_TYPES: + if split_type not in split_entries: + raise RuntimeError( + f"dataset_splits.json is missing split_type={split_type}. Delete it to regenerate cleanly." + ) + repaired = repair_persisted_train_subsets( + split_registry, + train_fractions, + split_json_path=split_json_path, + seed=seed, + ) + source = "repaired" if repaired else "loaded" + if dataset_name == "BUSI_with_classes": + sample_records = build_sample_records( + validate_image_mask_consistency(images_dir, annotations_dir)[0], + images_subdir=split_registry["images_subdir"], + annotations_subdir=split_registry["annotations_subdir"], + dataset_name=dataset_name, + ) + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + for split_type in SUPPORTED_SPLIT_TYPES: + validate_persisted_split_no_leakage(split_type, split_entries[split_type], source=source) + if repaired: + print(f"[Splits] Loaded and repaired persisted dataset splits from {split_json_path}") + else: + print(f"[Splits] Loaded persisted dataset splits from {split_json_path}") + return split_registry, source + + matched, missing_masks, missing_images = validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + images_subdir = images_dir.relative_to(dataset_root).as_posix() + annotations_subdir = annotations_dir.relative_to(dataset_root).as_posix() + sample_records = build_sample_records( + matched, + images_subdir=images_subdir, + annotations_subdir=annotations_subdir, + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = current_pipeline_check_path() + if pipeline_check_path is not None: + validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + split_registry = { + "version": DATASET_SPLITS_VERSION, + "dataset_name": dataset_name, + "split_policy": split_policy, + "dataset_root": _project_relative_path(dataset_root), + "images_subdir": images_subdir, + "annotations_subdir": annotations_subdir, + "seed": seed, + "train_fractions": list(train_fractions), + "split_types": {}, + } + + for split_type in SUPPORTED_SPLIT_TYPES: + if dataset_name == "BUSI_with_classes": + if split_policy == "balanced_train": + base_splits = build_balanced_train_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_stratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + else: + base_splits = build_unstratified_base_split( + sample_records, + split_type=split_type, + seed=seed, + ) + train_subsets = build_nested_train_subsets( + base_splits["train"], + train_fractions, + split_type=split_type, + seed=seed, + split_policy=split_policy, + ) + split_entry = { + "split_type": split_type, + "base_splits": base_splits, + "train_subsets": train_subsets, + } + validate_persisted_split_no_leakage(split_type, split_entry, source="created") + split_registry["split_types"][split_type] = split_entry + + save_json(split_json_path, split_registry) + print(f"[Splits] Created persisted dataset splits at {split_json_path}") + return split_registry, "created" + +def select_persisted_split( + split_registry: dict[str, Any], + split_type: str, + train_fraction: float, +) -> dict[str, Any]: + if split_type not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"Unsupported split_type: {split_type}") + + split_entries = split_registry.get("split_types", {}) + if split_type not in split_entries: + raise KeyError( + f"Requested split_type={split_type} is not available in dataset_splits.json. " + "Delete the JSON file to regenerate it with the new configuration." + ) + + subset_key = percent_label(train_fraction) + split_entry = split_entries[split_type] + train_subsets = split_entry.get("train_subsets", {}) + if subset_key not in train_subsets: + raise KeyError( + f"Requested train fraction={train_fraction} (key={subset_key}) is not available in dataset_splits.json." + ) + + return { + "dataset_root": resolve_dataset_root_from_registry(split_registry), + "split_type": split_type, + "train_fraction": float(train_fraction), + "train_subset_key": subset_key, + "train_subset_variant": 0, + "train_subset_source": "persisted", + "base_train_records": split_entry["base_splits"]["train"], + "train_records": train_subsets[subset_key], + "val_records": split_entry["base_splits"]["val"], + "test_records": split_entry["base_splits"]["test"], + } + +def apply_train_subset_variant( + selected_split: dict[str, Any], + split_registry: dict[str, Any], + *, + subset_variant: int, +) -> dict[str, Any]: + variant = int(subset_variant) + if variant <= 0 or float(selected_split["train_fraction"]) >= 1.0: + return selected_split + + split_policy = split_registry.get("split_policy") if current_dataset_name() == "BUSI_with_classes" else None + variant_subsets = build_nested_train_subsets( + selected_split["base_train_records"], + [float(selected_split["train_fraction"])], + split_type=str(selected_split["split_type"]), + seed=int(split_registry.get("seed", SEED)), + split_policy=split_policy, + subset_variant=variant, + ) + subset_key = str(selected_split["train_subset_key"]) + updated_split = dict(selected_split) + updated_split["train_records"] = variant_subsets[subset_key] + updated_split["train_subset_variant"] = variant + updated_split["train_subset_source"] = "variant_override" + return updated_split + +def export_selected_split_manifest( + pct_root: Path, + *, + percent: float, + split_source: str, + selected_split: dict[str, Any], +) -> Path: + variant_suffix = train_subset_variant_suffix(int(selected_split.get("train_subset_variant", 0))) + manifest_path = pct_root / ( + f"selected_split_{selected_split['split_type']}_{percent_label(percent)}pct{variant_suffix}.json" + ) + payload = { + "dataset_name": current_dataset_name(), + "dataset_root": str(Path(selected_split["dataset_root"]).resolve()), + "dataset_percent": float(percent), + "dataset_percent_label": percent_label(percent), + "split_source": split_source, + "split_type": str(selected_split["split_type"]), + "train_fraction": float(selected_split["train_fraction"]), + "train_subset_key": str(selected_split["train_subset_key"]), + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(manifest_path.resolve()), + "base_train_records": [dict(record) for record in selected_split["base_train_records"]], + "train_records": [dict(record) for record in selected_split["train_records"]], + "val_records": [dict(record) for record in selected_split["val_records"]], + "test_records": [dict(record) for record in selected_split["test_records"]], + } + save_json(manifest_path, payload) + return manifest_path + +def compute_busi_statistics( + dataset_root: Path, + sample_records: list[dict[str, str]], + cache_path: Path, +) -> tuple[float, float, str]: + filenames = [record["filename"] for record in sample_records] + if cache_path.exists(): + stats = load_json(cache_path) + if stats.get("filenames") == filenames: + print(f"[Normalization] Loaded cached normalization stats from {cache_path}") + return float(stats["global_mean"]), float(stats["global_std"]), "loaded_from_cache" + + total_sum = np.float64(0.0) + total_sq_sum = np.float64(0.0) + total_pixels = 0 + + for record in tqdm(sample_records, desc="Computing BUSI train mean/std", leave=False): + image_path = dataset_root / record["image_rel_path"] + img = np.array(PILImage.open(image_path)).astype(np.float64) + total_sum += img.sum() + total_sq_sum += (img ** 2).sum() + total_pixels += img.size + + global_mean = float(total_sum / total_pixels) + global_std = float(np.sqrt(total_sq_sum / total_pixels - global_mean ** 2)) + if global_std < 1e-6: + global_std = 1.0 + + save_json( + cache_path, + { + "global_mean": global_mean, + "global_std": global_std, + "total_pixels": int(total_pixels), + "num_images": len(sample_records), + "filenames": filenames, + }, + ) + print(f"[Normalization] Computed and saved normalization stats to {cache_path}") + return global_mean, global_std, "computed_fresh" + +def compute_class_distribution(sample_records: list[dict[str, str]]) -> dict[str, int] | None: + if not sample_records or not any("class_label" in record for record in sample_records): + return None + return { + "benign": sum(1 for record in sample_records if record.get("class_label") == "benign"), + "malignant": sum(1 for record in sample_records if record.get("class_label") == "malignant"), + } + +def format_class_distribution(class_distribution: dict[str, int] | None) -> str: + if class_distribution is None: + return "unavailable" + benign = int(class_distribution.get("benign", 0)) + malignant = int(class_distribution.get("malignant", 0)) + total = benign + malignant + return f"benign={benign}, malignant={malignant}, total={total}" + +def print_loaded_class_distribution( + *, + split_type: str, + train_subset_key: str, + base_train_records: list[dict[str, str]], + train_records: list[dict[str, str]], + val_records: list[dict[str, str]], + test_records: list[dict[str, str]], +) -> None: + if not any("class_label" in record for record in train_records): + return + section(f"Loaded Class Distribution | {split_type} | {train_subset_key}%") + print(f"Base train classes : {format_class_distribution(compute_class_distribution(base_train_records))}") + print(f"Train subset classes : {format_class_distribution(compute_class_distribution(train_records))}") + print(f"Validation classes : {format_class_distribution(compute_class_distribution(val_records))}") + print(f"Test classes : {format_class_distribution(compute_class_distribution(test_records))}") + +def print_split_summary(payload: dict[str, Any]) -> None: + unit_name = "Phase" if payload.get("phase_index") is not None else "Split" + section(f"{unit_name} Summary | {payload['split_type']} | {payload['train_subset_key']}%") + print(f"Dataset name : {payload['dataset_name']}") + if payload.get("dataset_split_policy") is not None: + print(f"Dataset split policy : {payload['dataset_split_policy']}") + print(f"Dataset splits JSON : {payload['dataset_splits_path']}") + print(f"Split source : {payload['split_source']}") + print(f"Split type used : {payload['split_type']}") + if payload.get("split_generation_mode") is not None: + print(f"Split generation mode : {payload['split_generation_mode']}") + if payload.get("phase_index") is not None: + print(f"Phase index : {payload['phase_index']}") + print(f"Phase val/test folds : val={payload['phase_val_fold_index']}, test={payload['phase_test_fold_index']}") + if payload.get("percent_sampling_mode") is not None: + print(f"Percent sampling mode : {payload['percent_sampling_mode']}") + print(f"Train fraction : {payload['train_subset_key']}% of frozen base train") + print(f"Train subset variant : {payload.get('train_subset_variant', 0)}") + print(f"Train subset source : {payload.get('train_subset_source', 'persisted')}") + if payload.get("sampling_chain_dataset_percents") is not None: + print(f"Sampling chain percents: {payload['sampling_chain_dataset_percents']}") + print(f"Base train samples : {payload['base_train_count']}") + print(f"Train subset samples : {payload['train_count']}") + print(f"Validation samples : {payload['val_count']}") + print(f"Test samples : {payload['test_count']}") + if payload.get("base_train_class_distribution") is not None: + print(f"Base train classes : {format_class_distribution(payload['base_train_class_distribution'])}") + print(f"Train subset classes : {format_class_distribution(payload['train_class_distribution'])}") + print(f"Validation classes : {format_class_distribution(payload['val_class_distribution'])}") + print(f"Test classes : {format_class_distribution(payload['test_class_distribution'])}") + print(f"Validation/Test frozen : {payload['val_test_frozen']}") + print(f"Leakage check : {payload['leakage_check']}") + +def print_normalization_summary(payload: dict[str, Any]) -> None: + mode = "ImageNet mean/std" if USE_IMAGENET_NORM else "Dataset train mean/std" + print(f"Dataset name : {payload['dataset_name']}") + print(f"Normalization mode : {mode}") + print(f"Stats cache path : {payload['normalization_cache_path']}") + print(f"Stats source : {payload['normalization_source']}") + print(f"Split type used : {payload['split_type']}") + variant_suffix = train_subset_variant_suffix(int(payload.get("train_subset_variant", 0))) + print( + f"Stats computed from : {payload['train_count']} train samples " + f"({payload['train_subset_key']}%{variant_suffix})" + ) +# ============================================================================= +# IMAGE PREPARATION + DATASETS +# ============================================================================= + +def _to_three_channels(image: np.ndarray) -> np.ndarray: + if image.ndim == 2: + image = image[..., None] + if image.shape[2] == 1: + image = np.repeat(image, 3, axis=2) + elif image.shape[2] > 3: + image = image[..., :3] + return image + +def _prepare_image(raw: np.ndarray, global_mean: float, global_std: float) -> np.ndarray: + img = raw.astype(np.float32) + img = _to_three_channels(img) + if IMG_SIZE > 0 and (img.shape[0] != IMG_SIZE or img.shape[1] != IMG_SIZE): + img = np.array( + PILImage.fromarray(img.astype(np.uint8)).resize((IMG_SIZE, IMG_SIZE), PILImage.BILINEAR) + ).astype(np.float32) + if USE_IMAGENET_NORM: + if img.max() > 1.0: + img = img / 255.0 + img = (img - IMAGENET_MEAN) / IMAGENET_STD + else: + img = (img - global_mean) / global_std + return np.transpose(img, (2, 0, 1)).copy() + +def _prepare_mask(raw: np.ndarray) -> np.ndarray: + mask = raw.astype(np.uint8) + if mask.ndim == 3: + mask = mask[..., 0] + if IMG_SIZE > 0 and (mask.shape[0] != IMG_SIZE or mask.shape[1] != IMG_SIZE): + pil_mask = PILImage.fromarray(mask) + if pil_mask.mode != "L": + pil_mask = pil_mask.convert("L") + mask = np.array(pil_mask.resize((IMG_SIZE, IMG_SIZE), PILImage.NEAREST)) + return ((mask > 0).astype(np.float32))[None, ...].copy() + +def print_imagenet_normalization_status() -> bool: + uses_imagenet_norm = bool(USE_IMAGENET_NORM) + if uses_imagenet_norm: + print("✅🖼️ ImageNet normalization is ACTIVE in `_prepare_image`.") + else: + print("⚠️🧪 ImageNet normalization is NOT active in `_prepare_image`.") + print("⚠️📊 Using dataset global mean/std normalization instead.") + if SMP_ENCODER_WEIGHTS == "imagenet" and not uses_imagenet_norm: + print("⚠️🚨 Encoder weights are set to ImageNet, but ImageNet normalization is disabled.") + return uses_imagenet_norm + +def _gaussian_kernel1d( + sigma: float, + *, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + if sigma <= 0: + return torch.ones(1, device=device, dtype=dtype) + radius = max(int(math.ceil(3.0 * sigma)), 1) + coords = torch.arange(-radius, radius + 1, device=device, dtype=dtype) + kernel = torch.exp(-(coords.square()) / max(2.0 * sigma * sigma, 1e-6)) + return kernel / kernel.sum().clamp_min(1e-12) + +def _smooth_displacement_field(field: torch.Tensor, sigma: float) -> torch.Tensor: + kernel = _gaussian_kernel1d(sigma, device=field.device, dtype=field.dtype) + if kernel.numel() == 1: + return field + radius = kernel.numel() // 2 + kernel_y = kernel.view(1, 1, -1, 1) + kernel_x = kernel.view(1, 1, 1, -1) + field = F.conv2d(field, kernel_y, padding=(radius, 0)) + field = F.conv2d(field, kernel_x, padding=(0, radius)) + return field + +def _apply_elastic_deformation( + image: torch.Tensor, + mask: torch.Tensor, + *, + alpha: float = 8.0, + sigma: float = 4.0, +) -> tuple[torch.Tensor, torch.Tensor]: + _, h, w = image.shape + if h < 2 or w < 2: + return image.contiguous(), mask.contiguous() + + device = image.device + dtype = image.dtype + dx = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + dy = _smooth_displacement_field(torch.randn(1, 1, h, w, device=device, dtype=dtype), sigma) * alpha + + yy, xx = torch.meshgrid( + torch.linspace(-1.0, 1.0, h, device=device, dtype=dtype), + torch.linspace(-1.0, 1.0, w, device=device, dtype=dtype), + indexing="ij", + ) + grid = torch.stack((xx, yy), dim=-1).unsqueeze(0) + grid[..., 0] = grid[..., 0] + dx.squeeze(0).squeeze(0) * (2.0 / max(w - 1, 1)) + grid[..., 1] = grid[..., 1] + dy.squeeze(0).squeeze(0) * (2.0 / max(h - 1, 1)) + grid = grid.clamp(-1.25, 1.25) + + image_out = F.grid_sample( + image.unsqueeze(0), + grid, + mode="bilinear", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + mask_out = F.grid_sample( + mask.unsqueeze(0), + grid, + mode="nearest", + padding_mode="reflection", + align_corners=True, + ).squeeze(0) + return image_out.contiguous(), mask_out.clamp(0.0, 1.0).contiguous() + +def _apply_minimal_train_aug(image: torch.Tensor, mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(2,)) + mask = torch.flip(mask, dims=(2,)) + if torch.rand(1).item() < 0.5: + image = torch.flip(image, dims=(1,)) + mask = torch.flip(mask, dims=(1,)) + if torch.rand(1).item() < 0.5: + k = 1 if torch.rand(1).item() < 0.5 else 3 + image = torch.rot90(image, k=k, dims=(1, 2)) + mask = torch.rot90(mask, k=k, dims=(1, 2)) + elastic_aug_prob = float(_job_param("elastic_aug_prob", 0.0)) + if elastic_aug_prob > 0 and torch.rand(1).item() < elastic_aug_prob: + image, mask = _apply_elastic_deformation(image, mask) + return image.contiguous(), mask.contiguous() + +class BUSIDataset(Dataset): + def __init__( + self, + sample_records: list[dict[str, str]], + dataset_root: Path, + global_mean: float, + global_std: float, + *, + preload: bool, + augment: bool, + split_name: str, + ) -> None: + super().__init__() + self.sample_records = [dict(record) for record in sample_records] + self.dataset_root = Path(dataset_root) + self.global_mean = float(global_mean) + self.global_std = float(global_std) + self.preload = preload + self.augment = augment + self.split_name = split_name + self._images: list[torch.Tensor] = [] + self._masks: list[torch.Tensor] = [] + self._raw_cache_bytes = 0 + + if not self.preload: + raise ValueError("PRELOAD_TO_RAM is mandatory in this RunPod runner.") + self._preload_to_ram() + + def _preload_to_ram(self) -> None: + desc = f"Preloading {self.split_name} ({len(self.sample_records)} samples) to RAM" + for record in tqdm(self.sample_records, desc=desc, leave=False): + raw_img = np.array(PILImage.open(self.dataset_root / record["image_rel_path"])) + raw_mask = np.array(PILImage.open(self.dataset_root / record["mask_rel_path"])) + if raw_img.shape[:2] != raw_mask.shape[:2]: + raise RuntimeError( + f"Image/mask spatial size mismatch for {record['filename']}: " + f"image={raw_img.shape[:2]}, mask={raw_mask.shape[:2]}" + ) + image = torch.from_numpy(_prepare_image(raw_img, self.global_mean, self.global_std)) + mask = torch.from_numpy(_prepare_mask(raw_mask)) + self._raw_cache_bytes += tensor_bytes(image) + tensor_bytes(mask) + self._images.append(image) + self._masks.append(mask) + + def __len__(self) -> int: + return len(self.sample_records) + + def __getitem__(self, index: int) -> dict[str, Any]: + image = self._images[index].clone() + mask = self._masks[index].clone() + if self.augment: + image, mask = _apply_minimal_train_aug(image, mask) + return { + "image": image, + "mask": mask, + "sample_id": Path(self.sample_records[index]["filename"]).stem, + "dataset": current_dataset_name(), + } + + @property + def cache_bytes(self) -> int: + return self._raw_cache_bytes + +class CUDAPrefetcher: + def __init__(self, loader: DataLoader, device: torch.device) -> None: + self.loader = loader + self.device = device + self._use_cuda = device.type == "cuda" + self._iter = None + self._stream = None + self._next_batch = None + + def __len__(self) -> int: + return len(self.loader) + + def __iter__(self): + self._iter = iter(self.loader) + self._stream = torch.cuda.Stream(device=self.device) if self._use_cuda else None + self._next_batch = None + self._preload() + return self + + def close(self) -> None: + self._next_batch = None + self._iter = None + self._stream = None + + def _preload(self) -> None: + if self._iter is None: + self._next_batch = None + return + try: + self._next_batch = next(self._iter) + except StopIteration: + self._next_batch = None + return + if self._use_cuda: + assert self._stream is not None + with torch.cuda.stream(self._stream): + self._next_batch = to_device(self._next_batch, self.device) + else: + self._next_batch = to_device(self._next_batch, self.device) + + def __next__(self): + if self._next_batch is None: + self.close() + raise StopIteration + if self._use_cuda: + assert self._stream is not None + torch.cuda.current_stream(self.device).wait_stream(self._stream) + batch = self._next_batch + self._preload() + if self._next_batch is None: + self._iter = None + self._stream = None + return batch + +class DataBundle: + def __init__( + self, + *, + percent: float, + split_payload: dict[str, Any], + train_ds: BUSIDataset, + val_ds: BUSIDataset, + test_ds: BUSIDataset, + train_loader: DataLoader, + val_loader: DataLoader, + test_loader: DataLoader, + ) -> None: + self.percent = percent + self.split_payload = split_payload + self.train_ds = train_ds + self.val_ds = val_ds + self.test_ds = test_ds + self.train_loader = train_loader + self.val_loader = val_loader + self.test_loader = test_loader + + @property + def global_mean(self) -> float: + return float(self.split_payload["global_mean"]) + + @property + def global_std(self) -> float: + return float(self.split_payload["global_std"]) + + @property + def total_cache_bytes(self) -> int: + return self.train_ds.cache_bytes + self.val_ds.cache_bytes + self.test_ds.cache_bytes + +def make_loader(dataset: Dataset, shuffle: bool, *, loader_tag: str) -> DataLoader: + num_workers = NUM_WORKERS + persistent_workers = USE_PERSISTENT_WORKERS and num_workers > 0 + pin_memory = USE_PIN_MEMORY and DEVICE.type == "cuda" + generator = make_seeded_generator(SEED, loader_tag) + return DataLoader( + dataset, + batch_size=BATCH_SIZE, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, + persistent_workers=persistent_workers, + worker_init_fn=seed_worker, + generator=generator, + ) + +def build_data_bundle(percent: float, split_registry: dict[str, Any], split_source: str) -> DataBundle: + pct_label = percent_label(percent) + pct_text = percent_text(percent) + selected_split = select_persisted_split(split_registry, SPLIT_TYPE, percent) + selected_split = apply_train_subset_variant( + selected_split, + split_registry, + subset_variant=TRAIN_SUBSET_VARIANT, + ) + pct_root = ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{pct_label}") + split_manifest_path = export_selected_split_manifest( + pct_root, + percent=percent, + split_source=split_source, + selected_split=selected_split, + ) + stats_cache_path = pct_root / ( + f"norm_stats_{normalization_cache_tag()}_{SPLIT_TYPE}_{pct_label}pct" + f"{train_subset_variant_suffix(int(selected_split.get('train_subset_variant', 0)))}.json" + ) + base_train_class_distribution = compute_class_distribution(selected_split["base_train_records"]) + train_class_distribution = compute_class_distribution(selected_split["train_records"]) + val_class_distribution = compute_class_distribution(selected_split["val_records"]) + test_class_distribution = compute_class_distribution(selected_split["test_records"]) + print_loaded_class_distribution( + split_type=selected_split["split_type"], + train_subset_key=selected_split["train_subset_key"], + base_train_records=selected_split["base_train_records"], + train_records=selected_split["train_records"], + val_records=selected_split["val_records"], + test_records=selected_split["test_records"], + ) + dataset_root = Path(selected_split["dataset_root"]).resolve() + global_mean, global_std, normalization_source = compute_busi_statistics( + dataset_root=dataset_root, + sample_records=selected_split["train_records"], + cache_path=stats_cache_path, + ) + + split_payload = { + "dataset_name": current_dataset_name(), + "dataset_split_policy": split_registry.get("split_policy"), + "dataset_splits_path": str(current_dataset_splits_json_path().resolve()), + "dataset_root": str(dataset_root), + "split_source": split_source, + "split_type": SPLIT_TYPE, + "dataset_percent": percent, + "train_subset_key": selected_split["train_subset_key"], + "train_subset_variant": int(selected_split.get("train_subset_variant", 0)), + "train_subset_source": str(selected_split.get("train_subset_source", "persisted")), + "selected_split_manifest_path": str(split_manifest_path.resolve()), + "base_train_count": len(selected_split["base_train_records"]), + "train_count": len(selected_split["train_records"]), + "val_count": len(selected_split["val_records"]), + "test_count": len(selected_split["test_records"]), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(stats_cache_path.resolve()), + "normalization_source": normalization_source, + } + + print_split_summary(split_payload) + print_normalization_summary(split_payload) + + train_ds = BUSIDataset( + selected_split["train_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=True, + split_name=f"train {SPLIT_TYPE} {pct_text}", + ) + val_ds = BUSIDataset( + selected_split["val_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"val {SPLIT_TYPE}", + ) + test_ds = BUSIDataset( + selected_split["test_records"], + dataset_root, + global_mean, + global_std, + preload=PRELOAD_TO_RAM, + augment=False, + split_name=f"test {SPLIT_TYPE}", + ) + + bundle = DataBundle( + percent=percent, + split_payload=split_payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=make_loader(train_ds, shuffle=True, loader_tag=f"{SPLIT_TYPE}:{pct_label}:train"), + val_loader=make_loader(val_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:val"), + test_loader=make_loader(test_ds, shuffle=False, loader_tag=f"{SPLIT_TYPE}:{pct_label}:test"), + ) + print_preload_summary(bundle) + return bundle + +def print_preload_summary(bundle: DataBundle) -> None: + section( + f"RAM Preload Summary | {bundle.split_payload['split_type']} | {int(bundle.percent * 100)}%" + ) + print(f"Train samples : {len(bundle.train_ds)}") + print(f"Val samples : {len(bundle.val_ds)}") + print(f"Test samples : {len(bundle.test_ds)}") + print(f"Train batches : {len(bundle.train_loader)}") + print(f"Val batches : {len(bundle.val_loader)}") + print(f"Test batches : {len(bundle.test_loader)}") + print(f"Global mean : {bundle.global_mean:.6f}") + print(f"Global std : {bundle.global_std:.6f}") + first = bundle.train_ds[0] + print(f"Sample image shape : {tuple(first['image'].shape)}") + print(f"Sample mask shape : {tuple(first['mask'].shape)}") + print(f"Sample image dtype : {first['image'].dtype}") + print(f"Sample mask dtype : {first['mask'].dtype}") + print(f"Estimated RAM preload : {bytes_to_gb(bundle.total_cache_bytes):.3f} GB") + +"""============================================================================= +MODEL DEFINITIONS +============================================================================= +""" + +def strategy_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + strategy = _require_supported_strategy(strategy) + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + if strategy == 2: + return "Strategy 2: Custom VGG + Segmentation Head (Supervised)" + if strategy == 3: + return "Strategy 3 Lite: Custom VGG + Segmentation Head + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + + if strategy == 2: + return f"Strategy 2: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) supervised" + if strategy == 3: + return f"Strategy 3 Lite: SMP {model_config.smp_decoder_type} ({model_config.smp_encoder_name}) + RL" + raise ValueError(f"Unsupported strategy: {strategy}") + +def _apply_omega_conv(omega_conv: nn.Conv2d, value_next: torch.Tensor) -> torch.Tensor: + weight = omega_conv.weight + value_next = value_next.to(device=weight.device, dtype=weight.dtype) + return omega_conv(value_next) + +def _conv3x3(in_ch: int, out_ch: int, dilation: int = 1) -> nn.Conv2d: + return nn.Conv2d( + in_ch, + out_ch, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=True, + ) + +class _ConvBlock(nn.Module): + def __init__( + self, + in_ch: int, + out_ch: int, + dilation: int = 1, + *, + num_groups: int = 0, + dropout: float = 0.0, + ) -> None: + super().__init__() + self.conv = _conv3x3(in_ch, out_ch, dilation=dilation) + self.norm = _group_norm(out_ch, num_groups=num_groups) if num_groups > 0 else nn.Identity() + self.act = nn.ReLU(inplace=True) + self.drop = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.act(self.norm(self.conv(x)))) + +def _group_norm(num_channels: int, *, num_groups: int = GN_NUM_GROUPS) -> nn.GroupNorm: + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + return nn.GroupNorm(groups, num_channels) + + +class _MultiScaleRefineBranch(nn.Module): + """Processes raw encoder features at each scale independently, then fuses + them into a single feature map. This gives the refinement head access to + multi-resolution spatial cues (edges at low levels, semantics at high + levels) that the 1x1 projection squashes away.""" + + def __init__( + self, + encoder_channels: list[int] | tuple[int, ...], + out_channels: int, + per_scale_channels: int = 32, + ) -> None: + super().__init__() + self._valid_indices: list[int] = [i for i, c in enumerate(encoder_channels) if c > 0] + self.scale_convs = nn.ModuleList() + for i in self._valid_indices: + self.scale_convs.append(nn.Sequential( + nn.Conv2d(encoder_channels[i], per_scale_channels, kernel_size=1, bias=False), + _group_norm(per_scale_channels), + nn.ReLU(inplace=True), + )) + total_ch = per_scale_channels * len(self._valid_indices) + self.fuse = nn.Sequential( + nn.Conv2d(total_ch, out_channels, kernel_size=3, padding=1, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2, dilation=2, bias=False), + _group_norm(out_channels), + nn.ReLU(inplace=True), + ) + self._init_small() + + def _init_small(self) -> None: + """Small-magnitude init so the branch starts as a near-zero residual.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + m.weight.data.mul_(0.1) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + parts: list[torch.Tensor] = [] + for idx, conv in zip(self._valid_indices, self.scale_convs): + out = conv(encoder_features[idx]) + if out.shape[-2] != h or out.shape[-1] != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + parts.append(out) + return self.fuse(torch.cat(parts, dim=1)) + + +class SelfAttentionModule(nn.Module): + def __init__(self, channels: int) -> None: + super().__init__() + mid = max(channels // 8, 1) + self.query = nn.Conv2d(channels, mid, 1) + self.key = nn.Conv2d(channels, mid, 1) + self.value = nn.Conv2d(channels, channels, 1) + self.gamma = nn.Parameter(torch.tensor([0.1], dtype=torch.float32)) + + def forward(self, f: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + b, c, h, w = f.shape + pooled = f + target_grid = max(int(_job_param("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID)), 1) + if target_grid < max(h, w): + stride_h = max(1, math.ceil(h / target_grid)) + stride_w = max(1, math.ceil(w / target_grid)) + pooled = F.avg_pool2d(f, kernel_size=(stride_h, stride_w), stride=(stride_h, stride_w)) + if target_grid >= 64 and target_grid not in _STRATEGY3_SAM_GRID_WARNED: + print( + "[Strategy3] Self-attention grid " + f"{target_grid}x{target_grid} requested; this implies a much heavier attention matrix " + "(for example 64x64 -> 4096 tokens). Lower strategy3_sam_attention_grid if this is too slow." + ) + _STRATEGY3_SAM_GRID_WARNED.add(target_grid) + + ph, pw = pooled.shape[-2:] + q = self.query(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + k = self.key(pooled).view(b, -1, ph * pw) + v = self.value(pooled).view(b, -1, ph * pw).permute(0, 2, 1) + attn = torch.softmax(q @ k / (q.shape[-1] ** 0.5), dim=-1) + out = (attn @ v).permute(0, 2, 1).view(b, c, ph, pw) + if ph != h or pw != w: + out = F.interpolate(out, size=(h, w), mode="bilinear", align_corners=False) + return f + self.gamma * out, attn + + def forward_features(self, f: torch.Tensor) -> torch.Tensor: + out, _ = self.forward(f) + return out + +class DilatedPolicyHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.classifier = nn.Conv2d(64, NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + bias = torch.full((NUM_ACTIONS,), -2.0, dtype=torch.float32) + keep_index = NUM_ACTIONS // 2 if NUM_ACTIONS >= 3 else NUM_ACTIONS - 1 + bias[keep_index] = 2.0 + with torch.no_grad(): + self.classifier.bias.copy_(bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class DilatedValueHead(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 512, dilation=1), + _ConvBlock(512, 256, dilation=2), + _ConvBlock(256, 128, dilation=3), + _ConvBlock(128, 64, dilation=4), + ) + self.readout = nn.Conv2d(64, 1, kernel_size=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.body(x) + return self.readout(features) + +def replace_bn_with_gn(model: nn.Module, num_groups: int = 8) -> nn.Module: + for name, module in model.named_children(): + if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)): + num_channels = module.num_features + groups = min(num_groups, num_channels) + while groups > 1 and num_channels % groups != 0: + groups -= 1 + setattr(model, name, nn.GroupNorm(groups, num_channels, eps=module.eps, affine=module.affine)) + else: + replace_bn_with_gn(module, num_groups=num_groups) + return model + +def _ensure_transunet_repo_on_path() -> None: + repo_dir = TRANSUNET_REPO_DIR.resolve() + if not repo_dir.is_dir(): + raise FileNotFoundError( + f"TransUNet repo not found at {repo_dir}. Expected the official repo in " + f"{TRANSUNET_REPO_DIR}." + ) + repo_str = str(repo_dir) + if repo_str not in sys.path: + sys.path.insert(0, repo_str) + + +def _load_transunet_components() -> tuple[Any, dict[str, Any]]: + global _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + if _TRANSUNET_VISION_TRANSFORMER is None or _TRANSUNET_CONFIGS is None: + _ensure_transunet_repo_on_path() + try: + vit_module = importlib.import_module("networks.vit_seg_modeling") + except Exception as exc: + raise RuntimeError( + "Unable to import the official TransUNet modules. Ensure the TransUNet repo is present " + "and dependencies such as ml_collections, scipy, and torch are installed." + ) from exc + _TRANSUNET_VISION_TRANSFORMER = getattr(vit_module, "VisionTransformer") + _TRANSUNET_CONFIGS = getattr(vit_module, "CONFIGS") + return _TRANSUNET_VISION_TRANSFORMER, _TRANSUNET_CONFIGS + + +def _transunet_tensor_changed(before: torch.Tensor, after: torch.Tensor) -> bool: + return not torch.equal(before, after) + + +def _load_and_verify_transunet_checkpoint( + vit_model: nn.Module, + *, + pretrained_path: Path, + img_size: int, + n_skip: int, +) -> dict[str, Any]: + checkpoint_path = Path(pretrained_path).expanduser().resolve() + if not checkpoint_path.is_file(): + raise FileNotFoundError( + f"TransUNet checkpoint not found at {checkpoint_path}. " + f"Expected ImageNet weights at {TRANSUNET_PRETRAINED_PATH.resolve()}." + ) + + weights = np.load(checkpoint_path, allow_pickle=False) + try: + missing_keys = [key for key in _TRANSUNET_REQUIRED_NPZ_KEYS if key not in weights] + if missing_keys: + raise RuntimeError( + f"TransUNet checkpoint {checkpoint_path} is missing required arrays: {missing_keys}" + ) + + position_embeddings = vit_model.transformer.embeddings.position_embeddings + root_conv = vit_model.transformer.embeddings.hybrid_model.root.conv.weight + block0_query = vit_model.transformer.encoder.layer[0].attn.query.weight + + pos_before = position_embeddings.detach().cpu().clone() + root_before = root_conv.detach().cpu().clone() + query_before = block0_query.detach().cpu().clone() + + posemb_source_shape = tuple(weights["Transformer/posembed_input/pos_embedding"].shape) + posemb_target_shape = tuple(position_embeddings.shape) + array_count = len(getattr(weights, "files", [])) + file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024) + + vit_model.load_from(weights=weights) + + pos_after = position_embeddings.detach().cpu() + root_after = root_conv.detach().cpu() + query_after = block0_query.detach().cpu() + + updated = { + "PosEmbed updated": _transunet_tensor_changed(pos_before, pos_after), + "ResNet root conv updated": _transunet_tensor_changed(root_before, root_after), + "ViT block-0 query updated": _transunet_tensor_changed(query_before, query_after), + } + + section("TransUNet Checkpoint Verification") + print("[TransUNet] OK Loaded R50+ViT-B/16 ImageNet checkpoint") + print(f"[TransUNet] File : {checkpoint_path} ({file_size_mb:.1f} MB)") + print(f"[TransUNet] NPZ arrays : {array_count}") + print( + "[TransUNet] PosEmbed shape : " + f"src {posemb_source_shape} -> tgt {posemb_target_shape}" + f"{' (interpolated)' if posemb_source_shape != posemb_target_shape else ''}" + ) + for label, status in updated.items(): + print(f"[TransUNet] {label:<22}: {status}") + print( + "[TransUNet] " + f"img_size={img_size}, patches.grid=({img_size // 16}, {img_size // 16}), " + f"n_skip={n_skip}, n_classes=1" + ) + + failed = [label for label, status in updated.items() if not status] + if failed: + raise RuntimeError( + "TransUNet checkpoint load verification failed. The following tensors were unchanged after " + f"load_from(...): {failed}. Training was stopped to avoid using a randomly initialized model." + ) + + return { + "checkpoint_path": str(checkpoint_path), + "array_count": array_count, + "file_size_mb": file_size_mb, + "posemb_source_shape": posemb_source_shape, + "posemb_target_shape": posemb_target_shape, + "updated": updated, + } + finally: + close_fn = getattr(weights, "close", None) + if callable(close_fn): + close_fn() + + +class _TransUNetEncoder(nn.Module): + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.transformer = transformer + self.out_channels = (3, 64, 256, 512, 768) + self._vit_token_cache: torch.Tensor | None = None + self._decoder_skip_cache: list[torch.Tensor] | None = None + + def _clear_cache(self) -> None: + self._vit_token_cache = None + self._decoder_skip_cache = None + + def decoder_inputs(self) -> tuple[torch.Tensor, list[torch.Tensor]]: + if self._vit_token_cache is None or self._decoder_skip_cache is None: + raise RuntimeError( + "TransUNet decoder was called before the encoder cache was populated. " + "Call the encoder first in the current forward pass." + ) + return self._vit_token_cache, self._decoder_skip_cache + + def forward(self, x: torch.Tensor) -> list[torch.Tensor]: + self._clear_cache() + if x.shape[1] == 1: + model_input = x.repeat(1, 3, 1, 1) + elif x.shape[1] == 3: + model_input = x + else: + raise ValueError(f"TransUNet expects 1 or 3 input channels, got {x.shape[1]}.") + + embedding_output, hybrid_features = self.transformer.embeddings(model_input) + hidden_states, _ = self.transformer.encoder(embedding_output) + if hybrid_features is None or len(hybrid_features) < 3: + raise RuntimeError( + "TransUNet hybrid ResNet features were not produced as expected." + ) + + deepest_skip, mid_skip, shallow_skip = hybrid_features[:3] + batch_size, n_patch, hidden_dim = hidden_states.shape + side = math.isqrt(n_patch) + if side * side != n_patch: + raise RuntimeError( + f"TransUNet token grid is not square: n_patch={n_patch}." + ) + vit_out = hidden_states.permute(0, 2, 1).contiguous().view(batch_size, hidden_dim, side, side) + + self._vit_token_cache = hidden_states + self._decoder_skip_cache = [deepest_skip, mid_skip, shallow_skip] + return [model_input, shallow_skip, mid_skip, deepest_skip, vit_out] + + +class _TransUNetDecoder(nn.Module): + def __init__(self, decoder_core: nn.Module, encoder: _TransUNetEncoder) -> None: + super().__init__() + self.decoder_core = decoder_core + self._encoder_ref = weakref.ref(encoder) + + def _encoder(self) -> _TransUNetEncoder: + encoder = self._encoder_ref() + if encoder is None: + raise RuntimeError("TransUNet encoder reference is no longer available.") + return encoder + + def forward(self, *features: torch.Tensor) -> torch.Tensor: + del features + hidden_states, skip_features = self._encoder().decoder_inputs() + return self.decoder_core(hidden_states, features=skip_features) + + +class TransUNetSMPAdapter(nn.Module): + def __init__(self, *, img_size: int, pretrained_path: Path) -> None: + super().__init__() + if img_size % 16 != 0: + raise ValueError(f"TransUNet requires img_size divisible by 16, got {img_size}.") + + vision_transformer_cls, configs = _load_transunet_components() + if TRANSUNET_VIT_NAME not in configs: + raise KeyError( + f"TransUNet config {TRANSUNET_VIT_NAME!r} not found in the official repo." + ) + + config_vit = copy.deepcopy(configs[TRANSUNET_VIT_NAME]) + config_vit.n_classes = 1 + config_vit.n_skip = TRANSUNET_N_SKIP + config_vit.classifier = "seg" + config_vit.patches.grid = (img_size // 16, img_size // 16) + + vit_model = vision_transformer_cls(config_vit, img_size=img_size, num_classes=1) + self.checkpoint_summary = _load_and_verify_transunet_checkpoint( + vit_model, + pretrained_path=pretrained_path, + img_size=img_size, + n_skip=TRANSUNET_N_SKIP, + ) + self.encoder = _TransUNetEncoder(vit_model.transformer) + self.decoder = _TransUNetDecoder(vit_model.decoder, self.encoder) + self.segmentation_head = vit_model.segmentation_head + self.classification_head = None + self.transunet_config = config_vit + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.encoder(x) + decoder_output = run_smp_decoder(self.decoder, encoder_features) + logits = self.segmentation_head(decoder_output) + return logits + +class HalfVGG16DilatedExtractor(nn.Module): + def __init__(self, *, dilation: int = 1, num_scales: int = 3) -> None: + super().__init__() + self.num_scales = num_scales + deep_dropout = 0.1 + + self.conv1_1 = _ConvBlock(3, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv1_2 = _ConvBlock(32, 32, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv2_1 = _ConvBlock(32, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + self.conv2_2 = _ConvBlock(64, 64, dilation=dilation, num_groups=GN_NUM_GROUPS) + + self.conv3_1 = _ConvBlock(64, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_2 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv3_3 = _ConvBlock(128, 128, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.conv4_1 = _ConvBlock(128, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_2 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + self.conv4_3 = _ConvBlock(256, 256, dilation=dilation, num_groups=GN_NUM_GROUPS, dropout=deep_dropout) + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + + @property + def out_channels(self) -> int: + return (32 + 64 + 128) if self.num_scales == 3 else (32 + 64 + 128 + 256) + + @property + def pyramid_channels(self) -> list[int]: + return [32, 64, 128] if self.num_scales == 3 else [32, 64, 128, 256] + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + x = self.conv1_1(x) + src1 = self.conv1_2(x) + x = self.pool(src1) + + x = self.conv2_1(x) + src2 = self.conv2_2(x) + x = self.pool(src2) + + x = self.conv3_1(x) + x = self.conv3_2(x) + src3 = self.conv3_3(x) + + if self.num_scales == 3: + return [src1, src2, src3] + + x = self.pool(src3) + x = self.conv4_1(x) + x = self.conv4_2(x) + src4 = self.conv4_3(x) + return [src1, src2, src3, src4] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + pyramid = self.forward_pyramid(x) + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + +class CustomVGGEncoderWrapper(nn.Module): + def __init__(self, *, num_scales: int, dilation: int) -> None: + super().__init__() + self.encoder = HalfVGG16DilatedExtractor(dilation=dilation, num_scales=num_scales) + self.projection = None + + @property + def out_channels(self) -> int: + return self.encoder.out_channels + + @property + def pyramid_channels(self) -> list[int]: + return self.encoder.pyramid_channels + + def forward_pyramid(self, x: torch.Tensor) -> list[torch.Tensor]: + return self.encoder.forward_pyramid(x) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.encoder(x) + +class SMPEncoderWrapper(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + depth: int, + in_channels: int, + proj_dim: int, + ) -> None: + super().__init__() + self.encoder = smp.encoders.get_encoder( + encoder_name, + in_channels=in_channels, + depth=depth, + weights=encoder_weights, + ) + if REPLACE_BN_WITH_GN: + replace_bn_with_gn(self.encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + self._out_channels = proj_dim + else: + self.projection = None + self._out_channels = raw_channels + + @property + def out_channels(self) -> int: + return self._out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h, w = x.shape[-2:] + features = self.encoder(x) + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + +class VGGDecoderBlock(nn.Module): + def __init__(self, *, in_channels: int, skip_channels: int, out_channels: int) -> None: + super().