* Refactor model runner methods for improved abstraction.
Moved slicing logic to a private `_slice_outputs` method and decoupled `_run_model` for clearer subclass implementation. Removed redundant `output` attribute in `ModelState` to streamline data handling.
* Add output parsing to model_runner and remove duplicate logic
Integrates an output parser directly into `model_runner` for streamlined inference and parsing. Removes redundant parser initialization from `modeld` to avoid duplication and enhance maintainability.
* Reordering
* linter
* linter
* Refactor model handling with `ModelData` abstraction
Introduce a `ModelData` class to encapsulate model and metadata logic, improving code clarity and modularity. Refactor `ModelRunner` to manage multiple models and add conditional handling for fallback scenarios. Adjust `TinygradRunner` to validate and use the new `ModelData` structure.
* Refactor model handling to use dictionary-based structure
Replaces the model list with a dictionary keyed by model type to improve clarity and maintainability. Updates related logic and ensures consistent handling of model metadata and inputs. Adds `slice_outputs` implementation to the `TinygradRunner` for proper output parsing.
* Refactor model runners to support policy and vision separation
Introduced `TinygradPolicyRunner`, `TinygradVisionRunner`, and `TinygradSplitRunner` to enable separate handling of policy and vision models. Updated `TinygradRunner` initialization and input preparation to accommodate modular processing. Adjusted `modeld` to utilize the new runners, ensuring compatibility with separated model workflows.
* Refactor model runner initialization and simplify logic
Introduce `get_model_runner` to centralize model runner selection logic, replacing multiple conditional instantiations. Simplify the handling of model metadata by removing fallback logic and restructuring output slicing to enforce proper loading of model data. These changes improve code maintainability and clarity.
* Refactor model data access for TinygradRunner initialization
Update references to access nested artifact properties and align with structural changes to the model data schema. This simplifies input shapes handling and ensures compatibility with updated model attributes.
* Refactor imports and clean up redundant code.
Removed unused imports and improved formatting for clarity and maintainability. These changes simplify the codebase by eliminating unnecessary dependencies and ensuring consistency.
* Refactor model output parsing with specialized parsers.
Introduce abstract `_parse_outputs` method to standardize parsing logic. Add `SplitParser` for specialized parsing in `TinygradVisionRunner` and `TinygradPolicyRunner`. This improves modularity and paves the way for easier parser customization.
* Add parser for model output processing in modeld_v2
Introduce a new `Parser` class to handle parsing and processing of model outputs, including MDN, binary cross-entropy, and categorical cross-entropy outputs. This modularizes the logic, improves clarity, and prepares for handling various types of model data.
* Add `input_shapes` property to model runners
Introduce a new `input_shapes` property in the abstract base class and its implementation in derived classes. This provides a standardized way to access the input shapes of models, improving clarity and consistency in the model runners.
* Refactor model runner to use private `_model_data` attribute
Replaced public `model_data` with private `_model_data` for improved encapsulation. Updated all references and property accessors accordingly. Simplified model type handling by using raw types where applicable.
* Remove debug print statement from model_runner.py
The unnecessary `print(model_type)` statement was removed as it served no functional purpose in the code. This improves code cleanliness and avoids unintended console output during execution.
* Refactor `_parse_outputs` call in `run_model`.
Replaced the use of `self.parser.parse_outputs` with `self._parse_outputs` for clarity and consistency. Updated method signature to align with the revised usage.
* Refactor model output parsing for clarity and scope separation
Moved specific parsing logic (e.g., lane_lines, lead) from `parse_model_outputs` to `parse_policy_outputs` to better align with functional responsibilities. This improves modularity and readability while maintaining existing functionality.
* Refactor model_runner to simplify result handling
Renamed variable `result` to `parsed_result` for clarity and removed unnecessary slicing during model output parsing. These changes improve code readability and maintain consistency within the `run_model` method.
* Adjust _parse_outputs method signature in model_runner
Updated the method signature of _parse_outputs to accept a single np.ndarray instead of a dictionary. This aligns with the intended data structure and ensures consistency across subclasses implementing this abstract method.
* Refactor ModelRunner to enforce abstract base class compliance
Updated `ModelRunner` and its subclasses to properly inherit from `ABC` while refactoring methods to ensure compliance with Python's abstract base class standards. Streamlined the handling of `_parse_outputs` and added a new `input_shapes` property for improved functionality.
* Fix buffer length issue in 20Hz model initialization
Adjusted `FULL_HISTORY_BUFFER_LEN` by adding +1 for `full_features_20Hz` to address compatibility issues with the current FoF model. Added a comment noting potential failure for other models with this adjustment.
