[DRAFT| DO NOT REVIEW YET] 🚀 feat(model): Add Dinomaly Model #2835
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…dation steps - Added detailed docstrings for the Dinomaly class and its methods. - Improved error handling in training and validation steps. - Updated pre-processor configuration to include crop size validation. - Refined output structure in the training step for clarity.
…zer configuration; enhance Gaussian kernel function
… SSLMetaArch implementation - Deleted `train.py`, `__init__.py`, and `ssl_meta_arch.py` files from the DINOv2 training module. - Removed unused imports and commented-out code in `vit_encoder.py`. - Streamlined the model loading process and eliminated unnecessary complexity in the architecture. - Ensured that the remaining code adheres to the latest standards and practices for clarity and maintainability.
…er and torch model classes
- Rearranged import statements for better organization and consistency. - Updated type hints to use the new syntax for optional types. - Simplified conditional checks and improved readability in various functions. - Enhanced logging messages for clarity during model loading and training. - Modified the `get_params_groups_with_decay` function to improve parameter handling. - Updated the `DinoV2Loader` class to streamline model loading and weight management. - Improved the `ViTill` class by refining feature processing and anomaly map calculations. - Adjusted the `simple_script.py` to utilize the new export types for model exporting. - Reduced the number of epochs in the training script for quicker testing.
… clarity and accuracy style: adjust training configuration in simple_script.py
…integration refactor: enhance training configuration and streamline model initialization in ViTill chore: add benchmark configuration and script for Padim model evaluation fix: update simple script for MVTecAD category and improve timing output
…related utilities refactor: update attention and drop path layers for improved efficiency and clarity
… timm library equivalents and clean up unused code
… clarity; remove unused layer files
…ding initialization
… handling - Added type hints and ClassVar annotations in model_loader.py for better clarity and type checking. - Enhanced error messages in model_loader.py to provide clearer guidance on model name and architecture issues. - Updated global_cosine_hm_percent and modify_grad functions in utils.py with type hints and improved gradient modification logic. - Improved documentation and type hints in vision_transformer.py, including detailed docstrings for methods and parameters. - Refined training configuration in lightning_model.py with type hints and assertions for better validation of input parameters. - Enhanced ViTill class in torch_model.py with static methods and type safety checks for architecture configuration. - General code cleanup and consistency improvements across all modified files.
…er; remove unused max_steps from training config
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Anomaly Maps