__init__() + self.block = nn.Sequential( + _ConvBlock(in_channels + skip_channels, out_channels, num_groups=GN_NUM_GROUPS), + _ConvBlock(out_channels, out_channels, num_groups=GN_NUM_GROUPS), + ) + + def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: + x = F.interpolate(x, size=skip.shape[-2:], mode="bilinear", align_corners=False) + return self.block(torch.cat([x, skip], dim=1)) + +class VGGSegmentationHead(nn.Module): + def __init__(self, *, pyramid_channels: list[int], dropout_p: float) -> None: + super().__init__() + if len(pyramid_channels) not in {3, 4}: + raise ValueError(f"Expected 3 or 4 VGG pyramid channels, got {pyramid_channels}") + + self.dropout = nn.Dropout2d(p=dropout_p) + self.num_scales = len(pyramid_channels) + + deepest = pyramid_channels[-1] + self.bridge = _ConvBlock(deepest, deepest, num_groups=GN_NUM_GROUPS) + if self.num_scales == 4: + self.up3 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[2], out_channels=128) + self.up2 = VGGDecoderBlock(in_channels=128, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + else: + self.up2 = VGGDecoderBlock(in_channels=deepest, skip_channels=pyramid_channels[1], out_channels=64) + self.up1 = VGGDecoderBlock(in_channels=64, skip_channels=pyramid_channels[0], out_channels=32) + self.out_conv = nn.Conv2d(32, 1, kernel_size=1) + + def forward(self, pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + features = list(pyramid) + x = self.bridge(self.dropout(features[-1])) + if self.num_scales == 4: + x = self.up3(x, features[2]) + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + else: + x = self.up2(x, features[1]) + x = self.up1(x, features[0]) + return self.out_conv(x) + +class PixelDRLMG_SMP(nn.Module): + def __init__( + self, + *, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = SMPEncoderWrapper( + encoder_name=encoder_name, + encoder_weights=encoder_weights, + depth=encoder_depth, + in_channels=3, + proj_dim=proj_dim, + ) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + features = self.extractor(x) + return self.sam(features) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class PixelDRLMG_VGG(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.extractor = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + ch = self.extractor.out_channels + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = DilatedPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.omega_conv = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) + nn.init.constant_(self.omega_conv.weight, 1.0 / 9.0) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.extractor(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + state, attention = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state), attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + + def neighborhood_value(self, value_next: torch.Tensor) -> torch.Tensor: + return _apply_omega_conv(self.omega_conv, value_next) + +class SupervisedSMPModel(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int = 5, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + self.dropout = nn.Dropout2d(p=dropout_p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + encoder_features = self.smp_encoder(x) + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + decoder_output = self.dropout(decoder_output) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + +class SupervisedVGGModel(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + +class RefinementPolicyHead(nn.Module): + A3C_NUM_ACTIONS = 1 + + def __init__(self, in_channels: int) -> None: + super().__init__() + self.body = nn.Sequential( + _ConvBlock(in_channels, 256, dilation=1, num_groups=GN_NUM_GROUPS), + _ConvBlock(256, 128, dilation=2, num_groups=GN_NUM_GROUPS), + _ConvBlock(128, 64, dilation=3, num_groups=GN_NUM_GROUPS), + ) + self.classifier = nn.Conv2d(64, self.A3C_NUM_ACTIONS, kernel_size=1) + nn.init.zeros_(self.classifier.weight) + if self.classifier.bias is not None: + nn.init.zeros_(self.classifier.bias) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.classifier(self.body(x)) + +class PixelDRLMG_WithDecoder(nn.Module): + def __init__( + self, + *, + arch: str, + encoder_name: str, + encoder_weights: str | None, + encoder_depth: int, + proj_dim: int, + dropout_p: float, + ) -> None: + super().__init__() + if _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + self.smp_model = TransUNetSMPAdapter( + img_size=IMG_SIZE, + pretrained_path=TRANSUNET_PRETRAINED_PATH, + ) + else: + self.smp_model = smp.create_model( + arch=arch, + encoder_name=encoder_name, + encoder_weights=encoder_weights, + encoder_depth=encoder_depth, + in_channels=3, + classes=1, + ) + self.smp_encoder = self.smp_model.encoder + if REPLACE_BN_WITH_GN and not _is_transunet_selection(encoder_name=encoder_name, decoder_type=arch): + replace_bn_with_gn(self.smp_encoder, num_groups=GN_NUM_GROUPS) + + raw_channels = sum(c for c in self.smp_encoder.out_channels if c > 0) + if proj_dim > 0: + groups = min(GN_NUM_GROUPS, proj_dim) + while groups > 1 and proj_dim % groups != 0: + groups -= 1 + self.projection = nn.Sequential( + nn.Conv2d(raw_channels, proj_dim, kernel_size=1, bias=False), + nn.GroupNorm(groups, proj_dim) if REPLACE_BN_WITH_GN else nn.BatchNorm2d(proj_dim), + nn.ReLU(inplace=True), + ) + ch = proj_dim + else: + self.projection = None + ch = raw_channels + + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.multi_scale_refine = _MultiScaleRefineBranch( + encoder_channels=list(self.smp_encoder.out_channels), + out_channels=ch, + per_scale_channels=32, + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + self.multi_scale_refine.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "decoder", "segmentation_head"): + module = getattr(self.smp_model, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_from_features( + self, + features: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in features: + if feat.shape[1] == 0: + continue + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + out = torch.cat(upsampled, dim=1) + if self.projection is not None: + out = self.projection(out) + return out + + def _encoder_concat(self, x: torch.Tensor) -> torch.Tensor: + return self._concat_from_features(self.smp_encoder(x), output_size=x.shape[-2:]) + + def forward_decoder_from_features( + self, + encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...], + ) -> torch.Tensor: + decoder_output = run_smp_decoder(self.smp_model.decoder, encoder_features) + logits = self.smp_model.segmentation_head(decoder_output) + if getattr(self.smp_model, "classification_head", None) is not None: + _ = self.smp_model.classification_head(encoder_features[-1]) + return logits + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.smp_model(x) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, Any]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + encoder_features = self.smp_encoder(x) + decoder_logits = self.forward_decoder_from_features(encoder_features) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_features=[feat[sample_index:sample_index + 1] for feat in encoder_features]: self.forward_decoder_from_features(sample_features), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.forward_decoder_from_features(encoder_features), + ) + return { + "base_features": self._concat_from_features(encoder_features, output_size=x.shape[-2:]), + "encoder_features": list(encoder_features), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype) * 0.0, + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + if encoder_features is not None: + ms_feat = self.multi_scale_refine(encoder_features, output_size=fused.shape[-2:]) + fused = fused + ms_feat + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + concat_feat = self._encoder_concat(x) + return self.sam(concat_feat) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +class PixelDRLMG_VGGWithDecoder(nn.Module): + def __init__( + self, + *, + num_scales: int, + dilation: int, + dropout_p: float, + ) -> None: + super().__init__() + self.encoder = CustomVGGEncoderWrapper(num_scales=num_scales, dilation=dilation) + self.segmentation_head = VGGSegmentationHead( + pyramid_channels=self.encoder.pyramid_channels, + dropout_p=dropout_p, + ) + ch = self.encoder.out_channels + self.refinement_adapter = nn.Sequential( + nn.Conv2d(ch + 5, ch, kernel_size=3, stride=1, padding=1, bias=False), + _group_norm(ch), + nn.ReLU(inplace=True), + _ConvBlock(ch, ch, num_groups=GN_NUM_GROUPS), + ) + self.sam = SelfAttentionModule(channels=ch) + self.policy_head = RefinementPolicyHead(in_channels=ch) + self.value_head = DilatedValueHead(in_channels=ch) + self.head_dropout = nn.Dropout2d(p=dropout_p) + self.use_refinement = True + self._strategy3_mc_cache: dict[tuple[Any, ...], tuple[torch.Tensor, torch.Tensor]] = {} + self._strategy3_mc_cache_fingerprint = "" + self._strategy3_mc_cache_fingerprint_sources: dict[str, Any] = {} + self._strategy3_strategy2_checkpoint_path: str | None = None + self._strategy3_eval_checkpoint_path: str | None = None + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def set_refinement_mode(self, enabled: bool) -> None: + self.use_refinement = bool(enabled) + self.refinement_adapter.requires_grad_(self.use_refinement) + + def clear_strategy3_mc_cache(self) -> None: + self._strategy3_mc_cache.clear() + self._strategy3_mc_cache_stats = {"ram_hits": 0, "disk_hits": 0, "misses": 0, "writes": 0} + + def _keep_bootstrapped_segmentation_in_eval(self) -> None: + if not (bool(getattr(self, "strategy2_bootstrap_loaded", False)) and bool(getattr(self, "freeze_bootstrapped_segmentation", False))): + return + for module_name in ("encoder", "segmentation_head"): + module = getattr(self, module_name, None) + if isinstance(module, nn.Module): + module.eval() + + def train(self, mode: bool = True): + super().train(mode) + if mode: + self._keep_bootstrapped_segmentation_in_eval() + return self + + def _concat_pyramid( + self, + pyramid: list[torch.Tensor] | tuple[torch.Tensor, ...], + *, + output_size: tuple[int, int], + ) -> torch.Tensor: + h, w = output_size + upsampled = [] + for feat in pyramid: + if feat.shape[-2] != h or feat.shape[-1] != w: + feat = F.interpolate(feat, size=(h, w), mode="bilinear", align_corners=False) + upsampled.append(feat) + return torch.cat(upsampled, dim=1) + + def forward_decoder(self, x: torch.Tensor) -> torch.Tensor: + return self.segmentation_head(self.encoder.forward_pyramid(x)) + + def prepare_refinement_context( + self, + x: torch.Tensor, + *, + sample_ids: list[str] | None = None, + mc_mode: Literal["train", "eval"] = "train", + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, + ) -> dict[str, torch.Tensor]: + if mc_mode not in ("train", "eval"): + raise ValueError(f"Unsupported Strategy 3 mc_mode {mc_mode!r}.") + pyramid = self.encoder.forward_pyramid(x) + decoder_logits = self.segmentation_head(pyramid) + decoder_prob = torch.sigmoid(decoder_logits) + if mc_mode == "eval" and sample_ids is not None and mc_cache_split != "train": + mc_variance, pred_entropy = _strategy3_cached_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_ids=sample_ids, + compute_sample_maps=lambda sample_index: _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob[sample_index:sample_index + 1], + sample_fn=lambda sample_pyramid=[feat[sample_index:sample_index + 1] for feat in pyramid]: self.segmentation_head(sample_pyramid), + ), + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + else: + mc_variance, pred_entropy = _strategy3_mc_uncertainty_maps( + self, + decoder_prob=decoder_prob, + sample_fn=lambda: self.segmentation_head(pyramid), + ) + return { + "base_features": self._concat_pyramid(pyramid, output_size=x.shape[-2:]), + "decoder_logits": decoder_logits, + "decoder_prob": decoder_prob, + "mc_variance": mc_variance.detach(), + "pred_entropy": pred_entropy.detach(), + } + + def forward_refinement_state( + self, + base_features: torch.Tensor, + current_mask: torch.Tensor, + decoder_prob: torch.Tensor, + mc_variance: torch.Tensor, + pred_entropy: torch.Tensor, + encoder_features: list[torch.Tensor] | None = None, + ) -> torch.Tensor: + del encoder_features + boundary = _differentiable_boundary(current_mask, kernel_size=3) + conditioning = torch.cat( + [ + decoder_prob.to(dtype=base_features.dtype), + current_mask.to(dtype=base_features.dtype), + boundary.to(dtype=base_features.dtype), + mc_variance.to(dtype=base_features.dtype), + pred_entropy.to(dtype=base_features.dtype), + ], + dim=1, + ) + fused = self.refinement_adapter(torch.cat([base_features, conditioning], dim=1)) + return self.sam.forward_features(fused) + + def forward_state(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + return self.sam(self.encoder(x)) + + def forward_from_state(self, state: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + state = self.head_dropout(state) + return self.policy_head(state), self.value_head(state) + + def value_from_state(self, state: torch.Tensor) -> torch.Tensor: + state = self.head_dropout(state) + return self.value_head(state) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if not self.use_refinement: + state, attention = self.forward_state(x) + return self.policy_head(state), self.value_head(state), attention + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + policy_logits, value = self.forward_from_state(state) + attention = torch.empty(0, device=state.device, dtype=state.dtype) + return policy_logits, value, attention + + def forward_policy_only(self, x: torch.Tensor) -> torch.Tensor: + if not self.use_refinement: + state, _ = self.forward_state(x) + state = self.head_dropout(state) + return self.policy_head(state) + context = self.prepare_refinement_context(x) + state = self.forward_refinement_state( + context["base_features"], + context["decoder_prob"].detach(), + context["decoder_prob"], + context["mc_variance"], + context["pred_entropy"], + encoder_features=context.get("encoder_features"), + ) + state = self.head_dropout(state) + return self.policy_head(state) + +def run_smp_decoder(decoder: nn.Module, encoder_features: list[torch.Tensor] | tuple[torch.Tensor, ...]) -> torch.Tensor: + signature = inspect.signature(decoder.forward) + parameters = list(signature.parameters.values()) + if any(param.kind == inspect.Parameter.VAR_POSITIONAL for param in parameters): + return decoder(*encoder_features) + if len(parameters) == 1: + return decoder(encoder_features) + return decoder(*encoder_features) + +def checkpoint_run_config_payload(payload: dict[str, Any]) -> dict[str, Any]: + return payload.get("run_config") or payload.get("config") or {} + +def _raw_decoder_rl_model( + model: nn.Module, +) -> PixelDRLMG_WithDecoder | PixelDRLMG_VGGWithDecoder | None: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return raw + return None + +def _uses_refinement_runtime(model: nn.Module, *, strategy: int | None = None) -> bool: + raw = _raw_decoder_rl_model(model) + if raw is None: + return False + if strategy is not None and strategy != 3: + return False + return bool(getattr(raw, "use_refinement", False)) + +def _policy_action_count_from_state_dict(state_dict: dict[str, Any]) -> int | None: + for key in ( + "policy_head.classifier.weight", + "policy_head.classifier.bias", + "policy_head.net.4.weight", + "policy_head.net.4.bias", + ): + tensor = state_dict.get(key) + if torch.is_tensor(tensor): + return int(tensor.shape[0]) + return None + +def _model_policy_action_count(model: nn.Module) -> int | None: + raw = _unwrap_compiled(model) + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d): + return int(classifier.out_channels) + return None + +def _set_model_policy_action_count(model: nn.Module, action_count: int) -> bool: + raw = _unwrap_compiled(model) + if isinstance(raw, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + classifier = getattr(getattr(raw, "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) == 1: + return False + policy_head = getattr(raw, "policy_head", None) + classifier = getattr(policy_head, "classifier", None) + if not isinstance(classifier, nn.Conv2d): + return False + if int(classifier.out_channels) == int(action_count): + return False + + new_classifier = nn.Conv2d( + classifier.in_channels, + int(action_count), + kernel_size=classifier.kernel_size, + stride=classifier.stride, + padding=classifier.padding, + dilation=classifier.dilation, + groups=classifier.groups, + bias=classifier.bias is not None, + padding_mode=classifier.padding_mode, + ).to(device=classifier.weight.device, dtype=classifier.weight.dtype) + nn.init.xavier_uniform_(new_classifier.weight) + if new_classifier.bias is not None: + nn.init.zeros_(new_classifier.bias) + policy_head.classifier = new_classifier + return True + +def _configure_policy_head_compatibility( + model: nn.Module, + state_dict: dict[str, Any], + *, + source: str, +) -> int | None: + action_count = _policy_action_count_from_state_dict(state_dict) + if action_count is None: + return None + if _set_model_policy_action_count(model, action_count): + print(f"[Policy Compatibility] source={source} num_actions={action_count}") + return action_count + +def _strategy3_checkpoint_layout_info( + state_dict: dict[str, Any], + run_config: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = run_config or {} + strategy = run_config.get("strategy") + has_legacy_policy_head = any(key.startswith("policy_head.net.") for key in state_dict) + has_new_policy_body = any(key.startswith("policy_head.body.") for key in state_dict) + has_new_policy_classifier = any(key.startswith("policy_head.classifier.") for key in state_dict) + has_refinement_adapter = any(key.startswith("refinement_adapter.") for key in state_dict) + is_strategy3_decoder_checkpoint = bool( + strategy == 3 + or has_legacy_policy_head + or has_new_policy_body + or has_new_policy_classifier + or has_refinement_adapter + ) + use_refinement = bool( + has_refinement_adapter or ((has_new_policy_body or has_new_policy_classifier) and not has_legacy_policy_head) + ) + return { + "strategy": strategy, + "is_strategy3_decoder_checkpoint": is_strategy3_decoder_checkpoint, + "has_legacy_policy_head": has_legacy_policy_head, + "has_new_policy_head": bool(has_new_policy_body or has_new_policy_classifier), + "has_refinement_adapter": has_refinement_adapter, + "requires_policy_remap": has_legacy_policy_head, + "policy_action_count": _policy_action_count_from_state_dict(state_dict), + "use_refinement": use_refinement, + "compatibility_mode": "refinement" if use_refinement else "legacy", + } + +def inspect_strategy3_checkpoint_compatibility(path: str | Path) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + layout = _strategy3_checkpoint_layout_info(payload.get("model_state_dict", {}), checkpoint_run_config_payload(payload)) + layout["path"] = str(checkpoint_path) + return layout + +def _configure_strategy3_model_compatibility( + model: nn.Module, + layout: dict[str, Any], + *, + source: str, +) -> None: + raw = _raw_decoder_rl_model(model) + if raw is None or not layout.get("is_strategy3_decoder_checkpoint"): + return + raw.set_refinement_mode(bool(layout["use_refinement"])) + if not bool(layout["use_refinement"]): + raw.refinement_adapter.eval() + if hasattr(raw, "multi_scale_refine"): + raw.multi_scale_refine.eval() + print( + "[Strategy3 Compatibility] " + f"source={source} mode={layout['compatibility_mode']} " + f"legacy_policy_head={layout['has_legacy_policy_head']} " + f"refinement_adapter={layout['has_refinement_adapter']}" + ) + +def _ensure_strategy3_refinement_adapter_compatible( + model: nn.Module, + state_dict: dict[str, Any], + *, + checkpoint_path: str | Path, +) -> None: + raw_model = _unwrap_compiled(model) + if not isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + return + target_state = raw_model.state_dict() + mismatched: list[str] = [] + for key, value in state_dict.items(): + if not key.startswith(("refinement_adapter.", "policy_head.classifier.")): + continue + target_value = target_state.get(key) + if target_value is None: + continue + if tuple(target_value.shape) != tuple(value.shape): + mismatched.append( + f"{key}: checkpoint={tuple(value.shape)} model={tuple(target_value.shape)}" + ) + if mismatched: + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected after the continuous Strategy 3 redesign. " + f"Checkpoint={Path(checkpoint_path).resolve()} mismatches={mismatched[:4]}. " + "Resume/eval from legacy S3 checkpoints is not supported; retrain Strategy 3 from the Strategy 2 bootstrap checkpoint." + ) + +def _configure_model_from_checkpoint_path( + model: nn.Module, + checkpoint_path: str | Path, +) -> dict[str, Any]: + checkpoint_path = Path(checkpoint_path).expanduser().resolve() + payload = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state_dict = payload.get("model_state_dict", {}) + _configure_policy_head_compatibility(model, state_dict, source=str(checkpoint_path)) + layout = _strategy3_checkpoint_layout_info(state_dict, checkpoint_run_config_payload(payload)) + if layout.get("is_strategy3_decoder_checkpoint") and layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected: the checkpoint uses a discrete multi-action " + f"policy head with out_channels={layout.get('policy_action_count')}. " + "The current Strategy 3 implementation requires a continuous 1-channel delta head." + ) + _configure_strategy3_model_compatibility(model, layout, source=str(checkpoint_path)) + layout["path"] = str(checkpoint_path) + return layout + +def _remap_legacy_policy_head_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]: + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + if key.startswith("policy_head.net."): + suffix = key[len("policy_head.net."):] + layer_idx, dot, rest = suffix.partition(".") + if dot: + if layer_idx in {"0", "1", "2", "3"}: + remapped[f"policy_head.body.{layer_idx}.{rest}"] = value + continue + if layer_idx == "4": + remapped[f"policy_head.classifier.{rest}"] = value + continue + remapped[key] = value + return remapped + +def _load_strategy2_checkpoint_payload( + path: str | Path, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + checkpoint_path = Path(path).expanduser().resolve() + ckpt = torch.load(checkpoint_path, map_location=DEVICE, weights_only=False) + saved_config = checkpoint_run_config_payload(ckpt) + if saved_config: + saved_model_config = RuntimeModelConfig.from_payload(saved_config).validate() + if saved_model_config.backbone_family != model_config.backbone_family: + raise ValueError( + f"Strategy 2 checkpoint backbone family mismatch: requested {model_config.backbone_family!r}, " + f"checkpoint has {saved_model_config.backbone_family!r} at {checkpoint_path}." + ) + return ckpt + +def _preview_state_keys(keys: list[str], *, limit: int = 8) -> str: + if not keys: + return "none" + preview = ", ".join(keys[:limit]) + if len(keys) > limit: + preview += ", ..." + return preview + +def _strict_load_strategy2_submodule( + target_module: nn.Module, + *, + checkpoint_state_dict: dict[str, Any], + checkpoint_prefix: str, + checkpoint_path: str | Path, + target_name: str, +) -> None: + extracted = { + key[len(checkpoint_prefix):]: value + for key, value in checkpoint_state_dict.items() + if key.startswith(checkpoint_prefix) + } + if not extracted: + raise RuntimeError( + f"Strategy 2 bootstrap failed for {target_name}: no checkpoint keys found with prefix " + f"{checkpoint_prefix!r} in {Path(checkpoint_path).resolve()}." + ) + + target_state = target_module.state_dict() + missing = sorted(set(target_state.keys()) - set(extracted.keys())) + unexpected = sorted(set(extracted.keys()) - set(target_state.keys())) + if missing or unexpected: + section(f"Strategy 2 Bootstrap Mismatch | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + print(f"Missing keys ({len(missing)}) : {_preview_state_keys(missing)}") + print(f"Unexpected keys ({len(unexpected)}): {_preview_state_keys(unexpected)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap failed for {target_name} from {Path(checkpoint_path).resolve()}. " + f"Missing keys={len(missing)}, unexpected keys={len(unexpected)}." + ) + + try: + load_result = target_module.load_state_dict(extracted, strict=True) + except Exception as exc: + section(f"Strategy 2 Bootstrap Strict Load Failure | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Target tensors : {len(target_state)}") + print(f"Checkpoint tensors : {len(extracted)}") + raise RuntimeError( + f"Strict Strategy 2 bootstrap load failed for {target_name} from " + f"{Path(checkpoint_path).resolve()}: {exc}" + ) from exc + + post_missing = list(getattr(load_result, "missing_keys", [])) + post_unexpected = list(getattr(load_result, "unexpected_keys", [])) + if post_missing or post_unexpected: + raise RuntimeError( + f"Strict Strategy 2 bootstrap reported residual mismatches for {target_name}: " + f"missing={post_missing}, unexpected={post_unexpected}" + ) + + section(f"Strategy 2 Bootstrap OK | {target_name}") + print(f"Checkpoint : {Path(checkpoint_path).resolve()}") + print(f"Checkpoint prefix : {checkpoint_prefix}") + print(f"Loaded tensors : {len(extracted)}") + print("Strict load : passed") + +def _use_channels_last_for_run(model_config: RuntimeModelConfig | None = None) -> bool: + model_config = (model_config or current_model_config()).validate() + if not USE_CHANNELS_LAST: + return False + if DEVICE.type != "cuda": + return False + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + if ( + model_config.backbone_family == "smp" + and "efficientnet" in model_config.smp_encoder_name.lower() + and USE_AMP + and amp_dtype in {torch.float16, torch.bfloat16} + ): + print("[MemoryFormat] Disabling channels_last for EfficientNet + AMP stability.") + return False + return True + +def build_model( + strategy: int, + dropout_p: float, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[nn.Module, str, bool]: + strategy = _require_supported_strategy(strategy) + model_config = model_config.validate() + if model_config.backbone_family == "custom_vgg": + if not bool(ENABLE_CUSTOM_VGG_BACKBONE): + raise RuntimeError( + "The legacy custom VGG backbone is feature-flagged off. " + "Set ENABLE_CUSTOM_VGG_BACKBONE=True to opt into the unused VGG code path." + ) + if strategy == 2: + model = SupervisedVGGModel( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_VGGWithDecoder( + num_scales=model_config.vgg_feature_scales, + dilation=model_config.vgg_feature_dilation, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.encoder, + checkpoint_state_dict=s2_state, + checkpoint_prefix="encoder.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.encoder", + ) + _strict_load_strategy2_submodule( + model.segmentation_head, + checkpoint_state_dict=s2_state, + checkpoint_prefix="segmentation_head.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.segmentation_head", + ) + if freeze_bootstrapped_segmentation: + model.encoder.requires_grad_(False) + model.segmentation_head.requires_grad_(False) + if hasattr(model.encoder, "projection") and model.encoder.projection is not None: + model.encoder.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + else: + if strategy == 2: + model = SupervisedSMPModel( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + dropout_p=dropout_p, + ) + elif strategy == 3: + model = PixelDRLMG_WithDecoder( + arch=model_config.smp_decoder_type, + encoder_name=model_config.smp_encoder_name, + encoder_weights=model_config.smp_encoder_weights, + encoder_depth=model_config.smp_encoder_depth, + proj_dim=model_config.smp_encoder_proj_dim, + dropout_p=dropout_p, + ) + model.strategy2_bootstrap_loaded = bool(strategy2_checkpoint_path is not None) + model.freeze_bootstrapped_segmentation = False + if strategy2_checkpoint_path is not None: + freeze_bootstrapped_segmentation = _strategy3_requested_bootstrap_freeze() + ckpt = _load_strategy2_checkpoint_payload(strategy2_checkpoint_path, model_config=model_config) + s2_state = ckpt["model_state_dict"] + _strict_load_strategy2_submodule( + model.smp_model, + checkpoint_state_dict=s2_state, + checkpoint_prefix="smp_model.", + checkpoint_path=strategy2_checkpoint_path, + target_name="strategy3.smp_model", + ) + if freeze_bootstrapped_segmentation: + model.smp_model.encoder.requires_grad_(False) + model.smp_model.decoder.requires_grad_(False) + if hasattr(model.smp_model, "segmentation_head"): + model.smp_model.segmentation_head.requires_grad_(False) + if hasattr(model, "projection") and model.projection is not None: + model.projection.requires_grad_(True) + model.freeze_bootstrapped_segmentation = bool(freeze_bootstrapped_segmentation) + model._keep_bootstrapped_segmentation_in_eval() + else: + raise ValueError(f"Unsupported strategy: {strategy}") + + use_channels_last_now = _use_channels_last_for_run(model_config) + if strategy == 3: + classifier = getattr(getattr(_unwrap_compiled(model), "policy_head", None), "classifier", None) + if isinstance(classifier, nn.Conv2d) and int(classifier.out_channels) != 1: + raise RuntimeError("Strategy 3 expects a continuous 1-channel policy head.") + _strategy3_bump_mc_cache_fingerprint( + model, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + model = model.to(DEVICE) + if use_channels_last_now: + model = model.to(memory_format=torch.channels_last) + + compiled = False + if USE_TORCH_COMPILE and hasattr(torch, "compile"): + try: + model = torch.compile(model, mode="max-autotune") + compiled = True + except Exception as exc: + print(f"[Compile] torch.compile skipped: {exc}") + return model, strategy_name(strategy, model_config), compiled + +def _unwrap_compiled(model: nn.Module) -> nn.Module: + return getattr(model, "_orig_mod", model) + +def count_parameters(module: nn.Module | None, *, only_trainable: bool = False) -> int: + if module is None: + return 0 + if only_trainable: + return sum(p.numel() for p in module.parameters() if p.requires_grad) + return sum(p.numel() for p in module.parameters()) + +def print_model_parameter_summary( + *, + model: nn.Module, + description: str, + strategy: int, + model_config: RuntimeModelConfig, + dropout_p: float, + amp_dtype: torch.dtype, + compiled: bool, +) -> None: + raw = _unwrap_compiled(model) + total_params = count_parameters(raw) + trainable_params = count_parameters(raw, only_trainable=True) + frozen_params = total_params - trainable_params + bn_count = sum(1 for m in raw.modules() if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d))) + gn_count = sum(1 for m in raw.modules() if isinstance(m, nn.GroupNorm)) + + section(f"Model Parameter Summary | {description}") + print(f"Strategy : {strategy}") + print(f"Model : {description}") + print(f"Dropout p : {dropout_p:.4f}") + print(f"Total params : {total_params:,}") + print(f"Trainable params : {trainable_params:,}") + print(f"Frozen params : {frozen_params:,}") + print(f"BN layers : {bn_count}") + print(f"GN layers : {gn_count}") + print(f"channels_last : {_use_channels_last_for_run(model_config)}") + print(f"AMP dtype : {amp_dtype}") + print(f"torch.compile : {compiled}") + print(f"Backbone family : {model_config.backbone_family}") + if strategy == 3: + print(f"S3 variant : {_strategy3_variant()}") + freeze_status = _strategy3_bootstrap_freeze_status(model) + print(f"S3 bootstrap loaded : {freeze_status['bootstrap_loaded']}") + print(f"S3 freeze requested : {freeze_status['freeze_requested']}") + print(f"S3 frozen now : {freeze_status['freeze_active']}") + print(f"S3 encoder state : {freeze_status['encoder_state']}") + if freeze_status["decoder_state"] != "n/a": + print(f"S3 decoder state : {freeze_status['decoder_state']}") + if freeze_status["segmentation_head_state"] != "n/a": + print(f"S3 seg head state : {freeze_status['segmentation_head_state']}") + + block_counts: dict[str, int] = {} + if strategy == 2: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + block_counts["dropout"] = count_parameters(getattr(raw, "dropout", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + elif strategy == 3: + if hasattr(raw, "smp_model"): + smp_model = raw.smp_model + block_counts["encoder"] = count_parameters(getattr(smp_model, "encoder", None)) + block_counts["decoder"] = count_parameters(getattr(smp_model, "decoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(smp_model, "segmentation_head", None)) + else: + block_counts["encoder"] = count_parameters(getattr(raw, "encoder", None)) + block_counts["segmentation_head"] = count_parameters(getattr(raw, "segmentation_head", None)) + block_counts["sam"] = count_parameters(getattr(raw, "sam", None)) + block_counts["policy_head"] = count_parameters(getattr(raw, "policy_head", None)) + block_counts["value_head"] = count_parameters(getattr(raw, "value_head", None)) + + for name, value in block_counts.items(): + print(f"{name:22s}: {value:,}") + +"""============================================================================= +METRICS + CHECKPOINTS +============================================================================= +""" + +_EPS = 1e-4 + +def _as_bool(mask: np.ndarray) -> np.ndarray: + return (mask[0] if mask.ndim == 3 else mask).astype(bool) + +def _tp_fp_fn(pred: np.ndarray, target: np.ndarray): + p, t = _as_bool(pred), _as_bool(target) + tp = float((p & t).sum()) + fp = float((p & ~t).sum()) + fn = float((~p & t).sum()) + return tp, fp, fn, float(t.sum()), float(p.sum()) + +def dice_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (2 * tp + _EPS) / (t + p + _EPS) + +def ppv_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, fp, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fp + _EPS) + +def sensitivity_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, fn, *_ = _tp_fp_fn(pred, target) + return (tp + _EPS) / (tp + fn + _EPS) + +def iou_score(pred: np.ndarray, target: np.ndarray) -> float: + tp, _, _, t, p = _tp_fp_fn(pred, target) + return (tp + _EPS) / (t + p - tp + _EPS) + +def _boundary_1px(mask: np.ndarray) -> np.ndarray: + m = _as_bool(mask) + if not m.any(): + return m + return m ^ ndimage.binary_erosion(m, iterations=1, border_value=0) + +def boundary_iou_contour_score(pred: np.ndarray, target: np.ndarray) -> float: + pb, tb = _boundary_1px(pred), _boundary_1px(target) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _biou_d(img_hw: tuple[int, int]) -> int: + """Resolve the Boundary IoU dilation width d for a given image size.""" + d = int(BOUNDARY_IOU_D) + if d > 0: + return d + height, width = img_hw + return max(1, int(round(0.02 * math.hypot(height, width)))) + +def _boundary_band(mask: np.ndarray, d: int) -> np.ndarray: + """Return the d-pixel inner boundary band used by paper-standard BIoU.""" + m = _as_bool(mask) + if not m.any(): + return m + eroded = ndimage.binary_erosion(m, iterations=max(int(d), 1), border_value=0) + return m & ~eroded + +def boundary_iou_score(pred: np.ndarray, target: np.ndarray) -> float: + """Boundary IoU from Cheng et al. CVPR 2021 using a d-pixel inner band.""" + height, width = _as_bool(target).shape + d = _biou_d((height, width)) + pb = _boundary_band(pred, d) + tb = _boundary_band(target, d) + inter = float((pb & tb).sum()) + union = float((pb | tb).sum()) + return (inter + _EPS) / (union + _EPS) + +def _surf_dist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + a, b = _as_bool(a), _as_bool(b) + if not a.any() and not b.any(): + return np.array([0.0], dtype=np.float32) + if not a.any() or not b.any(): + return np.array([np.inf], dtype=np.float32) + ba, bb = _boundary_1px(a), _boundary_1px(b) + return ndimage.distance_transform_edt(~bb)[ba].astype(np.float32) + +def hd95_score(pred: np.ndarray, target: np.ndarray) -> float: + distances = np.concatenate([_surf_dist(pred, target), _surf_dist(target, pred)]) + if np.isinf(distances).any(): + height, width = _as_bool(target).shape + return float(math.hypot(height, width)) + return float(np.percentile(distances, 95)) + +def compute_all_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + return { + "dice": dice_score(pred, target), + "ppv": ppv_score(pred, target), + "sen": sensitivity_score(pred, target), + "iou": iou_score(pred, target), + "biou": boundary_iou_score(pred, target), + "biou_contour": boundary_iou_contour_score(pred, target), + "hd95": hd95_score(pred, target), + } + +def checkpoint_manifest_path(path: Path) -> Path: + path = Path(path) + return path.with_name(f"{path.name}.meta.json") + +def checkpoint_history_path(run_dir: Path, run_type: str) -> Path: + if run_type == "overfit": + return Path(run_dir) / "overfit_history.json" + return Path(run_dir) / "history.json" + +def checkpoint_state_presence(payload: dict[str, Any]) -> dict[str, bool]: + tracked = [ + "model_state_dict", + "optimizer_state_dict", + "scheduler_state_dict", + "scaler_state_dict", + "log_alpha", + "alpha_optimizer_state_dict", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + "history", + "resume_source", + ] + return {name: name in payload for name in tracked} + +def write_checkpoint_manifest( + path: Path, + payload: dict[str, Any], + *, + extra: dict[str, Any] | None = None, +) -> dict[str, Any]: + run_config = checkpoint_run_config_payload(payload) + manifest = { + "checkpoint_path": str(Path(path).resolve()), + "run_type": payload.get("run_type", "unknown"), + "epoch": int(payload.get("epoch", 0)), + "strategy": run_config.get("strategy"), + "dataset_percent": run_config.get("dataset_percent"), + "backbone_family": run_config.get("backbone_family", "smp"), + "saved_keys": sorted(payload.keys()), + "state_presence": checkpoint_state_presence(payload), + } + if "resume_source" in payload: + manifest["resume_source"] = payload["resume_source"] + if extra: + manifest.update(extra) + save_json(checkpoint_manifest_path(path), manifest) + return manifest + +def checkpoint_required_keys( + *, + optimizer: torch.optim.Optimizer | None, + scheduler: CosineAnnealingLR | None, + scaler: Any | None, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + require_run_metadata: bool, +) -> list[str]: + keys = ["epoch", "model_state_dict"] + if require_run_metadata: + keys.extend( + [ + "run_type", + "best_metric_name", + "best_metric_value", + "patience_counter", + "elapsed_seconds", + "run_config", + "epoch_metrics", + ] + ) + if optimizer is not None: + keys.append("optimizer_state_dict") + if scheduler is not None: + keys.append("scheduler_state_dict") + if scaler is not None: + keys.append("scaler_state_dict") + if log_alpha is not None: + keys.append("log_alpha") + if alpha_optimizer is not None: + keys.append("alpha_optimizer_state_dict") + return keys + +def validate_checkpoint_payload( + path: Path, + payload: dict[str, Any], + *, + required_keys: list[str], + expected_run_type: str | None = None, +) -> None: + missing = [name for name in required_keys if name not in payload] + if missing: + raise KeyError(f"Checkpoint {path} is missing required keys: {missing}") + if expected_run_type is not None and payload.get("run_type") != expected_run_type: + raise ValueError( + f"Checkpoint {path} run_type mismatch: expected {expected_run_type!r}, " + f"got {payload.get('run_type')!r}." + ) + +def save_checkpoint( + path: Path, + *, + run_type: str, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: ReduceLROnPlateau | None, + scaler: Any | None, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": _unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + validate_checkpoint_payload( + path, + payload, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + torch.save(payload, path) + write_checkpoint_manifest(path, payload) + +def load_checkpoint( + path: Path, + *, + model: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scheduler: ReduceLROnPlateau | None = None, + scaler: Any | None = None, + device: torch.device, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + expected_run_type: str | None = None, + require_run_metadata: bool = False, +) -> dict[str, Any]: + ckpt = torch.load(path, map_location=device, weights_only=False) + validate_checkpoint_payload( + path, + ckpt, + required_keys=checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=require_run_metadata, + ), + expected_run_type=expected_run_type, + ) + + raw_model = _unwrap_compiled(model) + state_dict = ckpt["model_state_dict"] + load_strict = True + compat_layout: dict[str, Any] | None = None + _configure_policy_head_compatibility(model, state_dict, source=str(path)) + if any(key.startswith("policy_head.net.") for key in state_dict): + state_dict = _remap_legacy_policy_head_state_dict(state_dict) + if isinstance(raw_model, (PixelDRLMG_WithDecoder, PixelDRLMG_VGGWithDecoder)): + compat_layout = _strategy3_checkpoint_layout_info(ckpt["model_state_dict"], checkpoint_run_config_payload(ckpt)) + if compat_layout["is_strategy3_decoder_checkpoint"]: + if compat_layout.get("policy_action_count") not in (None, 1): + raise RuntimeError( + "Incompatible legacy Strategy 3 checkpoint detected during restore. " + f"Checkpoint={path} policy_head_out_channels={compat_layout.get('policy_action_count')}. " + "Resume/eval from pre-redesign Strategy 3 checkpoints is not supported." + ) + _configure_strategy3_model_compatibility(model, compat_layout, source=str(path)) + _ensure_strategy3_refinement_adapter_compatible(model, state_dict, checkpoint_path=path) + load_strict = bool(compat_layout["use_refinement"]) + + incompatible = raw_model.load_state_dict(state_dict, strict=load_strict) + if hasattr(raw_model, "clear_strategy3_mc_cache"): + _strategy3_bump_mc_cache_fingerprint(raw_model, eval_checkpoint_path=path) + if not load_strict: + missing_keys = [key for key in incompatible.missing_keys if not key.startswith(("refinement_adapter.", "multi_scale_refine."))] + unexpected_keys = list(incompatible.unexpected_keys) + if missing_keys or unexpected_keys: + print( + "[Checkpoint Restore] Non-strict legacy Strategy 3 load " + f"missing={missing_keys} unexpected={unexpected_keys}" + ) + + if optimizer is not None and "optimizer_state_dict" in ckpt: + try: + optimizer.load_state_dict(ckpt["optimizer_state_dict"]) + except ValueError: + if compat_layout is None or compat_layout.get("compatibility_mode") != "legacy": + raise + print( + f"[Checkpoint Restore] Skipping optimizer state for legacy Strategy 3 checkpoint at {path} " + "because the parameter layout differs from the refinement-capable model." + ) + if scheduler is not None and "scheduler_state_dict" in ckpt: + scheduler.load_state_dict(ckpt["scheduler_state_dict"]) + if scaler is not None and "scaler_state_dict" in ckpt: + scaler.load_state_dict(ckpt["scaler_state_dict"]) + if log_alpha is not None and "log_alpha" in ckpt: + with torch.no_grad(): + log_alpha.fill_(float(ckpt["log_alpha"])) + if alpha_optimizer is not None and "alpha_optimizer_state_dict" in ckpt: + alpha_optimizer.load_state_dict(ckpt["alpha_optimizer_state_dict"]) + restored = checkpoint_state_presence(ckpt) + restore_info = { + "restored_keys": restored, + "restored_at_epoch": int(ckpt.get("epoch", 0)), + "expected_run_type": expected_run_type, + } + write_checkpoint_manifest(path, ckpt, extra={"last_restore": restore_info}) + print( + f"[Checkpoint Restore] path={path} epoch={ckpt.get('epoch')} " + f"run_type={ckpt.get('run_type', 'unknown')} " + f"backbone={checkpoint_run_config_payload(ckpt).get('backbone_family', 'unknown')}" + ) + return ckpt + +"""============================================================================= +TRAINING + VALIDATION +============================================================================= +""" + +def _policy_log_probs_and_entropy(policy_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs = F.log_softmax(logits, dim=1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=1).mean() + return log_probs, entropy + +def _log_prob_for_actions(log_probs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor: + return log_probs.gather(1, actions.unsqueeze(1)) + +def sample_actions( + policy_logits: torch.Tensor, + stochastic: bool, + exploration_eps: float = 0.0, + keep_action_index: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + logits = policy_logits.float() + log_probs, entropy = _policy_log_probs_and_entropy(policy_logits) + if stochastic: + uniform = torch.rand_like(logits) + gumbel = -torch.log(-torch.log(uniform + 1e-8) + 1e-8) + actions = (logits + gumbel).argmax(dim=1) + if exploration_eps > 0: + keep_index = _keep_action_index(logits.shape[1]) if keep_action_index is None else int(keep_action_index) + keep_actions = torch.full_like(actions, keep_index) + random_mask = torch.rand(actions.shape, device=actions.device) < exploration_eps + actions = torch.where(random_mask, keep_actions, actions) + else: + actions = logits.argmax(dim=1) + log_prob = _log_prob_for_actions(log_probs, actions) + return actions, log_prob, entropy + +def apply_actions( + seg: torch.Tensor, + actions: torch.Tensor, + *, + num_actions: int | None = None, +) -> torch.Tensor: + num_actions = int(num_actions if