* Refactor TinygradRunner to remove abstract methods.
Simplified the TinygradRunner class by removing unnecessary @abstractmethod decorators and redundant method definitions. This streamlines the code and aligns it more effectively with its current usage and implementation.
* Refactor model runner classes and enhance type annotations
Simplified model runner implementations, added type annotations, and improved code readability and maintainability. Introduced new type definitions, updated metadata handling, and standardized input/output parsing across all runner classes. Minor comment update in `modeld.py` for clarity.
* Refactor model runner classes with detailed docstrings.
Enhanced class and function docstrings across model_runner.py for better clarity and maintainability. Descriptions now include detailed explanations of attributes, purposes, and workflows to aid understanding and future development.
* Refactor model runner classes and add TICI hardware optimization
Simplified and clarified class definitions, comments, and functionality for ModelRunner subclasses. Introduced the use of QCOM environment variable on TICI for potential hardware acceleration. Enhanced input/output handling and error reporting across Tinygrad and ONNX implementations.
* Update parser import and usage to use CombinedParser
Replaced the Parser class with CombinedParser in model_runner.py. This change ensures consistency with the updated parsing logic, aligning with the latest requirements for combined model output handling.
* Refactor TinygradRunner hierarchy for modular parsers
Reorganized the TinygradRunner and its specialized runners (Vision, Policy, and Supercombo) into a cleaner, modular structure using composable classes. This consolidates parser logic, removes redundancy, and simplifies initialization by leveraging a shared base class with a dictionary-based parser method.
* Refactor model runners to use ModularRunner as abstract base.
Introduce a new `ModularRunner` class to enforce a consistent interface across model runners. Updated existing runners, including `ModelRunner`, `SupercomboTinygrad`, `PolicyTinygrad`, and `VisionTinygrad`, to extend `ModularRunner`. Added abstract methods and properties to enhance modularity and code maintainability.
* Refactor model runners into modular components.
This commit separates the logic for Tinygrad, ONNX, and split runners into clearly defined modules and components. It introduces `PolicyTinygrad`, `VisionTinygrad`, `SupercomboTinygrad`, and centralized helpers for cleaner architecture. The changes improve modularity and maintainability of the model running and parsing workflows.
* Simplify imports and clean up unused code in ONNXRunner.
Removed unused imports and redundant environmental variables to streamline the codebase. Consolidated necessary imports and organized type definitions for improved readability and maintenance.
* Standardize imports and add model data validation.
Updated import paths to ensure consistency across modules by using `openpilot` as the base. Introduced validation in `_parse_outputs` methods to handle cases where `_model_data` is not initialized, preventing potential runtime errors.
* Remove unused import and fix whitespace in runners
The unused import `ModelData` was removed from `tinygrad_runner.py` to clean up the code. Additionally, extraneous whitespace was corrected in `onnx_runner.py` for improved readability and consistency.
* Remove unnecessary blank line in import statements
Cleaned up import section by removing an extra blank line. This helps maintain consistency and adheres to code style conventions.
* BROKEN!! Staging code but its not gonna work. Also I realized we need to run the split models in 2 stages because the output of one is immediately needed for the input of the other. We might handle it inside of the model_runner instead
* update smooth
* Revert "update smooth"
This reverts commit c335712e6e1ee189459ce34dfc9d4028feb9470f.
* match case made this very hard to read.
* shouldnt be there
* Refactor to allow TR (soon TM)
* TR 7 is .1
* metadata
* .2=3
* Remove redundant comments and clean up conditional blocks in modeld.py.
* Undoing wrong buffer
* Refactor model initialization and adjust ONNX runner import
Reorganized numpy input buffer initialization and updated `temporal_idxs` logic for better clarity and efficiency. Conditional import for ONNXRunner added for non-TICI platforms to optimize imports. These changes improve maintainability and compatibility across platforms.
* Update CURRENT_SELECTOR_VERSION to 4
Bump the CURRENT_SELECTOR_VERSION constant from 3 to 4 to reflect changes in the selector logic or requirements. This ensures compatibility with the updated selector version while maintaining the minimum required version as 2.
* Add output_slices property to model runners
Introduce output_slices property to provide access to the output slices for individual and combined models. This ensures consistent handling of output slices across vision and policy models, improving modularity and usability.
* Refactor imports to use SplitModelConstants consistently
Updated import references to use the renamed `SplitModelConstants` class for consistency across files. This change ensures clarity and better alignment with the updated class naming convention.