num_actions is not None else _job_param("num_actions", DEFAULT_STRATEGY3_NUM_ACTIONS)) + if num_actions >= 3: + deltas = _refinement_deltas(action_count=num_actions, device=seg.device, dtype=seg.dtype) + delta = deltas[actions.long()].unsqueeze(1) + return (seg + delta).clamp_(0.0, 1.0) + action_map = actions.unsqueeze(1) + return seg * (action_map == 1).to(dtype=seg.dtype) + +def _soft_dice_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + denom = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) + return (2.0 * inter + 1e-6) / (denom + 1e-6) + +def _soft_iou_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + inter = (pred * target).sum(dim=(1, 2, 3)) + union = pred.sum(dim=(1, 2, 3)) + target.sum(dim=(1, 2, 3)) - inter + return (inter + 1e-6) / (union + 1e-6) + +def _soft_recall_tensor(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + true_positive = (pred * target).sum(dim=(1, 2, 3)) + positives = target.sum(dim=(1, 2, 3)) + return (true_positive + 1e-6) / (positives + 1e-6) + +def _soft_boundary(mask: torch.Tensor) -> torch.Tensor: + return (mask - F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1)).abs() + +def _differentiable_boundary(mask: torch.Tensor, kernel_size: int = 3) -> torch.Tensor: + padding = kernel_size // 2 + mask_f = mask.float().clamp(0.0, 1.0) + eroded = 1.0 - F.max_pool2d(1.0 - mask_f, kernel_size, stride=1, padding=padding) + return (mask_f - eroded).clamp(0.0, 1.0) + +def _soft_iou_per_sample(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + pred_f = pred.float().clamp(0.0, 1.0) + target_f = target.float().clamp(0.0, 1.0) + inter = (pred_f * target_f).sum(dim=(2, 3), keepdim=True) + union = (pred_f + target_f - pred_f * target_f).sum(dim=(2, 3), keepdim=True) + return inter / (union + 1e-6) + +def differentiable_biou_loss( + pred: torch.Tensor, + target: torch.Tensor, + kernel_size: int | None = None, + *, + reduction: str = "mean", +) -> torch.Tensor: + if kernel_size is None: + height, width = int(pred.shape[-2]), int(pred.shape[-1]) + d = _biou_d((height, width)) + kernel_size = 2 * d + 1 + kernel_size = max(int(kernel_size), 1) + if kernel_size % 2 == 0: + kernel_size += 1 + pred_boundary = _differentiable_boundary(pred, kernel_size=kernel_size) + target_boundary = _differentiable_boundary(target, kernel_size=kernel_size) + inter = (pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + union = (pred_boundary + target_boundary - pred_boundary * target_boundary).sum(dim=(1, 2, 3), keepdim=True) + loss = 1.0 - (inter + 1e-6) / (union + 1e-6) + if reduction == "none": + return loss + if reduction == "mean": + return loss.mean() + raise ValueError(f"Unsupported differentiable_biou_loss reduction {reduction!r}.") + +def _strategy3_expected_next_mask( + policy_logits: torch.Tensor, + base_seg: torch.Tensor, + *, + action_count: int, +) -> tuple[torch.Tensor, torch.Tensor]: + logits_f = policy_logits.float() + base_seg_f = base_seg.float() + probs = F.softmax(logits_f, dim=1) + deltas = _refinement_deltas(action_count=action_count, device=logits_f.device, dtype=logits_f.dtype) + expected_delta = (probs * deltas.view(1, -1, 1, 1)).sum(dim=1, keepdim=True) + predicted_next = (base_seg_f + expected_delta).clamp(1e-4, 1.0 - 1e-4) + return predicted_next, deltas + +def _strategy3_action_targets( + seg_mask: torch.Tensor, + gt_mask: torch.Tensor, + deltas: torch.Tensor, +) -> torch.Tensor: + target_delta = gt_mask.float() - seg_mask.float() + return (target_delta - deltas.view(1, -1, 1, 1)).abs().argmin(dim=1) + +def compute_refinement_reward( + seg: torch.Tensor, + seg_next: torch.Tensor, + gt_mask: torch.Tensor, + *, + return_details: bool = False, +) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]: + seg_f = seg.float() + seg_next_f = seg_next.float() + gt_f = gt_mask.float().clamp(0.0, 1.0) + + r1_weight = float(_job_param("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT)) + biou_reward_weight = float(_job_param("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT)) + + target_dir = 2.0 * gt_f - 1.0 + progress = target_dir * (seg_next_f - seg_f) + room = gt_f * (1.0 - seg_f) + (1.0 - gt_f) * seg_f + r1 = r1_weight * progress * room + + biou_before = 1.0 - differentiable_biou_loss(seg_f, gt_f, reduction="none") + biou_next = 1.0 - differentiable_biou_loss(seg_next_f, gt_f, reduction="none") + biou_delta = biou_next - biou_before + r3 = biou_reward_weight * biou_delta.expand_as(seg_next_f) + + reward = (r1 + r3).clamp(-3.0, 3.0) + if not return_details: + return reward + return reward, { + "biou_before": biou_before, + "biou_next": biou_next, + "biou_delta": biou_delta, + } + +def compute_strategy1_aux_segmentation_loss( + policy_logits: torch.Tensor, + gt_mask: torch.Tensor, + *, + ce_weight: float, + dice_weight: float, + seg_mask: torch.Tensor | None = None, +) -> tuple[torch.Tensor, float, float]: + logits_f = policy_logits.float() + gt_mask_f = gt_mask.float() + num_actions = int(logits_f.shape[1]) + + if num_actions >= 3: + base_seg = seg_mask.float() if seg_mask is not None else torch.full_like(gt_mask_f, 0.5) + predicted_next, deltas = _strategy3_expected_next_mask( + policy_logits, + base_seg, + action_count=num_actions, + ) + action_targets = _strategy3_action_targets(base_seg, gt_mask_f, deltas) + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + boundary_dice_w = float(_job_param("strategy3_aux_boundary_dice_weight", 0.0)) + + threshold = float(_job_param("threshold", CONTROLLED_MASK_THRESHOLD)) + delta_large = float(_job_param("refine_delta_large", DEFAULT_REFINE_DELTA_LARGE)) + hard_margin = delta_large * 1.5 + with torch.no_grad(): + dist_to_thresh = (base_seg - threshold).abs() + hard_mask = (dist_to_thresh < hard_margin).squeeze(1) + + if ce_weight > 0: + action_ce_mix = float(_job_param("aux_action_ce_mix", 0.75)) + action_ce_mix = min(max(action_ce_mix, 0.0), 1.0) + + if hard_mask.any(): + logits_hw = policy_logits.float().permute(0, 2, 3, 1) + targets_hw = action_targets + action_ce = F.cross_entropy(logits_hw[hard_mask], targets_hw[hard_mask]) + else: + action_ce = F.cross_entropy(policy_logits.float(), action_targets) + + predicted_next_logits = torch.logit(predicted_next) + if hard_mask.any(): + gt_hard = gt_mask_f.squeeze(1)[hard_mask] + pred_hard = predicted_next_logits.squeeze(1)[hard_mask] + bce = F.binary_cross_entropy_with_logits(pred_hard, gt_hard) + else: + bce = F.binary_cross_entropy_with_logits(predicted_next_logits, gt_mask_f) + + ce_term = action_ce_mix * action_ce + (1.0 - action_ce_mix) * bce + aux_loss = aux_loss + ce_weight * ce_term + ce_loss_value = float(ce_term.detach().item()) + + if dice_weight > 0: + inter = (predicted_next * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (predicted_next.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + if boundary_dice_w > 0: + bd_loss = differentiable_biou_loss(predicted_next, gt_mask_f) + aux_loss = aux_loss + boundary_dice_w * bd_loss + + return aux_loss, ce_loss_value, dice_loss_value + + aux_loss = torch.zeros((), device=policy_logits.device, dtype=torch.float32) + ce_loss_value = 0.0 + dice_loss_value = 0.0 + + if ce_weight > 0: + if num_actions >= 3: + if seg_mask is None: + seg_mask = torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(seg_mask).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + ce_loss_value = float(ce_loss.detach().item()) + + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + inter = (probs_fg * gt_mask_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_mask_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + dice_loss_value = float(dice_loss.detach().item()) + + return aux_loss, ce_loss_value, dice_loss_value + +def make_optimizer( + model: nn.Module, + strategy: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + rl_lr: float | None = None, +): + raw = _unwrap_compiled(model) + encoder_params = [] + decoder_params = [] + rl_params = [] + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + if ( + name.startswith("extractor.encoder.") + or name.startswith("encoder.") + or name.startswith("smp_model.encoder.") + or name.startswith("smp_encoder.") + ): + encoder_params.append(param) + elif "decoder" in name or "segmentation_head" in name: + decoder_params.append(param) + else: + rl_params.append(param) + + decoder_lr = float(_job_param("decoder_lr", head_lr)) + rl_group_lr = float(_job_param("rl_lr", rl_lr if rl_lr is not None else head_lr)) + + param_groups: list[dict[str, Any]] = [] + + if encoder_params: + param_groups.append({"params": encoder_params, "lr": encoder_lr}) + + if decoder_params: + param_groups.append({"params": decoder_params, "lr": decoder_lr}) + + if rl_params: + param_groups.append({"params": rl_params, "lr": rl_group_lr}) + + try: + optimizer = AdamW(param_groups, weight_decay=weight_decay, fused=DEVICE.type == "cuda") + except Exception: + optimizer = AdamW(param_groups, weight_decay=weight_decay) + + return optimizer + +def infer_segmentation_mask( + model: nn.Module, + image: torch.Tensor, + tmax: int, + *, + strategy: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> torch.Tensor: + model.eval() + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + return threshold_binary_mask(torch.sigmoid(logits)).float() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="infer_segmentation_mask") + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta) + return threshold_binary_mask(seg.float()).float() + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + policy_logits = model.forward_policy_only(x_t) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + return seg.float() + +def train_step( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor, + alpha_optimizer: torch.optim.Optimizer, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + initial_mask: torch.Tensor | None = None, + decoder_loss_extra: torch.Tensor | None = None, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + if initial_mask is not None: + seg = initial_mask.to(device=image.device, dtype=image.dtype) + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + alpha = log_alpha.exp() + + total_actor = 0.0 + total_critic = 0.0 + total_loss = 0.0 + total_reward = 0.0 + total_entropy = 0.0 + total_ce_loss = 0.0 + total_dice_loss = 0.0 + accum_tensor = None + alpha_loss_accum = torch.tensor(0.0, device=image.device, dtype=torch.float32) + aux_fused = False + + optimizer.zero_grad(set_to_none=True) + + for _ in range(tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=True) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + + neighborhood_next = _bootstrap_value_target(model, value_next) + target = reward + gamma * neighborhood_next + advantage = target - value_t + critic_loss = F.smooth_l1_loss(value_t, target) + actor_loss = -(log_prob * advantage.detach()).mean() + actor_loss = actor_loss - alpha.detach() * entropy + step_loss = (actor_loss + critic_loss_weight * critic_loss) / float(tmax) + alpha_loss_accum = alpha_loss_accum + (log_alpha * (entropy.detach() - target_entropy)) / float(tmax) + + if not aux_fused and initial_mask is None and (ce_weight > 0 or dice_weight > 0): + aux_loss, ce_loss_value, dice_loss_value = compute_strategy1_aux_segmentation_loss( + policy_logits, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + if ce_weight > 0: + total_ce_loss = ce_loss_value + if dice_weight > 0: + total_dice_loss = dice_loss_value + step_loss = step_loss + aux_loss + aux_fused = True + + total_actor += float(actor_loss.detach().item()) + total_critic += float(critic_loss.detach().item()) + total_loss += float(step_loss.detach().item()) + total_reward += float(reward.detach().mean().item()) + total_entropy += float(entropy.detach().item()) + + if stepwise_backward: + if scaler is not None: + scaler.scale(step_loss).backward() + else: + step_loss.backward() + else: + accum_tensor = step_loss if accum_tensor is None else accum_tensor + step_loss + + seg = seg_next.detach() + + if not stepwise_backward and accum_tensor is not None: + if scaler is not None: + scaler.scale(accum_tensor).backward() + else: + accum_tensor.backward() + + if not aux_fused and (ce_weight > 0 or dice_weight > 0): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + logits_f = policy_aux.float() + aux_loss = torch.zeros(1, device=image.device, dtype=torch.float32) + if ce_weight > 0: + num_actions = int(logits_f.shape[1]) + if num_actions >= 3: + init_seg = initial_mask if initial_mask is not None else torch.ones_like(gt_mask) + seg_bin = threshold_binary_long(init_seg).squeeze(1) + gt_bin = gt_mask[:, 0].long() + aux_target = torch.where( + gt_bin == 0, + torch.zeros_like(gt_bin), + torch.where(seg_bin == 1, torch.ones_like(gt_bin), torch.full_like(gt_bin, 2)), + ) + else: + aux_target = gt_mask[:, 0].long() * (num_actions - 1) + ce_loss = F.cross_entropy(logits_f, aux_target) + aux_loss = aux_loss + ce_weight * ce_loss + total_ce_loss = float(ce_loss.detach().item()) + if dice_weight > 0: + probs_fg = F.softmax(logits_f, dim=1)[:, num_actions - 1 : num_actions] + gt_f = gt_mask.float() + inter = (probs_fg * gt_f).sum() + dice_loss = 1.0 - (2.0 * inter + 1e-6) / (probs_fg.sum() + gt_f.sum() + 1e-6) + aux_loss = aux_loss + dice_weight * dice_loss + total_dice_loss = float(dice_loss.detach().item()) + if decoder_loss_extra is not None: + aux_loss = aux_loss + decoder_loss_extra + if scaler is not None: + scaler.scale(aux_loss).backward() + else: + aux_loss.backward() + total_loss += float(aux_loss.detach().item()) + elif decoder_loss_extra is not None: + if scaler is not None: + scaler.scale(decoder_loss_extra).backward() + else: + decoder_loss_extra.backward() + total_loss += float(decoder_loss_extra.detach().item()) + + if scaler is not None: + scaler.unscale_(optimizer) + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + alpha_optimizer.zero_grad(set_to_none=True) + alpha_loss_accum.backward() + alpha_optimizer.step() + with torch.no_grad(): + log_alpha.clamp_(min=_alpha_log_floor(), max=5.0) + + return { + "loss": total_loss, + "actor_loss": total_actor / tmax, + "critic_loss": total_critic / tmax, + "mean_reward": total_reward / tmax, + "entropy": total_entropy / tmax, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": seg.detach(), + } + +def train_step_strategy3( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + gamma: float, + tmax: int, + critic_loss_weight: float, + log_alpha: torch.Tensor | None, + alpha_optimizer: torch.optim.Optimizer | None, + target_entropy: float, + ce_weight: float, + dice_weight: float, + grad_clip_norm: float, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + stepwise_backward: bool, + use_channels_last: bool, + current_epoch: int, + max_epochs: int, +) -> dict[str, Any]: + if not _uses_refinement_runtime(model, strategy=3): + raise RuntimeError( + "Legacy non-refinement Strategy 3 training is not supported after the continuous Strategy 3 redesign." + ) + + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + annealed_aux_ce_weight = _strategy3_annealed_aux_ce_weight(current_epoch) + del stepwise_backward, max_epochs, log_alpha, alpha_optimizer, target_entropy + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context(image, mc_mode="train") + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"] + base_features = refinement_context["base_features"] + encoder_features = refinement_context.get("encoder_features") + mc_variance = refinement_context["mc_variance"] + pred_entropy = refinement_context["pred_entropy"] + seg = decoder_prob.detach().to(device=image.device, dtype=image.dtype) + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + + decoder_loss = torch.zeros((), device=image.device, dtype=torch.float32) + decoder_ce_loss_value = 0.0 + decoder_dice_loss_value = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + decoder_ce = F.binary_cross_entropy_with_logits(dl_f, gt_f) + decoder_loss = decoder_loss + loss_weights["decoder_ce"] * decoder_ce + decoder_ce_loss_value = float(decoder_ce.detach().item()) + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + decoder_dice = 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + decoder_loss = decoder_loss + loss_weights["decoder_dice"] * decoder_dice + decoder_dice_loss_value = float(decoder_dice.detach().item()) + + optimizer.zero_grad(set_to_none=True) + + a3c_grad_clip = float(_job_param("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM)) + + refinement_base_features = base_features + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = mc_variance.detach() + detached_pred_entropy = pred_entropy.detach() + detached_encoder_features = [f.detach() for f in encoder_features] if encoder_features is not None else None + + step_action_hists: list[dict[str, float]] = [] + step_mask_deltas: list[float] = [] + step_reward_means: list[float] = [] + step_reward_pos_pcts: list[float] = [] + step_reward_zero_pcts: list[float] = [] + step_biou_deltas: list[float] = [] + advantage_maps: list[torch.Tensor] = [] + critic_targets: list[torch.Tensor] = [] + value_maps: list[torch.Tensor] = [] + + actor_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + critic_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + total_actor = 0.0 + total_critic = 0.0 + total_reward = 0.0 + total_ce_loss = decoder_ce_loss_value + total_dice_loss = decoder_dice_loss_value + effective_steps = max(int(tmax), 1) + final_refined_seg_for_aux: torch.Tensor | None = None + + for _ in range(effective_steps): + seg_before = seg.detach() + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg_before, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg_before.dtype) + seg_next = _strategy3_apply_delta(seg_before, delta) + reward_map, reward_details = compute_refinement_reward( + seg_before, + seg_next, + gt_mask.float(), + return_details=True, + ) + + with torch.no_grad(): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next.detach(), + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_encoder_features, + ) + value_next = model.value_from_state(state_next).detach() + + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + step_actor = -actor_advantage.mean() + step_critic = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + actor_loss_tensor = actor_loss_tensor + step_actor / float(effective_steps) + critic_loss_tensor = critic_loss_tensor + step_critic / float(effective_steps) + total_actor += float(step_actor.detach().item()) + total_critic += float(step_critic.detach().item()) + total_reward += float(reward_map.detach().mean().item()) + + step_action_hists.append(_strategy3_delta_distribution(delta)) + step_mask_deltas.append(float(delta.detach().abs().mean().item())) + step_reward_means.append(float(reward_map.detach().mean().item())) + step_reward_pos_pcts.append(float((reward_map.detach() > 0).float().mean().item() * 100.0)) + step_reward_zero_pcts.append(float((reward_map.detach().abs() < 1e-8).float().mean().item() * 100.0)) + step_biou_deltas.append(float(reward_details["biou_delta"].detach().mean().item())) + advantage_maps.append(actor_advantage.detach()) + critic_targets.append(critic_target.detach()) + value_maps.append(value_t.detach()) + final_refined_seg_for_aux = seg_next + seg = seg_next.detach() + + aux_loss_tensor = torch.zeros((), device=image.device, dtype=torch.float32) + aux_ce_loss_value = 0.0 + aux_dice_loss_value = 0.0 + if annealed_aux_ce_weight > 0.0 and final_refined_seg_for_aux is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refined_prob = final_refined_seg_for_aux.float().clamp(1e-6, 1.0 - 1e-6) + gt_f = gt_mask.float() + supervised_aux = torch.zeros((), device=image.device, dtype=torch.float32) + if ce_weight > 0: + refined_logits = torch.logit(refined_prob) + aux_ce = F.binary_cross_entropy_with_logits(refined_logits, gt_f) + supervised_aux = supervised_aux + float(ce_weight) * aux_ce + aux_ce_loss_value = float(aux_ce.detach().item()) + if dice_weight > 0: + inter = (refined_prob * gt_f).sum() + aux_dice = 1.0 - (2.0 * inter + 1e-6) / (refined_prob.sum() + gt_f.sum() + 1e-6) + supervised_aux = supervised_aux + float(dice_weight) * aux_dice + aux_dice_loss_value = float(aux_dice.detach().item()) + aux_loss_tensor = float(annealed_aux_ce_weight) * supervised_aux + total_ce_loss += aux_ce_loss_value + total_dice_loss += aux_dice_loss_value + + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + rl_loss = rl_loss_scale * (actor_loss_tensor + critic_loss_weight * critic_loss_tensor) + total_loss_tensor = decoder_loss + rl_loss + aux_loss_tensor + + if scaler is not None: + scaler.scale(total_loss_tensor).backward() + scaler.unscale_(optimizer) + else: + total_loss_tensor.backward() + + effective_grad_clip = a3c_grad_clip if a3c_grad_clip > 0 else grad_clip_norm + total_grad_norm = ( + float(torch.nn.utils.clip_grad_norm_(model.parameters(), effective_grad_clip).item()) + if effective_grad_clip > 0 + else 0.0 + ) + + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + adv_means = [float(a.mean().item()) for a in advantage_maps] + adv_stds = [ + float(a.std(unbiased=False).item()) + for a in advantage_maps + if a.numel() > 1 + ] + value_pred_errors = [ + float((target - value).abs().mean().item()) + for target, value in zip(critic_targets, value_maps) + ] + mean_value_pred = float(torch.stack([value.mean() for value in value_maps]).mean().item()) if value_maps else 0.0 + avg_action_dist: dict[str, float] = {} + if step_action_hists: + all_keys = set() + for h in step_action_hists: + all_keys.update(h.keys()) + for k in sorted(all_keys): + avg_action_dist[k] = float(np.mean([h.get(k, 0.0) for h in step_action_hists])) + + return { + "loss": float(total_loss_tensor.detach().item()), + "actor_loss": total_actor / float(effective_steps), + "critic_loss": total_critic / float(effective_steps), + "mean_reward": total_reward / float(effective_steps), + "entropy": 0.0, + "ce_loss": total_ce_loss, + "dice_loss": total_dice_loss, + "grad_norm": total_grad_norm, + "grad_clip_used": effective_grad_clip, + "final_mask": threshold_binary_mask(seg.detach().float()).float(), + "effective_steps": effective_steps, + "action_distribution": avg_action_dist, + "mask_delta_mean": float(np.mean(step_mask_deltas)) if step_mask_deltas else 0.0, + "reward_per_step": step_reward_means, + "reward_pos_pct_per_step": step_reward_pos_pcts, + "reward_zeros_pct": float(np.mean(step_reward_zero_pcts)) if step_reward_zero_pcts else 0.0, + "biou_delta_mean": float(np.mean(step_biou_deltas)) if step_biou_deltas else 0.0, + "advantage_mean": float(np.mean(adv_means)), + "advantage_std": float(np.nanmean(adv_stds)) if adv_stds else 0.0, + "value_pred_error_mean": float(np.mean(value_pred_errors)), + "mean_value_pred": mean_value_pred, + "rl_loss_scale_used": float(rl_loss_scale), + "annealed_aux_ce_weight": float(annealed_aux_ce_weight), + "alpha": 0.0, + } + +def train_step_supervised( + model: nn.Module, + batch: dict[str, Any], + optimizer: torch.optim.Optimizer, + *, + scaler: Any | None, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + grad_clip_norm: float, + ce_weight: float = 0.5, + dice_weight: float = 0.5, +) -> dict[str, Any]: + model.train() + _strategy3_keep_frozen_modules_in_eval(model) + image = batch["image"] + gt_mask = batch["mask"] + + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + else: + image = image.float() + gt_mask = gt_mask.float() + + optimizer.zero_grad(set_to_none=True) + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + logits_f = logits.float() + gt_f = gt_mask.float() + loss = torch.zeros(1, device=image.device, dtype=torch.float32) + ce_loss_val = 0.0 + dice_loss_val = 0.0 + if ce_weight > 0: + bce = F.binary_cross_entropy_with_logits(logits_f, gt_f, reduction="mean") + loss = loss + ce_weight * bce + ce_loss_val = float(bce.detach().item()) + if dice_weight > 0: + pred_f = torch.sigmoid(logits_f) + inter = (pred_f * gt_f).sum() + dice_l = 1.0 - (2.0 * inter + 1e-6) / (pred_f.sum() + gt_f.sum() + 1e-6) + loss = loss + dice_weight * dice_l + dice_loss_val = float(dice_l.detach().item()) + + if scaler is not None: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + total_grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_norm).item()) if grad_clip_norm > 0 else 0.0 + if scaler is not None: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + final_mask = threshold_binary_mask(torch.sigmoid(logits_f)).float().detach() + return { + "loss": float(loss.detach().item()), + "actor_loss": 0.0, + "critic_loss": 0.0, + "mean_reward": 0.0, + "entropy": 0.0, + "ce_loss": ce_loss_val, + "dice_loss": dice_loss_val, + "grad_norm": total_grad_norm, + "grad_clip_used": grad_clip_norm, + "final_mask": final_mask, + } + +@torch.inference_mode() +def validate( + model: nn.Module, + loader: DataLoader, + *, + run_dir: Path | None, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + gamma: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, +) -> dict[str, float]: + strategy = _require_supported_strategy(strategy) + model.eval() + effective_tmax = _resolve_test_iteration_tmax(tmax, context="validate") if strategy != 2 else max(int(tmax), 1) + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + losses: list[float] = [] + dice_scores: list[float] = [] + iou_scores: list[float] = [] + biou_scores: list[float] = [] + entropies: list[float] = [] + rewards: list[float] = [] + actor_losses: list[float] = [] + critic_losses: list[float] = [] + ce_losses: list[float] = [] + dice_losses: list[float] = [] + decoder_dice_scores: list[float] = [] + decoder_iou_scores: list[float] = [] + decoder_biou_scores: list[float] = [] + val_binary_flips_total: list[float] = [] + val_binary_flips_correct: list[float] = [] + val_binary_flips_wrong: list[float] = [] + prefetcher = CUDAPrefetcher(loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(loader), desc="Validating", leave=False): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + gt_mask = gt_mask.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred_prob = torch.sigmoid(logits).float() + pred = threshold_binary_mask(pred_prob).float() + bce = F.binary_cross_entropy_with_logits(logits.float(), gt_mask.float(), reduction="mean") if ce_weight > 0 else torch.tensor(0.0, device=image.device) + inter_prob = (pred_prob * gt_mask.float()).sum() + dice_loss = 1.0 - (2.0 * inter_prob + 1e-6) / (pred_prob.sum() + gt_mask.sum() + 1e-6) if dice_weight > 0 else torch.tensor(0.0, device=image.device) + losses.append(float((ce_weight * bce + dice_weight * dice_loss).item())) + ce_losses.append(float(bce.item()) if ce_weight > 0 else 0.0) + dice_losses.append(float(dice_loss.item()) if dice_weight > 0 else 0.0) + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + decoder_logits = refinement_context["decoder_logits"] + decoder_prob = refinement_context["decoder_prob"].float() + seg = decoder_prob.float() + loss_weights = _strategy3_loss_weights(model, ce_weight=ce_weight, dice_weight=dice_weight) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if loss_weights["decoder_ce"] > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += loss_weights["decoder_ce"] * batch_ce + if loss_weights["decoder_dice"] > 0: + dm_prob = decoder_prob.float() + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += loss_weights["decoder_dice"] * batch_dice + decoder_pred = threshold_binary_mask(decoder_prob.float()).float() + decoder_inter = (decoder_pred * gt_mask.float()).sum(dim=(1, 2, 3)) + decoder_pred_sum = decoder_pred.sum(dim=(1, 2, 3)) + decoder_gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + decoder_dice = (2.0 * decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum + _EPS) + decoder_iou = (decoder_inter + _EPS) / (decoder_pred_sum + decoder_gt_sum - decoder_inter + _EPS) + decoder_dice_scores.extend(decoder_dice.cpu().tolist()) + decoder_iou_scores.extend(decoder_iou.cpu().tolist()) + else: + fg_count = gt_mask.sum().clamp(min=1.0) + bg_count = (gt_mask == 0).sum().clamp(min=1.0) + pos_weight = bg_count / fg_count + _weight_map = torch.where(gt_mask == 1, pos_weight, torch.ones_like(gt_mask)) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + decoder_logits = model.forward_decoder(image) + seg = threshold_binary_mask(torch.sigmoid(decoder_logits)).float() + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + dl_f = decoder_logits.float() + gt_f = gt_mask.float() + if ce_weight > 0: + batch_ce = float(F.binary_cross_entropy_with_logits(dl_f, gt_f).item()) + decoder_loss += ce_weight * batch_ce + if dice_weight > 0: + dm_prob = torch.sigmoid(dl_f) + inter = (dm_prob * gt_f).sum() + batch_dice = float( + ( + 1.0 - (2.0 * inter + 1e-6) / (dm_prob.sum() + gt_f.sum() + 1e-6) + ).item() + ) + decoder_loss += dice_weight * batch_dice + else: + seg = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + decoder_loss = 0.0 + batch_ce = 0.0 + batch_dice = 0.0 + + batch_actor = 0.0 + batch_critic = 0.0 + batch_reward = 0.0 + batch_entropy = 0.0 + batch_loss = decoder_loss + decoder_ce_base = batch_ce + decoder_dice_base = batch_dice + aux_ce_total = 0.0 + aux_dice_total = 0.0 + effective_steps = effective_tmax + + if strategy == 3 and refinement_runtime: + refinement_base_features = refinement_context["base_features"] + detached_decoder_prob = decoder_prob.detach() + detached_mc_variance = refinement_context["mc_variance"].detach() + detached_pred_entropy = refinement_context["pred_entropy"].detach() + _enc_feats = refinement_context.get("encoder_features") + detached_enc_feats = [f.detach() for f in _enc_feats] if _enc_feats is not None else None + effective_steps = effective_tmax + rl_loss_scale = float(_job_param("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE)) + + for _step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_base_features, + seg, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + policy_raw, value_t = model.forward_from_state(state_t) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + state_next = model.forward_refinement_state( + refinement_base_features, + seg_next, + detached_decoder_prob, + detached_mc_variance, + detached_pred_entropy, + encoder_features=detached_enc_feats, + ) + value_next = model.value_from_state(state_next).detach() + actor_advantage = _strategy3_actor_advantage( + reward_map, + value_t, + value_next, + gamma=gamma, + ) + critic_target = _strategy3_critic_target(reward_map, value_next, gamma=gamma) + actor_loss = -actor_advantage.mean() + critic_loss = F.smooth_l1_loss(value_t.float(), critic_target.float()) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward_map.mean().item()) + batch_loss += float((actor_loss + critic_loss_weight * critic_loss).item()) + seg = seg_next + + batch_ce = decoder_ce_base + batch_dice = decoder_dice_base + batch_loss = decoder_loss + rl_loss_scale * ((batch_loss - decoder_loss) / float(max(effective_steps, 1))) + pred = threshold_binary_mask(seg.float()).float() + # Track binary mask flips between decoder and RL-refined prediction + flipped = (decoder_pred != pred) + gt_binary = (gt_mask.float() > 0.5) + correct_flips = flipped & ((pred > 0.5) == gt_binary) + wrong_flips = flipped & ((pred > 0.5) != gt_binary) + total_px = max(pred.numel(), 1) + val_binary_flips_total.append(float(flipped.float().sum().item() / total_px * 100.0)) + val_binary_flips_correct.append(float(correct_flips.float().sum().item() / total_px * 100.0)) + val_binary_flips_wrong.append(float(wrong_flips.float().sum().item() / total_px * 100.0)) + else: + for _ in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + x_t = image * seg + state_t, _ = model.forward_state(x_t) + policy_logits, value_t = model.forward_from_state(state_t) + actions, log_prob, entropy = sample_actions(policy_logits, stochastic=False) + log_prob = log_prob.clamp(min=-10.0) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]) + reward = ((seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2)) + x_next = image * seg_next + state_next, _ = model.forward_state(x_next) + value_next = model.value_from_state(state_next).detach() + target = reward + gamma * _bootstrap_value_target(model, value_next) + advantage = target - value_t + actor_loss = -(log_prob * advantage.detach()).mean() + critic_loss = F.smooth_l1_loss(value_t, target) + + batch_actor += float(actor_loss.item()) + batch_critic += float(critic_loss.item()) + batch_reward += float(reward.mean().item()) + batch_entropy += float(entropy.item()) + batch_loss += float(((actor_loss + critic_loss_weight * critic_loss) / float(max(effective_tmax, 1))).item()) + seg = seg_next + + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + policy_aux, _, _ = model(image) + aux_loss, aux_ce, aux_dice = compute_strategy1_aux_segmentation_loss( + policy_aux, + gt_mask, + ce_weight=ce_weight, + dice_weight=dice_weight, + seg_mask=seg, + ) + batch_loss += float(aux_loss.item()) + batch_ce = aux_ce + batch_dice = aux_dice + pred = infer_segmentation_mask( + model, + image, + effective_tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ).float() + + ce_losses.append(batch_ce) + dice_losses.append(batch_dice) + actor_losses.append(batch_actor / float(max(effective_steps, 1))) + critic_losses.append(batch_critic / float(max(effective_steps, 1))) + rewards.append(batch_reward / float(max(effective_steps, 1))) + entropies.append(batch_entropy / float(max(effective_steps, 1))) + losses.append(batch_loss) + + inter = (pred * gt_mask.float()).sum(dim=(1, 2, 3)) + pred_sum = pred.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.float().sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + dice_scores.extend(dice.cpu().tolist()) + iou_scores.extend(iou.cpu().tolist()) + pred_np = pred.detach().cpu().numpy() + gt_np = gt_mask.float().detach().cpu().numpy() + for idx in range(pred_np.shape[0]): + biou_scores.append(boundary_iou_score(pred_np[idx], gt_np[idx])) + if strategy == 3 and decoder_dice_scores: + decoder_pred_np = decoder_pred.detach().cpu().numpy() + for idx in range(decoder_pred_np.shape[0]): + decoder_biou_scores.append(boundary_iou_score(decoder_pred_np[idx], gt_np[idx])) + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + val_decoder_dice = float(np.mean(decoder_dice_scores)) if decoder_dice_scores else None + val_decoder_iou = float(np.mean(decoder_iou_scores)) if decoder_iou_scores else None + val_decoder_biou = float(np.mean(decoder_biou_scores)) if decoder_biou_scores else None + val_dice = float(np.mean(dice_scores)) if dice_scores else 0.0 + val_iou = float(np.mean(iou_scores)) if iou_scores else 0.0 + val_biou = float(np.mean(biou_scores)) if biou_scores else 0.0 + val_refine_score = 0.5 * (val_iou + val_biou) + + return { + "val_loss": float(np.mean(losses)) if losses else 0.0, + "val_dice": val_dice, + "val_iou": val_iou, + "val_biou": val_biou, + "val_refine_score": val_refine_score, + "val_decoder_dice": val_decoder_dice, + "val_decoder_iou": val_decoder_iou, + "val_decoder_biou": val_decoder_biou, + "val_dice_gain": None if val_decoder_dice is None else val_dice - val_decoder_dice, + "val_iou_gain": None if val_decoder_iou is None else val_iou - val_decoder_iou, + "val_biou_gain": None if val_decoder_biou is None else val_biou - val_decoder_biou, + "val_actor_loss": float(np.mean(actor_losses)) if actor_losses else 0.0, + "val_critic_loss": float(np.mean(critic_losses)) if critic_losses else 0.0, + "val_ce_loss": float(np.mean(ce_losses)) if ce_losses else 0.0, + "val_dice_loss": float(np.mean(dice_losses)) if dice_losses else 0.0, + "val_reward": float(np.mean(rewards)) if rewards else 0.0, + "val_entropy": float(np.mean(entropies)) if entropies else 0.0, + "val_binary_flips_total_pct": float(np.mean(val_binary_flips_total)) if val_binary_flips_total else None, + "val_binary_flips_correct_pct": float(np.mean(val_binary_flips_correct)) if val_binary_flips_correct else None, + "val_binary_flips_wrong_pct": float(np.mean(val_binary_flips_wrong)) if val_binary_flips_wrong else None, + } + +def _save_training_plots(history: list[dict[str, Any]], plots_dir: Path) -> None: + if len(history) < 1: + return + ensure_dir(plots_dir) + epochs = [row["epoch"] for row in history] + plot_specs = [ + ("loss.png", "Loss", [("train_loss", "Train"), ("val_loss", "Val")]), + ("dice.png", "Dice", [("train_dice", "Train"), ("val_dice", "Val")]), + ("iou.png", "IoU", [("train_iou", "Train"), ("val_iou", "Val")]), + ("reward.png", "Reward", [("train_mean_reward", "Train"), ("val_reward", "Val")]), + ("ce_loss.png", "CE Loss", [("train_ce_loss", "Train")]), + ("dice_loss.png", "Dice Loss", [("train_dice_loss", "Train")]), + ("lr.png", "Learning Rate", [("lr", "Head LR"), ("encoder_lr", "Encoder LR")]), + ] + for file_name, title, curves in plot_specs: + fig, ax = plt.subplots(figsize=(8, 4)) + has_data = False + for key, label in curves: + values = [(row["epoch"], row[key]) for row in history if key in row] + if not values: + continue + xs, ys = zip(*values) + ax.plot(xs, ys, label=label, linewidth=1.2) + has_data = True + if has_data: + ax.set_title(title) + ax.set_xlabel("Epoch") + ax.set_ylabel(title) + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(plots_dir / file_name, dpi=110) + plt.close(fig) + +RESUME_IDENTITY_KEYS = ( + "strategy", + "dataset_percent", + "dataset_name", + "dataset_split_policy", + "split_type", + "train_subset_key", + "train_subset_variant", + "best_checkpoint_metric_name", + "backbone_family", + "smp_encoder_name", + "smp_encoder_weights", + "smp_encoder_depth", + "smp_encoder_proj_dim", + "smp_decoder_type", + "vgg_feature_scales", + "vgg_feature_dilation", + "head_lr", + "encoder_lr", + "weight_decay", + "dropout_p", + "tmax", + "entropy_lr", +) + +PORTABLE_RESUME_PATH_KEYS = frozenset( + { + "base_split_manifest_path", + "subset_manifest_path", + } +) + +def _path_parts(value: Any) -> tuple[str, ...]: + if value is None: + return () + return tuple(part for part in Path(str(value)).parts if part not in {"", os.sep}) + +def _portable_path_token(value: Any) -> str: + if value in (None, ""): + return "" + path = Path(str(value)).expanduser() + roots: list[tuple[str, Path]] = [] + experiment_root = globals().get("EXPERIMENT_ROOT") + if experiment_root is not None: + roots.append(("EXPERIMENT_ROOT", Path(experiment_root))) + roots.append(("PROJECT_DIR", PROJECT_DIR)) + for label, root in roots: + try: + rel = path.resolve().relative_to(root.resolve()) + return f"{label}:{rel.as_posix()}" + except (OSError, ValueError): + continue + parts = _path_parts(value) + for marker in ("runs", "repeated_holdout", "manifests", "checkpoints"): + if marker in parts: + return "/".join(parts[parts.index(marker):]) + return "/".join(parts) + +def _resume_path_values_match(current: Any, saved: Any) -> tuple[bool, str]: + current_text = str(current or "") + saved_text = str(saved or "") + if current_text == saved_text: + return True, "exact" + + current_token = _portable_path_token(current_text) + saved_token = _portable_path_token(saved_text) + if current_token and current_token == saved_token: + return True, "portable-token" + + current_parts = _path_parts(current_text) + saved_parts = _path_parts(saved_text) + max_suffix = min(len(current_parts), len(saved_parts)) + for length in range(max_suffix, 2, -1): + if current_parts[-length:] == saved_parts[-length:]: + return True, f"suffix:{length}" + return False, f"current_token={current_token!r}, checkpoint_token={saved_token!r}" + +def _resume_value_matches(current: Any, saved: Any) -> bool: + if isinstance(current, (int, float)) and isinstance(saved, (int, float)) and not isinstance(current, bool): + return math.isclose(float(current), float(saved), rel_tol=1e-9, abs_tol=1e-12) + return current == saved + +def validate_resume_checkpoint_identity( + current_run_config: dict[str, Any], + saved_run_config: dict[str, Any], + *, + checkpoint_path: Path, +) -> None: + mismatches: list[str] = [] + for key in RESUME_IDENTITY_KEYS: + if key not in current_run_config or key not in saved_run_config: + mismatches.append(f"{key}: current={current_run_config.get(key)!r}, checkpoint={saved_run_config.get(key)!r}") + continue + if key in PORTABLE_RESUME_PATH_KEYS: + matches, reason = _resume_path_values_match(current_run_config[key], saved_run_config[key]) + if matches: + if str(current_run_config[key]) != str(saved_run_config[key]): + print( + f"[Resume] Accepted portable path match for {key}: " + f"current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}, reason={reason}." + ) + continue + mismatches.append( + f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r} ({reason})" + ) + continue + if not _resume_value_matches(current_run_config[key], saved_run_config[key]): + mismatches.append(f"{key}: current={current_run_config[key]!r}, checkpoint={saved_run_config[key]!r}") + + current_s2 = current_run_config.get("strategy2_checkpoint_path") + saved_s2 = saved_run_config.get("strategy2_checkpoint_path") + if current_s2 or saved_s2: + if str(current_s2 or "") != str(saved_s2 or ""): + # Warn but do not abort: when resuming, the model weights are fully restored + # from the resume checkpoint (not re-loaded from the strategy2 checkpoint), + # so a path change (e.g. file moved/renamed) does not affect correctness. + print( + f"[WARN] strategy2_checkpoint_path changed since checkpoint was saved " + f"(current={current_s2!r}, checkpoint={saved_s2!r}). " + f"Resuming anyway — model state comes from the resume checkpoint." + ) + + if mismatches: + raise ValueError( + f"Resume checkpoint identity mismatch for {checkpoint_path}:\n" + "\n".join(f" - {line}" for line in mismatches) + ) + +def load_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> list[dict[str, Any]]: + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + epoch_metrics = checkpoint_payload.get("epoch_metrics") + history: list[dict[str, Any]] = [] + checkpoint_history = checkpoint_payload.get("history") + if isinstance(checkpoint_history, list): + history = [dict(row) for row in checkpoint_history if isinstance(row, dict)] + history = [row for row in history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, list): + raise RuntimeError(f"Expected list history at {history_path}, found {type(payload).__name__}.") + file_history = [dict(row) for row in payload if isinstance(row, dict)] + file_history = [row for row in file_history if int(row.get("epoch", 0)) <= checkpoint_epoch] + if len(file_history) >= len(history): + history = file_history + if not history and isinstance(epoch_metrics, dict): + history = [dict(epoch_metrics)] + elif history and isinstance(epoch_metrics, dict): + if int(history[-1].get("epoch", 0)) < checkpoint_epoch: + history.append(dict(epoch_metrics)) + return history + +def train_model( + *, + run_type: str, + model_config: RuntimeModelConfig, + run_config: dict[str, Any], + model: nn.Module, + description: str, + strategy: int, + run_dir: Path, + bundle: DataBundle, + max_epochs: int, + head_lr: float, + encoder_lr: float, + weight_decay: float, + tmax: int, + entropy_lr: float, + entropy_alpha_init: float, + entropy_target_ratio: float, + critic_loss_weight: float, + ce_weight: float, + dice_weight: float, + dropout_p: float, + resume_checkpoint_path: Path | None = None, + trial: optuna.trial.Trial | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=description, + strategy=strategy, + model_config=model_config, + dropout_p=dropout_p, + amp_dtype=amp_dtype, + compiled=hasattr(model, "_orig_mod"), + ) + + strategy3_freeze_status = _strategy3_bootstrap_freeze_status(model) if strategy == 3 else None + strategy3_frozen_decoder = strategy == 3 and _strategy3_decoder_is_frozen(model) + decoder_head_lr = float(_job_param("decoder_lr", 0.0 if strategy3_frozen_decoder else head_lr * 0.1)) + encoder_group_lr = 0.0 if strategy3_frozen_decoder else encoder_lr + rl_group_lr = float(_job_param("rl_lr", head_lr)) + optimizer = make_optimizer( + model, + strategy, + head_lr=decoder_head_lr, + encoder_lr=encoder_group_lr, + weight_decay=weight_decay, + rl_lr=rl_group_lr, + ) + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_epochs, + eta_min=1e-6, # floor + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + + del entropy_alpha_init, entropy_lr, entropy_target_ratio + target_entropy = 0.0 + log_alpha = None + alpha_optimizer = None + + save_artifacts = run_type == "final" + save_history_incrementally = bool(run_config.get("save_history_incrementally", SAVE_HISTORY_INCREMENTALLY)) + write_epoch_diagnostic = bool(run_config.get("write_epoch_diagnostic", WRITE_EPOCH_DIAGNOSTIC)) + ckpt_dir = ensure_dir(run_dir / "checkpoints") if save_artifacts else None + plots_dir = ensure_dir(run_dir / "plots") if save_artifacts else None + history_path = checkpoint_history_path(run_dir, run_type) + diagnostic_path = diagnostic_path_for_run(run_dir) if run_type == "final" and write_epoch_diagnostic else None + history: list[dict[str, Any]] = [] + selection_metric_name = _strategy_selection_metric_name(strategy) + early_stopping_monitor_name = _early_stopping_monitor_name(strategy) + early_stopping_mode = _early_stopping_mode(strategy, early_stopping_monitor_name) + early_stopping_min_delta = _early_stopping_min_delta() + early_stopping_start_epoch = _early_stopping_start_epoch() + early_stopping_patience = _early_stopping_patience() + epoch_probe_mode = str(run_config.get("epoch_probe_mode", "fixed")).strip().lower() + best_model_metric = -float("inf") + patience_counter = 0 + best_early_stopping_metric: float | None = None + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + diagnostic_payload: dict[str, Any] | None = None + train_probe_batches: list[dict[str, Any]] = [] + val_probe_batches: list[dict[str, Any]] = [] + run_label = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ) + if diagnostic_path is not None: + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=0, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=0, + split_tag="val", + ) + else: + train_probe_batches = _fixed_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + ) + val_probe_batches = _fixed_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + ) + diagnostic_payload = empty_epoch_diagnostic_payload( + run_type=run_type, + run_config=run_config, + bundle=bundle, + train_probe_batches=train_probe_batches, + val_probe_batches=val_probe_batches, + ) + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + expected_run_type=run_type, + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + selection_metric_name = str(checkpoint_payload.get("best_metric_name", selection_metric_name)) + best_model_metric = float(checkpoint_payload["best_metric_value"]) + patience_counter = int(checkpoint_payload.get("patience_counter", 0)) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", run_type), + } + epoch_metrics = checkpoint_payload.get("epoch_metrics") + if isinstance(epoch_metrics, dict) and epoch_metrics.get("early_stopping_best_value") is not None: + best_early_stopping_metric = float(epoch_metrics["early_stopping_best_value"]) + if diagnostic_path is not None and diagnostic_payload is not None: + diagnostic_payload = load_epoch_diagnostic_for_resume( + diagnostic_path, + checkpoint_payload, + diagnostic_payload, + ) + print( + f"[Resume] {run_label} | {run_type} continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{max_epochs}." + ) + prev_params = _snapshot_params(model) if diagnostic_path is not None else None + start_time = time.time() + validate_interval = max(int(VALIDATE_EVERY_N_EPOCHS), 1) + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + + for epoch in range(start_epoch, max_epochs + 1): + epoch_losses: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_reward: list[float] = [] + epoch_entropy: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_loss: list[float] = [] + epoch_grad: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_effective_steps: list[float] = [] + epoch_mask_deltas: list[float] = [] + epoch_advantage_means: list[float] = [] + epoch_advantage_stds: list[float] = [] + epoch_value_pred_errors: list[float] = [] + epoch_reward_zero_pcts: list[float] = [] + epoch_biou_deltas: list[float] = [] + epoch_mean_value_preds: list[float] = [] + epoch_annealed_aux_ce_weights: list[float] = [] + epoch_rl_loss_scales: list[float] = [] + epoch_reinforce_losses: list[float] = [] + epoch_entropy_losses: list[float] = [] + epoch_entropy_bonuses_used: list[float] = [] + epoch_alphas: list[float] = [] + epoch_action_dists: list[dict[str, float]] = [] + + prefetcher = CUDAPrefetcher(bundle.train_loader, DEVICE) + progress = tqdm(prefetcher, total=len(bundle.train_loader), desc=f"Epoch {epoch}/{max_epochs}", leave=False) + for batch in progress: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=tmax, + critic_loss_weight=critic_loss_weight, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=ce_weight, + dice_weight=dice_weight, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=max_epochs, + ) + epoch_losses.append(metrics["loss"]) + epoch_actor.append(metrics["actor_loss"]) + epoch_critic.append(metrics["critic_loss"]) + epoch_reward.append(metrics["mean_reward"]) + epoch_entropy.append(metrics["entropy"]) + epoch_ce.append(metrics["ce_loss"]) + epoch_dice_loss.append(metrics["dice_loss"]) + epoch_grad.append(metrics["grad_norm"]) + if "effective_steps" in metrics: + epoch_effective_steps.append(float(metrics["effective_steps"])) + if "mask_delta_mean" in metrics: + epoch_mask_deltas.append(metrics["mask_delta_mean"]) + if "advantage_mean" in metrics: + epoch_advantage_means.append(metrics["advantage_mean"]) + if "advantage_std" in metrics: + epoch_advantage_stds.append(metrics["advantage_std"]) + if "value_pred_error_mean" in metrics: + epoch_value_pred_errors.append(metrics["value_pred_error_mean"]) + if "reward_zeros_pct" in metrics: + epoch_reward_zero_pcts.append(float(metrics["reward_zeros_pct"])) + if "biou_delta_mean" in metrics: + epoch_biou_deltas.append(float(metrics["biou_delta_mean"])) + if "mean_value_pred" in metrics: + epoch_mean_value_preds.append(float(metrics["mean_value_pred"])) + if "annealed_aux_ce_weight" in metrics: + epoch_annealed_aux_ce_weights.append(float(metrics["annealed_aux_ce_weight"])) + if "rl_loss_scale_used" in metrics: + epoch_rl_loss_scales.append(float(metrics["rl_loss_scale_used"])) + if "reinforce_loss" in metrics: + epoch_reinforce_losses.append(float(metrics["reinforce_loss"])) + if "entropy_loss" in metrics: + epoch_entropy_losses.append(float(metrics["entropy_loss"])) + if "entropy_bonus_used" in metrics: + epoch_entropy_bonuses_used.append(float(metrics["entropy_bonus_used"])) + if "alpha" in metrics: + epoch_alphas.append(metrics["alpha"]) + if "action_distribution" in metrics and metrics["action_distribution"]: + epoch_action_dists.append(metrics["action_distribution"]) + + pred_mask = metrics["final_mask"] + gt_mask = batch["mask"].float() + inter = (pred_mask * gt_mask).sum(dim=(1, 2, 3)) + pred_sum = pred_mask.sum(dim=(1, 2, 3)) + gt_sum = gt_mask.sum(dim=(1, 2, 3)) + dice = (2.0 * inter + _EPS) / (pred_sum + gt_sum + _EPS) + iou = (inter + _EPS) / (pred_sum + gt_sum - inter + _EPS) + epoch_dices.extend(dice.detach().cpu().tolist()) + epoch_ious.extend(iou.detach().cpu().tolist()) + + head_lr_now = float(optimizer.param_groups[-1]["lr"]) + enc_lr_now = float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else head_lr_now + progress.set_postfix(loss=f"{metrics['loss']:.4f}", iou=f"{np.mean(epoch_ious):.4f}", lr=f"{head_lr_now:.2e}") + + should_validate = epoch % validate_interval == 0 or epoch == max_epochs + val_metrics: dict[str, float | None] = { + "val_loss": None, + "val_dice": None, + "val_iou": None, + "val_biou": None, + "val_decoder_dice": None, + "val_decoder_iou": None, + "val_decoder_biou": None, + "val_dice_gain": None, + "val_iou_gain": None, + "val_biou_gain": None, + "val_reward": None, + "val_entropy": None, + } + if should_validate: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: running validation on {len(bundle.val_loader)} batches..." + ) + validated_metrics = validate( + model, + bundle.val_loader, + run_dir=run_dir, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + gamma=DEFAULT_GAMMA, + critic_loss_weight=critic_loss_weight, + ce_weight=ce_weight, + dice_weight=dice_weight, + ) + val_metrics.update(validated_metrics) + scheduler.step() # always step up the scheduler + grad_stats: dict[str, Any] = {} + param_stats: dict[str, Any] = {} + if diagnostic_path is not None: + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_epoch_action_dist: dict[str, float] = {} + if epoch_action_dists: + all_act_keys = set() + for d in epoch_action_dists: + all_act_keys.update(d.keys()) + for k in sorted(all_act_keys): + avg_epoch_action_dist[k] = float(np.mean([d.get(k, 0.0) for d in epoch_action_dists])) + + row = { + "epoch": epoch, + "train_loss": float(np.mean(epoch_losses)) if epoch_losses else 0.0, + "train_actor_loss": float(np.mean(epoch_actor)) if epoch_actor else 0.0, + "train_critic_loss": float(np.mean(epoch_critic)) if epoch_critic else 0.0, + "train_mean_reward": float(np.mean(epoch_reward)) if epoch_reward else 0.0, + "train_entropy": float(np.mean(epoch_entropy)) if epoch_entropy else 0.0, + "train_ce_loss": float(np.mean(epoch_ce)) if epoch_ce else 0.0, + "train_dice_loss": float(np.mean(epoch_dice_loss)) if epoch_dice_loss else 0.0, + "train_dice": float(np.mean(epoch_dices)) if epoch_dices else 0.0, + "train_iou": float(np.mean(epoch_ious)) if epoch_ious else 0.0, + "grad_norm": float(np.mean(epoch_grad)) if epoch_grad else 0.0, + "lr": float(optimizer.param_groups[-1]["lr"]), + "encoder_lr": float(optimizer.param_groups[0]["lr"]) if len(optimizer.param_groups) > 1 else float(optimizer.param_groups[-1]["lr"]), + "alpha": float(log_alpha.exp().detach().item()) if log_alpha is not None else None, + "train_effective_steps": float(np.mean(epoch_effective_steps)) if epoch_effective_steps else 0.0, + "train_mask_delta_mean": float(np.mean(epoch_mask_deltas)) if epoch_mask_deltas else 0.0, + "train_advantage_mean": float(np.mean(epoch_advantage_means)) if epoch_advantage_means else 0.0, + "train_advantage_std": _nanmean_or_default(epoch_advantage_stds, 0.0), + "train_value_pred_error": float(np.mean(epoch_value_pred_errors)) if epoch_value_pred_errors else 0.0, + "train_reward_zeros_pct": float(np.mean(epoch_reward_zero_pcts)) if epoch_reward_zero_pcts else 0.0, + "train_biou_delta_mean": float(np.mean(epoch_biou_deltas)) if epoch_biou_deltas else 0.0, + "train_mean_value_pred": float(np.mean(epoch_mean_value_preds)) if epoch_mean_value_preds else 0.0, + "train_annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "train_rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "train_reinforce_loss": float(np.mean(epoch_reinforce_losses)) if epoch_reinforce_losses else 0.0, + "train_entropy_loss": float(np.mean(epoch_entropy_losses)) if epoch_entropy_losses else 0.0, + "train_entropy_bonus_used": float(np.mean(epoch_entropy_bonuses_used)) if epoch_entropy_bonuses_used else 0.0, + "train_alpha": float(np.mean(epoch_alphas)) if epoch_alphas else 0.0, + "train_action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + "validated_this_epoch": should_validate, + **val_metrics, + } + if strategy == 3: + if row["train_advantage_std"] < 0.005: + low_advantage_std_streak += 1 + else: + low_advantage_std_streak = 0 + if abs(row["train_mean_value_pred"]) < 0.001: + low_mean_value_pred_streak += 1 + else: + low_mean_value_pred_streak = 0 + else: + low_advantage_std_streak = 0 + low_mean_value_pred_streak = 0 + if strategy3_freeze_status is not None: + row["strategy3_bootstrap_loaded"] = bool(strategy3_freeze_status["bootstrap_loaded"]) + row["strategy3_freeze_requested"] = bool(strategy3_freeze_status["freeze_requested"]) + row["strategy3_freeze_active"] = bool(strategy3_freeze_status["freeze_active"]) + row["strategy3_encoder_state"] = str(strategy3_freeze_status["encoder_state"]) + row["strategy3_decoder_state"] = str(strategy3_freeze_status["decoder_state"]) + row["strategy3_segmentation_head_state"] = str(strategy3_freeze_status["segmentation_head_state"]) + history.append(row) + + improved = False + early_stopping_improved_now = False + if should_validate: + selected_metric_value = _strategy_selection_metric_value(strategy, val_metrics) + row["selection_metric_name"] = selection_metric_name + row["selection_metric_value"] = selected_metric_value + improved = selected_metric_value > best_model_metric + if improved: + best_model_metric = selected_metric_value + if trial is not None: + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_ATTR, float(best_model_metric)) + trial.set_user_attr(OPTUNA_BEST_OBSERVED_OBJECTIVE_NAME_ATTR, selection_metric_name) + early_stopping_monitor_value = _early_stopping_monitor_value( + row, + strategy=strategy, + monitor_name=early_stopping_monitor_name, + ) + early_stopping_active = epoch >= early_stopping_start_epoch + if early_stopping_active and early_stopping_monitor_value is not None: + early_stopping_improved_now = _early_stopping_improved( + early_stopping_monitor_value, + best_early_stopping_metric, + mode=early_stopping_mode, + min_delta=early_stopping_min_delta, + ) + if early_stopping_improved_now: + best_early_stopping_metric = early_stopping_monitor_value + patience_counter = 0 + else: + patience_counter += 1 + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = early_stopping_monitor_value + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = early_stopping_active + row["early_stopping_improved"] = early_stopping_improved_now + row["early_stopping_wait"] = int(patience_counter) + if improved and save_artifacts and ckpt_dir is not None: + save_checkpoint( + ckpt_dir / "best.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + else: + row["early_stopping_monitor_name"] = early_stopping_monitor_name + row["early_stopping_monitor_mode"] = early_stopping_mode + row["early_stopping_monitor_value"] = None + row["early_stopping_best_value"] = best_early_stopping_metric + row["early_stopping_min_delta"] = early_stopping_min_delta + row["early_stopping_start_epoch"] = early_stopping_start_epoch + row["early_stopping_patience"] = early_stopping_patience + row["early_stopping_active"] = False + row["early_stopping_improved"] = False + row["early_stopping_wait"] = int(patience_counter) + + if save_artifacts and save_history_incrementally: + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + save_json(history_path, history) + + if diagnostic_path is not None and diagnostic_payload is not None: + epoch_alerts = _numerical_health_check(row, prefix=f"epoch[{epoch}]:") + if low_advantage_std_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] advantage_std collapsed - RL gradient near zero") + if low_mean_value_pred_streak >= 5: + epoch_alerts.append(f"epoch[{epoch}]: [WARN] critic degenerate - mean value prediction stuck near zero") + if int(grad_stats.get("n_nan", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected NaN gradients") + if int(grad_stats.get("n_inf", 0)) > 0: + epoch_alerts.append(f"epoch[{epoch}]: grad diagnostics detected Inf gradients") + + if epoch_probe_mode == "rolling_random": + train_probe_batches = _rolling_probe_batches_from_dataset( + bundle.train_ds, + num_batches=EPOCH_DIAGNOSTIC_TRAIN_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="train", + ) + val_probe_batches = _rolling_probe_batches_from_dataset( + bundle.val_ds, + num_batches=EPOCH_DIAGNOSTIC_VAL_BATCHES, + device=DEVICE, + epoch=epoch, + split_tag="val", + ) + + train_probe = _evaluate_probe_batches( + model, + train_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="train", + mc_cache_run_dir=run_dir, + ) + val_probe = _evaluate_probe_batches( + model, + val_probe_batches, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + mc_cache_split="val", + mc_cache_run_dir=run_dir, + ) + epoch_alerts.extend(train_probe.get("alerts", [])) + epoch_alerts.extend(val_probe.get("alerts", [])) + + diagnostic_payload["epochs"].append( + { + "epoch": epoch, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "is_new_best": bool(improved), + "best_metric_name": selection_metric_name, + "best_metric_value_so_far": float(best_model_metric), + "patience_counter": int(patience_counter), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value_so_far": best_early_stopping_metric, + "history_row": dict(row), + "train_batch_summary": { + "loss": _summary_stats(epoch_losses), + "actor_loss": _summary_stats(epoch_actor), + "critic_loss": _summary_stats(epoch_critic), + "reward": _summary_stats(epoch_reward), + "entropy": _summary_stats(epoch_entropy), + "ce_loss": _summary_stats(epoch_ce), + "dice_loss": _summary_stats(epoch_dice_loss), + "grad_norm": _summary_stats(epoch_grad), + "dice": _summary_stats(epoch_dices), + "iou": _summary_stats(epoch_ious), + "effective_steps": _summary_stats(epoch_effective_steps), + "mask_delta": _summary_stats(epoch_mask_deltas), + "advantage_mean": _summary_stats(epoch_advantage_means), + "advantage_std": _summary_stats(epoch_advantage_stds), + "value_pred_error": _summary_stats(epoch_value_pred_errors), + "reward_zeros_pct": _summary_stats(epoch_reward_zero_pcts), + "biou_delta_mean": _summary_stats(epoch_biou_deltas), + "mean_value_pred": _summary_stats(epoch_mean_value_preds), + "annealed_aux_ce_weight": float(np.mean(epoch_annealed_aux_ce_weights)) if epoch_annealed_aux_ce_weights else 0.0, + "rl_loss_scale": float(np.mean(epoch_rl_loss_scales)) if epoch_rl_loss_scales else 0.0, + "reinforce_loss": _summary_stats(epoch_reinforce_losses), + "entropy_loss": _summary_stats(epoch_entropy_losses), + "entropy_bonus_used": _summary_stats(epoch_entropy_bonuses_used), + "alpha": _summary_stats(epoch_alphas), + "action_distribution": avg_epoch_action_dist if avg_epoch_action_dist else None, + }, + "optimizer": { + "param_groups": _optimizer_diagnostics(optimizer), + "scheduler_last_lr": [float(value) for value in scheduler.get_last_lr()], + "target_entropy": float(target_entropy) if log_alpha is not None else 0.0, + }, + "grad_diagnostics": grad_stats, + "param_diagnostics": param_stats, + "probe_batches": { + "mode": epoch_probe_mode, + "train": _probe_batch_id_lists(train_probe_batches), + "val": _probe_batch_id_lists(val_probe_batches), + }, + "probes": { + "train_fixed": train_probe, + "val_fixed": val_probe, + }, + "probe_epoch_summary": { + "train_fixed": _format_probe_deterioration("train", train_probe, tmax), + "val_fixed": _format_probe_deterioration("val", val_probe, tmax), + }, + "alerts": epoch_alerts, + } + ) + save_json(diagnostic_path, diagnostic_payload) + + if save_artifacts and ckpt_dir is not None and SAVE_LATEST_EVERY_EPOCH and run_type != "trial": + save_checkpoint( + ckpt_dir / "latest.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + if save_artifacts and ckpt_dir is not None and CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0 and run_type != "trial": + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type=run_type, + model=model, + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + epoch=epoch, + best_metric_value=best_model_metric, + best_metric_name=selection_metric_name, + run_config=run_config, + epoch_metrics=row, + patience_counter=patience_counter, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + history=history, + log_alpha=log_alpha if strategy == 3 else None, + alpha_optimizer=alpha_optimizer if strategy == 3 else None, + resume_source=resume_source, + ) + + if trial is not None and should_validate: + reported_metric = row.get("selection_metric_value") + if reported_metric is None: + reported_metric = _strategy_selection_metric_value(strategy, val_metrics) + if reported_metric is None: + reported_metric = float(val_metrics["val_iou"]) + reported_metric = float(reported_metric) + trial.report(reported_metric, step=epoch) + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot( + trial, + current_best_value=best_model_metric, + ) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + if USE_TRIAL_PRUNING and epoch >= TRIAL_PRUNER_WARMUP_STEPS and trial.should_prune(): + raise optuna.TrialPruned( + f"Trial pruned at epoch {epoch} with " + f"{selection_metric_name}={reported_metric:.4f}" + ) + + if trial is not None and row.get("study_best_objective") is None: + study_best_value, study_best_trial_number = _current_optuna_study_best_snapshot(trial) + row["study_best_objective"] = study_best_value + row["study_best_trial"] = study_best_trial_number + + if VERBOSE_EPOCH_LOG: + tqdm.write(f"[{run_label}] Epoch {epoch}/{max_epochs}") + tqdm.write(json.dumps(row, indent=2)) + else: + tqdm.write( + f"[{run_label}] Epoch {epoch}/{max_epochs}: " + f"{format_concise_epoch_log(row, best_metric_name=selection_metric_name, best_metric_value=best_model_metric)}" + ) + + if should_validate and early_stopping_patience > 0 and patience_counter >= early_stopping_patience: + print( + f"Early stopping triggered at epoch {epoch}: " + f"monitor={early_stopping_monitor_name} mode={early_stopping_mode} " + f"best={best_early_stopping_metric} current={row.get('early_stopping_monitor_value')} " + f"min_delta={early_stopping_min_delta:.6g} wait={patience_counter}/{early_stopping_patience}." + ) + break + + elapsed = elapsed_before_resume + (time.time() - start_time) + if save_artifacts: + save_json(history_path, history) + if plots_dir is not None and run_type != "trial": + _save_training_plots(history, plots_dir) + summary = { + "best_model_metric_name": selection_metric_name, + "best_model_metric": float(best_model_metric), + "early_stopping_monitor_name": early_stopping_monitor_name, + "early_stopping_monitor_mode": early_stopping_mode, + "early_stopping_min_delta": float(early_stopping_min_delta), + "early_stopping_start_epoch": int(early_stopping_start_epoch), + "early_stopping_patience": int(early_stopping_patience), + "early_stopping_best_value": best_early_stopping_metric, + "best_val_iou": max((float(r["val_iou"]) for r in history if r.get("val_iou") is not None), default=0.0), + "best_val_dice": max((float(r["val_dice"]) for r in history if r.get("val_dice") is not None), default=0.0), + "best_val_biou": max((float(r["val_biou"]) for r in history if r.get("val_biou") is not None), default=0.0), + "best_val_iou_gain": max((float(r["val_iou_gain"]) for r in history if r.get("val_iou_gain") is not None), default=0.0), + "best_val_biou_gain": max((float(r["val_biou_gain"]) for r in history if r.get("val_biou_gain") is not None), default=0.0), + "final_epoch": int(history[-1]["epoch"]) if history else int(start_epoch - 1), + "elapsed_seconds": elapsed, + "seconds_per_epoch": elapsed / max(len(history), 1), + "device_used": str(DEVICE), + "strategy": strategy, + "run_type": run_type, + "resumed": resume_source is not None, + } + if strategy3_freeze_status is not None: + summary.update( + { + "strategy3_bootstrap_loaded": bool(strategy3_freeze_status["bootstrap_loaded"]), + "strategy3_freeze_requested": bool(strategy3_freeze_status["freeze_requested"]), + "strategy3_freeze_active": bool(strategy3_freeze_status["freeze_active"]), + "strategy3_encoder_state": str(strategy3_freeze_status["encoder_state"]), + "strategy3_decoder_state": str(strategy3_freeze_status["decoder_state"]), + "strategy3_segmentation_head_state": str(strategy3_freeze_status["segmentation_head_state"]), + } + ) + if resume_source is not None: + summary["resume_source"] = resume_source + if save_artifacts: + save_json(run_dir / "summary.json", summary) + return summary, history + +"""============================================================================= +EVALUATION + SMOKE TEST +============================================================================= +""" + +def _save_rgb_panel(image_chw: np.ndarray, pred_hw: np.ndarray, gt_hw: np.ndarray, output_path: Path, title: str) -> None: + img = image_chw.transpose(1, 2, 0) + img = (img - img.min()) / (img.max() - img.min() + 1e-8) + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + axes[0].imshow(img) + axes[0].set_title("Input") + axes[1].imshow(pred_hw, cmap="gray", vmin=0, vmax=1) + axes[1].set_title("Prediction") + axes[2].imshow(gt_hw, cmap="gray", vmin=0, vmax=1) + axes[2].set_title("Ground Truth") + for ax in axes: + ax.axis("off") + fig.suptitle(title) + fig.tight_layout() + fig.savefig(output_path, dpi=120) + plt.close(fig) + + +def _synchronize_device_for_timing(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize(device) + + +def _write_evaluation_timing_csv( + path: Path, + *, + timing_summary: dict[str, Any], +) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "scope", + "strategy", + "tmax", + "device", + "num_batches", + "num_samples", + "total_inference_ms", + "avg_batch_inference_ms", + "std_batch_inference_ms", + "avg_sample_inference_ms", + "std_sample_inference_ms", + "mean_per_image_inference_ms", + "std_per_image_inference_ms", + "mean_per_image_inference_seconds", + "std_per_image_inference_seconds", + ], + ) + writer.writeheader() + writer.writerow( + { + "scope": str(timing_summary["scope"]), + "strategy": int(timing_summary["strategy"]), + "tmax": int(timing_summary["tmax"]), + "device": str(timing_summary["device"]), + "num_batches": int(timing_summary["num_batches"]), + "num_samples": int(timing_summary["num_samples"]), + "total_inference_ms": f"{float(timing_summary['total_inference_ms']):.6f}", + "avg_batch_inference_ms": f"{float(timing_summary['avg_batch_inference_ms']):.6f}", + "std_batch_inference_ms": f"{float(timing_summary['std_batch_inference_ms']):.6f}", + "avg_sample_inference_ms": f"{float(timing_summary['avg_sample_inference_ms']):.6f}", + "std_sample_inference_ms": f"{float(timing_summary['std_sample_inference_ms']):.6f}", + "mean_per_image_inference_ms": f"{float(timing_summary['mean_per_image_inference_ms']):.6f}", + "std_per_image_inference_ms": f"{float(timing_summary['std_per_image_inference_ms']):.6f}", + "mean_per_image_inference_seconds": f"{float(timing_summary['mean_per_image_inference_seconds']):.9f}", + "std_per_image_inference_seconds": f"{float(timing_summary['std_per_image_inference_seconds']):.9f}", + } + ) + + +def evaluate_model( + *, + model: nn.Module, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + strategy: int, + tmax: int, + best_metric_name: str, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + pred_dir = ensure_dir(run_dir / "predictions") + pred_255_dir = ensure_dir(run_dir / "predictions_255") + + model.eval() + per_metric = {k: [] for k in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95")} + per_sample: list[dict[str, Any]] = [] + inference_total_ms = 0.0 + inference_batch_count = 0 + inference_sample_count = 0 + inference_batch_times_ms: list[float] = [] + inference_sample_times_ms: list[float] = [] + track_strategy3_mc_cache = bool(strategy == 3 and _uses_refinement_runtime(model, strategy=strategy)) + if track_strategy3_mc_cache: + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + + with torch.inference_mode(): + prefetcher = CUDAPrefetcher(bundle.test_loader, DEVICE) + try: + for batch in tqdm(prefetcher, total=len(bundle.test_loader), desc="Evaluating", leave=False): + image = batch["image"] + gt = batch["mask"] + sample_ids = [str(item) for item in batch["sample_id"]] + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + _synchronize_device_for_timing(DEVICE) + inference_start = time.perf_counter() + pred = infer_segmentation_mask( + model, + image, + tmax, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split="test", + mc_cache_run_dir=run_dir, + ).float() + _synchronize_device_for_timing(DEVICE) + inference_elapsed_ms = (time.perf_counter() - inference_start) * 1000.0 + inference_total_ms += inference_elapsed_ms + inference_batch_count += 1 + batch_size = int(pred.shape[0]) + inference_sample_count += batch_size + inference_batch_times_ms.append(float(inference_elapsed_ms)) + per_image_inference_ms = float(inference_elapsed_ms) / float(max(batch_size, 1)) + inference_sample_times_ms.extend([per_image_inference_ms] * batch_size) + pred_np = pred.cpu().numpy().astype(np.uint8) + gt_np = gt.cpu().numpy().astype(np.uint8) + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + for key, value in metrics.items(): + per_metric.setdefault(key, []).append(value) + per_sample.append( + { + "sample_id": sample_ids[idx], + **metrics, + "inference_time_ms": per_image_inference_ms, + "inference_time_seconds": per_image_inference_ms / 1000.0, + } + ) + mask_2d = pred_np[idx].squeeze() + PILImage.fromarray(mask_2d).save(pred_dir / f"{sample_ids[idx]}.png") + PILImage.fromarray((mask_2d * 255).astype(np.uint8)).save(pred_255_dir / f"{sample_ids[idx]}.png") + finally: + prefetcher.close() + del prefetcher + if track_strategy3_mc_cache: + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate: dict[str, dict[str, float]] = {} + for key, values in per_metric.items(): + values_np = np.array(values, dtype=np.float32) + aggregate[key] = {"mean": float(values_np.mean()), "std": float(values_np.std())} + batch_times_np = np.array(inference_batch_times_ms, dtype=np.float64) + sample_times_np = np.array(inference_sample_times_ms, dtype=np.float64) + timing_summary = { + "scope": "test_set_evaluation", + "strategy": int(strategy), + "tmax": int(tmax), + "device": str(DEVICE), + "num_batches": int(inference_batch_count), + "num_samples": int(inference_sample_count), + "total_inference_ms": float(inference_total_ms), + "avg_batch_inference_ms": float(batch_times_np.mean()) if batch_times_np.size > 0 else 0.0, + "std_batch_inference_ms": float(batch_times_np.std()) if batch_times_np.size > 0 else 0.0, + "avg_sample_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_sample_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_ms": float(sample_times_np.mean()) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_ms": float(sample_times_np.std()) if sample_times_np.size > 0 else 0.0, + "mean_per_image_inference_seconds": float(sample_times_np.mean() / 1000.0) if sample_times_np.size > 0 else 0.0, + "std_per_image_inference_seconds": float(sample_times_np.std() / 1000.0) if sample_times_np.size > 0 else 0.0, + } + + save_json( + run_dir / "evaluation.json", + { + "strategy": strategy, + "best_metric_name": str(best_metric_name), + "metrics": aggregate, + "per_sample": per_sample, + "timing": timing_summary, + }, + ) + + df_all = pd.DataFrame(per_sample) + avg_row = {} + for column in df_all.columns: + avg_row[column] = df_all[column].mean() if pd.api.types.is_numeric_dtype(df_all[column]) else "AVERAGE" + df_samples = pd.concat([df_all, pd.DataFrame([avg_row])], ignore_index=True) + df_summary = pd.DataFrame(aggregate).T + df_summary.index.name = "metric" + df_low_iou = df_all[df_all["iou"] < 0.01] + history_path = run_dir / "history.json" + df_history = pd.DataFrame(load_json(history_path)) if history_path.exists() else None + + xlsx_path = run_dir / "evaluation_results.xlsx" + with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer: + df_samples.to_excel(writer, sheet_name="Per Sample", index=False) + df_summary.to_excel(writer, sheet_name="Summary") + if not df_low_iou.empty: + df_low_iou.to_excel(writer, sheet_name="Low IoU Samples", index=False) + if df_history is not None: + df_history.to_excel(writer, sheet_name="Training History", index=False) + + csv_rows = [{"sample_id": row["sample_id"]} for row in df_low_iou.to_dict(orient="records")] + save_json(run_dir / "evaluation_summary.json", {"mean_iou": aggregate["iou"]["mean"], "mean_dice": aggregate["dice"]["mean"]}) + pd.DataFrame(csv_rows).to_csv(run_dir / "low_iou_samples.csv", index=False) + _write_evaluation_timing_csv( + run_dir / "timing.csv", + timing_summary=timing_summary, + ) + return aggregate, per_sample + +def percent_root(percent: float) -> Path: + return ensure_dir(RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}") + +def strategy_dir_name(strategy: int, model_config: RuntimeModelConfig | None = None) -> str: + model_config = (model_config or current_model_config()).validate() + if model_config.backbone_family == "custom_vgg": + return f"strategy_{strategy}_custom_vgg" + return f"strategy_{strategy}" + +def strategy_root_for_percent( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(percent_root(percent) / strategy_dir_name(strategy, model_config)) + +def final_root_for_strategy( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + return ensure_dir(strategy_root_for_percent(strategy, percent, model_config) / "final") + +def ensure_specific_checkpoint_scope(selector_name: str, selector_mode: str) -> None: + if selector_mode != "specific": + return + # Allow STRATEGY2 checkpoints with dict mapping for dynamic per-percent selection + if "strategy2" in selector_name.lower() and isinstance(STRATEGY2_SPECIFIC_CHECKPOINT, dict): + return + if len(STRATEGIES) != 1 or len(DATASET_PERCENTS) != 1: + raise ValueError( + f"{selector_name}=specific is only supported when exactly one strategy and one dataset percent are selected. " + f"Got STRATEGIES={STRATEGIES} and DATASET_PERCENTS={DATASET_PERCENTS}." + ) + +def resolve_checkpoint_path( + *, + run_dir: Path, + selector_mode: str, + specific_checkpoint: str | Path | dict, + purpose: str, +) -> Path: + run_dir = Path(run_dir) + if selector_mode == "latest": + checkpoint_path = run_dir / "checkpoints" / "latest.pt" + elif selector_mode == "best": + checkpoint_path = run_dir / "checkpoints" / "best.pt" + elif selector_mode == "specific": + ensure_specific_checkpoint_scope(purpose, selector_mode) + if not specific_checkpoint: + raise ValueError(f"{purpose}=specific requires a non-empty specific checkpoint path.") + checkpoint_path = Path(specific_checkpoint).expanduser().resolve() + else: + raise ValueError(f"Unsupported checkpoint selector mode '{selector_mode}' for {purpose}.") + + if not checkpoint_path.exists(): + raise FileNotFoundError(f"Checkpoint for {purpose} not found: {checkpoint_path}") + return checkpoint_path + +def resolve_train_resume_checkpoint_path(run_dir: Path) -> Path | None: + if TRAIN_RESUME_MODE == "off": + return None + return resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=TRAIN_RESUME_MODE, + specific_checkpoint=TRAIN_RESUME_SPECIFIC_CHECKPOINT, + purpose="train_resume_checkpoint", + ) + +def resolve_strategy2_checkpoint_path( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> Path: + if strategy != 3: + raise ValueError(f"Strategy 2 dependency checkpoint requested for unsupported strategy {strategy}.") + + specific_checkpoint = STRATEGY2_SPECIFIC_CHECKPOINT + if isinstance(specific_checkpoint, dict): + ctx = globals().get("CURRENT_FOLD_CONTEXT") + _phase_mode_fn = globals().get("using_fixed_phase_mode") + in_phase_mode = callable(_phase_mode_fn) and _phase_mode_fn() + if in_phase_mode and ctx is not None: + # Phase mode: key by phase index (split_repeat_index) + specific_checkpoint = specific_checkpoint.get(ctx.split_repeat_index, "") + else: + # Non-phase mode: key by dataset percent (float) + specific_checkpoint = specific_checkpoint.get(percent, "") + + checkpoint_path = resolve_checkpoint_path( + run_dir=final_root_for_strategy(2, percent, model_config), + selector_mode=STRATEGY2_CHECKPOINT_MODE, + specific_checkpoint=specific_checkpoint, + purpose="strategy2_checkpoint", + ) + + # Print which checkpoint is being used + checkpoint_label = run_identity_label(strategy=strategy, percent=percent) + ctx = globals().get("CURRENT_FOLD_CONTEXT") + if ctx is not None and abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12: + checkpoint_label = run_identity_label( + strategy=strategy, + percent=percent, + split_payload={ + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "dataset_percent": ctx.percent_fraction, + }, + ) + print(f"[Strategy 2 Checkpoint] {checkpoint_label} | Loading: {checkpoint_path}") + + return checkpoint_path + +def load_required_hparams(payload: dict[str, Any], *, source: str, strategy: int, percent: float) -> dict[str, Any]: + missing_keys = [name for name in REQUIRED_HPARAM_KEYS if name not in payload] + if missing_keys: + raise KeyError( + f"Incomplete hyperparameters for strategy={strategy}, percent={percent_text(percent)} from {source}. " + f"Missing keys: {missing_keys}. Required keys: {REQUIRED_HPARAM_KEYS}." + ) + return dict(payload) + +def load_saved_best_params_if_optuna_off( + strategy: int, + percent: float, + *, + model_config: RuntimeModelConfig, +) -> dict[str, Any]: + _, study_root, _ = study_paths_for(strategy, percent, model_config) + best_params_path = study_root / "best_params.json" + if not best_params_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=True, but no saved best params were found " + f"for strategy={strategy}, percent={percent_text(percent)} at {best_params_path}." + ) + params = load_json(best_params_path) + params = load_required_hparams( + params, + source=str(best_params_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using saved best parameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {best_params_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def load_manual_hparams_if_optuna_off(strategy: int, percent: float) -> dict[str, Any]: + key = manual_hparams_key(strategy, percent) + if key not in MANUAL_HPARAMS_IF_OPTUNA_OFF: + raise KeyError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but no manual hyperparameter " + f"JSON filename was found for strategy={strategy}, percent={percent_text(percent)} under key '{key}'. " + f"Required keys: {REQUIRED_HPARAM_KEYS}." + ) + manual_filename = MANUAL_HPARAMS_IF_OPTUNA_OFF[key] + manual_path = (HARD_CODED_PARAM_DIR / manual_filename).resolve() + if not manual_path.exists(): + raise FileNotFoundError( + f"RUN_OPTUNA=False and USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF=False, but the manual hyperparameter " + f"JSON file for strategy={strategy}, percent={percent_text(percent)} was not found at {manual_path}. " + f"Configured key='{key}', filename='{manual_filename}'." + ) + params = load_json(manual_path) + params = load_required_hparams( + params, + source=str(manual_path), + strategy=strategy, + percent=percent, + ) + print( + f"[Optuna Off] Using manual hyperparameters for strategy={strategy}, " + f"percent={percent_text(percent)} from {manual_path}." + ) + for name in REQUIRED_HPARAM_KEYS: + print(f" {name:20s}: {_format_hparam_value(name, params[name])}") + return params + +def resolve_job_params( + strategy: int, + percent: float, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if RUN_OPTUNA: + banner( + f"OPTUNA STUDY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return run_study( + strategy, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + if USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + return load_saved_best_params_if_optuna_off(strategy, percent, model_config=model_config) + + return load_manual_hparams_if_optuna_off(strategy, percent) + +def read_run_config_for_eval(run_dir: Path, checkpoint_path: Path) -> dict[str, Any]: + run_config_path = Path(run_dir) / "run_config.json" + if run_config_path.exists(): + return load_json(run_config_path) + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + return checkpoint_run_config_payload(ckpt) + +def run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> tuple[dict[str, dict[str, float]], list[dict[str, Any]]]: + strategy = _require_supported_strategy(strategy) + checkpoint_path = resolve_checkpoint_path( + run_dir=run_dir, + selector_mode=EVAL_CHECKPOINT_MODE, + specific_checkpoint=EVAL_SPECIFIC_CHECKPOINT, + purpose="evaluation_checkpoint", + ) + effective_run_dir = Path(run_dir) + if not (effective_run_dir / "run_config.json").exists() and checkpoint_path.parent.name == "checkpoints": + effective_run_dir = checkpoint_path.parent.parent + print( + f"[Evaluation] {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)} " + f"| checkpoint={checkpoint_path}" + ) + runtime_config = read_run_config_for_eval(effective_run_dir, checkpoint_path) + set_current_job_params(runtime_config) + model_config = RuntimeModelConfig.from_payload(runtime_config).validate() + if strategy == 3 and runtime_config.get("strategy2_checkpoint_path"): + strategy2_checkpoint_path = runtime_config["strategy2_checkpoint_path"] + elif strategy == 3: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + dropout_p = float(runtime_config.get("dropout_p", DEFAULT_DROPOUT_P)) + tmax = int(runtime_config.get("tmax", DEFAULT_TMAX)) + eval_model, _description, _compiled = build_model( + strategy, + dropout_p, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + checkpoint_payload = load_checkpoint( + checkpoint_path, + model=eval_model, + device=DEVICE, + ) + best_metric_name = str( + checkpoint_payload.get("best_metric_name") + or runtime_config.get("best_checkpoint_metric_name") + or _strategy_selection_metric_name(strategy) + ) + aggregate, per_sample = evaluate_model( + model=eval_model, + model_config=model_config, + bundle=bundle, + run_dir=effective_run_dir, + strategy=strategy, + tmax=tmax, + best_metric_name=best_metric_name, + ) + evaluation_json_path = effective_run_dir / "evaluation.json" + evaluation_payload = load_json(evaluation_json_path) + evaluation_payload["checkpoint_mode"] = EVAL_CHECKPOINT_MODE + evaluation_payload["checkpoint_path"] = str(checkpoint_path) + evaluation_payload["best_metric_name"] = best_metric_name + if checkpoint_payload.get("best_metric_value") is not None: + evaluation_payload["best_metric_value"] = float(checkpoint_payload["best_metric_value"]) + save_json(evaluation_json_path, evaluation_payload) + del eval_model + run_cuda_cleanup( + context=f"evaluation {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + return aggregate, per_sample + +def run_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + smoke_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + banner( + f"PRE-TRAINING SMOKE TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + smoke_root = ensure_dir(smoke_root) + if RUN_OPTUNA: + set_current_job_params() + elif USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF: + set_current_job_params( + load_saved_best_params_if_optuna_off(strategy, bundle.percent, model_config=model_config) + ) + else: + set_current_job_params(load_manual_hparams_if_optuna_off(strategy, bundle.percent)) + sample = bundle.test_ds[SMOKE_TEST_SAMPLE_INDEX] + image = sample["image"].unsqueeze(0).to(DEVICE) + raw_image = sample["image"].numpy() + raw_gt = sample["mask"].squeeze(0).numpy() + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + print_model_parameter_summary( + model=model, + description=f"{description} | Smoke Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + pred = infer_segmentation_mask( + model, + image, + DEFAULT_TMAX, + strategy=strategy, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=_use_channels_last_for_run(model_config), + sample_ids=[str(sample["sample_id"])], + mc_cache_split="test", + mc_cache_run_dir=smoke_root, + ).float() + pred_np = pred[0, 0].detach().cpu().numpy() + panel_path = smoke_root / "smoke_panel.png" + raw_mask_path = smoke_root / "smoke_prediction.png" + _save_rgb_panel( + raw_image, + pred_np, + raw_gt, + panel_path, + f"Smoke Test | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}", + ) + PILImage.fromarray((pred_np * 255).astype(np.uint8)).save(raw_mask_path) + del model + run_cuda_cleanup( + context=f"smoke {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + print( + f"[Smoke Test] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} passed. " + f"Saved panel to {panel_path.name} and mask to {raw_mask_path.name}." + ) + +"""============================================================================= +OVERFIT TEST +============================================================================= +""" + +OVERFIT_HISTORY_KEYS = ( + "dice", + "iou", + "loss", + "reward", + "actor_loss", + "critic_loss", + "ce_loss", + "dice_loss", + "entropy", + "grad_norm", + "action_dist", + "reward_pos_pct", + "pred_fg_pct", + "gt_fg_pct", +) + +def empty_overfit_history() -> dict[str, list[Any]]: + return {key: [] for key in OVERFIT_HISTORY_KEYS} + +def load_overfit_history_for_resume(history_path: Path, checkpoint_payload: dict[str, Any]) -> dict[str, list[Any]]: + history = empty_overfit_history() + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) + if history_path.exists(): + payload = load_json(history_path) + if not isinstance(payload, dict): + raise RuntimeError(f"Expected dict history at {history_path}, found {type(payload).