* Refactor buffer initialization and desired curvature handling
Refactored model input buffer initialization for improved clarity and consistency, leveraging dynamic shape calculations. Extracted `process_desired_curvature` method to encapsulate logic for handling 3D and non-3D cases. Simplified temporal index generation and related calculations for better maintainability.
* Refactor desire reshape dims logic in modeld.py
Adjust logic for setting desire reshape dimensions to handle `is_20hz_3d` separately. This improves clarity and ensures proper handling of different model runner configurations.
* Fix off-by-one error in full_desire buffer initialization
The full_desire buffer length was mistakenly set to full_history_buffer_len + 1. This change corrects it to match the intended full_history_buffer_len, ensuring proper alignment with other buffers.
* Simplify desire reshape logic by removing unused condition.
Removed the `is_20hz_3d` condition and associated reshape logic since it is no longer needed. This streamlines the code and avoids unnecessary checks for unused configurations.
* Refactor buffer initialization for 20Hz model variants
Reorganized buffer initialization logic to prioritize the 20Hz_3D condition. This improves clarity and ensures specific handling of different 20Hz configurations. Adjusted the order of conditions to streamline execution flow.
* Refactor: Move `get_action_from_model` and constants to `Model` class to improve encapsulation and readability.
* 12 line reshaping red diff
* .2 to match old models delay
* mypy fixes
* Revert "12 line reshaping red diff"
This reverts commit 8c7280f629043f0485749e2536a20af74c9209b2.
* mypy
* remove this
* Fix desired curvature for models which do not output desired_curvature
* fix FoF
* flip policy and vision outs to allow FoF and tomb raider to live in harmony using conditional `if 'this' in outs:'`
* noqa
* single
* sunnypilot modeld.py
* action
* overrides methodology
* combine split outputs to its own method
* comments
* Fix static checker line length
* static will fail on line length
lines:
286,
206,
70 - 77,
159,
168
* Address E501 line length violations
* This will make TR better while not effecting FoF/VFF at all
* Reduce this to one conditional and just call normally in vision/policy
* Align with upstream in our own way.
* check for desired curvature in outputs first
* outputs
* Use a cleaner import method
* Fix output
* Clean up some values
* Only call on init
* slight cleanup
* names!!!!!!!!!
* Refactor overrides structure to support key-value pairs.
The overrides structure now uses a list of key-value pairs instead of fixed lat/long fields. This change improves flexibility, allowing dynamic addition of override parameters. Code adjustments ensure backward compatibility and consistent behavior throughout the application.
* Refactor: Use local variable for SplitModelConstants
Introduce a local `constants` variable to replace repeated access to `SplitModelConstants`. This simplifies code readability and adheres to linter recommendations for line length.
* Refactor model constant handling to improve modularity
Replaced direct usage of model constants with dynamic access through model runners for better scalability and maintainability. This change centralizes constant definitions, reduces redundancy, and ensures clearer integration with different model types.
---------
Co-authored-by: discountchubbs <alexgrant990@gmail.com>
Co-authored-by: Discountchubbs <159560811+Discountchubbs@users.noreply.github.com>
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
* car: initialize sunnypilot interface immediately
* init
* Revert "car: initialize sunnypilot interface immediately"
This reverts commit 2e0fc4d87eb312a3a74a61bb8cbc780e33efbfc7.
* show int
* show int
* work properly with cereal
* unused
* in its own module
* bump
* not yet
* not yet
* list comp and use structs directly
* allow default key if needed
* lint
* send it with None
* bump
* Add enhanced HyundaiCAN extension
Introduced HyundaiCanEXT for improved object parsing and control integration, leveraging new fields like lead distance and relative speed in CarControlSP.
Refactored controlsd to utilize a modular design with ControlsdExt for additional sunnypilot specific functionality.
* self.cc_sp
* Refactor CarControlSP handling for improved state updates
* refactor
* bool
* Refactor controlsd SP communication logic
Update `publish_sp` method to include `CC_SP` parameter and refine SP SubMaster to exclude `radarState`. Remove custom lead vehicle state processing as it is no longer needed.
* remove in this pr
* bump
* start cleanup
* inherit instead
* even more!
* lint
* type hint
* use the same objects for submaster and pubmaster
* more
---------
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
* Fixes an issue on the long planner since Tomb Raider models, where the models are now meant to output the acceleration target and the "should stop" instead of it being calculated. However, older models (particularly those running on modeld_v2 from SP) do not output this. Leading to a "coasting" situation instead of braking when only e2e is used which is totally wrong.
infiniteCable