__name__}.") + for key in OVERFIT_HISTORY_KEYS: + values = payload.get(key, []) + if isinstance(values, list): + history[key] = list(values[:checkpoint_epoch]) + return history + + epoch_metrics = checkpoint_payload.get("epoch_metrics", {}) + if isinstance(epoch_metrics, dict): + for key in OVERFIT_HISTORY_KEYS: + if key in epoch_metrics: + history[key].append(epoch_metrics[key]) + return history + +def _grad_diagnostics(model: nn.Module) -> dict[str, Any]: + raw = _unwrap_compiled(model) + groups: dict[str, list[float]] = {} + total_sq = 0.0 + n_nan = 0 + n_inf = 0 + n_zero = 0 + n_total_params = 0 + + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + n_total_params += 1 + if param.grad is None: + n_zero += 1 + continue + grad_norm = float(param.grad.data.norm(2).item()) + if math.isnan(grad_norm): + n_nan += 1 + continue + if math.isinf(grad_norm): + n_inf += 1 + continue + total_sq += grad_norm ** 2 + group_name = name.split(".", 1)[0] + groups.setdefault(group_name, []).append(grad_norm) + + group_stats: dict[str, dict[str, float | int]] = {} + for group_name, norms in groups.items(): + group_stats[group_name] = { + "min": min(norms), + "max": max(norms), + "mean": sum(norms) / len(norms), + "count": len(norms), + } + return { + "global_norm": total_sq ** 0.5, + "groups": group_stats, + "n_nan": n_nan, + "n_inf": n_inf, + "n_zero_grad": n_zero, + "n_total": n_total_params, + } + +def _param_diagnostics(model: nn.Module, prev_params: dict[str, torch.Tensor] | None = None) -> dict[str, dict[str, float]]: + raw = _unwrap_compiled(model) + info: dict[str, dict[str, list[float]]] = {} + for name, param in raw.named_parameters(): + if not param.requires_grad: + continue + param_norm = float(param.data.norm(2).item()) + group_name = name.split(".", 1)[0] + entry = info.setdefault(group_name, {"norms": [], "update_ratios": []}) + entry["norms"].append(param_norm) + if prev_params is not None and name in prev_params: + delta = float((param.data - prev_params[name]).norm(2).item()) + entry["update_ratios"].append(delta / max(param_norm, 1e-12)) + + summary: dict[str, dict[str, float]] = {} + for group_name, values in info.items(): + norms = values["norms"] + ratios = values["update_ratios"] + summary[group_name] = { + "p_min": min(norms), + "p_max": max(norms), + "p_mean": sum(norms) / len(norms), + } + if ratios: + summary[group_name]["ur_min"] = min(ratios) + summary[group_name]["ur_max"] = max(ratios) + summary[group_name]["ur_mean"] = sum(ratios) / len(ratios) + return summary + +def _snapshot_params(model: nn.Module) -> dict[str, torch.Tensor]: + raw = _unwrap_compiled(model) + return { + name: param.data.detach().clone() + for name, param in raw.named_parameters() + if param.requires_grad + } + +def _action_distribution( + model: nn.Module, + image: torch.Tensor, + seg: torch.Tensor, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + *, + strategy: int | None = None, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> tuple[list[dict[str, float]], torch.Tensor]: + distributions: list[dict[str, float]] = [] + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_action_distribution") if strategy != 2 else max(int(tmax), 1) + refinement_context: dict[str, torch.Tensor] | None = None + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = seg.float() + for _step in range(effective_tmax): + if refinement_context is not None: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_raw, _ = model.forward_from_state(state) + delta = _strategy3_policy_delta(policy_raw).to(dtype=seg.dtype) + seg = _strategy3_apply_delta(seg, delta).to(dtype=seg.dtype) + distributions.append(_strategy3_delta_distribution(delta)) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, _ = sample_actions(policy_logits, stochastic=False) + seg = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + total_pixels = max(actions.numel(), 1) + step_dist: dict[str, float] = {} + action_count = int(policy_logits.shape[1]) + for action_idx in range(action_count): + step_dist[str(action_idx)] = float((actions == action_idx).sum().item()) / total_pixels * 100.0 + distributions.append(step_dist) + if refinement_context is not None: + return distributions, threshold_binary_mask(seg.float()).float() + return distributions, seg + +def _numerical_health_check(outputs_dict: dict[str, Any], prefix: str = "") -> list[str]: + alerts: list[str] = [] + for name, value in outputs_dict.items(): + if value is None: + continue + if isinstance(value, (int, float)): + if math.isnan(value): + alerts.append(f"{prefix}{name} = NaN") + elif math.isinf(value): + alerts.append(f"{prefix}{name} = Inf") + elif name == "train_reward_zeros_pct" and float(value) > 98.0: + alerts.append(f"{prefix}reward is degenerate (>98% zero-reward pixels)") + continue + if torch.is_tensor(value): + if torch.isnan(value).any(): + alerts.append(f"{prefix}{name} contains NaN") + if torch.isinf(value).any(): + alerts.append(f"{prefix}{name} contains Inf") + return alerts + +def _batch_binary_metrics(pred: torch.Tensor, gt: torch.Tensor) -> tuple[list[float], list[float]]: + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt.detach().cpu().numpy().astype(np.uint8) + dices: list[float] = [] + ious: list[float] = [] + for idx in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[idx], gt_np[idx]) + dices.append(float(metrics["dice"])) + ious.append(float(metrics["iou"])) + return dices, ious + +def diagnostic_path_for_run(run_dir: Path) -> Path: + return Path(run_dir) / "diagnostic.json" + +def _summary_stats(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "mean": None, "std": None, "min": None, "max": None} + arr = np.asarray(values, dtype=np.float64) + return { + "count": int(arr.size), + "mean": float(arr.mean()), + "std": float(arr.std()), + "min": float(arr.min()), + "max": float(arr.max()), + } + +def _nanmean_or_default(values: list[float], default: float = 0.0) -> float: + if not values: + return float(default) + arr = np.asarray(values, dtype=np.float64) + if np.isnan(arr).all(): + return float(default) + return float(np.nanmean(arr)) + +def _tensor_stats(tensor: torch.Tensor | None) -> dict[str, Any] | None: + if tensor is None: + return None + data = tensor.detach().float() + flat = data.reshape(-1) + if flat.numel() == 0: + return {"shape": list(data.shape), "dtype": str(tensor.dtype), "numel": 0} + return { + "shape": list(data.shape), + "dtype": str(tensor.dtype), + "numel": int(flat.numel()), + "mean": float(flat.mean().item()), + "std": float(flat.std(unbiased=False).item()), + "min": float(flat.min().item()), + "max": float(flat.max().item()), + } + +def _action_histogram(actions: torch.Tensor, action_count: int) -> dict[str, float]: + total_pixels = max(actions.numel(), 1) + return { + str(action_idx): float((actions == action_idx).sum().item()) / total_pixels * 100.0 + for action_idx in range(action_count) + } + +def _jsonable_action_distribution(distributions: list[dict[int, float]] | list[dict[str, float]]) -> list[dict[str, float]]: + jsonable: list[dict[str, float]] = [] + for step_dist in distributions: + jsonable.append({str(key): float(value) for key, value in step_dist.items()}) + return jsonable + +def _average_action_distributions( + distributions_per_batch: list[list[dict[str, float]]], + steps: int, +) -> list[dict[str, float]]: + averaged: list[dict[str, float]] = [] + if not distributions_per_batch: + return averaged + for step_idx in range(steps): + action_keys = sorted( + { + str(action_idx) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) + for action_idx in batch_dist[step_idx].keys() + } + ) + if not action_keys: + continue + step_summary: dict[str, float] = {} + for action_idx in action_keys: + values = [ + float(batch_dist[step_idx][action_idx]) + for batch_dist in distributions_per_batch + if step_idx < len(batch_dist) and action_idx in batch_dist[step_idx] + ] + step_summary[str(action_idx)] = float(np.mean(values)) if values else 0.0 + averaged.append(step_summary) + return averaged + +def _trajectory_degradation_summary(step_trace: list[dict[str, Any]]) -> dict[str, Any]: + if not step_trace: + return {} + ious = [float(step.get("iou_mean", 0.0)) for step in step_trace] + dices = [float(step.get("dice_mean", 0.0)) for step in step_trace] + ts = [int(step.get("t", idx)) for idx, step in enumerate(step_trace)] + init_iou = ious[0] + init_dice = dices[0] + best_iou = max(ious) + best_dice = max(dices) + best_iou_t = ts[ious.index(best_iou)] + best_dice_t = ts[dices.index(best_dice)] + first_worse_than_initial_iou_t = next((ts[idx] for idx, value in enumerate(ious[1:], start=1) if value < init_iou - 1e-6), None) + first_worse_than_prev_iou_t = next((ts[idx] for idx in range(1, len(ious)) if ious[idx] < ious[idx - 1] - 1e-6), None) + largest_iou_drop = max(best_iou - value for value in ious) + largest_iou_drop_t = ts[max(range(len(ious)), key=lambda idx: best_iou - ious[idx])] + return { + "steps_recorded": len(step_trace) - 1, + "best_iou_t": best_iou_t, + "best_iou": best_iou, + "best_dice_t": best_dice_t, + "best_dice": best_dice, + "final_t": ts[-1], + "final_iou": ious[-1], + "final_dice": dices[-1], + "delta_final_vs_init_iou": ious[-1] - init_iou, + "delta_final_vs_init_dice": dices[-1] - init_dice, + "delta_final_vs_best_iou": ious[-1] - best_iou, + "delta_final_vs_best_dice": dices[-1] - best_dice, + "first_worse_than_initial_iou_t": first_worse_than_initial_iou_t, + "first_worse_than_prev_iou_t": first_worse_than_prev_iou_t, + "largest_iou_drop_from_best": largest_iou_drop, + "largest_iou_drop_t": largest_iou_drop_t, + } + +def _average_rollout_traces(traces_per_batch: list[list[dict[str, Any]]]) -> list[dict[str, Any]]: + averaged: list[dict[str, Any]] = [] + if not traces_per_batch: + return averaged + max_steps = max(len(trace) for trace in traces_per_batch) + for step_idx in range(max_steps): + present = [trace[step_idx] for trace in traces_per_batch if step_idx < len(trace)] + if not present: + continue + reward_pos_values = [float(step["reward_pos_pct"]) for step in present if step.get("reward_pos_pct") is not None] + value_scores = [float(step["value_score"]) for step in present if step.get("value_score") is not None] + averaged.append( + { + "t": int(np.mean([float(step.get("t", step_idx)) for step in present])), + "dice_mean": float(np.mean([float(step.get("dice_mean", 0.0)) for step in present])), + "iou_mean": float(np.mean([float(step.get("iou_mean", 0.0)) for step in present])), + "pred_fg_pct": float(np.mean([float(step.get("pred_fg_pct", 0.0)) for step in present])), + "reward_pos_pct": float(np.mean(reward_pos_values)) if reward_pos_values else None, + "value_score": float(np.mean(value_scores)) if value_scores else None, + } + ) + return averaged + +def _rollout_probe_trace( + model: nn.Module, + image: torch.Tensor, + gt_mask: torch.Tensor, + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + sample_ids: list[str] | None = None, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_rollout_probe_trace") if strategy != 2 else max(int(tmax), 1) + rollout_trace: list[dict[str, Any]] = [] + batch_action_dist: list[dict[str, float]] = [] + reward_pos_pct = 0.0 + init_fg_pct = 0.0 + first_action_dist: dict[str, float] | None = None + first_policy_stats: dict[str, Any] | None = None + first_value_stats: dict[str, Any] | None = None + decoder_prob_stats: dict[str, Any] | None = None + first_entropy: float | None = None + selected_t = 0 + + def record_step( + *, + t: int, + seg_tensor: torch.Tensor, + seg_prev: torch.Tensor | None = None, + delta_map: torch.Tensor | None = None, + value_score: float | None = None, + action_distribution: dict[str, float] | None = None, + reward_pos: float | None = None, + reward_map_tensor: torch.Tensor | None = None, + step_entropy: float | None = None, + policy_stats: dict[str, Any] | None = None, + ) -> None: + pred_t = threshold_binary_mask(seg_tensor.float()).float() + dice_vals, iou_vals = _batch_binary_metrics(pred_t, gt_mask.float()) + step_data: dict[str, Any] = { + "t": int(t), + "dice_mean": float(np.mean(dice_vals)) if dice_vals else 0.0, + "iou_mean": float(np.mean(iou_vals)) if iou_vals else 0.0, + "pred_fg_pct": float(pred_t.sum().item()) / max(pred_t.numel(), 1) * 100.0, + "reward_pos_pct": None if reward_pos is None else float(reward_pos), + "value_score": None if value_score is None else float(value_score), + "action_distribution": action_distribution, + "seg_soft_stats": _tensor_stats(seg_tensor), + "entropy": step_entropy, + "policy_logit_stats": policy_stats, + } + if seg_prev is not None: + delta = seg_tensor.float() - seg_prev.float() + abs_delta = delta.abs() + # Binary mask flip tracking: how many pixels actually change in the thresholded output + binary_prev = threshold_binary_mask(seg_prev.float()).float() + binary_curr = threshold_binary_mask(seg_tensor.float()).float() + binary_flipped = (binary_prev != binary_curr) + flipped_to_fg = binary_flipped & (binary_curr > 0.5) + flipped_to_bg = binary_flipped & (binary_curr < 0.5) + gt_binary_local = (gt_mask.float() > 0.5) + correct_flips = binary_flipped & ((binary_curr > 0.5) == gt_binary_local) + wrong_flips = binary_flipped & ((binary_curr > 0.5) != gt_binary_local) + total_px = max(binary_prev.numel(), 1) + step_data["mask_delta"] = { + "mean_abs_change": float(abs_delta.mean().item()), + "max_change": float(abs_delta.max().item()), + "pct_pixels_changed": float((abs_delta > 1e-6).float().mean().item() * 100.0), + "fg_gained_pct": float((delta > 1e-6).float().mean().item() * 100.0), + "fg_lost_pct": float((delta < -1e-6).float().mean().item() * 100.0), + } + step_data["binary_mask_flips"] = { + "total_flipped_pct": float(binary_flipped.float().sum().item() / total_px * 100.0), + "flipped_to_fg_pct": float(flipped_to_fg.float().sum().item() / total_px * 100.0), + "flipped_to_bg_pct": float(flipped_to_bg.float().sum().item() / total_px * 100.0), + "correct_flips_pct": float(correct_flips.float().sum().item() / total_px * 100.0), + "wrong_flips_pct": float(wrong_flips.float().sum().item() / total_px * 100.0), + "flip_accuracy": float(correct_flips.float().sum().item() / max(binary_flipped.float().sum().item(), 1.0) * 100.0), + } + if reward_map_tensor is not None: + step_data["reward_stats"] = { + "mean": float(reward_map_tensor.mean().item()), + "std": float(reward_map_tensor.std().item()), + "min": float(reward_map_tensor.min().item()), + "max": float(reward_map_tensor.max().item()), + "pct_positive": float((reward_map_tensor > 0).float().mean().item() * 100.0), + "pct_negative": float((reward_map_tensor < 0).float().mean().item() * 100.0), + "pct_zero": float((reward_map_tensor.abs() < 1e-8).float().mean().item() * 100.0), + } + if delta_map is not None and seg_prev is not None: + gt_f = gt_mask.float() + ref_pred = threshold_binary_mask(seg_prev.float()).float() + gt_fg = (gt_f > 0.5).squeeze(1) + gt_bg = ~gt_fg + pred_fg = (ref_pred > 0.5).squeeze(1) + tp_mask = pred_fg & gt_fg + tn_mask = (~pred_fg) & gt_bg + fp_mask = pred_fg & gt_bg + fn_mask = (~pred_fg) & gt_fg + delta_squeezed = delta_map.squeeze(1).detach().float() + action_breakdown: dict[str, dict[str, float]] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + if pixel_mask.any(): + class_delta = delta_squeezed[pixel_mask] + action_breakdown[label] = { + "mean_delta": float(class_delta.mean().item()), + "mean_abs_delta": float(class_delta.abs().mean().item()), + "positive_pct": float((class_delta > 1e-6).float().mean().item() * 100.0), + "negative_pct": float((class_delta < -1e-6).float().mean().item() * 100.0), + } + else: + action_breakdown[label] = { + "mean_delta": 0.0, + "mean_abs_delta": 0.0, + "positive_pct": 0.0, + "negative_pct": 0.0, + } + step_data["action_on_class"] = action_breakdown + if reward_map_tensor is not None: + reward_squeezed = reward_map_tensor.squeeze(1) if reward_map_tensor.ndim == 4 else reward_map_tensor + per_class_reward: dict[str, float] = {} + for label, pixel_mask in [("tp", tp_mask), ("tn", tn_mask), ("fp", fp_mask), ("fn", fn_mask)]: + per_class_reward[label] = float(reward_squeezed[pixel_mask].mean().item()) if pixel_mask.any() else 0.0 + step_data["per_action_reward"] = per_class_reward + rollout_trace.append(step_data) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + record_step(t=0, seg_tensor=torch.sigmoid(logits)) + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": _trajectory_degradation_summary(rollout_trace), + "selected_t": selected_t, + "effective_tmax": effective_tmax, + } + + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + seg = refinement_context["decoder_prob"].float() + decoder_prob_stats = _tensor_stats(refinement_context["decoder_prob"]) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + selected_t = 0 + + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + state_t = model.forward_refinement_state( + refinement_context["base_features"], + seg, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, value_t = model.forward_from_state(state_t) + current_score = float(value_t.detach().mean().item()) + delta = _strategy3_policy_delta(policy_logits).to(dtype=seg.dtype) + + action_dist = _strategy3_delta_distribution(delta) + batch_action_dist.append(action_dist) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_value_stats = _tensor_stats(value_t) + first_entropy = 0.0 + record_step(t=0, seg_tensor=seg, value_score=current_score, step_entropy=first_entropy, policy_stats=first_policy_stats) + + seg_next = _strategy3_apply_delta(seg, delta) + reward_map = compute_refinement_reward(seg, seg_next, gt_mask.float()) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + step_entropy_val = 0.0 + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + seg_prev=seg, + delta_map=delta, + action_distribution=action_dist, + reward_pos=step_reward_pos, + reward_map_tensor=reward_map, + step_entropy=step_entropy_val, + policy_stats=_tensor_stats(policy_logits), + ) + seg = seg_next + selected_t = step_idx + 1 + + pred = threshold_binary_mask(seg.float()).float() + decoder_pred = threshold_binary_mask(refinement_context["decoder_prob"].float()).float() + decoder_dice_vals, decoder_iou_vals = _batch_binary_metrics(decoder_pred, gt_mask.float()) + decoder_baseline = { + "dice": float(np.mean(decoder_dice_vals)) if decoder_dice_vals else 0.0, + "iou": float(np.mean(decoder_iou_vals)) if decoder_iou_vals else 0.0, + "fg_pct": float(decoder_pred.sum().item()) / max(decoder_pred.numel(), 1) * 100.0, + } + final_dice_vals, final_iou_vals = _batch_binary_metrics(pred, gt_mask.float()) + rl_vs_decoder = { + "decoder_dice": decoder_baseline["dice"], + "decoder_iou": decoder_baseline["iou"], + "final_dice": float(np.mean(final_dice_vals)) if final_dice_vals else 0.0, + "final_iou": float(np.mean(final_iou_vals)) if final_iou_vals else 0.0, + "dice_gain": (float(np.mean(final_dice_vals)) if final_dice_vals else 0.0) - decoder_baseline["dice"], + "iou_gain": (float(np.mean(final_iou_vals)) if final_iou_vals else 0.0) - decoder_baseline["iou"], + } + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = selected_t + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": selected_t, + "effective_tmax": effective_tmax, + "decoder_baseline": decoder_baseline, + "rl_vs_decoder": rl_vs_decoder, + } + + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + seg = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + else: + seg = torch.ones(image.shape[0], 1, image.shape[2], image.shape[3], device=image.device, dtype=image.dtype) + init_fg_pct = float(seg.sum().item()) / max(seg.numel(), 1) * 100.0 + record_step(t=0, seg_tensor=seg) + for step_idx in range(effective_tmax): + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * seg + policy_logits = model.forward_policy_only(masked) + actions, _, entropy = sample_actions(policy_logits, stochastic=False) + action_dist = _action_histogram(actions, int(policy_logits.shape[1])) + batch_action_dist.append({str(key): float(value) for key, value in action_dist.items()}) + if step_idx == 0: + first_action_dist = action_dist + first_policy_stats = _tensor_stats(policy_logits) + first_entropy = float(entropy.detach().item()) + seg_next = apply_actions(seg, actions, num_actions=policy_logits.shape[1]).to(dtype=seg.dtype) + reward_map = (seg - gt_mask).pow(2) - (seg_next - gt_mask).pow(2) + step_reward_pos = float((reward_map > 0).float().mean().item() * 100.0) + if step_idx == 0: + reward_pos_pct = step_reward_pos + record_step( + t=step_idx + 1, + seg_tensor=seg_next, + action_distribution=action_dist, + reward_pos=step_reward_pos, + ) + seg = seg_next + pred = seg.float() + summary = _trajectory_degradation_summary(rollout_trace) + summary["selected_t"] = len(rollout_trace) - 1 + return { + "final_pred": pred, + "action_distribution": batch_action_dist, + "reward_pos_pct": reward_pos_pct, + "init_fg_pct": init_fg_pct, + "first_action_distribution": first_action_dist, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "decoder_prob_stats": decoder_prob_stats, + "first_entropy": first_entropy, + "rollout_trace": rollout_trace, + "rollout_summary": summary, + "selected_t": len(rollout_trace) - 1, + "effective_tmax": effective_tmax, + } + +def _format_probe_deterioration(label: str, probe_payload: dict[str, Any], tmax: int) -> str: + effective_tmax = int(probe_payload.get("effective_tmax", tmax)) + degradation = probe_payload.get("aggregate", {}).get("degradation", {}) + if not degradation: + return f"{label}: no degradation trace" + first_worse = degradation.get("first_worse_than_initial_iou_t") + first_step_drop = degradation.get("first_worse_than_prev_iou_t") + best_t = degradation.get("best_iou_t") + final_t = degradation.get("final_t") + delta_best = degradation.get("delta_final_vs_best_iou") + worst_t = degradation.get("largest_iou_drop_t") + worst_drop = degradation.get("largest_iou_drop_from_best") + return ( + f"{label}: first_worse={first_worse}/{effective_tmax} " + f"first_drop={first_step_drop}/{effective_tmax} " + f"best={best_t}/{effective_tmax} final={final_t}/{effective_tmax} " + f"final-best_iou={float(delta_best):+.4f} " + f"worst={worst_t}/{effective_tmax} drop={float(worst_drop):+.4f}" + ) + +def _optimizer_diagnostics(optimizer: torch.optim.Optimizer) -> list[dict[str, Any]]: + groups: list[dict[str, Any]] = [] + for group_idx, group in enumerate(optimizer.param_groups): + num_tensors = len(group.get("params", [])) + num_elements = int(sum(param.numel() for param in group.get("params", []))) + groups.append( + { + "index": group_idx, + "lr": float(group.get("lr", 0.0)), + "weight_decay": float(group.get("weight_decay", 0.0)), + "num_tensors": num_tensors, + "num_elements": num_elements, + } + ) + return groups + +def _probe_batches_from_indices( + dataset: BUSIDataset, + *, + indices: list[int], + device: torch.device, +) -> list[dict[str, Any]]: + batches: list[dict[str, Any]] = [] + for start in range(0, len(indices), BATCH_SIZE): + batch_indices = indices[start:start + BATCH_SIZE] + if not batch_indices: + continue + images = torch.stack([dataset._images[idx].clone() for idx in batch_indices], dim=0) + masks = torch.stack([dataset._masks[idx].clone() for idx in batch_indices], dim=0) + sample_ids = [Path(dataset.sample_records[idx]["filename"]).stem for idx in batch_indices] + batches.append( + to_device( + { + "image": images, + "mask": masks, + "sample_id": sample_ids, + "dataset": current_dataset_name(), + }, + device, + ) + ) + return batches + +def _fixed_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + return _probe_batches_from_indices( + dataset, + indices=list(range(max_samples)), + device=device, + ) + +def _rolling_probe_batches_from_dataset( + dataset: BUSIDataset, + *, + num_batches: int, + device: torch.device, + epoch: int, + split_tag: str, +) -> list[dict[str, Any]]: + max_samples = min(len(dataset), max(int(num_batches), 0) * BATCH_SIZE) + if max_samples <= 0: + return [] + if max_samples >= len(dataset): + indices = list(range(len(dataset))) + else: + rng = random.Random(SEED + stable_int_from_text(f"probe:{split_tag}:epoch:{int(epoch)}")) + indices = rng.sample(range(len(dataset)), k=max_samples) + return _probe_batches_from_indices(dataset, indices=indices, device=device) + +def _probe_batch_id_lists(fixed_batches: list[dict[str, Any]]) -> list[list[str]]: + return [list(batch.get("sample_id", [])) for batch in fixed_batches] + +def _reference_eval_payloads(project_dir: Path, percent: float) -> dict[str, Any]: + refs: dict[str, Any] = {} + pct = percent_label(percent) + strat2_dir = project_dir / "strat2_history" + strat3_dir = project_dir / "strat3_history_best" + strat2_candidates = [ + strat2_dir / f"evaluation_strat2_pc{pct}.json", + strat2_dir / f"evaluation_strat2_pct{pct}.json", + ] + strat3_candidate = strat3_dir / f"evaluation_{pct} (1)" + for candidate in strat2_candidates: + if candidate.exists(): + refs["strategy2_reference"] = load_json(candidate) + break + if strat3_candidate.exists(): + refs["strategy3_best_reference"] = load_json(strat3_candidate) + return refs + +def empty_epoch_diagnostic_payload( + *, + run_type: str, + run_config: dict[str, Any], + bundle: DataBundle, + train_probe_batches: list[dict[str, Any]], + val_probe_batches: list[dict[str, Any]], +) -> dict[str, Any]: + payload = { + "diagnostic_version": 1, + "run_type": run_type, + "strategy": int(run_config["strategy"]), + "dataset_percent": float(bundle.percent), + "run_config": run_config, + "probe_setup": { + "mode": str(run_config.get("epoch_probe_mode", "fixed")), + "train_probe_batches": _probe_batch_id_lists(train_probe_batches), + "val_probe_batches": _probe_batch_id_lists(val_probe_batches), + "train_probe_batch_count": len(train_probe_batches), + "val_probe_batch_count": len(val_probe_batches), + "tmax": int(run_config.get("tmax", DEFAULT_TMAX)), + }, + "epochs": [], + } + payload.update(_reference_eval_payloads(PROJECT_DIR, bundle.percent)) + return payload + +def load_epoch_diagnostic_for_resume( + path: Path, + checkpoint_payload: dict[str, Any] | None, + default_payload: dict[str, Any], +) -> dict[str, Any]: + payload = dict(default_payload) + checkpoint_epoch = int(checkpoint_payload.get("epoch", 0)) if checkpoint_payload is not None else 0 + if path.exists(): + loaded = load_json(path) + if isinstance(loaded, dict): + payload.update({k: v for k, v in loaded.items() if k != "epochs"}) + epochs = loaded.get("epochs", []) + if isinstance(epochs, list): + payload["epochs"] = [dict(row) for row in epochs if isinstance(row, dict) and int(row.get("epoch", 0)) <= checkpoint_epoch] + if "epochs" not in payload: + payload["epochs"] = [] + return payload + +def _evaluate_probe_batches( + model: nn.Module, + fixed_batches: list[dict[str, Any]], + *, + strategy: int, + tmax: int, + use_amp: bool, + amp_dtype: torch.dtype, + use_channels_last: bool, + mc_cache_split: Literal["train", "val", "test"] | None = None, + mc_cache_run_dir: Path | None = None, +) -> dict[str, Any]: + if not fixed_batches: + return {"n_batches": 0, "batch_details": [], "aggregate": {}, "alerts": []} + + effective_tmax = _resolve_test_iteration_tmax(tmax, context="_evaluate_probe_batches") if strategy != 2 else max(int(tmax), 1) + was_training = model.training + batch_details: list[dict[str, Any]] = [] + alerts: list[str] = [] + action_distributions: list[list[dict[str, float]]] = [] + rollout_traces: list[list[dict[str, Any]]] = [] + metric_lists: dict[str, list[float]] = { + key: [] + for key in ("dice", "ppv", "sen", "iou", "biou", "biou_contour", "hd95") + } + reward_pos_values: list[float] = [] + pred_fg_values: list[float] = [] + gt_fg_values: list[float] = [] + init_fg_values: list[float] = [] + decoder_dices: list[float] = [] + decoder_ious: list[float] = [] + iou_gains: list[float] = [] + dice_gains: list[float] = [] + + model.eval() + try: + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + _strategy3_bump_mc_cache_fingerprint(model) + _strategy3_reset_mc_cache_stats(model) + with torch.inference_mode(): + for batch_index, batch in enumerate(fixed_batches): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and image.device.type == "cuda": + image = image.to(dtype=amp_dtype) + + rollout_probe = _rollout_probe_trace( + model, + image, + gt_mask, + strategy=strategy, + tmax=tmax, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + sample_ids=sample_ids, + mc_cache_split=mc_cache_split, + mc_cache_run_dir=mc_cache_run_dir, + ) + pred = rollout_probe["final_pred"].float() + batch_action_dist = rollout_probe["action_distribution"] + reward_pos_pct = float(rollout_probe["reward_pos_pct"]) + init_fg_pct = float(rollout_probe["init_fg_pct"]) + first_action_dist = rollout_probe["first_action_distribution"] + first_policy_stats = rollout_probe["first_policy_stats"] + first_value_stats = rollout_probe["first_value_stats"] + decoder_prob_stats = rollout_probe["decoder_prob_stats"] + first_entropy = rollout_probe["first_entropy"] + rollout_trace = rollout_probe["rollout_trace"] + rollout_summary = rollout_probe["rollout_summary"] + + if batch_action_dist: + action_distributions.append(batch_action_dist) + if rollout_trace: + rollout_traces.append(rollout_trace) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + reward_pos_values.append(reward_pos_pct) + pred_fg_values.append(pred_fg_pct) + gt_fg_values.append(gt_fg_pct) + init_fg_values.append(init_fg_pct) + rl_vs_dec = rollout_probe.get("rl_vs_decoder") + if rl_vs_dec: + decoder_dices.append(rl_vs_dec["decoder_dice"]) + decoder_ious.append(rl_vs_dec["decoder_iou"]) + iou_gains.append(rl_vs_dec["iou_gain"]) + dice_gains.append(rl_vs_dec["dice_gain"]) + + batch_alerts = _numerical_health_check( + { + "pred": pred, + "gt_mask": gt_mask, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "reward_pos_pct": reward_pos_pct, + "first_entropy": first_entropy if first_entropy is not None else 0.0, + }, + prefix=f"probe[{batch_index}]:", + ) + alerts.extend(batch_alerts) + + pred_np = pred.detach().cpu().numpy().astype(np.uint8) + gt_np = gt_mask.detach().cpu().numpy().astype(np.uint8) + per_sample: list[dict[str, Any]] = [] + for sample_index in range(pred_np.shape[0]): + metrics = compute_all_metrics(pred_np[sample_index], gt_np[sample_index]) + per_sample.append( + { + "sample_id": sample_ids[sample_index] if sample_index < len(sample_ids) else f"sample_{sample_index}", + **{key: float(value) for key, value in metrics.items()}, + } + ) + for key, value in metrics.items(): + metric_lists.setdefault(key, []).append(float(value)) + + batch_details.append( + { + "batch_index": batch_index, + "sample_ids": sample_ids, + "pred_fg_pct": pred_fg_pct, + "gt_fg_pct": gt_fg_pct, + "init_fg_pct": init_fg_pct, + "reward_pos_pct": reward_pos_pct, + "action_distribution": _jsonable_action_distribution(batch_action_dist), + "first_action_distribution": first_action_dist, + "first_entropy": first_entropy, + "decoder_prob_stats": decoder_prob_stats, + "first_policy_stats": first_policy_stats, + "first_value_stats": first_value_stats, + "pred_stats": _tensor_stats(pred), + "rollout_trace": rollout_trace, + "rollout_summary": rollout_summary, + "decoder_baseline": rollout_probe.get("decoder_baseline"), + "rl_vs_decoder": rollout_probe.get("rl_vs_decoder"), + "alerts": batch_alerts, + "per_sample": per_sample, + } + ) + finally: + model.train(was_training) + if strategy == 3 and _uses_refinement_runtime(model, strategy=strategy) and mc_cache_split != "train": + mc_cache_stats = _strategy3_get_mc_cache_stats(model) + print( + "[Strategy3 MC Cache] " + f"ram_hits={mc_cache_stats['ram_hits']} " + f"disk_hits={mc_cache_stats['disk_hits']} " + f"misses={mc_cache_stats['misses']} " + f"writes={mc_cache_stats['writes']}" + ) + + aggregate_trace = _average_rollout_traces(rollout_traces) + rl_vs_decoder_aggregate: dict[str, Any] = {} + if decoder_dices: + rl_vs_decoder_aggregate = { + "decoder_dice": _summary_stats(decoder_dices), + "decoder_iou": _summary_stats(decoder_ious), + "iou_gain": _summary_stats(iou_gains), + "dice_gain": _summary_stats(dice_gains), + } + return { + "n_batches": len(fixed_batches), + "batch_details": batch_details, + "aggregate": { + "metrics": {key: _summary_stats(values) for key, values in metric_lists.items()}, + "reward_pos_pct": _summary_stats(reward_pos_values), + "pred_fg_pct": _summary_stats(pred_fg_values), + "gt_fg_pct": _summary_stats(gt_fg_values), + "init_fg_pct": _summary_stats(init_fg_values), + "action_distribution": _average_action_distributions(action_distributions, effective_tmax), + "rollout_trace": aggregate_trace, + "degradation": _trajectory_degradation_summary(aggregate_trace), + "rl_vs_decoder": rl_vs_decoder_aggregate, + }, + "alerts": alerts, + "effective_tmax": effective_tmax, + } + +def run_overfit_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + overfit_root: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + amp_dtype = resolve_amp_dtype(AMP_DTYPE) + use_amp = amp_autocast_enabled(DEVICE) + use_channels_last = _use_channels_last_for_run(model_config) + overfit_root = ensure_dir(overfit_root) + ckpt_dir = ensure_dir(overfit_root / "checkpoints") + history_path = checkpoint_history_path(overfit_root, "overfit") + run_config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": "overfit", + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "max_epochs": OVERFIT_N_EPOCHS, + "head_lr": OVERFIT_HEAD_LR, + "encoder_lr": OVERFIT_ENCODER_LR, + "weight_decay": DEFAULT_WEIGHT_DECAY, + "dropout_p": DEFAULT_DROPOUT_P, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "gamma": DEFAULT_GAMMA, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + if strategy == 3: + run_config.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(run_config["strategy3_freeze_bootstrapped_segmentation"]) + ) + run_config.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + run_config.setdefault("decoder_lr", 0.0 if bootstrap_freeze else OVERFIT_HEAD_LR * 0.1) + run_config.setdefault("rl_lr", OVERFIT_HEAD_LR) + run_config.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + run_config.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + run_config.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + run_config.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + run_config.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + run_config.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + run_config.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + run_config.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + run_config.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + run_config.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + run_config.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + run_config.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + run_config.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + run_config.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + run_config.setdefault("elastic_aug_prob", 0.3) + run_config.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + run_config.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + run_config.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + run_config.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + run_config.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + run_config.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + run_config.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + run_config.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + run_config.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + run_config.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + run_config.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + run_config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + run_config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + save_json(overfit_root / "run_config.json", run_config) + set_current_job_params(run_config) + + banner( + f"OVERFIT TEST | {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + effective_test_tmax = _resolve_test_iteration_tmax(DEFAULT_TMAX, context="run_overfit_test") if strategy != 2 else max(int(DEFAULT_TMAX), 1) + print( + f"[Overfit] Fixed batches={OVERFIT_N_BATCHES}, epochs={OVERFIT_N_EPOCHS}, " + f"head_lr={OVERFIT_HEAD_LR:.2e}, encoder_lr={OVERFIT_ENCODER_LR:.2e}" + ) + + fixed_batches: list[dict[str, Any]] = [] + for batch_index, batch in enumerate(bundle.train_loader): + fixed_batches.append(to_device(batch, DEVICE)) + if batch_index + 1 >= OVERFIT_N_BATCHES: + break + if not fixed_batches: + raise RuntimeError("Overfit test could not collect any training batches.") + if len(fixed_batches) < OVERFIT_N_BATCHES: + print(f"[Overfit] Warning: only {len(fixed_batches)} train batch(es) available.") + + model, description, compiled = build_model( + strategy, + DEFAULT_DROPOUT_P, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(overfit_root) + if resume_checkpoint_path is not None and strategy == 3: + _configure_model_from_checkpoint_path(model, resume_checkpoint_path) + print_model_parameter_summary( + model=model, + description=f"{description} | Overfit Test", + strategy=strategy, + model_config=model_config, + dropout_p=DEFAULT_DROPOUT_P, + amp_dtype=amp_dtype, + compiled=compiled, + ) + + optimizer = make_optimizer( + model, + strategy, + head_lr=OVERFIT_HEAD_LR, + encoder_lr=OVERFIT_ENCODER_LR, + weight_decay=DEFAULT_WEIGHT_DECAY, + ) + scaler = make_grad_scaler(enabled=use_amp, amp_dtype=amp_dtype, device=DEVICE) + log_alpha: torch.Tensor | None = None + alpha_optimizer: Adam | None = None + target_entropy = 0.0 + + history = empty_overfit_history() + prev_loss: float | None = None + best_dice = -1.0 + elapsed_before_resume = 0.0 + start_epoch = 1 + resume_source: dict[str, Any] | None = None + if resume_checkpoint_path is not None: + checkpoint_payload = load_checkpoint( + resume_checkpoint_path, + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + device=DEVICE, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + expected_run_type="overfit", + require_run_metadata=True, + ) + validate_resume_checkpoint_identity( + run_config, + checkpoint_run_config_payload(checkpoint_payload), + checkpoint_path=resume_checkpoint_path, + ) + start_epoch = int(checkpoint_payload["epoch"]) + 1 + best_dice = float(checkpoint_payload["best_metric_value"]) + elapsed_before_resume = float(checkpoint_payload.get("elapsed_seconds", 0.0)) + history = load_overfit_history_for_resume(history_path, checkpoint_payload) + resume_source = { + "checkpoint_path": str(Path(resume_checkpoint_path).resolve()), + "checkpoint_epoch": int(checkpoint_payload["epoch"]), + "checkpoint_run_type": checkpoint_payload.get("run_type", "overfit"), + } + print( + f"[Resume] overfit run continuing from {resume_checkpoint_path} " + f"at epoch {start_epoch}/{OVERFIT_N_EPOCHS}." + ) + if history["loss"]: + prev_loss = float(history["loss"][-1]) + prev_params = _snapshot_params(model) + start_time = time.time() + + for epoch in range(start_epoch, OVERFIT_N_EPOCHS + 1): + full_dump = epoch <= 5 or epoch % max(OVERFIT_PRINT_EVERY, 1) == 0 or epoch == OVERFIT_N_EPOCHS + epoch_losses: list[float] = [] + epoch_dices: list[float] = [] + epoch_ious: list[float] = [] + epoch_rewards: list[float] = [] + epoch_actor: list[float] = [] + epoch_critic: list[float] = [] + epoch_ce: list[float] = [] + epoch_dice_losses: list[float] = [] + epoch_entropy: list[float] = [] + epoch_grad_norms: list[float] = [] + epoch_action_dist: list[list[dict[str, float]]] = [] + epoch_reward_pos_pct: list[float] = [] + epoch_pred_fg_pct: list[float] = [] + epoch_gt_fg_pct: list[float] = [] + epoch_alerts: list[str] = [] + + for batch in fixed_batches: + if strategy == 2: + metrics = train_step_supervised( + model, + batch, + optimizer, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + use_channels_last=use_channels_last, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + ) + else: + metrics = train_step_strategy3( + model, + batch, + optimizer, + gamma=DEFAULT_GAMMA, + tmax=DEFAULT_TMAX, + critic_loss_weight=DEFAULT_CRITIC_LOSS_WEIGHT, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + target_entropy=target_entropy, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + grad_clip_norm=DEFAULT_GRAD_CLIP_NORM, + scaler=scaler, + use_amp=use_amp, + amp_dtype=amp_dtype, + stepwise_backward=STEPWISE_BACKWARD, + use_channels_last=use_channels_last, + current_epoch=epoch, + max_epochs=OVERFIT_N_EPOCHS, + ) + + epoch_losses.append(float(metrics["loss"])) + epoch_rewards.append(float(metrics["mean_reward"])) + epoch_actor.append(float(metrics["actor_loss"])) + epoch_critic.append(float(metrics["critic_loss"])) + epoch_ce.append(float(metrics["ce_loss"])) + epoch_dice_losses.append(float(metrics["dice_loss"])) + epoch_entropy.append(float(metrics["entropy"])) + epoch_grad_norms.append(float(metrics["grad_norm"])) + epoch_alerts.extend( + _numerical_health_check( + { + "loss": metrics["loss"], + "actor_loss": metrics["actor_loss"], + "critic_loss": metrics["critic_loss"], + "reward": metrics["mean_reward"], + "entropy": metrics["entropy"], + "grad_norm": metrics["grad_norm"], + "ce_loss": metrics["ce_loss"], + "dice_loss": metrics["dice_loss"], + }, + prefix="train:", + ) + ) + + model.eval() + with torch.inference_mode(): + image = batch["image"] + gt_mask = batch["mask"].float() + sample_ids = [str(item) for item in batch["sample_id"]] if "sample_id" in batch else None + if use_channels_last and image.ndim == 4 and image.device.type == "cuda": + image = image.contiguous(memory_format=torch.channels_last) + if use_amp and DEVICE.type == "cuda": + image = image.to(dtype=amp_dtype) + + if strategy == 2: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + logits = model(image) + pred = threshold_binary_mask(torch.sigmoid(logits)).float() + else: + refinement_runtime = _uses_refinement_runtime(model, strategy=strategy) + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + )["decoder_prob"].float() + else: + init_mask = torch.ones( + image.shape[0], + 1, + image.shape[2], + image.shape[3], + device=image.device, + dtype=image.dtype, + ) + if strategy == 3: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + init_mask = threshold_binary_mask(torch.sigmoid(model.forward_decoder(image))).float() + action_dist, pred = _action_distribution( + model, + image, + init_mask, + effective_test_tmax, + use_amp, + amp_dtype, + strategy=strategy, + sample_ids=sample_ids, + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + epoch_action_dist.append(action_dist) + + if strategy == 3 and refinement_runtime: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + refinement_context = model.prepare_refinement_context( + image, + sample_ids=sample_ids, + mc_mode="eval", + mc_cache_split="train", + mc_cache_run_dir=overfit_root, + ) + soft_init_mask = refinement_context["decoder_prob"].float() + state_t = model.forward_refinement_state( + refinement_context["base_features"], + soft_init_mask, + refinement_context["decoder_prob"], + refinement_context["mc_variance"], + refinement_context["pred_entropy"], + encoder_features=refinement_context.get("encoder_features"), + ) + policy_logits, _ = model.forward_from_state(state_t) + first_delta = _strategy3_policy_delta(policy_logits).to(dtype=soft_init_mask.dtype) + first_seg = _strategy3_apply_delta(soft_init_mask, first_delta) + reward_map = compute_refinement_reward( + soft_init_mask, first_seg, gt_mask.float(), + ) + else: + with autocast_ctx(enabled=use_amp, device=image.device, amp_dtype=amp_dtype): + masked = image * init_mask + policy_logits = model.forward_policy_only(masked) + first_actions, _, _ = sample_actions(policy_logits, stochastic=False) + first_seg = apply_actions(init_mask, first_actions, num_actions=policy_logits.shape[1]) + reward_map = (init_mask - gt_mask).pow(2) - (first_seg - gt_mask).pow(2) + epoch_reward_pos_pct.append(float((reward_map > 0).float().mean().item() * 100.0)) + + pred_fg_pct = float(pred.sum().item()) / max(pred.numel(), 1) * 100.0 + gt_fg_pct = float(gt_mask.sum().item()) / max(gt_mask.numel(), 1) * 100.0 + epoch_pred_fg_pct.append(pred_fg_pct) + epoch_gt_fg_pct.append(gt_fg_pct) + dice_values, iou_values = _batch_binary_metrics(pred.float(), gt_mask.float()) + epoch_dices.extend(dice_values) + epoch_ious.extend(iou_values) + epoch_alerts.extend( + _numerical_health_check( + {"pred": pred, "gt_mask": gt_mask}, + prefix="eval:", + ) + ) + model.train() + + grad_stats = _grad_diagnostics(model) + param_stats = _param_diagnostics(model, prev_params) + prev_params = _snapshot_params(model) + + avg_loss = float(np.mean(epoch_losses)) if epoch_losses else 0.0 + avg_dice = float(np.mean(epoch_dices)) if epoch_dices else 0.0 + avg_iou = float(np.mean(epoch_ious)) if epoch_ious else 0.0 + avg_reward = float(np.mean(epoch_rewards)) if epoch_rewards else 0.0 + avg_actor = float(np.mean(epoch_actor)) if epoch_actor else 0.0 + avg_critic = float(np.mean(epoch_critic)) if epoch_critic else 0.0 + avg_ce = float(np.mean(epoch_ce)) if epoch_ce else 0.0 + avg_dice_loss = float(np.mean(epoch_dice_losses)) if epoch_dice_losses else 0.0 + avg_entropy = float(np.mean(epoch_entropy)) if epoch_entropy else 0.0 + avg_grad_norm = float(np.mean(epoch_grad_norms)) if epoch_grad_norms else 0.0 + avg_reward_pos = float(np.mean(epoch_reward_pos_pct)) if epoch_reward_pos_pct else 0.0 + avg_pred_fg = float(np.mean(epoch_pred_fg_pct)) if epoch_pred_fg_pct else 0.0 + avg_gt_fg = float(np.mean(epoch_gt_fg_pct)) if epoch_gt_fg_pct else 0.0 + + avg_action_dist = _average_action_distributions(epoch_action_dist, effective_test_tmax) + + history["dice"].append(avg_dice) + history["iou"].append(avg_iou) + history["loss"].append(avg_loss) + history["reward"].append(avg_reward) + history["actor_loss"].append(avg_actor) + history["critic_loss"].append(avg_critic) + history["ce_loss"].append(avg_ce) + history["dice_loss"].append(avg_dice_loss) + history["entropy"].append(avg_entropy) + history["grad_norm"].append(avg_grad_norm) + history["action_dist"].append(avg_action_dist) + history["reward_pos_pct"].append(avg_reward_pos) + history["pred_fg_pct"].append(avg_pred_fg) + history["gt_fg_pct"].append(avg_gt_fg) + save_json(history_path, history) + + loss_delta = avg_loss - prev_loss if prev_loss is not None else 0.0 + prev_loss = avg_loss + if epoch_alerts: + print(f"[Overfit][Epoch {epoch}] Numerical alerts: {' | '.join(epoch_alerts)}") + + if full_dump: + current_alpha = float(log_alpha.exp().detach().item()) if log_alpha is not None else 0.0 + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} (delta={loss_delta:+.6f}) " + f"dice={avg_dice:.4f} iou={avg_iou:.4f} reward={avg_reward:+.6f} " + f"entropy={avg_entropy:.6f} alpha={current_alpha:.4f}" + ) + print( + f"[Overfit][Epoch {epoch:03d}] ce={avg_ce:.6f} dice_l={avg_dice_loss:.6f} " + f"grad_norm={avg_grad_norm:.6f} global_grad={grad_stats['global_norm']:.6f}" + ) + if avg_action_dist: + first = avg_action_dist[0] + last = avg_action_dist[-1] + print( + f"[Overfit][Epoch {epoch:03d}] action step0={first} step_last={last} " + f"reward_pos={avg_reward_pos:.2f}%" + ) + print( + f"[Overfit][Epoch {epoch:03d}] pred_fg={avg_pred_fg:.2f}% gt_fg={avg_gt_fg:.2f}% " + f"param_groups={list(param_stats.keys())}" + ) + else: + print( + f"[Overfit][Epoch {epoch:03d}] loss={avg_loss:.6f} dice={avg_dice:.4f} " + f"iou={avg_iou:.4f} reward={avg_reward:+.6f}" + ) + + row = { + "epoch": epoch, + "dice": avg_dice, + "iou": avg_iou, + "loss": avg_loss, + "reward": avg_reward, + "actor_loss": avg_actor, + "critic_loss": avg_critic, + "ce_loss": avg_ce, + "dice_loss": avg_dice_loss, + "entropy": avg_entropy, + "grad_norm": avg_grad_norm, + "action_dist": avg_action_dist, + "reward_pos_pct": avg_reward_pos, + "pred_fg_pct": avg_pred_fg, + "gt_fg_pct": avg_gt_fg, + } + if avg_dice > best_dice: + best_dice = avg_dice + save_checkpoint( + ckpt_dir / "best.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + if SAVE_LATEST_EVERY_EPOCH: + save_checkpoint( + ckpt_dir / "latest.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + if CHECKPOINT_EVERY_N_EPOCHS > 0 and epoch % CHECKPOINT_EVERY_N_EPOCHS == 0: + save_checkpoint( + ckpt_dir / f"epoch_{epoch:04d}.pt", + run_type="overfit", + model=model, + optimizer=optimizer, + scheduler=None, + scaler=scaler, + epoch=epoch, + best_metric_value=best_dice, + best_metric_name="overfit_dice", + run_config=run_config, + epoch_metrics=row, + patience_counter=0, + elapsed_seconds=elapsed_before_resume + (time.time() - start_time), + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + resume_source=resume_source, + ) + + peak_dice = max(history["dice"]) if history["dice"] else 0.0 + final_dice = history["dice"][-1] if history["dice"] else 0.0 + summary = { + "run_type": "overfit", + "strategy": strategy, + "peak_dice": peak_dice, + "final_dice": final_dice, + "description": description, + "resumed": resume_source is not None, + "elapsed_seconds": elapsed_before_resume + (time.time() - start_time), + "final_epoch": max(len(history["dice"]), start_epoch - 1), + } + if resume_source is not None: + summary["resume_source"] = resume_source + save_json(overfit_root / "summary.json", summary) + print( + f"[Overfit] {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)} | " + f"peak_dice={peak_dice:.4f}, final_dice={final_dice:.4f}" + ) + + del model + run_cuda_cleanup( + context=f"overfit {run_identity_label(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + return {**summary, "history": history} + +def run_configured_overfit_tests( + bundles: dict[float, DataBundle], + *, + model_config: RuntimeModelConfig, +) -> None: + banner("OVERFIT TEST MODE") + for percent in DATASET_PERCENTS: + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=strategy_root_for_percent(strategy, percent, model_config) / "overfit_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + +"""============================================================================= +OPTUNA + ORCHESTRATION +============================================================================= +""" + +def strategy_epochs(strategy: int) -> int: + strategy = _require_supported_strategy(strategy) + if strategy == 2: + return STRATEGY_2_MAX_EPOCHS + if strategy == 3: + return STRATEGY_3_MAX_EPOCHS + raise ValueError(f"Unsupported strategy for epoch selection: {strategy}") + +def suggest_hyperparameters(trial: optuna.trial.Trial, strategy: int) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + if strategy == 3: + rl_lr = trial.suggest_float("rl_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + return { + "head_lr": rl_lr, + "encoder_lr": ENCODER_LR_RANGE[0], + "decoder_lr": 0.0, + "strategy3_decoder_ce_weight": 0.0, + "strategy3_decoder_dice_weight": 0.0, + "strategy3_freeze_bootstrapped_segmentation": True, + "strategy3_variant": DEFAULT_STRATEGY3_VARIANT, + "rl_lr": rl_lr, + "weight_decay": trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True), + "dropout_p": trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]), + "tmax": trial.suggest_int("tmax", TMAX_RANGE[0], TMAX_RANGE[1]), + "smp_encoder_proj_dim": trial.suggest_categorical("smp_encoder_proj_dim", [64, 128, 192, 256]), + "critic_loss_weight": trial.suggest_float("critic_loss_weight", 0.10, 1.50), + "strategy3_mc_dropout_enabled": True, + "strategy3_mc_dropout_samples": trial.suggest_categorical("strategy3_mc_dropout_samples", [4, 8, 12]), + "strategy3_mc_dropout_p": trial.suggest_float("strategy3_mc_dropout_p", 0.05, 0.35), + "strategy3_delta_max": trial.suggest_float("strategy3_delta_max", 0.03, 0.20), + "strategy3_sam_attention_grid": trial.suggest_categorical("strategy3_sam_attention_grid", [16, 32, 64]), + "strategy3_r1_progress_weight": trial.suggest_float("strategy3_r1_progress_weight", 0.25, 2.0), + "biou_reward_weight": trial.suggest_float("biou_reward_weight", 0.0, 2.0), + "strategy3_advantage_normalize": trial.suggest_categorical("strategy3_advantage_normalize", [False, True]), + "strategy3_rl_grad_clip_norm": trial.suggest_float("strategy3_rl_grad_clip_norm", 0.5, 4.0), + "strategy3_rl_loss_scale": trial.suggest_float("strategy3_rl_loss_scale", 2.0, 50.0, log=True), + "strategy3_aux_ce_weight": DEFAULT_STRATEGY3_AUX_CE_WEIGHT, + "strategy3_aux_ce_anneal_start_epoch": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH, + "strategy3_aux_ce_anneal_epochs": DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS, + "strategy3_aux_ce_floor_fraction": DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION, + "threshold": trial.suggest_float("threshold", 0.35, 0.65), + "elastic_aug_prob": trial.suggest_float("elastic_aug_prob", 0.0, 0.5), + "epoch_probe_mode": DEFAULT_STRATEGY3_PROBE_MODE, + "early_stopping_patience": DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[3], + } + + head_lr = trial.suggest_float("head_lr", HEAD_LR_RANGE[0], HEAD_LR_RANGE[1], log=True) + encoder_lr = trial.suggest_float("encoder_lr", ENCODER_LR_RANGE[0], min(ENCODER_LR_RANGE[1], head_lr), log=True) + weight_decay = trial.suggest_float("weight_decay", WEIGHT_DECAY_RANGE[0], WEIGHT_DECAY_RANGE[1], log=True) + dropout_p = trial.suggest_float("dropout_p", DROPOUT_P_RANGE[0], DROPOUT_P_RANGE[1]) + params = { + "head_lr": head_lr, + "encoder_lr": encoder_lr, + "weight_decay": weight_decay, + "dropout_p": dropout_p, + "tmax": DEFAULT_TMAX, + "entropy_lr": DEFAULT_ENTROPY_LR, + "best_checkpoint_metric_name": BEST_CHECKPOINT_METRICS[2], + } + return params + +def _format_hparam_value(key: str, value: Any) -> str: + if isinstance(value, float): + if key in {"head_lr", "encoder_lr", "entropy_lr"}: + return f"{value:.3e}" + return f"{value:.6g}" + return str(value) + +def log_optuna_trial_start( + *, + study_name: str, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + trial_dir: Path, + params: dict[str, Any], + max_epochs: int, +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + lines = [ + "", + "-" * 80, + f"OPTUNA TRIAL START | {run_name}", + "-" * 80, + f"Study name : {study_name}", + f"Run dir : {trial_dir}", + f"Max epochs : {max_epochs}", + f"Objective metric : {_strategy_selection_metric_name(strategy)}", + ] + for key in sorted(params): + lines.append(f"{key:22s}: {_format_hparam_value(key, params[key])}") + tqdm.write("\n".join(lines)) + +def log_optuna_trial_result( + *, + strategy: int, + bundle: DataBundle, + trial: optuna.trial.Trial, + metric_value: float, + aggregate: dict[str, dict[str, float]], +) -> None: + run_name = run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number, + split_payload=bundle.split_payload, + ) + tqdm.write( + f"[{run_name}] completed: {_strategy_selection_metric_name(strategy)}={metric_value:.4f}, " + f"best_test_iou={aggregate['iou']['mean']:.4f}" + ) + +def study_paths_for( + strategy: int, + percent: float, + model_config: RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + pct_root = RUNS_ROOT / MODEL_NAME / f"pct_{percent_label(percent)}" + strategy_root = pct_root / strategy_dir_name(strategy, model_config) + study_root = strategy_root / "study" + trials_root = strategy_root / "trials" + return strategy_root, study_root, trials_root + +def manual_hparams_key(strategy: int, percent: float) -> str: + return f"{strategy}:{percent_label(percent)}" + +def reset_study_artifacts(strategy: int, percent: float, *, model_config: RuntimeModelConfig) -> None: + strategy_root, study_root, trials_root = study_paths_for(strategy, percent, model_config) + removed_any = False + for path in (study_root, trials_root): + if path.exists(): + shutil.rmtree(path) + removed_any = True + if removed_any: + print( + f"[Optuna Reset] Removed cached study artifacts for strategy={strategy}, " + f"percent={percent_text(percent)} under {strategy_root}." + ) + else: + print( + f"[Optuna Reset] No existing study artifacts found for strategy={strategy}, " + f"percent={percent_text(percent)}." + ) + +class _PlateauPruner(optuna.pruners.BasePruner): + """Prune a trial whose metric has plateaued (no improvement to its + own personal best within a patience window). + + Behaviour: + - During the first *n_warmup_steps* epochs: never prune. + - After warmup, track the trial's own best metric and the epoch + at which it was achieved. + - If *patience_steps* epochs pass without the trial beating its + own best, the trial is pruned (it has stagnated). + """ + + def __init__( + self, + n_warmup_steps: int = 80, + patience_steps: int = 40, + ) -> None: + self._n_warmup_steps = n_warmup_steps + self._patience_steps = patience_steps + + def prune( + self, + study: "optuna.study.Study", + trial: "optuna.trial.FrozenTrial", + ) -> bool: + step = trial.last_step + if step is None or step < self._n_warmup_steps: + return False + + post_warmup = { + s: v for s, v in trial.intermediate_values.items() + if s >= self._n_warmup_steps + } + if not post_warmup: + return False + + best_step = max(post_warmup, key=post_warmup.get) + epochs_since_improvement = step - best_step + return epochs_since_improvement >= self._patience_steps + + +def pruner_for_run() -> optuna.pruners.BasePruner: + if USE_TRIAL_PRUNING: + return _PlateauPruner( + n_warmup_steps=TRIAL_PRUNER_WARMUP_STEPS, + patience_steps=TRIAL_PRUNER_PATIENCE_STEPS, + ) + return optuna.pruners.NopPruner() + +def run_single_job( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + run_dir: Path, + params: dict[str, Any], + max_epochs: int, + trial: optuna.trial.Trial | None, + strategy2_checkpoint_path: str | Path | None = None, + resume_checkpoint_path: Path | None = None, + retrying_from_trial_number: int | None = None, +) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, dict[str, float]]]: + strategy = _require_supported_strategy(strategy) + params = dict(params) + params.setdefault("best_checkpoint_metric_name", BEST_CHECKPOINT_METRICS[strategy]) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + if strategy == 3: + params.setdefault( + "strategy3_freeze_bootstrapped_segmentation", + DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION, + ) + bootstrap_freeze = ( + strategy2_checkpoint_path is not None + and bool(params["strategy3_freeze_bootstrapped_segmentation"]) + ) + params.setdefault("strategy3_variant", DEFAULT_STRATEGY3_VARIANT) + params.setdefault("decoder_lr", 0.0 if bootstrap_freeze else params["head_lr"] * 0.1) + params.setdefault("rl_lr", params["head_lr"]) + params.setdefault("strategy3_decoder_ce_weight", 0.0 if bootstrap_freeze else DEFAULT_CE_WEIGHT) + params.setdefault("strategy3_decoder_dice_weight", 0.0 if bootstrap_freeze else DEFAULT_DICE_WEIGHT) + params.setdefault("strategy3_mc_dropout_enabled", DEFAULT_STRATEGY3_MC_DROPOUT_ENABLED) + params.setdefault("strategy3_mc_dropout_samples", DEFAULT_STRATEGY3_MC_DROPOUT_SAMPLES) + params.setdefault("strategy3_mc_dropout_p", DEFAULT_STRATEGY3_MC_DROPOUT_P) + params.setdefault("strategy3_mc_disk_cache_enabled", DEFAULT_STRATEGY3_MC_DISK_CACHE_ENABLED) + params.setdefault("strategy3_mc_disk_cache_read", DEFAULT_STRATEGY3_MC_DISK_CACHE_READ) + params.setdefault("strategy3_mc_disk_cache_write", DEFAULT_STRATEGY3_MC_DISK_CACHE_WRITE) + params.setdefault("strategy3_delta_max", DEFAULT_STRATEGY3_DELTA_MAX) + params.setdefault("strategy3_sam_attention_grid", DEFAULT_STRATEGY3_SAM_ATTENTION_GRID) + params.setdefault("strategy3_r1_progress_weight", DEFAULT_STRATEGY3_R1_PROGRESS_WEIGHT) + params.setdefault("biou_reward_weight", DEFAULT_BIOU_REWARD_WEIGHT) + params.setdefault("strategy3_advantage_normalize", DEFAULT_STRATEGY3_ADVANTAGE_NORMALIZE) + params.setdefault("strategy3_rl_loss_scale", DEFAULT_STRATEGY3_RL_LOSS_SCALE) + params.setdefault("elastic_aug_prob", 0.3) + params.setdefault("strategy3_rl_grad_clip_norm", DEFAULT_STRATEGY3_RL_GRAD_CLIP_NORM) + params.setdefault("strategy3_aux_ce_weight", DEFAULT_STRATEGY3_AUX_CE_WEIGHT) + params.setdefault("strategy3_aux_ce_anneal_start_epoch", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_START_EPOCH) + params.setdefault("strategy3_aux_ce_anneal_epochs", DEFAULT_STRATEGY3_AUX_CE_ANNEAL_EPOCHS) + params.setdefault("strategy3_aux_ce_floor_fraction", DEFAULT_STRATEGY3_AUX_CE_FLOOR_FRACTION) + params.setdefault("epoch_probe_mode", DEFAULT_STRATEGY3_PROBE_MODE) + params.setdefault("early_stopping_monitor", DEFAULT_EARLY_STOPPING_MONITOR) + params.setdefault("early_stopping_mode", DEFAULT_EARLY_STOPPING_MODE) + params.setdefault("early_stopping_min_delta", DEFAULT_EARLY_STOPPING_MIN_DELTA) + params.setdefault("early_stopping_start_epoch", DEFAULT_EARLY_STOPPING_START_EPOCH) + params.setdefault("early_stopping_patience", DEFAULT_STRATEGY3_EARLY_STOPPING_PATIENCE) + set_current_job_params(params) + if "smp_encoder_proj_dim" in params and int(params["smp_encoder_proj_dim"]) != model_config.smp_encoder_proj_dim: + model_config = RuntimeModelConfig.from_payload( + {**model_config.to_payload(), "smp_encoder_proj_dim": int(params["smp_encoder_proj_dim"])} + ).validate() + entropy_target_ratio = float(_job_param("entropy_target_ratio", 0.35)) + entropy_alpha_init = float(_job_param("entropy_alpha_init", 0.12)) + critic_loss_weight = float(_job_param("critic_loss_weight", DEFAULT_CRITIC_LOSS_WEIGHT)) + ensure_dir(run_dir) + run_type = "trial" if trial is not None else "final" + config = { + "project_dir": str(PROJECT_DIR), + "data_root": str(DATA_ROOT), + "run_type": run_type, + "run_name": run_identity_label( + strategy=strategy, + percent=bundle.percent, + trial_number=trial.number if trial is not None else None, + split_payload=bundle.split_payload, + ), + "strategy": strategy, + "dataset_percent": bundle.percent, + "dataset_name": bundle.split_payload["dataset_name"], + "dataset_split_policy": bundle.split_payload["dataset_split_policy"], + "dataset_splits_path": bundle.split_payload["dataset_splits_path"], + "split_type": bundle.split_payload["split_type"], + "train_subset_key": bundle.split_payload["train_subset_key"], + "train_subset_variant": bundle.split_payload.get("train_subset_variant", 0), + "train_subset_source": bundle.split_payload.get("train_subset_source", "persisted"), + "selected_split_manifest_path": bundle.split_payload.get("selected_split_manifest_path"), + "normalization_cache_path": bundle.split_payload["normalization_cache_path"], + "best_checkpoint_metric_name": _strategy_selection_metric_name(strategy), + "best_checkpoint_metrics": {str(key): value for key, value in BEST_CHECKPOINT_METRICS.items()}, + "save_history_incrementally": bool(SAVE_HISTORY_INCREMENTALLY), + "write_epoch_diagnostic": bool(WRITE_EPOCH_DIAGNOSTIC), + "head_lr": params["head_lr"], + "encoder_lr": params["encoder_lr"], + "weight_decay": params["weight_decay"], + "dropout_p": params["dropout_p"], + "tmax": params["tmax"], + "entropy_lr": params["entropy_lr"], + "max_epochs": max_epochs, + "gamma": DEFAULT_GAMMA, + "critic_loss_weight": critic_loss_weight, + "grad_clip_norm": DEFAULT_GRAD_CLIP_NORM, + "scheduler_factor": SCHEDULER_FACTOR, + "scheduler_patience": SCHEDULER_PATIENCE, + "scheduler_threshold": SCHEDULER_THRESHOLD, + "scheduler_min_lr": SCHEDULER_MIN_LR, + "execution_mode": EXECUTION_MODE, + "evaluation_checkpoint_mode": EVAL_CHECKPOINT_MODE, + "strategy2_checkpoint_mode": STRATEGY2_CHECKPOINT_MODE, + "train_resume_mode": TRAIN_RESUME_MODE, + "train_resume_specific_checkpoint": TRAIN_RESUME_SPECIFIC_CHECKPOINT, + } + config.update({key: value for key, value in params.items() if key not in config}) + config.update(model_config.to_payload()) + if strategy2_checkpoint_path is not None: + config["strategy2_checkpoint_path"] = str(Path(strategy2_checkpoint_path)) + if resume_checkpoint_path is not None: + config["resume_checkpoint_path"] = str(Path(resume_checkpoint_path)) + if retrying_from_trial_number is not None: + config["retrying_from_trial_number"] = int(retrying_from_trial_number) + save_json(run_dir / "run_config.json", config) + + model: nn.Module | None = None + try: + model, description, _compiled = build_model( + strategy, + params["dropout_p"], + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + summary, history = train_model( + run_type=run_type, + model_config=model_config, + run_config=config, + model=model, + description=description, + strategy=strategy, + run_dir=run_dir, + bundle=bundle, + max_epochs=max_epochs, + head_lr=params["head_lr"], + encoder_lr=params["encoder_lr"], + weight_decay=params["weight_decay"], + tmax=params["tmax"], + entropy_lr=params["entropy_lr"], + entropy_alpha_init=entropy_alpha_init, + entropy_target_ratio=entropy_target_ratio, + critic_loss_weight=critic_loss_weight, + ce_weight=DEFAULT_CE_WEIGHT, + dice_weight=DEFAULT_DICE_WEIGHT, + dropout_p=params["dropout_p"], + resume_checkpoint_path=resume_checkpoint_path, + trial=trial, + ) + + if trial is not None: + save_json( + run_dir / "summary.json", + { + "params": params, + "best_iou": float(summary["best_val_iou"]), + "best_model_metric_name": str(summary["best_model_metric_name"]), + "best_model_metric": float(summary["best_model_metric"]), + "resumed": bool(resume_checkpoint_path is not None), + "retrying_from_trial_number": retrying_from_trial_number, + }, + ) + return summary, history, {} + + del model + model = None + run_cuda_cleanup() + + aggregate, _per_sample = run_evaluation_for_run( + strategy=strategy, + percent=bundle.percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + return summary, history, aggregate + finally: + if model is not None: + del model + model = None + run_cuda_cleanup() + +def _save_best_params_so_far( + study: optuna.study.Study, + study_root: Path, + strategy: int, + *, + current_trial: optuna.trial.Trial | None = None, + current_best_value: float | None = None, +) -> None: + best, _best_value = _current_optuna_study_best_candidate( + study, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if best is None: + return + params = dict(best.params) + if strategy == 2: + params.setdefault("tmax", DEFAULT_TMAX) + params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", params) + +def run_study( + strategy: int, + bundle: DataBundle, + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, Any]: + strategy = _require_supported_strategy(strategy) + strategy_root, study_root, trials_root = study_paths_for(strategy, bundle.percent, model_config) + ensure_dir(strategy_root.parent) + strategy_root = ensure_dir(strategy_root) + study_root = ensure_dir(strategy_root / "study") + trials_root = ensure_dir(strategy_root / "trials") + storage_path = study_root / "study.sqlite3" + storage = RDBStorage( + url=f"sqlite:///{storage_path.resolve()}", + heartbeat_interval=OPTUNA_HEARTBEAT_INTERVAL, + grace_period=OPTUNA_HEARTBEAT_GRACE_PERIOD, + ) + sampler = optuna.samplers.TPESampler(seed=SEED) + study_name = ( + f"{MODEL_NAME}_{model_config.backbone_tag()}_{run_identity_slug(strategy=strategy, percent=bundle.percent, split_payload=bundle.split_payload)}" + ) + study = optuna.create_study( + study_name=study_name, + direction=STUDY_DIRECTION, + sampler=sampler, + pruner=pruner_for_run(), + storage=storage, + load_if_exists=LOAD_EXISTING_STUDIES, + ) + existing_trials = [trial for trial in study.trials if trial.state.is_finished()] + if existing_trials: + print( + f"[Optuna Study] Loaded existing study '{study_name}' with " + f"{len(existing_trials)} existing finished trial(s). Running {NUM_TRIALS} new trial(s)." + ) + else: + print(f"[Optuna Study] Starting new study '{study_name}' with {NUM_TRIALS} trial(s).") + + def objective(trial: optuna.trial.Trial) -> float: + trial_dir = ensure_dir(trials_root / f"trial_{trial.number:03d}") + params = suggest_hyperparameters(trial, strategy) + log_optuna_trial_start( + study_name=study_name, + strategy=strategy, + bundle=bundle, + trial=trial, + trial_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + ) + summary: dict[str, Any] | None = None + _history: list[dict[str, Any]] | None = None + aggregate: dict[str, dict[str, float]] | None = None + completed_successfully = False + pruned_by_optuna = False + run_cuda_cleanup() + try: + summary, _history, aggregate = run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=trial_dir, + params=params, + max_epochs=strategy_epochs(strategy), + trial=trial, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + metric_value = float(summary["best_model_metric"]) + completed_successfully = True + tqdm.write( + f"[{run_identity_label(strategy=strategy, percent=bundle.percent, trial_number=trial.number, split_payload=bundle.split_payload)}] " + f"completed: {summary['best_model_metric_name']}={metric_value:.4f}" + ) + return metric_value + except optuna.TrialPruned: + pruned_by_optuna = True + raise + finally: + current_trial = trial if (completed_successfully or pruned_by_optuna) else None + current_best_value = None + if summary is not None and summary.get("best_model_metric") is not None: + current_best_value = float(summary["best_model_metric"]) + _save_best_params_so_far( + study, + study_root, + strategy, + current_trial=current_trial, + current_best_value=current_best_value, + ) + if aggregate is not None: + del aggregate + aggregate = None + if _history is not None: + del _history + _history = None + if summary is not None: + del summary + summary = None + prune_optuna_trial_dir(trial_dir) + run_cuda_cleanup(context=f"trial {trial.number:03d} boundary") + + study.optimize(objective, n_trials=NUM_TRIALS, show_progress_bar=True) + + best_trial, best_observed_value = _current_optuna_study_best_candidate(study) + if best_trial is None or best_observed_value is None: + raise RuntimeError( + f"Study '{study_name}' has no trials with recorded best-observed values, so best params cannot be resolved. " + f"Finished trials={len([trial for trial in study.trials if trial.state.is_finished()])}, " + f"configured cap={NUM_TRIALS}." + ) + + best_params = dict(best_trial.params) + if strategy == 2: + best_params.setdefault("tmax", DEFAULT_TMAX) + best_params.setdefault("entropy_lr", DEFAULT_ENTROPY_LR) + save_json(study_root / "best_params.json", best_params) + optuna_best_value: float | None + try: + optuna_best_value = float(study.best_value) + except Exception: + optuna_best_value = None + save_json( + study_root / "summary.json", + { + "best_params": best_params, + "optimized_param_names": sorted(best_params.keys()), + "best_metric_name": _strategy_selection_metric_name(strategy), + "best_metric_value": float(best_observed_value), + "best_observed_value": float(best_observed_value), + "best_trial_number": int(getattr(best_trial, "number", -1)), + "optuna_best_value": optuna_best_value, + "best_iou": float(best_observed_value) if _strategy_selection_metric_name(strategy) == "val_iou" else None, + "finished_trials": len([trial for trial in study.trials if trial.state.is_finished()]), + "completed_trials": len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + "target_trials": int(NUM_TRIALS), + "ran_trials": int(NUM_TRIALS), + }, + ) + prune_optuna_study_dir(study_root) + if trials_root.exists(): + shutil.rmtree(trials_root, ignore_errors=True) + return best_params + +def run_final_training( + strategy: int, + bundle: DataBundle, + params: dict[str, Any], + *, + model_config: RuntimeModelConfig, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + strategy = _require_supported_strategy(strategy) + final_root = final_root_for_strategy(strategy, bundle.percent, model_config) + if SKIP_EXISTING_FINALS and (final_root / "summary.json").exists(): + print(f"Skipping existing final run: {final_root}") + return + save_json(final_root / "best_params.json", params) + resume_checkpoint_path = resolve_train_resume_checkpoint_path(final_root) + run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=final_root, + params=params, + max_epochs=strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + +def print_environment_summary(model_config: RuntimeModelConfig) -> None: + banner("RUNTIME SUMMARY") + images_dir, annotations_dir = current_dataset_dirs() + print(f"Project dir : {PROJECT_DIR}") + print(f"Data root : {DATA_ROOT}") + print(f"Runs root : {RUNS_ROOT}") + print(f"Dataset name : {current_dataset_name()}") + if current_dataset_name() == "BUSI_with_classes": + print(f"Dataset split policy : {current_busi_with_classes_split_policy()}") + print(f"Images dir : {images_dir}") + print(f"Masks dir : {annotations_dir}") + print(f"Dataset splits json : {current_dataset_splits_json_path()}") + print(f"Split type : {SPLIT_TYPE}") + print(f"Experiment mode : {EXPERIMENT_MODE}") + print(f"Device : {DEVICE}") + print(f"Device source : {DEVICE_FALLBACK_SOURCE}") + print(f"Model name : {MODEL_NAME}") + print(f"Seed : {SEED}") + print(f"PyTorch version : {torch.__version__}") + print(f"Batch size : {BATCH_SIZE}") + print(f"Use AMP : {USE_AMP}") + print(f"Num workers : {NUM_WORKERS}") + print(f"Pin memory : {USE_PIN_MEMORY}") + print(f"CuDNN deterministic : {torch.backends.cudnn.deterministic}") + print(f"CuDNN benchmark : {torch.backends.cudnn.benchmark}") + + if DEVICE.type == "cuda": + props = torch.cuda.get_device_properties(0) + print(f"GPU : {torch.cuda.get_device_name(0)}") + print(f"GPU VRAM : {props.total_memory / (1024 ** 3):.2f} GB") + print(f"AMP dtype : {resolve_amp_dtype(AMP_DTYPE)}") + print(f"Trial pruning : {USE_TRIAL_PRUNING}") + print(f"Backbone family : {model_config.backbone_family}") + if model_config.backbone_family == "custom_vgg": + print(f"VGG feature scales : {model_config.vgg_feature_scales}") + print(f"VGG feature dilation : {model_config.vgg_feature_dilation}") + else: + print(f"SMP encoder : {model_config.smp_encoder_name}") + print(f"SMP encoder depth : {model_config.smp_encoder_depth}") + print(f"SMP encoder proj dim : {model_config.smp_encoder_proj_dim}") + print(f"SMP decoder : {model_config.smp_decoder_type}") + print(f"Strategies : {STRATEGIES}") + print(f"Dataset percents : {[percent_text(value) for value in DATASET_PERCENTS]}") + print(f"Best metrics : {BEST_CHECKPOINT_METRICS}") + print(f"History incremental : {SAVE_HISTORY_INCREMENTALLY}") + print(f"Write diagnostics : {WRITE_EPOCH_DIAGNOSTIC}") + print_imagenet_normalization_status() + print(f"Trials per study : {NUM_TRIALS}") + print(f"Execution mode : {EXECUTION_MODE}") + print(f"Run Optuna : {RUN_OPTUNA}") + print(f"Use saved best params : {USE_EXISTING_BEST_PARAMS_WHEN_OPTUNA_OFF}") + print(f"Reset studies/run : {RESET_ALL_STUDIES_EACH_RUN}") + print(f"Load existing studies : {LOAD_EXISTING_STUDIES}") + print(f"Eval ckpt selector : {EVAL_CHECKPOINT_MODE}") + print(f"S2 ckpt selector : {STRATEGY2_CHECKPOINT_MODE}") + print(f"S3 bootstrap from S2 : {STRATEGY3_BOOTSTRAP_FROM_STRATEGY2}") + print(f"S3 freeze default : {DEFAULT_STRATEGY3_FREEZE_BOOTSTRAPPED_SEGMENTATION}") + print(f"Train resume mode : {TRAIN_RESUME_MODE}") + print(f"Verbose epoch log : {VERBOSE_EPOCH_LOG}") + print(f"Validate every epochs : {VALIDATE_EVERY_N_EPOCHS}") + print(f"Smoke test enabled : {RUN_SMOKE_TEST}") + print(f"Test iter control : {TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {TEST_ITERATION_T}") + print(f"Overfit test enabled : {RUN_OVERFIT_TEST}") + print(f"Overfit batches : {OVERFIT_N_BATCHES}") + print(f"Overfit epochs : {OVERFIT_N_EPOCHS}") + +def maybe_run_strategy_smoke_test( + *, + strategy: int, + model_config: RuntimeModelConfig, + bundle: DataBundle, + strategy2_checkpoint_path: str | Path | None = None, +) -> None: + if not RUN_SMOKE_TEST: + return + if EXECUTION_MODE == "eval_only": + return + run_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + smoke_root=strategy_root_for_percent(strategy, bundle.percent, model_config) / "smoke_test", + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +"""============================================================================= +REPEATED HOLDOUT INTEGRATION +============================================================================= +""" + +base = sys.modules[__name__] + +REPEATED_HOLDOUT_ROOT = base.RUNS_ROOT / base.MODEL_NAME / "repeated_holdout" +EXPERIMENT_ROOT = REPEATED_HOLDOUT_ROOT / FOLDS_EXPERIMENT_NAME +EXPERIMENT_DB_PATH = EXPERIMENT_ROOT / "experiment_state.sqlite3" +SPLIT_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "splits" +SUBSET_MANIFESTS_DIR = EXPERIMENT_ROOT / "manifests" / "subsets" +EXPORTS_DIR = EXPERIMENT_ROOT / "exports" +"""============================================================================= +RUNTIME STATE +============================================================================= +""" + + +@dataclass(frozen=True) +class PercentRepeatSpec: + percent_int: int + fraction: float + repeat_count: int + + +@dataclass(frozen=True) +class FoldRunContext: + split_repeat_index: int + subset_repeat_index: int + percent_int: int + percent_fraction: float + split_seed: int + subset_seed: int + repeat_root: Path + split_manifest_path: Path + subset_manifest_path: Path + + +@dataclass(frozen=True) +class RunKey: + split_repeat_index: int + dataset_percent: int + subset_repeat_index: int + strategy: int + + +CURRENT_FOLD_CONTEXT: FoldRunContext | None = None +LEDGER_CONN: sqlite3.Connection | None = None +PERCENT_SPECS_CACHE: list[PercentRepeatSpec] | None = None + +ORIGINAL_SAVE_JSON = base.save_json +ORIGINAL_PERCENT_ROOT = base.percent_root +ORIGINAL_STRATEGY_ROOT_FOR_PERCENT = base.strategy_root_for_percent +ORIGINAL_FINAL_ROOT_FOR_STRATEGY = base.final_root_for_strategy +ORIGINAL_STUDY_PATHS_FOR = base.study_paths_for +ORIGINAL_SAVE_CHECKPOINT = base.save_checkpoint +ORIGINAL_RUN_EVALUATION_FOR_RUN = base.run_evaluation_for_run + +BASE_RESUME_IDENTITY_KEYS = tuple(base.RESUME_IDENTITY_KEYS) +RUNTIME_ONLY_CONFIG_KEYS = frozenset( + { + "PERCENT_EXECUTION_MODE", + "SELECTED_DATASET_PERCENTS", + "SPLIT_EXECUTION_MODE", + "SELECTED_SPLIT_INDICES", + "PHASE_EXECUTION_MODE", + "SELECTED_PHASES", + "REPEAT_EXECUTION_MODE", + "SELECTED_REPEAT_INDICES", + } +) +PORTABLE_FINGERPRINT_FOLD_KEYS = frozenset( + { + "RESUME_FOLDS", + "REPEATED_HOLDOUT_ROOT", + "EXPERIMENT_ROOT", + "EXPERIMENT_DB_PATH", + } +) + + +"""============================================================================= +UTILITIES +============================================================================= +""" + + +def now_utc_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _jsonify(value: Any) -> Any: + if isinstance(value, Path): + return str(value) + if isinstance(value, dict): + return {str(key): _jsonify(val) for key, val in sorted(value.items(), key=lambda item: str(item[0]))} + if isinstance(value, (list, tuple)): + return [_jsonify(item) for item in value] + if isinstance(value, set): + return [_jsonify(item) for item in sorted(value, key=str)] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return repr(value) + + +def _summary_mean_std(values: list[float]) -> dict[str, float]: + arr = np.array(values, dtype=np.float64) + return { + "mean": float(arr.mean()) if arr.size > 0 else 0.0, + "std": float(arr.std()) if arr.size > 0 else 0.0, + } + + +def _phase_timing_summary_path() -> Path: + return EXPERIMENT_ROOT / "phase_timing_summary.json" + + +def _completed_run_rows_for_phase(phase_index: int) -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + AND split_repeat_index = ? + ORDER BY dataset_percent, subset_repeat_index, strategy + """, + (int(phase_index),), + ).fetchall() + ) + + +def _run_training_elapsed_seconds(row: sqlite3.Row) -> float | None: + run_dir = Path(str(row["run_dir"])) + summary_path = run_dir / "summary.json" + if summary_path.exists(): + try: + summary = base.load_json(summary_path) + if summary.get("elapsed_seconds") is not None: + return float(summary["elapsed_seconds"]) + except Exception as exc: + print(f"[Timing] Could not read {summary_path}: {exc}") + if row["elapsed_seconds"] is not None: + return float(row["elapsed_seconds"]) + return None + + +def write_phase_timing_summary_after_phase(phase_index: int) -> None: + if LEDGER_CONN is None: + return + rows = _completed_run_rows_for_phase(phase_index) + if not rows: + return + + run_entries: list[dict[str, Any]] = [] + phase_values: list[float] = [] + for row in rows: + elapsed = _run_training_elapsed_seconds(row) + entry = { + "phase_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": str(row["run_dir"]), + "training_elapsed_seconds": elapsed, + } + run_entries.append(entry) + if elapsed is not None: + phase_values.append(float(elapsed)) + + phase_stats = _summary_mean_std(phase_values) + existing_payload: dict[str, Any] = {} + summary_path = _phase_timing_summary_path() + if summary_path.exists(): + try: + existing_payload = base.load_json(summary_path) + except Exception as exc: + print(f"[Timing] Could not read existing phase timing summary {summary_path}: {exc}") + + phases_by_index: dict[int, dict[str, Any]] = {} + for phase_payload in existing_payload.get("phases", []): + if isinstance(phase_payload, dict) and phase_payload.get("phase_index") is not None: + phases_by_index[int(phase_payload["phase_index"])] = dict(phase_payload) + phases_by_index[int(phase_index)] = { + "phase_index": int(phase_index), + "completed_strategy_count": len(run_entries), + "completed_strategies": [int(entry["strategy"]) for entry in run_entries], + "runs": run_entries, + "training_elapsed_seconds_mean": phase_stats["mean"], + "training_elapsed_seconds_std": phase_stats["std"], + "updated_at": now_utc_iso(), + } + + phases = [phases_by_index[index] for index in sorted(phases_by_index)] + global_phase_means = [ + float(phase["training_elapsed_seconds_mean"]) + for phase in phases + if phase.get("training_elapsed_seconds_mean") is not None + ] + global_stats = _summary_mean_std(global_phase_means) + payload = { + "scope": "fixed_phase_training_time", + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": len(phases), + "training_elapsed_seconds_mean_across_phases": global_stats["mean"], + "training_elapsed_seconds_std_across_phases": global_stats["std"], + "phases": phases, + "updated_at": now_utc_iso(), + } + atomic_save_json(summary_path, payload) + print( + f"[Timing] Phase {phase_index:03d} training time summary updated -> {summary_path} " + f"(mean={phase_stats['mean']:.2f}s, std={phase_stats['std']:.2f}s)." + ) + + +def validate_hf_backup_settings() -> None: + """Fail fast at startup if backups are enabled but HF env vars are missing. + + Refuses to run rather than discovering hours into training (at the first + backup) that nothing can be uploaded. Disable by setting + ASYNC_REPO_BACKUP_AFTER_PHASE = False if you intentionally want no backups. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + missing = [] + if not repo_id: + missing.append("HF_REPO_ID (target repo, e.g. 'your-username/ADVAI24JUN-backup')") + if not token: + missing.append("HF_TOKEN (Hugging Face write token)") + if missing: + raise RuntimeError( + "Hugging Face backup is enabled (ASYNC_REPO_BACKUP_AFTER_PHASE = True) " + "but required environment variables are not set:\n - " + + "\n - ".join(missing) + + "\n\nSet them before running, e.g.:\n" + " export HF_REPO_ID='your-username/ADVAI24JUN-backup'\n" + " export HF_TOKEN='hf_xxxxxxxxxxxxxxxxxxxxx'\n" + "Or set ASYNC_REPO_BACKUP_AFTER_PHASE = False to run without backups." + ) + + +def _hf_backup_due(phase_index: int) -> bool: + """True only on every HF_BACKUP_EVERY_N_PHASES-th phase (0-indexed boundary).""" + n = max(1, int(HF_BACKUP_EVERY_N_PHASES)) + return (phase_index + 1) % n == 0 + + +def _hf_upload_project(*, label: str) -> bool: + """Create the HF dataset repo if needed and mirror PROJECT_DIR into it. + + Shared by the initial pre-training backup and the per-phase backups. + `upload_large_folder` is resumable and content-addressed: unchanged files are + skipped and an interrupted upload (e.g. a 503) can be safely re-run, so the repo + always converges to the latest project state. Retries with backoff to ride out + transient HF outages. Returns True on a verified successful upload. + """ + repo_id = HF_REPO_ID or os.environ.get("HF_REPO_ID", "") + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") + if not repo_id: + print(f"[Backup] Skipping HF backup ({label}): HF_REPO_ID is not set (export HF_REPO_ID=user/repo).") + return False + if not token: + print(f"[Backup] Skipping HF backup ({label}): HF_TOKEN env var is not set.") + return False + + try: + from huggingface_hub import HfApi + except Exception: + print(f"[Backup] Skipping HF backup ({label}): huggingface_hub not installed (pip install huggingface_hub).") + return False + + api = HfApi(token=token) + try: + api.create_repo(repo_id=repo_id, repo_type=HF_REPO_TYPE, private=True, exist_ok=True) + except Exception as exc: + print(f"[Backup] Could not ensure HF repo {repo_id} exists: {exc}") + + last_exc: Exception | None = None + for attempt in range(1, HF_BACKUP_MAX_RETRIES + 1): + try: + print( + f"[Backup] {label}: uploading project to " + f"hf://{HF_REPO_TYPE}/{repo_id} (attempt {attempt}/{HF_BACKUP_MAX_RETRIES})..." + ) + api.upload_large_folder( + repo_id=repo_id, + repo_type=HF_REPO_TYPE, + folder_path=str(PROJECT_DIR.resolve()), + ignore_patterns=list(HF_IGNORE_PATTERNS), + print_report=True, + ) + print(f"[Backup] {label}: HF backup complete -> {repo_id}.") + return True + except Exception as exc: + last_exc = exc + wait = min(60, 5 * attempt) + print(f"[Backup] {label}: HF upload attempt {attempt} failed: {exc}. Retrying in {wait}s...") + time.sleep(wait) + print(f"[Backup] {label}: HF backup FAILED after {HF_BACKUP_MAX_RETRIES} attempts: {last_exc}") + return False + + +def run_initial_hf_backup() -> None: + """Fresh backup BEFORE any training begins. + + Creates the repo and uploads the current project state synchronously, so the + entire backup pipeline (repo creation, token, upload) is proven before we commit + hours of compute. Later phase backups refresh this same repo. Runs in the + foreground on purpose -- if the first backup cannot complete, we want to know now. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE or not HF_BACKUP_ON_START: + return + print("[Backup] Running initial pre-training backup (this proves the backup pipeline before training)...") + _hf_upload_project(label="Initial backup") + + +def run_repo_backup_after_phase(phase_index: int) -> None: + """Refresh the Hugging Face dataset repo after a training phase. + + Fires only on every HF_BACKUP_EVERY_N_PHASES-th phase so we don't hammer HF. + Runs in a background thread at the call site. + """ + if not ASYNC_REPO_BACKUP_AFTER_PHASE: + return + if not _hf_backup_due(phase_index): + print( + f"[Backup] Phase {phase_index:03d}: skipping HF backup " + f"(uploads every {HF_BACKUP_EVERY_N_PHASES} phases)." + ) + return + _hf_upload_project(label=f"Phase {phase_index:03d}") + + +def atomic_write_text(path: str | Path, text: str) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_write_bytes(path: str | Path, payload: bytes) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(payload) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def atomic_save_json(path: str | Path, payload: Any) -> None: + atomic_write_text(path, json.dumps(payload, indent=2, sort_keys=True)) + + +def atomic_torch_save(path: str | Path, payload: Any) -> None: + target = Path(path) + ensure_dir(target.parent) + fd, tmp_name = tempfile.mkstemp(prefix=f".{target.name}.", suffix=".tmp", dir=str(target.parent)) + tmp_path = Path(tmp_name) + try: + with os.fdopen(fd, "wb") as handle: + torch.save(payload, handle) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_path, target) + finally: + if tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + + +def stable_hash(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def stable_int(text: str) -> int: + return base.stable_int_from_text(text) + + +def fold_seed(tag: str) -> int: + return int(base.SEED) + stable_int(tag) + + +def current_split_generation_mode() -> str: + mode = str(SPLIT_GENERATION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_GENERATION_MODES: + raise ValueError( + f"SPLIT_GENERATION_MODE must be one of {SUPPORTED_SPLIT_GENERATION_MODES}, got {mode!r}" + ) + return mode + + +def using_fixed_phase_mode() -> bool: + return current_split_generation_mode() == "fixed_stratified_phases_8_1_1" + + +def primary_unit_name(*, plural: bool = False) -> str: + if using_fixed_phase_mode(): + return "phases" if plural else "phase" + return "splits" if plural else "split" + + +def current_phase_execution_mode() -> str: + mode = str(PHASE_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PHASE_EXECUTION_MODES: + raise ValueError( + f"PHASE_EXECUTION_MODE must be one of {SUPPORTED_PHASE_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def phase_count() -> int: + if isinstance(NUM_PHASES, bool) or int(NUM_PHASES) <= 0: + raise ValueError("NUM_PHASES must be a positive integer.") + return int(NUM_PHASES) + + +def phase_val_offset() -> int: + if isinstance(PHASE_VAL_OFFSET, bool): + raise TypeError("PHASE_VAL_OFFSET must be an integer.") + return int(PHASE_VAL_OFFSET) + + +def phase_indices() -> list[int]: + return list(range(1, phase_count() + 1)) + + +def phase_execution_indices_to_run() -> list[int]: + indices = phase_indices() + if current_phase_execution_mode() == "auto": + return indices + + if not SELECTED_PHASES: + raise ValueError("SELECTED_PHASES must be non-empty when PHASE_EXECUTION_MODE='manual'.") + + selected: list[int] = [] + seen: set[int] = set() + max_index = indices[-1] + for raw_index in SELECTED_PHASES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + f"SELECTED_PHASES entries must be integer phase indices in the range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_PHASES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_PHASES contains duplicate phase index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def phase_fold_indices(phase_index: int) -> tuple[int, int]: + count = phase_count() + if phase_index < 1 or phase_index > count: + raise ValueError(f"Phase index must be in [1, {count}], got {phase_index}.") + val_offset = phase_val_offset() + if val_offset <= 0 or val_offset >= count: + raise ValueError( + f"PHASE_VAL_OFFSET must be in [1, {count - 1}] for {count} phases, got {val_offset}." + ) + test_fold_index = phase_index + val_fold_index = ((phase_index - 1 + val_offset) % count) + 1 + return val_fold_index, test_fold_index + + +def partition_seed() -> int: + if using_fixed_phase_mode(): + return fold_seed(f"phase_partition::{phase_count()}") + return int(base.SEED) + + +def split_generation_display_name() -> str: + if using_fixed_phase_mode(): + return "fixed stratified 10-phase 8/1/1" + return "repeated stratified holdout" + + +def cycle_index_label(index: int) -> str: + return f"{primary_unit_name()}_{int(index):03d}" + + +def cycle_identity_label(index: int) -> str: + return f"{primary_unit_name()}={int(index):03d}" + + +def all_split_repeat_indices() -> list[int]: + if using_fixed_phase_mode(): + return phase_indices() + return list(range(1, int(NUM_STRATIFIED_SPLIT_REPEATS) + 1)) + + +def max_subset_repeat_index() -> int: + return max(int(spec.repeat_count) for spec in percent_specs()) + + +def current_percent_sampling_mode() -> str: + mode = str(PERCENT_SAMPLING_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_SAMPLING_MODES: + raise ValueError( + f"PERCENT_SAMPLING_MODE must be one of {SUPPORTED_PERCENT_SAMPLING_MODES}, got {mode!r}" + ) + return mode + + +def current_split_execution_mode() -> str: + mode = str(SPLIT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_SPLIT_EXECUTION_MODES: + raise ValueError( + f"SPLIT_EXECUTION_MODE must be one of {SUPPORTED_SPLIT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_repeat_execution_mode() -> str: + mode = str(REPEAT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_REPEAT_EXECUTION_MODES: + raise ValueError( + f"REPEAT_EXECUTION_MODE must be one of {SUPPORTED_REPEAT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def current_percent_execution_mode() -> str: + mode = str(PERCENT_EXECUTION_MODE).strip().lower() + if mode not in SUPPORTED_PERCENT_EXECUTION_MODES: + raise ValueError( + f"PERCENT_EXECUTION_MODE must be one of {SUPPORTED_PERCENT_EXECUTION_MODES}, got {mode!r}" + ) + return mode + + +def split_repeat_indices_to_run() -> list[int]: + if using_fixed_phase_mode(): + return phase_execution_indices_to_run() + + split_indices = all_split_repeat_indices() + if current_split_execution_mode() == "auto": + return split_indices + + if not SELECTED_SPLIT_INDICES: + raise ValueError( + "SELECTED_SPLIT_INDICES must be non-empty when SPLIT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = split_indices[-1] + for raw_index in SELECTED_SPLIT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_SPLIT_INDICES entries must be integer split indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_SPLIT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_SPLIT_INDICES contains duplicate split index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def subset_repeat_indices_to_run() -> list[int]: + repeat_indices = list(range(1, max_subset_repeat_index() + 1)) + if current_repeat_execution_mode() == "auto": + return repeat_indices + + if not SELECTED_REPEAT_INDICES: + raise ValueError( + "SELECTED_REPEAT_INDICES must be non-empty when REPEAT_EXECUTION_MODE='manual'." + ) + + selected: list[int] = [] + seen: set[int] = set() + max_index = repeat_indices[-1] + for raw_index in SELECTED_REPEAT_INDICES: + if isinstance(raw_index, bool) or not isinstance(raw_index, int): + raise TypeError( + "SELECTED_REPEAT_INDICES entries must be integer repeat indices in the " + f"range [1, {max_index}]." + ) + if raw_index < 1 or raw_index > max_index: + raise ValueError( + f"SELECTED_REPEAT_INDICES entries must be in the range [1, {max_index}], got {raw_index}." + ) + if raw_index in seen: + raise ValueError(f"SELECTED_REPEAT_INDICES contains duplicate repeat index {raw_index}.") + seen.add(raw_index) + selected.append(raw_index) + return selected + + +def percent_specs_to_run() -> list[PercentRepeatSpec]: + all_specs = percent_specs() + if current_percent_execution_mode() == "auto": + return all_specs + + if not SELECTED_DATASET_PERCENTS: + raise ValueError( + "SELECTED_DATASET_PERCENTS must be non-empty when PERCENT_EXECUTION_MODE='manual'." + ) + + all_percent_ints = {spec.percent_int for spec in all_specs} + selected: list[PercentRepeatSpec] = [] + seen: set[int] = set() + for raw_percent in SELECTED_DATASET_PERCENTS: + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "SELECTED_DATASET_PERCENTS entries must be integer percentages in the range [1, 100]." + ) + if raw_percent not in all_percent_ints: + raise ValueError( + f"SELECTED_DATASET_PERCENTS entry {raw_percent} is not defined in " + f"DATASET_PERCENT_REPEAT_COUNTS. Available: {sorted(all_percent_ints)}." + ) + if raw_percent in seen: + raise ValueError(f"SELECTED_DATASET_PERCENTS contains duplicate percent {raw_percent}.") + seen.add(raw_percent) + spec_map = {spec.percent_int: spec for spec in all_specs} + for raw_percent in SELECTED_DATASET_PERCENTS: + selected.append(spec_map[raw_percent]) + selected.sort(key=lambda s: s.percent_int) + return selected + + +def validate_repeated_holdout_settings() -> None: + if using_fixed_phase_mode(): + if phase_count() != 10: + raise ValueError( + f"fixed phase mode requires NUM_PHASES=10, got {phase_count()}." + ) + phase_fold_indices(1) + if current_dataset_name() != "BUSI_with_classes": + raise ValueError( + "fixed phase mode currently supports DATASET_NAME='BUSI_with_classes' only." + ) + if current_busi_with_classes_split_policy() != "stratified": + raise ValueError( + "fixed phase mode requires BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"fixed phase mode requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + current_phase_execution_mode() + else: + if int(NUM_STRATIFIED_SPLIT_REPEATS) <= 0: + raise ValueError("NUM_STRATIFIED_SPLIT_REPEATS must be a positive integer.") + current_split_execution_mode() + current_percent_sampling_mode() + current_repeat_execution_mode() + current_percent_execution_mode() + split_repeat_indices_to_run() + subset_repeat_indices_to_run() + selected_percent_specs = percent_specs_to_run() + if using_fixed_phase_mode(): + if len(selected_percent_specs) != 1 or int(selected_percent_specs[0].percent_int) != 100: + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "If PERCENT_EXECUTION_MODE='manual', set SELECTED_DATASET_PERCENTS=[100]." + ) + + +def repeated_holdout_split_policy() -> str | None: + if base.current_dataset_name() == "BUSI_with_classes": + return base.current_busi_with_classes_split_policy() + return None + + +def active_specs_for_subset_repeat(subset_repeat_index: int) -> list[PercentRepeatSpec]: + return [ + spec + for spec in percent_specs() + if subset_repeat_index <= int(spec.repeat_count) + ] + + +def percent_specs() -> list[PercentRepeatSpec]: + global PERCENT_SPECS_CACHE + if PERCENT_SPECS_CACHE is not None: + return list(PERCENT_SPECS_CACHE) + specs: list[PercentRepeatSpec] = [] + if not DATASET_PERCENT_REPEAT_COUNTS: + raise ValueError("DATASET_PERCENT_REPEAT_COUNTS must contain at least one percentage entry.") + for raw_percent, raw_repeat_count in DATASET_PERCENT_REPEAT_COUNTS.items(): + if isinstance(raw_percent, bool) or not isinstance(raw_percent, int): + raise TypeError( + "DATASET_PERCENT_REPEAT_COUNTS keys must be integer percentages in the range [1, 100]." + ) + if raw_percent <= 0 or raw_percent > 100: + raise ValueError(f"Invalid dataset percent {raw_percent}; expected an integer in [1, 100].") + if isinstance(raw_repeat_count, bool) or int(raw_repeat_count) <= 0: + raise ValueError( + f"Invalid repeat count for percent {raw_percent}: {raw_repeat_count!r}. Expected a positive integer." + ) + repeat_count = int(raw_repeat_count) + if using_fixed_phase_mode() and raw_percent == 100 and repeat_count != 1: + raise ValueError( + "fixed phase mode requires DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got repeat_count={repeat_count} for percent 100." + ) + if raw_percent == 100 and repeat_count > 1: + print( + f"[Repeated Holdout] Percent 100 was configured with repeat_count={repeat_count}. " + "Collapsing to one effective repeat per split." + ) + repeat_count = 1 + fraction = float(raw_percent) / 100.0 + specs.append(PercentRepeatSpec(percent_int=raw_percent, fraction=fraction, repeat_count=repeat_count)) + specs.sort(key=lambda item: item.percent_int) + if using_fixed_phase_mode(): + if len(specs) != 1 or int(specs[0].percent_int) != 100: + configured = {spec.percent_int: spec.repeat_count for spec in specs} + raise ValueError( + "fixed phase mode only supports dataset percent 100. " + "Set DATASET_PERCENT_REPEAT_COUNTS={100: 1}. " + f"Got {configured}." + ) + PERCENT_SPECS_CACHE = list(specs) + return list(PERCENT_SPECS_CACHE) + + +def fold_experiment_summary(model_config: base.RuntimeModelConfig) -> None: + if using_fixed_phase_mode(): + base.banner("RUNNER FOLDS | FIXED STRATIFIED 10-PHASE 8/1/1") + else: + base.banner("RUNNER FOLDS | REPEATED STRATIFIED HOLDOUT") + print(f"Experiment name : {FOLDS_EXPERIMENT_NAME}") + print(f"Resume folds : {RESUME_FOLDS}") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Dataset name : {base.current_dataset_name()}") + print(f"Split type : {base.SPLIT_TYPE}") + print(f"Split generation mode : {current_split_generation_mode()}") + print(f"Generation display : {split_generation_display_name()}") + if using_fixed_phase_mode(): + print(f"Phase count : {phase_count()}") + print(f"Phase val offset : {phase_val_offset()}") + print(f"Phase execution mode : {current_phase_execution_mode()}") + print("Train percent mode : 100% of phase-train only") + if current_phase_execution_mode() == "manual": + print(f"Selected phases : {phase_execution_indices_to_run()}") + else: + print(f"Split repeats : {NUM_STRATIFIED_SPLIT_REPEATS}") + print(f"Split execution mode : {current_split_execution_mode()}") + if current_split_execution_mode() == "manual": + print(f"Selected split indices: {split_repeat_indices_to_run()}") + print(f"Sampling mode : {current_percent_sampling_mode()}") + print(f"Repeat execution mode : {current_repeat_execution_mode()}") + if current_repeat_execution_mode() == "manual": + print(f"Selected repeat idxs : {subset_repeat_indices_to_run()}") + print(f"Percent execution mode: {current_percent_execution_mode()}") + if current_percent_execution_mode() == "manual": + print(f"Selected percents : {[s.percent_int for s in percent_specs_to_run()]}") + print(f"Strategies : {base.STRATEGIES}") + print( + "Percent repeats : " + + ", ".join(f"{spec.percent_int}% x{spec.repeat_count}" for spec in percent_specs()) + ) + print(f"Execution mode : {base.EXECUTION_MODE}") + print(f"Run smoke test : {base.RUN_SMOKE_TEST}") + print(f"Test iter control : {base.TEST_ITERATION_CONTROL}") + print(f"Test iter target t : {base.TEST_ITERATION_T}") + print(f"Run overfit test : {base.RUN_OVERFIT_TEST}") + print(f"Run Optuna : {base.RUN_OPTUNA}") + print(f"Load existing studies : {base.LOAD_EXISTING_STUDIES}") + print(f"Repo backup enabled : {ASYNC_REPO_BACKUP_AFTER_PHASE}") + if ASYNC_REPO_BACKUP_AFTER_PHASE: + print(f"Repo backup target : hf://{HF_REPO_TYPE}/{HF_REPO_ID or ''}") + print(f"Repo backup cadence : every {HF_BACKUP_EVERY_N_PHASES} phases") + print(f"Initial backup on run : {HF_BACKUP_ON_START}") + print(f"Phase timing summary : {_phase_timing_summary_path()}") + print(f"Backbone : {model_config.backbone_display_name()}") + + +def config_snapshot(model_config: base.RuntimeModelConfig) -> dict[str, Any]: + base_config = { + name: _jsonify(getattr(base, name)) + for name in sorted(dir(base)) + if name.isupper() and not name.startswith("_") + and name not in RUNTIME_ONLY_CONFIG_KEYS + } + return { + "folds_runner": { + "SPLIT_GENERATION_MODE": current_split_generation_mode(), + "NUM_STRATIFIED_SPLIT_REPEATS": int(NUM_STRATIFIED_SPLIT_REPEATS), + "NUM_PHASES": int(NUM_PHASES), + "PHASE_VAL_OFFSET": int(PHASE_VAL_OFFSET), + "DATASET_PERCENT_REPEAT_COUNTS": _jsonify(DATASET_PERCENT_REPEAT_COUNTS), + "PERCENT_SAMPLING_MODE": current_percent_sampling_mode(), + "FOLDS_EXPERIMENT_NAME": str(FOLDS_EXPERIMENT_NAME), + "RESUME_FOLDS": bool(RESUME_FOLDS), + "REPEATED_HOLDOUT_ROOT": str(REPEATED_HOLDOUT_ROOT), + "EXPERIMENT_ROOT": str(EXPERIMENT_ROOT), + "EXPERIMENT_DB_PATH": str(EXPERIMENT_DB_PATH), + }, + "runner": base_config, + "model_config": model_config.to_payload(), + } + + +def portable_config_snapshot_for_fingerprint(snapshot: dict[str, Any]) -> dict[str, Any]: + portable = json.loads(json.dumps(snapshot, sort_keys=True)) + folds_runner = portable.get("folds_runner") + if isinstance(folds_runner, dict): + for key in PORTABLE_FINGERPRINT_FOLD_KEYS: + folds_runner.pop(key, None) + return portable + + +def config_fingerprint(snapshot: dict[str, Any]) -> str: + return stable_hash(json.dumps(portable_config_snapshot_for_fingerprint(snapshot), sort_keys=True)) + + +def dataset_fingerprint(sample_records: list[dict[str, str]]) -> str: + payload = { + "dataset_name": base.current_dataset_name(), + "records": [ + { + "filename": record["filename"], + "image_rel_path": record["image_rel_path"], + "mask_rel_path": record["mask_rel_path"], + "class_label": record.get("class_label"), + } + for record in sample_records + ], + } + return stable_hash(json.dumps(payload, sort_keys=True)) + + +def cycle_dirname(split_repeat_index: int) -> str: + return f"{primary_unit_name()}_{split_repeat_index:03d}" + + +def split_manifest_path(split_repeat_index: int) -> Path: + return SPLIT_MANIFESTS_DIR / f"{cycle_dirname(split_repeat_index)}.json" + + +def subset_manifest_path(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return SUBSET_MANIFESTS_DIR / ( + f"{cycle_dirname(split_repeat_index)}_pct_{percent_int:03d}_repeat_{subset_repeat_index:02d}.json" + ) + + +def repeat_root(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> Path: + return ( + EXPERIMENT_ROOT + / cycle_dirname(split_repeat_index) + / f"pct_{percent_int:03d}" + / f"repeat_{subset_repeat_index:02d}" + ) + + +def run_dir_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "final") + + +def overfit_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir / "overfit_test") + + +def strategy_root_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> Path: + strategy_dir = base.strategy_dir_name(strategy, model_config) + return ensure_dir(ctx.repeat_root / strategy_dir) + + +def fold_study_paths_for(strategy: int, ctx: FoldRunContext, model_config: base.RuntimeModelConfig | None = None) -> tuple[Path, Path, Path]: + strategy_root = strategy_root_for(strategy, ctx, model_config) + return strategy_root, ensure_dir(strategy_root / "study"), ensure_dir(strategy_root / "trials") + + +def select_sample_records() -> tuple[list[dict[str, str]], Path]: + dataset_name = base.current_dataset_name() + images_dir, annotations_dir = base.current_dataset_dirs() + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {base.DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + + matched, missing_masks, missing_images = base.validate_image_mask_consistency(images_dir, annotations_dir) + if missing_masks or missing_images: + raise RuntimeError( + "BUSI image/mask mismatch detected. " + f"missing_masks={len(missing_masks)}, missing_images={len(missing_images)}" + ) + + dataset_root = images_dir.parent.resolve() + sample_records = base.build_sample_records( + matched, + images_subdir=images_dir.relative_to(dataset_root).as_posix(), + annotations_subdir=annotations_dir.relative_to(dataset_root).as_posix(), + dataset_name=dataset_name, + ) + if dataset_name == "BUSI_with_classes": + pipeline_check_path = base.current_pipeline_check_path() + if pipeline_check_path is not None: + base.validate_busi_with_classes_pipeline_report(pipeline_check_path, sample_records) + return sample_records, dataset_root + + +def record_filenames(records: list[dict[str, str]]) -> list[str]: + return [str(record["filename"]) for record in records] + + +def duplicate_filenames(records: list[dict[str, str]]) -> list[str]: + counts = Counter(record_filenames(records)) + return sorted(name for name, count in counts.items() if count > 1) + + +def format_filename_preview(filenames: list[str], *, limit: int = 5) -> str: + preview = filenames[:limit] + suffix = "" if len(filenames) <= limit else f" ... (+{len(filenames) - limit} more)" + return f"{preview}{suffix}" + + +def overlap_preview(leaks: dict[str, list[str]], *, limit: int = 5) -> str: + if not leaks: + return "[]" + key = sorted(leaks.keys())[0] + return f"{key}: {format_filename_preview(leaks[key], limit=limit)}" + + +def validate_disjoint_record_sets( + record_sets: dict[str, list[dict[str, str]]], + *, + context: str, + expected_filenames: set[str] | None = None, +) -> None: + split_filenames: dict[str, list[str]] = {} + for split_name, records in record_sets.items(): + duplicates = duplicate_filenames(records) + if duplicates: + raise RuntimeError( + f"Duplicate filenames detected inside {context} {split_name}: " + f"{format_filename_preview(duplicates)}" + ) + split_filenames[split_name] = record_filenames(records) + + leaks = base.check_data_leakage(split_filenames) + if leaks: + raise RuntimeError( + f"Data leakage detected for {context}: {overlap_preview(leaks)}" + ) + + if expected_filenames is not None: + actual_filenames = set().union(*(set(values) for values in split_filenames.values())) + missing = sorted(expected_filenames - actual_filenames) + extra = sorted(actual_filenames - expected_filenames) + if missing or extra: + details: list[str] = [] + if missing: + details.append(f"missing={format_filename_preview(missing)}") + if extra: + details.append(f"extra={format_filename_preview(extra)}") + raise RuntimeError( + f"{context} does not match the expected dataset membership: {'; '.join(details)}" + ) + + +def validate_fixed_phase_dataset_requirements(sample_records: list[dict[str, str]]) -> None: + class_distribution = base.compute_class_distribution(sample_records) + if class_distribution is None: + raise RuntimeError( + "fixed phase mode requires class-aware records with class_label metadata." + ) + insufficient = { + label: int(count) + for label, count in class_distribution.items() + if int(count) < phase_count() + } + if insufficient: + raise RuntimeError( + "fixed phase mode requires enough samples to place every class in every phase. " + f"Need >= {phase_count()} samples per class, got {insufficient}." + ) + + +def build_fixed_stratified_phase_folds( + sample_records: list[dict[str, str]], + *, + seed: int, +) -> dict[int, list[dict[str, str]]]: + folds: dict[int, list[dict[str, str]]] = {index: [] for index in phase_indices()} + grouped = base.group_records_by_class(sample_records) + for class_label in sorted(grouped.keys()): + records = base.deterministic_shuffle_records( + grouped[class_label], + seed=seed, + tag=f"phase_partition::{phase_count()}::{class_label}", + ) + for record_index, record in enumerate(records): + fold_index = (record_index % phase_count()) + 1 + folds[fold_index].append(dict(record)) + + for fold_index in phase_indices(): + folds[fold_index] = base.deterministic_shuffle_records( + folds[fold_index], + seed=seed, + tag=f"phase_partition::{phase_count()}::fold::{fold_index:03d}", + ) + return folds + + +def validate_fixed_phase_folds( + phase_folds: dict[int, list[dict[str, str]]], + *, + sample_records: list[dict[str, str]], +) -> None: + if sorted(phase_folds.keys()) != phase_indices(): + raise RuntimeError( + f"Expected fixed phase folds for indices {phase_indices()}, got {sorted(phase_folds.keys())}." + ) + validate_disjoint_record_sets( + {f"fold_{fold_index:03d}": records for fold_index, records in sorted(phase_folds.items())}, + context="fixed phase fold partition", + expected_filenames={record["filename"] for record in sample_records}, + ) + + +def build_phase_base_split( + phase_folds: dict[int, list[dict[str, str]]], + *, + phase_index: int, + seed: int, +) -> dict[str, list[dict[str, str]]]: + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + train_records: list[dict[str, str]] = [] + for fold_index in phase_indices(): + if fold_index in {val_fold_index, test_fold_index}: + continue + train_records.extend(dict(record) for record in phase_folds[fold_index]) + return { + "train": base.deterministic_shuffle_records( + train_records, + seed=seed, + tag=f"phase::{phase_index:03d}::train", + ), + "val": base.deterministic_shuffle_records( + phase_folds[val_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::val", + ), + "test": base.deterministic_shuffle_records( + phase_folds[test_fold_index], + seed=seed, + tag=f"phase::{phase_index:03d}::test", + ), + } + + +def build_base_split_for_repeat(sample_records: list[dict[str, str]], split_seed: int) -> dict[str, list[dict[str, str]]]: + dataset_name = base.current_dataset_name() + if dataset_name == "BUSI_with_classes": + split_policy = repeated_holdout_split_policy() + if split_policy != "stratified": + raise ValueError( + "RUNNER_FOLDS.py requires BUSI_with_classes to use BUSI_WITH_CLASSES_SPLIT_POLICY='stratified'." + ) + return base.build_stratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + return base.build_unstratified_base_split( + sample_records, + split_type=base.SPLIT_TYPE, + seed=split_seed, + ) + + +def validate_base_split( + base_splits: dict[str, list[dict[str, str]]], + *, + split_repeat_index: int, + expected_filenames: set[str] | None = None, +) -> None: + context = f"{primary_unit_name()}={split_repeat_index:03d}" + validate_disjoint_record_sets( + base_splits, + context=context, + expected_filenames=expected_filenames, + ) + + +def validate_phase_coverage( + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]], + *, + sample_records: list[dict[str, str]], +) -> None: + expected_phase_indices = phase_indices() + if sorted(phase_splits_by_index.keys()) != expected_phase_indices: + raise RuntimeError( + f"Expected materialized phases {expected_phase_indices}, got {sorted(phase_splits_by_index.keys())}." + ) + + expected_filenames = {record["filename"] for record in sample_records} + train_counts: Counter[str] = Counter() + val_counts: Counter[str] = Counter() + test_counts: Counter[str] = Counter() + + for phase_index, phase_splits in sorted(phase_splits_by_index.items()): + validate_disjoint_record_sets( + phase_splits, + context=f"phase={phase_index:03d}", + expected_filenames=expected_filenames, + ) + train_counts.update(record_filenames(phase_splits["train"])) + val_counts.update(record_filenames(phase_splits["val"])) + test_counts.update(record_filenames(phase_splits["test"])) + + expected_counts = { + "train": phase_count() - 2, + "val": 1, + "test": 1, + } + counters_by_name = { + "train": train_counts, + "val": val_counts, + "test": test_counts, + } + for split_name, expected_count in expected_counts.items(): + offending = sorted( + filename + for filename in expected_filenames + if int(counters_by_name[split_name].get(filename, 0)) != expected_count + ) + if offending: + raise RuntimeError( + f"Invalid global phase coverage for {split_name}: expected each filename to appear " + f"{expected_count} time(s), offenders={format_filename_preview(offending)}" + ) + + +def build_subset_for_repeat( + train_records: list[dict[str, str]], + *, + percent_fraction: float, + subset_seed: int, +) -> list[dict[str, str]]: + if percent_fraction >= 1.0: + return [dict(record) for record in train_records] + subsets = base.build_nested_train_subsets( + train_records, + [percent_fraction], + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + return subsets[base.percent_label(percent_fraction)] + + +def build_incremental_subset_chain( + train_records: list[dict[str, str]], + *, + active_specs: list[PercentRepeatSpec], + subset_seed: int, +) -> dict[str, list[dict[str, str]]]: + fractions = [spec.fraction for spec in active_specs] + return base.build_nested_train_subsets( + train_records, + fractions, + split_type=base.SPLIT_TYPE, + seed=subset_seed, + split_policy=repeated_holdout_split_policy(), + subset_variant=0, + ) + + +def iter_manifested_contexts( + split_repeat_indices: list[int] | None = None, + subset_repeat_indices: list[int] | None = None, +) -> Iterator[FoldRunContext]: + selected_indices = all_split_repeat_indices() if split_repeat_indices is None else list(split_repeat_indices) + selected_subset_repeats = ( + subset_repeat_indices_to_run() if subset_repeat_indices is None else list(subset_repeat_indices) + ) + selected_subset_repeat_set = set(selected_subset_repeats) + for split_repeat_index in selected_indices: + for spec in percent_specs_to_run(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + if subset_repeat_index not in selected_subset_repeat_set: + continue + yield load_context(split_repeat_index, spec.percent_int, subset_repeat_index) + + +def validate_subset_records( + train_records: list[dict[str, str]], + subset_records: list[dict[str, str]], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + train_filenames = {record["filename"] for record in train_records} + subset_filenames = [record["filename"] for record in subset_records] + cycle_context = f"{primary_unit_name()}={split_repeat_index:03d}" + if len(subset_filenames) != len(set(subset_filenames)): + raise RuntimeError( + f"Duplicate filenames found in subset {cycle_context}, " + f"percent={percent_int}, repeat={subset_repeat_index}." + ) + outside_train = sorted(set(subset_filenames) - train_filenames) + if outside_train: + raise RuntimeError( + f"Subset contains filenames outside the base train split for " + f"{cycle_context}, percent={percent_int}, repeat={subset_repeat_index}: {outside_train[:5]}" + ) + + +"""============================================================================= +SQLITE LEDGER +============================================================================= +""" + + +def require_ledger() -> sqlite3.Connection: + if LEDGER_CONN is None: + raise RuntimeError("Ledger is not initialized.") + return LEDGER_CONN + + +def ledger_execute(sql: str, params: tuple[Any, ...] = ()) -> sqlite3.Cursor: + conn = require_ledger() + cursor = conn.execute(sql, params) + conn.commit() + return cursor + + +def setup_ledger(path: Path) -> sqlite3.Connection: + ensure_dir(path.parent) + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=FULL") + conn.execute( + """ + CREATE TABLE IF NOT EXISTS experiment_meta ( + experiment_name TEXT PRIMARY KEY, + config_fingerprint TEXT NOT NULL, + config_json TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + base_seed INTEGER NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS split_manifests ( + split_repeat_index INTEGER PRIMARY KEY, + split_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_count INTEGER NOT NULL, + val_count INTEGER NOT NULL, + test_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS subset_manifests ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + manifest_path TEXT NOT NULL, + train_subset_count INTEGER NOT NULL, + config_fingerprint TEXT NOT NULL, + dataset_fingerprint TEXT NOT NULL, + created_at TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS run_status ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + dataset_fraction REAL NOT NULL, + split_seed INTEGER NOT NULL, + subset_seed INTEGER NOT NULL, + split_manifest_path TEXT NOT NULL, + subset_manifest_path TEXT NOT NULL, + run_dir TEXT NOT NULL, + status TEXT NOT NULL, + stage TEXT NOT NULL, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at TEXT, + updated_at TEXT NOT NULL, + heartbeat_at TEXT, + completed_at TEXT, + last_epoch INTEGER, + latest_checkpoint_path TEXT, + best_checkpoint_path TEXT, + evaluation_path TEXT, + best_metric_name TEXT, + best_metric_value REAL, + elapsed_seconds REAL, + error_text TEXT, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy) + ) + """ + ) + conn.execute( + """ + CREATE TABLE IF NOT EXISTS final_metrics ( + split_repeat_index INTEGER NOT NULL, + dataset_percent INTEGER NOT NULL, + subset_repeat_index INTEGER NOT NULL, + strategy INTEGER NOT NULL, + metric_name TEXT NOT NULL, + mean REAL NOT NULL, + std REAL, + run_dir TEXT NOT NULL, + evaluation_path TEXT NOT NULL, + PRIMARY KEY (split_repeat_index, dataset_percent, subset_repeat_index, strategy, metric_name) + ) + """ + ) + conn.commit() + return conn + + +def fetch_one(sql: str, params: tuple[Any, ...]) -> sqlite3.Row | None: + return require_ledger().execute(sql, params).fetchone() + + +def load_run_status(key: RunKey) -> sqlite3.Row | None: + return fetch_one( + """ + SELECT * + FROM run_status + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + + +def upsert_experiment_meta( + *, + snapshot: dict[str, Any], + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO experiment_meta ( + experiment_name, + config_fingerprint, + config_json, + dataset_fingerprint, + base_seed, + created_at + ) VALUES (?, ?, ?, ?, ?, ?) + """, + ( + FOLDS_EXPERIMENT_NAME, + config_hash, + json.dumps(snapshot, sort_keys=True), + data_hash, + int(base.SEED), + now_utc_iso(), + ), + ) + + +def existing_experiment_meta() -> sqlite3.Row | None: + return fetch_one( + "SELECT * FROM experiment_meta WHERE experiment_name = ?", + (FOLDS_EXPERIMENT_NAME,), + ) + + +def ledger_row_count(table_name: str) -> int: + row = require_ledger().execute(f"SELECT COUNT(*) AS count FROM {table_name}").fetchone() + return int(row["count"]) if row is not None else 0 + + +def upsert_split_manifest_row( + *, + split_repeat_index: int, + split_seed: int, + manifest_path: Path, + train_count: int, + val_count: int, + test_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO split_manifests ( + split_repeat_index, + split_seed, + manifest_path, + train_count, + val_count, + test_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + split_seed, + str(manifest_path.resolve()), + train_count, + val_count, + test_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_subset_manifest_row( + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, + subset_seed: int, + manifest_path: Path, + subset_count: int, + config_hash: str, + data_hash: str, +) -> None: + ledger_execute( + """ + INSERT OR REPLACE INTO subset_manifests ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + subset_seed, + manifest_path, + train_subset_count, + config_fingerprint, + dataset_fingerprint, + created_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + split_repeat_index, + percent_int, + subset_repeat_index, + subset_seed, + str(manifest_path.resolve()), + subset_count, + config_hash, + data_hash, + now_utc_iso(), + ), + ) + + +def upsert_run_plan_row( + *, + key: RunKey, + dataset_fraction: float, + split_seed: int, + subset_seed: int, + split_manifest: Path, + subset_manifest: Path, + run_dir: Path, +) -> None: + existing = load_run_status(key) + if existing is not None: + return + ledger_execute( + """ + INSERT INTO run_status ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + dataset_fraction, + split_seed, + subset_seed, + split_manifest_path, + subset_manifest_path, + run_dir, + status, + stage, + attempt_count, + updated_at + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + dataset_fraction, + split_seed, + subset_seed, + str(split_manifest.resolve()), + str(subset_manifest.resolve()), + str(run_dir.resolve()), + "planned", + "manifested", + 0, + now_utc_iso(), + ), + ) + + +def mark_stale_running_as_interrupted() -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE status = 'running' + """, + (now_utc_iso(),), + ) + + +def mark_run_running(key: RunKey, *, stage: str) -> None: + row = load_run_status(key) + attempt_count = 1 if row is None else int(row["attempt_count"]) + 1 + started_at = row["started_at"] if row is not None else None + if not started_at: + started_at = now_utc_iso() + ledger_execute( + """ + UPDATE run_status + SET status = 'running', + stage = ?, + attempt_count = ?, + started_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + attempt_count, + started_at, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_stage(key: RunKey, *, stage: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET stage = ?, + status = 'running', + updated_at = ?, + heartbeat_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + stage, + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def update_run_checkpoint_progress( + key: RunKey, + *, + checkpoint_path: Path, + epoch: int, + best_metric_name: str, + best_metric_value: float, + elapsed_seconds: float, +) -> None: + column_name = "best_checkpoint_path" if checkpoint_path.name == "best.pt" else "latest_checkpoint_path" + sql = f""" + UPDATE run_status + SET {column_name} = ?, + last_epoch = ?, + best_metric_name = ?, + best_metric_value = ?, + elapsed_seconds = ?, + status = 'running', + stage = 'training', + heartbeat_at = ?, + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """ + ledger_execute( + sql, + ( + str(checkpoint_path.resolve()), + int(epoch), + str(best_metric_name), + float(best_metric_value), + float(elapsed_seconds), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_failed(key: RunKey, error_text: str) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'failed', + updated_at = ?, + error_text = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + error_text, + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def mark_run_interrupted(key: RunKey) -> None: + ledger_execute( + """ + UPDATE run_status + SET status = 'interrupted', + updated_at = ? + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def ingest_evaluation_into_db(key: RunKey, evaluation_path: Path, run_dir: Path) -> None: + payload = base.load_json(evaluation_path) + metrics = payload.get("metrics", {}) + ledger_execute( + """ + DELETE FROM final_metrics + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + (key.split_repeat_index, key.dataset_percent, key.subset_repeat_index, key.strategy), + ) + conn = require_ledger() + for metric_name, metric_payload in metrics.items(): + conn.execute( + """ + INSERT INTO final_metrics ( + split_repeat_index, + dataset_percent, + subset_repeat_index, + strategy, + metric_name, + mean, + std, + run_dir, + evaluation_path + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + str(metric_name), + float(metric_payload.get("mean", 0.0)), + float(metric_payload.get("std")) if metric_payload.get("std") is not None else None, + str(run_dir.resolve()), + str(evaluation_path.resolve()), + ), + ) + conn.commit() + best_metric_name = str(payload.get("best_metric_name", "")) + best_metric_value = None + if best_metric_name and best_metric_name in metrics: + best_metric_value = float(metrics[best_metric_name]["mean"]) + ledger_execute( + """ + UPDATE run_status + SET status = 'completed', + stage = 'done', + evaluation_path = ?, + best_metric_name = COALESCE(?, best_metric_name), + best_metric_value = COALESCE(?, best_metric_value), + completed_at = ?, + updated_at = ?, + heartbeat_at = ?, + error_text = NULL + WHERE split_repeat_index = ? + AND dataset_percent = ? + AND subset_repeat_index = ? + AND strategy = ? + """, + ( + str(evaluation_path.resolve()), + best_metric_name or None, + best_metric_value, + now_utc_iso(), + now_utc_iso(), + now_utc_iso(), + key.split_repeat_index, + key.dataset_percent, + key.subset_repeat_index, + key.strategy, + ), + ) + + +def completed_run_rows() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT * + FROM run_status + WHERE status = 'completed' + ORDER BY split_repeat_index, dataset_percent, subset_repeat_index, strategy + """ + ).fetchall() + ) + + +def metric_rows_for_completed_runs() -> list[sqlite3.Row]: + return list( + require_ledger().execute( + """ + SELECT + rs.split_repeat_index, + rs.dataset_percent, + rs.subset_repeat_index, + rs.strategy, + rs.run_dir, + rs.split_manifest_path, + rs.subset_manifest_path, + rs.evaluation_path, + fm.metric_name, + fm.mean AS metric_mean, + fm.std AS metric_std + FROM final_metrics fm + JOIN run_status rs + ON rs.split_repeat_index = fm.split_repeat_index + AND rs.dataset_percent = fm.dataset_percent + AND rs.subset_repeat_index = fm.subset_repeat_index + AND rs.strategy = fm.strategy + WHERE rs.status = 'completed' + ORDER BY rs.split_repeat_index, rs.dataset_percent, rs.subset_repeat_index, rs.strategy, fm.metric_name + """ + ).fetchall() + ) + + +def export_stats() -> None: + ensure_dir(EXPORTS_DIR) + rows = metric_rows_for_completed_runs() + split_manifest_cache: dict[str, dict[str, Any]] = {} + + def split_manifest_payload(path_text: str) -> dict[str, Any]: + cached = split_manifest_cache.get(path_text) + if cached is None: + cached = base.load_json(Path(path_text)) + split_manifest_cache[path_text] = cached + return cached + + raw_rows_by_run: dict[tuple[int, int, int, int], dict[str, Any]] = {} + for row in rows: + key = ( + int(row["split_repeat_index"]), + int(row["dataset_percent"]), + int(row["subset_repeat_index"]), + int(row["strategy"]), + ) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + raw_row = raw_rows_by_run.setdefault( + key, + { + "split_repeat_index": int(row["split_repeat_index"]), + "dataset_percent": int(row["dataset_percent"]), + "subset_repeat_index": int(row["subset_repeat_index"]), + "strategy": int(row["strategy"]), + "run_dir": row["run_dir"], + "split_manifest_path": row["split_manifest_path"], + "subset_manifest_path": row["subset_manifest_path"], + "evaluation_path": row["evaluation_path"], + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + }, + ) + if manifest_payload.get("phase_index") is not None: + raw_row["phase_index"] = int(manifest_payload["phase_index"]) + raw_row["phase_val_fold_index"] = int(manifest_payload["phase_val_fold_index"]) + raw_row["phase_test_fold_index"] = int(manifest_payload["phase_test_fold_index"]) + raw_row[f"{row['metric_name']}_mean"] = float(row["metric_mean"]) + raw_row[f"{row['metric_name']}_std"] = ( + float(row["metric_std"]) if row["metric_std"] is not None else None + ) + + raw_rows = list(raw_rows_by_run.values()) + raw_rows.sort( + key=lambda item: ( + item["split_repeat_index"], + item["dataset_percent"], + item["subset_repeat_index"], + item["strategy"], + ) + ) + + if raw_rows: + raw_fieldnames = sorted({key for row in raw_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=raw_fieldnames) + writer.writeheader() + writer.writerows(raw_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "raw_run_metrics.csv") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", raw_rows) + else: + atomic_write_text(EXPORTS_DIR / "raw_run_metrics.csv", "") + atomic_save_json(EXPORTS_DIR / "raw_run_metrics.json", []) + + grouped: dict[tuple[int, int], dict[str, Any]] = {} + for row in rows: + group_key = (int(row["dataset_percent"]), int(row["strategy"])) + manifest_payload = split_manifest_payload(str(row["split_manifest_path"])) + bucket = grouped.setdefault( + group_key, + { + "dataset_percent": int(row["dataset_percent"]), + "strategy": int(row["strategy"]), + "split_generation_mode": str( + manifest_payload.get("split_generation_mode", current_split_generation_mode()) + ), + "_phase_indices": set(), + "_metric_values": {}, + }, + ) + if manifest_payload.get("phase_index") is not None: + bucket["_phase_indices"].add(int(manifest_payload["phase_index"])) + bucket["_metric_values"].setdefault(str(row["metric_name"]), []).append( + { + "mean": float(row["metric_mean"]), + "std": float(row["metric_std"]) if row["metric_std"] is not None else None, + } + ) + + aggregated_rows: list[dict[str, Any]] = [] + for (_percent_int, _strategy), bucket in sorted(grouped.items()): + row = { + "dataset_percent": bucket["dataset_percent"], + "strategy": bucket["strategy"], + "split_generation_mode": bucket["split_generation_mode"], + } + metric_values: dict[str, list[dict[str, float | None]]] = bucket["_metric_values"] + row["n_runs"] = max((len(values) for values in metric_values.values()), default=0) + if bucket["_phase_indices"]: + phase_indices = sorted(int(value) for value in bucket["_phase_indices"]) + row["completed_phase_count"] = len(phase_indices) + row["completed_phases"] = ",".join(str(value) for value in phase_indices) + for metric_name, values in sorted(metric_values.items()): + means = [value["mean"] for value in values] + stds = [value["std"] for value in values if value["std"] is not None] + row[f"{metric_name}_mean"] = float(base.np.mean(means)) if means else None + row[f"{metric_name}_std"] = float(base.np.std(means)) if means else None + row[f"{metric_name}_within_run_std_mean"] = float(base.np.mean(stds)) if stds else None + aggregated_rows.append(row) + + if aggregated_rows: + aggregated_fieldnames = sorted({key for row in aggregated_rows for key in row.keys()}) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", delete=False, dir=str(EXPORTS_DIR)) as handle: + writer = csv.DictWriter(handle, fieldnames=aggregated_fieldnames) + writer.writeheader() + writer.writerows(aggregated_rows) + handle.flush() + os.fsync(handle.fileno()) + tmp_csv = Path(handle.name) + os.replace(tmp_csv, EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", aggregated_rows) + else: + atomic_write_text(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.csv", "") + atomic_save_json(EXPORTS_DIR / "aggregated_metrics_by_percent_strategy.json", []) + + +"""============================================================================= +BASE MODULE PATCHES +============================================================================= +""" + + +def patched_save_json(path: str | Path, payload: Any) -> None: + atomic_save_json(path, payload) + + +def _context_matches_percent(ctx: FoldRunContext | None, percent: float) -> bool: + if ctx is None: + return False + return abs(float(percent) - float(ctx.percent_fraction)) <= 1e-12 + + +def patched_percent_root(percent: float) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return ensure_dir(CURRENT_FOLD_CONTEXT.repeat_root) + return ORIGINAL_PERCENT_ROOT(percent) + + +def patched_strategy_root_for_percent( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return strategy_root_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STRATEGY_ROOT_FOR_PERCENT(strategy, percent, model_config) + + +def patched_final_root_for_strategy( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> Path: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return run_dir_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_FINAL_ROOT_FOR_STRATEGY(strategy, percent, model_config) + + +def patched_study_paths_for( + strategy: int, + percent: float, + model_config: base.RuntimeModelConfig | None = None, +) -> tuple[Path, Path, Path]: + if _context_matches_percent(CURRENT_FOLD_CONTEXT, percent): + return fold_study_paths_for(strategy, CURRENT_FOLD_CONTEXT, model_config) + return ORIGINAL_STUDY_PATHS_FOR(strategy, percent, model_config) + + +def patched_save_checkpoint( + path: Path, + *, + run_type: str, + model: base.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Any, + scaler: Any, + epoch: int, + best_metric_value: float, + best_metric_name: str, + run_config: dict[str, Any], + epoch_metrics: dict[str, Any], + patience_counter: int, + elapsed_seconds: float, + history: list[dict[str, Any]] | None = None, + log_alpha: torch.Tensor | None = None, + alpha_optimizer: torch.optim.Optimizer | None = None, + resume_source: dict[str, Any] | None = None, +) -> None: + payload = { + "run_type": run_type, + "epoch": epoch, + "model_state_dict": base._unwrap_compiled(model).state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "best_metric_name": best_metric_name, + "best_metric_value": best_metric_value, + "patience_counter": int(patience_counter), + "elapsed_seconds": float(elapsed_seconds), + "run_config": run_config, + "config": run_config, + "epoch_metrics": epoch_metrics, + } + if history is not None: + payload["history"] = [dict(row) for row in history] + if scheduler is not None: + payload["scheduler_state_dict"] = scheduler.state_dict() + if scaler is not None: + payload["scaler_state_dict"] = scaler.state_dict() + if log_alpha is not None: + payload["log_alpha"] = float(log_alpha.detach().item()) + if alpha_optimizer is not None: + payload["alpha_optimizer_state_dict"] = alpha_optimizer.state_dict() + if resume_source is not None: + payload["resume_source"] = resume_source + base.validate_checkpoint_payload( + Path(path), + payload, + required_keys=base.checkpoint_required_keys( + optimizer=optimizer, + scheduler=scheduler, + scaler=scaler, + log_alpha=log_alpha, + alpha_optimizer=alpha_optimizer, + require_run_metadata=True, + ), + expected_run_type=run_type, + ) + atomic_torch_save(path, payload) + base.write_checkpoint_manifest(path, payload) + + if run_type != "final" or CURRENT_FOLD_CONTEXT is None: + return + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(run_config["strategy"]), + ) + update_run_checkpoint_progress( + run_key, + checkpoint_path=Path(path), + epoch=int(epoch), + best_metric_name=str(best_metric_name), + best_metric_value=float(best_metric_value), + elapsed_seconds=float(elapsed_seconds), + ) + + +def patched_run_evaluation_for_run( + *, + strategy: int, + percent: float, + bundle: base.DataBundle, + run_dir: Path, + strategy2_checkpoint_path: str | Path | None = None, +) -> dict[str, dict[str, float]]: + if CURRENT_FOLD_CONTEXT is not None and LEDGER_CONN is not None: + run_key = RunKey( + split_repeat_index=CURRENT_FOLD_CONTEXT.split_repeat_index, + dataset_percent=CURRENT_FOLD_CONTEXT.percent_int, + subset_repeat_index=CURRENT_FOLD_CONTEXT.subset_repeat_index, + strategy=int(strategy), + ) + mark_run_stage(run_key, stage="evaluating") + return ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + +def install_base_patches() -> None: + base.save_json = patched_save_json + base.percent_root = patched_percent_root + base.strategy_root_for_percent = patched_strategy_root_for_percent + base.final_root_for_strategy = patched_final_root_for_strategy + base.study_paths_for = patched_study_paths_for + base.save_checkpoint = patched_save_checkpoint + base.run_evaluation_for_run = patched_run_evaluation_for_run + base.RESUME_IDENTITY_KEYS = BASE_RESUME_IDENTITY_KEYS + ( + "folds_experiment_name", + "split_repeat_index", + "subset_repeat_index", + "split_seed", + "subset_seed", + "base_split_manifest_path", + "subset_manifest_path", + ) + if RESUME_FOLDS and base.RUN_OPTUNA: + base.LOAD_EXISTING_STUDIES = True + + +@contextmanager +def activate_context(ctx: FoldRunContext) -> Iterator[None]: + global CURRENT_FOLD_CONTEXT + previous = CURRENT_FOLD_CONTEXT + CURRENT_FOLD_CONTEXT = ctx + try: + yield + finally: + CURRENT_FOLD_CONTEXT = previous + + +"""============================================================================= +EXPERIMENT PLAN MATERIALIZATION +============================================================================= +""" + + +def create_split_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + base_splits: dict[str, list[dict[str, str]]], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "base_splits": { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + }, + "counts": {split_name: len(records) for split_name, records in base_splits.items()}, + "class_distributions": { + split_name: base.compute_class_distribution(records) + for split_name, records in base_splits.items() + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + payload["phase_partition_seed"] = int(split_seed) + return payload + + +def create_subset_manifest_payload( + *, + split_repeat_index: int, + split_seed: int, + percent_int: int, + percent_fraction: float, + subset_repeat_index: int, + subset_seed: int, + split_manifest: Path, + base_splits: dict[str, list[dict[str, str]]], + subset_records: list[dict[str, str]], + subset_sampling_source: str, + sampling_chain_dataset_percents: list[int], + config_hash: str, + data_hash: str, + phase_index: int | None = None, + phase_val_fold_index: int | None = None, + phase_test_fold_index: int | None = None, +) -> dict[str, Any]: + payload = { + "experiment_name": FOLDS_EXPERIMENT_NAME, + "config_fingerprint": config_hash, + "dataset_fingerprint": data_hash, + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "split_generation_mode": current_split_generation_mode(), + "split_type": base.SPLIT_TYPE, + "split_repeat_index": split_repeat_index, + "split_seed": split_seed, + "dataset_percent": percent_int, + "dataset_fraction": percent_fraction, + "subset_repeat_index": subset_repeat_index, + "subset_seed": subset_seed, + "percent_sampling_mode": current_percent_sampling_mode(), + "subset_sampling_source": subset_sampling_source, + "sampling_chain_dataset_percents": [int(value) for value in sampling_chain_dataset_percents], + "parent_split_manifest_path": str(split_manifest.resolve()), + "train_records": [dict(record) for record in subset_records], + "val_records": [dict(record) for record in base_splits["val"]], + "test_records": [dict(record) for record in base_splits["test"]], + "base_train_records": [dict(record) for record in base_splits["train"]], + "counts": { + "base_train": len(base_splits["train"]), + "train_subset": len(subset_records), + "val": len(base_splits["val"]), + "test": len(base_splits["test"]), + }, + "class_distributions": { + "base_train": base.compute_class_distribution(base_splits["train"]), + "train_subset": base.compute_class_distribution(subset_records), + "val": base.compute_class_distribution(base_splits["val"]), + "test": base.compute_class_distribution(base_splits["test"]), + }, + } + if phase_index is not None: + payload["phase_index"] = int(phase_index) + payload["phase_count"] = phase_count() + payload["phase_val_fold_index"] = int(phase_val_fold_index) + payload["phase_test_fold_index"] = int(phase_test_fold_index) + return payload + + +def validate_materialized_phase_manifests(*, sample_records: list[dict[str, str]]) -> None: + if not using_fixed_phase_mode(): + return + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + for phase_index in phase_indices(): + manifest_path = split_manifest_path(phase_index) + if not manifest_path.exists(): + raise RuntimeError(f"Missing phase manifest for phase={phase_index:03d}: {manifest_path}") + payload = base.load_json(manifest_path) + if str(payload.get("split_generation_mode", "")).strip().lower() != "fixed_stratified_phases_8_1_1": + raise RuntimeError( + f"Expected fixed phase split_generation_mode in {manifest_path}, got " + f"{payload.get('split_generation_mode')!r}." + ) + if int(payload.get("phase_index", -1)) != phase_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_index={payload.get('phase_index')!r}, " + f"expected {phase_index}." + ) + if int(payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_count={payload.get('phase_count')!r}, " + f"expected {phase_count()}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_val_fold_index=" + f"{payload.get('phase_val_fold_index')!r}, expected {val_fold_index}." + ) + if int(payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Phase manifest {manifest_path} reports phase_test_fold_index=" + f"{payload.get('phase_test_fold_index')!r}, expected {test_fold_index}." + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + + +def validate_materialized_subset_manifests() -> None: + if not using_fixed_phase_mode(): + return + for phase_index in phase_indices(): + split_payload = base.load_json(split_manifest_path(phase_index)) + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + manifest_path = subset_manifest_path(phase_index, spec.percent_int, subset_repeat_index) + if not manifest_path.exists(): + raise RuntimeError( + f"Missing subset manifest for phase={phase_index:03d}, " + f"percent={spec.percent_int}, repeat={subset_repeat_index}: {manifest_path}" + ) + subset_payload = base.load_json(manifest_path) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=phase_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + +def validate_loaded_context_payloads( + split_payload: dict[str, Any], + subset_payload: dict[str, Any], + *, + split_repeat_index: int, + percent_int: int, + subset_repeat_index: int, +) -> None: + base_splits = { + split_name: [dict(record) for record in split_payload["base_splits"][split_name]] + for split_name in ("train", "val", "test") + } + validate_base_split(base_splits, split_repeat_index=split_repeat_index) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + validate_disjoint_record_sets( + { + "train_subset": train_records, + "val": val_records, + "test": test_records, + }, + context=( + f"{primary_unit_name()}={split_repeat_index:03d}, " + f"percent={percent_int}, repeat={subset_repeat_index}" + ), + ) + validate_subset_records( + base_splits["train"], + train_records, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + if set(record_filenames(base_train_records)) != set(record_filenames(base_splits["train"])): + raise RuntimeError( + f"Subset manifest base train records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(val_records)) != set(record_filenames(base_splits["val"])): + raise RuntimeError( + f"Subset manifest validation records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if set(record_filenames(test_records)) != set(record_filenames(base_splits["test"])): + raise RuntimeError( + f"Subset manifest test records do not match the parent {primary_unit_name()} " + f"{split_repeat_index:03d}." + ) + if using_fixed_phase_mode(): + phase_index = int(split_payload.get("phase_index", split_repeat_index)) + if int(split_payload.get("phase_count", -1)) != phase_count(): + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{split_payload.get('phase_count')!r}." + ) + val_fold_index, test_fold_index = phase_fold_indices(phase_index) + if int(split_payload.get("phase_val_fold_index", -1)) != val_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{split_payload.get('phase_val_fold_index')!r}." + ) + if int(split_payload.get("phase_test_fold_index", -1)) != test_fold_index: + raise RuntimeError( + f"Parent phase manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{split_payload.get('phase_test_fold_index')!r}." + ) + if int(subset_payload.get("phase_index", phase_index)) != phase_index: + raise RuntimeError( + f"Subset manifest phase_index={subset_payload.get('phase_index')!r} does not match " + f"phase={phase_index:03d}." + ) + if int(subset_payload.get("phase_count", phase_count())) != phase_count(): + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_count=" + f"{subset_payload.get('phase_count')!r}." + ) + if int(subset_payload.get("phase_val_fold_index", val_fold_index)) != val_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_val_fold_index=" + f"{subset_payload.get('phase_val_fold_index')!r}." + ) + if int(subset_payload.get("phase_test_fold_index", test_fold_index)) != test_fold_index: + raise RuntimeError( + f"Subset manifest for phase={phase_index:03d} has unexpected phase_test_fold_index=" + f"{subset_payload.get('phase_test_fold_index')!r}." + ) + + +def materialize_experiment_plan( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + ensure_dir(EXPERIMENT_ROOT) + ensure_dir(SPLIT_MANIFESTS_DIR) + ensure_dir(SUBSET_MANIFESTS_DIR) + ensure_dir(EXPORTS_DIR) + + snapshot = config_snapshot(model_config) + config_hash = config_fingerprint(snapshot) + data_hash = dataset_fingerprint(sample_records) + sampling_mode = current_percent_sampling_mode() + upsert_experiment_meta(snapshot=snapshot, config_hash=config_hash, data_hash=data_hash) + expected_filenames = {record["filename"] for record in sample_records} + partition_seed_value = partition_seed() + phase_splits_by_index: dict[int, dict[str, list[dict[str, str]]]] = {} + fixed_phase_folds: dict[int, list[dict[str, str]]] | None = None + if using_fixed_phase_mode(): + validate_fixed_phase_dataset_requirements(sample_records) + fixed_phase_folds = build_fixed_stratified_phase_folds( + sample_records, + seed=partition_seed_value, + ) + validate_fixed_phase_folds(fixed_phase_folds, sample_records=sample_records) + + for split_repeat_index in all_split_repeat_indices(): + phase_index = None + phase_val_fold_index = None + phase_test_fold_index = None + if using_fixed_phase_mode(): + if fixed_phase_folds is None: + raise RuntimeError("Fixed phase folds were not initialized.") + split_seed = partition_seed_value + phase_index = split_repeat_index + phase_val_fold_index, phase_test_fold_index = phase_fold_indices(phase_index) + base_splits = build_phase_base_split( + fixed_phase_folds, + phase_index=phase_index, + seed=split_seed, + ) + phase_splits_by_index[phase_index] = { + split_name: [dict(record) for record in records] + for split_name, records in base_splits.items() + } + else: + split_seed = fold_seed(f"split::{split_repeat_index}") + base_splits = build_base_split_for_repeat(sample_records, split_seed) + validate_base_split( + base_splits, + split_repeat_index=split_repeat_index, + expected_filenames=expected_filenames, + ) + + split_manifest = split_manifest_path(split_repeat_index) + split_payload = create_split_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + base_splits=base_splits, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(split_manifest, split_payload) + upsert_split_manifest_row( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + manifest_path=split_manifest, + train_count=len(base_splits["train"]), + val_count=len(base_splits["val"]), + test_count=len(base_splits["test"]), + config_hash=config_hash, + data_hash=data_hash, + ) + + if sampling_mode == "independent": + for spec in percent_specs(): + for subset_repeat_index in range(1, int(spec.repeat_count) + 1): + subset_seed = fold_seed( + f"subset::{split_repeat_index}::{spec.percent_int}::{subset_repeat_index}" + ) + subset_records = build_subset_for_repeat( + base_splits["train"], + percent_fraction=spec.fraction, + subset_seed=subset_seed, + ) + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="independent", + sampling_chain_dataset_percents=[spec.percent_int], + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + continue + + max_subset_repeat_index = max(spec.repeat_count for spec in percent_specs()) + for subset_repeat_index in range(1, max_subset_repeat_index + 1): + active_specs = active_specs_for_subset_repeat(subset_repeat_index) + if not active_specs: + continue + subset_seed = fold_seed(f"subset::{split_repeat_index}::repeat::{subset_repeat_index}") + subset_chain = build_incremental_subset_chain( + base_splits["train"], + active_specs=active_specs, + subset_seed=subset_seed, + ) + chain_dataset_percents = [spec.percent_int for spec in active_specs] + for spec in active_specs: + subset_records = subset_chain[base.percent_label(spec.fraction)] + validate_subset_records( + base_splits["train"], + subset_records, + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + ) + + subset_manifest = subset_manifest_path( + split_repeat_index, + spec.percent_int, + subset_repeat_index, + ) + subset_payload = create_subset_manifest_payload( + split_repeat_index=split_repeat_index, + split_seed=split_seed, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + split_manifest=split_manifest, + base_splits=base_splits, + subset_records=subset_records, + subset_sampling_source="incremental_chain", + sampling_chain_dataset_percents=chain_dataset_percents, + config_hash=config_hash, + data_hash=data_hash, + phase_index=phase_index, + phase_val_fold_index=phase_val_fold_index, + phase_test_fold_index=phase_test_fold_index, + ) + atomic_save_json(subset_manifest, subset_payload) + upsert_subset_manifest_row( + split_repeat_index=split_repeat_index, + percent_int=spec.percent_int, + subset_repeat_index=subset_repeat_index, + subset_seed=subset_seed, + manifest_path=subset_manifest, + subset_count=len(subset_records), + config_hash=config_hash, + data_hash=data_hash, + ) + + ctx = FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=spec.percent_int, + percent_fraction=spec.fraction, + split_seed=split_seed, + subset_seed=subset_seed, + repeat_root=repeat_root(split_repeat_index, spec.percent_int, subset_repeat_index), + split_manifest_path=split_manifest, + subset_manifest_path=subset_manifest, + ) + for strategy in base.STRATEGIES: + upsert_run_plan_row( + key=RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ), + dataset_fraction=ctx.percent_fraction, + split_seed=ctx.split_seed, + subset_seed=ctx.subset_seed, + split_manifest=ctx.split_manifest_path, + subset_manifest=ctx.subset_manifest_path, + run_dir=run_dir_for(int(strategy), ctx, model_config), + ) + + if using_fixed_phase_mode(): + validate_phase_coverage(phase_splits_by_index, sample_records=sample_records) + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +def validate_or_create_experiment( + *, + sample_records: list[dict[str, str]], + model_config: base.RuntimeModelConfig, +) -> None: + meta = existing_experiment_meta() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not RESUME_FOLDS and not ignore_resume_folds_gate: + if meta is not None: + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + if meta is None: + if ledger_row_count("split_manifests") > 0 or ledger_row_count("run_status") > 0: + raise RuntimeError( + f"Experiment DB {EXPERIMENT_DB_PATH} contains run state but is missing experiment metadata." + ) + materialize_experiment_plan(sample_records=sample_records, model_config=model_config) + return + + current_snapshot = config_snapshot(model_config) + current_hash = config_fingerprint(current_snapshot) + current_data_hash = dataset_fingerprint(sample_records) + stored_config_hash = str(meta["config_fingerprint"]) + stored_portable_hash = "" + try: + stored_config_json = json.loads(str(meta["config_json"])) + stored_portable_hash = config_fingerprint(stored_config_json) + except Exception as exc: + print(f"[Resume] Could not recompute portable config fingerprint from stored metadata: {exc}") + if stored_config_hash != current_hash and stored_portable_hash != current_hash: + raise RuntimeError( + f"Existing experiment config fingerprint does not match current configuration for {EXPERIMENT_ROOT}." + ) + if stored_config_hash != current_hash and stored_portable_hash == current_hash: + print( + "[Resume] Accepted existing experiment metadata with a portable config fingerprint match " + "(machine-specific paths/runtime resume flag changed)." + ) + if str(meta["dataset_fingerprint"]) != current_data_hash: + raise RuntimeError( + f"Existing experiment dataset fingerprint does not match current dataset contents for {EXPERIMENT_ROOT}." + ) + if using_fixed_phase_mode(): + validate_materialized_phase_manifests(sample_records=sample_records) + validate_materialized_subset_manifests() + + +"""============================================================================= +RUNTIME BUNDLE CONSTRUCTION +============================================================================= +""" + + +def load_context(split_repeat_index: int, percent_int: int, subset_repeat_index: int) -> FoldRunContext: + split_payload = base.load_json(split_manifest_path(split_repeat_index)) + subset_payload = base.load_json(subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index)) + validate_loaded_context_payloads( + split_payload, + subset_payload, + split_repeat_index=split_repeat_index, + percent_int=percent_int, + subset_repeat_index=subset_repeat_index, + ) + return FoldRunContext( + split_repeat_index=split_repeat_index, + subset_repeat_index=subset_repeat_index, + percent_int=percent_int, + percent_fraction=float(subset_payload["dataset_fraction"]), + split_seed=int(split_payload["split_seed"]), + subset_seed=int(subset_payload["subset_seed"]), + repeat_root=repeat_root(split_repeat_index, percent_int, subset_repeat_index), + split_manifest_path=split_manifest_path(split_repeat_index), + subset_manifest_path=subset_manifest_path(split_repeat_index, percent_int, subset_repeat_index), + ) + + +def build_fold_data_bundle(ctx: FoldRunContext) -> base.DataBundle: + split_payload = base.load_json(ctx.split_manifest_path) + subset_payload = base.load_json(ctx.subset_manifest_path) + dataset_root = Path(base.current_dataset_dirs()[0]).parent.resolve() + phase_index = ( + int(split_payload.get("phase_index", ctx.split_repeat_index)) + if str(split_payload.get("split_generation_mode", "")).strip().lower() == "fixed_stratified_phases_8_1_1" + else None + ) + cycle_token = f"phase{phase_index:03d}" if phase_index is not None else f"split{ctx.split_repeat_index:03d}" + normalization_cache_path = ( + ctx.repeat_root + / ( + f"norm_stats_{base.normalization_cache_tag()}_{base.SPLIT_TYPE}_{ctx.percent_int:03d}pct_" + f"{cycle_token}_repeat{ctx.subset_repeat_index:02d}.json" + ) + ) + + train_records = [dict(record) for record in subset_payload["train_records"]] + val_records = [dict(record) for record in subset_payload["val_records"]] + test_records = [dict(record) for record in subset_payload["test_records"]] + base_train_records = [dict(record) for record in subset_payload["base_train_records"]] + + base_train_class_distribution = base.compute_class_distribution(base_train_records) + train_class_distribution = base.compute_class_distribution(train_records) + val_class_distribution = base.compute_class_distribution(val_records) + test_class_distribution = base.compute_class_distribution(test_records) + + base.print_loaded_class_distribution( + split_type=base.SPLIT_TYPE, + train_subset_key=str(ctx.percent_int), + base_train_records=base_train_records, + train_records=train_records, + val_records=val_records, + test_records=test_records, + ) + + global_mean, global_std, normalization_source = base.compute_busi_statistics( + dataset_root=dataset_root, + sample_records=train_records, + cache_path=normalization_cache_path, + ) + + payload = { + "dataset_name": base.current_dataset_name(), + "dataset_split_policy": ( + base.current_busi_with_classes_split_policy() + if base.current_dataset_name() == "BUSI_with_classes" + else None + ), + "dataset_splits_path": str(ctx.split_manifest_path.resolve()), + "dataset_root": str(dataset_root), + "split_source": ( + "fixed_phase_manifest" + if phase_index is not None + else "repeated_holdout_manifest" + ), + "split_generation_mode": str(split_payload.get("split_generation_mode", current_split_generation_mode())), + "split_type": base.SPLIT_TYPE, + "percent_sampling_mode": str( + subset_payload.get("percent_sampling_mode", split_payload.get("percent_sampling_mode", "independent")) + ), + "dataset_percent": ctx.percent_fraction, + "train_subset_key": str(ctx.percent_int), + "train_subset_variant": int(ctx.subset_repeat_index), + "train_subset_source": str(subset_payload.get("subset_sampling_source", "repeated_holdout_repeat")), + "selected_split_manifest_path": str(ctx.subset_manifest_path.resolve()), + "sampling_chain_dataset_percents": [ + int(value) for value in subset_payload.get("sampling_chain_dataset_percents", [ctx.percent_int]) + ], + "base_train_count": len(base_train_records), + "train_count": len(train_records), + "val_count": len(val_records), + "test_count": len(test_records), + "base_train_class_distribution": base_train_class_distribution, + "train_class_distribution": train_class_distribution, + "val_class_distribution": val_class_distribution, + "test_class_distribution": test_class_distribution, + "val_test_frozen": True, + "leakage_check": "passed", + "global_mean": global_mean, + "global_std": global_std, + "normalization_cache_path": str(normalization_cache_path.resolve()), + "normalization_source": normalization_source, + "split_repeat_index": ctx.split_repeat_index, + "subset_repeat_index": ctx.subset_repeat_index, + "split_seed": ctx.split_seed, + "subset_seed": ctx.subset_seed, + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "folds_experiment_root": str(EXPERIMENT_ROOT.resolve()), + } + if phase_index is not None: + payload["phase_index"] = phase_index + payload["phase_count"] = int(split_payload["phase_count"]) + payload["phase_val_fold_index"] = int(split_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(split_payload["phase_test_fold_index"]) + + base.print_split_summary(payload) + base.print_normalization_summary(payload) + + train_split_name = ( + f"train {base.SPLIT_TYPE} {ctx.percent_int}% phase{phase_index:03d}" + if phase_index is not None + else f"train {base.SPLIT_TYPE} {ctx.percent_int}% split{ctx.split_repeat_index:03d}" + ) + val_split_name = ( + f"val {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"val {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + test_split_name = ( + f"test {base.SPLIT_TYPE} phase{phase_index:03d}" + if phase_index is not None + else f"test {base.SPLIT_TYPE} split{ctx.split_repeat_index:03d}" + ) + loader_prefix = ( + f"{base.SPLIT_TYPE}:{ctx.percent_int}:phase{phase_index:03d}" + if phase_index is not None + else f"{base.SPLIT_TYPE}:{ctx.percent_int}:split{ctx.split_repeat_index:03d}" + ) + + train_ds = base.BUSIDataset( + train_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=True, + split_name=train_split_name, + ) + val_ds = base.BUSIDataset( + val_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=val_split_name, + ) + test_ds = base.BUSIDataset( + test_records, + dataset_root, + global_mean, + global_std, + preload=base.PRELOAD_TO_RAM, + augment=False, + split_name=test_split_name, + ) + bundle = base.DataBundle( + percent=ctx.percent_fraction, + split_payload=payload, + train_ds=train_ds, + val_ds=val_ds, + test_ds=test_ds, + train_loader=base.make_loader( + train_ds, + shuffle=True, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:train", + ), + val_loader=base.make_loader( + val_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:val", + ), + test_loader=base.make_loader( + test_ds, + shuffle=False, + loader_tag=f"{loader_prefix}:repeat{ctx.subset_repeat_index:02d}:test", + ), + ) + base.print_preload_summary(bundle) + return bundle + + +def release_bundle(bundle: base.DataBundle | None) -> None: + if bundle is None: + return + del bundle + gc.collect() + base.run_cuda_cleanup(context="bundle release") + + +def checkpoint_candidates(run_dir: Path) -> list[Path]: + return [ + run_dir / "checkpoints" / "latest.pt", + run_dir / "checkpoints" / "best.pt", + ] + + +def resolve_resume_checkpoint(run_dir: Path) -> Path | None: + for candidate in checkpoint_candidates(run_dir): + if candidate.exists(): + return candidate + return None + + +"""============================================================================= +RUN EXECUTION +============================================================================= +""" + + +def strategy_requires_strategy2_checkpoint(strategy: int) -> bool: + return ( + base.EXECUTION_MODE == "train_eval" and strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2 + ) + + +def fold_param_metadata(ctx: FoldRunContext) -> dict[str, Any]: + subset_payload = base.load_json(ctx.subset_manifest_path) + payload = { + "folds_experiment_name": FOLDS_EXPERIMENT_NAME, + "split_repeat_index": int(ctx.split_repeat_index), + "subset_repeat_index": int(ctx.subset_repeat_index), + "split_seed": int(ctx.split_seed), + "subset_seed": int(ctx.subset_seed), + "split_generation_mode": str(subset_payload.get("split_generation_mode", current_split_generation_mode())), + "percent_sampling_mode": str(subset_payload.get("percent_sampling_mode", current_percent_sampling_mode())), + "subset_sampling_source": str(subset_payload.get("subset_sampling_source", "independent")), + "base_split_manifest_path": str(ctx.split_manifest_path.resolve()), + "subset_manifest_path": str(ctx.subset_manifest_path.resolve()), + } + if subset_payload.get("phase_index") is not None: + payload["phase_index"] = int(subset_payload["phase_index"]) + payload["phase_count"] = int(subset_payload["phase_count"]) + payload["phase_val_fold_index"] = int(subset_payload["phase_val_fold_index"]) + payload["phase_test_fold_index"] = int(subset_payload["phase_test_fold_index"]) + return payload + + +def finalize_run_from_artifacts(key: RunKey, run_dir: Path) -> bool: + evaluation_path = run_dir / "evaluation.json" + if not evaluation_path.exists(): + return False + ingest_evaluation_into_db(key, evaluation_path, run_dir) + export_stats() + return True + + +def execute_final_run( + *, + strategy: int, + ctx: FoldRunContext, + bundle: base.DataBundle, + model_config: base.RuntimeModelConfig, +) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None: + raise RuntimeError(f"Run plan row is missing for {run_key}.") + if str(row["status"]) == "completed": + return + if str(row["status"]) == "failed": + print( + f"[{split_generation_display_name()}] Skipping failed run " + f"{run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload)}." + ) + return + + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + + strategy2_checkpoint_path: str | Path | None = None + run_name = run_identity_label(strategy=strategy, percent=ctx.percent_fraction, split_payload=bundle.split_payload) + with activate_context(ctx): + banner_prefix = "PHASE RUN" if using_fixed_phase_mode() else "REPEATED HOLDOUT RUN" + base.banner(f"{banner_prefix} | {run_name}") + if strategy_requires_strategy2_checkpoint(strategy): + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + + if base.EXECUTION_MODE == "eval_only": + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + summary_path = run_dir / "summary.json" + if summary_path.exists() and not (run_dir / "evaluation.json").exists(): + mark_run_running(run_key, stage="evaluating") + ORIGINAL_RUN_EVALUATION_FOR_RUN( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + return + + if base.RUN_SMOKE_TEST: + base.maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + + params = base.resolve_job_params( + strategy, + ctx.percent_fraction, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = {**params, **fold_param_metadata(ctx)} + + resume_checkpoint_path = None + if str(row["status"]) in {"interrupted", "running"}: + resume_checkpoint_path = resolve_resume_checkpoint(run_dir) + + mark_run_running(run_key, stage="training") + base.run_single_job( + strategy=strategy, + model_config=model_config, + bundle=bundle, + run_dir=run_dir, + params=params, + max_epochs=base.strategy_epochs(strategy), + trial=None, + strategy2_checkpoint_path=strategy2_checkpoint_path, + resume_checkpoint_path=resume_checkpoint_path, + ) + finalize_run_from_artifacts(run_key, run_dir) + + +def reconcile_existing_artifacts(ctx: FoldRunContext, strategy: int, model_config: base.RuntimeModelConfig) -> None: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + return + run_dir = run_dir_for(strategy, ctx, model_config) + if finalize_run_from_artifacts(run_key, run_dir): + return + if (run_dir / "summary.json").exists(): + mark_run_interrupted(run_key) + mark_run_stage(run_key, stage="evaluating") + return + if resolve_resume_checkpoint(run_dir) is not None: + mark_run_interrupted(run_key) + + +def maybe_reset_study_artifacts(model_config: base.RuntimeModelConfig) -> None: + if not base.RESET_ALL_STUDIES_EACH_RUN or not base.RUN_OPTUNA: + return + if RESUME_FOLDS: + print( + f"[{split_generation_display_name()}] RESET_ALL_STUDIES_EACH_RUN ignored because RESUME_FOLDS=True." + ) + return + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + with activate_context(ctx): + for strategy in base.STRATEGIES: + base.reset_study_artifacts(strategy, ctx.percent_fraction, model_config=model_config) + + +def run_overfit_mode(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE OVERFIT TEST MODE") + else: + base.banner("REPEATED HOLDOUT OVERFIT TEST MODE") + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and base.STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = base.resolve_strategy2_checkpoint_path( + strategy, + ctx.percent_fraction, + model_config, + ) + base.run_overfit_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + overfit_root=overfit_root_for(strategy, ctx, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_eval_only_without_ledger(model_config: base.RuntimeModelConfig) -> int: + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EVAL ONLY | LEDGER BYPASSED") + else: + base.banner("REPEATED HOLDOUT EVAL ONLY | LEDGER BYPASSED") + print("[Eval Only] Skipping experiment ledger and evaluating directly from manifests and checkpoints.") + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + bundle = build_fold_data_bundle(ctx) + try: + with activate_context(ctx): + for strategy in base.STRATEGIES: + run_name = run_identity_label( + strategy=strategy, + percent=ctx.percent_fraction, + split_payload=bundle.split_payload, + ) + base.banner(f"EVAL ONLY | {run_name}") + base.run_evaluation_for_run( + strategy=strategy, + percent=ctx.percent_fraction, + bundle=bundle, + run_dir=run_dir_for(strategy, ctx, model_config), + strategy2_checkpoint_path=None, + ) + finally: + release_bundle(bundle) + return 0 + + +def run_pass_for_statuses( + statuses: set[str], + *, + model_config: base.RuntimeModelConfig, +) -> None: + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + pending_strategies = [] + for strategy in base.STRATEGIES: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is not None and str(row["status"]) in statuses: + pending_strategies.append(int(strategy)) + if not pending_strategies: + continue + + bundle = build_fold_data_bundle(ctx) + phase_had_error = False + try: + for strategy in pending_strategies: + run_key = RunKey( + split_repeat_index=ctx.split_repeat_index, + dataset_percent=ctx.percent_int, + subset_repeat_index=ctx.subset_repeat_index, + strategy=int(strategy), + ) + row = load_run_status(run_key) + if row is None or str(row["status"]) == "completed": + continue + try: + execute_final_run( + strategy=strategy, + ctx=ctx, + bundle=bundle, + model_config=model_config, + ) + except KeyboardInterrupt: + phase_had_error = True + mark_run_interrupted(run_key) + raise + except Exception: + phase_had_error = True + error_text = traceback.format_exc() + mark_run_failed(run_key, error_text) + print(error_text) + finally: + if using_fixed_phase_mode(): + write_phase_timing_summary_after_phase(ctx.split_repeat_index) + if not phase_had_error: + threading.Thread( + target=run_repo_backup_after_phase, + args=(ctx.split_repeat_index,), + daemon=True, + ).start() + release_bundle(bundle) + + +"""============================================================================= +MAIN +============================================================================= +""" + + +def run_repeated_holdout_main() -> int: + global LEDGER_CONN + + validate_repeated_holdout_settings() + if not str(FOLDS_EXPERIMENT_NAME).strip(): + raise ValueError("FOLDS_EXPERIMENT_NAME must be non-empty.") + if base.SPLIT_TYPE != "80_10_10": + raise ValueError( + f"RUNNER_FOLDS.py currently requires SPLIT_TYPE='80_10_10', got {base.SPLIT_TYPE!r}." + ) + + install_base_patches() + base.SAVE_LATEST_EVERY_EPOCH = True + + base.set_global_seed(base.SEED) + model_config = base.current_model_config() + fold_experiment_summary(model_config) + + if str(base.EXECUTION_MODE).strip().lower() == "eval_only": + select_sample_records() + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + return run_eval_only_without_ledger(model_config) + + sample_records, _dataset_root = select_sample_records() + ignore_resume_folds_gate = str(base.EXECUTION_MODE).strip().lower() == "eval_only" + + if not ignore_resume_folds_gate and not RESUME_FOLDS and EXPERIMENT_ROOT.exists(): + raise RuntimeError( + f"RESUME_FOLDS=False requires a fresh experiment root, but {EXPERIMENT_ROOT} already exists." + ) + if ( + not ignore_resume_folds_gate + and RESUME_FOLDS + and not EXPERIMENT_DB_PATH.exists() + and EXPERIMENT_ROOT.exists() + and any(EXPERIMENT_ROOT.iterdir()) + ): + raise RuntimeError( + f"Experiment root {EXPERIMENT_ROOT} already exists without a valid SQLite ledger at {EXPERIMENT_DB_PATH}. " + "Refusing to attach to ambiguous state." + ) + + LEDGER_CONN = setup_ledger(EXPERIMENT_DB_PATH) + try: + validate_or_create_experiment(sample_records=sample_records, model_config=model_config) + mark_stale_running_as_interrupted() + + for ctx in iter_manifested_contexts(split_repeat_indices_to_run()): + for strategy in base.STRATEGIES: + reconcile_existing_artifacts(ctx, int(strategy), model_config) + + export_stats() + maybe_reset_study_artifacts(model_config) + + if base.RUN_OVERFIT_TEST: + return run_overfit_mode(model_config) + + run_pass_for_statuses({"interrupted", "running"}, model_config=model_config) + run_pass_for_statuses({"planned"}, model_config=model_config) + export_stats() + if using_fixed_phase_mode(): + base.banner("FIXED PHASE EXECUTION COMPLETE") + else: + base.banner("REPEATED HOLDOUT COMPLETE") + print(f"Experiment root : {EXPERIMENT_ROOT}") + print(f"Experiment DB : {EXPERIMENT_DB_PATH}") + print(f"Raw metrics export : {EXPORTS_DIR / 'raw_run_metrics.csv'}") + print(f"Aggregate export : {EXPORTS_DIR / 'aggregated_metrics_by_percent_strategy.csv'}") + return 0 + finally: + if LEDGER_CONN is not None: + LEDGER_CONN.close() + LEDGER_CONN = None + +def run_single_run_main() -> int: + global DATASET_PERCENTS + banner("MLR ALL STRATEGIES BAYES RUNNER") + DATASET_PERCENTS = normalize_dataset_percents(DATASET_PERCENTS) + set_global_seed(SEED) + model_config = current_model_config() + dataset_name = current_dataset_name() + images_dir, annotations_dir = current_dataset_dirs() + if EXECUTION_MODE not in {"train_eval", "eval_only"}: + raise ValueError(f"EXECUTION_MODE must be 'train_eval' or 'eval_only', got {EXECUTION_MODE!r}") + if SPLIT_TYPE not in SUPPORTED_SPLIT_TYPES: + raise ValueError(f"SPLIT_TYPE must be one of {SUPPORTED_SPLIT_TYPES}, got {SPLIT_TYPE!r}") + if not images_dir.exists() or not annotations_dir.exists(): + raise FileNotFoundError( + f"Expected dataset directories for {dataset_name} under {DATA_ROOT}: " + f"images={images_dir}, masks={annotations_dir}" + ) + ensure_specific_checkpoint_scope("EVAL_CHECKPOINT_MODE", EVAL_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("STRATEGY2_CHECKPOINT_MODE", STRATEGY2_CHECKPOINT_MODE) + ensure_specific_checkpoint_scope("TRAIN_RESUME_MODE", TRAIN_RESUME_MODE) + + print_environment_summary(model_config) + split_registry, split_source = load_or_create_dataset_splits( + images_dir=images_dir, + annotations_dir=annotations_dir, + split_json_path=current_dataset_splits_json_path(), + train_fractions=DATASET_PERCENTS, + seed=SEED, + ) + + bundles: dict[float, DataBundle] = {} + for percent in DATASET_PERCENTS: + bundles[percent] = build_data_bundle(percent, split_registry, split_source) + + if RUN_OVERFIT_TEST: + run_configured_overfit_tests(bundles, model_config=model_config) + banner("OVERFIT TESTS COMPLETE") + return 0 + + if RESET_ALL_STUDIES_EACH_RUN: + if RUN_OPTUNA: + banner("RESETTING OPTUNA STUDIES") + for strategy in STRATEGIES: + for percent in DATASET_PERCENTS: + reset_study_artifacts(strategy, percent, model_config=model_config) + else: + print("[Optuna Reset] Skipped because RUN_OPTUNA=False.") + + try: + for percent in DATASET_PERCENTS: + banner(f"PERCENT STAGE | {percent_text(percent)}") + bundle = bundles[percent] + for strategy in STRATEGIES: + strategy2_checkpoint_path = None + if strategy == 3 and EXECUTION_MODE == "train_eval" and STRATEGY3_BOOTSTRAP_FROM_STRATEGY2: + strategy2_checkpoint_path = resolve_strategy2_checkpoint_path(strategy, percent, model_config) + + if EXECUTION_MODE == "train_eval": + maybe_run_strategy_smoke_test( + strategy=strategy, + model_config=model_config, + bundle=bundle, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + params = resolve_job_params( + strategy, + percent, + bundle, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + banner( + f"FINAL RETRAIN | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_final_training( + strategy, + bundle, + params, + model_config=model_config, + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + else: + banner( + f"EVAL ONLY | {run_identity_label(strategy=strategy, percent=percent, split_payload=bundle.split_payload)}" + ) + run_evaluation_for_run( + strategy=strategy, + percent=percent, + bundle=bundle, + run_dir=final_root_for_strategy(strategy, percent, model_config), + strategy2_checkpoint_path=strategy2_checkpoint_path, + ) + except Exception: + banner("RUN FAILED") + traceback.print_exc() + return 1 + + banner("ALL DONE") + return 0 + + +def main() -> int: + validate_hf_backup_settings() + run_initial_hf_backup() + if EXPERIMENT_MODE not in SUPPORTED_EXPERIMENT_MODES: + raise ValueError( + f"EXPERIMENT_MODE must be one of {SUPPORTED_EXPERIMENT_MODES}, got {EXPERIMENT_MODE!r}" + ) + if EXPERIMENT_MODE == "repeated_holdout": + return run_repeated_holdout_main() + return run_single_run_main() + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/experiment_state.sqlite3 b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/experiment_state.sqlite3 new file mode 100644 index 0000000000000000000000000000000000000000..35b5843018d69cca02cd1203ef4f08fe4ce109a9 Binary files /dev/null and b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/experiment_state.sqlite3 differ diff --git a/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.csv b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.csv new file mode 100644 index 0000000000000000000000000000000000000000..568f750d14c254e30ab41d7c50001e2eb31c63f5 --- /dev/null +++ b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.csv @@ -0,0 +1,2 @@ +biou_contour_mean,biou_contour_std,biou_contour_within_run_std_mean,biou_mean,biou_std,biou_within_run_std_mean,completed_phase_count,completed_phases,dataset_percent,dice_mean,dice_std,dice_within_run_std_mean,hd95_mean,hd95_std,hd95_within_run_std_mean,iou_mean,iou_std,iou_within_run_std_mean,n_runs,ppv_mean,ppv_std,ppv_within_run_std_mean,sen_mean,sen_std,sen_within_run_std_mean,split_generation_mode,strategy +0.14274893701076508,0.0,0.13207420706748962,0.4235888123512268,0.0,0.2640605866909027,1,1,100,0.7580860257148743,0.0,0.2920907735824585,15.369906425476074,0.0,21.321483612060547,0.6805214881896973,0.0,0.30149468779563904,1,0.847917914390564,0.0,0.2595667541027069,0.7454960942268372,0.0,0.310700386762619,fixed_stratified_phases_8_1_1,3 diff --git a/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.json b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.json new file mode 100644 index 0000000000000000000000000000000000000000..f1481d796582b82f96ae753e09465a62b8e78057 --- /dev/null +++ b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/aggregated_metrics_by_percent_strategy.json @@ -0,0 +1,31 @@ +[ + { + "biou_contour_mean": 0.14274893701076508, + "biou_contour_std": 0.0, + "biou_contour_within_run_std_mean": 0.13207420706748962, + "biou_mean": 0.4235888123512268, + "biou_std": 0.0, + "biou_within_run_std_mean": 0.2640605866909027, + "completed_phase_count": 1, + "completed_phases": "1", + "dataset_percent": 100, + "dice_mean": 0.7580860257148743, + "dice_std": 0.0, + "dice_within_run_std_mean": 0.2920907735824585, + "hd95_mean": 15.369906425476074, + "hd95_std": 0.0, + "hd95_within_run_std_mean": 21.321483612060547, + "iou_mean": 0.6805214881896973, + "iou_std": 0.0, + "iou_within_run_std_mean": 0.30149468779563904, + "n_runs": 1, + "ppv_mean": 0.847917914390564, + "ppv_std": 0.0, + "ppv_within_run_std_mean": 0.2595667541027069, + "sen_mean": 0.7454960942268372, + "sen_std": 0.0, + "sen_within_run_std_mean": 0.310700386762619, + "split_generation_mode": "fixed_stratified_phases_8_1_1", + "strategy": 3 + } +] \ No newline at end of file diff --git a/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.csv b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..1b6c20e3a76af7c8b621cf4a780f0f1af6d888de --- /dev/null +++ b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.csv @@ -0,0 +1,2 @@ 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b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..ebdcb35ac323c8ccc48a4a282bb9694de11b080e --- /dev/null +++ b/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/exports/raw_run_metrics.json @@ -0,0 +1,30 @@ +[ + { + "biou_contour_mean": 0.14274893701076508, + "biou_contour_std": 0.13207420706748962, + "biou_mean": 0.4235888123512268, + "biou_std": 0.2640605866909027, + "dataset_percent": 100, + "dice_mean": 0.7580860257148743, + "dice_std": 0.2920907735824585, + "evaluation_path": "/workspace/runs/Segformer_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/phase_001/pct_100/repeat_01/strategy_3/final/evaluation.json", + "hd95_mean": 15.369906425476074, + "hd95_std": 21.321483612060547, + "iou_mean": 0.6805214881896973, + "iou_std": 0.30149468779563904, + "phase_index": 1, + "phase_test_fold_index": 1, + "phase_val_fold_index": 2, + "ppv_mean": 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+scope,strategy,tmax,device,num_batches,num_samples,total_inference_ms,avg_batch_inference_ms,std_batch_inference_ms,avg_sample_inference_ms,std_sample_inference_ms,mean_per_image_inference_ms,std_per_image_inference_ms,mean_per_image_inference_seconds,std_per_image_inference_seconds +test_set_evaluation,3,6,cuda,5,65,4324.533731,864.906746,360.486130,66.531288,11.125726,66.531288,11.125726,0.066531288,0.011125726 diff --git a/runs/Unet_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/phase_timing_summary.json b/runs/Unet_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/phase_timing_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c555fb4b4e770dea9e1a30cedda6b3dd55c7d01c --- /dev/null +++ b/runs/Unet_B0_AB3_r3_only/repeated_holdout/stratified_holdout_v1/phase_timing_summary.json @@ -0,0 +1,30 @@ +{ + "definition": "training elapsed_seconds from summary.json, falling back to the ledger checkpoint elapsed_seconds", + "phase_count": 1, + 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